letter_digit_generator_v3_conv-CVAE.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Convolutional Conditional VAE on EMNIST\n",
    "Data source: [The EMNIST Dataset](https://www.nist.gov/node/1298471/emnist-dataset)\n",
    "\n",
    "EMNIST ByClass: \n",
    "* segmented digits and characters arranged by class\n",
    "* 62 classes comprising [0-9], [a-z] and [A-Z]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Import module and dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "from keras.layers import Lambda,Input,Dense,Flatten,Conv2D,Conv2DTranspose\n",
    "from keras.layers import Activation,BatchNormalization,Reshape,Concatenate,Dropout\n",
    "from keras.models import Model\n",
    "from keras.losses import mse, binary_crossentropy\n",
    "from keras.callbacks import Callback,ModelCheckpoint,ReduceLROnPlateau\n",
    "from keras.utils import to_categorical\n",
    "from keras import backend as K\n",
    "from keras.optimizers import Adam\n",
    "from keras import initializers\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import os\n",
    "import cv2\n",
    "from mnist import MNIST"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X_train shape: (697932, 784)\n",
      "X_test shape: (116323, 784)\n",
      "y_train shape: (697932,)\n",
      "y_test shape: (116323,)\n"
     ]
    }
   ],
   "source": [
    "emndata = MNIST('emnist_data')\n",
    "img_rows = 28\n",
    "img_cols = 28\n",
    "img_channel = 1\n",
    "orig_dimension = img_rows*img_cols\n",
    "\n",
    "X_train,y_train = emndata.load('emnist_data/emnist-byclass-train-images-idx3-ubyte',\n",
    "                               'emnist_data/emnist-byclass-train-labels-idx1-ubyte')\n",
    "X_test,y_test = emndata.load('emnist_data/emnist-byclass-test-images-idx3-ubyte',\n",
    "                             'emnist_data/emnist-byclass-test-labels-idx1-ubyte')\n",
    "X_train = np.array(X_train)\n",
    "y_train = np.array(y_train)\n",
    "X_test = np.array(X_test)\n",
    "y_test = np.array(y_test)\n",
    "\n",
    "print('X_train shape: {}'.format(X_train.shape))\n",
    "print('X_test shape: {}'.format(X_test.shape))\n",
    "print('y_train shape: {}'.format(y_train.shape))\n",
    "print('y_test shape: {}'.format(y_test.shape))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "label_str = '0123456789ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz'\n",
    "lebel_dict = {i:v for i,v in enumerate(label_str)}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Dataset samples"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x1080 with 25 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "np.random.seed(100)\n",
    "n_samples = 5\n",
    "n_cls = 5\n",
    "n_label = len(np.unique(y_train))\n",
    "fig, rows = plt.subplots(n_cls,n_samples,figsize=(4*n_samples,3*n_cls))\n",
    "\n",
    "for row,cls in zip(rows,np.random.choice(list(range(n_label)),n_cls)):\n",
    "    row[int(np.floor(n_samples/2))].set_title(lebel_dict[cls],fontsize=25)\n",
    "    for col_ax,img_idx in zip(row,[i for i,v in enumerate(y_train) if v==cls][:n_samples]):\n",
    "        img = X_train[img_idx].reshape(img_rows,img_cols)\n",
    "        img = np.transpose(img)\n",
    "        col_ax.imshow(img,cmap='gray')\n",
    "        col_ax.axis('off')\n",
    "plt.subplots_adjust(left=0.2, wspace=0.02)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "y_train shape: (697932, 62)\n",
      "y_test shape: (116323, 62)\n"
     ]
    }
   ],
   "source": [
    "X_train = X_train.astype('float32')/255. # for sigmoid\n",
    "X_test = X_test.astype('float32')/255.\n",
    "y_train = to_categorical(y_train,n_label)\n",
    "y_test = to_categorical(y_test,n_label)\n",
    "print('y_train shape: {}'.format(y_train.shape))\n",
    "print('y_test shape: {}'.format(y_test.shape))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "def sampling(arg):\n",
    "    arg = [z_mean,z_log_var]\n",
    "    dim = K.int_shape(z_mean)[1]\n",
    "    epsilon = K.random_normal(shape=(K.shape(z_mean)[0], dim),mean=0.0, stddev=1.0) # reparameterization trick\n",
    "    return z_mean + K.exp(0.5 * z_log_var) * epsilon"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "image_shape = (img_rows,img_cols,img_channel)\n",
    "batch_size = 128\n",
    "latent_dim = 6\n",
    "epochs = 100"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Convolutional Conditional VAE - Encoder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "__________________________________________________________________________________________________\n",
      "Layer (type)                    Output Shape         Param #     Connected to                     \n",
      "==================================================================================================\n",
      "image_input (InputLayer)        (None, 784)          0                                            \n",
      "__________________________________________________________________________________________________\n",
      "label_input (InputLayer)        (None, 62)           0                                            \n",
      "__________________________________________________________________________________________________\n",
      "concatenate_1 (Concatenate)     (None, 846)          0           image_input[0][0]                \n",
      "                                                                 label_input[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "dense_1 (Dense)                 (None, 784)          664048      concatenate_1[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "reshape_1 (Reshape)             (None, 28, 28, 1)    0           dense_1[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_1 (Conv2D)               (None, 14, 14, 16)   160         reshape_1[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_1 (BatchNor (None, 14, 14, 16)   64          conv2d_1[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "activation_1 (Activation)       (None, 14, 14, 16)   0           batch_normalization_1[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_2 (Conv2D)               (None, 7, 7, 32)     4640        activation_1[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_2 (BatchNor (None, 7, 7, 32)     128         conv2d_2[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "activation_2 (Activation)       (None, 7, 7, 32)     0           batch_normalization_2[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "flatten_1 (Flatten)             (None, 1568)         0           activation_2[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "dense_2 (Dense)                 (None, 64)           100416      flatten_1[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "z_mean (Dense)                  (None, 6)            390         dense_2[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "z_log_var (Dense)               (None, 6)            390         dense_2[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "lambda_1 (Lambda)               (None, 6)            0           z_mean[0][0]                     \n",
