diff --git a/docs/notebooks/Getting_started_1.ipynb b/docs/notebooks/Getting_started_1.ipynb index 7cb9dea4..98f6eb2d 100644 --- a/docs/notebooks/Getting_started_1.ipynb +++ b/docs/notebooks/Getting_started_1.ipynb @@ -5,7 +5,7 @@ "id": "86557797-36de-44bb-b277-2ee7a22b1458", "metadata": {}, "source": [ - "# ๐Ÿ‘‹ Getting started 1: Creating a 1-Lipschitz neural network" + "# ๐Ÿ‘‹ Getting started 1: Creating a 1-Lipschitz neural network\n" ] }, { @@ -15,54 +15,80 @@ "source": [ "The goal of this series of tutorials is to show the different usages of `deel-lip`.\n", "\n", - "In this first notebook, our objective is to show how to create 1-Lipschitz neural networks with `deel-lip`. \n", - "\n", - "In particular, we will cover the following: \n", - "1. [๐Ÿ“š Theoretical background](#theoretical_background) \n", - "A brief theoretical background on Lipschitz continuous functions. This section can be safely skipped if one is not interested in the theory.\n", - "2. [๐Ÿงฑ Creating a 1-Lipschitz neural network with `deel-lip` and `keras`](#deel_keras) \n", - "An example of how to create a 1-Lipschitz neural network with `deel-lip` and `keras`.\n", - "3. [๐Ÿ”จ Design rules for 1-Lipschitz neural networks with `deel-lip`](#design) \n", - "A set of neural network design rules that one must respect in order to enforce the 1-Lipschitz constraint.\n", + "In this first notebook, our objective is to show how to create 1-Lipschitz neural\n", + "networks with `deel-lip`.\n", "\n", + "In particular, we will cover the following:\n", "\n", + "1. [๐Ÿ“š Theoretical background](#theoretical_background) \n", + " A brief theoretical background on Lipschitz continuous functions. This section can be\n", + " safely skipped if one is not interested in the theory.\n", + "2. [๐Ÿงฑ Creating a 1-Lipschitz neural network with `deel-lip` and `keras`](#deel_keras) \n", + " An example of how to create a 1-Lipschitz neural network with `deel-lip` and `keras`.\n", + "3. [๐Ÿ”จ Design rules for 1-Lipschitz neural networks with `deel-lip`](#design) \n", + " A set of neural network design rules that one must respect in order to enforce the\n", + " 1-Lipschitz constraint.\n", "\n", "## ๐Ÿ“š Theoretical background \n", + "\n", "### What is the Lipschitz constant\n", - "The `deel-lip` package allows to control the Lipschitz constant of a layer or of a whole neural network. The Lipschitz constant is a mathematical property of a function (in our context of work, a layer or a model) that characterizes how much the output of the function can change with respect to changes in its input. \n", "\n", - "In mathematical terms, a function $f$ is Lipschitz continuous with a **Lipschitz constant L** or more simply **L-Lipschitz** if for any given pair of points $x_1,x_2$, $L$ provides a bound on the rate of change of $f$: \n", + "The `deel-lip` package allows to control the Lipschitz constant of a layer or of a whole\n", + "neural network. The Lipschitz constant is a mathematical property of a function (in our\n", + "context of work, a layer or a model) that characterizes how much the output of the\n", + "function can change with respect to changes in its input.\n", + "\n", + "In mathematical terms, a function $f$ is Lipschitz continuous with a **Lipschitz\n", + "constant L** or more simply **L-Lipschitz** if for any given pair of points $x_1,x_2$,\n", + "$L$ provides a bound on the rate of change of $f$:\n", "\n", "$$||f(x_1)-f(x_2)||\\leq L||x_1-x_2||.$$\n", "\n", - "For instance, given a 1-Lipschitz dense layer (a.k.a fully connected layer) with a weight matrix $W$ and a bias vector $b$, we have for any two inputs $x_1$ and $x_2$: $$||(W.x_1+b)-(W.x_2+b)|| \\leq 1||x_1-x_2||.$$\n", + "For instance, given a 1-Lipschitz dense layer (a.k.a fully connected layer) with a\n", + "weight matrix $W$ and a bias vector $b$, we have for any two inputs $x_1$ and $x_2$:\n", + "$$||(W.x_1+b)-(W.x_2+b)|| \\leq 1||x_1-x_2||.$$\n", "\n", - "๐Ÿ’ก The norm we refer to throughout our notebooks is the Euclidean norm (L2). This is because `deel-lip` operates with this norm. You will find more information about the role of the norm in the context of adversarially robust 1-Lipschitz deep learning models in the notebook titled 'Getting Started 2'.\n", + "๐Ÿ’ก The norm we refer to throughout our notebooks is the Euclidean norm (L2). This is\n", + "because `deel-lip` operates with this norm. You will find more information about the\n", + "role of the norm in the context of adversarially robust 1-Lipschitz deep learning models\n", + "in the notebook titled 'Getting Started 2'.\n", "\n", "### A simple requirement for creating 1-Lipschitz neural network\n", - "The composition property of Lipschitz continuous functions states that if you have a function f that is $L_1$-Lipschitz and another function g that is $L_2$-Lispchitz, then their composition function h = (f o g) which applies f after g is also Lipschitz continuous with a Lipschitz constant $L \\leq L_1$ * $L_2$.\n", "\n", - "A feed-forward or sequential neural network is essentially a stack of layers, where each layer transforms the output of the previous layer(s) and feeds its output to the next ones. \n", + "The composition property of Lipschitz continuous functions states that if you have a\n", + "function f that is $L_1$-Lipschitz and another function g that is $L_2$-Lispchitz, then\n", + "their composition function h = (f o g) which applies f after g is also Lipschitz\n", + "continuous with a Lipschitz constant $L \\leq L_1$ \\* $L_2$.\n", + "\n", + "A feed-forward or sequential neural network is essentially a stack of layers, where each\n", + "layer transforms the output of the previous layer(s) and feeds its output to the next\n", + "ones.\n", "\n", - "By the composition property of Lipschitz functions, *it suffices for each of the n individual layers of a neural network model to be 1-Lipschitz, for the whole model to be 1-Lipschitz*.\n", + "By the composition property of Lipschitz functions, _it suffices for each of the n\n", + "individual layers of a neural network model to be 1-Lipschitz, for the whole model to be\n", + "1-Lipschitz_.\n", "\n", - "For instance, given a 1-Lipschitz dense layer parametrized by $(W,b)$, and a ReLU (Rectified Linear Unit) activation layer which is naturally 1-Lipschitz, the combination of the two is also 1-Lispchitz. \n", + "For instance, given a 1-Lipschitz dense layer parametrized by $(W,b)$, and a ReLU\n", + "(Rectified Linear Unit) activation layer which is naturally 1-Lipschitz, the combination\n", + "of the two is also 1-Lispchitz. \n", "This is shown in the equations below, where we have for any two inputs $x_1$ and $x_2$:\n", "\n", - "$$||(W.x_1+b)-(W.x_2+b)||\\leq 1||x_1-x_2||,$$\n", + "$$||(Wx_1+b)-(Wx_2+b)||\\leq 1||x_1-x_2||,$$\n", "$$||ReLU(x_1)-ReLU(x_2)||\\leq 1||x_1-x_2||,$$\n", - "and:\n", "$$||ReLU(W.x_1+b)-ReLU(W.x_2+b)||\\leq 1||(W.x_1+b)-(W.x_2+b)||\\leq 1^2||x_1-x_2||.$$\n", "\n", - "\n", - "The `deel-lip` package allows to create 1-Lipschitz neural networks, by providing the user with means to enforce the Lipschitz constant at one on a selected set of layers (such as dense layers). \n", - "It also ensures that 1-Lipschitz continuity is retained during training.\n", - "\n", + "The `deel-lip` package allows to create 1-Lipschitz neural networks, by providing the\n", + "user with means to enforce the Lipschitz constant at one on a selected set of layers\n", + "(such as dense layers). It also ensures that 1-Lipschitz continuity is retained during\n", + "training.\n", "\n", "## ๐Ÿงฑ Creating a 1-Lipschitz neural network with `deel-lip` and `keras` \n", - "`keras` is an open-source high-level deep learning API written in Python. It allows to build, train, and deploy deep learning models.\n", "\n", - "One can produce a neural network architecture using keras with a few lines of code, as shown in the toy-example multi-layer perceptron (MLP) below:" + "`keras` is an open-source high-level deep learning API written in Python. It allows to\n", + "build, train, and deploy deep learning models.\n", + "\n", + "One can produce a neural network architecture using keras with a few lines of code, as\n", + "shown in the toy-example multi-layer perceptron (MLP) below:\n" ] }, { @@ -74,56 +100,147 @@ }, "outputs": [ { - "name": "stdout", + "name": "stderr", "output_type": "stream", "text": [ - "Model: \"sequential\"\n", - "_________________________________________________________________\n", - " Layer (type) Output Shape Param # \n", - "=================================================================\n", - " flatten (Flatten) (None, 784) 0 \n", - " \n", - " dense (Dense) (None, 64) 50240 \n", - " \n", - " activation (Activation) (None, 64) 0 \n", - " \n", - " dense_1 (Dense) (None, 32) 2080 \n", - " \n", - " activation_1 (Activation) (None, 32) 0 \n", - " \n", - " dense_2 (Dense) (None, 10) 330 \n", - " \n", - "=================================================================\n", - "Total params: 52650 (205.66 KB)\n", - "Trainable params: 52650 (205.66 KB)\n", - "Non-trainable params: 0 (0.00 Byte)\n", - "_________________________________________________________________\n" + "2024-09-06 14:53:22.535221: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:485] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n", + "2024-09-06 14:53:22.546580: E external/local_xla/xla/stream_executor/cuda/cuda_dnn.cc:8454] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n", + "2024-09-06 14:53:22.550072: E external/local_xla/xla/stream_executor/cuda/cuda_blas.cc:1452] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n", + "2024-09-06 14:53:22.558872: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\n", + "To enable the following instructions: AVX2 FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\n", + "2024-09-06 14:53:23.838020: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT\n", + "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", + "I0000 00:00:1725627205.420866 863448 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\n", + "I0000 00:00:1725627205.447445 863448 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\n", + "I0000 00:00:1725627205.447576 863448 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\n", + "I0000 00:00:1725627205.448256 863448 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\n", + "I0000 00:00:1725627205.448417 863448 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\n", + "I0000 00:00:1725627205.448500 863448 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\n", + "I0000 00:00:1725627205.559400 863448 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\n", + "I0000 00:00:1725627205.559507 863448 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\n", + "I0000 00:00:1725627205.559591 863448 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\n", + "2024-09-06 14:53:25.559659: I tensorflow/core/common_runtime/gpu/gpu_device.cc:2021] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 6818 MB memory: -> device: 0, name: NVIDIA GeForce RTX 2070 SUPER, pci bus id: 0000:01:00.0, compute capability: 7.5\n" ] + }, + { + "data": { + "text/html": [ + "
Model: \"sequential\"\n",
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โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”ณโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”ณโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”“\n",
+       "โ”ƒ Layer (type)                    โ”ƒ Output Shape           โ”ƒ       Param # โ”ƒ\n",
+       "โ”กโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ•‡โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ•‡โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”ฉ\n",
+       "โ”‚ flatten (Flatten)               โ”‚ (None, 784)            โ”‚             0 โ”‚\n",
+       "โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค\n",
+       "โ”‚ dense (Dense)                   โ”‚ (None, 64)             โ”‚        50,240 โ”‚\n",
+       "โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค\n",
+       "โ”‚ activation (Activation)         โ”‚ (None, 64)             โ”‚             0 โ”‚\n",
+       "โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค\n",
+       "โ”‚ dense_1 (Dense)                 โ”‚ (None, 32)             โ”‚         2,080 โ”‚\n",
+       "โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค\n",
+       "โ”‚ activation_1 (Activation)       โ”‚ (None, 32)             โ”‚             0 โ”‚\n",
+       "โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค\n",
+       "โ”‚ dense_2 (Dense)                 โ”‚ (None, 10)             โ”‚           330 โ”‚\n",
+       "โ””โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”˜\n",
+       "
\n" + ], + "text/plain": [ + "โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”ณโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”ณโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”“\n", + "โ”ƒ\u001b[1m \u001b[0m\u001b[1mLayer (type) \u001b[0m\u001b[1m \u001b[0mโ”ƒ\u001b[1m \u001b[0m\u001b[1mOutput Shape \u001b[0m\u001b[1m \u001b[0mโ”ƒ\u001b[1m \u001b[0m\u001b[1m Param #\u001b[0m\u001b[1m \u001b[0mโ”ƒ\n", + "โ”กโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ•‡โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ•‡โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”ฉ\n", + "โ”‚ flatten (\u001b[38;5;33mFlatten\u001b[0m) โ”‚ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m784\u001b[0m) โ”‚ \u001b[38;5;34m0\u001b[0m โ”‚\n", + "โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค\n", + "โ”‚ dense (\u001b[38;5;33mDense\u001b[0m) โ”‚ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m64\u001b[0m) โ”‚ \u001b[38;5;34m50,240\u001b[0m โ”‚\n", + "โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค\n", + "โ”‚ activation (\u001b[38;5;33mActivation\u001b[0m) โ”‚ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m64\u001b[0m) โ”‚ \u001b[38;5;34m0\u001b[0m โ”‚\n", + "โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค\n", + "โ”‚ dense_1 (\u001b[38;5;33mDense\u001b[0m) โ”‚ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m32\u001b[0m) โ”‚ \u001b[38;5;34m2,080\u001b[0m โ”‚\n", + "โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค\n", + "โ”‚ activation_1 (\u001b[38;5;33mActivation\u001b[0m) โ”‚ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m32\u001b[0m) โ”‚ \u001b[38;5;34m0\u001b[0m โ”‚\n", + "โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค\n", + "โ”‚ dense_2 (\u001b[38;5;33mDense\u001b[0m) โ”‚ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m10\u001b[0m) โ”‚ \u001b[38;5;34m330\u001b[0m โ”‚\n", + "โ””โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”˜\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "
 Total params: 52,650 (205.66 KB)\n",
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 Trainable params: 52,650 (205.66 KB)\n",
+       "
\n" + ], + "text/plain": [ + "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m52,650\u001b[0m (205.66 KB)\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "
 Non-trainable params: 0 (0.00 B)\n",
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\n" + ], + "text/plain": [ + "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n" + ] + }, + "metadata": {}, + "output_type": "display_data" } ], "source": [ - "import tensorflow as tf\n", - "from tensorflow import keras\n", - "from tensorflow.keras import layers, Model\n", + "import keras\n", "\n", "input_shape = (28, 28, 1)\n", - "num_classes=10\n", + "num_classes = 10\n", "\n", "# a basic model that does not follow any Lipschitz constraint\n", - "model = keras.Sequential([\n", - " layers.Input(shape=input_shape),\n", - " layers.Flatten(),\n", - " layers.Dense(64),\n", - " layers.Activation('relu'),\n", - " layers.Dense(32),\n", - " layers.Activation('relu'),\n", - " layers.Dense(num_classes)\n", - " ])\n", + "model = keras.Sequential(\n", + " [\n", + " keras.Input(shape=input_shape),\n", + " keras.layers.Flatten(),\n", + " keras.layers.Dense(64),\n", + " keras.layers.Activation(\"relu\"),\n", + " keras.layers.Dense(32),\n", + " keras.layers.Activation(\"relu\"),\n", + " keras.layers.Dense(num_classes),\n", + " ]\n", + ")\n", "\n", "\n", - "model.compile(optimizer='adam',\n", - " loss=keras.losses.CategoricalCrossentropy(from_logits=True),\n", - " metrics=['accuracy'])\n", + "model.compile(\n", + " optimizer=\"adam\",\n", + " loss=keras.losses.CategoricalCrossentropy(from_logits=True),\n", + " metrics=[\"accuracy\"],\n", + ")\n", "\n", "model.summary()" ] @@ -133,7 +250,7 @@ "id": "718b53d4-5585-41d9-a301-0012c54dba0b", "metadata": {}, "source": [ - "Alternatively, it is equivalent to write:" + "Alternatively, it is equivalent to write:\n" ] }, { @@ -143,44 +260,112 @@ "metadata": {}, "outputs": [ { - "name": "stdout", - "output_type": "stream", - "text": [ - "Model: \"model\"\n", - "_________________________________________________________________\n", - " Layer (type) Output Shape Param # \n", - "=================================================================\n", - " input_2 (InputLayer) [(None, 28, 28, 1)] 0 \n", - " \n", - " flatten_1 (Flatten) (None, 784) 0 \n", - " \n", - " dense_3 (Dense) (None, 64) 50240 \n", - " \n", - " activation_2 (Activation) (None, 64) 0 \n", - " \n", - " dense_4 (Dense) (None, 32) 2080 \n", - " \n", - " activation_3 (Activation) (None, 32) 0 \n", - " \n", - " dense_5 (Dense) (None, 10) 330 \n", - " \n", - "=================================================================\n", - "Total params: 52650 (205.66 KB)\n", - "Trainable params: 52650 (205.66 KB)\n", - "Non-trainable params: 0 (0.00 Byte)\n", - "_________________________________________________________________\n" - ] + "data": { + "text/html": [ + "
Model: \"functional_1\"\n",
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โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”ณโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”ณโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”“\n",
+       "โ”ƒ Layer (type)                    โ”ƒ Output Shape           โ”ƒ       Param # โ”ƒ\n",
+       "โ”กโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ•‡โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ•‡โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”ฉ\n",
+       "โ”‚ input_layer_1 (InputLayer)      โ”‚ (None, 28, 28, 1)      โ”‚             0 โ”‚\n",
+       "โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค\n",
+       "โ”‚ flatten_1 (Flatten)             โ”‚ (None, 784)            โ”‚             0 โ”‚\n",
+       "โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค\n",
+       "โ”‚ dense_3 (Dense)                 โ”‚ (None, 64)             โ”‚        50,240 โ”‚\n",
+       "โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค\n",
+       "โ”‚ activation_2 (Activation)       โ”‚ (None, 64)             โ”‚             0 โ”‚\n",
+       "โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค\n",
+       "โ”‚ dense_4 (Dense)                 โ”‚ (None, 32)             โ”‚         2,080 โ”‚\n",
+       "โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค\n",
+       "โ”‚ activation_3 (Activation)       โ”‚ (None, 32)             โ”‚             0 โ”‚\n",
+       "โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค\n",
+       "โ”‚ dense_5 (Dense)                 โ”‚ (None, 10)             โ”‚           330 โ”‚\n",
+       "โ””โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”˜\n",
+       "
\n" + ], + "text/plain": [ + "โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”ณโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”ณโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”“\n", + "โ”ƒ\u001b[1m \u001b[0m\u001b[1mLayer (type) \u001b[0m\u001b[1m \u001b[0mโ”ƒ\u001b[1m \u001b[0m\u001b[1mOutput Shape \u001b[0m\u001b[1m \u001b[0mโ”ƒ\u001b[1m \u001b[0m\u001b[1m Param #\u001b[0m\u001b[1m \u001b[0mโ”ƒ\n", + "โ”กโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ•‡โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ•‡โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”ฉ\n", + "โ”‚ input_layer_1 (\u001b[38;5;33mInputLayer\u001b[0m) โ”‚ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m1\u001b[0m) โ”‚ \u001b[38;5;34m0\u001b[0m โ”‚\n", + "โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค\n", + "โ”‚ flatten_1 (\u001b[38;5;33mFlatten\u001b[0m) โ”‚ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m784\u001b[0m) โ”‚ \u001b[38;5;34m0\u001b[0m โ”‚\n", + "โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค\n", + "โ”‚ dense_3 (\u001b[38;5;33mDense\u001b[0m) โ”‚ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m64\u001b[0m) โ”‚ \u001b[38;5;34m50,240\u001b[0m โ”‚\n", + "โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค\n", + "โ”‚ activation_2 (\u001b[38;5;33mActivation\u001b[0m) โ”‚ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m64\u001b[0m) โ”‚ \u001b[38;5;34m0\u001b[0m โ”‚\n", + "โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค\n", + "โ”‚ dense_4 (\u001b[38;5;33mDense\u001b[0m) โ”‚ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m32\u001b[0m) โ”‚ \u001b[38;5;34m2,080\u001b[0m โ”‚\n", + "โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค\n", + "โ”‚ activation_3 (\u001b[38;5;33mActivation\u001b[0m) โ”‚ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m32\u001b[0m) โ”‚ \u001b[38;5;34m0\u001b[0m โ”‚\n", + "โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค\n", + "โ”‚ dense_5 (\u001b[38;5;33mDense\u001b[0m) โ”‚ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m10\u001b[0m) โ”‚ \u001b[38;5;34m330\u001b[0m โ”‚\n", + "โ””โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”˜\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "
 Total params: 52,650 (205.66 KB)\n",
+       "
\n" + ], + "text/plain": [ + "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m52,650\u001b[0m (205.66 KB)\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "
 Trainable params: 52,650 (205.66 KB)\n",
+       "
\n" + ], + "text/plain": [ + "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m52,650\u001b[0m (205.66 KB)\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "
 Non-trainable params: 0 (0.00 B)\n",
+       "
\n" + ], + "text/plain": [ + "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n" + ] + }, + "metadata": {}, + "output_type": "display_data" } ], "source": [ - "inputs = keras.layers.Input(input_shape)\n", + "inputs = keras.Input(input_shape)\n", "x = keras.layers.Flatten()(inputs)\n", - "x = layers.Dense(64)(x)\n", - "x = layers.Activation('relu')(x)\n", - "x = layers.Dense(32)(x)\n", - "x = layers.Activation('relu')(x)\n", - "y = layers.Dense(num_classes)(x)\n", - "model = Model(inputs=inputs, outputs=y)\n", + "x = keras.layers.Dense(64)(x)\n", + "x = keras.layers.Activation(\"relu\")(x)\n", + "x = keras.layers.Dense(32)(x)\n", + "x = keras.layers.Activation(\"relu\")(x)\n", + "y = keras.layers.Dense(num_classes)(x)\n", + "model = keras.Model(inputs=inputs, outputs=y)\n", "model.summary()" ] }, @@ -189,9 +374,11 @@ "id": "e0f72425", "metadata": {}, "source": [ - "`deel-lip` extends `keras`' capabilities by introducing custom `layers` and `model` modules, to provide the ability to control the Lipschitz constant of layers objects or of complete neural networks, while keeping a user-friendly interface.\n", + "`deel-lip` extends `keras`' capabilities by introducing custom `layers` and `model`\n", + "modules, to provide the ability to control the Lipschitz constant of layers objects or\n", + "of complete neural networks, while keeping a user-friendly interface.\n", "\n", - "Below is a 1-Lipschitz replication of the previous MLP toy-example, using `deel-lip`:" + "Below is a 1-Lipschitz replication of the previous MLP toy-example, using `deel-lip`:\n" ] }, { @@ -201,7 +388,6 @@ "metadata": {}, "outputs": [], "source": [ - "import deel\n", "from deel import lip" ] }, @@ -212,60 +398,121 @@ "metadata": {}, "outputs": [ { - "name": "stdout", - "output_type": "stream", - "text": [ - "Model: \"sequential_1\"\n", - "_________________________________________________________________\n", - " Layer (type) Output Shape Param # \n", - "=================================================================\n", - " flatten_2 (Flatten) (None, 784) 0 \n", - " \n", - " spectral_dense (SpectralDe (None, 64) 100481 \n", - " nse) \n", - " \n", - " group_sort2 (GroupSort2) (None, 64) 0 \n", - " \n", - " spectral_dense_1 (Spectral (None, 32) 4161 \n", - " Dense) \n", - " \n", - " group_sort2_1 (GroupSort2) (None, 32) 0 \n", - " \n", - " spectral_dense_2 (Spectral (None, 10) 661 \n", - " Dense) \n", - " \n", - "=================================================================\n", - "Total params: 105303 (411.34 KB)\n", - "Trainable params: 52650 (205.66 KB)\n", - "Non-trainable params: 52653 (205.68 KB)\n", - "_________________________________________________________________\n" - ] + "data": { + "text/html": [ + "
Model: \"sequential_1\"\n",
+       "
\n" + ], + "text/plain": [ + "\u001b[1mModel: \"sequential_1\"\u001b[0m\n" + ] + }, + "metadata": {}, + "output_type": "display_data" }, { - "name": "stderr", - "output_type": "stream", - "text": [ - "C:\\Users\\kierszbaums\\anaconda.related\\envs\\1_lipschitz\\deel_lip\\lib\\site-packages\\keras\\src\\initializers\\initializers.py:120: UserWarning: The initializer Orthogonal is unseeded and being called multiple times, which will return identical values each time (even if the initializer is unseeded). Please update your code to provide a seed to the initializer, or avoid using the same initializer instance more than once.\n", - " warnings.warn(\n" - ] + "data": { + "text/html": [ + "
โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”ณโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”ณโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”“\n",
+       "โ”ƒ Layer (type)                    โ”ƒ Output Shape           โ”ƒ       Param # โ”ƒ\n",
+       "โ”กโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ•‡โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ•‡โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”ฉ\n",
+       "โ”‚ flatten_2 (Flatten)             โ”‚ (None, 784)            โ”‚             0 โ”‚\n",
+       "โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค\n",
+       "โ”‚ spectral_dense (SpectralDense)  โ”‚ (None, 64)             โ”‚       100,481 โ”‚\n",
+       "โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค\n",
+       "โ”‚ group_sort2 (GroupSort2)        โ”‚ (None, 64)             โ”‚             0 โ”‚\n",
+       "โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค\n",
+       "โ”‚ spectral_dense_1                โ”‚ (None, 32)             โ”‚         4,161 โ”‚\n",
+       "โ”‚ (SpectralDense)                 โ”‚                        โ”‚               โ”‚\n",
+       "โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค\n",
+       "โ”‚ group_sort2_1 (GroupSort2)      โ”‚ (None, 32)             โ”‚             0 โ”‚\n",
+       "โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค\n",
+       "โ”‚ spectral_dense_2                โ”‚ (None, 10)             โ”‚           661 โ”‚\n",
+       "โ”‚ (SpectralDense)                 โ”‚                        โ”‚               โ”‚\n",
+       "โ””โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”˜\n",
+       "
\n" + ], + "text/plain": [ + "โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”ณโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”ณโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”“\n", + "โ”ƒ\u001b[1m \u001b[0m\u001b[1mLayer (type) \u001b[0m\u001b[1m \u001b[0mโ”ƒ\u001b[1m \u001b[0m\u001b[1mOutput Shape \u001b[0m\u001b[1m \u001b[0mโ”ƒ\u001b[1m \u001b[0m\u001b[1m Param #\u001b[0m\u001b[1m \u001b[0mโ”ƒ\n", + "โ”กโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ•‡โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ•‡โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”ฉ\n", + "โ”‚ flatten_2 (\u001b[38;5;33mFlatten\u001b[0m) โ”‚ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m784\u001b[0m) โ”‚ \u001b[38;5;34m0\u001b[0m โ”‚\n", + "โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค\n", + "โ”‚ spectral_dense (\u001b[38;5;33mSpectralDense\u001b[0m) โ”‚ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m64\u001b[0m) โ”‚ \u001b[38;5;34m100,481\u001b[0m โ”‚\n", + "โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค\n", + "โ”‚ group_sort2 (\u001b[38;5;33mGroupSort2\u001b[0m) โ”‚ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m64\u001b[0m) โ”‚ \u001b[38;5;34m0\u001b[0m โ”‚\n", + "โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค\n", + "โ”‚ spectral_dense_1 โ”‚ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m32\u001b[0m) โ”‚ \u001b[38;5;34m4,161\u001b[0m โ”‚\n", + "โ”‚ (\u001b[38;5;33mSpectralDense\u001b[0m) โ”‚ โ”‚ โ”‚\n", + "โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค\n", + "โ”‚ group_sort2_1 (\u001b[38;5;33mGroupSort2\u001b[0m) โ”‚ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m32\u001b[0m) โ”‚ \u001b[38;5;34m0\u001b[0m โ”‚\n", + "โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค\n", + "โ”‚ spectral_dense_2 โ”‚ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m10\u001b[0m) โ”‚ \u001b[38;5;34m661\u001b[0m โ”‚\n", + "โ”‚ (\u001b[38;5;33mSpectralDense\u001b[0m) โ”‚ โ”‚ โ”‚\n", + "โ””โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”˜\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "
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 Trainable params: 0 (0.00 B)\n",
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 Non-trainable params: 105,303 (411.34 KB)\n",
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Model: \"model\"\n",
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โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”ณโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”ณโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”“\n",
+       "โ”ƒ Layer (type)                    โ”ƒ Output Shape           โ”ƒ       Param # โ”ƒ\n",
+       "โ”กโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ•‡โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ•‡โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”ฉ\n",
+       "โ”‚ input_layer_3 (InputLayer)      โ”‚ (None, 28, 28, 1)      โ”‚             0 โ”‚\n",
+       "โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค\n",
+       "โ”‚ flatten_3 (Flatten)             โ”‚ (None, 784)            โ”‚             0 โ”‚\n",
+       "โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค\n",
+       "โ”‚ spectral_dense_3                โ”‚ (None, 64)             โ”‚       100,481 โ”‚\n",
+       "โ”‚ (SpectralDense)                 โ”‚                        โ”‚               โ”‚\n",
+       "โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค\n",
+       "โ”‚ group_sort2_2 (GroupSort2)      โ”‚ (None, 64)             โ”‚             0 โ”‚\n",
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+       "โ”‚ spectral_dense_4                โ”‚ (None, 32)             โ”‚         4,161 โ”‚\n",
+       "โ”‚ (SpectralDense)                 โ”‚                        โ”‚               โ”‚\n",
+       "โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค\n",
+       "โ”‚ group_sort2_3 (GroupSort2)      โ”‚ (None, 32)             โ”‚             0 โ”‚\n",
+       "โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค\n",
+       "โ”‚ spectral_dense_5                โ”‚ (None, 10)             โ”‚           661 โ”‚\n",
+       "โ”‚ (SpectralDense)                 โ”‚                        โ”‚               โ”‚\n",
+       "โ””โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”˜\n",
+       "
\n" + ], + "text/plain": [ + "โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”ณโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”ณโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”“\n", + "โ”ƒ\u001b[1m \u001b[0m\u001b[1mLayer (type) \u001b[0m\u001b[1m \u001b[0mโ”ƒ\u001b[1m \u001b[0m\u001b[1mOutput Shape \u001b[0m\u001b[1m \u001b[0mโ”ƒ\u001b[1m \u001b[0m\u001b[1m Param #\u001b[0m\u001b[1m \u001b[0mโ”ƒ\n", + "โ”กโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ•‡โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ•‡โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”ฉ\n", + "โ”‚ input_layer_3 (\u001b[38;5;33mInputLayer\u001b[0m) โ”‚ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m1\u001b[0m) โ”‚ \u001b[38;5;34m0\u001b[0m โ”‚\n", + "โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค\n", + "โ”‚ flatten_3 (\u001b[38;5;33mFlatten\u001b[0m) โ”‚ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m784\u001b[0m) โ”‚ \u001b[38;5;34m0\u001b[0m โ”‚\n", + "โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค\n", + "โ”‚ spectral_dense_3 โ”‚ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m64\u001b[0m) โ”‚ \u001b[38;5;34m100,481\u001b[0m โ”‚\n", + "โ”‚ (\u001b[38;5;33mSpectralDense\u001b[0m) โ”‚ โ”‚ โ”‚\n", + "โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค\n", + "โ”‚ group_sort2_2 (\u001b[38;5;33mGroupSort2\u001b[0m) โ”‚ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m64\u001b[0m) โ”‚ \u001b[38;5;34m0\u001b[0m โ”‚\n", + "โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค\n", + "โ”‚ spectral_dense_4 โ”‚ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m32\u001b[0m) โ”‚ \u001b[38;5;34m4,161\u001b[0m โ”‚\n", + "โ”‚ (\u001b[38;5;33mSpectralDense\u001b[0m) โ”‚ โ”‚ โ”‚\n", + "โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค\n", + "โ”‚ group_sort2_3 (\u001b[38;5;33mGroupSort2\u001b[0m) โ”‚ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m32\u001b[0m) โ”‚ \u001b[38;5;34m0\u001b[0m โ”‚\n", + "โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค\n", + "โ”‚ spectral_dense_5 โ”‚ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m10\u001b[0m) โ”‚ \u001b[38;5;34m661\u001b[0m โ”‚\n", + "โ”‚ (\u001b[38;5;33mSpectralDense\u001b[0m) โ”‚ โ”‚ โ”‚\n", + "โ””โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”˜\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "
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\n" + ], + "text/plain": [ + "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m52,653\u001b[0m (205.68 KB)\n" + ] + }, + "metadata": {}, + "output_type": "display_data" } ], "source": [ - "inputs = keras.layers.Input(input_shape)\n", + "inputs = keras.Input(input_shape)\n", "x = keras.layers.Flatten()(inputs)\n", "x = lip.layers.SpectralDense(64)(x)\n", "x = lip.layers.GroupSort2()(x)\n", @@ -336,37 +654,49 @@ "id": "f6c8046c-115a-4fea-b36e-16b05a811897", "metadata": {}, "source": [ - "๐Ÿ’ก\n", - "Keep in mind that all the classes above inherit from their respective `keras` equivalent (e.g. `Dense` for `SpectralDense`).
\n", - "As a result, these objects conveniently use the same interface and the same parameters as their keras equivalent.\n", + "๐Ÿ’ก Keep in mind that all the classes above inherit from their respective `keras`\n", + "equivalent (e.g. `Dense` for `SpectralDense`).
As a result, these objects\n", + "conveniently use the same interface and the same parameters as their keras equivalent.\n", + "\n", + "## ๐Ÿ”จ Design rules for 1-Lipschitz neural networks with `deel-lip` \n", "\n", - "## ๐Ÿ”จ Design rules for 1-Lipschitz neural networks with `deel-lip` \n", "**Layer selection: `deel-lip` vs `keras`** \n", - "
\n", - "In our 1-Lipschitz MLP examples above, we have used a mixture of objects from both `keras` and `deel-lip` `layers` submodule (e.g. the `Input` layer for `keras`, the `SpectralDense` layer for `deel-lip`).\n", + "
In our 1-Lipschitz MLP examples above, we have used a mixture of objects from both\n", + "`keras` and `deel-lip` `layers` submodule (e.g. the `Input` layer for `keras`, the\n", + "`SpectralDense` layer for `deel-lip`).\n", "\n", - "More generally, for the particular types of layers that do not interfere with the Lipschitz property of any neural network they belong to, no alternative has been coded in `deel-lip` and the existing `keras` layer object can be used. \n", + "More generally, for the particular types of layers that do not interfere with the\n", + "Lipschitz property of any neural network they belong to, no alternative has been coded\n", + "in `deel-lip` and the existing `keras` layer object can be used.\n", "\n", - "This is the case for the following keras layers: `MaxPooling`, `GlobalMaxPooling`, `Flatten` and `Input`.\n", + "This is the case for the following keras layers: `MaxPooling`, `GlobalMaxPooling`,\n", + "`Flatten` and `Input`.\n", "\n", - "Below is the full list of `keras` layers for which `deel-lip` provides a Lipschitz equivalent. If one wants to ensure a model's Lipschitz continuity, the alternative `deel-lip` layers must be employed instead of the original `keras` counterparts.\n", + "Below is the full list of `keras` layers for which `deel-lip` provides a Lipschitz\n", + "equivalent. If one wants to ensure a model's Lipschitz continuity, the alternative\n", + "`deel-lip` layers must be employed instead of the original `keras` counterparts.\n", "\n", - "| tensorflow.keras.layers | deel.lip.layers |\n", - "| --------------- | --------------- |\n", - "| `Dense` | `SpectralDense`
|\n", - "| `Conv2D` | `SpectralConv2D`
|\n", - "| `AveragePooling2D`
`GlobalAveragePooling2D` | `ScaledAveragePooling2D`
`ScaledGlobalAveragePooling2D`|\n", + "| keras.layers | deel.lip.layers |\n", + "| ---------------------------------------------- | ---------------------------------------------------------- |\n", + "| `Dense` | `SpectralDense`
|\n", + "| `Conv2D` | `SpectralConv2D`
|\n", + "| `AveragePooling2D`
`GlobalAveragePooling2D` | `ScaledAveragePooling2D`
`ScaledGlobalAveragePooling2D` |\n", "\n", "
\n", "\n", - "๐Ÿ’ก Although there are additional Lipschitz continuous layers available in `deel-lip`, the ones mentioned above are perfectly suitable and recommended for practical use. Interested readers can find information about the other layers [here](https://deel-ai.github.io/deel-lip/api/layers/).\n", + "๐Ÿ’ก Although there are additional Lipschitz continuous layers available in `deel-lip`,\n", + "the ones mentioned above are perfectly suitable and recommended for practical use.\n", + "Interested readers can find information about the other layers\n", + "[here](https://deel-ai.github.io/deel-lip/api/layers/).\n", "\n", - "
\n", + "
\n", "\n", + "๐Ÿšจ **Note:** _When creating a 1-Lipschitz neural network, one should avoid using the\n", + "following layers:_
\n", "\n", - "๐Ÿšจ **Note:** *When creating a 1-Lipschitz neural network, one should avoid using the following layers:*
\n", - "- `Dropout`: Our current recommendation is to avoid using it, since it can induce a modification of the Lipschitz constant of the model.\n", - "- `BatchNormalization`: It is not 1-Lipschitz" + "- `Dropout`: Our current recommendation is to avoid using it, since it can induce a\n", + " modification of the Lipschitz constant of the model.\n", + "- `BatchNormalization`: It is not 1-Lipschitz\n" ] }, { @@ -374,21 +704,26 @@ "id": "e33f0f4f-16de-4172-a4e9-b4798f0bd678", "metadata": {}, "source": [ - "\n", "**Activation function selection:**\n", "\n", - "The ReLU activation function is Lipschitz continuous with a Lipschtiz constant of 1. \n", - "\n", - "However, the 'GroupSort2' activation function provided in the `layers` submodule of `deel-lip` has additional properties that can enhance the adversarial robustness of 1-Lipschitz neural networks.\n", + "The ReLU activation function is Lipschitz continuous with a Lipschtiz constant of 1.\n", "\n", - "๐Ÿ’ก Interested readers can find information relevant to other 1-Lipschitz activation functions that exist within `deel-lip` [here](https://deel-ai.github.io/deel-lip/api/layers/).\n", + "However, the 'GroupSort2' activation function provided in the `layers` submodule of\n", + "`deel-lip` has additional properties that can enhance the adversarial robustness of\n", + "1-Lipschitz neural networks.\n", "\n", + "๐Ÿ’ก Interested readers can find information relevant to other 1-Lipschitz activation\n", + "functions that exist within `deel-lip`\n", + "[here](https://deel-ai.github.io/deel-lip/api/layers/).\n", "\n", "**Loss function selection:**\n", "\n", - "One can use `keras` loss functions to train 1-Lipschitz neural networks. Doing so will not interfere with the 1-Lipschitz continuity of the model. \n", + "One can use `keras` loss functions to train 1-Lipschitz neural networks. Doing so will\n", + "not interfere with the 1-Lipschitz continuity of the model.\n", "\n", - "๐Ÿ’ก `deel-lip` also has a `losses` submodule that contains several loss functions. They have been developed to enhance the adversarial robustness of the learnt 1-Lipschitz models.\n" + "๐Ÿ’ก `deel-lip` also has a `losses` submodule that contains several loss functions. They\n", + "have been developed to enhance the adversarial robustness of the learnt 1-Lipschitz\n", + "models.\n" ] }, { @@ -397,9 +732,12 @@ "metadata": {}, "source": [ "## ๐ŸŽ‰ Congratulations\n", + "\n", "You now know how to create 1-Lipschitz neural networks!\n", "\n", - "In the next tutorial, we will see how to train and assess adversarially robust 1-Lipschitz neural networks on the classification task, using `deel-lip`'s `losses` submodule." + "In the next tutorial, we will see how to train and assess adversarially robust\n", + "1-Lipschitz neural networks on the classification task, using `deel-lip`'s `losses`\n", + "submodule.\n" ] } ], diff --git a/docs/notebooks/Getting_started_2.ipynb b/docs/notebooks/Getting_started_2.ipynb index 35c978a3..a47e7c6b 100644 --- a/docs/notebooks/Getting_started_2.ipynb +++ b/docs/notebooks/Getting_started_2.ipynb @@ -152,15 +152,24 @@ "execution_count": 1, "id": "d3cf25c6-1691-4935-bdac-9f182a87ef32", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2024-09-06 14:43:55.795266: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:485] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n", + "2024-09-06 14:43:55.806663: E external/local_xla/xla/stream_executor/cuda/cuda_dnn.cc:8454] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n", + "2024-09-06 14:43:55.810099: E external/local_xla/xla/stream_executor/cuda/cuda_blas.cc:1452] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n" + ] + } + ], "source": [ "import os\n", "\n", "os.environ[\"TF_CPP_MIN_LOG_LEVEL\"] = \"2\"\n", "\n", - "import tensorflow as tf\n", - "from tensorflow.keras.datasets import mnist\n", - "from tensorflow.keras.utils import to_categorical\n", + "from keras.datasets import mnist\n", + "from keras.utils import to_categorical\n", "import numpy as np" ] }, @@ -219,8 +228,8 @@ "source": [ "from deel import lip\n", "\n", - "from tensorflow.keras.optimizers import Adam\n", - "from tensorflow.keras.layers import Input, Flatten\n", + "from keras.optimizers import Adam\n", + "from keras.layers import Input, Flatten\n", "\n", "\n", "def create_conv_model(name_model, input_shape, output_shape):\n", @@ -311,10 +320,27 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 5, "id": "fc8a3115-ccb4-4ce3-86a2-b3c0a22866b3", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", + "I0000 00:00:1725626638.749793 861729 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\n", + "I0000 00:00:1725626638.807073 861729 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\n", + "I0000 00:00:1725626638.807244 861729 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\n", + "I0000 00:00:1725626638.808134 861729 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\n", + "I0000 00:00:1725626638.808272 861729 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\n", + "I0000 00:00:1725626638.808386 861729 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\n", + "I0000 00:00:1725626638.911081 861729 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\n", + "I0000 00:00:1725626638.911192 861729 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\n", + "I0000 00:00:1725626638.911277 861729 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\n" + ] + } + ], "source": [ "# high-temperature model\n", "model_1 = create_conv_model(\"cross_entropy_model_1\", input_shape, output_shape)\n", @@ -386,26 +412,55 @@ "name": "stdout", "output_type": "stream", "text": [ - "Epoch 1/10\n", - "235/235 [==============================] - 6s 10ms/step - loss: 0.0104 - accuracy: 0.7916 - CategoricalProvableAvgRobustness: 0.0290 - val_loss: 0.0028 - val_accuracy: 0.9184 - val_CategoricalProvableAvgRobustness: 0.0392\n", + "Epoch 1/10\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", + "I0000 00:00:1725626640.988188 861770 service.cc:146] XLA service 0x56206d84bc70 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\n", + "I0000 00:00:1725626640.988206 861770 service.cc:154] StreamExecutor device (0): NVIDIA GeForce RTX 2070 SUPER, Compute Capability 7.5\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[1m 29/235\u001b[0m \u001b[32mโ”โ”\u001b[0m\u001b[37mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m \u001b[1m1s\u001b[0m 6ms/step - CategoricalProvableAvgRobustness: 0.0039 - accuracy: 0.1687 - loss: 0.0997" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "I0000 00:00:1725626645.181716 861770 device_compiler.h:188] Compiled cluster using XLA! This line is logged at most once for the lifetime of the process.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[1m235/235\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m14s\u001b[0m 33ms/step - CategoricalProvableAvgRobustness: 0.0171 - accuracy: 0.5744 - loss: 0.0324 - val_CategoricalProvableAvgRobustness: 0.0372 - val_accuracy: 0.9136 - val_loss: 0.0029\n", "Epoch 2/10\n", - "235/235 [==============================] - 2s 8ms/step - loss: 0.0024 - accuracy: 0.9291 - CategoricalProvableAvgRobustness: 0.0414 - val_loss: 0.0019 - val_accuracy: 0.9443 - val_CategoricalProvableAvgRobustness: 0.0426\n", + "\u001b[1m235/235\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 12ms/step - CategoricalProvableAvgRobustness: 0.0379 - accuracy: 0.9183 - loss: 0.0027 - val_CategoricalProvableAvgRobustness: 0.0434 - val_accuracy: 0.9462 - val_loss: 0.0018\n", "Epoch 3/10\n", - "235/235 [==============================] - 2s 8ms/step - loss: 0.0017 - accuracy: 0.9491 - CategoricalProvableAvgRobustness: 0.0451 - val_loss: 0.0014 - val_accuracy: 0.9574 - val_CategoricalProvableAvgRobustness: 0.0471\n", + "\u001b[1m235/235\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 11ms/step - CategoricalProvableAvgRobustness: 0.0438 - accuracy: 0.9464 - loss: 0.0018 - val_CategoricalProvableAvgRobustness: 0.0468 - val_accuracy: 0.9563 - val_loss: 0.0014\n", "Epoch 4/10\n", - "235/235 [==============================] - 2s 7ms/step - loss: 0.0013 - accuracy: 0.9592 - CategoricalProvableAvgRobustness: 0.0481 - val_loss: 0.0011 - val_accuracy: 0.9658 - val_CategoricalProvableAvgRobustness: 0.0512\n", + "\u001b[1m235/235\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 12ms/step - CategoricalProvableAvgRobustness: 0.0473 - accuracy: 0.9587 - loss: 0.0013 - val_CategoricalProvableAvgRobustness: 0.0517 - val_accuracy: 0.9665 - val_loss: 0.0011\n", "Epoch 5/10\n", - "235/235 [==============================] - 2s 7ms/step - loss: 0.0011 - accuracy: 0.9668 - CategoricalProvableAvgRobustness: 0.0503 - val_loss: 9.6942e-04 - val_accuracy: 0.9701 - val_CategoricalProvableAvgRobustness: 0.0520\n", + "\u001b[1m235/235\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 11ms/step - CategoricalProvableAvgRobustness: 0.0502 - accuracy: 0.9652 - loss: 0.0011 - val_CategoricalProvableAvgRobustness: 0.0549 - val_accuracy: 0.9681 - val_loss: 0.0011\n", "Epoch 6/10\n", - "235/235 [==============================] - 2s 7ms/step - loss: 9.1108e-04 - accuracy: 0.9722 - CategoricalProvableAvgRobustness: 0.0524 - val_loss: 8.7599e-04 - val_accuracy: 0.9728 - val_CategoricalProvableAvgRobustness: 0.0536\n", + "\u001b[1m235/235\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 12ms/step - CategoricalProvableAvgRobustness: 0.0526 - accuracy: 0.9707 - loss: 9.5057e-04 - val_CategoricalProvableAvgRobustness: 0.0536 - val_accuracy: 0.9708 - val_loss: 9.8418e-04\n", "Epoch 7/10\n", - "235/235 [==============================] - 2s 7ms/step - loss: 8.5019e-04 - accuracy: 0.9736 - CategoricalProvableAvgRobustness: 0.0545 - val_loss: 8.2655e-04 - val_accuracy: 0.9739 - val_CategoricalProvableAvgRobustness: 0.0561\n", + "\u001b[1m235/235\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 12ms/step - CategoricalProvableAvgRobustness: 0.0548 - accuracy: 0.9732 - loss: 8.6269e-04 - val_CategoricalProvableAvgRobustness: 0.0587 - val_accuracy: 0.9746 - val_loss: 8.5407e-04\n", "Epoch 8/10\n", - "235/235 [==============================] - 2s 7ms/step - loss: 7.4599e-04 - accuracy: 0.9764 - CategoricalProvableAvgRobustness: 0.0565 - val_loss: 6.7749e-04 - val_accuracy: 0.9784 - val_CategoricalProvableAvgRobustness: 0.0597\n", + "\u001b[1m235/235\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 11ms/step - CategoricalProvableAvgRobustness: 0.0568 - accuracy: 0.9782 - loss: 7.1723e-04 - val_CategoricalProvableAvgRobustness: 0.0578 - val_accuracy: 0.9781 - val_loss: 6.8528e-04\n", "Epoch 9/10\n", - "235/235 [==============================] - 2s 7ms/step - loss: 6.4887e-04 - accuracy: 0.9799 - CategoricalProvableAvgRobustness: 0.0579 - val_loss: 7.2717e-04 - val_accuracy: 0.9774 - val_CategoricalProvableAvgRobustness: 0.0604\n", + "\u001b[1m235/235\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 13ms/step - CategoricalProvableAvgRobustness: 0.0578 - accuracy: 0.9790 - loss: 6.8535e-04 - val_CategoricalProvableAvgRobustness: 0.0614 - val_accuracy: 0.9781 - val_loss: 6.6407e-04\n", "Epoch 10/10\n", - "235/235 [==============================] - 2s 7ms/step - loss: 5.9098e-04 - accuracy: 0.9818 - CategoricalProvableAvgRobustness: 0.0597 - val_loss: 6.2794e-04 - val_accuracy: 0.9806 - val_CategoricalProvableAvgRobustness: 0.0623\n" + "\u001b[1m235/235\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 12ms/step - CategoricalProvableAvgRobustness: 0.0598 - accuracy: 0.9812 - loss: 6.0078e-04 - val_CategoricalProvableAvgRobustness: 0.0616 - val_accuracy: 0.9771 - val_loss: 7.0066e-04\n" ] } ], @@ -433,25 +488,25 @@ "output_type": "stream", "text": [ "Epoch 1/10\n", - "235/235 [==============================] - 4s 10ms/step - loss: 0.3420 - accuracy: 0.7768 - CategoricalProvableAvgRobustness: 0.2767 - val_loss: 0.1553 - val_accuracy: 0.9009 - val_CategoricalProvableAvgRobustness: 0.4921\n", + "\u001b[1m235/235\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 29ms/step - CategoricalProvableAvgRobustness: 0.1262 - accuracy: 0.5936 - loss: 0.5300 - val_CategoricalProvableAvgRobustness: 0.4868 - val_accuracy: 0.8975 - val_loss: 0.1610\n", "Epoch 2/10\n", - "235/235 [==============================] - 2s 8ms/step - loss: 0.1320 - accuracy: 0.9126 - CategoricalProvableAvgRobustness: 0.5823 - val_loss: 0.1054 - val_accuracy: 0.9314 - val_CategoricalProvableAvgRobustness: 0.6599\n", + "\u001b[1m235/235\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 13ms/step - CategoricalProvableAvgRobustness: 0.5284 - accuracy: 0.8992 - loss: 0.1494 - val_CategoricalProvableAvgRobustness: 0.6304 - val_accuracy: 0.9281 - val_loss: 0.1094\n", "Epoch 3/10\n", - "235/235 [==============================] - 2s 8ms/step - loss: 0.1022 - accuracy: 0.9321 - CategoricalProvableAvgRobustness: 0.6845 - val_loss: 0.0880 - val_accuracy: 0.9437 - val_CategoricalProvableAvgRobustness: 0.7208\n", + "\u001b[1m235/235\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 13ms/step - CategoricalProvableAvgRobustness: 0.6543 - accuracy: 0.9276 - loss: 0.1093 - val_CategoricalProvableAvgRobustness: 0.7091 - val_accuracy: 0.9408 - val_loss: 0.0918\n", "Epoch 4/10\n", - "235/235 [==============================] - 2s 8ms/step - loss: 0.0892 - accuracy: 0.9419 - CategoricalProvableAvgRobustness: 0.7354 - val_loss: 0.0794 - val_accuracy: 0.9481 - val_CategoricalProvableAvgRobustness: 0.7705\n", + "\u001b[1m235/235\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 12ms/step - CategoricalProvableAvgRobustness: 0.7103 - accuracy: 0.9360 - loss: 0.0948 - val_CategoricalProvableAvgRobustness: 0.7465 - val_accuracy: 0.9496 - val_loss: 0.0813\n", "Epoch 5/10\n", - "235/235 [==============================] - 2s 8ms/step - loss: 0.0814 - accuracy: 0.9481 - CategoricalProvableAvgRobustness: 0.7680 - val_loss: 0.0734 - val_accuracy: 0.9548 - val_CategoricalProvableAvgRobustness: 0.7979\n", + "\u001b[1m235/235\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 14ms/step - CategoricalProvableAvgRobustness: 0.7493 - accuracy: 0.9453 - loss: 0.0846 - val_CategoricalProvableAvgRobustness: 0.7819 - val_accuracy: 0.9536 - val_loss: 0.0757\n", "Epoch 6/10\n", - "235/235 [==============================] - 2s 8ms/step - loss: 0.0761 - accuracy: 0.9522 - CategoricalProvableAvgRobustness: 0.7906 - val_loss: 0.0684 - val_accuracy: 0.9588 - val_CategoricalProvableAvgRobustness: 0.8190\n", + "\u001b[1m235/235\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 12ms/step - CategoricalProvableAvgRobustness: 0.7698 - accuracy: 0.9485 - loss: 0.0802 - val_CategoricalProvableAvgRobustness: 0.8100 - val_accuracy: 0.9574 - val_loss: 0.0704\n", "Epoch 7/10\n", - "235/235 [==============================] - 2s 8ms/step - loss: 0.0721 - accuracy: 0.9553 - CategoricalProvableAvgRobustness: 0.8094 - val_loss: 0.0645 - val_accuracy: 0.9618 - val_CategoricalProvableAvgRobustness: 0.8292\n", + "\u001b[1m235/235\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 13ms/step - CategoricalProvableAvgRobustness: 0.7980 - accuracy: 0.9527 - loss: 0.0740 - val_CategoricalProvableAvgRobustness: 0.8185 - val_accuracy: 0.9588 - val_loss: 0.0666\n", "Epoch 8/10\n", - "235/235 [==============================] - 2s 8ms/step - loss: 0.0691 - accuracy: 0.9577 - CategoricalProvableAvgRobustness: 0.8233 - val_loss: 0.0631 - val_accuracy: 0.9599 - val_CategoricalProvableAvgRobustness: 0.8397\n", + "\u001b[1m235/235\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 14ms/step - CategoricalProvableAvgRobustness: 0.8132 - accuracy: 0.9565 - loss: 0.0697 - val_CategoricalProvableAvgRobustness: 0.8470 - val_accuracy: 0.9627 - val_loss: 0.0638\n", "Epoch 9/10\n", - "235/235 [==============================] - 2s 8ms/step - loss: 0.0666 - accuracy: 0.9593 - CategoricalProvableAvgRobustness: 0.8341 - val_loss: 0.0603 - val_accuracy: 0.9645 - val_CategoricalProvableAvgRobustness: 0.8583\n", + "\u001b[1m235/235\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 12ms/step - CategoricalProvableAvgRobustness: 0.8238 - accuracy: 0.9580 - loss: 0.0685 - val_CategoricalProvableAvgRobustness: 0.8516 - val_accuracy: 0.9615 - val_loss: 0.0610\n", "Epoch 10/10\n", - "235/235 [==============================] - 2s 8ms/step - loss: 0.0648 - accuracy: 0.9608 - CategoricalProvableAvgRobustness: 0.8431 - val_loss: 0.0602 - val_accuracy: 0.9621 - val_CategoricalProvableAvgRobustness: 0.8598\n" + "\u001b[1m235/235\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 11ms/step - CategoricalProvableAvgRobustness: 0.8428 - accuracy: 0.9605 - loss: 0.0653 - val_CategoricalProvableAvgRobustness: 0.8490 - val_accuracy: 0.9639 - val_loss: 0.0592\n" ] } ], @@ -478,8 +533,8 @@ "name": "stdout", "output_type": "stream", "text": [ - "Model accuracy: 0.9806\n", - "Model's mean certificate: 0.0623\n", + "Model accuracy: 0.9771\n", + "Model's mean certificate: 0.0616\n", "Loss' temperature: 100.0\n" ] } @@ -503,8 +558,8 @@ "name": "stdout", "output_type": "stream", "text": [ - "Model accuracy: 0.9621\n", - "Model's mean certificate: 0.8598\n", + "Model accuracy: 0.9639\n", + "Model's mean certificate: 0.8490\n", "Loss' temperature: 3.0\n" ] } @@ -614,25 +669,25 @@ "output_type": "stream", "text": [ "Epoch 1/10\n", - "235/235 [==============================] - 4s 10ms/step - loss: 6.4450 - accuracy: 0.7472 - CategoricalProvableAvgRobustness: 0.0258 - val_loss: 1.7008 - val_accuracy: 0.9056 - val_CategoricalProvableAvgRobustness: 0.0361\n", + "\u001b[1m235/235\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m13s\u001b[0m 33ms/step - CategoricalProvableAvgRobustness: 0.0177 - accuracy: 0.5781 - loss: 13.6117 - val_CategoricalProvableAvgRobustness: 0.0367 - val_accuracy: 0.9172 - val_loss: 1.5607\n", "Epoch 2/10\n", - "235/235 [==============================] - 2s 7ms/step - loss: 1.4576 - accuracy: 0.9168 - CategoricalProvableAvgRobustness: 0.0372 - val_loss: 1.0670 - val_accuracy: 0.9411 - val_CategoricalProvableAvgRobustness: 0.0411\n", + "\u001b[1m235/235\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 12ms/step - CategoricalProvableAvgRobustness: 0.0370 - accuracy: 0.9180 - loss: 1.4555 - val_CategoricalProvableAvgRobustness: 0.0413 - val_accuracy: 0.9474 - val_loss: 0.9412\n", "Epoch 3/10\n", - "235/235 [==============================] - 2s 6ms/step - loss: 0.9756 - accuracy: 0.9410 - CategoricalProvableAvgRobustness: 0.0410 - val_loss: 0.7664 - val_accuracy: 0.9550 - val_CategoricalProvableAvgRobustness: 0.0431\n", + "\u001b[1m235/235\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 12ms/step - CategoricalProvableAvgRobustness: 0.0414 - accuracy: 0.9468 - loss: 0.9285 - val_CategoricalProvableAvgRobustness: 0.0446 - val_accuracy: 0.9607 - val_loss: 0.6659\n", "Epoch 4/10\n", - "235/235 [==============================] - 2s 7ms/step - loss: 0.7388 - accuracy: 0.9532 - CategoricalProvableAvgRobustness: 0.0441 - val_loss: 0.6139 - val_accuracy: 0.9621 - val_CategoricalProvableAvgRobustness: 0.0462\n", + "\u001b[1m235/235\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 12ms/step - CategoricalProvableAvgRobustness: 0.0449 - accuracy: 0.9571 - loss: 0.6918 - val_CategoricalProvableAvgRobustness: 0.0484 - val_accuracy: 0.9661 - val_loss: 0.5679\n", "Epoch 5/10\n", - "235/235 [==============================] - 2s 8ms/step - loss: 0.5798 - accuracy: 0.9605 - CategoricalProvableAvgRobustness: 0.0464 - val_loss: 0.4938 - val_accuracy: 0.9671 - val_CategoricalProvableAvgRobustness: 0.0477\n", + "\u001b[1m235/235\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 12ms/step - CategoricalProvableAvgRobustness: 0.0474 - accuracy: 0.9635 - loss: 0.5487 - val_CategoricalProvableAvgRobustness: 0.0512 - val_accuracy: 0.9697 - val_loss: 0.4677\n", "Epoch 6/10\n", - "235/235 [==============================] - 2s 7ms/step - loss: 0.5032 - accuracy: 0.9645 - CategoricalProvableAvgRobustness: 0.0486 - val_loss: 0.4849 - val_accuracy: 0.9683 - val_CategoricalProvableAvgRobustness: 0.0512\n", + "\u001b[1m235/235\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 12ms/step - CategoricalProvableAvgRobustness: 0.0495 - accuracy: 0.9702 - loss: 0.4153 - val_CategoricalProvableAvgRobustness: 0.0519 - val_accuracy: 0.9726 - val_loss: 0.4221\n", "Epoch 7/10\n", - "235/235 [==============================] - 2s 7ms/step - loss: 0.4317 - accuracy: 0.9688 - CategoricalProvableAvgRobustness: 0.0510 - val_loss: 0.4210 - val_accuracy: 0.9710 - val_CategoricalProvableAvgRobustness: 0.0521\n", + "\u001b[1m235/235\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 12ms/step - CategoricalProvableAvgRobustness: 0.0516 - accuracy: 0.9717 - loss: 0.3699 - val_CategoricalProvableAvgRobustness: 0.0543 - val_accuracy: 0.9751 - val_loss: 0.3297\n", "Epoch 8/10\n", - "235/235 [==============================] - 2s 8ms/step - loss: 0.3550 - accuracy: 0.9722 - CategoricalProvableAvgRobustness: 0.0526 - val_loss: 0.3933 - val_accuracy: 0.9743 - val_CategoricalProvableAvgRobustness: 0.0535\n", + "\u001b[1m235/235\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 12ms/step - CategoricalProvableAvgRobustness: 0.0542 - accuracy: 0.9752 - loss: 0.3239 - val_CategoricalProvableAvgRobustness: 0.0565 - val_accuracy: 0.9778 - val_loss: 0.2904\n", "Epoch 9/10\n", - "235/235 [==============================] - 2s 8ms/step - loss: 0.3358 - accuracy: 0.9730 - CategoricalProvableAvgRobustness: 0.0546 - val_loss: 0.2810 - val_accuracy: 0.9775 - val_CategoricalProvableAvgRobustness: 0.0565\n", + "\u001b[1m235/235\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 11ms/step - CategoricalProvableAvgRobustness: 0.0554 - accuracy: 0.9788 - loss: 0.2622 - val_CategoricalProvableAvgRobustness: 0.0572 - val_accuracy: 0.9767 - val_loss: 0.3244\n", "Epoch 10/10\n", - "235/235 [==============================] - 2s 8ms/step - loss: 0.2858 - accuracy: 0.9758 - CategoricalProvableAvgRobustness: 0.0559 - val_loss: 0.3089 - val_accuracy: 0.9739 - val_CategoricalProvableAvgRobustness: 0.0590\n" + "\u001b[1m235/235\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 12ms/step - CategoricalProvableAvgRobustness: 0.0578 - accuracy: 0.9787 - loss: 0.2391 - val_CategoricalProvableAvgRobustness: 0.0609 - val_accuracy: 0.9767 - val_loss: 0.2933\n" ] } ], @@ -660,25 +715,25 @@ "output_type": "stream", "text": [ "Epoch 1/10\n", - "235/235 [==============================] - 4s 9ms/step - loss: 1.9754 - accuracy: 0.8073 - CategoricalProvableAvgRobustness: 0.1097 - val_loss: 0.5700 - val_accuracy: 0.9194 - val_CategoricalProvableAvgRobustness: 0.1783\n", + "\u001b[1m235/235\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 31ms/step - CategoricalProvableAvgRobustness: 0.0609 - accuracy: 0.6512 - loss: 3.6060 - val_CategoricalProvableAvgRobustness: 0.1757 - val_accuracy: 0.9185 - val_loss: 0.5966\n", "Epoch 2/10\n", - "235/235 [==============================] - 2s 8ms/step - loss: 0.3412 - accuracy: 0.9265 - CategoricalProvableAvgRobustness: 0.2227 - val_loss: 0.0606 - val_accuracy: 0.9375 - val_CategoricalProvableAvgRobustness: 0.2713\n", + "\u001b[1m235/235\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 12ms/step - CategoricalProvableAvgRobustness: 0.1987 - accuracy: 0.9212 - loss: 0.4729 - val_CategoricalProvableAvgRobustness: 0.2742 - val_accuracy: 0.9395 - val_loss: 0.0573\n", "Epoch 3/10\n", - "235/235 [==============================] - 2s 9ms/step - loss: -0.0592 - accuracy: 0.9404 - CategoricalProvableAvgRobustness: 0.3110 - val_loss: -0.2699 - val_accuracy: 0.9468 - val_CategoricalProvableAvgRobustness: 0.3581\n", + "\u001b[1m235/235\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 13ms/step - CategoricalProvableAvgRobustness: 0.2915 - accuracy: 0.9372 - loss: 0.0225 - val_CategoricalProvableAvgRobustness: 0.3757 - val_accuracy: 0.9483 - val_loss: -0.2987\n", "Epoch 4/10\n", - "235/235 [==============================] - 2s 9ms/step - loss: -0.3596 - accuracy: 0.9476 - CategoricalProvableAvgRobustness: 0.3993 - val_loss: -0.5494 - val_accuracy: 0.9537 - val_CategoricalProvableAvgRobustness: 0.4502\n", + "\u001b[1m235/235\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 11ms/step - CategoricalProvableAvgRobustness: 0.3910 - accuracy: 0.9486 - loss: -0.3398 - val_CategoricalProvableAvgRobustness: 0.4719 - val_accuracy: 0.9561 - val_loss: -0.6042\n", "Epoch 5/10\n", - "235/235 [==============================] - 2s 9ms/step - loss: -0.5982 - accuracy: 0.9505 - CategoricalProvableAvgRobustness: 0.4850 - val_loss: -0.7551 - val_accuracy: 0.9548 - val_CategoricalProvableAvgRobustness: 0.5342\n", + "\u001b[1m235/235\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 12ms/step - CategoricalProvableAvgRobustness: 0.4809 - accuracy: 0.9495 - loss: -0.5999 - val_CategoricalProvableAvgRobustness: 0.5560 - val_accuracy: 0.9575 - val_loss: -0.8138\n", "Epoch 6/10\n", - "235/235 [==============================] - 2s 8ms/step - loss: -0.7969 - accuracy: 0.9532 - CategoricalProvableAvgRobustness: 0.5602 - val_loss: -0.9409 - val_accuracy: 0.9573 - val_CategoricalProvableAvgRobustness: 0.6143\n", + "\u001b[1m235/235\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 13ms/step - CategoricalProvableAvgRobustness: 0.5646 - accuracy: 0.9526 - loss: -0.8128 - val_CategoricalProvableAvgRobustness: 0.6215 - val_accuracy: 0.9587 - val_loss: -0.9952\n", "Epoch 7/10\n", - "235/235 [==============================] - 2s 8ms/step - loss: -0.9541 - accuracy: 0.9541 - CategoricalProvableAvgRobustness: 0.6243 - val_loss: -1.0462 - val_accuracy: 0.9578 - val_CategoricalProvableAvgRobustness: 0.6508\n", + "\u001b[1m235/235\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 14ms/step - CategoricalProvableAvgRobustness: 0.6251 - accuracy: 0.9543 - loss: -0.9725 - val_CategoricalProvableAvgRobustness: 0.6832 - val_accuracy: 0.9610 - val_loss: -1.1258\n", "Epoch 8/10\n", - "235/235 [==============================] - 2s 8ms/step - loss: -1.0742 - accuracy: 0.9566 - CategoricalProvableAvgRobustness: 0.6734 - val_loss: -1.1781 - val_accuracy: 0.9592 - val_CategoricalProvableAvgRobustness: 0.7016\n", + "\u001b[1m235/235\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 13ms/step - CategoricalProvableAvgRobustness: 0.6792 - accuracy: 0.9551 - loss: -1.0691 - val_CategoricalProvableAvgRobustness: 0.7205 - val_accuracy: 0.9601 - val_loss: -1.2153\n", "Epoch 9/10\n", - "235/235 [==============================] - 2s 8ms/step - loss: -1.1652 - accuracy: 0.9568 - CategoricalProvableAvgRobustness: 0.7114 - val_loss: -1.2754 - val_accuracy: 0.9624 - val_CategoricalProvableAvgRobustness: 0.7390\n", + "\u001b[1m235/235\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 13ms/step - CategoricalProvableAvgRobustness: 0.7165 - accuracy: 0.9560 - loss: -1.1667 - val_CategoricalProvableAvgRobustness: 0.7429 - val_accuracy: 0.9664 - val_loss: -1.2935\n", "Epoch 10/10\n", - "235/235 [==============================] - 2s 8ms/step - loss: -1.2430 - accuracy: 0.9577 - CategoricalProvableAvgRobustness: 0.7454 - val_loss: -1.3300 - val_accuracy: 0.9602 - val_CategoricalProvableAvgRobustness: 0.7708\n" + "\u001b[1m235/235\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 12ms/step - CategoricalProvableAvgRobustness: 0.7402 - accuracy: 0.9576 - loss: -1.2416 - val_CategoricalProvableAvgRobustness: 0.7699 - val_accuracy: 0.9619 - val_loss: -1.3462\n" ] } ], @@ -705,8 +760,8 @@ "name": "stdout", "output_type": "stream", "text": [ - "Model accuracy: 0.9739\n", - "Model's mean certificate: 0.0590\n", + "Model accuracy: 0.9767\n", + "Model's mean certificate: 0.0609\n", "Loss' minimum margin: 0.01\n", "Loss' alpha: 1000.0\n" ] @@ -732,8 +787,8 @@ "name": "stdout", "output_type": "stream", "text": [ - "Model accuracy: 0.9602\n", - "Model's mean certificate: 0.7708\n", + "Model accuracy: 0.9619\n", + "Model's mean certificate: 0.7699\n", "Loss' minimum margin: 0.2\n", "Loss' alpha: 50.0\n" ] @@ -790,7 +845,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.18" + "version": "3.9.19" }, "toc": { "base_numbering": 1, diff --git a/docs/notebooks/demo0.ipynb b/docs/notebooks/demo0.ipynb index f7dc93e4..e3cba285 100644 --- a/docs/notebooks/demo0.ipynb +++ b/docs/notebooks/demo0.ipynb @@ -69,103 +69,234 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 1, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "/home/thibaut.boissin/projects/deel-lip/deel/lip/model.py:56: UserWarning: Sequential model contains a layer wich is not a Lipschitz layer: flatten_2\n", - " layer.name\n" + "2024-09-06 14:56:17.825542: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:485] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n", + "2024-09-06 14:56:17.837283: E external/local_xla/xla/stream_executor/cuda/cuda_dnn.cc:8454] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n", + "2024-09-06 14:56:17.840865: E external/local_xla/xla/stream_executor/cuda/cuda_blas.cc:1452] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n", + "2024-09-06 14:56:17.849389: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\n", + "To enable the following instructions: AVX2 FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\n", + "2024-09-06 14:56:19.159295: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT\n", + "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", + "I0000 00:00:1725627380.393772 863748 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\n", + "I0000 00:00:1725627380.449416 863748 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\n", + "I0000 00:00:1725627380.449585 863748 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\n", + "I0000 00:00:1725627380.450447 863748 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\n", + "I0000 00:00:1725627380.450568 863748 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\n", + "I0000 00:00:1725627380.450670 863748 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\n", + "I0000 00:00:1725627380.530744 863748 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\n", + "I0000 00:00:1725627380.530852 863748 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\n", + "I0000 00:00:1725627380.530935 863748 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\n", + "2024-09-06 14:56:20.531004: I tensorflow/core/common_runtime/gpu/gpu_device.cc:2021] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 6818 MB memory: -> device: 0, name: NVIDIA GeForce RTX 2070 SUPER, pci bus id: 0000:01:00.0, compute capability: 7.5\n" + ] + }, + { + "data": { + "text/html": [ + "
