Neural_net_mnist.ipynb

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      "display_name": "Python 3",
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    "colab": {
      "name": "Neural_net_mnist.ipynb",
      "provenance": [],
      "include_colab_link": true
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/phew-phew/Pattern_Recognition/blob/main/Neural_net_mnist.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jwRo_-KZxWtM"
      },
      "source": [
        "To Build a neural network with 1 hidden layer of 30 sigmoid nodes, and an output layer 10 softmax nodes from 1000 training images (100 images per digit).\n",
        "\n",
        "Training:  30 complete epochs, using mini-batches of 10 training examples at a time, learning rate η=0.1. \n",
        "\n",
        "Plots: Training error, testing error, criterion function on training data set, criterion function on testing data set of a separate 1000 testing images (100 images per digit), and the learning speed of the hidden layer (the average absolute changes of weights divided by the values of the weights).  "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FBMur0DlxWtM"
      },
      "source": [
        "import tensorflow as tf\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "from sklearn import metrics\n",
        "import pandas as pd\n",
        "%matplotlib inline"
      ],
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BvK7kzpRxWtN"
      },
      "source": [
        "mnist = tf.keras.datasets.mnist"
      ],
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Lc4SZb_yxWtN",
        "outputId": "26a55a54-8c0b-4b9d-bff6-26e2ba2ff584"
      },
      "source": [
        "(x_train, y_train), (x_test, y_test) = mnist.load_data()\n",
        "x_train = x_train / 255.0\n",
        "x_test =  x_test / 255.0\n",
        "print(x_train.shape,'xtrain shape')\n",
        "print(x_test.shape,'xtest shape')"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(60000, 28, 28) xtrain shape\n",
            "(10000, 28, 28) xtest shape\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ryL-BQh3xWtO"
      },
      "source": [
        "x_val = x_train[-10000:]\n",
        "y_val = y_train[-10000:]\n",
        "\n",
        "x_train = x_train[:x_train.shape[0]-10000]\n",
        "y_train = y_train[:y_train.shape[0]-10000]\n",
        "\n",
        "data = {}\n",
        "\n",
        "for i in range(10):\n",
        "    data[i] = list()\n",
        "\n",
        "for i in range(len(x_train)):\n",
        "    data[y_train[i]].append(x_train[i])"
      ],
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GaLgILW-xWtO"
      },
      "source": [
        "train_x = list()\n",
        "train_y = list()\n",
        "\n",
        "for i in range(len(data)):\n",
        "    for j in range(100):\n",
        "        train_x.append(data[i][j])\n",
        "        train_y.append(i)\n",
        "\n",
        "train_x = np.asarray(train_x)\n",
        "train_y = np.asarray(train_y)"
      ],
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aUoC7yzSxWtO"
      },
      "source": [
        "train_x=train_x.reshape(1000,784)\n",
        "x_val = x_val.reshape(10000,784)\n",
        "x_test = x_test.reshape(10000,784)"
      ],
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "60vc-gSPxWtQ"
      },
      "source": [
        "#For single Hidden layer"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JOSfX6EcxWtQ",
        "outputId": "90b3dbac-2afe-43a3-eb4c-bf414a5df6c0"
      },
      "source": [
        "from keras.models import Sequential\n",
        "import time\n",
        "from keras.layers import Dense, Activation\n",
        "model = Sequential()\n",
        "start_time = time.time()\n",
        "model.add(Dense(30, activation= \"sigmoid\"))\n",
        "model.add(Dense(10, activation='softmax'))\n",
        "weights=[]\n",
        "save_weights = tf.keras.callbacks.LambdaCallback(on_epoch_end=lambda batch, logs: weights.append(model.layers[0].get_weights()[0]))\n",
        "model.compile(optimizer=tf.keras.optimizers.Adam(learning_rate=0.1),\n",
        "              loss='sparse_categorical_crossentropy',\n",
        "              metrics=['accuracy'])\n",
        "m_h = model.fit(train_x, train_y,validation_data = (x_val,y_val), epochs=30, batch_size=10, callbacks = [save_weights])\n",
        "final_w = [model.layers[0].get_weights()[0]]\n",
        "weights = [weights]\n",
        "total_time = (time.time()-start_time)\n",
        "print(\"--- %s seconds ---\" % total_time)"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/30\n",
            "100/100 [==============================] - 2s 13ms/step - loss: 1.8836 - accuracy: 0.3636 - val_loss: 0.9342 - val_accuracy: 0.7101\n",
            "Epoch 2/30\n",
            "100/100 [==============================] - 1s 8ms/step - loss: 1.0309 - accuracy: 0.6730 - val_loss: 1.1493 - val_accuracy: 0.6275\n",
            "Epoch 3/30\n",
            "100/100 [==============================] - 1s 8ms/step - loss: 0.8347 - accuracy: 0.7159 - val_loss: 1.0075 - val_accuracy: 0.6881\n",
            "Epoch 4/30\n",
            "100/100 [==============================] - 1s 8ms/step - loss: 0.8093 - accuracy: 0.7351 - val_loss: 0.9118 - val_accuracy: 0.7054\n",
            "Epoch 5/30\n",
            "100/100 [==============================] - 1s 8ms/step - loss: 0.7595 - accuracy: 0.7451 - val_loss: 0.9616 - val_accuracy: 0.7238\n",
            "Epoch 6/30\n",
            "100/100 [==============================] - 1s 8ms/step - loss: 0.6335 - accuracy: 0.7927 - val_loss: 0.8666 - val_accuracy: 0.7407\n",
            "Epoch 7/30\n",
            "100/100 [==============================] - 1s 8ms/step - loss: 0.6283 - accuracy: 0.8126 - val_loss: 0.8180 - val_accuracy: 0.7519\n",
            "Epoch 8/30\n",
            "100/100 [==============================] - 1s 8ms/step - loss: 0.7701 - accuracy: 0.7532 - val_loss: 1.0078 - val_accuracy: 0.6978\n",
            "Epoch 9/30\n",
            "100/100 [==============================] - 1s 8ms/step - loss: 0.7233 - accuracy: 0.7940 - val_loss: 0.8751 - val_accuracy: 0.7563\n",
            "Epoch 10/30\n",
            "100/100 [==============================] - 1s 8ms/step - loss: 0.6555 - accuracy: 0.8084 - val_loss: 0.7781 - val_accuracy: 0.7730\n",
            "Epoch 11/30\n",
            "100/100 [==============================] - 1s 8ms/step - loss: 0.6184 - accuracy: 0.8005 - val_loss: 0.8939 - val_accuracy: 0.7452\n",
            "Epoch 12/30\n",
            "100/100 [==============================] - 1s 8ms/step - loss: 0.6279 - accuracy: 0.8051 - val_loss: 0.7699 - val_accuracy: 0.7879\n",
            "Epoch 13/30\n",
            "100/100 [==============================] - 1s 8ms/step - loss: 0.5295 - accuracy: 0.8277 - val_loss: 0.8855 - val_accuracy: 0.7269\n",
            "Epoch 14/30\n",
            "100/100 [==============================] - 1s 8ms/step - loss: 0.6976 - accuracy: 0.7897 - val_loss: 0.7984 - val_accuracy: 0.7740\n",
            "Epoch 15/30\n",
            "100/100 [==============================] - 1s 8ms/step - loss: 0.6118 - accuracy: 0.8285 - val_loss: 0.7904 - val_accuracy: 0.7908\n",
            "Epoch 16/30\n",
            "100/100 [==============================] - 1s 8ms/step - loss: 0.5974 - accuracy: 0.8244 - val_loss: 0.8326 - val_accuracy: 0.7579\n",
            "Epoch 17/30\n",
            "100/100 [==============================] - 1s 8ms/step - loss: 0.4655 - accuracy: 0.8470 - val_loss: 0.8168 - val_accuracy: 0.7795\n",
            "Epoch 18/30\n",
            "100/100 [==============================] - 1s 8ms/step - loss: 0.4583 - accuracy: 0.8484 - val_loss: 0.7413 - val_accuracy: 0.8046\n",
            "Epoch 19/30\n",
            "100/100 [==============================] - 1s 8ms/step - loss: 0.3535 - accuracy: 0.8883 - val_loss: 0.8913 - val_accuracy: 0.7623\n",
            "Epoch 20/30\n",
            "100/100 [==============================] - 1s 8ms/step - loss: 0.4857 - accuracy: 0.8440 - val_loss: 0.9495 - val_accuracy: 0.7542\n",
            "Epoch 21/30\n",
            "100/100 [==============================] - 1s 8ms/step - loss: 0.5373 - accuracy: 0.8382 - val_loss: 0.8529 - val_accuracy: 0.7681\n",
            "Epoch 22/30\n",
            "100/100 [==============================] - 1s 8ms/step - loss: 0.4511 - accuracy: 0.8655 - val_loss: 0.7447 - val_accuracy: 0.8129\n",
            "Epoch 23/30\n",
            "100/100 [==============================] - 1s 9ms/step - loss: 0.4551 - accuracy: 0.8739 - val_loss: 0.8550 - val_accuracy: 0.7851\n",
            "Epoch 24/30\n",
            "100/100 [==============================] - 1s 8ms/step - loss: 0.3724 - accuracy: 0.8931 - val_loss: 0.7772 - val_accuracy: 0.7955\n",
            "Epoch 25/30\n",
            "100/100 [==============================] - 1s 8ms/step - loss: 0.3955 - accuracy: 0.8930 - val_loss: 0.8229 - val_accuracy: 0.8026\n",
            "Epoch 26/30\n",
            "100/100 [==============================] - 1s 8ms/step - loss: 0.4610 - accuracy: 0.8510 - val_loss: 0.8251 - val_accuracy: 0.7966\n",
            "Epoch 27/30\n",
            "100/100 [==============================] - 1s 8ms/step - loss: 0.3640 - accuracy: 0.8794 - val_loss: 0.7900 - val_accuracy: 0.8020\n",
            "Epoch 28/30\n",
            "100/100 [==============================] - 1s 8ms/step - loss: 0.4996 - accuracy: 0.8495 - val_loss: 0.9082 - val_accuracy: 0.7659\n",
            "Epoch 29/30\n",
            "100/100 [==============================] - 1s 8ms/step - loss: 0.4307 - accuracy: 0.8748 - val_loss: 0.8873 - val_accuracy: 0.7543\n",
            "Epoch 30/30\n",
            "100/100 [==============================] - 1s 8ms/step - loss: 0.4716 - accuracy: 0.8465 - val_loss: 0.7886 - val_accuracy: 0.8029\n",
            "--- 25.17742156982422 seconds ---\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FpqvO6T4xWtQ",
        "outputId": "0cd044ce-0a45-421b-d3c8-b6515ef70ff9"
      },
      "source": [
        "predict_train = model.predict_classes(train_x)\n",
        "acc_train = np.where(train_y == predict_train)[0].shape[0]/train_x.shape[0]\n",
        "print('Training accuracy for 1 layer = {}'.format(acc_train))\n",
        "predict_test = model.predict_classes(x_test)\n",
        "acc_test = np.where(y_test == predict_test)[0].shape[0]/x_test.shape[0]\n",
        "print('Testing accuracy for 1 layer = {}'.format(acc_test))\n",
        "print('Total run time = {} seconds'.format(total_time))"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/sequential.py:450: UserWarning: `model.predict_classes()` is deprecated and will be removed after 2021-01-01. Please use instead:* `np.argmax(model.predict(x), axis=-1)`,   if your model does multi-class classification   (e.g. if it uses a `softmax` last-layer activation).* `(model.predict(x) > 0.5).astype(\"int32\")`,   if your model does binary classification   (e.g. if it uses a `sigmoid` last-layer activation).\n",
            "  warnings.warn('`model.predict_classes()` is deprecated and '\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Training accuracy for 1 layer = 0.875\n",
            "Testing accuracy for 1 layer = 0.7911\n",
            "Total run time = 25.17742156982422 seconds\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "id": "f34Xi0I7xWtQ",