      "                                                                 z_log_var[0][0]                  \n",
      "==================================================================================================\n",
      "Total params: 770,236\n",
      "Trainable params: 770,140\n",
      "Non-trainable params: 96\n",
      "__________________________________________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "w_init = initializers.random_normal(stddev=0.02)\n",
    "gamma_init = initializers.random_normal(mean=1.0,stddev=0.02)\n",
    "\n",
    "img_inputs = Input(shape=(orig_dimension,),name='image_input')\n",
    "label_inputs = Input(shape=(n_label,),name='label_input')\n",
    "encoder_inputs = Concatenate()([img_inputs,label_inputs])\n",
    "\n",
    "x = Dense(orig_dimension,kernel_initializer=w_init,activation='relu')(encoder_inputs)\n",
    "\n",
    "x = Reshape(image_shape)(x)\n",
    "x = Conv2D(16,3,strides=2,padding='same',kernel_initializer=w_init)(x)\n",
    "x = BatchNormalization(gamma_initializer=gamma_init)(x)\n",
    "x = Activation('relu')(x)\n",
    "x = Conv2D(32,3,strides=2,padding='same',kernel_initializer=w_init)(x)\n",
    "x = BatchNormalization(gamma_initializer=gamma_init)(x)\n",
    "x = Activation('relu')(x)\n",
    "\n",
    "before_flatten_shape = K.int_shape(x)\n",
    "x = Flatten()(x)\n",
    "x = Dense(64,kernel_initializer=w_init,activation='relu')(x)\n",
    "\n",
    "z_mean = Dense(latent_dim, name='z_mean')(x)\n",
    "z_log_var = Dense(latent_dim, name='z_log_var')(x)\n",
    "\n",
    "z = Lambda(sampling, output_shape=(latent_dim,))([z_mean, z_log_var])\n",
    "\n",
    "encoder = Model([img_inputs,label_inputs],[z_mean,z_log_var,z],name='encoder')\n",
    "encoder.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Convolutional Conditional VAE - Decoder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "__________________________________________________________________________________________________\n",
      "Layer (type)                    Output Shape         Param #     Connected to                     \n",
      "==================================================================================================\n",
      "latent_inputs (InputLayer)      (None, 6)            0                                            \n",
      "__________________________________________________________________________________________________\n",
      "label_input (InputLayer)        (None, 62)           0                                            \n",
      "__________________________________________________________________________________________________\n",
      "concatenate_2 (Concatenate)     (None, 68)           0           latent_inputs[0][0]              \n",
      "                                                                 label_input[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "dense_3 (Dense)                 (None, 64)           4416        concatenate_2[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "dense_4 (Dense)                 (None, 1568)         101920      dense_3[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "reshape_2 (Reshape)             (None, 7, 7, 32)     0           dense_4[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_transpose_1 (Conv2DTrans (None, 14, 14, 32)   9248        reshape_2[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_3 (BatchNor (None, 14, 14, 32)   128         conv2d_transpose_1[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "activation_3 (Activation)       (None, 14, 14, 32)   0           batch_normalization_3[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_transpose_2 (Conv2DTrans (None, 28, 28, 16)   4624        activation_3[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_4 (BatchNor (None, 28, 28, 16)   64          conv2d_transpose_2[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "activation_4 (Activation)       (None, 28, 28, 16)   0           batch_normalization_4[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_transpose_3 (Conv2DTrans (None, 28, 28, 1)    145         activation_4[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "flatten_2 (Flatten)             (None, 784)          0           conv2d_transpose_3[0][0]         \n",
      "==================================================================================================\n",
      "Total params: 120,545\n",
      "Trainable params: 120,449\n",
      "Non-trainable params: 96\n",
      "__________________________________________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "latent_inputs = Input(shape=(latent_dim,),name='latent_inputs')\n",
    "decoder_inputs = Concatenate()([latent_inputs,label_inputs])\n",
    "\n",
    "x = Dense(64,kernel_initializer=w_init,activation='relu')(decoder_inputs)\n",
    "x = Dense(before_flatten_shape[1]*before_flatten_shape[2]*before_flatten_shape[3],activation='relu',kernel_initializer=w_init)(x)\n",
    "x = Reshape((before_flatten_shape[1],before_flatten_shape[2],before_flatten_shape[3]))(x)\n",
    "\n",
    "x = Conv2DTranspose(32,3,strides=2,padding='same',kernel_initializer=w_init)(x)\n",
    "x = BatchNormalization(gamma_initializer=gamma_init)(x)\n",
    "x = Activation('relu')(x)\n",
    "\n",
    "x = Conv2DTranspose(16,3,strides=2,padding='same',kernel_initializer=w_init)(x)\n",
    "x = BatchNormalization(gamma_initializer=gamma_init)(x)\n",
    "x = Activation('relu')(x)\n",
    "\n",
    "x = Conv2DTranspose(img_channel,3,activation='sigmoid',padding='same',kernel_initializer=w_init)(x)\n",
    "outputs = Flatten()(x)\n",
    "\n",
    "# instantiate decoder model\n",
    "decoder = Model([latent_inputs,label_inputs],outputs,name='decoder')\n",
    "decoder.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Convolutional Conditional VAE - complete model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "outputs = decoder([encoder([img_inputs,label_inputs])[2],label_inputs])\n",
    "vae = Model([img_inputs,label_inputs], outputs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "beta = 1 # 1 --> regular VAE\n",
