Linear Regression Notes.ipynb

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  "metadata": {
    "colab": {
      "name": "Loocv_vs_K_Fold.ipynb",
      "version": "0.3.2",
      "provenance": [],
      "collapsed_sections": [],
      "include_colab_link": true
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    "kernelspec": {
      "display_name": "Python 2",
      "language": "python",
      "name": "python2"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/mlcow/islr_experiments/blob/master/Linear%20Regression%20Notes.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "metadata": {
        "colab_type": "code",
        "id": "dTaKPsqNroKf",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import tensorflow as tf\n",
        "import random\n",
        "import matplotlib.pyplot as plt"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "5uRh1DA-SCEi",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "#Linear Regression Notes\n",
        "\n",
        "- Nan loss happens\n",
        "  - Just implies your a learning rate that is out of control\n",
        "  - Decrease the learning rate till loss in successive steps starts to decrease\n",
        "- Too small learning rate\n",
        "  - Takes forever to learn, the result will be approximately close"
      ]
    },
    {
      "metadata": {
        "colab_type": "code",
        "id": "GwplXF5HsA-Z",
        "outputId": "f1888ea2-b54b-4be2-dc3f-a0ee9fe1ae88",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 286
        }
      },
      "cell_type": "code",
      "source": [
        "def true_model(x):\n",
        "  return 10 + 2*x + 7*x**2 + 9*x**3 # (10 + 2*x + 7*x**2 + x**3)\n",
        "\n",
        "def noise(x):\n",
        "  return np.random.normal(loc=0.0, scale=250.0, size=x.shape)\n",
        "\n",
        "def population(x):\n",
        "  return true_model(x) + noise(x)\n",
        "\n",
        "x = np.arange(-10, 10, 0.1)\n",
        "plt.plot(x, true_model(x), label=\"y\")\n",
        "plt.plot(x, population(x), label=\"y+noise1\")\n",
        "plt.plot(x, population(x), label=\"y+noise2\")\n",
        "plt.legend(title=\"true model data\")"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x11415f810>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 2
        },
        {
          "output_type": "display_data",
          "data": {
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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "colab_type": "code",
        "id": "eNR4Pr34vbBy",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "def model_fn(features, labels, mode, params):\n",
        "  order = params.get(\"order\")\n",
        "  b = tf.get_variable(\"b\", shape=[1], dtype=tf.float32, initializer=tf.zeros_initializer)\n",
        "  w = tf.get_variable(\"w\", shape=[1, order], dtype=tf.float32)\n",
        "  print b\n",
        "  print w\n",
        "  print features\n",
        "  print labels\n",
        "  #print \"A>>>\", tf.matmul(features, w)\n",
        "  \n",
        "  preds = b + tf.reduce_sum(features * w, axis=-1)\n",
        "  \n",
        "  print \"P\", preds\n",
        "  \n",
        "  if mode == tf.estimator.ModeKeys.PREDICT:\n",
        "    return tf.estimator.EstimatorSpec(\n",
        "      train_op = None,\n",
        "      loss = None,\n",
        "      predictions = {\"x\": features, \"preds\": preds, 'coeff': tf.convert_to_tensor(w), 'b': tf.convert_to_tensor(b) },\n",
        "      mode=mode\n",
        "    )\n",
        "  \n",
        "  error = labels - preds\n",
        "  print \"E\", error\n",
        "  loss = tf.reduce_mean(error**2)\n",
        "  \n",
        "  train_op = tf.group(tf.train.GradientDescentOptimizer(params.get('lr')).minimize(loss),\n",
        "                      tf.assign_add(tf.train.get_global_step(), 1))\n",
        "  \n",
        "  if mode == tf.estimator.ModeKeys.TRAIN:\n",
        "    return tf.estimator.EstimatorSpec(\n",
        "      train_op = train_op,\n",
        "      loss = loss,\n",
        "      predictions = preds,\n",
        "      mode= mode\n",
        "    )"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "colab_type": "code",
        "id": "yQoTR_ZOs1MW",
        "scrolled": false,
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "def run_model_with_params_and_plot(lr, log_steps, num_epochs):\n",
        "    try:\n",
        "        estimator = tf.estimator.Estimator(model_fn=model_fn, params={\"order\": 3, \"lr\": lr},\n",
        "                                           config=tf.estimator.RunConfig(log_step_count_steps=log_steps))\n",
        "\n",
        "        def feature_vector(x, order):\n",
        "          return np.transpose(np.stack([x**i for i in range(1, order+1)])).astype(np.float32)\n",
        "\n",
        "        x = np.arange(-10, 10, 0.1)\n",
        "        y = population(x)\n",
        "\n",
        "        estimator.train(input_fn=tf.estimator.inputs.numpy_input_fn(feature_vector(x, 3), y.astype(np.float32),\n",
        "                                                                    batch_size = x.shape[0],\n",
        "                                                                    num_epochs=num_epochs, shuffle=True))\n",
        "\n",
        "        gen = estimator.predict(input_fn=tf.estimator.inputs.numpy_input_fn(feature_vector(x, 3), y.astype(np.float32),\n",
        "                                                                    batch_size = 1,\n",
        "                                                                    num_epochs=1, shuffle=False))\n",
        "\n",
        "        res = []\n",
        "        # Collect output\n",
        "        for i in gen:\n",
        "            _y = i['preds']\n",
        "            res.append(_y)\n",
        "            # print _x, _y, true_model(_x), _y - true_model(_x), i['b'], i['coeff']\n",
        "            \n",
        "        plt.plot(x, y)\n",
        "        plt.plot(x, res)\n",
        "    except:\n",
        "        print \"\\x1b[31m\\\"{}\\\"\\x1b[0m\".format(\"Failed with error\")"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "dRpFXdXfMgxZ",
        "colab_type": "code",
        "outputId": "a6a16fac-38fb-472e-d790-7e2c14b997f4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 377
        }
      },
      "cell_type": "code",
      "source": [
        "run_model_with_params_and_plot(1e-3, 10, 100)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:Using temporary folder as model directory: /var/folders/_n/7gq_v92s6xq549134xk8hyts_mxyxb/T/tmpajFw2a\n",
            "INFO:tensorflow:Using config: {'_save_checkpoints_secs': 600, '_session_config': None, '_keep_checkpoint_max': 5, '_task_type': 'worker', '_global_id_in_cluster': 0, '_is_chief': True, '_cluster_spec': <tensorflow.python.training.server_lib.ClusterSpec object at 0x1140f1350>, '_evaluation_master': '', '_save_checkpoints_steps': None, '_keep_checkpoint_every_n_hours': 10000, '_service': None, '_num_ps_replicas': 0, '_tf_random_seed': None, '_master': '', '_device_fn': None, '_num_worker_replicas': 1, '_task_id': 0, '_log_step_count_steps': 10, '_model_dir': '/var/folders/_n/7gq_v92s6xq549134xk8hyts_mxyxb/T/tmpajFw2a', '_train_distribute': None, '_save_summary_steps': 100}\n",
            "INFO:tensorflow:Calling model_fn.\n",
            "<tf.Variable 'b:0' shape=(1,) dtype=float32_ref>\n",
            "<tf.Variable 'w:0' shape=(1, 3) dtype=float32_ref>\n",
            "Tensor(\"random_shuffle_queue_DequeueUpTo:1\", shape=(?, 3), dtype=float32, device=/device:CPU:0)\n",
            "Tensor(\"random_shuffle_queue_DequeueUpTo:2\", shape=(?,), dtype=float32, device=/device:CPU:0)\n",
            "P Tensor(\"add:0\", shape=(?,), dtype=float32)\n",
            "E Tensor(\"sub:0\", shape=(?,), dtype=float32)\n",
            "INFO:tensorflow:Done calling model_fn.\n",
            "INFO:tensorflow:Create CheckpointSaverHook.\n",
            "INFO:tensorflow:Graph was finalized.\n",
            "INFO:tensorflow:Running local_init_op.\n",