Model: \"sequential\"\n",
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โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”ณโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”ณโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”“\n",
+       "โ”ƒ Layer (type)                    โ”ƒ Output Shape           โ”ƒ       Param # โ”ƒ\n",
+       "โ”กโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ•‡โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ•‡โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”ฉ\n",
+       "โ”‚ spectral_conv2d                 โ”‚ (None, 28, 28, 16)     โ”‚           321 โ”‚\n",
+       "โ”‚ (SpectralConv2D)                โ”‚                        โ”‚               โ”‚\n",
+       "โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค\n",
+       "โ”‚ scaled_l2_norm_pooling2d        โ”‚ (None, 14, 14, 16)     โ”‚             0 โ”‚\n",
+       "โ”‚ (ScaledL2NormPooling2D)         โ”‚                        โ”‚               โ”‚\n",
+       "โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค\n",
+       "โ”‚ spectral_conv2d_1               โ”‚ (None, 14, 14, 16)     โ”‚         4,641 โ”‚\n",
+       "โ”‚ (SpectralConv2D)                โ”‚                        โ”‚               โ”‚\n",
+       "โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค\n",
+       "โ”‚ scaled_l2_norm_pooling2d_1      โ”‚ (None, 7, 7, 16)       โ”‚             0 โ”‚\n",
+       "โ”‚ (ScaledL2NormPooling2D)         โ”‚                        โ”‚               โ”‚\n",
+       "โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค\n",
+       "โ”‚ flatten (Flatten)               โ”‚ (None, 784)            โ”‚             0 โ”‚\n",
+       "โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค\n",
+       "โ”‚ spectral_dense (SpectralDense)  โ”‚ (None, 32)             โ”‚        50,241 โ”‚\n",
+       "โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค\n",
+       "โ”‚ frobenius_dense                 โ”‚ (None, 10)             โ”‚           640 โ”‚\n",
+       "โ”‚ (FrobeniusDense)                โ”‚                        โ”‚               โ”‚\n",
+       "โ””โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”˜\n",
+       "
\n" + ], + "text/plain": [ + "โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”ณโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”ณโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”“\n", + "โ”ƒ\u001b[1m \u001b[0m\u001b[1mLayer (type) \u001b[0m\u001b[1m \u001b[0mโ”ƒ\u001b[1m \u001b[0m\u001b[1mOutput Shape \u001b[0m\u001b[1m \u001b[0mโ”ƒ\u001b[1m \u001b[0m\u001b[1m Param #\u001b[0m\u001b[1m \u001b[0mโ”ƒ\n", + "โ”กโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ•‡โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ•‡โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”ฉ\n", + "โ”‚ spectral_conv2d โ”‚ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m16\u001b[0m) โ”‚ \u001b[38;5;34m321\u001b[0m โ”‚\n", + "โ”‚ (\u001b[38;5;33mSpectralConv2D\u001b[0m) โ”‚ โ”‚ โ”‚\n", + "โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค\n", + "โ”‚ scaled_l2_norm_pooling2d โ”‚ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m16\u001b[0m) โ”‚ \u001b[38;5;34m0\u001b[0m โ”‚\n", + "โ”‚ (\u001b[38;5;33mScaledL2NormPooling2D\u001b[0m) โ”‚ โ”‚ โ”‚\n", + "โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค\n", + "โ”‚ spectral_conv2d_1 โ”‚ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m16\u001b[0m) โ”‚ \u001b[38;5;34m4,641\u001b[0m โ”‚\n", + "โ”‚ (\u001b[38;5;33mSpectralConv2D\u001b[0m) โ”‚ โ”‚ โ”‚\n", + "โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค\n", + "โ”‚ scaled_l2_norm_pooling2d_1 โ”‚ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m16\u001b[0m) โ”‚ \u001b[38;5;34m0\u001b[0m โ”‚\n", + "โ”‚ (\u001b[38;5;33mScaledL2NormPooling2D\u001b[0m) โ”‚ โ”‚ โ”‚\n", + "โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค\n", + "โ”‚ flatten (\u001b[38;5;33mFlatten\u001b[0m) โ”‚ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m784\u001b[0m) โ”‚ \u001b[38;5;34m0\u001b[0m โ”‚\n", + "โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค\n", + "โ”‚ spectral_dense (\u001b[38;5;33mSpectralDense\u001b[0m) โ”‚ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m32\u001b[0m) โ”‚ \u001b[38;5;34m50,241\u001b[0m โ”‚\n", + "โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค\n", + "โ”‚ frobenius_dense โ”‚ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m10\u001b[0m) โ”‚ \u001b[38;5;34m640\u001b[0m โ”‚\n", + "โ”‚ (\u001b[38;5;33mFrobeniusDense\u001b[0m) โ”‚ โ”‚ โ”‚\n", + "โ””โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”˜\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "
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Devices:\n", + "I0000 00:00:1725627382.892568 863830 service.cc:154] StreamExecutor device (0): NVIDIA GeForce RTX 2070 SUPER, Compute Capability 7.5\n", + "2024-09-06 14:56:22.938074: I tensorflow/compiler/mlir/tensorflow/utils/dump_mlir_util.cc:268] disabling MLIR crash reproducer, set env var `MLIR_CRASH_REPRODUCER_DIRECTORY` to enable.\n", + "2024-09-06 14:56:23.125054: I external/local_xla/xla/stream_executor/cuda/cuda_dnn.cc:531] Loaded cuDNN version 8902\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[1m16/30\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m \u001b[1m0s\u001b[0m 12ms/step - MulticlassKR: 0.0607 - accuracy: 0.1946 - loss: 6.8997" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "I0000 00:00:1725627386.455230 863830 device_compiler.h:188] Compiled cluster using XLA! This line is logged at most once for the lifetime of the process.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "Model: \"hkr_model\"\n", - "_________________________________________________________________\n", - "Layer (type) Output Shape Param # \n", - "=================================================================\n", - "spectral_conv2d_4 (SpectralC (None, 28, 28, 16) 321 \n", - "_________________________________________________________________\n", - "scaled_l2norm_pooling2d_4 (S (None, 14, 14, 16) 0 \n", - "_________________________________________________________________\n", - "spectral_conv2d_5 (SpectralC (None, 14, 14, 16) 4641 \n", - "_________________________________________________________________\n", - "scaled_l2norm_pooling2d_5 (S (None, 7, 7, 16) 0 \n", - "_________________________________________________________________\n", - "flatten_2 (Flatten) (None, 784) 0 \n", - "_________________________________________________________________\n", - "spectral_dense_2 (SpectralDe (None, 32) 50241 \n", - 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val_MulticlassKR: 0.2486\n", + "\u001b[1m30/30\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - MulticlassKR: 0.5051 - accuracy: 0.9237 - loss: -0.1753 - val_MulticlassKR: 0.6148 - val_accuracy: 0.9410 - val_loss: -0.3100\n", "Epoch 5/30\n", - "30/30 [==============================] - 1s 35ms/step - loss: 0.6889 - accuracy: 0.8338 - MulticlassKR: 0.2546 - val_loss: 0.5352 - val_accuracy: 0.8650 - val_MulticlassKR: 0.2835\n", + "\u001b[1m30/30\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 17ms/step - MulticlassKR: 0.6573 - accuracy: 0.9358 - loss: -0.3483 - val_MulticlassKR: 0.8202 - val_accuracy: 0.9465 - val_loss: -0.5050\n", "Epoch 6/30\n", - "30/30 [==============================] - 1s 37ms/step - loss: 0.5256 - accuracy: 0.8609 - MulticlassKR: 0.2879 - val_loss: 0.4442 - val_accuracy: 0.8805 - val_MulticlassKR: 0.3166\n", + "\u001b[1m30/30\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 12ms/step - MulticlassKR: 0.8781 - accuracy: 0.9404 - loss: -0.5443 - val_MulticlassKR: 1.1200 - val_accuracy: 0.9502 - val_loss: -0.7696\n", "Epoch 7/30\n", - "30/30 [==============================] - 1s 34ms/step - loss: 0.4469 - accuracy: 0.8735 - MulticlassKR: 0.3186 - val_loss: 0.3349 - val_accuracy: 0.8911 - val_MulticlassKR: 0.3470\n", + "\u001b[1m30/30\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 17ms/step - MulticlassKR: 1.2211 - accuracy: 0.9425 - loss: -0.8410 - val_MulticlassKR: 1.5829 - val_accuracy: 0.9510 - val_loss: -1.1627\n", "Epoch 8/30\n", - "30/30 [==============================] - 1s 34ms/step - loss: 0.3493 - accuracy: 0.8835 - MulticlassKR: 0.3480 - val_loss: 0.2641 - val_accuracy: 0.8961 - val_MulticlassKR: 0.3787\n", + "\u001b[1m30/30\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - MulticlassKR: 1.7310 - accuracy: 0.9469 - loss: -1.2617 - val_MulticlassKR: 2.2681 - val_accuracy: 0.9522 - val_loss: -1.7127\n", "Epoch 9/30\n", - "30/30 [==============================] - 1s 34ms/step - loss: 0.2722 - accuracy: 0.8938 - MulticlassKR: 0.3818 - val_loss: 0.2122 - val_accuracy: 0.8993 - val_MulticlassKR: 0.4127\n", + "\u001b[1m30/30\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 13ms/step - MulticlassKR: 2.4758 - accuracy: 0.9445 - loss: -1.8480 - val_MulticlassKR: 3.2075 - val_accuracy: 0.9535 - val_loss: -2.4585\n", "Epoch 10/30\n", - "30/30 [==============================] - 1s 34ms/step - loss: 0.2036 - accuracy: 0.9013 - MulticlassKR: 0.4153 - val_loss: 0.1330 - val_accuracy: 0.9079 - val_MulticlassKR: 0.4487\n", + "\u001b[1m30/30\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 13ms/step - MulticlassKR: 3.4430 - accuracy: 0.9466 - loss: -2.5462 - val_MulticlassKR: 4.2359 - val_accuracy: 0.9485 - val_loss: -3.2346\n", "Epoch 11/30\n", - "30/30 [==============================] - 1s 35ms/step - loss: 0.1472 - accuracy: 0.9059 - MulticlassKR: 0.4505 - val_loss: 0.0799 - val_accuracy: 0.9126 - val_MulticlassKR: 0.4861\n", + "\u001b[1m30/30\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 17ms/step - MulticlassKR: 4.4586 - accuracy: 0.9465 - loss: -3.4071 - val_MulticlassKR: 5.2198 - val_accuracy: 0.9510 - val_loss: -4.0375\n", "Epoch 12/30\n", - "30/30 [==============================] - 1s 35ms/step - loss: 0.0939 - accuracy: 0.9103 - MulticlassKR: 0.4915 - val_loss: 0.0371 - val_accuracy: 0.9142 - val_MulticlassKR: 0.5313\n", + "\u001b[1m30/30\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 13ms/step - MulticlassKR: 5.3493 - accuracy: 0.9480 - loss: -4.0844 - val_MulticlassKR: 5.9408 - val_accuracy: 0.9533 - val_loss: -4.6767\n", "Epoch 13/30\n", - "30/30 [==============================] - 1s 40ms/step - loss: 0.0499 - accuracy: 0.9100 - MulticlassKR: 0.5346 - val_loss: -0.0211 - val_accuracy: 0.9206 - val_MulticlassKR: 0.5729\n", + "\u001b[1m30/30\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 17ms/step - MulticlassKR: 5.9736 - accuracy: 0.9475 - loss: -4.5796 - val_MulticlassKR: 6.3789 - val_accuracy: 0.9528 - val_loss: -4.9476\n", "Epoch 14/30\n", - "30/30 [==============================] - 1s 39ms/step - loss: -0.0216 - accuracy: 0.9162 - MulticlassKR: 0.5760 - val_loss: -0.0682 - val_accuracy: 0.9200 - val_MulticlassKR: 0.6219\n", + "\u001b[1m30/30\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 18ms/step - MulticlassKR: 6.3452 - accuracy: 0.9484 - loss: -4.9115 - val_MulticlassKR: 6.7228 - val_accuracy: 0.9541 - val_loss: -5.3107\n", "Epoch 15/30\n", - "30/30 [==============================] - 1s 35ms/step - loss: -0.0666 - accuracy: 0.9168 - MulticlassKR: 0.6248 - val_loss: -0.1301 - val_accuracy: 0.9236 - val_MulticlassKR: 0.6742\n", + "\u001b[1m30/30\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - MulticlassKR: 6.6690 - accuracy: 0.9513 - loss: -5.1483 - val_MulticlassKR: 6.9589 - val_accuracy: 0.9536 - val_loss: -5.4324\n", "Epoch 16/30\n", - "30/30 [==============================] - 1s 35ms/step - loss: -0.1223 - accuracy: 0.9197 - MulticlassKR: 0.6778 - val_loss: -0.1777 - val_accuracy: 0.9270 - val_MulticlassKR: 0.7275\n", + "\u001b[1m30/30\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 13ms/step - MulticlassKR: 6.9024 - accuracy: 0.9507 - loss: -5.3773 - val_MulticlassKR: 7.1334 - val_accuracy: 0.9547 - val_loss: -5.6325\n", "Epoch 17/30\n", - "30/30 [==============================] - 1s 36ms/step - loss: -0.1605 - accuracy: 0.9199 - MulticlassKR: 0.7291 - val_loss: -0.2426 - val_accuracy: 0.9272 - val_MulticlassKR: 0.7900\n", + "\u001b[1m30/30\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - MulticlassKR: 7.0586 - accuracy: 0.9496 - loss: -5.4738 - val_MulticlassKR: 7.2874 - val_accuracy: 0.9570 - val_loss: -5.7759\n", "Epoch 18/30\n", - "30/30 [==============================] - 1s 36ms/step - loss: -0.2278 - accuracy: 0.9218 - MulticlassKR: 0.7886 - val_loss: -0.2883 - val_accuracy: 0.9305 - val_MulticlassKR: 0.8471\n", + "\u001b[1m30/30\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - MulticlassKR: 7.2025 - accuracy: 0.9535 - loss: -5.6918 - val_MulticlassKR: 7.3852 - val_accuracy: 0.9587 - val_loss: -5.8997\n", "Epoch 19/30\n", - "30/30 [==============================] - 1s 40ms/step - loss: -0.2246 - accuracy: 0.9170 - MulticlassKR: 0.8478 - val_loss: -0.3104 - val_accuracy: 0.9183 - val_MulticlassKR: 0.9070\n", + "\u001b[1m30/30\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 17ms/step - MulticlassKR: 7.3015 - accuracy: 0.9535 - loss: -5.7211 - val_MulticlassKR: 7.4868 - val_accuracy: 0.9568 - val_loss: -5.8655\n", "Epoch 20/30\n", - "30/30 [==============================] - 1s 34ms/step - loss: -0.3066 - accuracy: 0.9213 - MulticlassKR: 0.9085 - val_loss: -0.3778 - val_accuracy: 0.9284 - val_MulticlassKR: 0.9754\n", + "\u001b[1m30/30\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 19ms/step - MulticlassKR: 7.3952 - accuracy: 0.9556 - loss: -5.7836 - val_MulticlassKR: 7.5595 - val_accuracy: 0.9594 - val_loss: -6.0809\n", "Epoch 21/30\n", - "30/30 [==============================] - 1s 39ms/step - loss: -0.3736 - accuracy: 0.9241 - MulticlassKR: 0.9739 - val_loss: -0.4258 - val_accuracy: 0.9280 - val_MulticlassKR: 1.0388\n", + "\u001b[1m30/30\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 19ms/step - MulticlassKR: 7.4631 - accuracy: 0.9549 - loss: -5.8790 - val_MulticlassKR: 7.6517 - val_accuracy: 0.9587 - val_loss: -6.1018\n", "Epoch 22/30\n", - "30/30 [==============================] - 1s 35ms/step - loss: -0.4180 - accuracy: 0.9229 - MulticlassKR: 1.0337 - val_loss: -0.4805 - val_accuracy: 0.9302 - val_MulticlassKR: 1.1069\n", + "\u001b[1m30/30\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 19ms/step - MulticlassKR: 7.5415 - accuracy: 0.9547 - loss: -5.9190 - val_MulticlassKR: 7.7233 - val_accuracy: 0.9602 - val_loss: -6.1897\n", "Epoch 23/30\n", - "30/30 [==============================] - 1s 38ms/step - loss: -0.4624 - accuracy: 0.9234 - MulticlassKR: 1.1055 - val_loss: -0.5607 - val_accuracy: 0.9312 - val_MulticlassKR: 1.1803\n", + "\u001b[1m30/30\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step - MulticlassKR: 7.6045 - accuracy: 0.9556 - loss: -6.0076 - val_MulticlassKR: 7.7263 - val_accuracy: 0.9608 - val_loss: -6.1816\n", "Epoch 24/30\n", - "30/30 [==============================] - 1s 35ms/step - loss: -0.5279 - accuracy: 0.9257 - MulticlassKR: 1.1797 - val_loss: -0.5866 - val_accuracy: 0.9275 - val_MulticlassKR: 1.2456\n", + "\u001b[1m30/30\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 13ms/step - MulticlassKR: 7.6273 - accuracy: 0.9557 - loss: -5.9860 - val_MulticlassKR: 7.8158 - val_accuracy: 0.9609 - val_loss: -6.3125\n", "Epoch 25/30\n", - "30/30 [==============================] - 1s 38ms/step - loss: -0.5482 - accuracy: 0.9218 - MulticlassKR: 1.2388 - val_loss: -0.6441 - val_accuracy: 0.9310 - val_MulticlassKR: 1.3125\n", + "\u001b[1m30/30\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 17ms/step - MulticlassKR: 7.6798 - accuracy: 0.9566 - loss: -6.1091 - val_MulticlassKR: 7.8554 - val_accuracy: 0.9623 - val_loss: -6.3072\n", "Epoch 26/30\n", - "30/30 [==============================] - 1s 35ms/step - loss: -0.6375 - accuracy: 0.9263 - MulticlassKR: 1.3103 - val_loss: -0.6890 - val_accuracy: 0.9295 - val_MulticlassKR: 1.3795\n", + "\u001b[1m30/30\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 14ms/step - MulticlassKR: 7.7116 - accuracy: 0.9566 - loss: -6.0760 - val_MulticlassKR: 7.8706 - val_accuracy: 0.9629 - val_loss: -6.3750\n", "Epoch 27/30\n", - "30/30 [==============================] - 1s 42ms/step - loss: -0.6668 - accuracy: 0.9230 - MulticlassKR: 1.3719 - val_loss: -0.7413 - val_accuracy: 0.9271 - val_MulticlassKR: 1.4496\n", + "\u001b[1m30/30\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 18ms/step - MulticlassKR: 7.7647 - accuracy: 0.9561 - loss: -6.1461 - val_MulticlassKR: 7.8945 - val_accuracy: 0.9620 - val_loss: -6.4019\n", "Epoch 28/30\n", - "30/30 [==============================] - 1s 35ms/step - loss: -0.7483 - accuracy: 0.9264 - MulticlassKR: 1.4371 - val_loss: -0.7748 - val_accuracy: 0.9296 - val_MulticlassKR: 1.5096\n", + "\u001b[1m30/30\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 16ms/step - MulticlassKR: 7.7952 - accuracy: 0.9579 - loss: -6.2021 - val_MulticlassKR: 7.9388 - val_accuracy: 0.9613 - val_loss: -6.4203\n", "Epoch 29/30\n", - "30/30 [==============================] - 1s 49ms/step - loss: -0.7495 - accuracy: 0.9229 - MulticlassKR: 1.4900 - val_loss: -0.8622 - val_accuracy: 0.9332 - val_MulticlassKR: 1.5644\n", + "\u001b[1m30/30\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 17ms/step - MulticlassKR: 7.7879 - accuracy: 0.9578 - loss: -6.1679 - val_MulticlassKR: 7.9699 - val_accuracy: 0.9603 - val_loss: -6.3747\n", "Epoch 30/30\n", - "30/30 [==============================] - 1s 35ms/step - loss: -0.8047 - accuracy: 0.9246 - MulticlassKR: 1.5530 - val_loss: -0.8732 - val_accuracy: 0.9297 - val_MulticlassKR: 1.6220\n" + "\u001b[1m30/30\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - MulticlassKR: 7.8354 - accuracy: 0.9571 - loss: -6.2022 - val_MulticlassKR: 7.9560 - val_accuracy: 0.9628 - val_loss: -6.4307\n" ] } ], diff --git a/docs/notebooks/demo1.ipynb b/docs/notebooks/demo1.ipynb index ac65966a..908e7748 100644 --- a/docs/notebooks/demo1.ipynb +++ b/docs/notebooks/demo1.ipynb @@ -5,17 +5,22 @@ "metadata": {}, "source": [ "## Demo 1: Wasserstein distance estimation on toy example\n", - "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/deel-ai/deel-lip/blob/master/docs/notebooks/demo1.ipynb)\n", "\n", - "In this notebook we will see how to estimate the wasserstein distance with a Neural net by using\n", - "the Kantorovich-Rubinestein dual representation.\n", + "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/deel-ai/deel-lip/blob/master/docs/notebooks/demo1.ipynb)\n", "\n", + "In this notebook we will see how to estimate the wasserstein distance with a Neural net\n", + "by using the Kantorovich-Rubinestein dual representation.\n", "\n", "### Wasserstein distance\n", "\n", - "The wasserstein distance measure the distance between two probability distribution. Wikipedia article gives a more intuitive definition of it:\n", + "The wasserstein distance measure the distance between two probability distribution.\n", + "Wikipedia article gives a more intuitive definition of it:\n", "\n", - "> Intuitively, if each distribution is viewed as a unit amount of \"dirt\" piled on M, the metric is the minimum \"cost\" of turning one pile into the other, which is assumed to be the amount of dirt that needs to be moved times the mean distance it has to be moved. Because of this analogy, the metric is known in computer science as the earth mover's distance.\n", + "> Intuitively, if each distribution is viewed as a unit amount of \"dirt\" piled on M, the\n", + "> metric is the minimum \"cost\" of turning one pile into the other, which is assumed to\n", + "> be the amount of dirt that needs to be moved times the mean distance it has to be\n", + "> moved. Because of this analogy, the metric is known in computer science as the earth\n", + "> mover's distance.\n", "\n", "Mathematically it is defined as:\n", "\n", @@ -23,25 +28,27 @@ "W_1(\\mu,\\nu) = \\inf_{\\pi \\in \\Pi(\\mu,\\nu)}\\underset{x,z \\sim \\pi}{\\mathbb{E}}\\parallel \\textbf{x}-\\textbf{z} \\parallel\n", "$$\n", "\n", - "where $\\Pi(\\mu,\\nu)$ is the set of all probability measures on $\\Omega\\times \\Omega$ with marginals $\\mu$ and $\\nu$. In most case this equation is not tractable.\n", + "where $\\Pi(\\mu,\\nu)$ is the set of all probability measures on $\\Omega\\times \\Omega$\n", + "with marginals $\\mu$ and $\\nu$. In most case this equation is not tractable.\n", "\n", "### KR dual formulation\n", "\n", - "In our setup, the KR dual formulation is stated as following:\n", - "$$ W_1(\\mu, \\nu) = \\sup_{f \\in Lip_1(\\Omega)} \\underset{\\textbf{x} \\sim \\mu}{\\mathbb{E}} \\left[f(\\textbf{x} )\\right] -\\underset{\\textbf{x} \\sim \\nu}{\\mathbb{E}} \\left[f(\\textbf{x} )\\right] $$\n", + "In our setup, the KR dual formulation is stated as following: $$ W*1(\\mu, \\nu) = \\sup*{f\n", + "\\in Lip_1(\\Omega)} \\underset{\\textbf{x} \\sim \\mu}{\\mathbb{E}} \\left[f(\\textbf{x}\n", + ")\\right] -\\underset{\\textbf{x} \\sim \\nu}{\\mathbb{E}} \\left[f(\\textbf{x} )\\right] $$\n", "\n", "This state the problem as an optimization problem over the 1-lipschitz functions.\n", "Therefore k-Lipschitz networks allows us to solve this maximization problem.\n", "\n", - "[1] C. Anil, J. Lucas, et R. Grosse, ยซย Sorting out Lipschitz function approximationย ยป, arXiv:1811.05381 [cs, stat], nov. 2018.\n", - "\n", + "[1] C. Anil, J. Lucas, et R. Grosse, ยซย Sorting out Lipschitz function approximationย ยป,\n", + "arXiv:1811.05381 [cs, stat], nov. 2018.\n", "\n", - "We will illustrate this on a synthetic image dataset where $W_1$ is known." + "We will illustrate this on a synthetic image dataset where $W_1$ is known.\n" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ @@ -50,33 +57,35 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "Matplotlib created a temporary config/cache directory at /tmp/matplotlib-_d1mx9in because the default path (/home/thibaut.boissin/.config/matplotlib) is not a writable directory; it is highly recommended to set the MPLCONFIGDIR environment variable to a writable directory, in particular to speed up the import of Matplotlib and to better support multiprocessing.\n", - "2021-09-08 18:20:20.330918: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcudart.so.11.0\n" + "2024-09-06 15:02:24.948218: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:485] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n", + "2024-09-06 15:02:24.959398: E external/local_xla/xla/stream_executor/cuda/cuda_dnn.cc:8454] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n", + "2024-09-06 15:02:24.962773: E external/local_xla/xla/stream_executor/cuda/cuda_blas.cc:1452] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n", + "2024-09-06 15:02:24.971253: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\n", + "To enable the following instructions: AVX2 FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\n", + "2024-09-06 15:02:26.305343: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT\n" ] } ], "source": [ - "from datetime import datetime\n", - "import os\n", "import numpy as np\n", "import math\n", "\n", - "import matplotlib.pyplot as plt \n", + "import matplotlib.pyplot as plt\n", "\n", - "from tensorflow.keras import backend as K\n", - "from tensorflow.keras.layers import Input, Flatten, ReLU\n", - "from tensorflow.keras.optimizers import Adam\n", - "from tensorflow.keras.models import load_model\n", + "import keras\n", + "from keras.layers import Input, Flatten, ReLU\n", + "from keras.optimizers import Adam\n", + "from keras.models import load_model\n", "\n", - "from deel.lip.layers import SpectralConv2D, SpectralDense, FrobeniusDense\n", - "from deel.lip.activations import MaxMin, GroupSort, FullSort\n", + "from deel.lip.layers import SpectralDense, FrobeniusDense\n", + "from deel.lip.activations import FullSort\n", "from deel.lip.losses import KR, HKR\n", "from deel.lip.model import Model" ] @@ -87,18 +96,22 @@ "source": [ "### Parameters input images\n", "\n", - "The synthetic dataset will be composed image with black or white squares allowing us to check if the computed wasserstein distance is correct. One distribution will be the set of black images, while the other will be the set of images with a square on it. these two distribution are diracs, and the wasserstein distance can be analyticaly computed:\n", + "The synthetic dataset will be composed image with black or white squares allowing us to\n", + "check if the computed Wasserstein distance is correct. One distribution will be the set\n", + "of black images, while the other will be the set of images with a square on it. these\n", + "two distribution are diracs, and the Wasserstein distance can be analyticaly computed:\n", "\n", - "In the case to the two diracs the wasserstein distance is then the L1 distance between the two images." + "In the case to the two diracs the Wasserstein distance is then the L1 distance between\n", + "the two images.\n" ] }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 3, "metadata": {}, "outputs": [], "source": [ - "img_size = 64 \n", + "img_size = 64\n", "frac_value = 0.3 # proportion of the center square" ] }, @@ -106,69 +119,71 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### Generate images" + "### Generate images\n" ] }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 4, "metadata": {}, "outputs": [], "source": [ - "def generate_toy_images(shape,frac=0,v=1):\n", + "def generate_toy_images(shape, frac=0, v=1):\n", " \"\"\"\n", " function that generate a single image.\n", - " \n", + "\n", " Args:\n", " shape: shape of the output image\n", " frac: proportion of the center square\n", " value: value assigned to the center square\n", " \"\"\"\n", " img = np.zeros(shape)\n", - " if frac==0:\n", + " if frac == 0:\n", " return img\n", - " frac=frac**0.5\n", - " #print(frac)\n", - " l=int(shape[0]*frac)\n", - " ldec=(shape[0]-l)//2\n", - " #print(l)\n", - " w=int(shape[1]*frac)\n", - " wdec=(shape[1]-w)//2\n", - " img[ldec:ldec+l,wdec:wdec+w,:]=v\n", + " frac = frac**0.5\n", + " # print(frac)\n", + " l = int(shape[0] * frac)\n", + " ldec = (shape[0] - l) // 2\n", + " # print(l)\n", + " w = int(shape[1] * frac)\n", + " wdec = (shape[1] - w) // 2\n", + " img[ldec : ldec + l, wdec : wdec + w, :] = v\n", " return img\n", "\n", "\n", - "def binary_generator(batch_size,shape,frac=0):\n", + "def binary_generator(batch_size, shape, frac=0):\n", " \"\"\"\n", " generate a batch with half of black images, hald of images with a white square.\n", " \"\"\"\n", - " batch_x = np.zeros(((batch_size,)+(shape)), dtype=np.float16)\n", - " batch_y=np.zeros((batch_size,1), dtype=np.float16)\n", - " batch_x[batch_size//2:,]=generate_toy_images(shape,frac=frac,v=1)\n", - " batch_y[batch_size//2:]=1\n", + " batch_x = np.zeros(((batch_size,) + (shape)), dtype=np.float16)\n", + " batch_y = np.zeros((batch_size, 1), dtype=np.float16)\n", + " batch_x[batch_size // 2 :,] = generate_toy_images(shape, frac=frac, v=1)\n", + " batch_y[batch_size // 2 :] = 1\n", " while True:\n", - " yield batch_x, batch_y\n", + " yield batch_x, batch_y\n", "\n", "\n", - "def ternary_generator(batch_size,shape,frac=0):\n", + "def ternary_generator(batch_size, shape, frac=0):\n", " \"\"\"\n", " Same as binary generator, but images can have a white square of value 1, or value -1\n", " \"\"\"\n", - " batch_x = np.zeros(((batch_size,)+(shape)), dtype=np.float16)\n", - " batch_y=np.zeros((batch_size,1), dtype=np.float16)\n", - " batch_x[3*batch_size//4:,]=generate_toy_images(shape,frac=frac,v=1)\n", - " batch_x[batch_size//2:3*batch_size//4,]=generate_toy_images(shape,frac=frac,v=-1)\n", - " batch_y[batch_size//2:]=1\n", - " #indexes_shuffle = np.arange(batch_size)\n", + " batch_x = np.zeros(((batch_size,) + (shape)), dtype=np.float16)\n", + " batch_y = np.zeros((batch_size, 1), dtype=np.float16)\n", + " batch_x[3 * batch_size // 4 :,] = generate_toy_images(shape, frac=frac, v=1)\n", + " batch_x[batch_size // 2 : 3 * batch_size // 4,] = generate_toy_images(\n", + " shape, frac=frac, v=-1\n", + " )\n", + " batch_y[batch_size // 2 :] = 1\n", + " # indexes_shuffle = np.arange(batch_size)\n", " while True:\n", - " #np.random.shuffle(indexes_shuffle)\n", - " #yield batch_x[indexes_shuffle,], batch_y[indexes_shuffle,]\n", - " yield batch_x, batch_y" + " # np.random.shuffle(indexes_shuffle)\n", + " # yield batch_x[indexes_shuffle,], batch_y[indexes_shuffle,]\n", + " yield batch_x, batch_y" ] }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 5, "metadata": {}, "outputs": [], "source": [ @@ -177,11 +192,10 @@ " Display an image\n", " \"\"\"\n", " if img.shape[-1] == 1:\n", - " img = np.tile(img,(3,))\n", + " img = np.tile(img, (3,))\n", " fig, ax = plt.subplots()\n", - " \n", - " imgplot = ax.imshow((img*255).astype(np.uint))\n", - " " + "\n", + " ax.imshow((img * 255).astype(np.uint))" ] }, { @@ -190,12 +204,12 @@ "source": [ "Now let's take a look at the generated batches\n", "\n", - "#### for binary generator" + "#### for binary generator\n" ] }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 6, "metadata": {}, "outputs": [ { @@ -208,119 +222,109 @@ }, { "data": { - "image/png": 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+ "image/png": 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", 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", 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" ] }, - "metadata": { - "needs_background": "light" - }, + "metadata": {}, "output_type": "display_data" } ], "source": [ - "test=binary_generator(2,(img_size,img_size,1),frac=frac_value)\n", - "imgs, y=next(test)\n", + "test = binary_generator(2, (img_size, img_size, 1), frac=frac_value)\n", + "imgs, y = next(test)\n", "\n", "display_img(imgs[0])\n", "display_img(imgs[1])\n", - "print(\"Norm L2 \"+str(np.linalg.norm(imgs[1])))\n", - "print(\"Norm L2(count pixels) \"+str(math.sqrt(np.size(imgs[1][imgs[1]==1]))))" + "print(\"Norm L2 \" + str(np.linalg.norm(imgs[1])))\n", + "print(\"Norm L2(count pixels) \" + str(math.sqrt(np.size(imgs[1][imgs[1] == 1]))))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "#### for ternary generator" + "#### for ternary generator\n" ] }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 7, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Norm L2(imgs[2]-imgs[0])35.0\n", - "Norm L2(imgs[2]) 35.0\n", - "Norm L2(count pixels) 35.0\n" + "Norm L2(imgs[2]-imgs[0]): 35.0\n", + "Norm L2(imgs[2]): 35.0\n", + "Norm L2(count pixels): 35.0\n" ] }, { "data": { - "image/png": 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", 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", 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", 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", 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" ] }, - "metadata": { - "needs_background": "light" - }, + "metadata": {}, "output_type": "display_data" } ], "source": [ - "test=ternary_generator(4,(img_size,img_size,1),frac=frac_value)\n", - "imgs, y=next(test)\n", + "test = ternary_generator(4, (img_size, img_size, 1), frac=frac_value)\n", + "imgs, y = next(test)\n", "\n", "for i in range(4):\n", - " display_img(0.5*(imgs[i]+1.0)) # we ensure that there is no negative value wehn displaying images\n", + " display_img(\n", + " 0.5 * (imgs[i] + 1.0)\n", + " ) # we ensure that there is no negative value wehn displaying images\n", "\n", - "print(\"Norm L2(imgs[2]-imgs[0])\"+str(np.linalg.norm(imgs[2]-imgs[0])))\n", - "print(\"Norm L2(imgs[2]) \"+str(np.linalg.norm(imgs[2])))\n", - "print(\"Norm L2(count pixels) \"+str(math.sqrt(np.size(imgs[2][imgs[2]==-1]))))" + "print(\"Norm L2(imgs[2]-imgs[0]): \" + str(np.linalg.norm(imgs[2] - imgs[0])))\n", + "print(\"Norm L2(imgs[2]): \" + str(np.linalg.norm(imgs[2])))\n", + "print(\"Norm L2(count pixels): \" + str(math.sqrt(np.size(imgs[2][imgs[2] == -1]))))" ] }, { @@ -329,38 +333,38 @@ "source": [ "### Expe parameters\n", "\n", - "Now we know the wasserstein distance between the black image and the images with a square on it.\n", - "For both binary generator and ternary generator this distance is 35.\n", + "Now we know the wasserstein distance between the black image and the images with a\n", + "square on it. For both binary generator and ternary generator this distance is 35.\n", "\n", - "We will then compute this distance using a neural network." + "We will then compute this distance using a neural network.\n" ] }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 8, "metadata": {}, "outputs": [], "source": [ - "batch_size=64\n", - "epochs=5\n", - "steps_per_epoch=6400" + "batch_size = 64\n", + "epochs = 5\n", + "steps_per_epoch = 6400" ] }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 9, "metadata": {}, "outputs": [], "source": [ - "generator = ternary_generator #binary_generator, ternary_generator\n", - "activation = FullSort #ReLU, MaxMin, GroupSort" + "generator = ternary_generator # binary_generator, ternary_generator\n", + "activation = FullSort # ReLU, MaxMin, GroupSort" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "### Build lipschitz Model" + "### Build lipschitz Model\n" ] }, { @@ -372,81 +376,121 @@ "name": "stderr", "output_type": "stream", "text": [ - "2021-09-08 18:20:38.075170: I tensorflow/compiler/jit/xla_cpu_device.cc:41] Not creating XLA devices, tf_xla_enable_xla_devices not set\n", - "2021-09-08 18:20:38.076265: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcuda.so.1\n", - "2021-09-08 18:20:38.116402: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:941] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n", - "2021-09-08 18:20:38.116842: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1720] Found device 0 with properties: \n", - "pciBusID: 0000:01:00.0 name: GeForce RTX 3080 computeCapability: 8.6\n", - "coreClock: 1.83GHz coreCount: 68 deviceMemorySize: 9.78GiB deviceMemoryBandwidth: 707.88GiB/s\n", - "2021-09-08 18:20:38.116868: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcudart.so.11.0\n", - "2021-09-08 18:20:38.119558: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcublas.so.11\n", - "2021-09-08 18:20:38.119602: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcublasLt.so.11\n", - "2021-09-08 18:20:38.120389: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcufft.so.10\n", - "2021-09-08 18:20:38.120583: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcurand.so.10\n", - "2021-09-08 18:20:38.122025: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcusolver.so.10\n", - "2021-09-08 18:20:38.122661: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcusparse.so.11\n", - "2021-09-08 18:20:38.122768: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcudnn.so.8\n", - "2021-09-08 18:20:38.122832: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:941] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n", - "2021-09-08 18:20:38.123234: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:941] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n", - "2021-09-08 18:20:38.123588: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1862] Adding visible gpu devices: 0\n", - "2021-09-08 18:20:38.124825: I tensorflow/compiler/jit/xla_gpu_device.cc:99] Not creating XLA devices, tf_xla_enable_xla_devices not set\n", - "2021-09-08 18:20:38.124895: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:941] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n", - "2021-09-08 18:20:38.125224: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1720] Found device 0 with properties: \n", - "pciBusID: 0000:01:00.0 name: GeForce RTX 3080 computeCapability: 8.6\n", - "coreClock: 1.83GHz coreCount: 68 deviceMemorySize: 9.78GiB deviceMemoryBandwidth: 707.88GiB/s\n", - "2021-09-08 18:20:38.125241: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcudart.so.11.0\n", - "2021-09-08 18:20:38.125254: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcublas.so.11\n", - "2021-09-08 18:20:38.125266: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcublasLt.so.11\n", - "2021-09-08 18:20:38.125278: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcufft.so.10\n", - "2021-09-08 18:20:38.125289: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcurand.so.10\n", - "2021-09-08 18:20:38.125300: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcusolver.so.10\n", - "2021-09-08 18:20:38.125311: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcusparse.so.11\n", - "2021-09-08 18:20:38.125323: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcudnn.so.8\n", - "2021-09-08 18:20:38.125366: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:941] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n", - "2021-09-08 18:20:38.125711: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:941] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n", - "2021-09-08 18:20:38.126022: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1862] Adding visible gpu devices: 0\n", - "2021-09-08 18:20:38.126048: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcudart.so.11.0\n", - "2021-09-08 18:20:38.409201: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1261] Device interconnect StreamExecutor with strength 1 edge matrix:\n", - "2021-09-08 18:20:38.409221: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1267] 0 \n", - "2021-09-08 18:20:38.409225: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1280] 0: N \n", - "2021-09-08 18:20:38.409352: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:941] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n", - "2021-09-08 18:20:38.409615: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:941] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n", - "2021-09-08 18:20:38.409848: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:941] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n", - "2021-09-08 18:20:38.410069: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1406] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 9056 MB memory) -> physical GPU (device: 0, name: GeForce RTX 3080, pci bus id: 0000:01:00.0, compute capability: 8.6)\n", - "2021-09-08 18:20:38.493063: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcublas.so.11\n", - "2021-09-08 18:20:38.861293: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcublasLt.so.11\n", - "2021-09-08 18:20:38.861380: I tensorflow/stream_executor/cuda/cuda_blas.cc:1838] TensorFloat-32 will be used for the matrix multiplication. This will only be logged once.\n" + "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", + "I0000 00:00:1725627748.457893 865036 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\n", + "I0000 00:00:1725627748.481452 865036 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\n", + "I0000 00:00:1725627748.481614 865036 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\n", + "I0000 00:00:1725627748.482359 865036 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\n", + "I0000 00:00:1725627748.482506 865036 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\n", + "I0000 00:00:1725627748.482608 865036 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\n", + "I0000 00:00:1725627748.588870 865036 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\n", + "I0000 00:00:1725627748.588979 865036 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\n", + "I0000 00:00:1725627748.589062 865036 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\n", + "2024-09-06 15:02:28.589130: I tensorflow/core/common_runtime/gpu/gpu_device.cc:2021] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 6818 MB memory: -> device: 0, name: NVIDIA GeForce RTX 2070 SUPER, pci bus id: 0000:01:00.0, compute capability: 7.5\n" ] }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "Model: \"model\"\n", - "_________________________________________________________________\n", - "Layer (type) Output Shape Param # \n", - "=================================================================\n", - "input_1 (InputLayer) [(None, 64, 64, 1)] 0 \n", - "_________________________________________________________________\n", - "flatten (Flatten) (None, 4096) 0 \n", - "_________________________________________________________________\n", - "spectral_dense (SpectralDens (None, 128) 1048833 \n", - "_________________________________________________________________\n", - "spectral_dense_1 (SpectralDe (None, 64) 16513 \n", - "_________________________________________________________________\n", - "spectral_dense_2 (SpectralDe (None, 32) 4161 \n", - "_________________________________________________________________\n", - "frobenius_dense (FrobeniusDe (None, 1) 65 \n", - "=================================================================\n", - "Total params: 1,069,572\n", - "Trainable params: 534,785\n", - "Non-trainable params: 534,787\n", - "_________________________________________________________________\n" - ] + "data": { + "text/html": [ + "
Model: \"model\"\n",
+       "
\n" + ], + "text/plain": [ + "\u001b[1mModel: \"model\"\u001b[0m\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "
โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”ณโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”ณโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”“\n",
+       "โ”ƒ Layer (type)                    โ”ƒ Output Shape           โ”ƒ       Param # โ”ƒ\n",
+       "โ”กโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ•‡โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ•‡โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”ฉ\n",
+       "โ”‚ input_layer (InputLayer)        โ”‚ (None, 64, 64, 1)      โ”‚             0 โ”‚\n",
+       "โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค\n",
+       "โ”‚ flatten (Flatten)               โ”‚ (None, 4096)           โ”‚             0 โ”‚\n",
+       "โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค\n",
+       "โ”‚ spectral_dense (SpectralDense)  โ”‚ (None, 128)            โ”‚     1,048,833 โ”‚\n",
+       "โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค\n",
+       "โ”‚ spectral_dense_1                โ”‚ (None, 64)             โ”‚        16,513 โ”‚\n",
+       "โ”‚ (SpectralDense)                 โ”‚                        โ”‚               โ”‚\n",
+       "โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค\n",
+       "โ”‚ spectral_dense_2                โ”‚ (None, 32)             โ”‚         4,161 โ”‚\n",
+       "โ”‚ (SpectralDense)                 โ”‚                        โ”‚               โ”‚\n",
+       "โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค\n",
+       "โ”‚ frobenius_dense                 โ”‚ (None, 1)              โ”‚            65 โ”‚\n",
+       "โ”‚ (FrobeniusDense)                โ”‚                        โ”‚               โ”‚\n",
+       "โ””โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”˜\n",
+       "
\n" + ], + "text/plain": [ + "โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”ณโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”ณโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”“\n", + "โ”ƒ\u001b[1m \u001b[0m\u001b[1mLayer (type) \u001b[0m\u001b[1m \u001b[0mโ”ƒ\u001b[1m \u001b[0m\u001b[1mOutput Shape \u001b[0m\u001b[1m \u001b[0mโ”ƒ\u001b[1m \u001b[0m\u001b[1m Param #\u001b[0m\u001b[1m \u001b[0mโ”ƒ\n", + "โ”กโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ•‡โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ•‡โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”ฉ\n", + "โ”‚ input_layer (\u001b[38;5;33mInputLayer\u001b[0m) โ”‚ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m64\u001b[0m, \u001b[38;5;34m64\u001b[0m, \u001b[38;5;34m1\u001b[0m) โ”‚ \u001b[38;5;34m0\u001b[0m โ”‚\n", + "โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค\n", + "โ”‚ flatten (\u001b[38;5;33mFlatten\u001b[0m) โ”‚ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m4096\u001b[0m) โ”‚ \u001b[38;5;34m0\u001b[0m โ”‚\n", + "โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค\n", + "โ”‚ spectral_dense (\u001b[38;5;33mSpectralDense\u001b[0m) โ”‚ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m) โ”‚ \u001b[38;5;34m1,048,833\u001b[0m โ”‚\n", + "โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค\n", + "โ”‚ spectral_dense_1 โ”‚ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m64\u001b[0m) โ”‚ \u001b[38;5;34m16,513\u001b[0m โ”‚\n", + "โ”‚ (\u001b[38;5;33mSpectralDense\u001b[0m) โ”‚ โ”‚ โ”‚\n", + "โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค\n", + "โ”‚ spectral_dense_2 โ”‚ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m32\u001b[0m) โ”‚ \u001b[38;5;34m4,161\u001b[0m โ”‚\n", + "โ”‚ (\u001b[38;5;33mSpectralDense\u001b[0m) โ”‚ โ”‚ โ”‚\n", + "โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค\n", + "โ”‚ frobenius_dense โ”‚ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m) โ”‚ \u001b[38;5;34m65\u001b[0m โ”‚\n", + "โ”‚ (\u001b[38;5;33mFrobeniusDense\u001b[0m) โ”‚ โ”‚ โ”‚\n", + "โ””โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”˜\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "
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 Trainable params: 534,785 (2.04 MB)\n",