    "reconstruction_loss = binary_crossentropy(img_inputs,outputs)\n",
    "# reconstruction_loss = mse(img_inputs,outputs)\n",
    "reconstruction_loss *= img_rows*img_cols\n",
    "\n",
    "kl_loss = -0.5*beta*K.sum(1+z_log_var-K.square(z_mean)-K.exp(z_log_var),axis=-1)\n",
    "vae_loss = K.mean(reconstruction_loss+kl_loss)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "__________________________________________________________________________________________________\n",
      "Layer (type)                    Output Shape         Param #     Connected to                     \n",
      "==================================================================================================\n",
      "image_input (InputLayer)        (None, 784)          0                                            \n",
      "__________________________________________________________________________________________________\n",
      "label_input (InputLayer)        (None, 62)           0                                            \n",
      "__________________________________________________________________________________________________\n",
      "encoder (Model)                 [(None, 6), (None, 6 770236      image_input[0][0]                \n",
      "                                                                 label_input[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "decoder (Model)                 (None, 784)          120545      encoder[1][2]                    \n",
      "                                                                 label_input[0][0]                \n",
      "==================================================================================================\n",
      "Total params: 890,781\n",
      "Trainable params: 890,589\n",
      "Non-trainable params: 192\n",
      "__________________________________________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "learning_rate = 0.001\n",
    "vae.add_loss(vae_loss) ## in this case, we set y value to NONE during the fit\n",
    "vae.compile(optimizer=Adam(lr=learning_rate))\n",
    "vae.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Callback functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "class NEpochPrint(Callback):\n",
    "    def __init__(self, display_step):\n",
    "        self.epoch = 0\n",
    "        self.display_step = display_step\n",
    "    def on_epoch_end(self,epoch,logs={}):\n",
    "        self.epoch += 1        \n",
    "        if self.epoch == 1 or self.epoch % self.display_step == 0:\n",
    "            print('Epoch: {}/{} ..... {}: {:.4f} - {}: {:.4f}'.format(self.epoch,\n",
    "                                                                      self.params['epochs'],\n",
    "                                                                      self.params['metrics'][0], \n",
    "                                                                      logs.get(self.params['metrics'][0]),\n",
    "                                                                      self.params['metrics'][1],               \n",
    "                                                                      logs.get(self.params['metrics'][1])))\n",
    "NEpochPrinter = NEpochPrint(display_step=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "learning_rate_reduction = ReduceLROnPlateau(monitor='val_loss',patience=5,verbose=1,factor=0.5,min_lr=0.00001)\n",
    "\n",
    "saving_folder = 'best_weight_ldg_v3_conv-cvae'\n",
    "file_name = 'ldg_v3_conv-cvae'\n",
    "os.makedirs(saving_folder,exist_ok=True)\n",
    "best_model_weight_path = os.path.join(saving_folder,file_name+'-best-wiehgts'+'-{epoch:03d}-{loss:.3f}-{val_loss:.3f}.h5')\n",
    "save_best_model = ModelCheckpoint(best_model_weight_path,monitor='val_loss',verbose=0,save_weights_only=True,save_best_only=True,mode='min')\n",
    "def train_or_load_weights(flag,weight_name=None):\n",
    "    if flag == 'train':\n",
    "        hist = vae.fit([X_train,y_train],\n",
    "                       epochs=epochs,\n",
    "                       batch_size=batch_size,\n",
    "                       validation_data=([X_test,y_test],None),\n",
    "                       verbose=0,\n",
    "                       callbacks=[save_best_model,learning_rate_reduction,NEpochPrinter])\n",
    "        return hist\n",
    "    if flag == 'load':\n",
    "        vae.load_weights(os.path.join(saving_folder,weight_name)) # load all the weights for encoder and decoder when loading for vae"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 1/100 ..... loss: 173.1963 - val_loss: 163.0941\n",
      "Epoch: 2/100 ..... loss: 158.8446 - val_loss: 158.7980\n",
      "Epoch: 4/100 ..... loss: 155.6530 - val_loss: 155.0976\n",
      "Epoch: 6/100 ..... loss: 154.2887 - val_loss: 154.1175\n",
      "Epoch: 8/100 ..... loss: 153.4639 - val_loss: 153.5485\n",
      "Epoch: 10/100 ..... loss: 152.9135 - val_loss: 153.0157\n",
      "Epoch: 12/100 ..... loss: 152.4837 - val_loss: 152.7149\n",
      "Epoch: 14/100 ..... loss: 152.1543 - val_loss: 152.5864\n",
      "Epoch: 16/100 ..... loss: 151.8832 - val_loss: 152.0458\n",
      "Epoch: 18/100 ..... loss: 151.6498 - val_loss: 151.9309\n",
      "Epoch: 20/100 ..... loss: 151.4739 - val_loss: 152.1617\n",
      "Epoch: 22/100 ..... loss: 151.2972 - val_loss: 151.7075\n",
      "Epoch: 24/100 ..... loss: 151.1526 - val_loss: 151.6446\n",
      "Epoch: 26/100 ..... loss: 151.0245 - val_loss: 151.5502\n",
      "\n",
      "Epoch 00028: ReduceLROnPlateau reducing learning rate to 0.0005000000237487257.\n",
      "Epoch: 28/100 ..... loss: 150.9075 - val_loss: 151.5079\n",
      "Epoch: 30/100 ..... loss: 150.1691 - val_loss: 150.8077\n",
      "Epoch: 32/100 ..... loss: 150.0978 - val_loss: 150.7700\n",
      "Epoch: 34/100 ..... loss: 150.0385 - val_loss: 150.6594\n",
      "Epoch: 36/100 ..... loss: 149.9853 - val_loss: 150.7441\n",
      "Epoch: 38/100 ..... loss: 149.9484 - val_loss: 150.7487\n",
      "\n",
      "Epoch 00039: ReduceLROnPlateau reducing learning rate to 0.0002500000118743628.\n",
      "Epoch: 40/100 ..... loss: 149.5794 - val_loss: 150.4517\n",
      "Epoch: 42/100 ..... loss: 149.5229 - val_loss: 150.3501\n",
      "Epoch: 44/100 ..... loss: 149.4988 - val_loss: 150.3307\n",
      "Epoch: 46/100 ..... loss: 149.4701 - val_loss: 150.3811\n",
      "Epoch: 48/100 ..... loss: 149.4490 - val_loss: 150.2892\n",
      "Epoch: 50/100 ..... loss: 149.4222 - val_loss: 150.3684\n",
      "Epoch: 52/100 ..... loss: 149.4088 - val_loss: 150.2795\n",
      "Epoch: 54/100 ..... loss: 149.3921 - val_loss: 150.2428\n",