        "outputId": "1a948a9f-d83c-49bb-fd49-35d4659e84d7"
      },
      "source": [
        "df = pd.DataFrame()\n",
        "df['training_loss'] = m_h.history['loss']\n",
        "#df['accuracy'] = model_history.history['accuracy']\n",
        "df['validation_loss'] = m_h.history['val_loss']\n",
        "df.plot(grid=True)"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fee38b77780>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 18
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 284
        },
        "id": "q27EcVhqxWtR",
        "outputId": "80d1867c-222f-4383-f49d-32d5ae14f390"
      },
      "source": [
        "df = pd.DataFrame()\n",
        "df['accuracy'] = m_h.history['accuracy']\n",
        "df.plot(grid=True)"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fee30c69128>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 19
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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lFRe0iGTx/1zJ/94+mKFJbRyX6M+6dVgCsTERvP7V/mrLlZS5eeLDLSS2bcG0sX0ap3G14E+yTwAO+Xx2ebf5ehq4S0RceK7qf+izr6e3e+ffIvKN+jTWmFDw56W7WZJxjCe+1Z/RvTrUun7vTrHcnpzImysPkHm6MKBt23gwm7teXU1cy0jmT72s1j9EzVGr6AjuGJHEJ+lHOJZbVGW5V1fsY/fxfH5504BaPzDWGKSmfigRuR0Yr6r3ez/fDVyqqtN8yjziPdbvRWQU8CowEIgEWqvqKRFJBj4EBqhqboVzTAWmAsTHxyf7Tg9cW/n5+V9bJKS5c1o84LyYAhnPzuxyfrO6iMu6hjN1UHSdr5ZPFbp5bHkho7pE8P1Bte/KqSym3afL+f26IlpHCtNHxtC+RfOZWqu+39HxAjePLSvkxl6R3Nrn/FFFJwvdPL6ikIHtw/nR8MYZDVgxprFjx65X1RFVVqhuLgXvD8EoYLHP5xnAjApltgJJPp/3Ap0qOVYqMKK681U2N0595oto7pwWj6rzYgpUPKfPlOjo33yhVz7/peYVldb7eM/8a6v2nL5Qdx6t/fwrFWNatz9LB8z8VK98/kvNzC6od9saWyC+o++/vkaHz/qs0jl17v/7Wu33xCfqasS/TW3nxvHnp3kt0EdEeopIFDAJSKlQ5iBwNYCI9AdigBMi0tF7gxcRuRDo4/0hMMb4UFUe/zCdY7lF/HHSsIAMm3xobG9aRkXwu8921Os46/Zncc+rq+kYG80/p46ia5sW9W5bczRldE9OnSlhYdrXZ8P8POMYSzKO8eNr+pDQhP82NSZ7VS0DpgGLgW14Rt1sFZFZInKTt9hPgQdEJA2YB0zx/tJcCWwWkU3AAuBBVc1qiECMac7eXefi481HeOTavn7N3eKPdq2imHrlhSzeeoyNB2teGrMya/dnce/cNcRfEMO8By6jc5xzHlisrct7t6d3p9a8/tV/h2EWlpTzVMpW+nRqXe28/E2BX51uqrpIVfuqai9Vfda7baaqpnjfZ6jq5ao6RFWHqupn3u3vqeoA77bhqvqvhgvFmOZpz4l8nkrZyuhe7Xnwyl4BPfb3r+hJ+1ZR/O+n22s9P9Lqvac8iT4uhvlTQzvRg2cY5pTRPUjPzGGD98fzT1/uIvN0Ib+6eSCR4U37HkbTbp0xDldcVs6P528kJjKMF+4Yem4my0BpFR3BD6/qzaq9WSzfddLvetuzypnymmc64vlTLwvKeqxN0a3DPcMwX/vPfnYfz+Pl5Xu5bXgil17YPthNq1GzmC7BGKf6/Wc72ZKZy5y7kxvsynnypd14ZcU+vvf6WjrFRtPxghjiY6PpdEE08bExdLogmk4+/+48lscL64vo3r41bz9wGR1jG+bBrOaoZVQEky5JYu5/9nMwq4CWURHMuKFfsJvlF0v2xgTJsp0nmLPM86RrQy46Eh0Rztwpl/D+hkyO5xVxIq+YA6cKWLM/i9MFpZXWSWgtzJt6Wa1nigwF94zqwSsr9rHZlcOztwxsNn8jS/bGBMHJ/GIeeSeNPp1a84sbGn6u877xsUy//vwr0KLSck7kFXM8r5gTeUUcyy2moKSchJKDzSaJNbakdi2ZOKQrR3KKmFzNildNjSV7YxqZqvLogs3kFpXy5vdHBvVpy5jIcJLatTzvSdjU1ENV1DAAf5g0rNktqGQ3aI1pZH//aj9fbj/O49f3o3+XC2quYJqk5pTowZK9MfVyKr+Y3dme5fD8se1ILr/+ZDtX9evEvaN7NGzjjPFh3TjG1NGSjGP87N00cgpLeXbNYnp1bM2ghDgGdL3A829C3NeehC0sKedH8zYS1yKS3/o5P70xgWLJ3jRre0/k85fUPfTrHMttwxMbZem7kjI3//vpdl5dsY8BXS/gzr5hRHXsxpbMHL7ac5IPNmYCIAI9O7RiUEIcA7vGsfVwDruO5/Pm90c22DzzxlTFkr1ptj7alMnj76dT6lYWrHfz/Kc7GD+wM5NHduOyCxtmlaRDWQVMe3sDaa4cpozuwYwb+rFyxXLGjPnvgtTH84rYkpnDlsxc0jNzWLMvi482eZZ4mHrlhXyjT8eAt8uYmliyN81OYUk5T6ds5Z/rDnFJj7a8OHkYOYWlzF9ziPc3uEhJO0zPDq2YdEkStyUnBmwI4SfpR3j0vc0A/O2u4Ywf2KXScp1iY7iqXwxX9Ys/t+1kfjH7Tp5heLfqVzoypqFYsjfNys5jeUx7ewO7juczbWxv/ueaPkSEh9ElrgVP3zSA6df3Y1H6EeatOchvPtnO7z7bwbiL45k8shuX9+pQp+kIikrL+fWibbyx8gBDktrw58nDar1oR4fW0TZu3QSVJXvTLKgq7653MfOjLbSOjuCN742stDskJjKcW4cncuvwRHYfz2PemkO8t8HFovSjJLVrwY2DuzI4sQ2DEuPoGhdTY1fPvpNnePitDWQcyeWBb/Tk59f1IyrCBrGZ5seSvWny8ovLePLDLXywMZPRvdrzh+8M9Wtirt6dYnlywsX8/LqLWLz1KPPXHOKlZXspd3tmf2zXKurcyJlBCXEMTIgjsW2Lcz8AZ+8JREaE8co9I7jm4vjqTmdMk2bJ3jRpGYdzmfb2BvafOsNPrunLtKt6E17LrpiYyHAmDk1g4tAECkvK2XY013sDNYf0zFzmLNtLmfcHoE3LSAZ2jaNFVDhLMo4xorvnnkCoLthhnMOSfRP2yvK9vLu6kGEjS4lrGRns5jQqVeWt1QeZtTCDNi0ieev+yxjVq/7TyLaICmd4t7Zfu1FaVFrOjqN5pHt/ALYcziHNVcBDY3rxyLi+RDTxecqN8Ycl+yaq3K28vHwvx3LdTH1zHW98fyTREU1vxfqGUO5WZry/mXfWufhm3468cMeQBh2XHhMZzpCkNgwJ0ApRxjRFfl2yiMh4EdkhIrtFZHol+7uJyFIR2Sgim0XkBp99M7z1dojIdYFsvJOt2ZfFsdxiLu0czup9Wfz83c243bVbaag5KncrP1+QxjvrXPzwqt68NuUSewDJmACoMdl7FwyfDVwPXAxMFpGKc7I+gWdt2mF4FiT/i7fuxd7PA4DxwF/OLkBuqpeSdpiWUeF8b1A0j46/iJS0w/y2ngtHNwRV5Q+f7+TBN9dzMr+4Xscqdys/ezeN9zdk8tNxffnptRcFfOUmY0KVP904I4HdqroXQETmAxOBDJ8yCpydvi8OOOx9PxGYr6rFwD4R2e093soAtN2xSsrcfLLlCOMujic6PIf/981euLIL+WvqHhLatOCuy7oHu4mAJ9H/etE2Xl6+DxFIc53mb3cl16k7pKzczU/fTeOjTYf5+XUX8fDY3g3QYmNClz/dOAmA7+TWLu82X08Dd4mIC1gE/LAWdU0Fy3ed4HRBKTcN6Qp4plKdddMArurXiZkfbeGLbceC3EJPon9moSfRTxndg5SHryBMhG+/tJJ31tZuLvSycjc/eceT6B8b388SvTENQGpacV5EbgfGq+r93s93A5eq6jSfMo94j/V7ERkFvAoMBF4EVqnqP7zlXgU+UdUFFc4xFZgKEB8fnzx//vw6B5Sfn0/r1q3rXL8p+FtaEekny/nj2JYUFZw5F09RmfLcmiIOn3EzfWQMF8YFp0dMVXl7ewlLDpQxrnsE3+0XhYiQV6L8La2IrafcjEmK4M7+UURW0g3j+x2Vu5WXNhez5mg5d1wUyQ09G34is0Bzwv/mKnJaTE6LB86PaezYsetVdUSVFVS12hcwCljs83kGMKNCma1Aks/nvUCnimWBxcCo6s6XnJys9bF06dJ61Q+2guIy7f/kJzr9vTRVPT+eY7mFevlzX2jyM5/pgZNnGr19brdbZ36Yrt0fW6i/TNmqbrf7a/tLy8r114sytPtjC/WW2Sv0aE7hecc4G1NJWbk+9I/12v2xhTrn33sao/kNorn/b64yTovJafGonh8TsE6rya3+dOOsBfqISE8RicJzwzWlQpmDwNUAItIfiAFOeMtNEpFoEekJ9AHW+HHOkPX5tmMUlJRzo7cLp6JOsTG8ft9ISsuVKa+vIftMSaO1TVWZ+dFW/r7yAA98oydPTuh/3nQDEeFhzLi+P7O/O5ztR/OY8KcVrN2fdd6xSsvd/GjeRj5OP8IT3+rPA1de2FhhGBOSakz2qloGTMNzVb4Nz6ibrSIyS0Ru8hb7KfCAiKQB84Ap3h+brcA7eG7mfgo8rKrlDRGIU6SkHaZTbDSX9qz6AaLenVrz8j0jcGUX8sAb6ygqbfg/qdutPPHhFt5cdYAfXHkhj99wfqL39a3BXfjgoctpFRXO5DmreGPl/rP/dUeZW5n29gY+2XKUmRMu5v5vWKI3pqH5Nc5eVRepal9V7aWqz3q3zVTVFO/7DFW9XFWHqOpQVf3Mp+6z3noXqeonDROGM+QUlvLvHSeYMLhrjVMCjOzZjhfuGMK6A9n89J20Bh2D73Yrv/hwC2+tPsiD3+zF9Ov7+TVX/EWdY/lo2hV8s29HZn60lZ+9u5m8olJmbypm8dZjPH3jxXzvip4N1m5jzH/ZE7RNyOItRykpd3PT0Mq7cCqaMLgrh08X8utF2+naJoZffKvi4w/153Yrj3+Qzvy1h3hoTC9+ft1FtVoUJK5FJC/fM4I/frGLP36xi8Vbj5JfXM6siQO4Z1SPgLfXGFM5S/ZNSEraYbq3b8mQxDi/6zzwjQvJzC7k5eX7SO7ejvEDOwesPW63Mt07bcEPr+rNI+P61mn1p7Aw4Sfj+jI4MY6nUrZya68wS/TGNDKb4amJOJ5XxFd7TnLj4K61SqgiwswbB5DQpgVvrT4QsPaoKo+950n0P7q6T50Tva+r+8ez4rGruKpbaE3qZkxTYMm+iVi0+Qhuxe8uHF/hYcJtwxNYsfskR3IKA9KelXtO8e5617mZHxtiPVdjTOOxZN9EpKQdpl/nWPrGx9ap/m3JiajCBxszA9KeeWsPcUFMBD+6uk9AjmeMCS5L9k3AoawCNhw8Xaer+rO6t2/FJT3asmC969wQx7rKOlPC4i1HuXV4IjGRNm+dMU5gyb4J+Ndmz7xxNw6ue7IHuD05kb0nzrDx0Ol6Hef9DS5Kyt1MHtmtXscxxjQdluybgJRNhxnerQ1J7VrW6zg3DOpCTGQY76131fkYqsq8NQcZ1q0NF3WuW5eSMabpsWQfZDuP5bH9aN65GS7rIzYmkvEDOpOSdrjOT9WuO5DNnhNn7KreGIexZB9kKZsOEybwrXp24Zx1e3ISeUVlLMmo2zTI89YcJDY6ggmDuwSkPcaYpsGSfRCpKilphxndqwMdYwOz9N6oXu3pGhfDextq35WTU1DKx5uPcNPQrrSMsuftjHESS/ZBlObK4WBWQUC6cM4KDxNuGZ7Asp0nOJZbVKu6H27KpLjMbswa40SW7IMoZdNhosLDuC6AUxwA3DY8EXctx9yfvTE7KCGOgQn+T9dgjGkeLNkHSblbWbj5MN+8qCNxLQI7fcCFHVuT3L0t79VizH2aK4ftR/OYNDIpoG0xxjQNluyDZPW+UxzPKw5oF46v24Ynsut4PptdOX6Vn7f6IC0iwxusPcaY4LJkHyT/SjtMy6hwrukf3yDH/9bgLkRHhLHAjzH3+cVl/GvzYW4a0pXYGJukzBgnsmQfBCVlbhalH2XcxfG0iGqY6QjiWkRynXfMfXFZ9WPuUzYdpqCk3LpwjHEwv5K9iIwXkR0isltEpley//9EZJP3tVNETvvsK/fZV3Ht2pC0fNcJcgpLG7zL5PbkRHIKS/li2/Fqy81bc5B+nWMZmtSmQdtjjAmeGgdTi0g4MBsYB7iAtSKSoqoZZ8uo6k98yv8QGOZziEJVHRq4Jjd/KWmHiWsRyTf6dGzQ81zeuwOdL4hhwXoXNwyq/CGpLZk5pGfm8PSNF9s0xsY4mD9X9iOB3aq6V1VLgPnAxGrKT8az6LipRE5hKUsyjnHDoM5ERTRsL9rZMff/3nmC43mVj7mfv/Yg0RFh3DIssUHbYowJLn+yTQJwyOezy7vtPCLSHegJfOmzOUZE1onIKhG5uc4tdYjnP91OUWk5d17avVHOd9vwRMrdykcbD5+3r6CkjI82HuZbg7oQ19JuzBrjZHrvWQMAABTySURBVIF+Jn4SsEBVfe8IdlfVTBG5EPhSRNJVdY9vJRGZCkwFiI+PJzU1tc4NyM/Pr1f9hrQru5y3VhdxbfcITu7aSOqumusEIp4L48L4+7Lt9C4/8LWumuWuUvKKy7go8lSj/s2a8ndUF06LB5wXk9PigTrEpKrVvoBRwGKfzzOAGVWU3QiMruZYrwO3V3e+5ORkrY+lS5fWq35DKS4t13EvpOro33yh+UWlftcLRDxvrtyv3R9bqOmu01/bfsvsFXrV75aq2+2u9zlqo6l+R3XltHhUnReT0+JRPT8mYJ1Wk1v96cZZC/QRkZ4iEoXn6v28UTUi0g9oC6z02dZWRKK97zsAlwMZFeuGgjnL9rDzWD6zJg6gVXTjTjJ24+CuRFUYc7/jaB4bDp5m8shudmPWmBBQY7JX1TJgGrAY2Aa8o6pbRWSWiNzkU3QSMN/7C3NWf2CdiKQBS4Hn1GcUT6jYd/IML365mxsGdebqBnqIqjpxLSMZd3E8H23KpKTMDXiGW0aFh3HrcLsxa0wo8OsSU1UXAYsqbJtZ4fPTldT7ChhUj/Y1e6rK4++nEx0RxtM3DghaO25PTuTjzUf4cvtxxlzUkQ82ZnLtgHjatYoKWpuMMY3HJi1vYO9tyGTl3lP86uaBdLogJmjt+EbvDnSKjWbBehdFpeXkFJbyXZvK2JiQYcm+AZ3KL+bZjzNI7t426Ik1IjyMW4Yl8MqKfRw+XUj39i257ML2QW2TMabx2Nw4DejZj7eRX1zGb24dRFhY8G+C3pbsGXOfcSSX71yS1CTaZIxpHJbsG8iKXSd5f2MmP7iyF33jY4PdHAD6xscyJDGOiDDh9mS7MWtMKLFunAZQVFrOLz5Mp2eHVky7qnewm/M1v5w4kP0nz9ApNnj3D4wxjc+SfQN48YtdHDhVwNv3X0pMZMNMYVxXQ5Pa2OyWxoQg68YJsO1Hc5mzbC+3JycyuneHYDfHGGMAS/YB5XYrM95P54IWkfzihv7Bbo4xxpxjyT6A3lp9gI0HT/PEt/rT1h5WMsY0IdZnX4PjeUXkFpbVWC6/uIznP93BFb07cMuwSmeANsaYoLFkX43M04WM/W0qJeVuv8pHR4Tx7C0DbWIxY0yTY8m+Gu+sPUSp283ztw0mxo+Fwft3jqV7+1aN0DJjjKkdS/ZVKHcrC9a7uKJ3B+64JCnYzTHGmHqxG7RV+M/uk2SeLuQ7luiNMQ5gyb4K/1x7iLbeeeCNMaa5s2RfiawzJXyWcZRbhiUSHdG0noA1xpi6sGRfiQ82ZlJartaFY4xxDL+SvYiMF5EdIrJbRKZXsv//RGST97VTRE777LtXRHZ5X/cGsvENQVX559qDDE1qw0Wdm8ZslcYYU181jsYRkXBgNjAOcAFrRSTFdy1ZVf2JT/kfAsO879sBTwEjAAXWe+tmBzSKANp06DQ7j+Xzm1tDejVFY4zD+HNlPxLYrap7VbUEmA9MrKb8ZGCe9/11wBJVzfIm+CXA+Po0uKG9s+4QLSLDmTC4S7CbYowxAeNPsk8ADvl8dnm3nUdEugM9gS9rW7cpOFNcRsqmw0wY3IXYmMhgN8cYYwIm0A9VTQIWqGp5bSqJyFRgKkB8fDypqal1bkB+fn6d6y93lXKmpJy+ESfr1YZAqk88TZXTYnJaPOC8mJwWD9Q+Jn+SfSbgOywl0butMpOAhyvUHVOh7nmtU9U5wByAESNG6JgxYyoW8Vtqaip1rf/nv37FhR0juf/mbzaZ+W3qE09T5bSYnBYPOC8mp8UDtY/Jn26ctUAfEekpIlF4EnpKxUIi0g9oC6z02bwYuFZE2opIW+Ba77YmZ/fxPNYdyGbSJUlNJtEbY0yg1Hhlr6plIjINT5IOB+aq6lYRmQWsU9WziX8SMF9V1adulog8g+cHA2CWqmYFNoTAeGedi4gw4dbhthC3McZ5/OqzV9VFwKIK22ZW+Px0FXXnAnPr2L5GUVLm5v0NLq7u34kOraOD3RxjjAk4e4IW+HL7MU7mlzDpkm7BbooxxjQIS/Z4Jj3rfEEMV/btGOymGGNMgwj5ZH8kp5B/7zzB7cmJhIfZjVljjDOFfLJfsM6FW+GOETbpmTHGuUI62bvdyjvrDzG6V3u6tW8Z7OYYY0yDCelkv2rvKQ5l2WpUxhjnC+lkP3/tIeJaRHLdgM7BbooxxjSokE32OQWlfLr1KDcP7UpMpK1GZYxxtpBN9h9uyqSkzM0d1oVjjAkBIZnsVZX5aw8xKCGOAV3jgt0cY4xpcCGZ7Ldk5rLtSK5d1RtjQkZIJvuUtEyiIsK4aUjXYDfFGGMaRUgm+z0nztC7Y2viWthqVMaY0BCSyd6VXUBi2xbBboYxxjSakEv2qooru5AES/bGmBAScsn+dEEpBSXlJLa16RGMMaEj5JK9K7sQwLpxjDEhxa9kLyLjRWSHiOwWkelVlLlDRDJEZKuIvO2zvVxENnlf561d29hc2QWAJXtjTGipcVlCEQkHZgPjABewVkRSVDXDp0wfYAZwuapmi0gnn0MUqurQALe7zv57ZW/dOMaY0OHPlf1IYLeq7lXVEmA+MLFCmQeA2aqaDaCqxwPbzMBxZRcQGxNhwy6NMSHFn2SfABzy+ezybvPVF+grIv8RkVUiMt5nX4yIrPNuv7me7a03V3ahXdUbY0JOjd04tThOH2AMkAgsE5FBqnoa6K6qmSJyIfCliKSr6h7fyiIyFZgKEB8fT2pqap0bkp+fX239Ha4COrYMq9c5GlNN8TRHTovJafGA82JyWjxQ+5j8SfaZgO8kMonebb5cwGpVLQX2ichOPMl/rapmAqjqXhFJBYYBX0v2qjoHmAMwYsQIHTNmjN8BVJSamkpV9VWV7C8Xc83gJMaMGVDnczSm6uJprpwWk9PiAefF5LR4oPYx+dONsxboIyI9RSQKmARUHFXzIZ6rekSkA55unb0i0lZEon22Xw5kECQ5haWcKSm3kTjGmJBT45W9qpaJyDRgMRAOzFXVrSIyC1inqinefdeKSAZQDvxcVU+JyGjgJRFx4/lhec53FE9js5E4xphQ5VefvaouAhZV2DbT570Cj3hfvmW+AgbVv5mBYWPsjTGhKqSeoD17ZZ9kV/bGmBATcsk+NjqCC1oEahCSMcY0DyGW7AtIaNsCEQl2U4wxplGFWLK3B6qMMaEpZJL92Xns7easMSYUhUyyzy0sI7+4zJK9MSYkhUyyP2TDLo0xISxkkr09UGWMCWUhlOztyt4YE7pCKNkX0jra5rE3xoSmkEr2iTbG3hgTokIo2RdYF44xJmSFTLLPtAeqjDEhLCSSfU5hKXnFZSS0sSt7Y0xoColkbyNxjDGhLkSSvY2xN8aEthBL9nZlb4wJTX4lexEZLyI7RGS3iEyvoswdIpIhIltF5G2f7feKyC7v695ANbw2XNkFtIoKp01LG2NvjAlNNa7iISLhwGxgHOAC1opIiu9asiLSB5gBXK6q2SLSybu9HfAUMAJQYL23bnbgQ6na2amNbYy9MSZU+XNlPxLYrap7VbUEmA9MrFDmAWD22SSuqse9268DlqhqlnffEmB8YJruP5va2BgT6vxJ9gnAIZ/PLu82X32BviLyHxFZJSLja1G3wdkDVcaYUBeoxVgjgD7AGCARWCYig/ytLCJTgakA8fHxpKam1rkh+fn5X6t/plTJKyqjKOsIqakn63zcYKkYjxM4LSanxQPOi8lp8UDtY/In2WcCST6fE73bfLmA1apaCuwTkZ14kn8mnh8A37rntU5V5wBzAEaMGKFjxoypWMRvqamp+NbPOJwLXyznmyMGMmZQlzofN1gqxuMETovJafGA82JyWjxQ+5j86cZZC/QRkZ4iEgVMAlIqlPkQb1IXkQ54unX2AouBa0WkrYi0Ba71bms09kCVMcb4cWWvqmUiMg1Pkg4H5qrqVhGZBaxT1RT+m9QzgHLg56p6CkBEnsHzgwEwS1WzGiKQqtgDVcYY42efvaouAhZV2DbT570Cj3hfFevOBebWr5l158oupGVUOG1tjL0xJoQ5/gnasyNxbIy9MSaUhUCyt6mNjTEmBJK9jbE3xhhHJ/vcolJyi2wee2OMcXSyz7SROMYYAzg82dvUxsYY4+HwZG8PVBljDDg+2RfSIjKcdq2igt0UY4wJKocnextjb4wx4Phkb/PYG2MMhESyt5E4xhjj2GSfV1RKTmEpCXZlb4wxzk32madt2KUxxpzl2GTvyrIHqowx5iznJnsbY2+MMec4ONkXEhMZRnsbY2+MMc5O9oltW9oYe2OMwc9kLyLjRWSHiOwWkemV7J8iIidEZJP3db/PvnKf7RXXrm0wrtM2tbExxpxV47KEIhIOzAbGAS5grYikqGpGhaL/VNVplRyiUFWH1r+ptePKLmRoUpvGPq0xxjRJ/lzZjwR2q+peVS0B5gMTG7ZZ9ZNfXMbpglIS2thIHGOMAf+SfQJwyOezy7utottEZLOILBCRJJ/tMSKyTkRWicjN9WmsvzJtamNjjPkaUdXqC4jcDoxX1fu9n+8GLvXtshGR9kC+qhaLyA+A76jqVd59CaqaKSIXAl8CV6vqngrnmApMBYiPj0+eP39+nQPKz89nd0EMf9hQzJOXxdCrTXidj9UU5Ofn07p162A3I6CcFpPT4gHnxeS0eOD8mMaOHbteVUdUWUFVq30Bo4DFPp9nADOqKR8O5FSx73Xg9urOl5ycrPWxdOlSff0/+7T7Ywv1eG5RvY7VFCxdujTYTQg4p8XktHhUnReT0+JRPT8mYJ1Wk1v96cZZC/QRkZ4iEgVMAr42qkZEuvh8vAnY5t3eVkSive87AJcDFW/sBpwru4DoiDA6tLYx9sYYA36MxlHVMhGZBizGc9U+V1W3isgsPL8kKcCPROQmoAzIAqZ4q/cHXhIRN577A8/p+aN4Au7s1MY2xt4YYzxqTPYAqroIWFRh20yf9zPwdO9UrPcVMKiebaw1m9rYGGO+zpFP0J5docoYY4yH45J9UZmSXWDz2BtjjC/HJftThZ6hpNaNY4wx/+W4ZH+i0A3YA1XGGOPLccn+5Lkre0v2xhhzliOTfXREGB1bRwe7KcYY02Q4MNm7SbAx9sYY8zWOS/anCtVuzhpjTAWOS/YnC93WX2+MMRU4KtkXlJSRVwoJbSzZG2OML0cle5vH3hhjKueoZO86l+ytz94YY3w5LNkXAJBkV/bGGPM1Dkv2hUSEQQcbY2+MMV/juGTfIUYIC7Mx9sYY48thyb6ADi0cFZIxxgSEozKjK7uQDi3sqt4YYyryK9mLyHgR2SEiu0VkeiX7p4jICRHZ5H3d77PvXhHZ5X3dG8jG+yooKePUmRLaW7I3xpjz1LgsoYiEA7OBcYALWCsiKZWsJftPVZ1WoW474ClgBKDAem/d7IC03kdRqZubhnSlZ+SpQB/aGGOaPX+u7EcCu1V1r6qWAPOBiX4e/zpgiapmeRP8EmB83ZpavXatonhx8jAGdvBrWV1jjAkp/iT7BOCQz2eXd1tFt4nIZhFZICJJtaxrjDGmAQXqMvhfwDxVLRaRHwB/B67yt7KITAWmAsTHx5OamlrnhuTn59erflPjtHjAeTE5LR5wXkxOiwfqEJOqVvsCRgGLfT7PAGZUUz4cyPG+nwy85LPvJWBydedLTk7W+li6dGm96jc1TotH1XkxOS0eVefF5LR4VM+PCVin1eRWf7px1gJ9RKSniEQBk4AU3wIi0sXn403ANu/7xcC1ItJWRNoC13q3GWOMaUQ1duOoapmITMOTpMOBuaq6VURm4fklSQF+JCI3AWVAFjDFWzdLRJ7B84MBMEtVsxogDmOMMdXwq89eVRcBiypsm+nzfgae7p3K6s4F5tajjcYYY+rJUU/QGmOMqZwle2OMCQHiuYnbdIjICeBAPQ7RATgZoOY0BU6LB5wXk9PiAefF5LR44PyYuqtqx6oKN7lkX18isk5VRwS7HYHitHjAeTE5LR5wXkxOiwdqH5N14xhjTAiwZG+MMSHAicl+TrAbEGBOiwecF5PT4gHnxeS0eKCWMTmuz94YY8z5nHhlb4wxpgLHJPuaVtNqjkRkv4ike1f/Whfs9tSWiMwVkeMissVnWzsRWeJduWyJd86kZqOKmJ4WkUyfldpuCGYba0NEkkRkqYhkiMhWEfmxd3uz/J6qiac5f0cxIrJGRNK8Mf3Su72niKz25rx/eucuq/o4TujG8a6mtROf1bTwzK5ZcTWtZkVE9gMjVLVZjg8WkSuBfOANVR3o3fY8kKWqz3l/lNuq6mPBbGdtVBHT00C+qv4umG2rC+8khl1UdYOIxALrgZvxzG/V7L6nauK5g+b7HQnQSlXzRSQSWAH8GHgEeF9V54vI34A0Vf1rVcdxypV9fVbTMg1EVZfhmRjP10Q86x3g/ffmRm1UPVURU7OlqkdUdYP3fR6eGWsTaKbfUzXxNFveGYzzvR8jvS/Fs2bIAu/2Gr8jpyR7p66IpcBnIrLeu8CLE8Sr6hHv+6NAfDAbE0DTvCu1zW0uXR4ViUgPYBiwGgd8TxXigWb8HYlIuIhsAo7jWd51D3BaVcu8RWrMeU5J9k51haoOB64HHvZ2ITiGd8GF5t+PCH8FegFDgSPA74PbnNoTkdbAe8D/qGqu777m+D1VEk+z/o5UtVxVhwKJeHoy+tX2GE5J9plAks/nRO+2Zk1VM73/Hgc+wPMlN3fHzi524/33eJDbU2+qesz7f0Y38DLN7Hvy9gO/B7ylqu97Nzfb76myeJr7d3SWqp4GluJZQbCNiJydpr7GnOeUZF/jalrNjYi08t5gQkRa4Vnla0v1tZqFFOBe7/t7gY+C2JaAqLBS2y00o+/Je/PvVWCbqr7gs6tZfk9VxdPMv6OOItLG+74FnoEo2/Ak/du9xWr8jhwxGgfAO5TqD/x3Na1ng9ykehGRC/FczYNnkZm3m1tMIjIPGINndr5jwFPAh8A7QDc8s5ve0ZxWL6sipjF4ugcU2A/8wKe/u0kTkSuA5UA64PZufhxPP3ez+56qiWcyzfc7GoznBmw4ngv0d1R1ljdHzAfaARuBu1S1uMrjOCXZG2OMqZpTunGMMcZUw5K9McaEAEv2xhgTAizZG2NMCLBkb4wxIcCSvTHGhABL9sYYEwIs2RtjTAj4/6Ro1/wLWMKEAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "id": "6X_ll54SxWtR",
        "outputId": "0bfe3089-a923-4179-c367-0cb82508833f"
      },
      "source": [
        "df = pd.DataFrame()\n",
        "for i in range(0,len(weights)):\n",
        "        w = weights[i]\n",
        "        W = final_w[i]\n",
        "        learning_speed = [0]\n",
        "        for i in range(1,len(w)):\n",
        "            wold = w[i-1]\n",
        "            wnew = w[i]\n",
        "            diff = wold-wnew\n",
        "            d = wnew\n",
        "            m = np.median(d[d>0])\n",
        "            d[d==0] = m\n",
        "            diff = abs(diff)/d\n",
        "            if diff.size:\n",
        "                avg = abs(np.mean(diff))\n",
        "            else:\n",
        "                avg = 0\n",
        "            learning_speed.append(avg)\n",
        "        df['learning speed'] = learning_speed\n",
        "        df.plot(grid=True)"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "c913ePMTxWtR",
        "outputId": "0c9f37f7-b862-4dfd-b44d-a94f775791cf"
      },
      "source": [
        "print(model.summary())"
      ],
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "dense (Dense)                (None, 30)                23550     \n",
            "_________________________________________________________________\n",
            "dense_1 (Dense)              (None, 10)                310       \n",
            "=================================================================\n",
            "Total params: 23,860\n",
            "Trainable params: 23,860\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n",
            "None\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_iERPv8DxWtR"
      },
      "source": [
        "#For 2  and 3 Hidden layers"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7S-OkUaOxWtR"
      },
      "source": [
        "2 Hidden layers"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IaZErJN6xWtR",
        "outputId": "ef11e29b-6f9b-4daf-b9b8-59fe423d8fc3"
      },
      "source": [
        "from keras.callbacks import LambdaCallback\n",
        "model2 = Sequential()\n",
        "start_time = time.time()\n",
        "model2.add(Dense(30, activation= \"sigmoid\"))\n",
        "model2.add(Dense(30, activation= \"sigmoid\"))\n",
        "model2.add(Dense(10, activation='softmax'))\n",
        "weights1 = []\n",
        "weights2 = []\n",
        "save_weights1 = LambdaCallback(on_epoch_end=lambda batch, logs: weights1.append(model2.layers[0].get_weights()[0]))\n",
        "save_weights2 = LambdaCallback(on_epoch_end=lambda batch, logs: weights2.append(model2.layers[1].get_weights()[0]))\n",
        "model2.compile(optimizer=tf.keras.optimizers.Adam(learning_rate=0.1),\n",
        "              loss='sparse_categorical_crossentropy',\n",
        "              metrics=['accuracy'])\n",
        "m_h2 = model2.fit(train_x, train_y,validation_data = (x_val,y_val), epochs=30, batch_size=10,callbacks = [save_weights1,save_weights2])\n",
        "final_w = [model2.layers[0].get_weights()[0],model2.layers[1].get_weights()[0]]\n",
        "weights = [weights1,weights2]\n",
        "total_time = (time.time()-start_time)\n",
        "print(\"--- %s seconds ---\" % total_time)"
      ],
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/30\n",
            "100/100 [==============================] - 1s 10ms/step - loss: 2.3803 - accuracy: 0.1414 - val_loss: 1.8414 - val_accuracy: 0.3026\n",
            "Epoch 2/30\n",
            "100/100 [==============================] - 1s 8ms/step - loss: 1.6601 - accuracy: 0.3406 - val_loss: 1.6241 - val_accuracy: 0.3949\n",
            "Epoch 3/30\n",
            "100/100 [==============================] - 1s 8ms/step - loss: 1.5635 - accuracy: 0.4132 - val_loss: 1.4968 - val_accuracy: 0.4044\n",
            "Epoch 4/30\n",
            "100/100 [==============================] - 1s 8ms/step - loss: 1.5340 - accuracy: 0.4384 - val_loss: 1.3542 - val_accuracy: 0.4588\n",
            "Epoch 5/30\n",
            "100/100 [==============================] - 1s 8ms/step - loss: 1.3848 - accuracy: 0.4751 - val_loss: 1.5247 - val_accuracy: 0.5430\n",
            "Epoch 6/30\n",
            "100/100 [==============================] - 1s 8ms/step - loss: 1.2801 - accuracy: 0.5591 - val_loss: 1.3618 - val_accuracy: 0.5228\n",
            "Epoch 7/30\n",
            "100/100 [==============================] - 1s 8ms/step - loss: 1.3167 - accuracy: 0.5290 - val_loss: 1.1710 - val_accuracy: 0.5610\n",
            "Epoch 8/30\n",
            "100/100 [==============================] - 1s 8ms/step - loss: 1.1137 - accuracy: 0.5793 - val_loss: 1.2231 - val_accuracy: 0.5279\n",
            "Epoch 9/30\n",
            "100/100 [==============================] - 1s 8ms/step - loss: 1.1228 - accuracy: 0.5936 - val_loss: 1.1736 - val_accuracy: 0.5855\n",
            "Epoch 10/30\n",
            "100/100 [==============================] - 1s 8ms/step - loss: 1.1345 - accuracy: 0.5906 - val_loss: 1.2652 - val_accuracy: 0.5363\n",
            "Epoch 11/30\n",
            "100/100 [==============================] - 1s 8ms/step - loss: 1.1596 - accuracy: 0.5747 - val_loss: 1.2771 - val_accuracy: 0.5817\n",
            "Epoch 12/30\n",
            "100/100 [==============================] - 1s 8ms/step - loss: 1.1651 - accuracy: 0.6081 - val_loss: 1.2680 - val_accuracy: 0.5254\n",
            "Epoch 13/30\n",
            "100/100 [==============================] - 1s 8ms/step - loss: 0.9808 - accuracy: 0.6547 - val_loss: 1.0652 - val_accuracy: 0.6515\n",
            "Epoch 14/30\n",
            "100/100 [==============================] - 1s 8ms/step - loss: 0.8642 - accuracy: 0.6934 - val_loss: 0.9774 - val_accuracy: 0.7137\n",
            "Epoch 15/30\n",
            "100/100 [==============================] - 1s 8ms/step - loss: 0.9126 - accuracy: 0.6966 - val_loss: 0.9833 - val_accuracy: 0.7079\n",
            "Epoch 16/30\n",
            "100/100 [==============================] - 1s 8ms/step - loss: 0.9951 - accuracy: 0.6784 - val_loss: 1.0342 - val_accuracy: 0.6835\n",
            "Epoch 17/30\n",
            "100/100 [==============================] - 1s 8ms/step - loss: 0.9150 - accuracy: 0.7012 - val_loss: 0.9769 - val_accuracy: 0.6988\n",
            "Epoch 18/30\n",
            "100/100 [==============================] - 1s 8ms/step - loss: 0.8106 - accuracy: 0.7326 - val_loss: 1.0499 - val_accuracy: 0.6766\n",
            "Epoch 19/30\n",
            "100/100 [==============================] - 1s 8ms/step - loss: 0.9952 - accuracy: 0.6772 - val_loss: 1.0488 - val_accuracy: 0.6516\n",
            "Epoch 20/30\n",
            "100/100 [==============================] - 1s 8ms/step - loss: 0.9404 - accuracy: 0.6841 - val_loss: 1.1511 - val_accuracy: 0.5863\n",
            "Epoch 21/30\n",
            "100/100 [==============================] - 1s 8ms/step - loss: 1.0223 - accuracy: 0.6528 - val_loss: 1.0136 - val_accuracy: 0.6823\n",
            "Epoch 22/30\n",
            "100/100 [==============================] - 1s 8ms/step - loss: 0.8653 - accuracy: 0.7351 - val_loss: 1.0185 - val_accuracy: 0.7025\n",
            "Epoch 23/30\n",
            "100/100 [==============================] - 1s 9ms/step - loss: 0.9645 - accuracy: 0.6866 - val_loss: 1.1247 - val_accuracy: 0.6418\n",
            "Epoch 24/30\n",
            "100/100 [==============================] - 1s 8ms/step - loss: 0.9589 - accuracy: 0.6933 - val_loss: 0.9856 - val_accuracy: 0.6689\n",
            "Epoch 25/30\n",
            "100/100 [==============================] - 1s 8ms/step - loss: 0.8918 - accuracy: 0.7317 - val_loss: 0.9891 - val_accuracy: 0.6144\n",
            "Epoch 26/30\n",
            "100/100 [==============================] - 1s 8ms/step - loss: 0.8325 - accuracy: 0.7428 - val_loss: 0.9362 - val_accuracy: 0.7035\n",
            "Epoch 27/30\n",
            "100/100 [==============================] - 1s 8ms/step - loss: 0.7303 - accuracy: 0.7915 - val_loss: 0.9399 - val_accuracy: 0.6990\n",
            "Epoch 28/30\n",
            "100/100 [==============================] - 1s 8ms/step - loss: 0.9201 - accuracy: 0.7049 - val_loss: 0.9105 - val_accuracy: 0.7028\n",
            "Epoch 29/30\n",