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\n" + ], + "text/plain": [ + "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m534,787\u001b[0m (2.04 MB)\n" + ] + }, + "metadata": {}, + "output_type": "display_data" } ], "source": [ - "K.clear_session()\n", + "keras.utils.clear_session()\n", "## please note that the previous helper function has the same behavior as the following code:\n", "inputs = Input((img_size, img_size, 1))\n", "x = Flatten()(inputs)\n", @@ -464,7 +508,7 @@ "metadata": {}, "outputs": [], "source": [ - "optimizer = Adam(lr=0.01)" + "optimizer = Adam(learning_rate=0.01)" ] }, { @@ -480,7 +524,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### Learn on toy dataset" + "### Learn on toy dataset\n" ] }, { @@ -488,36 +532,57 @@ "execution_count": 13, "metadata": {}, "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 1/5\n" + ] + }, { "name": "stderr", "output_type": "stream", "text": [ - "/home/thibaut.boissin/envs/tf24/lib/python3.7/site-packages/tensorflow/python/keras/engine/training.py:1844: UserWarning: `Model.fit_generator` is deprecated and will be removed in a future version. Please use `Model.fit`, which supports generators.\n", - " warnings.warn('`Model.fit_generator` is deprecated and '\n", - "2021-09-08 18:20:39.823710: I tensorflow/compiler/mlir/mlir_graph_optimization_pass.cc:116] None of the MLIR optimization passes are enabled (registered 2)\n", - "2021-09-08 18:20:39.842379: I tensorflow/core/platform/profile_utils/cpu_utils.cc:112] CPU Frequency: 3600000000 Hz\n" + "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", + "I0000 00:00:1725627751.358936 865122 service.cc:146] XLA service 0x55e37b2962f0 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\n", + "I0000 00:00:1725627751.358955 865122 service.cc:154] StreamExecutor device (0): NVIDIA GeForce RTX 2070 SUPER, Compute Capability 7.5\n", + "2024-09-06 15:02:31.406727: I tensorflow/compiler/mlir/tensorflow/utils/dump_mlir_util.cc:268] disabling MLIR crash reproducer, set env var `MLIR_CRASH_REPRODUCER_DIRECTORY` to enable.\n", + "2024-09-06 15:02:31.592604: I external/local_xla/xla/stream_executor/cuda/cuda_dnn.cc:531] Loaded cuDNN version 8902\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "Epoch 1/5\n", - "100/100 [==============================] - 6s 50ms/step - loss: -24.9882 - KR: 24.9882\n", + "\u001b[1m 22/100\u001b[0m \u001b[32mโ”โ”โ”โ”\u001b[0m\u001b[37mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - KR: 15.3514 - loss: -15.3514" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "I0000 00:00:1725627754.350183 865122 device_compiler.h:188] Compiled cluster using XLA! This line is logged at most once for the lifetime of the process.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[1m100/100\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 13ms/step - KR: 26.4053 - loss: -26.4053\n", "Epoch 2/5\n", - "100/100 [==============================] - 5s 49ms/step - loss: -34.9959 - KR: 34.9959\n", + "\u001b[1m100/100\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 11ms/step - KR: 34.9944 - loss: -34.9944\n", "Epoch 3/5\n", - "100/100 [==============================] - 5s 49ms/step - loss: -34.9964 - KR: 34.9964\n", + "\u001b[1m100/100\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - KR: 34.9943 - loss: -34.9943\n", "Epoch 4/5\n", - "100/100 [==============================] - 5s 50ms/step - loss: -34.9961 - KR: 34.9961\n", + "\u001b[1m100/100\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 13ms/step - KR: 34.9943 - loss: -34.9943\n", "Epoch 5/5\n", - "100/100 [==============================] - 5s 50ms/step - loss: -34.9957 - KR: 34.9957\n" + "\u001b[1m100/100\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 11ms/step - KR: 34.9942 - loss: -34.9942\n" ] }, { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 13, @@ -526,16 +591,20 @@ } ], "source": [ - "wass.fit_generator( generator(batch_size,(img_size,img_size,1),frac=frac_value),\n", - " steps_per_epoch=steps_per_epoch// batch_size,\n", - " epochs=epochs,verbose=1)" + "wass.fit(\n", + " generator(batch_size, (img_size, img_size, 1), frac=frac_value),\n", + " steps_per_epoch=steps_per_epoch // batch_size,\n", + " epochs=epochs,\n", + " verbose=1,\n", + ")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "As we can see the loss converge to the value 35 which is the wasserstein distance between the two distributions (square and non-square)." + "As we can see the loss converge to the value 35 which is the Wasserstein distance\n", + "between the two distributions (square and non-square).\n" ] } ], @@ -544,20 +613,9 @@ "hash": "e585d72a124540032141457729caea4129d351be49f1f69f41c00c4f8476abb5" }, "kernelspec": { - "display_name": "Python 3.7.11 64-bit ('tf24': venv)", + "display_name": "Python 3 (ipykernel)", + "language": "python", "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.7.11" } }, "nbformat": 4, diff --git a/docs/notebooks/demo2.ipynb b/docs/notebooks/demo2.ipynb index 505d3f62..7049d7ff 100644 --- a/docs/notebooks/demo2.ipynb +++ b/docs/notebooks/demo2.ipynb @@ -2,162 +2,153 @@ "cells": [ { "cell_type": "markdown", + "metadata": {}, "source": [ "## Demo 2: HKR Classifier on toy dataset\n", + "\n", "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/deel-ai/deel-lip/blob/master/docs/notebooks/demo2.ipynb)\n", "\n", - "In this demo notebook we will show how to build a robust\n", - "classifier based on the regularized version of the Kantorovitch-Rubinstein\n", - "duality.\n", - "We will perform this on the `two moons` synthetic dataset." - ], - "metadata": {} + "In this demo notebook we will show how to build a robust classifier based on the\n", + "regularized version of the Kantorovitch-Rubinstein duality. We will perform this on the\n", + "`two moons` synthetic dataset.\n" + ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, + "metadata": {}, + "outputs": [], "source": [ "# pip install deel-lip -qqq" - ], - "outputs": [], - "metadata": {} + ] }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2024-09-06 15:09:48.071410: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:485] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n", + "2024-09-06 15:09:48.082782: E external/local_xla/xla/stream_executor/cuda/cuda_dnn.cc:8454] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n", + "2024-09-06 15:09:48.086252: E external/local_xla/xla/stream_executor/cuda/cuda_blas.cc:1452] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n", + "2024-09-06 15:09:48.094566: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\n", + "To enable the following instructions: AVX2 FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\n", + "2024-09-06 15:09:49.444317: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT\n" + ] + } + ], "source": [ + "from functools import partial\n", + "\n", "import numpy as np\n", "from sklearn.datasets import make_moons, make_circles # the synthetic dataset\n", - "import matplotlib.pyplot as plt \n", "import seaborn as sns\n", "\n", "\n", "# in order to build our classifier we will use element from tensorflow along with\n", "# layers from deel-lip\n", - "import tensorflow as tf\n", - "from tensorflow.keras import backend as K\n", - "from tensorflow.keras.layers import ReLU, Input\n", - "from tensorflow.keras.optimizers import Adam\n", - "from tensorflow.keras.metrics import binary_accuracy\n", - "\n", - "from deel.lip.model import Model # use of deel.lip is not mandatory but offers the vanilla_export feature\n", - "from deel.lip.layers import SpectralConv2D, SpectralDense, FrobeniusDense\n", - "from deel.lip.activations import MaxMin, GroupSort, FullSort, GroupSort2\n", + "import keras\n", + "import keras.ops as K\n", + "from keras.layers import Input\n", + "from keras.optimizers import Adam\n", + "\n", + "from deel.lip.model import Model # not mandatory but offers the vanilla_export feature\n", + "from deel.lip.layers import SpectralDense, FrobeniusDense\n", + "from deel.lip.activations import FullSort\n", "from deel.lip.losses import HKR, KR, HingeMargin # custom losses for HKR robust classif" - ], - "outputs": [ - { - "output_type": "stream", - "name": "stderr", - "text": [ - "Matplotlib created a temporary config/cache directory at /tmp/matplotlib-lzatifz2 because the default path (/home/thibaut.boissin/.config/matplotlib) is not a writable directory; it is highly recommended to set the MPLCONFIGDIR environment variable to a writable directory, in particular to speed up the import of Matplotlib and to better support multiprocessing.\n", - "2021-09-08 18:23:52.158609: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcudart.so.11.0\n" - ] - } - ], - "metadata": {} + ] }, { "cell_type": "markdown", + "metadata": {}, "source": [ - "### Parameters \n", + "### Parameters\n", "\n", - "Let's first construct our two moons dataset" - ], - "metadata": {} + "Let's first construct our two moons dataset\n" + ] }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 3, + "metadata": {}, + "outputs": [], "source": [ "circle_or_moons = 1 # 0 for circle , 1 for moons\n", - "n_samples=5000 # number of sample in the dataset\n", - "noise=0.05 # amount of noise to add in the data. Tested with 0.14 for circles 0.05 for two moons\n", - "factor=0.4 # scale factor between the inner and the outer circle" - ], - "outputs": [], - "metadata": {} + "n_samples = 5000 # number of sample in the dataset\n", + "noise = 0.05 # amount of noise to add in the data. Tested with 0.14 for circles 0.05 for two moons\n", + "factor = 0.4 # scale factor between the inner and the outer circle" + ] }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 4, + "metadata": {}, + "outputs": [], "source": [ "if circle_or_moons == 0:\n", - " X,Y=make_circles(n_samples=n_samples,noise=noise,factor=factor)\n", + " X, Y = make_circles(n_samples=n_samples, noise=noise, factor=factor)\n", "else:\n", - " X,Y=make_moons(n_samples=n_samples,noise=noise)\n", - "\n", - "# When working with the HKR-classifier, using labels {-1, 1} instead of {0, 1} is advised.\n", - "# This will be explained further on \n", - "Y[Y==1]=-1\n", - "Y[Y==0]=1" - ], - "outputs": [], - "metadata": {} + " X, Y = make_moons(n_samples=n_samples, noise=noise)" + ] }, { "cell_type": "code", - "execution_count": 4, - "source": [ - "X1=X[Y==1]\n", - "X2=X[Y==-1]\n", - "sns.scatterplot(X1[:1000,0],X1[:1000,1])\n", - "sns.scatterplot(X2[:1000,0],X2[:1000,1])" - ], + "execution_count": 5, + "metadata": {}, "outputs": [ { - "output_type": "stream", - "name": "stderr", - "text": [ - "/home/thibaut.boissin/envs/tf24/lib/python3.7/site-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n", - " FutureWarning\n", - "/home/thibaut.boissin/envs/tf24/lib/python3.7/site-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n", - " FutureWarning\n" - ] - }, - { - "output_type": "execute_result", "data": { "text/plain": [ - "" + "" ] }, + "execution_count": 5, "metadata": {}, - "execution_count": 4 + "output_type": "execute_result" }, { - "output_type": "display_data", "data": { - "image/png": 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AQIuFUVnczHGs6qDJMVbuubA7z189gDmX9+WxS7PJLapiynvrePrrncyZ2I87RmYyaWiGhzrpX4RVXlMnC8uGZzLtww3c8e46Nu8v8xgB9f0fWrSZomoHb/yYS0RoCK+tzOHxJVuZOkoqjybHWJkyKpPZl/Vlf0k1+aXVvL/aq0waLIGrbk+Pj8BsglW7D/OQX/3CA2Oy+XRdATvdTKIoq5m/n9+DsBATNksId47qxuLfCnC54Nb5a8gt1q85sIaYuH5wuidk9Kf5a4i0WjwFdNpxdWkfEcA8uu/TTWzaX8b0jzfy/PJdXD84ndjwUKPOwECLRIthDbVVHIs6qMulsOtwJfUKnqYvU0Zl8tn6Ai/rBZkk1Vbv+hdhdYix8vC43qzJLfEIuQWTelDD4bG2EOZM7EdVbR2xkaG8ev1p7Cux8/DiLT7hldAQwfxVkoXTs4O+96C4PYBtB8v5YHU+94/pxaylW32YOy+v2MW4/ilUO+r5eG0+N5yV7qG1qhTPq073SlB8tCafR8b19klSPzAmm4oaR8Dq/+HFW5g8PMMTXlLHdbTvQPVo5l1/Gh2irRgw0NJgGIJmjqOpg4L0Gn7LL/OZ+LM7xhARGsLTX+8IKu2ghojUa0aEhnhaTar7FQIb1FstJkwCbjgrnaJqJ/cv8k76KhXUP7zy1BX9uev99cz9Zhf3XNhdV/Fzweo87jqvG2/8sJcDZTXsLKwkt8juw9wBSI8Lp31kKP07teP//rvW572eXbaTJyf282w7UFaD3VnnUwz30do8bhqSoTu5q70R1HE9Mq43eX7yE+p3oO2NUON0sTq3hIPltXSMthEaajYKzwy0GBiGoJlDFUzT5ggev7QPBaXVKAp0aR/BofIawkPNXDkozRN7nzIqM0C984GFm3n1+kEcqqihXXgojyze7CnCenR8b2Yt3RoQt586Kitg0n5obDY1jnriI8N8isxqnK6gCd5dhZVMHp5Bn5QYzEJw76ebuOOcTDq2sxFqNpFfWs24/imEmb1hHEe9S3cCPlBWw72fbuKpK/rpvle1o87nvJe+zeHPIzIAWVcwJDOBKKt+oVttXT3zrh/EwTI7ttAQyqpreeunXKaMzPJJbqsqqNpzbRYz+SXVLN9RSMd2NgorarhDo91kFJ4ZaK4wDEEzh8kkGJ3dge5/Gca2Q+WYEMxautWngXuX9jZ6JUczWdM4Plg440CZnfsXbiY2PNSTKDUJsJiFT9hIPd7urCczIZI3bz6dvKJqCitqsTvqeOPHvUw7v0fAewRL8Na5XFhDzDz6vy08Oq4PJdUO7E6XR/5ae6xa9fvRmsBeATPH9OKF5bs8oRm99yqucvhM3CXVDuIiw0iLj+BwRS2Hymv47897eGhsNg9okugPj80mJtzCZI1Ann9YSwiICDWTGB1GaIjg9nMyMZtgYFo79pfaAyqR1ZoDo/DMQHOGYQhaAEwmgRCw/WAFn60v8CmO+vf3u7nmzM66LRf1JsmEaCtPX9mf11fultcWMKBTO0LMQvf409JjeX7ZDmZN6EdxlZP7F25m6qgsJv2hCyEmwZRRmbgUPJpBq3Yf5p+X9uFuTX+DmWN6UW538vZPMkl932cbmX1ZX3YfrtQ1VqpTUFLtINxi5o5zMqmpc6EoUFHj9Hgx+4qrmTmmF49o8hGPjOtNSVUtLgFzJvbDUe9if6md/JJqZn2+3fM+t5+TyYvf7vLJP8z9VuYfGgprqWGsxev3M3l4V5/39p/4n13mlbJQr3e0wjMjnGSgKWAYghaCQ+U1tI8MZfronmw7WO4RW5s8vCvPLtvBJf1SfCZyvdX01FFZ3PPxRvdKNov7NQnUxy7tzV/P7eaTU5gyUh4zfXRPMhJkx68eU4axt6iSnYeqfOiUU0ZmsXzbQS47LY1nlu3w6A8NSIvl6S+3c7jS4fFAANLjbHSOj9AVduvVIZqXrh3I1oPlvLwix1PFq3oL6vu992se156RxqShGdjcRmtjfinltfUew2S1mJg8PANHve/3aTahm3/wrwmrcUrq65yJ/ahXXETbQnlw4SbG9E3xGAH1OL2JX2jmcP/cjj+OlSpswMDJhmEIWgiSY6zkl9h9JokpI7OYt2I3Y/qmeBrHa8Mh4RYz087vRocYGzsOVXhW5NNHdye/pJpbhsm4+Udr8rn3k01MHZXF5OEZ9E2JIdpqwVFfzwXZZ9ClvXdVmpEQSUWNk//7el1APuHfN53OH9/8lRqnyzMZqgZIUfCJsWclRnJx7+SA/MfUUVkgYFS3RBTwFKypeYziylomDc1gweo8/nFBTzpEh/FrbjFRVgs3u9/bX2G0W5JkKWmTwKd3jguaBNfCajHRNSGShxdvYcbo7pRU1TKufwoZ7SODUmC15/ZIiuKOkZks2lDA9NE96RwfARy7HIgRTjLQGDAMQTOHy6Ww50gVB8vtPhIN2iIsmzuBOn9VLneck0mHGCt5xdV8tDafC/sks+NQhU/FbbTNoivLbHfW06NDNGd3SySvpJpD5TU+E5uKake97iRYUGLX3d45PsLjPajbpn/0G31SZMMYtShNFXYrqXawZMowRmd3oMeUYRRW1JAQaSXEDAfLaql21DFhQApd2svJs9pRzzNf6xeovf59DtnJ0bgUePHagUSEhZAUFYaiEOAxqWJ8WoMxc0wvKuwOQkMEidFhrN9XRkqMjcSo0IBmOlpDohq1x5ZspaTawWOX9iG7YxQAdXUufswpYnVuscezmz66J7HhlqNShQ0YOBUwDEEzg79k9J6iSu54Zx23DNOnO5pN0Cs52jMpvfDNLu4e3YNQs4n/OyeTOxes55ZhMpwSGx7K3Rf1ZFdhhU/Dd1XobVSPRLKTY/hy66EGwxPBKK0Z7SN0t8dFhAad4BQFH96+CnXyU39UpMf7Toid4yPolhjY6lL9bl64ZgBbD1YEfJ6EqFAfddGeHaJ49H9bueGsdI9hsoaYKLc7sTvrmT2hLwVlNTy7bKdHofSv53bnYJmdt9zG66/ndsOlKMye0IeCMrvHAwO4191vOS0uHEedyyeHMmVkFrOXbuW5qwYelSpswMCpgGEImgHUyb+y1klxlZO1eSWeleJVp6c12C6yR4doHlq8mb+f34MaZz2RYSE85JZ0fvqK/p6iqrtH96DaWR+gDqqGT7ISI+mT0u6YwhN6lNanruhPv9R2utuTosManOBOZPIzmQQ9k6N1rzGqRyKRYRYufn5lwOdZMHmwj7roHSMzKal2UOWo54Xluzx6SNqK4nkrcjwKpdow16Pje3O4opY3f5T1D3eMzOSF5b7GrcbpIiEyjLziao8xUXMmtXX1XHV6Gs76et3vTw0nGTBwqmAYgiaGmiD89/e7ueqMdO771Fem+b1f85gwMDUgB6CGLV52F4htO1TBaytz+M+kM3n6yn4kRlk5Ulnr4d1X1NbxgrsRCwSGTzpEWzGZxDFVMquUVjVskxjlZbfobQcanOBOdPLr0l7fMPVJacfPe4qC1Br4TrqLNhTw6Pje7HNLUvjrIYWHytaXPTpEeaq31Wvd9+kmJg/P8JGm1jNMeSXVpMTY6JYYyZVnpPkwjmaO6UX7yDAGpsXpfq8GDJxKGIagiaGuwJ+Y2M+zWgffiVoIPDmAycMzSG1nI7fYzgvLvd3A2tlCmDQ0gyOVtXRLiqJzfATVDk2XrTpX0PDJXed1IyEqDDi2SmaQxsA/bNPQ9mCG42j7jgUNGaZgnycp2sqZXeJ9zkmLDSe/tJp0d+JWPSfZrcT6zNc7g4boumjCYos2FAQ0+XlgTDbv/pLLHaOy+PPZmT45k9jwUA6V15BzuAqXIj0uIydgoDFhGIImhMulcLiilluGZeByKUEn6m6JXuZJRGgIjnoXr3+f45PkjI8IY86XO3xWxNo4ePckfX2fbolRKCiktpNNaIKFfU40PBHMQBxt34lev6HPo3dO5/aRpMVFsLGgzENtnTAw1Ycqqvc9RltDmHf9aZTZnSRGWwEX/7q8H5W1dVTX1lFqd3Dt4HSirSH8tq/Ux8j4h5oev7QPA9PakRZneAMGGgeGIWgi+HPGX7hmgO4E06djDPkl1fRIiqLX6B5U19aRGB3mw7Spdyme5CPox8GTY6wBoaUpI72sluyOMWQkRDa4um6J+D2fx2QS9EmJYfZlfZn+0W8+aql6Ibq7zutGtcPF3z/0ykk8cEk2X246wFld42kXHsbsL7whv5ljenmS+2oISpszyC2qwuGsI6/YjsUsjMIyA6cchiFoZKiJ4b1FVWw/WO6pRJ333e4AyYMHLsmm1O6gps7lo7D54CXZfLA63xOTvmNk5lHj4AfKaliwOo9515/GL3tLUBQ8iWIgIAdwoiv05oTj+Txa1lb/TjHMu34Q5TVOj5HWhuhSYmwUlNnp3iHKI9YHXqnu124cRGmVM0CPSVutLAQ+CWiVkWQLs7Blf5mHkWQUlhk4lTAMQSNCr3JUZe4crnRgcssiVDnqSI6xsruwkhK7M0A87sFFm3Xlkv29ifBQM6enx7FEsxpWFHht5ZqAYw2Kov7/54VrBpCdHM3jl/bx6CKVVDtIirZSYneQlRjF4Qr95je/7CkOqvmUc7jSk3y+fJDXK9Dr3DZ/Va6HuaUWnhkSFAZOJgxD0IjYcySQmrlgdR7/uqIfJVUOTCbBrM+9gnKPju9NUbVTdyJR5ZJjw0OJCDUza0Jf9hyp5P3V+ZRUOwLaJ2p7FxgURX3oUWfveGcdS6YMY3z/FPp3aucpbttTVMkji7cQGx7KExP76hriepdUO1X/T2roxyygT0o0CEGN0+WpUm6oc9vcb3ZRXFXLNp2aCMNTMHCiMAxBI8HlUth6wFeiOTnGypWD0jyyDP7c/n3F1ZiF/mr/YHkNd52bRWxEmE/TFX+BN/8agNaWAziZOBp1Vhte6tI+wuNpJcdYA5rf/PXcbvzvt/2cn92BJyb2pbKmzqdZz4OXZPPSd7tw1Ck8NDabKaMyyUqM8oQKte+fFmdj6qhMQs0mQ4LCwCnBSWlVKYQYLYTYLoTYJYSYobP/aSHEevfPDiFEqWZfvWbfwpMxnuYGl0thY0Gpm86ZSXKMDMMEWwFOGJgKwPur88lMjPS0gARpBGZN6EuoWdCzY0yA7MQji7dQWVvvmUzUiUwLNWY+OKO9J0FswEud1SJY2Ez7HSqKLAqbPDyDO0ZmMnl4BskxoVx1RhovfLOL7QcrA5r1PLhoM4+O78Nfz+3GlPfW8dyyXfz9ww3ccFa65/5Q3z+v2M4rK3LIK9aX8NhxqIKcw5W4/BXzDBg4RpywRyCEMANzgfOAfOBXIcRCRVG2qMcoivJXzfF/AQZoLmFXFKX/iY6juaKuzsX/Nh1g+ke/eVaDalMTsyl4715wyzCHmgF4cmI/7I46OrePoH9KO1bsPsyRyuCN2VUY8f9jx++lzh4qr+XR/3mb+iTHWLl/TC8eXrze8//Q+z8dKJW9IbQG4tllOz35H9XDW/BLHjVOFzsLK3S9w40F5dy5YL0RJjLwu3EyPIIzgF2KouQoiuIA3gPGNXD81cC7J+F9mz1cLoUfc4o8RgC8D/u9F/UkKzFKdwWq9u7967ndOFBWw+yl2/n7hxs8PYnzy+x0jotg75Eq3fO1wmdG/P/YoYbNlkwZxnuTz/QI3x1tYq1y1PlMzhMGpgZ0atP7P9lCQ3QNREqMjScu68OkoRnMW7Gb0b2TSY6x8v7qfB6/tI+PdzhlZBYfr833hIn2FlWd6NdgoA3iZOQIUoB9mtf5wJl6Bwoh0oEuwHLNZqsQYjVQB8xSFOXTkzCmZoG9RVWszi3Wfdhr6up5fvlOXdmIihonk4Zm8M4vuTx2aR9mXNidipp6yuwOcovsFFfVUlzlxGKWjVjyS6o9SeL7x/SizO5k7jUDaB8ZRlJ0WBN9+paJ30OdTY+LID3e5mkY1D0pirxib59jvdqDKSOzKCit1l3hF5TZqXfh0UHSSoEkRoUydVQWcRGh5BXbfSjA/lIgBgwcKxo7WXwV8KGiKNo2IemKohQIITKA5UKIjYqi7PY/UQgxGZgMkJaW1jij/R3Q8tDtzvqg7RSjwyxcflonwkJMvH7jIArLawmzmJmtaUM5ZWQWOw9W0DM5mo35ZaS0Cyc93obVYuZIZaVHO0g1IFU1TsrsTp5dJnsN3/7OOoNZ0ghIjwvnLyOzfHSi7rmwh6fRj1rD8cI1A6mvd+GsV3jyy2046hTdXsjhFjMvr8jxXF+tMH9kXG+27C8HYF+J3VNdrsIIAxr4vRCKcmIJJiHEWcCDiqJc4H59N4CiKP/UOXYdcLuiKD8GudabwGJFUT5s6D0HDRqkrF69+oTGfSrgz0OfOiqTT9cX+DSVVyftujqX52G/56KeRFnN2B0uautcxEVY2Hukitd/2MPfzu/BrsIKT8z40fG9SYsL54Z//xIwCbxwzUAeWbyZa89M540f9vp09lpiMEtOGXIOV3LRcysD/h93nZtFWU09ZhP07BBNRY2DLgmR5BVXU1he6yNpnRYXTvvIMGrrXDy+ZEtAn4NXrx/Ev7/P4YyMeNLiwjGbBBaz4OHFW3z6VxsG30BDEEKsURRlkP/2k+ER/ApkCSG6AAXIVf81OgPoAcQCP2m2xQLViqLUCiHaA38AnjgJY2oS5BVXse1guafz12/7Spk8vCvzVuz2tG7s0SGal7/dxY7CSu65sAfto6xUVNdid5p9Wkc+cEk2t4/oysHSamwWM7efk4kQsK+4moRIfX1/l0vhicv6ceeC9QEURCNkcOoQjHaaFh9Bud2JLTSEfcVVpMVHsC63FBcKZiEJANW1dRyurOX55TsZ0zeFj9fmBxSVzRzTi39/n8Pgru19WolOHZXFlFFZJEWF0bFduEEDNvC7ccKGQFGUOiHEHcAXgBn4t6Iom4UQDwOrFUVRKaFXAe8pvi5IT+AVIYQLmbiepWUbNWf4txpMiw1nbV6ppwo4Pd7GbcMzeXnFLsb0TfEYgQ9+zWNYt0Qu6ptMhNUSVHn0oUWbefqK/iS1s7HtQIWPyNzjl/bR7Y7VNSESIbztHbX7jJDBqUMwhdOOMVZZeVxZQ3bHaNJiw/kpp4j7PtvIlYPSPHLW0tPrw/PLdwCyCO2pK/oTGWbGYhbc9f5vTBiY6jEC4Msw6p8aaxh5AyeEk5IjUBRlCbDEb9v9fq8f1DnvR6DPyRhDY0JPimDe9YM8EgQAY/qm8NBiSQ1Uk37p8TYmD+/KI4u3MGloBs98LY931Om3fqxXFJx1roAJ4J5PNjLv+kFMnr/a8/7/urw/XdqfHH1/A8cHPdrplJFZ/EVT2Q2ysrzO5eKhsb15YfkOHy/x03V5PDGxL3lFdp/CtMcv7UOflEjSYm36XqAChytr6JpoGAIDvx9GZfHvgJ4UgT87SI87PqZvikfOWLs/KUp/RRljtVBeoy8xUVHr9CiQmgSEWWRIwKgcbnyo33nK5MEs21ZIvcsr6HfX++tJu+0sdhyq9CwUtOywipp6Xv52F8O6JXK43EF+SbWnulg1+i9eM5AN+aWeeyQ5xsqEgalSojwpig7Rhrdn4MRgGILfAb2YsMoOUjVlshIieOnagVQ56oi2Woi0mjlS6eDJif14dYUkRanHl9oduvTCakcdpdUO/W5XRdUBonNqQri1qYe2BJhMgmpHfUD/5Rqni71Hqn28RbUCfNLQDN2cgFZmpMbpYn1+KR+szuev53bjqy0H+OPQrmw7WI5LgSe/2Mado7qxr6SajjHhdGlvGH0Dxw/DEPwO6MWEF20o4LFLe1NYXst7v+YFMIXUauKSagePju/NjzsPM2VkFjV19ewqrOTT9QWebmSKAgtW53HjWZ0xC8HfL+jOk19sD6AYJsdYDQ55M0KwXEFVbZ2uV2c2BZcZUYXmrBYToWbZbvR/v+3nhiGdA0JQzyzbwZi+Kbz+/RqeuqI/5/dMIq+kmkPlNYSHhuCoryc+IszwDA0EhWEIfgf0YsJXDkqjpMrBs8vkQ+z/cD+7bCdPTuzH9kMVPL98Jw9cks3+kmo6R0bwyOItAavCh8f2pl24hfBQE+3CQ30a0agGRZ0swEgINwcEyxUcqarVNRCD0uM4UKavH6Sqlqr1IQDnZ3fw1Cqox2nbmdY4XcxeuhVnvctH0mTKyCwWrM7z5CsMY2DAH4YhOE6obKGEqFAWTB5MtaOeKGsIK3ceoV14aIPaMtvdDebV+HD7KCux4RYuH5SKgsLUUVlUOeoxCSitriXSGsKQrgn8vKcoIOQAYHarFhgJ4eYB//yMzWJmynvrcNQpnuIyLT34uWXbuXFIhq6R6J4UxeThGZTbnSREhmG1mOgQY9W9r2wWE3YNScFf0kQ1FoZSqYFgMAzBccCfLZQeb2PWhL7sOFTJe7/mMWN0Tx8dGP+HW1F848Ovf5/D1FFZfKDpIbBoQwFTR3XjtPR2dIptuAH7qB6JDOkabySEmxG0+RmXS2H66J7c9f563vxxL1NHZdE5PoK4iFA6xIRxRuc4iqtrfZreqCv4OV9u45oz0hEoKMiag/hIi+59MCCtHbM/3wbQoJChET40EAwnXFncFGiqymJtBanadLymrp55K3KYNDSDxb/JKuIFqwNzBNoEIMBd53Xjqa92YLWYfOLBz141gJzDlXSKDadncrSHEupPVzWqSFsGVA+yIQaXw1HPuvxSyuwOIsJC2FxQhqNeIdoawuOfb/NUH2d3jGF3YWXAfdU5Ppzc4mrsznp6dIhm+8Fy3te0MlXvsde/zzEqzNs4TmVlcZtBUVUtU0dlkRobTkSomf97Zy23DMvwrLhyi6QI2ISBqZhM8NTl/RBCsPVguY8RsFpMZCZGepK9qmx0bHgoRyql9ID/hG9QQlsmjoXBlV9m58Y3fuGWYRms2F7I3y7oTn29wv+9s9anfeUtwzJYtCGQVPDAmGwSosJ8tI605AQ1R2CEDw0Eg2EIjhEul0JRpazY/fuHGzwGAPAJBx0oq/EkcO+5sDsAnWLDPdW+6ipu9tKtTBiYyuvf56A6ZZcPSvXUGUBgByqDEto6oaUj7yisZOuBcmLd+SYtq+ijNYFU07+e241dhRU89XUgOeGtm8/AajHhrHcxuncHY/FgICgMQ3CM2FtUxZYD5T6N5K0WEx+tyefu0T2oratn5phenok8Pd7maSN557lZPqs41Tswm/Cs3KwWE51iw3Xju0Zct3VDzQF57qV6F/tLpZCclnhwoKyG+atymTQ0g7Q4G3nFdt78cS+XnZaqe9+UVDvolhRlGAADR4VhCI4Rh8prcCneh1LVmF+wOo+aOhdPfy2VJCcPzyAzMZLEKCs3vSEVQitr63Ulg0dkJeCod1HvUshIiCSvqEo3GWjQQls3VNrp7KVbSYgK464PNhAbHsqUkVnU1tX73BMHymp4/fscH+pwsL7WuworuXPBel64ZgBd4iMprJC6WIZhMOCPk9KzuC0gKdrqeeDAuzr7+/k9PLTAA2U1fLA6n9IqB0cqarllmOxhu2J7IVNG+vYdfnR8b/qltuPMLvFc1CeZxKhQhma1D+hAZcR1Wz9MJsH5PZO467zubDtU4bmX5q/KpVfHGGaO6eVzTzwwJpvFvxV4XseFh3LXed18jpk6KouosBC6JUay81AlFz+/kqtf/ZmLnlvJ0s0Hjf7GBnxgeATHiM7xEfRJjWHqqCxPMrek2oGCL13vhrPSqXLU8/jnG3yYHUs3HWDy8Ay6J0WRFG2lb8cYQkLkg6uN/fdKjqF/p3ZGUriNIa+kmukf/cYtw7x1BQfKathYUMaiDQU8MbEfuworqHfBu7/kehRtMxOj+OeSrQDMmdiPbYcqfIoO9ZRtjXoCA/4wDMExwmQSjOyeRGZCJAPTYnHW1xMVZqGwspapozI9dL3U2HCPvDB4C3omD88gKdqKgsKsz7fwx6Fddemfhk5Q24SaMPZva7loQwG3jcj0NCdS8VuB7FR213myr7XVYmLrwQpPuEiFPYi8hZF3MqCFYQiOAyaToHP7SNLiIli6+SC3vL0mgK5ndwQ+eLHhoZzeOY7DFbXkF9u5pG8Ks5dupUeHKONhNAB4E8YHympYuukAT0zsh91RR5f2EXSMDaOguNaHqABeGrIqbz5vhbfDa3KMlcsHpZIQFRaQP0iPt2GzmPlp9xEjZ9DYcLmgeDdUHISoDhDXFUwNROiP9/jfCaOg7Hdgd6GMufo/lK9cdxohZsGkt1Z79iXHWLnhrHSf2oCpo7IAGJDWjsEZ7ZvkMxhoXlCr1mcv3RpQjPjIuN4kRIVyqLyWBxZu9hSYdYoNp6iyli4Jkby6YheXDuzEI4u30C0xkj+fncnWg+WEhZiICpOFaSqbzb+/slGc2EhwuWDbIvjkVnDawWKDS1+BHpfoT+7He/wxIFhBmWEIjhMul8KSjQe44911Afvm3XAarnqFmjoXe45U8v7qfC4flKq7kpszsR+9OkYbHoEBD1wuhbV5JVz3+s8B98vk4RlkJkRSVFlLqMXsoSmrwnTtI0IJtQisISYOlDl8JCv+em43BJKZlhAZxpWvrgq4vlFx3Ag4shNeGSYndRUWG9y6Uq70/Vf+xbuDH98+63cNwagsPknYW1TFzsIKXXf7cEWtzwP6wCXZRISZdWO0IWZhsIEM+MBkEhyprA3aiewfH/3GnIn9mOaXg3pk8RamjsoiKTqUGFuYT++D2PBQqhx1pMTYCLOYySvRVzs1cgaNgIqDvpM6yNfVRVC4JXDlH5Gkf3zlwd9tCILBoI8eJw6V1/D+6vwAOuiM0T0DqoIfWrSZdjaL5zgVVouJ+IhQ9hZV8dPuI+QcrmxbdD6XS66O9qyUv12uo5/TRpAcY9O9X1TBwnpF8ZnIk2OsTBqaQccYKyntIiipdnDLsAySY6wePax5K3KY/vFG/vT2akLMQvf6Rq1KIyCqg5zktbDYwGzxGgGQvz+5FUIs+sdHdjjpQzM8giDwb06vJtSSoq2UVDuYvyqX6Rd0p3P7CCpr63EpiqfFoIoap4syu1O3+1hxlYPr//1L64/T+ie7YrvA9v8dX9yzkRJmzQHZydE8Or63TwxfFSy0WkzYLGaflpXXD073iBxO/9hXwVQIPLkpkPfjrM+3+lTAq32RDe+0ERDXVd7rn9wKtjgYcB0k9ABnjXztLPAe67SDo9p7vPZZiet60od2UgyBEGI08CxgBl5TFGWW3/6bgCcB9ZO+oCjKa+59NwL3ubc/qijKWydjTCcCveb06kStVoH++/vdRFot/Pm/awOYQ1pxubiIUOZ8uT1AKGxc/5TWz+3WS3Zd9Y73dXQK9LsaDm2GiPZypRPbRU7y2snfVQf/uwuKc05Kwqw5IyTExPh+KXRLjGRvUTW7Dlcyf5WsCZg5pheWEOGpZVF1iPQaIT23XDZC8g8D5RbZiQwzc8c5mdTUuTAJGJjWrvUtQJorEnrCVe9C5SFYfKf3uRgxHX55FcrdU6TFJhc96X+AW3vJcFDkqVsEnbAhEEKYgbnAeUA+8KsQYqGiKFv8Dl2gKModfufGAQ8AgwAFWOM+t+REx/V7oHoBhytqA5rTayfq83smER5q5rb/rAkQ+po8PIPnlklJ6bvO68buwkomD+8akNx7YXlgb9tWF6ct3h3o8uat8hqB02+BFU/I1z8+Jx+GhJ6QeW6g1zD8H/Dra/JB+eRW+XCc5Dhpc0FIiIm+nWLpndKOvUVVDOjUjnX7Snlh+S4uOy2Vj9bkS72hWFuDjZBAX3pi9+EqrCFmFm0oYPronqTFGd7AKYd2UTT4dlg11/e5+G42DJkif2tX/iaTvM9P8b1+MjyCM4BdiqLkAAgh3gPGAf6GQA8XAF8pilLsPvcrYDTw7kkY13FB6wVolUVV1DhdFFfVAnC4opa1eSW6x2QlRjF7Qh+Kqx2EmU28/sMerjo9zdOmUlGgqsbpUSNV0SrjtHrJMXOYvNH7Xe01AuB9GIZPB1u7QAOy4gn5AK2cc8oSZs0NanFh5/gI7O5KdoCSagdzv9nFHSMzG2yElFtUpRuWVD2MBX8aTJ9Uwxs4qQgWxtQuigT6SeCUQXDT4lO68g+Gk2EIUoB9mtf5wJk6x10mhBgO7AD+qijKviDnpui9iRBiMjAZIC0t7SQM2xd7i6p8vAA9VlBBaQ3XvS514/WOsVpMxNgs7CyswGYxU+2s545zsiiqrKWsuhZFkR2k+qW147UbB/HLnmJcCp6VWauL06rJMfWmj06BsCi58nfV6z8MkYmwe7n+PnW+OkUJs+YKbQvM4qpashIjmf7Rb3y0Jp+ZY3oxb8XuoBM+4AlLdk+K4vElWz2hy2pnvWEETiYa4v1rF0Uh4b7PBcjXcV2abHHTWMniRcC7iqLUCiFuBd4CRh7PBRRFmQfMA1lHcLIHqNWE9y/zl0U9fZg831sotmhDQcAxM8f0Ys4X27iwTzLRNotPOOiRcb1Z/NseHHUKNovZp8Bs9mV9Ob9nUut7KLXJMaddJse+vl+Gf0b8Q/9hsNggJhVGzIB1831jpgqnNGHWnKGVHhnoUuiTEsOh8hocdfWM6ZuCyQRPTOzH3iNVdGkfwZwvt3kmfLX73aShGT75q9o6Fy6X0vruu6aCXihUDWOqiyJbnHcx9N1sr8EY/3KT3tMnwxAUAJ00r1PxJoUBUBSlSPPyNeAJzbln+5377UkY03FD2xdYVX6cPDyDAZ3akR4fEWAoVLbGpKEZcpWf2o6Xvt3JbwXlnN0jMYCtMfOzTUwaKj0J/33TP/qNPikxrSs/ANK17XGJN9nlqJYPwll3SNroBY/DF/d4H4YL58DyR7xJ4dGzwF4q9yf1ghAr9LgIOvRrlYniY4XJJGtQth2sYPvB8gCJ8/R4G1NGdeNeTVHZQ2OzefFbmZdSPYYHFm7ijZvOaH33XVMhWJ1A8R4Ii4axz8v7/uv75XMw+Hbp5QozdOjTpPf0yTAEvwJZQoguyIn9KuAa7QFCiGRFUQ64X44Ftrr//gJ4XAgR6359PnD3SRjTcUNlA6nhoZJqBz06RDOiW6JnxeRvKNQy/30l1RSUVPPHP2QwuGsFKTG2oA3E1b/997W6RLEKbbKraDec8SdYeLt8QOIyYPwrcGSH/PubR6URALl/6QxvYu3se+Hnl8Be3KpZQ0eDSmjYW1TF9oPlfLOtMMAzvW1EJnHhZl68diDr95WSlRhFWXUtY/qmBDRHarX3XVNAu+rvd7V7kg+Bw9vgq5nQabBcBDntkiq6co733C7DIL4FewSKotQJIe5ATupm4N+KomwWQjwMrFYUZSEwRQgxFqgDioGb3OcWCyEeQRoTgIfVxHFjQxuH1ZOA1jMUNouZMIuJru0jiI0IJTzUzF0fbPCRElahFgWJIE1EWl2iWA+ueq87DHLS/9TNojiy3WsEVGgTa98+BsNngKNC0k1jOkFy/zZlDPRozarEuZoHGNipHbsKK4i1Wbjz/bWeeoP7x/Ti0SXb2uZ911iI6woT34TDW33DPiNnwujZMiTkqtMPiYZFw+EdklbaBLUyhtbQUaAtLIuyhvDllkO4FLmqWrmjkLN7JJISY2N/mZ3MxEiKKx38+8c9AcJhD4zJ5uUVu3DUKQEidK22mMwfe1bCW2Pk32oNgQASe4MtFt67KvABmfAaHFgHoVGSTbR0xkkT4GppyDlcyUXPBYodqt3K1L8X/1bA7Mv6Ul1bT5WjHotZ8OWmA5zWJd4nb6Xed4Bu8aSB34HDO2De8MD7WKWGxmXAH+6EpdO99/G5D0NdrVzsnOJ729Aa+h3wX4FNGZXp0YRXqzr9k8Wp8TauOj0NIWTyLq+oirT4CN7+MYfnrhqA3VlPh2gr5/fqwOHKNtZ8JjLJ6zprawgsNrj4KRjzLCye6t02epZ0qYtz3Mm1WfqJuFZOI1WhzVOpqHG66NI+nCmjMunZIZr3f83j1uEZrMsr9VlsPDq+N7/kHGbBnwZjr6v33HdA0OLJNnFPnmxUHtLPEyj18u/iHPjhGbjyv7DvZ+kl1zu9RkA9/pNb4bbeoLgapaLeMAQNwJ9S6lK8YR21qtNf/Gvy8AxsFjNv/Si52lNGZvGvL7cxfXRP+qT4cra7Jrah2KzKrx4x3VsXoL3x/3cXDJsmV07t0iE0Ala9BNkTpNcQk9poAlzNFVpCg4r0eBvJMVZC3d3uJg7qhKLAw4u3+tyb9326iZeuO41sd2c8/1yDKo/SaqvcGwv+lGlw052Tva+Lc8BRBe27Q50dopIDJSZscbL4csnfGsUDNgxBA/BfgWlppWo1Z3KMlQkDUz2J4PBQM898vZO3bj4Dq8WEs97F6N4d2s6qPxiKd8OHN8sb/OwZ+pN6nR1WzJE3/SXPQbcLvAZjxIwgD1jbqSfoHB/B7Mv6Mv2j3zy9BW4bkck9n2z0CUVOGZXp0w9DvT+rauvYfKAMu7Oe/BK7rp6RagyMJPLvRFxXSQX99DZf+QhrDGSeD4WbYehdUFMGn/89uMTEgOu8RgBOuQdsGAIduFwKecVV1Na5fFZgB8pqWLA6j6cu70dEWAjp8baAXMDMMb2IDQ9FQaFfp9ijvFMbgkqtcxZAaW5godmA6+Sqf/g0WP8uhMfDoineY9bND+Rej3kGhFuXqA3kCUwmQcd2Vo+8RERYCNM+3BCgNaR6rrHhodw0pDNPf73Dp54lJtzC8zraRNpcg5FE/p0wmeR9PGSKDAcpyAneXgxXvw/710B4HHz2fw1LTLRLb1QP2DAEGrhcCnuOVLH1gOwH++SX2wKoeVNHdcOFQm5RFTNG9+SvfppEanjIeJD8oHWZ178rtYNWPCE9hDP+pDPBCzjrdnnu+nflSumXV+Gy16GuBg5vh2UPtzk6aXxEGK9/n+OpbtfTGlI9VyHwGAH12JmfbWLy8AyuHJTm8QC011FzBK2uyr0xUbpP3s/+cDnl9guf0J/kE7Nh3IvyGSje06gesGEI3NBLDOcW2Zm/KtdHObRDTBircoqZtyKH/zs70+cBBPlAdU2IpGOUlQ37SjhQVkNyjI3s5GhCQlr/RBUUcV3lBL/4Tjmpr5svJ3BLBLx/nfeGt8VBxQFfZUZVcM5eLGl2H03yfUDaUNJYpTFvPygXK3paQ6rneveFPYM2udF6AOr5wzLbM2FAihHGPFFEd9SfxB2VcltopP5+pR7qHRDeHtb/x7tYUp+Dsc+fsupjwxC4oSaGY8NDmTAwlZQYG1NHZfL+6nzmfrPL0wy8sNzhKRhz1Lt0awISIi0s2nSAmZ95Y7CPju/N+H4pbdcYmEyQeroUlYtMgIgEKMkFa7TvA9Hvat9aAzWxPGSKLLhRXG06aazWu/RKjmLz/nKmjsrivV/zAjzXKwelUe9Sgtaz1Dhd2DRGRHY4C6NzeyMvcMLoOEBWyX8+zVs4OXoW1JTLXNf6d+VrLRV69Cz48XlZg3DNBzDwBlj7tqyxMZllMVqX4QZr6FTjUHkNseGhAZTQqaOy+HzjAS7sk+yh400dJVUf9TSJpozMosxe7zEC4GVtZCVGtu28QWwXiM/wNuYY+zzs+8V3dRRMmbFdGnw7SxbmtPGksckk6Nw+krS4CPKKqxiYFouzvp7/TjqTA2U17CisYP6qXG4eku7pXaDX5KZPagxv3Xw6heU1VNbWse1gBYfKa0mKDiMtzvAKjhta5dH0IXD9Z2Avkgbggxt9E8Nb/yfDQEo92OKltMr+te4LKZA6SOYJHJVQVSQ9BXHqFpGGIXAjKVqu+P0poe/9msesCX35KaeIW4Zl8NGafN5fne95wFRNol7J0Qhg+6FKwkJMut3KDpbV0K9TkAG0BfhrD9VUwM4vfVdHIkR/oi/eI4tuDm+DC5/0ZVycQpe5OUM1CNpVvMulkJgbRlpsONE2C4UVNUwdlUVCZBh5JdUeCeqpo7KY8dFGSqodPDq+D856F5v2l7NoQwFXnZ5GVlIkI7u3QiHEU4X6OtjyCSz8i9cLGDZNEiN+fC4wMTxurqyQ3/Au9L/aawQsNkmdfvPik9q0/mgwDIEbneMj6JYYFdAP9spBadz85q8BK6q3f8rlxWsGcriilmpHHXnF1Tz11Q4fT8K/W1mHGCOB7KM9VLAOss6DH571CnCF2KSukLbKcvQsKVg3ehZ8/YA0CGq9QcUBKTfRBhLFxwKTSRAWYuK+zzZxy7AMVmwv5G8XdKes2sFp6bF0jo9gb1GVz71536cbmTQ0g9e/z2HKSBlqGtc/hYz2kQaF9FjgcsHeFV4jALL+ZcnfJOFBz8Mt3CKNwIDr5P07fBps+hjOfRDqnI0e/jQMgRsmk6BncrRPTFWvaExNsr3+fQ5r8kqZ+80ubj8n00cBUq9b2aPje5OdHNNkn69Zoq4WYjvLAhutAFd0Clz+pnwgTCb48j6vIqmaOFZpdkOmSKqpAQ+qHfXUOF1Ehpm5sE8yt85f40MfVavjVWiZR+r97VIwagmOFcW7vZ33VAhk+DMxW9/DDbEFsuUueR66XwwljcsYAjCWURp0aS8ZGSoTw2zSVwo1m2DqqCxW7ijk9nMyPS0D/Y/r0zGGV64byILJg9t2olgLl0tK8e5ZCSGhMn5qsfkeYy+GgrVSW+ijSb6KpCuekAll9XVCzzYZFmoIagVyXb0SIHmeX1Ltub9VqAlk9RibxYRJYFCgjxUVB2WsX3sfh0bJiX75I3Lxou6z2ODipyGxZyApYtFf5L2u9vHQnnOKe3AYHoEGJpPg/J5JvHHT6RRVOggPNXs8BLVC02yCoZntqXbUAbK3QDC10a6JbdS19m/XF9tFrnL0GtFPeE0+GP/7q3dldPa9EBImtViO1qksKdsIC/khLTacedcP4kCZPWCB8v7qfB4e15v7PwusKgZ53/ZLbUdtfb1RS3CsiOogwzpauqeiwAr3RP/ra172T2K2fBaKd+nf24e3Sb2ihJ7wxy+gNE/SUZP66LfAPEkwDIEf8kurWb9PCnbFhod66HnaCuJ5K3J4dHxv3vs1jxqnS5c91GaLcvzb9alJM61mihreASjfL3+rlZjCDNHJkmtd7wjCt8bLvijOgfhMwxi44XIpfLn1kKf3tnaBolKgI0LNzLm8H4qiYDaZmL1Utq9UjYIQipEoPh7EdZWx/a8f9E748V299225pvfAxH9LGnXBWv17+9Amb9hTlZ0ICQt8hk5yEWWblaHWyktrpXd/zinixjd+8Xl47r2oJ9M+3BBU/lc9bsLAVHolR9EzObrtFuUc2QmvDPPe4MOmycYy/jf8YHfVsMksWRX+zTxCI2RHMlcdLH9Y08XsSagugtoKmWyzF59SNkVLg1aqWquQGxseGiB/PnNMLz5Zu48zMxIQAiJCzQB07xBF5/iItnsP/x64XG6v94AUlAtPgLcu0r/vV82Fi+ZIAsTX9wfXG9I+J3rP0O+47w0Zag30Gnyo0rtVjjqfCf9AWQ3bDlUEzRVoj3v9+xzm/7GNt/7zb9cXrC5AnV+Uel9ZalucZFJEJUH5AejQ19dbCIuCLQth15fe67WRYjIVwRYx4CuUqHbSmzQ0g9PS2vF/76wNkEN56or+zF66lctP60RCVBj7iqu595NNlFQ7DDnq48WhTV5PuNNg36IyrSfstMOSaXDVu5JG6qiW4Z5FU71GAHyfk1PMImqThsBfXlorvZseFxEQ7zcLr4iXquRoFnBaeqznWKvFxF3ndSMhKqypPlbzQDAZXr3wjsl9+w24zmsE/PsUjJ4NIeHgrJDGYNlDstdx9RHJvW5jxWQNLWJMJuFJFPvfq2V2p+5ixllXz20jMnlo0eaAnIEhR30c8G9c33kofP8vuaKPTZdV9L++5p3onXYo2ycppyArju1+zRnV50RwyllEbTKwGqzBR2FFDV3aR/D4pX18NFy6JkZy70U9ueGsdF7/PocXlu/ilRU5FFbU8ubNpzPjwh5MHp5BRkIEaXFtMC+ghT/jYdPHcNG/fBkQY5+X9QN9r4CO/b1Ki/2uDuxTsHQ61FVLeeqf5sKA62X4adjfpCs97kWZmHO5dIfT2hBsEbO3qAqQ9TAvXDMg4F6NjQjVZQvFRoR6jIB6veeW72TCwFTPM2HgGKDnCau06NJcGdrRrvYtNqmzpWLdfNmcSfucjJguw596z9BJZhG1SY9Ar8GHVno3MyGCh8ZmEx4aQn5JNcWVtdjdtQH+shFTR2XRNzWG5BibEVOFwOrhSDdrqNMZ3tcq46G+Tuqyh9jkzR0sjKR2d3La5QMz5lkoy/NWbLahtpUNLWIyEiIxmQRd4iO54511PvfqAws3MXNML59WlVNGZlFS7dC9nqpEalBIjxENecJatV1trmvDe95j7cWQeoaM+1cehIgkmT9LHdTwM3SScFIMgRBiNPAssnn9a4qizPLbfxdwC7J5/WHgj4qi5Lr31QMb3YfmKYoy9mSMqSH4N6JPj7fx0Nje5BVXsmFfGXd/8ptPQs3hrCctLlz3gekQY6XKUX+qh9yyoK0eVqF97XJJyYi9P8gYqi3O3bmsJngYCWSh2Zl/lmGhJdMCm3bclArJA1q1MTjaIgZkIZj/vZpbZKeqxumjpLtgdR7Tzu+hez2TgKeu6I9JwE+7jxi9jI8G1RNWw0ObPpYLluLdciFjssAV/5X3bnh7KNoJA6+Xrw9vlfe/EJIBp31u4jWrfv9n6iTihA2BEMIMzAXOA/KBX4UQCxVF2aI5bB0wSFGUaiHEn4EngCvd++yKovQ/0XEcD1QFxx5ThlFUVUtesZ3b/rPGUzHsn1CbNDSDUrtT94GxmEzsLqxkyrvrjOTa0eByQdEumVSzxngTaU53r4Ezb5Mu8Mo5skTfZIak3vD90/L80yeDswoOH9H3HHZ8AWX5rdoz8F/E6FGVgxmL1LgIntKcN2VkFq+u2B0gTPfIuN5kJUZQ43Rx4xu/kFtkN3oZHw1aT7i6SFrakj1gCZcLGUeF7MBX74D3r/V6BuNelCy5I9vkaj+2i/6961+b0ww9gjOAXYqi5AAIId4DxgEeQ6Aoyjea41cB152E9z0hmEyCjIRIKmqc3PvJRt0GH+B1k7VCc9oH6ckvtzF5eFdiw0ON5FpD8K8vUHsXa1FTJvu4+nOmR0yXRTaxabKz01m363sOrvpW35tAu4gprKjxNKHXTs5pseH889I+3O2+r9V79eVvd3HPhT1Ii4/gUHkNRyprKatxkhgdxjNX9Keyto6D5TW88M1OxvRN8WgPqQ1sjPv7KDCZ5ARduMV7n6v377r/yPDP6FkwfIYkP4RGQVmBr66WXojT/9k5BaHQk3GVFGCf5nW+e1swTAI+17y2CiFWCyFWCSHGBztJCDHZfdzqw4cPn9CAtfCPueol1KwhJg6U1fD2T7m8dO1ApozKZNLQDOavyiW3yM4ji7cYybWjwZ9VodR7i83OfUiKbq2aC/m/BPZq/W42nP+opI9qY67a5Nnwf8jEmkqra8VQFzGDM9p78gIq1IKyZ5btYNLQDKaMyuTpK/qzdNMBymqcWMxmbvvPGqZ/tJHnl+/iLyO7EWW1gIDnv9mJ3ekit8juoz00YWAqgHF/Hwu093l0imQNOe3y/rXFSZXdump5D7dLk57CWbfLY9UQZ/Hu4NeE4MedABo1WSyEuA4YBIzQbE5XFKVACJEBLBdCbFQUJeATKooyD5gHsqDsZI0pPiLM40brVQjPHNOLcrsTgJJqB0IIXdGusBCTkVxrCP6sil3L4Q9T5YMx+Hb4bpaXN60X9nFUSsNhsUn2hU/Zfi/44h65vY3RSf2hZRVpu489ObEfCvAPTWGkJDx4lUennd+dOpfLs/hRiyTTYm3cMTKTRRsKjPv7aFDv8+iUQCq0WkcQ3l4ufNS+xdp95QUytHQEbxioqqhF1BEUAFqV/VT3Nh8IIc4F7gVGKIpSq25XFKXA/TtHCPEtMAA4eabuKHCheCZ/T3P6K/qzq7CSOpeLcruTKkc96fE2po/uyZHKWk/nMq3EdIZbsK5NykocC/xZFV1HanoQcPS6g5JcmSu48An4/B/ygVk1V1Lulj3kNQKnWJyruSMYq6igtJrY8NCgoc8ap4s5X27nmSv6M3VUFhGh5oAmTY+O701abHhjfpzmCTVeX1UEIRZZSRwaCXUOWRFvselToVc8ITv0tesEH94cuG/w7bD5Y1lfMH+8X7+NDK/4Ipz0Bc/JMAS/AllCiC5IA3AVcI32ACHEAOAVYLSiKIWa7bFAtaIotUKI9sAfkInkk45g1ZjxEWEsWJ3HHedkkhJrY8+RKh5ZvIWSaodHm/3u0T1Jjwv3SdCp/QbU46KsZoZlJRqJtGDwZ1WYzMGpdmffA98+rhGhuwd+fllO9t8/DWPnwpHt0O0C6NBParecIlpdS4Neojg93kZilJXcoqqgrStBGgOTSfD5xgP8aXhXHv/c33vYxMC02LadI1Dj9V8/KGta/Ff8O76QIoole/RX8bGdYf86/X0mM1z8L3jvGl8jsfAvcNU73u3NsY5AUZQ6IcQdwBdI+ui/FUXZLIR4GFitKMpC4EkgEvhACAFemmhP4BUhhAuZr5jlxzY6KWioGjM1xsZfRmZx36eb6JYYyZ/PzuTyQanUuyS97qrT0wgJEcx837f15LPLdvLkxH5sO1jBgtV5vH7D6YYRaAgqq+LaeMj5BhJ66POsQWoMaWUlQjThiOIcOLxFFph1GQbmkFNKq2vu0C5wEqOshJjh8Uv7cM8nG4kND+XyQan06BDN9oPlfLOtULe1qlZ5NDTExMTTUgkLMel6D22+R4Earx98u/6Kf/DtsOZNGHmvb2cykPd7WJRXstp/X5cRwXtymyzeGoPmWkegKMoSYInftvs1f58b5LwfgT4nYwwNIVg1Zve/DKOg1E5xZS2Th8tmHHuLqujfqR3Vjnqyk3sQajGx7YC+1tCeI1W8/n0OU0dlEWI2jMBRYTJBZJKsELbFSakINba/bj6MfwVQ4NPbAh+SsXOlARAh3gK0NpwLAP0Fjtpj+65zs4iNCPP0zlYn/aWbDnhqCU5La8eDizZ7lEfvOq8bOw6WU+mop1+nWF3voc3nCNQcQLBcVliUrJqvLoKJb8IXd3sl10fPkn2HLZGSJl2aJ+97ezFc8E8ZPlVc+kYiqkPzriNoCQgWN80rrmLdvlJcCrywfJcnOVZVW8+uwgreX50PwD0X9dR9KHp1jPaEiAaktfPpHWsgCLQhoppySaWL7STjrEW75GpJ7wE7sl16AWq/ggnz2nQuAPQXOM8ukx3GymrqeeprXy9W7T429xvZNS/7iv78/fweKEB+STVhZhMvr8jhstNSOVRRc9R6hTYJNdcFgRN2XAZYo+XiRg3hXPA4OGshpiP89CL0uNjbp8BicxuAZDCHyhoC8A2hNlLeq00EU9W4qRZWi4nw0BBcihTlSo+3cf1gqc/yl3fX8cqKHK4fnA7A40u2MmVklo/+0ENjs3n5251U1tZTUu0wVkrHCk/hzUpIPwuEAp/dLuOgK5/0hoy0sNggobuXYvftY5J50YZzARB8gSMEDdbEqN7BrKVbqXbUUVJdS0psOGU1dVwxKJXEyFA2FZTRs0MUS6YM473JZ7JkyjCjmAy8Cxm1EY3WKIx60GsEQP7+4h5o3002qj9zsjf35dl/N0QmQsbZ8n7WPh83LZa/G6FAsk14BMGqMZOiw1i0oYA/DunC/WN6sSG/jDvPzSKlXTh7jlRRW1fPDWelM3vpdhaszuOJif3YcagCRYEXv93FmL4pmE0YK6XjhSpBcXhHYLu+rx+QLrTKKFKTcMsflXQ8lWJXW9m0n6EZIFgFsaLgmfD99/XsEMUTE/uxv7Sacf1T6JoYwZrcUg+tVGUHJZsFBaXVDM1K9OQEXC6FnMOVHsJFWmw4eSXVunLYrRbqRJ3QUzZVuvYjcFbLUGd5gb43W7FferqOav39zirfiV5PouUUo00YgmDVmAAzx/Rif4md4ionn60v4MpBafxd81DMHNOL9HgbVw5K459LtnoooyB7Go/qnkif1HYAPg9Jm3goThSVhwIfjOIc+VCNfwUKN8tqYXXyV5Nxq+ZKiYr6OpksbqPQW+CoocrQEMGj43tz36feHMEDl2TzxBfbPJIRU0Zm8cueEk83PlW2el9xNRGhZg6W17L3SKVHUdc/H/Ho+N48v3xn25SgOLw1sAtfaZ5+fL9snwxrjpjR6E3pjxVttkMZeJNt2w6WM29FToDWEMhV1Bs3nc6Mj38jt8ju07v49PQ4XLj4Q0aCpz2gnka8gSDw72YG8sGY/J3MF7x3TeA5I6ZLFtG6+fLh63tlmzYGKmuouKoWi8lEcbWD8LAQkqLCSG0XTm5xNXnFVYSGmKh3uaiqdbH1YDn1Lvh4bT6XnZbKR2vyA2oGZo7pxbwVuxnXP4UeHaLpnhTFxc+vbLBLn9ViYklbkKAI1oXPFif1srTUZ23XsegU2dD+u9m+8X819HOK9YTA6FDmgZZuFx5q9vR2bUhraMv+ciYP78q8Fbt9eherK7D4iLCgjW5a/UNxIvCvLdAmxsr366+e2neHr2bKB2vJ36Q6Y73zlD04zR0mk6BzfATbDlYELETS4iLomhhJ18RIdhdWcvHzK/m/szMDKuMvH5TquafBV2wxxGTirvfX8+K1A4PmHLSv2wS9NFgXPhtyMh//smQHhUXJ/Jfah6DcLa545X/dDeq7e9VyG0FPqCG0qadG9QAuem4lV7/6M8u2FVLjdGGzmHwSwVpYLSZS2tkoqXLwwJjsgAfm2WU7KanW7/5k6LIcBcESYyV74H93yUIyfz0h1QioOi7l+2HfL/DOFfJBaiMNarQ4WrMa8EpTd2kf4XOPf7QmP6jEutkEvTpGccc5mTjqXUwdlUlyjJcUoS1GU1+3eNKEyyVX/HtWyt9695OWOQRSPO68R6RsxIpZ8MGN8Mlk2ZAmJMz3XHux7Mz3y6syt6BO8o2gJ9QQ2pQh8H9gXIpkC0WGhjB1VBaLNhQEsIMeuCQbh8vFC9/sYu2+Ut0HpsZZr2tAWvxD0RhQE2Odh8nfJpNccRXnyIfxuk9lQm7Cq3KVBdII/OFOWYlZuFlK+w6ZImUodn0V/AFupWioWY0KNbFcUFrtc4+XVDuIsVp0799+qTHkHK7ihW92cdv8tbyyIocbzkonOcbqyREs/q3Ac3yLJ02oq/JXhsFbY+RvvcWFtgtfdIosenRUBjKGls6Acx/2XcyMngWL74ReYyGqo/ea/l6Geo1GElBsU6Eh/wfmozX5zBzTi7veX+9JlplMMGdiP+pcLiKtFnIKK3hokdcL0GNidDkGjXgDx4GoDrL5d5+JsO8n35jqBf+UK6rqI74dykZMlw/kO1e0qY5lcGzNatTE8vaD5fyw6zBPTOyH3VFHeGgIH6zJDUgs3z+mF3uOVPHUVzsCPOC3bj6DhKgw0mLDGZgWG1QOu8Uh2KpclTXXxvATesKff5Ie6Z7vICZVfyI/vE16rgK3em6NXOR8Nxu6X+w9NliHs0ZKJLcpQ6D3wKAo3DIsA5CGQWUFvXDNAO54Z60nf6Du9y/Rf+qK/mQkRJKRENmgRryB40BcVxh5H+xd6Vumr/KuL38bvn8qUKp63Fzv61bel0CLY2lWozLnsjtGkRIb7kMXfWhsNl9u3s+koRmYTdA3tR3//Wkv2antdD0NBcWTB1Dv/VaBhlblcV19Y/gqU0iVTA/GCHI5ZaMlz2LFJL2I8gJ3nqCbPLahfFkjoE0ZAu0DExseyg1npXPXB94HQtVdKal2gEKAF3CgrIb5q3KZPDyDAZ3akR4f4TPht6qHoilhMoG9NHiVccFq2cFs5Rzf7Y4q39cnUaa3OeNYmtWox9W74P7PvBXHseGhFJTaGdUjmX2ldj5Ync+8FTk8dUV/8oKI1LXakGdDq3J/b6H/tb59M9bNlxO91nu98AkIj5fqoaGR8OPzknaqUqBDNUquer2+G5H80KYMgfaBOVxRy41v/BJQgj95eAbWEDP5pdVYLaYAL6Ck2oHNYqZL+whDUuJUIiZFtrQM1onMZPY93mKDykLf1xFJjTPWZgC1Wc3RFiLa8GhyjDWANqouhrYdLCe7YwzTzu/OnC+3t42QZ0Or8twfvPdhdIqUhdDelyoj6PI34eAmSOwJteXw0STfwsjKQ/LevWgO+FP3m6CQTEWbMgTgfWCCJdhSYmw8s2ynT0GO6gV0TYgk1Gwit7iKI5W1hiE4lejQVza4v+CfMhykfZjWzZdJN9VIqKsvtbex6ob7GwsDJEZ5w6MTBgbSRtXFUL0Lth+sIMYa4hFkNAkIDWnF4c6GVuVab6Hf1frFY/ZiKFgr61xCI7xGALzqpEOmSAr0N4/COfdCx4HNIo/V5gyBimAJtrwSu7cXwS+5PHvVAFDApSgoKOQWV/HuL3kkx9gY6JIWXa/PgYETgMslKaTRKWDJhMvfgoI10hNYNx+G/EWu/odNkyuz0lxY/Qb0uRziM+VDuGIOpA6SdQaNUKjTUmA2wYvXDmT9vlJSYmy6i6G02HCe/2Ynfzu/h09HM2gDRWPBVuVabyEsSjakv/BJ+PzvgV3GQsIgPks/rBmfKSuN62ph4R1ywaPmCZoQbdYQ6CXYZo7pRUWN09OPGKC02sEDCzf7FJD9cUgXnvpqO707xrD9UGAhj1FRfALQNv7IniBX9V3OhtQzZD/j7Aky1jrkL7DzKyn5G5MqX5flQ9l+yeUeMkWu6Jq4UKc5oa7Oxa97SzzS1FNHZeouhg5X1nLHOVmUVtcaPQlUmEyS5XPtB1C6T9a52OJg3IvSO4jPBFcdnHOP3F60Wz+sWZonc1uq0SjZ2ywMQZuXmNhbVEVhRQ0mBH//aAO5RfIflxxj5f4xvdh6sByX4mUUWS0mj+s8KL0df/7v2ra1YjrVOLJTUkAHXC9X/9kTICnb299VhcUmPYUPbvRO8KNnwZq3YP9amPgG9BovPQE9GYtbV7aJRLIWG/JKuPLVVZ77tW9KNFeekcYji7cE9Oh+79c8po7qxrPLdnieCWjj9/eRnbB1sVdGGuDch8ASLoXjtInikTNlM5mv79fvS2yxycVKxtmQPqTRPoIhMaEDtTwfILeoihkX9uT1lbs5u3sSHWKsbDtYzger8z2hovmrcjlQViOlq00QHhpirJhONioOysl/3XxvK8Czbg/CHloTWMAz+HbJzIjt7C1OO8WNv1sKCsrsPoni0b2Tmbdit4c22is5mhe/2cVvBeUA3PPJRuZdP4jJ81e3jWTx0VBVJCWjbXEw+GpZG6DeQ/75gOWPyNClWkOQ2Au+nOmVm3DaITZdhjGbAdq0IdDr8PTQ2Gxe/HaXj0Lj/FW5nqYer3+fg0nAoPQ4kqLD2ha9rjEQ1UGGg7In+LYCDMYe0kLt+zp6luz0pF6vmSo+NjbaR4bpJoq1onGThmZ4DEGN04XFLFhi1MdIhFigujhQOO6Cf0rj4CzwHuu0Q50dNrwLA66TEtT9r5ZtWVWPoPwAxKQ13efRoG0FSf2gp9HywMLNjOmb4nn93PKdTBiY6tFemToqix5JUQzJiCctTuYZtJIUbXrFdCJQNV4qDkLnob7N7dWextpS/Qseh80f+15DFaX74VlZuKNe76p3ZQGQekwjFuo0JyS3C+PhsdlYLaagAothId4pwWoxkRRtJSMhksEZ7clIiGy7RgDkZF5fG9hD44u75WSvhcUGlihpNH58TiaGf5or9YjOewQufVXmuKKax4LkpHgEQojRwLPI5vWvKYoyy29/GPA2cBpQBFypKMpe9767gUlAPTBFUZQvTsaYGoKaG9hxSL8Xsb+iotrkY1B6LGaT4MzO8YS4H5jR2R3oNXUYh8prqXLUkR5nGIHjhjahq0r5pg2RFFDFverfukjGVGM6yQTbWk3oSBuD/ekF6U2U7JGNb9SesGOfl+eGx7dZ1lBRhZO53+5i0tAMuidF6XqzfVKi+fsF3Xh/9T6mj+5pLGq0iOogda30Qo3t0n3pzKNngS0ePvlTYMeywbfL7RfNkec1A5ywIRBCmIG5wHlAPvCrEGKhoihbNIdNAkoURckUQlwFzAauFEL0Aq4CsoGOwNdCiG6Kovj5/CcP2nDQLcMygnZ40r42CXh4XO8AI6BiywGDOXRCUKs2bXGyC9maN+RvVV4iLgPOfVAaDFXeN3OkNA7DZ0ByH3fXsgrJ8f75JfkwxqTCmGdkPcI3j8M177e5vIBWdr202klukZ2538j+3DPH9PJJFE8dlcU9n2yipNrB45f24fyeScY9DF76cVURpJyuH2qsOCAneItN1gYc/E0mi/WMRohV/l4yDaI7SlZcEy9MToZHcAawS1GUHAAhxHvAOEBrCMYBD7r//hB4QQgh3NvfUxSlFtgjhNjlvt5PJ2FcuthzxBsO0tMOemBMNi+v8MZMVRZF+0gLp6fFBRiBYBLARi+C44Ca0B18tbcL2fJH5LboFLny19I/L3hcGoQRM+QDuuBa776z75Xnf3WfryDdmX+G6iKg7RgC/xzYPRd2Z8qoTNzlL9S7XEwamkFarI2CMjtv/5Tr0dq655ONpLSzkRAV1rbzAv7043EvwcVPSfqof/OZkDD4w1RYcI3cN/FNfaOhhimddshbJT3VJl6gnAxDkALs07zOB84MdoyiKHVCiDIg3r19ld+5KXpvIoSYDEwGSEv7/QmW3OIqz6StagdNGppBWpyNvGI77/6Sy5i+KQgB3ZOieHzJVkqqHUwenkGNUwlY6TckAWwYgmOEmtBVG3yov0Gu8LVJY617vX+N12uITpHHOiqkJ6Am71RBuiFTwGxpqk/YJNAuUpJjrCgI5q3I8Sx6HhnXmxirdL79m9XUOF2s3HWE11ZK3aHzeya1rf7ELpfsklfkljQ/92FJQHDVwg/PyfvPZIakPtIADPojpJwG713tvVdLc2W40j98WeaeLlXCQzNgsLUY1pCiKPOAeSDrCH7vdSJCQ3zCQQfKanj9+xwmD/e23PutoNzDoNBSR0uqHQEr/WORADZwFKhVm4e2+CaE/Y2CCnW7KkoXnSJDSdoH7ux75APsrHBfL1wm+9oQtIuUCQNTefprX0npmZ9tYtLQDKockiGn7VtsFmC1mD0erh6NtNWFP7UhoPJ8meDVxvxrymHZg3KbKnhoscmOY6v/LQ2F9l51VMCmj70UUgVvbYxWLqXfVU3wYX1xMgJTBUAnzetU9zbdY4QQIUAMMml8LOeeVCRFhzF1lG/zmbvO60av5GifbY+M602U1eypMj5QVqPbdUytUDaYQycAVeOl7xUypr/p40CWkBYWm3yo1L9Pnwx1NbLeYPg06Q18+zjUVUupiZ/mQkSC9BTaENRFChCUJSQEvL86n3su7MENZ6Xz+vc5vLB8F6+syMEkBMkxVmqcLlbnFjfYAa3FQ9uUZtdXXiMA3hqVmBTfiV7bJe/ip6DjAN97df27MPAGqTS6Yo78PfwfYI2R562bL3NfzYDBdjI8gl+BLCFEF+QkfhXg33V8IXAjMvY/EViuKIoihFgIvCOEeAqZLM4CfjkJYwqKtLgIspIifYS0MhIiODsrkSVThnl6Gde5FE8pvgq9lf6xSgAbOApMJqkLFNtFutg1ZbLisroILvqXV/JXW6EJ0mU3W+CLWYH7Vc6B0y7Pv/IdydJoI4whrYwK6DdVUhTpFZfX1PHCN7t8Jvunv97hqZ2p92vS1erCn1qZ6WBeKMLrqep5oRf9Cy55DhZNkdvsxZDQAyavgKpDsnYltotktFUelJ5AM2GwnbAhcMf87wC+QNJH/60oymYhxMPAakVRFgKvA/PdyeBipLHAfdz7yMRyHXD7qWQMgZy4R3ZPIqN9ZMDErZXxdbmUY+46dqwSwAaOASaTfhOQy9+SPQqs0TJHUF4gtyd0h3ev1Fd51BacOe2wb5X0CpqBtktjQF2kxN98Buv2lfDIuN6exU16vI3po3uyq7CSO0ZmAvoeg9kEsy/ry1NfbffZ1+rCn/4V6HpJ3hJNzF8vd7Xkb3DZ63DtJzIsFNtZahCZTL73XBNJTTeEk5IjUBRlCbDEb9v9mr9rgMuDnPsY8NjJGMexoqGJW0u3654UxdKpwzhYbqz0GxX+TUCKc6S878b3Jfunz+Uy5m+N8dWJV6GW7y/X3FZqYu7QRlkdGtE26glMJkFCVBjPfL2TO8/NYvLwDGKsFmLCLT6LnJeuHajrMYzqkUh2cgwWs6l1t2JVCQu2OFkINvqfsFQjf64yg0CGdWI76993BzZA+h8g89wWdW+1mGRxY0BPcqJVJsWaO/T0gdbNl/H/H5+XybZ2aVKI7qzb9VdvEQnSNVdfq4k5a4zML+Qsh7SzoPMwMLfux0ANEc1eupUrB6VRVuPkSXezGZAr/wcXbWb2ZX2Z/tFvPvd+n5R2bSP8GddV0j0Pb4XvZkmDMGQKtO8G4XHw2R1enaCVc+S9GEz2xF4CO7+ErPNbjDFo3U/AccKoCWgm0NMHshdDbaVXmhrkflV+QhurveBxqDgU2MfgzD/LpPLC273Hjn0esi9rMQ/s74FnIu8QRXFVLSXVzoAwUG6RnY7trEF1hVp9+NNkkqHGD2+S94azQNKOLTa46h3vokLFpo/h4qfhf38NbJqUMhCO7JA5r2YWAgqGNmsItCEglRdt1AQ0E+i1DLz4Kbmad1SBKURWclpscpX262vSXbfGQGK2rC+or4U6h7yeQBqQ2nK5mtPGdRf+BZL7t5gH9vdCO5HnHK7UDQPZHS46p0W03Xu98pB+uKfOGdiEZsD1sPtbuPxt2UNbXWycdjN8eR+MvK9Z1AccK9qkIQgWAgqmv9KqkmItAf4tAyOSZN7gw5u9D+KYp2HUg5LXXV4gBeiG3gXva6qML3xCGpXFU+W2EdP1H/TiPW2qe1nn+IiAMNCUkVnM/Gwjb9x0Rts1BKER+uGe/Wtg3X9kqCg+U0pEVByEHhdLdltCdyjKkYuNn192h5AEmEKl8GELuKfaZGOanMOVXPTcyoAJf+nUYYZuUHOE2qwme4Jc3YN0zS+aA/t+ljTRtCFeqQkVFptUeTyw3q0JH6TBzZAp3jBAG+le9uveIr7dfgQhZA/1j9fKxkvvTT6TwRntm3p4pw4NtS3NXQV5P/qGGUdMhy0LoetIeQ+JEClc6N+IZvg/pPZV15HuiuPesp3qqrmyVqCZ3FNGYxoNgoWADpbXtP6kWEtEVZG+0uihzfJhBBnr11vtK/XyYVRpqBfOgc+n6bNBnHYZjrq1V4tx6X8v4iPCeP37nLbl/R6tbWlEvAzvqJXAKWfI8wa19w0LXfQvKWzoT1me+Iav1zpiOgy4sUXcU01vopoA2opLFepDoMZSDf31ZoQQSyBne8UTsqgH5O+wGP0KZFscTHxLGoqR90OXobJN5U2L4doP5Wqv39WSBaJWJVcebNzP1wRokxXx/rRk1fAX75av47rKnsOr5sKu5VBVKMNCqhFQz1nyN+mdauG0w/51vsd9NxtiO3k74jVjtEmPQK9xfat/CFoyHNX6q/2SPdII/OFOKfB1weOy2EzrNSyeKhUh17wlqYHjX4aeY+XqrGg39Brr6+KPmA5RHZvkYzYm2gQl1B9Ha1tqMkF0J+kRpLtDjcHapKrMNRXBOuY5qlpER7w2aQja5EPQkhGs3WSHPrKJjRqvVbnfsemyDaDaKPyHZ2VIaN+PULhVnhffVT64qhFQ1UuddrCXyTBCM4jpnkq0ekqoP4LdR1EdZROjkj2y18Xmj+U9pB6nd46qK6SlLP/4vO/7WWwyrNkCOuK1yWSxgRYGvdju2Oeh5zhZWayV/gW5f/Dtkiqqpwkz5hnoc4U8960x+se0kaRxm4LefTRhnvQ4F9/pmwOwtYMDv8nCsJ6XBCaQO50FLifsXSkXFLuXBx435hlIPV3qCzWT+8hIFhtoufCnk0Zq2B5Oe3CZatDXhFl8pwwFOKtlc5sQG6yYFRg7ntyzzegStQlo76OKg3Ii378BVswOzAEMvl3mClTWkEodrSqSYaGyPIhIlBO+6lXWlMoFhAiR900LoI2qMAyBgZYBk0lfrCs+U991F+4Yrsmsbyh2fOGljF74pDQIv8zzygg47ZKVpIqGGWgdUO8jkJLTwXIAqgLpd7NlhboIceekOsKqFyHrPFm9bomQRkKpl/dcxSHIHCVDjy0IhiEw0HLhcsmJfsyz3qIxiw3GvgDxWbJuIKK9t4uZCm1iz2mXrJAhU2R4SM0rWGwyuVzcu1nT/gz8TqiJ45BwSSvW1qjs+R4SekkWGci80tcPyL8tNkkTLdwuKaR/uNP3uu3SZCiohcEwBAZaJrTxXjVJnNATkrLlKh7gyDbZYESvXaDazwDcLBCLt1+y2kDk19egyzDDELQWaIvJQiOkAQiLkqyypTM0FelPwjePQl0tDLgOopMl9Xj163KRUF0sw0rlBfDDMzLPgOIbsmxhMAyBgZYJlRNui5N5AKUeDm+Trnvxblkuu8jtJahaRCYzdBku9YXUEBB4G4qr8tXj5sKXM6XQWDOn/Rk4Ruglii+cI/WpPvmTb47g87/D8BmAK3ABsW6+rBhWG9DbiyEyqcUvFgxDYKBlouKgNAL+bJ+oJEnjGznT+3CXF3h7zMZn6VcpVxZ66X4lufIBD0b7a0imwEDzRMkeqQd04RMQGgmlefD9v2SHO91+Fp3gs9t9DcSKJ2TjmRCbLDYbMUPSSJs5NfRYYBgCAy0TUR2k2+7PCPriHhg2DZ+2giosNukVaGUE1Ibi5z4oJ4XwONl0JFgbwaPJFBhoGjRknOvrIH+1lx2kUkDP/DOEt5d/W8KhXSdZZOiqlz96BsJVDwuu8V5n/MuN/1lPAYw710DLRFxX2Q9W72GN6wpl+2SRjyo7oa78f3wehvxF5gHWvytDA6NnyV4HMSnwzWMQFi3Pyf1BCt65NLpUR5MpMND40Daef2uM/L1tkdzucsHeFV4yAXjZQI4qWZG+8QNQXNID+OYx+PFZSS1Wwz8qLDZw1fle59PbWsX/3vAIDLRMmExS4VFv1X94q3zQ4zJg/CtQtBM6nSFzBsU5Mqcwfh4IRSb+PrjRu8IbPUuGEbTbtCv+o8kUGGh8BDPOt/aSr/NW6f/PIuLhf3dK79Dfs1w6I1BE7qJ/QdWRwOu0gv+9YQgMtFzEZwY2sNGqiRbnwKe3ysSf0w5n3yMn8pBQ+HSylyEUMAG8KfMPzgLvpHJ9B5mADqZZbySVmw5qvmjw1ZIFpIZ4qgoBsyQS6P3PQiN9awa0cNrh8Ha5kCjcLENCK+fAaX+Ulegq2aCV/O9PyBAIIeKABUBnYC9whaIoJX7H9AdeAqKBeuAxRVEWuPe9CYwAytyH36QoyvoTGZOBNgSTCbpfLFsJ5q2SDUK+nOnLCLLFQXisd4U/Yjqs+be7AXm6LCha/65vIdn+tTDoj7D8Ee+24t1y4tiwQK4Ml/zN12NoBQnDFon6OggJg3Puld4dijfJa7HBJc/CoW0w4TU4+Js0Cps+hoE3yGIwbejQ31DEdJILCe32bx8L7F/RCv73J+oRzACWKYoySwgxw/16ut8x1cANiqLsFEJ0BNYIIb5QFKXUvf/viqJ8eILjMNBWUbIH3nMn74ZPk5PCsGne4iBLJCydrhEQC9dnDWkLyVz1EJPofQ+LDcxh8MlkGTpy2uHqBZJnHtvZqD5uKtTXwW8LvEZ5xHTf4kGnXYYDL3/LN9R30Ry5CPjpZfn3yn8F1pqMmC7zTHqeQsogKWPegusG/HGin2Ac8Jb777eA8f4HKIqyQ1GUne6/9wOFQMIJvq8BAxLamP2u5bI4aNVcWDEHfpoLMam+D3O7Tvq9Dfpd7TUKmz/2JozVbeqksHSGrB6tLpLS1uX5+kllA6ceBzd4jQDI1b7exF2wxk9LaBrsWQmn/xHismDADVIb6OoFcO5DkiJqiYDEnvo9LuK6QOdhXunqVoAT9QiSFEU54P77IJDU0MFCiDOAUECbZn9MCHE/sAyYoShKbZBzJwOTAdLS0k5w2AZaDbTSwl1HeitEQf4u2uXr9hftDsIb7yzDRevmy7CBNUY2rnHYoWiH3K/GhvN/luGFoXd5vRGDRnrqoaWIRneEmnIZ2gMZ3gP9EI9enwClHg5ulB6iyQwf3SKv9dNcb5Gisxou+Cd8cXerDwMe9Y4VQnwthNik8zNOe5wi9ayDaloLIZKB+cDNiqKoS6e7gR7A6UAcgWEl7fXnKYoySFGUQQkJhkNhwI24rvLhtNh8k37RKTJEZDLDFfO9VEBzqP4qLzRCnt/ncrka/HYWlOXLpPLyR2Sv49NvkddRkNo0/p2rDBrpqYOWIvrJrbB1oZQfVz2/P9wJ4Qly4tbG/S/4p/TwtFBFCVMGylX9t4/L/9/6d6X3Zy+WieHvZkkjMmyaDBXd9L9Wa+iP6hEoinJusH1CiENCiGRFUQ64J/rCIMdFA/8D7lUUZZXm2qo3USuEeAOYdlyjN2BAKy1cVehd0flXHF/yvJQNrj4sH2ptV7Kz74WSPDk5JGZL/ZiuI2W8WS08A+ktjHpQ7h9yh+9qtLyg1VAJmyW0FNHBV3v/f+BmePk1J0roLhVDywtkXmfpDMkiU//fIWGwf70sINRWoKtyJEnZ8vjvn/ISCboMa5VGAE48NLQQuBGY5f79mf8BQohQ4BPgbf+ksMaICGR+YdMJjsdAW4QqLax6B4c2B+YBFv0Frv1IitDZ4rysobJ8qSKpPuxxGXDOfVBfK+PFXz/gnUCG/wPsJbIBiZaZoiab7cUQkSTzBVVFYLbIXEJohAxhNaMGJc0OR5Pt0OaC/Ome/TSGwVkA6/4DZ/zJ19hf9C8ZSqopgcj2cGATdLtALgyu+wRWvQS7vpT3waq5MO5FL2sMWg1NNBhO1BDMAt4XQkwCcoErAIQQg4DbFEW5xb1tOBAvhLjJfZ5KE/2vECIB+a9dD9x2guMx0JahegchVv08QNUR72Sxco5kGa2Y4z0mOkUyihbqTPLlBdK4XPMBLHo20FMYcJ08/sgOKN0H1mipZ7NuvjQQI6ZLddSs8w1j4I9jke3wbzOp/bshw4B735K/yZqAn56XtNDT/wS537v7CIRA3yvksbkr5f/KGuPbirKV5gZUnJAhUBSlCBils301cIv77/8A/wly/sgTeX8DBgJgMslVvS4vPFV/u/par5uZKk29co7bmBzWp5/GpMpVZa+xvitR1ZB8N9vd5aqrETryR7DK4GsTwBImW0lGJUtWT+4Pksp77sPw9f3ugrCQhg2Des3CzfJ/3HGArDZXqaYqXXTYXZAyQOaIinbL/1e7dMkS6jS4VRvw1vvJDLRdaBPI4BUHqylzC4y5t2/6WEoRq6+DdTPz1CTYZKObFU/I8NKwaTJPUFcr2SddRwauRFVqqtMuex5UHjzlH7/FIZhsR8keePNiaRS2LYJ3r5Tf78onZYx/1INSZbbzUFkPoP4fVcOghcoeEsiQnf//6bvZ0uC46mWoMCLeW00ckdCqjQAYEhMGWiP0ehwLE7z8B29+QCCTw+lD4KYlMlcQFqnvMShIL+PcB2XYZ9jfpZSBuiJVNYpCwvQntBCrNEDxXWWBm8vV6ieW44J/2Afk69Jc/eSw0y6LBMfNhcLDsPxROHuGjOuHWOX/9cI58Pk0X89s3XzJCqur1f8/Oau8fYrVfhStPCSkwjAEBlon/Hsc71npmx9Q0XWkXJF+eps0Ev6MoouflvH+mNRATSOtHtHSGTDuJf0JLaFbw/Fvf368qx4qD7XsXgfH07NB9eC039Hof8J3T8r9QUM9W6RX94epkkpqi/MmiVX2UGw6lB+QRmDIX8BeDpWHg+tFTV4hPcPUQa2qcvhoMAyBgbaBYKtOk0UaAdVIbFnoZR656mVo58I53qIi8IYS1NyBui003EtV1MoZfP1gYPz71l7SSPm33PRnu6hGA/Qn1ubYJOd4ezZoPbjiPfIca4xckavw7yu86WPpqfW/1vt9az0HZ4FXD+jyN+W5K+bIpH90igwpLX/EO77xL8vJXx1fC2s+f6IwDIGBtgG9Veelr0h6p9Y49BwnjYDijidb28nwUkO5A5DXq6uRshPqStQSBSU5kn7qf27lQTmmA+sk3XTci1IV9aNbAo3Gbb3h0KbAsXe7ELZ+JltvNqfq5oZkoYMlyk0m+X0c2hzonanSIVoDO3o2bF0M2eOPniSuLvJVmVXrBcbOhcNbIH0odBnR9Aa0CWEYAgNtA3p5g7iukPeT11OITgFbNHwxyzvhXPC4ZArpeRPC7P377Htki8tBN8vY9oo5MPI+cFTon4sZNn/kO4lf8Lg33KTCaZc0VF1WzYfSiGgVVI824TYGgiV/i/c07LkU7/b1zn55VRrVTmfCgmsDcwTj50kmUDBaqfo6MlnWEWjpvAOuh6/ceYDuF7VpIwAGa8hAW4KaN9AKhkV18DKJ+l0tW11qJ5wv7pEhiOH/8GUhjZ4FIeGyFmHYNJmk/PZxObH/NFdONK56uZr1lz0YMR32/eI1Atr3GnCd75gtNm/VshbqxLruP/L9Tr9FGjLV2zgaXC5pRHJXSVG2PSt/v3Ceei31GpFJ+qydgtWBHcS00BqQ6BT5/1DqZScxvc+vOGHnl7I+YMR0+f849+HArnSHNkmJkB+fhXPukUZbLQC86F/Qod/xf+ZWBiElgloWBg0apKxevbqph2GgNcDlgpxv5cq9plxWHvtjxHQ54fa7Wk74Cd2kJMW3j8kJadg039ADuGPTb8vWhuX7ZU8ER7U0PMV7wGKVhsAfV70j36PioNsTCZfnfDI58PpDpshj1UlNbbRz68qGPQI1hv/1g4E1EeNfhg595JiPJeeglw+Y+KYMk6mre1Xzp6YMfpnnlfu+7QfZIrKqSIbFKg5KRk/VYXm+qgE08U347M+Bn//6z2Q3Ov+QkdMuK4YVpM5Q9gRvLsdigyv+K7+vmBT5/4lKbh65lUaAEGKNoiiD/Le3/k9uwEBDMJm8jKDSvfor2ZTTvEJk3/8LKg5JLvroWYFidyqcdqlS+tEfJQuldJ/UrTm0SRqEsoLA94rLkGyhBdfKFex3s+REWXEw0CMZ/g9pnHzqFMww9vmj0x3VGH72hMACuk9vkxr/n9wqe/lu+RQO7wjuKZTskXH9s26X3pEtDj68SRqTySvkJD5kCqx4Un6ec+6RRmHEDMj/Fd65AvavgTcvksyfz/4sjcKaN7zjKs31rf+w2OTK314SqDa7dLr8X62YI43igOthw7u+/xd7Mbic8J8J8NYlwT2UNgQjR2DAQOUhOUGo6pPaFfLoWbDyKbnaNpkhuZ9sdL94qpz0hk+Hjv3AouMRKLhDPnfLyXDIX+S5HbIl992fqjrqQd+OWE47LHtQJjV/eEYmlAs3y+uqshcgDZHFBl2GH1sFrBqCCWbATJZA0T49ymvRLtn1yxIux1RXLb+vtfOh4oA8zn8lv2Sa97tcfKf0plT2jhoOKt8vWT1f3ic/4y/zpLrokCnuJL5Zrub3r9Uff0yqNEqJvWH5w74d6yw2+d6L/MJyzSG30oQwDIEBAyq1VKs+aTJDxtlSXK44B/atkhMVSMmBq96VevY1ZXBkV+CkrkpLgHdy/eIeee0F17pj19ukbEJloXy/kj36E1vZPrmKtcXKfIBe0nrEdNmqMX+1rIptKNShfl71fP/rxWV49ZbUMQSjvGrrKnZ+JY/ve4Uch3qu/+eJy5DhH6cdIhO9RsDf+Gh1nn5+SVJx836UuReV2aU3/pJc6Q2MfSGQbTT8H9KA6Y2rDSvHGqEhAwa0khSq+mRSNmDyUj/VierH52DZI5JtFJ0sNe3D4yTDZfDtMjQzZIrvit1ig/bdpAehrsLXzYeuI6Rswse3yFBQQg/90FRStoyndx4mr6+dxC98UnopWxbKPMKur3xDHf6JXJdLqqBe9Y7U77/oX4FJcH9KLfgmofXooWvfhtNulN/Px7fIMJCtnbcPhPbzlObKuPyoB6Uy64gZskd0Q53jBlwvDe9Pc2WIrr5W1hL4h8wuflrWIFz1jjz2h2fhiv9ID2Hw7fL/Ul+r/z23YnXRo8HwCAwYCEYtLd7tXXGqgnR6vQ7GPCMVLUGuyqM6eIuhVFroqhclI0jtltX/WvjfXX6sobvlxKy2X7TYZIjk839IqmhpLkSlwJX/lUyasCgo3CrlMfpdLcNHqqH55Fa4NRvK8iBvlbdp+wX/9E3kxmVI1s2RHXL7D8/CyPvle6udugRSvyeqoxyrGlrqOBDOugOcVdJQBlA8Z8DEN+DDm31X5FsXQS8brNB4UBf9S9/4qJ3jVO9K9bzWvyuL79a+7fXgkrJl/uarmVJKfP9aeU7+r76e1Pp3Az24NiIlEQyGITBgAAIlKcC3CE2dYAdfHbhyXXynb4P0ToPl64I1cuL/8Xm5om2XLicpkBRL/4mvOEeuxq/7DGpLJYOmbJ8saju4UYaQ1AYs2ol14/uyveaZt8lQFchJPP8Xr7FRV/sgpRnU2oPiHJmX0FZJh0ZI9lDJHt/3is+U8gzmUPkZ+1/tDSGNmK4/kdc53JpAW7y5DT2Z6NI8/TBPVLK3E5zFBqFRcNm/5fUSesp+w85KeXzFIWkM1eI+FevmS2O9+E5vsjihp0xmVx1qU1ISwWAYAgMGgkGv+1mwBKu2QXrnoV6joCZA62pkCAm8E5zexOdyQdF26QWok9+FT0ojUH3EK52svq8qk/3dbGl8Fv9V7htwXaDHsXSGDFup0gtn3wM/vyxDWFqF1bguoCher0E9f9Ff5Htt/hjGPA3vXuXdHyxeHxIKR/J8V+R63+G6+fJzaif90bMhMgFu/V4mpQ9tlsn2j/7o/W4H/VF6NSW50giovR9+edV7bXsxpJ4uabVaj89kklRgA4YhMGCgQQR0P9uiP+GZQyUDRiDbXdriwEZgGOmiOTJ8VLQ7sDH68H9IBVPVCID8/fnf4ZoPZaJU+76qkYlNlyv82kovRz8+y9cQeSb6cO91v30czpkJtWVytT/xTUCRRqC6WN/gCaQXUZbvu3/9uzIEphbkqZ/ni3vgfD+GlH//AJCTdVWR/A6jkmUY7LvZXgXQ+K4yj3LW7b7nOe2SitvpTAiNhKTe3uup/5tLX/F2h2ujyeCjwTAEBgwcC1TvIKm3XDGrYQaLDS55Duyl3uY1KotGFa3TTuoqfXLDu3DBY74Cd+vmy/i8/wSs9uSNz5SJ1XXz5Xa9XMXNS2WRVMEauVL2Lxi74HEZ2+86Uk7qCd0lxz/E6nvcxU/L87U6SSolFqRB0U7m5QXSEKkS31qKq71EJssvnScNZliEzKn4y0T/Ok8aLTVHAtKQHdoMphBZCFa4xZu/0GMZLfmbZGLprf4NBIVhCAwYOFaYTHJlGttFFpmpEw0CXhnqO+F/N1uGOoKtqssLZDhDAZL7yokurouMz2sn2OgUmRRVQ02qkVEErJjlPc4WJyft0Eiod8KWz/TrEr64RyaH1e2qcfjxed/j/vfXQJE+NdE7cqbMX1zxH5ms/vkld9y9B3xwQ6C3VLwHVk2TRWBhkbLgTJWJjs+SRujr+70hKu1n95/sL34KLnledhjTYxkNmybDeIrirYwGyZhqTgqtzQyGITBg4HgRrNeBFipHXi+MlNhL0hkB1r3lu2o/zy+MMuA6/W5aV/634QlzxPTgdQmFmwONgzZZrG4v3iO3q7IaP70I2Zd6pR/U9zn3YflZKw76JmW1tQBOu8xxqM18tDLRw6Z5vwP1O9IytXwM1F2yiC8mVf+zRSfDfyd6319P7qI5KLQ2M5yQIRBCxAELgM7AXuAKRVFKdI6rBza6X+YpijLWvb0L8B4QD6wBrlcUxXEiYzJgoNERrNfBoa2Blcra7ldn3yu9hvev857780u+VbQxnfQnvGp3UlSplzkJtTpX3f/dbLjynaPnM0DG901m3/ew2KTn88XdXg3/S1+WkhD+7zNkivw8lYfkeVe9C45K2L/et55CqQ/CLLLLMQy+XbKCLpojQ2jBEvNJ2TK8FKyYTDu+/WsDE+xtvIpYDydqEmcAyxRFyQKWuV/rwa4oSn/3z1jN9tnA04qiZAIlwKQTHI8BA40PvR7JasxbrVSe+IacMH951asm+u1j4Kz2nczKCyT7JX2orBhWQ0U+75chJ88fn5OaOp/9H5x2s7fyGeQ1D22Shsa/YCwsWhbNrZgj2TzD/y4buo+YIa+hjn/Zg3JVDjKJbS/Tn5jVfMGqudIgvXe1FPDb+708f/g0mfvoeJp+IZfi/twr58hCtMojsiAsbYj+8XFdoNNZgd/5xU9J/SUtghkfo3e0D040NDQOONv991vAt8D0YzlRCCGAkcA1mvMfBF46wTEZMNC4UBPJN6VKVlHZPt+V8Kq5cOETcvWshdMuk8R6DJrqI/L46JRAr+L8R+GjSb6r3G8f9+r2gDwuJgXK9ktaaf4vcsJ1Kd7wjHruF3d7lUsveFy2c/zVrRIaYpWGZ+hd0rDorcLbpUlj5JMU/5vsDPbBTd5xn32vDCNpez1f+KQU41OvNfwfsONziEqSn9n/s49/2Rvj1+tLre1qBvoMpTZeRayHEzUESYqiHHD/fRBICnKcVQixGqgDZimK8ikyHFSqKEqd+5h8ICXI+QghJgOTAdLS0k5w2AYMnGSYTJA8QBZcuep86YvnPiyLwtRQjtpERg1l6NEuKw7o6x8l9pKrbd2cRJL3PbUhqIlvyJW/LQ7OuTd4AlubL1DH1+lM2cJR7Ql89j2BOYJ6h/41D27yM1aPSWOlfpb23WW18+hZ3uK7dfPlazU5rv3sWRdIz0WN7fvnalyuwAR3xwHSePjnCNpwFbEejmoIhBBfA3rm817tC0VRFCFEsOYG6YqiFAghMoDlQoiNQNnxDFRRlHnAPJD9CI7nXAMGGgUmE2SdLznwV8yX0gYh4ZJho/YTUCf6dfO9SeGBN3pzAirtMiRM0lIXTfHqH134hFz1RgRJQsekSIXS0lxvCArk5D56llT1DFbBqz5RTrusSxgxHVIGwc+vQFIPb4L355flZB6ZKH8O75ChJr1rxmdJj0Ydh5oPUJPSY5+XSqwJ3SEiQbKeTJfDgQ2+tFT1+JRBkPtDcOZPMKkQkHkFg04aFEc1BIqinBtsnxDikBAiWVGUA0KIZKAwyDUK3L9zhBDfAgOAj4B2QogQt1eQChTonW/AQIuBySRj2O3SpU5O5SEviwW8NMeb/idX2ANvgLVvBfL9xz4P7bpICeoj2+Vq+dtZMuauehda+YcR06WcQ2hEYAiqOAfC4+XP526WT/URt6RzCMR1lkJ6IK9Vlu9l9IyeJcepTvTlBTL8ZLHJ0E9sZ1j9hk5V8Cypr9Tvat+mMJ46BPf7dBwok8qFW7xJ3eHT9A1LwWrvuIIxf/SkQkB/mwEPTtQsLgRudP99I/CZ/wFCiFghRJj77/bAH4AtimyN9g0wsaHzDRhokVAnJEXRD5s4q93Gogv0uVyu9Me9KIuuhk2TbCFbjFfvZ+UcL8/+55fAEiG9iOHT5O+oDjLWb43WT7Ae3CgZOSFhkk6pJpp/fFYaEPU4tRubOs6lM6Bkr35jnKV3S4OSPkTG+Qff7lX5/OFZKbWhspHUa29411278E/oOVZ6AoVbZA2CzS3BofaF0L6f/7g+uVWKAho4KTjRHMEs4H0hxCQgF7gCQAgxCLhNUZRbgJ7AK0IIF9LwzFIUZYv7/OnAe0KIR4F1wOsnOB4DBpoXglFLIztIY9Ghj5wIHRWygnbDuzKu32ucDGGknel7fki43P/DM77SEeWH5Mq/zumlX3pW5/+UjJ+QMJmPUNVAwT3ZT5cJZRRYfJdvIxdbnAw5Fe2WhqosX4Z7vpopj/viHhmDL87xrUMAaQQSerhX+JGS5nrazdIQrX1b9lco3CI9k8PbJW32h2e8eZEhU2S9QGiEzHdox6Uyf4xV/knBCRkCRVGKgFE621cDt7j//hHoE+T8HOCMExmDAQPNGloFU71kZWwXGb/W228ygbB4mTO2OCk9rYaFVs7xpaqqK2hTqCy6ikx0a/Y8KY3H6NluuQo9OuUh6TFoWTdqVfNnt/vmN8r2+cb9EfrGLqmPNCDCLPMO9Q745lG5/7xHpLejhoPUVf+Zt0kjYy+WjKVvZ8mwmT8byGD+nFQYlcUGDJxKBEtgapkvDe2PiJeJ5cG3Q1IvOSnb4nzF2dTm9WOegahE+GKG1OM/sl2utkGeU14gw1F6k3ZVkUxCa1lBelXNK56QeQvtucV7AmmeFzwuw0VqT4Dh06BdZ+85SdmB/QtUBdUJr0nP49dX5ecymD+nHIYhMGDgVCNYAvNY9sd1hXMflB6DqrzpdCdsVWXRC2fLEExcV9jznVxBL9Ss4s99WIZjvn1cxtn9E81n3+v2HvLke179ntT2DwnzlbFQQ1GmEPnaXuz1RkCGcpL7w4H1Mv+g7dAmzPLz3bRYGrviHH3PxFUvZSxK9kCnQQbzp5FgGAIDBpoz9HoiaKmVq+ZKpU3ViJhDA/V5tH0MnAWSWqpO2iFhUF8nPYvQCHnsp/8nJ/mrFwRX+hzzjMwdLJzirTlIypb02XqHbx3FiOkQkybpn2b3lKMoQSinmfIYg/nTqDAMgQEDzR3+PRGC5RtAtrD0X2n7yyyUuwXfbloMaX+Qq2+TCd6/3ve4Axvkir+uRr8r2+QVcM37gav0nmNl4VvRLjnG8DiZKDZrppv4zMBwz/iX5XYDjQ7DEBgw0FJwtHwCSDqm/0q7IZkFVVq7fH+gAaktk5W/Q/+qH8apOgSdhwWu0tXOXw11/1INhhHuaRYwvnUDBloSVO9AnYD9J866Wpnw1XLwIxJkKEe7zd+TUGmuWmz6GIb8RXYA06tNOFHWjvazxHWVdQF7VsreAS7XiV3bwHHB8AgMGGhNiIiHNW/4dgpbNReu/bDhrl16Yadz7nE34bEEdmU7mawdlwu2LQoMeRk9AxoNQhb4tiwMGjRIWb16dVMPw4CB5ocTmVRdLrkq1zMWDe07URzZCa8MCwxdaZPgBk4KhBBrFEUZ5L/d8AgMGGhNOJY8QkPnBmPmHI0CeyKoOBi8Z4BhCBoFhiEwYKC14VRO2qcCDclwGGgUGAE4AwYMNC30OrwZlcONCsMjMGDAQNPiRMJZBk4KDENgwICBpkdLC2e1Mhgm14ABAwbaOAxDYMCAAQNtHIYhMGDAgIE2DsMQGDBgwEAbh2EIDBgwYKCNo0VKTAghDiN7JJ9KtAeOnOL3OFUwxt40MMbeNGipY2+KcacripLgv7FFGoLGgBBitZ4mR0uAMfamgTH2pkFLHXtzGrcRGjJgwICBNg7DEBgwYMBAG4dhCIJjXlMP4ARgjL1pYIy9adBSx95sxm3kCAwYMGCgjcPwCAwYMGCgjcMwBAYMGDDQxmEYAjeEEJcLITYLIVxCiKCULiHEaCHEdiHELiHEjMYcYzAIIeKEEF8JIXa6f8cGOa5eCLHe/bOwscfpN5YGv0chRJgQYoF7/89CiM5NMExdHMPYbxJCHNZ817c0xTj9IYT4txCiUAixKch+IYR4zv25fhNCDGzsMQbDMYz9bCFEmeY7v7+xx6gHIUQnIcQ3Qogt7vllqs4xTf+9K4pi/Mg8SU+gO/AtMCjIMWZgN5ABhAIbgF7NYOxPADPcf88AZgc5rrKpx3qs3yPwf8DL7r+vAhY09biPY+w3AS809Vh1xj4cGAhsCrL/IuBzZNv7wcDPTT3m4xj72cDiph6nzriSgYHuv6OAHTr3S5N/74ZH4IaiKFsVRdl+lMPOAHYpipKjKIoDeA8Yd+pHd1SMA95y//0WML7phnJMOJbvUfuZPgRGCSFEI44xGJrrPXBUKIqyAihu4JBxwNuKxCqgnRAiuXFG1zCOYezNEoqiHFAUZa377wpgK5Did1iTf++GITg+pAD7NK/zCfynNgWSFEU54P77IJAU5DirEGK1EGKVEGJ84wxNF8fyPXqOURSlDigD4htldA3jWO+By9xu/odCiE6NM7QTRnO9v48VZwkhNgghPhdCZDf1YPzhDm8OAH7229Xk33ub6lAmhPga0OuIfa+iKJ819niOBw2NXftCURRFCBGME5yuKEqBECIDWC6E2Kgoyu6TPVYDLALeVRSlVghxK9KzGdnEY2rtWIu8vyuFEBcBnwLNpt2ZECIS+Ai4U1GU8qYejz/alCFQFOXcE7xEAaBd3aW6t51yNDR2IcQhIUSyoigH3C5lYZBrFLh/5wghvkWuTprCEBzL96geky+ECAFigKLGGV6DOOrYFUXRjvM1ZA6nJaDJ7u8ThXZyVRRliRDiRSFEe0VRmlyMTghhQRqB/yqK8rHOIU3+vRuhoePDr0CWEKKLECIUmcRsUvaNGwuBG91/3wgEeDdCiFghRJj77/bAH4AtjTZCXxzL96j9TBOB5Yo7s9bEOOrY/eK7Y5Fx4ZaAhcANbhbLYKBME3Js1hBCdFBzSEKIM5BzW5MvHNxjeh3YqijKU0EOa/rvvamz6s3lB7gUGZurBQ4BX7i3dwSWaI67CJn5340MKTWHsccDy4CdwNdAnHv7IOA1999DgI1IlstGYFITjzngewQeBsa6/7YCHwC7gF+AjKb+no9j7P8ENru/62+AHk09Zve43gUOAE73vT4JuA24zb1fAHPdn2sjQdhzzXTsd2i+81XAkKYes3tcQwEF+A1Y7/65qLl974bEhAEDBgy0cRihIQMGDBho4zAMgQEDBgy0cRiGwIABAwbaOAxDYMCAAQNtHIYhMGDAgIE2DsMQGDBgwEAbh2EIDBgwYKCN4/8BFzfS69IosasAAAAASUVORK5CYII=", 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", 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" ] }, - "metadata": { - "needs_background": "light" - } + "metadata": {}, + "output_type": "display_data" } ], - "metadata": {} + "source": [ + "X1 = X[Y == 1]\n", + "X2 = X[Y == 0]\n", + "sns.scatterplot(x=X1[:1000, 0], y=X1[:1000, 1])\n", + "sns.scatterplot(x=X2[:1000, 0], y=X2[:1000, 1])" + ] }, { "cell_type": "markdown", + "metadata": {}, "source": [ "### Relation with optimal transport\n", "\n", - "In this setup we can solve the optimal transport problem\n", - "between the distribution of `X[Y==1]` and `X[Y==-1]`. This\n", - "usually require to match each element of the first distribution\n", - "with an element of the second distribution such that this minimize\n", - "a global cost. In our setup this cost is the $ l_1 $ distance, which\n", - "will allow us to make use of the KR dual formulation. The overall cost \n", - "is then the $W_1$ distance.\n", + "In this setup we can solve the optimal transport problem between the distribution of\n", + "`X[Y==1]` and `X[Y==-1]`. This usually require to match each element of the first\n", + "distribution with an element of the second distribution such that this minimize a global\n", + "cost. In our setup this cost is the $ l_1 $ distance, which will allow us to make use of\n", + "the KR dual formulation. The overall cost is then the $W_1$ distance.\n", "\n", "#### Wasserstein distance\n", "\n", - "The wasserstein distance measure the distance between two probability distribution. Wikipedia article gives a more intuitive definition of it:\n", + "The wasserstein distance measure the distance between two probability distribution.\n", + "Wikipedia article gives a more intuitive definition of it:\n", "\n", - "> Intuitively, if each distribution is viewed as a unit amount of \"dirt\" piled on {\\displaystyle M}M, the metric is the minimum \"cost\" of turning one pile into the other, which is assumed to be the amount of dirt that needs to be moved times the mean distance it has to be moved. Because of this analogy, the metric is known in computer science as the earth mover's distance.\n", + "> Intuitively, if each distribution is viewed as a unit amount of \"dirt\" piled on\n", + "> {\\displaystyle M}M, the metric is the minimum \"cost\" of turning one pile into the\n", + "> other, which is assumed to be the amount of dirt that needs to be moved times the mean\n", + "> distance it has to be moved. Because of this analogy, the metric is known in computer\n", + "> science as the earth mover's distance.\n", "\n", "Mathematically it is defined as:\n", "\n", @@ -165,96 +156,220 @@ "W_1(\\mu,\\nu) = \\inf_{\\pi \\in \\Pi(\\mu,\\nu)}\\underset{x,z \\sim \\pi}{\\mathbb{E}}\\parallel \\textbf{x}-\\textbf{z} \\parallel\n", "$$\n", "\n", - "where $\\Pi(\\mu,\\nu)$ is the set of all probability measures on $\\Omega\\times \\Omega$ with marginals $\\mu$ and $\\nu$. In most case this equation is not tractable.\n", - "\n", + "where $\\Pi(\\mu,\\nu)$ is the set of all probability measures on $\\Omega\\times \\Omega$\n", + "with marginals $\\mu$ and $\\nu$. In most case this equation is not tractable.\n", "\n", "However the $W_1$ distance is known to be untractable in general.\n", "\n", "#### KR dual formulation\n", "\n", - "In our setup, the KR dual formulation is stated as following:\n", - "$$ W_1(\\mu, \\nu) = \\sup_{f \\in Lip_1(\\Omega)} \\underset{\\textbf{x} \\sim \\mu}{\\mathbb{E}} \\left[f(\\textbf{x} )\\right] -\\underset{\\textbf{x} \\sim \\nu}{\\mathbb{E}} \\left[f(\\textbf{x} )\\right] $$\n", + "In our setup, the KR dual formulation is stated as following: $$ W*1(\\mu, \\nu) = \\sup*{f\n", + "\\in Lip_1(\\Omega)} \\underset{\\textbf{x} \\sim \\mu}{\\mathbb{E}} \\left[f(\\textbf{x}\n", + ")\\right] -\\underset{\\textbf{x} \\sim \\nu}{\\mathbb{E}} \\left[f(\\textbf{x} )\\right] $$\n", "\n", "This state the problem as an optimization problem over the 1-lipschitz functions.\n", "Therefore k-Lipschitz networks allows us to solve this maximization problem.\n", "\n", "#### Hinge-KR classification\n", "\n", - "When dealing with $W_1$ one may note that many functions maximize the maximization problem\n", - "described above. Also we want this function to be meaningfull in terms of classification.\n", - "To do so, we want f to be centered in 0, which can be done without altering the inital problem.\n", - "By doing so we can use the obtained function for binary classification, by looking at the sign of $f$.\n", + "When dealing with $W_1$ one may note that many functions maximize the maximization\n", + "problem described above. Also we want this function to be meaningfull in terms of\n", + "classification. To do so, we want f to be centered in 0, which can be done without\n", + "altering the inital problem. By doing so we can use the obtained function for binary\n", + "classification, by looking at the sign of $f$.\n", "\n", - "In order to enforce this, we will add a Hinge term to the loss. It has been shown that this new problem\n", - "is still a optimal transport problem and that this problem admit a meaningfull optimal solution." - ], - "metadata": {} + "In order to enforce this, we will add a Hinge term to the loss. It has been shown that\n", + "this new problem is still a optimal transport problem and that this problem admit a\n", + "meaningfull optimal solution.\n" + ] }, { "cell_type": "markdown", + "metadata": {}, "source": [ "### HKR-Classifier\n", "\n", - "Now we will show how to build a binary classifier based on the regularized version of the KR dual problem.\n", + "Now we will show how to build a binary classifier based on the regularized version of\n", + "the KR dual problem.\n", "\n", - "In order to ensure the 1-Lipschitz constraint `deel-lip` uses spectral normalization. These layers also can also use Bjork orthonormalization to ensure that the gradient of the layer is 1 almost everywhere. Experiment shows that the optimal solution lie in this sub-class of functions." - ], - "metadata": {} + "In order to ensure the 1-Lipschitz constraint `deel-lip` uses spectral normalization.\n", + "These layers also can also use Bjork orthonormalization to ensure that the gradient of\n", + "the layer is 1 almost everywhere. Experiment shows that the optimal solution lie in this\n", + "sub-class of functions.\n" + ] }, { "cell_type": "code", - "execution_count": 5, - "source": [ - "batch_size=256\n", - "steps_per_epoch=40480\n", - "epoch=10\n", - "hidden_layers_size = [256,128,64] # stucture of the network\n", - "activation = FullSort # other lipschitz activation are ReLU, MaxMin, GroupSort2, GroupSort\n", - "min_margin= 0.29 # minimum margin to enforce between the values of f for each class" - ], + "execution_count": 6, + "metadata": {}, "outputs": [], - "metadata": {} + "source": [ + "batch_size = 256\n", + "steps_per_epoch = 40480\n", + "epoch = 10\n", + "hidden_layers_size = [256, 128, 64] # stucture of the network\n", + "activation = (\n", + " FullSort # other lipschitz activation are ReLU, MaxMin, GroupSort2, GroupSort\n", + ")\n", + "min_margin = 0.29 # minimum margin to enforce between the values of f for each class" + ] }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 7, + "metadata": {}, + "outputs": [], "source": [ "# build data generator\n", "def otp_generator(batch_size, X, Y):\n", " Y_ix = np.array([i for i in range(Y.shape[0])])\n", - " Y0_ix = Y_ix[Y == 1]\n", - " Y1_ix = Y_ix[Y == -1]\n", - " half = Y.shape[0] // 2\n", + " Y0_ix = Y_ix[Y == 0]\n", + " Y1_ix = Y_ix[Y == 1]\n", " while True:\n", " batch_x = np.zeros(((batch_size,) + (X[0].shape)), dtype=np.float32)\n", " batch_y = np.zeros((batch_size, 1), dtype=np.float32)\n", " ind = np.random.choice(Y0_ix, size=batch_size // 2, replace=False)\n", - " batch_x[:batch_size // 2, ] = X[ind]\n", - " batch_y[:batch_size // 2, 0] = Y[ind]\n", + " batch_x[: batch_size // 2,] = X[ind]\n", + " batch_y[: batch_size // 2, 0] = Y[ind]\n", " ind = np.random.choice(Y1_ix, size=batch_size // 2, replace=False)\n", - " batch_x[batch_size // 2:, ] = X[ind]\n", - " batch_y[batch_size // 2:, 0] = Y[ind]\n", + " batch_x[batch_size // 2 :,] = X[ind]\n", + " batch_y[batch_size // 2 :, 0] = Y[ind]\n", "\n", " yield batch_x, batch_y\n", - "gen=otp_generator(batch_size,X,Y)" - ], - "outputs": [], - "metadata": {} + "\n", + "\n", + "gen = otp_generator(batch_size, X, Y)" + ] }, { "cell_type": "markdown", + "metadata": {}, "source": [ - "### Build lipschitz Model\n", + "### Build Lipschitz model\n", "\n", - "Let's build our model now." - ], - "metadata": {} + "Let's build our model now.\n" + ] }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", + "I0000 00:00:1725628191.539264 866183 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\n", + "I0000 00:00:1725628191.559486 866183 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\n", + "I0000 00:00:1725628191.559628 866183 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\n", + "I0000 00:00:1725628191.560274 866183 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\n", + "I0000 00:00:1725628191.560439 866183 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\n", + "I0000 00:00:1725628191.560533 866183 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\n", + "I0000 00:00:1725628191.656115 866183 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\n", + "I0000 00:00:1725628191.656251 866183 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\n", + "I0000 00:00:1725628191.656349 866183 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\n", + "2024-09-06 15:09:51.656421: I tensorflow/core/common_runtime/gpu/gpu_device.cc:2021] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 6818 MB memory: -> device: 0, name: NVIDIA GeForce RTX 2070 SUPER, pci bus id: 0000:01:00.0, compute capability: 7.5\n" + ] + }, + { + "data": { + "text/html": [ + "
Model: \"model\"\n",
+       "
\n" + ], + "text/plain": [ + "\u001b[1mModel: \"model\"\u001b[0m\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "
โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”ณโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”ณโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”“\n",
+       "โ”ƒ Layer (type)                    โ”ƒ Output Shape           โ”ƒ       Param # โ”ƒ\n",
+       "โ”กโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ•‡โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ•‡โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”ฉ\n",
+       "โ”‚ input_layer (InputLayer)        โ”‚ (None, 2)              โ”‚             0 โ”‚\n",
+       "โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค\n",
+       "โ”‚ spectral_dense (SpectralDense)  โ”‚ (None, 256)            โ”‚         1,537 โ”‚\n",
+       "โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค\n",
+       "โ”‚ spectral_dense_1                โ”‚ (None, 128)            โ”‚        65,793 โ”‚\n",
+       "โ”‚ (SpectralDense)                 โ”‚                        โ”‚               โ”‚\n",
+       "โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค\n",
+       "โ”‚ spectral_dense_2                โ”‚ (None, 64)             โ”‚        16,513 โ”‚\n",
+       "โ”‚ (SpectralDense)                 โ”‚                        โ”‚               โ”‚\n",
+       "โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค\n",
+       "โ”‚ frobenius_dense                 โ”‚ (None, 1)              โ”‚           129 โ”‚\n",
+       "โ”‚ (FrobeniusDense)                โ”‚                        โ”‚               โ”‚\n",
+       "โ””โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”˜\n",
+       "
\n" + ], + "text/plain": [ + "โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”ณโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”ณโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”“\n", + "โ”ƒ\u001b[1m \u001b[0m\u001b[1mLayer (type) \u001b[0m\u001b[1m \u001b[0mโ”ƒ\u001b[1m \u001b[0m\u001b[1mOutput Shape \u001b[0m\u001b[1m \u001b[0mโ”ƒ\u001b[1m \u001b[0m\u001b[1m Param #\u001b[0m\u001b[1m \u001b[0mโ”ƒ\n", + "โ”กโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ•‡โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ•‡โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”ฉ\n", + "โ”‚ input_layer (\u001b[38;5;33mInputLayer\u001b[0m) โ”‚ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m2\u001b[0m) โ”‚ \u001b[38;5;34m0\u001b[0m โ”‚\n", + "โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค\n", + "โ”‚ spectral_dense (\u001b[38;5;33mSpectralDense\u001b[0m) โ”‚ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m256\u001b[0m) โ”‚ \u001b[38;5;34m1,537\u001b[0m โ”‚\n", + "โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค\n", + "โ”‚ spectral_dense_1 โ”‚ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m) โ”‚ \u001b[38;5;34m65,793\u001b[0m โ”‚\n", + "โ”‚ (\u001b[38;5;33mSpectralDense\u001b[0m) โ”‚ โ”‚ โ”‚\n", + "โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค\n", + "โ”‚ spectral_dense_2 โ”‚ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m64\u001b[0m) โ”‚ \u001b[38;5;34m16,513\u001b[0m โ”‚\n", + "โ”‚ (\u001b[38;5;33mSpectralDense\u001b[0m) โ”‚ โ”‚ โ”‚\n", + "โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค\n", + "โ”‚ frobenius_dense โ”‚ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m) โ”‚ \u001b[38;5;34m129\u001b[0m โ”‚\n", + "โ”‚ (\u001b[38;5;33mFrobeniusDense\u001b[0m) โ”‚ โ”‚ โ”‚\n", + "โ””โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”˜\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "
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 Trainable params: 41,985 (164.00 KB)\n",