      "Epoch: 56/100 ..... loss: 149.3640 - val_loss: 150.2370\n",
      "Epoch: 58/100 ..... loss: 149.3496 - val_loss: 150.2783\n",
      "Epoch: 60/100 ..... loss: 149.3245 - val_loss: 150.2607\n",
      "\n",
      "Epoch 00061: ReduceLROnPlateau reducing learning rate to 0.0001250000059371814.\n",
      "Epoch: 62/100 ..... loss: 149.1342 - val_loss: 150.1176\n",
      "Epoch: 64/100 ..... loss: 149.1138 - val_loss: 150.0780\n",
      "Epoch: 66/100 ..... loss: 149.0966 - val_loss: 150.1078\n",
      "Epoch: 68/100 ..... loss: 149.0857 - val_loss: 150.1002\n",
      "Epoch: 70/100 ..... loss: 149.0797 - val_loss: 150.0683\n",
      "Epoch: 72/100 ..... loss: 149.0664 - val_loss: 150.0632\n",
      "Epoch: 74/100 ..... loss: 149.0569 - val_loss: 150.0678\n",
      "Epoch: 76/100 ..... loss: 149.0509 - val_loss: 150.0695\n",
      "Epoch: 78/100 ..... loss: 149.0430 - val_loss: 150.0389\n",
      "Epoch: 80/100 ..... loss: 149.0297 - val_loss: 150.0772\n",
      "Epoch: 82/100 ..... loss: 149.0155 - val_loss: 150.0411\n",
      "\n",
      "Epoch 00084: ReduceLROnPlateau reducing learning rate to 6.25000029685907e-05.\n",
      "Epoch: 84/100 ..... loss: 149.0042 - val_loss: 150.0683\n",
      "Epoch: 86/100 ..... loss: 148.9026 - val_loss: 150.0035\n",
      "Epoch: 88/100 ..... loss: 148.8993 - val_loss: 149.9943\n",
      "Epoch: 90/100 ..... loss: 148.8991 - val_loss: 149.9920\n",
      "Epoch: 92/100 ..... loss: 148.8912 - val_loss: 149.9757\n",
      "Epoch: 94/100 ..... loss: 148.8835 - val_loss: 149.9651\n",
      "Epoch: 96/100 ..... loss: 148.8811 - val_loss: 149.9682\n",
      "Epoch: 98/100 ..... loss: 148.8728 - val_loss: 149.9754\n",
      "Epoch: 100/100 ..... loss: 148.8684 - val_loss: 149.9847\n"
     ]
    }
   ],
   "source": [
    "hist = train_or_load_weights(flag='train')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "def training_plot(hist,model_name):\n",
    "    loss = [hist.history['loss'],hist.history['val_loss']]\n",
    "    plt.plot(loss[0],color='b',label='training')\n",
    "    plt.plot(loss[1],color='r',label='valid')\n",
    "    plt.title(model_name,fontsize=18)\n",
    "    plt.legend(loc='best',fontsize=13)\n",
    "    plt.xlabel('epoch',fontsize=15)\n",
    "    plt.ylabel('loss',fontsize=15)\n",
    "    plt.tick_params(axis='both',labelsize=13)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "training_plot(hist,'ldg_conv-CVAE_v2 training')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_five_reconstruction(input_set,label_set,random_idx,encoder,decoder):\n",
    "    fig, axes = plt.subplots(2,5,figsize=(15,5))\n",
    "    for row,flag in zip(axes,['in_img','out_img']): \n",
    "        for col,idx in zip(row,random_idx):\n",
    "            original = input_set[idx]\n",
    "            if flag == 'in_img':\n",
    "                original_img = original.reshape(img_rows,img_cols)\n",
    "                original_img = np.transpose(original_img)\n",
    "                col.imshow(1-original_img,cmap='bone')\n",
    "                col.set_title('idx = {}\\nlabel = {}\\noriginal'.format(idx,lebel_dict[np.argmax(label_set[idx])]))\n",
    "            if flag == 'out_img':\n",
    "                encoded_z = encoder.predict([np.expand_dims(original,0),np.expand_dims(label_set[idx],0)],batch_size=1)[2]\n",
    "                predict = decoder.predict([encoded_z,np.expand_dims(label_set[idx],0)])\n",
    "#                 predict = (0.5*predict) + 0.5 # image back to 0-1 from -1 - 1\n",
    "                decoded = predict.reshape(img_rows,img_cols)\n",
    "                decoded = np.transpose(decoded)\n",
    "                col.imshow(1-decoded,cmap='bone')\n",
    "                col.set_title('reconstructed')\n",
    "            col.axis('off')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Traing set reconstruction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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NsRrAUgBf7cpz9SvOudj8ANgJYMkR7vsSgPtTYgfgbSnx5wA8Hf79UQDXpdw3CEADgEkp207v5n1fBuAj4d8vBrCtr3+f/On5n7jmLIDzAZQBGJRy210Abuzr3yl/ev4nrnmbsu+f6OvfIX967yeu+QpgbPiYqwGMQXDiuxzA9/v6d8qfnv2Ja86m7juAeQBqEEy4rgewtK9/r135ie03PiIyU0QeEpEyEakF8P8QHDRS7Un5+y4EBxkAmATgp4dntgAqAQiAcT24y3ci+F9IAPgA+G3PgBOznB0LYI9zrsPbn558j1A/FLO8jdofGkBilq+N4Z8/d87tc84dAPAjAJf10PNRPxSznP0n59w6AA8B+Peefq7uFNuJD4D/A7AJwAzn3DAEX/WJN2ZCyt8nAigN/74HwKedc/kpP0Odcy929qQicquI1B3hZ/1RNv0LgMUiMh7BV5ac+Aw8ccrZUgATUq8jDveH154PPHHK28Nc114aJVBs8tU5VwWgBMzXgS42ORvh2wA+iRj9p2icJz65AGoB1InISQA+GzHmayJSICITEKw+cU94+60AvikicwFARPJE5NquPKlz7jPOuZwj/Mw9ynYVCK6HvA3ADufcxq6+UEqMOOXsywDqAXxdRNJFZDGAKwDc3dUXS4kRp7wlilu+3gbgiyIyUkQKEFzm9FDXXiolRNxyNvUxtob78i9dGd8fxHni81UEl4wdAvAbvJkEqf4B4BUArwF4GMDvAMA5dz+C5SPvDr9WXAfg7b2wz3ciuC6S3/YMTLHJWedcC4Arw+c4AOCXCGrUNvXUc1K/FZu8JUL88vW7AFYBKAawEcCrAL7fw89J/Uvcctb3HQDZnY7qJyQsVCIiIiIiIkqsOH/jQ0RERERE1CWc+BARERERUeJx4kNERERERInHiQ8RERERESXegJr4hGuW39DdYzt5nMki4kQk7UQfiwYe5izFDXOW4oh5S3HDnD0+XNWth4nIZAA7AKQ759r6dm+IOsecpbhhzlIcMW8pbpKQswPmGx8RGdzX+0B0LJizFDfMWYoj5i3FDXP2+MV+4iMis0VkqYhUi8h6EbkyvP0PIvJ/IvKIiNQDuDC87Xsp235dRPaJSKmIXB9+fTc9ZfvvhX9fLCIlIvJvIrI/3ObjKY9zuYi8KiK1IrJHRG7s3d8CxQlzluKGOUtxxLyluGHO9rxYT3xEJB3AgwCeADASwBcB/FlEZoVDPoCgA3IugGXetm8D8BUASwBMB/CWTp5uNIA8AOMAXAfgFyJSEN5XD+AjAPIBXA7gsyJy9Qm9OEok5izFDXOW4oh5S3HDnO0dsZ74ADgbQA6Am51zLc65ZwA8BOD94f3/cM4td851OOeavG3fA+A259x651wDgJs6ea5WAN9xzrU65x4BUAdgFgA455Y6594In2ctgLvQedLRwMScpbhhzlIcMW8pbpizvSDuE5+xAPY45zpSbtuFYAYLAHs62zYlPtpYADjoFXI1IEhQiMhCEXlWRCpEpAbAZwCM6MoLoAGHOUtxw5ylOGLeUtwwZ3tB3Cc+pQAmiEjq65gIYG/496MtWbcPwPiUeMIJ7MedAB4AMME5lwfgVgByAo9HycWcpbhhzlIcMW8pbpizvSDuE5+XEVyL+HURSReRxQCuAHB3F7a9F8DHw0KyLAD/dQL7kQug0jnXJCILEFyHSRSFOUtxw5ylOGLeUtwwZ3tBrCc+zrkWAFcCeDuAAwB+CeAjzrlNXdj2UQA/A/AsgK0AXgrvaj6OXfkcgO+IyCEEyXbvcTwGDQDMWYob5izFEfOW4oY52zvYwDQkIrMBrAOQGdemTDSwMGcpbpizFEfMW4ob5uyRxfobnxMlIteISEa4hN//AHiQCUL9GXOW4oY5S3HEvKW4Yc52zYCe+AD4NIAKANsAtAP4bN/uDlGnmLMUN8xZiiPmLcUNc7YLeKkbEREREREl3kD/xoeIiIiIiAYATnyIiIiIiCjx0jq5n9fBDSxJaFDFnB1YkpCzAPN2oElC3jJnBxbmLMVNZM7yGx8iIiIiIko8TnyIiIiIiCjxOPEhIiIiIqLE66zGh3pZe0fHUeMoGWn8ZyQiIoqLlrbO+0rys52o+/EbHyIiIiIiSjxOfIiIiIiIKPE48SEiIiIiosTjxIeIiIiIiBKPlXN9zF+84N6XX1bxqkdXmW3yivJU/KkPX2XGjMnP74a9IyIiomPR4XSfzHUlJWbMX+59QsVpGfZ0zP9s5+c6+Vrb2zsdM0h0H8/Bgwb2dx4D+9UTEREREdGAwIkPERERERElHic+RERERESUeKzx6WOlVVUqvuN7f1TxU0/pGAAmTpyr4ne84wIzhtcCEw08fm1BU0uLGVPd0KDi+uZmFWdnZppt8rOyVJwVMaYrjqdB8/GIuoZ9oF/XTr1nz8GDKv7uV35qxjzyyG9UPGvmAjPmXddcpGJ+rlNJZaWKf3vHA51uM+usWSp+9wKba+mDB5/Yjh1Bo/cZdKip0YzxP3OGpGeYMd15/OYnARERERERJR4nPkRERERElHic+BARERERUeJx4kNERERERInXJ4sb+AW4gG2wNFDsr61V8aZNK1Tc0qILjwGgvb21R/eJiOLJX8xge8V+M2blxi0qrthToeKiCUVmmwWzZ6h4zrjxZozzF1ZotQsrVNbVq7imUS+00