            "100/100 [==============================] - 1s 8ms/step - loss: 0.7474 - accuracy: 0.7638 - val_loss: 0.9295 - val_accuracy: 0.7182\n",
            "Epoch 30/30\n",
            "100/100 [==============================] - 1s 8ms/step - loss: 0.7150 - accuracy: 0.7846 - val_loss: 0.9166 - val_accuracy: 0.6970\n",
            "--- 25.19829750061035 seconds ---\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wnNTFqUixWtS",
        "outputId": "783631e3-c5e4-4f13-936e-1199d9cebb3b"
      },
      "source": [
        "predict_train = model2.predict_classes(train_x)\n",
        "acc_train = np.where(train_y == predict_train)[0].shape[0]/train_x.shape[0]\n",
        "print('Training accuracy for 2 hidden layer = {}'.format(acc_train))\n",
        "predict_test = model2.predict_classes(x_test)\n",
        "acc_test = np.where(y_test == predict_test)[0].shape[0]/x_test.shape[0]\n",
        "print('Testing accuracy for 2 hidden layer = {}'.format(acc_test))\n",
        "print('Total run time = {} seconds'.format(total_time))"
      ],
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/sequential.py:450: UserWarning: `model.predict_classes()` is deprecated and will be removed after 2021-01-01. Please use instead:* `np.argmax(model.predict(x), axis=-1)`,   if your model does multi-class classification   (e.g. if it uses a `softmax` last-layer activation).* `(model.predict(x) > 0.5).astype(\"int32\")`,   if your model does binary classification   (e.g. if it uses a `sigmoid` last-layer activation).\n",
            "  warnings.warn('`model.predict_classes()` is deprecated and '\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Training accuracy for 2 hidden layer = 0.764\n",
            "Testing accuracy for 2 hidden layer = 0.6837\n",
            "Total run time = 25.19829750061035 seconds\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "id": "ma56QBV9xWtS",
        "outputId": "01a9c506-0d7d-4817-b37a-5e0a56668716"
      },
      "source": [
        "df = pd.DataFrame()\n",
        "df['training_loss'] = m_h2.history['loss']\n",
        "#df['accuracy'] = model_history.history['accuracy']\n",
        "df['validation_loss'] = m_h2.history['val_loss']\n",
        "df.plot(grid=True)"
      ],
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fee30bcc630>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 25
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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UVNYX1N1qccq3pRHUr5hqaSTLGoUQovigrpSaoZQ6oZTaXMh5b6XUL0qpDUqpLUqpCs9ukxrQBU7ugRNbLx8L8XbBzclegroQokYryUh9JtCniPPjga1a6yigJ/CuUsqp/E0rXJp/J1BXT8EopYwcMDL9IoSowYoN6lrrBOBkUUUAT2Wkf/PIL1v2Df1KIMfJB+p1KTCv3ijAXRJ7CSFqNEvMqX8ENAOOAJuAx7TWJgvUW7Tm/SF1O6TuuHwoPNCDI2cyOZ9Vob9ThBCi2lIlWdetlAoDFmqtI82cGwR0BZ4EGgF/A1Fa67Nmyo4FxgIEBQVFx8bGlqnRGRkZ1HLMosvKe9kXNoIDYUMASDyWy0fJWbzS2YUwb+varzQjIwMPD4+qbobF2Fp/wPb6ZGv9Advrk7n+xMTEJGmt2xV6UVF73V16AWHA5kLO/Qp0u+Lrf4AOxdVpkT1KP79R66ldLx/feeysrj9hoZ677lCZ664q1rz/pTm21h+tba9PttYfrW2vT+b6QyXsUXoQuB5AKRUERAB7LVBv8Zr3h+ObIH0PAPX93LG3U+w5UfocxEIIYQtKsqTxO2AlEKGUSlFK3aeUGqeUGpdf5L9AF6XUJmAxMEFrnVZxTb5Cs9uMj9sWAODkYEd9PzdZ1iiEqLGK3SRDaz2smPNHgN4Wa1Fp+NSFkLbGKpjrngDyE3vJskYhRA1lfU+UXqt5fziyHk4fBIwVMAfSz5OTV/ELcIQQorqxgaCePwWz1ZiCaRTgQU6e5uDJC1XYKCGEqBrWH9RrNYTglpcfRJKt7YQQNZn1B3UwpmBS1sCZwzQKMDaelnQBQoiayEaC+gDj4/aFeLo4EuTlLCN1IUSNZBtB3b8xBDS7agpmT6qsVRdC1Dy2EdTBmII5sALOHSc8wIM9JzJkazshRI1jW0EdDdsX0ijQg4ysXI6fzarqVgkhRKWynaAe2Az8wmHrfMIDZGs7IUTNZDtBXSljtL5/GeGexghdbpYKIWoa2wnqYAR1nUdAyiI8nR1kpC6EqHFsK6gHtwKf+qhtC2gU6CEjdSFEjWNbQf3SFMzeeCJraQnqQogax7aCOhgPIply6aUSOXEui7OZOVXdIiGEqDS2F9TrtAWvUFqeXQIgG1ELIWoU2wvqSkHz2/A/tgwPLsgUjBCiRrG9oA7QvD/KlE1vh2QJ6kKIGqXYnY+sUmgH8AhmSM56RizrSp5J89gNjfF0cazqlgkhRIWyzZG6nR00u5WOeUnc1caPL5bv4/p3lzA/+bDkgxFC2DTbDOpgTMHkZjKp+RF+fqgrwd4uPBabzJ2frWLHsXNV3TohhKgQthvU63cB9wBY/y1RdX2Y91BXXr+9JTuOn6Pvh0v578KtnJPljkIIG1NsUFdKzVBKnVBKbS6iTE+lVLJSaotSaollm1hGdvbQ6SHY/TfsS8DeTjG8Yz3inurJkHZ1mbF8H73eXcLP62VKRghhO0oyUp8J9CnspFLKB5gK3Ka1bgEMtkzTLKDTQ+BdF/78D5hMAPi6O/HGwJb8/FBXQrxdePz7ZIZ+tortx85WcWOFEKL8ig3qWusE4GQRRYYDc7XWB/PLn7BQ28rP0QWufxmObYSNsVedujQl8+bAluw6fo7b/recncdlrl0IYd1USaYelFJhwEKtdaSZc+8DjkALwBP4QGs9q5B6xgJjAYKCgqJjY2PNFStWRkYGHh4eJSusTbRd9wzOWadY3fETTPbOBYqczjIxIeEibQLtGRflUqY2lVep+mQFbK0/YHt9srX+gO31yVx/YmJikrTW7Qq9SGtd7AsIAzYXcu4jYBXgDvgDu4AmxdUZHR2tyyouLq50F+xfrvXLXlrHv11okdd/3aobTFyo96VmlLld5VHqPlVzttYfrW2vT7bWH61tr0/m+gMk6iJiqyVWv6QAf2qtz2ut04AEIMoC9VpO/S7QtB8s+z84d9xskfu6NcDR3o5P4vdUcuOEEMJyLBHU5wPXKaUclFJuQEdgmwXqtawbX4W8LIh7zezpQE8X7mxfl5/WpXD49MVKbpwQQlhGSZY0fgesBCKUUilKqfuUUuOUUuMAtNbbgD+AjcAa4HOtdaHLH6uMXyNoPwbWfw3Ht5otMrZHI5SCT5fIaF0IYZ1KsvplmNa6ttbaUWsdqrX+Qms9TWs97Yoy72itm2utI7XW71dsk8uhxwRw9oS/XzR7uo6PK3e0DSV27SFOnMus5MYJIUT52e4Tpea41YLuz8DuRbB7sdki43o0IjfPxOdL91Vy44QQovxqVlAH6DAWfMPgrxfBlFfgdJi/O7dFhfDNqgOcOp9d+e0TQohyqHlB3cEZbngFTmyB9d+YLTI+JpwL2Xl8uVxG60II61LzgjoY+5iGdjBWwmQV3ESjcZAnfVoE8+WK/bLHqRDCqtTMoK4U3PQaZByHFR+aLfJwr3DOZeby9coDZXuPnEzY/itIsjAhRCWqmUEdoG4HaHE7LP8Qzh4pcDqyjjcxEQF8sWwfF7JzS1//ig8hdjjs+ssCjRVCiJKpuUEdjLl1nQf/TDZ7+uFe4Zw8n83s1QdLV29eLiR+aXy+6pNyNVEIIUqjZgd13zBjNUzybDi6scDp6Pq16NzQj+lL95KZU3ClTKF2/AbnjkC9LrA3Dk5UvwdshRC2qWYHdYDuT4OrD/z1H7Pz34/0Cuf42Sx+TEopeZ1rPzfyuA/5ChxcYPW04q8RQggLkKDu6gs9JsK+BNjzT4HTnRv50aaeD5/E7yEnz1R8fak7Yd8SiB4FHoHQaghsiIULRaWkF0IIy5CgDtBuNHjVgSVvFRitK6V4pFc4h09fZH5ywRuqBaz9HOydoO1I4+uOD0JuJiTNtHy7hRDiGhLUwXgg6bon4NBq2Btf4HRMRCDNa3sxNW43eaYilihmZcCG74x18B4BxrGg5tCgB6yZDnmy5l0IUbEkqF/S9h7wDCl0tP5wr3D2pp3nt01HC69j0xzIOmtkg7xSp4eMG6db51dAw4UQ4l8S1C9xcIZuT8LBlcac+DX6tAgmPNCDj+N2YzI3Wtca1n4BwS2NNfBXatwbajWUG6ZCiAonQf1Kbe42RuvxBUfrdnaK8TGN2H7sHIu3m9lb++AqOL4Z2t9vPLF69cXQcRykrIWUxArsgBCippOgfiVHF2Nu/eAKYzXMNW5tFUKYnxsv/ryZY2euybe+djo4e0PLQebrbj0cnL3kYSQhRIWSoH6ttveAZ22zc+sO9nZMHRFNRlYuo2eu5dylZF/njsPWBdBmBDi5m6/X2dOoe+vPcOZwBXdCCFFTSVC/1qXR+oHlsH9pgdPNQ7z4eERbdh4/x/jZ64216+tmgSkH2t1XdN0d7gdtMpY9CiFEBZCgbk7bkeARbMytm9GjSQCv3x5Jws5UXp6XjE76EhrGgH940fX6hkFEX2PNevYFizdbCCEkqJtzebS+DPYVHK0DDG1fj4djwklftwB19rAxCi+JTg/BxZPG8kchhLAwCeqFic4frS8xP1oHeKp3E570WcJh7ceCC5Elq7d+F2PZ46ppkmtdCGFxxQZ1pdQMpdQJpdTmYsq1V0rlKqUKWf5hZRxd4brHjXn1/cvMFlFpu4i4sI6lXrfy9E9bWbU3vfh6lTJG66nbzD69KoQQ5VGSkfpMoE9RBZRS9sBbgG3tCBE9CjyCIP5N8+cTvwA7R/reM4F6fm6MnZXI7hPniq838g5wD5DljUIIiys2qGutE4DiUgw+AvwEmHkqx4o5ukLXS6P15Vefy8ow8rC3GIBXQAhfjmqPk4M9o75cS+q5rKLrdXA2Vsrs+hPSdldc+4UQNY7SJZjXVUqFAQu11gUmjpVSdYDZQAwwI7/cj4XUMxYYCxAUFBQdGxtbpkZnZGTg4eFRpmtLyy4vi46rH+CCW102tP7v5eO1j/xJxM6prGvzJme9mwGw70web6zJpI67HRM7uODsoAqrFqesU3RaNYYjITexu/HYSu1TZbC1/oDt9cnW+gO21ydz/YmJiUnSWrcr9CKtdbEvIAzYXMi5H4BO+Z/PBAaVpM7o6GhdVnFxcWW+tkxWfKT1y15a719ufG0yaT21i9ZTuxqfX2HR1mO6wcSF+r6Za3RunslMZVeYO07rybW1vnCq8vtUwWytP1rbXp9srT9a216fzPUHSNRFxFZLrH5pB8QqpfYDg4CpSqkBFqi3+ogeDe6B/86tH1pt5HnpMKZAnpfrmwUx6bYWLNp2gkm/bLn0i8+8TuMg5zys/6YCGy+EqEnKHdS11g201mFa6zDgR+AhrfXP5W5ZdeLkBl0fM7I3Hlhp5EZ39oaWg80Wv7tzGGO7N2TWygP8vvlY4fXWjoL6XWH1pyhTKfZAFUKIQpRkSeN3wEogQimVopS6Tyk1Tik1ruKbV420u9dYsfLXf4y86K2HF57nBXj2pghahHjxyoItnM0sYnOMTg/CmYP4pa+pgEYLIWqakqx+Gaa1rq21dtRah2qtv9BaT9NaF0gOrrUepQu5SWr1Lo3WDycZeV7aF53nxcHejjcGtiQtI4u3/9heeMGIvuBdjzqHf7Nwg4UQNZE8UVoa7e415tYb9QL/xsUWbxXqw6guDfh29UGSDhSyKtTOHloPx+f0JjiTYuEGCyFqGgnqpeHkDmPj4I4vSnzJU72bEOLtynNzN5GdazJfKOpOFBo2lG2JpxBCXCJBvbS8Q8GtVomLuzs78Gr/Fuw8nsH0pXvNF6rVgNPeLYxNqyUfjBCiHCSoV4LrmwXRt2UwHyzexb6082bLHAvuBem7Zbs7IUS5SFCvJK/c2gJnBzv+M2+T2bXrqQFdwMEVkr+tgtYJIWyFBPVKEujlwoQ+TVmxJ5256wpuZ5fn4AbNb4MtcyEn00wNQghRPAnqlWh4h3pE1/dl8q9bOXk+u2CBqGGQeQZ2yPJGIUTZSFCvRHZ2ijcGtiQjK5fJv24tWKBBd/CqY9wwFUKIMpCgXsmaBHkyrkcj5q47zLJdaVeftLOHVkNh9yI4V0R6ASGEKIQE9SowPiacBv7u/OfnTWTmXJPzpfVw0CbYKHuYCiFKT4J6FXBxtOe12yM5kH6B//2z6+qT/o0htL2sWRdClIlDVTegpurSyJ9B0aF8umQvt0aFXH0yahj8+iQc3QAhraumgaWRfQHSdkLqduN1YjvRR3bC0QjwqQ++9cGn3r+fO3tWdYuFsFkS1KvQf/o245/tJ3hu7iYeaXbFqDxyIPzxnLFdXnUK6rlZcGIbpO4wNs4+sd34eOoAkN9+O0fwCyfH0cs4vi8BsjOurse1lhHkfesbgb71cAhsVundEcIWSVCvQr7uTrzYrxlPfL+BWOVAmw7Z+Lo7gasvNO0Lm36A3pPBwamqmwoXTsLnN8DJPcbX+cGbkDYQNRwCIozAXKsh2DuyMT6enj17GlNIF07C6f1GkD99IP/jQTi+Bbb/CkfWw6iFVdk7IWyGBPUqNqB1HZbuSmPuusMseWMxt7epw+iuDYiIGg5b5sGuv6BZv6ptpMkEc8fCmUPQfyqEtrscvIulFLj7Ga860QXPx70BS96Cs0fBq7bl2y5EDSM3SquYUor3hrTmta6uDGwbys/Jh7np/QTuincnyyUAXR3SBiS8A7v/hj5vQpsRxqi8JAG9JFoOArTxJK0QotwkqFcTdTyNTTVWTryeCX2asic9k5kZHcjb8Sez/0niXFG7J1Wk3Ysg/g1odaeRT97S/BtD7dbGVJMQotwkqFczvu5OPNizEQnPxtD0prE4kMfOxTPp/MY/vLJgC/sLyfJYIU4fhJ/GQGBz6Pd/BTbZtpiWg4x59fQ9FVO/EDWIBPVqytHejh7dekLtKJ4NXscNzQL5ZtUBev9fAptSzlR8A3KzYM49YMqDoV8b2/lVlBYDAQWbbHMnRCEqkwT16i5qOG7pm3k/xomlE2LwdnPk+XmbyDMV8WDS2aMQOwJ+nwDZZRzZ/z7BGD0P+AT8GpWtjpLyrgNh1xlTMPLAlRDlUmxQV0rNUEqdUEptLuT8CKXURqXUJqXUCqVUlOWbWYO1HGwsH0yeTW1vV17s15xNh88wa+V+8+V3/gXTuhpz4aunwafdISWp0Oq11qRnZF19MHk2JH1pbLRdWStvIu+A9OY2YvIAACAASURBVF1wbGPlvJ8QNqokI/WZQJ8izu8DemitWwL/BT6zQLvEJe5+0OQmIxdMXi63tqpN9yYBTPlzB0fPXPy3XG42/PkfmD0YPGvDA0vhngVGbvYvboT4tyAvt0D17y/aRfTkRfzf3zsxmTQc2wQLn4CwbtDrpcrrZ/P+xi8vuWEqRLkUG9S11gnAySLOr9Ban8r/chUQaqG2iUuihsH5E7DnH5RSTO4fSa5JM2lBfvre9D0wozes/Aja3w9jFkNAE2jYAx5cboyC41+HGTdddTMyYWcqH/6zi7q1XPlg8S4e+TKevNi7wcUHBs0A+0p8jMGtFoRfD5vnGuvihRBlosxtrVagkFJhwEKtdWQx5Z4GmmqtxxRyfiwwFiAoKCg6Nja2tO0FICMjAw8PjzJdW10V1SdlyqHLitGc8m3F1hbPArBwTzY/7sphaqM19D72KVrZsyPiYdICOputI+DEUprsnIadKYfd4fey1fdGXlqZibeT4sXOrixPyabb7reIsU9mUcR/candosL6U5jA4wk03/Yu61u/zhmf8r1/RbC1f3e21h+wvT6Z609MTEyS1rpdoRdprYt9AWHA5mLKxADbAL+S1BkdHa3LKi4urszXVlfF9um3Z7V+1V/rCye11lpnnT+rf5s8UOuXvXTu9Bu1PnWw+Dc5c1jrr27T+mUvnfj6Dbrbi9/pXcfPGeeWvqf1y156yqTHdNMXftfzkw9XbH/MycrQenKw1gseK9d7VxRb+3dna/3R2vb6ZK4/QKIuIrZaZPWLUqoV8DnQX2udbok6xTWihkFetjE9cWwzTjN60SdnMR/mDuCt4CngU7f4OrxC4K55LKr/JC0y1/O3y0TC0+Nh7xJY/Cq0uJ27H3uDFiFePPrdel77dSu5eWWbCjGVZRWLkztE9IWtPxv3CIQQpVbuoK6UqgfMBe7WWu8sf5OEWbWjjIeAEqbA9F6QeQZ1z3yOtHmKL1aksPXI2RJVE7crjTE72jG1yRc416oL34+A2UON5Fy3/Y9Ab1dm39+JkZ3rM33pPu76YjVp166OMSPPpEncf5I3ft/GDe8t4aFFF/h5fcENtovVcjBcPAV740p/rRCiREsavwNWAhFKqRSl1H1KqXFKqXH5RV4C/ICpSqlkpVRiBba35lIKWo+Ac0eMvUzHLYeGPZh4c1N8XEuwdh04cvoiT36fTNNgTx4a0s+4odrtKfAMhqHfXM5z7uRgx6T+kbw7OIr1B09z6/+WkXzodIH6zmfl8sfmozw1ZwPtX1vEoGkr+WLpPoK8nAn1tOPx75N564/txqqakmrUy8hSKatghCiTYpc3aK2HFXN+DGD2xqiwsI7jjFS39TqDnfH72MfNiRfy0/fOXn2AuzuHmb00J8/EI9+tJzvXxNQRbXFxtAfs4fqXjJcZd0SHEhHsybhvkhgybSX/HdCCbo0DWLztOIu2nWDlnnSy80x4uTgQ0zSQG5oF0b1JAN6ujiz6J47Fp/35JH4Pu45n8P6drfFwLsFqGgcnY3njxjnGg1NO7mX8ZglRM0nqXWti7wBhXQscHtC6Dj8mpfD2Hzu4qUUwgV4uBcpM+XMHSQdO8eGwNjQMKPnqgMg63vzy8HU8GrueCT9tuny8vp8bd3euzw3NgmgX5ouj/dV/9DnYKV6/PZKmwZ68unArd0xdwecj21G3VgnSDbQcDEkzYcfv+VkcRYldPAVOnpW7HFVUK5ImwAYopZg8oCVZeSYmLdxa4Pzibcf5NGEvIzrW47Zrt84rAV93J2aO7sCk21ow8eamLHqyO/FP9+TFfs3p3MivQEC/sl0ju4Tx1egOHD1zkds+WsaqvSW4j16vC3iGSC6Y0jp9ED6Igvnjq7ologpJULcRDfzdeTgmnF83HiVux4nLx1NOXeDJORtoEeLFi/2al7l+ezsjQI/r0YjwQE9UKTI2XtfYn/kPX4evuxN3fb6a2asPFn2BnZ2xpd/uRcauSaJ4JhPMexAyz8DG741dpUSNJEHdhjzQoyENA9x58efNXMzOIzvXxMOz15Nn0nw8/NI8etVo4O/OvIe60iXcn+fnbeKVBVuKXi7ZcjCYcmDbgsprpDVb9TEcWAa9XwNnL4h7vapbJKqIBHUb4uxgz+u3tyTl1EU+/GcXb/2xneRDp3l7UCvC/Kv+hqO3qyMzRrbjvusaMHPFfkZ9uZYzFwrZ/KN2lLHMUqZgind8i/GcQcQt0Hk8dHkYti+Ew+sqtx15uZC2u3LfUxQgQd3GdGrox6DoUD5L2MsXy/YxsnN9+rasPnt/Otjb8WK/5rx9RytW70tnwNTl7E3NKFhQKWO0vn8ZnD1S+Q21FrlZMPcBcPGGWz8wvm8dx4FrLfhncuW2ZcEj8FE7OLqhct9XXEWCug16vm8zfFwdaRXqzfO3NKvq5pg1pH1dZt/fiTMXcxj3TRLZuWamYiLz9y/dLPuXFirudTi+CW77H3gEGMdcvOC6x2HPYjiwonLaseMP2DAb0MZm4qLKSFC3QbXcnfjrie7MeaAzzg5VN49enPZhtZgyuBU7j2cwNd7Mn+3+4bJ/aVEOrIDlH0DbkRBx89Xn2t8PHkHGaL2iNx65eAp+eQwCW0D3Z2Hn73C48Bz+omJJULdRfh7OVXpjtKR6NQ2if+sQPo7bzc7j5woWaDkYjibLXO21Ms/CvAfAtz7cZOamqJMbdHsaDiyHvfEV25bfJ8KFNBgwFbo+akz9yI3aKiNBXVS5l/o1x8PZgWd/3Fgw1UFk/v6lm+WG6VX+fA7OpMDtn4FzIQ+TRY8Er1D4578VN1rf/htsjDXSTYS0NlJNdH3MWI56cHXFvKcokgR1