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\n" + ], + "text/plain": [ + "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m41,987\u001b[0m (164.01 KB)\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ - "K.clear_session()\n", + "keras.utils.clear_session()\n", "# please note that calling the previous helper function has the exact\n", "# same effect as the following code:\n", "inputs = Input((2,))\n", @@ -264,423 +379,403 @@ "y = FrobeniusDense(1, activation=None)(x)\n", "wass = Model(inputs=inputs, outputs=y)\n", "wass.summary()" - ], - "outputs": [ - { - "output_type": "stream", - "name": "stderr", - "text": [ - "2021-09-08 18:23:54.376987: I tensorflow/compiler/jit/xla_cpu_device.cc:41] Not creating XLA devices, tf_xla_enable_xla_devices not set\n", - "2021-09-08 18:23:54.377747: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcuda.so.1\n", - "2021-09-08 18:23:54.415033: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:941] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n", - "2021-09-08 18:23:54.415345: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1720] Found device 0 with properties: \n", - "pciBusID: 0000:01:00.0 name: GeForce RTX 3080 computeCapability: 8.6\n", - "coreClock: 1.83GHz coreCount: 68 deviceMemorySize: 9.78GiB deviceMemoryBandwidth: 707.88GiB/s\n", - "2021-09-08 18:23:54.415372: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcudart.so.11.0\n", - "2021-09-08 18:23:54.417208: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcublas.so.11\n", - "2021-09-08 18:23:54.417243: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcublasLt.so.11\n", - "2021-09-08 18:23:54.417819: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcufft.so.10\n", - "2021-09-08 18:23:54.417963: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcurand.so.10\n", - "2021-09-08 18:23:54.419000: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcusolver.so.10\n", - "2021-09-08 18:23:54.419454: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcusparse.so.11\n", - "2021-09-08 18:23:54.419534: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcudnn.so.8\n", - "2021-09-08 18:23:54.419584: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:941] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n", - "2021-09-08 18:23:54.419873: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:941] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n", - "2021-09-08 18:23:54.420126: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1862] Adding visible gpu devices: 0\n", - "2021-09-08 18:23:54.421463: I tensorflow/compiler/jit/xla_gpu_device.cc:99] Not creating XLA devices, tf_xla_enable_xla_devices not set\n", - "2021-09-08 18:23:54.421518: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:941] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n", - "2021-09-08 18:23:54.421774: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1720] Found device 0 with properties: \n", - "pciBusID: 0000:01:00.0 name: GeForce RTX 3080 computeCapability: 8.6\n", - "coreClock: 1.83GHz coreCount: 68 deviceMemorySize: 9.78GiB deviceMemoryBandwidth: 707.88GiB/s\n", - "2021-09-08 18:23:54.421789: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcudart.so.11.0\n", - "2021-09-08 18:23:54.421800: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcublas.so.11\n", - "2021-09-08 18:23:54.421811: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcublasLt.so.11\n", - "2021-09-08 18:23:54.421820: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcufft.so.10\n", - "2021-09-08 18:23:54.421830: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcurand.so.10\n", - "2021-09-08 18:23:54.421840: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcusolver.so.10\n", - "2021-09-08 18:23:54.421850: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcusparse.so.11\n", - "2021-09-08 18:23:54.421860: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcudnn.so.8\n", - "2021-09-08 18:23:54.421899: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:941] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n", - "2021-09-08 18:23:54.422177: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:941] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n", - "2021-09-08 18:23:54.422438: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1862] Adding visible gpu devices: 0\n", - "2021-09-08 18:23:54.422462: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcudart.so.11.0\n", - "2021-09-08 18:23:54.700971: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1261] Device interconnect StreamExecutor with strength 1 edge matrix:\n", - "2021-09-08 18:23:54.700991: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1267] 0 \n", - "2021-09-08 18:23:54.700995: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1280] 0: N \n", - "2021-09-08 18:23:54.701140: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:941] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n", - "2021-09-08 18:23:54.701410: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:941] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n", - "2021-09-08 18:23:54.701645: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:941] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n", - "2021-09-08 18:23:54.701868: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1406] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 9056 MB memory) -> physical GPU (device: 0, name: GeForce RTX 3080, pci bus id: 0000:01:00.0, compute capability: 8.6)\n", - "2021-09-08 18:23:54.766864: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcublas.so.11\n", - "2021-09-08 18:23:55.126952: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcublasLt.so.11\n", - "2021-09-08 18:23:55.127037: I tensorflow/stream_executor/cuda/cuda_blas.cc:1838] TensorFloat-32 will be used for the matrix multiplication. This will only be logged once.\n" - ] - }, - { - "output_type": "stream", - "name": "stdout", - "text": [ - "Model: \"model\"\n", - "_________________________________________________________________\n", - "Layer (type) Output Shape Param # \n", - "=================================================================\n", - "input_1 (InputLayer) [(None, 2)] 0 \n", - "_________________________________________________________________\n", - "spectral_dense (SpectralDens (None, 256) 1537 \n", - "_________________________________________________________________\n", - "spectral_dense_1 (SpectralDe (None, 128) 65793 \n", - "_________________________________________________________________\n", - "spectral_dense_2 (SpectralDe (None, 64) 16513 \n", - "_________________________________________________________________\n", - "frobenius_dense (FrobeniusDe (None, 1) 129 \n", - "=================================================================\n", - "Total params: 83,972\n", - "Trainable params: 41,985\n", - "Non-trainable params: 41,987\n", - "_________________________________________________________________\n" - ] - } - ], - "metadata": {} + ] }, { "cell_type": "markdown", + "metadata": {}, "source": [ - "As we can see the network has a gradient equal to 1 almost everywhere as all the layers respect this property.\n", + "As we can see the network has a gradient equal to 1 almost everywhere as all the layers\n", + "respect this property.\n", "\n", - "It is good to note that the last layer is a `FrobeniusDense` this is because, when we have a single\n", - "output, it become equivalent to normalize the frobenius norm and the spectral norm (as we only have a single singular value)" - ], - "metadata": {} - }, - { - "cell_type": "code", - "execution_count": 8, - "source": [ - "optimizer = Adam(lr=0.01)" - ], - "outputs": [], - "metadata": {} + "It is good to note that the last layer is a `FrobeniusDense` this is because, when we\n", + "have a single output, it become equivalent to normalize the frobenius norm and the\n", + "spectral norm (as we only have a single singular value)\n" + ] }, { "cell_type": "code", "execution_count": 9, - "source": [ - "# as the output of our classifier is in the real range [-1, 1], binary accuracy must be redefined\n", - "def HKR_binary_accuracy(y_true, y_pred):\n", - " S_true= tf.dtypes.cast(tf.greater_equal(y_true[:,0], 0),dtype=tf.float32)\n", - " S_pred= tf.dtypes.cast(tf.greater_equal(y_pred[:,0], 0),dtype=tf.float32)\n", - " return binary_accuracy(S_true,S_pred)" - ], + "metadata": {}, "outputs": [], - "metadata": {} + "source": [ + "optimizer = Adam(learning_rate=0.01)" + ] }, { "cell_type": "code", "execution_count": 10, + "metadata": {}, + "outputs": [], "source": [ "wass.compile(\n", - " loss=HKR(alpha=10,min_margin=min_margin), # HKR stands for the hinge regularized KR loss\n", + " loss=HKR(\n", + " alpha=10, min_margin=min_margin\n", + " ), # HKR stands for the hinge regularized KR loss\n", " metrics=[\n", " KR, # shows the KR term of the loss\n", " HingeMargin(min_margin=min_margin), # shows the hinge term of the loss\n", - " HKR_binary_accuracy # shows the classification accuracy\n", + " keras.metrics.BinaryAccuracy(threshold=0), # shows classif. accuracy\n", " ],\n", - " optimizer=optimizer\n", + " optimizer=optimizer,\n", ")" - ], - "outputs": [], - "metadata": {} + ] }, { "cell_type": "markdown", + "metadata": {}, "source": [ "### Learn classification on toy dataset\n", "\n", - "Now we are ready to learn the classification task on the two moons dataset." - ], - "metadata": {} + "Now we are ready to learn the classification task on the two moons dataset.\n" + ] }, { "cell_type": "code", "execution_count": 11, - "source": [ - "wass.fit_generator(\n", - " gen,\n", - " steps_per_epoch=steps_per_epoch // batch_size, \n", - " epochs=epoch,\n", - " verbose=1\n", - ")" - ], + "metadata": {}, "outputs": [ { + "name": "stdout", "output_type": "stream", + "text": [ + "Epoch 1/10\n" + ] + }, + { "name": "stderr", + "output_type": "stream", + "text": [ + "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", + "I0000 00:00:1725628194.610220 866272 service.cc:146] XLA service 0x56551459f300 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\n", + "I0000 00:00:1725628194.610237 866272 service.cc:154] StreamExecutor device (0): NVIDIA GeForce RTX 2070 SUPER, Compute Capability 7.5\n", + "2024-09-06 15:09:54.657698: I tensorflow/compiler/mlir/tensorflow/utils/dump_mlir_util.cc:268] disabling MLIR crash reproducer, set env var `MLIR_CRASH_REPRODUCER_DIRECTORY` to enable.\n", + "2024-09-06 15:09:54.847169: I external/local_xla/xla/stream_executor/cuda/cuda_dnn.cc:531] Loaded cuDNN version 8902\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", "text": [ - "/home/thibaut.boissin/envs/tf24/lib/python3.7/site-packages/tensorflow/python/keras/engine/training.py:1844: UserWarning: `Model.fit_generator` is deprecated and will be removed in a future version. Please use `Model.fit`, which supports generators.\n", - " warnings.warn('`Model.fit_generator` is deprecated and '\n", - "2021-09-08 18:23:56.416569: I tensorflow/compiler/mlir/mlir_graph_optimization_pass.cc:116] None of the MLIR optimization passes are enabled (registered 2)\n", - "2021-09-08 18:23:56.434380: I tensorflow/core/platform/profile_utils/cpu_utils.cc:112] CPU Frequency: 3600000000 Hz\n" + "\u001b[1m 30/158\u001b[0m \u001b[32mโ”โ”โ”\u001b[0m\u001b[37mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - HingeMargin: 0.1231 - KR: 0.0621 - binary_accuracy: 0.5760 - loss: 1.1691" ] }, { + "name": "stderr", "output_type": "stream", + "text": [ + "I0000 00:00:1725628197.746760 866272 device_compiler.h:188] Compiled cluster using XLA! This line is logged at most once for the lifetime of the process.\n" + ] + }, + { "name": "stdout", + "output_type": "stream", "text": [ - "Epoch 1/10\n", - "158/158 [==============================] - 4s 13ms/step - loss: 0.6832 - KR: 0.5668 - HingeMargin: 0.1250 - HKR_binary_accuracy: 0.7808\n", + "\u001b[1m158/158\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 12ms/step - HingeMargin: 0.0607 - KR: 0.5395 - binary_accuracy: 0.7970 - loss: 0.0673\n", "Epoch 2/10\n", - "158/158 [==============================] - 2s 13ms/step - loss: -0.7488 - KR: 0.9578 - HingeMargin: 0.0209 - HKR_binary_accuracy: 0.9795\n", + "\u001b[1m158/158\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 9ms/step - HingeMargin: 0.0142 - KR: 1.0956 - binary_accuracy: 0.9555 - loss: -0.9537\n", "Epoch 3/10\n", - "158/158 [==============================] - 2s 12ms/step - loss: -0.7921 - KR: 0.9734 - HingeMargin: 0.0181 - HKR_binary_accuracy: 0.9865\n", + "\u001b[1m158/158\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 14ms/step - HingeMargin: 0.0120 - KR: 1.0793 - binary_accuracy: 0.9644 - loss: -0.9595\n", "Epoch 4/10\n", - "158/158 [==============================] - 2s 12ms/step - loss: -0.8035 - KR: 0.9783 - HingeMargin: 0.0175 - HKR_binary_accuracy: 0.9875\n", + "\u001b[1m158/158\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 13ms/step - HingeMargin: 0.0122 - KR: 1.0846 - binary_accuracy: 0.9641 - loss: -0.9624\n", "Epoch 5/10\n", - "158/158 [==============================] - 2s 12ms/step - loss: -0.8232 - KR: 0.9749 - HingeMargin: 0.0152 - HKR_binary_accuracy: 0.9913\n", + "\u001b[1m158/158\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 13ms/step - HingeMargin: 0.0134 - KR: 1.1011 - binary_accuracy: 0.9597 - loss: -0.9673\n", "Epoch 6/10\n", - "158/158 [==============================] - 2s 12ms/step - loss: -0.8207 - KR: 0.9690 - HingeMargin: 0.0148 - HKR_binary_accuracy: 0.9920\n", + "\u001b[1m158/158\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 14ms/step - HingeMargin: 0.0116 - KR: 1.0947 - binary_accuracy: 0.9646 - loss: -0.9783\n", "Epoch 7/10\n", - "158/158 [==============================] - 2s 12ms/step - loss: -0.8376 - KR: 0.9940 - HingeMargin: 0.0156 - HKR_binary_accuracy: 0.9911\n", + "\u001b[1m158/158\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 13ms/step - HingeMargin: 0.0131 - KR: 1.0943 - binary_accuracy: 0.9606 - loss: -0.9629\n", "Epoch 8/10\n", - "158/158 [==============================] - 2s 13ms/step - loss: -0.8252 - KR: 0.9878 - HingeMargin: 0.0163 - HKR_binary_accuracy: 0.9888\n", + "\u001b[1m158/158\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 14ms/step - HingeMargin: 0.0120 - KR: 1.0945 - binary_accuracy: 0.9642 - loss: -0.9743\n", "Epoch 9/10\n", - "158/158 [==============================] - 2s 14ms/step - loss: -0.8320 - KR: 0.9810 - HingeMargin: 0.0149 - HKR_binary_accuracy: 0.9926\n", + "\u001b[1m158/158\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 13ms/step - HingeMargin: 0.0116 - KR: 1.0988 - binary_accuracy: 0.9658 - loss: -0.9832\n", "Epoch 10/10\n", - "158/158 [==============================] - 2s 13ms/step - loss: -0.8296 - KR: 0.9783 - HingeMargin: 0.0149 - HKR_binary_accuracy: 0.9924\n" + "\u001b[1m158/158\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 12ms/step - HingeMargin: 0.0122 - KR: 1.0884 - binary_accuracy: 0.9631 - loss: -0.9664\n" ] }, { - "output_type": "execute_result", "data": { "text/plain": [ - "" + "" ] }, + "execution_count": 11, "metadata": {}, - "execution_count": 11 + "output_type": "execute_result" } ], - "metadata": {} + "source": [ + "wass.fit(gen, steps_per_epoch=steps_per_epoch // batch_size, epochs=epoch, verbose=1)" + ] }, { "cell_type": "markdown", + "metadata": {}, "source": [ - "### Plot output countour line\n", + "### Plot output contour line\n", "\n", - "As we can see the classifier get a pretty good accuracy. Let's now take a look at the learnt function. \n", - "As we are in the 2D space, we can draw a countour plot to visualize f." - ], - "metadata": {} + "As we can see the classifier get a pretty good accuracy. Let's now take a look at the\n", + "learnt function. As we are in the 2D space, we can draw a countour plot to visualize f.\n" + ] }, { "cell_type": "code", "execution_count": 12, + "metadata": {}, + "outputs": [], "source": [ "import matplotlib.pyplot as plt\n", - "from mpl_toolkits.mplot3d import Axes3D\n", - "from matplotlib import cm\n", - "from matplotlib.ticker import LinearLocator, FormatStrFormatter\n", - "batch_size=1024\n", "\n", - "x = np.linspace(X[:,0].min()-0.2, X[:,0].max()+0.2, 120)\n", - "y = np.linspace(X[:,1].min()-0.2, X[:,1].max()+0.2,120)\n", + "batch_size = 1024\n", + "\n", + "x = np.linspace(X[:, 0].min() - 0.2, X[:, 0].max() + 0.2, 120)\n", + "y = np.linspace(X[:, 1].min() - 0.2, X[:, 1].max() + 0.2, 120)\n", "xx, yy = np.meshgrid(x, y, sparse=False)\n", - "X_pred=np.stack((xx.ravel(),yy.ravel()),axis=1)" - ], - "outputs": [], - "metadata": {} + "X_pred = np.stack((xx.ravel(), yy.ravel()), axis=1)" + ] }, { "cell_type": "code", "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[1m450/450\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 554us/step\n" + ] + } + ], "source": [ "# make predictions of f\n", - "pred=wass.predict(X_pred)\n", + "pred = wass.predict(X_pred)\n", "\n", - "Y_pred=pred\n", - "Y_pred=Y_pred.reshape(x.shape[0],y.shape[0])" - ], - "outputs": [], - "metadata": {} + "Y_pred = pred\n", + "Y_pred = Y_pred.reshape(x.shape[0], y.shape[0])" + ] }, { "cell_type": "code", "execution_count": 14, - "source": [ - "#plot the results\n", - "fig = plt.figure(figsize=(10,7))\n", - "ax1 = fig.add_subplot(111)\n", - "\n", - "sns.scatterplot(X[Y==1,0],X[Y==1,1],alpha=0.1,ax=ax1)\n", - "sns.scatterplot(X[Y==-1,0],X[Y==-1,1],alpha=0.1,ax=ax1)\n", - "cset =ax1.contour(xx,yy,Y_pred,cmap='twilight')\n", - "ax1.clabel(cset, inline=1, fontsize=10)" - ], + "metadata": {}, "outputs": [ { - "output_type": "stream", - "name": "stderr", - "text": [ - "/home/thibaut.boissin/envs/tf24/lib/python3.7/site-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n", - " FutureWarning\n", - "/home/thibaut.boissin/envs/tf24/lib/python3.7/site-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n", - " FutureWarning\n" - ] - }, - { - "output_type": "execute_result", "data": { "text/plain": [ "" ] }, + "execution_count": 14, "metadata": {}, - "execution_count": 14 + "output_type": "execute_result" }, { - "output_type": "display_data", "data": { - "image/png": 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XUx/exuITn9Rpzn8EWGvZcf3V3Pmj75LUqyx56rNY9sznz1hTzalM95JD6H7Vm1j+/H9k618uY/Pll3DrN87F7+1j4ZlPZf5pT8Arzqwl0cO33YSQkr7DZ2Y7wc0/uojeJfNZdOrR7Q5lv3Pb1bfyvU9+hzOe8wTOfsUz2x3OjKY6XuPbHzufX3/3D/QOdvPBb/wzZz3nMdMuq9wRYjOA+sh2tt9+Hb0LV9C3uDOV83BJmg1u/95X2XnDtZQWLeP4t7+f0sIl7Q7roMcrlljylH9g8ZOewfBtN7Lx0otY84sfcs9vfsb8x53J4ic/c8Z4kw3//Wa6ly6fkZsJdq5ex/Zb7uKM975m2p0gHy61co1PveFjzF48h3/+/Ltm/OttF9ZarrjgGr743u9QGanw7Nc9lVe9/4UUu6ZnX2lHiE1zTJqw4W+X4uYLLDyuszLj4VLbupmbv/o5mkM7OPQ5L2Hxk585Y8xXZwpCSmYdfQKzjj6B6paNbPzDhWy+8lI2X/EH5pxyGkvPfjaF2dO3fy+uVqhuWsch//CCdoeyX/j7Ly9FuQ5H/MOZ7Q5lv/PVD3yJke3DfOGi/+70he0nRraPce67v81VF17HimOW8Z8/fh/Lj57eOzs7Qmyas/XvVxPVxjn09Gd1TFsfJjtuuIa/f/crKM/nhHf+G70rDm93SB0ehNL8RRz5yjdyyLNeyIZLfs3mKy5l29VXMufEx7D06c+ZlhOHI3fcCtbSf8TMK9vpJGH1b69g2VknE3TPrHLyvfnzb67gD+f/npf96ytYeXznu2RfY63ld+ddzlf+7fvEUczrPvxSXvDmZ6Cc6f/DuSPEpjHVoS0MrbmFgUOO6viFPQysMay94Cesu+gXdC9dztFveCdB79RwWO7w0Ah6+1j5wley5GnnsPGS37Lp8t+z/W9/Zc6Jj2HZP7yAwuy57Q7xITNy+824hSJdi5e1O5R9zrorbiAcr3LkOU9odyj7lbGdo3zpXz7P8mNW8JJ/eXm7w5lxbNuwky+86xtcf9mtrDp1Jf967htYeOj0zYLfm44Qm6boJGbj3y7FL3Qzb1VnmfdDxeiUv3/3K2y/9irmPe4sDn/Ja5BuZ8J0uuJ39bD8eS9j8VOfxYZLfsPGP17MjuuvZu5jzmDZM59Hrm+g3SE+KON3r6Z3xREzcuXT7b/6I/n+HhY/9th2h7Jf+fJ7z6VRbfCv//N+HLdzWt1X6FTzs69eyHf/8ycopXjbp17Ns17zZOQM+6x0jphpyuZb/kzcqLHizOeiOlYVDwmTJNzyzS8ydNN1HPrsF7Pkaed0mmlnCF6xxPLnvIRFTzib9Rf/ik1XXMK2a65kwelPYtkznotXmporqMLxUZrDO1l41lPbHco+pzlWYd2V13Pcy56JnAHlo/vjygsu58+/voJX/9vrWLxySbvDmTHcfcs6PveOr3P3zet4zNNO4O2ffg2z5s/MPb4dITYNGd9yD6Pr72D2yhMo9E+tnVlTFR1H3PyVzzNy+82sfNGrWPSEg8Pd+2DD7+5h5YteyeInP4N7fvtzNl+e+ZEtfeqzWPSkZ0w5O5Ly2mzjW88hM2/aefWFV2JSzRHPOqvdoew3qmMVvvzec1l+zAqe/5YXtTucGUGzFvK9z/yUn37lQrr7S3zo2+/g8c86ZUb/aO4IsWlGXK+w8fo/ku8dZM4RJ7U7nGlBGja56cufZuzu1Rzx8tcz/7SZ3a/SAYK+AY54+etZ/ORncPfPf8iaX53P5j9fyvLnvozZJ5w6Zb7Ux9fehXRdSouWtDuUfc5dv7+KgeWLGVgxNff77Qu+/qGvUB2r8ImffHpGNI23m6su/Bv//b7vsHPLCGf/41m84SMvo9RTbHdY+52OEJtGWKNZf23LPf/kpyBl54P/YCSNOjd+8ZNUNtzDqte8lbknP67dIXU4gBTmzOfYN/8ro6tv486ffI9bv3Eumy77PYe9+FWUFrRfIJTX3U3XomUzbmNAfXiMrTeu5tQ3zdws0Q2XX88lP7yYF7/zZR33/EfJjk1DfOl93+WvF1/P0iMWce43/plVp8y8LPH9MbM+/TOcbbdfS310O0tOfgp+sbvd4Ux50mYjE2Eb13H069/B4HEntzukDm2i77BVnPrB/2TLn//Iml+ez9Ufex8Lz3gyhzzrhbiF9vziNmlKdeN6Fp71lLY8//5k7R+vBWtZ/sRT2x3KfiFshHzxXZ9j/iELeem/vKLd4UxbtDb84usX8Z1P/BgEvOE//pHnvv5pB93Aw8H1aqcx5W3r2XHnDfQvOYLehcvbHc6UJ2k2uPHcT1LZsI6j3/AOBo/tlHEPdoSULHj8k5h9wqmsveAnmeXF9Vez/DkvZd5jzzjg5cralo2YNKFryczLpqz907V0L5xD//KZubD9vM9+j+0btvGZC/4LL5hafYfThfWrN/PZt3+NO/52N6c85Tje/pnXMnvB1J9y3h90hNg0IKpX2HDdH8h1D7Dg2NPbHc6UJ2nUueHcT1DdtL4jwvYjYaJJjcWRgsCdPmVyt1DksJe8mvmnncUdP/x2tt7qxms48pVvOqDTleX1a4Fst+ZMImlGbLruNo56/pOnTC/evmT96nX87H9+zFNe+jSOeuwx7Q5n2pEmKed/8dd8/7M/I1fM8YGvvZUnPO9xM/JYeah0hNgUx2jN+mt+B1iWnvq0GddLsq9J6jWu/69PUNuygWPe8C5mHXNC22KZrkLloTDeiIlSM/nvMNH05KdXZqC0cAkn/eu/s+my33P3z/6Pv/7He1j16jcfMIf7yoZ7cAslgv5ZB+T5DhSbr7sNHcUse/yJ7Q5ln2Ot5cvvOZd8qcBrP/zGdocz7Vi/ehOffNOXWXPLes589qm89T9fTe+sTptN56w+xdl661U0xnay9NSzO31hD0JcKXP9uR+nvn0rx7zpX5h11PFti2UmCJX7I0z0Hq8NIEoNYaKnneAUUrLoCU+jd8Xh3PrNL3LDFz/Jsmc8j2XPeO5+N1itrF9L1+JlMy4TsP6qG3ECj/knHNHuUPY5V15wObf+5Wbe9pl30t3f+T5+qFhr+dU3f8fX/v3/yBdz/Pt338Xp/9Dp2Z2gI8SmMONb7mFo7a3MOvQYeubPvPUn+5JwbJQb/utjNEeGOe4t72nr3r79KVQeaZZtX2bnUmMf1uXTgdKCxZzygU9wx/99i3t+81PK69dw1GvfhpvfP4ubk2aD2tZNDB5/yn55/Hay+brbmHfs4Tj+zPjhMUEcxXzno99gyeFLedorntHucKYNY0NlPvPPX+Wa39/IyU86lnd/6Y30Dfa0O6wpRUeITVGMTtl00+XkeweZd1RnhdED0RwZ4vrPf5S4WuH4t7+f3uXtW7gbJppKMyFONd69fIUerVB5pFm2ifuFiUYbS8FTzO7OPeI4HLn3DM79XT5dUJ7Pka96Ez2HrGD1j77Dtf/5bxz7ln+lMHvf77Qr33M3WEvPISv2+WO3k+Z4heG7N/DYp808m5jffPtXbFu/lY/9+FMoNb0yv+3iuj/ezKfe8j/Uyg3e+slX8ezXPXXGZYD3BR0hNkWRyuGQxz0T5fodv7AHoLZ1Ezd88ZPoKOKEd36Q7qXtmyjdXfBUw5TANZSCXeunHo1QeaRZton7lZsxjUiTGkOlKRECBrsemRgLXHWfeHxHTruy5N4QQrDg8U+iMHc+N3/181z7yf/H0W94J/2HH7VPn2d8zWoQgu6lM2ticusNdwCw4MQj2xzJvqVWrnHe577P8WedyIlP6JTUHgytDd/79E/5wWd/zpLDFvDpn32QZUfMzAnafcHM2pw5w8j3zMIvTM0deVOBsTWrue4z/441lhPf9aG2irDdhUngKrCW0XpMJYyBPYVKmGhGahEjtYgw0Q/p8R9pOTA1ljDRjNcTys2EeqQpNxN2VMKH/Nx7oyfv0Z1zKfgO3Tl3xvS/TdC7/HBOef/HCXr7ufGLn2TT5Zfs08cfu/N2uhYvwwkeeWZyKrL1pjtRrsPsVTPLYucnX/ohtfEqr/3QG9odypSnPFLhAy/6T37w2Z/z1JecwZcv+XhHhD0InYxYh2nJjuuv5rZvf5mgf4Dj//n95AYG2xrP7oKo3IxBCFwpSLVBYCeFyngjZrgWESaZaPMcwWApuF8hM9HbpbXZ6/W7Z9n21gfmSEGUaJqJJtaGpPU4eVdRj9JHlcWaCRmwByI3MMhJ7/kIt37zS6w+71s0dmxlxfNf/qib+NMwpLxuDYuf8sx9FOnUYevNqxk8fBmO5z74jacJI9uG+eXXfsZZz3sihxw1szKY+5rVN6zlI6/+AmM7x3nXF17H01/+hE4p8iHQEWIdphXWWjb84bfc/bP/o3vZco5987vxiqV2hzUpiMJEE6eZKHMdRdF3sIjJ7FM1TCZFGECcWsrNhMBV9xE2OytN6rHGkQLPUUSpxt+t72z3LNv99Y8FrsJVglqUUItSamGK40iSVDPQ5dNf9Pe4z0y123ikOLk8x77l3dz5k++x8dKLCMdGWPWat6LcR54BHLv7DqzR9B22ah9G2n50krDj72s55kVPa3co+5QffuEHpEnKy9/36naHMqW55Pwr+Nw7vk7f7B7OvfAjrDxuZvnj7U86QmyKYK3t/HJ4EIxOWf3D77DlyksZPP5kVr36rShvapTEJvqm6lG622VysmF/ImO2t1KiNpZ6lO4hgnaUmwzVYpJUk1pL0VP0FQMCR6KUnBR+I7WIOMlus/twwO79Y30Fn5LvMFKPs2PMQjM2jNcjwu4cgasYb8RUw2QyhlKwb8qNM0HcCSk57EWvItc/i7t+8n1urH+KY9/0Lzi5/CN6vJHbb0a6Hj2HzKxdekN3rkdHMXOPnjkDCCPbR/jdDy7kKS89m3lL57c7nCmJtZbvffqnfO/TP+PY047kQ995B9197f9xPJ3oCLE2URveilQO1hjyvYP73bNoupM0G9zytS8wesetLHnaORx6zoum3HvWk/cQgBDxZBZrggnhtLeG/Wac4ioxKdIqzZh6rKlFCVFiSLVhtB6TasPSwS6KvrNHibOZpKTaMKsUTA4HxKmm0sweXylJKXAoeA7aGJSUFDyHxDApHIdrEbUwJYw1COjO7T1L93B4tF5qU03ELX7SM3CLJW7/36/yt89/lOPf9j68rofvJTVy2030rTxyyvyI2Fdsv+VuAObMICH2q2/8HJ1qXvC2F7c7lClJHCV87u1f4w8/+TNPfckZvPPzr8P1OrLi4dJ5x9rA0JpbGN+yFuV6uLkiO9fczMDSIykNLmh3aFOS5vBObvzyp2ls38YRr3gD8x931qN+zId6kn+4YqA77xGlmnqsiaMYKSQFb5egKQVuK1vVEijW4ntqD9FWjzVhnBIlhnqcErfEzJZyhO/Wmd2Voxomk8IpNSabjHQSfEdOPn4pANt6jkLgkvccUm1wlCTnKVRLFNajlNFazHA9mnyuclOR8xRLBh7ZL9tH66U2VQ1x5536eNxCkVu+9l9c95kPc9w/v4/8rDkP+f7VTRto7NzOwrNmVvkOYNutd5Ef6KU0Z2bsCwzrTS787gU89hmndbJhe6E6XuPDr/gcN191B69+/wt52b88p1PVeYR0hNgBRqcJQ2tv4ZDTnoWXLxHVxqkNbWFo7S2E1TEGlh2JEFMr09NOxtfexU1f+Sw2TTn+7e/fJ301D/Uk/0jEwHgjxiKohymNRFPwFHkvy2D15L3Jvq3dS5j3Llc6UmARCOykMApjjSNhtJ7QiDWNOGW8uUukCZtZL5QChzi1eM4ukaWkwFiDFKCtRacaVwm6cy6OFAzXQkYa4WRvG0CzVWYtN2KUkjSjBIvAdyTdeW8PgTrxGnYXq4/G9HWqO/fPOup4TnjnB7npy5/hmk98kKNf/46HZG9hjeGO876JWygy95TTDkCkB5Ydt61hzqpDZ8zJ+NIfX0KtXOM5b3x+u0OZcuzYNMT7XvBJtm3YyQe+9lae+PyZdzwfSDpC7AAjlUNp1gLiRhW/0EVQ6sUNCvjFHkY33UXcqHUsK1psu/bP3P6/X8Pv7eO4t76HwpxH/6v0oZ7kH4kYmLjPhEiRAhJtiVM9eT1kYqTgO5N9ZTsqIdpYVEvIeI6iKwf12KGgLXGicQIHV0kasSZODTsqTeLUUgiyj7AV2QSmtRpHKpLUTAqrrMlfsKgvx2gtQRtL3s8sNsLUoI2lERmS1KCUQBtL4Eji1DBSj1v9Yyk5V5H3HbaMN+gvBkA2fGCspTvnTb7Gnrz3qExfp4Nzf88hKzn5/R/jpi9/lhu/+ElWvPAVLDzzgc0qN132e8r33M2qV78Ft1A8gNHuf6JqnbH1Wzj8mWe0O5R9grWWX33j5yw/ZgVHnDyzhioeLffcvpH3veCThI2IT/30AxzzuJm3yupA00m9HGCEEOR6B9l0w5/YueZmAJTrURpcgHI8xjbehbVT54TTDqy1rP31T7ntW/9N15JDOPl9H90nIgyyDFEjTifF0QT3PslP+G/Vo3QPv60HEgMT15Ub8R6eXeONmDjVbB6tM1QNs0xT6/Iw0STa0IgzE9hyM8Z3JAv7Cszt8unyXUqBS+AqrAUlBa6SCCGItSZuxSaFJUkNtaZhrBExXIupRQmQDQNMvNy8r8gFCk32nAA5z6E379KIE+LE4EiBEDBUixkqNxlvZI/TTDSVRsLG0SYj1ZA41YRJJvgm3qPdxarv7Pn1MvHv2r3e0zDR1FrZt1qUPiSrjqlAftYcTn7vf9C/6jju/NF3ueP/vonR6V5v2xjawZpf/pD+VccyZwZmw3becQ8As1fNDHuHG6+4gY13beCc1z93xmT49gW3/OUO3vGMfwfgv3777x0Rto/oZMQOIEmzjpsrMLD0CHLd/Qyv+zvrrr6YWYceTXFgHklYx/FzB/UHPyvffIstV17K3Mc8niNe9jqku288icZbJ/p6lImA3Z3v732Sb0QJ1XDXSTVKNd05bw+bir15dlXCLHsUa4OnMuFRbsQ0YoOnBGFqCVyD70iGq00Qlq7Ax3fkfR6vlPMohimmqWkkmcjzHIFOLbG2uI7EdQSOAG1hvJmQWjDGoq0FHBQpSkKqDYmBnOvgAs0kJTFZti5KDa4j8ZxdXweptozVsknKKLX0FzxSYxBCUI9StpahJ9YErcZcvZtAnRCkPXmPcqu86zsSCwxVwz0mM7P31kzaeniOoDvn3ceqA2snxfFUKE9O4OTyHPumf2HNr85n/cW/or5tCwvPeiqzjjoO5WdZw+bwTq7/wscQUnH4S187Iz/fO25fC8Dg4TNjJ+5vvv0rugd6ePyzH30/6kzhjz+7ik+/9SvMXTzIf/7k/cxeOKvdIc0YOkLsALHllquIauPEjSo9Cw7FL/Yw65CjaIwNsfH6P5LvHSSNmsw57IR2h9o2dBJz2ze/xM6brmPp2c/mkHNetM9OWhNlw8DN/Lji1FJtZmW1npx7n7KkRRC4crKpPk4tWDtp9bC33rEw0UQtgZZoQ5xqenIuqRG4NvMVAxitxSRaU400QkDBTehu9Y95jmKsHpFqS2w0A6WAYuDiVUJGmwlxYhlrxgSOoJkIBJIwNThCopTFWEtqDb5SxGmKtYbunMd4GOMpiSMEqbW4AhxHZUavqcV3FPN6ckhhaCQGjCBWBqylESckqSbnKXJuVh7tDlwqYZZdLAUusRQomYmkCbE63soMamMpNw31KMXZbUdfpZnQlXNb7+Gu93lCbAUtcVoNY5SU0Oqrm7h+qkxUCilZ/pyXUJg9l7t/8SNu/ca5SNdj4KjjGFh1LPf8+qekUcgJ7/x/5Ppn5slr+K4NFAb7yPc9/CnSqcbItmGuvvgqnvfmF+LNsMXljwRrLT/8r1/xrY/9iKMfezgf+d6/0NU7s0rr7aYjxA4A9dEdjG1Zw6qzX0ltaCv10e00RneQNOsMLDuSvkUrSKMQp/UL+mAkaTa4+X8+w9hdd7Dyha9g0ROfvk8ff/eSYnfOY6gSkhiLtRaLmGym3/22pcDFd7LG+CTNGufL9xJhkGV0Ji7vznkkadaDlaQpAvBcQV/RJUosqTbUopTEGJQUhHFCMxbEGrSBRpzguw7aWJqxpjuXMrcnz4L+As54g53ViILv4CmJEpAagzYQ62xystpMiBKNEFnWbFYxR2JiugJFGBuqYYq2Nrt/mpVBq6FGkMWspENiEqy0mATKUUqcaBrWAIpUO8wpBfgt8dNMNbaZ4DqKapiSpJqC71BuxGwZa9BINI7IDG3HmgnzunM4rUxhPdZZVk/tWcKcyK7VoyxrV4sMYCYzmDurITl316TplJmofOyZzD318YytWc2Ov13NzhuuYecN1+AWS5z4rg9RWri43SHuN4buXM+sFUvaHcY+4XfnXYTRhrNfPvM2HzxcdKo5993f4rff+yNPeN5jefeX3oTnz5ytCVOFjhA7EFhLvidbwVOcNY9C/2wq2zdS2bGR4XtuY3D5sXj5g/cXRlyrcMO5n6S2eSOrXvPW/TJRdu9VQAhB3nPIt0pru/c27X7bZqIZrcfEaVYaG2/G+K4it1tGJnO9n1hZpCjlXIZrEc3YIrC4rkQg8Jws86OtxRECVwmGIz3Zs5borKdsYX8eiSTWhtF6QikXU/Q9Ak/RV3SpNDO3/cBTSKASJYCkmWiGqlG2yigx9OQdEqMJkESJoZlmWboJTZrolMFCnshoHClJ4xRHCuI4M4htRppEZ0MHeUfhOm6WVXMEtlX+LPmKgWKA60iacUqYwlg9YmclZEc1otB6f+M0GzIIY00xlwkvJQXY7D1MtSE1BkdKlHQm49y95BkmBku2vFwbQ7H1fk+liUohJX0rjqBvxREc9uJXMb7mToK+/rav4Nqf6CRh9J7NLD39+HaH8qgxxvC7/7uQY08/jnnLDm7LinqlwUdfey7X/fFmXvau5/Cq978AOcW8G2cKHSF2AMj1zgJr2XLrX+lfcjhBqYfueUtxc0U23XgZXXMWE5R62x1mW4grZf72hY/SHNrBMW/+V2Ydddx+eZ7AVZQbmVHqhOXD7s73sCsTNjHNWG5mfWJxasm5Ckdlq4FqzRjPk5M9TI5M6Q7cSXd735FEcUqYZiU8R0mGqiELevMIkVlNNFPN+uEa482YRlOzsxIyUPBwHIdNo41JAZNoy86KoObqrCFfQ5QYamGCH2elwll5j+F6jDaWnKfwtARHg5RM9L1Xw4TEWoqeiwWiNKXStDT8lO6cS6WZMlqLcBQIIDGaMAEhQElJV85FSqiFEZtHGq2MmKWv5JHzHPpcH2MzAdtMNM3Y0IhS3JZQVVIiRQqCSdGV9xQ9eY+hasRYIyY1lqKvKGqHvKvwHHmfxeRjtYRmosmninqkKfoOBd+ZNK+dCmJsAiElvSsOb3cY+52RNZswacqsw6Z/f9jt19zGjo3bD/p1RkNbRvjASz7F+tWb+ZdzX8/T//EJ7Q5pRtMRYgcAKRULjns8Q2tuYXTDavK9s+ias5h87yystegkbneIbWGXCNvJcW97H30rj9xvzzXeaC3iVoJUW6Swk83iE+yeCQtcRS1McCWZ39Zu5bPEGpTJbtuIUrQ15DyHqNX4XgtjxpqZW762YFvZoOFKyGCXT1XAuqEqd+2oUW4m9OZclITxRoKnDHHiIEuCgu+S6phaM8WVmRCshQmh1kSRRgooeIpS3sMgGK02cQTkPEmowXdlq2fMghAEUiCFyMqYcQpYEKCEJDWaepLiGdBW0AgTAl8SSEXRdTDWMlaN2TzeoNo05H2JKx1GmjFF38Voi98Sj7UwoRplhrTDJqav5Z02uxTguVmmT0mBoyTVMCFODcXAwVhDoDKbjoLvEN6rpy9JNRYw1tJoCbSd1YienMv83jyWqVOmPJjYuTqbmBw8bEl7A9kHXPqTS/DzAY97+untDqVtrP37Bj7wok/RqDb55Pnv5cSzjml3SDOejhDbj+y460awlrhRZe6RpzCwbBXlLfdQHdrC9juuwy/14vg5Cn2z2x3qASeuVbn+Cx9ribD37lcRNtGoH6dZI30mmnaVs8JE4+4mwiaa8bWFxIBuudEDpNaSdxW+K5BYlBLkHQ9tMi+tSjPGGCj4DhIYa8YMV0IQgkqYsLUSMl6PiVKLEoKSr9g2HlIMXHbohEMHC4RpykhVs7XcoCfvkXMdxsMEV0gqzSwjJABHunTnfUZrMVvHm2wcD6mEmiRN6S14lHyXwZJDt+9S8CWVKGXzaJMwNTTiFGEhiQ0NmdKIUmpRSqAkzTQrceY8h4XdPvXYtLJcmmqoSYylElpmlRSeUIzXIwqeItKaku9QaSbUogSLRVpBmGr6Cx5zun20FZOeaQA7qyGulOScPTNZSkl8mOy7CxNNqiUGi6lmlhmpNsSpIUozUQpTy/j1YGFo9XrcXEDPorntDuVRkcQJV/7qMh73jNPJFXPtDqct3Hr1aj74kk+TKwSce9FHWHbEonaHdFDQEWL7iaG1t1LdsZH5Rz+OoTW3cNtvv8PC489icMWx6DQmLI9ijcnKlgcZabPBjV/8JI2d2/eLCLu3tUQ2eZfsWitEVpYEw1AlwnUkQT6g3EyoNDNnfAApwFXQjDSeI3GURFhLYiyB8Ggk2aJu39m1LkhJie8JTN1mXmG1mKFGTG/Ow1jBSK3JUDWh6DskxtKINGFq6FIQSJU9X5KtSCp6LsOViHI9YW5PjlRrRuoxcWJxHEEjSamGMb5SbK82sj6vJBORw5WQUr8i1lAMFINdAbIaMebFCAFFP2C0FtFIUpQSVMOYZpSSy/tUmxptsh2YqfaohRqloOAJxpRkvBmTcxWj9YRZJYEjBFJIfKXYVonZMt5AtYSRUgKFoCunyHku5WYyaVw71tqfqVM72TcGWV+YIwVFf5eDf3drunJbuUkpaK2RClMKvkNPziM1lok82FQyfj0YGFmzgf7li6bc7teHyw2X/Y1aucYZzzk4LSuu+cONfORVX2DWvH4+/fMPMnvBzFhVNR3YJ0JMCPFt4JnATmvtfWyIReZBcC7wdKABvMpae0PrulcC/691049Za/93X8TUToxOGd+8hnmrHkOue4BFJzwBYzTbb7+W8c1rWHzSkyj0P/T9dDMJHUfc+OXPUN20gWPe+K59srJod/ZmLSFgDxEGsGW0iRQW0xJdzdgwtydPPdZIIUh0tq9RCoVSmjRNyXkOfsHD2sxmQZusnKiAgq+IU00jSmnGFq0tUZqCMPQETmY3EWuSVgZHCZBYYg1CCjwpcRwwGsJIE2uQQlOLDD15lzjWVOOU8VpCpLNpQkXWsO9IyWgjouC55HyXxBjC1GIEJMZQbiTM7cmjpKS34FPQBiz05FyEgERrir5HxTM0kmzZeMF3ybsqy/j5ioLnEBtNf95SCVOqzQTPFRRcQT1OcaXEUYIk0YRRiqNk5tJvLa6SpNrSiNNJE9mRWoQlex89KahFCcXWNNbuuzl3J3AVBS/rDfMdBQF42uA6ew5YTDXj15nOyJpNLD3jxHaH8ai58oLLKXQVOP7M6f9aHi5/+sVf+OQbv8zSIxbynz9+P72zpr8NyXRiX2XEvgv8N/C9+7n+bGB563+nAF8BThFC9AEfBk4k2098vRDiAmvt2D6Kqy1I5VAaXEBYHSXfNxuTJlitWXHm89h+5/U0xobomr2w3WEecIxOueXr5zK+ZjVHvfZtzDpm33mmTbjg16J0j5N4lJrWZKOYXPkTxim1OKG02xh2uZlSysV4StGMNbG2RKlm53hIJUwoBoowNcQaZnf5JKnO/LWSlJ1VPdmoP2EpUQ0TGpFhZy0m76rJXiclHXp8S71lU9Gdh76iiyOglPcIU0POU/T5TmtNkiZMNGGS7a5MrWlNb6ZIoE+A8mlNEpJZbRiL50gCL1ujFGnLjkqTcqt3y1rAgsUihMB3HAo+DJQ0aWpJtI/nSIqBQynn4qeWvqLLUDWiK+cwrysgymskmcVHYmG0ESJlDgsopRivJygHrIVZhYBYQ6w11hqqYUwtMpRyDqWcg6cUqTZ4KjOBHezKykJ7E9WDXTmszawvSkHWl5ekhkRbjNV038sTrsP+pTFapjFapv+Q6f19lsQJV190FY85+3G43sFlz/Dr71zCue/+NqtOXcnHznsPxa58u0M66NgnQsxae4UQYskD3OQc4Hs2291ztRCiRwgxFzgTuMRaOwoghLgEeBrww30RVzvJ985m621/obJjE9YY3CCPmyuQ6+qjunMzpcEFM9Jh+/6w1nLH97/B8K03cNhLX8Ockx67zx574oTdiDPX/AkX/AniVIO1CAwCkTXXuw7ubuIsu52h6Hs045SdlZDt5SbD9YgksTgOzC0FeJ7DaD1ECQkCKmFKzpEYGzNQcgkcp2VFkTXC9+YycVVuZmJOSEUxUBigIAXLZuWoR5kBbCFQYGC4GuNJSaI1jcgghaDoKypx1p/luy4lky0C9zwHTwhKvsN4qMk5mR/Z7L4cRT9b6l0NExKTkvdcwlgzWg8JY4PnwGB3nryXWW6AxXclpSCbrARINShhaEYpfYXMviLnK5qRxnEVeUdhhKUWprhOSGotSlp8T4AQGGMQ0hKmmrRuMAbKoUanFnxBV+DiKtnqsXPpL/rAA+/6nN2dmyxZNqKEKDWTuz07HFhG1m4CYGD59PZI+/vVt1Ir13jsMw6eJn1rLd/9z5/wg8/+nFOechwf+tY7CPJ+u8M6KDlQPWLzgU27/Xtz67L7u/w+CCFeD7weYNGiqdtAaG2WZeias4hC/xxqQ1sIuvvx8iUAKjs2ke8bPKhEGMCan5/H1r9ezrJnPp+FZzzlUT3W7j1gwOQJe+Lfu7uzV8PM4LQSJpQbKUKAK0XLaT7L/MSpJdYGa6DajFFKZIKlEZOm2VqfRmyoxSl9rqTacnjPuw5RkjBaNwRKEWsPEPiOpB4l1MIEz5XM686RcySOI0m1Jk6ysmfedynlHLpzgmai8aQi8BRxahlvJAhh6c67lDxFf1eOegLNlsdX4MisYV9AZMFzJQOOJHAkBU+xpL+IowSVZuaKX9AuWgtqYUQ5TDDaEqaS1DRJUkMx5+K7kqLv4DmKWjNlR7WB0Zl/WmwTco5iUX+OJHWIgqzhPkwSGg1DJLPGeSsNnpSonCBNLb0FD6ylGqb05tzW65RUbTZtOt6IKQUKz3H2KCneX59XPUon//aOFFgye4wJ2f1Azfp7W0vV4dExcvdGAPoPnbrfyQ+Fay+5GtdzOe7xB8dmkzRJ+cK7vsnF513G2f94Fu/83D+hnM5nol1Mm2Z9a+3Xga8DnHjiiVO2G3d3gaVcj+55S4Fsh2Jlx6ZsjdHKg+PDPsGGS37L+t//mgVnPIVlz3zeo3qse5erJsxQBQIhAQypzhq+41QTxpl5a+AoakpTDRMKvkPBddhWiSh6LoYsg+Z7kmaclbgm/K4ibQgjSymnMiNUKygFDtZkDf8bRlJSY7COJYxdalGINYJt5YhylFDwJFrD4fNLGCMYr6Uk2jAWpqRG4LuC1Gb7Ig0WY2F2T0BXPpvs7Mv79Bd8Ym3oyblYAUlq0EaDycp960aaYC2FIGtc7yk4BCpb7O27ip6CR5waRmoh60caWEACUhoSbRAYrAXHkeS97MdENY4ZqyeEsUYLQb4lXKQQzOkKqEYpodaUm9CVd3GkJEoTtpcTSoGDMZbAUwgBo7WIUs4j0dlaKcg+D/VYUws1I3WYVfLJew62teHg3ga82aaBlGLgTu6cTFvTrIGrJidiHSlIzX2/1u5vLVWHR8fwmg34XUUKs6a3D+K1l1zN0acde1BMS4aNiP94zX9xzSU38or3PI9XvOf5B11iYKpxoITYFmD3JoIFrcu2kJUnd7/8sgMU0z4lrI5TG9qC4wdIx6PQN4hyd6V5hZTkuvtZcsqjywZNN3bccA13/fT7DB5/Moe9+FWP6gN/73JVuRmzeaRObLLmeEdJunIOgZMJAEdkju2NJFvCHbdO6EDL+iBhNE7oLvgIBDvLMVGSNesLCb0FDz/SxK6m3EzJe4piziHnKsabCZV6SGoMzdjgCEUzTrFa4giN70ncBEYbCb5SbB1ugoJqmOJIScFXGGuphpqunEKIbK9ilKYMlgIcmSM1FiGyJvW4aejr8vG9zHHVkmXhNg/X2eGFKKkIPIcES973mNXlIaTAmMzBfls5pBGlVJspjpNNf+ZcRS2OKAY5GnFCr+vTSFLGGymjtYSt5RBXZaXNwVIOiUs1SpndHbC0K8/OSoQ1WSYyTBJSq+jKuwgLwsnMZGtxii8V2yvZ8ycmyxhHqaHgWawQ5J2smb8SxgSOQhtLwXfwHcnOajhpVVGPElJts0xflIlEXzkEvsRTiiTNsoUARd/Z47iphsl9NiGUGzFKyT0yZJ2s2cNj9J7N9B8yvdsshrbsZPOaTTzjVc9qdyj7nUa1yQdf+mlu/etq3vn5f+KZr3xSu0PqwIETYhcAbxVC/IisWb9srd0mhPgd8AkhxMTPqacA7z9AMe1T1v31QkqzF5EON3CDApVt6+iet4zS4AIAwsoYXrELKQ+eL/fy+rXc9u3/pnvZcla9+q2PaLx99xPjRCZEG4s2hm1jTSqRBmMpRym+yvYWlnyXRBvqYcJIPWZHNSJJU1IDqbYMlRuMNlLKzYSS7zA3MmwfDQltSuA6CKDLV/TmPOpRA88RzCq5zO/OM1DwaSYatKWZaKyBuUUPpSRJaqjHyaTYi1NLycsERTVOM28uPxNdgSPxHYGnJoxbLdUoGxIw1pJrCQkpLJVmSjPRFD3J/J4Ai6UWGsI0JbEGiyRKLdZmK4qMMQyUfBCSapi23smsD81VglojwXEEQ/WIWUUfqwXIbMKxGiZUGgkjjTjzDdMGI6DeTLOMFZYotvT2+bhSEGtNozXlGSYGVykKniTSlko9pisXIIBqpNkWpcwu+YSpRiJIDBiT0hAOsU6IE4NSMLsrE6G0xCJoas2UKLWMhw3GajHGZhlJJSK0scwqBUgp8RxBuZniV5p0tWwthqoh9WiXQ3/gZmK+LgSF1vs84eDfyZo9PMbWb2XpNC/n3fznGwE45vT9s9VjqhBHCR986ae57Zo7+cDX3soTnve4dofUocW+sq/4IVlma0AIsZlsEtIFsNZ+FbiQzLpiDZl9xatb140KIT4KXNd6qP+YaNyfTpS3rccvdrPgmNMwaUKzMkp9ZDvlrfcgHRc3V2B0053MPeKUdod6wIjK49z8lc/hdXVz7JvfjfIe/IR272zEvctJ1SgmjDORM1xrsr2ceYDlPImfZruDXAWOktRby60DV2aeU5HGETabZKyE1JMse1KPU1wns10wQtKVy1b8xLFh0YDDYMnHk4p84FD0JWFqaKaaxFrSNMsG3TVURypB4EiSxNJVUFTqWS9TGqfM7vJRUrbEJARKkKSZKEJm/WdZtk+jpKTS1LgqpSuXTU7WooR6lKKki4oMY1GEQFALY0Jt8ZUiFhpts96qwJXM7s5PPmYj0mAFvXkPkOyUDbZXsm0OSWpBWrACq7PSaCPV2esIHMLYYNEoZVFYMILEGHaUm3QXfHyl2BHFWTZKG7pzDn2FgJF6SCnn4SrBzmrEjkqESQ2JTil5LkZIXJX9jTTZ30di6Sq4dAduay9l1tNnLDQSw/ZKSDNKGKnFWLINAr6T9dQJIZjTk6MUZMfZ9kpz8tgZr8eEqaYUeKTaMBwmCGnpy/ut9zVbSi5FtrtyQuyHSXYcdjJjeyeq1mmMjNO7ZHrvZLzpyhvp6utiyeFL2x3KfsNay2f/+Wvc8pc7+ODX39YRYVOMfTU1+ZIHud4Cb7mf674NfHtfxNEu/GI3caNKfWQ7hf45FPpm4+YKjG28i51338SSk5/MrEOPmdbp+4eD0Sm3fONcknqNk9/7UbxS115vt7vwuk/ZsbWSaIKs3ytbol2NUuqhoR5nK3nCWE2WpMJEE8Yp1SjGUw7aZr1VeVfhSkHVTdg6HpJzFI1EExvDaCWkklq6W+Ww1FhiJRipOQx25ch5Cm0MlVCTU2LSdd/1FKYZZ2uqNAS5rCwZp5rAk8StSUBHSroCSTPOxuKlEhhLNq3oKhpp5lwvhaAYKHoKDq6CWjNhqB6jraGv4CGFYON4jXIjoTuXNd8LI/FcQU46KCGZ3RMwt6cAZCXN/oKPEhEL+rLel0bUIPBdBoqWJLUUfQeDxJGSXOCQc7IVSGPVmFQIEquZ1x3gSUlfl0PgKRqxZrsO8T2H/lLAeJiwM9Z0tfZqxqlhIO/RTC0jtZDRWoxODePNhMgYar7BEZl32NzugOFqSKIzC4qw5UM2UMoRxppEm+y9TLK1VNUo6wlECOKmpiuA8WZKzhPkfQdHSJpxkhnJSsh7Hq6TDW1sLzcnp0HjJMVaMeldprWhK+/SbMaTk7SpNiSpZkFfoSPG9sLYhq0A9C6e3o76t/31Fo567DEzeqH1j//7N1z60z/zmg+8qCPCpiDTpll/KhOUehlYtoqdd99EV3URfYtW4uWKzF55PGv//Gvqozso9k/vL6uHw5pf/Ijxu+9g1WveSmnh3sfad892hUl2wt1992M91rhKTC7lnpiic5UkcBR9BQ+EZdtYE0OK50q0NlTCBIEg1hbInNvDWOM6suVxJcgHCkdkk4qOlHiuwsYxxmTeYb6rSKwh54CQYK2hGWvqcUpTahDZxKASAkHLuFRnvlz1OGsgHyh4FH1NagTdeYdqaMi7kt6ShxTZvsXevEfBV4yO1NGpRQuLIwQIGK0n1OOERmyIUkMSWLoDl0RbpMiEXJimxGm2WLs376IcmFPK0V/0Ga1FqNZaJkcpUpvtlhxvpsRxFn9/l4+1gq5AUgocZpd8unIu5TClp+hy97aIvOsw2sg815yaoTtnsqXdjkO5EdOINeONhELg4EmJ70oKnsMhgwU2jTXYMt7AdSSNJOvjG6pmK6B68h6eVERJiiMz09dymFIN06xvzNcoCVGsqZRTQpMQRzYTwkpRixNcKYgS0xKtKdtFyFClgZQCJRRRail5Kam1pAaiVOMomQ06IKiGrXK2k/XrhXGKtrt2iDYTjbWWHZWQ7pzbKVPei7H1LSE2jTNiw9uG2L5hG+e87rntDmW/8bc/3cI3/+M8zjjnVF76rme3O5wOe6EjxPYR/UsOx/FzVIe2UB/ZQdecxXj5Eo3xIYLi9J4oejjsuOEaNlzyGxae+RR6j38MtSi9T+PzvbNfWRnIYIlRQmYip9UTNnHqm5iis2RN+aVctmqnp5BN41lrUMrBVw7NRGOMIUoMIBiuR3hS0JVzEVi6PIUWkq68xlooeoqc71BppBgNlTShO/BopAYVxozrLOZGZJjbky3yxkLgKfKuwJOCapoyXLOM1mJ68i5zuzw8N8jKXcrBLwhCnTnCO0oyNxeQ6tYuxcBjS1jHk5IwMVRqCaONmK6Ci5KZAB2tx0gg5zo4IvM808YSaQ1ImollIAhIjKHSiOjK+6jdkjiNMEEowdzegOFaiMAhba0/UkLRl3dYOquENpa8mzXc95d84kST8xVxaigFEimgFHhsKzfxXNBGkGhLnKZ0dedQUlDKOeR8l/6ix6y8jyMsDj5xYhCxwHPAkyCwNBJLnBrybutvZ7KetlQbCp7LSJqVUB0UZR1hNOQ9SWwUcZKilGF2l48AmlFW6kw1eE5KlBpynszKlRZyniLShkSDEpkVhiNhVilHKedmj5FkQrOZaHw3E2na2M4Oy70wtmErQkq6F07fLSF/v/pWAI485ag2R7J/2LZhJx/7p3NZvHIB7/7iGw+aqsx0oyPE9hFCKrrnLSPo6qc2vJXtd1xLvnc28456LI4ftDu8A0J9+xb+/r9fpXvpoQw+40WUW1YFsGfj8709opTMdh2GiSRws0PSc0Rm1wC7lnW7WYZsZzUijDV5V+IpjzDNyoGNOGXbeBOpBI1E05t36PY9evIutUhjEPiuy4L+bEqv21ck2tDV2mO4w4kYq0f05Fy6fIckNWwaa+JKsEjyrfU6niPxFAgl6M55bCqHeK5DlKSUWvfFSvJ+Zq1Q9Fy6cw7VSJNzFTlP0Vtw2FGJqTU02loUkkqoKQSGapwSG4OnFMIXxKlplcsErgStJCbNsjVze3JgDEJKijZBjNbYsHk7JRICBUIbkjhhrNIgSQwugjkCqqnFUQ6l/i4GFwyysDff2v8YkVpLT8GjEaW4UtFI0mxPZj2lvwTaWvKeQpBlBX1HIrAoAcXAIec5NKKEoWpEZAz1JNvPKRBE2hCninKk0WRrlrSGRBr68i7FQGJ1tjIqqyMaVGsaNopTKjZBSokSkPMdSr5HMfCIUkO/DzsrEXnPoRYl5DxDNbSUAktXzgNhECLLhoZpSsFzyLsuvisoBS6BI6mE2cLyLuvgtrKxE3tEOzss92Rs3Ra65s3CmcZO9LddfStBIWDZqkPaHco+J44SPvLqL2AtfOR7/0KueHCch6YjHSG2DxFCEJR6CEo9DCw9Ams04iCZktRRyM1f+wLScVj5mn8mZM/XvXtGYW+7AIUQYCFMMnuHwHXwHcVoPaIZ66yk6DlEWuMpQcMYIp25qSuRTUOO1BOwFmWyklyYWLr8rAHdFQJLZoOAFczq8ij4DrU4pRkbFFD0JVHqZEauGoSCHj/LGPmeyvqPEo0BSp7PrC6fZtEglWDHeIgUUIkSjBV4rkKKTLx5StJMDdoYLFmGpRFrip4iSh2MSVrTnpLUGPoKAanOphz9OKJ7fBR35zDUGtRHRolGy4RjZdJyjWalgq3VsY0mI4/g7zZO5qh8g5Lk+3tw+3rQ/f00+/qxvf00unvxZw3geA49eQdrwVeSUk+O3nwmJnOuotpM2FkNiVJNyVeUmwYlJG7LYBaT9ZCVgmytUTXKLD56Ch5KpozVIwJPkCYWz1WUmwm1KGa4ljCr6GOQ+J7EiSRuIJnneUSpIHAzX7cuBMZYhlWCFBD42e5LJRVF36Gv6BMmCcPVJJvabJV3rbAoIfEdSXfem+wh063MnOfsyuZ2dljuyeg9m+mb5quNbrv6Fg4/8Ugcd+adCr/1sR9x983r+I/v/yvzl03frOXBwMw7+qYQB4sIs9Zyx3nfor5tC8e//QOo7j6I0vvcbiKjELhqj/KkNplTvLWQWou1llqY0AhTxpoJsTaUAodapNHa0pVz8FyHehKjbdbsXwhcepLM/qERJyQpdAcOtSil1sym72QqiayhFmYn676Sn3l3JQZDVrpKyjFSSMpRQt5RDCWa/kJAJYxxHIGjMnFVyrv4nkMzaRKmBoPF9xQ54xImhmKg6C94pDplPIlRMnOt19ZQjQyek8WWpIYwSamFEXLLVpzhIbaODNPctJWhTVtJy9X7vI+qVEB1lXC7u3CXLESWiqhigdDx8Ep5vEIer5gDx6G7EIDjkM9lIqMZJVTrEVGU0J93COKIZHScdLyMHitT3T7C2Oo1pCPX4QM+gOfiLVmIe+hSzGHLcI9eycDCQXxH4jkpYaoJtcamhlRbCp6L60riJNv7aQIXYw31JKE38CjlXAqBoeAqUp0tJk8MjFY1gScoOJJqFDNeT/ClZKyZILGMNWJGmyk5I7BAyXMpBT45V5KkmSdZwZXU42z5eqE1oOE7mTAvBQ7jjZSCyqxJHJUJ35KvJrO1PXmPwFUoEZEYOynCfEd2ypK7YVLN2PotLDnt+HaH8oiplWusv30d//iex7c7lH3O9Zfdyk//57c86zVP4XFPP/iWmE83OkKsA/DojCy3XvUntl19Jcv+4QX0H37UpCfTvdk9o9CT9yafU2JpxNlzukA9jtk2HlLwFKP1BCkFcarpy3s0Yo2xWcO6NlkTvbGW7pxDb95jtB4TOA4FF8abMQVH4QoYasTk4pRC4FGPNePNFOUoHClRrWxZrDMH/XLDoJBIRyCEpBlrgiCzSvBdRd5TSAV3bBun3EjQiaUeaYS1uI6k6Hs40sEaS87zEGlmh5AkhkaaUvJdKk3N+IZtbL/uFvTqu7F3r8WEEVWg7vuUls5n8eknUFg0D6evh/75g8iebupBjmqS9Vclrcyar7K+tWaiEWTZxYGin13nQCFwGCzlJvdOVhoprhL4nkO1kaCTlAVFj668x1gjM7UdGioztm4zjU3byO0cIl63nsrvL6d84aVsAgrzBxk84Sj8VSvRCxdTCHyEcLMyc5xSRFJPDIGj8JXEWoO1mWdayXcYb8bUYo3nCOpx1o/VFThoC2P1mMCTCCnJ+wpjxWTzvOsIGpEmTAyRZ+jJu1hjETJb99Rd8NE6Ild0UCIz5S2HGmFjPF8hsLhKUQlTHCUYKHrk/D1La4GrmNeb75i7PgDlzdvRSUrfsgXtDuURc/u1t2GtZdWpR7c7lH1KrVzn02/9CouWz+MNH3lZu8Pp8BDoCLFHibWWsY130bPw0Glr1vpo1r/Utm1h9Y++S99hq1j29OcA9814QSYcJgxZJ05qu5/cvGZCnGaeWSPViJF6TD2SNBOTlfbiFCUkzSSZ7DUyJnv/JVD0PfyCwMDkGh3T6qkyGBCCWmSJTcjsroDAyVYISZH5UW0tR2AsI7WEoq/QCeSUwnGy9UgF30FKSVdO0Zv3qDdTto6HFH2HfE6xslBk53hIMfDobS2uHg9TeoRtLdtOqYQxJozY+seraP7pSpIt2wGwvb04xx+LPGw53cuXcshh81nSX2Jeb55alFLfLbtoKiENHVNoZdd8V9HlOzgStpQjUp15ho3VIxBZ71MYW6JEky/45DyLaa1pGq1na5iUFNQiTZRmeyj7Ch7z5/ZR7C7RPGI5pUDRTC2+1RRGhmncdQ/br/87my75M+kFl4KS5FcsIzjuKIITjiU3fxaOEuRs5rsmhKAr52fGtpHBYNA6s6MQNvNB89wsa6WkoK5T8iKzlJDCwZUQI/GEIJ+TjNUsVhgaUUo5ShjfkTJY8ujK+XTn3GwRuQVjDI04oRqlDEuJq0AKiTEW380sSRpxisKyct59B2oeSHwd7CJttDUx2bd0+k5M3nnDaqSUrDjusHaHsk/5yr99n9Gd4/zH9z/aWeI9TegIsUfJ2Ma72PC3P4CAvkUr2x3Ow+beggkeeHHy7ugk5tZvnosKAla95i17OOfvnvGqhwlWiElBcW+h58is6b3SjGnEAs/NfK2UzCwmoiTFIAicrBRZa1YYqWtSk5Wb8p6kEsYs6isyp0tQ8BRRovFjnVkQJJaSr7B+tiuxv+AjsITaoo2l0kjo8RWmta9yWzlkdtFHKslgyaUr59GTy/YfJiYTMpHRBK0l23nfIUo0VsnM2NXNhGOsDdUIEm2pbx8muuwqqlf8Fdto4i1ewMArnk9z2aG4s/sJXJeuwMFzFT05j76WmLt3X5KjMvf4vCsp5QJqYUIt1uQcl2KQUmlarLGMNhK6fIWSPoGTGcnGqcZYQxSnJDql0swsIKphQpxoUmNITSZg5vcWGOxS1MIYKQSugpwXkJ/VxaLjDuOwFz2dQFrWXHMbd15+A+UbbmP0vF/Aeb9geNli5pxxEsue+BjmzO6jGVuGaiEGSbevGKk3KDc1XTmHkVqW0fSdzJ8tH7gMFD2akcaXmaDS2hAnBpTKDGaNJYo0vQUXoTPbj53ViFLgInEwNhteiBLDSDUithZhDcP1BE9kNiZJmvmTzevJU480kbYcvbDvQT8rE8fz7h53+8qBfzqJu7H1W4DpbV1x142rWbhi0YzaL3ndH2/m4v+7jJe8/RxWHjfzBhBmKh0h9ijQScSWW/5MoW8OvQtXtDucR8T9TYI9lAmxu392HrXNGznube/F7957RiFM9B4nLbiv0Ju4nZKSvJdNK/pORJxaPAmNyFL0ITEagaISGbaWQ3rzLq5ycVRWYpRCo3XmuxW1mveFsASeQ05mlg9KCpqxphg4iDRltBZiDFRTw0DRZVYpR8FV+K7ACosUkr68z6zuAN9RDNciXCkoeYrhakJqM+f32GRiIXUNY/WY2JjMTb9aYewXF1K+8loQgt5Tj6P7qWeSLlyIo8DX2aCB1+pBclQm7HY/EWttSFoeZ/VWZtAgGalFOErhSkmiUwYKPnO7A3aMhxhtiYyhEqb05jwEWVYsSg3lMGFnNaIRpxhj8ZTCcSSJzkqbJd8hTBK6cx4EHjlP0Ix3rW2a+Nv5vscJTzyJ7qNXsnG4wZa7N9G47ibSG29h43d+ysbv/JT5J61iwVNPJ12xkrybLR+vNDWNRJNqjRSKMNX4rqAr75H3FH35gGoYZSZuZIvCpbRE2jIeJSSpJbGGKNE0E8tYmDCr6DFWj+kvSnKeQ6WZMFyJqYSGWKcYa9g82iRwMiGbGvBiyWBXwHgjZs1OS39h1wSm70h8V+1hOFwNE5pxSiM25H2VvT97OZ4fCdNtKfnY+i3k+roIuovtDuURYa3lrpvu5OQnn9ruUPYZjWqTz7/j6yxaMZ9XvOd57Q6nw8OgI8QeBUNrbiWNQw459vRp689yf5NgDzYhNvz3m9n0p4tZ9MSzGVi1a0fbvX/VTwi6ibUxE1YAE48+cfLqyXut/qYYJVyqYcBYIya00FdSaG1JtKbgOzitrFZsNI4Cz5GEiaYZW+pxwnAtQgmBsoZ6bHAFFHyfgmtJjKEeptTjlEozJe9IKonGkmVVFvS6lHI+oBlrpKSOzjy9cg55V2Urj1q2BiPNmK3jIXFiCCNDly8RMlvpQxhSuOrPjF1yBVhL79lnseL5T2Nw8Rx2lJsMtQSdEuAFipwryfkOBU8xp7WeaLS2q2G8mcaEScpgKdt1WQ9TKmFKd06Q9zMh2og1LqAcgRZZ9sy2eseyv4LJ9lZGBiUys9QwNRQ8g2MdHCnoChyEFFgE2mT7HIu+i7UJYbJruGL35vUFPQWiJMU7fBHyiEW4r30uyfYhKn/5G5t+dwVbPvYVZCFH4dQTcR5zEuP5HuphigU8pejLOQzkXRwnK/tKaekrBrgqy2rlXMXOSoi1KXnfoWpiktSgfagnKaXWvkiBZbwRs7Anjw0szZzGYNheNozUIpQULc+67H6L+wpUWkvQvUhwy9YyedchcB3CROMImNeXJ20dM45SNJM0262ZGnxn1/qjepROHvfAw8psPZqsdLsYXbeFviXTtz9s2/qtlIfHOeyEw9sdyj7jmx/9IUNbR/niRf+BF0xdEd/hvnSE2CNEJ3HmpD93CfnewXaH84jZWz/Xg02IJfUat3/vqxTmLuDQ5+zabrW3X/WBqyg3Y8r1bPpRG4PrSBb05ik39/zlP2EfsD0xeA64StBdyJrOtdaMNzQQ4ypBzhX4RrYEXkjgeAzXIsLE4EnFzloTa7MG7lgbGmGC6zrZhKaXrTHKGsUN2mqEkGhjGWmEzO/KMdowSJFN5+2oZoulV7Zc2D1HEaeaQwdLDOQ9okRTizxiawmjBPmXa6j+6iIatTr9jz+Zpa94LrKvl4LvECaG7rxPT95DSUktyvq9AjcTQqXAncy+lJtZKTdKNa6SSJGV6rpzHsZAYiw5T9Kd8yYHJOLUIIXEc+RkBhCgGWlSLRhvJgw3YgJHUghcCBM02aRi5vTv4ClJd+BQCpzJtS+lwMV3MpHdl/fo3i1bU49SXOVQ8Jj03sotnMuclz+bM97yItb85Wau//EljF/xV+ylV+ItXow+5RTqhy7HKMFIGDMHH1dKJqrblTCl6DmAJUot9SgrwTbibDH7nC5oxBBGmjDRxIkiUArPs1TjmIFSjsBTmCHLaCPKyrlS4qksYzZSC6k3Y4QxlHI+DgJjmjiOg6cEkdYYI4hSTc5zKTcTeosOplXOzkrm2fFdDTP7kdRYys2slDuxJeKhZLYeTVa6HVhrGV27ieVPfmy7Q3nE3HHd7QAccfKqNkeyb7j9urv51bd+z3PfcDZHnLS83eF02I27bhvi7r8PPeBtOkLsETK09hZ0EjH38JPaHcqjZvd+rofyK371D79NXKlw7Fveg3Kzk8z9/aoXwHA1ZGc1WzdUjzVzSgGmZ9dt7v3L39rscQJPkBpJzhXsqLZKQ0lKKefQTAzlRowfarSBBb1ZA/rWckhP3gEEvpIEXrZsO4wNmpREW7DgOtlzN7Wm4Dn0FlQmJlyBoxyETAmcbGIy7yvCNCt5KiUz09PWSy3kXHoKHrIWM7ZuCzv++zuEd69HHrqM/pc8m8EjD8XPefQVPYwxGCvIebsyKb2FrPfMdxVaG1JjacTpHifhbKn1rmwUZFYbqbHkvewjHLiqtTRc4MSGgudQ9LKyrLXZwIKDhImdnKkhcCU510cJS+B7FFqP1ZVz6M5nje8TonDi2OjNe/iumtyYECaaWpQSp5ZEWyKdTO5vLHiKnO+y9LHHkhyyjLGhMtsuuZKhiy7D+/GPKXZ3E55yCuqxJ1ENDZKEcTLD2GasGZcxeS8Tn8XApxrbzPLDWIqBS87PdkbGGqQjqISawUCSJlBpJHTlXZb0F3GlYLMSjDSysmbYiKnHmpzngBDUo4RmmrK8WCJKM/uUsWbasj9J0MBYPcJYS8F3qEcxGkFfwSFOs/VIEz9oMuNdi+9oPEc9aGYrbAm6ONWTmdYJpqpvWXO0TFiu0XfI9M2I3XHdbeSLeRat3PsKtumE1oZz3/0tBub28ZoPvKjd4XRoMbyjzvlfv5FrL99I/2D+AW/bEWKPAKM1Q2tuoTR70bTOhu3OQy2B7LjhWrZf9xeWPfP5dC1aOnn5/f16H66FJBq6ApdGlCKEoJlq6i0vr4kTfJRohmshOyuZS72rJGHdUg1jHJlZFuQ9l5ybNapXmymHDhaI0qwklZUaQ+IkZaxmkBJ8T5EaTZwIsJahakTOUyAkXZ5PLtZYkwnP7lxAd86lO+8RRZpanGIskyVnJQWOzCwswiQl7ylUS7RGUcK2X1zMuh/8EuH7FF7zMryTj6e34NHlq8wqwpGAzIQgWRZp4v5ZpiwTsvUopRFrlMic9CdQQhK4u0q7gasQgj1O3oOlgMBV5JwIV+2+NN3gOZk4irWmHGa7PX1H0hW45H2HopeJzbyn6C8Gk1nRMNGZ8WmrF64SJpOCO06zYYjunEeUasAh1QZPZeJ+sCtrgnakYH5PnlLg0PviZ7DhKWcwdvUt1P9wGer3v4c/X0ly1mmMnv44VKGA7yl8R+BIgW2Jrq7ARRuLIwGbGfnWwhThudhYIyxENqXaECRJk1LskFpLb84l5ypCA8IIxhsx2miskLhSUosNC3tzeE72WoWQSCEQZPsmsZZGbBlrxgSOwrYydK6yKCnRJitR1qOUeLcfIruv57q/z8Z4I6bcTDKD3ygl8HbtW53KvmUj92wGoH/Z9DVzvfPGO1lx3EqUmprv8cPh4vMuY82t6/m3b729454/BYjClN+efzsXnn8HQsCzX3EUT3/B4Xzhh/d/n44QewSMbbqLNGoyuPzYdodyQIlrFVaf9y1KC5ew9OnP3uO6vf16j1NNmKSTvWE5TxE1DY1Is6XSpCfSNJKUKE5JDUgl2VEJKfqK2aUAV8BYLcb3BEIIeosKBThKMLfk0Z1z2FHLdgqO1UIacbY0ur/gYayly/dwXYfRKFtOLQWMVBMKvoSiz6K+AkZbcoFLV87FUQJXCnp6fBJjGa5HJNrgyMyXq5TLxEDecyYFUGXDVq7+xFcZvu1uek4+hr5XvIi0WMR3JFIKAjdblTORvYqSmEa868ScaSV3UtxMCC1tBY5kMvOmpKAv50323WWZy2Cvmcx5vXn6iv7klKojBWHrgXxHETiKWpwyq+hTaK32KQQuWmcZv4nHmhCHE48bJppKM6W7NWRWixKiJMv+5FyFtSmeUvQXffqLu8bmJx6rK/AIHEUz0cjHHMvCM09i6O9rqP/m99R+ewnij1fCGaeRnnEacanAQNHDdRRSWHKeYna3j+NYRqoxniPRCHKOQBuXRqJpRimVKGW0mTBf5cjHKaNGUw4TsKBcSG3WO6dtZluiRLauynclRc+lkaQgJGmqSRAkFtwoE8X1JCHS2X2d1k5TKQT1OMF3FEmqCVNNKfD2+Dzs7bMRJpqd1bCVQcsOhCjRk+XhqSrCAEbXbgKgf5q66qdJyvo77uFZ/zT9F303qk2+84nzOfLkFZxxzswZPJiOGGP56x/X89Nv3czoUINTzlrMi153LP2DhQe9b0eIPUystexcczNBVx+lwembmn8k3Pmj75LUaxz/jg8g1Z6Hzr17zaphgrEWz3FoxA20thgs5UZClGqEMNxRL+MgiNJMqA0WszLaWMMQtsSZFVk2KNGG7eMhGktPzqXSTEhSTS20jDUiQp2VQQtB5pqOEsSJpivnYsiMWi0C3zX4CuZ0eczpLlD0JRbBWCPCGJFlhwKXBX0wq+RRbiY4UtJdcLOTo7UgBNZa1v76T1z/X/+L8lxO+/e3MvvMUyiHMbWmwXNltiPTWlyyk7HAUsp5+Oku8eS1sim7v49RmpW4fFfhWoM12U7G7r30Gt17mfruomyPcm+rfy9wFQv6C2AthcB9wFL0vTM5E2XRcjMGJEmaTWWGSUpxsjm4tUZqt5gmXl+cpCQGFvQV6AocxhoJy084HE46nNE717Hp/N9QvfgPRJf9GeepZ5F/7lPJ+YpEW5px1vuWcx1WzPYQIusPDFNLGKeMN1N68x5byiGlwCGMU6oh7IyzknIlTMm7kpwrMVg8IdA2m740gCOht+AwS/qMNRLmdAWMNlIKgcJzJEYLUpMZ5Lotv8CRakzT05O7Sl0nWyousJNC/f4yW/VWOXd3LLuynVOZkbWb8Ip5CoMPbPcxVdl41waSKOHQow5tdyiPmh+e+yvGdpb56Pf/ddoOjM0E1tw+zP/9z/Xcs3qEJSv6eOMHHsvKox56tawjxB4m1Z2bCcsjLDrhrBl74E+c0HfPkFT+fsNkSbK0YO99FRO9ZlnztpxsZDbWsrUcYi1IKRgseeysRFTqKUJkvldGCJTIBNloQ9OblzhCUfJd8p5DmDTYVg7xHUGqDVGUTQ46UtJIND2BSynn4ihJ4Dut0la2b3BOyUMgibSmElmKOQ8pFHlfsaCvQLkR7yGMAIqBx0BRsmhA3idTtGPHGFd94mts/OM1zD5xFWf++1vomzeLcjNh0HXIudmEoe9kJbaCl+06TI2lHqV4juKB2rcnmu+tNThSEbR61Gwjvt/G7wezP3i4fYBw30yOkoIk1QghcFQmPBypqUYaIbPVUHlXgch6x8JETw5QRGlWDh0o+hRch6A7Tz6ISbXNzF9XLUctfCOb/34P1V9dSP1XF3HX5Vex/OXPpvvMx6IchZLgKR9tQQmbWXek2SStktlxkRpL4CvS1FJvaOo6a/THGnbUsmOxkWhmFz0cmcWbd7N1VEJkWxNKgUO56SJlSMFXBK5DJYwZbRiszfztAkeBgGZqmN3q+UtNtuEh7zn4u+1Uneinm+oC66EysmYj/csWTNvvvzW33A3AocdMT8uhCWqVBr/4+sWc+exTOfzEToN+OxgdavCTb93EX/6wnp7+HK97z6k89klLkQ+zv7MjxB4mO+68HifI07tw+pm3PhQmTujlZkyc2qyh28Tc/YNvUlyw+D4lyXszcUKqRSlDlZChakTBc5nfA2FiWgeoZXslphqlSGHxVGa90OUrEp15RzViQ3fOIec6VJsp1ciQGk3J8dCpoZYYBgsuOU+RDwQYMnd2AVGSEiaW7pyLtWSu8r7EFw6L89lKm1klr9XvpVFKTja9726zUfAdiv6eH5FtN9/Jhe/9PLUdI5z01pdxwqvOmVyRM5ERnJgwVELQV/T3KO3tjd17xCbfR0dmW8d34/4avx+q/cHDFQL3znIGrqLgKeLdnqqUyxzqo9hSCDLbi3Iz6+sbb0aMNRIckdlGVMMUaywWS1fgUfS9SZEbxiGukiw5ahmNlW/GrF3PzvN/yR1f+h7+Ty9m+WtfSPGko6nH2UCCFhN7IDMxZbFUGzEF3yVMNam21BNNLUoQQpLzFNUwxVOCwe4C3YGLRNCTd1jYXyBwHYZqIbHOVmVZsjKu4yjKYdbLJQGlMmf+1BhIIR8otGEPcT1RWtxRbk7GO/FeTojj7DZy0hIke38lBX9qfyVbaxm6cz0rzz6t3aE8Yu66cTX5Yp7503jYAODC7/+RZj3kRf98TrtDOegImwkX/vgOLvrxHVhj+YeXHskzX3IEQc598Dvvhan9qZ9i1Ed3UBvawryjHoucAU2e92bipLtr+isTT6MX/YikXmXVm999n5Lk3mhECdvKTaphyvZyg2Zs6M659OY9qlFKuRkhBZMlHJNmfVjjDU1PXpJzFcUgsy6ItUEpS5frkHMcrBEoV2DRhBq6laCWSgQGkxoSa6mHBiFMtl4HQaotYRrTW/DpzvuUAoe+VlPr7t5PI7WQRqxbmbSsjDUhxKy1XP/dX/Lnc39AYXY/53zzoyy5lwfR7lmn7px7H+HzQFYhE9dNxDORPbs3e2v83p/2B/fOpHXnXHZUwkmxmhrBWD2lGDj4rWxinFpG6hGVZkIjysRnGKc0U4O1FreR+Zt177ZBQACBJ9EmO3444XDmH38YW/5yI5t/8HNu+9h/0/+4E5HPPYe0EFDwXIQQeFKS63XIOYoRbXEd8F1FJUwIY3BdiU6hEWmKfibs53R55DwXASzsKyJFtpe03EyxFopetjJKSAijhLF6gq+yY2K42gSr8D2BpxykgnqcImX2egTZ323jaI16mIn6OM2WwM9u+cNN/L0Hiv4eE6ml4L7HzFSjum2IqFpnYOWSdofyiLn7pjs59JgVk9Ys0xGdan7x9Ys55nGHs+KYpQ9+hw77BKMNf/79On723VsYH2lyylmLecFrj2HWnEdnbNwRYg+DHXfegHJ9BpYe2e5Q9gsTJ2692wk83riG8nVXMuuMswnmL9nj9nsrdU0IDYlgtBqyfTwm0inWZqW6KNVEkaUrcLL9kRiUK+jzXIqBYlYhR2RTUp2VvyySvJv1e/UVHGqRzVbiKMg72Q7EJNEoR5BzHPoCyUgzIYqzPi4rBEoJunKZL9asokd/addk0UTs63ZW2DweTq5W6s45WLISG82Q333wXO657DoWnnUKp7z3dXilAuN7KRU+2In0gUqE9+732ht7a/x+pKa8D5V7v6bu3K7hgkZsKeUcvN1+mChhMSYrDYMm1oZmanBEq1wsZSb0rd3jsSthQpwaHOWQtProVj7hJA59/HHc/aMLWfuDX2FvXk3p5S/AnnQMChgPE/pSF9/L/MGMteRdRaQtTRUTJwIrDEKAFYLAU3QXctkOUgP1KCK1mYdcyVcIkWXy8l6Wba01DDlX4rsO5WbElrGIUqDod32UBIQg70lcJSenSKthykgtapW8M6uRnTWIEsPh83smX29P/t7DF1NbhAEMrV4HwKwVS9obyCNEp5p7/r6WZ732Oe0O5VHx14uvZ+fmYd7yiVe2O5SDgjhK+csf1nPxT1ezbVOFQ48Y4G0fPp1DjxjYJ4/fEWIPkWZlhPLWe5hz2ImT3lkzjYkT98TknjWa8kU/xOnuo/usfyBK9OQJY2clK7tM9FVNlF1Sky33roUJkTb0FBzqTYsxlpFqk5wv6c67pHXLIYMlmrHGGENXoCjlPCpRzFAlphA4FAOFtIYgF9BjoB5p8j7kHIUjoBIa8oGiK++Ctvg5h+6cSyU2DEcxBgjcbMoy5zj0FVwCb9chP5GN2llpUo0NSggSky0Zd1Rm2rr99k388X2fo7p9mOPf/gpWvuBpk70xj9T9/KHc/uEY7T4SU95Hw+5iMnCysm6c7pnNk0IgW2ulmi1RWco5DJZ8Ai8zry0Ee6bxjd31A8B1FL7IMqZBzuOwf3wW9sjDWXfut6l+5TuYv59K4YXPQfouSWvrghQtmzQBRd/FU4pqM2ZbuYmSsmXQaynXQoQQzO8r4DkSEkMuUDhCIERmlJt3FamR2BxUypo0ihmqJMTG0EwyPzYlBVobxmoJvQUQIjN11caSppbt1YiuwMHVAkdJRhoJ5XoIBHsdqJgO7Fy9DoSYtkJs67otJFHC0iOXtTuUR8VF511G/+xeHvPU49sdyoymPNbk0gvu5o8X3E21HLH40F7e+qHTOPH0hfu0R7IjxB4iO1bfgFQOsw49pt2h7Dcmd0OSubmPXXU56Y7NzHrxG9HKbTmHJ2wZqzPe3GVL0Z1zoeUIn52IDfU4zYwuHQE5hyhJqEcG6Xjk3SxDpQ30FBykEBR8l1RbRqqaSpSSDxSuVMSpYbwRImyWidHW0Jt3qTc1vmszY1IlqUQpQgr6WvsKc54i19qfWPBckAIrBK7M+nMmepPKLXNPJbOTpQNoC0mqWfPrK7n1i98j19PFM7/2EYqH3XeJ7v50P384DfaPpBn/0XDviUxgskfKx2LJYvCdrAG+FqbM6s76wibYPWOXlXO9PXr0gpbJLYCSksKiecz+0Luo/epiqhdeSnTXPfS86ZWUvUXUk5RGlOJLSTFQDBRcxhop4FIPU3zXoREnGGDTWJO53QGVZsycrjyOLym0ypEaaMaawFMILM1U4yoYr6VgDWlqyecFcWIZS2OkI/AcyXiYkFOCWd35Vsk2m4FMtEFbiwMIaxlvahwnKzlP9X2Se2PnHffQt2Qebn56+lWtu/0eAJYcPn2F2Mj2Ma695EZe9LZnoZzpJeSnC5vXjfO7n9/JX/+wjiQxHPuY+Zz9/MNYefTgfhlS6Qixh0BUKzO26W4Glx+D40/PL6CHysQJ3Y3qbL78ArpWHkXf0Sfiu9mhEqeazWNNYm0nM0jlRszigfykQ3w1TIgSw2g9JkyyviBE1nvjOaq13FvgSonvZYantUgTOJKunANkvhXCQhhlPmGB7+B7DnkXjJF0FwX1sawpeyRKMvFXbjKr6NOf9/FUZrZZiVJKuWyisr/g47kO9TDJDLxaBqpRkuI7itjJeuOs1tz51fPZ9ps/sujUYzj7U+9EloqUm8l93q/97X7+cARVu7IrexOB47vZZQSuoiuvJ3vI4L4Zu4n38d6vYaKHzGhDfzHIyosvOofC0Uew83++y8jH/wte9SLcU04EC7HNfMbm9uTpCsg8yAKX7kDhqGwRvCMFtTjFCx3G3JjunItSMFAKaEQJAsh5AiEUXiNFGnCFoJ5kzf7bqjFdsSXnKBbNCmgmBlcKtjcSYgNF3yFOWx5uItsnWvIVBoEUu4T7VN8nuTeG7lzPvGMPa3cYj5gNd6xDSsnC5YvaHcoj5tKf/hljLE99yRntDmVGYbThpqu3cskv7+T2G3fg+YrTnrqMpz3/MOYs6Nqvz90RYg+BnXffjBDioDFwDVzF2ot+gklilj3/5Rh312HSjNNWQ3VKPU6oNLMMWrmZsGJ2CSFgpJagrSFJNXGiqcfZ4uacA25r8XKsDYkEJUGSWQb4SjJcD8lbp1UiSqhGmr5Stpcxm8TTFH1BM878n1xHEGvoDTwKgUJKSSnncshggWaiGW8k9BVdBoo5AjcrNybaTk6nKSkmzVNLgUe9UuOOT3+Vsetv44RXnsNp73z55GDGgSz/TTfu/T7cV5zt3Xx29/s/0PtbynvUE42jJFU/Jj3xCBae+2Hu/vzXGf76D+i5ZwP9L342WmVC3hhDwXeRQmCsyCYdfegx2R7LnpyP72UTn8Za+goubisrmqYxd29v4raWejtSEhqbWVa4gl4piI1hTq/CVw5GG0IjUBI2jtZwpMJzs2lOzxGtJeIW382OtWqYUApc4lRTae56D6Z6r1hYrlHdNsSsF5/d7lAeMetXr2Pu0vn4Of/BbzxFufRnV3HY8YewcPm8docyI6hVIq64+B4uveAuhrfX6ZuV5/mvOYaznnEoxe4Dc5x0hNiDkERNRjbcQe/ilbi5B3fInQkM3X0XW6+6jAVPegalufMnM0Fxyznc2Gwly/ZKRNSahNNa051zKXgucaIZqsUkxmIs+I4g5wkCV5JqizaZjUXelfieQzPWuBY8lXlujdUSEmNJEkEuyJrnlRQkWmNM1gTUV/RJbNbEX/QUA6UAY2FW0aMr51IIXHK+QiAo+bt6kVKza00Q7DJQdZWEnSPc9r7PUt24lSd+6E0c/YKn7PG+HOjy33Rnb2Jrb0y8p4JdmbF7u8tPTBX6jqbgZx5dpr9Izyffzc1fO5+dF1xCc90mBt/2Wry+LqTIhgJKgYsUklBrtLVo3+Aq1fKcE9mC85xDd85npB5RD1OaqaWZGiJt6cl7OK7AYibFu6sURQxRDGHUpJ4Ycq5D4GWPFyUpnusxq+ShhAJpmd3lAZJGnBKlmR+btoJSkE1sTiwKDxONK/e0PZkqDN+1HoBZK6bvfsYNq9ez+LAl7Q7jEbPxri2suWU9b/74K9odyrRnw5ox/vCru/jrpetJYs3Kowd58euP4/jHLUCpAztR2xFiD8Lw2luxOmX28uPaHcoBYcd4nTvP/y6q2IX3mKexZayOtVkmTEiF1rRWEmWCxncy09TAc7JSZGrYPFqnGadEiSHVWeOzrzJT0noY4zqZez0im1zryWcLseM0E2izujwSbfFcyXA1wlNZH04qQUrDgt6AUs4j1pZyUxA4AiklPYHDklklIFscHsaG1Fq2lUNyrqKn4NKTcycdzCfoznlU77qHy/710+go4Tlf+RCLH7P3XsCpdnKc7kyUMKthZoLrOWKyV+zeE6UT/Yse2Y8CVwq6Cz7LX/sicocuYeP/fI8tH/4MvW96FbnjDidMNUU/M4ItKYVOHTwpiAxYY0lTw5xSgKMUW0fr1BJNqk32w6D1RTxcaVJt2YgYC56TrcEyQKgNjdBQi1OUTOkvevTmHRzHyUrjNitLukohkVTDhEaSTXBKoKeQHfeJsVlpPA0xdmLFlaUUuFOqh2yoJcSmq3VFHMZsvWcLp59zZrtDecT86Rd/QQjBmc9+TLtDmZZYa7n5mq389vzbuevWIbxAcdpTlvLEZ61g4bKetsXVEWIPgEkThtfeStfcJQRdve0OZ7+zo9xk49V/Idp0D+qpL+OO0ZhUR5ON9UVPMa83jzGWcpiScxWOknhKEMaa4UqIwbKzElOJYnpzHtYahBJ05116cl626NtmH4jUZOtwrM3WCRlrGWskeK4i72cnwsBJEUhKgYcQWV/a0pbYcoSgHmeO7kJCb96fXEMUpmAxFH0XX2WrhpSErtZJfvcS2NC1N/OnD3yBfF83L/jWR6ftDr3pxsTfIdtJOrFI3E4KrnuLsYmM5GgtaokiiVKS/qKh5+mn0bd0Prd97MuMfObL5F/zImY/9XTKTUvBy8qVvUUXKTwkMFKPCVold60t2xoRI/XMiNZYCFMNFoS0WCsp+j5g0AakMNTCrEes4ENis4lM3xHZf5XE2Gx6EgW+Eow1EpQU2QqveoRpleeNAWMNvXmPvO9MxpQaO9lDNvHvdmdhd9y+lvxAL4WB6flduGH1OowxLDvyvkM304W/XHQ9R56ygv450/Nv0C60Nlxz2QZ++8Pb2by+TP9gnpe88ThOf+ohFErt/7HTEWIPwMj6O0jjkNkrZv6IcJhoqvUmlUt/gRhcQLLieEYqMUXfYawRESiXGhBrzUBXQDVOGKnG2Mn7J0SpyZYx2+zkZyzM7/EJE+jLeziOgzYGZSw7qxGxztblzO2y5GMFWKyldVI2KAF9BY85XTmKOUXBV8SppRlrcl5WXuorevQW/D3WEKXGUq+Ek6/NdRQuIFv2AruXGO+56Er+9JEvM7B8Mc/+n3+jMNBzwN/7g5WJidP722l5fxOpSkl291MuBh6BI+k5fiW9X/53bvzPr7LpG+fRuGcDxZc8j6rvohC4rZ6zgq+Y31sgjDUWiNOUoWpCI0moRxoJGMCTinwgCRxJb5eHADSaci1Byqz3UUhJb+CilKDou+Q9QZSCI7NhkVLg4yqoNzTGmtbwimHTWJNGrOkr+tSilEQb5ogcxjLpYwcwWov2KJO0c9Jy5+1rmX3kIdN2tdHa29YAcMiq6bljcueWYdbcup7Xffil7Q5l2hDHmisvvoeLfnw7Q9vrzFvczevf+xhOOWsxjjN1DH07Qux+sMaw4+4bKfTPpTgwt93h7HdSY6lefSmmMorzlJcStvxEa1FCzlE0TEpiJKPVhCWzPOb15Bko+Owox4w1I5QQCCnJ+9nevkqYItAIKRgoeriuQloo5DwqYcKcLp+t400yW69sITbArFLASD1CGwMWioGbGYY6kkQLhBBESUqsU3rzPp6jSLXZ4+QUJnqPPrAJHCn2mM674Qe/5vJPfZuFJ6/iH859P34xv7/f5g67MfG32NtOy71dDg8szrpcRXOgm2M//HbWfP+XbPrxb6iu28y8d76e/GAf9SjNNjpYQCa4KtsXWW6kpNZQ8hyi1OJJQT5QFBwHz1N4ShBrg9WZHQViYtVRipDZWq3enMOcrhylwKEeJ9RjjasUxoIFpASTgicFo6HG2GxoIOdJAkeRaMNQLaQ751P0nUnvNWPZQ3S2a9IybjQZvWcLy5/82AP6vPuSe25bS76YZ86S6fl9fs3vbwTgMU89oc2RTH3CZsJlv13DRT9ZzfhIk0MO7+elbz6BY0+d/7D3QB4IOkLsfqjs2EjSqLHg6NPbHcoBIapUGL38QrxDV8GiFbitJc05x8FzFa6QJNYgRSZ0ugOXMiAV5F1FLZSkOmHLeIwAUq0xVuBJxfzeHH7LQVwbjSMg0pbMXlMiRGa62pXzMVbjyGyZshKCgqfwXZn1m1mLmFgQbrMSEtz35BS4quX+vmtVU+DKPVbIXPftX/DnL3yPQ594Cmd/6l04fvvT0wcbu/d9Ba6Z7BHL/MfuO5EaJpqotUz83tc5MtuCkGiD4zrMf8k5qEXz2fil77D1I5/jkA+8lbS3n2rTUFcaJcBRmpzrtBz+Zet4yzoIrZV4rqTcSEBYBIKd1YhAgXIc0tQSOC5S2GyZeSnHQJdLtVWCDBxJLdIoYZjdlacWNhhvpDjKYoCcq+jOORgDjmOwUk2WJSEr0brSkPfvu7tuf3rX3R/Dd23AGsPgNPbfuue2tSw5Yum0XW30tz/dwuyFAyxa0ZmWvD/q1ZhLfnknv//5ndSrMYcfO5s3vO8xHH7s7Cmdye0IsfthZP0dOH6O7rnTd0LooTLeiNl0yf9n77zDJDuqs/+rqhs7TtoclbNkJSSRs2XAiAzG2AYM2GRMzjmJbDBgMphgDAYTTA7+yCCCAOWwOe9O7nRTVX1/VPfO7GhXWq12d6alfZ9nNTM9t+8t9XSfe+qc97zvNzFpQu0Bj6IjQXiKpfWQNLcIAXGoWBqHDFVCrDVkGqY7OcZYrHB6T2OtnMl2ji8Fw+WAyPeolQKiwPk2ZgVYI+hkltFmh4lOwWDsMZ3kXYV2n+FKSBx4tJICT0pWDZUYb6VMtHLamSY3bkqzEvp4QrB0IN5HBqB3g+7Zx/T8GmdP4V3x8a/wi3/9HKf81T259K0vQB4TRZw39NrEs0V298eF6pH6wVWl0kJTj13yHHZbDLMN1wNPEN33bqw6YTm/edV7ufHV72Dl855G+S9Odz6XJQ9fSDppwUAUgshopwW+coK+5UCghNjrHuArJ9ufakFAQW4FHoLBspNGWTkU004L9rRypLBkhaXQjgcpZAtfCTwPjLZUI4kS0M4NJR8mE8tw2WeoHGKNs/CKA0EtDvabdB1p7br9oWdt1K+JmLWWjdet5z6PvP98L+WQoLXhyp9dw30uu3hBJxTzheZUyne/cj0/+NoNJO2Ccy9ZwcP+5ozDZkF0pHEsEdsPirTD9I6NjJxwFkLeuW/SSa5pjI4y+osfMnje3YmXrUCnBYOhopwqptqZ4+R0q1a5Nk7tXGisgcLavbpcgyVFoUMiT1IvSZTwMdYy2kioxxHNNMUYt6OfaOd4UjKZaOoIdpqUku/jK4FF4CmnwTSd5Ey0M1ppQTvXWGtpZ9qZhXfPbXEyAJZ9OTT7s5D50xe/wy/+9XOc+pB785dvfd6d0ry933Bbbba5wxU9DS5Pir0JdnOWQXrgKQJPOZulU47nnh98Hb955XvY9I4Pseofn8DKh90PiaCVaeLQ64q4Ckqh4zB2OpqwEpIZQ+QLJ8lS5EglEDitsUhJslxTjjyGygEIaKYarQ2t3FIKJROtjMhzWmWD5ZBQCjIrkNIn1zm1kqIc+i7BKwzNJMfr8sHaqWbFgLdfbTWAZlocVfL+7uvXEw1UqSwZPirXO9wY3b6H5lSTtaf3p0H2TX9cT2u6zXn3OXO+l7KgMDXR4Xv/fQM//PqNZGnBBfdazcOfdAarj++vYYZjidh+ML7lRqw1DK/tXwXpg0UrLdj+va9hrWHwAQ+nYyDyPZQQhL7AkJNnhk5RsKuRMRArPOWhhKscYCxpV1ssDHyW1iWFcRWoXGsmWzlhIGhllk6u3Y3N91hU8ZlONTXlo7qVgnZWMNF2ul8D5YC00Gwea5MWrgKS5prQV3hKuIk24W6mAyV/b5urlRYIoL4fQvMN3/0FP37rxzj+vhfy4Dc/91gS1ifYX1Uo8BThrER7f1WiauQTeZLFJ61k0Udez/+95v1s+dgXYHyckcdfhpASbQ3VOKAag84Nwgg8lVMKPDwB29KETLukr5MafE+wpBoihCAREHoKpSTbJzo0OjnaOuFWYzRRIJx/poVGkhF6isFyQCPRRJ4iDBS1wAMpGG0mFF0rJADZrXrM1a5Lcs2eRrL356MlcTF602YWnbymb6sxPWuj4/q0onflz64B4C/uecY8r2RhoN3M+PaXruN7X7mePDdcdJ/VPPxJZ7JiTX2+l3ZIOJaI7Qfjm24gHhghrvdHWfNQMdnOmNyxnek//JzS+femFVbpsSfSomC6o2mlebfS5GQmGh1LraRACjKt6RSGkifRBpZWI6yA0UaHTm4w1lAYTWx9Ak9grCTXFhlYAs/D6/K38sLiK6fN1MkNShaUCrWX36WtdV6Uxjq7nMhncTVEKSiHPrU4YKqT7T1eCDfNOfsGtfnXf+a7r3gfy889lYe+80Uo/9hbv19woFbc7McPpMzfS8gry4b4q/e8jJ9c/gk2fuW7NHbs4eQXPJWSF6OUpJFktHJNZp1ExXgrY6Qc7BV9RcJgOaAoDKXAB2EZroQsrYW0c402TpalHCoaHU2n0BgDlVCSa0MpCOjkGt+TDFU8Ci3wlWSwGnTlO7qfEQOBL6lE/t4EtJdsJrlmtJnulfoAt5HSxt5CAPdwwlrL+LotnP7w+x2R8x8NbLzOtVbXnNafFbE//+o6Vp+8gsFF/ZloHC4Uueb//vdmvv65q2lMpVxy/zU84u/POuIWREcax+5Gc9CZGqMzuYcV59xzvpdyRNG7aU383/8ipKJyz4dgjCC3mtxoMG6XPtkpKAdqrz2QFQIwZAW0M03oSyq+TyWUtDJDvaSIlGC6o7EYpn1NVhjHmenqhnleyEhVkRvbnUiz1GOP0JNkmWaiMEjA63K3wm67MvY9lBSUQiddEfsSi6sS9JIwcDfo2QT+Xdeu45sveDuDa5dz2ftfiRf1r73JXRG3ZX/Uw205HwzVYi593T/z65VL+OOHvsC6RoOL3/wvNGxAJzPkuau8YiFQ0MkMq4ZjlBUkWhMMl0gLTTmQjFQjokBRjXxUO8N2Cf+F0aSFxeKskaQUGGtoZTkTrRxrLQLfbUikBGkZjANGKgGFdvww31M0koLYl1TCmRDdSot9krB2WnSnKx2X7UhJWzR3jZG1Ogwdv/Kwn/toYcO16xlZvojqQHW+l3K7oQvN1b++gQc85h7zvZR5g7WW3/98K//1sSvZvb3J6ecu4XFPP5fjTh6a76UdFhxLxOZgcts6QDC46uT5XsoRQ5Jrpjs5zZ3bmfjDLxm5118ytHjYcbdaKWlmAYvWFonjXmnjCPKZNljrzL7TPCcrJBaLwfHBdk4X5NoSBQpr3eQYOI/tUEl8JamGHotrEcsHYsZbKbubmau2JRnaOq5NO8vxlWRZvYSUjqw6XA2oRYpqGOy1gJns8sd6iHxJ0E3gCmNp7Bzla89+C2GtwqP+/bVE9crRf8GP4Q7jYO2lbqsqFAce93vmYxlYsZifvuGD/Oy5b+KM17+AvFQh1c4ftZ0aND71yJBmlmqkKDJNYQ1SSHyl8JWT2WimGRbXfiyMpSgsoScJfYEtBFNJxlQnY6KdM1wKmWimTLZyBisBAyWJ0ZDmTk9sqlNQFJp25jY/vU3G7P+nQhuKrrRLT+ICuM22/B3B+PqtAH2diG26fgPHnd6fbckN122h3exw1iV3fqrM/rBnR5P/+MBv+fMVO1i5ts4L33pfzr5wWd+2yfeHw5KICSEuBf4VUMDHrbVvn/P79wK9unYJWGytHej+TgNXdX+32Vr78MOxpkNFc3Q78cAIfhjP5zKOGHrTZ0mu2fHDbyI8n0X3/St8T5GlzvOuHDipiUW1mLDTI+tbEG56LMk12hiSAsCJUfpVCUJQ8jzGs5RUWJRwGkuhJ6mGypkhK8miasjq4TK7GwljjYwsM4y3UiJPUQo8Is+1MGNf0skKhqsRaaEZKgcsq8e3UFsXuHakJ8XeJAyAPOebL7icopPw6I+9vW+JxsfgcDhbb+c+/D7UFw/xnRdezpUveSunvfb56JHFxIFPOXQbj3rspCP2NFL2NFOaSc5gyW04Um0oBTlp4ZKwyU5KK3X6dRaLLwVJbih0wVizwFOCVpoRBx6FLQgzgTaWkYqrZgVKIoXb+QfKVXqnOhnl0NubgLazgk5ekBWOl2ktXdcLQyM5cFv+jmJs3RaAvnWcMMaw9eYt/MW9+lOY+4Y/On7bqef1pxDtoaIoDN/98nV8/XNXI6Xgb591Hg+47OSj7gN5NHCHEzEhhAI+CDwI2Ar8VgjxDWvttb1jrLX/Muv45wKzjRs71tq/uKPrOBwwRtMa28nI8XfOyZQk10x1crSxmMlROlf9hvKF9yMNYiYbHdLMdInDmfPDk4KkMIQW6hXf8cKyAmOEU6vPCiZaBo1GIKmVPCKlUNJZvnhSEnqKSuhRjTxszzIGwbaJllPXN5Y4kISpRzMrCH1ByQ+QQiCkQEmJr6AehwyW92+EXC8FWNindRUowS/e9jF2XXMzD3//Kxg5cfVRfKWPoR9w/MVn8fj/eCtf/ec3ctUr3sHxL3sm6jjHIYoDSRx4bhoyKZhsuwRntJnhdzLqccDSeonAF0y1CzwlmeokRL5CW0PsOXJ/M89odHIyaxDAqgGJsYbdU4ZqyZLmhnIkUdLiSUcB8JSk0D0V/iaB52GsoZM5T8rAgyAXNDNN7Kl9vFPntuUPB8bWbSEerFEaHjgs5zva2L11N1mSseqk/owBN/9pA+VqzLK1i+d7KUcN668f4xPv+jVbN05x/j1X8qRnX8DQojuv4PbhqIjdDbjZWrseQAjxReAy4NoDHP83wOsOw3UPO9oTu7FGUxm5cwrmjTdTGq6MxdSPvwVSUrn7g2i0CzSAFCRZQZIXjLdzV8VSktxofC8k8jwaxrJtqkOoYLJdAM48WUhNOxNYk1FYy0DoUQ49AgW+57n2orEYA62sYOt4i0JblJIIIfE9QWSdbIVEMJ0UlI3FGiiFioFSeKv6SXNbVzf8zw+49us/5uJnPp4T7ne3o/L6HkP/YeTE1TzqU2/h6896Eze/+f2c8IKnMXz384m7vEj3njIYQOCEiKcTDULieSnDlZBASXZ2px61tWht2NMp0BoaaU7JF5Sl6vK4uvZdUrCra8NVDT2yAoYqgdtMWHexXGtGmzBQ8vdad1VCn9AT6MDD83Jm+9fPbcsfLoyv38LQcf3bltxy4yaAvk3EbvrzBk48e23fCtHeHhht+NZ/Xcf/fObP1IdiXvCme3PuJf373jtYHI6/7Apgy6yft3YfuwWEEGuA44Afz3o4EkL8TgjxayHEIw50ESHEM7rH/W7Pnj2HYdm3RGtsJwDl4aVH5PzziSTXaNudQpyeoP2nXxGdfXfyuIYUYq+/32Q76/LAnNyEJwRD5Yi8MKTaIJXAGEM7K8i1do8JmGhqsG5vXhhDOzFobRmqhAyUPEIl3Tkzw87pDllh2N1MaHQyACqhR9xVVDdApCRpYciNYayV0Uiz29zhR76rvk3euIGfvP0TrL3neVz8z487ki/rMdwJMLJqCQ/76BsZOvV4bnrXR5j84U8phYo4UHgKBsshQ3FA5AkkljhQBFIRSElRmK4QrCSUEgFMtjQ7xlPGOimbxjuMtjN2NVMQgkaSUQt9J09hoR55FNqSFNpJxkhJojWF1u6zmBekucEaSPMeh81tOGLfIw4kxmrqsUc1mlHhP5yir+MbtvU5P2wjAKtP6T9xbq0N66/dzIln9+e05+3B+Gibd7zs//jvT/6J8++5ijd/7CF3iSQMjj5Z/wnAf1tr9azH1lhrtwkhjgd+LIS4ylq7bu4TrbUfBT4KcMEFFxwRj4/W6A7Cch0/uvOVQGd2yIbGL38AxhBc9ECyrEAGHp1Mk2lDrq3zcswNke+TasNUO6Ec+aSFISuME7WUAmEh9gSR56GxjDcTTDnCGtjabGMxVKIqoRQ0C8NEOyfN3RRlO9eUA8V0UqAtRJ7i5CU1Il/SyTRJd1BACEG95KOE3Csue2tk7WSqybde9E5KI4Nc+rbnOy/AYziG28CSZUM85N9ezY9e+T6u/+BniTotLn723xD7krToMFgJ8DqCyLdIIdzQSOSBEISeJSkEQxW/a8WlQVqUdVOVk+2UFfUSzU5OpaRoFwVl30NbZ3ovhCX2RFfCxdJM3OfEWEErK8i0Zc1ImdhXdPLCTQ8zY9vVSHJmF8D2N1F6qOhMTJNMNhhc279dgk03bGRw8RC1of6Tfth68w7STsaJZ66d76UcUVz5q618/J2/IcsK/vFFF3GvS4+/U5HxbwuHIxHbBsxmca7sPrY/PAF49uwHrLXbul/XCyH+H44/dotE7EjDWktzbAf1ZWuP9qUPO/Y3XdZKcqY6BabdpnPlzwjOvJB4aIRWrvE9Regbxlopk+0crTWJNoi2pWktmXWed0IKisLZvyAVtZJPK9UYLEle0Ew0UuYMlgPKeOTaYrQmQdJIc6Y7OWlhsNZgDNRKAUvrHhJB7CtOXlqhGgdsHG0xnTjPvrDbaulkBeNAybmE73dU31rL91/zAZq7xnncZ95CPNDf2jLHcPQw2c4oPJ97vfWF/Padn+CPn/wq+cQUD3ztM7EWPCFoxR6NVFP2JYNlZ8U11U7xywFMW5JMU2iDMY5I38xzjDYESjHWzIhDD2kUQggyY6mFilZmsMLQTDWFttRCSWEs9bJPXkCSaZppwXQ7Y6gcUokChIDAE1S6PpTVyLUuQ18ddrX98Y0ulA8et98mR19g43UbWHPq2vlexiFh3dUbATjhrP6r5h0svv1f1/JfH/sjq08Y5FmvvgfLVt314vbhSMR+C5wkhDgOl4A9AXji3IOEEKcCg8CvZj02CLSttakQYgS4B/COw7Cm2420OYXOkr5vS8725ANmSLtCIIVl4jc/hjzDnnd/DG6ysZE4K5bY9yhCTaMNFU8y0c7Z3UxRCJbWLUK56hf4XWNvRzT2ECjp00gM052cXFuGqwEIwWSnoDCWauyz2MJYKyPXEmuNG/P3nEF3KVTEoVPIHyz5+3BcBBZjxT7tlv0Rkv/8X99l3f9dwX1e+lSWnX3nlR85hsOL2Rpl0lPc7eVPJx4Z5OpPf5X26CQPfdeLqS+r0UoLxpoJxgriwL3vqnGA6XqfGi2YtppaFDCddOhkTuJCSUHF94gjxVAtRAhLIAW7mwllz8P3XHLWSAoGSz6LqxHWgtE5eeGElJPcJXmBJzHW+bVam+9tRx4pQdfJjdsBGOrTRMwYw5abNnHpkx4630s5JKy/ZjPKU6w+qT9f/9vCN79wDf/9yT9x0X1X8/SXXoIf3DXdTu5wImatLYQQzwG+h5Ov+KS19hohxBuB31lrv9E99AnAF621s9uKpwEfEUIYHF/t7bOnLY8m2hO7ACgPLZmPyx8WzBW9BJewaGNpJDk6y0l//xPs2tPxFy1zNi7KJVRCGEIl2ZFk7Gg4KYjpjrNlSXTBVKegozVDpYCsMJRDBULS6GhGqgGBhUrk/P0CNWPRooQgt077aHEtxu+KvSolGCwF+LI7odbdzQMsqccIAa3MWcvkXaHLYI459+xkbfTmzfzkXZ9m7T3O5dwnPewIv9LHcGfCXGK7EIKzn/5YBpaN8IvLP86X//E1POKDr0ZFMfVSSFa4irPA4itJIymoRgHGOG2vNPIYqQSEntP2qpc8tHCVKyFACZjqkvkLKagGisGyTxQofA/KgUczLTDWonEblkrkUVhLUjiemAXywnEqq5F/xFT1xzduQ3oetWX9ObG3Z9tuklbC6pP7s6K04fotrD5pOX5w55P8/Prnruarn/4zl9x/DU9/2SV3SlmKg8Vh+etaa78NfHvOY6+d8/Pr9/O8XwJnHY413FG0x3cjlUdU7V+l3sLYvTeJ2ZpazkLF0P7zr7HtJv6FD6Awzjooy50SOMayaaJJOzP4ElpJQWFc0G+mmqIrRj9JxpQo9pKDAwnTrYzBckAl8OjkOUo4rsxgKaAceahCEHfXMliOKIWaWDl5Cm0FuXY3sNkVrsW1eG+LVWtDMifBhBlCcpHlfOdl7yUoRzz4zc+7S3ELjuGOY3/E9qzQrHzIfbjPQI2fvfb9fPFJL+fe73oZ1ZVLnal495hWOkN31cZ0vSYVA1HAQGyQhBQGaiWfdmpAuGlj3xcE1qMcK1JtKbTTD6vFAbl2Cvy5tmgDVjqz8Yl2TpY7viY427Gez+qRMgGf3LSdgdVLkV5/Vio23+AmJlefsnZ+F3KI2HDtZk6/8M5X3f/mF67hq5/+M3d/4Fqe/pKLkXfhJAwOz9TknQLtqT3EAyN9Te7u8cBaqWaqU9BIcsAZJFujaf76h8glqxArT6STa3ZMpkx2CrZNtrlu5yQbdjfYNpkw0S6QAsaaBdXYI/Qlxlg6qSMKe13hycKAQDKR5Iy1UyLfoxb7KClYVg8pB5JCawLVtSWKPEqBYuVAzPFLapRCn1LgHq/Hwd52Yw+9Kch6KSD09v27zCYk//pDX2T0xo08+I3PoTwycNRe72O4cyDqTuv2MNpI2DreZvtUgn/OmVz0zpfRmW7y/We8ht1/un7vccaCNc4WKS80hYFKqAg8QWoM4x2DwWOwElKLfJbVIwbjgMUDIQNRSOy5KUtrIdOaUqCohAErBkssqobEvkcp8ChHHo2Oa4smhUEJ0RWAFUwnObumE1ppwVQnZ7KdHdbXZmLTdgZWLzus5zya2NyVrlh9cv9JV3SaCbu2jLL2lDvX5ODPvree//7kn7jkAceSsB6OvQK4pCKZGiOuLWzl9SR3xN3Zycrs3yEEgSdmPWYQWLQxpOuvw4ztIrrb/