NzaZrbpisx0/VGWNzTLjMnPztbbpOltuCACHY9DTU3mtr88/KyKly69x4wZPnycij/y9c+bMVOLRp7g3lGcRZ0rbykrU/ETd/+j08epr16i4stPPdWMSR869Bj3Lpq/z9v2l6v4tR27zDZjhxeoePbYcWZMdy7swaM6ERERERElHic+RERERESUeJz4EBERERFR4nV7jU9zq60/Wb1jh4r3VB40Y6447TQVZ2cO6d4d66f8Bn4dXWjol52tr3X0r2+n+Ihq4Ojf1txm6x52VOi6jFZvTHqazYlCr8bBj4Hjb0xJvS8qdyrrdQ3NinWbzZhn73xGxbt36JqfiVN0PQ8AdHxAP9eUIlsHVFWv63W277f1RRuKd6q4pLhExfU1ev8BAP517hH1oNl5OpfHz7Q1SNOm6ttGDhum4uE5OWYb/z3C9we1trer+O5nnjdjbvn2TSquqakwY65+72dU/LGrLjFjmG8Dm98IFwB++e3fq3jNK493+jh79xareNaCWWbMxy/RzXKPt77R3+eb/u1nKl7+wv1mmyFD9HH2o1/+ihnzzc9+UMUZEec4XcVvfIiIiIiIKPE48SEiIiIiosTjxIeIiIiIiBKPEx8iIiIiIkq8bq+K31JeZm772se+oeL9+20Do7Lv/peKP/XOt6s4CUV+UY3Onl+2RsW1tQdUnJlpm0otueoaFUcVGlP/5BfGllVXmzF+gbrf5BYAlj7ykoobDunCcr/YGwAmzZ2k4gVzbYHjSWPGqFgiCsn9okc2eewbUYsbVDfo3Nm13h5r16/VTZJLSvQCCP4xCAAmzZ2s4n2nnWzGrCjWiyS89sxrZszGletUvHv3RhU3NNSYbfzGqFE5mZWVp+Jx4+wCDRNnTlVx0Xh93Jx22jSzzdlz9HtkzjjbWI+SzW/I+MaePSr+y0/uMtv476nx4+2xdsHlC1ScO2RgLOhER9biLVL08LKVZozfDLe5xZ5X+kpLt6p49WOrzZj3X3ieiruywFjUZ9DStfoYv+z5+1RcVqYXOwOAtLR0Fe/dsteMaW3XvxsubkBERERERHQUnPgQEREREVHiceJDRERERESJ1wMNTG2zxfJyfU3f9u1rzZgf//t/qHjuydNVfPG8ed2wd33r5p/dbm778Y3/puLmZl2rceml15ltvvSlD6l4oDR77e/8+p2miGa+lXV1Kn70RXut7e6Nu1Vcscc2v3tp6WMqbmjQdUB+k1sAmDXrTBXXXGHri5rPO13FQzIyzJiCrCwVj87Xz5U+eLDZhnpHS5vOQb/2CwDq6vS/e329zp2qqnKzzb5t+1Ts1/MAwOrHVql42eO2sZ5f01Nfr/elvd1+fvics9eVDxqkP8pKS+3+bdxYoOLhw3W9zhm732K2yc7VuX7S2LH2uSNqjig5/IaM//21W1T8/PP3mm3y80eq+As3fduMed+F56uYtZIDy76I+t5n129Q8Z0//J0ZU1m5z9zWGf+YeXCvreOsb25WcdR5ZYM3ZnuFbVL93D3P6cett6+zs/2r2ld5XPvXVXynERERERFR4nHiQ0REREREiceJDxERERERJV631/iMHDbM3DZr1kIV79y5zowpL9+p4kfufkrF596ga36A/l/b4l8PufLJZWZMh3dN+0ne7+qaL77bbDO2oMDcRj3L7+UA2GtO73pKX9u64SV9vS4AbF6jaxxWrnzIPm697mXS0dFuxgwZYvv0pNqzZ5O5betW3TPq2Wdt/4lfDBuh4uzsPDNmwoTZKr7m8+9S8cL5+n4AmD5KX/Pe39+7cRBVE1CUm6viGafbfjYzXta1XtXVuqZnv3csBoD77/2Fip985M9mjF8r1Nhg+09l5+h6sPnzL1TxrHmnmm1yC/Vr2vL6RjNm715d0xPVD0hE/74yM3X9TuFoe1wdX1ioYtbzJJtfpwnYXipRx03fhRe+X8XXXn6hGcO+PQPbwbpD5rbiV4pVvHmz7ePTEdE7pzP+sa9gTKEZk92FXpkVh/Q+v/SGPc9Y8YKu7Wxqqjdjemr/uorf+BARERERUeJx4kNERERERInHiQ8RERERESUeJz5ERERERJR43b64gd/MEACu+PTVKl637gUzZt++bSp+6h/3q/i6z+gCagCYN37C8exir1m1fbuKly+/34wpKtKv4Ybf/1DFV56mG0oCbBDZG/zFDPyFDABgW7kuCn/iD0+oeP36F802+/fvUnFNjW1OmjY4XcW5ubbQb/bsc8xtqfxibwBobW1ScUODLa7cs0cXjvv7Atj3ana2Lj5veFej3aFFOo9PmTjRjqFjErW4QX62XvRi2tTxZsyEGZNVvH69XsCipsY2t6ut1U0c6+qqzBi/QDUjwxZvjxqln/uM889T8aJrFpltJo3QC26s3TjXjCkpLlFxfY0tqBVvYYJhI/RCPLPOmtXpc1Oybdi719z291/+RcX+++PCCz9gtvnmD76g4gnDh3fD3lGSNDS3mNuqyvRxtbraNgi17MJLwNEXYRmaM9TcljZIn1dGLfTxyHLdpPrBX/3djCku1mPaIx7HPHeabpQ+cuJIMyZ9cPdNV/iNDxERERERJR4nPkRERERElHic+BARERERUeJ1e41PVP3JO85boOJXrrDXxN7zpx+puL6+WsXNrbrRZ3/T0mb37/EHdC1TVCOn/ILRKp4zTl+Tn9WNTZuo6/yaHr+eBwCeXa4bgq5a9ZiK/aa8ANDaqh83P99ey+rXfU2efLIZc9F7L1HxoEH6mt49m3XNAwDUVOimjpvXvWbGrF79qIpbWm1tU0uV/l34rztKVq6+pnjOuHFmDGvXTlxmmj6kRzWUHjFO160MG6brD/w6NABoa9PXo4vYjw6/Fm306ClmzPxTL1DxKYvnq/js6dPMNqOG6RqkCYW25q3mdF3305XPi8x0/RoKs3PMmOE59jZKDv9z+967Hzdjli37m4rz8vT75+rPXWu2OXmCPoaz8S2VVun6nWeeXWXGbFilP5OjmpdbneeW/zjFr9om0Puq9Tl3bVOTGfP3X+h6tzWvPmnGdFbTk5lp64umTNHnOBdfbms9M9JY40NERERERNRlnPgQEREREVHiceJDRERERESJx4kPERERERElXrcvbhDFL7DNLbAFo37hf2urLqY9WFfX/TvWjfzCMABY8+xKFQ8Zkm3GLLnqGhVPG1nUvTtGnWqIaE76u/t1wf7Se541Y9as0YV9fmPPnJwCs82UKbqY+7p//4oZc/pZc1Q8tcgugDAuosA7VVtEgWF5jV7c4MVi2+T02bvOUvH2jZvMmM2bX1bxnj16TFRzy/p63Sz17eecacaMLdC/r6gGnXR0/u+sMNsecybO1s1jJ07U+VZWphsvA3ZRjuzsPDNm1qyFKvabkwJ2MYNz5s9Wsb+QAQAMydDN7fw8AYBReXa7YxWVb8zBZNtYWqrilWcdr8wAACAASURBVE8uM2P8wvDFi9+n4su9xZsALtRC9rziL48/r+Jbv/c9s01Z2Q4Vd3R0mDEZGZlebBcLqKvT56P+47z22tNmm+fXfVDFh6rsObe/mEFVVZkZ4/MbR0+beqoZc8m73qPiM6fahXG6E4/qRERERESUeJz4EBERERFR4nHiQ0REREREidcrNT7+NX5dceCAbsD4/OMvmzGXnGwbO/YW//rNvz6y1Izxm0EuWnSNGfOxT71TxdmZQ0585+io2r3rXSvrbWPZ5ffpa71fffUpM6a8XF+P69f0zJ59jtnmtEXnqvjiCxeaMVOKdJ3XUK/GAej8GvKo+8fk56t48ZzZZkzBJ3VNyKsvR1xre48OS0t1bVN9va4lAoCSks0qrmlsMGNGe/vHq+SPnanxybE1PrNnTFLxptNOUvGOHWvNNv4147kR9Wuz5ulrtxddY5vQ+Q1K/Zoev54HiGj+GPF5wloc6syhiIaMf/j1fSpetuw+M6ZohG4qfv1/fVTFE4brBsBEgK1LX//iehVXVdmm6H4tZdS588giXaOZF9EEfePGl1Ts1/g4Z2uHmpt0XX1FSYUZ09Ki30NRNUi+jHRdkzRnrj0vmnOOPhfJSEvv9HFPBD8tiIiIiIgo8TjxISIiIiKixOPEh4iIiIiIEo8THyIiIiIiSrxeWdzA6MJiB21tutCqvrp/NTDdUq4L0/74w1vMmMpK3dxp3sLTzJiTxozp3h2jTnVlcYNNm1apeP/+3WZMS4suRJw8WRcd+gsZAMBF71ms4hmjRpkxfoG3Ke4+TpnpumAwqunjWVOnqtj/XQHA1ld041NZ2j37R90vqkh0srd4xsyzZql40ytzzTb79+9ScZpXsAoAw4brRtX+8wB2MYOsTPs4RD1hzY4d5rbH/qZXamlutouuFBTqz+jxhXphj+46PlN8tUY0DH9omW5g//e7f6Xi2tqDnT5uYeFoc9vnb7xJxQf2HjBjtt38mor9vB482J76v/HcGyp+4O7fmzGHDlWq2DlnxpiGpdP0ee/nbvqE2eaMKXoRpZ5uAMxvfIiIiIiIKPE48SEiIiIiosTjxIeIiIiIiBKvV2p8MtL007z9PReZMX/9g64tKCvbruKODnstYW/ZdcBeQ/ndr/xUxcWbV5ox/vWZfrNSwNZdUO9raW01tx06pK+/bWlpNGP8a1lPPf1CFS+62jZwPH/WTBX3ZY1DVNPHOq/J364dpWbM3p263mnQIH097ogR48w2C85fouJJI2z9R09f1zsQRf1OR3u1XWeerGt8Np9ua3w2e8e3tjb7nmk4pK8jP9Ro3zPtEdeEE/UE/3P75q/82IwpLta1nLNmLTBjfnr3L1U8Z9x4M4YGFr/2tay62ox56Nf/UHFlpf0s9fnnFOPGzTRj3naxbgC6v7bWjLnntzpH/fPpqJrlP/zy+yr2m1YD0TU9vvHj9efJJ2/4mooXTtNNrIHePw/iNz5ERERERJR4nPgQEREREVHiceJDRERERESJ1yd9fMYW5Jvb8vNHqri0VPcKKX51o9nGv84yqmbhePiP++iLq82Y5567V8WtXt8hAFiw4B0qnjPO1j5Q3/OvqwVs3UrUmA4vTyrK96p4z+Y9Zps9Y3VPiILsHDOmp2pd/LyO6tGzvWK/iotXF5sxu3atU7Fz+nHS04eYbXIL9OvMYD1PrDU01JjbSrbtVPH2HXvNmNljx6o4y+tZ1V3HcBpYoo5l/uf2qlUPmzH+cf38i68wY06dNEnFbRE9WzoTldfM9fhqaWtTcWl1lRmzadMKFbd7eTMo4t8/N7dQxdd++jozZsZo3fsv6nHy8kaouKJC1/Q0N9v6y0OH7GvoTEaGrc1529UfUvHHrrpExf2hdxvfeURERERElHic+BARERERUeJx4kNERERERInHiQ8RERERESVenyxuENW8cOEFF6t448YXVVxcbBuE9tTiBg0teqGCNU+tMWOqq3UReGHhGDPmik9frWI2aOyf0tPs2yA7Wy/AEVWw395ep+ItXjO8nL8XmG0GD9Y5Ovwyu7jB8NxcFUctBBC12EKqZq/4EgCq6+tVXOnFAPDy8rUq3rDqNTOmokIv2uD/bnJy7OvOKdCvqbP9p57T4hXZVjfoxqP1NTYv2lqb9Zh6u7jB3r16QZqS4hIzpsZrjjrKa6bKgm86Hv5nNmA/t/3PbMAuqjR57iQz5q4nn1Pxlld0nruIhRXEy+O5i2xT4PdfeJ6KszPtZwz1vY6Ipp1lNbq5546KCjOmpaXZ3JYqPS3D3JaXp8+NR08ZZcZkpOmm9/4CMYA9H83MzFJx1OIGgP867Wd0erp+rmHDRpgxY6aM7nT/+ho/ZYiIiIiIKPE48SEiIiIiosTjxIeIiIiIiBKvT2p8MiNqKvwGhyJ6ThZ1vaRfx5AR8bjHY82OHSp+4ckHzRi/aeNZZ11mxly26Kxu2R/qXn6eTB850oy57ltfVvHSe541Y9aseVLFe/ZsUnH5/l1mm+XL/6biJ+98uxkzftpkFWcPyzZjBg0+eo3Moao6c5vfYLKkZLMZU1q6VcX19dVmTEeHrhG56CLdsOySD9r3wsUXLlRxd71X6eiiGjv6tV5+o1E/TwCgofGQftx2W0PmNzWNqhVqbrXbEZ2o7ftt/c6Lzzyq4rTB6WbM2WdfqeItr2w1Yx6+/3cqrqouV7F/LgDY85dx42aYMbj1Jyr8+CUXmSGseet7pVW2see3vvRjFa999Xkzprx8h7kt1chRk81tSy57r4rftvAMM8avFW9qtfVtfj1bS0vTUfelq0aPnqriJW9/rxnziQ/r91R//Kznu4qIiIiIiBKPEx8iIiIiIko8TnyIiIiIiCjxOPEhIiIiIqLE6zdVR9n5enGDNK+504EDthnergO6adS88ROO67kbveZnjz/wgop37lxntiko0E2a/GalADC2wDZypP5nSESDrcXnnqZiF9HEzLffW8wgqqCwqkoXxq5c+bAZs379MBX774WuaGuzBY8NDbpAvSGiCWVbe6t+7oiC4LwC3WTt5HNPV/EZZ9lmfVOKbNNi6nlRzfdqGnXD0r1b9OIG/gIXgM0dor7U6jXhXb7Gfkbv27dNxUVF9vzg7Z94h4pv/fb/mjEVB3TD5sGD9WmTfy4AAFVVZSr2m/sCwPrl61XcvmSxGcPFDfrextJSc9uLyx5QcdQxs61Nf