            "INFO:tensorflow:Done running local_init_op.\n",
            "INFO:tensorflow:Saving checkpoints for 0 into /var/folders/_n/7gq_v92s6xq549134xk8hyts_mxyxb/T/tmpajFw2a/model.ckpt.\n",
            "INFO:tensorflow:loss = 11350641.0, step = 1\n",
            "INFO:tensorflow:global_step/sec: 128.738\n",
            "INFO:tensorflow:loss = inf, step = 11 (0.080 sec)\n",
            "ERROR:tensorflow:Model diverged with loss = NaN.\n",
            "\u001b[31m\"Failed with error\"\u001b[0m\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "5ueXERF_Mgxd",
        "colab_type": "code",
        "outputId": "3440bbce-2589-477f-f922-f18688e32e26",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1095
        }
      },
      "cell_type": "code",
      "source": [
        "run_model_with_params_and_plot(1e-5, 10, 100)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:Using temporary folder as model directory: /var/folders/_n/7gq_v92s6xq549134xk8hyts_mxyxb/T/tmppgJ2GQ\n",
            "INFO:tensorflow:Using config: {'_save_checkpoints_secs': 600, '_session_config': None, '_keep_checkpoint_max': 5, '_task_type': 'worker', '_global_id_in_cluster': 0, '_is_chief': True, '_cluster_spec': <tensorflow.python.training.server_lib.ClusterSpec object at 0x102cc3390>, '_evaluation_master': '', '_save_checkpoints_steps': None, '_keep_checkpoint_every_n_hours': 10000, '_service': None, '_num_ps_replicas': 0, '_tf_random_seed': None, '_master': '', '_device_fn': None, '_num_worker_replicas': 1, '_task_id': 0, '_log_step_count_steps': 10, '_model_dir': '/var/folders/_n/7gq_v92s6xq549134xk8hyts_mxyxb/T/tmppgJ2GQ', '_train_distribute': None, '_save_summary_steps': 100}\n",
            "INFO:tensorflow:Calling model_fn.\n",
            "<tf.Variable 'b:0' shape=(1,) dtype=float32_ref>\n",
            "<tf.Variable 'w:0' shape=(1, 3) dtype=float32_ref>\n",
            "Tensor(\"random_shuffle_queue_DequeueUpTo:1\", shape=(?, 3), dtype=float32, device=/device:CPU:0)\n",
            "Tensor(\"random_shuffle_queue_DequeueUpTo:2\", shape=(?,), dtype=float32, device=/device:CPU:0)\n",
            "P Tensor(\"add:0\", shape=(?,), dtype=float32)\n",
            "E Tensor(\"sub:0\", shape=(?,), dtype=float32)\n",
            "INFO:tensorflow:Done calling model_fn.\n",
            "INFO:tensorflow:Create CheckpointSaverHook.\n",
            "INFO:tensorflow:Graph was finalized.\n",
            "INFO:tensorflow:Running local_init_op.\n",
            "INFO:tensorflow:Done running local_init_op.\n",
            "INFO:tensorflow:Saving checkpoints for 0 into /var/folders/_n/7gq_v92s6xq549134xk8hyts_mxyxb/T/tmppgJ2GQ/model.ckpt.\n",
            "INFO:tensorflow:loss = 15670659.0, step = 1\n",
            "INFO:tensorflow:global_step/sec: 110.138\n",
            "INFO:tensorflow:loss = 5289269500000.0, step = 11 (0.094 sec)\n",
            "INFO:tensorflow:global_step/sec: 453.37\n",
            "INFO:tensorflow:loss = 1.0312552e+18, step = 21 (0.023 sec)\n",
            "INFO:tensorflow:global_step/sec: 354.084\n",
            "INFO:tensorflow:loss = 8.657798e+22, step = 31 (0.025 sec)\n",
            "INFO:tensorflow:global_step/sec: 403.648\n",
            "INFO:tensorflow:loss = 9.8772864e+27, step = 41 (0.025 sec)\n",
            "INFO:tensorflow:global_step/sec: 416.407\n",
            "INFO:tensorflow:loss = 9.1363464e+32, step = 51 (0.024 sec)\n",
            "INFO:tensorflow:global_step/sec: 253.235\n",
            "INFO:tensorflow:loss = inf, step = 61 (0.040 sec)\n",
            "INFO:tensorflow:global_step/sec: 340.494\n",
            "INFO:tensorflow:loss = inf, step = 71 (0.029 sec)\n",
            "INFO:tensorflow:global_step/sec: 381.797\n",
            "INFO:tensorflow:loss = inf, step = 81 (0.026 sec)\n",
            "INFO:tensorflow:global_step/sec: 591.022\n",
            "INFO:tensorflow:loss = inf, step = 91 (0.019 sec)\n",
            "INFO:tensorflow:Saving checkpoints for 101 into /var/folders/_n/7gq_v92s6xq549134xk8hyts_mxyxb/T/tmppgJ2GQ/model.ckpt.\n",
            "INFO:tensorflow:Loss for final step: inf.\n",
            "INFO:tensorflow:Calling model_fn.\n",
            "<tf.Variable 'b:0' shape=(1,) dtype=float32_ref>\n",
            "<tf.Variable 'w:0' shape=(1, 3) dtype=float32_ref>\n",
            "Tensor(\"fifo_queue_DequeueUpTo:1\", shape=(?, 3), dtype=float32, device=/device:CPU:0)\n",
            "None\n",
            "P Tensor(\"add:0\", shape=(?,), dtype=float32)\n",
            "INFO:tensorflow:Done calling model_fn.\n",
            "INFO:tensorflow:Graph was finalized.\n",
            "INFO:tensorflow:Restoring parameters from /var/folders/_n/7gq_v92s6xq549134xk8hyts_mxyxb/T/tmppgJ2GQ/model.ckpt-101\n",
            "INFO:tensorflow:Running local_init_op.\n",
            "INFO:tensorflow:Done running local_init_op.\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "hs8V_OzgMgxg",
        "colab_type": "code",
        "outputId": "7869dabc-d907-4b80-9cac-98a1a4d867f7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 782
        }
      },
      "cell_type": "code",
      "source": [
        "run_model_with_params_and_plot(1e-6, 100, 100)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:Using temporary folder as model directory: /var/folders/_n/7gq_v92s6xq549134xk8hyts_mxyxb/T/tmp7Lv2eL\n",
            "INFO:tensorflow:Using config: {'_save_checkpoints_secs': 600, '_session_config': None, '_keep_checkpoint_max': 5, '_task_type': 'worker', '_global_id_in_cluster': 0, '_is_chief': True, '_cluster_spec': <tensorflow.python.training.server_lib.ClusterSpec object at 0x1140f1850>, '_evaluation_master': '', '_save_checkpoints_steps': None, '_keep_checkpoint_every_n_hours': 10000, '_service': None, '_num_ps_replicas': 0, '_tf_random_seed': None, '_master': '', '_device_fn': None, '_num_worker_replicas': 1, '_task_id': 0, '_log_step_count_steps': 100, '_model_dir': '/var/folders/_n/7gq_v92s6xq549134xk8hyts_mxyxb/T/tmp7Lv2eL', '_train_distribute': None, '_save_summary_steps': 100}\n",
            "INFO:tensorflow:Calling model_fn.\n",
            "<tf.Variable 'b:0' shape=(1,) dtype=float32_ref>\n",
            "<tf.Variable 'w:0' shape=(1, 3) dtype=float32_ref>\n",
            "Tensor(\"random_shuffle_queue_DequeueUpTo:1\", shape=(?, 3), dtype=float32, device=/device:CPU:0)\n",
            "Tensor(\"random_shuffle_queue_DequeueUpTo:2\", shape=(?,), dtype=float32, device=/device:CPU:0)\n",
            "P Tensor(\"add:0\", shape=(?,), dtype=float32)\n",
            "E Tensor(\"sub:0\", shape=(?,), dtype=float32)\n",
            "INFO:tensorflow:Done calling model_fn.\n",
            "INFO:tensorflow:Create CheckpointSaverHook.\n",
            "INFO:tensorflow:Graph was finalized.\n",
            "INFO:tensorflow:Running local_init_op.\n",
            "INFO:tensorflow:Done running local_init_op.\n",
            "INFO:tensorflow:Saving checkpoints for 0 into /var/folders/_n/7gq_v92s6xq549134xk8hyts_mxyxb/T/tmp7Lv2eL/model.ckpt.\n",
            "INFO:tensorflow:loss = 9116013.0, step = 1\n",
            "INFO:tensorflow:Saving checkpoints for 101 into /var/folders/_n/7gq_v92s6xq549134xk8hyts_mxyxb/T/tmp7Lv2eL/model.ckpt.\n",
            "INFO:tensorflow:Loss for final step: 105732.92.\n",
            "INFO:tensorflow:Calling model_fn.\n",
            "<tf.Variable 'b:0' shape=(1,) dtype=float32_ref>\n",
            "<tf.Variable 'w:0' shape=(1, 3) dtype=float32_ref>\n",
            "Tensor(\"fifo_queue_DequeueUpTo:1\", shape=(?, 3), dtype=float32, device=/device:CPU:0)\n",
            "None\n",
            "P Tensor(\"add:0\", shape=(?,), dtype=float32)\n",
            "INFO:tensorflow:Done calling model_fn.\n",
            "INFO:tensorflow:Graph was finalized.\n",
            "INFO:tensorflow:Restoring parameters from /var/folders/_n/7gq_v92s6xq549134xk8hyts_mxyxb/T/tmp7Lv2eL/model.ckpt-101\n",
            "INFO:tensorflow:Running local_init_op.\n",
            "INFO:tensorflow:Done running local_init_op.\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "z0f-sgYEMgxj",
        "colab_type": "code",