UeX8PJx55bYWJB86zcwV+68+6RUi+5dea9tCWP8NXPcE1OtYeDkHZ+jxrDEVsvMPy7fjwklY+DgEtTT+Krikw/3gHgBxr1n+PUWxJKiLauG2qBB6NQ1kyp87OHTywtUnWw6C9N2yqgIg4wT88igEt4IeE4sv33o4+DaAf16z/I5Sv0+AC+nGtIuD07/HndyNXzj7lhirl0SlkqAuqgUj1UEk9naK5+ZuurTxiqHZbbJ/KRh/qSx4BLIyYOD0qwNpYewdIeZ5Y4XMtvmWa8v2X2HTHOj+DNRuVfB8u3vBI9j4ZSJ/YVUqCeqi2gjxcWXCzU1ZtjuNH5JS/j3hVgvCb8jfvzSv6hpY1dbNMqZRbpwEgU1Lfl3kHRDQ1Lh5aYnv34WT8MvjENzSmEs3x9HVOHdwheTGr2QS1EW1MqJDPTqE1WLywq2cOJf574m29xi55Be9UmVtq1In98Ifz0GDHtDhgdJda2dvjNbTdhopjcvrt2fg4kkY8Inxl0BhLt2ojXtdRuuVSIK6qFbs7BRv3tGSzFwTL8+/IilV077Q7j5Y8SFstJFpmNOHjDnng6sgJRGOrIejG+H4VkjdCel74NQBY5XLvHFg52DMX9uV4b9t01uNefj4NyCvkNQMJbHtF2MlUo8Jxki9KA7O0P1pSFkLu/4u+3uKUpGky6LaaRjgweM3NObtP3bwx+aj9InMT3PQ5004sQ0WPAz+jY0ldNZqXwJ8OxhyM4sve8nAz8E7tGzvZ2cHvV6E2YNh/dfGnHdpnU+HhU8YeXmue6Jk17S5C5b9n7ESpvGNRhoDUaEkqItq6f5uDVm44Sgvzt9C54b+eLs5GjcGh8yCz3pC7AgYG//vo/HW5MBKY19Y3wbQJ/8hHVMemHKveOVdfcwrxLivUB6Nb4TQDrDkHYgaXvrrf38GLp6Ge+YXPe1yJXtHY1nl/PGw4zdoekvp31eUigR1US052tvx9qBW9P94Oa/9tpW3B0UZJzwC4M5vYEYf+GFk6QJMWR3fAgsepYmpFnTtBI4Fd5YqsZREY4TuVQdGLgCPQMu1szhKQa8XYNZtkPQlUIq8QFvnw+afjOuDWpTufVvdCUvfM+bWm9xctukjUWLy3RXVVmQdb8Z2b8icxBSW7Ur790RIG7jtI+MR+D9KsFa7rLSGVdPgsxhI303I0b9gZt+yZ408sh6+Hgju/pUf0C9p2APCusHSd7HLK+HUz/k0WPikkYenawmnXa5k7wA9J8LxzZZdVinMkqAuqrXHrm9MA393npu3kQvZuf+eaDUYujwCaz+HpK8s/8YZqTB7CPwxARr2hIcT2dziOUjdAZ/2KP0j8Mc2wde3g6s3jPzFmE6pKr1ehPOphKYsNH5xZWUYN22PbjTm+rfON76ny96Hv1+G74ZB1tn81S5l/OM+8g7wjzAyONbkZamVoNifkFJqBtAPOKG1jjRzXgEfAH2BC8AorXUlZ+cXtsrF0Z43B7Zk6GerePevnVdvyXfDJGNq5NenjHXYRT0yXxq7FsHP44ykWX2nQPsxoBRpAZ2gR3+IHQ4zb4FbpkD0qOLrO7EdZvUHRzcjoPvUtUw7y6peR2jcmwa7voX/fmfM2RfGzhFcfeHmtyCo7NshYmdvjNZ/HG08b9BqcNnrEkUqya/dmcBHwKxCzt8MNM5/dQQ+yf8ohEV0bOjHXZ3qMWP5Pu5oG0rzEC/jhJ09DJphTI98f5dx49S7TtnfKCcTFk+CVVMhsDncs6BgIAtsBvf/Az/eZ6SbPbrRWJVT2NOdabuNOWw7RyOg+4aVvX2W1OdNDl94kdAGEUbQdvUxPrr4XP21o5vlVqw0HwCBU4xllS1uL/uoXxSp2OkXrXUCUNTuv/2BWdqwCvBRSlWfrXaETXimd1O8XBx584/tV59w9YVh30HOBSOw55RiieCVTmyHz683AnqHB4zAXdjI1NUXRvxgZCNM/MIYhWecKFju5F746lbQJmMO3a9R2dpWEfwasbvxWLjhZSNdbtt7oNmt0KAbBEcaSyed3C27BNHODmKeg5N7jM2xRYVQugRPeimlwoCFhUy/LATe1Fovy/96MTBBa51opuxYYCxAUFBQdGxsbJkanZGRgYdHIelGrZSt9aki+vPHvhxid2TzdDsXIv2vzhXvn7qKyC1vcCwohu1NHyt5MNKakCN/0GjPDPLsXdne9FFO+rUzW9RcnwKPJxCx43/kOHqyOfJ5MjzDAXDOPEGb9c9jn5dJcuvJnPcIK3V/zckzac7nQEaO5nyO/vdjNpzP/zzbZJTL05CnwaQhzwR5Ov+YyTju7ZjHA63dcXGoxLXjWhOd9BQOuRms6TAVbedw1Tk7Uw72eZnY5128/Mqzd+O8R/0SVV8T/h/FxMQkaa3N/yOlkoP6ldq1a6cTE4ssUqj4+Hh69uxZpmurK1vrU0X0Jys3j+vfXYKXiyMLH7kOO7trglHcG7DkTbjpDej8UMEK8nKNrILnU41NHc6nGRtb7/zdWAPefyp4BpW+T0c3GOvmz6fCrR8aqYJn9jV2BBr5i/GwTjn8vfU4r/+2jbRzWZzLKnz+204Zm3u7OtrjYG+Hg53CwV5hb2eHo70yvrazw8FeYacUy3anMea6BrzQrxxz5WWx80/jJnRAfv6arAzIPgfZ5wuf3+/4INz4arFJzGrC/yOlVJFB3RKTWoeBK+/8hOYfE8KinB3seeamCB6LTWb+hsPc3uaapyt7TDCWzf31gvEx80x+EE8zAm7m6YKV2jsZvwQ6jiv7+unaUcZ8/g+jYN5YY+efvBxjDX05A3ryodM8PHsdYX7u3BEdio+bI75uTvi4OeLj5oSPq+Plzz2dHQr+oivCqI//ZMbyfQxoU4fIOt7lamepNO5t3Hw+uc/Y4MPp0ss9/2vPKz53h51/wepP4NAqGPQl1GpQeW21QpYI6guAh5VSsRg3SM9orY9aoF4hCri1VQjTl+5lyp87uTmy9tVb9tnZwe3TjKc1d/0Fbv7GmvDgyH8/d/e/4vMA8KxtbNRcXu7+cPc84xfKph/grh8hNLpcVR46eYExX60l0MuZ2fd3xM/DufztvMLgJk5sOW3Pc3M3Me+hLjjYV9IKZ6XglndLXj78BmOuf/54+LS7scl2iwEV1z4rV5Iljd8BPQF/pVQK8DLgCKC1ngb8hrGccTfGksbRFdVYIezsFM/f3Izhn69m1sr9jO1+zc1HZ08Y/VuVtA17R2PpX583y32D8WxmDvfOXEtWronYsZ0sHtAB3B0VL9/anIdnr+erlQe477ryjYDPXMgh8cBJYiICS/UXQ4k0y09I9uNo40ni/WOg92vle7rXRhUb1LXWw4o5r4HxFmuREMXoEu5PTEQAH/2zmyHt6uLjVoLNIipTOQN6Tp6J8d+uY1/aeWbd24HwQE8LNaygW1rW5qeIFN79awd9IoOp4+Napnoyc/IYNXMN6w+epkeTAN4dEoW/pX8R+daH0X8Yy05XfgSHVsPgr6rXqqJqQJ4oFVZp4s3NyMjK5aN/dld1UyxKa81L8zezdFcarw9sSZdw/wp9P6UUr/aPRGt4ef5mSrJw4lomk+bpHzaQfOg0d3Wqx8q96fT9YCkrdqcVf3FpOTjBTa/BsFgjJfGn3Y2b3eIyCerCKkUEezIoOpRZKw8U3NPUin2WsJfv1hxifEwjhrSrnCdP69Zy48kbm7Bo2wn+2Hys1Ne/9/dOFm48ysQ+TZk8oCXzx3fF08WBEV+sZsqfO8jNs/DeqAARN8O4ZUZysZ/ugwWPQs7Fkl9vw5t2yCNdwmo9cWMTFmw4wpS/dvDBnW2qujnl9sfmo7z5x3b6tarNUzdGVOp7j+4axs/Jh3l5wRa6NvbHy6VkmS9/SDzER3G7ubN9XcZ2bwhAs9pe/PLIdbyyYAsfxe1m1d50PhjWpsxTO4XyDoVRvxq52pf9H6QkUt+tNfy1GLIzIOuc+Vd2hrGBR9QwY+OVgCaWbVcVk5G6sFq1vV2577oGzE8+wqaUM1XdnHJJPnSax79PpnVdH6YMjrL8jcZiONjb8cbAlqRlZPH2tU/tFmLlnnSen7eJruF+/HdAJOqKewluTg68PSiKD+5szfZj5+j7wdIy/RVQLHtHuOEVGPETnD9Bg/3fwprPjB2aUhLzM2oqI4FaaDtofht0GAvhN0LiDPi4vfHU79YFxnMMZZF9AXYvNpLL7Vtq7OFahWSkLqzaAz0a8d2aQ7z+2zZm39/xqsBiLVJOXWDMV4kEeDoz/Z52Vy/TrEStQn0Y2SWMmSv2c3ubUKLr+xZadk9qBuO+SaK+nztTR0TjWMhyyP6t6xAV6sMj361n3DdJ3NO5Ps/3bWb5Pja+AZ7czpIl8fToVcLNRDJSYf0sSPwS5twNniFGgrbokeAZXPh1pjzjgbO9cbA33tiOMC/76jJedYypoaDIfz/6hVdKvhsJ6sKqebk48mivcF75ZSvxO1OJiaiCHOXl8O/SxTxix3a0/IqRUnqqdwR/bD7G83M3sfDR68wG65Pns7l35loc7BRfjmqPt2vRUzVh/u789GAX3v5jO58v28eafSf5aHhbwgMt/Di/vcPVaQeK4xEA3Z6Cro8bzzWs/RziX4eEt40llO3HQP2uxmqmk/v+DeL7EoynhcEI1h3GQsMYCIiAtB1G5tDjW+DYZtjzz79Pydo7Q2BT45rm/aHJTZbtfz4J6sLqDe9Yn5kr9vPmb9vp3jgA+0qeuiirS0sX96ZW/NLFkvJwduDV/pHcPyuRzxL2Mj4m/KrzWbl5PPB1IkfPZPLd/Z2oW8utRPU6OdjxQr/mdAn34+kfNnLr/5bRNdwfBzuFff7rys8vfW1np/D3cGZEx3oVt3TVzt648Rpxs7HZd+IMWP8NbJkH/k0gNwtOHzDKetWBiFuMHPsNexTc6MSn7tXbDuZmQ9pO4wnn45uNYL/rbyNbpwR1IcxzcrDj2T5Neejbdfy0LqXcq0a01lzMyePUhRxOX8jm9IUczl7MITvLcismtNa8+LOxdPHtO1pV+NLF0rixeRB9WgTz4eJd9GtVm/p+7oDR5ok/bWLt/lP8b1ibIqdnCtOraRC/PdqNVxduYX/aBfJMmlyTCZOGXJOJvDxtJB4zaXJNxseMrFw+XbKHh2LCGdUlrGKnp/waGUsme71g5H1Png0u3tD5YWgUY0yhlGaKz8HJeKI5+Jq0WWWdvy/JW1ZYzUJUopsjg2ld14d3/9rBra1CcHUq/D++1prdJzJYte8kWw6f4eT5bE5f/DeAn76QQ7aZZXi1XBStos/TwN+9XG3VWvPygi3Erj3EwzHhDGlfxZtmmPHKbS1Y/l4a/5m3ma/v64BSig8W72Le+sM83bsJt0aVfeemYG8Xpo4oeQqF7cfO8tbv23nz9+18tWI/T97YhIFtQyv2LzJHV2gzwnhVhAqcW5egLmyCUorn+zZjyKcrmbF831XTBiaTZteJDFbvS2fV3nTW7DtJWoZxY6uWuxMBHs74uDnSwN8dXzcnvC8lzXLNT5rl5kh2ronx36xhyKcr+e7+jmWeKjEeLtrC16sO8ED3hjzVu3oupwv2duHZPhG8OH8L89Yfxk4p3l+0izvahhaYkqloTYO9+HJ0B1buSefN37fxzI8b+WLZPib0aUrPiACrvDlekSSoC5vRoUEtbmwexCfxe2gfVoutR86wau9J1uw/ycnzRhAP8Xahe+MAOjasRaeGftSr5VbioDCxgyvvbzAx9NNVfDOmI81qly4RmMmkeWnBZr5ZdZAHejRkYp+m1TogjehYn7nrDzPpl61czM6jY4NavDGwZZW1uXMjP34e35VfNx3lnT93MHrmWjo1rMVzNzcjqq5PlbSpOpKgLmzKhD5Nuen9BIZ8uhKAOj6uxEQE0ik/iIf6upY5KNXxsOP7sR0YPn01w6av4pv7OpY4Za3JpHlh/mZmrz7IuB6NmNAnoloHdDCSp70xsCX9PlxG3VpuTLsrGieHqn20RSlFv1Yh9G4ezHdrDvLh4l30/3g5/VrV5pmbKveBrepKgrqwKeGBHnwyoi1nM3Pp1LAWob4lW51RUg0DPJjzQGeGTV/F8Omr+OreDrSpV/QNQ5NJ85+fN/PdmoM81LMRz9xU/QP6JU2DvZj7UBdCfFzxda8+idOcHOwY2SWMgW3rMD1hL9OX7uPPLcfoUtue+pHlv+9hzeSJUmFzercIZlB0qMUD+iX1/Nz4/oFO+Lg5cfcXa1i7v/AnCI2Avonv1hxkfIx1BfRLWoX6VPn6+cJ4ujjyZO8IljzTkzvb12P5kVx6vRvPg98kseGQmU1RagAJ6kKUQaivG3Me6EygpzMjZ6xhxZ6CGQlNJs3z8zbx3RpjlcvTva0voFuLQC8X/jsgknd7uPFQz0Ys351G/4+Xc+dnK4nfcaJM2SetlQR1Icoo2NuF2Ac6UcfHldFfriVhZ+rlcyaT5rm5m4hde4hHeoXzVO8mEtArgbez4pmbmrLiuev5T99m7E+7wKgv19L3w2XMTz5cMRkjqxkJ6kKUQ6CnC7FjO9EwwIMxXyXyz/bjmEyaiXM38n3iIR69vjFP3igBvbJ5ODtwf/eGJDwbw9uDWpGdm8djscn0nBLPVyv2czE7r6qbWGEkqAtRTn4eznx3f0ea1vbkga+TGPnlGuYkpvCYBPQq5+Rgx5B2dfn7iR5Mv6cdQV4uvLxgC13eXMz7i3ZeXupqSySoC2EBPm5OfDOmIy3reLN0VxqPXd+YJ26sng8W1UR2doobmwfx04Nd+GFcZ6Lr+/L+ol10eXMxL8/fbFMbrciSRiEsxMvFkW/GdGTb0bNE169V1c0RhWgfVov2YbXYdfwcnyXsZfaag3y96gC3tArhge4NS/zsQXVVopG6UqqPUmqHUmq3UmqimfP1lFJxSqn1SqmNSqm+lm+qENWfm5ODBHQr0TjIk3cGR7H02V7c360hcdtP0O9/y7j7i9Us25VmtStmig3qSil74GPgZqA5MEwp1fyaYi8Ac7TWbYA7gamWbqgQQlSEYG8XnuvbjBXP9WJCn6ZsP3aOu75YTb//LWPBhiNWt2KmJCP1DsBurfVerXU2EAv0v6aMBi4lwvAGjliuiUIIUfG8XBx5sGcjlk2I4a07WnIxJ49Hv1vPdW/F8f6inRw/m1nVTSwRVdyfGEqpQUAfrfWY/K/vBjpqrR++okxt4C/AF3AHbtBaJ5mpaywwFiAoKCg6Nja2TI3OyMjAw8PCu6ZUMVvrk631B2yvT7bWH7Bsn0xasyE1j8UHc9mcloedgjaB9vSq60gzPzvsKmFVk7n+xMTEJGmt2xV2jaVulA4DZmqt31VKdQa+VkpFaq2v+rtFa/0Z8BlAu3btdM+ePcv0ZvHx8ZT12urK1vpka/0B2+uTrfUHLN+nXsATwIH088xefZA5iYdISsykgb87IzrW4462oRWaE6cs/SnJ9Mth4Mos/qH5x650HzAHQGu9EnABqs9WLkIIUQ71/dx5rm8zVj53Pe8PbY2fuxOTf91GxzcW8+ScZNYdPFVtbqyWZKS+FmislGqAEczvBIZfU+YgcD0wUynVDCOopyKEEDbExdGeAW3qMKBNHbYdPcu3qw8wb91h5q47TNNgT65vFkjPiEDa1PXBwcym3ZWh2KCutc5VSj0M/AnYAzO01luUUq8CiVrrBcBTwHSl1BMYN01H6erya0sIISpAs9peTB7Qkok3N2N+8mHmrz/CtCV7+ThuD14uDnRrEkDPJgH0iAgg0NOl0tpVojl1rfVvwG/XHHvpis+3Al0t2zQhhKj+PJwdGNGxPiM61ufMxRyW704jfscJ4nek8uvGowC0CPGiZ0QAMRGBtK7gUbw8USqEEBbi7epI35a16duyNlprth49S/yOVJbsSL1qFP9Ir8bc371hhbRBgroQQlQApRQtQrxpEeLN+JhwzlzMYdkuYxQf5F1x0zES1IUQohJ4uzpyS6va3NKqdoW+j2RpFEIIGyJBXQghbIgEdSGEsCES1IUQwoZIUBdCCBsiQV0IIWyIBHUhhLAhEtSFEMKGFLtJRoW9sVKpwIEyXu4PpFmwOdWBrfXJ1voDttcnW+sP2F6fzPWnvtY6oLAL3UAIcgAAA+9JREFUqiyol4dSKrGonT+ska31ydb6A7bXJ1vrD9hen8rSH5l+EUIIGyJBXQghbIi1BvXPqroBFcDW+mRr/QHb65Ot9Qdsr0+l7o9VzqkLIYQwz1pH6kIIIcyQoC6EEDbE6oK6UqqPUmqHUmq3UmpiVbfHEpRS+5VSm5RSyUqpxKpuT2kppWYopU4opTZfcayWUupvpdSu/I++VdnG0iqkT68opQ7n/5ySlVJ9q7KNpaGUqquUilNKbVVKbVFKPZZ/3Cp/TkX0x5p/Ri5KqTVKqQ35fZqUf7yBUmp1fsz7XinlVGQ91jSnrpSyB3YCNwIpwFpgWP7G11ZLKbUfaKe1tsqHJpRS3YEMYJbWOjL/2NvASa31m/m/fH211hOqsp2lUUifXgEytNZTqrJtZaGUqg3U1lqvU0p5AknAAGAUVvhzKqI/Q7Den5EC3LXWGUopR2AZ8BjwJDBXax2rlJoGbNBaf1JYPdY2Uu8A7NZa79VaZwOxQP8qblONp7VOAE5ec7g/8FX+519h/IezGoX0yWpprY9qrdflf34O2AbUwUp/TkX0x2ppQ0b+l475Lw30An7MP17sz8jagnod4NAVX6dg5T/IfBr4SymVpJQaW9WNsZAgrfXR/M+PAUFV2RgLelgptTF/esYqpiqupZQKA9oAq7GBn9M1/QEr/hkppeyVUsnACeBvYA9wWmudm1+k2JhnbUHdVl2ntW4L3AyMz//T32ZoY47Peub5CvcJ0AhoDRwF3q3a5pSeUsoD+Al4XGt99spz1vhzMtMfq/4Zaa3ztNatgVCMmYmmpa3D2oL6YaDu/7dzvy4NRWEYx78vDkEsQ7CJQbDaBcOSXRBREIz+DxZBsIpdtPmDgb/2DxiMBoOC1WLYktXiazjvYMFtYcJ8L8+n7O7cbZzDyx7uPedwe97PRVtq7v4Rrx3gllLM7Nox79md/+yMuT8jc/d2/Om+gROS1Snmaa+Bc3e/iea0dfptPNlr1OXun8ADsAzUzawWp4ZmXrZQfwIWYzV4EtgEWmPu00jMbDoWejCzaWAVeB38rRRawE4c7wD3Y+zLn+iGX1gjUZ1iEe4UeHP3o55TKevUbzzJazRrZvU4nqJsCHmjhPt6fGxojVLtfgGILUrHwARw5u6HY+7SSMxsgXJ1DlADLrKNycwugQblMaFtYB+4A5rAPOURyxvunmbhsc+YGpTbegfegd2e+eh/zcxWgEfgBfiO5j3KPHS6Og0YzxZ5a7REWQidoFxwN939IDLiCpgBnoFtd//q+zvZQl1ERPrLNv0iIiIDKNRFRCpEoS4iUiEKdRGRClGoi4hUiEJdRKRCFOoiIhXyA1gwujdFnIV6AAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "id": "8u8HFGbwxWtT",
        "outputId": "7be32918-7384-4eee-93ef-fb3ffb957ab0"
      },
      "source": [
        "df = pd.DataFrame()\n",
        "df['accuracy'] = m_h2.history['accuracy']\n",
        "df.plot(grid=True)"
      ],
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fee30a47ac8>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 26