5ZmZugGtQsX2kWAzrnqXBV35Rwyb2iWuW3cuJne/un8a2qyC8/4DUsHRzROP/nkC1S84PKFZsz4wsIj7Wq/wXcVERERERElHic+RERERESUeJz4EBERERFR4vVJjU9UQ6MLLtXXCv7xp+NVXFa23WxzPM3wSiorzW0PPL9CxUv/oRufRdVL+A1Lo5qV+o2mqH8aJLYZ6LRRo/QNi043Y8TbbvPmlSr2a34A4NAhnX9+ozEAqKnRtWv+80Td5jfMGxRxbXiH18wyqvFeXp6uxYm6Ln7KlFNUfPoS/buZMdpe8z40oo6K4suvcwCArKw8FWfn2ca7men9pqyUYqysWjdWfujX/zBj/GPrmDHTzJgpU8epeOLE2WbM5s0vqzgzU9dUzJhxptlm7Vrd8DqqpsJFNBemvtfSps8rn3tshRmzf/9uFfv1PFEmTzlZxZ+96eNmzJlTdIPQrtR4jRw2zNx20pm6znbLltUqPnhwX6ePO2bMdHPb1Z9/p4ovWWDPi+JQl9b/95CIiIiIiOgEceJDRERERESJx4kPERERERElHic+RERERESUeP2m0nR4jm5gmp6ui6GjCrEP1tWpOKpZX12TbiL5k5/facb86RbdtKyyShd++c2gAOCaL7xLxWxWmiy5Q3SzsfkTJ5oxM70i/vmnzFDxmlUbzDYvPeAtpLH0LjOmvq7a3ObzCyUnTZqn4lmn2Saigwb7CyLYRRNmLThJxbNnTLLP7TUj9RuWxaG4caCIWmBl0gj97+cvLFO+SzfZBWyheFRD34UXXHzUx4167qiFbog6s7+2VsX+AgSAPb6d/ZZLzZhzZuhjds1/ftSMGTdNH/vXrnxJxf6CMMFzDzpqDADC42S/5B/b6qvrzJj29s4XM/Dzb9SoySqeUDjcbHM8iwBFfY4PG64XPBgxQi/isXu3PTfxF2iIWujjtJP0ggcjcnPMmDjgO4+IiIiIiBKPEx8iIiIiIko8TnyIiIiIiCjxYnOBdXu7bVb68F1PqTjvuqFmzM1f/YWKH33012ZMS4uuA/KvbfyPX/7IbPOxS9+qYjYrTbaoJqdZmZkq9puPDc/JNdv41942NBwyY0pKNne6P2+98moVzzhD16GddYqu1QHsa4i6NthvhlaYY5tQZqSlq5g1PfGS6dXV+P/mReNtzUJBgW7oG5W3uQX6eu/C7Ijc4XGSeoDfnDlKVEPdLK+m4t0LFpgxi2fr84FHXta1a/f+yNYNt7Q0qnjcuBlmzNxFug6Tx9H+oaaxQcUlm0vMmI6O9k4fx290O2WmzqO8LHu+ejw5EFUnecWVb1Fxmtc4esuWV8w2fpPdGXPmmzETR+i6pKEZmWZMHPCdRkREREREiceJDxERERERJR4nPkRERERElHj9psYn07sGMTs73xth6xFWPP20it9YYa9bfPHF+1Xs1/MAtqbnS//9PRV/4K0XmG1Y00O+HK/3z4xRo8yY/LP1db8dHbYfyt4tezt9riWXnaviSSNGqHhMvv/+ia7p8fnXGPO68+Tx/039HmpT5k8x28x5/WwVl5ZsN2PyRuqcG5KRbsZ0JQeJjtWgiOOUX/ezfuWrZswzG3Q/k6jj3SN361rix++7V8Xbt79mtsnN1b3NvnDTt82Y9y4+r9Pnpt6X7dXuTphte/ilP6Q/60VazJgLzr9WxV/6z4+reESurq0EomuJj8c4r7feSWfqGuCPffGbZpusXH1ucu17LjFj/H3urv3tbXynERERERFR4nHiQ0REREREiceJDxERERERJR4nPkRERERElHjinC2uTnHUO7tTfbNedOA/v3uriv/vh/9htolaqKAz/kIGgF3M4Lpr3qbiXK9oPcHiWamm9VrOdof2iMZ7HUd/TwLg4hopkpCzQB/mbUNzs4q3V+w3Y1as0011S7eWmjGzvALaC06yTXT9ZqkDuKA7CXnbZzlbUlmp4i9/+v+ZMQ888Atzm29kkS1c91VU7FFxa5suZPeb+wLAkks+rOL/+dlXzRh/QZoYGJA5+/ymTea2b173LRU3NtaZMZ/9/tdU/LFLLlJxb36G++cZUecdvqjGqDEUmbMD9lOHiIiIiIgGDk58iIiIiIgo8TjxISIiIiKixOs3NT6+5zZuVPHn3v1JM6a4eJWK09IyzJipU+ar+JM3fM2MGcA1Pb4BeQ0vxVoSchbow7z1a8qaWmwzvsr6ehXXNDSYMYVeI9TC7GwzJjPdNjUdoJKQt/0mZ/3zBQD4gnfOULxltRnjXBdqHTKGqvi8896l4qs/pxtVAsDl5y1Q8YThw82YGDZ/jN0ORzjmnPXryQDg93c+pOL21jYz5tprL1bxvPETjvWp6cSxxoeIiIiIiAYmTnyIiIiIiCjxOPEhIiIiIqLE48SHiIiIiIgSr98ubuA31bvz2RfMmPXL1qk4Oz/HjLn48kUqPmvqVDMmKzPzeHYxiQZk8SLFWhJyFmDeDjRJyNt+k7P++QJgzxnWL19vxjivkaNELDjgn1dc+55LVDxn3DizTUIbTDNnQ63t7Z2OSWgOxA0XNyAiIiIiooGJEx8iIiIiIko8TnyIiIiIiCjx+m2Nj6+9wzYai7rNl5GW1hO7k1S8hpfiJgk5CzBvB5ok5G2/zln//KAr5wtdMYDPKZizFDes8SEiIiIiooGJEx8iIiIiIko8TnyIiIiIiCjxOPEhIiIiIqLEi83iBtQrWLxIcZOEnAWYtwNNEvKWOTuwMGcpbri4ARERERERDUyc+BARERERUeJx4kNERERERInXWY0PERERERFR7PEbHyIiIiIiSjxOfIiIiIiIKPE48SEiIiIiosTjxIeIiIiIiBKPEx8iIiIiIko8TnyIiIiIiCjxOPEhIiIiIqLE48QnRkTkRhG5o6/3g6irmLMUR8xbihvmLMVNX+UsJz4pRGSpiFzfg4+/U0SW9NTj08DDnKU4Yt5S3DBnKW6Ys9H6dOIjIml9+fzHKm77S90vbjkQt/2lnhG3PIjb/lL3i1sOxG1/qfvFLQfitr/dpdcnPuEM8RsishZAvYhMFJG/iUiFiOwQkX9JGTtYRL4lIttE5JCIvCIiE8L7zhWRVSJSE/55bsp2S0XkuyKyPNzuCREZEd43RETuEJGDIlIdbjtKRL4P4HwAt4hInYjcEo53IvJ5EdkCYIuITA5vS/Oe7/qU+JMisjF87g0icrqI3A5gIoAHw8f/ejj2bBF5MdyX10VkccrjTBGR58LHeRLAiJ74N6GjY84yZ+OIecu8jRvmLHM2bpizMcxZ51yv/gDYCeA1ABMAZAN4BcB/AcgAMBXAdgCXhmO/BuANALMACIBTAAwHUAigCsCHAaQBeH8YDw+3WwpgG4CZAIaG8c3hfZ8G8CCALACDAZwBYFjKdtd7++sAPBk+51AAk8Pb0lLG/HM7ANcC2AvgrHCfpwOYlPLal6RsNw7AQQCXIZiEXhzGReH9LwH4EYBMABcAOATgjt7+NxvoP8xZ5mwcf5i3zNu4/TBnmbNx+2HOxi9n++pSt5855/YAmBf+Qr7jnGtxzm0H8BsA7wvHXQ/gP51zm13gdefcQQCXA9jinLvdOdfmnLsLwCYAV6Q8x23OuWLnXCOAewGcGt7eiiDRpjvn2p1zrzjnajvZ3/92zlWGj9WZ6wH8r3NuVbjPW51zu44w9kMAHnHOPeKc63DOPQlgNYDLRGQigkS7wTnX7Jx7HkFyU99gzgaYs/HCvA0wb+ODORtgzsYHczYQi5ztq+v79oR/TgIwVkSqU+4bDOCF8O8TEMxyfWMB+L/4XQhmm4eVpfy9AUBO+Pfbw8e9W0TyAdwB4D+cc61d2N+uONI+R5kE4FoRSU3udADPIniNVc65+pT7doWPT72PORtgzsYL8zbAvI0P5myAORsfzNlALHK2r77xceGfewDscM7lp/zkOucuS7l/WsT2pQh+wakmIvg67uhP7Fyrc+4m59wcAOcCeAeAj3j7daT9BYDD/2hZKbeNTvn7kfY56vH3ALjde/3ZzrmbAewDUCAi2SnjJx7hcannMWffHMucjQ/m7ZtjmbfxwJx9cyxzNh6Ys2+O7fc529fLWa8EUBsWhg0NC7/michZ4f2/BfBdEZkhgfkiMhzAIwBmisgHRCRNRN4LYA6Ahzp7QhG5UEROFpHBAGoRfE3YHt5djuCazCNyzlUgSMYPhfv7Ceik+C2Ar4rIGeE+TxeRwwntP/4dAK4QkUvDxxoiIotFZHz4VeJqADeJSIaInAf9tSf1DeYsczaOmLfM27hhzjJn44Y5G4ecdX1TCJZaDDUWwF0IvsarArDi8P0IviL8TwA7EBRBrQIwPrzvPARFZDXhn+elPOZSpBR0AfgYgGXh398PYDOCWW45gJ8hLOoCcA6A4nA/fubeLASb7r2Gt4f7VA3g/wPwnPd8nwmfow7AOgCnhbdfBWB3uN1Xw9sWhttXAqgA8DCAieF9UxF8RVqHoBjtFrB4kTnLnOUP85Z5m8Af5ixzNm4/zNn45ayEO0NERERERJRYfX2pGxERERERUY/jxIeIiIiIiBKPEx8iIiIiIko8TnyIiIiIiCjxOPEhIiIiIqLES+vkfi75NrBIX+9AN2DODixJyFmAeTvQJCFvmbMDC3OW4iYyZ/mNDxERERERJR4nPkRERERElHic+BARERERUeJ1VuNDPazD6UtOnRf79wPA4EF6vjpIknDpLRER0cDQ3tGh4qjPev+z3f/sJ6Jjx3cRERERERElHic+RERERESUeJz4EBERERFR4nHiQ0REREREidcrixs0trSoOKoYP23wYBWLNyaOBfx+sWJdU5MZs7W8XMUb9pSouLmh2Wxz3vw5Kp46cqQZk+79Polo4PELqIHOF1Txj71Azy2o4u9f1P4ez/5FjYnjZwj1f1GLEjS3tqp4f22tGbNs02YVb1yx0Yw59YL5Kr50vo5zhwzp8n5SMkTl27HqzWOhv7+tbW1mTFtHu4rTBtnz1/Q0PV05kdfAb3yIiIiIiCjxOPEhIiIiIqLE48SHiIiIiIgSr9trfFoirt8rq6lWcUfEJYp5Q4eqODszU8WZ6elmm/50zXbUdZdNXm3TjooKM+bv9z2t4mUPPavinOx8s03eDbkqnjh8uBnDGh+igae1XV8rXd9sawTLqvXxuLK+XsXDhtq6gdF5+jjkH5+Brh2P/f2paWxQ8aEmu79+jU9UHVBBdlan+zc0Q982xPtMiWoO2Z8+Y6h/8ut5AKC4rEzFDz+2zIy55xe/UXF19X4zprHuMypeNHOmilnjkyz+sS3qfNo/xkfln1/jODRDH+sy0uz5dHedM/qv4WDdIRVvLbd5fuCQHjN91CgzZvKIESrOijjGdxW/8SEiIiIiosTjxIeIiIiIiBKPEx8iIiIiIko8TnyIiIiIiCjxun1xgzav8AoAymp0866qujozZuIIXaA/rqBQxRlpEbvajwpP/QJcAGjyis72VlaaMW+8sE7FxcUrVTxh/Elmm+YW/bgswCUaeKIWVPGbw/kLGQDASxt048S9xXtVXDhWH3sBYOKkMSoeW1BgxmR6x+i6Ztuweft+vcBL2Q5dBN5Y12i2aW3Wx7uGmnozpmhCkYpHTrRNnedMmqDi8d6iMP4CO4D93IlaAIEGFr942y/MBoC//uVJFd/zq1vNmN271qt43PiZZsy4GeNU7L/HKL6iFmlp8BbEijp+b9pXquK6iAVhxubrxWjys7NVHNX0/niaVEd9BvkL2Ly8bZuKH7/rGbNNTUWNis9753lmzLBFZ6mYixsQEREREREdBSc+RERERESUeJz4EBERERFR4vXKBaMHvWtgN7+xzY6ZrMfkzdUN6XIiGnX19xadh5r0Ne5rV28yYzZufFHFdXX6ms7MTP17AIA8r1mf36yK+oeo61+jauB8/rW/UdfaRj12qqias67kSZrXxIxNHePFryssra4yY3as3a7iXRt2q3hosa11qZima3NKJtkGc37O7XxjhxlT/MoWFe/evUHFDQ26HjTqtqysYWZMfr7enwmTbL3E1gWzVHzOW89U8WmTJ5tt/Lqf/t5Im7qff6ytbdR1aPc9aZuT/ulnP1JxeflOM6Zo5CQVX/n+j5kxSy7QdQ25EXVoFA9+HkWdC+yv1ce6Vdu2mzHFrxSr2LXbWqEt6frUfs4CXSs+LqJG02/o7CKOa/45RFTz1B0V+rPimfteUPELjz9sthnknWdMnT/FjGk86xRz2/HiNz5ERERERJR4nPgQEREREVHiceJDRERERESJx4kPERERERElXrcvbuBgi6r92/xiWgAo21mu4tOn6uKmUXl53bB3PSeqGVVVvW60V7xqsxlz4ECJitPTdVOmidNtke7EESNUzKZ6fcMvVvQL/Q5GNOotr9GNuvyGZYBtANYRkVv+IgTNbfq50wd3/tYeGlGoXZCTo+JCr/EZAGR7jcOyMjJU7BcqAsfXHI2Onf+7r6yzzT73btENS4vXvariDmfzbeem0SoeHtEAr65G53tp6VYzpqxML3hQV6cXX2hqsvvrvP3JzLAF3rW1B1RcURHxGVO609yWKu0Su1zOyRN009Phg+2YQRG3UXL4RegvbdELdNx280/NNn7uFxSMNmPe/6kvqviq9y4xY6YU6ca86cy12PLPEaM++7fv36/idcvWmTG71u9UcUujfZx2b8GDEeP1OWPdFNtcemiGPh8YJJ2fV/rnuADw6ha9IMO6FWtUXFGxx2yTn68/TzKz7WJmfjPpE8EzZiIiIiIiSjxOfIiIiIiIKPE48SEiIiIiosTrlQamza1tKq6vtdcF1hzQtQ+7Dx5U8eQifY0iYOscerNuoCvNqPZV62akxevXmjENDbpx6/jxusnewssXmm0ms8an10XVcPm1OBtLde3EmnX6WnAAKF6tm4811DaYMft2lKq4rc1ew+s3Emtvb/PutznR6DWCHJZXZMakpenrfKdENBI75ULdSOzM6dNUXJSba7bxGxCzEWTPyPCOiXlZtgHyoSpdi1NdoxvONTbqYxJga2+qqsrs4x6qVPGBA3vNGP+xW1ubzRifn8vtHW1mTGOjfk2trfY9M9ire9u+VtcbTZyjG0oCwIxRujFqVGNgSo6o4/yuA7p+7Lff+ZOKN21cYbbJy9Of0e/+0OfMmE9+8p0qjmwq6dVPUnz554h+XgHAsidXqnjNsyvNmP3lu1Rc31BjxvjnB3mP6Br5yZPHmm1Oz9Cf9blDMs2YDu/w558DAUDpNn3+Ula2U8X+sRoARo3Sx97cghwzZlg3Nu/lGTMRERERESUeJz5ERERERJR4nPgQEREREVHi9UqNT1uHvraxcl+lGVNZqa8L3LZVr/W9cJquIwCAoRn2GsS+0txmrzvfsUNf415SYvv4+H035s07X8XnnXmy2cbvo0Ldz+/JU9Vga3FWb9fr1T9y+xMqfu6xB8w2/hr2zc32cf2+V1H1OunpXu8c0bUdUXUQfh2Qi7ie3e/hkrV8mBmz+lldd7b5ct1/4uQL5pltLph1koqLhtnHZT+UYxNVE+XX++VFXBedk6d7M/k5WF2te0kAtt9ORkQvHb8OKCq3/Z5UQ4fqerCcHFvnkJOTr2K/VgcAhgzxX1OjfZxsfZ27fx18lHbnvxdZh5Ykfq2u32cNAH75i3tU/NRTf1RxVO/CCy54r4o//aX3mzGTvR49UbW6rHuMLz+3mrxzijXF28w2G1ZsVHHUOaPf+zGqz59/nCrZoc9V/MqX0QAAGUVJREFUtm+1vXTmjBunYr9eFLDn8tv228+K1Y+tVvH+/bomqaPdnpsUFU1U8eiJo8yY7jzv5Tc+RERERESUeJz4EBERERFR4nHiQ0REREREiceJDxERERERJV63L26QNsgWRAl0oZVf7AQApaVbVVyxWxdNRRWV9mXhn9/obHtEkdeyvy1TcWXlPjMmN3e4is++4mwVTygsNNuwYWn3ao1oPlvmFbm+tNU2I334V4+o+IlHblexX4QI2AaIfsNQwBZqZ2QMiRhjG3ylam1tMrcN8t6bbW2tZkyLV6DuN6UEgI0bXtJjavWYXet3mm3SPq+f+6I5c8yY/Gz9ulnYe+Iy0uwhftSU0Sr288JfpACwCwpELR7gNyN1zhbd5ubq49msWXqhjFnzdHNcAJh6il7YJjPLFrlm5+vc2faaLRyuLNV5WjRBF5fneI8BAGnesZY5mSxNLbrR7cMrVpsxd/76xyquq9ONyefOPc9s88XvXa9iv3AcANK5mEui+Z/1+2t1A/E1T64x22x440UV7yvbbsb4i8ZELTTjL5pVXa2bVNdHNE73F1+I0uC9X7Zs3mnGbN70soprvMVyCgrHmG1mnjpXxVHvl+487+UZNBERERERJR4nPkRERERElHic+BARERERUeJ1e42Pf20hABTm6HqExsY6M6byoG5getBrctoWUYfRl1q8+ohVb9hGUyuWP6zi1tYWM2bePH198KJFp6o4N6IBIXWv1ojms6u85qSP/e5xM+bxh/+k4oqK3Z0+V1aWbtw5atRkM2bihNkqHjdpqhmTnuk1MB2kaw/a2219RXurfp21lbZZ3+bilSqOqkurq9fXuO/es0HFHc6+V6cu069h3vjxZkxeVpa+gfUUx8w//g7NyDBjRk/WNT6Z3jXiHR3238+/za8LAgB417T79TwAcO4516j4ys9freLTT5puthnpNbv1r50HgDSvXmLfqbaGbMMeXXOXmaHr66aOHGm28XOSDUzjK6qWc/WOHSq+5d//nxnjN50eM0Yfy/71BzeYbc6ervOY9TwDT4t3XrGtvFzFm199w2xTvn+nirtSbxlVJ+xra9P1l/t325r0Tfv0Z71f3wgA1XV6f1Y//ooZ478Gv8HvjBlnmG3mX3CyiscW2EbWrPEhIiIiIiI6Bpz4EBERERFR4nHiQ0REREREiceJDxERERERJV73L24QUfyZ6TXRy8rKNWPaO7xCsDeKVVzbZBsyDs/Vj9ObzeVqGnQDv7VLXzdjyst3qthvTAkAp5x3lopnjB6lYhZFdr+OThqLAcCGlZtUvOrFp8yYqqoyFftFhwUFuogcAObOWaTis9+22IyZPHeyioePsoV+6RGNKVNFLdjQ2qaLe1ua7GIbG17UReGvv2Ab+r32+jMqbvIWKykvtw2KN760UcU73nqmGeMXl7NR77Hzj4HZmbbZZ2uz/ndvbrHNSH3t7TqfohY3yMjUiySMGjXFjDnz0gUqPmfeSSqOKmodkt558a6fK1GLOhR6DXL9Znz+IjwAMNR7bjYwja+qeruo0q3f1wvUbNq0wozJyclX8fs++S8qvupcndMAkNmFnKXk8M8pAOBQkz6urnz+NRWvX7/cbFPtNftMS7PHsezsPBUL7DHJX2DowAG9eFjxK/rzGAAmzZmk4vRMm8Nl2/UCCKtfetqMqa096O2vfv/MP9u+X2bPmKzirIjjd3fimQURERERESUeJz5ERERERJR4nPgQEREREVHidXuNT5RhXhPOMWPstd+vvKKvSdy6dY2Kdx2oMNtMKSrqhr3rXNT1m2U1uvnjK8tfMGP85lNjxkwzY05fcrqK87NsHRB1r+ZW3Xz2lZ07zZg3XtDNxUpLt5oxflPH4cPHqXjhwsvNNm/9wCUqvuCcU80Y//3SlRqHqKaOPj+P/QZrADC6SDedzCsaZsaU7tum4p079e+q3mtwCgAlJfr3t3t3mRnTNHOmijM6qWMiy2+w6Tf2BIC2Vp23fgPTqCadXcmvIUN0jcz06aebMbPPmqXiMfn6+u+oa7v919CVJqK5Q4aY2/x6nfYO3eQ36ncVdRvFg3+cf2TVq2bM00/cqeI2rzE5AFx4oW66+5FPXKXi/Gz7mc1asIHFP5YAwNZyXa/zypNec/CDuu4GANq9Jrt5eXlmzASvwXlLRI1mTc0B73F1Xre06IamAFBfo89Xm+ptXf221/Rnf0nJZjPGfw9NmTJfxadeZM95/HN5vxF3d+M3PkRERERElHic+BARERERUeJx4kNERERERInHiQ8RERERESVer1QP+0XK+VENGdN1UWtdXZWKSw/qGIgouO2hgsKoZpCPP/mSiteufc6M8YtwZ848y4yZM14XxLNpY89r9QoIy/fahTPK9+kmnI2Nh8yY9HTdHHLsWL14xewF88w2c+bpMX5xN2CbL/oNgIHuKZ5t8X4PgG2YW3mybfo31lukY/fuDSpubbWFk1VVuvFZZVmlGdPUqhtK+os80LHLiCjOz8nXxdg5ufp4LGKPQf6xtq3NNr/1G/iOGDvCjBk/fLiK/YU7ohYTOK5jYsT7wz8ed6XFJIvU4yFqAaKSKn3O8OebbzNjKiv1cclfoAYALv2IXqRmTL4uOI/KEX9/mEfJVt9sP/Pe2LRdxRs36ua4UY2j/XOKqVPtQgALL7xIxYcq7blJ8frXVVxaukXFNTV64QUAKF5VrOKD++x50YYNuumqf54OAEOH6kVuTlt4nornz7CLm+VlZam4p8+DeZZNRERERESJx4kPERERERElHic+RERERESUeN1e4xPVXC47U1+3WDja1vgI9HZ+U6bKfbYmwG/A6NdGHC+/GdXmMtts8a+3/kHFTU22FqKwcIyKF115vhkztkDXeHSlOR+dmDavtqVij72WtaJij74h4hryvPyRKp44ca6O50w024wcphuCRtXv+I0Wu6uJon/deVrEa2r3bquvsXlt6if869kjrs9NT9cNJaPyvKPzHpl0jKL+LQrH6DqbUaP0NdeDB9tmzID+x+mIaNiXlqaPv1G1nH6DUj8PevL4xzqL5GqMaMj498eeV/GKFQ+aMX4T6tNOW2LGTJmlj+Nt7Tr3o+o7/DyOqrVL9479zM/48OuES6tsrcumlZtUfDCiYakvP3+Uis+9+GIzxj+vaGm09ZZ1Vfpz26/pOVRrz6dfX7VMxRUVu82YigMlKo5qbD1m9FQVn37xGSr2m5UCttazp98L/MaHiIiIiIgSjxMfIiIiIiJKPE58iIiIiIgo8Xqlj49/vV7G0EwzRrxr0Rsb9TWKpVv3mm0aW/S1jZnptjNDV64V9Gsfaht1fdHttz1gtlm/Xl8PGdX74vTT9fWZ573lDDMmb6hev5zX+fY8v+4hJy/bjGltadLbDLZvlfb2VhV3dOias8yhtubMv/a7OaJHVGb6sb8t/bq06BoaPaaqvsGMWfrqGyp++o5nzJgNG15UcaNX3zZsmK4hAYDhw8eqeOy0MWaMXwtIx84/fkT1Q8gaon/Pufm5Ks7IsP2TGhp0r4io2iH/NhdRtOX3RPNzkqgr/M/skkpbY/G3W/+s4qieIwUFo1V88rmnmTF+Tejmfbr3T1Or/hwAbM3C9FGjzJjhObrfSRaPf7FR16TPD1YXbzVjVi/V54iNDbUq9nv2AMC0aTr/Zp4504wZO1HnUlVVrRmTP1L3mhpcrPPR3xcAqD2k636qa2ztc4t3XjRkiD13OnPhpSo++7Q5Ks4Zout9gd7vX8lvfIiIiIiIKPE48SEiIiIiosTjxIeIiIiIiBKPEx8iIiIiIkq8XlncwG/UlR1RTO4XejV4xVcHS23DpQZvcYO8rCwzxjRbjOA3YVpXops0PfDnP5ptmpt1YXhRkW1WefVn36Pi2ePGmjH+74Z6nl9IN6woz4wZmqUbjUY16qqv1zlaVrZDxSXFdkGO0WNGqLimwS4wMGyoLi7PHWqLzf0Gko1egW1UQ7+6Jn3by8teM2Oe+9vTKn5j3fNmTFVVuYoHD9aFk35DTAA4eeGZKj5p4ngzJqqZKx0bv+g7yuBB+pjY3qZzyUUuONB5A9NBg3STxsFptmmjv+hGO7vW0nHwm4a+8Pp6M8ZfgCgjwxZVz517nopHTbKLEOzbpRuYr96qG1E21enFkABgkNewdN7588yYBbNnqHj22HFmTG8XfZPlNysFgN0HD6p41WOrzZg9ezaquKVV52xWll5UBgCmztALAYwYaxcK6krT+9xC/diDvXz09wWw57QtLTav09L0Z/2UySebMYuuOVfF4woL9WNENPPt7UW9+K4iIiIiIqLE48SHiIiIiIgSjxMfIiIiIiJKvG6/qD7qWr2sDN3IsWhCkRmTmanrGAZ5DUEPVeoGeoBtHOY3cQSir3/0HfKaUT31qG7QWFKy2WyTlqZf04IFl5kx8+dOV3HuEFurwYalvS/Nu266YLit8Zk9d6GKy8t3mjG1XoOv7dtfV/FDt9v6hZcfW67ioUPtdb7Dvet6o647F1Onoa9D3rdNN9kDgIpSfa2634gUACor9Xb+db+AbVo2dcopKr7gMvteuOhdb1HxzDG2gWkGa3xOmH88iaqbGjVM57t/Pbh/HTcQ3aDZ1+ZdN97S1GLG+DULfr0Rj4fUFX5t5FN/esqMqa09oOLCQnvMycvTNZeVZRG1xLX6uba+rs8HDh2y23R06OOx/xiAPTeZWjTSjGFT077n15MBwEuvbdDx00+aMeXlu1Ts107m59t/7znn6hqfaSPtmBG5+ngtsMfM/FEFKs7M1PXvUfU7DQ01Km5vt83Vs7P0Z8dpCy40Y06dP0vFeV6Ncn+oW+v7PSAiIiIiIuphnPgQEREREVHiceJDRERERESJx4kPERERERElXq9UE/sFtkVFBWbM6NFTVVxZqQuxd++0CwxsKtWNxEYOG2bGZHoN/do6bDOqjd7jPHH3/SpuadGLHwBAUdEEFZ916dlmzOQRunDy/2/v7p6ivq84jn/Dsjwsy7LAysPyoAgsIBJUnuJjSMiY1qTVaTNppje9aae96W3/nPYPyLTTzDRXTetkphexqYLUNDFRVHwAERSRRRQWe9Fe5JzznUKswvLl/bo7O+cnyH73x+83/M73w/B2ftABWl1pGyw7/MGwqKfvXDc9Fy+eFbUecvVtHqAHx/UmGc7ZoD29mYBzNkBybU0OIi4vZ80xeh37BhyLiuQgYnW1/dlkMoOiHv7RSVEPnjhgjulr2SPqMoZ2N4Vvc5fiqFyDqUZ5nkokZO2cc/PzM6L2BZjqULzF+UXTs/BYrjnyS7EROph3Ykaux/Of280NtHS63bymh77n7syZnqlrMtBcn1v1JgrOOZdToZdfj18yPXoTmyeHB00PmxtsPr3W7i0smJ4Ln1wQ9eTkl6ZHbwwUjcr3sq31kDlmb2+rqNOV9lq5MCL/XuH7XVpVJ0NDUykZGD41NWGOce6B/Dqea5O6enmd3v92v+lpq5WbMenr3nzYwIa/+AAAAAAIHjc+AAAAAILHjQ8AAACA4G3K0ImeqehpajQ9nd