        "outputId": "03d4a52f-a9b8-40e0-824d-07df06f3a3b8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 782
        }
      },
      "cell_type": "code",
      "source": [
        "run_model_with_params_and_plot(1e-7, 100, 100)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:Using temporary folder as model directory: /var/folders/_n/7gq_v92s6xq549134xk8hyts_mxyxb/T/tmpxXYe4P\n",
            "INFO:tensorflow:Using config: {'_save_checkpoints_secs': 600, '_session_config': None, '_keep_checkpoint_max': 5, '_task_type': 'worker', '_global_id_in_cluster': 0, '_is_chief': True, '_cluster_spec': <tensorflow.python.training.server_lib.ClusterSpec object at 0x11430ee50>, '_evaluation_master': '', '_save_checkpoints_steps': None, '_keep_checkpoint_every_n_hours': 10000, '_service': None, '_num_ps_replicas': 0, '_tf_random_seed': None, '_master': '', '_device_fn': None, '_num_worker_replicas': 1, '_task_id': 0, '_log_step_count_steps': 100, '_model_dir': '/var/folders/_n/7gq_v92s6xq549134xk8hyts_mxyxb/T/tmpxXYe4P', '_train_distribute': None, '_save_summary_steps': 100}\n",
            "INFO:tensorflow:Calling model_fn.\n",
            "<tf.Variable 'b:0' shape=(1,) dtype=float32_ref>\n",
            "<tf.Variable 'w:0' shape=(1, 3) dtype=float32_ref>\n",
            "Tensor(\"random_shuffle_queue_DequeueUpTo:1\", shape=(?, 3), dtype=float32, device=/device:CPU:0)\n",
            "Tensor(\"random_shuffle_queue_DequeueUpTo:2\", shape=(?,), dtype=float32, device=/device:CPU:0)\n",
            "P Tensor(\"add:0\", shape=(?,), dtype=float32)\n",
            "E Tensor(\"sub:0\", shape=(?,), dtype=float32)\n",
            "INFO:tensorflow:Done calling model_fn.\n",
            "INFO:tensorflow:Create CheckpointSaverHook.\n",
            "INFO:tensorflow:Graph was finalized.\n",
            "INFO:tensorflow:Running local_init_op.\n",
            "INFO:tensorflow:Done running local_init_op.\n",
            "INFO:tensorflow:Saving checkpoints for 0 into /var/folders/_n/7gq_v92s6xq549134xk8hyts_mxyxb/T/tmpxXYe4P/model.ckpt.\n",
            "INFO:tensorflow:loss = 11522218.0, step = 1\n",
            "INFO:tensorflow:Saving checkpoints for 101 into /var/folders/_n/7gq_v92s6xq549134xk8hyts_mxyxb/T/tmpxXYe4P/model.ckpt.\n",
            "INFO:tensorflow:Loss for final step: 194376.8.\n",
            "INFO:tensorflow:Calling model_fn.\n",
            "<tf.Variable 'b:0' shape=(1,) dtype=float32_ref>\n",
            "<tf.Variable 'w:0' shape=(1, 3) dtype=float32_ref>\n",
            "Tensor(\"fifo_queue_DequeueUpTo:1\", shape=(?, 3), dtype=float32, device=/device:CPU:0)\n",
            "None\n",
            "P Tensor(\"add:0\", shape=(?,), dtype=float32)\n",
            "INFO:tensorflow:Done calling model_fn.\n",
            "INFO:tensorflow:Graph was finalized.\n",
            "INFO:tensorflow:Restoring parameters from /var/folders/_n/7gq_v92s6xq549134xk8hyts_mxyxb/T/tmpxXYe4P/model.ckpt-101\n",
            "INFO:tensorflow:Running local_init_op.\n",
            "INFO:tensorflow:Done running local_init_op.\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "3fsr3apEMgxo",
        "colab_type": "code",
        "outputId": "fbc32202-1eab-477c-c281-d914fe52a2f4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 782
        }
      },
      "cell_type": "code",
      "source": [
        "run_model_with_params_and_plot(1e-8, 100, 100)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:Using temporary folder as model directory: /var/folders/_n/7gq_v92s6xq549134xk8hyts_mxyxb/T/tmpWU1iD1\n",
            "INFO:tensorflow:Using config: {'_save_checkpoints_secs': 600, '_session_config': None, '_keep_checkpoint_max': 5, '_task_type': 'worker', '_global_id_in_cluster': 0, '_is_chief': True, '_cluster_spec': <tensorflow.python.training.server_lib.ClusterSpec object at 0x1152f8990>, '_evaluation_master': '', '_save_checkpoints_steps': None, '_keep_checkpoint_every_n_hours': 10000, '_service': None, '_num_ps_replicas': 0, '_tf_random_seed': None, '_master': '', '_device_fn': None, '_num_worker_replicas': 1, '_task_id': 0, '_log_step_count_steps': 100, '_model_dir': '/var/folders/_n/7gq_v92s6xq549134xk8hyts_mxyxb/T/tmpWU1iD1', '_train_distribute': None, '_save_summary_steps': 100}\n",
            "INFO:tensorflow:Calling model_fn.\n",
            "<tf.Variable 'b:0' shape=(1,) dtype=float32_ref>\n",
            "<tf.Variable 'w:0' shape=(1, 3) dtype=float32_ref>\n",
            "Tensor(\"random_shuffle_queue_DequeueUpTo:1\", shape=(?, 3), dtype=float32, device=/device:CPU:0)\n",
            "Tensor(\"random_shuffle_queue_DequeueUpTo:2\", shape=(?,), dtype=float32, device=/device:CPU:0)\n",
            "P Tensor(\"add:0\", shape=(?,), dtype=float32)\n",
            "E Tensor(\"sub:0\", shape=(?,), dtype=float32)\n",
            "INFO:tensorflow:Done calling model_fn.\n",
            "INFO:tensorflow:Create CheckpointSaverHook.\n",
            "INFO:tensorflow:Graph was finalized.\n",
            "INFO:tensorflow:Running local_init_op.\n",
            "INFO:tensorflow:Done running local_init_op.\n",
            "INFO:tensorflow:Saving checkpoints for 0 into /var/folders/_n/7gq_v92s6xq549134xk8hyts_mxyxb/T/tmpWU1iD1/model.ckpt.\n",
            "INFO:tensorflow:loss = 8927242.0, step = 1\n",
            "INFO:tensorflow:Saving checkpoints for 101 into /var/folders/_n/7gq_v92s6xq549134xk8hyts_mxyxb/T/tmpWU1iD1/model.ckpt.\n",
            "INFO:tensorflow:Loss for final step: 5099280.0.\n",
            "INFO:tensorflow:Calling model_fn.\n",
            "<tf.Variable 'b:0' shape=(1,) dtype=float32_ref>\n",
            "<tf.Variable 'w:0' shape=(1, 3) dtype=float32_ref>\n",
            "Tensor(\"fifo_queue_DequeueUpTo:1\", shape=(?, 3), dtype=float32, device=/device:CPU:0)\n",
            "None\n",
            "P Tensor(\"add:0\", shape=(?,), dtype=float32)\n",
            "INFO:tensorflow:Done calling model_fn.\n",
            "INFO:tensorflow:Graph was finalized.\n",
            "INFO:tensorflow:Restoring parameters from /var/folders/_n/7gq_v92s6xq549134xk8hyts_mxyxb/T/tmpWU1iD1/model.ckpt-101\n",
            "INFO:tensorflow:Running local_init_op.\n",
            "INFO:tensorflow:Done running local_init_op.\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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iO0VkhYicWF83Ccj1eFieXdZRuVJKdVldUyvXPf4x9z6/nZRBoSyemMiEYVG4DcwdOZhAZx/oii05AP++xup3MG648Tm46TVInNDroXjt1RKRCOBl4F5jTDXwGDAKmAYUAL/14rFuF5FMEcksKSnx1tMqpfqIxhYXJTVNp5W73YbvvbiDrccq+NnVk1jz7flEhgS2nT1cMNrPm5pqCuGN71hnD7mbYdHP4a6NMPZS8NFscK8kCREJxEoQTxtjXgEwxhQZY1zGGDfwN6zmJIB8wPMq4sl2WUflpzHGLDfGZBhjMuLj+2gnlFLqnP1i1T4u/eMHNLa42srW7SvihuUf8+buQv7nsvHcNHcEIYFOwEoOAQ7x36Gv9eWw5sfwx2mw9SnIuBW+uRXOvwcCgnwaWrc7rsVa7OTvwD5jzO88yocaYwrsu58DdtvbK4FnROR3WB3X6cAmQIB0EUnDSg7LgC90Nz6lVP9ijNUhXVrbzLp9xVw+ZSgvbcnjey/uIDk2lAevnMCXz0896TELxyfw8f0LiI/0s6GvTbWw8TH46BGrU3rK9XDx/TAozdeRtfHG6KYLgJuAXSKy3S77H+BGEZkGGOAIcAeAMWaPiLwA7MUaGXW3McYFICL3AKsBJ7DCGLPHC/EppfqRrOJaCqoaAXhpSy5hQU5++PJO5o2OY8WXZxEUcHoDiYj4V4JoqoUtT8L6P0B9KYy9HD7zgE/6HDrT7SRhjFmPdRZwqlVneMzPgZ+3U77qTI9TSqkPDlr9kJ+bnsTr2/PZcKiM8UMjeexLM9pNEH6loRI2LbfOHhrKIW0+LPgxJGf4OrIO6dpNSqk+5f2DJYxOiOAbnxnNa9vzSU+I4N+3zSEyJNDXoXWstgQ2PgqbnoDmGkhfDBd9D1Jmd/5YH9MkoZTyO8YY3jtQwvwx8TgcnzZU1Da18klOOTfNHcHI+AhW3j2P1Lgw/00QZYfgk8dh67+gtREmLIULvwtDp/g6si7TJKGU8jsbD5dz6z8288iN07lq6rC28r++f4jmVjdLp1llk5OjfRVix4yBnA+sJqWDb4EjwOqQnvdtiEv3dXRnTZOEUsrvHCisBqxhrSeSxPHKBpZ/cJirpg5jSrIfzppuaYTdL1nJoWg3hMXB/B9Axm0Qmejr6M6ZJgmllN/JKq4F4L0DJbS63ORVNPCt57ZhgB8sGevb4E5VmgVb/gHbn7E6oxMmwtJHYdK1EBji6+i6TZOEUsovNLe62ZVfxcwRsWQX1+J0CFUNLTyxPodH1mUR4BAeWTaN5NgwX4dqnTXsW2klh6MbrCalcZdDxlesEUv96FrZmiSUUj7X0Ozizn9v4f2DJTz91TlkF9eyZOIQVu8p5Jdv7id1cBjP3j6XodGhvgvSGDi+DXY+Dzueg8ZKiE2DhT+FaV+AiATfxdaDNEkopXpcU6uLzCMVNLa4yEgdRHTop6ORCqoauOeZbWw9VkGAQ3h5Sx5ldc1MS4mhvrmVHXlV/OPW2b5LEOU5sOslKzmUZYEzyDprmHkrpF4IDj+fm9FNmiSUUj3uqY+O8ItV+wG45bwR/HTpJAAOFtWwbPlGGltc/PnGGTy3+Rj/2XkcgNGJEdwwOwXjhuiwXh7iWlNkNSftehFy7UWtR8yD878BE66C0NgzP74f0SShlPKqxhYXl/3xQ+qaW5mVOog/3TidPcerGRIVwpDoELbnVrbVffjN/bjchv98Yx6j4iMoqGrgw6xSAEbHRxDVm/MfKo7C/jdg70o7MRiIHwcLHoTJ10FMSqdP0R9pklBKeUV+ZQPDokPYfKScw6V1pCdE8MbOAh64fAIHi2oZNzSS9IQInvr4KC0uN3uPV7NufzHfWzSGUfERAFw8NoGH/ruP0EAnSTE93LxkDBTtseYy7PsPFNhLzyVOsq78Nv5KSJjQrzqhz4UmCaXUOSmpaeLGv23kDzdMIzY8iPm/epcHr5pIfkUDAQ7hgSsmcMuKTWzPreRQSS0XpscxcVgUza1usotreWRdFjFhgdzisWLrqPhwkmNDiQ0LOmmmtdc0VsPh9yB7DWSthRqraYukDKsDevyVMHiU94/bh2mSUEqdk/XZJWQX1/Lm7gJGJ0TQ6jY8v/kYDhGmD48hY0QsIrBqVwHNrW7SEyKYlGTNkF6zt4h3DhTzjUtGn7Skhojw2+um4vRWgnC1QP5WOPIBHH4fjn0M7lYIjoJRl8Doz8LohRA11DvH64c0SSilzknmkQoANudUUFnfAsDufGum9LcWpBMeHMCo+Aje3lsIQHpiJGmDwwkPcvLX9w9hDFwzM/m0550zcvC5B+VqgYKdVlLI+RCObYSWOmtf4mQ47x5IX2QtrOf00/We/IwmCaXUOdly1EoS23MrKa1rYnJSNPsKqml1G84fZX3QT06KJtuePZ2eEIHDIUwcFs2mI+XMHBHLiMHh3QuiphDyNlu33M3WPIbWBmtf/Dhr/kLahdbIpPBuJJ8BTJOEUl50vLKBQKfjnC5ws3ZvEa/vOM4jy6YhHp2lFXXNrNlXxHUzk08q7ymPvXeIcUMjuWRsx5PDqhpaOFBUw+SkaHblV3G4pI5vLxzD0OgQNmSXMn24NUR0UlI0r27LJykmlPBg6+NmYlIUm46U8/kZSWcXWEMFFO6Gwl12YsiEqmPWPkcgDJ0KM78MKbOs+Qv9dHJbb9MkoZQX3frkZo5XNvDwtVO4bPLZtXO/tCWPt/YUctfFoxg/NKqt/C/vZfO3D3PIGBHLSHsUUE95Y+dxHn5rP+kJEVw8Jr7DpLTtWAXGwJ3zR3H3M1sByEiN5cY5KRRVNbVd/GfSMOv3GJP4adyfHZ/IR9llXDF52OlPDOB2Q0WOlQyKdn+aGKrzPq0TlWwlg7l3QvIsGDKlX6yT5I80SSjVRc9vPsbckYM7bCI58e06PMjJXU9v5dW7zm/7Rt0ZYwxbj1nNN+/sL25LEm634Y2d1qXiDxbVkBwbxjv7i1k4PoEAp3dn+hZXN/KjV3cTHuQkq7iW3fnVJy3FXVzTSGV9CyMGh/HegRKcDuHisfGMSYwgu7iWaSkxhAcHkBD56Yf1xKRoAhzCOI+kd/7oOFZ/+yJrpFH+TijNtmYylx60tssPWddeABCntbz2iPOsoalDJll9C314VdW+RpOE6ldKapqIDAkgJNDZVlbT2HLGi9I0NLt45J0svjovjcER7TcTvX+whB++vIvrM5L51bVTT9r3o1d3MSkpmhR74bnfXj+Ne5/fxktb8rqcJAqqGimuaQLg3f3F3H3JaAAyj1a0Xc/5YFEtpbXNPPDabj4/PYnfXDe13WGibrfhte35VDe0MDEpmlmpg7oUw8tb86lqaOGVu85n2fKNPLPpKKHbAkiLC+O6jBSue/xjjpbVI2JNMZg5Ipbw4ACuz0hhX0FNW3NSm5YGIqpzefPyJpLkQ1j7PFQeg6pcqDgCtUWf1hUnxI6AwenWqKP4sTBkMsSP1zMEH/O7JCEiS4A/Ak7gCWPML30ckvKRwqpGli3/mD9/YUbb0MkzaWxxsej377N0WhI/uWoiAL9evZ+/vHeIpVOH8b3FY9tWEN1fWM1LmXncMX8Ur23L57H3DjEkKoQbZqVw73PbGRIdwnUZyUwcFo3Lbfi/VfsA2JBdhjGmrRmmodnFc5tzGXGorG2kznkjB7NowhD+u6uAB6+c2KXrLm87Zs1Cnj8mng+zSqisbyYmLIiVO/IJCXQQHRrIgaIagpwOAhzCK9vyiQ4L5MErJ572XO8eKOY7L+wArHlgv7pmCtdlfDpbOLu4huOVjVw0Jv6kx+3Kr2T4oDBmDI/ls+MTeXZTbttzvH+wlKNl9Xx34WhMUzVTY5rJiGuCXS/x1YAiiC2CVx+zPvhriqC2EOrLAGi7zI4jAKKTITrFGnoaN9pKCnHp1kJ5AUGdvk6q9/lVkhARJ/Ao8FkgD9gsIiuNMXt9G5nyprqmVoIDHAQ4HTz9yVHiI4JZNHHIafVW7ynkSFk97x0o7lKSeO9AMRX1LazaVcCPr5jA8g8P8+i7h5iVGsubuwtZs7eIO+aP4mhZPa9tz8flNhwoqmFfQQ0AO/OqGJ0QwVt7CnGI1Ufw8f2f4a3dhewvrGHe6DjWZ5dypKyetDiryWlvQTUut+FwaR2vb88ndXAY0WGBfG56Eit3HOf9gyV8dkLnTSNbj1UQHODg7ktG8/7BEp7ccIQxiZG8ujWfBeMTaWpxc7CwhmaXmwXjExgWE8qTG45w/qi4tuevb24lLCiAFzJziYsI4j/fmMcPXtrJ91/aSUlNAzdMjWP5ul2s3naYEBoZuWw8yeGG5oZagtyNpB7ZwsJoYN16HnQW8vmIo0weZCgvLSL4UDV/DGsgfEMtGPfpv4AzCCISrc7i2BFWf0F0MsSMsJJCzHCIHAIO5+mPVX5NjDG+jqGNiJwH/MQYs9i+fz+AMeb/OnpMRkaGyczM7KUIVXcV1zRy2R/Xc8nYeH585QRmPrSWkXHhvHXvRafVvWXFJt4/WMKSiUN4/KaZpz+ZMVbbdUsDtDby4MuZbDyYTwjN/HDhCJ54Zy+zhkdyx7zhVNQ28NwnORwurCQ8CGYmRxAb4uC9fQU4cTEoRAgNMIxLCOeTnDI+N20Yr23P4+Ix8ezMrSAwwMGi8Qk8u+kYF6XHMWlYJBjD7vxKNhwqQwDBMDo+nEvGxuNyu3lh8zGGRgVz8dh4K1bjBsxJ2w3NrVTWN7M3vxKHwPwxg/ngQDEV9c04MMSGBpAxIoZj5XUcLq5BMIxLjGB4TCA7jpTgam0iIcxBa3MTLc2NJIQ7qa2vJyYYogINxtVMS1MjQaap638kcUBIDITGQGgsdY4I9lUGMGHkcMKiBluL251ICCd+hsYO+OUr+hoR2WKMyei0np8liWuBJcaYr9r3bwLmGGPuOaXe7cDtAMOHD5959OjRXo9VnZ3c8nqaWt38YtU+3tlfTJDTwX0LR/DE6s0Mkmr+feNo1mbuJtzUcunoCFrqK3n5432EmzriApuZmxRIVUUZrQ1VhJpGwh0tn3Zu9hA3Yn2eIzgcDkSg1Q0gBDodIEKzy+AyIIDLQGCAkyCnE0RoaHXT3GqICg1ExGF/iIr1IWxvlze00tjqxiCEBwcSExaEQWhsNbS6ISIkCBGhpslFQXUTBiFlcDhhIaE0GieHy5upahbEGQQBQZTUu2kmgIUTk4mOCAdnEMYRwLbCZo7WwAUThpMwaBDP7yhn1YFq6k0w9YQwY/Qw3s6q5ZGb5zFn3Ih+v/y16udJwpOeSfgZt9tqi645DtXWrbUynzc2bCHaVcEgqWF4cD1BzRWES8ffbl3OYMpbg2kNjKS4OZjkIQlsLnDR5AyjojWYZeeNISQsHAJCIDCUbQWN/HNzEXcvmsRru8vZcryRi8Yn8/UFE6y2cEeANcPW4fz0vuPT++9mV3Dbv7bjRvjS3BE8dPVk/r3xKA+8tpu0uHDWfmc+Tofww5d28tr2fC4aE88dF43k/ld2MXxQGFNTYvjdmoO8eOd5bR3F245V8Lm/fMSvrp3CtTOSySmr41hZPReMjiMowMGBwhoW/+EDrpuZTGx4EF+YPZzUuPZHTu0rqObSP35IgEPY/dPFJ3XMn9DY4uJr/8wk0OlgxZdnnfHPlF/ZwCW/fo+M1FiKa5raJrzteHDRSdd6UP1XV5OEX/VJAPmA53q8yXaZ8if15dbolIoc64IsFUesW+UxqCkAV/NJ1Z04mGNikKgE3KFDiRmaxH+ym9lfHczksaN463Azpe5IjrdEUGXC+c6VGWw/Xs+buwr407XT+co/MhnVEM5xGvnT9dP51j8zSR09i1aX4T87j/P766fx16e3si2igt9etIC0yHz+/tpuHr7sIhjctUtdThoeiBvr23PGCOtD/urpSfzz4yN8a8GYtrWEbjk/ldLaJrYdq+TOf2+hrK6Zy6cM5aa5IwgKcDDDYzTTtJQY64pqm47x/ObcthnKN2Sk8MtrJvO3Dw8TGujkR5ePJybszJ22I+PDcTqECcOi2k0QACGBTv512xy68sUvKSaUNd+5iMSoEP6+Podfrz5AWly4Jgh1Gn9LEpuBdBFJw0oOy4Av+DakAcrtgsqjULwfSvZDyQEoPQDlh6HILRgeAAAbz0lEQVSx6uS64QkQm2pNaopOgshhEDUMopIgaijf/W8+72RVsPk7C61mGsC9LZ/lL+7grSUX8vrqg2zYU8iM4TGEtrj5w7tHKa1t4pbzRjA9xfrQPVRSx1VThzFn5CBEYEduFZuOlLEhu4yrpyfx/sESrpmZhMMhfH5GEksmDTl9SOYZxEcGMyw6hONVjcwcYR0zIjiAt789/6R6E4ZF8fcvz2Lv8Wqu+vN6jIGpyTHEhgdx5/yTVw8VEa6ensQf1mYRFODgx1dMILeinic3HOF4VQMfHyrji3OGd5ogAIIDnFw5ZShTU2I6rdvVWdkn5ntcPT2JX68+0KXBAWrg8askYYxpFZF7gNVYQ2BXGGP2+Dis/q++3FpL//h2KN4HJfugNOvkNv+oJGvs+uTrrOGKsakwKM0avRLc/izgbccqiGkO4u39u7hs8pC2BAGwdNow5qXHERcRzAWjB/PWnkKuy0ihvtnFz97Yy/ThMfzP5eMJDrCuK5Bf2cDV04cRGRJIekIE67NL2oaNPvDqbhpaXCyaYI2QEpGzShAnTB8RixyrJDm28+sYTBgWxb0L0/nTO9ln/OC+YVYKW45W8PWLR3H+qDhcbkNueQOfHC7juoxkvv3ZMV2O7w/Lpne57tlIignlV9dMOWninFIn+FWSADDGrAJW+TqOfqu+3FoE7URSKNhuNROdEJUMCeMgbb61QFr8OCs5hESd9DRut6GhxUVdcyuNtfXERwYTGvRpM0htUyvX//VjWlxW08elpyxRISLE2RPXlk5Poqyumc9NT6LF5aairpmbzxtBcID1fNNSYmhscXFhujWuf2pyDC9usZZomJQUxe78aiKDA5jbndVDgZ9eNZHaxtYufxO/+5LR3HRe6hmbaIZGh/Kv2+a03Xc6hOU3zcRtjNdnTHfH9bMG5lXXVOf8LkkoL3K7raUOcjfCsU+stfQrcj7dH5sGSTMh4zYYNs1aIK2da/f+8+MjlNYU8M0F6QQ4HWQX17Js+ceU1p7c9zAtJYY/f2E6ybFh7M6vosVluGD0YIyBC0bFdRhmVEgg9y60vlGHBDr53uKxJ+1/8KoJ1DS2tp2JTE2xkkRMWCA/v3oySx/dwCXjEro0ae1M4iKC2xJXV4jIObXhOxyCAx0uqvoGTRL9idsNRbusi6sc3WBdp7fB6iwlLA6Gz7VWyRw23U4InbdvG2P40zvZlNQ0sTO/ij/dOJ2H39pPY4ub+y8dR3hwAEEBDvIrGlixIYerH/2I526fw848qynokWXTO1zqoqsSIkNIiPz0/jS7eefiMfFMTYnhgcvHnzZ7WCnlHZok+jJjrDODw+9ZiSHnA2got/YNHg3jLofh50HKXOuSjOcw2Sm/soGSmiYuTI/jw6xSLn9kPcfK6/neojHccUpH7ZVTh7L0zxv4+/ocqhtbSYoJ7XaCaM+4IZEsnTaMm+3LXn71wpFeP4ZSyqJJoq9pbbLOEg6uti7gXnHEKo8cBmOWwMj5kHaRNbrIC050Dv9wyTgq6pu56+mtxEcG85V5aafVHZ0QyYLxiazeU0RooJOpKT3TERrgdPDHHurEVUqdTJNEX1BbDFlvW0nh0LvQXGtNIkubb12OceTF1plDN5dFeOqjI8SGB3HV1E8TzNZjFYQEOhg7JJJAp4M1355Pi8tNWFD7b53LJg9h5Q7r4vI3nTeiW/EopXxPk4S/qimCfSthz2vWmQPGOluYfJ11xpB2EQR1baLYqU5MtvIcxeNyG36xah9NrW7yKuq562JrqeptxyqZkhzT1mk8JPrMyzbPH5NAaKCThhYXU3RIpVJ9niYJf1JTCHtXwt7X4OhHgIG4sTD/B1b/wpAp3T5baHW5mffwu9x9yShuOi+1rfxoWR1NrW6GDwrjV28dYMSgcBZOSGDv8WpunZfa4fOdKjTIyWfGJfDfXQU6OUupfkCThK8118P+/8KOZ+Hwu9bqoPHjYP4PYeLVkDDeq4fLKa2jsLqRNfuKT0oSB4us5bL/sGwaP125hwde28W+ghE0u9xts5676vuLx7JoYiJRZ7jQj1Kqb9Ak4QtuNxz7yEoMe16H5hqIHg4XfhcmXWtNZush+wqtZLD1aAUut2lbk2h/YQ0iMH5IFL+9fiqXPbKeP7+bzYXpccw/y+GlqXHhHS5Up5TqWzRJ9KbaYtj2L9jylLUuUlAETLgapi6DERd4fXnmDdmlZKTGts1cBthfUG2F0tTKvoLqtiahg0U1jBgURmiQk9EJkfzrK7Mx0O1ZzEqpvk2TRE8zBo6sh8wVsO8/4G6B1AvhMw9Y/QxBPfONO7e8ni8+8Qn/74oJ3OYxXHV/YQ1xEUGU1jazKae8LUkcKKxhTOKnM9bmaHJQSqFJouc0VsP2Z6zkUHoAQqJh9tdg5q0Q3/VF3c7VsfJ6AD7KLj05SRRUM290HJuPVPBJThmjEyJIHRzOkbJ6Lj9lfSWllNIk4W1VefDJ41aTUlM1JGXA0r/ApM9DYOeri3pLfkUDAJ/klNPqchPgdFBV38LxqkbGDY1CRHh1Wz6r9xQRGRKAy20YMySyk2dVSg00miS85fh2+PjPsOdVq4lp4tVw3t3WAno+kF9pJYnaplY2HSnnhc25xIZb1y0YNySSKUnRFFU3snB8In96JwuAsYmaJJRSJ9Mk0R3GWMNWP/wdHPnQ6oiefQfMvRNihvs0tPzKBiKDA6hpauXe57ZTXPPppULHD40iMSqE80dbK7POS49j1a4CRsW3f10IpdTApUniXBgD2Wvh/Ychb7M1E/qzP4OZt1h9D34gv6KBMUMiqWtqZX9hDTfOTiE6NIjDJbUkRJ686N6YxMiTOq2VUuoETRJnwxhrYb33H4bjWyE6Ba74PUz7IgR4f7XTrnC5DW/sPE5hVSNTU2LahqzmVzYwNSWGUfHhNLe6+dHlE4g4h6u1KaUGNv3U6ApjrAX23nkICndal+y86k8wZRkEdH594p705IYcHvrvPgBiwwLZ+D8LCHQ4KKhq4LLJQ7l34Ri++Zl0HA69yI1S6ux1a/aWiPxaRPaLyE4ReVVEYuzyVBFpEJHt9u1xj8fMFJFdIpItIo+IvcqciAwSkTUikmX/PLu1IHpK7mb4x+XwzPXQVGONVPrGFphxs88TRHldM39cl8VFY+J54uYMKupbeGt3ISW1TbS4DEn2tZo1QSilzlV3p/iuASYZY6YAB4H7PfYdMsZMs293epQ/BnwNSLdvS+zy+4B1xph0YJ1933dKDsJzX4S/L4TSLLj8t3DPZpj+RXD6x5pEj6zLor7Zxf+7fDyfGZfA8EFhPP3JMfLs4a9JMWdesVUppTrTrSRhjHnbGNNq390IJJ+pvogMBaKMMRuNtV71P4Gr7d1Lgafs7ac8yntXdQGs/Cb8ZY51xbdLfgTf3Aazvuo3yQGgrqmVFzJz+dz0JNITI3E4hBtnD2dTTjnv7C8CICnm3JYSV0qpE7zZJ/EV4HmP+2kisg2oBh4wxnwIJAF5HnXy7DKARGNMgb1dCCR6MbbOtTbBxr/A+78GV7M1lPWi70F4XK+G0Zl39hcRGRLIkdI66ptdLJuV0rbvhlkp/PWDQ/zlvUMAbc1NSil1rjpNEiKyFhjSzq4fGWNet+v8CGgFnrb3FQDDjTFlIjITeE1EJnY1KGOMERFzhphuB24HGD7cC/MRDq6Gt+6D8sMw9nJY/BAM8r/rJtc1tXLPM9twG0NSTCgj48KZOeLTrptB4UH87vqpfOUfmUSHBupoJqVUt3X6KWKMWXim/SLyZeAKYIHdhIQxpglosre3iMghYAyQz8lNUsl2GUCRiAw1xhTYzVLFZ4hpObAcICMjo8Nk0qnSLHjrfsheA4PT4Usvw+gz/ro+9dbuQuqbXUQGB3CopI7vLx570tXlAD4zLpEfLBlLcXVTB8+ilFJd162vmiKyBPgBMN8YU+9RHg+UG2NcIjISq4P6sDGmXESqRWQu8AlwM/An+2ErgVuAX9o/X+9ObJ1a97+w4RHrWtGLfg6zb/f5aKX21De3tl1P+pVteQwfFMajX5jBn9/N4gaPpiZPJy49qpRS3dXd0U1/BiKBNacMdb0I2Cki24GXgDuNMeX2vruAJ4Bs4BDwpl3+S+CzIpIFLLTv9xxXM0y9Ab65Fc6/xy8TxD825JDx0FoOFtVwvLKBjw6V8fkZSUxOjuavN2UQF+GbCXxKqYGjW2cSxph2v7IaY14GXu5gXyYwqZ3yMmBBd+I5K5/9WbevF92Tapta+aM9xPVnb+wl0OnAKcI1M844gEwppbxq4PZs+nGCAHhyfQ4V9S1cOzOZl7ZYA8J+tnQiKYN0WKtSqvcM3CThxxqaXTyxPoeF4xP5v89PJre8nvFDo/jS3BG+Dk0pNcBokvBDb+w8TlVDC1+9MI1Ap4Pn7zjP1yEppQao7nZcqx7w9CfHGBUfzpy0Qb4ORSk1wOmZhB/ZlFPOzrxKtudW8v+umHDaHAillOptmiT8REFVA8uWf4zbQGRwANfMSOr8QUop1cM0SfiJV7bm4zbw8tfPZ0xiBJEh/rOYoFJq4NIk4QeMMbyYmcvckYNOWotJKaV8TTuufWTP8Sre3lMIwOYjFRwpq+e6me0vs6GUUr6iZxI+8N+dBXznhe00u9y88vXz+cPag0SGBHDp5PYW21VKKd/RJNHL3tpdwD3PbmXG8FjyKur58pObqWpo4Refm9y2kJ9SSvkLbW7qRTtyK7n3+e1MS4nh6a/O4f9dMYGqhhYuGD2YG2drU5NSyv/oV9de0upy890XdzA4PJi/3ZxBSKCTyycPJeBLwuy0wTonQinllzRJ9JJnN+eSXVzLX2+a2bbEt4iwZNJQH0emlFId0+amXlBc3cjv1xxkTtogFk3o3Ut3K6VUd2iS6GHHyuq59vGPaWpx8eCVE7VZSSnVp2iS6EGNLS5u/ccmqhtbeOZrc5kwLMrXISml1FnRPoke9Nu3D3CopI5/3zaHqSkxvg5HKaXOWrfOJETkJyKSb1/feruIXOax734RyRaRAyKy2KN8iV2WLSL3eZSnicgndvnzIuJ/F53uoofe2MvUn77N3z7M4YtzhjMvPc7XISml1DnxRnPT740x0+zbKgARmQAsAyYCS4C/iIhTRJzAo8ClwATgRrsuwMP2c40GKoDbvBBbr2tqdfH0J8dIiwvn/kvH8cDlEzp/kFJK+ame6pNYCjxnjGkyxuQA2cBs+5ZtjDlsjGkGngOWitWb+xngJfvxTwFX91BsPWpTTjkNLS6+uWA0d8wfRWiQ09chKaXUOfNGkrhHRHaKyAoRObGEaRKQ61Enzy7rqHwwUGmMaT2lvM9570AJQQEOzhupTUxKqb6v0yQhImtFZHc7t6XAY8AoYBpQAPy2h+M9EdPtIpIpIpklJSW9ccgue/dAMXNHDtYzCKVUv9Dp6CZjzMKuPJGI/A14w76bD3guRpRsl9FBeRkQIyIB9tmEZ/32YloOLAfIyMgwXYmvN2QX13K4pI6b5o7wdShKKeUV3R3d5LmmxOeA3fb2SmCZiASLSBqQDmwCNgPp9kimIKzO7ZXGGAO8C1xrP/4W4PXuxNbbckrr+PKTmwgLcrJooi75rZTqH7o7T+JXIjINMMAR4A4AY8weEXkB2Au0AncbY1wAInIPsBpwAiuMMXvs5/oh8JyIPARsA/7ezdh61Tee3UpdUyvPfm0uSTGhvg5HKaW8oltJwhhz0xn2/Rz4eTvlq4BV7ZQfxhr91OeU1jaxO7+aHy4Zp5PmlFL9is647oZ1+4pIig0lu7gWgLkjB/k4IqWU8i5NEueosKqRO/+9hTGJkUxLiSE8yMnkpGhfh6WUUl6lSeIcPbkhhxaXYc/xanJK65idNogAp66XqJTqX/RT7RxUN7bw9CfHWDwxkbiIIOqbXZw3crCvw1JKKa/TJHEOVu0soLaplXsuSefm81IBuGC0zrBWSvU/2tx0Dg4W1RIW5GRSUhRjh0QyfXgMk7Q/QinVD2mSOAc5pbWkDg5HRAgKEC5Mj/d1SEop1SO0uekc5JTWkRYf7uswlFKqx2mS6CJjDG63obnVTW5FAyPjNEkopfo/TRJd9K+NR5n9i7UcLKrB5TakDtYkoZTq/zRJdEGLy81j7x2itLaZZzYdA9DmJqXUgKBJogve2l1IQVUjAK9szQPQ5ial1ICgSaITxhieWJ9D6uAwFo5PpLHFTUxYIDFhQb4OTSmlepwmiU48/ckxduRWcsf8USwYnwBAmp5FKKUGCJ0ncQaHS2p56L97uTA9jhsyUiiuaQI0SSilBg5NEmfw53eyCXQ4+M11U3E4hCHRIXx/8VjmpOmS4EqpgUGTRAfqm1t5a08hS6cNIzEqpK387ktG+zAqpZTqXdon0YE1e4uob3Zx9bQkX4eilFI+o0miA69ty2dYdAizUrVpSSk1cHUrSYjI8yKy3b4dEZHtdnmqiDR47Hvc4zEzRWSXiGSLyCMiInb5IBFZIyJZ9s/Y7v1q525/YTUfZJWydHoSDof4KgyllPK5biUJY8wNxphpxphpwMvAKx67D53YZ4y506P8MeBrQLp9W2KX3wesM8akA+vs+72uscXFvc9tJzYsiK/OS/NFCEop5Te80txknw1cDzzbSb2hQJQxZqMxxgD/BK62dy8FnrK3n/Io7xUut+GPa7O48k/r2V9Yw6+vm8LgiODeDEEppfyOt/okLgSKjDFZHmVpIrJNRN4XkQvtsiQgz6NOnl0GkGiMKbC3C4HEjg4mIreLSKaIZJaUlHjlF3hnfzG/X3uQmLBAfn3tFC4Zm+CV51VKqb6s0yGwIrIWGNLOrh8ZY163t2/k5LOIAmC4MaZMRGYCr4nIxK4GZYwxImLOsH85sBwgIyOjw3pn49lNx0iIDOaZr80l0Kn9+UopBV1IEsaYhWfaLyIBwOeBmR6PaQKa7O0tInIIGAPkA8keD0+2ywCKRGSoMabAbpYqPptfpDvyKxt470Axd108WhOEUkp58MYn4kJgvzGmrRlJROJFxGlvj8TqoD5sNydVi8hcux/jZuDE2chK4BZ7+xaP8h7V2OLi92sOYoAbZqX0xiGVUqrP8MaM62Wc3mF9EfC/ItICuIE7jTHl9r67gH8AocCb9g3gl8ALInIbcBSrI7xHVdQ1c81jH3G4tI5bL0glZVBYTx9SKaX6lG4nCWPMl9spexlrSGx79TOBSe2UlwELuhvP2Xh5ax6HS+v4+y0ZLBjfYT+5UkoNWAO2Ad4Yw/Obc5k+PEYThFJKdWDAJoltuZVkFddyfYb2QyilVEcGbJJ4MTOX0EAnV0wZ6utQlFLKbw3YJDF8UDhfviCVyJBAX4eilFJ+a8BeT+LrF4/ydQhKKeX3BuyZhFJKqc5pklBKKdUhTRJKKaU6pElCKaVUhzRJKKWU6pAmCaWUUh3SJKGUUqpDmiSUUkp1SKxLTfddIlKCtbT4uYgDSr0Yjrf4a1zgv7FpXGdH4zp7/hrbucY1whgT31mlPp8kukNEMo0xGb6O41T+Ghf4b2wa19nRuM6ev8bW03Fpc5NSSqkOaZJQSinVoYGeJJb7OoAO+Gtc4L+xaVxnR+M6e/4aW4/GNaD7JJRSSp3ZQD+TUEopdQb9PkmIyHUiskdE3CKSccq++0UkW0QOiMjiDh6fJiKf2PWeF5GgHojxeRHZbt+OiMj2DuodEZFddr1Mb8fRwTF/IiL5HvFd1kG9JfbrmC0i9/VCXL8Wkf0islNEXhWRmA7q9cpr1tnvLyLB9t85234/pfZULB7HTBGRd0Vkr/0/8K126lwsIlUef98f93Rc9nHP+HcRyyP267VTRGb0UlxjPV6L7SJSLSL3nlKnV14zEVkhIsUistujbJCIrBGRLPtnbAePvcWukyUit3QrEGNMv74B44GxwHtAhkf5BGAHEAykAYcAZzuPfwFYZm8/Dny9h+P9LfDjDvYdAeJ6+fX7CfC9Tuo47ddvJBBkv64TejiuRUCAvf0w8LCvXrOu/P7AXcDj9vYy4Ple+NsNBWbY25HAwXbiuhh4ozffU135uwCXAW8CAswFPvFBjE6gEGs+Qa+/ZsBFwAxgt0fZr4D77O372nvfA4OAw/bPWHs79lzj6PdnEsaYfcaYA+3sWgo8Z4xpMsbkANnAbM8KIiLAZ4CX7KKngKt7Klb7eNcDz/bUMXrIbCDbGHPYGNMMPIf1+vYYY8zbxphW++5GILknj9eJrvz+S7HeP2C9nxbYf+8eY4wpMMZstbdrgH1AUk8e04uWAv80lo1AjIj09gXpFwCHjDHnOlm3W4wxHwDlpxR7vo86+jxaDKwxxpQbYyqANcCSc42j3yeJM0gCcj3u53H6P9BgoNLjw6i9Ot50IVBkjMnqYL8B3haRLSJyew/Gcap77FP+FR2c3nbltexJX8H61tme3njNuvL7t9Wx309VWO+vXmE3b00HPmln93kiskNE3hSRib0UUmd/F1+/p8A64+voC5svXjOARGNMgb1dCCS2U8err12/uMa1iKwFhrSz60fGmNd7O572dDHGGznzWcQ8Y0y+iCQAa0Rkv/1to8diAx4Dfob1T/0zrOawr3T3mN2N68RrJiI/AlqBpzt4mh55zfoSEYkAXgbuNcZUn7J7K1ZzSq3d3/QakN4LYfn138Xue7wKuL+d3b56zU5ijDEi0uPDU/tFkjDGLDyHh+UDKR73k+0yT2VYp7kB9re/9up4JUYRCQA+D8w8w3Pk2z+LReRVrGaObv9jdfX1E5G/AW+0s6srr6XX4xKRLwNXAAuM3RjbznP0yGt2iq78/ifq5Nl/62is91ePEpFArATxtDHmlVP3eyYNY8wqEfmLiMQZY3p0jaIu/F165D11Fi4Fthpjik7d4avXzFYkIkONMQV281txO3XysfpNTkjG6pM9JwO5uWklsMwedZKG9U1gk2cF+4PnXeBau+gWoKfOTBYC+40xee3tFJFwEYk8sY3Vcbu7vbredEo78Oc6OOZmIF2skWBBWKfpK3s4riXAD4CrjDH1HdTprdesK7//Sqz3D1jvp3c6SmzeYvd5/B3YZ4z5XQd1hpzoGxGR2VifCT2avLr4d1kJ3GyPcpoLVHk0s/SGDs/qffGaefB8H3X0ebQaWCQisXbz8CK77Nz0dA+9r29YH2x5QBNQBKz22PcjrFEpB4BLPcpXAcPs7ZFYySMbeBEI7qE4/wHceUrZMGCVRxw77NserCaX3nj9/gXsAnbab9Chp8Zm378Ma/TMod6Izf575ALb7dvjp8bVm69Ze78/8L9YSQwgxH7/ZNvvp5G98BrNw2om3OnxOl0G3HnivQbcY782O7AGAJzfC3G1+3c5JS4BHrVfz114jEzshfjCsT70oz3Kev01w0pSBUCL/Rl2G1Y/1jogC1gLDLLrZgBPeDz2K/Z7LRu4tTtx6IxrpZRSHRrIzU1KKaU6oUlCKaVUhzRJKKWU6pAmCaWUUh3SJKGUUqpDmiSUUkp1SJOEUkqpDmmSUEop1aH/D13FwAtLT3YMAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "5CU_bAitMgxs",
        "colab_type": "code",
        "outputId": "66320949-7819-40fc-c8a1-cfca6440ac5c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 816
        }
      },
      "cell_type": "code",
      "source": [
        "run_model_with_params_and_plot(1e-8, 100, 200)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:Using temporary folder as model directory: /var/folders/_n/7gq_v92s6xq549134xk8hyts_mxyxb/T/tmpzI5xf9\n",
            "INFO:tensorflow:Using config: {'_save_checkpoints_secs': 600, '_session_config': None, '_keep_checkpoint_max': 5, '_task_type': 'worker', '_global_id_in_cluster': 0, '_is_chief': True, '_cluster_spec': <tensorflow.python.training.server_lib.ClusterSpec object at 0x1156c67d0>, '_evaluation_master': '', '_save_checkpoints_steps': None, '_keep_checkpoint_every_n_hours': 10000, '_service': None, '_num_ps_replicas': 0, '_tf_random_seed': None, '_master': '', '_device_fn': None, '_num_worker_replicas': 1, '_task_id': 0, '_log_step_count_steps': 100, '_model_dir': '/var/folders/_n/7gq_v92s6xq549134xk8hyts_mxyxb/T/tmpzI5xf9', '_train_distribute': None, '_save_summary_steps': 100}\n",
            "INFO:tensorflow:Calling model_fn.\n",
            "<tf.Variable 'b:0' shape=(1,) dtype=float32_ref>\n",
            "<tf.Variable 'w:0' shape=(1, 3) dtype=float32_ref>\n",
            "Tensor(\"random_shuffle_queue_DequeueUpTo:1\", shape=(?, 3), dtype=float32, device=/device:CPU:0)\n",
            "Tensor(\"random_shuffle_queue_DequeueUpTo:2\", shape=(?,), dtype=float32, device=/device:CPU:0)\n",
            "P Tensor(\"add:0\", shape=(?,), dtype=float32)\n",
            "E Tensor(\"sub:0\", shape=(?,), dtype=float32)\n",
            "INFO:tensorflow:Done calling model_fn.\n",
            "INFO:tensorflow:Create CheckpointSaverHook.\n",
            "INFO:tensorflow:Graph was finalized.\n",
            "INFO:tensorflow:Running local_init_op.\n",
            "INFO:tensorflow:Done running local_init_op.\n",
            "INFO:tensorflow:Saving checkpoints for 0 into /var/folders/_n/7gq_v92s6xq549134xk8hyts_mxyxb/T/tmpzI5xf9/model.ckpt.\n",
            "INFO:tensorflow:loss = 13109204.0, step = 1\n",
            "INFO:tensorflow:global_step/sec: 544.742\n",
            "INFO:tensorflow:loss = 7364271.5, step = 101 (0.185 sec)\n",