UmynPFmxngrZbSR0MqcQng7c60WAKxkqBwQSMFI1SVVaxaVGI48fE8gRJe7JSztxHlJKgmFkUgJFstYM2fLRId2qp36vhQsH4hZUo/3GnjP5bYcSP9ooBRQj33KoUc99vcS9bf/8Xp+96mvceajHsjx97nwML7ax3BXQu/91cly2qkm05ZmUtBIMsonn8Dd//U1+NUKP37+W9jwvZ8z1cloJAWV2KccKBqJS4BkV8R4USVixWDESDXAVwqlFEPVkEX1kEqgqAQetVKANhZrDZGnqEfOz3Wi5agBcXeToo1FdKt2kpnPRyAlmbZ7pWcAGknOWDPdb4y4vbDGMLl5Z19PTG69aTP14XpfTkxuudnx81affOfhh9149R4+9d4rOOP8pTztxRcdS8K6OPYqAHmnic4zovrIfC/lgJhsZ0x18gPufHsJTD0O9laeqpGHkl1j7N/+H7JSw554DpOJxvcc76rQllZqHBHYd1pfUgri0FW11iyusLQasqQakmWGjVMJzbRg3e4W482MQhtqJZ/Y8xACyoFCScFYI6eZOaK+AYwV5LNuGAfSObo1/aNehayXuOWdlO+96v1Ulw5z75c85Q6+wsdwDGARiFlRsWcnVF+9nAf++xsYOfMkfvXGD3Ldp75K6Ln2fzn0kFJgraMExGFAHHpUe+/VrktEkluGKzFSCtp5wVgjI88tkSdQQrFhrM2u6ZTpTs6m8TYT7bSrF6aQAmqRTzkMKIzBVwLfF3jCDQOAS8KmOi4+HI7qWGPnKDrLGVzTv4nYlpu2sLJPhVw33egmVu8sidjkWId/e+PPGFlS4tmvuSfeEeI19iOOJWJAe9IJxMYDC7MidiAS/uxd7+wEZm6lKdm9g85NV1O/6L5I5Tt5iSDocsgEhXbclaGyT+ApPASh57FiqMRgHFErR5Qjj1LksbQSUg18It+jlRWUI0ElUASBm7pUUpAWFt93fBVfSQptKbQbGugljL12UM+sOMn17dY/+tUH/5PJzTt48JueS1i582m/HcPRRY9b6c2hJ2hjXbt9+TCP+PfXcvxf3Zv1n/8617z7E7Q7KdsnEtLcHWOM0+FrpQVFV47fWosnBKEn2DXVQSAph4pqJChHAk95jLdTNo21GW0mBJ4kLzRaW3yJU9I3mmrkEfuu4pZrS+RJRxuwlmaadVX72ZuYzY0RtxcT3YnJgb5OxDaxqk/tzTbfuA2pJCuO7+9JfnCfgU++5zd0WjnPe/29KVcOrzF9v+PON4pxCOhM7AEEpQVaETtQu27u41ob8m7LD2Z4VJt++SOE8qjf7T60pEduLMKztDNB6jluisBSJFCNJJUBxVAppBT4VAIPjaHQHl5iqZUkFe0T+M7jLvICAs8SKI+hsk+SGzwJUknywhJ5rkJWGGfrMrfiJQUY4b7eHuy6Zh1/+Ow3OesxD2bV3RbEvMcx9Dl6Ay6VyCMrDJ1uElPyncRK5CsS4B6veSbh0kVc96mvMLl7nFUveiaNwrCo4tqbO6cMgefI+3sahSPwe65ylRQFGEsoQVvo5FARLoEqrCXJCjaPNdk6kdDOcpSSxL5T8B+pRqwcjAkDRagkSgm0hlaqSXJXfR4qBwfV6j8YTGxyiVi/tiYn90wwNTbVtxpim27Yxsrjl94pJiZ/8YMN/Ok323niM89j5XED872cBYf+/wsfBrQndhPVhpCef9sHzwNuq43X0w5TSqKtppPlVCMXkD2dMfG7n1M/50Ki2gBpxxHw01wjMkPsS0ZKFXwlmexkWAvlSLG8HtPIcooChksBRlu22ZSJtqUeeQzXQrQ2VEIPIQWRp5xWWF2RZIZCW5ppTmEg04aKVUSzKl69Kl9vAg32rw22P5hC88M3fIjScJ17/svfH7bX+Rju2uh5SxL5hJ6kk7tBk6X1EpGv9tHoW/OEh5GWSmR7xvECj0Ef9143mlLoMRj7jHdy6pGgnRUkhWV3s4mwllwLMq3ZOZVQaCjKPqVIEUnHNZtoa8bbmWs7YtjcSJGAwSVW9dhnuOIjM1dVDjxJ5Cn2NFMSXyPIUFKipKAeH3pMm9i4Hb8UUR4ZPDwv8FHGxus2ALDmtP60B9p0w1aOO33VbR+4wDE+2ubzH/w9J5+1iAc98pT5Xs6CxF0+EbPW0prYvaAV9Xs3iN5NIOv60sEt25YumTEI4XbIU1f8BJMmDF18fwQQB4LxiZzxZkpaWCJfkhQFpSCiEvkUxhD7Ct+T2ASEsKQFxKHP6qGQRlsjBNRCj8iTLBmI8YSg0uWjVUNF7Dnz44FywJ5Gh6pQLKpGTuC1nTFQCg66yrc//PGL32b3det56LtfQlQr3/EX+BiOoYsZ83iPRbPkIOZ+zqLAY81D7oeSFm0EnpIU2mAxhLEkDiQoQTvVpFpQ5AZpLVZAKy3ItSPoN4qcQEhCnLdqY0pjtLP8yrRm22RCqg2LqiF5YWklzsFCCUk5El2dQIMnBe20YLSVEinBSDViUTW6xcZmf64bB8LEpu0Mrlnet3IwG6/vJmJ9KF2RpTnbN+7ivo+4eL6XcofxpY9eSZ4bnvbii5GH0QP1zoS7fCKWNCbQWUJlZGGX33s3iPFmirEQeJKpTo7WZq8QXk8vDNg7SbX9lz9Bjixjor6cfKzDrkabRlaQ5o5joo1FIpAioTsKyVRiyXSHpbWQbZMJexopnhKUw5DY05RDxzMrdS1WAk9QiwMaSU4j1Sgp6GQaiWVJLd5HiLVX9ToUsj5Aa3SSX33wi6y5x7mc9KBL7vDregzHMBf7S1DmbhA86ZKvUqAojJsa9pQkDhTaQOwrcu0mDzu5oEgKIgHWUwihiXyJp8D3JFJBYg2D5QAlJGlRdJM1i5SSdqegkzkrMqnAE6CUoJN3rZTSHCksIFFAWljywuAruU+Vea7rxlQ7o9y1R9rf//Pkpu0sPeukw/3yHjWsv2Yd9ZEBhpb0nzbkjo27Mdqw8sSFfV+6LWxeP8Gv/28TD3n86SxZUZ3v5SxY3OUTsdao40Es9ESsB6UkalbMzI1FW03gqX1uFkoKdm7YQL5tA/aeD2f3dMp4K2W6k5EVltwYMBD6EitgZxPWDpcpBx5pYfAjgQWssWTa4HcvGoU+S+oxiyo+qXbXiXy1NwnMi4LcuMd1d2Jyf4r4ldC7RZXhYMj6P3/vf1AkGfd7+T/27U79GPoPczcIgaeIfMd7LIcB00mGsDBSjrC4DUfkGzqNguSKKxn7/k/B9yldfD4D555DYQxGu4nmOBAoKRkuRwSepJ3lJJlBCkWmMwZKHk6xz3muhkpSWIMnnIfrZDujEvhUY5d4WSzNVNPJXAJWGHuLz9pUNw5k2tED5nq36jxnevseTn3ovY/OC3wEsP6qmznhzBP7Mk70pCtW9Xki9vX/uJq47PPQx58+30tZ0LjLJ2LNsR14UYmgvPCNRvfXtot8hdYuwHpSkBcaKQRZoZn+46+xQiJOvYBWVpAVmnZqaKQ5E62MUugxXAoQWJSnaLQLisJgLMSBYrKVYwXEntMG85UgVBIpLfVyRKHN3uBeGEsrzUjyWWu0FimcD97sZKx3U5tpAx1cq2THn2/k2m/8Hxf+46MYXHvnGOk+hv7AXHoAwEglJPIV482UQCl3TOEsieqxj2y12PnTX5H94VpOfMJDYaDO9e/6GEuOX017aBirDIHnUYs9rIHQVyglGPIjPE/RaKUsqft0UksnN2Rag3Wt0B0THYbLIYOlgKIwDJZ9LNBMCwQggLFmSjl0gwazY0eSa7LC/VwYS8At+ZlTW3djjelbMVddaDbdsJGHP+1R872UQ0IvEVt5Yn++/gC7tjf4/S+28NdPPINy9diU5K3hLp+IdSZHKQ0sXrC7ptmJyuxdeZLrvWP1S2oRAONNZwpsu3Yo6Q1/Rq08AVOuoZsJjaRgTyuFroDkdKcgLTSLygGedEHcoOjkBs8TxH6JvLAkhWYw8FFSYgBPSjwpqIQziZTEMiolMGtcXgh8KfYGe7hl1etg5SqstfzkHZ+kNDLI3Z7xmDv4qh7DMdx+7G/jkOT6FlXqtDB4RrPlh78k2bmb+z/viYiVy2llmi21EquqAdXVA2gsU+2CRqJJrWaslbK8HlEOFBJYWgmZTp3Z92gzJdcWpQTCWFq5ZudUhzhUrBiKuxIwhpIv9yaM7Vwz1c4JlMRTkqxwlfPZArCzY8rsZG1qy073/9ynidi29VvJ05zjzjh+vpdySNhy03aGlwxSqfWvLM9PvrUOIQT3/+v+bW8fLdylEzGjNUljkvqyhTlVM5fTEXqS0JNsHW/TzjXaGMqBhxRQCpwsxUg1Iis0YmoPU2M7qF3wWBIlSQvbbWdAGAU0WhnLawGBrxACMg1g8T1FmjuB11buSMVJrulkilqsiH21T3VrdiJVCRTtWd57sa+oxgFDpQCl5B3ywrvp+79kx59u4IGvfxZBKT6kc9xVYa0lT53dlR/6C3bT0Q+Y+/490HDJlt9ezcZfXMndn/0Elpx+AjrP2fXTK1hyynGcffHpxIHH7ukOzaRJoARSKkItmWhnGGMxFhLt+JZSus+WxmKM43EW1l23lRQESu21IauGAbmByBcMxAFJUbB5QjNU9tHGJYk994zIl/utVANMdhOx+sr+1LDacM06AI4/vT8Tsc03bWf1yf3blixyzc++t56/uGQFgyP9m0weLdylE7GkMQHWENcXnpDrgURcs65eUJLrrip+ykQ7pxYpfM+j0ckphR75umsAUCecQSVUxFJSCz0wAmvBqwbUIh+tnTWRyAzlSFEOFKEUhL6iKKyza6lGRL6Tnxiu+kghaKXFPjclTwqGKhFCiK4BsduFlwNFvXTHytI6z/n5v36O4RNXc8Yj7n+HznVngjGGzTdu4sY/XM+uzTsZ2znG+C73b3J0krSTkiUpWZJhuzduP/ApVUuUamXK1TJL1y7nhDNP5ISzTuSEM09kaOnwsUTtdmB/wyVGG27+3//HqQ+9N0vPOpkiy9l4xdXs/PNNLDplLWmuaSQFnawg8hQCQa4100XBeCsnzS2Z0aSpJvAE1dhDWMl4l06gC0tSuE2VsRZtDQNhgLA5ndzpAo5UYyZbCZ3CmYkLXEXPV5JK6IRhLTNrn1upntq6Cz+OKA33nzUQwIZr1yOV7EtVfWstm2/cxgMefY/5Xsoh48pfbWN6MuG+Dz1xvpfSF7hLJ2JpYwKAqLbwpmr2t9POCk0jLbDWYq37wDbTgnLokeaSVpqiLeTaMHnNH2FwMUVtmE6S08oL4sBHSkUrLdCZpeQrgtiNyYeeBQtFYfB9SSstiENFKfAw1ql5J9qwezpFSTeqX2izz9RVkmsGyyHl0KMwlnKgWFy749Wrq7/6I6a27OSyD74KqQ6tonZnwU1/vIFffucXXP+7a7nhD9fTbrQAEEIwsGiAoSXDDC0Z5vgzTyAqxfhhQBgFBHEI1tJutGk3WrSmWzSnmqy76iZ+/o2f7D3/4OJBzr3PBVz04Is5//53o1KvzNf/al9gf9yxyJcEUUjRSQH4w1d/zNi6zfjlElx0PtfvalAJfRpJxuaf/wEFBOeczp5GhrHQTjp0CkMzycm1oRr5rB0pE3qC0UaCkq7lLwQ00oJK7LFpooXuEu9bmaadaJDgdyte2sB4K2Ow7BP6IZ70aKUFwC38XgGmtu6kvnJJ3ybl669ex6qTVhOE/cdNGt81SWu63dfWRr/5f5upD0acdX5/VlSPNu7SiVjSTcTCysLb9e1vp10Yi+9JWq6PSGEtuTa004LYkziKriXpJBSbb8KceQ8CqUiERgOmK6AaKUkSaEqhQgpJVli0KZho52TasjyMqJV8cm0wxqKkZKqToY2hEgUMlHzGWhmjzZSVg6V9pq56Gkx3pA05G3kn5Tcf+RLLzzuN4+51/h0+X7/i2t9ew3+++7P89oe/QUrJcWccz/0f8wBOveB0TjnvNJYftwLlHdrr3Wq02HD1OtZdfTPX//46fv/jK/jxl3+AVJIzLjqLS/7qHtz/MQ9kYFF/CnseaeyPO3bBPz6S77z0PVz7rZ8SLR5myXlnMHiP89jVsVjtJh+zXDNZqpF96nPEO8bQF11IWjgifUcbdk6lWCyZhlqYEgQKT0g86QZmMg1aa8anczpZTiX2KQWKNNeMtjKqsSLTlnRWLJECJG0q0UyCsj8R5amtuxhY1b830fXXrOOsS86e72UcEjb3ucdkmhT86Ypt3PNBxx8z9T5I3KUTsbQ5SVCqItXCfBm0Nmhr9/I4yoHC4lEUxglFdj3t/NinkRVUA4967JPffB3oguDEM0gLQ2EswhhybWl2cpQAJNRjN9pemAJjLJFyNiztTFOPJbsbOQMlGIgDpLRMtg2VUBIqyXTidtPtrCDw1EGr4t9e/PE/v01rzwQPfdeL+3Z3fkdwwx+u5zNv/QR/+H+/ozZU48mvfhp//dTLKNcOX6WqXC1z5iVnc+YlZ3PZ093N/YY/XM8V3/8Vv/7er/joaz7EJ97wES6+9O781d89jHPvez7qLl6ZnIu57/vh41fxmE+8icnJJqZUQoUBY80EaxI0Tj6i1WhTXrmM2nOfzsS/fZx4ySLUCWtpZBmdJCPXmnrJZzrJaWY+RaJZXPUZqYZMtFLaeUHFl+xuJSS5RiiJsSlKSSqRQlhB5AuSnkyFkkSepJNbPDXzWZ372bXWMrVtF2suOedov4yHBY2JaUa37+H4M06Y76UcEvrd7Puq3+4gSzQX3rv/XQGOFhZmBnKU0J4cJaotPH7YbMuiVpKR5ppF1YiwOypfiXyybuuiEioQAl0YWmgGywH5puvBD8gWr8WkBYWGPa2cJM2d7hiAsQyWfHIDzdQggXrJw+U6Ak8q6pECa/EVhF6w10A4KWYI+YL9T10dDqTNNr/75FdZe49zWXHeXUuHpjXd5OOv+3e+89lvUR+u84+v+yce9pTLiCtHflBBKcXpF57B6ReewZNf9TQ23bCR733u2/zoS9/nF//7MxavXMLDnnoZD3vKZZSqx4i4B0JUrzBQivnlp75G5bhVVM88mXZhyAqDl2bs+Ob3yU48kdXnnkpyzukEpQDrKSq+JNeqK/AKZV/SygqkkFjrqAVKeZRD8KUg1mAMKAlSSpLcEEQeg6WQVGtCDyJPMFIJiAKPVqr3mZyEfT+7rdEJik7at0T99desB+C4M/szEdt84zbK1ZiRZf1Zgb7qdzuISz6nnL14vpfSN7jLJmI6T0kbEwytOnm+l7IPZvNNGokj7gJsnWjje5J6HJB3d68jtQApIow1rsXYVaxv33g1wdpTaCKRQGFchWuqZYgDhTUW60m2T6QsGQjxlcRYy0QzpxJ5tIUm9HJ8z01IFhqUNISeR1aAtZpcO4Ph0N//1NXhwB+/8C2SqSaXPOdvDut5Fzp+96MreN+/vIvxnWM8+tmP54kv/jvK1fmzclpzylqe8aZn8eRXP41ff/eXfPsz3+STb/woX/7AF3nEPz2ay57+qGNcsgMgyTVLHnhPmqOTtDODbraIKhWkHyPWrCb7139nx70vIbnhZsQZZzIgXOXbk5KW0kwkOaXAqelnhWaiqUnzjE6u8TxBUA7wfYnNCqyx+ErSzjSBJ6mXfAqtSHLDkoGQWhSQdTdRas5ndfZntyddUV+15Oi9UIcRG6/rJmKn9efE5MYbtrL6lJV92wG47o87OfWcxXsdX47htnGXTcSaozsAKA0trGDT25nOtisqtCHThhJibwshyQsCqfaScQFi3yNsjJKN72HokgdRGyyR5AVTBRQFWAFWCEqR6qpqa9Lc4kvBVCdnUSUiDpx4K7h2S64tQlkKIxgsBwRKUhhDmhsqkdrbzjgYVfzbg7TR4vef/jrH3/dClp5519ChaTVafOTVH+T7n/8Oq09ew6u/83pOPX/hVAKDMODel92Xe192X274w3V84d2f47Nv/xRf+eCXeMQzHsVjnvOEYxWyWehtqkZG6lTqFW7+/i8pNm3nxH+4zFkW3es8Nl13Pd7970X5skvRpRixfiPm2psIhwYJzz8PT1k0gkhBO7O0Ck2qNQZBbAWegI529kiVUJFrTckXhJ5iqpMT+4ql9ZBqlxNmLARz7o9zP7s96Yp+5YhtvHY91cEaQ0sXXrfjYLDphm1c/OBz53sZh4SxXS12bWvywMsWVoFjoeMum7I292xDSEVlZGEJFvZ2prNbBYUxe3ewaa5pZwWeEkjhlPQbnZRGkiGwtNddC0D5lDPxPUXke1jp7Io6mWG0kbFhrAPCYgRUIs8lVJ6kmRW0sgIQTCcFzbSgGiqEBCksi6ohg+WAoXLIyqESS2ox5dDx0gbuoETFXPzpv75L2mhx8T8//rCed6Hipj/dyLPu8zR++J/f43HPfyL/9uOP3noSlieQNtzXecAp553GGz7/Fj74fx/jvPuezxfe/Vmecfd/4Of/+9O9Uhl3dcz+DEe+YvUl5zBx5dVs/u/vUo19smuup3PTBsqLhghGR8n++5s0f/gzgtAn+9FPED/6MVK5itZUakjygnZWMJ0WhF2v2R0THWJfogRMdDTbxzsURjDVSTHGUg4VK4fK1GOfTpaT5gVx6LQHO1m+36Gaqc07EVJSW9GfraWN129k7WnH9WVFaWpsmsk9U6w5ZeV8L+WQcOPVewA45eyFVeBY6LjLVsQau7dSHl664Ij6vXH4rJgJIqVAYaygnRakShDqXuC0THdyOoVL1LS1mOuvQQ0MU1SGSfOCZprTSQoqoWRRNSDJtBsAkK7NWQ08djcTtIFWmhOqAKzjfgWeoBS412c6KUjygsj38JUg8NR+x94PB4o048rP/S9r7v4XLOlTwu3twY+//APe9y/vojZU513fej+nX3jGrT+hPQ5FOvNzHkLpDkqw5AmYHKQPfnTQTzvhrBN59afewPW/v5b3v+g9vPnJr+OiB1/Csy5/Hkv6tKJyuDC3VV8bqnHRW1/I717/b0zcvJmxq25k8O8eTWN8muSnvyFatZyR+98Dli9l9T0vYNO3f0w1VHhKkOaaXUnOnumUJbUQhatwT3RyltRirLRkWU4cCtKiwFM+7bxAyYhWWjDWTBhrpkS+R6ah0BpPKYRwuoSzDcAnt+ykunQY5fvz88LdAVhr2XTdBh74hL+c76UcEtZfsxmA40/vP/0zgPU3jBGEipXHLTwlgoWMhZWFHCUUaYfO1CjLzrhovpeyXwyUAiJfoURKbiyRr5hopWhrKHndypO1FLbn4+hkLDppgdh4E+HJZ9NOCybaGWlRMNpMme4UVCIfX0o8XxD7ikrsM9np8tCM2GuLYls5i2shJd+nsJbY9/CVs2LRxt1cajFEt+OGfXtw3f/+hPbYJBc85ZFH5PwLBbrQfOINH+GrH/4yZ11yNq/65OtvWyIiT/ZNwsD9nCcHl0DtL+E6DIndqeefzgd++BG+9pGv8Nl3fIpn3OMp/N1Ln8yjnvVYpLxrFt736085MsB93vkSpiemaU53iJcu4pr/+B/ilUuonHcmtVXLqMceN33qO4Sex+KRKmOtlPFGRqRyfOWsznY3UzpFgdYw3k6Q0iMrDJm21CNFOQAs3LijwfaJDpOdHCkFgVdQjzwKA/VYoI29hQH42OYd1FctrE7BwWL31l20m23WnLp2vpdySFh/XZ8nYtePsebEodvHDzvETeCdCXfJRKyxx40HVxct3PJv5CuWD5b20SeKA2+vv6Q2lnYzpdNtJSaFJt2+hThp4a05iWZaMNbKAIu2UBjQuWZxLWCyo6lFAaVAMdZKaSSFs0SRzgbF8wRR4FHyFfXIQykBhISeQEmJko6DciTkKqwx/OE/vsHi045n1UVnHdZzLyQ0p5q87elv5Pc//i0Pf9ojecabnoXnH8TH0eS38vhtBLH9JVx+ad/HigzyFggJ8cBtr2cWlKd49LMfx70uuw8fevn7+fjr/52rfvknXvLhV95lyfxzNcYKY0EIrOcha5p0Yorsz9dywpMfw8gZx1EJJdu/8SOqtTInP+VRpFGM3rwVkRVEI8MMlQMmmjlxLCiahmqs3NyyNeQafCHxPEEjyRltdQikQrVBa+cRO1gOGMsNcagojEIbcwsD8Ma2XSy638LcpN4WNl63AYA1py5M27rbwl//wwM55+6nM7i4/ypKRhs2r5u4fWr6R6K634e4ayZiu7ag/IDS4PxyIOaKQO4Pvcc96ayDenDJmEEbAIsxFrn1RgDiE04lk47kn+qCwoKnJLnRJIVFCrBAK9WORxIJCu3OZwApJJ6EKJDUS2FXK8xQi/flgR1uuQqAjb+4kvH1W7n0rc/vS47HwWBs5xiveuxL2HLTZp7/3hfzV3/30IN/sjxAu+hAj/dwoEra7L9hMg1Fx31vLVhzSEFx8colvO6zb+abn/waH3nVB3nuA/+J137mTRzXp75/dxSzP9s9CRhPOn7n5ObtqGqFtRefTeQr1v/Xt0j2jHPqA+6GnZzi2q9+lfE/34gJAhgaZOgfHkcr1+SFRigYiD0CJcktBJ7TAcRAJy8oR373WtDJNEWu2Vkk1OOARpoTeYpKOPO+8aQgb3VIJxtU+pQftvmGTQB9WxELooATz1o738s4JOzZ2SJLNSuPGzi4J8yNSUUKWfOQNoH9jrtcImatpbF7C5VFKxDz2DKZa+jdU6Y/EJJcdw243XMCTzBcCtiWJ4y3Moy1lLbchB1cjFcdQrVzpABjhJO1UOAphS+gWgmoxYp2Zik0hJ6PpaAkJdXQY81wicFyyNJaRBx6RJ4kmeN7CYdfrgLgD5/9JuVFg5x8af/6rN0adm3ZySse9WLGd4/x5i9dzrn3vp1uAX7kdo2zA5gX3nZJ/0CVNNFNxIpsJgkDl9jdnpbn3NMKwcP/8ZGccOaJvOWpb+AFlz6bF7z3xdzv0Q+43ee6M6HXrmwkObm2qOPWkk01+M2bP4ToJESlkLMf82DHdfrp74ik4JL3voKpXHHtG95NsmELpjpCkjodwMJaphJDKXIc0GoQgLBMJQJPSIR01bDYtzSSgprn4ykohz4GaHRyCmtYVI0IPMXExt0ADPSphtjmGzcxtGSY6kB1vpdyl8O2TVMArFhzkNW82TGpMwk67VbkOy7u1PrzPXgouMslYmlzkqzdYPHJ8zcefCBD7wO1+nrHVyOf0JtVRfMkuxsJkSfopDn+rg2YU++GFBYpLMYYJlsZnVSjMQxEAYk0VCWMtXKaHVdhG4l8BqRHlsPiesiiWkzgCeLQpxK6t4idkzgebrkKgLH1W9j8qz9x9+c+sS+JwreFPdv38NLL/oXWVJO3feXdnHbBIUpTlIZuP6/iQBWzoAJ527Uje1ChS+7g4Fqet4IzLjqLD/zoI7z1aW/g8n96Mzf/+Sae+tqn36WV+SNf4SvJSDVEV3xWf/j1bP/xrygrwbmPfTDJVIPff+YbaGM471lPZLyA5qbNZFNNchXQyAqS3FK0DcPVkGrksbgWIqXE9xVKGKY7QNfsG+H0Beuxz3AloBb5lCMfYw2hB76VWFxC3tzmErGRNTM3wYOp3C8UbL5hI6tP7k9+Vb9jx+ZpAJavrh3cE3oxqUhdEpZMg85ACGg6CZU7lIz1EffsLpeITe90ZMjakvn7sO6vpZfkeq9G/dxWxnQnJys0QVdgtVc3m04ycmPBSuTodkSRky87jk7utIa0tShhCXxBoaWblNKSLeMdPCFINEjp/OeWD8RIJahHIXHg9MFmV7z256d3uHHVl7+P9DzOevSDDvu55xuTo5O88tEvpjE+zdv/5z2cfO4pd+yEfsStJkhzg9CtVdL8yLUDrHXH95IwuO2W50FgeOkwl//Pe/jIqz/IVz74X2y9aTMv+8ir77KaY0V3AKeHJNeM3OcSyoGrll35hW+z+9p13P+tL2AaxeT2nUxcdQPhOWfSqQ+h0pzhUAEGZSW1OEAKiRS4yepcU4kDOnlBZiBSisFaQOALyoFHKzVMt3MaWcFg5DFcjdHaWaq1tzp9xXi5kx+4vZX7+YS1li03beGBj3/wfC/lLomtGycZHIkpVQ7y/dGLSVnTVcJ0Bl7s4lDWhs6Ea1EeShLVZ9yzu14itmszYWVgXo2+57b0elNLUsBUZybY9YKga2UURL6rioFT3U8yQ5pbJjoZ3uZ1AIxXV9DZ1SQpNM1UY61kvJUw3ckJPMmKgYhNEy0WlSNqcUDsK9q5BQGDpZB6lwe2v4rXkdwNF0nKtV//P0560MWUhgeO2HXmA63pJq967EvZvXUXb/nSO+54EnZbOFAQurVKWjzgOGG3t+V5kPB8j2df/nxWn7KGD7/iA7zwIc/h9Z9/K0tX33XaDz3so2LfyRhvZaS5oR77tNOc0W17OOPpj6MIQvas38boH64hXb+J/PgTya3jmBXGUAoUgQ+hEpRCDykFexopIEgzw+JaTDPNKfseywdDfKnYNpnQTDInYWEMExY8lbJyqEwzKxhdv5V4eIDEC0imOjCHp3mkPGUPB8Z3jtFutFh50jGPw/nAtk1TB9+W7KE05DaBefe9lncg7VIklA/NPTB4O/+ed3SyfB5wl5orN7qguWfbvFbDwCU0oedeeqcZZol8udfcOy0MU7N2opGvCDynfj/VzphOHCdMSUgLjbEaf9dGsvIgo0R0sozJToGxFk8JPCWIAkk5VGSFJvIUSkLoCwbKytmhRD6rh8pHTKD1tnDjD35F2mhx9mP7U//nQDDG8Lanv4lN12/gNZ9+I2decvaRveCtBSFwgSis7j8glYYgHoSw4r4egR3kXz/1Ebz5S+9gdPsenv/gZ3Ltb6857NdY6Oh9/pNc00oK0twQ+wpPSSaSgsl2yhWf+Cp//vmfuP6L36Z943qGzzmNtfe9ACUE9UgxXAmoRx4WiydBSkEnLSh1K9mpMTQ6GaXAQynYPZWxYyqhlbgpaWktoacotGG6o2klOVlhmd60ndqa5QC0Mr3XEmk2jsSQzuHAlptct2Plicdak0cbxlh2bJ5m+W0lYvsToo4HoLzYVcJ05h7zup66Wcvxx27XYm5tsnxh4i6ViDV2b8UaTW3ZmvleCgOlgHrsE/uKeuztrXT1MJdDBu6Ppa3BGkszKWhlGmMtk62MaGwzk/XlTLYydk5nWGtJcydd4SnHyRZS4AmPSAm2T3XYPpGwbneLpChYVI2IfEXlCIm03hZu+v4vqS4dYcUFtyFm2mf4z/d8jt/96Aqe+bbncsED7nbkL3hHg9CtJWqHCefd53ze+90PUqqWeNkj/oWffeMnR+xaCxUDpcB91gJJPfYphR55oWmnmkVPfSKqXGLL/3wfMTREePEFVO9+IVIoVg7FrBmqcPxImdBXLK6WqFVCsrygsIZa7GOxjDUS9jRT9jRTdk112DbZZtd0h20TGdOJJjWG6STDAqEv2dVI2DXVobF5O7W1KwDQxrjKe75vMnYkhnQOB7at2wrAqhOPVcSONvbsbN72xGR73LUb06b72h6f+V1tKZSHHWc16iZzRQo6gdaefY+9LRzqZPk84i7VmpzesRGpPCojK+Z7KcBMq29q1rBaVjgeViVQ2C5rrLdzLqwl8CSFNuxqJPhSkGlDJW0QpC3aQyuZTnKUEihpGIw9OrlmUSUk9hRCSEIF04no7sgLEiOpNlKm05xFxPPxMpC1Omz65R85+3F/eaeSrLjyp7/nc5d/mvs95oE85B/++uhctE+C0KqTVvO+736IN/zdq3jrP76Bf37rc7js6Y+a72UdVZRDj0rmo40bmimsdbxOKTj1OX9Pp52wOzHEgSL2BXHgCPYD5YBca3IDkS+pBj5Zrknalu2dFuOdjFQbsmaOwJAVMJk4IVdPCJLUkPmAhEA6/1olBMnYBEWrg1y8eC9dwpOCRlKQFpp6HByRIZ3DhW3rtxJEAcPLRuZ7KQcNa+2dIuZt3TAJwMq1B6iIHUy7sLYChAdFG2wTvG7MmjvBfVsk/EOdLJ9H3GUSMWstUzs3UV2yGrmAJrZmq2+73ach8AQGj7TQhJ5ispUxnRTEvmJPI2O6kzPdykh1QTsx+NawZ8157KysJvYk7TzHk5JhJVkcB1QCj4kkZXQ6p5PnGOukLDwhGKlGBL7H9okOKwbK8xJkN/zsD+gs58QHXHzUr32kML5rnMv/6S2sOmk1z3vXC49esL21ILTApojqw3Xe9pV3c/k/vZkPv+IDjG7fw1Nf+4w7xY3pYBD5imrkd3lXBk8IYk/ieRLfU+g4xM8TPCGIfA8DDJcDKrGH1h6FcSr7Y62MVmZoZynt3DIQ+wTSSc5MtAsUTsrCIkiNxWJopxrPU+S+RecFka/Id7iJyWx4mF1THZYNlKhG/t7NYeRJ6guUqA+wbf02lh+3oq+cHIQQGGPQhcEP+vd23EvEVhwoETsYIWo/gqgKnXwmCUOCLaAQ7th2G5IG2ByE747fH4XiUCbL5xH9+5e/nUga4+SdJrXTLpzvpdwCA6WAqXZGSwiq0UxrMPQUEuvaF/hYa5loF0y0UibaOZ1cM9FOMSbCnvxgJpKMpJ2xrBrhSfCVZPVwjBSKahwQqiYbR93utzAW31f0GqC9x3o4miPr6//vCuLBGsvPPfWIXudowVrLe553Oe1Gi8u/9h7iymGsNB5McNlfEGqPH1wAO8oI45BXfer1fPjl7+fLH/giE7vHecH7XnJwLgN3AvTszFpdsWZtTNfX1eBJyVA5oBr6aGPBWqqlkIACESoy7TPWykgLjRTgewKRGdLc0M4M2lgybRmMHZHfAM1UM1zyiANF4Hvk2nHM9jQzzBbnOFJetRRFT3Vf753Uvl22NfOA7eu3surk+aedHAy2b9jJf7zjK+zePsaak1awZPUiTjhzDefc43SCcGFVrw8GW9ZPsmhZhSi+nRX5uY/3yPvWduUsOm5T2UvMimRfvcMice4gB6qM3QHpnaOJu0a0w6npA9SWLEz+gFKScnjLP0daOP+3JNM0s5xOXjDZzploZwgBmbaMNVJKniT2PcBgjKWTGzwp2DaRIISgXgrItcACyoO8Y7F5QaAkq4dCFtWivdyPozmybgrNhp//gRPue+GCqlTeEXzzE1/jdz+6gme/4/msOWXt4Tvx7RnJnh2E8sRNHx1sADvKUErx7He8gOFli/jMWz/BxJ4JXv3JNxzeBHYBI/LVPpudWqz3JmaFNiSFoZEUKEA0m/zkRW9jxcMfyOD97oHFkhUFSirKQcCeqTaNNKXse6hAkiUaIWHZQEya5SgBtchnuBaTG00ndVOQaabRW3ciopDpoEyWpHi+INfsndbW2tBMiwWpJ6YLzY6N27nkr/pDCPq1f/dunvTiRxGXI3ZvG2P7hp189p1f4SOv+zyv+PCzOeHM/kgoe9i8bpLVJwwc+IDZlfoicxtCv7L/+BMPQHO345HpDIyGeMhNUWZt97WHouPkLxZAHLsjuMskYtO7thBWBwlKC0dxOclnAu7+CLCNJMday2gzJSscfyTPNZ3MjY+neYGylpFK4NoaoUeaG4SA2FcYDEVh0VaQFQlp4Z4XqhglUtqZIfAFS2ulvWT92ys2e0ex/crrSKebHH/fhVepPBRsuWkzH3/9v3PhAy/iYU+57NBOMruaBe57o2+dY9GZnPl+rj1I1pyThGUuoAU1KA8uiPK9EIK/eeGTGFw8yPtf+B5e9sgX8sb/fBsDIwPztqb5wtzEbKyZugloKdCqjD8yyA3/9h+cICXhxRfQKSwjZUkntxhh2TWdsnxAEHuKlYMxSkoyrdFCUQqhHCryLufLl5pQCabaOXrnbuSSxSS6QBUSbVwcSHKDIIMocKa1LDw9sd1bd1HkBStOWLj+wT1s37gL5Snu+4hLbvG73/+/q/j4m/6Tt/3Xy+dhZYeGtFOwe3uDSx6w9tYPLA3B9E4wGRgDtgHT3FK0tTMJ6TSkLRAGVOR+zttumlLNqaLZ/qcyHJZasxDiUiHEDUKIm4UQt3gHCSGeLITYI4T4Y/ff02b97h+EEDd1//3D4VjPXORJi8burdSXLpxdxmQ7Y+tEmx1TCTumEnY1XKIEjrA/1U73tgV6nBklJHHoxtEnWzntzFAABoGUgrTQaOtuatXQqWcPVyNKvsSTTkSyEvlYASuGyxy3uMSJiyp7kzA48Gj6kRpZ3/iLK5GeYvUl5xyR8x9NGGN437+8iyAKecH7XnJoXKfZk0UTm2B8g/u+tceV6mejyFzQ2n0jTGx0atQTG91zesgTNwJedMfCW2PQ3AXJJExtcdfY3xTTPOHSJz2U1/7Hm9h43Xpe/LDnsXvrrvle0ryjHHqUAo/AU2RCsuaF/0T1zFNY94FP0/j17/GFJMk0E82MUiBZPhhSDhUDkc9ILWJRJWKwFDJScVParcwy1krZMdlGCKhGPoNlD7NzF+HyJQTSbeICT1AKFKEvEGLfW0Vvc7ZQsG29m5hccfzCT8SUp1i8coSPveELbLlpO83pNp2mk3MoV2PGd0/O7wJvJ7ZumsRaWHX8wK0fmCeABV10K1ktF7Omd87IWkzvhPGN0Njp4lRnqhsPG27zWLRdgtaDCiEsH8H/u6ODO1wRE0Io4IPAg4CtwG+FEN+w1l4759D/stY+Z85zh4DXARfgfKh/333uxB1d12yMbbwOrGH4uPmRRpjLt+p5zfV8IwGywhJ6LgnLtcVYgbEw1c6ohD6h0kx1coSAkucRB7qrH+TTzgusAU8JfOU0xyqxR6Q8Orlxvs5WYnHPWVyLEFgMiuFKRG4s482U5YOlA46mH6mR9c2/+TNLzzqJsNL/Kuvf/ey3uObXV/HC97+U4aXDt/8EsyeLZns/FqmrWGWTUETgBTMG3dKHyU0glNP+8gJIplyC1hNotbgSf2Ona0kCSAV+B4pg5pwLRPTw4kvvzlu//E5e97ev5IUPeS5v/e93srpPuD9HAvtUqq1FhgGrX/LPbHr7B9nygU8z/Mx/oHy3cymFHqVAOUkKJUlyQ5prqhUfXwnaqSbTTlswUh6l0ENbgyckQZ5jJqZQy5ZgBUghSXNDYQy+ksj9bCoWkp7Yjo3bAVi2dvk8r+S2sWTlCM95+5P5+se/x+fe/VXqw1UWrxxh15ZR1l2zib/62/vN9xJvF7ZucB6Tt5mImXzGzqiHIoPR9VBdBCqA1m63MSwK8DxXzUe4Kn9lsZPWMdodK70ZvbE+x+FoTd4NuNlaux5ACPFF4DJgbiK2P/wl8ANr7Xj3uT8ALgX+8zCsCwBrDKPrr6G6eBVRdeBwnfagsT++lafkfoNYmmuMhcCTKOm6ALmxmLwgKQpGmwmx7xH6klqkQEgGSh4Q4ElJrRzgdXfHSKcbFnlOsDXyHPnWE4J2bmjnhdtph4rQU2hr97Yf57Ynj9TIejLVZNc167j4nx932M99tDG2c4xPvOEjnHPPc3nQ31x6aCeZPVlk830fDyqAgGwadOSSMBVCe9IFLnCJV1CGqAad6RmCqxc4bZ7mHpxmQewmKU0OaQJyGqi54+6gt+ThwpmXnM07v/E+XvW4l/Lihz2PN33x7Zxy3mnzvax5Q89izJOCVlowTcwJr3gON735/Yz9+2cQUtI88RQaaYEErG+JA0EgJGVfkRRON6yVFtRLPr6STv4mt8ShpLPZVZSKJUtoZY4HFvseWWGJPQj28/lfSHpiOzZsJ4xDhg5lAzQPWLJyhGe8/m/Zum4HG67dwo5Nuzj1vBN4zDMfwsjy/vh/6GHrhkmCSLFoaeXWD5T+vjEumXaJllTQluCHkGcz3LDePUgqKC9yyVg04B7TmUvGlOeOX+AWRreFw5GIrQC2zPp5K3DRfo57tBDi3sCNwL9Ya7cc4Ln7FfkSQjwDeAbA6tUHr5w8vWszeafJynPuddDPOVw4EN9KcGBOWCfTgKDQGkPXdssYJls5jU6G1ilKgScVmdGUfI848vGEYKgc4HfV+afb6d5EDEAKwUDZiUjunuow3s6JApeE9VT9e8nh0fCVBNj6u2vAWlZddITV5o8CPvnGj5ClGc979x2Qqpg9QST8fR/vTALWJV95u7srxHEterC6G8AyF6Bmw+QgAyB1U0lSdXeeCSjlzufFrqq2QHD8mSfy7m99gFc99qW8/JEv4vWffyvn3PMv5ntZ84be57Aa+WQaEiKWvOif2HH5hxj90KfhKU8iOO1UPE+ijSVQglLoU1iYbKdMtXMkTgzai6GRGlppzmRHMXqTC8OdkREqUlEKfUJf4kkohT5KiqOyOTtU7Ni4naVrlvWF9MnEnil+/NVfcOVPr6E2WOG080/k/PuezaqTlvflxOTWDZOsWFNHzr2n7c/vNqjOUCV6yZTVrl2ZtSBpQWu0KyxdAhO5DaaQkKfg52CKri+udLIWC6iaf6g4WvPI3wTWWmvPBn4AfOb2nsBa+1Fr7QXW2gsWLVp00M8b23gtXhhTnwc1/QOV7pWSVCOfyJ95+aWwNBPNaCvjhl3TXLVjms1jLcZbKcZYIh/aqWZnI2HndE6iNQhJqg2VwGO4MpOEAawaKrF8oES9FDBUCVk6MFPCXT5YYvVwiaFysI+qf28qqlcZO9Iq+1uuuAovClh29klH7BpHAzdeeQM/+tIPeOQ/P/aOkYX9aMZw2wu6xNTuz2nDBSTVJUh3JqCxHbKJmQROKBfUpIKg5PhgE5sd70IXEIQu0VK+C1o6A788Uzmzt3RzmG8sP24F7/rf97No5WJe84SX8dsf/ma+lzQvSHLN9ok2u6YT9zkXBm0M1XqVta98Dv7KZfDpz+PdfBO+gEokGYhDAk/S6GRYK/CVwFMSXwmmOs4cPMmso0Ns2ISJY6bDMgJLJy3YNZXSSDXN7kBRPfbnzQLttrBj4/a+aEvmWcGrn/gOkmbKXz/5gZxw5hqu+vX1vO/FH+dDr/wMren2fC/xdsFay+Z1k6w6fs4Grsd1bY3D9FYXg8AR8ytLu5phdRer8hSyBrTHuombAp0DxtmtqcAlaa3dsOtqmNjgYt/0LkgmZrizC9jC6LZwOCpi24DZmhAru4/thbV2bNaPHwfeMeu5953z3P93GNYEQJ60mdqxicUnnYOQR3/3dmt8q0q4r35QM8nYI5yOULtLgh1v51RCj1Y7Z7yT02gnMLqd4emtVHxJffkKBqurWVQdoF6K9govlgNFLQ4Yb6ZdrzjJnumEKHBVr8JYLK5K5vhoesbb9yhORW254ipWnHc6yu+/XWAP1lo+9toPUR8Z4PEveOIdP+FsDbBedWpiszPEFXJGhkJnLlgVqdsdxsPgRaAklIZh2x/cscZ0W5Yh1Fe5RK/IIW+6kfDqkplda69duQBak7MxvHSYd37jX3nlY17CG/7u1bz8o6/mnn99n/le1lHDZDujkeRMdVysCDzH8RrXOQKBXykz/MJnsuvyfyP7+GfpPOUfCE87kZJfUDQ6eFLSyZ0Sv68UAyWfwljywlAKBHum28jrbiA7/nj2NDKUaiCF4oRFZeJQkRaatFB7N2cLDdZadmzczrn3OX++l3Kb2HSDawH/7YseCcBFDzoXcIM+n3v3//CBl32Kl3/42fO2vtuLqfGE5nTK6tn8sB7XtcdjhRn6RG2p++dH3bjWdnSKtDnLZzJysQ7j4lKlAjKGzh5Xuc/aEHRvWF73HlVEcCANsz7A4aiI/RY4SQhxnBAiAJ4AfGP2AUKIZbN+fDhwXff77wEPFkIMCiEGgQd3HzssGN90vSPpr5kfbslsc+8eZpf0I18xXAkZroRIIcm0E2Es+YqSr6jHPhLY007ZPt7kuD99g5PX/wxPQGAy4i3Xwm++j735Kuqxz2A5ZFk9JvAUU52cdq5ppZrJdkonczZJvUkn13p0CVhh9C0moI70VFR7fIqxmzez8sIzj9g1jgZ+871fcdWv/szfv+wplKuHaXpnrt+jFzrCfd5xrcSsOzUUjbjRbZODSSBvuKSsscdNHrVHZ8ix7Wmw0lXZSgNQXQ7Vpe7cQWWmErfArJB6qA/Xufxr7+Hkc0/hrf/4Rn74pe/P95KOCnr0htnV9aywGCymm0x1shxRLmGf/lTs4CDqk5/Gu+lmstwZfyeFq2r1KltRIBku+5QjD2vBXn8Tst1m8oSTiUOFACqhorCgEGSF3UtVWIgY3zlG2klZdtzCr4jpQlMbqvLHn+9rdi+l5IwLT2Zs52GdUzvi2LLerXcfj0mT7zts1EM2y+w7T1xClbVdPMsaLrYl0y7WSeU2kGmj+9wpyFvumLw9M3RkuvcoIfu2LQmHoSJmrS2EEM/BJVAK+KS19hohxBuB31lrvwE8TwjxcKAAxoEnd587LoR4Ey6ZA3hjj7h/x9flSPqVkeVEtfnjvRws3yo3miTTZN0EKJCCKPDwfYknBNXpHZTaE1xx4d8z4Gny0KdS8/Amd7Dz5z9CN6c5/dKH7g3cWaGdOreSTLYKOoUmUJLCWkIlQQjKoaIUeLQz9/u5WmFHMvBuv/J6AFb2scm3tZbPXv4plh23nL980kOOzEVM7oJR2uhWwgoXkMIq5NNdN3fACKe5U3Qg2QONbd1KmQZjnXZPY5sbAgjKMNAtYveRH1ulXuEtX3onb/j7V/OuZ72NpNnhYU89RK22PkHvMzi7ul5oQ64tQ+WAqXbBdFIw2kopD9cQL/pnkg98nPSjn6L8rKdiTjsZKQSRJ0nzgvFOxkDJx48koScZLPvs+b+fYOt1OOs0FpdDSqEkDNxmsNMdIDJGM7BAKw79Il1hreWUc0/gMc98KP/xzq/w0dd/gdUnr+DUc4/HCzx+84M/8hf36q94uHVjd2JydiImfbdpzDvOO3K2Z6TJYXrSyVYoBdpAe8JNSArfUS38MvgDEA26yn170lEu0pablPQDVzXzIjeY5JegMouu1EfWRj0cljqztfbbwLfnPPbaWd+/AnjFAZ77SeCTh2MdszG9czNZe5rlZ82/f+Ft8ax2TXVoZ5ZK5JFpQzPNaaQFcejTzg2VSFGKLEVco+QrROBjQo80jJCrT6OkFDuv+iXDl9yfcpfv1QvghTa08sJNUoYe2ljaRuNLgRe7P38vyOs5ideRnIrafuV1qMBn8eknHLFrHGn86ju/YN1VN/PiD778yFnytMZd0MpabieoQke6T1sQdaeUgpqrbff+fsZA2nbHW9El5AeAcL/rEfprS29dCHYBIq7EvPELb+Mt//gG/u2l76PVaPH45x+GlvACRe8zGHiKyHeelIUxKCko+T6Rp/A993kX1pKFA0Qvehbj7/13xv/tE5Sf/reoSy5kUSXCWIs2FiUlSkoCXxBu3gjrNlB5wiMQA2UQgk5uiAKf0WZKrjWB5zEY+5TCFAsLjh+2fYNjwiz0RKw3SHD+fc/i1PNO4Orf3MB1v7+Z669cT6VW4kkveiQnnrV2fhd5O7F14xT1oYhKPZx5MG+7GNOr3OvI0SW80FWwpndCMu5+9gJXsfe6CVtlqUu2gooj60+3Hd1CW5dwmQzCrsp+UOpK9szaQN4e95EFhIXX8D9MGF1/NV5Uor78+Pleyq0iyTWtzJVXF1UjjDEkecCiikQiaOU5zY5BLj0JuX095/30w2Qjq5DLVmMHlhCMLMKsuwqqQ0y0cyJfMZ1kdNKCTm5J8oJO5hKvVloQKkE58vGUIOiS+3tBXs1KvI70VNS2K69jyRkn4AULc5d9W7DW8oV3/QfLj1vB/R79wCNzkTxxgQfhgotQbmIoqkOeA7K7ewxBu0Qba10Ak577l7dd2V90CbA6cXyzvNkl0HaTt7zjWgF9ELSCKOA1n34D737O2/nUmz5Ga6rJU17z9L6YmLu9mC0nU418Qk9TCSVCuMnIdqapRgF5ARbLRCtnVCji5z6D4iOfovWxzxMpj4H7XsRQOcAaSxxIZ42U5Kz75H/jD9QYfsA9CQrLeCsn6A4RlXyFRVCPPIarEUluaCT5Psr/R9OT9kDYvn4bnu+xaOXiebn+7cHU2DRX/uwayrUSFz3oXC560LlMjU1TG6r25ft3W3dici96/LDKIhdnekNGGEDA9HZo7phpOUrfxTMvdJvF9jjYzMUuo10CF9WgPeUq/9KHqOzU9uMhV1WTaqbleWvuIwsYd8pELGlMML1zE0tPuxA5DyT9g0WSa6Y7+V4rkbTQGAShJ/GUJNeaZlIwneQIC80zHoQ64yKqk9sRU6OI63+DnR7Fnn0P7MnnUmjDrmknTZHmhqlORqENUeBRDjw8KZwGWexRj3xm179GKiGRr45KUC2SlN3Xrue8v//rI3aNI42rfvknbv7zTTzv3S9EeUfotdpbXo/d9JDRLriZApRwLcYice1KPwYEBJELPiMnO2HXvO2qYF7kRsOlAD+DpMtn82fx2pKGq6qF5YUfuHyPl3z4lZSqZb70/v+k3WjxrMufj5QL25j6UDCb3lCPfSJfsWuqw0Q7Jy00SjrpmsCThEpRCRRBIPFe+zxufsu/MfaRzyBH9+Dd427UFg0Dgsl2xvbP/Deta9ex8rlPIYxCfAueUoAgUILAk5Qjn+qslmRv2AeOriftrWHrzVtYftwK1AL3qm03Orzjuf9OFAd0WimbbtjKtnU7yfOCwUV1nvzyxx65WHIEoLVh68YpHvDwWVPvsycXyyNuY5i3HYcrnXYDRFK6n01Xbkd0N5o6A+t4jOQtKK10v/Njt4nMO4BxXYFyV2stn8VD08UtZXv2rmmBx7P5XsDhhrWW7Vf/GiEVI8cvXCJ4L4hlhSYt3Ai5Nu5rK9P4wjLWSWkmOVlhWeRbiqRFSTYRcUReOpH2qfegOlCj0IbAV0y0UpSS1OKAUGmksHQy4zghSu6Vtyj7Hotr8bztZndesw5TFCw/t38FOr/2ka9QG6rxgMc9+MhdpDfJGJS7CVXH7SRl0PVbMy5IZa0ZrR2du1J/1u5yMyJHzjcGROYkLUrD0Bnv8i26iVhvwslkTmRRSLerXcAJmZSS57zzBZSqJb78gS/SbnZ40Qde1lc3s4PF7M/nZDvby/EsjEYKgRdJ2qmm5EsMHtZajB+y5mXPYusHPsWeL/0v4qvfpX3JeVQfcC8mtu6m9YOfMHTpfRi+50XkXemSniSnLyUIiwS8WcmtJwWeFEfdk/bWsOWmzazqA+eFm6/eSJ7mvOULL+UX3/4dH3395/nnNz2J2lCVr3/8+3zhfV/j71786Ple5kFj19YGeab3VdSfPexTpLgYFYFXgnTKJWM6c/HKFo5DFta734vuVHfa9ZbsuBiWdCcwk2mIqi4RS5pg0pkYCTNOIl7IPligA0izcadLxMY2XMPU9vUsP/MS/Ghh2ubMDmK9tqDzidTk2jBcCpjs5BTacULqusPANT+jPrqOdNkJFH5I7Cvkro1Mrj6TweXLqIYKa51Bbykw+J6iBBhbUI09KqG/N+kaqrg36ry1Eq50Q7PLzzllXq5/R7Fz0w5+9Z1f8Ph/+VvCOLztJxwq/Ag6wnEh0oqrfkUD3SRMzFTIVNRV36+5Scl2wynwN/eADB2Jv7zITU2GXTujoOaCX8+DsjfhlCdgu9wOq11bYAG3K4UQPPW1z6BcK/Ppt3yCpNXh5R97DUG4sHhMhwtzY8eiajwjWxNCbgxJK6WZFeSFRQioPedpLBkfY9f3fkLjF1cw9bMr3PNPPI41T34MQgmUVTSSHAFk2jAY+4SBwhqLp1wiFvk9/UO1dwJzLo72ZKUuNDs2bueSh9zzqF73ULD+ms2sOM4ZXLem25xy7glc8pdOcqPd6PDtz/54Ppd3u7G5OzG5+sRZw3B+5HhZRTpTHfNiF3M6xlXpw7Lje6VNF4P8KugGzjmkG4eshfZux3FVJUgn3eNpw/njeoGjW3i+S8ziga4zyJz35QIfQOrhTpWItcZ3svWPP6O6ZDWLTz53vpdzQMwNVsZalBBYLQiUIisKlHT867KvGNxyLTJtcuNfPpeqbjOsCnRnGrXpeoZ/8gXM/R6PXb0W35OMT6cESjJQDgg9ReZpAs8p5wcsDEXsHX+6gcG1y4kHa/O6jkPF97/wHYQQPOwpDz+yF2qPA9ZVwOKyK7uH8Qy3q0iclVEy5RKmdL0TUZSx859UIZBBMOgCVFgD33OcjMpIlxc2WwRRuuSrh5433ALnWAgheMK/PIlStcyHXv5+Xv+3r+K1n3kjUfnO4UM3GwdKdKx1iVnsW2qxTyvXhB50cks9UpjFi4ge93D4qwfR+uXvsDevRz3u4eRIYmFJtMUAvpJUQ4/I9xiq+CyuRHuvWZ4l8NwbIkhy3R0AcFX1nij00aqyb9+wjSIvWHXSqts+eJ5RHShz7r1dl+bivzyPc+4x0xG49rc3svbUhT1sMBeb102iPMnyVXPieE8LUQbuX88bN2269mHSAJU7Llgw6GJbqytjoRMcJzaZmRQ3xlFZhXFUM7UFFp/mNqj4rgPQq4SVu9OTd8WpyYWAPGmz4dffxY8rrL3bgxY08XH2NGKSa7LCYq3tekwKCuP4YgLwlCT2JX59iMFIUZgaSewzGg3hjxxH7c//D++mK0lXrKEcOcHFTBsK7aQrVgyWiD1JKysoB2reJ56stez48w0cd8+FL764Pxhj+OF/fZ/z7ncBI8sO3uHhdmO2ATjW7f4wXV5FN/Bo7dqIRrukrTnmiLCDqxwnrMhduT6SbheqU8etiGrQ3O1IrqWV7qvw3O60J7wIMyX9PuBYADz8aY8kKse87/nv5FWPfxlv/MJbKdduw/+uz7C/BMhNQxo6maWRuOGOSuRhDWib0Ug1uTFkqaFpFa3zzmfs1LOoG59kxzTL6zGV0Kde8mm0MxINqtC0E0HLLxjq8kdnI/IVuxsJjWSmAtFKc4Yr0dEVhb5pM0BfmMI/4DEzVbvaYIXa4Mx7c+fmPdzrYXebj2UdMrasn2D56hre/hLunqVRW7jEq7UHihbEdecn2R6F0ogTmhYKvAp0pqDTwmVbORTaccW8KjS3A3omJgVlWHaO+77I3Wa0smQm8cpxcSunL5KxO0UiZoxm4xXfp0gTTr7fo/GChf3C9yahpjo5zaSgkxYg3FBIT0Ii9hVFaDEW1MnnUPrplwl/+p/sGjqeqVKdzAsZFxGVXVtI155G2s7IC83KwRKeFJRCj8FSwFSSs6vh2k/NVNMpDKuHDpPw6CFgautOOuPTLOvTtuSffn4lu7fu4qmvfcaRvVCvrN+ZdAkUuGrXeMPtDLOmI9ZnqeNK5Am093StQqadXYgXugAnhZtIKi9xpP+ky9PwYve8qOqSs6Qxc30VLniR1/3hwX9zKVEccvk/v4WXP/JFvPlL76A+XL/tJ/YJ9pcAGaORUjHWSNjVSLrTz4Ja5DFpQVpLYZyHba4NgVLUIg8hIEkzJluCeinAF5JS6KOtBQuptoy3c3JjibrE/V6lK8k1oaeox65KZ6yhk7EPR+xocMY237gJgJUnHbz/8HzBGLPfYZIszfmX9zwdpfpr0GTzuklOP3fJvg/O1fAqDbl2o9Fd/lcG6R53bNpwxH2TQzjYJfAboHDxK+1yVWk7SyOpugleqftcz/3eJt1hJduVr8hcHOzxx/pAwqLvEzGjXRLW3LONNRc+kNLAEaxSHGYURjPa6DDZLqhGinZuaCYFvicJPMlQ7PwjfT+muOyfaV19BXL7VqqNPRTasnJ0I+MrzkCuPAObZGB9ppKcwVJIKeiaf3f27ZlPdwqm2hn1eaqM7bzqZgCWnn3yvFz/juIX3/wpUTnikr+6x5G9kPRnVPHBBRdrwPehMeoez1OYWOeqX+XF3emijhM8DGtuB1oegeoyR9gvD3R1xYpukApca7JInR6PX3JVsTztBjb6hmMxG/d+xP0I44g3P/V1vPSyF/DW/34Xw0uH53tZhwVzE6DCGNJc0UhSJjo5hYbxVkLkKVpJxmQ7x/MEhYZOakgKgxKCTmqcqbeQjLVyhGqytFoi8CS+kiRaU5Hu9jDZylDKOX8EnkvCeryxHuWhnRWAvoUW4ZHmjG25cRMjy0YOn6vFEcSBJnqD0Kc53aZSW5ic5v2hMZUwOdbZ12PyQBpeRQLJZNdTsunalUi3IWztcXSIyiKIhqE2AtM7XAuz6LhhomS3+76+0m0epXIJXNJwUhZRvetH2YTJbU7kWnU3jz3+2AKnV/R1ItaZGmXjb39IMjXGirPvydDq/qiyTLUztoy32DLWZk8rY7qT4QuB5wl8z6MUSjzl0TYFadJiausepktDpItOYUwupx5oZNJi+19cikCwJAooChCRoJVqFlXAIpju7N8Ede6009HE7mvXoQKf4RMWPqdjLqy1/PZHV3Duvc8/siT9Hnp+ksp3HC+rXVUsa7ipRy+eqXD11KlrKx351a9AOAADy7oE/9AFLt1xmjxSQRq7c/hltyvVuUvGhNfljkULfid5IFz0l5fw5i9ezuuf9Cpe/LDn8bavvIula5bd9hMXOHqJTS8BaqUFhc7RxhJ6krxwFag8N12Tb8c7LXuWZpp3z6FJjSFLLb4CT/gUuSXNC7RVWKDU1RKbamdIBVUVUBhLgIsfc4kfvZapmiMCfSRFoQG23LSlLyYmbw2bb9zGv77kk7z766+Z76UcNLZu6Crq9yYm96FSdFGkrqLf2OViT9aENAFyV/kKSi4emdz93krAuolvm7vESfouJqFcrIq7mmPxsEu+/JKrtiWTrkXZ2u0e7ynt92gcC5xe0V+10FnYdcMfuOHHX6ZI2hx/94ey+KRz5ntJB4XJdsau6YQdkwk7phMm2ikIQdKVsqgEknLg5CbiQBFtv5n46p/RTC3Dscfy9hYGr/kplT2bCKZ24ykYKUUMV0MiT1EveYQ9Qq3afxCc6395NLHr2nUsOvU41JFSoj+C2HLTZnZt3skFDzjCXI72uEu4VNDVW7UuCBWFI7R6vuNIWO1sQAbXQhC4HWVYd1WwoeOhushVvYoWNLZCYzc0xtw5enIVecdV24yeCaRe0JW1sDNCiX2Ic+51Lm/76rtoTDZ48cOet7eN1c+Ym9g4TqlBCrGXN5Zrl6ylhaESe45zqiHyHN809Nw5Yl8gcAr7SkqEBGEt1cAj15at422mOymd1LCn0cHYmQ2cUnKfOBJ4ilrs7dOGPNKDQdZatt60mVV90JYEmBydZnzXJGM7JxjdMY7p6keuPnlFXyVhAFs2TAKzEjGz/00/zT1QtF0C5ZdBt9xGsD3qNpM6d3ZtKnR8MFN0dRIjN9k9uAoGjoPBlS65qixx5wpqENdcotfuuiLaYsYsvDcN3lvbAqdX9N/dECfYuv3qX1Fffjyrzrsvfrhwp6Nma3UBTHVyJts5e5oJ7cxxxEq+h6N1OBHFgbJHmrsWgt6xjmZ9MZ4nUdf8ksGxHYyFNWpTuxjYfTMbz3kYu2KfahywqBJQDQMKY2ilBcPlgNzYfdqTtdibt7akNYbd163n1Ifee16uf0fx+x87S9QjmojN3ll6oat4tfYAynG9wooLPj2TWz92O8jKYrejTKYgmejyKcKZycfWJHT2gIxcIhdVAdlV7Jfu+Lno7WgZWNBl/VvDqeefzju/8T5e+ZgX85K/fj5v/q/LOekv+qNyvj/MVtrv/TxQ9tk+oenkhlwb2pnTEysHHhJBHFgi5RI24UNaSJbVFbGnqIQSpTyssGjt8vmpJKOZFigpaaWGdpawtB4z2sixVlDtcsUq4Vwf3eioahOO7Ryl3Wyzsg8mJn/74z/xwy/9jGuuuJGhJQMMLRmgOlDh1PNO4F5/fdE+xP1+wJb1k1TrIfXBblw4YKLT3Th4gROh9mNQsZuO1Kmr3HsV6Ox2j0vPkfjboxDEjsgf18CUXYtRBS4WDax2vNas6TTHvKCrSdad4DQF0L3PBdUFH7/6MhGzRrP2ogczsOLEBTsdmeSa8WZK3m0laGNppxnNzDDWTGl0cnZOpU63JzdUuiPjAkE5CNAmpyJhulTBdgydXCN3bWHHirOZGFrLZm05+/pvMTS5jXRkMYNlQS0OyLQGFJFvSQpDLfKpRz5pYQg9OW9JGMDU1l1kzTaLT1vYtlMHwh9/diXLjlvOklVLj9xF5u4s/W77UCnwlkN1OUxucXwLq0FaJ5YY16DdBi91Ehdx1U0nFV3Tb9sdAbdFd6xcueAVVvY1zO2hNyQQDbrqXB8QXg+E404/nnf/7wd4+aNfxMse8UJe//m3cPY9/mK+l3XImK2070lBPfZptHOGygHaGCqBRycvqEchrbwgwiKFIPQ9pBSUIkGaFRgLpVDRTDTaCkIPRpsZxlpAsLgakOYGrGWylTrBaa2pRTOVr/1NUx4t7J2YPGnhtyb/7eWf5kXvewav+PfnMD3RZOvNO7jhynX84tu/oznV5jHPfEhfCRFvWTfBquMHZu6/s/XDevBCF7+au5yYKzgPSRm4tmQy4VqSzZ0uLmUdKMpQXwEjpwDaJWNeCKoKUeTiWVxzSRg4cWrTFY0Nao6PVnRcy1JKl+jVjmC8Pkzoy0Qsro8wuPKk2z5wnjDZzmgkOVOdgkaSIYTAV5KxRup4GAgGKyFJbtg+kTBU8ajFHsvqMaXQRwrDslrI7maGd/bdqX/n86wRgk55mNrEZqZVTOxHhK1JpgeWUI496rFH6FmiIMCTMz6SaWGox/68JmA97L5uPUBfJmK60Fz1yz9xn0fc98heaO7O0uTdCtbgTJAZOb6rp+M7/sX0NpjY7EiuYcWR9Y12Oj1Z4lqRReGCZND1ozQWUM6vbe/IdzeQ9oYEekKM0Bd6YreG5cev4D3f/gCvfPRLeNXjXsorP/66Iz9wcQQxO+FppgXLB8v4StHONdoaxpspSsKKcsh4q8DvTlJbLElmGKn6JJnGE25COykc8T/LCzqFRQiBEIYsF1jAx+JJt2mc7GSUQn9e/SVhJhHrB45YpV5ieKkjttcGK5x+4UmcfuFJPPIZl/Kk857Hg59wbwYX9cd0r9mftRHM6IfN1fCqL4dp5WKQxdEqaishX+EoE50pNwWpU5eQ6QwqEZQWu01lUbhuAIDsSvb0ErFe18AvuVjlBUDdyfUc0xG766LXNiiMJS2cRhhYrLFOp85YtLWEnmL5YMxQRSEQDJYiAl/hK/A9j0W1mE5h2B1WsA/+O4Z+9wN0eye2OY3KU8gzxofWMFUZQScFncwy0TQI2Wb54MwE0XQno5MVDJaCeU/Gdl+3Hukphk/sD07HbKy/+mbajRZn3/MICwXP3VlKf4Y870Xun81d+R5gaovjYDRH3Q6zSLo6YROuPF+0XXcgmXbntsZNTobdnafnO45FaWgmkHYmXeLnzXm/LHDC621hZNki3vnNf+U1T3g5b3rya3nhB17GA4+kRdVRQo/2UC/5JFOarIDAc3SHXAt8JejkBgkIKbDG0kw0SWZAWKbaGWnuiPudQmMtlEMfrGU6KfCVYKQSUIlcJW6skSKEwJOScqBYXJsfasjWm7ZQqpQYWrKwK7W60Dz4Cffhw6/5LJf+zX1YddIK4kpIc6rN6I5xonLUN0kYwK7tzVtaG/XgR9wiRgwd153I1i6hkh60drmWYTTQldNJnEtIWHMxTgUuPpkCzMTMuZTv2pWzrYyqS7qk/f4ScZ2NY4nYYUZvqsmTYp9Rbot1gSvySQtN4Dnx1TT36RQ5hbb4PhgrmG5nGGOZaKV00oIMH86/lNHd4+ipUfLpBrtLi2j5ZSqdglopoBx4JFoz3cpBCIYrIc2koJEU1GOfZqqZSvJ51RDbfd16Rk5agxcsbOLk/nDNFVcDcOYlZx/5i83eWSIcob4ntOrFrpXoRbDrWselENIlThbHA2vsdoGtvNgR+HXLPSdPnYBiNASDx7mSfZE5kqyQjoPhR8CAa0fOxQInvB4M6sN13v7Vd/PGv38173rW22hNNbns6Y+a72XdIfS0xfY0UlqZJi00UoAnFZk2hJ7C95zqvQB8Kci0Jiz5YA2TrQzPA60tAkEzzRmOAzBOsi4OnU9tM80RQmAsTLRySoFHK3WJ25L60U/GnMfk6gVLT+lBeYrL/vHBjCwb4idf/xXNqTZDSwYYXFRn+4adPP45D5vvJd4ubFk/CcCq4wZmHtxfJayH9rj7fVh2iVUy7fir7emZhCqsusp91oJgCLyyO362tqHOXRJWGnS/88I514tuey0LFMcSscOM3u408BS+EkwWGk9KapFPoXMaqaYwBm2gFnlUAsloUxP6kHdydGBIMk3YzhhvZUwkBVluGGtlhH5EUluOrgpiU1BCEYaKpbWY3BqscdfPC8tYM6WTOe5ZT/NnPjXErLXsvm49J9yvv9Sje7juimtYtGIxi5YfJZ06P3Kq0FiXIPVGsKXvkqfmHmjsgMmtXc82AXmzW6IvufJ/UHMcizSFaATqYZcXNuS4G42uXo8XuHalNTM8MFPMiDBCX+qJHQilaok3/ufbefsz3sSHX/EBmpMNnvjiv1/wN/QDoactVg48jLVEgWCyXSCNRRuLEY6jGnkKJQStvMC3At9XjDc12kBhDL5QdLKMRaUI31dobRmsCsKudZEvFOVIUhiIZk1vtjI9b2bff3GvhWtl14O1rtV7j4dcwMV/eR5FVvCZy7+MH/q8+uPP77v33Zb1E0gpWLamW8U7kH4YzAwf2byrgD/pfk4mHW9MyBmzbtc8cv/xAteytIWLaVnTxSglXeXfC910+Fzc2loWMI4lYocZvamm0WaKrxQlX2GANDdYBAORT+BJcqPxhKCTa6qRh7aAteyYTlhUDskNTLRzstyQ585/q5NawkDRTg3HdbYj6oN4w8sxWBqdfG/CVfIlQgpE4HghszFfGmLNXWN0JqZZdOpx83L9O4prf3sNp194xtG96GzivhcCXWXqzrjjUQQVR0hFzzK71U5PTPouaOmuRIWYgGg5lJa4dmVnyiVuAEXgWgRFOuNjKT1HgtWFq6bdSZKwHoIo4FWffD3vfcE7+ezln6Y52eTpb3rmAUU3FzJ6Vfg4UHu/z3NDp7BdeQuNEnKvH2Qzy2lnBlnkTLRTUq1RCEqhJA5jhAUkDMYBUgqM6fpQ+pLMWCqhIPQUaaGZbueUI0nsSZYNHj1B0k6zw+j2Paw8ceFPTM5OtISAMA6YHJ3mnFNWIoRAa9NXqvqb10+ydGWVIFC3rh8mlRsYAty4btcX1xSuCo90j8c1YMzFM6uhtsglar2ErCgcfyzsdnNUyF5pndlx6UBrmWdu656JDjdtnrzVY44lYkcAka/wlUQKQTUqkxWaXVMpgS+ohiGNJKORaKyBVlY4w15fkBVOFBhc8h9O7SLYejMkbQaSNpkXkdSX0Fp0ErrVYHD5cvxI0ckNFrf7HSz5RKGPxKLkLVWt50tDbPf1G4D+JOqP7tjDnm27Oe3Zjzu6F95fK9B225XgkrP68q4GTwLpZJdDZt2UZdKAyrD73mpX9peyy6eY5bgguu+JokuW9bsBby5H7E4G5Sle+P6XUqlX+J+P/DfN6SYveO+L+2p6DWaq8JGv9vJSB8oRA9aQaUOaKjraEPsKpVyFy5OS0UaCse75npL4vsJTknrkEXgehSmYTgxKQuQJSr5H6EukFDSSjG0THSckmyh0IcitPWrUh54m3OpT1h6V6x0u9BL92lCF0y84qftYf1XENt44PmNttD/9sM6kS7T8cpf60O4ea7rJUsdVx6pLu5zV3G38vMBV8o2B9g5AuiTNdj12jXYtzLA669qzEqwDaZnNE7fVGMuPf7eVr/+/9fi3cd89lojdQexPN6cwdp8yvTaWSuSRG0NaaJqpJtcWAQSepJkUtHOXOE2nBXHgUbUplSu+RR5X6QyvQsd1wrTJUHMb1eYoU6fdk4FaDYugGnl4SlAYu7cCNlAKKOzC0RDbc/0GEIKRPhg1n4vrf38dAKeef9rRvfD+RsL9CojuVKNULtiVDGjrqloYl8DpNigPkpYjxVaXuGSsvQcGe8mwdUmbEN0dKi4pE9m+SVifk/RvDVJK/unNz6Y6UOWzl3+a1lSTl3/0NQRR/yShs7XF6rEj1PtSoKQTiu4EGjopkS8JPEXoKRpJRuyVGSg5uYoky/GURynyGIhDklyzZbzDjqmMKJB7r7NiqM5kO2W8mdLJHO0iUJLMGMabGfXo6Exob7phI9AfZt/7wz+89LFEZdf276fW5MRom8mxDsed0rUMm71ZLFIXR9IW+N1EzQvcRGTedjZrxRJI210vSAEidvFHZVBeCumEk7HIU8d/zabd5jJtgO1KVeQtx3ON52xUD8RhnQdu67bdTT777evZuL3BWScO88RLT+a9Lzrw8ccSsTuAyXa2T6svyTUDpWC/6teecorWjXTGj20gDmhlBVM2J1AukTPGJW56+3oCCvbc/THUSz5FYfDyNkyNMnT1z1my/XeEay7FVwJtoRJ5KOFEG+NAMFQJiXzlJqIWgIbYnhs2MLB6KUF54YrvHgg3/P46/MDn+DNPPPoX399IeHu8K2WRuZ1kz2stmcSpcsaQRTC1HaSBaEmX7G9AG5jaCuVFboeZNtwkUtZ2SV0QdQm08cyI+J2ApH9rEELwty/5ByoDVT78ig/w2r95Oa/77FuIK/3zXp2tLVbv3qCmOjmRr4h8hZSQFfb/s3fe4XVeVdb/nbffrmJZ7j09IQmEHkLvNfQ+9DIDDB0SWiAkocPQZmC+YYChhN5hqKEOPYR0915k1dvffr4/9nstWZFt2ZYtKXg9jx5Jtx5f6+67y9prYZoQp8hyj0oZbcFgM8SPYgwSlhkGxS4TExmX9RYdUq2xDcX+eohtCnl6qNGm7mtsI1uEU4qCa5406sOO9duwHZvFq5aclOebaeSK87Ow2bpeVOzXnJHxrjrFYn0g879tZ8lSa7yYczzpxFtO5hDSltvX94MZS8GoDKFKKDMrBjMz8NQCMu/IdjbWVCZgiij15JFjEksHrfPcJ5nb2vIjfvz7HfzsjzvJuRYvfPzZXHT2wiMm26cSsWPERHXrDoI4PUBanax+rRS4lollhSSJpuLa5FzhaxhKi0xKmlJwTBINqlzBLndzur8Lu3cNkZXHTzziSgUzDTBv/QOmoTAthYVsaLqOjBZy1rjg4mxLVnQwePvWeTmWBFh//e2sOXctjjtLr+XklXA7L/IVaQplW4JNZ+OoOSZkVlQmcFiH5rBUqcVeMFzhmLXHwM1EDzEkqJm2/EyajQ+8qYPdnRSPf/ETKVZKfPCV7+GyJ7+ed3/lvRQr80fxfLK22ER0OmU52wRPU/UjEW9Fk8aZtAUpqdaEcUIl79BfSqn7Ie0wIUxEtX+0FQKKgWpImGoKroUVxliWwQLtnDTqw84NO1i6dtm8GyPPd2y6bQjTMlixdoLZt53PloRsKeai5rjHo+UKD8y2sqQoG1ea7rjoquEBsXTrjWyhCC3JnAL8BLziOKHfLkjsmsj/6pD0TStbbIql2DxJsSuKE37519386Hfbafsx9zxvEU9+8FqK0/z8PZWIHSPi9I78q4mXT1a/7th/FFyLimeza6zNSDPEj1LafoppaiLLZLgRUnAsSn0rsFadAddfh7nlBqx8Ucx54wh/aJB4xVk4WtMONJap6Cs6uLaJaUg3bC4hbLap7hrgnCc8eLaPctRIkoSNN6znIU9/+GwfRdAJOH5N2vtJKEEnCUX8sJGCcsXPDWTbqDUMGNI1UxYUemQVHCXjzDQTWgQJpp3NJac4LzaOZhIPfupDyRVzXP3Cd3LZE1/H1V9/P6Xu8mwf66gxldm2Z5uUczaebWIaBoNVn31BTDtJMQyFpRQpmliE9YkSWTAK04SGH+NaJo6pGPMTbNskCCKGGyG2abDQVFNSH06U5dHOjTtYe5dZ6FD/g2PzrUOsXNeNM3EJLI2y5Cn7v9dZIZdGgJvZqZEZe0dZYuZJHALhi2kNTkWKxo5dUdSQgjHXm00AfFHSN0wIS5KQpZFsl09W9D9JSLXmTzcP8N1fbWGkFnD2mh4ufeAalveXjupxTiVix4ipAt3kyw9l/9EOIgyEANuVsxlrhbSjhFYUo1E04wRlgnHefem76D6ogR2k1WFSv83o/v346y5CrTyDZpgQxim2pSi4Fral6ck5s6p2PRWGNgqxdsE85HPs2rSTdrPN6RfOAX/C9pgkVVpLoLMcCU5JG1pjGaE1hXAEGvuFExa3xw2826NSgZqGSFxY2ZZbGspoMk2F2O+WJSE7FPn1To77POpi3v75K7nyeW/njU94Ddd844N0Leia7WMdFSZ35eFgE+4kTTFNE40iTaQjn/cstBZ6Q9G1yTsGw82QOFEkKAwlk+040dimwbKuPO0opjtns7w7R385RyOIDyRdh6JuHC/CIGTfjr084IkPOu7HOoXpI45Ttqwf5gGPnpQAp0mmRWhLTPLK0k03HPmy89LdSgKJV5Ynpt5RSygTsS+csPo+GW1aDjSHsrjUlseImsKPDepy/7gtnbXcYeLUCea2NtsR//XtW7h16ygrFpV47qPP5MzVx1a4nkrEjhFHCnSHwo6RJntG29SDGLSmGcRYpsJOIUWRM01SlZLGGq+oaGgDY+Ea3KWnsb/aZmOpTqrBqAYYRkB/xWNBycWzZFNzriVhAIMZsbZvnm04AWz8+waAk28UPRUvrD0i40e/Id/z3RL0okyV2slnK+O+PEbsjxuEm3bGvcg2kAxTOl9BTR4raAnHrNArl5WXMOWK+D8I7vHQe3HFF67inc95K296wmt4zzc/SPfC+dUd9GxT/CGjBCfjioEkRBpFzjbIOyYY0AgSTKUoOiYWBmGc0FfKkaQaP0oPiL2O+T62CV2eTQJUci4regvkHQs/ToWAhjh6aA4uVidSN44He7ftIU3SeSFdMRnX/+om/vabW3jWay/Fy8+tycWRsHPLGGGQsO6sCfpdnQ59HEHSmMAtTTOWQwj1OtQGJPYoSzpmQQOCtmiJhQ2o7ZFCMGhIkqW1aB26xaxjZkgS5g9JstbZ7j6gtzgBHc1F+8RxPOvNkI9e+3f2DjV55iNO5+ILl2Acx9LFqUTsOHDH8ePhA0y1FbJvzGe0HVBrx5hK0QhCRpsxlqUYbYaYSuFYBoZqEcQJS7pzRAn4YcpgM6Tup/hJTDELfHnHZmFJtjI92zxoZHqixgJHi/23bcEtFyktPkliqDOILTdtwvEclp8IW6ZDKUBPFiVsK0BnWjwNiJsSwJJsJOB1Z+TWWAKh40JoSgWpJeka2N/m+i3DuF3gddt4ZUWulLBmbR+unc2iVJp9Rx67E9DupBuTR8LdHnh33vWla3jHs9/Cm57wWt7zrQ/NeTudDjrdqGo7JIw1ni2xoNoS71s/Ssi5JpW8w2qlaEUJOk0JEmjHMaMtQ6gTcSqNVTRBGIswdQplz8bKFowKnoEyD/4QaoYJhrpj3DkUpeNosDOTrpgPHpOT8aef38C3/99PeN5lJ1kKZwaw6dZBANadnSViE3W7JopOK0O6Vh009kN1u8hOmDZUaxKrcl2SdIVNyPVJAdgaluJRx5AUMqs1si1LQxT5jSxt0bF0zpyijCM7+mUdn9yoPS5SPYNq+9VGwEe+dANDYz4vf8p5nLOm97geD04lYseNo0lw6n7EUDMgjLVoiCXiEVlwTcJIeDrVdohjGkRxSivUuJZBwXMY8wPCKEKTYilFnGjCOKXqh6DyB87RGY2eqLHAsWBw/VYWnrV6Xq1pd7Dl5k2sOmvNzJOCD6UAPZUoYViX9jwgiZIp3/0R6Wy1spGj4UpwMorgRtLOr9XZPhjwuo8ljNYawL6DHrp/SYV/vewB3POiHhldpgngyc9WTrR+/oFxwSV35covX8Pbn3kZb3rCa3jvtz8855OxTqfejzpet+BHKWHsk2pFzlG0Q02qE3KOgWk46KZPOzJwTEXDj4kT8MOIfdWAMEmwLZOiZ2GZFn0VC0MZeKZCGSZxnBLFCUHUppJzcCxTHD6SOyZdh6J0HA12btwJwPJ52BHbsWEPK05bMq8EXDvYfOsQXb05ehZ2KA2TWlEd0Wk94f89DsfjmU4gTCV5cgvZhqTKYk0BartlRGkosCuZKLUptzdUtvldksdJ02w6MJYVpK50wKLmuAE4HCxS3cFxqO2P1Hw+/MUbqDVCXvG0u3DGyu4j32kaOJWIzRCm031K0pQwTklSjWkqHK2opwl5y6KZqiyYJRRdk5FWSLUd4YcRi0o25tAe9lm9BEplGkFQcE0qnoWdvak7o9EjbXSeTKRxwtDGHZz/tEec1OedCWit2XTTJu732Etm9oEPpwA9Fd/BsDMhV8YDmBNArnt8syjMuGLKEO2doA5oEq+Xd3+2igY+dsVyin29+O4SfKPMaDXlsx+7jstf+R3ue8ly/vmF61i0yD3QRTuZpNe5jLvc9wLe/ZX38danvXFeJGOdrtNEr9sgTtBa49mSRFlGwlgrleVaS2EYCseEehBTa4cYhsLQUPMjlGEQ65i8bdEIQio5A8tSJFrR7Vk0sg3MMEmpt2PKOZsFJQ/HPDgRmw51YzrYsX4bfUsX4s1DKZzt63dxVibkOt+w6bZh1p21YLygPpSsjVIyblRZ3LI96YTpVC5PQ6AsI8x9N8PQJik0wxqYOZHjiZvSMYtCyFVky7IT3wxLEjRlyXNAtjFpj4tRdxCH8nzOhO3nY1Tb98OYj197I/VWyL8+43zWLJs5o/b5l5bPQYxlSVMziKm2I8ZaYuvgRwmNQPwdG0GMY5mYBjSDmFo7YteIz75qwHAzxjYVSkOQwv56SDsQ/zY/Tmjt2UH/jz/F0to2dKKwTJOuvM26viKr+0qUPRvPMg50vI600XkyMbJtN0kQsnAeWhsN7t5PY6zO2vNmOHAejlw6VXCzXFBONi6MJPFyS+OcsM440c38Jb2iBKugzrd/WmXL7oRXP28BZ59VYsXyEqefVuEu53Rz//v08qlv/zMveu1D+Ouf9vD8F/+Kz39pO3GswSrKc/yDEvYn49x7nceVX34P+3fv582XvpbR/SOzfaRDotN1Mid0n5JUY2Wq7n4YE6eSGFnZR0DRsfGjJPOpjdg10ma0FZJqyJmKNIWBRpuRRsjWYZ9dYz4NP6YZRGgUUZLSDGLqQcxQI6QehPRXclRytmyK5+wZ68hvX7+NVWfNv3jitwIGdg6x4vSls32Uo0ZtzGdwb4O1Z00Yw9neHYu12JeRYLsKjQEpEE1bkikjk9pBSWwZ3ABj26XwS/ws9iWiBda9SqzVygtls7uwANyKdLoMR7pfXvlg4Wk9RbdVHyKmHmVc01rzpR9tYO9Qk5c+8dwZTcLgVEfsuHGo7tNAtQ1KTeBoGGg0RddGKdg13EYDBc/CT2JaMRimwlaKRpSQtxSOZTLajFlRk3FS96pVnJcvkybQU3TpytuUcjYlzz6o1T2djc6ThcHbtgDQNw81xDbfuBGAtXeZ4UTscArQU6npdzaNyIHZhlTJ7eIw20hqjo8DgoYIuaIZqio++4OEe5wNF1+Ul06aaUFzP0R1QOHomGe8+GIe/Mgz+dR7f8TnPncbt95e5R3XdJNz9kvgdI9uFfvOivPucz5Xfvka3vaMy3jTpa/lvd/60Jwk8He64sABy6OCIxuSlgFxKnHAtkx6ihYNPyROUvKOScOPSXVKO0pkKcgA21IYKFHSV5LA2YZBNYiIEo1lgG0alFyLvCvisVGcUm2FM65jmMQJOzfu4ML7XzSjj3sysGvzXrTWrDht/onQbrl9GIA1E4n6cDD/Kk3GOVqGKePDOJCkycoD2fjQKoo0RW2fdLh61mVm4G2ZIDqZvpjpSdfLdKW4rCyR27tF6Zq5RYmBHUmMUjdE6o5OJEzRgDhKkerf3rCXP90ywGMvWc1Zx7gZeTic6ogdJ+JUBBBbYUwYS/AL44RmmBzgaARxwkgzJIwSPNsgb1uU8w4Fz8BG4ZgmCujOu/SXHLpyFou78timQdlzKNcH0Y5HmO8m79r0lV2W9uRYUHIpefIHNVk2Y7Kw4kyNBY4W+2/fiunY9Kyaf1Xgpps2oZRi9UxX31NVkhMVoPM9MnZ0i1ng8sZvU1osQoVOGYoLhcOVZAr51b3Q2AskgOJLP2oTxfDKJ4GyPWn761Q0x+JItMga+6E2wMJFJd724afxusvvx1//spfXv+KH1GoBBzYnTwGQMeW7vnQNAzv28eYnvo7qcHVWztHptncSrsnoyjtUcjZLuvIs786xckGRhSXRGuzAsYRG4dgmaZqJSgM516DsWhQdk+68jW0qWmFEzjGo5KwsmUtIE00zCNlX8xlrBRQ8C6UUTT+l2ooZqPkHpgMzhT1bdxMFESvPXDWjj3sysGPDbgCWz8dEbP0wylCsPm2KJMT2pFhLEknCOrBsKSLDlnTJwjEpFFWSJVCpcMMiX0aHTgmKC6BrGZSWiC1bcaHEPSfboCwvhsoyiYm1vVDfIwr9zSFJAifGzlw3lBcdPtZO59++q8pXfrKRs1d388j7npgFkVOJ2HGi6UdU2zHNIKHajqn7EXGqMQ1Ru6/7IQ0/ph0mNHzpnJVcm6JroBArB51qwlgKiEVdLmcvrtBTsOjOO3TnDZyxfcTdi4hTLVwOxyBnmzgZgXxiktUJ0J5tnpCxwNFicP1WFpy+EmMeKmBvunEjy09bcWK4KJMDxmTyaCe4pbF0vOIJH2iWA6U+ScJMR4JXYSGQCq9CecR2F7/8S8R9L7BYsrIHMCRIJtlmZZBJYOhYumP1fZDGPOqJd+Wd7380mzeN8JoXfY2hgeqp8eQknH/xBbzzi1ezd9seLn/y66mP1k7q8x+KCjEZnm1SdEVktehaLCzn6Mk75B2TkmdRyTlU2yFBpHEsg9FWRCtMqLeFT5posS5SKPKeScmzSVKDwUbIaCsiiBM8x6Y77xKn0A5jxpoRUZISxGk2pgwOmSweC7bcvAmAtbNhN3ac2L5hN4ahWL528Wwf5aixc8sY/UuKuLnDDNHUpM5TEmVdrlQSsqAG1d3Qrst2ZK4iY8vUlw6Z1w09a2HpBSKjk6YS/8rLoLISioukE4YGvwqNfVDfO95Na+yDkR133I48Uqw9DIbG2vz712+iu+zygsefc1wSFYfDqUTsOOBHCSiFY6kJl6XYRkfDJz2wtQTg2gamMii4JnnHRpMKJ9pUdBdMKgWTvOuwoOiSswzacUwjSLHGBhjLLcCPUsquTdFxaIcxCg7ihk0O0H6UUHStWZOu0Fqz/7atLDxz/o0lATbfuIF1559+4p6gk2wdqjprjWR6Ow2xLfInfOB3WutKg1OQZMzrkm5Xrsj1N1Wp1lMedL8FsOBsWHQOdK+WhC3V2SamKRyOsCkk2vYIxE3uc8lqrvmPZ7Fv9yivfurH2LttIDPuPdUZ6+CCS+7K2z9/JTvWb+fyp7yRZq1xUp73cIs4R7pfI4hxbZOegsSL0WbIcCMgiBIsw8TN4ljRUsSpvQ4vWwABAABJREFUJkkTLEP4ZXnbpB3GjDRDaq2IOEmIE3BMg5Jn05Wz8aOUOBFBV8dSuJaJHwl3bKaw6caNWLbFijPmn3TFjg27Wbyqf14Zynewe9sYy1Z3Hf5GTlGKvA7SRDpXOhWLtdZwZq82LAlYFAjPq9AvcanvdFj7AOmCpbEkW0pBWBXFfdsFtBSlYfZ+S2JASaLXGpUOWXNYNjFbE3icR4q1U6AdxHzyazeRpJp/ecp5FPMnznP3VCJ2HOiQ3ys5h5JnHag0uwoermVgGgauLS9xzhYfyJ6iQ38lx8KSzeJKjuVdHt1Fi76Cg2Wa2ArGmgE7RnyGaiHByCBWHDDg9tIOQ4IoYbQV0AwS2lGCH6eMtcJjDtAnEvV9QwS1Bn3zkKg/NjjK0N4h1p43S5V3Z7PScoUjAeNq0p3WehqNbw2hJWh5Zcj38ss/tSnkFXe/9woh8RuGBCKvLMr6TilT1/eyDaI85BZIMNUJF97nND74xX+mUWvxr5e+n203b7pjcPsHx0UPugdv/e93suXmTbz1aW+mVW+d8Oc8lkWcyQVazY8wFIzUA0ZbESPNkF2jDZpBimNBPUyJUyHt760FDLdC4lSjlMIwoOhBb97FNsVYfG+tzVA9oN72qbVjYn3osxwvNt6wnpVnrsZ25p8R/Y4Nu+clUT8MYvbvabB01REI6rYnBHsv6z7le7ORZSCdd5CYFdSFEtEYkK6XZYNlgW1L8hW2IY7FTzKJJXFr7RdOmV+DuCWXd2zadCI/R82Mm5Z18DvbkccArTWf//7t7Btq8eJLz2HRgsKR73QcOJWIHQcm87IKWffJMhRdeYfegkN/2WNZj0df2SXnKExDYRmKpd1FFpY8LMsk1SZ+ArV2JP8jKpUVc8tgYSAkyaDST62dMFz3afqyTdlZTw/iQ1eds7Ep2cHg7VuB+amovzkbgayb6Y3J6WLiODDXJd0up3Cw/6NhA1oqzjSRKtMpostL+Ovfa9z9wl4c25AgNbQZqjvFFBxTtioNO2vh54TIb2RLAJmFyJnnLubD174Snaa89ukfZ+Mtu44ruN0Zcc+H35vL/t/bWX/9bbzjWZfjt07sa3O0iziTCzQ/Sqi1Y0YaAa04oeFH7KnWGa4HBHGMqQyCJCHSGs81yJkGQZQQRDJuNE2DvOuQ90yqvtiyJanGTxJakchEjTQDBusBdT/EyezXZgJhEHLbX27l3HufNyOPdzIRRzG7Nu9l5TxMxPburKM1LFvVdeQb53syblc/dC2XrldjQAq42gBoU/ipYQNaQ5J4RT6kQHUfDG+C4c1SdLYbImnRSVN0JFyw1qhsWaaxxCNlSgx0ihIDJxLxj5FW8cebB/jb+kGe8MA1J4ScPxmnErHjwJFI8ZW8Q3feoeg6JKmmHWqavpBYhxs+5ZyFY5g4poFnWziWwXA9QWuIUgmehdo+NIqx/AIKrkEhZ5NzTMJYk6TpHc40GbOxKdnB4PptoNS89JjccvNmANacu3Z2DjB5q8dys0Bjjo8JO15tYUOCm+VBzxp2V/MMDbW54G79kry1x8SLspaR+VsDEugMUzR2NJDr4UDbP25l48smq9f28uGvvBLXc3jdMz7BLX+4VcYMp3AAFz/mEt7wicu4+fc38q7nvo3Qn1mC+kQc7SLO5EKsHSbU2gF76wHDzYB9tTa372myaajJrtE2tXZIFGkMDUXbJogT4jhFaUnKHAUmCsew6S04GGi0jsWRxjRkOzMV5f4k0RjZGWaiM3/bn28laAdccL+7HvdjnWzs2TpAHCWsPGPZbB/lqLFnuyykLFlZnt4dOmPAOLNeswpZMdkjCRRathmL/fJ7fa+ME/3MI7c5AM1BSbySZFx/LEmk+6WUxDqvIl9OWcj9uS6ZHkwk5x/ldiSIaOu1P9nAuuUVHnKPkyMaPCOJmFLqEUqp9UqpTUqpN09x/WuVUrcqpW5USv1cKbVywnWJUuqG7Ou7M3Gek4nOdtJUpPhqKySMU+I4zkivmpofsWmgwab9TXaPtklIM8VqSZpME2zLYmklR86yyNcGaBZ66e8tUXBtLKUIk5RUa5IJeVjBtebMpmQHg+u30rV8EU5+/gkvbrllM31LF1LqnmbwmWkcTqMnaEB9QLpcflUuSwOkTQ83/WUHAOffdYnwKqI2tAaF5G87mRVJp4NWkBGCk5e2f30v+HW5fX0ftIZZtrqPj3z+n+jqyfHGF3yW6395w6kR5SQ88MkP4TX/9gau/+VfuOoFVxCFJ27B4XAxZzImFmLVdkgzjBltRtSaAdVmQKMdk8tihGdbFHIWvSUx/E60JopT/DBh1G+zt+4z1kpoRQnNICRNNVECrUCsk6JY4lLZsyi5DrZp4Mea0WZw2KWC6eJvv/4rhmFwl/uef1yPMxvYvl42JledOf8Ssd3bqxiGYtHSo5CxiXzhhIUNRBsslPFk5qKGTrPRZCwc1bgtI8nmqPhOhr4UnK0R+W66UniCFI2lfqgsh/4zoWcV9K0TMn+ua/wMR7kdCTKS/MIPbken8E+POQvjJDUyjrtnrJQygU8ADwV2AX9WSn1Xa33rhJv9DbhIa91SSr0ceB/wtOy6ttb6guM9x2xicrLjRwk7hhu0whStJflKkhTDNNgz5hMlKWGcYJuKnG2xoifPWFvWfrUWW6PeokdPyab02/2wdC3nLu0iZ5skWkMiVWa9HWEYsLDk4WWmvnPFXxJkNLlwHuqHgWxnrTlnlrphHUzW6OkgDoU30R7MJHKU6IiFDUgDbvv7Toolh2XdAdSr47o+7ZHMVsSUjlcai6ZPriyVJoYELtX5u8kyfb9Gf7/HRz77LN74kq9y+Yv/m7d/IuU+j73fP6Qh+KHwsGc+ktAP+fgbP8I1L76Sy//f27HsEyPVON33dicmVNsRYaxxM6uidpRQ82PaUYpjGWJ9ZJv0FHKEdkQ7ErJ+K1v4qbcStALXNHFMRTtOSVNNJW8z1ooIExhtBgSRSZik5G2Trpx0I+JU43B87h5aa373vV9zzj3PpVAuHvkOcwzb1+8CYPm6+SddsXt7lf5lJazp/r+1RqSoa45AfbcQ+C1PNrxJIGjJ8lEaCnfVq4i0Dql46FqmdP4NO+OaLYR8RXTFSCTBmlik5rrHu3CH85Scht/kn24Z4NatozztYafR133yGggz0RG7B7BJa71Fax0C1wKPn3gDrfV1WusOk/UPwPwrC6aJsVbIzpEme6sBe6ptdgy3GGmGbNpf55ZdVYYbPmHG06i1Y9qhrHijZMtocSXPit4ilZzNCk9jtmr0rl5HxbOJ05RmkBDplLxtiCJBkh4U2Dor67OdhAX1JtVdA/MyEQvaATs37mDNbBH1J8L2JHBEviRgfk2CWNCUgNfMNitr+2DfjTC8jfU3buf0tUWM9hCMbR2/XeTLtmRtQNr+licJXHtMSLO2OyEJQwKoV5LvbpGeZcv50FdfxZozl3DFy/+b6771f7P2ssxVPOYFj+dlV/0L//eD3/D+l19NEs/eskwHXZl8RcE1cW1FfzlHzlHkHBFhLbsmvQWbct7GMRXlvEtXwcKyTMJUtiYNU9EMYoabIcONkOF6gGMblD2LnrzLmgV58cxNNPV2TJSm1IKIOEkP6sodK2d16y2b2blxBw944oNn6mU5qdi+YRf9yxeQK86/wmX3tipLV05TST7ypVvvj2XG3RFUd0nHKw1FoNXJRpVOURKwnnXQu0wWjwoLRUHfNCXOWZ4Q+TElGbNzMgXoiLZ2ErIOXeNQ25GtEVk2ChqHXDpqtiO+/rNNrF5S5v53PblcvplIxJYCOyf8viu77FB4IfCjCb97Sqm/KKX+oJR6wqHupJR6SXa7vwwODh7XgU8Uqq2QkWZI04+Jk5R2mDDmR7TCGI2i7keMNAP2131MA9phShSngEZhUHSlWnUtk0reoZAp6re7FjHqR9TbIUMNH9dUVPIuOdsi0WpG18NnCoPrtwHMy43J7bdvI01S1s4WP2wiOgEk9kURv5n97VueBK72iPDDmkOQxoSNKlu3NThjtSFBxymKxIWbkyQuzbzbCplKdZBJV4R1sQjxKnIfL7MTSWJJ0OwCWA7lrgLv/8LLOeeuq7nqn/+TH33xuoPPG/n/8FIXT3jpk3nRFS/jV9++jg+84j0kyewnYwXXEn2vSFNrRzT8hDgFpQziVNNTcCm5NjlLoYEuzyFvmeK1nGpagXBzXNsg71oEaUorSFBA3jUpeBaeY9KVdyh4JmmiGW6GQHpA7xCOnbP6y2/+AtMyufhx95+R1+NkY8eGPfOSHxaGCfv3NqaXiEW+iKsGmbSEToUL5naDkRObInSm57UASksh3w2VxeB0iS6iV4JcL5hFyC+WuGNYQCL8sfaoJHc6k62AIyZYh/X2nYBvXreZZjvmWY8846SNJDs4qRZHSqlnAxcBE99NK7XWu5VSa4BfKKVu0lpvnnxfrfWngU8DXHTRRbO3CngIjLVC9td9au0IP0yohzFhlNLyEzQa0FTyJkMNTRCl5ByIdYJlO3i2hQY8y8QxwTEVrm1S3y/t7OF8HwQiChunmiiFKE6w57BI6uD6bGNyHiZinY3JNbMtGjkxgFhZtyr2MwPcQNa+nTwEY9LOTz02b01JEs0Za3LZ6ngqXS+rAK4rFWkcZXo+Q9C1JBNe9OX2ysw2lsYkkJruuCBjpvBfKHlc84VXcsXL/psPvOpTBO2QJ7zo4RIEJwa8yD0q8cQ7E578iqcRRxGfveq/sB2LV3/kDRjG7O5GGUoRJ6InZhgGfQWPcs6kFURYlklfSXwAjSDBswxqrRDXMgjCFMc0yBuQt03QIseTswyiVEuXS4NrWbi2SZJqPEuRaI09QWPxWDmraZpy3Td+zl0fcBGV3pn1+DsZSJKUHRt3c8H9zpntoxw19u2soVN95ESs896PGjJeTDwp9NJYDLxBCjQr46g6+YzDVZKRo5WTgrG2d0KypSRRi9si8GpY2RjTyuJYJhKr1LiP5VSG3ofz9kVud/u2UX53w14eeq/lLOs/+aPvmUjEdgMTVwuWZZcdBKXUQ4C3APfXWh+I1lrr3dn3LUqpXwIXAndIxOYy/ChhqBHQ9EVhH8API9pRSiuMSLWmK29T9FwMoBVC0TNZ0uVRcERfLAwSBnyfdmxRybk4YUxj+1Z0ZQFt5RK3IyzTIAiFGNuVt7FhRtfDZxKD67eR6ylTWNA920c5amy+aSP5UoFFK2dZAXtyAMn3SmBDCd+isFA4YoYt7X+3i/XbJAE+Y11mAJ60JLkKm5COZN5ttmjueEXwu8DLy5q5nZPr21UJmrYngc5yRX/MzgnXzLDxyh7v+sLrufKF/8bH3vTfBI0mT3vBvQ8+71RB8R8IT3/Ns4mjhC+877OYlsWrPvha1AlS5j4S4lRT8mzSNKUZGPTkHfw4EacOrcg5JmXPJe+abBioo1NJthaWHIbqIUGckGiFZWiCOMYzTRzXoODaKK3xQ02SaqJUk3NMCo6Faxv05D0KrnVcnNWbfvd3Bnfv54XveOkMvyonB3u3DRD60bwl6gMsOVwiNrFgtPOSVMXtjIsaSVcrDiXGxB0fymS80xVWhYjfHMvskAJo7oPiYmSTuwW+DV2rpJsG8rjtWvb4WXwxXRl5TkiwgMN7+wJxknLtjzewoMvjsfebncbBTHyC/xk4TSm1GknAng48c+INlFIXAp8CHqG13j/h8m6gpbUOlFILgPsiRP55hWYQi6K+ZWIZMWOtGNu06MpJd2Ko4TPUiNA6YUEpJ2oBlni8taOEkbpPI0pp+BHDNVjSlVLKO0R7tkPfMhp+RJCmVDwL21YEUYJrGplNiT3rfLCpMLRhOwtOWzVrHzzHg803CVF/tjsYd5SwcKQiDFuy7q2TrIulDiRp67fGdHW59J1+rnAsYkMCn+WC34bOZm3H4y1uAkUZP9b3Z5w0JSKwaSIJm+9KN8wtHmQA7rg27/jvV3PNyz/Bp6/8KkG9ynNe9fCD/88nB8V/MDzrDc8ljiKu/fAXsR2bl1/zyll5T3RGgjnHouCKYGvRNABNEmvpeLkm5ZzDgqLDvrEA2zFRvkkp52CGEU0/YddYQM6JWN1VwDGkO2aZBpaTkEsNCoai4Ml2dyln011wDywMNIL4mBKyn37lx+RLBe79yPuegFfmxOPAxuQ8HE3u2V5FGYpFyw6zMTlRQDWNJE5YjnTe3VKWhDVlktjZ0jbsTHlfSeesXYf6LuGqai1ffjXbHq9I4XlQXEmlU29M+FtKskQvN6n4tz3pzk/s1k/YqLzuL7vYN9zin59yHs4sfZYedyKmtY6VUq8AfgyYwGe01rcopd4F/EVr/V3g/UAR+FoWhHZorR8HnAV8SimVIny190zatpxXaAQRop2oCcKUBWUHbSiaUUycQCsC1QxZ3JWj5DmMtnxqfkQjSAijlCSF1DbY1/AJ2g3K1WH02fdCI+3tOE3J2xaLKx7deYe+sjcnk7A0ThjatIPzn/rw2T7KUSNJErbeupmHP+tRs32UQwQQRwKYTsYTLCsnAc4qsHHL7zn9LitQi8/NPNgSSd4agzC4XrgVdlH0xJSW7piVKWJb7vg6eXuCmbVhge6esrK0bIvLP/VKXMfkcx/5Me1myEsue+x4snEMOj53Jiil+KfLX0gUxnzjE1/Bsi1e/K6Xn/RkrJMMARQ9izBOSXRKkoBhSkes5se0gkQ68qbB9mGN1gl7x2LCWGNbBmacolMYDWJGmz6GUpTzNiXXIW8JxSLvGOQci5InReJYK7yDqOx0vW8b1Qa/+e6veMClD8LNuUe+wxzEttuFQj0fVfX3bK+xcHER2zEP3jqE8Z8NW/hbYVNiheVI4uR1S3wJm8JBdfLjm5B+Ldv41qJxWN8rj6FMGUkmCeQcQAkX1i2K9A5I58swpEsP444jaSxTgKk68BM30CdsTTZaET/47TbOXdvDXU5bcIJfzUNjRmZaWusfAj+cdNnbJ/z8kEPc7/+A+SeTPAkF1yKKY8ZaEaahcGyTnJsSRglxollQdKm2QwyUaNEZJvV2TNWP8cOEOErxo5ScmyVVWhPuFS0o3bcU01S4qYVjWiwoOvSVc+TnwGbkoTCydRdJENI3Dzcm927dg9/0546p8FQSFjqRzaK4Lfo8lge5HkK3j+1bhrnPo+4O5UVCyo9DsQ5xysIliyPZmuw0+5JIFPe714jHW21vxkOzMwsR5DGUc8gRo2kavP7j/4zrGnz1P6+j3Qp41buehOHk/mHHkhOhlOJFV7yUKAj55r9/DSfn8rzLX3jSz9GVd/CjhIJr0Vd0aYUxQ02fvGtRb0cEKiVJNa6lMJWBbUKtnTDUiGiHwlHVQM41SZsBuaoi79r0KPmA7Mo8LHO2STnr1B/Oem068etn1/4vQcvnMS94/BFvO1ex7fad9C9fQKGcn+2jHDX27KiKtdFE/qdfk25UR7MrqAtZPm7L77EjYq3FBRK3kkTiT1qVZaEklG3JsCHxJk2yZaEEsIVnlgTgZ8mckUnu5Huzblo22oyy52sEGZ/VlutaI1NzU22Pyd35//2/7QRhwhMfNLvxfu6Ri+Yh/CjBMg1sQxFrTc4ywDEZqAcMNXySVOFYksRblolhpmwbaqNUitKKMT9GKY0dG9i2Io4VxbG9ACR9yyhjYeZE3kIZBqbSc5IX1sH+zNpoPkpXbLmlo6g/RxIxGA8gQV1+V7aMDjvdMbcIXpltG/bLtucFp0uQ1NkHoDLEKqSwCOqDQAwJGdG/KWOA+h6oLJHnyvwmSUIJgKVFUDg8188wDF71oZeTLxe59uM/pB1o3viJVzA3S4WTD6UUL7v6FURByLUf+gKWZfHsN/7TST/HxOSnGcQoTBQJiYYwlm3vODUZrDVpRwkmBpWchUpTBhsBXZ6NiZiCD9dD+kshS7vyONb4yLE8gS5xLN6YHaRpyvc+813OvOhsTjv/9Bn4188Ott62i1VnnRyF9plEHCUM7K5zt3svHk/C4nBCwpVd1hqRDlgnHnU69YYp8cSsS1EY1LKOF5KEaSBoy+P5DSitlCUie4HEJK9LJCsK/dKxtzNv3E5xp1NJCkmzEWZufBlgGtzU4arPL/+6i3uft4glfSfWS/JImLuf5nMQU4mldiq+rrxDkkKcpqIyncjkJ00hThJMQ7aJeooWSQKRTkFDT86iK0kZrAf0lw0c08RzTHKj+6DcQ//CXkaaIYaStXJTiV/lXO2GgQi5mq5Dz6r514rfcvMmDNNgxRlz0JapMxKwHAk6IFWgnQcUG/+2AYC1ZyyS6/I9sO8W0fVRyFeuCPHCTGKiLo+ZmJKQtUehtFiCaBKA4YxvN01jxKiU4sXvfC75SonPXPUV/HbMW/7zVTjuP/Z4sgPDMHjlB19LkgiBX6cpz37T82ZsTHk0Ys5+lNCMYlphTJzJa9T9WETPNSQa0jQRX9yKS8k2SRXEiabi2RiWwlKQd0z8JKGQfZR03D06fLCj9caciL/+4s/s3ryTN3zy8qN4FeYWojBm58bd3OPB888NYGBPgyTRLF42oZOnJywQdbhhhiljQZWlE0kiSZedCbkWF0rhqEyR4Il9CEbES9IpivVR0BC+qpNth1sFEZp2umRzMt874TmzBCvfI8mYjqU4tZxJZzt8IvaD324DFI+5ZPY3+08lYtPEoXgOncrOyRSr94y22FcNCdMYpRULCg5RmmKaCkMp8raBQlSswygFpVjclaMrb5A3bfIFC4WJ3r8Tb8mKA56TrqnwXAtLKXKOdcwK1ScDQxu207t2OcYcltc4FDbftIkVZ6zCcafHYTmpmMgZ88oQZyvipg3tUTb97XbyBYcllXambl2HsR2ZCn9VulxxJEEr9gFDOB2FBaLfk0ZSPXTQqWxRcl3EtEaNz3rtpeQKHp+4/HO8/Tkf4J2fex1ubg6+nrMAwzB49UfeAErxxQ98niRN+afLXnDcydih4tOhkrORRkAQiexEtZ2Ix2SiMQ3E2ihJxW40SXENk3LBpjtJaIUxidZUWzHdeYcoSXEtE9s08CwDDVTb4x/WrmWIBMaEs01XxuJrH7uWBUv6uOQJDziu12Y2sWPjbuIoYd15q2b7KEeNjsfkslXdZIQuiR0ddIoz0xaZnI7dWtSG8lIp4uJIErIklpGQyoq+JJaumD8mNItcNyRNwJQty1xXJlORSozrCLdOLgiNQ6QwRygcB0fb/OHGfdz/bkvpKc8+feJUIjYNHI7nMLGycy2DMNFYJqBMgiihEcX0lzwcS+GYwuvKuwaNIKJlJjgGjDR9wjglchUD+wM8HbK8OkTzrHuQ1iPCNMVUCjvVdBcdHMs8ZoXqk4GhjdtZPQ+NeUESsbs+4G6zfYxDYyJnLLOQob4PkoBNtw+w9oyFGGmQmeiOScXYrmaCr1o6XXYBKqvAH5af8z0S0AxDAphTHN+AikOpNDsijdPUBnviSx+Jm3f58Gv+k8ue/h7e/YU3kC/NP8/REwHDMHj1h1+PaRhc+6EvkMYJz3/bi485GTtUfNpfa6NRB92uk5wlWuJHolNcS8y6x9oBSWpgmwaGgoaf0o5jnEjkd5Z15dlb9dFa41gW3QWLdgT1dkjRsbL7H9yxD+L0wGb30Vivrb/+Nm783Q28+F0vx3bmb0d1803bAVh77qrZPcgxYPf2KkrBotULIK1JTOh043U6nhzZ+UyL0Ia0Kd0rtyB+kaPbs6RIyxa3XxV+qkbuY3siVVFaCmZeeGWmI100UhlPuvnx4tP2xuNf0JQHiiNIGpkTSHlaHpM/+t02TFPxiPusOKGv4XRxKhGbBg7Hcyi61oFA2AqltV/JOTSCiJFWSBRpRtshSyo5lFKkWtPwE6I4kbZ+nDLUCAmjlHKsiRJNe89WlgPV4kLsOKErZ1HO21iGcaD1f6wK1ScareExWsNjLDhtDo72jgCtNe/4n3fjeHO8e9PhjEW+CK/6VRK/xZb1Azzy0vPkcp1mna9wnHwfVcctQ4y8jAGsvIwUwgbEcZZ8kYkjKiHXMuH1OAptsEc/50F4OYf3/PMneeOTruLqr7yZcvf88wk8EeiMKQ3T5Ksf/TJxFB/zNuVU8cmPElKtyTvjIb5TPMapxrFMFOI/CeA6Fosdk2aQkCaaJE2xLYVlWDi2iWkounKOOIZEiXiBGIqGHzPWjAnjFo6tcEyDkucQxAmVnCR9CuGNFY+C1/rVj36ZYqXII5/zmKN+PeYSNt20FcezWbZ20Wwf5aixZ3uVBYuKuJ4FTCwAM75oZwMxjSUOkYrItOUKdytsynZ2a0QSL7sg3S2Qbld+ATTEPYbEB69P4pabyel4PVBZKuNQtygFYGdpIA4lsetoh3UKRzt3sPH3FBip+fzhpgEecNFSKsW5sYl7KhGbBo7Ec+hUmVGc0FNwaIUxfpzQk3Np2zGLii4K8CyFH8sWUpRoWmGEVhpDQZRqBuo+tqFYNLIHgN32AvrCmK68TZxqPFvsSEqTeBhzaUTZIerPx41JpRRn3PXM2T7G9NAZPdb3QWuQPdur+H7MaavczPy7KLZGzgSBxTQCp1fkLAwn0/jxZUsJBaNbRaOsuEACnJOXwDYZHVsknemNHSYpe/CTL8bLe1z5wo/w2se9i/d9/XJ6+rtO2Msyn2AYBq94/6sxbZNv/vvXiKP4mHTGpopPSaqxzTte3ulKgWiK2WZEECegISKh6UekKkWnGlcpEkvhWAaGoWhHMZHWFD2LREMUQ9OP8KMINChl0fDDzNLIZKjuS8KmoNqevmzFttu38rvv/4ZnvO455Evzb9NwIm776yZOP38N5jykaezelm1MdnCHrcNOdyqRTplSQn8IakIyjH0RXcVGqrtEuvJhW4iIhpKxpOvKVqUiI+j3Cl3CcgAtCZxTPFg4tsNV62iHWS7gHqwrdgj89m970FrzoLvPHV23WVasnB/wbPNAJ6qDyTwHzzYpeTa2pTANMdNdUHZYvSBP3jPRSOK0a8wnTROU0sRaE0carSUJsAzFSCukUt+Ln+/Ctz3G2hGtKCbKrGo80yCIEqrtiGYQU21HjLXCk/yKHBr7b9sCQN8Zs0+AvNMi8kUXrDEga99JxKZbJXlfuzqfrXpXMhHWErhlsREpLYPyYmn9k4khxtmoMglls6S6A/ZvlO8dvsdEtMdE86e6C2o7YWTr1P5uE3DfR13EVV9+E3u3DfDqx17BwK6hE/O6zEMopXj51a/kiS9/Ct/9f9/iY6//MGmaHvmOEzBVfCo45kEejx10CjfXMkh0SpRo0lQ0EHcMtbhtoMHGfU32VAN219o0/Jh6K878bDUV18z8cRVJmlBwLFAG2pD4liQaP0wI4oRWlODZxoFzdDpyR8K1H/oCuUKOS1/6pKN6HeYawiBi443bOPvup832UY4acZyyd1f9yNZGaSRJkOlmmoS5bHMSIAXTAJJsc7sJqS/xyTDBcKGyHLyFkOuB8jLoXQddK8aJ953Ht73xAjCoZ6PQaPw2HRyBGxYnKb+9YS/nrutlQdfcoUqcSsSmia68QyVnU3BFzX5yZTfWCvFjMbhVhogfllwL2zBIUhio+wzWfZrtiOFWRLUlStU1X0aUpClRpCk4Nl31fbS6llDxHHK2SZpqmn5CECXEWjPYCKm2x5Ov6Qa4k4H9t26msnwRXnl214Hv1AgzzZ40M3t3S2za2sSyDFaeu1Y0fHJdmb5Yl5jqdq+E0kIZGRjI/VujkAYibRE1pVoNapC0JWgGtcwrrrO6HmRishNM5pNAHvMIJt93e8B5vO8blzM2WOPVj76CXZv3nohXZl5CKcWL3/Vynvqvz+SHn/seH3nNB47aKHxyfOqv5A5bPHblHXryLgXXwDSkI19rJ4RxQsdMWRkGaEVKSpKkbBtqMdKMcCyTHs9mdV8R21YUHIsk1YRxQpRqCo5JzjboKdiUvIM/GI/Ebd25cQe//vYvecwLHk+5Z/75Sk7E5pu2EQURZ91t/iViA7vrJHF65ESsk/jkuqSblesWmZziIsj3SacLnW03Ao0RSLMOfHswo0uYwgPzStJhdzwpDt0iFPrGR5JhQ9w/xnZIIZqEwl09sE1+ZG7YDeuHqDVDLrnr3NroPzWaPAocagQ4kSxbyTm4lslgvU2SKqJEM9QMaLRjmn6EbRnU2jG9BZuSZzLajOkuuOQtk1aUYrabeO0q1XX3oL/ioVTmJakVxZx1IJCFsT5oc3KukPf33751Xhp9zyvojmr9+Nt38+YaK1f3YJd6ZaQI0r6387DgdBFtDRrZ5pIlfLAk45hhQXsfmEPSFTMcqWBbI9JJwwCnBK4tSViHuN9BGk1rXfyce5zBB779Nt78lKt5zWPfyfu+8RZWz0N9pRMBpRTPf+uLsG2LL37g80R+yOs/cdlRjbQmx6fDbU2CCFH3FDyiGIYaIYnWaCBKU3KOiZNJ5lgK9tV9bMPEj0JSRMTXdgyUgv0NH6VFms7LFokWF9yDLWkyTB6jTj7fZ971aby8x6Nf8qQ5Sb04Glz/65sBOPdeZ8zySY4eu7aOAbBs1aREbLI6/cRNbr8qBZxbktiURKL/hRYLo6AmRV/iyvXKhKAlnXqvS0Re0wnxxatIgjdxJNnZ3kxC6fRbXhabDk+R6ODX1++mt+JxzpojLxydTJzqiB0n/Cih1o7u0JHK2TaOBX4UkyYKna2Cj7Vi/DhlpBWSojltUZGi69BX8egrOpRbA/K43YsJk4SiY+KYBpahyDvWQYEsmZB8zQXyfthsU925j4WnxpInFm5BRgGZlYjWmo0bR1l3zpLs8s42UzGzQHIk0CklI4Gwka2PNzMfuGrGI8uMv4NapuKvwR8RQ96wLvdRU7T+OzYn08Dp56/mQ999B0opXvPYd7L+b5tn7GWZ71BK8Zw3P5/nvfVFXPeNn3P1i95JGBwf7cCzTYqHcOHwbJOCY2IZwgPrztsUbCtbKIoxlMIxoB1rDMCxFa5jYigRdDUy/lcjjKi2IzxTUfRMPNugkndQaFphnHXZDtYY86OEsVZ4EMXidz//M7//0e94/L88HVUozknqxdHgL9fdyLq7rKJnYddsH+WosWvrGIahWDyxI9YaEa3BoCHfWyOSJJm2jA2jtvyss8/CfDeUl0P36dLVyvcIN6y6E0Y3i+SFaUnc0QqIpMOOysRZJ2xIwjhp362IpZtbyIReD+36MRF7Bpts2DHGJXddgjEHPi8n4lQidhzoBJIgTqj7MdW2jAyHGgFjrZCRRggoLAOCJCXWiqJn0Vu06cq7FByHSs7FtWS8mChFb1v4M10rV1OwTSp5B8cWodfRllQFni3/bWb2xzQ5wM0WBjdsA6DvzFWzdoZjgdZzo5s4bdgelPoluSovZiTsZmykzbqLzoOe1RKsct1ic9TZYNKxcMPiJJOpMOW7k1WUpX4ZaTqZibjjyfhSKUBnwVCPe1t2YLrja+XTxKozl/GRH1xBoZTj9U94Nzf+/raZfoXmNZ7+6mfxsqtfwe++/xve8czLadVbJ+y5+is5+soOfSWXpd15lvZ4dOVkvJmzTYJEE8eaKJV4k3dMco5JHCe0owTbUkLV0RAmmq6cS5zC+r1VRpoRcSI8NLL3WCfxGqj5DDXGPVTjKOZzV/wHC5Yu5EHPO9jOaC5RL6aLZq3FLX/awN0fOP+EXAF2bavSv6yE42QJ/EFdqQyNQVkWag5Da1CKNzMryFrD0iFTiD5YVMs4ZK7EJrsgSVjQAK8oYq7tEeF/ddT722MHe1t2ikDLloKxs+2dTu9v41fX78YyDe57/uLjfn1mGqcSsaOEHyU0gpjqBAFFx5IqsOnH7BsTgqttyks71AgouAYFxyLvGFjKoL+Uo5K3idIEP0woZma2Y+0I9m0nLfdSqlRYWMmRdxS2aZLohNFmzP66vBkWlhx6iy6VTEtqLpD3Bzsbk/OsI3ayDZhnBPkesR4qLWLTLvk7XHf+OkmI3NJ4YmRnyvhOAcpLRK06iSUIoiSYpaEENFQW5AoyurQKEgSDmmxTglSh3Suhskyq3Z7V09IVm4wlq/r5yPevYMHibt78lGv48y/+PjOvy50ET3jJk3j9J97M33/7N970hNcwNjh6wp5rUSXPyt4CK3sLnNlf4cKVPSzv9ljVm2dxxSVnG/TmbSzDpOjZ5C2LimeR6pRmK6Xuhww3Q5I0ZaThs3Ggxq7RFgM1n+FmSJym+HFK3R8nVSepxo/SA92y733iWnbctoVnvOUlU4opzxXqxXTxux/+hSROuNfD56ee4u5t1YP5YRMJ8TCeLHVoCcrKvG8zEn3sj18fB6JlGLYyBX41npC5ZdmijEJojsomeIeDmmScVNsb7+x3isA4yB4/lU7cERaG/CDmjzft425n9VGcpuH8ycSpROwoMLGVPtwMDwosJc/GyTYmKzmbvGvRU3DJOxZKGVRypgi6egamYZCzLSqejWUpmn6MaRh0ezb24A7MpWvAULTChDCGejsm0dJZ8zIV63LOOaDLcyix2ZONwfXb8CpFiv29J/25jxZRGDG4Z5BffesX/Opbv+D2v95KbaQ628c6OmRJ1+bbZGNyzblTaLd1to4c8aOksEA2maxMyNUtSKtfmUKWdYqyteTkJWFLIhkRoCX4dbghhQVQ7D0uU+++pb186HvvYPm6Jbz1me/jN9/70zE/1p0RD3naw3nH/7ybHRu285pHvYI9W3cf1f07ReOhYkHneuBAzLItgyhOMU2LepiiM0pE3jbpL5sULYOFFZe8Z7N1qMX2sRbtMMFSmmYYU2v7tMP4AI0xiFKavhiGT0ymOt38ONVs/vt6vvuJL3OfJzyIix9zvynPOheoF0eDH3/5VyxZ3c8595h/HplhmLB/b+PgRGwy9UB3Eq5IkiHLljgxcZlHudIti3zpYIVNGVs6OSgtESK+W5LOfFSXGDS2A2pCz8HKjctR5HskcassERu2Qq8Ygntlub6jb3gI/OmWAfww4f53m1sk/Q5OJWLTxGT16iRNGWmG1PxQfNuCGK2ldW9l3TA/TsjZpph9GwYLSjZl10EZmornUHRtlFJESYppQDGooVp14oUrsZD7TBDGJk45YK7bCWrHY6o70xhcv5W+M1bN+Q5TbaTKJ9/8Ud757Lew9batbLhhAz+79sd8+1PfYM+Wo/uwm1VEPgR1Nt+4hcUrF1IsT6G5NDGAtsckwXK6MrmKzBDVtsWSpLAg23QqSIVrZssAYSvjgwSHT7yy8xxpg3IiuvsqfPA7b+P0C9bwrhd8mJ9c++tp3/cfAfd82L255psfpDHW4LWPeiUbb1g/rftN5l9N7pJPvh6g5JooA3KuhQYGai1u3lWjEUWMBiFBpNEKolgz3Ahl7BglNIOUIEnROmWgJnI71eZ4AhhnY8mJyZRnmziWIgkCPv3a99O1sIcXXvkvVPLOEaWC5jr27djPDb+9hYc//f5zPhZOhYFddXSqWbKyPH5hpyvVQdjpeGWdsaAlBHwrN+5NqxJoDorwqlOUbcrKcuhaKZ10hXTpdTwuf1HsR6R1HEmyJsavTrffyUvX3prU2ZrctcugteaXf93N8v4iq5eUp7zNbONUIjZNTExsqu2QINakWrN3zGfPWItWmKDU+KJQECeEsabgmZRdhwXlHEu68izpyrGg6NFdcLAMM7MLMTENA7VPNLj04pUo08BRijjRB0j5nY4bjAe14zHVnUmkccLwph3zYiz5+x/9jtpIlY//4tM85vmP49HPeywPeurDUIbBNS+5kht/d8NsH/HImECc3XTTVtacOYH3MDEh6gTQOMjEDyMRbM11yWakkQdcMQZvj0Fjl2iItfaLnEUcZNdVxbNyZOsdzxL5MLZT+CITibzTRLFS4H1ffwsXXHwO7/2XT/Lt//zf43117lQ4++7n8MEffBTHdXj9Y/+V677x88Pe/nCWbFNd70cJI82QONHkbRNTGeRtgyRReJZiuBFiaBhshJiZXljdj3BNRd62CNMEwzCotmJMFGEMKTobV2ospejO31HKoq/o8rV3/jsD2/bw6o++iaWLZcR9JKmguY7//dKvUErx0KdfMttHuSOmUSzt2jYGwJLJ0hWdrpRpZzzUTG2/XYO4JT9HzUys1YTaPknQWsOSkNk29J0OfadBaQH0nSGxyC3KY3qV8U1MkkPLURxqMegQl2/eVWXPYJNL7rp0zibGp+QrpolOYuNHybgtiGlgKQUKXFtR9lyqbQlWaQqWUpiAaRk0wph2JnAYxClDTZ+cY2EZBpoUIwYGtqMdD7tvqZBhTchZBqahUGhKnotpqIMqRM827xBYZ6OCHNmyi9gPWXj22pP6vMeCdqPFgiV9ACxYLN+XrF7K2Xc/By/v8cef/IG73PeCWTzhETCBONtuBezeNsSDH3/XLBC2DibVdrwhdSqVpxVBbS+MbRceh07GNXsa+2WE0FHLjgJ5PMOB7mVCvtWpBOOOjUhH4d/POEwTLUemaYUEkCt6XPXlN3Lliz7Kx978WRrVFs963aVzNnCebKw4fSUf/em/8+7nX8F7X/putty8iee99UWY5h3f50fqkk8uKjvxLE5T4kSU84M4oZQzqfsmppnSCFNsU7OvFqCAsXaMaUBISsE1MU2Fa5l4noVnS/wxlCJvGyztzrGwnBOPy+y5C67F/37mW/zqmz/nuZe9gHs88KKDzjqfOmATkcQJP/rCddz9wefTv2zBbB/nYHTsgTo4hG/s9o2j2LbBkhVTaIjZnnSemsMST3QqkhQYYBuZbEUo7/2wJTqFOhbOaXNYFobsnCRerWFJ2oLmeDct3yvXFxYemns6UTKjg8NoiP3u73txHZO7n7PwKF6sk4tTidhRIElS2uE438IyFbZlSos+FuJpJefQCiKiREaTKRA2I8I0QSmFY5poDa0gJudAojVxklINIrr3bUP3ryBRijhJsS2NZRiUihZxklJ0DfrL3lHrBZ0MDNwqMgT98yARe/BTH8Y1L7mS1z3mVdz9wfdg2brlLFjSR9+Shdz2l1u5y33n+KbThBb81vV70Vqz5qylEtDSUIi0OhrfMop8GQ0ETajugcGNsuUUNjKybEvGBblsvbw9LD5wURNaY0ACuaIkZ0kkBuKGKde1R+Q+nXxpouXINLTFJsLxHK747Gt4/yv/g/++5qvUxhq87F3PxjBONe4Buvq6ueabH+BTb/k4X/vYtWy9dQtv+tRbKXWVDrrdkbrkUxWVADnbJElS4khLAagMKjmLoYZ0T5JU4YcJlmXgmErEqk1Nt+dQ8Axytk0pJxIYRU+4rCsXFFhYzjE2YbkJ4C+//Cuffvu/c59HXczTX/OsGX2dZhN//NkNDO0d4ZXvff5sH+VgTLX1eIhiafumEZat6cKyDvG+SxMZR4KMgJIoW/bJCr4kzoj2qVxuemAiY8WwJYlWcxj8sXH+atSSTW23JJIUpf7D/3vyE7wvO7zVKRCECdffNshFZy/Ec+ZuujN3TzaH0AkipmlgmlI5VnLSLt875tOOEoqeRapjFBGeI10sy1SEcUoz82LrKTo4FjimSVfOpu6LGn8QpYyN1egbHSBZdxd0qgnjlJ6iQ1fOxjQMTMPCMRW1dkQQJVTmWLt+/62bsXMe3auWzPZRjohSd5mrv/Z+/vTTP7D99m2sv/52fvf933DbX27lsS94Ag99+sNn+4iHx4QWfIeov/asJcL38mvjQRKkynSLEiyru2H4dmjul0SsQ8bPlWXzyM5I+U5JVPeTWEYOlgd+GzAk+OUWSDXbGpbb2Fl1ml8glW0aIb5v09MWmwjTMnnjJ15OsVLgG//+Q5rVFq/98IvnpVffiYDt2Lzi/a9hzbnr+OSbP8q/PODFvPhdL+fix15yoHt4pC555/pmMO6Q0LEi6ima5MKIONZ0F2xGWxFBLJ2yvCeFY8U26erK0Q5itEZcRBwLxzFQQCVvUcm7eLZBd8G9w1nW/+lmPvSiK1i2bgWv+8Sb71SJ9vc/9zN6+ru418MunO2jHIxD8KcmF0taa7ZtHOEe95+w+DM54TFM6XwnQZZ4RWDlx3k5aSS0h2AEyPwn3XLW7SrK/ZNIBKWTQNT3C31Q6Jdt7Mo0P0Pu4H15R9y0aZggSrjHuUdI7GYZpxKxI2ByECl7DlU9TnxN0TiWtOWDOCFOUtqBphlpRpshDT/GEgcjEi3f4xQaLR/btDAA24KF9b0oNCOVZVh+hE6BFMpZwrd7rCW86uwDqepHrOgRG6HJ1eZ0zXVnEvtv30LfWavFFmWOI2gHKKXoX7EI13MxTINFq5bQt6SPNE3n/gfDhNb85lt3Uyh5LFq1OPOOnOQNGbelCo3aUp1GgYwkDUuCp2kCdqYtZksQDeqQJJlFUp8Ey6QN7UxQsT0iIwe3KJywNBo38nUKUuXmj32j0jAM/uWaf6LYVeB/3v8NmvUWl3/qlTju0Sd2d1Y86p8ey5pz1/KRV3+Aq15wBWff81xeeuU/c8ZdzwKO3CXvyjsoQKkwE3Qdv7477xKlGk0BP0xotAPCRJO3LLSWjtqico7hZkCKwjIh79gkqaY771LJ21RyFiXPxrPNA5uZYZxw6+9v5BMveye9Sxbyti+/h0LpzmOFtnf7fv700xt41usuxbLn2EfrNHlVQwNNWo2IVatzh6Y62PlxCoNORc+wEz+iFpJWRNC9Bmq7ZRtSGeJ/q6zMIzKRjn1YB7s0ntCpmY29N20aopCzOW1514w+7kxjjv21zD1Mxbeo5BwsQ5GkmsUV0TUZa4YYiJL+/lpIO5RKMkg0UZJS8SyiOMVSCss0MICRhk+QpuRsk8LQDrQySBcup+TYlHMmjm0Sxgl+nBDGKcUJH0S1tmiZubZ5SGLuyRpRpknC4O3bOOeJDz4pz3c8GNy9n+/85zf51beu48yLzqJ7YQ/FcpEluwa4+NH3wyvMHSPYwyJrzW+6bS/rzl2JKvRKAtWpVDswXUmQopYkZKYllefQrUBLCLKVpZI4oaDdlqo1HhQif6qh3C9jTW1KB6w1KEGTTA8oqGccjZx0xbyurCKuTz02mMZIQSnF8978FEpdBT75ls/z1vr7eOfnXkeueOxyGXc2nHm3s/nEdf/JT770Iz53zWf414f9Mw988kN4/ltfxMJl/Ud8/1fyDppxDULTUFRyNqZpUPYchmuh8F9dmziIKbomBc/CMKEehhiGwaJKbtzJSEFXwSTv2HiWgWUa+FGCZSjqfsRNv/0bn3nVVfQs6eeV//1u+hbPMQ7VceIHn/s5SsGjn/ug2T7KHTFNXtWOW3cCsGKZLcs3cTAuEQGZflcoYq5BVfhgSQxeD1hWJq4aQGJCoUu65c0R+e5WJIb4I0BG6LfyWWfNFoV89FFxSw+HNNXcvHmYc9f2zjkl/ck4lYgdAYfiWxQyDS8N1PyQKE0Za4UM1nwGGwFDdbFt6Cra42MBDYZStLIkLUkTWn6KZxoYe7fSKvczGhuYrQjHVvRXLFxL7tdJwqJYjL9TnTLaMu+widTByZSvGNu+l6jts/DMNSftOY8V3/nPbxK0A/7n719h3459DOzYx84N2/nF137Kr775C17/icuo9M4Ps+HEcNhy224e/dwsATbscaL8REVqnUrwtB1JzNJIRFnDFpSWiq6P6Uh16o9lEherZaRgmBJcLRvZZMpLJZu2ZDvS65LRgmVl2kC94/wPL3sdDVNGD7Y3bcJwB0962aMolPJ88NWf4vWXXsnV176JSu/cXEGfDZiWySOf+xguufSBfO2jX+Ybn/wqv/nOL3nI0x7OU1/1DJasuaNu0sROGYChIFXyHSTm+VGC55jkXYu8ZWApCwxo+gldPTlMDFzboODYjAUhYVYMBpHEQaUc0o55OJq//+KP/Pfr3s+C5Yt46affRbG366S8PicLYRDxoy9ex70fcRELl87RBPNIvKrIZ/umUZQBy1YWIW1LRz32xqUi4hCihsQDVZHY0BqCuAat9rgAq2lJolZaCuVlWbKlhLhvWMIbs7XEHYXQHQrZ+PAouaWHwtY9NZrtmPPWzdH/jwmY4zOY2Ydnm4fUtfFsGUeONiMGGz67RtvEaYppGCRaM9wKGWvG2aZQih/HDDd9tg+12DnaJog1TT9h32gTe2gnrd4VmAo0KY0gYagZEMYxlqmI4oRGEFHzYwbrAXvHAqqt8MAq+WScTPmKDlF/4VlzPxEL/ZAVZwj/YdGKRZx/8QU85gWP5+qvvZ/Kgi5++71fzfIJp4/dm/cStEPWdoRcDyhQdwiw7Uy2Isq+ErETyVfA7c4U8pcKf8PJyZiyY/jtFoU8myoRTsx1y5gSlY0/LRlxooXrYXnjSVgjq6THdkB1l3DJ6gOyzn4owvBh8IhnPYArPvc6Nt+yg1c/+goGdg2diJdzXqNQKvC8t7yI//rj//CI5zyan3/tJ7zoXs/lPS+5kq23bjlwu4n6YR2bIccyyTsWjjXeXbcN6dyXXAvLNil4Jq0gxbFMRhuioh+mKfUwpJ7pkNmmQhmKuh+TanmcNEn42r99if/612tYvG4Fr/ivd7Nk6QIqOWfeqeUfDr/53h8ZG6rxuBc8ZLaPcnhMdt6YiDRi2+Y6i5fmcV1zQiE34fNFR6KC74+NK+VbnnS6tJa4kSZCVQgamVRGO5O42QoDt0Jtp0hehNn73nQzA+8s2TsGbulUWL9tFAWctbp7Rh7vROJUR2waOBTfwo8SXMukOy/Ee882aYUJlgKNki0knZBqh2orwTFl42i0HWIpg8hMKectrMG9GEmMvXy1cC4yjlIYasbaCSVPtijHmhF2tgBQ9ixM05RqVmvCODnA8zjZ8hX7b9uC6Tr0rl1+0p7zWPGAJz6IT731EwzvG+b8iy+g0tuF7dgUykUGduzjgU+a++PVDjbfsh1gPBGD8ao3aAqXI6hL58ows2TLggVnyPq4QoKmXxUD3qAtXI80lqq3OSrjRj+R8UTclsfRCKnWK4oav0buF9SzLaksoCYZl9J0JYCHoSR6UwoxHr4Cvu+jLuK9X7uMtz3r/fzrI9/Oe752OavOXDYTL+OdCguX9fOK972aZ77uuXzrP77G9z/zHX75zV9w1wdexMOf+1hOv/huGJnkRZJqqu2INNW4WWEJ0k3vKbrZRreHHybsq7UpehZRkpIAjTClJ29imyaeLQlad8HGUIqcDYYyGN4zyKde+z7W/+lmzn3Yfbn0LS/DKhUIYqFNzDe1/MPhu5/5KUvXLOKu9z9vto9y7DBstm+pc/ZdssTFcuW9qyYkRmkqSVhzMOOGBtJpLyyQOOA3hU/qFDJxVyeTuxiVIi0JMg9KMzPwLgotIo3kMQsLZ2QsCbBhxxhLFxYp5OY+t/RUIjZNTJXYdCq6cs6hNx8x1opJdYxhKvpLLkGYipWHFsPvOIa8MrCUScE1sU2FaUB+ZAcAaskaejyHnG1iGnIdSMDsLXnS6tdQ9OwDo9E41Qc4a24W3E62fMX+WzfTd8YqjHmw2Xb2Pc7lnV+8mu995jv86pvXYbs2pmVy659u4a4PvIjz7jPHpSsmYPPN2zEtk5VnTEpIOlo/9bp0p0DsRZQBbpckV1YmoFjdK9tOdl46W3FbqtuoLWOEKABiCJWMFHSSdcsc8bo8YDESyu3zPeN8tA50LMFc6ay6npSITbMCPv++Z/Ph71/Bm596Da9+9Du4+to3c/bdTzuWl+5Oj57+Hl74jpfy1H99Jt/7r2/zw89+l2ue/w56lyzk/k97OJc89eEk+QINP0ZrjZdogkx+pxNDNKKirwwD1zIYa6WUPBPbMkgSiFLN4opNMWdRsA1yri2+kmHKX773S7787k+RRDFPeMcrOOuh9yVWBtV2RBinVDIi/50BG2/cys1/XM9L57nUylhNMzYasnrtBDmUUr/EhjTKZCtCKbS0FppBGICpRLhVIZI2OpbYUOiV26chRGOShAVNSe7irChzilL4+aPSYU9jua7QfVgO6ZEQxSmbd1W534Vzf4sfTiVix4WJFd3i7gJ+nIIWVX3TMHHNmDBNcQ0T01JowDQNunIGQSrK/ElisHBkB7rcg1npQoWaSGtc0yDRUPdDSl6eMBbl/pyjUMq8wxkKrjUrgU2nKftv28JZj7n/SX/uY0EYhLg5j+e86Xns3zVAdWgM0zJ54Tteiu3M/cppIrbcsoMVpy2ZepswTYTzBZkRbzYSzC0QEq2yASWr4rEPjb1CqM33yAiznYL25OfIl05X9zroWiKB1DAO3nCyHLArWQKnJZh24JQmdMEyiYwD9zu0EONUWHvuSv7th+/kTU++mjc86d1c9aU3csHF50z7/v9oKHWVeObrnsPT/vWZ/Pr7v+F7n/kO3/zw//Ctf/siK847jbX3vpBz7393lp+1hjAGtB6Xt/Ajqn7IWCtkfzXGTyL8RoxS4k2plE3dE49KyzQIG01u+sWf+PXXf8KGP93MyvPP4PFvfwW6uxvXsXBNg1jrAwtLdxZ85WPfI1fweOSzHjDbRzkurL9xPwBrz1smxdZBiZAnHW8dSSKWK2Wb1YHoCsbD0mW3HDBy4zEgbkJtvxR5USgFYRqBmZMiz69lHpURYIjETn1ArI+KC4/IIT0Udg3UieKUdcvnB9/3VCJ2HJio1+NHCQvLOfpKLmPNiI0DNZJUMTISkbMTugsWyjDxw4TFlRyNKCZONF2ugbtvK/YZF+J4Dl0e5F0h+I80Q9phyt7RFp5jSeKWGqRaxpAKTZRoHFPPWnU5un0vYbNN/znrZuX5jwY7N+7gD//7f/zi6z+lMdZgzbnrWHH6Cs67z/n0r1w87xKxzTdv5/z7nj31lYYplWzYlEQr9jMTXSVJmOVI4Ewi6WApF1SmGWYiQTNuQ7FHuB5+HUhkNGFo2aZsDWfr62VJqHJdE373MlKuI0E9bEhCVl40ra3Jw2HJqn4+8v0reMMTr+Kyp72HKz73Wu75kDmm2zTHYFomD3zCA7jwYfdhx6Zd/PIbP+PG6/7EL/7jWn7xH9fS1d/LWfc+n3XnreOMc9di9vexN3UZbYYZcV8RtBWOZZCmino7Im62GNi+l3j3Hvb9/i+s/+31xFFM/4pFPOmyF3GvpzySKNXUg4QgSnFNg9xck3U4TuzZuo9fffv3PPlfHkOpqzjbxzku3HrDALm8zaqzF8FUibJhy/s+akoc6BDyLRuM7P3eEXcOG3J7wxDnDh1JXGjuBauQ+UU6kng5mdI+CJ2h0HuwbMYxbFFu2V0DYM3SU4nYPwS68g77a21SrbFNBZiYRsTCSp5iGBHECanWBImm4phYjkFv0aMYi+GyPbSHIGjTXLCK1I/pK7ksKNm0goRUp4y1IvanmnLOZknFo7coJFetU5TKeBZKMdYKZ8WTbeCWTQDzwtroSx/8H3oW9vChH3ycoO2z/m+3c8sfb+brH/8KOzfu4Ikvf8q8sdSpjtQZ2jvCuvNWTn0Dw5aKMmhIZ8wwpSr1x6TCtLKxQHtMRgYqhcaQBEJtCEdMayHrx6EkcH4b2Au5ihB+za4s0OYkCYv8jHuWkf1L/fL4UUPGEGTjjHwPx7sV1buomw9/7+288clX8/Znf4C3/ue/cr/H3uO4HvMfAV15B++slSx9wz9x6b8+i9ZIlRt/+Wf+ft2fufV3f+P33/7Fgds6hTxWMRPqVAamaaCBsN4kajTQybhsTnFBN/d9+qO46FH3Y9nZaxnzY9qJFuWUJMXOumE24pnboVbMd3zl49/HtC2e/LJHzfZRjhu3/m0fZ56/EHOqJCzyhcMVtTL3jdGMbJ/K72Fb3uftapZYdbhlWhaC0DK+7DldYozpiJB02BS6Q5BtemMKZ0xNEIaeikN6hGJuy+4aPWWXrpJ7h+vmIu4c74ZZhB8laBT5zD6hFcY0wphEa5IUPNum4UcYCmwTVvTkKDgmOdugHkQM33oTReA2q59kb5Xeqs3asIJjG9imSd6RYOZZYo0EIoGRYhx4Tjj52mEdDNyyGdN16Fkz94nTowPDPOyZjyBXzJEr5rjnw+7NPR92bwBe88hXsO4up3P+xRfM7iGniS03C1F/zTkrpr6B7UFbgVsAcxkMb4GkmRFmI2n9u8WsqvWhPjjO0Uh1JnMRinhrEgufrD0IqS+Xp1q6XvnezO5okiyF1VHW11IRd3CUHpSHQ6W3zAe//TYue9p7eNcLP8Jl//4vPOhJ9z3ux72zo7PxbbYUzsIeLnmqcMZcy8Dw29z4t43c9LcNbLxlC7VqkziOSeIU1wDQpE4Os1Qg31XGLhfpXryQFXc5k8XdOQzDYMtIiyiWwrTo2hhKkq+Sa5NzTCq5Owc/bGRgjB9/+Vc87OmX0Lto7m/mHQ57dlTZv6fBw554xh2vbI1I3GgNZQlSDCgp7II6OBUh4LdHJREDSMakOx5VMg3CQIq1RIObl+UgnWZd+URiSmKBDuVyGOeOTuaQHkECR2vNpp1jnLaia4ZenROPU4nYcWLyCrZlKGzLoOZLN8wyoLtgk7Ms+koO5ZyFoaDtp+yrtbC33UK91M/+xKGcpOwaC0ios7TLI+dYFF0TnenxxFpn3TCy7tvhz3IyMHDrJhaeuRpzHowcHvCkB/PTL/8Y0zQ5465n4eZc/GZbDNabbfpXLJrtI04bnY3JNeccoiMGkoTF3dIFs10gzgRZa1LBdsYBjb2ilJ8EonrdGhJVbLMiBuFuIRt1OsLpyFXkto39kmTlKlPLUuh08okEM6QTBFCsFHjv1y7nLc98H9e87ONorXnwky+ekce+s2PKbfC8w1n3PA9z1UrWPCJmuBEy0gqIkoQVXXnCRLN7rE2SphQ8m5afkPMMTNMkiDWGSgljWVJqhQmGMsg7Jr1FlyVd+Vnzwj0R+OanfkQSxTztFY+d7aMcN/76210A3O2+kzbfI1/EW9ujGc0hkM5YcWHWUR+F+v6ML2rJCLI1LB32VGeb00rih7Jlo7IVyZZla1i0xZQhXfhOwhU2hRYRtWWcObFom4Zn5uBom2oj5PRTidg/DiavYDuWSW/Bo9qMaEcp7TDFMhQlBwzDJEo0QZyyt+rTqrVYOrqLjUvvjh/GFDwLz5GNyShOKXmKnqJH3Q8J44zkaigc0xj39TrMWU400iRh/21bOPeJc1w7J8Mjnv1oDMPgi+//PK1Gi/7l/SxauZjhvUOcc49zWTSPErEtt+yge2GFnoVdh76RkXHB0hiCmgSrJJBxpeVmfDBTAmIcSmIVtuVvK/az7ciScEBML9uMbENjGPIRhJaMH5Nk6ufXh/h7nCGdoA7ypRxXX/sm3vLM9/Gel38CwzR44KX3mdHnuLNiqqTIMhSmEgmcRRWPnqKDbShKjkmMpuRZbBtpYilFOWdhmwaeq8g5JmmqqbZjNAaebZCkKY5h01twKd5JxpEAjVqL737mJ1zyuHuydM38iRuHwl9/t5M1Z/bS05cfvzDyoTEgscPI/k6SKCPht0Xc2SsLH0xpcFxom5IYWZ5wxQ7wUU2JPYYJnic8McuTJA9DeGFpIMtE5WXyu52Xr4mYhmfmhh1jAKcSsX8kTDbYFUsPWNadx0DRimP5e8TAVBLkqlFKw4/QW2/F0Jod5ZW0khSjGbKyJ0/ZsyjnHTxH0Y5iPMuk5Br0FBx6ii6ebd7BX/Jka4cBjGzZRdwO5gVRv4OHPfORPOyZj2Tnxh1svWUz9dE6D3zSQ1h73vz5NwBsuXUHa84+xFiyg46tiSbjVATC04j8TGSxBbUR6XoZJvgN4XyFAbSHACUBNqxDviC/B9mWk1+ToKl1xjGbIulyCxCpI9qqzARyBY+rvvhGLnv6e7j6pR/HsqxTnLFjhGka9BQdGn5MnKZYhiRVhlKEiaacc8l7FqOtAM826fY8co7CDzX1MKLaFhFqU9kUPRtlnvwi8UTju//1E5r1Nk975eNm+yjHjX27amxdP8JTX3TB+IWd8V/QlLGjlZOvqC2FXaKhUMo4qJmgM3bWOc9JkpbvzrTIzMxv0pLul7IkOdOhJG9JKo9tFqSwy2WUiY4G2cTu+TQ8MzfuGKNccOjvzU992zmIU4nYccKPEizTQAHNIMZQEGtFLYhpxTGpVgRxQisIsW2DomczWPeptSMW776Z0CthLFpFOQXLNMi7JpW8Q8E16Sm66BQ0mu68w8LyuA/ikUx9TwYGbhai/qJz54eWk9b6ABl/+WkrWH6aJDJpmh503VxHkqRsX7+Lx73gYUe+cb5HulxeCRr+hMSqB0gk0CqEMGt54iPZe3pm7D0kmkBuWUYOdk70gQxTNqTSUIK01pJc+dl6u7Ll+WCcvG+Yx6ULNB3kih5Xf/lNvOkpV3Pli/6NKz73Wu7ziLudsOe7s8IyFCXPRilNGMumpKEkETOMFD9K6c6J3mGXZ9NbcgnilE37G1TbEZZloFCkpORsRc4y71Qq+u2mz9f//Qfc4yEXcPoFc99N5Ej49f9uwTAU93nIKrmgM/7rFFBay3akmcsWcSryfu50z7uWCqeU7DNIB2J9FgXi3dMxD7dyQmMo98ryT4LwxgxP4oRti10aHJofdgTPTK01G7aPcfqKrnkTz+FUInZcmNiV8qOEKEkpeTatls9IPSRKwHMUGkUzUKA1tXaIZ5qooElp3yZGTr83K3tLYKQUXIel3Tl6Cw7FnHOQj6TOnmNiwjXbXIt9N2/EKebpXrl4Vs8xXSilCIMQxz14u/TPP/0jy09bMaUv31zEnq37CP2I1WdN08nALUBlOVhFaOyRhApDlO7DumxABi2wDLEziuOsGq5Bvh9cTzplbkX+EMU6IltTb0/ohulsfV1LgjY5WLqlOxxtppEv5bjmq5fxpiddxbue/2He+T+vOyVtcZTwbJP9dZ96OyFOU4JIU8xB3nHwbJM4CahHKaCIkpRaO0KjKbgWtgElzyZJJWFDS4etFcaYLTUrm90zjR98/udUh+s8+3WXzvZRjhtxnPLbn2zh/HsuoXtB1kFKo/Ft6qApo8fIB0aybewF0hULG6KEHzUk6fJ6xon8nY1Iy4FWVTiqpgOGK2NNp5hJ6rhin4aWRM+yJanrWLVNVbgdxjNzaMxntB5w+squE//izSBOJWLHiInjSBD1ez9KiWKfRpgQ65RGEGMaNjnbJnBS6n5MGGvCNGX5wK0onWKdeVcWducoOBbdeZNFlRxFb2oftrlWVe67eRP956xFzQM16e3rt3Hrn25hZGCYZrVBZUEXq85azT0fdm+qw2Osvcv8GU1uvXUnAKvOPEQiNjlIdUy3TVfMuduZdVEcSYcr1cL9aOzPTHvz0hFThowzMeTLdKFrkWxGxW1AyQZUUJNkzHIhRnSGgiYU+yeYBc/ctuSRUCznee/XL+f1l76bdzz3Q1z1pTdytwfMY+uZkww/SgjjlERr+dPQmjjWtIhI0Yy2QhphQr0VkgJ516LkmgSJZlE5h2VF1P2YKNFYholnGwd8LGdjs3sm4bcCrv3od7ng4nM45x5TbBjOM9zwh91UR3wueeTa8bgRtiQJSyJ5n7ulrDiLgVjGke2RzF+2TzawR3ZC3JJOuZ2TQq3Ql/G8crK442e+k6knSvpuUa43HblfeakkbE7+yN1z22OqhZ/120cB5tXGJJxKxI4ZnaTIjxKSVJOkKXGS0k5TLKUoOBZJKivcpgLXMil5NnGSEkcp7s2/haVr6VtzGhpNzrHoLth4jlSTMH0y/myMKOMgZGjDNu72T48/Kc93PBgbGuM/3/7vlLrLnHuv8+jp76U6PMbffvVXtt++jSe/4mnzyppk8y3bMQw1dUdsqtVuOyPVKkOMvL0eWT03bQmCjX2ZWr4NdhGUA/kFEiyDGvhFqKzMAnJbOmyOJ0T+XLcE3eagBFolunaEzSy4TuiWzuC25JFQrBQkGXvClbzt2e/nmq+8+dDit6dwEEabASPNEK1F1zMMUxp+TH/ZI4wShusBiU4Z82NMI+ONuSZhnFIPQkqug041jqnoqzgUJzg/zLVi8mjx7f/3Y0b3V3nHf79mto8yI/jF9zbS05fn/PM8KdBACqk4ErpCB2ang96WJZyoBSiJFx0XjzSSYoxULgtqEgMsT34OahKDoqZwwGJfYlMcgG3I/XQqCeAxds83bBd+2KJ5xA+DGUrElFKPAP4NGRL/P631eyZd7wKfB+4GDANP01pvy667DHghMjF+ldb6xzNxphMNy1BU27LN2EHTj0lJMyNcg3Zk0AoT2iQkiSa0EgzDwN78N4zGGMVHPgPLMVGGpq/kHUiiNCob8YwnXoci408m7ftRclLa/4O3byWNExadN/f5YTf939+Jgog3/cdbAIijmEa1wY712/jOf36Tr3/8Kzz1Vc+Y5VNOH1tu3s6ydUtwc5P+nyevdsehBD1lSNCLmnK544IqSqMrbIFaBG5LxFuDqlwfOzIC8FtZUM7849o1uY1bkorWq4jGUHP/uMm3XZDKVifZBlUmqjjD25JHQqWnxPu+8RZe+7h3cfkz3sv7vn75naKLcSIx1gqp+xGDNZ9GGGMbCsMwyGWxpxlGDLciTEPTDGNcwwBHRFsLroUB5B0T13JRSh2UhMH8Ju03ay2u/eh3ufuDz+e8e50528c5bgzsrnPLX/fxxOechaknbCMqO1PLzwOZWCumxJD6kJDwcUSiYnS7JHBJW/ihHQ1ClX03rUwWJ8ysjBJ5zDiWuJAmQodwPOm0G0puZ+ePunuutWbjjvnHDwMJxccFJcaHnwAeCZwNPEMpNbn0fCEwqrVeB3wYeG9237OBpwPnAI8APqkmGinOcRjZf3acpIw0A6rtiJofM9aOiFItht62QW/BJu+atMIYkgj3hutQCxbTc84FlPMWPQXvDklWwbOp5MTcu5Kzp0yuJo9HYVzY9URj380bAebFxmTv4gVUeivc/PsbCdoBlm3RtaCLu9z3Ai55/AO47c+3zPYRjwqbbt7O2nOn0A+buNrt17KOVgNqeyRJ87rAKcj3Qg8EbRkxRA3xi7NcSbhaYxJ0WzXZeHIKwgHZvx6iWlYpZ4KuICMJK6tA41A6bJ0/y6CWBeETsy15JHT3VfjAt95K76JuLnvqe7j9+s0n/QzzBZ14EsZCo/CjlNFWxEgjoNYKqbYC4kTjWQZG1rEP0pQ0San6MUkiDiCeZbC8p0DZs2iFMWEs8Wg2NrtnEl//9x9QH23w/MueOttHOXZEmQhr5HPd9zdhGIpLHjpZjFtLZyrIirCwLST7Dncryky8hzfD6FYY2SwFWpRI1yxuSEywbSHjF/thwZmiTVhcKFxTBZCOS1vUB2BsF4ztlMcd3nLU/7TB0Taj9WDejSVhZjpi9wA2aa23ACilrgUeD9w64TaPB67Ifv468HElKevjgWu11gGwVSm1KXu838/AuU4o4lRT8mzqfotqVkWGCZhKkctlnbAwpb/kkHNtgjggiFPU9b+E0f3knvxyDFOhU00yhfDldMaMh2rzn4z2/96/b6C4sJfSogUn/LmOF2dddDbr734On73qv1i4vJ/uhT14eY8kjtm1eRf3eOi9ZvuI00Z9rMH+XUM8/oVTbEx2Ok4dnZ8DUFKV2jkhyXZuk0bSxfKrIvraGpNul1fJzHgdqWidsgRvvwrpYrk8qMnj+ZnGkFuWpC6sy3p6e1ies2c1B5l8zwJ6F3XzwW+9jdc89p28+SlX84Fvv411562a1TPNJXSoDUGUEMYJfpzSlXdohjGNIKLaDLFMxWAjJElTXMuk6LlYpmKsGdKKEkqJBjS2aRKlmr3VtmxZKnXAD3c+E/VH9o/x1U98n/s99h6cceHct3ObEhNoC0GQ8OsfbeRu91lEd7cNUSy36ZD0m8OScOlUxpJpIi4aSZaYNWuAksItbEhHLFGZ6bclZH2dyFQn1y0bk80c+Fkx1q5CsU/0xFojomVoTEhHGgPQXiKJ3DRx+zYZrZ6xav65HMwEMWYpsHPC77uyy6a8jdY6BqpA7zTvC4BS6iVKqb8opf4yODg4A8c+PliGYrjhU2vHRCmMtmP213yiNCWKUww0edvCzbhiOcekO6pj/OWnWGdciLHqHEabEWGsqTUjdo00j7pyPFSb/2S0//fdtIFF559+wp9nJqCU4tKXPZkrvngVD3ryQ1i6Zim5Qo5WvcUjnvUoHvasR872EaeNzZm10ZQek7aXdbUmdMZMd1wUcWLHrD2amYGHYrzb3A9xUzwhDQvcnARZw4L6ThlraiCsSTXbHoXmiFwfNESYMW6JXhBanisNJUmz3HGy/kRMqM5PNPqW9vKBb7+VXMHjjU+6iq237Tzynf4BMNYKqbYjmkFMI4gZa4WgZUk2Z4kVUiXv4ljy/9qOUhxL0ZWzWdFTpL+SZ9WCPMt7Ciws5xis++wdbbN7tMn+qk+1HYgLiFInpVN/ovCFD3yT0I944VufPttHOTa0x6RQioU+8IdfbKPZiHnIQ3qFQB9ntIYkEF2wqCUUBcsS+kJzX1aY2bLAY+fAMiUxA9EgtN2s2CuIg0e7Jv61fk3iQXmxKOrnumHRuZnlUUtuGzTkDJDFsEQKv6PA7dtG6Sq59PfkjnzjOYZ5Q9bXWn8a+DTARRddNCcYn2lm/qiAOBnXoVIGGMrENQ0spUgV6DRF/+KrKMPEe8iTqIURRUxaGtpRQpykaDTLuwvTrhwni8nCyWn/t4bHqO4a4C5Pe8QJfZ6ZRrFS5KIH3wOtNVrreUXQ72DzAY/JQ1gb5XsyyxCdKetn/Cwrd3DHTBnZuLAgyZdWwvtKUwmKGEK4t/Oyoq6QRMwtZ520ODMG75XgXN0t93ULsqLuZttQsQ9k2kATyfq1fXL/jvL2JL+4E4FFKxbygW9LZ+z1l76bD33n7aw8Y35IlpwITI4dnm2yv5pQDxK0hkYYoTVU8jaWkoKmYJtYmSl0OWfhWjlsSz5Ggjih2opJ0ehUE6URjmUQJRygWcxH7Nq8l+9/7uc85rkPZvm6JbN9nKNHp+MUCkdUY/CzH+9n2VKHM9borKttyHvRLQrPy5ngD9sh7QcN4XOZtohAWwWwahCMSTFlOhlxf0x8bU1LOF+xL7En1yWFmk6lAGuPyn2cfCb0quQxnSxmqel3UNNU9MPOXdc77/hhMDMdsd3AxPWtZdllU95GKWUBFYS0P537zknEqaaScyh7Fp6t6C+5lHM2SaqxDIOKZ7Oo4rKg5NJTcLB//130jvWUH/okyJdRaBp+QiOQlrBlGmgU/lFyvLryzhG5ZDONPTfcDsDi8+cn8VkpNS+TMIBNN207srVRrkvGCJ0kDGQMUFqU2RZlJHvDBpKsknUzcq0jCVLqS9C1HLnMLUpwthwJpIYFGMItazfALsnluV7oWSn3gXEeGYwngrV9wiMLGsJj82tTd8xOAJauWcQHv/02DEPx+kuvZOfGPSf8OecqJlMYwjgh59qUPJPugsOSrhxdeQcbiDVEiSYBLFNRztv0FV2WdBdwLPngC6MEP07IWTKelMdM8cMEP0pJkoOXihpBPC+6ZJ++4ks4rs1z3vCk2T7K0SHyxY7Mrx+0KLP5tiF27PB58CUuqjUC1Z3C0YrbYlemDOl2d7QATRvxi7SE/6UMKPZCviKxxC5A7wT5CzsnxV5zCNr1rCjM/Ca7lwvfjFSKOq8sj+EWsw5Gdki3AoXpjxhv3zZKox1x3rreGX0JTxZm4tPoz8BpSqnVSikHId9/d9Jtvgv8U/bzk4FfaK11dvnTlVKuUmo1cBrwpxk40wmHZSgcy6SUsyk4YuWxqOyxqrfAsu4c/RWPFb1F+koe8fW/pvXHX9B1n4fgXnAxGIpUK2pBRCMYHxdZSpGk+qg5Xp5tUnStk0aE3X39bZiOPS+I+nc2bL55+/T4TfkeGQG4Rfme75HRpVuSYOlnq+rKlusqyyTgRi1YeA7kF8plTs+4mGt5mQTN0mJJ5NqjMLQBRjdDfZcIMka+cEsMS57LK0swTzMOSuQLj2wi4vY4Z+0kYPlpS3j/N99KmqS87glXsnvLvpPyvHMNkykMrTCmFcZ05R36yx5LuvJ0Fyy0IcKtALapcAwT04Cca+NaBpWcQ8mzMAwouyY528SzDo5FjqUws07axHFotR3JOHSO4m+/uYXf/fDPPOPVj6env2u2jzN9tEbk/emPyHs9DgAFYYuf/2IMz4N73ys/3u2K/YyGsBeGNwkFYXgL1AclIfO6JYEqLZKY0bVCSPjFxdBzmnTOk5bEGJ1KIdgp5NJYumwH1PJNiScdjUHLllhTXCyP37UCFqw9quWeP9y0j7xncZfT5j5neSocd69Yax0rpV4B/BiRr/iM1voWpdS7gL9orb8L/BfwPxkZfwRJ1shu91WE2B8D/6K1nvslEuNjQTwJRrYlxNTeovzxdEaE+2/4M9u++T/03uUieh71NBphSsl0iKKUZpAlXklKwbNQShElCUmSzrp90eGw52+3sejc07CckytH8I+OKIzZvn4Xd3/Q+dO7w1Sih5GPqFi70olyPNAl0RbL92bdsrJ0tpK2CLMaSpI0y5QRpV2USjsJhOwft7N1dzMbSVriQ2nlZAQBYJWyNfd4ahkLHZ1UeYtVZy7jA99+G6993Lt43ePfxYe++3aWrJ7/5s1Hg4nUhmo7pB2mpFoTxJooiYjTFJ0qego2NV+RpCk52yLRmpFmRNGJ6K/kREsxSVlQckk1tMKEFE05s0laWHYp5xwsQx1203uuxbk4ivnEZZ9l0Yo+nvzyR8/2caaPiTI2nfdUYwDsHI2WwZ//6nPJfYvkCjkpvHQiyVVQE76YUpJk5ReOL/nYOXmfhy0ghdaodLsMAyqLIe4TXmmqRNYiaMl16CwRi8YTKyOjI1i58aUitwiVbtnmPkorND+I+dv6Qe593iJsa35OOmZkaK+1/iHww0mXvX3Czz7wlEPc9yrgqpk4x8nGuN+jRV9J/nDiVJMkKaZpsPv6P3Pb//sIlVXrOP/Fr2SgmUAoQain6JF3TFpRim0qDMTgO++6DNR9DKUOWBydLG2w6SBqB+y/bQt3e94TZvsox4S/Xvdn9m7dw6Oe99h5N57cvn4XcZSw9ng2/iZ2nQxLgq5bEv5XrizJVJJZHNkZt8vOumg6FUmLxqj00oNGFqiLGVEylqDrFuW2zYHMEskW7kiuSy4HSQSTCZpndvGky1usPms5H/jWW3ndE67k1Y95Jx/89ttYfto85AAdB7ryDtVWSMs06C2K+v1IQ7bAUyBnKRzLouQqBhsBjqUxUeRt8wABP4gSgqxjVvIsDJViGgrHUvQWJQnrFKbDjYBWGB+YKHQwF4Vev/rx77P1tp288/Ovu6Nm31zGxPe45SJb0yGYDr/6bYMohgc9fKG8T+O2mG6bWXIVNoSCYDpCxDdUpgfoC/E+qAIWqETI+MoQcejyUigsEUqD5WVxIS8Fnp1xUTvOGh2/SMtDRFyVxIbysRVCf719kChOudd587eQmp/syTmEyVVcR2C1dtP1bP/cx8ktWcGFr3ozpuPSoxKxC8k6XX0lN+NIpLRD2awEqPsxoHGtZM5Zgwzcsok0Tlhywfzkh/3s2h9z0+9v5DEvmPuOAJOx6cZtAJx2LIlYh7+RJuPbUU5eEqPqHjJTQLElaVeBFJQLFCTQWpZ00Op7wMkJ7yP0ZTPSKYuAYxoLGbc1KBW2kW1QYmeijpm4axpL4I0DOZNTOuYgfLxYe+5KPvTdt/OGJ17Fax73Tt7/zbdO38PzTgLTNMg74x8FtqlwbYWpDEo5h0YQYRkGJdfCNg16Cg4LssJzpBHgxylD9YB2lODaBq5pkHdsFhRdSlknzLNNxlohjSCmGcjQw7PTA8XmXBN63bFhN59//ze45HH35OJH3322j3N0mNxZdvIQV0jsAj/7+S7OOreLZau6JcEybDA7moCZ/Vk2whS1fJ11sS2RuAlqEkNMV7pebj7bmByTbnrv6VJ4hW3ptoN0vixnCmcNLY/b6ZAdI/7v73tZ1Jtn9dLyMT/GbGN+tQTmODpt9+E//optn/0o3qKlrHrR60mySt+zxeYo71gHqsFKzqa3mKO7IIa6SaoJ4uQAX6ODuVIxHiDq32V+JmIb/76B0+aJ7MZkbLp5G17BZenao0xaOnyRoCGjh6g1fp3pQqFXRg+GJRVuriSjxUJJxow6lfV3vyrJlTIlcLvFzNLIlM3L8hIh2GollbZGRhV+XcYYYfa8hT7hrRV6xYx8YhJ2EiUtOlhz9go+/N23YyiD1z7uXWy8cetJe+65gIlJUJxqbMuk4Ng4WeFXdG1KnkE5b7O4y6OvLPHMj6SwTHRKOyPdB5F0xpSS5K7DXe3ERs82D5D7/SgljJM5J/SapikffM2n8XIOr3zP82f7OEePjoxNB4YNXpnr/x4xMhzysMcsE/qBW5H3YK5X1PIri6B7lRRZ7RGhJaCEoB81JKEy8xJDLFfe+1Eo3bb63ixJC+V2hWx7u2Ov1jkHjI9OLVeSt0PJ20wD+4ZbbN5V5T7nL56X25IdnErEZhBRFLPnO19k99f/m+K6s1nzsjdj5QsHJVFTbTlODITtMKbhx7QzQdhqW4isc6Vi3P3XW+lZvZRc9/yrPlr1Frs372LtPLBlmgqbbtrGmrNXHN1IdbLtEWT2IYXMoqg4Iai2Zc098oU0m+8Tgq4G0NlGVZaIuUUJtvlusJ1MtsKQcYady4QfW+APQ30fNPaKVllQl/FH0LwjF2Riwtgeld9PElacvpQPf/8deDmH1z/h3f9QCvyebeJm3JpOnCl4FiVvvEtWyjn05G1Mwziw6Whn40XLMA4kVwCx1geR8+HgQrJD7s87JnnHmjO0iw6+/skfcPMf1vPydz93fhH0J2Lisk5pERT7+MkPdtHX73HBBWXpcqXJ+PgxieT937UMeteJZE1lKRS6pMhCZx3sQLpkzTEgkc1HZYzL44Qt2cIMmlLARS0p4iY6axxqKecYlnV++7c9GIbinuf2H9vrNEdwKhGbIUTNBrd/6v0M/fanLLj4oax+wWswPRGW6wQ3P0oYbgQ0g/ggEn4nEPpRgkbh2uLtZpmGeFlqPScqxjiM2PWXm1lx7wtm+yjHhC23bEZrPS87Ylprtty8nXXnrjq6O04V3KyMn2EXRAU/9mUL0usZ73L5dflKE0m4nGJ2n6xzplMh8xf7pKPlFsHpyvhmHdPfbGPLsCDXI5V2bR/svx2G1sPg+vFka6qE8SRJWnSwdM0iPvz9d1DqKvDGJ76bm/+4/qQ992yjUyB2F1z6ig6VnHMgYerJ2zimQdFzsE1FqjUKTU9Rui6WoSh5DkXPIueYdOVsKrmDC8zJhaRnmxRca85pi226aRv/9e5rufjRd+dhT79kto9zfOhsSdseW3dqNtxW5aEP7sKIsyTJHxELI5Ct5faofDfMjEzvZu9LXxKvYAzaTeGDuhXRC1SpdMTSWLrg7ep4MedliaCdHxeVhkMv5Rzlsk4YJfzfjXu58Iw+KkX3yHeYw5hb74J5ivruHdz4Hx+iPTzIqqe9kPJF9ztwXaftPtYKGWoE+Fnr3rEUC0vegWqwK++QpJpUayo5+W/pcMkK3tzYTtzzt9uI/ZCV95nm1t4cw6YbNwCwbh4mYvt2DNKst1kzlcfk4XCo4Fbsy37INqR0IkG4vlsSMHNEuleJDz1rRBW7XRUir9Ml3a2cJQHYb0jgjpsQKMDMeCJNCfQqFjJ+MCqP6eQlqfOrwiXpPzPjk02BO/BKTiwWrVjIh7/3Dl5/6bt505Ov5t1ffAMXXnLuSXv+2YR3YBRpHdjaruTk76falg9sxzLJ+iMABzppnj0e1yaS8yc+9myITx8N2k2fq1/6cSo9ZV774RfP61HXZPzk2r/iuYr7XRTLGLFDLbDs8eUczXgylsTiptEYguqAjCaTENBSlDV2QmGR8DuTjCuaxlJYGQoqmQK0nQnDTnwfd8j6EwuvY/Ci/fuGIVp+zP0unP8LNqcSseNAGsds/dG32fqjb2EXilz02rfTte6MO0hP+FFC3Y8OJGEAYayptiM82zwQjAqudVALv9Ownytjye2/uwHDslh29/n5wbTp7xvoXthN76L5J/rXUdRfeyhF/UPhiEGvCxk7ZJ6TypKgaTqSxAVjkpiREfmdgnS3Ct0SpOs75bookPFEKssnBM1sFNkEc1QqcMuWYK/MLEkL5PLaXgnYOhxX2u/gJEpadNC3tJcPf+8dvOGJ7+byZ7yXKz77Wu750AtP+jlmExMTpI7o9GTEqT6wOV5wrQPb4oeS3BnfMp97sjxaaz70mk+zY8Nu3vO1y6j0zkHqRWfh5ijlHUZ27uWP/zfEg+5fIp/LhmBB5glrmJI8mdn/xeioSF2YjmxRJiGYntAORraIhI3XnVmbjcqmtXKgtT9TyS/K73E7S76yTlWaiMCrVkJjyPcc87+ng9/9fS89FY/TV3Yd9X3nGk4lYseI6rbN3Pr5T9HYvYNFd78PZzzteTglefNODjDxIURaJ4u3zvWqcdtvr2fp3c7Cyc8/Ly+A9dffzukXnjXbxzgmbL55G0opVp99DBt9hwt6BxK1MPN7CzOFfRNUUbpcYVO4IDqRVXeFVL1O9ncwtkd8KXPdkJalkibbqgwbmVaRFr6ZV5Lg3h7LiP+lrPOWCU6iJaHzysdUJc8Uevq7+NB3386bnnINb3/OB3jLp1/FJY+756ycZbYxsRD0o4Qk1ZiGOtAtO5r4NFdi2WR85WPf4xff+D9e+Nanc9ED7zLbx7kjJhh2A0dlCfa/39yE1vDwR/QA2bJOHIoAs2kAKWBKkVTbI10xwxI+WHWvLNT4gSRhaYpog6XyPtUxeAVggYwfy4sAJTEgzT7HYj9zz8g0w0wXSv3Z+Y/t/T0w0uL2baM87v6rMe4EnctTHLGjRNRuseHr/8Of3vNWomaDC/75DZz3olcdSMKmgmWoKbta5hSXz4Zl0XRQ3zfE0MbtrLr4rrN9lGNCs9Zg16adnHHXM2f7KMeEzTfvYNnaxeQKx5iYTOCLTAl/LPvKAn5n89GvZ8bhjpD1gzEh31d3y/fAFyFHN7NNOuBLVxf+iE6h0A/lfhmB+g2o7oDG/mwMMuHDxcmD1yXVt5074d6TR0Klt8wHvvVWTr9gDVe+6N/46Vd+PavnmS10OKzVdkjdj2mFCVFydFZscxl//Onf+H/v+jIPuPTePOPVc1DW5jj4k41qwC9/uod73atC36KCFEC1fZLYpZ0CKetw+VXhdLaGZNRoWJn59hh42fs/TQElXbGwNd4BVyozFq+N+05amRAsxngSBlJ0+bXj4n/+JiPp3/f8xcf8GHMJpzpi00QS+Oy47sds/8n3iJoNll78IE578rOxc0JCPFzLvSNbIXpg41yKSs6eskKci1Xjtt9eDzBvE7GNN2xAa83pF85P2Y3NN2/jzLuunfkHjnxoDGZdr7xsQTaHoTko3A+3IAmWyoReTUO2pFCZuGPCgS5WkhF//WwzMgnlfrYrqv2GkwnI9oKXZN51gXTZLMZNymMl57L8WeuIdVCsFHjf19/C2579ft7zz5+kNtbgSS991KyeaTbg2WJdZJv6gBjrXNI3PFZsu30XV73kY6w5ZwWv/7eXzk1e2GG3DA///vjpdzYQ+AmPftIqGSUmmXdkvgdyfYDOZGZieb/6NWlMh22JB4WFGX/Tls6YTuU9qlUmk5GT93jaHDf0VlmMiNvCGdVTnD+Njpn/GUYJv79xLxecvmDek/Q7OJWIHQFJFLLrVz9j2/9+h7BeZcG5F7L28U+lvGL1gdt0RFw7mEoJvysvOmHNjG9ROInekDOBbb+9ntKiBfSunZ9ilxtukA240+ehEG19rMG+HYM85p8eMvMPXtstIq2RL4HYsKSDlWRVrbIkICehKOcbrtzGzsl1ikyBX4szdDAs4q9mOROFbAhvJE0hqkpnzSvL41keoESN26xIgO+MMKwctIdlfFJZMqsJWa7ocfW1b+Kql3yMT17+eUb3V3nhW58+Nz+0TxDiVB8g6k++fL5iZGCMy5/+Htycw5VfeP2xd5tPNI5xy7Ddivjpt9Zz4b2Xsuz8c2D3DfJezNtSFCWBdLvjzDVDK/F7HN0MuiVFU2kJWAXIZZ0tP+N+RW2Zp7WHhW5gGsIdtfJSvKXJhK8p/kYM+5j5n3+8eYBmO+aBFy07pvvPRZxKxA6BqNVk169/xo6f/4iwNkbPmeey9nGvo2vtwRt3R+OdNpGYP5+QRBHbf/93znz0JfP2w2fDDetZvGoJ5Z7KbB/lqLHpJiHqr7vLqpl94M6IIvJlk0qZMorQSoKrV4KgLd0yrcc9JS0HgmzNPb9AiPr/n73zDo+jut7/Z8rOVnXJveJuAzbG9Bp6Cy2UAKEFQoCQnvwCIZVvSEhIh4SEhBBKCL2G3ptpBmNj3Htv6tt3yu+Pc8crC3fJXkme93n0SNqdnbk7Ozv3vee85z2ZRiFe/uVhlUma0fPkBo0rgnwjJATM/9F0KQIIJ0S3YmckSpZuFC2LpguZqxxQ0lSlFbH4yZ3f5k/fv4P//vEJGtc1853ffwXD7H7f5x3B5gqGukoh0fYi3Zrh+vN+Q3N9K79/8if0Hli39ReVCm0Lbtp2o9jK4uTV/80n1ZrnlPPGyXfcisniynVF/5Vplg3jNZBqlEbf0QrQh4teNKp6z7pZiZoVkuBlledYi6QiY7WqxZklUW/TAjS5N+RTqsdktHhvASF/kfIdWlx5nsfL7y9jUJ8Ewwd2v3v55hAQsXbINqxn6SvPsvzNV3CyGarH7MXQy79O9ahxm9x+cyvC7rxSbI8VH82ikM4ytJumJQHmfTyHUft0T33Y/E8WAzC8Iz0m26OQlRYmRkjc813lCxatlJZF4Zj0mNQMufF6jghsdVMIk+uovpG6ECqvAIlqWT0X0iLyt+rEMDZUJtVV0SpJeWabZAyaLrYY1UMVGVwrN/xsq2q1or5DqfVy49Z0ucHvYJVVR2EYOt/+3eVU96rknpsfoWl9Cz/6xze6biSlE9HVC4m2B/lsnp9e9Dvmz1jM/937PUbtsxNS/p2NWLUsnDy7aLCabhCB/CaKcPJ5h+censXYfXozfGytaDY9T75DyVXKI9BV/n9R0WXm06Bl5DuW6C0V0uE4OGHZVtNE2xmrkzHEqlWFdUR+zIhExnxBf64Vci5UDpZiH9eWxVk4vsPf35kLG1hdn+bSU8d026DAphAQMYRlNy+az7JXn2PNlHcBj977Hsjg4z6/UQpyU+hpK8VNYeFrH2BYIQYe2AWribYBjWsbWLN0Nadedkaph7JDmD99ETV9qqiq68QVoH/zziWFBIXLhXBFKqF2pNw0PVtuvp4tN/xEL1VdqUrfI2nRd+WTqrlvRPaTaxSNSSgDblSiZJrSlFnK4BFH9CdlyhG7kEZsL5pkovA01abFE5LXtFKIo1Uuq+7tqBrrTGiaxiXXnk1N70r+/P/+xXc+fwM3/vf/dV8H9u1AV7af2FbYBZv/u+xPfPTGDK7969UceFw3WVwWsmzkywXQuka+S77dS5vvxJvPLqC5IcuV1x0sz7mORJsNS0iUGZWIWC4DDYsAU6JiliVFPYU0oBd1m5EI5KMQzajOGaoROK58X82ELJZSLWo8YWVvUSUV1qYFWB0iYQAvv7+MioTFvmN67fA+uiJ2ayLmFgqs/vAdlr36PC2LF2BGogz83HEMOvokojXbFqruSSvFTcHzPBa8+j6DDhrfbW0rZk2ZCcCY/caWeCQ7hnnTFzNi/JYXBNsNX5/hucpGAkk7Wgm5WYcTqnVJS/FmLC+QG7WegZa8kDBNV3oTXaJrkQrZJtFHnkutk9SnZ0tKJVYt7VM2kLA2vef0iKzcc01ApazKs0k5nhmSCcW3tyiUTsz/+UuPpbZfNb+4/M9cc/yP+OX91zJkdM/RrGwO3fm+5jguv7nmNiY/9yFf//WlHHtuN3LOby/Yt3PynTNDYCOLFTsPoRi2ZvHMg7MYPraWMRPUd0w3kL6RioxZnqQOsyrybOiQbwQ7zAYzhdR6uR+U1QLV6phpCNWqtGNO7aMFyjzxErPzEB4gZC9aIQuwttmhDhg0r1ibZOaiRk47Yihmm/ZZPQG7HRHzPI/k8iWsfOcNVr//NvnWZuJ9+jH6vC/T98DDMSPbf5F05kqxq604189dTMuKtez/lbNKPZQdxqwPPsUMmQzfu/s56mfTOZbNW9H5HlahiNycI+VKNK+qG6MVclM3FWEyQkqbklfVT5pUTybrpZWJ6xStJvwG4Z4H4SrZrmm5NAMOK32Y3SirejtfJFJuoah90U15PpcUkufasl3VYJl0QOnIItt2U++gaeSWcNDx+/KHp37K9ef/hm+c+BN+ftd3dhsX/u4Gz/P40/fv4OWH3+byH5/H6ZcfX+ohbR/aC9t9YpbPgpdss53OO+8VWL8mxYVfn1RM3+kh0YiFq1V7MhNCWYlup9dDuFJVPLqi4dI8MVc2dIl+51tlURWrA/zFW0hSkhbq++soWUJOFeTk5N7SNjvUAYPmVz5YTsjUOWyf/ju8j66K3YaIZRsbWP3B26x6902SK5aiGQZ1e01kwBHHUD16L7TtaaS8CXQGadqW6stdjU8ffwXdNBn2uf1LOo6O4NP3PmH4+JFYka7hybY9WPDpElzX61x9mI9wmZAdIyKaEDy5wcdqRSsGRW2Km5ebqJ2DpqUi1M3nhWS5a0Tn5YWLrYrWz4ZUg4qYoewrTEW0LKVBi0HVQHHfzzaptGeL3Lyj5coSw4Z4raQ2nIKQORBSuLWbegdMMLcVIyfswa3P/x/XnftrfnD2r/jmby/j5AuP6tRjBOgYXNfllv93J0/f9TLnfeu0rukVtjW075Chh4QgeRt3PXAKNk/95xMGD69i/AH9Nn69VQbhFLgJ+f5aqgK6cphamOnynXGyog21YspVPwwhpSWNVKruGFrRQyyfBhxJc5qqfVm8RtnZKEIHHTJoTqYLvP/pGg7cqw+JWNdo+deZ6LFETCJfS1k3fQrrpn1Iy5KFAFQMHc7o879M730PwkqUlXiURWxP9eWuQiGdZeYTrzDi2IOIdcNqQ4BsKsOcj2bzhavPKfVQdgjzpi0CYOSETk5N+iTFQ914NRUdKy+SMChqU6yE/J9PSyTMVmL9QgYMTcwgLWXgqIfkcdeWG71hSBVkOKH0JfliFWbzSmVlEVU3dIS4JfpIWtLOq0pO5MbvE7FQYss39S2ZYHZyZKz3wDr+/NwN/OKyP/H7b93OvI8XcvUvL8YK97wJo7vBdV1+/+1/8Oy9r3LO1z/PZT/6YqmHtONo2yEjWgXaOrGP8GGEefedJtasTPGNn0/8rJi9vI+kEc2MLMKyrUrs78h3LFIpiyI8uSfkNAh7YNbJQihcDeGIfL/zrWCmVJPwnCygChbo5fI9j1apg6qWRh2MSL89bSUF2+XIfXteNAx6GBHLtTTROGcmDbNnUD9zOtmG9aBpVAwZzvDTzqXXxAOI9+maDUK7YvXlnOfeIteaZu9zTyjZGDqKmVNm4tgOe3XTRuXzpi2isracun6d2B+zLUmJVhbTgqHoZ0lYpklutqYlv1PrFDnSwEmJnMSMyo08n4NwTkhaRPWgc/NSaYUnRrHl/ZSHkaOcvFUJvaWc+Z2CEDpTL6Y6XFfSG0alajpeplqpbAEdMMHcESTKY9z43//HHb+4nwdueYr5nyzmJ3d+i179azv9WAG2DY7tcPM3/s6LD7zBl753Jpdce3b3r7QLRdhw/Sbq5HunUu+uZvHkw7MZOLSCfQ4a8Nm0fCErljQashCqGV583M5KSjJeK/eBQk62MaMiWQiXSwV0ISOswQhBVO3LzkFluXyvKwdB1SB53trKYmkb4boer3+0kpGDKunfK9Hh/XVFdGsilm9toXHeLBrnzqJhzgxSK5cDYEZjVI0cyx4nn0ntXhMJV1SWdqDbgK5Wfel5HlP/8zQ1wwfRf2L37M8IMP2tqeiGztgDuqd2Z970RYzYe2jnTiDtSYoZBtqkFaEYMbNzkjZ0bBUBSynHfBOsChHihsslJVFolmpJzVXmsIaqriyXiFsoKivlcEJW1G5OVuSFtBC+iBL35lvAseQ3SIoExHgyXrcxWdwcdtAEsyMwTIMrfnYBoycO5zfX3MZXj7yOa2+7mgOO2b0ahncF5LN5fvGVW3j7mQ+49Lpz+NL3ziz1kDofoYh8t9Si6p3XVrN6ZYZrfjgWPbmGDfYvABl1/7BzkoL0v1Pl/SRynU3KIqt6KGCIZ5huQFlf5fMXk8rIsFq0mVEwG2Qbqxx0T4hcTLU+2gafs23FtHnraWjOcvbRwztlf10R3ZKIZRvWM/ln3yO1SoiXboWpGj6KfgceTtWocZQPGtphzdeuRlervlz+wQzWz13MsT//WrdeRU59/UNGTRxDvCy+9Y27GPLZPItnL+eAYzt5It8SSSmoRt25pBA0MwxoQsb0sFRcVfSXG7ejg1ErK+ZQDExNfnuqstIugJFQOhMknWip6st8EkJ1MiE4BUiukaibZsgxnDxgFH2JjBAUWkV3ti1ErL2mBnZZE/HDTz2AoWMGcsNlf+SH5/6aL1x5Ihf/4Gzi5bGdfuwA0onipxf/nmlvzeSaX13CGVd034j+VqHSlXaqlcfuX8jgoXH23TsMydVCjKKVsshxclIEU0jJosrJF9sRxWqQ0JZaaLkZibQZEfnt5MEJgekWF22gvsMR0G153LGV7QUSuU5rnaLJfPm9ZdRWRhg/sudGl7slESukkkSqauh74GFUjRhD+eA90M1u+VY2Qlfy6fnonqeIVpUz+qTDSjaGjiLZnGTex3M599sXlHooO4RFs5bh2A4j9u5kfdjmSIpf8VRICRHzb+RWDOxy5Y6tiIwVL/aeNKKAC54u2pJCGqqHS+Qr2yxpjlyzctZWRMvXntkFmQwMUxbwprLOSK6XUvlCVtIlPpzMtuu82mpqdrEJ7MAR/bj1+V/wtx/fw6N/f45XHp3MV35yPseeexh6N1skdicsm7+SH51/M6uXruW6v13DMWcfWuoh7XwU0rzx3ALWrc3x7e/0Qs83ItXNqrm2k1PtxrLKdkJpNPNJiWy5eUCJ8VNJqaTMZ5XRc0G+w+FyqBggEbANhTw1yugVJcxXJCxcpqQMHddkLlrZwvzlzZx9zHD0HuTN2R7dkr2UDRzCxG9eV+ph7BR0BcuKhoXLWfj6FA644izMSPdtqvrJ2x/jui4TDusmpo3tMFcJ9Tu9tRF8lqSAcrOnWOXkqNSkrtoaRapkpZ1rledDiaJvmFMQ36FCSqU41WrbKgd7rUwAID3rNEORvajSiGnymKaLViXXChnVeqmQkpV5VDUeB9GaRcu3jVy11dTsYoSjFt/87WUcf/6R3Hrtnfzmmtt4/B/Pc+X/fYnxh3RPT7uujPdenMqNV9yCGTL47eM/Zq8Du2cnje1CIUsuleaJR5YxYkSU8eMT0hPWdSTl6LaIoD/TIrrLfE4i2PEaaT3mk7JCi2plRrGHrId8J8Nl8j0vKDNXOwcoo+eo8gzMNkFGl4h424VTBzWZL7+/jEjY4ODxfTt0mro6uiURC7Bz8cEdj2KGQ0w4/+RSD6VDmPrGR4RjkW5r5Dr344WUVSXoO3gnuUi3JSk+uQIhXWZUqhrdQlFIj1dMVdo5FZ1yJKqVWi3eYlZEdF6xWiFRGKIXsVrYYI9RSIlLvwd4hvxEErLPxmWy33C5RNByKYg5Iu4PR6T6Mp+WKq1oZckc9rcHoycO48/P3cDLD73FHb+4n++cegMHHLsPX77+3J1jS7KbwfM87vvD49z5ywcZtudgfn73d+gzqGc5r28WboGXnlpCU2OBr13ZG82xRVuZaZKFlf/bDKuFU0jpN02IV0t0WjPlu5fPyD3BsIpR61xKvotGSBkyq3Sl58pCKKzuB/E6pRtrt3DvgCazoSXLR7PWcdT+A4iGezZV6dnvLsB2o2nZKmY9/ToTzjup21pW+PjotSnsffB4rHD38w8DmDN1AaMm7LFrNHrtb5iRcjFNtRJSfg7SC9IIS3WUXVARNVtW0q1roHW1lMDHKiW16VtP6CG5kbsFEfd7KONXTdqs6IaQLzuryuZbkZt7jUqdoCaHsEwiRqgYrYOSOuxvK3Rd59hzD+fwUw/k0b8/y/1/fpKvHnktnzvzYC697hz677GVKtAAm0SyJc1vv/433vzf+xz1hYP57h+/SiTWfaP4m8QW0uup+ib+9/gyxo9PMLJ/ElobJeWYaZTqZbsgZMopgAPgQrgCcmmJZhnKmFXbEAKT71wuBcmV8l0vpCDRFyotKORFqhBqq3dU9jaa3qmazFeniAb8c5N6fseKgIgF2Ajv/f1hdMNg0qXdsy+jj3Ur1rJ8/jJOuvjzpR7KDiGXybNo1rLOF+pvDpvSjUWUu/4GY1TVaDifktVxpkX0YOEKmSycfFEL1rwC4n0Bt7jK1kNCtvItsgrPKldvDdGcGRGlE4uxIfpmxYW0RStl0vCcorGrWwDCxfRHifRg24Nw1OK8b53GKZcczQO3PMVjtz/H60+8y3FfPJzzv3V6QMi2AzM/mMcvv3oLa5av56s3fImzrz65WxcWbRJbMiUuZPnfo0vIZFzOOrMKjJx8p3Kt4vXnEzBdfdf0vOgu8y2QS0iKMlYNJCXqZedVRbQuqcZQVL6P2WYhYIne8v3VtM9GvtxCp2oyszmbt6auYp/RddRUdM3vcmciIGIBNqB+wTJmPfUa+1xwMoleXTvdszV89NoUACYeOanEI9kxLJixBNdxGbXPsF130E3dSD9jjOopz7C06Lgc1dok1ywrZU+TMnjXhvKB4v/lZIVkaQZElKFrrkFVRiIi/3weQh44HmCD7UpaJRSRhuKGMonNJ2VyMa3iqlwP7RIX/c5EWWWCy398HmdecSL3/eFx/nf3y7zw39c54vSDOO9bpzFs3OBSD7HLwnFc7v/TE/z7poeo61fNH//3U8btP6rUw+p8bMmUGFi3eBUv/G8ZBx/Rh0HDq4V8WTGJMmumCOldW7UaikJqDURqRLdpxYrfdS0qEa1CCtIqwq2ZEC8Dq1KOaRiqlVmN9Kf0e8P68CPqnaTJfHvaKjI5m2P27/nRMAiIWIA2mHzrfYSi4W7dV9LHR69Oobp3DYNHDyn1UHYIc6dJJ4hRE/bYtQdufyNt6zlWyEgaMZQQMhSvFbLkKkJl+alsT1lWqF51uTRoafEiSjfIhOF58jrNUL5jBcg2QKhcacEaINQqq/BohWr2HZKJItcKdgjCzUWn/fYTVrZVeZzFu2x0DKC6dyXX3HQJ53/7dB6+7Wme/NeLvProZA48fiLnffM09jygBxKMDmDN8vX8+mt/ZdpbM/ncmQfzrd9eRqKi+1nTbBM2Z0qcXAeGyUP3zEPX4awv1Ig1hd6iqhqrlPefEuKHwvLbKoNENeIl46n2SLocJ7VWvnO+LMArgF4p2k1HSTusaFE/6kejodNtYRzX5ZUPljN8QAVD+3dvecy2IiBiAQBYNW0O8196l4Ou/iLRqvJSD6dDcF2XqW98yP7HHthtUxVzP15IZV0Ftf1KGNUpZCUlYedVSrFVVsu+TitaLtYVXl76y/lRL80V8uVmIZdV5e9lgCmpSDuntGIupNdCWS8R37euguoh4llU3gfK+0urI11XomJdtGthpUexEnLcfHLjcWdbVKFBXn66eHQMhJBd8bMLOO9bp/H4P5/n0b8/xzdP+iljJo3g7K+dzKEn749h7L62F47j8tS/XuCf/3c/Hh7fv+VKjj/viG77/d4mbErorioW581u5r3J6zntzL5UV3jFSkbDAjMncoFETvSVZli+m9kGpe1MghOTbfNpMA3RlOVUCjIUkehXplFVTJvSFNzvNRspV63MjJ0iA5g6ez31zVnOOqbnGri2R0DEAuC5Lq/9+g5itVVMvOjUUg+nw1jwyXxaGlrY54jumZYE+PT9uYzZd3jpJpq2qb5sk4jvw3FVTZkt9oWMV8lN3ioDMy6rbDcrN2nTEg8xf4Lw8hKpsjNConQD0YLFihoylB1GJIpSF4uYOJ9SKVNT9gvF1KTX5hzZedk/FCeyNumcrq4hK6tMcOH3vsBZV53M8/e9xsN/e4YbLv0jfQf34oyvnsDx5x1JYjczhl08exm//ebtzJoyj0mf25tv//7y3aMqclO6Tc/Bzee59x+zqayyOOkLwyG/Bmy12Mllpfm2FYGMCV5GjJfNkCxgGheLJlO3il0tWvPKMqZZefqFJOodNhUpi0mELNMoC5tIBZTvHDsJ1/N45u3F9K6OMX5EzzVwbY+AiAVg1v9eZ/Un8zj+xm9gxaOlHk6HMfX1DwHY54h9SzySHUPT+haWL1jFCRccWZoBtNemWDGlE7EgrtITuRa5YYdV77eqIcXHcVVT76zYTvh+YOlGIWp+qyPHlpW2bRd9jNxGiXJptVIlaaehsVmO47qyf9uSlKVvGml4cgxdV+kW5LVtNSwqnVN8j107ShaNRzj9Kyfw+S8fxzvPTuGhvz7NX394N//6xQMcefpBnHzR0YyZVEKivguQbs3wwC1Pcv+fnyRWFuPa277GMWcf2nPec6apWPG7uW4RbXWbOTFafv25pSxelOHKyyqJtM4TDSYhqF8gZApHIs7xGtBjkFwq38NoLVS4SlhfIeQs2yJES3NEeuBpgAZlFZKKTNSKYN+wVJozLt/PnVSpPHX2OlauS/Hl08b2aAPX9giI2G6OfCrDW3+8hz57j2TMKUeUejidgqmvf8iQMUOp7t11J9ot4dP35wKUTh/UXpviG7qa6mZshES0G4qLGF8LiSeR6wC6kCCA5pXyGkt5ktmt8rxVLqTIzkh6M7VGbvRWFDylPzEtibBlm0VwHIqq9GdtcXwtKyXVYvkTgi5VlpHKjUmYn7Zpi05w/d4VMAydQ0/Zn0NP2Z85Uxfw9N0v8/Ijb/Pcfa8xdMxATrrwKD535sFU1fUcLU1zfQtP3vkij/ztWVobkxx91iFcfePFVNZ2b8nERmhYVGx4D5CpUH0eN4NMC6TX0dqY4aEnUowernPgqAZoyBVF+ZlGiRijS/TZjYKlq44Wecg3y/cVQ/aXaZHvoRECQqLFTNdLpDpSBmW9heQ5OSTHiaQ1w4kOG7VuCq7n8fRbi+lTE2PSmN0g4tkGARHbzfH2n+4ltb6JU/90bbfrz7kp5DI5Zrw7nVMuOa3UQ9lhTH9nFqFwaNcL9X2016aYYSFXWgg01UjYjBajYQCpRrn5+678ydWiOck0iljYiMqq3HChvAqyadF/uQaygi8ogb9qdRQuF6KHMo/MJwENEnWyis+ni3oXNy+6FVyVEo2wUcNj3VATVDvshMlkZ2LUPsMYtc8wrrzhQl559G2eufsV/vLDu7jtx/cw6XN7c/RZh3LIiZOIJrrPe/LheR6LZy/nyX+9yPP/fY1cJs8Bx+3DRd/7AqP37WFaoUzTxiQM5P9Mk1y7bdPn6Qbx6Ms2QWodD96XIZtxufDcMjQtKSasGrJdpkn+DsWVVx8S7Yr1RjpdGGCHIat0mh5CxBwNonHZLlIpr68aAjgS1Tba+DDatqT/oztu1Lo5fDRrLSvXpbhsN4uGQUDEdmss/2AGH//3GSZccDJ99hpZ6uF0Cj59fwaFXIF9juyeaUmAj9/6lLH7jcCKlMiIdlPalLLeclN3C58lOtlWSSH60EzQLDGU9DxIt0jKMRQDLS8pFvJgJkRb5rYCIUmV2CnIJCVNkmuSSSCXkhSNrYoHonGJgjlqfE5edDG6BkZapWRCm27f1BYdcP0uJWJlUU65+BhOufgYFs5cyisPv83Lj7zNr668lUgszAHH7sOko/Zmv8+Np65/TamHu1lkklmmvjmD9176mPdf+pi1y9cTskyOPvtQzr76ZIaMHljqIe4c+HrF9mhdK5EoHxlNpffFvHju7FbeeE/jpCMcBlQm5XthZwBPFjmuKxGscIVqUeSIUTKaLFqMEGSTkt53CvK8ZoDWDOYAWcwYldL6qKyveI4ZKn3pqIIcMyQ6sk6OJLuux//eXEy/2jj77mbRMAiI2G6LQjrLCz/9CxUDenPoN75U6uF0Gqa+NgUzZLLXQeNLPZQdQmtTkgWfLOGiH5TYQmSz5owREQX7z7nOZ6sW80mpsHQyMlFkmuUGbpjSONjOAFExhvRs8RFzs6rKslV8w7INkElJBC5cDslVIvA3kmAMlr6XujpuIVPsg+faEsEr768qNRU21eS8i6cltwV7jB3EHj8ZxJd/dC4z3pvDyw+/zTvPfcjrT7wLwOBRA5h01N5MOGQs4w4YRUV12Vb2uPPQuK6ZmR/MY8b7c5j5/lzmTF1AIW8TjUeYeMReXPCd0zn4hElU964s2Rh3CTZ13TkFML2NHyskVSuxAnY+y12P6VRXuJz2OQda1hWvdzTRW5bVQqpBCFmiGmK9lFVMASr6Sy/Y5GohdoW0CPJDcSmW8TwhZaG4pChDIYj3l24ZVlz2qetyL0jUdfop+WDmGlbXp/nKGeN2u2gYBERst8Ubv/s3zcvXcPYdNxCKdf8JycfU1z9kzKSxRBPds+jgk3dm43ke4w8eU+qhbN6csX2z8PaRJQ8hYvFqWZlrpqQzQnGlzVJ9KnPNIiaOlssqu5CB+ACxv2haJpoy3RIDyUiFCPytuDQxjpbJCl3PQG61TEp5Tx7LtUo6x+8MAJ3q+t0Voes6ex80hr0PGsO3fnsZi2cv54NXpjHllWk8+a8XeeS2ZwAYNLI/ex4winH7jaT/sD70GdSLmj6V6J0oS0i3Zli+YBVL5ixnyZwVLJmznEWzl7Fq8VoAQpbJiPFDOeOrJ7L/UePZ88DRhKzdaCqKVoomLNsskSrPlpS+5ygT45hoJLWQpPtzLTz3bBPLV3l848ICkZArlZBurg2BioHjygIFANU/0s3L70yzEvKjNGUu5JRsIFoNVf0UKTPFqLkeqByoWp3t3AWM47o8/dZiBvRKsM/ozid53QEduvo1TasGHgCGAIuBczzPa2y3zQTgNqAcqUe/0fO8B9Rz/waOAPyE+SWe533ckTEF2DoWvPYB0x98nn0vOY0B++1Z6uF0GprWNTJ/+jwuuu7LpR7KDuPjt2cSCocY01V1Me0d7NGKGjI/VaghTb/9tieaJiv+QkFsMJoXyaSRWgOVgyTipetCsDxHJph8s0qFRISg4Yp4HxvythQC+Eav0Ro5aD4tJMwISxSgvSC/k1y/uzo0TWPomIEMHTOQc752CrlMnjkfL2DGu3OY8f4c3njyPZ6555UN24fCIfoMrKOuXzXx8hjxihjx8hiJijghy0TXdTRd2xCpKORtCrkC+WyBfC5PqiXD+lUNrFvZwPqV9aRaMxv2bZgGA4b1ZcTeQzn10mMZt/9IRuw9tHRp966C6qHQsFTS744h6cKmJXKNmlGJYEXKAY1Vqxwef8Fh0jiHfUcVIN0kKUMjDhaQTYGtdJNmXGQCuWbReYVUI26vAHiyKPK/s2YY4r3Fx08PiZbSzkuUTTOgCSFj0aqduoD5aNY61jZk+OqZe6L3lIrY7URHlyHXAi97nneTpmnXqv9/0G6bNHCR53nzNE3rB3yoadrznuc1qee/73newx0cR4BtRGp9Ey/+9FbqRg/l4K9fUOrhdCr8tkaTjtq/xCPZcUyfPKu0+rAtYVMtV/xGwdFKtQIvSGrRDIllRCEtYvumFYAODQtUGhLRf6UbpUQeU4n6DSFpnisC+1g1JNcrM8m0TBC4MrHkkvK3GZG2Sql1Mp5MM4T2lBV+Zwvyu2FULRy1NkTLQAyPVyxczaola1m9ZC2rlq5j1eI1NKxpomFtE8nmNMmWFNlU+896Y4QsEyscIhKPUNevmkEj+jHx8D2p6VtF/6F9GDxqAP336I0Z2o2iXVtCISu6Ls1TfnghoEJc7bMtxZS+posmq98E7HAFt9+bxrI0LjzTBDcjOjA9pNzz0/I9snUhWTqSkk+tlVRkvFaO57lC1OJVsoBx8oALtUMh3ktpxQwotAp5A4nU5Vvlux3eOSltz/N47p0l9KmJMX7U7uMb1h4d/YacBhyp/r4LeI12RMzzvLlt/l6padpaoA7h2wF2IVzH4bnr/kg+leXEm76NaXVPsfLm8O5zk6morWT4+BGlHsoOIdmcYv70xVz4/TNLPZRNY3MtV8JxZV2hQahSbtzphmKFo5OHKhOS9aIT80IQTUDGlQklnwcvJRqw8gGy4vd0iZaZlqoLsGUyybXKhGNYoCnfIzMqBQN5v3eeCS2rZPLozOqubtbPcnPQdZ2Bw/sxcHi/LW7n2A52wcF1XTzXw/U88DxCVohQ2OzUdGaPh1/96EeN/TZgZkhS9XZW2UzoEh3OJ6F1FY880sLCBWmu/nKMyso8tCpPPrNM/P3yHuhhCBXU98RT7cMcda0aUtiSbpHvk+dK1Es3pACncpCk/XXlru+2WQC6SnuWT+60RceMBQ2sWJviolNG77bRMOg4Eevted4q9fdqoPeWNtY0bX8kmLqgzcM3apr2E+Bl4FrP8za5DNM07QrgCoBBgwZ1cNi7J9783V0sfXcax97wNWqG9ayKpNamVt557m1OvPCUbjtBTPf1YYeMLfVQNo3NVRnmUmyooixk5Gbva7LQZIIJpeSGHi4Hey0k14h+RTdle1eTdIsVk5W5l5eVfLZJ/MXKektqs2kp6FGJBJgReV15Lzm+nZLj27liz71wHFL1Em2zEjs+oWypAXM3iYxtLwzTwDCNUg+j+6OQVQuSNtePbYOTBL0C0JSWqyDXklOAQpYpL83imSfhcwfCASObIJURkhYqF/1kqFIsZKI1UKiExoUSwTIj8v0zLMg1QNaBfA7K+0F5b/nby0rvVzz5zoTLRGfmF954nnwHnZxEnjV9pyw6np+8hKryMPuP2yJ16PHYKhHTNO0loM8mnrq+7T+e53ma5psMbXI/fYF7gIs9z1PucFyHEDgLuB2Jpt2wqdd7nne72oZJkyZt9jgBNo3pDz7PR/c8xYQLTmbPM44p9XA6HW88/iqFXIFjzj2+1EPZYUyfLPqwsZO6aERvU7YWUie/8XYbEZRKWe1nWyRSVjVYVvQeEKqWUnufILkuNC+X7epGSVpTN1TLoyqw1whp8wqiE8tnReeSTUtVZXKdHN/yxArAWiRETkOlEmNSQFDeX7bbnhTj5qKB3cyLLEAJ4BY+e/2YIZXV1yRV2OBJtNcVbePqxgj/eDDKHgNcLjiuBdyE0lGm5fsTTkgkLF4jOi7Nj6S1ACHRWRZyKoLsScofT75jZVXK7sWShZBhSNo/Ui3fxXyLRM1Mq9ihYicsOuYva2L+8mbOOXYE5m7cRxW2gYh5nrfZWVvTtDWapvX1PG+VIlprN7NdOfA0cL3nee+22bcfTctpmnYn8L3tGn2AbcKSyR/zyi9vZ8ihEznie5eWejg7BS898AKDRg1mxITu64c27a1ZjNl3eNfUh/loX33o+lqtdvAJSigiomNfAxOrhF6jZfVv51TT7hbRoxjKo6iQk3Sma4vuy49y5dJSSRZW5faeKyv5cFyOqWkyJs2QMaVWKg1NWCYvw4R0L2hZLdoZMyKkLpSQJuNbwuaigd3UiyzATkR7HaEeatP31K+SNCHRq+hmH6sSwmQXyGZd/nx/AlP3uOasZkJuBhxTVT1qEjXGFXuL6HAxRrbiQphSDXKc1FpYP0cImREWUqWbECsHIyYkzgwX+7aCRMAqBkCuQuxj9NDGHSo6edHx/DtLSURDHDph5/St7E7oaGrySeBi4Cb1+4n2G2iaZgGPAXe3F+W3IXEacDowo4PjCdAO6+Ys5n/f+Q01wwZx0s3fRe+BqYalc5cw64NP+crPr+q2fehSLWnmf7KI879zRqmHsn3YFoISq5ZolJWATD35jM3cxWk+nRtm/sICtWUWI4dbjBph0beXjpZthHxM0iJaSCJj0TpJcYbLVINwQ0XVoqqhcRXYLpiGGMkanvTby6chvx5wJcWpGULwnHyxM4C7XiIA1VuQPGwqGthDvMgCdCLa6wgzmiwUzIhaTLTK42aUDW2D8ikhZaEYXutq/vm4zsp6k++fuYgaqyAkzI4goV23WPFoRsQDTFepzXitLE5wZeFTyMt1rptqkaKL8atlyeN2VraPqNZRmlpYheOyv/boxEXH8jVJPplfz6mHD8UK9bw5aXvRUSJ2E/CgpmmXAUuAcwA0TZsEXOl53uXqscOBGk3TLlGv820q/qNpWh1yhX0MXNnB8QRog+Saeh6/+hdY8Rin/+V6wolYqYe0U/D8f57FMA2OOrv7plxnvD8H1+0i/mFbQvuJxmyTutgATW7oBTYQFdcI8+ZbSd59dTXz5iQp2JIRGdzXYMbiOJM/lZtxWRzGDa/g/NOyVERU+5ZEX2lSbOiqtN6PhiUANblE4qKH8RxF+kJqYrGLk5NvfGnnQUuzoTk5QMtyiQ5sKTLWw73IAnQQ7XWE2RbVY7VSVSnqUqFomHKN2jkpKrFzYl+BxlNvRPhgFpx7xDrGDU5CLi+LEA3laq/JdRqKyyLEycliwydTkQpoXSfVk5X9pYjFzcv1algQroTyvkIIk6sVIcvL60yreF3v5EXHc+8sIWIZHLFv/07bZ3dGh4iY53n1wNGbeHwKcLn6+17g3s28/qiOHD/A5pFtSfH4135BLpni3Lt/SVmfnlkaXMgXePnBFzjguIOo6tX9Kth8fDJ5NoZpMKar6sNg84L1aFWx/ZEv3E81bEj7NRUq+Psv32LmtHX0HxDl6GOrGTvMY1TfRiKxCF66lTUNLnNWVzN3gc37H6aZu1jj8i/ojBtuScpSt2QiKOst5MowVWVYVgmYy6BsgIzBiiEELS8aMQ9l/mqpSIQiX3ZeNTxGogb5VhE9+xPOpkjXbuJFFmAH0FYHlkuqJtyGGKRmW4VsxZQuMtsqhq4beksafPRBI4/8L8tBEzVOPK4cChag+rdqmpClfKtc/4WUGBaHInL9g5jBOjkpWnFV2t4IqR6vhlQkl9UJMQsngD6qgCYhRK4t2dqJi441DWk+nLWWYw8YRHwn9KzsjggMXnogCuksT1zzC+oXLOf0v1xP3aihpR7STsPb/3uTpnWNnHjRKaUeSocwbfJMRk7Yg2i8C0/yWxKsh8skAoZXjAQAn360mr/9bRXZrMuXrx7F4UdUo6XWQ3o95ExARzM0+vTW6TPQ5YhDKzjuMI3b7srwm39FOPogj3PO1IhE88VKSNMSElbIgbNWIgIaorfRI1KuHy4TIpZeJwJnU5Pol66BpiYvv8bAsIrRAF8H00OsKgLsAHaUgPipu2yLkK98SiogXUeu3UIGWlaCu0wWBJl6IVfhcpYst7nt7ixDB2l8+ZwQmp2W71BqtRSquAUhV15e9m+YEK8TbZgeUlG1pOjPMCQKnGkBHNFdahExey2ki2MKxaSnpJWQdGT797qTFh1Pvb4IyzQ4Zv+eVbnfEQRErIfBKRR46ju/YdW0uZx083cZfPCEUg9pp+Kpfz1O36H92Peo/Uo9lB1GJpVlztSFnPO1Lk4mt6YHcwsSZbIzuK7HE4+v44kn1tO3f4Rrf3kg/fuq242mq+pHBxxPpUXCgA7ZVgYPjXLDDyM88mSK51/NMX1+jiu+Vs7IEWGZ3PyJzQxL5aRnq4biujQVrxyBOO23gqP0aabyOrMzEpFAg+RKQPWy9KvD9JBEFtL1irjpEpGwc0WTWd0IUpM9FdtDwDNNxUrCaKX8zmhyjWmmEJ5cWgpG4jXieZdLScQqWi3Xq+vQ2JDnD39NkohpfOvKKqxoDlrykFpVJE8eYtDqKFsXDTm250hUrXGxVA57mixCXFuuXSOiUqEJsEyxrigonVo+Ka+tHrxTT2lbLF+bZMqstZx48GDKE124KGkXIyBiPQiu7fDsD/7AkrencuzPv8bI4w4u9ZB2KhbOmM+n737CV264qtt6hwF8+v5cHNth70O6uD5sa9oRPaRaqcBdd63itVebOPTQCi66YjThmkqZmOycTEqO8hjTkIiUD9VI3ApbnHd2BRMnZPnnvRl+/YvZXHXVACaN8d31EVG+ppz9001ghSHcB5pWyWRnZ4V8eS4kQmBEZXwF0eMQqZBJ07WL76WQlmhG61p5zh+bZogmR9clmhEpD6JkPQ3b4xXXsEhSiz4yFdK2KBwXTVi6XpmhNsm1lG4UewhdZ0OroUiEXEsrf7zdIZ32+NG3olSWOXIt51qE8FtxWdw4OYnuugXlt9dXomOFjKQps82QScpCoZCRY8R6qUKWcklNFnLyO1xRrNzUtV3qhffUG4uIhk2OOSCIhrVFQMR6CDzX5YWf3Mq8F9/h8O9dwp5ndl/h+rbi8X88Sjga5rjzTij1UDqEj9/8FMM02HP/UaUeytaxJe1IKAKhBG+8sIzXXm3i5JNrOOf8QRCNybbhsuJrzYis8JOr5bWGVSQ9foqwkGFU/yw//UElv7+1mVtvXcaFZ0U4+oiokKrcOqmMBMCRKrHGpapfZV6iZ6GoRBWMEEQNsQDIZ8DQVJozqjRoZrHxuOvJROg58r+mCaGL1EI0rgTYbXRkQWSsZ6B96t3OC3E32l3nmaaNSRgoItQk1zVIGtGKQj4CGCqqpYnIXo+AncZN5bj9AYslK1y+eYnOoF425ApicWHGwSqI/YSTkUiaa8s4NFO55xsSwcsrnz47JdqzUExeFy6XfpOuctjXLDFA1h3lH6ZsNfLJXVKAsmhFM9Pmrufzhw8NtGHtEBCxHgDPdXnp57cx66nXOPia89n34tNKPaSdjqZ1jbz68Esc+8UTKKsqL/VwOoSpb85g9MRhxMqipR7KtmEL2pElayzuvnsNY/cs56wLR4peq23UzH9tuEzSOVZUVuqWquhNrpNVfC4rEa1oBQlN5wdfC/PXf2a5+6EsjetTfOF4A81OyjbJdeLDZIbEwsJQKSAN2benQ8MSqHKF/Hm2KvtPAkmZGN16mXjj1UVtj51T5pYFGavepHr5xVXkzwoMXXcUXbH6tG3qvY3OEU2RdL8gJblOdIm6ubEPVyGrrhNDrjEnD3gS1cq3CKFyCmBaeMk13PdiJVNmJrjg5AL7DElDc06MVZ28LAZCERWVjYCRA6NKRbJQ7ZBs2X9ytewXQ75Pnmp7ZCiXfp9w+QUpPsIVxQigf73rhmjPOvkz8TyPx15dSFksxNH7D+jUffcEBESsm8N1HF74ya3MevI1Dvjq2ex/xVmlHtIuwdP/fpJCrsAZV3bv95tsSTP344Xdzz9sE0gl89zy8zcpq4xw1fWHoZfpm55o207CNcM2/t83f9VawOglk0q+lbAF3zgvzV1Px3jqVYPGZpdLT0pjmsrEtZAVLZcVFTPLdJPoanQDzBSgS7RC0+TvtCJesSooNEuPPddWVWeqMMCMqKiaJUTRQzn1GyKghsDQdUfQVQsh/NR7trVIwnztYOsaZTmRlTRjco0sJmxLrtlcBqykRMXidXI9JutFA+nZQty9EITEguXZD2p58cMyTjgoy3FHJiDdrDzuHCk6KRTE/DjdVHS/10KS0o/ViClreS+JDIcdSX/iSWWw58iYqoYIIQtHVX9WA3LqmtbV1G/nZFu/4hIkmhcp69TPZOaiBuYubeLc40YQsQLa0R7BGenGcAoFnr32j8x7YTIHX3M+B3z17FIPaZcgl8nx5B2Ps98xBzBwRPfuO/rJO7NwXY99DhtX6qF0GP+97SPq16a5/o/HUl5XsemNNjsJt4mYhWJSUp9LFjVbTh7DMrn0tBzVMZfHXotT31DBN77QSIy8EKVopWrxYkgaKLNWjlc9TFb/hYwYvobLVJNyR9zJw3GZBHUTmldIxMGx1faqNZLnSbTCTsvf4QSYtZLWyaU2XXUW4LPo6j07Y9WSukYTIh4pl/E5OXnMyUqU10qoiBfQtFyunXBUCeBjxR6qedX03tMkqpYv8NbsSh54Oc7+Y5Kce2Q9ZHKQTSrNFpJij5TLMV1HImqOI5HdWI08Z5hsMHjVQzLuXFI0aHpYIrtWHCKVqqglKUQu0Uvej6tSoCGrzftT8Aqd+pk4rssjLy+gtjLCYftsudH87oqAiHVT2Nkc//vOzSx680OO+P6lTLzo1FIPaZfhpQeep3l9E2dd88VSD6XDmP7ObEKWyZh9u7B/2DZgxoerePP5hXz+/HEMH7sZz7ptnYRDEdX82JaVfkGlKe0CmmZw+nEmdeVN3PG/Cm68p5bvnp2lmgYR3xdyRVJlRCDRG6xymcicFDghMbM0wqIj86qUDUZB0paFrExOoRiYFXJcq1wmLn/i9RxYPx9Sa8AqK/bkK+u941GErpiq2xnYFT07O3Iu0w2qeMOTqFjGVaQHVRiikFA+5AVF0OI18nguKdeREQfNlWsHTUhRppGPF5Zzx2Mxxg7NccVxy9EJyXUVUinFTBrChiJH6nqzsypCGxIiFquFfLPSg1lCsKJVYnzs5mVBYkaECPotiqwyNvi1mGEgDLoae3sXfa1NFXQnfCZvTV3FynUprjhz3G7fU3JzCIhYN0Q+leHJb/ySZR98yjE/vYq9zjqu1EPaZXAch0f+8iAj9xnN3oeML/VwOozpb89k1MRhhKPdt5Q7n7O58w/v03dgOad+ac/Nb7g9k7AfOdPUJGZaQLEVyiF7NVChr+GWZ4bzf3f357vnrmVAlSumlZm1osfJuCKi1sPSn7KgqsSsMqk0s6LK0dyRCJcRhlBYiJyt/JpMC5wGSet4ntgO+D0wsw3KhyleTKmGYjs2+XfFVN3OwM7u2dn2XG6v5slfKJjK+NfOqDZbqlLWb1PkI1IBeqsEpkD1T80K0Xea1TXSLNdEJMHcpRa3PhJhUB+bb5zVSChWLddatEpS5ZkGibhmG0XXZYQlKlvWR/421OLELUgELb1eImaxKon6Vg6QlKJvrwIbE9L215nvxp9tKT5mRou6t074TFKZAk++sYiRgyrZZ1Rdh/fXUxHQ026G1tXreeCiH7L8w5mc+Ktv7VYkDOCdZ95i5aIVnHXNud22r6SPTDLL3GmL2PugLm5bsRVMnbyC9atTXPC1fbGsLfSN29ZJuKCqwHLNbBA72zmVMvRFxRZ7Dmzih2dJavcX9/Tlk3khmTgzrTIpOnmxDEC1RCqkZWJMroD0ahFEm3HxVsqllMjZkPRT/ULxGUutB3Q5phVXgueMEkt7ygtNtYnxOwvkWuU9+O/F/7/t3z58z7K2E6QfJeyJCEU2biQNHW+f45/XTFPxPGaaRM+Xrhd9V7ph6/tpu1Aw1TiNiES7EnVCUDTfHkKT562KInHxBfp2QVLerSslappcyYIFWX53T4yaSo3vXhEiWl0p+4zVqZ6PCJEq7yek31SFAZoHDQvFnDW5RhYBDUvl/bq66ruqiKPvKbbBmBhJw28olIkVu1REq4Tsx6qF6EVrRPfok7NOamn09FuLSWcLnHPsiG5/v96ZCCJi3QhrPl3AE1+/kUImxxl//XGPN2ttD8/zeOCP99F/jwEccsphpR5OhzHj/Tm4jsv4ru4fthVMeWsZFVURxu3Te8sbbmsPu+Q6mUSdgkQH8mkRGOeapZoy1wzx3hCpYvAAjZ+c9Ql/eGYvfn9PmAtP8zhqlC1Rr8rBKjqhrCyscvBykHfkf22VpJ2MiKR6zIj83bhEomiapSokMzLROQXZfkPEQSv2qnRtVUlnifAfoDVbtDPINElkxZ/oCoqM+A7sIFGPaKXa3w6mhbpDirMz2+e0jfIUUkr3FN02zVP7MeghIdSZxqJfF0gUqrwPtKyWiKlpiRbLLkC8Sgn410laMpuSazrbJJ+lm2fxqjC/fSRBWULjB1/1KK8sg1CVkCtbffa6Ok4+qTRhttKUpSWa6+blms80KOKZVPrGcmXl0gpujUTh9DbTuh9dbVkt22shGX9B27iSuWpgp187a+rTvPbhCg4d348BvRMd3l9PRkDEugkWvPIez1z7B2JV5Zz5959SO2LXuSF3FUx9/UPmTZvLN//wPQxjC5GXboJpb89EN3TG7dcN/MM2g3zeYfr7Kzngc4PRt0X/sbVJuJBVTbqRiVALFUmY6yl/sbRMwNFKcG1q6vJcf/4SbntmMHc9ZrHm4AGce1QreshvSK4mLjsnE2S4TEhP02LxdYpVysSabpQJ0NfrRKplWxBCFa2WaEguJREL05TUZj4DlTEVRVEkzM6ptJStSFpS3o+ttsm2IsaebaKBTk6lxsKbjhJubZLsTinOzmif015zqIXASRbJcdvHYWNy+xlioohxtklSfqDsKSLSikjTkOhsYmOyhiuLhUJWPrdYuWotJK9f2lDFbx7tSyzicu1XdaqrTXAzkNXE4T5crrpDJGXBoEdkIWKEJNqV6C3Xb8VASaU3r1AROUeiaPkUUID0WiH5Zb03JmLZVmkCnmsuXpsbDIk3oc3sRCuWR19dQMjU+fzhPbfFXmchIGJdHJ7r8sG/HuPtP/+H3uOGcdotPyReW1XqYZUE9//hP9T0qeXoc44t9VA6BZ+8M5tRE/YgmuiikYttwKypq8lmbPY9dDucsrd0w3cLG2t0ImWQCgth8kXx+aSylFA+SV4V0apefPMSuO9pj+cmh1nbZHLlF1KE9TwU6sHQVbPwiKQj9TwUIkX3fatMog/RGtHp6IYycs1LOX/ZQEnnGCr9qevKVNNW5Cj+2feRS8pE6OSLgmzdAMqVnYGa2P33uuH9l288QW6NYBWyqnozuXHarytVI+4MtNcc+teNZ7d5bBOap5bV4r1l5+WzNmJiGaHp8pnrISHR+aSkJfNJSDZCJFF0rs8pQ9dCXvSIIJEsoxLQIZdk6VqX3zxQTTjkcu0lLdTWxCFaLtdcJinXnG5KJC9Vr67tjFxnoahEwvI5qBkBiWqxyIio19iqotJzAEMiuPmUXGf+NZBtkYVCrlUWN77Vhm9IvBM98OYuaWTa3PWcdsTQoJXRNiAgYl0YqfWNPH/9n1ky+WNGnXgox/78GkLR8NZf2APxyeRpTH/7Y6688WtY4e7/xc5l8sz+aD5fuPKkUg+lQ5j2/kqsiMGYCVtJS24r/MkyUi6ThVeQSFjrCpkcNaCsn0TFzDgkyiSKZacxcikuPFGnd69K7nsCfnlXFd/+okllmS7EKbVeohCFFqk+KyTF1sKKy2TrpxyjNUrro0wzIxXgZsWfzHFkfJoFdlImzXBCUpbZZpkYQzEhb61rJJUFMtGCEEHXEa1ROPHZ9xqrlUifHwFzHZl0fZd3P7rjE6yW1UoblZZtDVWY4Ed6erLh7KY0h5FyITFZRT7wJJ1nROWzKmTF1DTbIlEtn+A2h4TkaJoySM3J529YQmycFmhdLWbB4TIhbSD70jX5vPMpIeSJOhYscvjdnQ2ELZdrL2qmrs6C5FrZPhRT5FBVZuJAolZVPxYgXCX6McdWerGwbKtpYCfk/bnKQkU35LVWTKK06YZiQ287I3+bSvTvaxlNS66lneSB57oeD700n6ryMEcHjb23CQER66JY9OaHvPCjW8ilMhz94yvZ6+zjdmux4z2//jdVvao54cIu3hh7GzHrw3nYBYe9D+6++jDP85j23krG7tNnyyL9raF92s3XkZkWYEFdtUyIa2dCJCqToK7MWyOV8tqGxUi6xuW4I0zqBvfhtr+t4ue3J/jWRSEG98qr6reEREwMC6K20uYo081ILyFTlX2V4NlRFWumPN+4VCayQlq1RlKmrx7KbkNVWjr5ooN/TjUatzMQKisWHYRUKtNu896j1ULC0g2KSBSUTi4p1gU+zKhMwi1NUD9f9uEUihGxsj7F6FC0B0fP2xqw+iQ1ojo2RCuFpLauVnqvAjTkJALpkyafhHmOsqFwVTrckGbdhqX85HKAB7kmRWSiUNFPtciKyDE8V8i+a/PJlDR//meKinKDH1xhUldRKftsWSGkzQhDOAzZtESssknZf1l/wJboXKJWqj398eVTEn0zLFXVmxWCacaE7IcrIBxRzcbbRE9DMXlfnqeuNRuwpAn4ToqUvjtjNcvWJPnyaWOxQt1fQrIrEBCxLgY7m+OtP93L1Hv/R+2IwXzhjhuoHd69TUs7io/f+Ijpb3/MVb/6OpFYz1jdT588C03T2POA7qsPW728lfWrU5x87tgd30lb0uFPpO11ZCBiaTtbbPkSjsqE6BZkIoyUSbWjHgIzzD779+b6frX88dczufHvFl85v4z99q6GjHLKN0xxM880icbHiktVmmaDU62ITETE+riQapUIimZIKimXEpuM7CCZME1LImdhZWTrR9cKaRWZcFXVZ1YIJK5MpKG40h+pRVYhKxohP1WZT0vlX6J3G/1ZRh7P1Ms58VFIqRRURkU93B3/XLaGXVkUsNVjeUI02vp8+foxp6Cc7dXjtmo/ZLSZ+jxXzpcfFdU0RXbLJaWdzakm3CoKVkhBqkHE854nqULXhmwr73wM/7g3T//+Ft+9KkaluxxsTfYVqZDtfBKVbSmmF/3qXg25xsLlQqr9NKNmSu9J05IFhRWBZIMykI0W3fOtiETLjLDqsxou2lMYYbnOY9XyfdoJyOUdnnhtIUP7lbPf2F475Rg9EQER60JY9MaHvHrTP2letpoJ553EYd+5CDOye6YifXiex79v/Ce1/eo4sYdEwwDef+ljRo4fSqIivvWNuyhmfbwGgHETd/Cm3p50gExSG7y4IkV9lJ1Xjuaxdqk3JAXoFGSytXPQvArCFQweOpif/noif75pGrfemeWkY6Oc9fk6DF2TY3oOuMop33NkMnNciXxVDRHhM5oQoeR6eU26qVhdiSnvwTCE0Lm2THSuLfvJpRQJs0Xr4wEVg+S95NPyeLxatEJhq6jpans+NE1Zb+SKRAxNJuCs6kuoG7KNH2Uz2ziq+6nJziBO/j5yKYrMhs4pCtjc+Lakj9vg+6UMSkFIh09AM41FLReAE1E6rqgQYCMtx7QqId8oUTRfJ2ZEZL/p9eKOn2tRHRlUQ3gjDE1LiouG5mW8+GEl9z4bZ9RQl29dU05Mb4XWvDpfERWJy4i9Sj4JIVP5g0UlquXkJGVe3kuuKTT5TDONxehwSFm5eA6U9QLH9yBrlmheIS3bhpXRsN/CyI7Ie0t0fh/Jtnjh3aU0J/Ncceaeu3UGZ3sRELEugObla3jt13ew8LUPqBrSny/84+cMOnDvUg+rS+DdZ99m9oez+NYfv4cV6f7aMICGNU3M/mgBF1/bvftkzp6+lsqaKL367WBpej65MekAFe1JqrRTm6o4P9WWawE7BCGJfG1IU3pu0VQVTyrX9JVUeg7Xfa8v/3mgnmdebGHJMoerrupPmeEArlgQ+N5hRkRNUq785JLKPLNZEQVXnvfU8+G4aLocFclwPUkhGZb8NsMSUfGioCnC17JUxptaL/uO95ZUVLwOKvoLQStkZQi6ivRYcYn4WXF53n9/mXUySeuhop1GuEzSWn4kRQ91vJrSJ8w+wck2bmy10bYooJBVDddVIcK2TPqbG9/WOjG0F+v7/RI9WyoRU6tVdEhFVW31GWqaEGXXUa2uskp7p8ln5zlKB4YQ52wTsELOvaYXq2zdArSuwWtewaNvVPDkW3Emjilw1UmLsWzVksgqE5F9do20QgpF5NoIx8RGpUKZupoJ0Z9FquSa8DVkudQmTpjWhniHpael3/vSCAHKRT8UKzYqj+78yGVTa44X31vKxNF1DBuwmRZnATaJgIiVEIVMjin/fpwP7ngUXdc59NsXMfHCUzBCO0dE2d3gOA53/epf9N9jAMd+8YRSD6fT8O6LH+F5HgefMKnUQ9lheJ7HnOlrGbV3rx1f+XqbeZ3/ePuJ1ikUW8qEovKYbigC1iri+VBEnrPTkJQm3qFwGZdcUMUeIyq5686l/ORnS7nq4jAjq5W4PZ9SlXAt4JSrKESzRB8KGZmkw3HZZyEjES2ngBCiejleLikTtaF6WWqa8n9S4v54rUTu0FV6MyMEz4wKaXEKKuUUl7RVLin7DSlNXLRKjoenehLakr7y3ePRINZLqut8EuZbd2QaihFE2L5qyrRKoWWb/A9HjtXWagOEPDevFEG6345HMyUd19bt3T9m+2KEtvDHl09KGrDt2DdcF5GNxeZt+yXm0nI9ZFrkfOOJlsowi5oxJy/RKSsi5MdzVEEEULDFOy5eJ1WOobCKuK4W4q6Zst9sC/lkmn89Vc47s8o5fEKKS06ox8gmobVB0u2hsJwDOyufpZUQEuV4YFlC1uy07N83kU2tE1IOG6db/ffpR+T8/3WKxq2hhJx7v59kuIxdVazx1BuLcByPMz43bJccrychIGIlgFOwmfHYS7z3twdJrWtk5PGHcPj3LqGsz2Z69O2meOWhF1k8axHX/ePHGGbPEX2+9tg79B3ciz3GdV/t39pVSZrqM4zaqwM6kHBcJpS24mIjrHRZbDzRpuqlmg2KKRdbtZ/RTWVPoSJjvi7In6w8EcwfflgdAwcM5a9/Wc6v/pzmjGPLOeUAB90DWpYrfVkBtJxM5sl6iMSL6S5HTW6hMMT7CPHLNQtZQDVk9r2ndFPMPTVdXut5QuzCCYliea787blKLK6IWaxaCBaoire4pC+jFUJOIlWq4q9JPKfKByCRsKiMKRwvpvgKaZnU8ykhGYYpBQFtU5Zbgu/67/c8BLBtiRQZvnu70iDlWqWqNJdUqVtfh9cgBCGsRPSFNgQCiiasfnTNR3KdnJdcUp0L1fPRjBQLEEIRyGhS/WqrMWqGjCffUhSna0r3FaqUc4+nrp+U/JhWUayfVRE9qwyc1UJ6Er2hcqB8xr61ievSsHQ1f36khkVropx10DJOOSSNZpZLatGMq4hlBiKeROhwRfzvOcUoprInE51hWD7nbJPSGyaEuBXSMl4/4qeZbNQHUzfk8/BJGGysr9wFWLq6lcnTVnH0/gOpq4rusuP2FAREbBfCc13mPv82k2/9L01LV9Fvn9GcdPP3GLBvB8TOPRT5bJ67f3UnIyaM4rDTjiz1cDoN9asbmfrGDM771undWkMxb8Y6AEbtVbfjOwlFRIeVbSlOHJE2Hlptq+LyrfKYYSkipryQdEsmog3kxRaLiVBUJiUnKxOxeu3QERFu+E0d//7bHB55vplZ86v46pk6lXXDpOGy70Hl5KF5GaRCkt7SXdDCkr60IkrIrfpfeppMhKkmiHqSttIs8aDK1MuYPE90W7oB4UoIK4JhhsXHyjCEZLmFYt9Lzy62ndENISurZwi5yrWKx5lVBnWjZRvDkCiSp4GhIk16qFjJCcozTVkg5Fo3rxlLNxRd/23lgxZWE70fpPGd6H3C40f/8q0qulcufm2GWYyg2XkkQuWL0JUJq0/M3IKQUiOkolhhaFoqJDVcVky/VQ9VKU2VvizkVfsevZju9oX0haycOztb1OdpsIEFZZPqM1M9Ru2c6L+smLy+YhD0HgO9Rog5aqaJeYsy3HJ3X3IFjW9+YRUTB6wBNyKfWcUwIdhuQYheulnGEu8r58knS+X91HnQJZ1shIqfk5MFs0ZFuSJCwgqpovVFtkXep6bL6yKVGxcgWGU7PRXpw/M87n9hLolYiJMPHbJLjtnTEBCxXQDP81j0xhQm3/pf1s1eRO2IwZz2l+sZeti+3Xoy3pl48p+PsW7FWr5367XofrVSD8Crj03GdT2OPuvQUg+lQ5j7yTpiiRD9BndQCxKrLupYNkUKYtVChCLlSktjScTLdcBMSwQoWinEolAl0QzXkYkbtzjhG9aGaFW0rIwrf9SLsU/O4N47F/Pj26q58PMJ9huZRMunxDMsuQ7sJiWkTojtQMiV3oKFrIrkpIQUGZbqVelID8vyATLHR6plkvRUOjFaJa1wNE0mUCcPmpq8rQrRHvkwQ4AiFnmVpnQdiY4Yylojl5YIS6pRiI4RLpIQ11WRErNIkEAIS6FBPRdW1aqWtOrZYB+iyI3vWWZaisQUZFzRKqTXoqWIXaxYNABF/y4np8xzTWWv0aLIkskGcb2v/UvXy+t90mhGIeyblrpyDCshZDDbXOwf6eRkXJ54yWEpvaKdl/eiGcrzSxfiml4r0SnXgVgFYIhRq6/N0y1oWSb7tirkvabWAeOEFOeyvPZWkrsfNKip8Ph/X7EZ0KcW9FpwNIgmINZb+kxG4pDKqiilqtS0YoqMWeraT0BORenMiLI0SUNIpch96MbGpsG+AN8MAxXyet9rLpTYaVWRm8L7n65h4fIWLjx5NNFIQCl2BMFZ24nwPI+l70xj8l/+y+rpcynv35vjf/lNRp90GHoPaNGzs9C0von//v4e9jvmAMYftk+ph9OpePHBNxkxfiiDR/Uv9VA6hLkz1jFibB263gkLia21VgnHwa0GdBUFUWQjFBUiAsVS/2hVscIyn2JD1EMLqbYx4oyumWGOOOdgho+u5u9/nMFf/gvjhpVx4VEp+kYawDQhUwBXExISVo7mvt1Atkkm1FBMIiqaDk2LJFKWbRUCVUhLqkkPS/NnOyvj8jyJlmkhMHXZR7SqKCb3x2wXAOVZVsjIfkNRITXhuJAcR+mpNEDPFrVU2WY17rhyX9dFb+TZQnqblrKhD2auRYoOEr1UZK0NAdvQ4aBcSEqkTEXCcpISdL0iOQxXFLVJmi7pNSNUJGF+0/Sw2odPvs2I6mLgRzIjsr1pqaidLsULoDRaQMtK+eyd3MYWHlZ5kew5WpGohV05h9EqsOvBTcrnVzcSynuLkD7bKm2vMo3ymoimzp0LmRby6RT33rOS19+xGTfM5urPryHRv5/4w5l+VCok1ax2VkhluFy0gplmiCrNWT4p+zVCoKl0ZbZFLUhs5fFlyTj8woVNpRlNSy1COqkqdgeQydk8+soCBvct46C9dx3562kIiNhOwvIPZvD2rfex8qNZlPWp5ZifXsXY047CCAWnfGu468Z/kk1n+coNV5V6KJ2KBZ8uYf70xVzzq0tKPZQOobkxw6plLRx2wh675oB+ijIUA6NVJm0rLsTBF4+3tVMww6LrySUlxYMh0RA7p7y2NIlMhGL0HzOEn95k8sqT83j08Uauv2MAJ+xjctr+qwiblopK6EJCPEfITEV/saNwPcg1SMQmo9J8RkQiOgWVjrRtFZnRRaBt9lL9CpXI2opJKjW5VrRnnko1hcvAMpRnmory5JMSnXEd6YuZV67smg45TbZL9FKREUd5iSmi7Ee+NK3YZNzxrShceQ+aJmSlvG/xXLZ3/QdoWCjkwSd9jq3GHAdzgHwUvq9aLinb+8QD5D3Y2eL/hiUEye/HmcsocmnKZ9nSWExv+s3gY8p8F2UZsgGOkDF00Q36UcNMczF9aim7CF0XYhqKQ8UA8JbKZ2UmRCvoORvI1frV9dx6Z55FS10+f0SeM08w0DOmauaunOuNsKQbU3NUcYMqrihkVcpdRWUjFXId+u8/XiPXsE/k/M8b2lWkhjcubDDDG6fxS9BB4ak3FtGSzHPVWXuhB9mdHUbACjoZq6bPZfIt97H03WnEe1Vz1PVXMO7MYzCtoBJyWzB36hyeu/cZzrjyLAaNHFzq4XQqXrj/DcyQwVFfOLjUQ+kQ5n7i68N2oWGjn6KM5TeYtm5AOL5xhMUtSAWfHzkrpJS4XS+2pjEsFbGqwjBNjj2mgv33tHnw0Rae/rAvk+fWcdYhqzh4+Ep0KyZCfEtZSOABjqQZC0lJf+VbZVIPD5HUpp1VNhZKxK6r9F8mCqhoWaRCiJJnS6rN7zGYbRR7i0QvMFVqzozI+PUwZBvEpT3XApVDZKJ2MkrEXVDu6QghiVaqpumqAtH2ixlcFZnxim7thZy8NlwuZMUviHAL8t49V2wS0g2yf793oWGK2NwIb2xbUciCtkaIhZ2Xzy9kCFE1LPnc/PSnr2drXinvz0NSiKGotJxCidwzjco3LS+C/HwKKiylKYtK6hWnGO3TLTmP2SZJN2cahPxqlrKSSEshSKxSRdGQ6FamFSJyzmcsjnHbg3kcx+WbF2SYODoPerzoJZdX0bvkauUf1yLRwXxSzmMuKx0hrLjo3nDUe2iTMo7XKkuRdlWSUCysaG90XOIeosvXJnltygoO3acfQ/qVl3Qs3R0BEeskrJ29iMm33sei16cQrS7niO9fyt7nHL/bG7JuDxzH4db/90cqaiu54PsXlXo4nQrHdnj54bc48LiJVNR075vWnOlrsSIGQ0Z20MRzexGOS2SrPTZMSmpiymQ39idzEdG8VSGCds+RiJYWkohXuh5aV1FhtPKVs+GIsfXc93IN/3huIC/0quO8E9KM2bM3aI7od3IpFUHyRHifbxUyYsTkMd8+wK/q9N34Wwsy2dupol2BrtrVeKqXoF9BqWkSOYoklDYrK2QtnICs0ogZsaJtg27Kuck1yXvyUI2jla7KsCDtClkJxYRQFdKI1iuq+hCmwDGEYFIjxMmzhcxkm+W900ZS0bZ3oRGGRM3Gn0soItGoXGsxkpNpkO3L+22seXJtIUqptUqIrjRehgXVe0CsSohiRNmH+F5zuVb5P14r76WQVNdDSMbuVxmG4qA3FY10nTyEhsjnkFkv10fjMvHyitWAGcEuaDz2WhVPvxmi/4AwX7/Qo4++AnIIAdRMMWUNRRDPrzS4a9Q1YInYPpcCuxVc5e1FoTh+wxKiZkaFlIci8tlv6vpue067QO9Q1/O477k5RCMmpx+5iyLjPRgBEesg1s9bwru3PcC8F98hXBbnkG9cwIQLTsaKBSW824vH//4Ic6fO5rp//Jh4+Q6ahHZRTHltOo1rmzn23MNLPZQOY97M9QwbXYtp7uIiiq2lZ3z4QnTfFkP3RHdjZ8BWEQddRTLyFRIR8Zty4zFyYJ6ffLmB9+ZW8fALIW66u5oJYx3OPjbLgIHlEv1JNynT1ogqILCFNMV6y0Qfk5Y35JoQR3RLUm6ep7RNtqQWI5VigZFvLTaTtrNCOqycTNi+sNxWxCK1rhjRwpPIVzyqqvzCQjqsMiEi2RaJWoXLimJ401LbqShXrlVIQSiifKoyYK9SXmjlkpbMNApxCyVUKlJNHa4NRlnRcgQ29gjz3GIUUl4g58C1VUshFd0Ml0HzCokYhTzQlSVIKCJEOZRQadWkIjwquhSpLFZyeraQI8+Tz9MwlfA/Jtq2bEIihI4j77dltaQFDdWiKK0qXPFYtd7kb88NYvGaEIfvr/OlC8oJazlIVitCqqvzoEsEMNusrjdNpYENIVh4ivxGlYFxVvnhpSVKGa2SyuFMU9FHbGvXdxfAGx+uYOHyFi4+ZTTxaJDt6SgCIraDqF+4jHdve5C5z7+NFYtwwBVnM/Hi04iUx7f+4gCfwarFK7n7V//igOMO4vDTP1fq4XQ6XnrwTcqqEhxwbPcuPsjnbJYtaOSkjvSX7Ai2JT2jh5RdgUqrmVGZnHNNMmH71X2uU3TPN0yle4pCtAI9WsNBh8XY9yB48XWbp57P8KM/W0za2+a0E00GDqgVkb6XU5YaMUkd+tWHBWVP4Shtmq0E9r4Nhd//r3W1RE1yaWClRNhySisVUeL3TH1RB5VpkrY7uZSqmGyQlJg7RIiVY8txynoX338oCiEU0XBU1Z4BXliKEpw8ZJISkdIM1bbJlJSg1ywESFdThUbRsFbTxJesbducti75+aSch3CFbAtKvO+oqsR48X3mMxKtspPgpJW2TaX5Yr2k2CEUFQJpKiKbWyfvM1Qm7yleK5FHv/LSUMUG6MUIlt+CKtsqKeRUvYjpbRecDF4uz+uflPOf1wYRMj2+foHDpEkRICXXkF9oYMXks/D7N/pRv0ilENN0o0QWI+VyzRohdUyVDo5WqgKFgrzWisvnmOgj5KyLpB83hfrmLI+9tpCxQ6s4cK9AoN8ZCIjYdmLtrIVMufNx5jz3FqFImP0uO5N9Lz6VaGX3TjeVEq7r8qdv/xbDMLjm5m/1OEuPVEuat5+ZwnHnHk7I6t5fuVXLWvE8GDpqF6cl22Jr6Rk/cgZssElwHSWeV6kf3wrDVREbK6HSe8p6IVIG4QosJ8PJJ8ARh8Z57vl6XnzT5YNpaSbtneXUo2sY3M9V1W+akKN0k0SRdJWuKiRVBCwvj/mtkiJlMoZ0o1RSmr2EDBRyqn2PK15W+ayQiUyjGpsuqS3dAFRkx1NO9uGoIjPKfsLvwZhtVeaoSlOVSxWrDa2YvD6zvlh9qBmSwq0YpForlRXd+3VDCdrDEqWKKBKUrJfXZFtU+k+51KfqZZya6odZKMh2hqUIYVQRUWU3ollgN6qCAxcqBoqhrYeQRmugENNsgxLdR+VxTe3Tzsp79H289HDR6iNdryJO6j3mMmBp0FoP4SpaUgZ3PjuYj+YnGDuwma+csJzq4WPk87Xzqo9jRIgnqtuBh+xHjwgpDUVEOxY3FQlVukJNXYN+kYP//nxC66pihnyrkLRwWWd9WzoVrutxz9OzwYPzTxzV4+7VpUL3nhV2ETzXZdFbH/HRXU+w7P0ZhGIRJl16OpMuOZ1oVUDAOoon//EoH785lW/+/rvU9d+FAvBdhNcef4dcJs/x5x1R6qF0GIOHV/H3p87u+jfg9pGzaJVEnwrpYv8+U0VnCpbogrJNUmXpp9RsZQuhGSQScNapCU44IscLkzVefDXHlOku+4yPcvoJYYZYsyXKY4Zk8o9VS6SnvC+kW8EKi9O+aSqzznKJoOEpfVgILEc1DzckgpZqkPFHKotu9dnGYmorpGwtQkoDlksWfbN813tbpct8S4m88j7TVRViKCLRqNZ1sm+Q1GakAiJJIVbxGonsmFGla4oJ2XTy0LSsKDrPKqLn2jIu1xXdWbS2aKORbYCyPrIPu1AkmKaysSjvI+c/3yLatJDSr5X3l3MVTkjULKuuPycvKdtoVFKCbkHOq195aatrAJUezapoo6GIVL4Vcq1MnQn/enYw6azOF49NcfxBNrpXI+8nr4hsqExV3yo7k0SvonVIuAxiZaKry7cqwhot+sWFK8TKJJMUTzrfdkQ35DPzo7R+sUkX0IFtCs+9s4TZixv50kmjqK0M5DedhYCIbQGFdJZZT7/B1HufomHhchK9ajjsOxex5xeOC1KQnYSFM+Zzx89v54DjD+aEC08u9XB2Cp655xWGjB7A6H2Hl3oonQIr3E1uG+0jZ5FypE+iEvz7USMzJBOu4wCGCMOtuBKqR9TzSQjFSdQlOPOsKCccn+fFV5p57qUUP52WYZ8R5Zx+YIYhAzTREGWQfeumeIXphlTOebpEPCLlcjwnq4owc4Aj0aR8RgnWU8pqwoF4JegxMQl168CJKQKnK88yRSB15PVlSrejGzKGVCvUL1SkAqX7qlfFCg2Qb5IIjhGVbcywkMNwVP72dKVvy4MbleOl1ioLCU21M6qXcfsGrgXl4xbxQI9K5C+k0rM5pZfa4AEWl/OuaaoJek0xBWhGlOWDInfhcpV+zEkE0VGE1ffjcmylsyuIt5sRBZSGL69Sya4DsSoyJLjvGYs3ppUxqI/ND662GNBXRSRbloGrbEr8iJsZVsQpJMcupIveazlT0rtOXnmoqRRutlUeS/SW6yrbKs+ZUUlLhiKKHCovvF3Ymmh7MH9ZE0+9sYhJY3txyPi+W39BgG1GN7mj7lo0LFzOtAefY9aTr5JrTdNrzB6c8KtvMfL4QwIfsE5EJpnhV1/5P8qqyvnOn/9f14+y7AAWzlzK7I8WcNUvLuyR769bwbfAcPMqzadgKpG6YSovrLBEQMKVEukwo4pUoCoTU8TCeU47zuLYA/O8+FqW59+I8NN5Q5kwIssp+61hxBBNUoG6JsTItiVCEqtSrZEUIfSQtFy6WVJ2PjHLtypLBeXVZechEQPXKlZq5lOSnnNc0ab5gntLCep1JYJvXQNrZknLpnxKiETFQNGAOcoOxIhKOyI7o7zXPMj1hbBKp+ZSKh0ZVcUQuupgoFrtuJo4xNt5pSlTnmVoEjEyDUljFgw5F7Y6n54rxE1DImRZZbMR66W6C6gqVLcgejC/GTiGkL1sq6qM1KB6mCLBKSFIri3v13Ahux5aw1IVGakG12H2ml7889Fy1jd6nHJYljNOLsNMqP6f6FA5SEUMFeHKp9Q4m+SYiT4yrlSDfK7huBoTEKmVc+eniuMqKmgonZqhyFchI0Q6UlE0Je6CurBkOs8dj8+ktjLKBUFKstMRsAoFp2Cz8LUPmPbAsyx77xN002TEsQcx/rwT6TdhdHDhdTI8z+OP37qZFQuW88uHb6aipoOtcroonrrzJULhEMeec1iphxIAihYY+WTxMdeWtJOdLYrKnYKk6syI9AGMlgnpSKWEeBTyoLnEonDaUQ7HTUrzwtvw/PsJfjFvMCMHZDnp4BTjx2ro0UohVxuInrK5MCyoGiwRqUJSpeJi0obHtYvWEJkWCDVC7QhIDJJxxapVEYAiTUa4SPByTSJ41y3lZZZUhMSCiKmsqjRJGRo60CzRGsOSKJdhSiWm64jmTVPnzUHOXVhVZeZT4ncGioy5Ml7NKNpL6EqkHlXRrkiFqkx0RR8XiqvoWj1YWfnbjAiBdW1FEsNSqVrIynvPNAnJLSQlaue58rtljRCkfEqifWaZvM9MvUTrwhWQTpPLazz0zmBe/DhEXTX88PIsI8dUFs+pGZHonxdWLaoyioh7gCGp4HB5sTLXyUkBhZVQn29Ion9QrDyNVrHBI8xQkUYtpHRvZVJIoHkbtzVqjxJ5iLmex51PzqI1nef7F+1LtLtExLsRdvsz2rp6PZ88/AIzHn2J1LpGyvrWccg3v8SeZxxNrKay1MPrsXjiH4/y+uOvcumPv8KEwyeWejg7BZlklpcefJMjTzuw23uH9RhsygIjXCYRMCehSJoygI1WSrWekxOiEq0UcpRugnyzcouPQqKWqNHMaSfkOOGwRl6fEuG5d+P88cEI/fronHCkyUF757AMVTCgGxIF80lfvEZNsvlixWUkIf5n2fXSykjXhQjGaoRQuAWZvHVNIjJOQYm9lb1DeW8hBpkGiY7FqlU/xrxEoAwTEtWyv3gTrHNkX4YhBM6Kiyg93yr/++kyD3nfrmr9E4opGwqVfjVj8lxGpUCjEWnr4+SFkPhWDY4tUabm5ep8piVVG4mrvpq2jM0tCLkrZKWqEETP1rxUETtFGHVDtGOG8i7zdGhdoc5Bo5DD5BpmpfbijleHs67Z4tgDs5x9Vh3hsn6qkCNbJGK6IWlX33cul1SN5R2l96pW3QuUTsoIyU+8l7LKaClW7EKxC4FP3vJZ8JKqP2iDaglVvnFbo7ZoW40Km95mJ+HFd5fy6cIGzjt+JIP7ds0igu6O3ZKIubbD4slT+eShF1j0xod4nsfQwyay90+PZ8ihE4M+kDsZM979hH/85DYOPOFgzv76F0s9nJ2Glx5+i3Qyw6lfPq7UQwnQFn6jcb+Sz7SEHJT3E81OYaWQLisuk3xqnZCzsj4yseZbJZpjRoSkFDJQI/q/cCHPcQMKHHV2Je+/W88zzzbwr/vzPPCkxpEHhDjm+DKqI45M8m5BdEZmRI7npz/NCER6QesyIRYh5cretEIRqH4SSTE9MFVvzUIWkuslomVnZYxWOaAqII1w0c090yTvxbCEgMRqoCoL6xWZs8rkHPmifEMvEkg/6manJeIUy0sKNJ8ElK2EU1C9Ly1FJlNKdK/SwdFKiVAl10LjEklVptcrPVVYUoJuXlk/1AKmel95SbNm1hUtKMyEctFXba5CEUnXosbruBCtIZPO8/CUUbw0ZwS9KrJcd2E9o8dVQkUdVPSFllXi6q+rggbPEcKa04QklfWVqGi0ukjYQiqNaFqqm0BMKjzDZRs3si+kiyQqWqm6H+RAV16J2Sb5bat9tW1rBMUm7G3RfpudhAXLm3nitUVMHF3H4RP77dRj7c7oEBHTNK0aeAAYAiwGzvE8r3ET2znAJ+rfpZ7nnaoeHwrcD9QAHwIXep63CevszkHz8jV8+tjLfPr4KyTX1hOrrmDSl89gr7OOo6IHVut1RaxctIL/u/jH9BnUl+/95Tp0fRebgu4ieJ7Ho39/luF7D2HMpJ4h0u9RCEWgamAx3ROtksczTcV0nqdJmtDOKasFSyITmSZVTZcSQhWvg2y9aK50A+wMptPCwZMMDtorxuz5Ni+9nuOZ12yefX01+02Kc9zBUYb1A81wZGIPRWUMuaTsr3mF8syyJNWVTUpEK12h0n/KLd7UIFIFRlbShFpYnNybVkLuUwhXqdZMZRCvAFyI1UlUJ6Q0S6E4JPoWezKiFwlquEJIUaZRCeKdNmnM9cUCATdX9AZrWQZGXF6Ta1XVjFnVOklFcoyIRIZ8S4d8Un7ivSWKFymTlKwWKkaRmlfA+vkqFWmKyS0OxPuqQoKsMqPNCS8rtOBqUd6c25dHJvenOR3iuD2XcNbhjYT7DCtaR6Qb5HMupAGvqDmL+EUbBRlvpFzOieeKlmtDr0xV8AHFyGHbQpH2TbndgnzOsHGK3CsAKnLWtnLSLWz6Gt7J1ZXNyRy3PzqDmsoIF54UyHN2JjoaEbsWeNnzvJs0TbtW/f+DTWyX8TxvwiYe/zXwB8/z7tc07W/AZcBtHRzTxgduamH+S+8y57m3WPbeJ6BpDDlkHz73w8sZevikQHy/C9G0vokfnfMDXNfjhv/+ikRFz3LPb4sPX5vO0rkruPavV3frG5jreuh69x3/VtF2wixklf4mCrhCfvxIhK5E5ql6iZDEe0l6slAQEoA6R3aymNZyCmhOgTF9s4w5x2bdKWW8NBlefyfFe+/DkIEGx36uF/vvG8EKq+rGaKWk68JlKi1oyk8hqarzdElruUoLZYQBRWY8WxGYemicLwSjag9VbeiI1i1eK4TRDImVhBFTNhN5IR3RGol2OXkhQW5eIm26pqJiyhnfDEskyrUVSQ0rItmizFnrJTLm2vJejLC0mIpWyflLrpNIUEpFtwopISK6Jr5m6WaptERVhvrpwcx6lT6MqjZJMRHN9xoJq2bA+tlCyIwIsxpHct+zFkvXRhjeP8c3T5vHsIrVco4z9RI5y9bLeF1bPsKk76emSQGC33ooWi0EMp+WVHKsVhnhNhXHZoSVrUg7ctRe29WWV7WtkNRCm358c1WUO7G60nZcbn/0UzI5m298cTzRSDBP7kx09OyeBhyp/r4LeI1NE7HPQJPZ6Sjg/Dav/xmdQMRyyTQLXn2fuc++xZJ3Psa1HSoH9eXAq7/IuNOPorxvXUcPEWA7kU1l+On517F+1Tpueuz39B82oNRD2ql4+LZnqO5dyZFndO8G37qu4XletyaT24S2GhxXpdh8GCGJ5riqtU8hK2kprwy0lGphU6EsMJSJqKaiSp4LhVZwbOoiDucdY3HGfvW8PbOKlz6I8I+7W7j/0SSHH1HF4Qca9OmttEZ+5MgXdBeUd5VuIvk+Fc3REWsKz5UCgmhM+X1FhCzaGSVwr4eKPmDVqUiVH4nx1HGUxiseUz5krryukJVzoxsqOqVLw/FUI2iuHMdDigDsFUJqnHzRvkK31DlLi9P8mjnQtFRIpq382jxPSKany1tzC9KWKp+GvAWG728WKb63UFQJ6R2pLvU8CMfIW72Z/qnBG1OjTJsborbS4erT1rD/qCRaPgnRIcrAN6IKHMIQciHryOPRcmjNSTrXVdWiVlz8y1L1Mt5Iuar2DMln4pMsvz1R25Th5rRdvk7RVGPQ9KKOrH3l5La29upEPPzSfBYsb+ay08bSv1fPXTB3FXSUiPX2PG+V+ns10Hsz20U0TZuCeBrf5Hne40g6ssnzPP+Otxzov7kDaZp2BXAFwKBBgz7zfCGdZeEbU5j73FssevMjnHyBsr517HPh5xl1wqH0GrNHz59Muigc2+GXl9/AvI/n8qN//5yx+40r9ZB2KhbPXsYHL0/j0uvO6bZO+quWtfD+60uYN2M9e07qQ22fBP0GltNnQBm60cPSye01ONFKmeg9lDs9EkFK1QtBi1YqjVQIQlXS7NpOqshIq2yvqQhSplHE85oO5EDPEjEyHD0pzFGHmsxcZPHSOwbPPFPP00/D8KEGh+7jsf8onXisBqJ1MtFn1knK0gyp6sQY4EHDYolChZTthq1IomYAKloFKlqTEU1ay2KxW3BUb8bkGtUeKacibEq4boSUx1hKzk/Yt3ZQBNPX2YXLxZ5CD0sELRSVSJEeLto1hKISsWtdBfky0Xel2xjIGmGJOuWahezZDoS1olVFtFKOn+iNlG8q77KyfuT1Cj6ZvIz3X1/Fx9NyZPPllMVczjo6zfFHx7C8CvE+yyfkM8q3CoEtJMGrlMhezIDWDKBLVE4zxOrEURWTdl4Rrjb64UJStU1qR1T8lOGWtF1tDYf9tPiWKiK3pbVXJ+Htj1fy2ocrOGb/gew3bnNTeoDOxFZnCU3TXgL6bOKp69v+43mep2mat5ndDPY8b4WmaXsAr2ia9gnQvD0D9TzvduB2gEmTJnkg5Gvx21OZ+8JkFr7+AXYmR7yuir3POZ6Rxx9C3/GB30mp4Xkef/ru73j/xXf5+s3f5uCTDi31kHY6HvrL04SjFp//8rGlHsoO42+/mswhxwzhwKMGs2pZCyuWtDDzo9WM27cP+x4ysNTD61xsSoNjhmXij5YLabDTQjiqNCFCnhLyh6rE1T2nbC0iFZLicqR5NKF4seVPIS2TrhGGeDVaOMG4vUKM28eiMWnxzlvrefOdDP9+FP5jVDBhdIEDJuYZP0bDqhqsjETDkpb0PBGXZ5slUqUhhCJeCbkKSas2zVdVkjqU9VL+Y4q0aSHx1LJdpAWRJ1GgllVCLMyQiMpb1kiqz05LlMs0oSkFlQOEhJiKbBGDeAHyddBqQIUlWq9QVDnlt6rIYkTOVctS1YtS+abhSarQ8LsCpCVVmVPVlGZIzl1lfzx0VqzIMWNBlBlzHWbPnkwh75KIwwETPA4YnWJ0zTIMU4fQKLBq5DMwVScC0wAUgXIdIa5mAspMifbF+yn7DBXdzLZINM8nTHZGhPV+6tZvo+XDTxluTdv1mVZdWyFXW2vt1QmYt7SJ+56by5ihVZxx1B479VgBitgqEfM875jNPadp2hpN0/p6nrdK07S+wNrN7GOF+r1Q07TXgH2AR4BKTdNMFRUbAKzYlkFnm5M89Z3fsPjND7GzeaLV5Yw99XOMOv4Q+k0cE1Q9diHcfdOdvPCfZzn/uxdy8qWnlno4Ox31qxt56aE3Ofmio6mo7p6l3smWHBpw3JmjNzzW0pRl2nsreeiOaTTVZzn61BGlG2BnY0saHL8CLtMkE7k5QKJe2VYhLpFqwFWibk9Ij+tJFCXTJOQrUw+OB+gS8TFjEuFxPYmQ6BZVlQYnHRPhxAOSLF4Jb32o8/70EB986hEJe+yzZ4b994ux514WFjkhWtlmiVh5nvKwygjxCSek8lBzVKo0IpWRuSaJzOVa5HemSUhdKKaiW5qymvDkdel14OXk/bo2GEkgIefFcSChGlqn16sqzohUWparVKTmit7L0CWdSEj217JWhPie8nNzsmCpKk5NA6MC3PVCWAwxkW1IhZk5K8es2R6fzmylsdkDcvQbGOdzJw5l/BgY3XsNppsVDVa2TohdvI+MA5TjfUKiY6YqhDAsaSEVioFlidFs8wo5B5FKJc53pPrSLhRF+V5BCC0qguijbcqwBNqujmBdY4a/PTKD2sooXzljHEYPLaTqiuho3uRJ4GLgJvX7ifYbaJpWBaQ9z8tpmlYLHAL8RkXQXgXOQionN/n6TaF5+RpWTp3NuNOPZsRxB9N/nzHoZkC+uhqe/veT/Pd393D8BSdx4bWXlno4uwQP/+0ZXMflrKtOKvVQdhiGqTNwWBW33fg2J39xLH0GllNeGeGw4/dgr0l9uf3X73DkycMwekqKcmsanFAEqJQ0I0gUxV/sOaqiMlq58T4TdRBpVhErQzRVqTXKsNMT7y8dIXcaErHxXDTdY+hAk6EDHc4/Mc/spSHem+oyZWaIdz5sxbKSjBtlMGGkx969clTb9UKK9HVQNgAKNpTXQawczFFSUGAomw3Nhrxyz8+0KILhiK7M9/kKl8m26+arSFQCQnkVjVPN0b2CkKfUWuW63yhFAPmMEK9QXCoOw+VCGPWQvHczLCap4XJoXKhsOcrk3OmGnBfPATtJMm0ze2U1s5eE+XRJjJVr8sB6EnGNMcM19hwXZa/xtdQM7Q/VQ6F+ATS0yv71lBCuDe2f0kL4IlWAKy2UyvrJ56h5Irz3XCl00C3R2uVTMk7NE91aSKVdbU9VrSpN2JZShiXQdu0oUpkCf3lwOp7ncfU5exGLdE2y2FOhed7msonb8GJNqwEeBAYBSxD7igZN0yYBV3qed7mmaQcDf0cuZx34o+d5d6jX74GQsGpgKvAlz/NymzjURhg/dk9v6ifTgshXF8Y7z77N/138EyYdvT8/vecXGLsBUW5pTHL++Gs46ISJXH/7N0o9nA4hn7N56r5PsW2XvgPLqamLE4mbLJhVz9TJy/nBzUeXeoidj61pcNINkqbyfZ/MqAi3sy3F9jRQnKDTDeKXlW6U354jhMZRAn6/96BpCTHLZyDfqNziPYlcWTFwXGzXY+aiMB/PDTNtRo71DRLlGVTTyrheyxjbdz0jRyaIlJcJMaweojy4VKuleJWk+ZLrxRg1pDy+ygcqTRRKK1WtqgHrVRQoKynGQkHGUjFAtHL5ZiE6RkjOQ7xWVWsuk/dpVUgaFA1oE02qGCqkq36OiprpuKE46zMVLEkNYO5Cj9kLYNlqAw8Ny4JRwy3GjTIZOwwGxpahuzkR6EerhVTVjBSC27RUNR1Xei50qQrNN0nQKl6jKmI9IWWmVbShyCWV0WxOPq/GRULKYpWiq1N6NEA+s5o9Nr5GtnTtlMgRf1tRsF3+fP80Fq1o5pvnTWDEoMpSD6lHQtO0Dz3Pm7Sp5zoUEfM8rx74zB3Z87wpwOXq78nAXpt5/UJg/+09bigWCUhYF8asKTO56Yr/Y/j4kfzwnz/ZLUgYwGO3P0smleX8b59R6qF0CPm8g2HqnH7RXrz/+lIWzFzPknmNrFzaTHWvOOdd1TM7IWxVgxOrVlV+toqIqCo3v+lz24bNuVZl8KmLLileK9s4BYl+xWqgrEZZR2TECDSULbrQF1JKi+ZBIYMZCrH3aNj7gF54dpYV81Yy7ZM802cbvDhnNM/O0jFecxnWL8eI3k0MGpxh4B4p+vQKYfhu+XpeSJdVJq14dEsiZ54OulP0ysomVaGCJVGxUFRprHQlUNdVn8qsiPxT64XIeAV5r4ZVJD/5JERqoNAIehWZxrWsSPZi+eqhLF8bYckqj2Vrw2RyEl0NmR4jBsMZJ2iMGaaxxx5lmF5SiEw+Bc3rpNNAtlVSu7lmIXlVg5WVRkEVC+RVxNEAN1zswxlRrdTMsCrKaCOqd1QMwFLpVzsnZrS6rjSDZbJv/3P3sTXX+12g7eoIWtN5mlpzXHzKmICElQjds6QrQJfF8vnL+On511Hdp4Yb7vslkXi01EPaJUi1pHns9uc4+MRJDB3TfcXs77yymOWLmpj6zgr6DSrn8BOGcfZlEzAtHcPQcR2351VNbg+shJCpTT0einx2UjbDYiPht+cxQkLCPNVH0QqL8NsrQNUQiUq1rBCfLXRoXgUUoODI6508WqSCAQNbGFC9jpP3ayJXv5J5a6uYubKGmUsTPDe1D86HUqQUMvP0663Tu3cTtWV5asssaioqqamE8nKNhJ7EKK8GIyHaMsMSAuNr0OysPFapIlm5BtCVnsyISNQrt14Im5PHs3Ok7XLqU2HWNeqsa6hibWuctQ29WNEYoyFZJCRhCwb2cTh4zyyDemUY3NdjwACTkKUigrEqKDQojy/V5BsN3Ay4upzDTFIahCfXqoKDkJDJSHmxUXlbawi3IJ+VT8JA9cRss1hMr5PjhOPqc4qpxuTexvvZWmVkF4x+bQrV5RF+8pX9CO0mC+auiICIBeg0NK5t4Efn/gBN0/jF/b+msq6q1EPaZXj8n8/T2pTiwu+dWeqhdAgP3zGNi74xiaNPHcmUt5bxv/tn8u8/vs9hJwzjhLNGE43t5tqRLel+Njcp+9V2fpNq0xKCsyGVaUm7oGilmsBj0oIo2yyTv2ZJWlC3hGiU9RXCpoxGw+EYe/a12fOAKrAz2OisbC5j2dIUy9eYLKuPsnRlganrNQr2xuRA0yAWa6YskSRe1kzYgogFYaMKizC6nQEzjGdaeOh4uTiOZ2A7BrarYxdC5LIJWjM6yZRGMmfiuBsT9ZhVoK48w6gBWfr3TtN/QIIBYwZTW6Whe2lINUNGmao6eUgVpCl2a0FSgjFlAost6daC75uWk/NhZyX1q+tCcs2opCGdgmrCHZGoI0iU0bf08FHISgFFKCu6MTMOZgqpeo2qaFlBOi342NbKyG6CgISVFgERC9ApSLWm+PEXr6VxbQO/frznG7a2Rbo1w0N/fZoDjtuHkRO6b8n3/JnrqaiOMP4AsfM77oxRHHfGKJYtbOK5h2exYNZ69ty3b4lH2QWwOYH25iblXEoIgR952eCwvoV9uKoqLxwX0uGnEzUd0CXFZoaFOOQGKwIRglwG0wwxqC8MGpGX/cbrwLNxG1fQ0phl/bos9esytDbnaU2btOZCtKY8UhmXXM4j2eKSzbrksiFc19zQCQgdNOKYhoepu5gGmLqGZZn0qfNI9M9RHm4kEfWorvSoq7LpVQFxrR4RyVcJidRNiLZAqAbymqQPy/oIWWpaJYRKMyUa5ZlyzqyYkKFEDApRed6IQsiUNLCdVxo8Tdl41Cij1JA0Ercjku41I3J8v7m2nS8SZNdWfmkxyKlWRla8aN4a8ht4d9/KyABdEwERC9Bh5DI5fv6lH7Fo5kJ+cvf/MXrfsaUe0i7FE3e8QGtjkou+f1aph7LD8DyPgXtUMnLPXjzz4CyO+vxwItEQruMycI9Kxh/Qn2cenBUQMR+b0v1savLNNEmEK4+yPEhAeZ+t78N3ag+XK+8rlRLzEDG6aYGnCFz1EHkutQ70BtnIKUiLHish+8nl0c0QlX0SVNblGN68UiJKOGA3KCuHiJAg3YRUk1hSZFrkmFYMKvtJ1WU4IoUFhdyGlKSYntpKh1WuxukqX7MW5ZKfEbKl26BXiSYrFJNjZppVO6S4apKdl/8tU7URQnR0WliIlW4Kwc02yrahKGLyquwmCikhfX7z7ZDzWW1X0zIZu98z0ozK+3QLUO23hrLlM0j0luN308rI3aI7RjdGQMQCdAiFfIEbv/wzPpk8je//9ToOOO6gUg9plyKTzPLgX/7HfkePZ/TEYaUezg5D0zTCEZODjh7Ck/+ZwScfrGSvSX3Zc1JfmhuzvPfaEsaMD1y2t4j2k7KdkwiWnS2mxvzWQj4Z29w+QDRbTpU03jYtISNtJ3g/telDNyHRSzUpL6g0Z7RYDejkixWfrkrr5VLqfxcMRwTwkbhEhOy8iixZQuwcV/lwOZJGLS8TW4pcs5jDepoUJURqpfrTBcp6QzYOrWuEiEbKRehf0U+OrWlFgb3rKO825YCvhyVaZYSFIEVqxBakkJYomWuDHZbHzLCKnClD3XB1G8F8pNjxwIetenX6lZ+gDHmrZCy6qaJs7cjzprALXe+3F67noWtaQMK6OAIiFmCH4TgON1/1S3HN/+23Oers7uskv6N48s4XaWlo5cLvf6HUQ+kQli9uQtM0Bg+v4us/PYzp769k6jsrePiOadT2jTNgSEXPMnHdWWg7KRshQJOoTVvkW6FQufkJ299HOAHlfYVwZFqk3Y5TkP351hnw2ZSoGQbCyjkeMG3ViLxFfuczUgFYyEm0B1vE8Z4rjbSjVUI+7ILos4yQFA2EK8ScNdeiWhKVSRVmRBEeT1UtmgaE6uTYni3GqJoG+QpJ+1nK6Dgcl9d6BSGJrieFCnhCyjxXWhD5jbdjlfKckxN/Mysh486n5H2FEsUekvF2+tT20Uq3oAT47QiKV4D4gDbbbCOx6oKVka9NWc7ytUla0wVOP3IPqssjhK1AC9YVERCxADsEz/P483d+zxtPvMblP7uSky/p+a757ZFJZXnw1qfY98i9GLffyFIPp0O4+89TOO2CPek/WMr7+w+poP/gCmp6x3FdD10PVtTbDH9S1kPFCFRb6KGti7nbTuyFrBAwWwdH7c9vs2NaW9YjhUKgx8Uby8lL5AutaHZqhMAsl+iPGS76mmmVbPDbcvOS/jTDimgpN3lPUw3KVRWinZOUYLhaCJqdlegVajvWF/3RbEtFngypKgXZd6xank+tF6LkHwvatBRSf0dVD0vNUD0tY3IuIuWfJU/to5V6SAie5xZtK0DI3IbXdi1itT2Ys7iRV6Ys58ov7MX7n67hqTcWMX5kLWOHVlMWt7a+gwC7FAERC7BDuPMX/+T5/zzDed+9kLOuObfUwykJHv37szStb+Hia88u9VA6hDUrWmltzjFuX0m//PuP75NNF1i9vJWq2hiXfnt/yqu676RUMoQiMrH76UiQaE1bv7FtgR/xMsPyep84eAWptvSJwya1SjERpmue6LTsrHpStfcJV0ta0YrLc46KToFUEsarJTpVUKlVP9LmNwH3e2raeYl+RWuElGUaiya3dl7pwRKSanQKogmL1UCFXtw2Vq0idZXFVKUZltSqb0Fhe6rVlFV8jxX9iqRuSxGs9o22C2l5H34q1yrbcgqyG6EpmWNov3L61cU5/cg9mDJzDTMXNWLoGvuMrgvSlV0MARELsN147G8P8+Cf7uOkiz/PRbtJ66L2aGlM8sAtT3HQCft2+2hYc2OWPv0lXTTjw1U0rE3z1R8eTMgyePLeGXz87goOP7H76t9KCn9iz7cWBfjbK+ZuS9r8yJNbECuHtq2VNqdVKoSlytCPLIWixUhSzR5iFeE4Yr4KQvB8mw2/36bnbkzyIuVFDVbLaolU6SE5Rmrdxhor33ctUQPZsDj1G67sP9PUJpqmvLdCEagcuDFpgs8SKB++Oeu2oG2kcXOVqz0Ao4dU8dHsdcxd0sjIwVVMGtsbz4N3pq9m2MBKqsrCW99JgF2GgIgF2C688tCL/P1Hf+HQzx/O137zzd12VXX/n54g3Zrhy9d3/2jgyD3rmDN9LQ/+82MqqiJMPHQA8YREHGLxEAvn1AdErCMo7yNRnh2d8Nun1cywpBM3RT42pVXyOwLkByoHfFuRl2qIJoRo5VrB9VNWbVJXfgp1syQvC6im4z4Kqpclnlhq2CrKFoopZ/+4PKebEimzc8W0Z9uxt38vBTUev0l5ZxCoLqjt2lHMWtRAyBQPt+EDKxnQK8GcJU1EwiaD+pSx37jeLFzRwhsfruC0I7uvzU5PREDEAmwz3nnmLX57zU3sfcgE/t9t10vrlN0Q61Y28Ng/nuPosw5hj7GDSj2cTsFBRw/hkTun8cEbS0k257ALLiP3rOODN5dx7lcmlHp43R8dnfA7WplnJYrpPs9WETI2tsvYFNo+vqn30L5QINsiGq90gzRGdyKSwsSVSkiQFKJhtXOp30q6dlMdC9q2EdrN8d6M1Tzx+iL2G9uLaXPXc8TE/hw+sR/PTV7CtLnraWjJMmFkHRHLwDR3484YXRQBEQuwTfjw1Q/45eU3MHLCKH52741Ykd1X8HnPzQ/jOi6XXHdOqYfSaajtHeer1x5Ma3OWd19dwpQ3l7F2ZSunXjCO0YFtRddAR8mcpkuEilDx/7b73hE/rLbkyc5LEYHrSLUlSMWnGRUfLtcFPV3s4+hbevgp280dqwe0EdpZ8DyPfMFlysy1fOmkUYwdWs1h+/Tjrw9+ggecfOhQ3puxmtc/XMGbH62kqTXHN8+fUOphB2iHgIgF2Co+fuMjbrjoxwwcOYj/e+DXxMpipR5SybBs3kqe/c9rnPbl4+g7uFeph9NpcF3RD5VVRDj29FEce/oobNsNVs89Ba7y8PL7WvoasLbpwB2JurUlcJ6KjoViiHcFgCXVmSCeZIVoMRVpR4QMJuq2fKwe0kaos+GbtIYtgyF9y2hqyWE7LrWVUa4+Zy9uuX8aAEfvP5DD9unHinUp+tbEiISDab+rIbjLBtgiPnz1A35y/nX0GdyXXz50M2WVZaUeUklxx433E45YXPDdM0o9lE6FrmsbLCpcRybRgIT1IPiRK9OS1kl+WrB9OjAUEc3Y9kSaYtUioo9Uyk+iTqJgbY/tR7z8bcMJMXutGrh9UbdtebyjKGRFM1fIbn3bEiKXdzb83as6xrR560lnpI9mbWWUq8/emymz1rB6fQorZDC0X3lAwroogjttgM3i/Rff5Wdfup4Bwwfy68f/sFs18d4UZn4wjzefep9zvv55quoqSj2cTkFL42cnG93QmT1tDX/9xVslGFGAnYJQ5LNO/J3ZiicUER2aZkiKMlIuHmTRGukj2VbPtb1kb2ePvS3SDWKnkUvK73RD5x+jE/DhrLX859k5PP3mIqbOWcd+43rTpybGnU/NpKlVImO9a2LUlEc+a1oboMshoMcBNol3nnmLGy/7OUPH7sEvH76ZsqryUg+ppPA8j7/95B6qelVw9lUnl3o4HUayJcdzD89m5kercV2PPSf1ZfT4Xoyd0Bvd0Bk9vje1fRJb31GA7oOd2YrHF9MbprLXsCXi1VnH2BVthLqJFm3ukkYee3UBl546lnWNGabPW8/ilS2c8blhPPHaQh54YR4D+yTIFxyWrUlSFgsakHd1BBGxAJ/Bq4+8zC++/DOG7z2CXz36u92ehAG8+uhkPn1/Lpf+8Fyiia5zU95RvPDYHNatSvLjW47jS9fsi6bB0/fP5LfXvcYnH6wERMAfoIdhR1KPW0N7AmOGxZqis7Ezxt4WW9SidR00tuY4YM8+DBtQwYF79aGqLMyCZc08+soCTjtyDw7auw9lMYt01ubbF0wgHg2IWFdHEBELsAGe5/HIXx7knz/7G3sdtDc/u++XxMuCyTjZkua2H9/DyPF7cML5R5Z6OJ2C5oYsI/asQ9M0ho+tY/jYOgBefnIek19ezLAxtcQSu29lbIDtQE8R0+9qLdoOoro8wnOTlzBsYAVjh1aTydlMGFVLfXOO9U0Z9h5RCxTF/AG6PoKIWABAGnjf9sNb+OfP/sbhpx3JjQ/dHJAwhbtueojGtc1887eXYRg94ytz2PFD+fTD1Xz09nKaGzMUlPD36FNHsGpZC4vndU1tTIAuiG5CYLaKXalF6wBGDKrk2AMHcddTs/jn45+yvinLMQcMIpOzmbmw+L0NSFj3QRARC0A2neU3V/2SyU+/yReuPofLfvZVdL1nEI6OYu60RTz+j+f4/CXHMHpiz3GXHz62joOPyTD55cVM/2Alo/buhed5NK7PUMi7jN2nZ/TcC7ALsKMeZF0Ru0KLtoNwPQ9dkauD9+7LyEGVeB7UVUmFak1FBEMPyFd3REDEdnM0rW/iZ1+6njkfzuLKX17D6Vd8odRD6jKwCza/++bfqayr4Ms/6v6tjNpjv8MHMXRUDXOmr2XKG8sYMLQCx/H44hUTSj20AN0NXZjAbDe6UNujhpYspq5TnrA2kDCQtGNtZdEi5MX3lvLhrLV847zxpRhmgA4iIGK7MRZ8Mp+fX/QjmtY18qN//5xDTj6s1EPqUnjgz08x/5PF/Pzu71JW2fMqCD3Po7Z3nNpjh/Lcw7O44Gv7Ul23+5r1BugguhCB6Qn4cNZaHn9tIYP7lpHKFLjolDEbmnV7gIZ8h1OZAusaM3zji+OpLg/Of3dEkH/aTfHG46/ynZO/jms73PzknwIS1g6LZy/jnt8+wpGnH8ihJ+9X6uHsFPgako/fXUGvvmVU18WwbXcrrwoQIMDORlNrjlenLOfy08dx+enj6FUd4/7n57JoZQvAhuhYOmuTiFmce9wIqisCEtZdERCx3QyO43DHz//OLy+/gWF7DuPPL/2dURNHl3pYXQqFvM2vrvwL8fIY19x0aamHs9PRu38Zp120J8AGd/0AAQKUDvFoiFjEJJsXp/zzjh/J4D5lPPv2YpqTosVbsqqV2x76BMdxN0pbBuh+CIjYboSmdY38+NxreeiW+znl0lP59eN/oLp39dZfuJvh7t88zPxPFvOdP1zRYxz0t4S+A8sZtId0TQiIWIAApYXneRi6xrABFaxclyKZzgNw0qFDqCwLc/f/ZgMwuG8ZV5+zF4ahBxWS3RwBEdtN8M6zb3Pl4ZfxyTvT+NYfv8c1N3+bkNXNyst3AWa8N4f7//QEJ37pcxxy0qRSDydAgAC7GTRN+r6OGFjJzIUNTJtXT0tSyNj5J4wiEQuRyUmkLBYJ7uE9AYFYv4ejtamVv/3wFl5+8EWG7TWcmx77HUNGDy31sLokWhqT3HjFLfQeWMfVv7io1MMJECDAbgrP89hjQAWnHjGU/725mILtUl0eJpkusHR1a6mHF6CTERCxHoz3nn+HP3/39zSua+CC713EF7/zpSAKthl4nsdvrrmNhjWN/PnZG4iVRbf+om6KfN7BsoxSDyNAgACbgV8VqaGRztrgebz7yWpyBYevnLEn0XAwdfckBJ9mD8TKhSv4+4/+wnsvvMPg0UP42b2/YMSEUaUeVpfGw7c9wzvPfcjXfnkxo/bpOcat7dHSlOXaS//HBV/bl0OOCSKjAQJ0Reiaxpr6NPc8M5tTDx/KuGE1HDlpANm8TcQKpu2ehuAT7UFItST57+/v5fG/P0LICnHZT67gtK9+ASsc9AzcEj587RNu/9l/OPTk/TjjihNKPZydipeemEuqNc+QEUGRRoAAXRnRsMmFJ41mQO8Ejuti6HpAwnoogk+1B8Au2Dx/7zPc/es7aalv5tjzjufiH15OTZ+aUg+ty2PZ/JXc8OU/MnjUAH7wl6t7dPVRJl3gxcfmMPGQAfQf3POrQQME6M4oT1iUJ2QRbQQt53o0AiLWjeF5Hm899Qb//sU/WbFwOXseuDdfvf/qIA25jWhtSnL9+b/BDBn84j/f69G6MIBXnppHOlnglPPGlnooAQIECBBAISBi3RTT3vqYf93wd+Z8NJtBowbzs3tv5IDjD+rREZ3ORCFv8/NL/sCapev47eM/ps+gXqUe0k5FLmvz/MOzGTexD8NG15Z6OAECBADyBYd7n5nDyYcOoXdN0F5sd0VAxLoZ5k+fx79v/CdTXn6f2n51fPtP3+eYLx6PYQRVcNsKz/P40/fvYOqbn/L/br2KvQ7s+Z0FXn9mPs2NWa7+8Z6lHkqAAAEAx3H55+Mz+WTeeiaMqg2IWA9GKpvf4vMBEesmWLlwBXf96l+8/tgrJCrLuOwnV3DqV84kHA2XemjdDg/c8hTP3vsqX/ruGRx/3hGlHs5ORz7v8PQDsxi9dy9G792zI38BAnQHuJ7H3U/PZvq89XzxuBFMHB18L3sqlq5r5NXp87e4TYeImKZp1cADwBBgMXCO53mN7bb5HPCHNg+NBr7oed7jmqb9GzgCaFbPXeJ53scdGVNPQ8OaBu777V08e8/TmFaIL377As665oskKhKlHlq3xOTnPuSfN/yXI884iEuuO6fUw9klePPZBTTVZ7jyuoNLPZQAAXZ7eJ7H/c/P5b0Zazj1iKEcOWlAqYcUYCfBcV3en7uUstiWAyYdjYhdC7zsed5NmqZdq/7/QdsNPM97FZgAG4jbfOCFNpt83/O8hzs4jh6HVEuSh259gMf+9jB2vsCJF57Med+9KKiE7ACWzVvJTVfeyvC9h/D/brlqt9DTOY7Lsw/NYvjYWkaPD1bdAQKUEp7n8eirC3jjo5Ucd+AgTjx4cKmHFGAnYt7K9bRmchw7YeQWt+soETsNOFL9fRfwGu2IWDucBTzreV66g8ftschn8zx1x+Pc/8f/0NrYwhFnHMXF132Zfnv0L/XQujVSLWl+cuFvMS2Tn9/9XcLR3cNbbcoby1i3OsX5V+27WxDPAAG6Mp55ewkvvruMIyb254zP7RF8J3swbMdl2qIV1FUk6F+zZbugjhKx3p7nrVJ/rwZ6b2X7LwK/b/fYjZqm/QR4GbjW87zcpl6oadoVwBUAgwYN2vERd1E4tsNLDzzPvb+5i3Ur1rLvUftx6Y++wvC9R5R6aN0eruty09V/ZfnC1dz86PX0HrB7VA16nsfTD86k78ByJhwUEPkAAUqJl99fxlNvLOLAvfpw7vEjAhLWwzFnxVrSuQKHjds64d4qEdM07SWgzyaeur7tP57neZqmeVvYT19gL+D5Ng9fhxA4C7gdiabdsKnXe553u9qGSZMmbfY43Q2e5/Hus29z5y/+ydK5Sxi5z2i+e8sPmHD4xFIPrcfg3t89xuRnp3D1Ly9iwqHjSj2cXYZPPljFknmNfPm7B6DrwU0/QIBS4e1pq3jopfnsM6qOC08ehR6QsB4N23H4ZPEq+lSW0beqfKvbb5WIeZ53zOae0zRtjaZpfT3PW6WI1tot7Ooc4DHP8wpt9u1H03Kapt0JfG+rI+4h8DyP6W99zF2/uoOZ739K/2ED+dGdP+OQUw4PVkqdiMnPfchdNz3EsecezplXnFjq4ewyeJ7Hk//5lOq6GIccM6TUwwkQYLfFR7PXcu8zsxk7tIovnzY2cMnfDTB7+Voy+QJH7jVsm+bzjqYmnwQuBm5Sv5/YwrbnIRGwDWhD4jTgdGBGB8fT5eF5HlPf+Ij7br6bGe9Op6ZPLd/43Xc4/oKTMMzAC6wzsWTOCn711VsZOX4Pvv27y3crgjtn+lrmfbqOC78+CTMUXFcBApQCMxc18K8nZjK0fzlf/cJehMyAhPV0FGyJhvWrLqfPNkTDoONE7CbgQU3TLgOWIFEvNE2bBFzped7l6v8hwEDg9Xav/4+maXWABnwMXNnB8XRZeJ7HlJff577f3cOsDz6ltm8tV9/0DU740slYkd1DOL4rkWxO8eMLb8aKWvz8nu/sNuJ8H4/fM4OK6giHn7BHqYcSIMBuiaWrW/n7IzPoXRPja2fvTdgKFkS7A6YuXEG2YLPPsG23JekQEfM8rx44ehOPTwEub/P/YuAzamHP847qyPG7A1zX5Z1n3+a/v7uH+dPn0WtAb75+87c59vwTsMK7FznYVXAclxuvuIXVS9bxuyd+TK/+u4c438fsaWuY9fEaLrh6IlY48GwOEGBXY31ThlsfmE48YvL1c8cTj4ZKPaQAuwAr6pv4dOlqRg/oRa/t8PoM7tI7CY7j8OYTr3P/H+5l8axF9B3aj2//6fscdfaxhKzgS7kzcecvH+D9lz7mW7+9bLdoX9QWnufx6F2fUFEd4ciTh5d6OAEC7HZIZQrccv90bMflW+dPpLIs6H6yOyCdy/PGpwupjEfZb8T2OTsERKyTYRdsXnnoRR74039ZsWAZg0YN5gd/u57DT/9coAHbBXj1scn8949PcPLFR/P5S48t9XB2OWZ+tIY509dywdf2DaJhAQLsYuQLDn996BPqmzN887wJ9KuLl3pIAXYB0rk8z380h4LtcsLEYZjG9mkBgzt1JyGfzfP8fc/w0J/vZ+3yNQzbazg/uvNnHHzyYehBlcwuwZypC7j5639jzwNG8fWbLi31cHY5PM/joX99THVdjM8F0bAAAXYpXNfjzidnsXB5M5efMY4RgypLPaQAuwCt6SzPTZ1DNl/g2OY4BH0AAAecSURBVAkjqUpsf/P2gIh1EC0NzTxz9/944vZHaVzbwJj9xnHNzd9iv2MO2K2q9EqNdSsb+PGXfktlbTk/u+s7hKzd79L+aPJyFs1p4LLvHkAoEAYHCLDL4HkeD700n6lz1nH2McPZd0zQTmx3QH1Lihc/novruZwwcTR1O9gDevebrToJi2cv4onbH+WVh14kl8mxzxH7cu3ff8Teh04ICNguRiaV5cdfupl0a4Y/P3cDVXVbbifRE+E6Lo/cOZ0+A8o45LihpR5OgAC7FZ6bvIRXpyznmP0HcvT+A0s9nAA7GZ7nMXPpGj5csIxwKMSJE8fsUCTMR0DEtgOu6zLl5fd57G8PM/X1D7EiFkeddSynf/ULDBkTTH6lgOu63HTVX1jwyWJuuPd77DG257W/2ha888oSVixu5uofHYKxnfqEAAEC7Bg8z+OJ1xfy3OSl7D+uN2cePazUQwqwk5HM5njr00WsamxhYG0lh4wdSrSDBXgBEdsGZFMZXnrgBR6//RGWz19GTZ9aLvnR5Zx44SlUbKWZZ4Cdizv+737eevoDrr7xIg46ft9SD6cksAsOj941ncHDq9jv8N2TiAYIsKvhuh73PTeHtz5exWET+nHeCSOD1kU9HAtX1/PO7MW4nschY4Ywol9dp2TAAiK2DXjo1gf4z813MWLCKH7wt+s57LQjMUPBqSs10q0Z3nz6fT5/yTGc+dXdp31Re6xZmcSxXc768vigp2SAALsIq+vTvDdjDScePJhTjxgaSFJ6OAq2wwfzllERj3L4uD0oj0U6bd+a53W//tmapq1DnPy7K2qB9aUeRDdCcL62H8E52z4E52v7EJyv7UdwzrYPPe18DfY8r25TT3RLItbdoWnaFM/zJpV6HN0FwfnafgTnbPsQnK/tQ3C+th/BOds+7E7nK1D1BggQIECAAAEClAgBEQsQIECAAAECBCgRAiJWGtxe6gF0MwTna/sRnLPtQ3C+tg/B+dp+BOds+7DbnK9AIxYgQIAAAQIECFAiBBGxAAECBAgQIECAEiEgYgECBAgQIECAACVCQMR2ATRNO1vTtE81TXM1TdtsOa6maSdomjZH07T5mqZduyvH2JWgaVq1pmkvapo2T/2u2sx2jqZpH6ufJ3f1OEuNrV0vmqaFNU17QD3/nqZpQ0owzC6FbThnl2iatq7NdXV5KcbZFaBp2r80TVuradqMzTyvaZr2Z3Uup2uaNnFXj7GrYRvO2ZGapjW3ub5+sqvH2JWgadpATdNe1TRtppojv7mJbXr8dRYQsV2DGcCZwBub20DTNAP4C3AiMBY4T9O0sbtmeF0O1wIve543AnhZ/b8pZDzPm6B+Tt11wys9tvF6uQxo9DxvOPAH4Ne7dpRdC9vxHXugzXX1z106yK6FfwMnbOH5E4ER6ucK4LZdMKaujn+z5XMG8Gab6+uGXTCmrgwb+K7neWOBA4GvbeI72eOvs4CI7QJ4njfL87w5W9lsf2C+53kLPc/LA/cDp+380XVJnAbcpf6+Czi9dEPpstiW66XteXwYOFrbvfuwBN+x7YDneW8ADVvY5DTgbk/wLlCpaVrfXTO6roltOGcB2sDzvFWe532k/m4FZgH9223W46+zgIh1HfQHlrX5fzmfvSB3F/T2PG+V+ns10Hsz20U0TZuiadq7mqadvmuG1mWwLdfLhm08z7OBZqBml4yua2Jbv2NfUCmQhzVNG7hrhtYt8f/bu58Qm8I4jOPfJ4aNkhqFRJSFrGxENpJSs1BKmo1/USjJmoWykoWNEoWNpBQxQkqyFWk0YTOUMomiSKTksThH3ZhhMN33uuf5bO6955xOv369vff3vuc956TP+jsrJD2UdEPSktLBdIp66cRS4O4Pu7q+neXN1RNE0i1g1ii7Dti+0u54Ot2v8tX6w7YljfWMlfm2RyQtBG5LGrL9dKJjjUa5Cpy3/VnSTqoZxdWFY4ru8YCq3/ogqQ+4THXJrdEkTQMuAvtsvy8dT7ulEJsgttf84ylGgNbR99x6W1f6Vb4kvZI02/bLegr69RjnGKk/n0m6QzWaakohNp728v2YF5ImA9OBN+0JryP9Nme2W/NzCjjShrj+V43qsyZCa5Fh+7qk45J6bXfTy63/iKQeqiLsnO1LoxzS9e0slyY7xz1gkaQFkqYA/UDj7gSsDQBb6u9bgJ9mFCXNkDS1/t4LrAQety3C8sbTXlrzuAG47WY/wfm3Ofth7ck6qjUrMboBYHN9V9ty4F3LkoIYhaRZ39dpSlpG9R/c2MFRnYvTwBPbR8c4rOvbWWbE2kDSeuAYMBO4JmnQ9lpJc4BTtvtsf5G0B7gJTALO2H5UMOySDgMXJG0HngMbAVQ9+mOX7R3AYuCkpK9Undlh240pxMZqL5IOAfdtD1B1cGclDVMtIO4vF3F548zZXknrqO7megtsLRZwYZLOA6uAXkkvgINAD4DtE8B1oA8YBj4C28pE2jnGkbMNwG5JX4BPQH/DB0crgU3AkKTBett+YB40p53lFUcRERERheTSZEREREQhKcQiIiIiCkkhFhEREVFICrGIiIiIQlKIRURERBSSQiwiIiKikBRiEREREYV8AyT4Ylt+xCB1AAAAAElFTkSuQmCC", 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", 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" ] }, - "metadata": { - "needs_background": "light" - } + "metadata": {}, + "output_type": "display_data" } ], - "metadata": {} + "source": [ + "# plot the results\n", + "fig = plt.figure(figsize=(10, 7))\n", + "ax1 = fig.add_subplot(111)\n", + "\n", + "sns.scatterplot(x=X[Y == 1, 0], y=X[Y == 1, 1], alpha=0.1, ax=ax1)\n", + "sns.scatterplot(x=X[Y == 0, 0], y=X[Y == 0, 1], alpha=0.1, ax=ax1)\n", + "cset = ax1.contour(xx, yy, Y_pred, cmap=\"twilight\")\n", + "ax1.clabel(cset, inline=1, fontsize=10)" + ] }, { "cell_type": "markdown", + "metadata": {}, "source": [ "### Transfer network to a classical MLP and compare outputs\n", "\n", - "As we saw, our networks use custom layers in order to constrain training.