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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iss0Y85Qx5uoKi94IJLv/PFCqwfx5SRoFJWU8Pq6XZWdV9IkL5zcXdWHuhkyW7sxp8Nffe6iIpakH+eWQDgT6efaXzpiesfRv14LXvkk/a759+WyXzfj5Gp7T2S4NyqODj0RkoYh0F5EuIvKs+7HHRWRBhWWeFJGH6qtQpSqzM/sY76/exy1DOxDfKszb5ZyX313SlfjYMB7+dAv5xxt2GObkNMZbhrT3eB1jDDMui+dAfgnvr9532nPvrtJhF2/RI0qVZYkIT32+nbAgf+4d3d3b5Zy3isMwTzXgMExhqYN/r8/gyr6tiTnHU+sO79qSC7u25K8pP1FY6gDK9/pfWLSTpPhorkvQYZeGpqGuLOur7Tl8/9Nh7hvTnYiQup/C6A194sL57aguzPuh4YZhPv0hk4JSB1OGd6zV+g9cFk9e0QneXr4bl0uYMXcz/r4+PDexrw67eIGGurKkUoeTZ7/YQffYUG4+hyEDK/jdxd24oFUYD82r/2EYl6v8bIz92rWo9cUl+rVrwdherXh7+R7+vCSNtXvzeHxcz0oPXlL1T0NdWdLsFXvZn3ecx8f1atTXzqyNAD8fXr6uH4eLTvDH/26r19davusQu3OLmDL83E5Odqb7L+vO8RMOXl+6i4sviGGSDrt4jb3+N6gm4eCxEt5Yms6YnrFc2K2lt8upF73blg/DfPpDFku2198wzJyVe2gZGsgVfWqexlidrjFh3Di4PZEhAfzpGp3t4k0a6spyXlycSplTePSKHt4upV6dHIZ5ZH79DMNkF7lYlprLzUPaezyNsTrPjO/NdzOSdNjFyzTUlaVsyjjK3A2Z3HZhJzq2PP/LqjVmFYdhHp6/meMnHHW6/W/2l+Hva+rsMwkfH0NooF53x9s01JVliAhPfr6N6LBApl/c1dvlNIjebcO5b0x3Fm7JZuSLKby3ai8nHFUflu+pwlIHyzMdXNnn3KcxqsZNQ11Zxmcbf+bH/Ud54LL4JrVHeHdSV+b9ZhidW4bw+GfbGD3zWz7bmIXLde4Hb4sIP+w/woy5myhxwpQRneqhYuVNTed/hrK0olIHz325gz5tw5k0sOnNrEjoEMnHdw4lJS2XFxelck/yRt76djczLotnVHx0jR9MZuQdZ/6PWcz/MYs9h4oI9PPh0g5+9G+nFyizGw111eiJCM99uYOcY6X89eaB+DTRa1gaY0iKj+GibtF8vvlnXvkqjalz1jG4UyQPjr2AhA6nzzM/VlLGws0H+PTHLNbuyQNgaOdIfnNRFy7v04oNq1d6ow1VzzTUVaP3zsq9vL96P9NGdiahw7ldF9OOfHwM4/u35fLerfl43X5e+2YX1/7te8b0jOXe0d3JOVbCvB8y+Xp7DqUOF51bhnD/pd2ZMKAtcRHB3i5f1TMNddWofbUtm6e/2M7YXq14aOwF3i6nUQnw82HysI5cmxDH7BV7+Pu3u7li+3IAIoL9uWFQOyYOjKNfXLjOG29CNNRVo7UlM597kjfSt204f76hf5MddqlJcIAf0y/uxs1DOjB3QyYdooIZFR9DgJ/Og2iKNNRVo5R1tJjb3l1HZEgA/7g1kWYB1rqakTdEhARwx8jO3i5DeZn+KleNTkFJGb+es46SE07emTqImDCdR62Up3RPXTUqDqeL6R/+SPrBQuZMHUT3WGtf+EKphqZ76qrREBGeWLCNb9NyeWZCb37RLdrbJSllORrqqtH454o9fLBmP3de1JmbBtvrHOlKNRQNddUoLNqazbMLd3B571Y8eJlOXVSqtjTUlddtyjjK/338I/3iWujURaXOk4a68qrMI8f59bvraRkayD9+lUiQv05dVOp86OwX5TUlZU5uf3c9pQ4nH90xhOiwQG+XpJTlaagrr3ntm3R2ZhfwztRBdNOpi0rVCR1+UV6xJTOfWd/t5vrEOJLiY7xdjlK2oaGuGtwJh4sH5m4iKiSAR6/s6e1ylLIVj0LdGDPWGJNqjNlljHmoimWuN8ZsN8ZsM8Z8WLdlKjt569uf2JldwDMTehPezN/b5ShlKzWOqRtjfIE3gTFAJrDOGLNARLZXWKYb8DAwQkSOGGP072lVqbScAl5fms5V/dpwaa9W3i5HKdvxZE99MLBLRHaLyAkgGRh/xjJ3AG+KyBEAETlYt2UqO3C6hAfmbiYsyJ8nr9JhF6XqgxGp/uK1xphJwFgRud19fzIwRESmV1jmP0AaMALwBZ4UkUWVbGsaMA0gNjY2ITk5uVZFFxYWEhoaWqt1Gyu79VRZP1/uKePj1BPc1TeQoW2sN/GqKbxHVme3nirrJykpaYOIJFa5kohU+wVMAt6ucH8y8MYZy/wXmA/4A52ADKBFddtNSEiQ2lq2bFmt122s7NbTmf3syS2U7o8ulF/PWScul8s7RZ0nu79HdmC3nirrB1gv1WSrJ8MvWUC7Cvfj3I9VlAksEJEyEdlD+V57Nw+2rZoAl0t4cN5mAvx8ePaa3nppNaXqkSehvg7oZozpZIwJAG4EFpyxzH+AUQDGmJZAd2B3HdapLOzDtftZsyePx67sQWxzveCFUvWpxlAXEQcwHVgM7AA+EZFtxpinjDFXuxdbDBw2xmwHlgEPiMjh+ipaeUd6TgFHik6c0zpZR4t5buEOLuzakusT29W8glLqvHj0aZWILAQWnvHY4xVuC3Cf+0vZ0NasfMa/uRI/H8M1A9oyZURHLmjVvNp1RIRHPt2CAM9N7KPDLko1AOtNQVANrszp4oG5m4kKCeCSHjHM/zGL5HUZDO8SxZThHbmkRyy+lZwu99Mfsvg2LZcnr+pJu8hgL1SuVNOjoa5q9FbKT+w4cIxZkxO4tFcrHhx7AcnrMnjv+71M+9cG2kcG86thHbh+UDuaB5UfIXq01MVT324nsUMEvxrW0bsNKNWE6LlfbEhEWLwtm+z8kvPeVnpOAa8v3cW4vq1PHQHaIjiAuy7qwnczkvjrzQNp1TyIZ77YwdA/fcPjn23lp9xC3t9+guIyJy9M6qsXvVCqAemeus04XcKTC7bxr9X76NQyhE9/M5yIkIBab+uBuZsJCfTlj1f3Out5P18frujTmiv6tGZrVj7vrNxL8toM3lu1D4AHx15Al2j7HAiilBXonrqNlJQ5ufuDH/jX6n1M6N+GrCPF3Pn+Bkodzlpt752Ve9iYcZQnr+5FVGj1F7Do3TacV67vx8qHLube0d0Z1c6PO37RqVavq5SqPQ11m8gvLuNXs9eyaFs2j13Zg1dvHMBL1/Vl7Z48Hpq35eSRvx7be6iIl79KZXSPGK7u18bj9aLDArlndDem9ArEz1f/eSnV0HT4xQYO5BczZfY6dh8q5C83DTgVwuP7t2X/4eO88nUaHaKC+b/R3T3ansslPPTpZvx9fHhmgk5FVMpKNNQtLj2ngFtnr+VYiYM5UwczomvL056ffnFX9h4+zqtL0ukQFcw1A+Jq3OZH6/azencez0/sQ6twPQJUKSvRULew9Xvz+PW76/H39SF52lB6tw0/axljDM9N7EPW0ePMmLuZNuHNGNI5qspt/ny0mOcW7mRE1yhuGKRHgCplNTroaVFfbcvm5rfXEBkSwPzfDq800E8K8PPh77ck0i4ymDvf38Du3MJKlxMRHp2/BadLeH5iXx12UcqCNNQt6MM1+7nr/Q1c0CqMuXcN8+hozfBgf+ZMGYyvMdw2Zx15lZzD5T8bs1iWmsuMsfF6BKhSFqWhbiEiwqtL0nhk/hZGdo/mo2lDa5xqWFH7qGBm/SqRn/NLmPbeekrK/jfVMbeglD9+vp2EDhHcqkeAKmVZOqbeCGzOPMryzDJ2Ld/NseIyjhaXkX/G1zH39zKnMCkhjucm9sG/FlMGEzpEMPP6fkz/8EdmzN3Mazf2xxjDEwu2cvyEkxeu1SNAlbIyDXUvO1hQwrV/+54yp8DWHRgDzYP8CW/2v6824c1o7r7dLSaUiQPbntd497i+bdh3+DgvLU6lY1QwPds0Z+GWbB64LJ6uMXoEqFJWpqHuZf9en0mZU3h0SBDXjx1JWKBfg+wp/3ZUF/YdLuIvS3cRGuhHrzbNmTayc72/rlKqfumYuhc5XcKHa/YzvEsU3SJ8CW/m32BDH8YYnr2mDyO6RlFS5uTFSX1rNZyjlGpcdE/di75LyyXraDGPXNED8lIb/PX9fX2YPWUQB4+V6mwXpWxCd8286IM1+2gZGsiYnrFeqyHQz1cDXSkb0VD3kqyjxSzdeZAbBsUR4Kdvg1KqbmiaeMnHa/cjwI2D2nu7FKWUjWioe0GZ00XyugxGdY/WoQ+lVJ3SUPeCb3bkcLCglJuHdPB2KUopm9FQ94IP1uynTXgQSRfEeLsUpZTNaKg3sL2HiliefogbB7fHVw/HV0rVMQ31BvbR2v34+hg9V7lSql5oqDegUoeTT9ZnMKZHLLHN9YpCSqm651GoG2PGGmNSjTG7jDEPVfL8FGNMrjFmo/vr9rov1foWbc3myPEybh6q0xiVUvWjxtMEGGN8gTeBMUAmsM4Ys0BEtp+x6MciMr0earSND1bvp0NUMCO6tKx5YaWUqgVP9tQHA7tEZLeInACSgfH1W5b9pOUUsHZvHr8c3F7PV66UqjdGRKpfwJhJwFgRud19fzIwpOJeuTFmCvAckAukAfeKSEYl25oGTAOIjY1NSE5OrlXRhYWFhIZa67zf728vJSXDwcykYJoHnB3qVuypOnbrB+zXk936Afv1VFk/SUlJG0QkscqVRKTaL2AS8HaF+5OBN85YJgoIdN++E1ha03YTEhKktpYtW1brdb3heKlDej+xSH7/0Q9VLmO1nmpit35E7NeT3foRsV9PlfUDrJdqstWT4ZcsoOL8uzj3YxV/MRwWkVL33beBBA+222R8vvlnCkocegSpUqreeRLq64BuxphOxpgA4EZgQcUFjDGtK9y9GthRdyVa3wdr9tMtJpRBHSO8XYpSyuZqnP0iIg5jzHRgMeALzBaRbcaYpyj/M2AB8HtjzNWAA8gDptRjzZayNSufTRlHefKqnud1XVGllPKER1c+EpGFwMIzHnu8wu2HgYfrtjR7+GDNfoL8fbhmYJy3S1FKNQF6RGk9Kigp47ONWVzdrw3hzfy9XY5SqgnQUK9H/9n4M8dPOPUDUqVUg9FQryciwger99G7bXP6xoV7uxylVBOhoV4PHE4Xn238mZ3ZBdw8pIN+QKqUajAefVCqauZwuli9O48vtvzM4m055BWdoE14EFf3a+Pt0pRSTYiG+nlwOF2s2n2YhVsOnAry4ABfLukRy5V9WjEqPoYgf19vl6mUakI01M/RySD/YvMBFm8rP5Xu/4K8NaPiozXIlVJeo6F+DkodTq56fQVpOYWEuIP8Cg1ypVQjoqF+Dv676QBpOYU8Nb4X1ye20yBXSjU6GuoeEhHeXrGH7rGhTB6qM1qUUo2TTmn00Krdh9lx4Bi3jeikga6UarQ01D00e8UeIkMCmDCgrbdLUUqpKmmoe2DPoSK+2XmQW4a013F0pVSjpqHugXdW7sHfx4dbhuk5XJRSjZuGeg3yj5fx7/WZXN2/DTFhQd4uRymlqqWhXoOP1u2nuMzJbSM6ebsUpZSqkYZ6NcqcLuas3MvwLlH0bNPc2+UopVSNNNSrsXDLAbKPlfDrC3UvXSllDRrqVRARZq/YQ+eWISTFx3i7HKWU8oiGehU27DvCpsx8po7oiI+PHmyklLIGDfUq/HPFHsKb+XNtgl4wWillHRrqlcjIO87ibdn8ckh7ggP09DhKKevQUK/EnO/34mMMtw7r6O1SlFLqnGion6GgpIyP12VwZd/WtArXg42UUtaioX6Gj9dlUFjq0GmMSilL0lCvwOkS5ny/l0EdI+gb18Lb5Sil1DnzKNSNMWONManGmF3GmIeqWe5aY4wYYxLrrsSG89W2bDKPFOteulLKsmoMdWOML/AmcDnQE7jJGNOzkuXCgHuANXVdZEP554o9tItsxpierbxdilJK1Yone+qDgV0isltETgDJwPhKlnsaeAEoqcP6GsymjKOs33eEqcM74asHGymlLMqISPULGDMJGCsit7vvTwaGiMj0CssMBB4VkWuNMSnA/SKyvpJtTQOmAcTGxiYkJyfXqujCwkJCQ80wx0cAAAuWSURBVENrtW5V3tpUwqZcJzNHBdPMr+FDvT568ia79QP268lu/YD9eqqsn6SkpA0iUuUQ93kfWWOM8QFmAlNqWlZEZgGzABITE2XUqFG1es2UlBRqu25lDuQXs/6rZUwZ3onLR581stQg6ronb7NbP2C/nuzWD9ivp9r040moZwHtKtyPcz92UhjQG0hxX5C5FbDAGHN1ZXvrjYnD6WLuhkxeXZIOwK3DO3q3IKWUOk+ehPo6oJsxphPlYX4j8MuTT4pIPtDy5P3qhl8aCxFh0dZsXvoqld25RQxo34LXfzmAdpHB3i5NKaXOS42hLiIOY8x0YDHgC8wWkW3GmKeA9SKyoL6LrEsrdx3ixUU72ZSZT7eYUGZNTmBMz1jcf2UopZSleTSmLiILgYVnPPZ4FcuOOv+y6t6WzHxeXLyT5emHaBMexEuT+jJxYJzOdFFK2YrtT0G4O7eQV75K44stB4gI9ucP43py85D2BPn7ers0pZSqc7YO9ZcXp/K3b38i0M+H31/SjTt+0YmwIH9vl6WUUvXGtqFeVOrgjWW7GN0jluev7UPL0EBvl6SUUvXOtif0Sj9YCMD1iXEa6EqpJsO2oZ6WXQBA99gwL1eilFINx76hnlNAkL+Pzj1XSjUptg311JwCusaE6pRFpVSTYttQT88p1KEXpVSTY8tQzy8uI/tYiYa6UqrJsWWop+eUf0gar6GulGpibBnqaTnl0xm7xdrnvMpKKeUJm4Z6ASEBvrRt0czbpSilVIOybah3iw3TMy8qpZoc24Z6dx16UUo1QbYL9cOFpRwqPKEzX5RSTZLtQv3kh6Qa6kqppsh2oZ5+0D2dsZWGulKq6bFdqKdmF9A8yI+YMD0zo1Kq6bFdqKfnFBLfSme+KKWaJluFuoiQ6p7OqJRSTZGtQj23oJT84jI9PYBSqsmyVajr6QGUUk2drUI9VU/kpZRq4mwV6uk5BUSFBBCl1yRVSjVRtgr11JwCPehIKdWk2SbURcR9tSMdT1dKNV0ehboxZqwxJtUYs8sY81Alz99ljNlijNlojFlhjOlZ96VW7+f8EgpLHTqdUSnVpNUY6sYYX+BN4HKgJ3BTJaH9oYj0EZH+wIvAzDqvtAZpOXp6AKWU8mRPfTCwS0R2i8gJIBkYX3EBETlW4W4IIHVXomfSsstDvXuMhrpSqukyItXnrzFmEjBWRG53358MDBGR6WcsdzdwHxAAXCwi6ZVsaxowDSA2NjYhOTm5VkUXFhYSGnr62Pk/Npey7bCTV5OCa7VNb6usJyuzWz9gv57s1g/Yr6fK+klKStogIolVriQi1X4Bk4C3K9yfDLxRzfK/BN6tabsJCQlSW8uWLTvrsXF/WS63vL261tv0tsp6sjK79SNiv57s1o+I/XqqrB9gvVSTrZ4Mv2QB7Srcj3M/VpVkYIIH260zLpeQflCnMyqllCehvg7oZozpZIwJAG4EFlRcwBjTrcLdK4Gzhl7qU8aR45SUuXQ6o1KqyfOraQERcRhjpgOLAV9gtohsM8Y8RfmfAQuA6caY0UAZcAS4tT6LPpNe7UgppcrVGOoAIrIQWHjGY49XuH1PHdd1Tk5OZ9Q56kqpps4WR5Sm5RTQtkUzQgM9+h2llFK2ZZNQ19MDKKUU2CDUHU4XPx0spLseSaqUUtYP9X15xznhdOmRpEophQ1C/dTpAfRDUqWUskGo5xRiDHSN0TF1pZSyQagX0D4ymGYBvt4uRSmlvM4Woa5DL0opVc7SoX7C4WLPoSKdzqiUUm6WDvU9h4pwuET31JVSys3SoZ6aozNflFKqIkuHenpOAb4+hs7RId4uRSmlGgVLh3pqdgEdo4IJ9NOZL0opBRYP9fSDhXqhaaWUqsCyoV5S5mTf4SK66ekBlFLqFMuG+q6DhbgE3VNXSqkKLBvq6QdPznzROepKKXWSZUM9NbsQf19Dhyid+aKUUidZNtTTcwroEh2Kv69lW1BKqTpn2URMzSnQa5IqpdQZLBnqJQ4h80gx8TqerpRSp7FkqP9c6ALQPXWllDqDJUM9yx3q8RrqSil1GkuGemahi0A/H9pFBnu7FKWUalQsGepZhUK32FB8fYy3S1FKqUbFmqFe4KK7nh5AKaXO4lGoG2PGGmNSjTG7jDEPVfL8fcaY7caYzcaYb4wxHeq+1HL5xWUcKRW66+kBlFLqLDWGujHGF3gTuBzoCdxkjOl5xmI/Aoki0heYC7xY14WetEtPD6CUUlXyZE99MLBLRHaLyAkgGRhfcQERWSYix913VwNxdVvm/6RmFwJ6tSOllKqMnwfLtAUyKtzPBIZUs/yvgS8re8IYMw2YBhAbG0tKSopnVVaQneOgb6SQvnENu4x9PigtLCys1c+jsbJbP2C/nuzWD9ivp9r040moe8wYcwuQCFxU2fMiMguYBZCYmCijRo0659cYBQxMSaE26zZmKTbryW79gP16sls/YL+eatOPJ6GeBbSrcD/O/dhpjDGjgUeBi0Sk9JyqUEopVSc8GVNfB3QzxnQyxgQANwILKi5gjBkA/B24WkQO1n2ZSimlPFFjqIuIA5gOLAZ2AJ+IyDZjzFPGmKvdi70EhAL/NsZsNMYsqGJzSiml6pFHY+oishBYeMZjj1e4PbqO61JKKVULljyiVCmlVOU01JVSykY01JVSykY01JVSykaMiHjnhY3JBfbVcvWWwKE6LKcxsFtPdusH7NeT3foB+/VUWT8dRCS6qhW8FurnwxizXkQSvV1HXbJbT3brB+zXk936Afv1VJt+dPhFKaVsRENdKaVsxKqhPsvbBdQDu/Vkt37Afj3ZrR+wX0/n3I8lx9SVUkpVzqp76koppSqhoa6UUjZiuVCv6SLYVmOM2WuM2eI+u+V6b9dTG8aY2caYg8aYrRUeizTGfG2MSXd/j/Bmjeeiin6eNMZkud+njcaYK7xZ47kyxrQzxixzXyB+mzHmHvfjlnyfqunHsu+TMSbIGLPWGLPJ3dMf3Y93MsascWfex+5ToFe9HSuNqbsvgp0GjKH8snrrgJtEZLtXCzsPxpi9lF+027IHTBhjRgKFwHsi0tv92ItAnog87/7lGyEiD3qzTk9V0c+TQKGIvOzN2mrLGNMaaC0iPxhjwoANwARgChZ8n6rp53os+j4ZYwwQIiKFxhh/YAVwD3Af8KmIJBtj3gI2icjfqtqO1fbUa7wItmp4IvIdkHfGw+OBd92336X8P5wlVNGPpYnIARH5wX27gPJrI7TFou9TNf1YlpQrdN/1d38JcDEw1/14je+R1UK9sotgW/qNpPxN+8oYs8F9YW67iBWRA+7b2UCsN4upI9ONMZvdwzOWGKaojDGmIzAAWIMN3qcz+gELv0/GGF9jzEbgIPA18BNw1H2xIvAg86wW6nZ0oYgMBC4H7nb/6W8rUj7GZ51xvsr9DegC9AcOAK94t5zaMcaEAvOA/xORYxWfs+L7VEk/ln6fRMQpIv0pvxb0YOCCc92G1ULdo4tgW4mIZLm/HwTmU/5G2kGOe9zz5Pinpa9dKyI57v9wLuAfWPB9co/TzgM+EJFP3Q9b9n2qrB87vE8AInIUWAYMA1oYY05epa7GzLNaqNd4EWwrMcaEuD/kwRgTAlwKbK1+LctYANzqvn0r8JkXazlvJ4PP7Ros9j65P4T7J7BDRGZWeMqS71NV/Vj5fTLGRBtjWrhvN6N8QsgOysN9knuxGt8jS81+AXBPUXoV8AVmi8izXi6p1owxnSnfO4fy68V+aMV+jDEfAaMoP01oDvAE8B/gE6A95adYvl5ELPHhYxX9jKL8T3oB9gJ3VhiLbvSMMRcCy4EtgMv98COUj0Nb7n2qpp+bsOj7ZIzpS/kHob6U73B/IiJPuXMiGYgEfgRuEZHSKrdjtVBXSilVNasNvyillKqGhrpSStmIhrpSStmIhrpSStmIhrpSStmIhrpSStmIhrpSStnI/wdy3EqBb/9nXQAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 513
        },
        "id": "sf1jDvYvxWtT",
        "outputId": "ab447ed5-f958-4ffb-a8cd-35704dbdd6cd"
      },
      "source": [
        "df = pd.DataFrame()\n",
        "for i in range(0,len(weights)):\n",
        "        w = weights[i]\n",
        "        W = final_w[i]\n",
        "        learning_speed = [0]\n",
        "        cnt=0\n",
        "        for i in range(1,len(w)):\n",
        "            wold = w[i-1]\n",
        "            wnew = w[i]\n",
        "            diff = wold-wnew\n",
        "            d = wnew\n",
        "            m = np.median(d[d>0])\n",
        "            d[d==0] = m\n",
        "            diff = abs(diff)/d\n",
        "            if diff.size:\n",
        "                avg = abs(np.mean(diff))\n",
        "            else:\n",
        "                avg = 0\n",
        "            learning_speed.append(avg)\n",
        "        df['learning speed'] = learning_speed\n",
        "        df.plot(grid=True)"
      ],
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hq3Fmsh8xWtT",
        "outputId": "b33bedee-b0b2-4ede-c264-971612bc8317"
      },
      "source": [
        "print(model2.summary())"
      ],
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"sequential_1\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "dense_2 (Dense)              (None, 30)                23550     \n",
            "_________________________________________________________________\n",
            "dense_3 (Dense)              (None, 30)                930       \n",
            "_________________________________________________________________\n",
            "dense_4 (Dense)              (None, 10)                310       \n",
            "=================================================================\n",
            "Total params: 24,790\n",
            "Trainable params: 24,790\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n",
            "None\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "owskd1nfxWtT"
      },
      "source": [
        "# For 3 Hidden Layers"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UiGYXRfTxWtT",
        "outputId": "98be3209-7e35-487b-a751-3d76482a69a9"
      },
      "source": [
        "model3 = Sequential()\n",
        "start_time = time.time()\n",
        "model3.add(Dense(30, activation= \"sigmoid\"))\n",
        "model3.add(Dense(30, activation= \"sigmoid\"))\n",
        "model3.add(Dense(10, activation='softmax'))\n",
        "weights1 = []\n",
        "weights2 = []\n",
        "weights3 = []\n",