            "INFO:tensorflow:Saving checkpoints for 201 into /var/folders/_n/7gq_v92s6xq549134xk8hyts_mxyxb/T/tmpzI5xf9/model.ckpt.\n",
            "INFO:tensorflow:Loss for final step: 3919769.2.\n",
            "INFO:tensorflow:Calling model_fn.\n",
            "<tf.Variable 'b:0' shape=(1,) dtype=float32_ref>\n",
            "<tf.Variable 'w:0' shape=(1, 3) dtype=float32_ref>\n",
            "Tensor(\"fifo_queue_DequeueUpTo:1\", shape=(?, 3), dtype=float32, device=/device:CPU:0)\n",
            "None\n",
            "P Tensor(\"add:0\", shape=(?,), dtype=float32)\n",
            "INFO:tensorflow:Done calling model_fn.\n",
            "INFO:tensorflow:Graph was finalized.\n",
            "INFO:tensorflow:Restoring parameters from /var/folders/_n/7gq_v92s6xq549134xk8hyts_mxyxb/T/tmpzI5xf9/model.ckpt-201\n",
            "INFO:tensorflow:Running local_init_op.\n",
            "INFO:tensorflow:Done running local_init_op.\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "yq6XKtuGMgxu",
        "colab_type": "code",
        "outputId": "3b1bf562-c7d3-433a-a6f8-800f4a8f8c4b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 884
        }
      },
      "cell_type": "code",
      "source": [
        "run_model_with_params_and_plot(1e-8, 100, 400)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:Using temporary folder as model directory: /var/folders/_n/7gq_v92s6xq549134xk8hyts_mxyxb/T/tmp48rFQ3\n",
            "INFO:tensorflow:Using config: {'_save_checkpoints_secs': 600, '_session_config': None, '_keep_checkpoint_max': 5, '_task_type': 'worker', '_global_id_in_cluster': 0, '_is_chief': True, '_cluster_spec': <tensorflow.python.training.server_lib.ClusterSpec object at 0x115a2ce90>, '_evaluation_master': '', '_save_checkpoints_steps': None, '_keep_checkpoint_every_n_hours': 10000, '_service': None, '_num_ps_replicas': 0, '_tf_random_seed': None, '_master': '', '_device_fn': None, '_num_worker_replicas': 1, '_task_id': 0, '_log_step_count_steps': 100, '_model_dir': '/var/folders/_n/7gq_v92s6xq549134xk8hyts_mxyxb/T/tmp48rFQ3', '_train_distribute': None, '_save_summary_steps': 100}\n",
            "INFO:tensorflow:Calling model_fn.\n",
            "<tf.Variable 'b:0' shape=(1,) dtype=float32_ref>\n",
            "<tf.Variable 'w:0' shape=(1, 3) dtype=float32_ref>\n",
            "Tensor(\"random_shuffle_queue_DequeueUpTo:1\", shape=(?, 3), dtype=float32, device=/device:CPU:0)\n",
            "Tensor(\"random_shuffle_queue_DequeueUpTo:2\", shape=(?,), dtype=float32, device=/device:CPU:0)\n",
            "P Tensor(\"add:0\", shape=(?,), dtype=float32)\n",
            "E Tensor(\"sub:0\", shape=(?,), dtype=float32)\n",
            "INFO:tensorflow:Done calling model_fn.\n",
            "INFO:tensorflow:Create CheckpointSaverHook.\n",
            "INFO:tensorflow:Graph was finalized.\n",
            "INFO:tensorflow:Running local_init_op.\n",
            "INFO:tensorflow:Done running local_init_op.\n",
            "INFO:tensorflow:Saving checkpoints for 0 into /var/folders/_n/7gq_v92s6xq549134xk8hyts_mxyxb/T/tmp48rFQ3/model.ckpt.\n",
            "INFO:tensorflow:loss = 13147750.0, step = 1\n",
            "INFO:tensorflow:global_step/sec: 707.204\n",
            "INFO:tensorflow:loss = 6884557.0, step = 101 (0.144 sec)\n",
            "INFO:tensorflow:global_step/sec: 499.314\n",
            "INFO:tensorflow:loss = 4926316.5, step = 201 (0.198 sec)\n",
            "INFO:tensorflow:global_step/sec: 858.642\n",
            "INFO:tensorflow:loss = 2468414.0, step = 301 (0.118 sec)\n",
            "INFO:tensorflow:Saving checkpoints for 401 into /var/folders/_n/7gq_v92s6xq549134xk8hyts_mxyxb/T/tmp48rFQ3/model.ckpt.\n",
            "INFO:tensorflow:Loss for final step: 1627857.9.\n",
            "INFO:tensorflow:Calling model_fn.\n",
            "<tf.Variable 'b:0' shape=(1,) dtype=float32_ref>\n",
            "<tf.Variable 'w:0' shape=(1, 3) dtype=float32_ref>\n",
            "Tensor(\"fifo_queue_DequeueUpTo:1\", shape=(?, 3), dtype=float32, device=/device:CPU:0)\n",
            "None\n",
            "P Tensor(\"add:0\", shape=(?,), dtype=float32)\n",
            "INFO:tensorflow:Done calling model_fn.\n",
            "INFO:tensorflow:Graph was finalized.\n",
            "INFO:tensorflow:Restoring parameters from /var/folders/_n/7gq_v92s6xq549134xk8hyts_mxyxb/T/tmp48rFQ3/model.ckpt-401\n",
            "INFO:tensorflow:Running local_init_op.\n",
            "INFO:tensorflow:Done running local_init_op.\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "pRuFLCg-Mgxx",
        "colab_type": "code",
        "outputId": "ae7b0bf7-b8a5-48c2-cd37-d35560f4cfbc",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1021
        }
      },
      "cell_type": "code",
      "source": [
        "run_model_with_params_and_plot(1e-8, 100, 800)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:Using temporary folder as model directory: /var/folders/_n/7gq_v92s6xq549134xk8hyts_mxyxb/T/tmp_YZERu\n",
            "INFO:tensorflow:Using config: {'_save_checkpoints_secs': 600, '_session_config': None, '_keep_checkpoint_max': 5, '_task_type': 'worker', '_global_id_in_cluster': 0, '_is_chief': True, '_cluster_spec': <tensorflow.python.training.server_lib.ClusterSpec object at 0x114f87510>, '_evaluation_master': '', '_save_checkpoints_steps': None, '_keep_checkpoint_every_n_hours': 10000, '_service': None, '_num_ps_replicas': 0, '_tf_random_seed': None, '_master': '', '_device_fn': None, '_num_worker_replicas': 1, '_task_id': 0, '_log_step_count_steps': 100, '_model_dir': '/var/folders/_n/7gq_v92s6xq549134xk8hyts_mxyxb/T/tmp_YZERu', '_train_distribute': None, '_save_summary_steps': 100}\n",
            "INFO:tensorflow:Calling model_fn.\n",
            "<tf.Variable 'b:0' shape=(1,) dtype=float32_ref>\n",
            "<tf.Variable 'w:0' shape=(1, 3) dtype=float32_ref>\n",
            "Tensor(\"random_shuffle_queue_DequeueUpTo:1\", shape=(?, 3), dtype=float32, device=/device:CPU:0)\n",
            "Tensor(\"random_shuffle_queue_DequeueUpTo:2\", shape=(?,), dtype=float32, device=/device:CPU:0)\n",
            "P Tensor(\"add:0\", shape=(?,), dtype=float32)\n",
            "E Tensor(\"sub:0\", shape=(?,), dtype=float32)\n",
            "INFO:tensorflow:Done calling model_fn.\n",
            "INFO:tensorflow:Create CheckpointSaverHook.\n",
            "INFO:tensorflow:Graph was finalized.\n",
            "INFO:tensorflow:Running local_init_op.\n",
            "INFO:tensorflow:Done running local_init_op.\n",
            "INFO:tensorflow:Saving checkpoints for 0 into /var/folders/_n/7gq_v92s6xq549134xk8hyts_mxyxb/T/tmp_YZERu/model.ckpt.\n",
            "INFO:tensorflow:loss = 14153971.0, step = 1\n",
            "INFO:tensorflow:global_step/sec: 501.766\n",
            "INFO:tensorflow:loss = 9269026.0, step = 101 (0.201 sec)\n",
            "INFO:tensorflow:global_step/sec: 946.244\n",
            "INFO:tensorflow:loss = 4793717.0, step = 201 (0.106 sec)\n",
            "INFO:tensorflow:global_step/sec: 764.31\n",
            "INFO:tensorflow:loss = 2830751.2, step = 301 (0.131 sec)\n",
            "INFO:tensorflow:global_step/sec: 450.067\n",
            "INFO:tensorflow:loss = 1603347.9, step = 401 (0.225 sec)\n",
            "INFO:tensorflow:global_step/sec: 528.966\n",
            "INFO:tensorflow:loss = 1048698.6, step = 501 (0.185 sec)\n",
            "INFO:tensorflow:global_step/sec: 723.071\n",
            "INFO:tensorflow:loss = 627295.7, step = 601 (0.139 sec)\n",
            "INFO:tensorflow:global_step/sec: 615.801\n",
            "INFO:tensorflow:loss = 381997.84, step = 701 (0.162 sec)\n",
            "INFO:tensorflow:Saving checkpoints for 801 into /var/folders/_n/7gq_v92s6xq549134xk8hyts_mxyxb/T/tmp_YZERu/model.ckpt.\n",
            "INFO:tensorflow:Loss for final step: 336505.12.\n",
            "INFO:tensorflow:Calling model_fn.\n",
            "<tf.Variable 'b:0' shape=(1,) dtype=float32_ref>\n",
            "<tf.Variable 'w:0' shape=(1, 3) dtype=float32_ref>\n",
            "Tensor(\"fifo_queue_DequeueUpTo:1\", shape=(?, 3), dtype=float32, device=/device:CPU:0)\n",