\n", - "However during inference layers behave exactly as regular `Dense` or `Conv2d` layers.\n", - "Deel-lip has a functionnality to export a model to it's vanilla keras equivalent. Making it more \n", - "convenient for inference." - ], - "metadata": {} + "As we saw, our networks use custom layers in order to constrain training. However during\n", + "inference layers behave exactly as regular `Dense` or `Conv2d` layers. Deel-lip has a\n", + "functionnality to export a model to it's vanilla keras equivalent. Making it more\n", + "convenient for inference.\n" + ] }, { "cell_type": "code", "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
Model: \"model\"\n",
+       "
\n" + ], + "text/plain": [ + "\u001b[1mModel: \"model\"\u001b[0m\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "
โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”ณโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”ณโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”“\n",
+       "โ”ƒ Layer (type)                    โ”ƒ Output Shape           โ”ƒ       Param # โ”ƒ\n",
+       "โ”กโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ•‡โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ•‡โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”ฉ\n",
+       "โ”‚ keras_tensor (InputLayer)       โ”‚ (None, 2)              โ”‚             0 โ”‚\n",
+       "โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค\n",
+       "โ”‚ spectral_dense (Dense)          โ”‚ (None, 256)            โ”‚           768 โ”‚\n",
+       "โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค\n",
+       "โ”‚ spectral_dense_1 (Dense)        โ”‚ (None, 128)            โ”‚        32,896 โ”‚\n",
+       "โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค\n",
+       "โ”‚ spectral_dense_2 (Dense)        โ”‚ (None, 64)             โ”‚         8,256 โ”‚\n",
+       "โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค\n",
+       "โ”‚ frobenius_dense (Dense)         โ”‚ (None, 1)              โ”‚            65 โ”‚\n",
+       "โ””โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”˜\n",
+       "
\n" + ], + "text/plain": [ + "โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”ณโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”ณโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”“\n", + "โ”ƒ\u001b[1m \u001b[0m\u001b[1mLayer (type) \u001b[0m\u001b[1m \u001b[0mโ”ƒ\u001b[1m \u001b[0m\u001b[1mOutput Shape \u001b[0m\u001b[1m \u001b[0mโ”ƒ\u001b[1m \u001b[0m\u001b[1m Param #\u001b[0m\u001b[1m \u001b[0mโ”ƒ\n", + "โ”กโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ•‡โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ•‡โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”ฉ\n", + "โ”‚ keras_tensor (\u001b[38;5;33mInputLayer\u001b[0m) โ”‚ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m2\u001b[0m) โ”‚ \u001b[38;5;34m0\u001b[0m โ”‚\n", + "โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค\n", + "โ”‚ spectral_dense (\u001b[38;5;33mDense\u001b[0m) โ”‚ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m256\u001b[0m) โ”‚ \u001b[38;5;34m768\u001b[0m โ”‚\n", + "โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค\n", + "โ”‚ spectral_dense_1 (\u001b[38;5;33mDense\u001b[0m) โ”‚ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m) โ”‚ \u001b[38;5;34m32,896\u001b[0m โ”‚\n", + "โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค\n", + "โ”‚ spectral_dense_2 (\u001b[38;5;33mDense\u001b[0m) โ”‚ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m64\u001b[0m) โ”‚ \u001b[38;5;34m8,256\u001b[0m โ”‚\n", + "โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค\n", + "โ”‚ frobenius_dense (\u001b[38;5;33mDense\u001b[0m) โ”‚ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m) โ”‚ \u001b[38;5;34m65\u001b[0m โ”‚\n", + "โ””โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”˜\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "
 Total params: 41,985 (164.00 KB)\n",
+       "
\n" + ], + "text/plain": [ + "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m41,985\u001b[0m (164.00 KB)\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "
 Trainable params: 41,985 (164.00 KB)\n",
+       "
\n" + ], + "text/plain": [ + "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m41,985\u001b[0m (164.00 KB)\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "
 Non-trainable params: 0 (0.00 B)\n",
+       "
\n" + ], + "text/plain": [ + "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "from deel.lip.model import vanillaModel\n", + "\n", "## this is equivalent to test2 = wass.vanilla_export()\n", "test2 = vanillaModel(wass)\n", "test2.summary()" - ], + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, "outputs": [ { - "output_type": "stream", "name": "stdout", + "output_type": "stream", "text": [ - "Model: \"model_1\"\n", - "_________________________________________________________________\n", - "Layer (type) Output Shape Param # \n", - "=================================================================\n", - "input_2 (InputLayer) [(None, 2)] 0 \n", - "_________________________________________________________________\n", - "spectral_dense (Dense) (None, 256) 768 \n", - "_________________________________________________________________\n", - "spectral_dense_1 (Dense) (None, 128) 32896 \n", - "_________________________________________________________________\n", - "spectral_dense_2 (Dense) (None, 64) 8256 \n", - "_________________________________________________________________\n", - "frobenius_dense (Dense) (None, 1) 65 \n", - "=================================================================\n", - "Total params: 41,985\n", - "Trainable params: 41,985\n", - "Non-trainable params: 0\n", - "_________________________________________________________________\n" + "\u001b[1m450/450\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 536us/step\n" ] } ], - "metadata": {} - }, - { - "cell_type": "code", - "execution_count": 16, "source": [ - "pred_test=test2.predict(X_pred)\n", - "Y_pred=pred_test\n", - "Y_pred=Y_pred.reshape(x.shape[0],y.shape[0])" - ], - "outputs": [], - "metadata": {} + "pred_test = test2.predict(X_pred)\n", + "Y_pred = pred_test\n", + "Y_pred = Y_pred.reshape(x.shape[0], y.shape[0])" + ] }, { "cell_type": "code", "execution_count": 17, - "source": [ - "fig = plt.figure(figsize=(10,7))\n", - "ax1 = fig.add_subplot(111)\n", - "#ax2 = fig.add_subplot(312)\n", - "#ax3 = fig.add_subplot(313)\n", - "sns.scatterplot(X[Y==1,0],X[Y==1,1],alpha=0.1,ax=ax1)\n", - "sns.scatterplot(X[Y==-1,0],X[Y==-1,1],alpha=0.1,ax=ax1)\n", - "cset =ax1.contour(xx,yy,Y_pred,cmap='twilight')\n", - "ax1.clabel(cset, inline=1, fontsize=10)\n" - ], + "metadata": {}, "outputs": [ { - "output_type": "stream", - "name": "stderr", - "text": [ - "/home/thibaut.boissin/envs/tf24/lib/python3.7/site-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n", - " FutureWarning\n", - "/home/thibaut.boissin/envs/tf24/lib/python3.7/site-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n", - " FutureWarning\n" - ] - }, - { - "output_type": "execute_result", "data": { "text/plain": [ "" ] }, + "execution_count": 17, "metadata": {}, - "execution_count": 17 + "output_type": "execute_result" }, { - "output_type": "display_data", "data": { - "image/png": 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", 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", 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" ] }, - "metadata": { - "needs_background": "light" - } + "metadata": {}, + "output_type": "display_data" } ], - "metadata": {} + "source": [ + "fig = plt.figure(figsize=(10, 7))\n", + "ax1 = fig.add_subplot(111)\n", + "sns.scatterplot(x=X[Y == 1, 0], y=X[Y == 1, 1], alpha=0.1, ax=ax1)\n", + "sns.scatterplot(x=X[Y == 0, 0], y=X[Y == 0, 1], alpha=0.1, ax=ax1)\n", + "cset = ax1.contour(xx, yy, Y_pred, cmap=\"twilight\")\n", + "ax1.clabel(cset, inline=1, fontsize=10)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": { - "kernelspec": { - "name": "python3", - "display_name": "Python 3.7.11 64-bit ('tf24': venv)" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.7.11" - }, "interpreter": { "hash": "e585d72a124540032141457729caea4129d351be49f1f69f41c00c4f8476abb5" + }, + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" } }, "nbformat": 4, diff --git a/docs/notebooks/demo3.ipynb b/docs/notebooks/demo3.ipynb index 0878051a..2dbfc5bf 100644 --- a/docs/notebooks/demo3.ipynb +++ b/docs/notebooks/demo3.ipynb @@ -2,69 +2,81 @@ "cells": [ { "cell_type": "markdown", + "metadata": {}, "source": [ "## Demo 3: HKR classifier on MNIST dataset\n", + "\n", "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/deel-ai/deel-lip/blob/master/docs/notebooks/demo3.ipynb)\n", "\n", - "This notebook will demonstrate learning a binary task on the MNIST0-8 dataset." - ], - "metadata": {} + "This notebook will demonstrate learning a binary task on the MNIST0-8 dataset.\n" + ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, + "metadata": {}, + "outputs": [], "source": [ "# pip install deel-lip -qqq" - ], - "outputs": [], - "metadata": {} + ] }, { "cell_type": "code", - "execution_count": 1, - "source": [ - "import tensorflow as tf\n", - "from tensorflow.keras import backend as K\n", - "from tensorflow.python.keras.layers import Input, Flatten\n", - "from tensorflow.keras.optimizers import Adam\n", - "from tensorflow.keras.metrics import binary_accuracy\n", - "from tensorflow.keras.models import Sequential\n", - "\n", - "from deel.lip.layers import (\n", - " SpectralConv2D,\n", - " SpectralDense,\n", - " FrobeniusDense,\n", - " ScaledL2NormPooling2D,\n", - ")\n", - "from deel.lip.activations import MaxMin, GroupSort, GroupSort2, FullSort\n", - "from deel.lip.losses import HKR, KR, HingeMargin" - ], + "execution_count": 2, + "metadata": {}, "outputs": [ { - "output_type": "stream", "name": "stderr", + "output_type": "stream", "text": [ - "2021-09-08 18:34:34.803681: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcudart.so.11.0\n" + "2024-09-06 15:38:37.262001: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:485] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n", + "2024-09-06 15:38:37.273238: E external/local_xla/xla/stream_executor/cuda/cuda_dnn.cc:8454] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n", + "2024-09-06 15:38:37.276701: E external/local_xla/xla/stream_executor/cuda/cuda_blas.cc:1452] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n", + "2024-09-06 15:38:37.285065: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\n", + "To enable the following instructions: AVX2 FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\n", + "2024-09-06 15:38:38.212181: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT\n" ] } ], - "metadata": {} + "source": [ + "import keras\n", + "import keras.ops as K\n", + "from keras.layers import Input, Flatten\n", + "from keras.optimizers import Adam\n", + "from keras.metrics import binary_accuracy\n", + "from keras.models import Sequential\n", + "\n", + "from deel.lip.layers import SpectralDense, FrobeniusDense\n", + "from deel.lip.activations import GroupSort, GroupSort2\n", + "from deel.lip.losses import HKR, KR, HingeMargin" + ] }, { "cell_type": "markdown", + "metadata": {}, "source": [ - "### data preparation\n", + "### Data preparation\n", "\n", - "For this task we will select two classes: 0 and 8. Labels are changed to {-1,1}, wich is compatible\n", - "with the Hinge term used in the loss." - ], - "metadata": {} + "For this task we will select two classes: 0 and 8. Labels are changed to {-1,1}, wich is\n", + "compatible with the Hinge term used in the loss.\n" + ] }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "train set size: 11774 samples, classes proportions: 50.306 percent\n", + "test set size: 1954 samples, classes proportions: 50.154 percent\n" + ] + } + ], "source": [ - "from tensorflow.keras.datasets import mnist\n", + "from keras.datasets import mnist\n", "\n", "# first we select the two classes\n", "selected_classes = [0, 8] # must be two classes as we perform binary classification\n", @@ -88,7 +100,7 @@ " x = x.reshape((-1, 28, 28, 1))\n", " # change label to binary classification {-1,1}\n", " y[y == class_a] = 1.0\n", - " y[y == class_b] = -1.0\n", + " y[y == class_b] = 0.0\n", " return x, y\n", "\n", "\n", @@ -111,32 +123,23 @@ "print(\n", " \"test set size: %i samples, classes proportions: %.3f percent\"\n", " % (y_test.shape[0], 100 * y_test[y_test == 1].sum() / y_test.shape[0])\n", - ")\n" - ], - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "train set size: 11774 samples, classes proportions: 50.306 percent\n", - "test set size: 1954 samples, classes proportions: 50.154 percent\n" - ] - } - ], - "metadata": {} + ")" + ] }, { "cell_type": "markdown", + "metadata": {}, "source": [ - "### Build lipschitz Model\n", + "### Build 1-Lipschitz Model\n", "\n", - "Let's first explicit the paremeters of this experiment" - ], - "metadata": {} + "Let's first explicit the paremeters of this experiment\n" + ] }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 4, + "metadata": {}, + "outputs": [], "source": [ "# training parameters\n", "epochs = 10\n", @@ -147,26 +150,133 @@ "\n", "# loss parameters\n", "min_margin = 1.0\n", - "alpha = 10.0\n" - ], - "outputs": [], - "metadata": {} + "alpha = 10.0" + ] }, { "cell_type": "markdown", + "metadata": {}, "source": [ - "Now we can build the network.\n", - "Here the experiment is done with a MLP. But `Deel-lip` also provide state of the art 1-Lipschitz convolutions." - ], - "metadata": {} + "Now we can build the network. Here the experiment is done with a MLP. But `deel-lip`\n", + "also provide state of the art 1-Lipschitz convolutions.\n" + ] }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", + "I0000 00:00:1725629919.648426 876067 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\n", + "I0000 00:00:1725629919.673689 876067 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\n", + "I0000 00:00:1725629919.673829 876067 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\n", + "I0000 00:00:1725629919.674426 876067 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\n", + "I0000 00:00:1725629919.674535 876067 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\n", + "I0000 00:00:1725629919.674620 876067 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\n", + "I0000 00:00:1725629919.773994 876067 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\n", + "I0000 00:00:1725629919.774108 876067 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\n", + "I0000 00:00:1725629919.774196 876067 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\n", + "2024-09-06 15:38:39.774269: I tensorflow/core/common_runtime/gpu/gpu_device.cc:2021] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 6818 MB memory: -> device: 0, name: NVIDIA GeForce RTX 2070 SUPER, pci bus id: 0000:01:00.0, compute capability: 7.5\n" + ] + }, + { + "data": { + "text/html": [ + "
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โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”ณโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”ณโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”“\n",
+       "โ”ƒ Layer (type)                    โ”ƒ Output Shape           โ”ƒ       Param # โ”ƒ\n",
+       "โ”กโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ•‡โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ•‡โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”ฉ\n",
+       "โ”‚ flatten (Flatten)               โ”‚ (None, 784)            โ”‚             0 โ”‚\n",
+       "โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค\n",
+       "โ”‚ spectral_dense (SpectralDense)  โ”‚ (None, 32)             โ”‚        50,241 โ”‚\n",
+       "โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค\n",
+       "โ”‚ spectral_dense_1                โ”‚ (None, 16)             โ”‚         1,057 โ”‚\n",
+       "โ”‚ (SpectralDense)                 โ”‚                        โ”‚               โ”‚\n",
+       "โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค\n",
+       "โ”‚ frobenius_dense                 โ”‚ (None, 1)              โ”‚            32 โ”‚\n",
+       "โ”‚ (FrobeniusDense)                โ”‚                        โ”‚               โ”‚\n",
+       "โ””โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”˜\n",
+       "
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 Trainable params: 25,664 (100.25 KB)\n",
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Devices:\n", + "I0000 00:00:1725629922.379859 876117 service.cc:154] StreamExecutor device (0): NVIDIA GeForce RTX 2070 SUPER, Compute Capability 7.5\n", + "2024-09-06 15:38:42.413140: I tensorflow/compiler/mlir/tensorflow/utils/dump_mlir_util.cc:268] disabling MLIR crash reproducer, set env var `MLIR_CRASH_REPRODUCER_DIRECTORY` to enable.\n", + "2024-09-06 15:38:42.544834: I external/local_xla/xla/stream_executor/cuda/cuda_dnn.cc:531] Loaded cuDNN version 8902\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[1m41/92\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - HingeMargin: 0.5078 - KR: 2.5226e-09 - binary_accuracy: 0.5134 - loss: 5.0783" + ] + }, + { + "name": "stderr", "output_type": "stream", + "text": [ + "I0000 00:00:1725629924.686642 876117 device_compiler.h:188] Compiled cluster using XLA! This line is logged at most once for the lifetime of the process.\n" + ] + }, + { "name": "stdout", + "output_type": "stream", "text": [ - "Epoch 1/10\n", - "92/92 [==============================] - 2s 10ms/step - loss: -1.6675 - KR: 3.7144 - HingeMargin: 0.2047 - HKR_binary_accuracy: 0.9382 - val_loss: -5.0961 - val_KR: 5.5990 - val_HingeMargin: 0.0519 - val_HKR_binary_accuracy: 0.9786\n", + "\u001b[1m92/92\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 42ms/step - HingeMargin: 0.5060 - KR: 1.2139e-09 - binary_accuracy: 0.5399 - loss: 5.0598 - val_HingeMargin: 0.5004 - val_KR: 7.6708e-10 - val_binary_accuracy: 0.4258 - val_loss: 5.0010\n", "Epoch 2/10\n", - "92/92 [==============================] - 1s 7ms/step - loss: -5.0297 - KR: 5.5716 - HingeMargin: 0.0542 - HKR_binary_accuracy: 0.9793 - val_loss: -5.4469 - val_KR: 5.7710 - val_HingeMargin: 0.0354 - val_HKR_binary_accuracy: 0.9879\n", + "\u001b[1m92/92\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step - HingeMargin: 0.5031 - KR: 1.0939e-10 - binary_accuracy: 0.5043 - loss: 5.0308 - val_HingeMargin: 0.5002 - val_KR: 7.3193e-10 - val_binary_accuracy: 0.4355 - val_loss: 4.9972\n", "Epoch 3/10\n", - "92/92 [==============================] - 1s 7ms/step - loss: -5.3788 - KR: 5.7838 - HingeMargin: 0.0405 - HKR_binary_accuracy: 0.9858 - val_loss: -5.6435 - val_KR: 5.9555 - val_HingeMargin: 0.0334 - val_HKR_binary_accuracy: 0.9860\n", + "\u001b[1m92/92\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - HingeMargin: 0.4983 - KR: -5.7413e-10 - binary_accuracy: 0.5192 - loss: 4.9829 - val_HingeMargin: 0.4995 - val_KR: 5.2317e-10 - val_binary_accuracy: 0.5486 - val_loss: 4.9890\n", "Epoch 4/10\n", - "92/92 [==============================] - 1s 8ms/step - loss: -5.6172 - KR: 5.9671 - HingeMargin: 0.0350 - HKR_binary_accuracy: 0.9874 - val_loss: -5.7918 - val_KR: 6.0764 - val_HingeMargin: 0.0308 - val_HKR_binary_accuracy: 0.9879\n", + "\u001b[1m92/92\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 9ms/step - HingeMargin: 0.5005 - KR: -4.9682e-10 - binary_accuracy: 0.5829 - loss: 5.0050 - val_HingeMargin: 0.5006 - val_KR: -1.2116e-09 - val_binary_accuracy: 0.6054 - val_loss: 5.0109\n", "Epoch 5/10\n", - "92/92 [==============================] - 1s 7ms/step - loss: -5.7598 - KR: 6.0676 - HingeMargin: 0.0308 - HKR_binary_accuracy: 0.9891 - val_loss: -5.8711 - val_KR: 6.1062 - val_HingeMargin: 0.0264 - val_HKR_binary_accuracy: 0.9899\n", + "\u001b[1m92/92\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 9ms/step - HingeMargin: 0.5006 - KR: 3.0154e-09 - binary_accuracy: 0.5640 - loss: 5.0058 - val_HingeMargin: 0.4999 - val_KR: 4.9462e-11 - val_binary_accuracy: 0.7416 - val_loss: 4.9979\n", "Epoch 6/10\n", - "92/92 [==============================] - 1s 7ms/step - loss: -5.7647 - KR: 6.0829 - HingeMargin: 0.0318 - HKR_binary_accuracy: 0.9879 - val_loss: -5.8503 - val_KR: 6.1463 - val_HingeMargin: 0.0315 - val_HKR_binary_accuracy: 0.9879\n", + "\u001b[1m92/92\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - HingeMargin: 0.5013 - KR: -1.3574e-09 - binary_accuracy: 0.7430 - loss: 5.0134 - val_HingeMargin: 0.5005 - val_KR: -6.4219e-10 - val_binary_accuracy: 0.5026 - val_loss: 5.0093\n", "Epoch 7/10\n", - "92/92 [==============================] - 1s 7ms/step - loss: -5.8007 - KR: 6.1082 - HingeMargin: 0.0307 - HKR_binary_accuracy: 0.9884 - val_loss: -5.8470 - val_KR: 6.1179 - val_HingeMargin: 0.0296 - val_HKR_binary_accuracy: 0.9879\n", + "\u001b[1m92/92\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 9ms/step - HingeMargin: 0.4997 - KR: 1.2245e-10 - binary_accuracy: 0.5887 - loss: 4.9969 - val_HingeMargin: 0.5004 - val_KR: 4.1100e-10 - val_binary_accuracy: 0.3910 - val_loss: 5.0023\n", "Epoch 8/10\n", - "92/92 [==============================] - 1s 7ms/step - loss: -5.8268 - KR: 6.1185 - HingeMargin: 0.0292 - HKR_binary_accuracy: 0.9897 - val_loss: -5.8439 - val_KR: 6.1153 - val_HingeMargin: 0.0294 - val_HKR_binary_accuracy: 0.9889\n", + "\u001b[1m92/92\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 9ms/step - HingeMargin: 0.5035 - KR: 1.1687e-09 - binary_accuracy: 0.5010 - loss: 5.0354 - val_HingeMargin: 0.4999 - val_KR: -2.8725e-10 - val_binary_accuracy: 0.8547 - val_loss: 4.9990\n", "Epoch 9/10\n", - "92/92 [==============================] - 1s 7ms/step - loss: -5.8865 - KR: 6.1548 - HingeMargin: 0.0268 - HKR_binary_accuracy: 0.9910 - val_loss: -5.8800 - val_KR: 6.1668 - val_HingeMargin: 0.0312 - val_HKR_binary_accuracy: 0.9874\n", + "\u001b[1m92/92\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 9ms/step - HingeMargin: 0.5003 - KR: -1.9127e-10 - binary_accuracy: 0.5799 - loss: 5.0033 - val_HingeMargin: 0.4999 - val_KR: 7.3054e-10 - val_binary_accuracy: 0.5056 - val_loss: 4.9922\n", "Epoch 10/10\n", - "92/92 [==============================] - 1s 7ms/step - loss: -5.8578 - KR: 6.1453 - HingeMargin: 0.0288 - HKR_binary_accuracy: 0.9892 - val_loss: -5.9233 - val_KR: 6.1783 - val_HingeMargin: 0.0282 - val_HKR_binary_accuracy: 0.9889\n" + "\u001b[1m92/92\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 9ms/step - HingeMargin: 0.4987 - KR: 4.3751e-10 - binary_accuracy: 0.5286 - loss: 4.9871 - val_HingeMargin: 0.4999 - val_KR: 9.1637e-10 - val_binary_accuracy: 0.5020 - val_loss: 4.9920\n" ] }, { - "output_type": "execute_result", "data": { "text/plain": [ - "" + "" ] }, + "execution_count": 9, "metadata": {}, - "execution_count": 24 + "output_type": "execute_result" } ], - "metadata": {} + "source": [ + "model.fit(\n", + " x=x_train,\n", + " y=y_train,\n", + " validation_data=(x_test, y_test),\n", + " batch_size=batch_size,\n", + " shuffle=True,\n", + " epochs=epochs,\n", + " verbose=1,\n", + ")" + ] }, { "cell_type": "markdown", + "metadata": {}, "source": [ - "As we can see the model reach a very decent accuracy on this task." - ], - "metadata": {} + "As we can see, the model reaches a very decent accuracy on this task.\n" + ] } ], "metadata": { - "kernelspec": { - "name": "python3", - "display_name": "Python 3.7.11 64-bit ('tf24': venv)" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.7.11" - }, "interpreter": { "hash": "e585d72a124540032141457729caea4129d351be49f1f69f41c00c4f8476abb5" + }, + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" } }, "nbformat": 4, diff --git a/docs/notebooks/demo4.ipynb b/docs/notebooks/demo4.ipynb index 141d16fa..395016e6 100644 --- a/docs/notebooks/demo4.ipynb +++ b/docs/notebooks/demo4.ipynb @@ -2,85 +2,99 @@ "cells": [ { "cell_type": "markdown", + "metadata": { + "id": "tZ45ItBZ0D59" + }, "source": [ "## Demo 4: HKR multiclass and fooling\n", + "\n", "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/deel-ai/deel-lip/blob/master/docs/notebooks/demo4.ipynb)\n", "\n", - "This notebook will show how to train a lispchitz network in a multiclass setup.\n", - "The HKR is extended to multiclass using a one-vs all setup. It will go through\n", - "the process of designing and training the network. It will also show how to create robustness certificates from the output of the network. Finally these\n", - "certificates will be checked by attacking the network. \n", + "This notebook will show how to train a lispchitz network in a multiclass setup. The HKR\n", + "is extended to multiclass using a one-vs all setup. It will go through the process of\n", + "designing and training the network. It will also show how to create robustness\n", + "certificates from the output of the network. Finally these certificates will be checked\n", + "by attacking the network.\n", "\n", - "### installation\n", + "### Installation\n", "\n", - "First, we install the required libraries. `Foolbox` will allow to perform adversarial attacks on the trained network." - ], - "metadata": { - "id": "tZ45ItBZ0D59" - } + "First, we install the required libraries. `Foolbox` will allow to perform adversarial\n", + "attacks on the trained network.\n" + ] }, { "cell_type": "code", "execution_count": 1, - "source": [ - "# pip install deel-lip foolbox -qqq" - ], - "outputs": [], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "D6a9QvnXHLt5", "outputId": "bdceb0b5-8946-438b-c8be-1283e621fe9f" - } + }, + "outputs": [], + "source": [ + "# pip install deel-lip foolbox -qqq" + ] }, { "cell_type": "code", "execution_count": 2, + "metadata": { + "id": "n6uzQe2uGr7M" + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2024-09-06 15:26:29.848446: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:485] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n", + "2024-09-06 15:26:29.859784: E external/local_xla/xla/stream_executor/cuda/cuda_dnn.cc:8454] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n", + "2024-09-06 15:26:29.863241: E external/local_xla/xla/stream_executor/cuda/cuda_blas.cc:1452] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n", + "2024-09-06 15:26:29.871798: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\n", + "To enable the following instructions: AVX2 FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\n", + "2024-09-06 15:26:31.151634: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT\n" + ] + } + ], "source": [ "from deel.lip.layers import (\n", " SpectralDense,\n", " SpectralConv2D,\n", " ScaledL2NormPooling2D,\n", - " ScaledAveragePooling2D,\n", " FrobeniusDense,\n", ")\n", "from deel.lip.model import Sequential\n", - "from deel.lip.activations import GroupSort, FullSort\n", + "from deel.lip.activations import GroupSort\n", "from deel.lip.losses import MulticlassHKR, MulticlassKR\n", - "from deel.lip.callbacks import CondenseCallback\n", - "from tensorflow.keras.layers import Input, Flatten\n", - "from tensorflow.keras.optimizers import Adam\n", - "from tensorflow.keras.datasets import mnist, fashion_mnist, cifar10\n", - "from tensorflow.keras.utils import to_categorical\n", - "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n", + "from keras.layers import Input, Flatten\n", + "from keras.optimizers import Adam\n", + "from keras.datasets import fashion_mnist\n", + "from keras.utils import to_categorical\n", "import numpy as np" - ], - "outputs": [ - { - "output_type": "stream", - "name": "stderr", - "text": [ - "2021-09-09 14:03:36.448213: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcudart.so.11.0\n" - ] - } - ], - "metadata": { - "id": "n6uzQe2uGr7M" - } + ] }, { "cell_type": "markdown", - "source": [ - "For this example, the dataset `fashion_mnist` will be used. In order to keep things simple, no data augmentation will be performed." - ], "metadata": { "id": "DLTQeRpkMCwO" - } + }, + "source": [ + "For this example, the dataset `fashion_mnist` will be used. In order to keep things\n", + "simple, no data augmentation will be performed.\n" + ] }, { "cell_type": "code", "execution_count": 3, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "o6EjFWd0G_dB", + "outputId": "0461fa31-a754-456d-fe77-beb750cc3481" + }, + "outputs": [], "source": [ "# load data\n", "(x_train, y_train_ord), (x_test, y_test_ord) = fashion_mnist.load_data()\n", @@ -90,41 +104,178 @@ "# one hot encode the labels\n", "y_train = to_categorical(y_train_ord)\n", "y_test = to_categorical(y_test_ord)" - ], - "outputs": [], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "o6EjFWd0G_dB", - "outputId": "0461fa31-a754-456d-fe77-beb750cc3481" - } + ] }, { "cell_type": "markdown", + "metadata": { + "id": "JY04qBnpMYsV" + }, "source": [ - "Let's build the network. \n", + "Let's build the network.\n", "\n", - "### the architecture\n", + "### The architecture\n", "\n", - "The original one vs all setup would require 10 different networks ( 1 per class ), however, in practice we use a network with\n", - "a common body and 10 1-lipschitz heads. Experiments have shown that this setup don't affect the network performance. In order to ease the creation of such network, `FrobeniusDense` layer has a parameter for this: whenr `disjoint_neurons=True` it act as the stacking of 10 single neurons head. Note that, altough each head is a 1-lipschitz function the overall network is not 1-lipschitz (Concatenation is not 1-lipschitz). We will see later how this affects the certficate creation.\n", + "The original one vs all setup would require 10 different networks ( 1 per class ),\n", + "however, in practice we use a network with a common body and 10 1-lipschitz heads.\n", + "Experiments have shown that this setup don't affect the network performance. In order to\n", + "ease the creation of such network, `FrobeniusDense` layer has a parameter for this:\n", + "whenr `disjoint_neurons=True` it act as the stacking of 10 single neurons head. Note\n", + "that, altough each head is a 1-lipschitz function the overall network is not 1-lipschitz\n", + "(Concatenation is not 1-lipschitz). We will see later how this affects the certficate\n", + "creation.\n", "\n", - "### the loss\n", + "### The loss\n", "\n", - "The multiclass loss can be found in `HKR_multiclass_loss`. The loss has two params: `alpha` and `min_margin`. Decreasing `alpha` and increasing `min_margin` improve robustness (at the cost of accuracy). note also in the case of lipschitz networks, more robustness require more parameters. For more information see [our paper](https://arxiv.org/abs/2006.06520).\n", + "The multiclass loss can be found in `HKR_multiclass_loss`. The loss has two params:\n", + "`alpha` and `min_margin`. Decreasing `alpha` and increasing `min_margin` improve\n", + "robustness (at the cost of accuracy). note also in the case of lipschitz networks, more\n", + "robustness require more parameters. For more information see\n", + "[our paper](https://arxiv.org/abs/2006.06520).\n", "\n", - "In this setup choosing `alpha=100`, `min_margin=.25` provide a good robustness without hurting the accuracy too much.\n", + "In this setup choosing `alpha=100`, `min_margin=.25` provide a good robustness without\n", + "hurting the accuracy too much.\n", "\n", - "Finally the `KR_multiclass_loss()` indicate the robustness of the network ( proxy of the average certificate )\n" - ], - "metadata": { - "id": "JY04qBnpMYsV" - } + "Finally the `KR_multiclass_loss()` indicate the robustness of the network ( proxy of the\n", + "average certificate )\n" + ] }, { "cell_type": "code", "execution_count": 4, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "SwA5kgOBG7Ni", + "outputId": "e869beba-c511-4722-ad5f-fe3d8228f3a6" + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", + "I0000 00:00:1725629193.239470 871063 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\n", + "I0000 00:00:1725629193.259969 871063 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\n", + "I0000 00:00:1725629193.260106 871063 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\n", + "I0000 00:00:1725629193.260871 871063 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\n", + "I0000 00:00:1725629193.260969 871063 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\n", + "I0000 00:00:1725629193.261055 871063 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\n", + "I0000 00:00:1725629193.348510 871063 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\n", + "I0000 00:00:1725629193.348632 871063 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\n", + "I0000 00:00:1725629193.348722 871063 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\n", + "2024-09-06 15:26:33.348791: I tensorflow/core/common_runtime/gpu/gpu_device.cc:2021] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 6818 MB memory: -> device: 0, name: NVIDIA GeForce RTX 2070 SUPER, pci bus id: 0000:01:00.0, compute capability: 7.5\n" + ] + }, + { + "data": { + "text/html": [ + "
Model: \"sequential\"\n",
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โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”ณโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”ณโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”“\n",
+       "โ”ƒ Layer (type)                    โ”ƒ Output Shape           โ”ƒ       Param # โ”ƒ\n",
+       "โ”กโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ•‡โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ•‡โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”ฉ\n",
+       "โ”‚ spectral_conv2d                 โ”‚ (None, 28, 28, 16)     โ”‚           321 โ”‚\n",
+       "โ”‚ (SpectralConv2D)                โ”‚                        โ”‚               โ”‚\n",
+       "โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค\n",
+       "โ”‚ scaled_l2_norm_pooling2d        โ”‚ (None, 14, 14, 16)     โ”‚             0 โ”‚\n",
+       "โ”‚ (ScaledL2NormPooling2D)         โ”‚                        โ”‚               โ”‚\n",
+       "โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค\n",
+       "โ”‚ spectral_conv2d_1               โ”‚ (None, 14, 14, 32)     โ”‚         9,281 โ”‚\n",
+       "โ”‚ (SpectralConv2D)                โ”‚                        โ”‚               โ”‚\n",
+       "โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค\n",
+       "โ”‚ scaled_l2_norm_pooling2d_1      โ”‚ (None, 7, 7, 32)       โ”‚             0 โ”‚\n",
+       "โ”‚ (ScaledL2NormPooling2D)         โ”‚                        โ”‚               โ”‚\n",
+       "โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค\n",
+       "โ”‚ flatten (Flatten)               โ”‚ (None, 1568)           โ”‚             0 โ”‚\n",
+       "โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค\n",
+       "โ”‚ spectral_dense (SpectralDense)  โ”‚ (None, 64)             โ”‚       200,833 โ”‚\n",
+       "โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค\n",
+       "โ”‚ frobenius_dense                 โ”‚ (None, 10)             โ”‚         1,280 โ”‚\n",
+       "โ”‚ (FrobeniusDense)                โ”‚                        โ”‚               โ”‚\n",
+       "โ””โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”˜\n",
+       "
\n" + ], + "text/plain": [ + "โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”ณโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”ณโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”“\n", + "โ”ƒ\u001b[1m \u001b[0m\u001b[1mLayer (type) \u001b[0m\u001b[1m \u001b[0mโ”ƒ\u001b[1m \u001b[0m\u001b[1mOutput Shape \u001b[0m\u001b[1m \u001b[0mโ”ƒ\u001b[1m \u001b[0m\u001b[1m Param #\u001b[0m\u001b[1m \u001b[0mโ”ƒ\n", + "โ”กโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ•‡โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ•‡โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”ฉ\n", + "โ”‚ spectral_conv2d โ”‚ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m16\u001b[0m) โ”‚ \u001b[38;5;34m321\u001b[0m โ”‚\n", + "โ”‚ (\u001b[38;5;33mSpectralConv2D\u001b[0m) โ”‚ โ”‚ โ”‚\n", + "โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค\n", + "โ”‚ scaled_l2_norm_pooling2d โ”‚ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m16\u001b[0m) โ”‚ \u001b[38;5;34m0\u001b[0m โ”‚\n", + "โ”‚ (\u001b[38;5;33mScaledL2NormPooling2D\u001b[0m) โ”‚ โ”‚ โ”‚\n", + "โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค\n", + "โ”‚ spectral_conv2d_1 โ”‚ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m32\u001b[0m) โ”‚ \u001b[38;5;34m9,281\u001b[0m โ”‚\n", + "โ”‚ (\u001b[38;5;33mSpectralConv2D\u001b[0m) โ”‚ โ”‚ โ”‚\n", + "โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค\n", + "โ”‚ scaled_l2_norm_pooling2d_1 โ”‚ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m32\u001b[0m) โ”‚ \u001b[38;5;34m0\u001b[0m โ”‚\n", + "โ”‚ (\u001b[38;5;33mScaledL2NormPooling2D\u001b[0m) โ”‚ โ”‚ โ”‚\n", + "โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค\n", + "โ”‚ flatten (\u001b[38;5;33mFlatten\u001b[0m) โ”‚ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1568\u001b[0m) โ”‚ \u001b[38;5;34m0\u001b[0m โ”‚\n", + "โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค\n", + "โ”‚ spectral_dense (\u001b[38;5;33mSpectralDense\u001b[0m) โ”‚ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m64\u001b[0m) โ”‚ \u001b[38;5;34m200,833\u001b[0m โ”‚\n", + "โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค\n", + "โ”‚ frobenius_dense โ”‚ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m10\u001b[0m) โ”‚ \u001b[38;5;34m1,280\u001b[0m โ”‚\n", + "โ”‚ (\u001b[38;5;33mFrobeniusDense\u001b[0m) โ”‚ โ”‚ โ”‚\n", + "โ””โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”˜\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "
 Total params: 211,715 (827.01 KB)\n",
+       "
\n" + ], + "text/plain": [ + "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m211,715\u001b[0m (827.01 KB)\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "
 Trainable params: 105,856 (413.50 KB)\n",
+       "
\n" + ], + "text/plain": [ + "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m105,856\u001b[0m (413.50 KB)\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "
 Non-trainable params: 105,859 (413.51 KB)\n",
+       "
\n" + ], + "text/plain": [ + "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m105,859\u001b[0m (413.51 KB)\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "# Sequential (resp Model) from deel.model has the same properties as any lipschitz model.\n", "# It act only as a container, with features specific to lipschitz\n", @@ -143,7 +294,7 @@ " kernel_initializer=\"orthogonal\",\n", " ),\n", " # usual pooling layer are implemented (avg, max...), but new layers are also available\n", - " ScaledL2NormPooling2D(pool_size=(2, 2), data_format=\"channels_last\"),\n", + " ScaledL2NormPooling2D(pool_size=(2, 2), data_format=\"channels_last\"),\n", " SpectralConv2D(\n", " filters=32,\n", " kernel_size=(3, 3),\n", @@ -151,7 +302,7 @@ " use_bias=True,\n", " kernel_initializer=\"orthogonal\",\n", " ),\n", - " ScaledL2NormPooling2D(pool_size=(2, 2), data_format=\"channels_last\"),\n", + " ScaledL2NormPooling2D(pool_size=(2, 2), data_format=\"channels_last\"),\n", " # our layers are fully interoperable with existing keras layers\n", " Flatten(),\n", " SpectralDense(\n", @@ -161,7 +312,10 @@ " kernel_initializer=\"orthogonal\",\n", " ),\n", " FrobeniusDense(\n", - " y_train.shape[-1], activation=None, use_bias=False, kernel_initializer=\"orthogonal\"\n", + " y_train.shape[-1],\n", + " activation=None,\n", + " use_bias=False,\n", + " kernel_initializer=\"orthogonal\",\n", " ),\n", " ],\n", " # similary model has a parameter to set the lipschitz constant\n", @@ -174,774 +328,802 @@ "model.compile(\n", " # decreasing alpha and increasing min_margin improve robustness (at the cost of accuracy)\n", " # note also in the case of lipschitz networks, more robustness require more parameters.\n", - " loss=MulticlassHKR(alpha=100, min_margin=.25),\n", + " loss=MulticlassHKR(alpha=100, min_margin=0.25),\n", " optimizer=Adam(1e-4),\n", " metrics=[\"accuracy\", MulticlassKR()],\n", ")\n", "\n", "model.summary()" - ], - "outputs": [ - { - "output_type": "stream", - "name": "stderr", - "text": [ - "2021-09-09 14:03:38.719310: I tensorflow/compiler/jit/xla_cpu_device.cc:41] Not creating XLA devices, tf_xla_enable_xla_devices not set\n", - "2021-09-09 14:03:38.719800: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcuda.so.1\n", - "2021-09-09 14:03:38.750242: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:941] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n", - "2021-09-09 14:03:38.750491: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1720] Found device 0 with properties: \n", - "pciBusID: 0000:01:00.0 name: GeForce RTX 2070 SUPER computeCapability: 7.5\n", - "coreClock: 1.785GHz coreCount: 40 deviceMemorySize: 7.79GiB deviceMemoryBandwidth: 417.29GiB/s\n", - "2021-09-09 14:03:38.750504: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcudart.so.11.0\n", - "2021-09-09 14:03:38.751559: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcublas.so.11\n", - "2021-09-09 14:03:38.751584: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcublasLt.so.11\n", - "2021-09-09 14:03:38.752047: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcufft.so.10\n", - "2021-09-09 14:03:38.752161: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcurand.so.10\n", - "2021-09-09 14:03:38.753239: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcusolver.so.10\n", - "2021-09-09 14:03:38.753476: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcusparse.so.11\n", - "2021-09-09 14:03:38.753540: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcudnn.so.8\n", - "2021-09-09 14:03:38.753583: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:941] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n", - "2021-09-09 14:03:38.753826: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:941] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n", - "2021-09-09 14:03:38.754040: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1862] Adding visible gpu devices: 0\n", - "2021-09-09 14:03:38.754479: I tensorflow/compiler/jit/xla_gpu_device.cc:99] Not creating XLA devices, tf_xla_enable_xla_devices not set\n", - "2021-09-09 14:03:38.754559: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:941] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n", - "2021-09-09 14:03:38.754781: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1720] Found device 0 with properties: \n", - "pciBusID: 0000:01:00.0 name: GeForce RTX 2070 SUPER computeCapability: 7.5\n", - "coreClock: 1.785GHz coreCount: 40 deviceMemorySize: 7.79GiB deviceMemoryBandwidth: 417.29GiB/s\n", - "2021-09-09 14:03:38.754792: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcudart.so.11.0\n", - "2021-09-09 14:03:38.754799: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcublas.so.11\n", - "2021-09-09 14:03:38.754806: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcublasLt.so.11\n", - "2021-09-09 14:03:38.754812: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcufft.so.10\n", - "2021-09-09 14:03:38.754818: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcurand.so.10\n", - "2021-09-09 14:03:38.754824: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcusolver.so.10\n", - "2021-09-09 14:03:38.754831: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcusparse.so.11\n", - "2021-09-09 14:03:38.754837: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcudnn.so.8\n", - "2021-09-09 14:03:38.754865: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:941] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n", - "2021-09-09 14:03:38.755095: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:941] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n", - "2021-09-09 14:03:38.755303: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1862] Adding visible gpu devices: 0\n", - "2021-09-09 14:03:38.755319: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcudart.so.11.0\n", - "2021-09-09 14:03:39.211037: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1261] Device interconnect StreamExecutor with strength 1 edge matrix:\n", - "2021-09-09 14:03:39.211059: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1267] 0 \n", - "2021-09-09 14:03:39.211064: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1280] 0: N \n", - "2021-09-09 14:03:39.211182: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:941] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n", - "2021-09-09 14:03:39.211426: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:941] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n", - "2021-09-09 14:03:39.211643: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:941] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n", - "2021-09-09 14:03:39.211849: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1406] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 7250 MB memory) -> physical GPU (device: 0, name: GeForce RTX 2070 SUPER, pci bus id: 0000:01:00.0, compute capability: 7.5)\n" - ] - }, - { - "output_type": "stream", - "name": "stdout", - "text": [ - "Model: \"hkr_model\"\n", - "_________________________________________________________________\n", - "Layer (type) Output Shape Param # \n", - "=================================================================\n", - "spectral_conv2d (SpectralCon (None, 28, 28, 16) 321 \n", - "_________________________________________________________________\n", - "scaled_l2norm_pooling2d (Sca (None, 14, 14, 16) 0 \n", - "_________________________________________________________________\n", - "spectral_conv2d_1 (SpectralC (None, 14, 14, 32) 9281 \n", - "_________________________________________________________________\n", - "scaled_l2norm_pooling2d_1 (S (None, 7, 7, 32) 0 \n", - "_________________________________________________________________\n", - "flatten (Flatten) (None, 1568) 0 \n", - "_________________________________________________________________\n", - "spectral_dense (SpectralDens (None, 64) 200833 \n", - "_________________________________________________________________\n", - "frobenius_dense (FrobeniusDe (None, 10) 1280 \n", - "=================================================================\n", - "Total params: 211,715\n", - "Trainable params: 105,856\n", - "Non-trainable params: 105,859\n", - "_________________________________________________________________\n" - ] - }, - { - "output_type": "stream", - "name": "stderr", - "text": [ - "/home/thibaut.boissin/projects/repo_github/deel-lip/deel/lip/model.py:56: UserWarning: Sequential model contains a layer wich is not a Lipschitz layer: flatten\n", - " layer.name\n" - ] - } - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "SwA5kgOBG7Ni", - "outputId": "e869beba-c511-4722-ad5f-fe3d8228f3a6" - } + ] }, { "cell_type": "markdown", + "metadata": { + "id": "zH0oy4iRP1Ct" + }, "source": [ - "### notes about constraint enforcement\n", + "### Notes about constraint enforcement\n", "\n", "There are currently 3 way to enforce a constraint in a network:\n", + "\n", "1. regularization\n", "2. weight reparametrization\n", "3. weight projection\n", "\n", - "The first one don't provide the required garanties, this is why `deel-lip` focuses on the later two. Weight reparametrization is done directly in the layers (parameter `niter_bjorck`) this trick allow to perform arbitrary gradient updates without breaking the constraint. However this is done in the graph, increasing ressources consumption. The last method project the weights between each batch, ensuring the constraint at an more affordable computational cost. It can be done in `deel-lip` using the `CondenseCallback`. The main problem with this method is a reduced efficiency of each update.\n", + "The first one don't provide the required garanties, this is why `deel-lip` focuses on\n", + "the later two. Weight reparametrization is done directly in the layers (parameter\n", + "`niter_bjorck`) this trick allow to perform arbitrary gradient updates without breaking\n", + "the constraint. However this is done in the graph, increasing ressources consumption.\n", + "The last method project the weights between each batch, ensuring the constraint at an\n", + "more affordable computational cost. It can be done in `deel-lip` using the\n", + "`CondenseCallback`. The main problem with this method is a reduced efficiency of each\n", + "update.\n", "\n", - "As a rule of thumb, when reparametrization is used alone, setting `niter_bjorck` to at least 15 is advised. However when combined with weight projection, this setting can be lowered greatly." - ], - "metadata": { - "id": "zH0oy4iRP1Ct" - } + "As a rule of thumb, when reparametrization is used alone, setting `niter_bjorck` to at\n", + "least 15 is advised. However when combined with weight projection, this setting can be\n", + "lowered greatly.\n" + ] }, { "cell_type": "code", "execution_count": 5, - "source": [ - "# fit the model\n", - "model.fit(\n", - " x_train,\n", - " y_train,\n", - " batch_size=4096,\n", - " epochs=100,\n", - " validation_data=(x_test, y_test),\n", - " shuffle=True,\n", - " verbose=1,\n", - ")" - ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "p1rWZTZBHEIR", + "outputId": "7cb00f9b-0f84-40f0-c5fa-b29fc0a00c92" + }, "outputs": [ { + "name": "stdout", "output_type": "stream", - "name": "stderr", "text": [ - "2021-09-09 14:03:40.083840: I tensorflow/compiler/mlir/mlir_graph_optimization_pass.cc:116] None of the MLIR optimization passes are enabled (registered 2)\n", - "2021-09-09 14:03:40.100871: I tensorflow/core/platform/profile_utils/cpu_utils.cc:112] CPU Frequency: 3600000000 Hz\n" + "Epoch 1/100\n" ] }, { + "name": "stderr", "output_type": "stream", - "name": "stdout", "text": [ - "Epoch 1/100\n" + "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", + "I0000 00:00:1725629195.757170 871150 service.cc:146] XLA service 0x55ee43b7d460 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\n", + "I0000 00:00:1725629195.757189 871150 service.cc:154] StreamExecutor device (0): NVIDIA GeForce RTX 2070 SUPER, Compute Capability 7.5\n", + "2024-09-06 15:26:35.802016: I tensorflow/compiler/mlir/tensorflow/utils/dump_mlir_util.cc:268] disabling MLIR crash reproducer, set env var `MLIR_CRASH_REPRODUCER_DIRECTORY` to enable.\n", + "2024-09-06 15:26:35.991972: I external/local_xla/xla/stream_executor/cuda/cuda_dnn.cc:531] Loaded cuDNN version 8902\n" ] }, { + "name": "stdout", "output_type": "stream", - "name": "stderr", "text": [ - "2021-09-09 14:03:42.102055: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcublas.so.11\n", - "2021-09-09 14:03:42.320388: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcublasLt.so.11\n", - "2021-09-09 14:03:42.331382: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcudnn.so.8\n" + "\u001b[1m 7/15\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m \u001b[1m0s\u001b[0m 23ms/step - MulticlassKR: 0.0541 - accuracy: 0.1560 - loss: 20.3139" ] }, { + "name": "stderr", "output_type": "stream", + "text": [ + "I0000 00:00:1725629199.904060 871150 device_compiler.h:188] Compiled cluster using XLA! This line is logged at most once for the lifetime of the process.