19or58+e+ivn79n+aYzz8dE3V7XZ3pKVHPs0/O2Wd4P/zdx6L+5sp5Ucdidnbo6NEzoj4+Yp91rI7HRZ0PwU1wLqrWY1N1tekZ6u0S9c1TI6Zn8ZF8Jnbypgw1882Gab6A0MdL8pniB8/sPIUO+C2IqOdoPWtNP7NbW9tielpaekTdMzRgenqHXxV1376MqJs9P89EHoaY7QS+n3O8RM6QFcfkM+KRyEYCTO2a1LMP83fnTc8z9fx8JA+e90b+0+tmblHOj2Wzdq3pNZtI2PNSskYGMpbGbch4eq+c542Vy57bV2ww6hfjMqj6q68+Mz1VVXIWIvtzG5RZEZMzSPkwHxG6VTWfdXFy0vR8cf4fop6fv2t69Hxlbc1uUfcelSHpzjm3r6FB1OWldj3m1Hylb3Y8uUuu68pdcm4zGrXzO/q8X15uPy9tbXIuqbunzfTowFLfnOlW4+oDAAAAQPC48QEAAAAQPG58AAAAAASPGx8AAAAAwduUzQ30gG0yZgMZ9/TIQevIR/Jby2YfmmPGzo6KujHTYHoKi+TA1thfx0zPnz/6UNQrK09F3d19zBxz6pfviPpA827TU1JkB8iQf3wBYK0qhOu9n5y0PQdk2JheW+OfyQ06nHNudlaG4c3N3TE9udyKqH0BpjZoVK790lK5sYZzzrV37xd197H9pqelo1nUGc+GIbsS5aKOF8th+ahn2FKfAxjS3TqF6r3Qw6exmHx/fT162Pw/r62t26M33WCTC2yEHui+PyevB9Y8weRFKjDSd05M1sqAyOSupOkpiMi1n6iWmx0tztuw6OyS/P6WlmwIpr42WVy2m+GYzxDnzZfufla+n+N/sxtrTU/LAFDfAH+N2sxgcOhdUR9666A9JiHXVrHnd6n+LFTE7AYIDTVyM4NUg9yoIB63wah6cxpf4G/nQLeo22prTI8OyM7H3/X81gEAAAAQPG58AAAAAASPGx8AAAAAwduUGR8t5pl9GRqRwUh/+K0MRpqZuWGOmZmRwVLnPrYzFQ+mZcjk+PinpmdhYVbUjY0doh4+/X1zTG+bnEnyzYnk47ONsHxzBjpwc3+jDd3VQZ0DKsjz0g8Pm2Me3X8k6qmJKdPz8J4M46uqt0FiZQkZbNeg5tuq43ZOQ3+/+v/onF3HvmeM9QwP6zx/+d4b/R43dcqARt+z3VeuXBD16uqK6SkpkTMUOkTPOf96Atbjmxf7Nhuw69wrBTKoOpu1czYry3IdR4tteG9JmZxh1DM9/xqVgefOObekvlZTU5fpOXLmiKir4naWMx/DH0P3cEmGis/emjU9T588FnVpqf19W1e3R9Q9r8tw8IEOe57V4Z++axP9mp6xdc7+rm/vk9cmd67aYPKaOTnf29LVYXq6XpPrOFWeMD3bYW4z/79DAAAAAPg/ceMDAAAAIHjc+AAAAAAI3pY8cF0YiZjXutJyRqGz8zVRz87eNsdMTMjclFs3vzQ9C4/ui3p19anpaW7eJ+qf/vpXoj58wu633lRVJWrf/wnbl56N0HvTO+dcUj3LqmdmdqfkXvrOObem9uBfOmbX40puVX7tQvu1CyPyaxepHt8shV6jvvkP/Uw58zvhKVX5Ie3N8tybOWjnEUZH5fku55nxSaXkv1PfmjY9FbGYeQ34rqpTMm8nmbR5ItmsnJW8enXU9JSVyRmFx4uPbU+FXLPXLl0T9d27180xiQp57j/54/dMz9EBOfNRXmJnLjn/bj6dk+OTSMj3tyxu85/aO/pE3X+8V9T1SXuMnp/dyIyX79qzVM3RZzIyU+jhmwfMMctq7bf3Z0zP4Q75mm9efzvgLz4AAAAAgseNDwAAAIDgceMDAAAAIHjc+AAAAAAI3pZsbuAb2NLBTToo6fJlGbLnnHM31WYGDx5Mr/u1qqsbTM+RN06J+sQbMtyptcYOTuohXYLGdp6oHipUtW9DBK3K89raOmF9zjH0iucXjcjTvg676/9evzlm+tr7op6ZvmV6ugfkJjBDb/WZnqoyGdJYsA3C7rD19O/X+qQMx81kbCDjnNoQaeHhPdNz7tyfRD029hfTU1wc+5+175pi4PVhUb/7/ojpaa+rE3UR4b55QQc8+4b8F+aGRR2rsOGzg6cGRT3U2ipqX4D4RsI/9fWBL9xXb3iQVhsp7O/vNMc8XZWbKnWm602P/txt1+tefusAAAAACB43PgAAAACCx40PAAAAgOBtyUOlvvmEWLEMQjr5g2OizuVy5phPfv9HUd++/Y3pKYvJgLJjb542PWd+8Y6ou9IyeK+8pMQcs5EwSOB5sJbwMulzV6o8Luq+1r3mmMrffCDq5RUbYBpX58nuBjv7oGcjN/JMO6BnCdLJSlGf/Nnb5phnKojy4vhZ0/NIBZwXFNhLonhcfq22tkOiPnr6hDlmZETOd3TU2zBfPUvCZyE/1CTkNePIcTvzWNci57MaKitNj56dTKr5xud9v/X1gS/ANKHOxUWqxzdPZubh43HTo4PStys+aQAAAACCx40PAAAAgOBx4wMAAAAgeNz4AAAAAAjeK77wo29ZP0nxBVlRmxcsLi+L+sbsrDlm/PoNUV8dvWp6djWmRH3w1Q7Ts08N4epgqR00dBjCVP2mrVnkhRDWrHNbuG51IN6qZyMZ/VrOF5qnzpO+AdoddC5dTwjrdsvWrA5bnFlYMD1fT0+J+tIle30wMzkj6mixHd6uTssh9X1dLaLu9GxcUKkG2QP5LOyINavPh088G7no61V97nPObjqwmQG164Wc+s7xObUZiG/ThG24qZf3G9x2nzwAAAAA+K648QEAAAAQPG58AAAAAAQvb2Z8nsd6zzE6Z0OZfLbBc4qbJYQfRF6vWbxwIaxZ51i3O00I6zav16y+PvBZ5/rHOWevIXbw9UII//G8XrN44ZjxAQAAALAzceMDAAAAIHjc+AAAAAAIHjc+AAAAAIK3eYlKL4EZMty5Q4cAAOC/NrQJAdcMwI7DX3wAAAAABI8bHwAAAADB48YHAAAAQPDWCzAFAAAAgG2Pv/gAAAAACB43PgAAAACCx40PAAAAgOBx4wMAAAAgeNz4AAAAAAgeNz4AAAAAgvdvXmTGgMyWBOkAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1080x360 with 10 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "np.random.seed(100)\n",
    "random_idx = np.random.randint(len(X_train),size=5)\n",
    "plot_five_reconstruction(X_train,y_train,random_idx,encoder,decoder)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Test set reconstruction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 10 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "np.random.seed(100)\n",
    "random_idx = np.random.randint(len(X_test),size=5)\n",
    "plot_five_reconstruction(X_test,y_test,random_idx,encoder,decoder)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Generating arbitrary handwritten letters/digits"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "def find_idx_from_label_dict(search_str):\n",
    "    for i,val in lebel_dict.items():\n",
    "        if val == search_str:\n",
    "            return i    \n",
    "def letter_digit_gen(input_str,th=None):\n",
    "    gap = 20\n",
    "    img = np.zeros((img_cols,gap*len(input_str)+(img_cols-gap)))\n",
    "    for idx,l in enumerate(input_str):\n",
    "        if l == ' ':\n",
    "            img[:,gap*idx:gap*idx+img_cols] += np.zeros((img_cols,img_cols))\n",
    "        elif l not in label_str:\n",
    "            pass\n",
    "        else:\n",
    "            cls_idx = find_idx_from_label_dict(l)\n",
    "            latent = np.random.randn(latent_dim)\n",
    "            latent = np.expand_dims(np.random.randn(latent_dim),0)\n",
    "            generated = decoder.predict([latent,np.expand_dims(to_categorical(cls_idx,n_label),0)])\n",
    "#             generated = (0.5*generated) + 0.5\n",
    "            generated = generated.reshape(img_rows,img_cols)\n",
    "            generated = np.transpose(generated)\n",
    "            img[:,gap*idx:gap*idx+img_cols] += generated \n",
    "    if th != None:\n",
    "        for i,v in np.ndenumerate(img):\n",
    "            if v >= th:\n",
    "                img[i] = 1\n",
    "            else:\n",
    "                img[i] = 0\n",
    "    return img        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "img = letter_digit_gen('TesT 123?9')\n",
    "plt.imshow(1-img,cmap='gray')\n",
    "plt.axis('off')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "img = letter_digit_gen('TesT 123?9',th=0.5)\n",
    "plt.imshow(1-img,cmap='gray')\n",
    "plt.axis('off')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "def sample_images_gen(input_str,rows=5,cols=3,th=None):\n",
    "    images = []\n",
    "    for _ in range(rows):\n",
    "        current = []\n",
    "        for _ in range(cols):\n",
    "            current.append(letter_digit_gen(input_str,th=th))\n",
    "            current.append(np.ones(shape=(img_rows,10)))\n",
    "        images.append(np.concatenate(current, axis=1))\n",
    "    combined_img = np.concatenate(images)\n",
    "    plt.figure(figsize=(15,20))\n",
    "    plt.imshow(1-combined_img,cmap='gray')\n",
    "    plt.axis('off')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "sample_text = 'HeLlo 2?468'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x1440 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sample_images_gen(sample_text)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x1440 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sample_images_gen(sample_text,th=0.5)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.6.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}