            "None\n",
            "P Tensor(\"add:0\", shape=(?,), dtype=float32)\n",
            "INFO:tensorflow:Done calling model_fn.\n",
            "INFO:tensorflow:Graph was finalized.\n",
            "INFO:tensorflow:Restoring parameters from /var/folders/_n/7gq_v92s6xq549134xk8hyts_mxyxb/T/tmp_YZERu/model.ckpt-801\n",
            "INFO:tensorflow:Running local_init_op.\n",
            "INFO:tensorflow:Done running local_init_op.\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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C++Fw7Zvg4VnFXhsPPZNQSjVJOUXleAh8syMNuwEfLw82Hsqlb9tQBrYL5/N7Ljr9LOLQz/DZVGjR3TGSydvPPcG7kCYJpVSTY4xh6tvrSMsrJSzQh24tg+gfE8qn64/Qv63jGodBp140d3gdfHQdhETDLfPBr4pmqEZIm5uUUk3OtuQ8dh3NJ7+0goSMQib1acUNg2IQgaEdm5++QfJG+OhXENwKpn11wd1drjb0TEIp1eTMWX8Yf29PPr/nIj5ed4gpg2OIaObLDw9fRttw/5Mrp2yCD6+BwAhHggiqYmrwRkzPJJRSjdr8zclsTPrfbWkKyypZtO0ok/q0olebEP5+bR8imvkCENM84OSO6qNbHQnCP9SRIIJbuzp8t9MkoZRqtMor7Tw6fwd/XLATx21s4IO1SRSX25gyOKbmjQ+thfevBN9gmLYYQtvWXL+R0iShlGq0dqTkUVZpZ29aATtT8knPL+U/3ycwtkcUA9vVcLe4hOWOM4hmLeD2byCsneuCbmC0T0Ip1WhtsJqZfLw8eH9tknVltTlxf+oq7V4IX0yHFt3gli+hWaRrgm2gNEkopRqt9Yk5dIwMpG90KF9sSgbg8Yndadc8sOoNtnwEi34D0YPgprmNasrv81Xr5iYRaSsiK0Vkt4jsEpHfWuVPiUiKiGy1HhOdtnlURBJEZJ+IXO5UPt4qSxCRxjsZilKq3tntho1JOQxuH86MER0Z3CGcj+8cwl0jqpjK2xj48QVYeD90HAlTv9QEYamLM4lK4CFjzGYRCQI2icgya91LxpgXnCuLSA9gCtATaA0sF5Eu1upXgbFAMrBBRBYZY3bXQYxKqSZmX3oB+aWVDO4QTvdWwcy9+6KqK9oqYPGDjrOI3tfD5P+Al69rg23Aan0mYYxJNcZstpYLgD1Amxo2mQzMMcaUGWMSgQRgsPVIMMYcNMaUA3OsukopVSO73XDPh5v4envqibIf4jOBKq6cdlZyzHGR3JaP4NJH4NrZmiBOUaejm0SkPdAfWGcVzRSR7SLyjogcH0rQBjjitFmyVVZduVJK1WhDUg7f7krjkXnbSTlWQqXNzodrDzGkQzhtwwOq3ij3ELxzuWM+pqtfg8sea9SzuZ6vOksSItIMmAc8aIzJB14DOgH9gFTgxTp8rRkislFENmZmZtbVbpVSF6hF247i7+2JMYbff7aVeZuTSTlWwu2XdKh6g8Qf4c3LoCAVps5vtDcMqgt1MrpJRLxxJIiPjTHzAYwx6U7r3wQWW09TAOerUqKtMmooP4kxZjYwGyAuLs7UwVtQSl2gKmx2luxIZWyPKEZ0ieT/vtjGusQcosP8Gdsj6uTKxsDPr8DyJ6F5rGMm14hY9wR+gah1khDHNexvA3uMMf90Km9ljDneQHgNsNNaXgR8IiL/xNFxHQusBwSIFZEOOJLDFEDTu1KqRj8lZJFbXMGVfVsztkcU3VsF8a/l+5ncrzWeHk7NR2WFsGgm7PrScbOgq/8LvkHuC/wCURdnEpcAU4EdIrLVKnsMuFFE+gEGSALuBjDG7BKRucBuHCOj7jfG2ABEZCawFPAE3jHG7KqD+JRSjUBGQSk5ReV0axl8oqyorJK/fb2HiGY+jOgSAUDP1iG8eWvcyRtnxsPcWyFrH4z5M1zyW+1/OEu1ThLGmJ9wnAWcakkN2/wV+GsV5Utq2k4p1XQ9Nn8nK/amc9/ITvx2dBcMhj98sY0DmYV8OH0Ivl5V3CHOGNj8PnwzC3wCHPeB6HSZ64O/gOkV10qpBqe0wsZrqw5w14iONPP1oqzSxpqELFoE+fLqygN8uTkFfx9PDmQW8djEblzSOeL0nRTnwFcPwJ6vHBfIXf26434Q6pxoklBKNTjLdqfz7xX7aR3qxw2DYtiUlEtJhY2Xb+yPr5cH//k+gZzict69bRCXdaviBkCJq2H+DCjKhLF/gYtmgofOZ3o+NEkopRqcdYnZAKw9kM0Ng2L4cX8WXh7CRZ2a08zXixFdqpl0r6wAlv8ZNrwJ4Z3gzmXQur8LI298NEkopRqc9YmO2Vt/OZiDMYYf4zMZ2C6MZr41/GQlrICvfgt5yTDkHhj1BPg2c1HEjZeefymlGpSconLi0wuJCQ8gLb+UNQnZ7E7Nr/7soTgHFtwHH10LXn5wx1KY8A9NEHVEk4RSqkE5fhYxc1RnAO79eBP+3p5c1feUW4fabbDxHXhlIGybA8Mfgnt+gpghrg65UdMkoZRqUNYlZuPr5cHkfq1pEeRLQWklj03sdvIcTIfXOabVWPw7aNEd7v4BRv8JvP3cF3gjpX0SSim3Scgo4G9L9nLfyE7EtQ+ntMLG0p1pDGofjq+XJzcMasvBrCJuHmLdPvTYEfj+Gdg+B4Jaw6/ehl6/0gvj6pEmCaWU27y7Jonv92awal8GvxkVS5CfF0fzSnn+130BeGhcV0fFwkxY/SJsfBsQGPZ7R/OS9jvUO00SSim3OD4x35juUQT7e/HvFfsRgWGdI/53cVxpPqx9Fdb+ByqKod/NMHIWhES7N/gmRJOEUsotftrvmJhvyqC2jO7egi5RQbzxwwFmTegGRVmw7nVYPxtK86DHZLjsjxDZ5cw7VnVKk4RSyi0WbTtKiL83I7pEIiLcc2kn7u7rjaz9G2x6HypLofskR7OSXhDnNpoklFJ15oO1Sby5+iCD2zfnt6NjiWkeQHmlHQ8BL8//DaYsKK3g251pXN2/NT6eAklrYMObyJ6vHBX63OCYqTWyq3veiDpBk4RSqs4s2JJCYWkl3+5MZdOhHB6+vCuPfLEdP29PBrQLI7OgjJuHxFBhM0hFEfcFrYbX7oGMXeAXAoPvhqH3QmjbM7+YcglNEko1MHklFTTz9Tr5hjkN2GurDrAnNZ9//KoPO1LymD6sI2O6t+CmN9cx85MtdGsZRKcWzdhzNJ+KykoWL/yMG33XsNFvLQFrSqBlb7jyZej9a8d03qpB0SShVANSXmln5PMruWtER+4b2dnd4ZxRaYWN1384QF5JBRd1ak6FzTC4Qxhx7cP595R+LN2Vxp+v6klIQQLs/Brb1jl4eiZTUOFPcpvL6TL+Pmg7WK9zaMD0imulnBSUVnDZC6v45WC2W15/X1oBucUVLN+dfubKDcDSXWnklVQA8PzSfYjAwHbhYAwTwo7yr+YLCHlrKLx2Efz0TzyjurEx7nmuC3yPFre86ZhCQxNEg6ZnEuqC9MWmZEZ0iaBFUN1OwxCfXkhiVhE/7c9iaMfmdbrvs7EjJQ+Abcl5FJRWEOTn7bLXNsYQn17IkZxi+rQNOe3YVtUBPXfjEaLD/GkV4sf+pMPcFX6AkO+WQML3UHAUPLygwwi4eCZ0vQKCoogDlk5y2dtStaRJQl1wknOLefjzbTwwqjO/H1e3o1+O5BQDcCCzsE73e7aOJwmb3bA+MYfR3aNqtb/ySjt/WbybaRe3p3OL6q9OttkNs+Zt5/NNyQBc3jOKN6b+7z7RZZU2fv36WkrKbXxy11Aig3xZvjke74PLealjFh0KNhDuuwuPYgN7Qhx3gus6EbpcDv5htXoPyr00SagLzt7UAsfftII63/f5JAljDHM3HmH+5hRevXkAEc18a6x/rLic/JJKYpqf3km7MyWPuHZh7EjJ46eErFoniY1JOXz4yyH2puUz9+6LEKemHWMMu47m8/OBLH5KyObH+EzuHtGRlGMlrNiTQWmFjS2Hj5FXUsG2pAwqU7Yx0OswP/37NeI89jGqLJExPgZz1Atby/58UjqF/qN+Rc+4y8BTf1oaiwb3SYrIeODfgCfwljHmWTeHpBqYfen1lyQOW0kiMauISpv9pKaVU5VX2lmTkMVnG47w7a40AN5bk8TDl9d8dvPAnK1sSMxhwf2X0LVl0En725dWwO3D2uPn7ckP8ZkkZBTQKbLZiR/3XUfz+M/3CbxgzW303s9JTOrTinbNAwHYk5pPblE5F1vTWqw5kAXAhqRcvt6RyqQ+jum2K212fv3GWrYcPgZAeKAPsyZ0457hHfhl+06yd25nz8LtpOz4ma4mkcskmUd8KwEorvRjt2c34lvdwfDRV+HTbhBePoHccg7HWV04GlSSEBFP4FVgLJAMbBCRRcaY3e6NTDUke1LzAccPemFZZc13KztHx5NEhc1wJLeEDhGB5BSVk5hV6OiQtby6MoHZPx4kr6SCYD8vfjemC7uO5vHB2iTuHdmJQF8v0vJK8RBoEfy/tv2UYyWs3p+JMXD3hxtZOHMYIf6Ofof49ALKbXZ6t3H0B/xl8W7G/PPHE81qpRU2fjtnKwkZhVwf15aMglKeX7qPl5bFM2tCN6YP68B9H28mMauI6cM68OiEbvyUkE2/tqGUV9p59pu9TOjZEs/yPDZs2UFI8mre6uXNxc2LCChIgt0H4KcDDK0oZqgPsBNiCKI8shdbfUfQZ9Bw/NsOICC8I3F6v+gmo0ElCWAwkGCMOQggInOAyYAmCXXC3rQCAnw8KS63EZ9ewICYumvzTs4toX3zAJKyi0nIKKRDRCB//moXX207ypLfDqdby2CKyyt54bt9DGofzt0jOjI8NhIfLw+2HM7lu93pvLQsnktiI5j58WZ6tglh7t0XsWx3Osm5xeSXVGIM/OuGfjz8+TZ+99lW3ro1Dg8PYXuyoz+id5sQJvYKYEiHcB7/cgff7U7n9+O68s9l8SRkFCJi2J6YSs6xY3TzP0a/lj58/e03dCltTZecbVwfaSdv7SLWH/Hk5rQUBkTaCfcoIrfoKPztGNhKuAi4yAdIAA54Qlh7aN7Z0cncvBP/3mb4JMGX/j268/qtcbSqsyOsLjRijHF3DCeIyHXAeGPMndbzqcAQY8zM6raJi4szGzdudFWIys1KK2z0fHIpV/VtzZdbUvjbNb25ql9rnv92L9uS8/j0rqEkZhXxr+XxtAnz5+Yh7egcGchrS7cS7VfClV38oSQXygqhosQxs2hlKVQUYysr5t1Vu+nb0peEtGP0jw6ifbgv3+08ioexEdXMi7iYYPKKStl6KJverQNp7u8JxgAGjJ0DGfkcKy7HA2M97PRsHcyBjHwqK20IhkAfD9qG+pFXUkZWQRnhAV6E+XuSVVBGeWUlrYJ9EWt/RWXlFJdVEBbgTUFxOQGelXjbS/HgzP9uy4w3uTQjKKwF/sERfH/Ehgluw0X9+/DY8mwG9u7FtPGXQLMo8Dx5FNW3O9OY+clmFtx/Cb3ahNTTp6ncSUQ2GWPizlSvoZ1JnBURmQHMAIiJiXFzNOp82ezmnK8qTsgoxGY3jO7egh93H+Hgns08uXI+Jj+ViyWX5I8/pDTzMPcWZRBCIeGbijBSxL3G5tjBqur37YFwo6cPXvkBtPcCr2xvynK96I2NZgG+5BTZKUxvhs0uhEo5zTy8we4N4uF4eHjSsVUE+aU2ckoq8ffxZndqAWkmhKQKL5r5eZNXaqNvizAI9SdYPDhwOI+dx8q4rG0LfsrOol1EM1rHhDuuHRAPSosr+W5nOhH4k24rY2Kf9uzMKGdTajn5Nh9G9mrHZb3a8e6GDL5LKOTSvrHcc3kcmfZARr+8nnKbYdvMcXh4ebLhmz28tTqRYQcj+KEyk9+MHAEhQVUei/G9WrLpj2MJCXDdEFzVMDW0JJECOE/aEm2VncQYMxuYDY4zCdeEpurSD/GZ3P7uelqF+DOkQziDOoTzzc40Qv29efyK7mTklxHo60nH5v6QcxAy90LOQQL2becT790M/i6XSZIKSdYOfRx/Cg4FYreH4RvWGr/gWL5KLKfMO4RcE0CmLYCBXTtyw4g+FOHHB5syiWkRwbCeMYQ0C2L1wXxufXcDn00bykvL4zmSU0JZpY3u7YJ5e9ogJv7jewZFhhHs5823hWlsmTH2tAvBBAixHvmlFUz/83e0LQjgcEUx394/nM6BvkQG+Z6o2yK3mOufX0XrFH8Olxcz/+qLwan5LMxueCF+Gbm5FXSNCmLqtcPJ3ZrCK59tA+CKoRdBh3DGRhez7IvtTBzTB0IDiAReuWkgaXkl+Hp5AjBlUAxv/niQDUk5/H5sF2Kjqk4Qx2mCUNDwksQGIFZEOuBIDlOAm9wbkqotYwx//XoPw2IjGNm1BQCfbzxCsL83A9qFsWxPOvPadZ9qAAAbfElEQVS3pNAxGMqKE3hl5/t0l8P09jqM3SsZj8qSE/tq4RXKMY9IPDpeyvKMZnx12IerhscxenA/PtxVxhNLEvHyENZMH0WLIF++eWsdPx/I5ndjumByi3li21EumdSPeZtSeGl9KpBG5/WFLHlgOIdzHa8T0zyAvtGh/HIwhw4RgTw0ris+Xh6M6R7Foq0ptA0PoGfr4JOGk1Yl2M+bHq2C2XU0n4hmvnSNCjptm+iwAK7q25r5W1KICQ+gf9vQk9Z7eAgXd47g6+2p/GpgG0TkRB+Mt6fQJzrkxH4+uWvoSdte2iXypOcdIgJZ/JvhtA71IzTA5yw/PdXUNagkYYypFJGZwFIcQ2DfMcbscnNYTd78zcnYDVw38PzuBrZ0Vxpv/ZTIwm1H+eEPI/EQ4fu9Gdzay4dZPRKpDF5LReJa/LJ3I16OZqEyryB2VLZlocdYLp84loA2vcjzb8tF/9rE2B5RDLi2P7HZRYxIymXUgDYgwuTBFfxjxRHG9ogiyhpR9MzVvXj7p0TuGNae/NJKFm9P5dH5O9iRksfobi2Y3L8ND3y6hQ/WJpGeX4qPpwdRQX48NK4rdwzrcGI/AGN7tODT9YfZm1bAjBEdz+q9D2ofzq6j+Qzr3LzapHL3pZ34cmsKV/dvU2WdK3q34sf4TK7u3waAmPAAmgf6ENM8AD9vz3P5KOjROvic6ivVoJIEgDFmCbDE3XGo//nPygRScksYHhtx0o/m2ai02Xlu6T4ig3wpLchl5YJ36VW2mcV8T8fdabAbvLz88WozELo/CG0GQsve+Ia0pXh/Fg+/t4G/LfPh0QktOJCZTXG57cTEd+2aB564PgAc/3Nf8sBwwpv973/JHSOb8ddregMQ5OfNw5d35S+LHYPl7h/VmQExYczblMwL3+2jwmboEx2Ch4fg4yGnvdeLO0Xg7+1JSYWNnmf5YzukQzjv/ZzEsNjIaut0bRnEkgeG0yEisMr1E3u3YnzPlnhY/Tciwt+v7a1nA8olGlySUA1Lpc3O4exiKu2GV77fzzNX9z6n7ZeuXsOYnDnc23IfwRVb8dxtpwQ/Nnp0p93YmXi2vxha9jltdA3AiC6RLLjvEh79cju/n+togx/bI+qkC9BOVdVVzM5uu7g9y3enE+zvdaLZ5olJ3Zn69nrGdI/iwTGx1W7r5+3J8NgIvtudftZJYnT3KJ68sgeT+tQ8iLR7q5r353FKB/+4ni3P6vWVqq0GNQT2fOgQ2PqVmFXEZS+sokWQLzlF5az6w0haBPnx1k8HuXlwO0ICvNl1NI/ichudI5sRFuAN6Tth15fY9yzGI2sfAKZlHwpjRvHW0Xa8cyiSXw/uxJ+u7HFWMdjthlXxGSzensp9IzvXOAfR2e4PTv/hPRtrErJ47+ckXr9l4AVzvwelqtKoh8Cq+rM