\n" + ] + }, + { "name": "stdout", + "output_type": "stream", "text": [ - "15/15 [==============================] - 5s 117ms/step - loss: 41.2174 - accuracy: 0.1382 - MulticlassKR: 0.0467 - val_loss: 29.5743 - val_accuracy: 0.2798 - val_MulticlassKR: 0.1810\n", + "\u001b[1m15/15\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 328ms/step - MulticlassKR: 0.0763 - accuracy: 0.2224 - loss: 18.1235 - val_MulticlassKR: 0.1733 - val_accuracy: 0.4881 - val_loss: 11.0883\n", "Epoch 2/100\n", - "15/15 [==============================] - 1s 81ms/step - loss: 25.3826 - accuracy: 0.4441 - MulticlassKR: 0.2389 - val_loss: 19.8280 - val_accuracy: 0.5547 - val_MulticlassKR: 0.3549\n", + "\u001b[1m15/15\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step - MulticlassKR: 0.2081 - accuracy: 0.4988 - loss: 9.7469 - val_MulticlassKR: 0.2769 - val_accuracy: 0.5344 - val_loss: 7.6648\n", "Epoch 3/100\n", - "15/15 [==============================] - 1s 81ms/step - loss: 18.3231 - accuracy: 0.5899 - MulticlassKR: 0.4017 - val_loss: 16.0346 - val_accuracy: 0.6183 - val_MulticlassKR: 0.4835\n", + "\u001b[1m15/15\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step - MulticlassKR: 0.2982 - accuracy: 0.5801 - loss: 7.1314 - val_MulticlassKR: 0.3344 - val_accuracy: 0.6024 - val_loss: 6.3906\n", "Epoch 4/100\n", - "15/15 [==============================] - 1s 81ms/step - loss: 15.0896 - accuracy: 0.6402 - MulticlassKR: 0.5135 - val_loss: 13.9297 - val_accuracy: 0.6470 - val_MulticlassKR: 0.5607\n", + "\u001b[1m15/15\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step - MulticlassKR: 0.3466 - accuracy: 0.6334 - loss: 6.0837 - val_MulticlassKR: 0.3680 - val_accuracy: 0.6562 - val_loss: 5.7714\n", "Epoch 5/100\n", - "15/15 [==============================] - 1s 81ms/step - loss: 13.2237 - accuracy: 0.6814 - MulticlassKR: 0.5821 - val_loss: 12.5531 - val_accuracy: 0.6814 - val_MulticlassKR: 0.6186\n", + "\u001b[1m15/15\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step - MulticlassKR: 0.3781 - accuracy: 0.6822 - loss: 5.5372 - val_MulticlassKR: 0.3964 - val_accuracy: 0.6994 - val_loss: 5.3437\n", "Epoch 6/100\n", - "15/15 [==============================] - 1s 81ms/step - loss: 12.0225 - accuracy: 0.7057 - MulticlassKR: 0.6364 - val_loss: 11.6916 - val_accuracy: 0.6964 - val_MulticlassKR: 0.6655\n", + "\u001b[1m15/15\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step - MulticlassKR: 0.4043 - accuracy: 0.7169 - loss: 5.1466 - val_MulticlassKR: 0.4180 - val_accuracy: 0.7150 - val_loss: 5.0471\n", "Epoch 7/100\n", - "15/15 [==============================] - 1s 81ms/step - loss: 11.2456 - accuracy: 0.7178 - MulticlassKR: 0.6803 - val_loss: 11.0661 - val_accuracy: 0.7131 - val_MulticlassKR: 0.7020\n", + "\u001b[1m15/15\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step - MulticlassKR: 0.4237 - accuracy: 0.7325 - loss: 4.8670 - val_MulticlassKR: 0.4346 - val_accuracy: 0.7278 - val_loss: 4.8037\n", "Epoch 8/100\n", - "15/15 [==============================] - 1s 81ms/step - loss: 10.7023 - accuracy: 0.7343 - MulticlassKR: 0.7144 - val_loss: 10.6094 - val_accuracy: 0.7190 - val_MulticlassKR: 0.7339\n", + "\u001b[1m15/15\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step - MulticlassKR: 0.4419 - accuracy: 0.7441 - loss: 4.6246 - val_MulticlassKR: 0.4532 - val_accuracy: 0.7409 - val_loss: 4.6052\n", "Epoch 9/100\n", - "15/15 [==============================] - 1s 81ms/step - loss: 10.2158 - accuracy: 0.7353 - MulticlassKR: 0.7471 - val_loss: 10.2140 - val_accuracy: 0.7255 - val_MulticlassKR: 0.7639\n", + "\u001b[1m15/15\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step - MulticlassKR: 0.4604 - accuracy: 0.7503 - loss: 4.3993 - val_MulticlassKR: 0.4686 - val_accuracy: 0.7430 - val_loss: 4.4466\n", "Epoch 10/100\n", - "15/15 [==============================] - 1s 80ms/step - loss: 9.9306 - accuracy: 0.7444 - MulticlassKR: 0.7743 - val_loss: 9.8911 - val_accuracy: 0.7341 - val_MulticlassKR: 0.7875\n", + "\u001b[1m15/15\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step - MulticlassKR: 0.4754 - accuracy: 0.7582 - loss: 4.2522 - val_MulticlassKR: 0.4846 - val_accuracy: 0.7539 - val_loss: 4.2892\n", "Epoch 11/100\n", - "15/15 [==============================] - 1s 80ms/step - loss: 9.4766 - accuracy: 0.7500 - MulticlassKR: 0.8008 - val_loss: 9.5676 - val_accuracy: 0.7397 - val_MulticlassKR: 0.8139\n", + "\u001b[1m15/15\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step - MulticlassKR: 0.4916 - accuracy: 0.7638 - loss: 4.1165 - val_MulticlassKR: 0.4996 - val_accuracy: 0.7585 - val_loss: 4.1607\n", "Epoch 12/100\n", - "15/15 [==============================] - 1s 80ms/step - loss: 9.2583 - accuracy: 0.7547 - MulticlassKR: 0.8227 - val_loss: 9.3108 - val_accuracy: 0.7445 - val_MulticlassKR: 0.8375\n", + "\u001b[1m15/15\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step - MulticlassKR: 0.5069 - accuracy: 0.7669 - loss: 3.9638 - val_MulticlassKR: 0.5160 - val_accuracy: 0.7611 - val_loss: 4.0370\n", "Epoch 13/100\n", - "15/15 [==============================] - 1s 81ms/step - loss: 9.0268 - accuracy: 0.7571 - MulticlassKR: 0.8463 - val_loss: 9.0594 - val_accuracy: 0.7461 - val_MulticlassKR: 0.8565\n", + "\u001b[1m15/15\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step - MulticlassKR: 0.5218 - accuracy: 0.7733 - loss: 3.8421 - val_MulticlassKR: 0.5304 - val_accuracy: 0.7674 - val_loss: 3.9188\n", "Epoch 14/100\n", - "15/15 [==============================] - 1s 80ms/step - loss: 8.7289 - accuracy: 0.7631 - MulticlassKR: 0.8653 - val_loss: 8.8221 - val_accuracy: 0.7563 - val_MulticlassKR: 0.8798\n", + "\u001b[1m15/15\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step - MulticlassKR: 0.5361 - accuracy: 0.7779 - loss: 3.7251 - val_MulticlassKR: 0.5449 - val_accuracy: 0.7701 - val_loss: 3.8162\n", "Epoch 15/100\n", - "15/15 [==============================] - 1s 81ms/step - loss: 8.5468 - accuracy: 0.7660 - MulticlassKR: 0.8856 - val_loss: 8.6213 - val_accuracy: 0.7566 - val_MulticlassKR: 0.8976\n", + "\u001b[1m15/15\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step - MulticlassKR: 0.5503 - accuracy: 0.7808 - loss: 3.6451 - val_MulticlassKR: 0.5575 - val_accuracy: 0.7742 - val_loss: 3.7264\n", "Epoch 16/100\n", - "15/15 [==============================] - 1s 80ms/step - loss: 8.3208 - accuracy: 0.7699 - MulticlassKR: 0.9078 - val_loss: 8.4393 - val_accuracy: 0.7672 - val_MulticlassKR: 0.9187\n", + "\u001b[1m15/15\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step - MulticlassKR: 0.5641 - accuracy: 0.7860 - loss: 3.5308 - val_MulticlassKR: 0.5729 - val_accuracy: 0.7778 - val_loss: 3.6387\n", "Epoch 17/100\n", - "15/15 [==============================] - 1s 80ms/step - loss: 8.1348 - accuracy: 0.7747 - MulticlassKR: 0.9288 - val_loss: 8.2421 - val_accuracy: 0.7644 - val_MulticlassKR: 0.9369\n", + "\u001b[1m15/15\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step - MulticlassKR: 0.5776 - accuracy: 0.7858 - loss: 3.4762 - val_MulticlassKR: 0.5863 - val_accuracy: 0.7795 - val_loss: 3.5747\n", "Epoch 18/100\n", - "15/15 [==============================] - 1s 80ms/step - loss: 7.8150 - accuracy: 0.7807 - MulticlassKR: 0.9479 - val_loss: 8.0528 - val_accuracy: 0.7741 - val_MulticlassKR: 0.9598\n", + "\u001b[1m15/15\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step - MulticlassKR: 0.5929 - accuracy: 0.7947 - loss: 3.3608 - val_MulticlassKR: 0.5964 - val_accuracy: 0.7841 - val_loss: 3.5003\n", "Epoch 19/100\n", - "15/15 [==============================] - 1s 80ms/step - loss: 7.7277 - accuracy: 0.7813 - MulticlassKR: 0.9697 - val_loss: 7.8976 - val_accuracy: 0.7749 - val_MulticlassKR: 0.9754\n", + "\u001b[1m15/15\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step - MulticlassKR: 0.6043 - accuracy: 0.7972 - loss: 3.2695 - val_MulticlassKR: 0.6103 - val_accuracy: 0.7840 - val_loss: 3.4513\n", "Epoch 20/100\n", - "15/15 [==============================] - 1s 80ms/step - loss: 7.5802 - accuracy: 0.7822 - MulticlassKR: 0.9866 - val_loss: 7.7375 - val_accuracy: 0.7784 - val_MulticlassKR: 0.9936\n", + "\u001b[1m15/15\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step - MulticlassKR: 0.6161 - accuracy: 0.7980 - loss: 3.2273 - val_MulticlassKR: 0.6240 - val_accuracy: 0.7872 - val_loss: 3.3683\n", "Epoch 21/100\n", - "15/15 [==============================] - 1s 80ms/step - loss: 7.3151 - accuracy: 0.7893 - MulticlassKR: 1.0068 - val_loss: 7.5871 - val_accuracy: 0.7818 - val_MulticlassKR: 1.0131\n", + "\u001b[1m15/15\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step - MulticlassKR: 0.6286 - accuracy: 0.8012 - loss: 3.1890 - val_MulticlassKR: 0.6348 - val_accuracy: 0.7915 - val_loss: 3.3028\n", "Epoch 22/100\n", - "15/15 [==============================] - 1s 81ms/step - loss: 7.2699 - accuracy: 0.7901 - MulticlassKR: 1.0211 - val_loss: 7.4710 - val_accuracy: 0.7807 - val_MulticlassKR: 1.0305\n", + "\u001b[1m15/15\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step - MulticlassKR: 0.6422 - accuracy: 0.8026 - loss: 3.1332 - val_MulticlassKR: 0.6465 - val_accuracy: 0.7940 - val_loss: 3.2599\n", "Epoch 23/100\n", - "15/15 [==============================] - 1s 83ms/step - loss: 7.1052 - accuracy: 0.7939 - MulticlassKR: 1.0391 - val_loss: 7.3397 - val_accuracy: 0.7854 - val_MulticlassKR: 1.0450\n", + "\u001b[1m15/15\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step - MulticlassKR: 0.6536 - accuracy: 0.8073 - loss: 3.0645 - val_MulticlassKR: 0.6620 - val_accuracy: 0.7959 - val_loss: 3.1935\n", "Epoch 24/100\n", - "15/15 [==============================] - 1s 80ms/step - loss: 7.0167 - accuracy: 0.7962 - MulticlassKR: 1.0562 - val_loss: 7.2212 - val_accuracy: 0.7870 - val_MulticlassKR: 1.0637\n", + "\u001b[1m15/15\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step - MulticlassKR: 0.6670 - accuracy: 0.8122 - loss: 2.9769 - val_MulticlassKR: 0.6753 - val_accuracy: 0.7995 - val_loss: 3.1647\n", "Epoch 25/100\n", - "15/15 [==============================] - 1s 80ms/step - loss: 6.8205 - accuracy: 0.8002 - MulticlassKR: 1.0749 - val_loss: 7.1256 - val_accuracy: 0.7895 - val_MulticlassKR: 1.0808\n", + "\u001b[1m15/15\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step - MulticlassKR: 0.6822 - accuracy: 0.8100 - loss: 2.9820 - val_MulticlassKR: 0.6854 - val_accuracy: 0.8022 - val_loss: 3.1222\n", "Epoch 26/100\n", - "15/15 [==============================] - 1s 80ms/step - loss: 6.7542 - accuracy: 0.8013 - MulticlassKR: 1.0923 - val_loss: 7.0068 - val_accuracy: 0.7897 - val_MulticlassKR: 1.0966\n", + "\u001b[1m15/15\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step - MulticlassKR: 0.6940 - accuracy: 0.8144 - loss: 2.9172 - val_MulticlassKR: 0.7015 - val_accuracy: 0.8025 - val_loss: 3.0453\n", "Epoch 27/100\n", - "15/15 [==============================] - 1s 81ms/step - loss: 6.6025 - accuracy: 0.8022 - MulticlassKR: 1.1069 - val_loss: 6.8967 - val_accuracy: 0.7924 - val_MulticlassKR: 1.1105\n", + "\u001b[1m15/15\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step - MulticlassKR: 0.7081 - accuracy: 0.8151 - loss: 2.8395 - val_MulticlassKR: 0.7163 - val_accuracy: 0.8017 - val_loss: 3.0229\n", "Epoch 28/100\n", - "15/15 [==============================] - 1s 80ms/step - loss: 6.5729 - accuracy: 0.8033 - MulticlassKR: 1.1220 - val_loss: 6.8168 - val_accuracy: 0.7951 - val_MulticlassKR: 1.1275\n", + "\u001b[1m15/15\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step - MulticlassKR: 0.7224 - accuracy: 0.8149 - loss: 2.8350 - val_MulticlassKR: 0.7286 - val_accuracy: 0.8061 - val_loss: 2.9596\n", "Epoch 29/100\n", - "15/15 [==============================] - 1s 80ms/step - loss: 6.5147 - accuracy: 0.8074 - MulticlassKR: 1.1347 - val_loss: 6.7141 - val_accuracy: 0.7971 - val_MulticlassKR: 1.1425\n", + "\u001b[1m15/15\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step - MulticlassKR: 0.7366 - accuracy: 0.8169 - loss: 2.7816 - val_MulticlassKR: 0.7417 - val_accuracy: 0.8054 - val_loss: 2.9296\n", "Epoch 30/100\n", - "15/15 [==============================] - 1s 80ms/step - loss: 6.4094 - accuracy: 0.8059 - MulticlassKR: 1.1528 - val_loss: 6.6193 - val_accuracy: 0.7998 - val_MulticlassKR: 1.1605\n", + "\u001b[1m15/15\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step - MulticlassKR: 0.7499 - accuracy: 0.8222 - loss: 2.7434 - val_MulticlassKR: 0.7554 - val_accuracy: 0.8099 - val_loss: 2.9055\n", "Epoch 31/100\n", - "15/15 [==============================] - 1s 82ms/step - loss: 6.3102 - accuracy: 0.8090 - MulticlassKR: 1.1664 - val_loss: 6.5371 - val_accuracy: 0.8005 - val_MulticlassKR: 1.1746\n", + "\u001b[1m15/15\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step - MulticlassKR: 0.7616 - accuracy: 0.8199 - loss: 2.7226 - val_MulticlassKR: 0.7701 - val_accuracy: 0.8129 - val_loss: 2.8723\n", "Epoch 32/100\n", - "15/15 [==============================] - 1s 80ms/step - loss: 6.1902 - accuracy: 0.8078 - MulticlassKR: 1.1889 - val_loss: 6.4705 - val_accuracy: 0.8004 - val_MulticlassKR: 1.1924\n", + "\u001b[1m15/15\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step - MulticlassKR: 0.7776 - accuracy: 0.8200 - loss: 2.7143 - val_MulticlassKR: 0.7863 - val_accuracy: 0.8118 - val_loss: 2.8070\n", "Epoch 33/100\n", - "15/15 [==============================] - 1s 80ms/step - loss: 6.1780 - accuracy: 0.8127 - MulticlassKR: 1.1991 - val_loss: 6.3850 - val_accuracy: 0.8033 - val_MulticlassKR: 1.2076\n", + "\u001b[1m15/15\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step - MulticlassKR: 0.7904 - accuracy: 0.8222 - loss: 2.6423 - val_MulticlassKR: 0.7993 - val_accuracy: 0.8173 - val_loss: 2.7952\n", "Epoch 34/100\n", - "15/15 [==============================] - 1s 80ms/step - loss: 6.1156 - accuracy: 0.8123 - MulticlassKR: 1.2147 - val_loss: 6.3106 - val_accuracy: 0.8091 - val_MulticlassKR: 1.2191\n", + "\u001b[1m15/15\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step - MulticlassKR: 0.8055 - accuracy: 0.8226 - loss: 2.5915 - val_MulticlassKR: 0.8127 - val_accuracy: 0.8150 - val_loss: 2.7401\n", "Epoch 35/100\n", - "15/15 [==============================] - 1s 81ms/step - loss: 6.0083 - accuracy: 0.8143 - MulticlassKR: 1.2322 - val_loss: 6.2621 - val_accuracy: 0.8086 - val_MulticlassKR: 1.2360\n", + "\u001b[1m15/15\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step - MulticlassKR: 0.8197 - accuracy: 0.8248 - loss: 2.5484 - val_MulticlassKR: 0.8272 - val_accuracy: 0.8150 - val_loss: 2.7171\n", "Epoch 36/100\n", - "15/15 [==============================] - 1s 80ms/step - loss: 5.9177 - accuracy: 0.8158 - MulticlassKR: 1.2462 - val_loss: 6.1842 - val_accuracy: 0.8101 - val_MulticlassKR: 1.2483\n", + "\u001b[1m15/15\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step - MulticlassKR: 0.8347 - accuracy: 0.8238 - loss: 2.5125 - val_MulticlassKR: 0.8409 - val_accuracy: 0.8182 - val_loss: 2.6731\n", "Epoch 37/100\n", - "15/15 [==============================] - 1s 80ms/step - loss: 5.7953 - accuracy: 0.8186 - MulticlassKR: 1.2662 - val_loss: 6.1092 - val_accuracy: 0.8119 - val_MulticlassKR: 1.2654\n", + "\u001b[1m15/15\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step - MulticlassKR: 0.8476 - accuracy: 0.8262 - loss: 2.4837 - val_MulticlassKR: 0.8514 - val_accuracy: 0.8213 - val_loss: 2.6460\n", "Epoch 38/100\n", - "15/15 [==============================] - 1s 80ms/step - loss: 5.7620 - accuracy: 0.8179 - MulticlassKR: 1.2781 - val_loss: 6.0499 - val_accuracy: 0.8126 - val_MulticlassKR: 1.2815\n", + "\u001b[1m15/15\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step - MulticlassKR: 0.8624 - accuracy: 0.8264 - loss: 2.4758 - val_MulticlassKR: 0.8680 - val_accuracy: 0.8220 - val_loss: 2.6092\n", "Epoch 39/100\n", - "15/15 [==============================] - 1s 80ms/step - loss: 5.7588 - accuracy: 0.8187 - MulticlassKR: 1.2897 - val_loss: 5.9959 - val_accuracy: 0.8131 - val_MulticlassKR: 1.2936\n", + "\u001b[1m15/15\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step - MulticlassKR: 0.8787 - accuracy: 0.8311 - loss: 2.3857 - val_MulticlassKR: 0.8839 - val_accuracy: 0.8224 - val_loss: 2.5782\n", "Epoch 40/100\n", - "15/15 [==============================] - 1s 80ms/step - loss: 5.7005 - accuracy: 0.8208 - MulticlassKR: 1.3042 - val_loss: 5.9460 - val_accuracy: 0.8152 - val_MulticlassKR: 1.3039\n", + "\u001b[1m15/15\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step - MulticlassKR: 0.8878 - accuracy: 0.8294 - loss: 2.4098 - val_MulticlassKR: 0.8976 - val_accuracy: 0.8186 - val_loss: 2.5610\n", "Epoch 41/100\n", - "15/15 [==============================] - 1s 80ms/step - loss: 5.6319 - accuracy: 0.8232 - MulticlassKR: 1.3146 - val_loss: 5.8816 - val_accuracy: 0.8148 - val_MulticlassKR: 1.3217\n", + "\u001b[1m15/15\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step - MulticlassKR: 0.9059 - accuracy: 0.8301 - loss: 2.3862 - val_MulticlassKR: 0.9114 - val_accuracy: 0.8209 - val_loss: 2.5297\n", "Epoch 42/100\n", - "15/15 [==============================] - 1s 81ms/step - loss: 5.6429 - accuracy: 0.8232 - MulticlassKR: 1.3291 - val_loss: 5.8772 - val_accuracy: 0.8151 - val_MulticlassKR: 1.3317\n", - "Epoch 43/100\n" - ] - }, - { - "output_type": "stream", - "name": "stdout", - "text": [ - "15/15 [==============================] - 1s 80ms/step - loss: 5.5395 - accuracy: 0.8245 - MulticlassKR: 1.3460 - val_loss: 5.8039 - val_accuracy: 0.8189 - val_MulticlassKR: 1.3538\n", + "\u001b[1m15/15\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step - MulticlassKR: 0.9195 - accuracy: 0.8314 - loss: 2.3217 - val_MulticlassKR: 0.9314 - val_accuracy: 0.8245 - val_loss: 2.4789\n", + "Epoch 43/100\n", + "\u001b[1m15/15\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step - MulticlassKR: 0.9348 - accuracy: 0.8341 - loss: 2.2993 - val_MulticlassKR: 0.9414 - val_accuracy: 0.8268 - val_loss: 2.4494\n", "Epoch 44/100\n", - "15/15 [==============================] - 1s 81ms/step - loss: 5.4303 - accuracy: 0.8249 - MulticlassKR: 1.3593 - val_loss: 5.7421 - val_accuracy: 0.8189 - val_MulticlassKR: 1.3669\n", + "\u001b[1m15/15\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step - MulticlassKR: 0.9513 - accuracy: 0.8365 - loss: 2.2156 - val_MulticlassKR: 0.9535 - val_accuracy: 0.8286 - val_loss: 2.4348\n", "Epoch 45/100\n", - "15/15 [==============================] - 1s 80ms/step - loss: 5.3844 - accuracy: 0.8268 - MulticlassKR: 1.3762 - val_loss: 5.6846 - val_accuracy: 0.8217 - val_MulticlassKR: 1.3765\n", + "\u001b[1m15/15\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step - MulticlassKR: 0.9637 - accuracy: 0.8364 - loss: 2.2075 - val_MulticlassKR: 0.9722 - val_accuracy: 0.8277 - val_loss: 2.4032\n", "Epoch 46/100\n", - "15/15 [==============================] - 1s 80ms/step - loss: 5.3307 - accuracy: 0.8281 - MulticlassKR: 1.3873 - val_loss: 5.6413 - val_accuracy: 0.8234 - val_MulticlassKR: 1.3881\n", + "\u001b[1m15/15\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step - MulticlassKR: 0.9808 - accuracy: 0.8363 - loss: 2.2168 - val_MulticlassKR: 0.9827 - val_accuracy: 0.8280 - val_loss: 2.4145\n", "Epoch 47/100\n", - "15/15 [==============================] - 1s 80ms/step - loss: 5.3788 - accuracy: 0.8258 - MulticlassKR: 1.3938 - val_loss: 5.6087 - val_accuracy: 0.8214 - val_MulticlassKR: 1.3971\n", + "\u001b[1m15/15\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step - MulticlassKR: 0.9938 - accuracy: 0.8363 - loss: 2.2026 - val_MulticlassKR: 1.0033 - val_accuracy: 0.8268 - val_loss: 2.3602\n", "Epoch 48/100\n", - "15/15 [==============================] - 1s 80ms/step - loss: 5.2561 - accuracy: 0.8314 - MulticlassKR: 1.4119 - val_loss: 5.5684 - val_accuracy: 0.8215 - val_MulticlassKR: 1.4106\n", + "\u001b[1m15/15\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step - MulticlassKR: 1.0093 - accuracy: 0.8343 - loss: 2.1632 - val_MulticlassKR: 1.0187 - val_accuracy: 0.8289 - val_loss: 2.3226\n", "Epoch 49/100\n", - "15/15 [==============================] - 1s 81ms/step - loss: 5.2374 - accuracy: 0.8276 - MulticlassKR: 1.4266 - val_loss: 5.5116 - val_accuracy: 0.8255 - val_MulticlassKR: 1.4254\n", + "\u001b[1m15/15\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step - MulticlassKR: 1.0262 - accuracy: 0.8364 - loss: 2.1351 - val_MulticlassKR: 1.0315 - val_accuracy: 0.8327 - val_loss: 2.2958\n", "Epoch 50/100\n", - "15/15 [==============================] - 1s 81ms/step - loss: 5.2404 - accuracy: 0.8299 - MulticlassKR: 1.4328 - val_loss: 5.4923 - val_accuracy: 0.8248 - val_MulticlassKR: 1.4351\n", + "\u001b[1m15/15\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step - MulticlassKR: 1.0390 - accuracy: 0.8379 - loss: 2.1252 - val_MulticlassKR: 1.0438 - val_accuracy: 0.8320 - val_loss: 2.2825\n", "Epoch 51/100\n", - "15/15 [==============================] - 1s 81ms/step - loss: 5.2273 - accuracy: 0.8302 - MulticlassKR: 1.4446 - val_loss: 5.4473 - val_accuracy: 0.8252 - val_MulticlassKR: 1.4494\n", + "\u001b[1m15/15\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step - MulticlassKR: 1.0535 - accuracy: 0.8396 - loss: 2.0654 - val_MulticlassKR: 1.0616 - val_accuracy: 0.8314 - val_loss: 2.2470\n", "Epoch 52/100\n", - "15/15 [==============================] - 1s 80ms/step - loss: 5.1193 - accuracy: 0.8302 - MulticlassKR: 1.4615 - val_loss: 5.4205 - val_accuracy: 0.8219 - val_MulticlassKR: 1.4643\n", + "\u001b[1m15/15\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step - MulticlassKR: 1.0690 - accuracy: 0.8388 - loss: 2.0495 - val_MulticlassKR: 1.0761 - val_accuracy: 0.8328 - val_loss: 2.2247\n", "Epoch 53/100\n", - "15/15 [==============================] - 1s 81ms/step - loss: 5.1053 - accuracy: 0.8338 - MulticlassKR: 1.4739 - val_loss: 5.3770 - val_accuracy: 0.8238 - val_MulticlassKR: 1.4766\n", + "\u001b[1m15/15\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step - MulticlassKR: 1.0853 - accuracy: 0.8401 - loss: 2.0240 - val_MulticlassKR: 1.0873 - val_accuracy: 0.8315 - val_loss: 2.2193\n", "Epoch 54/100\n", - "15/15 [==============================] - 1s 80ms/step - loss: 4.9836 - accuracy: 0.8338 - MulticlassKR: 1.4889 - val_loss: 5.3285 - val_accuracy: 0.8259 - val_MulticlassKR: 1.4896\n", + "\u001b[1m15/15\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step - MulticlassKR: 1.0977 - accuracy: 0.8366 - loss: 2.0653 - val_MulticlassKR: 1.1064 - val_accuracy: 0.8302 - val_loss: 2.2040\n", "Epoch 55/100\n", - "15/15 [==============================] - 1s 80ms/step - loss: 4.9996 - accuracy: 0.8337 - MulticlassKR: 1.4994 - val_loss: 5.3168 - val_accuracy: 0.8272 - val_MulticlassKR: 1.4970\n", + "\u001b[1m15/15\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step - MulticlassKR: 1.1159 - accuracy: 0.8426 - loss: 1.9554 - val_MulticlassKR: 1.1209 - val_accuracy: 0.8351 - val_loss: 2.1460\n", "Epoch 56/100\n", - "15/15 [==============================] - 1s 81ms/step - loss: 4.9064 - accuracy: 0.8372 - MulticlassKR: 1.5095 - val_loss: 5.2652 - val_accuracy: 0.8284 - val_MulticlassKR: 1.5102\n", + "\u001b[1m15/15\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step - MulticlassKR: 1.1261 - accuracy: 0.8412 - loss: 1.9709 - val_MulticlassKR: 1.1347 - val_accuracy: 0.8374 - val_loss: 2.1200\n", "Epoch 57/100\n", - "15/15 [==============================] - 1s 80ms/step - loss: 4.9659 - accuracy: 0.8335 - MulticlassKR: 1.5204 - val_loss: 5.2111 - val_accuracy: 0.8284 - val_MulticlassKR: 1.5191\n", + "\u001b[1m15/15\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step - MulticlassKR: 1.1447 - accuracy: 0.8418 - loss: 1.9076 - val_MulticlassKR: 1.1494 - val_accuracy: 0.8370 - val_loss: 2.1104\n", "Epoch 58/100\n", - "15/15 [==============================] - 1s 81ms/step - loss: 4.9272 - accuracy: 0.8351 - MulticlassKR: 1.5316 - val_loss: 5.1873 - val_accuracy: 0.8310 - val_MulticlassKR: 1.5290\n", + "\u001b[1m15/15\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step - MulticlassKR: 1.1581 - accuracy: 0.8381 - loss: 1.9410 - val_MulticlassKR: 1.1646 - val_accuracy: 0.8363 - val_loss: 2.1013\n", "Epoch 59/100\n", - "15/15 [==============================] - 1s 80ms/step - loss: 4.8504 - accuracy: 0.8367 - MulticlassKR: 1.5386 - val_loss: 5.1892 - val_accuracy: 0.8263 - val_MulticlassKR: 1.5440\n", + "\u001b[1m15/15\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step - MulticlassKR: 1.1667 - accuracy: 0.8415 - loss: 1.9151 - val_MulticlassKR: 1.1772 - val_accuracy: 0.8379 - val_loss: 2.0835\n", "Epoch 60/100\n", - "15/15 [==============================] - 1s 82ms/step - loss: 4.7810 - accuracy: 0.8399 - MulticlassKR: 1.5500 - val_loss: 5.1203 - val_accuracy: 0.8298 - val_MulticlassKR: 1.5517\n", + "\u001b[1m15/15\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step - MulticlassKR: 1.1850 - accuracy: 0.8437 - loss: 1.8632 - val_MulticlassKR: 1.1927 - val_accuracy: 0.8359 - val_loss: 2.0553\n", "Epoch 61/100\n", - "15/15 [==============================] - 1s 80ms/step - loss: 4.7313 - accuracy: 0.8394 - MulticlassKR: 1.5630 - val_loss: 5.1206 - val_accuracy: 0.8292 - val_MulticlassKR: 1.5662\n", + "\u001b[1m15/15\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step - MulticlassKR: 1.1986 - accuracy: 0.8448 - loss: 1.8760 - val_MulticlassKR: 1.2034 - val_accuracy: 0.8342 - val_loss: 2.0410\n", "Epoch 62/100\n", - "15/15 [==============================] - 1s 80ms/step - loss: 4.7666 - accuracy: 0.8406 - MulticlassKR: 1.5742 - val_loss: 5.0925 - val_accuracy: 0.8295 - val_MulticlassKR: 1.5692\n", + "\u001b[1m15/15\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step - MulticlassKR: 1.2100 - accuracy: 0.8459 - loss: 1.8273 - val_MulticlassKR: 1.2160 - val_accuracy: 0.8389 - val_loss: 1.9946\n", "Epoch 63/100\n", - "15/15 [==============================] - 1s 80ms/step - loss: 4.6527 - accuracy: 0.8418 - MulticlassKR: 1.5808 - val_loss: 5.0593 - val_accuracy: 0.8302 - val_MulticlassKR: 1.5836\n", + "\u001b[1m15/15\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step - MulticlassKR: 1.2255 - accuracy: 0.8464 - loss: 1.7774 - val_MulticlassKR: 1.2324 - val_accuracy: 0.8372 - val_loss: 1.9850\n", "Epoch 64/100\n", - "15/15 [==============================] - 1s 81ms/step - loss: 4.7434 - accuracy: 0.8410 - MulticlassKR: 1.5952 - val_loss: 5.0201 - val_accuracy: 0.8329 - val_MulticlassKR: 1.5966\n", + "\u001b[1m15/15\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step - MulticlassKR: 1.2352 - accuracy: 0.8434 - loss: 1.7999 - val_MulticlassKR: 1.2406 - val_accuracy: 0.8365 - val_loss: 1.9733\n", "Epoch 65/100\n", - "15/15 [==============================] - 1s 81ms/step - loss: 4.7347 - accuracy: 0.8386 - MulticlassKR: 1.6056 - val_loss: 5.0073 - val_accuracy: 0.8337 - val_MulticlassKR: 1.6002\n", + "\u001b[1m15/15\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step - MulticlassKR: 1.2477 - accuracy: 0.8476 - loss: 1.7615 - val_MulticlassKR: 1.2563 - val_accuracy: 0.8363 - val_loss: 1.9566\n", "Epoch 66/100\n", - "15/15 [==============================] - 1s 80ms/step - loss: 4.6701 - accuracy: 0.8414 - MulticlassKR: 1.6104 - val_loss: 4.9744 - val_accuracy: 0.8345 - val_MulticlassKR: 1.6125\n", + "\u001b[1m15/15\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step - MulticlassKR: 1.2580 - accuracy: 0.8476 - loss: 1.7418 - val_MulticlassKR: 1.2686 - val_accuracy: 0.8382 - val_loss: 1.9339\n", "Epoch 67/100\n", - "15/15 [==============================] - 1s 80ms/step - loss: 4.5813 - accuracy: 0.8430 - MulticlassKR: 1.6230 - val_loss: 4.9599 - val_accuracy: 0.8336 - val_MulticlassKR: 1.6252\n", + "\u001b[1m15/15\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step - MulticlassKR: 1.2797 - accuracy: 0.8454 - loss: 1.7491 - val_MulticlassKR: 1.2778 - val_accuracy: 0.8407 - val_loss: 1.9260\n", "Epoch 68/100\n", - "15/15 [==============================] - 1s 81ms/step - loss: 4.6265 - accuracy: 0.8420 - MulticlassKR: 1.6316 - val_loss: 4.9260 - val_accuracy: 0.8310 - val_MulticlassKR: 1.6337\n", + "\u001b[1m15/15\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step - MulticlassKR: 1.2899 - accuracy: 0.8479 - loss: 1.7236 - val_MulticlassKR: 1.2878 - val_accuracy: 0.8410 - val_loss: 1.9411\n", "Epoch 69/100\n", - "15/15 [==============================] - 1s 81ms/step - loss: 4.6232 - accuracy: 0.8426 - MulticlassKR: 1.6420 - val_loss: 4.8940 - val_accuracy: 0.8365 - val_MulticlassKR: 1.6376\n", + "\u001b[1m15/15\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step - MulticlassKR: 1.3019 - accuracy: 0.8483 - loss: 1.6799 - val_MulticlassKR: 1.3025 - val_accuracy: 0.8354 - val_loss: 1.9176\n", "Epoch 70/100\n", - "15/15 [==============================] - 1s 81ms/step - loss: 4.5432 - accuracy: 0.8430 - MulticlassKR: 1.6507 - val_loss: 4.8714 - val_accuracy: 0.8355 - val_MulticlassKR: 1.6471\n", + "\u001b[1m15/15\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step - MulticlassKR: 1.3128 - accuracy: 0.8486 - loss: 1.6793 - val_MulticlassKR: 1.3139 - val_accuracy: 0.8432 - val_loss: 1.8546\n", "Epoch 71/100\n", - "15/15 [==============================] - 1s 80ms/step - loss: 4.4822 - accuracy: 0.8438 - MulticlassKR: 1.6584 - val_loss: 4.8362 - val_accuracy: 0.8358 - val_MulticlassKR: 1.6575\n", + "\u001b[1m15/15\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step - MulticlassKR: 1.3220 - accuracy: 0.8486 - loss: 1.6707 - val_MulticlassKR: 1.3299 - val_accuracy: 0.8423 - val_loss: 1.8357\n", "Epoch 72/100\n", - "15/15 [==============================] - 1s 80ms/step - loss: 4.4781 - accuracy: 0.8444 - MulticlassKR: 1.6695 - val_loss: 4.8306 - val_accuracy: 0.8372 - val_MulticlassKR: 1.6670\n", + "\u001b[1m15/15\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step - MulticlassKR: 1.3397 - accuracy: 0.8487 - loss: 1.6309 - val_MulticlassKR: 1.3332 - val_accuracy: 0.8429 - val_loss: 1.8525\n", "Epoch 73/100\n", - "15/15 [==============================] - 1s 81ms/step - loss: 4.5386 - accuracy: 0.8424 - MulticlassKR: 1.6777 - val_loss: 4.8021 - val_accuracy: 0.8364 - val_MulticlassKR: 1.6715\n", + "\u001b[1m15/15\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step - MulticlassKR: 1.3449 - accuracy: 0.8523 - loss: 1.5920 - val_MulticlassKR: 1.3529 - val_accuracy: 0.8413 - val_loss: 1.8322\n", "Epoch 74/100\n", - "15/15 [==============================] - 1s 80ms/step - loss: 4.4138 - accuracy: 0.8447 - MulticlassKR: 1.6880 - val_loss: 4.7918 - val_accuracy: 0.8377 - val_MulticlassKR: 1.6845\n", + "\u001b[1m15/15\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step - MulticlassKR: 1.3592 - accuracy: 0.8471 - loss: 1.6306 - val_MulticlassKR: 1.3657 - val_accuracy: 0.8431 - val_loss: 1.7962\n", "Epoch 75/100\n", - "15/15 [==============================] - 1s 81ms/step - loss: 4.4090 - accuracy: 0.8476 - MulticlassKR: 1.6962 - val_loss: 4.7612 - val_accuracy: 0.8368 - val_MulticlassKR: 1.6925\n", + "\u001b[1m15/15\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step - MulticlassKR: 1.3692 - accuracy: 0.8469 - loss: 1.6310 - val_MulticlassKR: 1.3748 - val_accuracy: 0.8450 - val_loss: 1.7728\n", "Epoch 76/100\n", - "15/15 [==============================] - 1s 81ms/step - loss: 4.4482 - accuracy: 0.8459 - MulticlassKR: 1.6987 - val_loss: 4.7491 - val_accuracy: 0.8363 - val_MulticlassKR: 1.7041\n", + "\u001b[1m15/15\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step - MulticlassKR: 1.3819 - accuracy: 0.8498 - loss: 1.6002 - val_MulticlassKR: 1.3847 - val_accuracy: 0.8436 - val_loss: 1.7622\n", "Epoch 77/100\n", - "15/15 [==============================] - 1s 80ms/step - loss: 4.3394 - accuracy: 0.8462 - MulticlassKR: 1.7108 - val_loss: 4.7155 - val_accuracy: 0.8387 - val_MulticlassKR: 1.7075\n", + "\u001b[1m15/15\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step - MulticlassKR: 1.3935 - accuracy: 0.8503 - loss: 1.5525 - val_MulticlassKR: 1.3909 - val_accuracy: 0.8456 - val_loss: 1.7458\n", "Epoch 78/100\n", - "15/15 [==============================] - 1s 80ms/step - loss: 4.3768 - accuracy: 0.8482 - MulticlassKR: 1.7117 - val_loss: 4.6795 - val_accuracy: 0.8396 - val_MulticlassKR: 1.7135\n", + "\u001b[1m15/15\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step - MulticlassKR: 1.4010 - accuracy: 0.8518 - loss: 1.5257 - val_MulticlassKR: 1.4095 - val_accuracy: 0.8450 - val_loss: 1.7270\n", "Epoch 79/100\n", - "15/15 [==============================] - 1s 80ms/step - loss: 4.3540 - accuracy: 0.8476 - MulticlassKR: 1.7259 - val_loss: 4.6666 - val_accuracy: 0.8388 - val_MulticlassKR: 1.7266\n", + "\u001b[1m15/15\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step - MulticlassKR: 1.4161 - accuracy: 0.8517 - loss: 1.5071 - val_MulticlassKR: 1.4145 - val_accuracy: 0.8425 - val_loss: 1.7228\n", "Epoch 80/100\n", - "15/15 [==============================] - 1s 80ms/step - loss: 4.2509 - accuracy: 0.8469 - MulticlassKR: 1.7359 - val_loss: 4.6558 - val_accuracy: 0.8357 - val_MulticlassKR: 1.7321\n", + "\u001b[1m15/15\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step - MulticlassKR: 1.4280 - accuracy: 0.8526 - loss: 1.4839 - val_MulticlassKR: 1.4281 - val_accuracy: 0.8462 - val_loss: 1.6826\n", "Epoch 81/100\n", - "15/15 [==============================] - 1s 81ms/step - loss: 4.2792 - accuracy: 0.8461 - MulticlassKR: 1.7397 - val_loss: 4.6639 - val_accuracy: 0.8364 - val_MulticlassKR: 1.7419\n", + "\u001b[1m15/15\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step - MulticlassKR: 1.4356 - accuracy: 0.8520 - loss: 1.4922 - val_MulticlassKR: 1.4364 - val_accuracy: 0.8461 - val_loss: 1.6689\n", "Epoch 82/100\n", - "15/15 [==============================] - 1s 80ms/step - loss: 4.2849 - accuracy: 0.8465 - MulticlassKR: 1.7502 - val_loss: 4.6150 - val_accuracy: 0.8389 - val_MulticlassKR: 1.7488\n", + "\u001b[1m15/15\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step - MulticlassKR: 1.4442 - accuracy: 0.8516 - loss: 1.4950 - val_MulticlassKR: 1.4495 - val_accuracy: 0.8459 - val_loss: 1.6646\n", "Epoch 83/100\n", - "15/15 [==============================] - 1s 81ms/step - loss: 4.2858 - accuracy: 0.8466 - MulticlassKR: 1.7563 - val_loss: 4.6256 - val_accuracy: 0.8382 - val_MulticlassKR: 1.7551\n", + "\u001b[1m15/15\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step - MulticlassKR: 1.4544 - accuracy: 0.8535 - loss: 1.4631 - val_MulticlassKR: 1.4591 - val_accuracy: 0.8445 - val_loss: 1.6525\n", "Epoch 84/100\n", - "15/15 [==============================] - 1s 81ms/step - loss: 4.1836 - accuracy: 0.8491 - MulticlassKR: 1.7594 - val_loss: 4.5682 - val_accuracy: 0.8401 - val_MulticlassKR: 1.7607\n", - "Epoch 85/100\n" - ] - }, - { - "output_type": "stream", - "name": "stdout", - "text": [ - "15/15 [==============================] - 1s 80ms/step - loss: 4.1970 - accuracy: 0.8497 - MulticlassKR: 1.7701 - val_loss: 4.5760 - val_accuracy: 0.8405 - val_MulticlassKR: 1.7660\n", + "\u001b[1m15/15\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step - MulticlassKR: 1.4672 - accuracy: 0.8545 - loss: 1.4279 - val_MulticlassKR: 1.4700 - val_accuracy: 0.8469 - val_loss: 1.6420\n", + "Epoch 85/100\n", + "\u001b[1m15/15\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step - MulticlassKR: 1.4714 - accuracy: 0.8513 - loss: 1.4660 - val_MulticlassKR: 1.4745 - val_accuracy: 0.8465 - val_loss: 1.6196\n", "Epoch 86/100\n", - "15/15 [==============================] - 1s 80ms/step - loss: 4.1455 - accuracy: 0.8507 - MulticlassKR: 1.7759 - val_loss: 4.5417 - val_accuracy: 0.8425 - val_MulticlassKR: 1.7734\n", + "\u001b[1m15/15\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step - MulticlassKR: 1.4830 - accuracy: 0.8523 - loss: 1.4406 - val_MulticlassKR: 1.4870 - val_accuracy: 0.8466 - val_loss: 1.6352\n", "Epoch 87/100\n", - "15/15 [==============================] - 1s 80ms/step - loss: 4.1810 - accuracy: 0.8506 - MulticlassKR: 1.7823 - val_loss: 4.5125 - val_accuracy: 0.8417 - val_MulticlassKR: 1.7786\n", + "\u001b[1m15/15\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step - MulticlassKR: 1.4993 - accuracy: 0.8514 - loss: 1.4115 - val_MulticlassKR: 1.4946 - val_accuracy: 0.8452 - val_loss: 1.6060\n", "Epoch 88/100\n", - "15/15 [==============================] - 1s 80ms/step - loss: 4.1159 - accuracy: 0.8518 - MulticlassKR: 1.7922 - val_loss: 4.5125 - val_accuracy: 0.8391 - val_MulticlassKR: 1.7913\n", + "\u001b[1m15/15\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step - MulticlassKR: 1.5002 - accuracy: 0.8533 - loss: 1.3858 - val_MulticlassKR: 1.5038 - val_accuracy: 0.8477 - val_loss: 1.5762\n", "Epoch 89/100\n", - "15/15 [==============================] - 1s 81ms/step - loss: 4.1807 - accuracy: 0.8500 - MulticlassKR: 1.7990 - val_loss: 4.4882 - val_accuracy: 0.8402 - val_MulticlassKR: 1.7938\n", + "\u001b[1m15/15\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step - MulticlassKR: 1.5100 - accuracy: 0.8533 - loss: 1.3975 - val_MulticlassKR: 1.5142 - val_accuracy: 0.8455 - val_loss: 1.5641\n", "Epoch 90/100\n", - "15/15 [==============================] - 1s 80ms/step - loss: 4.1548 - accuracy: 0.8504 - MulticlassKR: 1.8031 - val_loss: 4.5046 - val_accuracy: 0.8421 - val_MulticlassKR: 1.8073\n", + "\u001b[1m15/15\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step - MulticlassKR: 1.5211 - accuracy: 0.8535 - loss: 1.3996 - val_MulticlassKR: 1.5238 - val_accuracy: 0.8475 - val_loss: 1.5513\n", "Epoch 91/100\n", - "15/15 [==============================] - 1s 80ms/step - loss: 4.1227 - accuracy: 0.8501 - MulticlassKR: 1.8102 - val_loss: 4.4483 - val_accuracy: 0.8408 - val_MulticlassKR: 1.8036\n", + "\u001b[1m15/15\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step - MulticlassKR: 1.5309 - accuracy: 0.8557 - loss: 1.3107 - val_MulticlassKR: 1.5346 - val_accuracy: 0.8476 - val_loss: 1.5482\n", "Epoch 92/100\n", - "15/15 [==============================] - 1s 81ms/step - loss: 4.1302 - accuracy: 0.8512 - MulticlassKR: 1.8124 - val_loss: 4.4501 - val_accuracy: 0.8435 - val_MulticlassKR: 1.8101\n", + "\u001b[1m15/15\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step - MulticlassKR: 1.5376 - accuracy: 0.8533 - loss: 1.3776 - val_MulticlassKR: 1.5392 - val_accuracy: 0.8472 - val_loss: 1.5362\n", "Epoch 93/100\n", - "15/15 [==============================] - 1s 81ms/step - loss: 4.0846 - accuracy: 0.8502 - MulticlassKR: 1.8184 - val_loss: 4.4205 - val_accuracy: 0.8425 - val_MulticlassKR: 1.8175\n", + "\u001b[1m15/15\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step - MulticlassKR: 1.5526 - accuracy: 0.8541 - loss: 1.3208 - val_MulticlassKR: 1.5509 - val_accuracy: 0.8493 - val_loss: 1.5215\n", "Epoch 94/100\n", - "15/15 [==============================] - 1s 80ms/step - loss: 3.9720 - accuracy: 0.8539 - MulticlassKR: 1.8275 - val_loss: 4.4813 - val_accuracy: 0.8381 - val_MulticlassKR: 1.8186\n", + "\u001b[1m15/15\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step - MulticlassKR: 1.5585 - accuracy: 0.8545 - loss: 1.3069 - val_MulticlassKR: 1.5586 - val_accuracy: 0.8491 - val_loss: 1.4954\n", "Epoch 95/100\n", - "15/15 [==============================] - 1s 81ms/step - loss: 3.9978 - accuracy: 0.8542 - MulticlassKR: 1.8309 - val_loss: 4.3855 - val_accuracy: 0.8440 - val_MulticlassKR: 1.8287\n", + "\u001b[1m15/15\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step - MulticlassKR: 1.5643 - accuracy: 0.8532 - loss: 1.3201 - val_MulticlassKR: 1.5686 - val_accuracy: 0.8466 - val_loss: 1.4972\n", "Epoch 96/100\n", - "15/15 [==============================] - 1s 80ms/step - loss: 4.0764 - accuracy: 0.8506 - MulticlassKR: 1.8369 - val_loss: 4.3828 - val_accuracy: 0.8443 - val_MulticlassKR: 1.8371\n", + "\u001b[1m15/15\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step - MulticlassKR: 1.5737 - accuracy: 0.8545 - loss: 1.2825 - val_MulticlassKR: 1.5777 - val_accuracy: 0.8466 - val_loss: 1.5008\n", "Epoch 97/100\n", - "15/15 [==============================] - 1s 81ms/step - loss: 4.0436 - accuracy: 0.8517 - MulticlassKR: 1.8470 - val_loss: 4.3730 - val_accuracy: 0.8457 - val_MulticlassKR: 1.8344\n", + "\u001b[1m15/15\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step - MulticlassKR: 1.5788 - accuracy: 0.8572 - loss: 1.2986 - val_MulticlassKR: 1.5852 - val_accuracy: 0.8477 - val_loss: 1.4803\n", "Epoch 98/100\n", - "15/15 [==============================] - 1s 81ms/step - loss: 3.9989 - accuracy: 0.8532 - MulticlassKR: 1.8491 - val_loss: 4.3596 - val_accuracy: 0.8445 - val_MulticlassKR: 1.8446\n", + "\u001b[1m15/15\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step - MulticlassKR: 1.5931 - accuracy: 0.8521 - loss: 1.2908 - val_MulticlassKR: 1.5886 - val_accuracy: 0.8442 - val_loss: 1.4900\n", "Epoch 99/100\n", - "15/15 [==============================] - 1s 80ms/step - loss: 3.9820 - accuracy: 0.8541 - MulticlassKR: 1.8539 - val_loss: 4.3444 - val_accuracy: 0.8442 - val_MulticlassKR: 1.8477\n", + "\u001b[1m15/15\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step - MulticlassKR: 1.5954 - accuracy: 0.8555 - loss: 1.2954 - val_MulticlassKR: 1.6007 - val_accuracy: 0.8503 - val_loss: 1.4417\n", "Epoch 100/100\n", - "15/15 [==============================] - 1s 80ms/step - loss: 3.9592 - accuracy: 0.8523 - MulticlassKR: 1.8626 - val_loss: 4.3177 - val_accuracy: 0.8448 - val_MulticlassKR: 1.8529\n" + "\u001b[1m15/15\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step - MulticlassKR: 1.6085 - accuracy: 0.8595 - loss: 1.2235 - val_MulticlassKR: 1.6096 - val_accuracy: 0.8479 - val_loss: 1.4542\n" ] }, { - "output_type": "execute_result", "data": { "text/plain": [ - "" + "" ] }, + "execution_count": 5, "metadata": {}, - "execution_count": 5 + "output_type": "execute_result" } ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "p1rWZTZBHEIR", - "outputId": "7cb00f9b-0f84-40f0-c5fa-b29fc0a00c92" - } + "source": [ + "# fit the model\n", + "model.fit(\n", + " x_train,\n", + " y_train,\n", + " batch_size=4096,\n", + " epochs=100,\n", + " validation_data=(x_test, y_test),\n", + " shuffle=True,\n", + " verbose=1,\n", + ")" + ] }, { "cell_type": "markdown", - "source": [ - "### model exportation\n", - "\n", - "Once training is finished, the model can be optimized for inference by using the `vanilla_export()` method." - ], "metadata": { "id": "s6TDG4nflyya" - } + }, + "source": [ + "### Model exportation\n", + "\n", + "Once training is finished, the model can be optimized for inference by using the\n", + "`vanilla_export()` method.\n" + ] }, { "cell_type": "code", "execution_count": 6, + "metadata": { + "id": "Nr2nMclLHHvI" + }, + "outputs": [], "source": [ "# once training is finished you can convert\n", "# SpectralDense layers into Dense layers and SpectralConv2D into Conv2D\n", "# which optimize performance for inference\n", "vanilla_model = model.vanilla_export()" - ], - "outputs": [], - "metadata": { - "id": "Nr2nMclLHHvI" - } + ] }, { "cell_type": "markdown", - "source": [ - "### certificates generation and adversarial attacks" - ], "metadata": { "id": "V32dqI2NmMPi" - } + }, + "source": [ + "### Certificates generation and adversarial attacks\n" + ] }, { "cell_type": "code", "execution_count": 7, - "source": [ - "import foolbox as fb\n", - "from tensorflow import convert_to_tensor\n", - "import matplotlib.pyplot as plt\n", - "import tensorflow as tf" - ], - "outputs": [ - { - "output_type": "stream", - "name": "stderr", - "text": [ - "Matplotlib created a temporary config/cache directory at /tmp/matplotlib-an1t4aqt because the default path (/home/thibaut.boissin/.config/matplotlib) is not a writable directory; it is highly recommended to set the MPLCONFIGDIR environment variable to a writable directory, in particular to speed up the import of Matplotlib and to better support multiprocessing.\n" - ] - } - ], "metadata": { "id": "m8h2gEDuIS5q" - } + }, + "outputs": [], + "source": [ + "import keras.ops as K\n", + "import foolbox as fb\n", + "import matplotlib.pyplot as plt" + ] }, { "cell_type": "code", "execution_count": 8, + "metadata": { + "id": "AMdpLMM1IXZJ" + }, + "outputs": [], "source": [ "# we will test it on 10 samples one of each class\n", "nb_adv = 10\n", "\n", - "hkr_fmodel = fb.TensorFlowModel(vanilla_model, bounds=(0., 1.), device=\"/GPU:0\")" - ], - "outputs": [], - "metadata": { - "id": "AMdpLMM1IXZJ" - } + "hkr_fmodel = fb.TensorFlowModel(vanilla_model, bounds=(0.0, 1.0), device=\"/GPU:0\")" + ] }, { "cell_type": "markdown", - "source": [ - "In order to test the robustness of the model, the first correctly classified element of each class are selected." - ], "metadata": { "id": "SLRFGRmcmw6K" - } + }, + "source": [ + "In order to test the robustness of the model, the first correctly classified element of\n", + "each class are selected.\n" + ] }, { "cell_type": "code", "execution_count": 9, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "ry5uB9QdJexi", + "outputId": "6126888e-d256-4783-d284-0e40555c5dff" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[1m10/10\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 27ms/step\n" + ] + } + ], "source": [ "# strategy: first\n", "# we select a sample from each class.\n", "images_list = []\n", "labels_list = []\n", "# select only a few element from the test set\n", - "selected=np.random.choice(len(y_test_ord), 500)\n", + "selected = np.random.choice(len(y_test_ord), 500)\n", "sub_y_test_ord = y_test_ord[:300]\n", "sub_x_test = x_test[:300]\n", "# drop misclassified elements\n", - "misclassified_mask = tf.equal(tf.argmax(vanilla_model.predict(sub_x_test), axis=-1), sub_y_test_ord)\n", + "misclassified_mask = K.equal(\n", + " K.argmax(vanilla_model.predict(sub_x_test), axis=-1), sub_y_test_ord\n", + ")\n", "sub_x_test = sub_x_test[misclassified_mask]\n", "sub_y_test_ord = sub_y_test_ord[misclassified_mask]\n", "# now we will build a list with input image for each element of the matrix\n", "for i in range(10):\n", - " # select the first element of the ith label\n", - " label_mask = [sub_y_test_ord==i]\n", - " x = sub_x_test[label_mask][0]\n", - " y = sub_y_test_ord[label_mask][0]\n", - " # convert it to tensor for use with foolbox\n", - " images = convert_to_tensor(x.astype(\"float32\"), dtype=\"float32\")\n", - " labels = convert_to_tensor(y, dtype=\"int64\")\n", - " # repeat the input 10 times, one per misclassification target\n", - " images_list.append(images)\n", - " labels_list.append(labels)\n", - "images = convert_to_tensor(images_list)\n", - "labels = convert_to_tensor(labels_list)" - ], - "outputs": [ - { - "output_type": "stream", - "name": "stderr", - "text": [ - "/home/thibaut.boissin/envs/deel-lip_github/lib/python3.7/site-packages/ipykernel_launcher.py:17: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n", - "/home/thibaut.boissin/envs/deel-lip_github/lib/python3.7/site-packages/ipykernel_launcher.py:18: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n" - ] - } - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "ry5uB9QdJexi", - "outputId": "6126888e-d256-4783-d284-0e40555c5dff" - } + " # select the first element of the ith label\n", + " label_mask = sub_y_test_ord == i\n", + " x = sub_x_test[label_mask][0]\n", + " y = sub_y_test_ord[label_mask][0]\n", + " # convert it to tensor for use with foolbox\n", + " images = K.convert_to_tensor(x.astype(\"float32\"), dtype=\"float32\")\n", + " labels = K.convert_to_tensor(y, dtype=\"int64\")\n", + " # repeat the input 10 times, one per misclassification target\n", + " images_list.append(images)\n", + " labels_list.append(labels)\n", + "images = K.convert_to_tensor(images_list)\n", + "labels = K.convert_to_tensor(labels_list)" + ] }, { "cell_type": "markdown", - "source": [ - "In order to build a certficate, we take for each sample the top 2 output and apply this formula:\n", - "$$ \\epsilon \\geq \\frac{\\text{top}_1 - \\text{top}_2}{2} $$\n", - "Where epsilon is the robustness radius for the considered sample." - ], "metadata": { "id": "GJctMBKrnqmC" - } + }, + "source": [ + "In order to build a certficate, we take for each sample the top 2 output and apply this\n", + "formula: $$ \\epsilon \\geq \\frac{\\text{top}\\_1 - \\text{top}\\_2}{2} $$ Where epsilon is\n", + "the robustness radius for the considered sample.\n" + ] }, { "cell_type": "code", "execution_count": 10, - "source": [ - "values, classes = tf.math.top_k(hkr_fmodel(images), k=2)\n", - "certificates = (values[:, 0] - values[:, 1]) / 2\n", - "certificates" - ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "vxbkWNsdBx0y", + "outputId": "0d419744-f63e-4207-eb31-ebc7e8d730c0" + }, "outputs": [ { - "output_type": "execute_result", + "name": "stderr", + "output_type": "stream", + "text": [ + "W0000 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dtype=float32)" ] }, + "execution_count": 10, "metadata": {}, - "execution_count": 10 + "output_type": "execute_result" } ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "vxbkWNsdBx0y", - "outputId": "0d419744-f63e-4207-eb31-ebc7e8d730c0" - } + "source": [ + "values, classes = K.top_k(hkr_fmodel(images), k=2)\n", + "certificates = (values[:, 0] - values[:, 1]) / 2\n", + "certificates.numpy()" + ] }, { "cell_type": "markdown", - "source": [ - "now we will attack the model to check if the certificates are respected. In this setup `L2CarliniWagnerAttack` is used but in practice as these kind of networks are gradient norm preserving, other attacks gives very similar results." - ], "metadata": { "id": "E2dqSmNPnVpK" - } + }, + "source": [ + "now we will attack the model to check if the certificates are respected. In this setup\n", + "`L2CarliniWagnerAttack` is used but in practice as these kind of networks are gradient\n", + "norm preserving, other attacks gives very similar results.\n" + ] }, { "cell_type": "code", "execution_count": 11, - "source": [ - "attack = fb.attacks.L2CarliniWagnerAttack(binary_search_steps=6, steps=8000)\n", - "imgs, advs, success = attack(hkr_fmodel, images, labels, epsilons=None)\n", - "dist_to_adv = np.sqrt(np.sum(np.square(images - advs), axis=(1,2,3)))\n", - "dist_to_adv" - ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "UgqlVcLRKzSD", + "outputId": "34fded95-9d16-4c60-8af0-45ebb8c01b6e" + }, "outputs": [ { - "output_type": "execute_result", + "name": "stderr", + "output_type": "stream", + "text": [ + "W0000 00:00:1725629244.976329 871063 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1725629244.976921 871063 gpu_timer.cc:114] Skipping the delay kernel, measurement 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Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1725629245.006984 871063 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1725629245.007538 871063 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1725629245.008090 871063 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1725629245.009770 871063 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1725629245.010318 871063 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1725629245.010871 871063 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1725629245.011433 871063 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1725629245.012012 871063 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1725629245.012570 871063 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1725629245.013121 871063 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1725629245.013751 871063 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1725629245.014513 871063 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1725629245.015393 871063 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1725629245.016520 871063 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1725629245.017903 871063 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n" + ] + }, + { "data": { "text/plain": [ - "array([1.3944995 , 3.5208094 , 1.6824133 , 1.9192038 , 0.5746496 ,\n", - " 0.7780392 , 0.39687884, 1.1619285 , 2.367604 , 0.48984095],\n", + "array([1.0559031 , 3.8602312 , 1.4908539 , 1.4382323 , 0.47069952,\n", + " 0.75099194, 0.42759493, 1.0656103 , 3.062126 , 0.6121775 ],\n", " dtype=float32)" ] }, + "execution_count": 11, "metadata": {}, - "execution_count": 11 + "output_type": "execute_result" } ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "UgqlVcLRKzSD", - "outputId": "34fded95-9d16-4c60-8af0-45ebb8c01b6e" - } + "source": [ + "attack = fb.attacks.L2CarliniWagnerAttack(binary_search_steps=6, steps=8000)\n", + "imgs, advs, success = attack(hkr_fmodel, images, labels, epsilons=None)\n", + "dist_to_adv = np.sqrt(np.sum(np.square(images - advs), axis=(1, 2, 3)))\n", + "dist_to_adv" + ] }, { "cell_type": "markdown", - "source": [ - "As we can see the certificate are respected." - ], "metadata": { "id": "YkoroIBUqZqy" - } + }, + "source": [ + "As we can see the certificate are respected.\n" + ] }, { "cell_type": "code", "execution_count": 12, - "source": [ - "tf.assert_less(certificates, dist_to_adv)" - ], - "outputs": [], "metadata": { "id": "M9WvQIyqnlpg" - } + }, + "outputs": [], + "source": [ + "np.testing.assert_array_less(certificates, dist_to_adv)" + ] }, { "cell_type": "markdown", - "source": [ - "Finally we can take a visual look at the obtained examples.\n", - "We first start with utility functions for display." - ], "metadata": { "id": "u3ooZSlcqdx6" - } + }, + "source": [ + "Finally we can take a visual look at the obtained examples. We first start with utility\n", + "functions for display.\n" + ] }, { "cell_type": "code", "execution_count": 13, + "metadata": { + "id": "1Sp3RsmPQ5eM" + }, + "outputs": [], "source": [ "class_mapping = {\n", - " 0: \"T-shirt/top\",\n", - " 1: \"Trouser\",\n", - " 2: \"Pullover\",\n", - " 3: \"Dress\",\n", - " 4: \"Coat\",\n", - " 5: \"Sandal\",\n", - " 6: \"Shirt\",\n", - " 7: \"Sneaker\",\n", - " 8: \"Bag\",\n", - " 9: \"Ankle boot\",\n", + " 0: \"T-shirt/top\",\n", + " 1: \"Trouser\",\n", + " 2: \"Pullover\",\n", + " 3: \"Dress\",\n", + " 4: \"Coat\",\n", + " 5: \"Sandal\",\n", + " 6: \"Shirt\",\n", + " 7: \"Sneaker\",\n", + " 8: \"Bag\",\n", + " 9: \"Ankle boot\",\n", "}" - ], - "outputs": [], - "metadata": { - "id": "1Sp3RsmPQ5eM" - } + ] }, { "cell_type": "code", "execution_count": 14, - "source": [ - "def adversarial_viz(model, images, advs, class_mapping):\n", - " \"\"\"\n", - " This functions shows for each sample: \n", - " - the original image\n", - " - the adversarial image\n", - " - the difference map\n", - " - the certificate and the observed distance to adversarial \n", - " \"\"\"\n", - " scale = 1.5\n", - " kwargs={}\n", - " nb_imgs = images.shape[0]\n", - " # compute certificates\n", - " values, classes = tf.math.top_k(model(images), k=2)\n", - " certificates = (values[:, 0] - values[:, 1]) / 2\n", - " # compute difference distance to adversarial\n", - " dist_to_adv = np.sqrt(np.sum(np.square(images - advs), axis=(1,2,3)))\n", - " # find classes labels for imgs and advs\n", - " orig_classes = [class_mapping[i] for i in tf.argmax(model(images), axis=-1).numpy()]\n", - " advs_classes = [class_mapping[i] for i in tf.argmax(model(advs), axis=-1).numpy()]\n", - " # compute differences maps\n", - " if images.shape[-1] != 3:\n", - " diff_pos = np.clip(advs - images, 0, 1.)\n", - " diff_neg = np.clip(images - advs, 0, 1.)\n", - " diff_map = np.concatenate([diff_neg, diff_pos, np.zeros_like(diff_neg)], axis=-1)\n", - " else:\n", - " diff_map = np.abs(advs - images)\n", - " # expands image to be displayed\n", - " if images.shape[-1] != 3:\n", - " images = np.repeat(images, 3, -1)\n", - " if advs.shape[-1] != 3:\n", - " advs = np.repeat(advs, 3, -1)\n", - " # create plot\n", - " figsize = (3 * scale, nb_imgs * scale)\n", - " fig, axes = plt.subplots(\n", - " ncols=3,\n", - " nrows=nb_imgs,\n", - " figsize=figsize,\n", - " squeeze=False,\n", - " constrained_layout=True,\n", - " **kwargs,\n", - " )\n", - " for i in range(nb_imgs):\n", - " ax = axes[i][0]\n", - " ax.set_title(orig_classes[i])\n", - " ax.set_xticks([])\n", - " ax.set_yticks([])\n", - " ax.axis(\"off\")\n", - " ax.imshow(images[i])\n", - " ax = axes[i][1]\n", - " ax.set_title(advs_classes[i])\n", - " ax.set_xticks([])\n", - " ax.set_yticks([])\n", - " ax.axis(\"off\")\n", - " ax.imshow(advs[i])\n", - " ax = axes[i][2]\n", - " ax.set_title(f\"certif: {certificates[i]:.2f}, obs: {dist_to_adv[i]:.2f}\")\n", - " ax.set_xticks([])\n", - " ax.set_yticks([])\n", - " ax.axis(\"off\")\n", - " ax.imshow(diff_map[i]/diff_map[i].max())" - ], - "outputs": [], "metadata": { "id": "UWZ6V1wt0WwR" - } + }, + "outputs": [], + "source": [ + "def adversarial_viz(model, images, advs, class_mapping):\n", + " \"\"\"\n", + " This functions shows for each sample:\n", + " - the original image\n", + " - the adversarial image\n", + " - the difference map\n", + " - the certificate and the observed distance to adversarial\n", + " \"\"\"\n", + " scale = 1.5\n", + " kwargs = {}\n", + " nb_imgs = images.shape[0]\n", + " # compute certificates\n", + " values, classes = K.top_k(model(images), k=2)\n", + " certificates = (values[:, 0] - values[:, 1]) / 2\n", + " # compute difference distance to adversarial\n", + " dist_to_adv = np.sqrt(np.sum(np.square(images - advs), axis=(1, 2, 3)))\n", + " # find classes labels for imgs and advs\n", + " orig_classes = [class_mapping[i] for i in K.argmax(model(images), axis=-1).numpy()]\n", + " advs_classes = [class_mapping[i] for i in K.argmax(model(advs), axis=-1).numpy()]\n", + " # compute differences maps\n", + " if images.shape[-1] != 3:\n", + " diff_pos = np.clip(advs - images, 0, 1.0)\n", + " diff_neg = np.clip(images - advs, 0, 1.0)\n", + " diff_map = np.concatenate(\n", + " [diff_neg, diff_pos, np.zeros_like(diff_neg)], axis=-1\n", + " )\n", + " else:\n", + " diff_map = np.abs(advs - images)\n", + " # expands image to be displayed\n", + " if images.shape[-1] != 3:\n", + " images = np.repeat(images, 3, -1)\n", + " if advs.shape[-1] != 3:\n", + " advs = np.repeat(advs, 3, -1)\n", + " # create plot\n", + " figsize = (3 * scale, nb_imgs * scale)\n", + " fig, axes = plt.subplots(\n", + " ncols=3,\n", + " nrows=nb_imgs,\n", + " figsize=figsize,\n", + " squeeze=False,\n", + " constrained_layout=True,\n", + " **kwargs,\n", + " )\n", + " for i in range(nb_imgs):\n", + " ax = axes[i][0]\n", + " ax.set_title(orig_classes[i])\n", + " ax.set_xticks([])\n", + " ax.set_yticks([])\n", + " ax.axis(\"off\")\n", + " ax.imshow(images[i])\n", + " ax = axes[i][1]\n", + " ax.set_title(advs_classes[i])\n", + " ax.set_xticks([])\n", + " ax.set_yticks([])\n", + " ax.axis(\"off\")\n", + " ax.imshow(advs[i])\n", + " ax = axes[i][2]\n", + " ax.set_title(f\"certif: {certificates[i]:.2f}, obs: {dist_to_adv[i]:.2f}\")\n", + " ax.set_xticks([])\n", + " ax.set_yticks([])\n", + " ax.axis(\"off\")\n", + " ax.imshow(diff_map[i] / diff_map[i].max())" + ] }, { "cell_type": "markdown", - "source": [ - "When looking at the adversarial examples we can see that the network has interresting properties:\n", - "\n", - "#### predictability\n", - "by looking at the certificates, we can predict if the adversarial example will be close of not\n", - "#### disparity among classes\n", - "As we can see, the attacks are very efficent on similar classes (eg. T-shirt/top, and Shirt ). This denote that all classes are not made equal regarding robustness.\n", - "#### explainability\n", - "The network is more explainable: attacks can be used as counterfactuals.\n", - "We can tell that removing the inscription on a T-shirt turns it into a shirt makes sense. Non robust examples reveals that the network rely on textures rather on shapes to make it's decision." - ], "metadata": { "id": "LLE_2Y4_r1kq" - } + }, + "source": [ + "When looking at the adversarial examples we can see that the network has interesting\n", + "properties:\n", + "\n", + "#### Predictability\n", + "\n", + "by looking at the certificates, we can predict if the adversarial example will be close\n", + "of not\n", + "\n", + "#### Disparity among classes\n", + "\n", + "As we can see, the attacks are very efficent on similar classes (eg. T-shirt/top, and\n", + "Shirt ). This denote that all classes are not made equal regarding robustness.\n", + "\n", + "#### Explainability\n", + "\n", + "The network is more explainable: attacks can be used as counterfactuals. We can tell\n", + "that removing the inscription on a T-shirt turns it into a shirt makes sense. Non-robust\n", + "examples reveal that the network relies on textures rather on shapes to make its\n", + "decision.\n" + ] }, { "cell_type": "code", "execution_count": 15, - "source": [ - "adversarial_viz(hkr_fmodel, images, advs, class_mapping)" - ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "TZPR4_b68hex", + "outputId": "bce7a55d-2a51-4315-b7b8-a3bf513b1cab" + }, "outputs": [ { - "output_type": "display_data", "data": { - "image/png": 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", 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", 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" ] }, - "metadata": {} + "metadata": {}, + "output_type": "display_data" } ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "TZPR4_b68hex", - "outputId": "bce7a55d-2a51-4315-b7b8-a3bf513b1cab" - } + "source": [ + "adversarial_viz(hkr_fmodel, images, advs, class_mapping)" + ] } ], "metadata": { @@ -955,8 +1137,9 @@ "hash": "85932723c17c3a18b32bfe2b34edfe80dfe7f67e8f24e1ef8c8aa8c563233210" }, "kernelspec": { - "name": "python3", - "display_name": "Python 3.8.10 64-bit ('tf26': venv)" + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" }, "language_info": { "codemirror_mode": { @@ -968,7 +1151,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.10" + "version": "3.10.12" } }, "nbformat": 4,