/vYBnvt7Dyzf2J8Tfm4PWHEZPTOrBbz7dwoItKbSPCOS5b/dhjON/9le8/BOtyeLGgHXcF74Zz6w9IJ6khgzgzYpbufL6uxjYtw9BwO+A39js5/QD6+EhjOoWxahutZvHyHl/5+uSzhFcYk15oVRToElCnWTF3gx+iM9k8faj3DykHQcziwAYHhtBXLswFm9PpW24o0ln6bbDRCd/zRyfOQz12A12OFLSm7ZXvMiRVuO44q099O8UxsC+fU56jZrmQ1JKNSyaJNRJDmQ4zhwWbElxJImsIsIDfQgN8OGKPq3481e7KU4/wFP+q5iU+z0Rx/LJ8GkFwx/nuaN9mL3DztP2Xrz12UE8PISnJ/d08ztSStWGJokm7khOMeGBPgRak+QlWM1LG5JySc4t5mCmY/4ijGFy+GHaer/AKI8tCMJ39gF8ZBvDDddMZVLfaG4vKGNB0k889uUOvD2Fj6YPOWn0kVLqwqNJogkrrbAx8eXV3BDXlj9O6oExhoSMQkZ2jWTVvkwWbj1KYmYB97WKh7efJDx5PYO9Q/jU+3puuu9PvP3pEfak5vNmD8fIncggX378v8s4nFOMv48nrUL83fwOlVK1pUmiCfvlYDYFpZVsPpwLQGZBGQWllYzsEondVknSyvf4lC/odCQVQtvBxBcwXa9nvKcf0syXZ38VSk5R+UkXdHl5etAxsnajj5RSDYcmiSYkv7SC5JySE1fdrtybAcCe1AJsdsdZBBiGlP7MzSX/xttjH3vsMWwd8k/6jZsGnl44zwfaMbIZHau/Rkwp1QjoMJMm5MWl+7j2tTWUV9oxxrBibwY+Xh6UVNhIzCqkeM9SvvJ5nO6r78MbO/tHvMLD4f+h9bCb9XaUSjVR+i+/CVkVn0lphZ2DWYV4iJCcW8K0i9qx+pe1NJt3E2PSfyRZIjGT/4v0uYFYTy++HuXuqJVS7qRJook4klPMoWzHrTn3pRWQnl9KMEU8zAf80ect7Jm+fBxyJ/O9JjGv/2VujlYp1VBokmgiVu/POrG8LzWfiAPz+cH/DYK25LPMbyyve93Mnlw/ruzT3I1RKqUaGk0STcTq/Zm0CvEj1ieLidvup1fpJpL8exI29VVWrvNi07rDRDTz4oEaJrhTSjU92nHdSBWVVTL17XWsT8yhrNLGuoQMHgtdwZuFD9C+ZDd/rLid9Zd9Cq37c1HH5vh7e/LG1IG0CdVrG5RS/6NnEo3UD/GZrN6fRVJ2ETe0L+Ed+x/pl36QxPDh3HT0elJpzvcdHE1LV/ZtzbieUSduc6mUUsdpkmikVuzJwM9bGJH3FdN3fYTd2x+ueZcEuZjUDzcR0cznpJvcaIJQSlVFk8QFzhjDrqP5J91z2WY3bNu7ny+C36JX0Vo2eg2g+70fQvNoulojnOLahZ/xHs1KKaVJ4gK3JiGbW95ex8s39mdir5bM25xMTM7PfGr7A+ElpTD+HwwcfBfi4ThTiA7z55LOzbm6f2s3R66UuhBokrjArdibDsD7PyeRX1RC9pJn+LXnl8SbaHynLSa4XV+czxc8PISP7xzqnmCVUhecWo1uEpHnRWSviGwXkS9FJNQqby8iJSKy1Xq87rTNQBHZISIJIvKyWG0eIhIuIstEZL/1N6x2b61p+GFfJj6eHhw4dJj2S2/nt17z2dZ8AkuGfkRwu77uDk8pdYGr7RDYZUAvY0wfIB541GndAWNMP+txj1P5a8BdQKz1GG+VzwJWGGNigRXWc1WDw9nFHMwq4um4Mpb4Ps4gdnH4kmfp/5tP+P3Efu4OTynVCNQqSRhjvjPGVFpPfwGia6ovIq2AYGPML8YYA3wAXG2tngy8by2/71SuqvHD/kyu8VjNDTvuIsjfm896v0XM2HtBO6SVUnWkLvsk7gA+c3reQUS2APnAH40xq4E2QLJTnWSrDCDKGJNqLacBdXPX+0bK2G0Erfk7L/l8iokZTtD1H3BrQLi7w1JKNTJnTBIishxoWcWqx40xC606jwOVwMfWulQgxhiTLSIDgQUictY3OzbGGBExNcQ0A5gBEBMTc7a7bTwqSjj63m1cXfAte1tdTbdb3gQvH3dHpZRqhM6YJIwxY2paLyK3AZOA0VYTEsaYMqDMWt4kIgeALkAKJzdJRVtlAOki0soYk2o1S2XUENNsYDZAXFxctcmkMdmRnMfTi3cRF1HJ1KRHaVW4i/cC72DqnS+Cp86uopSqH7Ud3TQe+D/gKmNMsVN5pIh4WssdcXRQH7Sak/JFZKg1qulWYKG12SJgmrU8zam8ybPbDX9csIPC1P3cvOMOwgriecrvEYZN+wuemiCUUvWotn0S/wF8gWXWSNZfrJFMI4CnRaQCsAP3GGNyrG3uA94D/IFvrAfAs8BcEZkOHAKur2VsjcaCrSmUp2zny+AX8PUw2G5cwtMxg9wdllKqCahVkjDGdK6mfB4wr5p1G4FeVZRnA6NrE09jVFph47tvvuRzv7/j6xuKTF2AV2QXd4ellGoitK2igVvzzcf8q/zPeARFIXcsBU0QSikX0iTRgJVv+4JLNz9Iind7/Gd8B6Ft3R2SUqqJ0STRUO2cj9eCGWyyx5J57edIs0h3R6SUaoI0STREO+fDvDtJ9O/Jw96PM6R7e3dHpJRqojRJNDAlW7/AzLsT2g7mPvMoPdu30fs+KKXcRpNEA2J2fonPgrvYaOvExmGz2ZdriGuvk+EqpdxH7yfRUMQvxcy7k832ztxW/ggtvzoIwKD2Oh+TUsp99EyiITi0FjP3Vvaatrwc9Tcu7tGeA5lF+Ht70qN1sLujU0o1YXom4W5pO+GTG8jziWJq4f/x9lWDyCupYNnudPrHhOKt024opdxIk4Q75SRi++AaxCeA6eWP0bF9DP3ahmK3G0Z0iWRSn1bujlAp1cRpknCXgnQq359MQVExt9ifYldFEK9f2QFw3If6gzsGuzlApZTSJOEe5UXwya+xF2RwR8WjtOs5gJYVdsb2qOq2HUop5T6aJFzNbqPyi+l4pu1glucsQmIv4r83D3R3VEopVSXtFXWxvEWP4hX/DS9738H8wl7cOLgJ3llPKXXB0DMJV9rwNiFb3+AD23jmeU0iJhxGdWvh7qiUUqpamiRcJWE5ZskfWGXvz56+s1h5bT8q7XYd4qqUatA0SbhC5j7sc2/jqE97ZubNZOGIznh6CJ4enu6OTCmlaqRJop6t3HaA2EXX4lcp3FD+ILeM6EnnFkHuDksppc6KJol6ZLPZ8FhwNy3NUT7v+Sofj5pM+4hAd4ellFJnTZNEPTqy8GkuNRvY1ecxbrz2JneHo5RS56xWvaYi8pSIpIjIVusx0WndoyKSICL7RORyp/LxVlmCiMxyKu8gIuus8s9ExKc2sbld/HfEbP83XzGcTpMecnc0Sil1XupiaM1Lxph+1mMJgIj0AKYAPYHxwH9FxFNEPIFXgQlAD+BGqy7AP6x9dQZygel1EJt7ZB/AzJvOXtOODb3+hJ+PnrAppS5M9TX+cjIwxxhTZoxJBBKAwdYjwRhz0BhTDswBJovj1mujgC+s7d8Hrq6n2OqF3W4wxkBFCXw2lQrjwYzy3zG+f0d3h6aUUuetLpLETBHZLiLviMjx26i1AY441Um2yqorbw4cM8ZUnlJeJRGZISIbRWRjZmZmHbyF2imvtDP51TU8On8HfPsoZOzi85gnyPKKYmA7vbOcUurCdcYkISLLRWRnFY/JwGtAJ6AfkAq8WM/xAmCMmW2MiTPGxEVGRrriJWv05uqD7EjJo3TrF7DpXbj4AT7M6sLAdmH4eum1EEqpC9cZG8uNMWPOZkci8iaw2HqaArR1Wh1tlVFNeTYQKiJe1tmEc/0G7XB2Mf9esZ9RLYp5Om82WaF98Rw6i73fr+LhcV3cHZ5SStVKbUc3Od8V5xpgp7W8CJgiIr4i0gGIBdYDG4BYaySTD47O7UXGGAOsBK6ztp8GLKxNbK6ycGsKprKc1/1fQcSDp7x/x7rDBQAM7djczdEppVTt1HbYzXMi0g8wQBJwN4AxZpeIzAV2A5XA/cYYG4CIzASWAp7AO8aYXda+HgHmiMgzwBbg7VrG5hJrDmTxXMg8fNK3sbz7cyze4sP+ZfH4e3vSJzrU3eEppVSt1CpJGGOm1rDur8BfqyhfAiypovwgjtFPF4ySchtBh1dyjddCGHQXF192OxMqdrBibwYju0Ti46WT9ymlLmw6gL8WtuxN4G+er1MY0pVm454h1NuH124ZSEm5DU8PcXd4SilVa5okzpcxhK98hBAKsV03G7z9Tqzy99ERTUqpxkHbQ87X9s/olruSz4On4d+2n7ujUUqpeqFnEufj2BHM1w+z0d6F7D53uzsapZSqN3omca7sdlhwLzabjYcq7uWSLnr7UaVU46VJ4lytex2SVvNVq5lke7fWYa5KqUZNk8S5yD6AWfFniL2cl3MvZmjH5nqPaqVUo6a/cGfLbid/7r0UVnpwV+5UErOLubhzhLujUkqpeqVJ4izt+foVgtPX8V+f2/k5wxuASzrrtBtKqcZNRzedBduxZNpuepYtnr2573dPcVNJJduT8+jWMtjdoSmlVL3SJHEmxpA5535CTCXHRr9IkL8PQf4+tA0PcHdkSilV77S56QzMznm0TFvF+343c+nQC2pqKaWUqjU9k6hJUTb2Jf/HDntH/Ifdj4fOx6SUamL0TKImy55ASvN4pGIGAzvqRXNKqaZHk0R1Dv0MWz9mTYsbOezVgW4tg9wdkVJKuZw2N1XFVgFfPwQhbXnFdg19ogPw0ovmlFJNkP7yVWXdG5Cxm/Kxf2NLahn9Y8LcHZFSSrmFJolTFGcdxr7ybxB7OTuaXUKFzdA/RudnUko1TdrcdIr9HzxA1/Jy/lJ2C/u/jQfQJKGUarJqdSYhIp+JyFbrkSQiW63y9iJS4rTudadtBorIDhFJEJGXRUSs8nARWSYi+62/Lm/jqYxfTt/8lXzq+2sWHfIl5VgJt13cnhZBfmfeWCmlGqFanUkYY244viwiLwJ5TqsPGGOqumXba8BdwDpgCTAe+AaYBawwxjwrIrOs54/UJr5zUllGxVcPccQeRZsrZrG9TwxW/lJKqSarTvokrLOB64FPz1CvFRBsjPnFGGOAD4CrrdWTgfet5fedyl1j3Rv4FyTxV3M7w7q30QShlFLUXcf1cCDdGLPfqayDiGwRkR9EZLhV1gZIdqqTbJUBRBljUq3lNCCqjmI7s8JMzI/Ps8ZjIHQeS4CPdtUopRScRXOTiCwHWlax6nFjzEJr+UZOPotIBWKMMdkiMhBYICI9zzYoY4wREVNDTDOAGQAxMTFnu9vqrXwGU17Mn0qncH/vqt6qUko1TWdMEsaYMTWtFxEv4FpgoNM2ZUCZtbxJRA4AXYAUINpp82irDCBdRFoZY1KtZqmMGmKaDcwGiIuLqzaZnJW0HZjNH/CZTMCvVXeu6tu6VrtTSqnGpC6am8YAe40xJ5qRRCRSRDyt5Y5ALHDQak7KF5GhVj/GrcDxs5FFwDRreZpTef0xBr59lBKPZjxXMpm/X9tbr6xWSiknddH4PoXTO6xHAE+LSAVgB+4xxuRY6+4D3gP8cYxq+sYqfxaYKyLTgUM4OsLr174lkLSaf1RMY/JFvegTrddDKKWUs1onCWPMbVWUzQPmVVN/I9CrivJsYHRt4zlrlWWYpY9z2COGZQFXsHRcF5e9tFJKXSiabNuKfd0bSG4iT5TeyJ8m9yHIz9vdISmlVIPTJMd6GmPYseEHsm39uGjcDYzv1crdISmlVIPUJJOEiLAx7gXy8vL4/chO7g5HKaUarCaZJACmD+vg7hCUUqrBa7J9Ekoppc5Mk4RSSqlqaZJQSilVLU0SSimlqqVJQimlVLU0SSillKqWJgmllFLV0iShlFKqWuK4i+iFS0Qyccwaez4igKw6DKeuNNS4oOHGpnGdG43r3DXU2M43rnbGmMgzVbrgk0RtiMhGY0ycu+M4VUONCxpubBrXudG4zl1Dja2+49LmJqWUUtXSJKGUUqpaTT1JzHZ3ANVoqHFBw41N4zo3Gte5a6ix1WtcTbpPQimlVM2a+pmEUkqpGjT6JCEivxaRXSJiF5G4U9Y9KiIJIrJPRC6vZvsOIrLOqveZiPjUQ4yfichW65EkIlurqZckIjusehvrOo5qXvMpEUlxim9iNfXGW8cxQURmuSCu50Vkr4hsF5EvRSS0mnouOWZnev8i4mt9zgnW96l9fcXi9JptRWSliOy2/g38too6I0Ukz+nz/VN9x2W9bo2fizi8bB2v7SIywEVxdXU6FltFJF9EHjyljkuOmYi8IyIZIrLTqSxcRJaJyH7rb1g1206z6uwXkWm1CsQY06gfQHegK7AKiHMq7wFsA3yBDsABwLOK7ecCU6zl14F76zneF4E/VbMuCYhw8fF7Cnj4DHU8rePXEfCxjmuPeo5rHOBlLf8D+Ie7jtnZvH/gPuB1a3kK8JkLPrtWwABrOQiIryKukcBiV36nzuZzASYC3wACDAXWuSFGTyANx/UELj9mwAhgALDTqew5YJa1PKuq7z0QDhy0/oZZy2HnG0ejP5MwxuwxxuyrYtVkYI4xpswYkwgkAIOdK4iIAKOAL6yi94Gr6ytW6/WuBz6tr9eoJ4OBBGPMQWNMOTAHx/GtN8aY74wxldbTX4Do+ny9Mzib9z8Zx/cHHN+n0dbnXW+MManGmM3WcgGwB2hTn69ZhyYDHxiHX4BQEXH1zehHAweMMed7sW6tGGN+BHJOKXb+HlX3e3Q5sMwYk2OMyQWWAePPN45GnyRq0AY44vQ8mdP/ATUHjjn9GFVVpy4NB9KNMfurWW+A70Rkk4jMqMc4TjXTOuV/p5rT27M5lvXpDhz/66yKK47Z2bz/E3Ws71Meju+XS1jNW/2BdVWsvkhEtonINyLS00Uhnelzcfd3ChxnfNX9h80dxwwgyhiTai2nAVFV1KnTY9co7nEtIsuBllWsetwYs9DV8VTlLGO8kZrPIoYZY1JEpAWwTET2Wv/bqLfYgNeAv+D4R/0XHM1hd9T2NWsb1/FjJiKPA5XAx9Xspl6O2YVERJoB84AHjTH5p6zejKM5pdDqb1oAxLogrAb9uVh9j1cBj1ax2l3H7CTGGCMi9T48tVEkCWPMmPPYLAVo6/Q82ipzlo3jNNfL+t9fVXXqJEYR8QKuBQbWsI8U62+GiHyJo5mj1v+wzvb4icibwOIqVp3NsazzuETkNmASMNpYjbFV7KNejtkpzub9H6+TbH3WITi+X/VKRLxxJIiPjTHzT13vnDSMMUtE5L8iEmGMqdc5is7ic6mX79Q5mABsNsakn7rCXcfMki4irYwxqVbzW0YVdVJw9JscF42jT/a8NOXmpkXAFGvUSQcc/xNY71zB+uFZCVxnFU0D6uvMZAyw1xiTXNVKEQkUkaDjyzg6bndWVbcundIOfE01r7kBiBXHSDAfHKfpi+o5rvHA/wFXGWOKq6njqmN2Nu9/EY7vDzi+T99Xl9jqitXn8Tawxxjzz2rqtDzeNyIig3H8JtRr8jrLz2URcKs1ymkokOfUzOIK1Z7Vu+OYOXH+HlX3e7QUGCciYVbz8Dir7PzUdw+9ux84ftiSgTIgHVjqtO5xHKNS9gETnMqXAK2t5Y44kkcC8DngW09xvgfcc0pZa2CJUxzbrMcuHE0urjh+HwI7gO3WF7TVqbFZzyfiGD1zwBWxWZ/HEWCr9Xj91Lhcecyqev/A0ziSGICf9f1JsL5PHV1wjIbhaCbc7nScJgL3HP+uATOtY7MNxwCAi10QV5WfyylxCfCqdTx34DQy0QXxBeL40Q9xKnP5McORpFKBCus3bDqOfqwVwH5gORBu1Y0D3nLa9g7ru5YA3F6bOPSKa6WUUtVqys1NSimlzkCThFJKqWppklBKKVUtTRJKKaWqpUlCKaVUtTRJKKWUqpYmCaWUUtXSJKGUUqpa/w/4Bq+J5x1D1QAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "vz-IEZD1Mgx0",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}