        "save_weights1 = LambdaCallback(on_epoch_end=lambda batch, logs: weights1.append(model3.layers[0].get_weights()[0]))\n",
        "save_weights2 = LambdaCallback(on_epoch_end=lambda batch, logs: weights2.append(model3.layers[1].get_weights()[0]))\n",
        "save_weights3 = LambdaCallback(on_epoch_end=lambda batch, logs: weights3.append(model3.layers[1].get_weights()[0]))\n",
        "model3.compile(optimizer=tf.keras.optimizers.Adam(learning_rate=0.1),\n",
        "              loss='sparse_categorical_crossentropy',\n",
        "              metrics=['accuracy'])\n",
        "m_h3 = model3.fit(train_x, train_y,validation_data = (x_val,y_val), epochs=30, batch_size=10, callbacks = [save_weights1,save_weights2,save_weights3])\n",
        "final_w = [model3.layers[0].get_weights()[0],model3.layers[1].get_weights()[0],model3.layers[2].get_weights()[0]]\n",
        "weights = [weights1,weights2,weights3]                                                                              \n",
        "total_time = (time.time()-start_time)\n",
        "print(\"--- %s seconds ---\" % total_time)"
      ],
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/30\n",
            "100/100 [==============================] - 1s 9ms/step - loss: 2.2963 - accuracy: 0.1405 - val_loss: 1.9160 - val_accuracy: 0.1883\n",
            "Epoch 2/30\n",
            "100/100 [==============================] - 1s 8ms/step - loss: 1.9601 - accuracy: 0.1684 - val_loss: 1.8662 - val_accuracy: 0.2117\n",
            "Epoch 3/30\n",
            "100/100 [==============================] - 1s 8ms/step - loss: 1.8918 - accuracy: 0.1999 - val_loss: 1.9242 - val_accuracy: 0.2272\n",
            "Epoch 4/30\n",
            "100/100 [==============================] - 1s 8ms/step - loss: 1.9226 - accuracy: 0.1918 - val_loss: 2.3381 - val_accuracy: 0.1276\n",
            "Epoch 5/30\n",
            "100/100 [==============================] - 1s 8ms/step - loss: 2.0426 - accuracy: 0.1668 - val_loss: 1.8334 - val_accuracy: 0.3121\n",
            "Epoch 6/30\n",
            "100/100 [==============================] - 1s 8ms/step - loss: 1.9271 - accuracy: 0.2088 - val_loss: 2.0002 - val_accuracy: 0.2142\n",
            "Epoch 7/30\n",
            "100/100 [==============================] - 1s 9ms/step - loss: 1.8937 - accuracy: 0.2613 - val_loss: 1.6901 - val_accuracy: 0.3069\n",
            "Epoch 8/30\n",
            "100/100 [==============================] - 1s 9ms/step - loss: 1.7314 - accuracy: 0.2953 - val_loss: 1.7733 - val_accuracy: 0.2525\n",
            "Epoch 9/30\n",
            "100/100 [==============================] - 1s 9ms/step - loss: 1.7605 - accuracy: 0.2773 - val_loss: 1.7557 - val_accuracy: 0.3185\n",
            "Epoch 10/30\n",
            "100/100 [==============================] - 1s 9ms/step - loss: 1.7165 - accuracy: 0.2997 - val_loss: 1.6577 - val_accuracy: 0.3318\n",
            "Epoch 11/30\n",
            "100/100 [==============================] - 1s 9ms/step - loss: 1.6406 - accuracy: 0.3333 - val_loss: 1.8415 - val_accuracy: 0.2677\n",
            "Epoch 12/30\n",
            "100/100 [==============================] - 1s 9ms/step - loss: 1.7452 - accuracy: 0.3179 - val_loss: 1.7692 - val_accuracy: 0.3075\n",
            "Epoch 13/30\n",
            "100/100 [==============================] - 1s 9ms/step - loss: 1.7365 - accuracy: 0.3216 - val_loss: 1.7066 - val_accuracy: 0.2776\n",
            "Epoch 14/30\n",
            "100/100 [==============================] - 1s 8ms/step - loss: 1.6211 - accuracy: 0.2898 - val_loss: 1.6194 - val_accuracy: 0.3521\n",
            "Epoch 15/30\n",
            "100/100 [==============================] - 1s 8ms/step - loss: 1.6263 - accuracy: 0.3828 - val_loss: 1.7581 - val_accuracy: 0.3490\n",
            "Epoch 16/30\n",
            "100/100 [==============================] - 1s 8ms/step - loss: 1.6712 - accuracy: 0.3611 - val_loss: 1.6742 - val_accuracy: 0.3251\n",
            "Epoch 17/30\n",
            "100/100 [==============================] - 1s 8ms/step - loss: 1.7327 - accuracy: 0.3209 - val_loss: 1.7148 - val_accuracy: 0.3284\n",
            "Epoch 18/30\n",
            "100/100 [==============================] - 1s 8ms/step - loss: 1.6422 - accuracy: 0.3667 - val_loss: 1.6822 - val_accuracy: 0.3814\n",
            "Epoch 19/30\n",
            "100/100 [==============================] - 1s 8ms/step - loss: 1.6905 - accuracy: 0.3488 - val_loss: 1.6359 - val_accuracy: 0.3453\n",
            "Epoch 20/30\n",
            "100/100 [==============================] - 1s 8ms/step - loss: 1.5704 - accuracy: 0.3642 - val_loss: 1.7483 - val_accuracy: 0.3839\n",
            "Epoch 21/30\n",
            "100/100 [==============================] - 1s 8ms/step - loss: 1.7679 - accuracy: 0.3190 - val_loss: 1.5688 - val_accuracy: 0.3591\n",
            "Epoch 22/30\n",
            "100/100 [==============================] - 1s 8ms/step - loss: 1.5954 - accuracy: 0.3586 - val_loss: 1.4712 - val_accuracy: 0.4365\n",
            "Epoch 23/30\n",
            "100/100 [==============================] - 1s 8ms/step - loss: 1.4757 - accuracy: 0.4466 - val_loss: 1.5314 - val_accuracy: 0.3607\n",
            "Epoch 24/30\n",
            "100/100 [==============================] - 1s 8ms/step - loss: 1.4937 - accuracy: 0.3791 - val_loss: 1.4886 - val_accuracy: 0.4019\n",
            "Epoch 25/30\n",
            "100/100 [==============================] - 1s 8ms/step - loss: 1.5343 - accuracy: 0.3457 - val_loss: 1.4884 - val_accuracy: 0.3783\n",
            "Epoch 26/30\n",
            "100/100 [==============================] - 1s 8ms/step - loss: 1.5703 - accuracy: 0.3977 - val_loss: 1.5872 - val_accuracy: 0.3531\n",
            "Epoch 27/30\n",
            "100/100 [==============================] - 1s 8ms/step - loss: 1.4966 - accuracy: 0.3842 - val_loss: 1.6916 - val_accuracy: 0.2677\n",
            "Epoch 28/30\n",
            "100/100 [==============================] - 1s 8ms/step - loss: 1.5395 - accuracy: 0.3248 - val_loss: 1.6025 - val_accuracy: 0.3396\n",
            "Epoch 29/30\n",
            "100/100 [==============================] - 1s 8ms/step - loss: 1.5720 - accuracy: 0.3495 - val_loss: 1.6485 - val_accuracy: 0.3270\n",
            "Epoch 30/30\n",
            "100/100 [==============================] - 1s 8ms/step - loss: 1.4492 - accuracy: 0.3728 - val_loss: 1.5850 - val_accuracy: 0.3715\n",
            "--- 25.547838926315308 seconds ---\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Eb3skloExWtU",
        "outputId": "9e1be4de-916c-4476-a290-54282897f83e"
      },
      "source": [
        "predict_train = model3.predict_classes(train_x)\n",
        "acc_train = np.where(train_y == predict_train)[0].shape[0]/train_x.shape[0]\n",
        "print('Training accuracy for 3 layer = {}'.format(acc_train))\n",
        "predict_test = model3.predict_classes(x_test)\n",
        "acc_test = np.where(y_test == predict_test)[0].shape[0]/x_test.shape[0]\n",
        "print('Testing accuracy for 3 layer = {}'.format(acc_test))\n",
        "print('Total run time = {} seconds'.format(total_time))"
      ],
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/sequential.py:450: UserWarning: `model.predict_classes()` is deprecated and will be removed after 2021-01-01. Please use instead:* `np.argmax(model.predict(x), axis=-1)`,   if your model does multi-class classification   (e.g. if it uses a `softmax` last-layer activation).* `(model.predict(x) > 0.5).astype(\"int32\")`,   if your model does binary classification   (e.g. if it uses a `sigmoid` last-layer activation).\n",
            "  warnings.warn('`model.predict_classes()` is deprecated and '\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Training accuracy for 3 layer = 0.4\n",
            "Testing accuracy for 3 layer = 0.3614\n",
            "Total run time = 25.547838926315308 seconds\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WXFKPlpZxWtU",
        "outputId": "7571168b-d3eb-4877-81c6-6388ea7db7f5"
      },
      "source": [
        "# evaluate the model\n",
        "_, train_acc = model3.evaluate(train_x, train_y, verbose=0)\n",
        "_, test_acc = model3.evaluate(x_test, y_test, verbose=0)\n",
        "print('Train accuracy: %.3f, Test accuracy: %.3f' % (train_acc, test_acc))\n",
        "print('Total run time = {} seconds'.format(total_time))"
      ],
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Train accuracy: 0.400, Test accuracy: 0.361\n",
            "Total run time = 25.547838926315308 seconds\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "id": "GD5zSf5AxWtU",
        "outputId": "d04710ec-3859-49d0-f95f-cd65ef7482b8"
      },
      "source": [
        "df = pd.DataFrame()\n",
        "df['training_loss'] = m_h3.history['loss']\n",
        "#df['accuracy'] = model_history.history['accuracy']\n",
        "df['validation_loss'] = m_h3.history['val_loss']\n",
        "df.plot(grid=True)"
      ],
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fee305b2550>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 38
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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052NJUhIgqFvJn7n4aSmNqlrySF4IIe4zY7JftgMGI5aqPRS968GoxStaeFTKSN3ZD3IzIesmOLjd37YJIUQJpEyAIYULj+6s+1KocAGSTMEIIaoICeqGFC48urNEQCFZVSqEqGIkqBuSUkZQd5aRuhCiapGgbkhpq0kLyfSLEKKKkaBuSEoC2DrdvuNRcYWlAmT6RQhRRUhQNyQ5Thull5auqNODk4+M1IUQVYYEdUNK2hzjTrIASQhRhUhQNyQl3rigLtMvQogqQoJ6afJy4eal0nPUCzn7QWqi4WOEEOI+kaBemtJ2PLqTi2/BsbKjjBCi8klQL01KgvbqVsZ+Hy7+kJcFWSWWlxdCiPtKgnppUuK017JG6kULkGQKRghR+SSol6ZwpF7WnLqLr/aaerli2yOEEEaQoF6a5Hht4ZGDh+HjCjeglgwYIUQVIEG9NCnxhhceFZLpFyFEFSJBvTTG5KgDOHlppQJk+kUIUQVYXFDffPIKb25N51padsXeyJjVpKCVCnD2lekXIUSVYHFB3dPJjkvpKltPVeB0h7ELjwrJAiQhRBVhcUG9eYA7rnaw6UQFjoyNXXhUqHABkhBCVDKLC+o6nUILHxu2nEokL7+CVnEau/CokIs/pMlIXQhR+SwuqAO09NWTnJHDgdjrFXMDYxceFXL21So1SqkAIUQls8ig3tRHj16nEH6igkbHxi48KuTip5UKyEyumPYIIYSRLDKoO9sqhNT2JPxkBc2rG7vwqFDRAiSZghFCVC6LDOoAPYJ9OZqQwuWUzPK/eGGOelkLjwo5S6kAIUTVYLFBPayRtpJzy8kKGB0XriY1VuFIXXZAEkJUMosN6sHVXKnm5lAxqY0pCeBuZOYLaHPqINMvQohKZ7FBXVEUwoJ92X7mKtm5+eV34bxcuHnRtJG6oxcoepl+EUJUOosN6qBNwaRm5RJ54Vr5XTT1Mqj5xpUIKKTT3UprFEKISmTRQT20vg+2eoXN5TmvnhKvvZoS1EFbVSrTL0KISmbRQd3Z3oYOQd6El+e8emFQNzZHvagxfjL9IoSodBYd1AF6NPLl9JVUYq+ll88FkwtH6ibMqYOWASNFvYQQlczig3pYsJZ5srm8FiKlJICts/ELjwq5FJTflVIBQohKZPFBva6PM7W9nAgvr3n1lDjjdjy6k7Mf5GVD5o3yaYcQQpjB4oO6oiiENfIl4uxVMnPy7v2CKQmmz6dDsQVIMgUjhKg8Fh/UQZuCyczJZ9e5pHu/WLKR29jdyaWgVIDsgCSEqERWEdQ71vXGwVZ376mNebmQesm8oF60AbVkwAghKk+ZQV1RlFqKooQrinJMUZSjiqK8XMIxiqIo0xVFOaMoyiFFUdpUTHNL5mCrp3M9HzaduIJ6Lw8qixYemZj5AjL9IoSoEowZqecCr6mq2gToCExUFKXJHcf0BRoU/BkLzCzXVhohrJEvMdfSOX81zfyLFOWom1D3pZCjp1YqQKZfhBCVqMygrqrqRVVV9xX8903gOHDn/MQA4EdVswvwUBSlerm31oAeBVUb76nAV4qZOepQrFSAGdMvsXtwTL9o+nlCCHEHG1MOVhQlEGgN7L7jowAgttjPcQXv3RapFEUZizaSx9/fn82bN5vU2EKpqaklnlvDWWHlrpPUz4sx67o1Y7dSH9h+6Dy5tqZPo4TgRPaFExw2pV9qHqE7RlDbpTGbne7r78EKVdr/RpbM2vpkbf0B6+uTOf0xOqgriuICLAdeUVU1xbSmaVRV/Q74DqBt27Zqjx49zLkMmzdvpqRzH007xryIaNp16oKzvUm/rzR//gUxznR56FHT89QB4upCelKJbStV7F7Ykopb9mXTzqviSvvfyJJZW5+srT9gfX0ypz9GZb8oimKLFtAXqaq6ooRD4oFaxX6uWfDefRUW7EdOnsqOM1fNu4C5C48KOfuZXqnx7EYAHDMuQn455NkLIR5oxmS/KMBs4LiqqtNKOWw1MKIgC6YjkKyq6n2fJG5bxwsXexvzV5eau/CokIufVqnRlAycs5sA0Kk5kBxn/r2FEALjRuqhwHCgp6IoBwr+9FMUZZyiKOMKjlkDnAPOAN8DEyqmuYbZ2ejoUt+HzSfNTG00d+FRIRcTSwVk3IC4SKgTqv2cdMb8ewshBEbMqauquh0wOB+hahF0Ynk16l6EBfvy59FLnLh0k8bV3Yw/8V4WHhUqWoB0BdXBgys3s/B3cyj9+PNbQc2D9mPhwg5IOgv1e5l/fyHEA88qVpQWV5jaGG5q1cbUS+YvPCpUsFdp2rUExi6IosNHG5n210ny80v51nB2I9i7QfCj5OodZaQuhLhnVhfU/d0caFLdjc0nTJxXT0nQXs1ZeFSoIKhPXbGd8BNXCK3vzfRNZxi/KIq0rNzbj1VVOLMJgrqB3pYMxxoS1IUQ98zqgjpoUzBRMddJTs8x/qTCh5T3MFJfe17LXnHJSWLx2I4sHNOBf/dvwvpjlxk8M+L2jTySzkJyDNTrCUC6U4AEdSHEPbPKoN4z2I+8fJVtZ0wYrReO1M2YU8/Ozefd1UeZsPI8uegZ19aVdoFeKIrC6C5BzBvVnoQbGQz4ZsetSpIFqYyFc+gZjjXgRgzkZpl8fyGEKGSVQb1VLU88nGwJN2UKJiW+YMcjd5PudTklk2e+38W8iGhGd6mH3tUP5+xrtx3TraEvqyaG4uFky3M/7Oan3TFwZiN41QXPQADSnWoAKlw7b9L9hRCiOKsM6nqdQrcGvmw5daX0h5R3SonXctRNWHi0+1wSj07fztGEFKYPa82/+jdBcfYtsahXXV8XVk0MJbS+D++u3Ef22S3k1e1Z9HmGY8G0j0zBCCHugVUGddDm1a+mZnM4Ptm4E5LjjZ5PV1WVH7ad45kfduPmYMOvk0J5vGXBuS7+pa4qdXOwZc7IdrzTKhW7/EymnavF9bRsoHCkjgR1IcQ9sdqg3q2BL4oCaw4bubA1JQHcys58ScvK5aXF+/ngj+P0CvZj1aRQGvq73jrAxXCpAL1O4Vnv0+QrNvx0uTYDvtnBqcs3ybNx1vLcJagLIe6B1QZ1bxd7Hmrsz7dbz/HGsoN3pxQWV7TwyPBIPerCNR77ejtrDl/k732C+XZ4CG4Otrcf5OxbdqmAs5vQ1e7I7P8LIyMnj4Hf7OBYUh5419eyYoQQwkxWG9QBZjzbholh9VgaFUe/6dvYH3O95AMLFx6VUvclLSuXd1cfZcisnWTl5LNwTAfG96iHUtL8u4s/5OdARmn3ugKXDkG9MNrU9mT1pFD83RxYeCwL1buejNSFEPfEqoO6rV7H648E88vYTuTmqQyZtZPpG0+Tm5d/+4EG0hm3n77KI19sZV5ENCM61uGvKd3oXN+n9Ju63CoVUKKz4dprQSpjdXdHJoTVJyFN5QLVtYesmUY+BxBCiDtYdVAv1D7IizUvd6V/i+pMW3+Kod/tun0hUEGlRDxqF72VnJHD35cd4rnZu7HT61g6rhPvDWhWdp12Z1/ttbRt7c5uAidvqNay6K3+LarjYgtrL7pob8gUjBDCTJYX1PPz8L5658ZLZXN3tOXLp1vz5dOtOHXpJn2/3MbyqDjUM5tgy3+h6UDwaQjA+mOX6f35FpZGxTKuez3WvNyVdoFext2oaAPqEoJ6fr4W1OuGadvfFXCw1dOtpi2rYhy1NySoCyHMZMb2QJVs/wKaH/kIajhD50kmnz6gVQAhdTx59ZeDfL5sA484/gtH70boB3xDUlo27/52jN8OJhBczZXvR7SlRU0P025gaPrlylFtBF9CJcawWjZsjPZDRUGReXUhhJksL6i3Hs6VXb/g99c/wckLWj1j8iVqejqxeFRLrn31Mnk383g6ZRJhERf5Ydt5bmbmMOWhhozvUQ87GzO+yDh4gM6m5OmXMwWlAer1vOsjXycdoY0CiI/2pfrV0+hNv7MQQlhgUNfpOd74Vfxc7eDXSdqy/uBHTbuGqqL/Ywq+qac432cOSTu8+PTPk7Ss5cGng1vQqJpr2dcotX26gm3tSihRcHYj+DUF12olnjq8Ux3Onq2GS/wJTPx+IIQQgCUGdUDV2cLQRfDj47B0FDy3HIK6Gn+BPd/BoZ8h7J8EdRrE7yG57Dl/ja4NfNHrzNyftDgXX0i9fPt72WkQsws6/F+pp3Vv4MtK+1rYJW/S8tzN3StVCPHAsrwHpYXsXeDZZVpBrMXDIGG/ceddiIB1b0GjftD1bwA42dnQo5Ff+QR00B6W3jn9Er1D2+quXuk7G+l0CtXqNsNJzeDkWXlYKoQwneUGddDm1IevBEcPWDgYrp42fHxKAiwZof0iGDjrtgyUclXS9MvZjWDjCLU7GTy1ZcsQADZHRFRM24QQVs2ygzpoq0CHrwIUWDDw1mYXd8rNgl+GQ06GNnVjYoldk7gUVGrML7bI6ewmCAwFWwN7lgIuNYIBiDtzmOQMEzb5EEIIrCGoA/jUh+ErtJWYCwZCWtLdx6x9A+Ij4YkZ4Bdcse1x8Yf8XMi8of18IxaunjI49VLEvSb5ensC8hNYFlXKLyghhCiFdQR1gOotYdhibfegRUMg6+atz6LmQ9Q86DIFmgyo+LYUriotzFU/W3oq4110enRedWntdJWFuy4YXw9eCCGwpqAOENgFhsyFiwfh52e1KZe4SFjzNy2g9vzX/WlH0QKkggyYs5u0ujK+jYw737seje2ucP5qGtvPXK2YNgohrJJ1BXWA4H4w4Bs4vwWWPK/No7tWg8GzQXeflvQUlgpIS9TK+p7brP1SMTZF0bs+rumx+Drp+XHnhQprphDC+lhfUAdoNQwe+RhOrdVK4A5dpGXK3C/Fp18S9mlz/cZMvRTyro+Sn8MLLWzZdOIycdfTyz5HCCGw1qAO0GkCDJgBz/wM1Vvc33s7eoLOVpt+ObsJFB3U7WH8+d71ARgcmAnAot0x5d9GIYRVst6gDtD6WdOCaXlRFG1ePS1Rq/dSo41p3xQKgrpPZiy9Gvvzy95YMnPyKqixQghrYt1BvTI5+2ppjPGRpk29ADj7gL07JJ1hRKc6XEvLNn6vVSHEA02CekVx8YO4vdo2eSWU2jVIUaBga7vQej7U9XGWB6ZCCKNIUK8ohWmN9m4Q0Nb08ws2odbpFJ7rWIcDsTc4HGch29zl5eKUJs8BhKgMEtQrinNBUA/qBnozimF614fkWMjJYHBITRxt9fy4M7o8W1hxDiyi/d6X4PSGym6JEA8cCeoVpXCkburUSyHveoAK187j7mjLE60DWH0wgetp2eXWxApzrmBz7T+maCWHhRD3jQT1ilKtuTb10uAR884vyIChYGu7EZ3qkJWbz9Ko2HJqYAVRVYjeTqpzHa1kQ/hHld0iIR4oEtQrSmAXeDNGqyJpDu962mtBUG9c3Y12gZ4s3BVDXlWuB3P1FKQlElfzMQgZBbtmQPy+ym6VEA8MCeoV6V52LrJ3BZdqkHRrs4znOwcScy2ddUcvlUPjKkj0NgBueDSDh9/Tni38NhnypIywEPeDBPWqzLt+0UgdoG+z6tT1debLDaerbvXG6O3gVpNMh2pazfp+n8Glw7Dzm8pumRAPBAnqVVlBrnohvU5hcs8GnLx8s2qO1gvm0wnscutbSpPHIbg/bP4Erp2r3PYJ8QAoM6grijJHUZQriqIcKeVzd0VRflMU5aCiKEcVRRlV/s18QHnXh/SrWlGyAo+1rEFdH2e+3FgFR+sF8+kEdrn9/X6fgc4Gfp+iBX4hRIUxZqQ+D+hj4POJwDFVVVsCPYD/KYpid+9NE7cyYG6NcPU6hUk963Pi0k3+Ona5khpWivNbtdc7g7pbDXjoHa0E8cGf73uzhHiQlBnUVVXdClwzdAjgqiiKArgUHJtbPs17wN2R1ljo8ZY1CPR2YvrG06hVaeRbMJ+OZ+Ddn7UdA7U6wLq3IE02/hCioijGBAVFUQKB31VVbVbCZ67AaiAYcAWGqqr6RynXGQuMBfD39w/5+WfzRm2pqam4uLiYdW5VVVKflPwcum19igt1hhAd9Oxtn22Pz+GHw9lMbm1PG38zVqyWN1Wlc8TzXPNqzYnGU0rsj1NaDG0jp5DoG8rxJq9WUkPNZ23/7qytP2B9fSqpP2FhYXabF8gAACAASURBVFGqqpZee0RV1TL/AIHAkVI+GwJ8DihAfeA84FbWNUNCQlRzhYeHm31uVVVqn75oqapLRt71dk5untrt001qvy+3qvn5+RXbOGNcPq6q77ipatSPqqoa6M+mD7XjTq+/f20rJ9b2787a+qOq1tenkvoDRKoGYmt5ZL+MAlYU3O9MQVAPLofrCrgrrbGQjV7HpLD6HE1IYcPxK2ZfPiYpnW/Cz5Cdm38vrSzKT79rPv1OXV8Dn4baQ1MpISBEuSuPoB4D9AJQFMUfaARI7lp5KajWWFLWyMDWAdT2cuLLjafMmlu/npbNiDm7+WzdSRbuusfSvobm04uzsYfHpheVEIi7nk5KpixMEqK8GJPSuBjYCTRSFCVOUZQxiqKMUxRlXMEh/wE6K4pyGNgI/F1VVXkSVl6860FOGty8Oy+9cLR+JD6FTSdMG61n5ebxfwuiSLiRSXA1V77adJrkDDODa0n56YbU6QQho1B3zeDvX81n/MKoqvXAVwgLZkz2yzBVVaurqmqrqmpNVVVnq6o6S1XVWQWfJ6iq2ltV1eaqqjZTVXVhxTf7AVJKBkyhgW0CqOXlyJd3ZsJsnQob3oWrp+86R1VV/rH8MHuir/HZky2Y+mRLrqfnMGvL2buONUriSS2fPqir8ec8/B4Zdt68lTuT3Wcus/lkonn3FkLcRlaUVnVlBHXbgtH6objkW4Hxwk7Y9B/Y/jl83RZm94Z9P0LWTQC+3nSGFfvjmfJQQwa0CqBZgDtPtKrBnO3nSbiRYXobjZ1PL0a1d+Mz/Ria6i7wd7f1fLTmOLl59zivL4SQoF7luQWAjUOpQR1gUJua1PR05IuNp1Hz8+Gvt8G1Okw+AA+/r61IXf0STG1IzJzn2bphNYNa1WByr/pF13itdyNUFT5ff8r0NkZvB/da4FHH6FMiziYx91oLYqs/wpicxTglHmBJZJzp9xZC3EaCelWn04FXvduqNd7JVq9jYlh9Dsbe4NjGH7XNrnu+DV5BEPoyTNwDYzaQGPQ4XhfWsdT+faZeHo2y7X+QkgBALS8nnu9ch2X74jhxKcX49pk6n15g7o5ovJ3t8B02E8WtOt85zeDbv/aTmnUf163l5xn8exXCEklQtwR3FPYqyeA2NQl0t8F758eo/k2h5bBbHyoKMU5NeeTsEIY4zyWt79fo3GpoUzSfN4VfnoOcDCaG1cfV3oZP1p4wvm2F8+kmTL1cSEpj44nLPNuhNg5u3ihD5uCXn8jr2d/wnbnz+ubY+wN8FQKxe+/fPYWoYBLULYF3fbh+HvJKH8Xa2ej4X9AequVf4nCTv4FOX/RZcnoOo+btIS9fZcaorjh3GA6j/oDJ+6HTJDj+G+xfiIeTHZN61mfzyUQizhiZwGTGfPr8iAvoFYVnOxZM19Rqj9Lzbfrrd3N9+/dcSs40+lr35PAyQIX1/5JCY8JqSFC3BN71IT8XbhjIJU+/RpvoH9iltOLdo/5FmTA5efmMXxRFzLV0vh0eQl3fYkuOvepC7/9ArY6wYzrk5TCiUyABHo58vPaEcVUgo7eZNJ+empXL0shYHm1RHX83h1sfhL5CZq1u/FOZx6Lf/jTqWvckOQ7i9oBfE4jZCSfXVPw9K1p+HnwXBhFfVXZLRCWSoG4JijJgDExNbPsfSlYKVzu/zb6YG2w/cxVVVXl75REizibx8aAWdKzrXfK5XaZAcgwcWYGDrZ7XejfkcHwyvx1KMNwuM+bTl0fFcTMrl1GhQbd/oNPhMHQ2ubau9D/1T07EVHAFymOrtdcn54F3A1j/juXvznRmAyTsK/gGIh5UEtQtQRlpjVw7D7u/hVbP8HBYT6q7O/DlhtPM2nKOXyJjmRRWnyEhNUu/foPe2oh1++eQn88TrQJoXN2Nz9adJCs3r/TzEk9AepLRUy/5+SrzIqJpVcuDVrU87j7AxQ8GfUsjXRwJS6YYdU2zHVsF/s3Bt5G27V7SaS3t05Ltna29Xjp0Ww1+8WCRoG4JnLzAwaP0oL7xfdDbQtg/sbfRM6FHPSIvXOe/f56gf4vqvPpwQ8PX1+kg9BVIPA6n/0KnU3irXzBx1zNYsNPAlE/0du3VyKC+5VQi56+mMSo0sNRjXJr05kCdUfRM/YNj6+cZdV2TJcdD7G5oMkD7uVE/qN0ZNn9clMtvca5fgNN/QWBXUPPhQkRlt0hUEgnqlkBRSi3sRexeOLoCOr+kbUYBPNWuFrW9nAip48nUJ1ui0xkxNdJsELjX1kbrQNcGvnRt4MPX4WdKLx8QvU07p6x6LwXmRkTj72ZPv+bVDR7X+JlPOKJrRJ0d/yAv6bxR1zbJ8YKpl6ZPaK+Koj1bSEu03PnoqHlaPx6fDjaOtzYsEQ8cCeqWorCwV3Gqqi00cvaDzpOL3ra30bP25a4s+b9OONjqMYreFkInQ+wubUUq8GbfYJIzcpi5uYS5/OLz6UY4cyWVracSea5DHWz1hv/Z2ds7cOXhb8hT4caC4ZCbbVwfjHV0Ffg1BZ8Gt96r2RaaDtSCegl1dqq03GzYvwAa9tEeftfuCOe3VXarRCWRoG4pvOtDShxkp99678TvWhAOewvsby+k72xvg96YEXpxrZ4FJx/YPg2ApjXcGdg6gDk7zhN/Z/kAE+fT50Wcx85GxzMdaht1fFjHtsxyfwXvG4fJ2fC+Sd0wKCVB+zsrHKUX1+vf2sPS8I/K7373w/HV2reMtmO0n4O6wpWjkCr1dB5EEtQthXc97fVaQVXj3GxY/2/wDYbWw8vnHnZO0HGcNjd7Sdtn/LXejQCY9tcd5QNMmE9PTs9heVQ8A1rWwNvF3qimKIpCz0EvsjC3F7a7voLTG4zvhyGFWS9NSgjqXnWh3QvaqPeKCQuwistM1tqafx/r2ETO0abA6vXUfg7qrr1Gy2j9QSRB3VLcmQETNVcL8A+/D/py3M6u3Qtg5wI7vgAgwMORUZ0DWbE/jmMJxcoHFM2nl52fviQyloycPEYaeEBakraBXuxp+DdOqrXJXzEWUi6adH6Jjq3SMn18S3l43O11rf8b3jH92slxMPsRWDQYFg4qn/aW5cpxuLADQkZpD7wBqrcCO1cJ6g8oCeqWwquu9pp0BjJuwOZPIKiblo5Ynhw9oe0oOLJcS5UEJvSoj5uDLZ/8WTB6NWE+PS9fZf7OaNoHedG0hrvJzXm1X0sm50wmNzMNVo69txFwykWI2VXyKL2Qszd0fRVO/XnbvHTizSxOXsvjzyOXWLwnhhmbz/DRmuO8vvQgL8yP5LXpC7n6RVdSEy+w0GYw+TE7YWZnbbVuRYqcC3o7aP3crff0NlCnszwsfUBJULcU9i7gWkN7WLr9cy0PufcHJhXRMlrHiaCzKcoEcXey5aWe9dl6KpHwk1e00aGR8+nrj10m7noGo00cpRcK9HGmU8fOvJv9rBakjq4w6zpAQdaLWvJ8enEdxmnVMdf/C/LzWX0wgW6fhvPxnkzGLYziHysO8+mfJ5kfEc2201fxvRLBf66/jl6vY3bDmXypPMOzyqfkuNbU6uqsfgmyUs1vd2my0+DgYu2XlLPP7Z8FddMGACllLCATVqcKbEMvjOZdD2IitBFni6FQvWXF3MetOrR8GvYvhB5vgosfwzvVYeGuC7w4P5IZDSLpDUYF9XkR5wnwcOShxv5mN2dyrwb02Pcw4203USv8Qy2/XG9r+oWOrgLfxtqCI0NsHbUql6vGs3LhV0w5Vp+2dTzp4ZtBWOe2eDrZ4elkh6OdHg4shtXvgW8jnJ5dysvuATyUkMxTs3IZmPUuyztuwX7XdO2bzeAfICDEvL+EkhxeBlkp0G7M3Z8Vblhyfhu0HFp+9xRVnozULYl3fbgerY3Oe75dsfcKfQXysmHXTEBLk1wxIZTBbWqSe24bF/Hlz3h7g9vQHb+Ywq5z1xjRqQ42ZaQxGuLlbMekng15N22Q9hxh/wLTL3LzklbjpaxReoGrdZ8g2qYuIWe+YnSH6vz0Ykea+ehpWsOdGh6OONrqYOtnsGqcNtUxei24BwBa1tDM50I4kZjF6Lh+5Dy3WnuwPbu3dk6+gVW6xlJViJytPR+o1eHuz/2bawvWZArmbqpq+SUhDJCgbkkKH5Z2HA8etSr4XvW0EfHeH7SMDrTg+t9BzXjY6QxHbJszbmEUo+ftJSYpvcRLzN1xHkdbPU+3My6N0ZAxXeqiNOxLZH5DMjd+DDkm7tB0rGDqxdB8eoEDsTd47JudvJ81lNq6RP5dbSd2NsX+r5KXC7+9DJs+0L4xPbscHG5/XtCtoS+fDG7BjjNJvBHlhjpuOzR+XDtn3qPaCtB7Eb8PLh6EtqNLnoLT6bTRenTJQX1ZVBzvRGSQnG69wa1Ue3+Az+rDzQquL1RJJKhbkkZ9tSDSpYLrohTq8or29T5y7q33Ek9gm3WNnn0H86/+Tdhz/hoPf76F6RtP31YnJiVbZdWBBAa1CcDdyYypkjvodQrTn2nNcs8xOGRc4eL6L027wLFVWvqnX7DBw37eE8NTs3ai1ym8Om68lia45dNbtVSyUuHnYbBvPnR9DQZ+CzZ2JV5rSEhN/ta7ISv3x/Pp1sswZI52/KUjMKsLHFpiWh+Ki5wNts7av4fSBHaDGzHat7tijsQn89aKw1xIyWdpVKz5bbBEebmw40vIvFG0HsPaSFC3JN71YNB3d40KK0yN1lA3DHbNgJyCGucF+en6oK6M6RLExtd68HATf6atP0WfL7ax7bS24GVLbA7ZufmM7BxYbs1xsrPh1RdGsUvXGsc9XxF30ciVnzcvabVQDIzSs3Lz+MeKQ7y54jAd6nrx26QuNAtw11JGM5Nh2zRss2/A/P5aNcT+n2uLlcp4UD0xrD7PdKjNzM1nWbDrgvasYvx28GsMK16EP/5mei339GtadlKLp8DBrfTjgrppr8WmYFIyc5iwaB9eznYEuemYvzOaPGNKLFuL46shOVb7BR85B25Y3y81CerCsC5TIPUyHPxJ+zl6G3jcyk+v5u7A18+0YcGY9gAMn72HiT/tY1NMLl0b+NDA37Vcm+Prak+NwR/jQSqb5/7LuOmD479hKOsl4UYGT327i8V7YpnQox7zRrXH07lg9F2tubaL1O5vabPvdW2np6cXa9MeRlAUhfcfb8pDjf349+qjrDt6SVsoNHKNtkHJ3u/hj9dMC+wHF0NuZskPSIvzbaSVkChIzVRVlb8vO0T8jQy+fqY1/eraEnstg00nrhh/b0u3awZ4BsEzBd+Stn5Wue2pABLUhWFB3aBGG+0ra16OttAlsOtdh3Vt4Mufr3TltYcbsuHYZa5nqQarMd6L2k07kVTnUQZm/cob89cbLg8McOxX8GmkjY7vsPNsEo99tZ0zl28y67k2vNEn+O7yCj3/CYqCPi8LRv4OjfqY1F4bvY6vhrWhZU0PJi/eT9SFa1ouee8PtJo9kbNhzevGBXZV1UaYNdtrv3AMURRtXv38VlBV5kdEs/bIJd54pBFtA71o46enursD8yOiTeqPxYrdC3F7tWdSnnUgZKSW4WVl+9RKUBeGKYq2GOd6NGz5r8H8dHsbPS/1asD6Kd15obkdPRr6VVizvB97H0ddLh3j5/Hm8sOlZ+GkXtF+ERWW2S1wLS2b//x+jOdm78bDyZZfJ3WhT7NSqke614QXNhDZ9nOzUxId7fTMfr4t1d0dGDM/krOJqdrf7cPv3xqxr/172YH9/FYt/9zIbwoEdYPUS5w4EsWHa47TK9iPF7tqC9n0OoXnOtZh+5mrnL5soSWHTbFrBti7azWOQHsmorfT/l1bEQnqomyNHtV2B9o6Vfu5TqjBw2t7O9ElwNa4kr/m8qmPrvVzjLDdyN4DB5i2/lTJxx1frdUXL5h6Sc/O5ZvwM3T/NJy5O84zpE1NVk0Mpb6fS8nnF6rWnGz7UnaOMpK3iz3zR7fHRqfw/Jw9XLmZWVD29wMtsO/5Fv5803Bgj5ytrfptOtC4mxbMq//26xL8XB3431O3l2Ie1r42djY65ln7aP1GrPaNLWTEreJ3rtWg/YvaA2tza/1UQRLURdl0Oi0TBvW2+fRK1/3v6HR6vvRfy1ebzrBkbwkPvY6uAu8G5HgHs2j3Bbp/tpnP1p2kYz1v1r3Sjf8OaYGrw71n5xirjrczs59vR1JqNqPn7SU1K/dWYO84AXbPgj//UXJgv3kJTvyhjTRtHe7+vASqRyBJej+aZR/g62da4+F0e6aOl7MdT7SqwYp98aXXzbcGe77TXtv/3+3vh76i1frZbGGVOQ2QoC6M0/wp7QFfg0cquyW3uAegtH+RNsl/8XRQOv9YeZitp4qVm01NRL2wgzO+D/HIF9v458oj1PFyYtm4Tnw/om25P8Q1VstaHsx4tg3HL97k4Wlb+HbLWVKycuGRj6DDeNg9E9b98+7Avu9HbQNyY6degNk7ognPDibM/iSta5acNfV850AycvJYGml9mSCAloYaNR+aPH73+g5nb+g0QRvFXzxYOe0rZxLUhXFs7GB8BPT5uLJbcrsur6LYOvMf15U08HNhwqJ9RdUkz21bjKLmM+lgHfQ6hR9GtGXpuE60DfSq5EZDWLAfC8a0J9DbmY/XnqDzx5v4zx/HievwL200uesbbQOUwsCel6vtblQ37FYZ5jLsi7nOJ2tPcLN6JxxybsCVYyUe17SGO+0Dvaw3vfHAT5CVrNU0KknHCdrq200fmn+P3KxbZbErmQR1YTw7Z/NqrlQkZ2/o/BK2p/5gYV89LvY2jJ63l+fn7OFixM9cUGowZtCj/PlKNx5q4o9SEQXQzNS5ng+Lx3bk95e68FBjP+ZFRNN96hZeuvE0V5s8Dzu/1oqKqSqcXgcp8WWnMRa4npbNpEX7qO7hwODBz2hvGigZMDI00DrTG/PztW8+NdtBrXYlH+Pooe36dXodxO4x/R7ZabBgIHwVAtE77q295UCCurB8nSaAkw8+u/7L3FHtSM3KJSbmAp30xwnoPIwn29U2fReo+6hZgDtfPN2abW+EMaZLEJtPJtJ2X2/WOfWHiK9Q178De2eDa3Vo2LfM6+Xnq7y65ABXU7P55pk2uPkHaqWbDQT13k38rTO98dSf2gi643jDx3UYB86+sOk/pl0/Ow0WPanVFXL20xaUpV8zv73lQIK6sHz2rlp62vktNE6PYuNr3VnzyA105GPT3MgskSqghocjb/VrTMQ/evL2o014P280C3IfQon4Es5uJCbwSS7cyCInz3BN+W+3niP8ZCJv929Mi5oe2ptB3bTUzrzcEs+x0eusM71x1wxwqwmNBxg+zs4Zuryq/eI7t8W4axcP6IN/gGGLtRTa3yabvkq4HElQF9ah7WhwrwUb38ff1R7HU7+BVz3wb1bZLTOZq4MtL3Sty5Y3wnAf8iV/ODxKsurEk3sb0v2zzTR6ey2hn2ziqW938tqSg3y+/hTLouLYfS6J9ccuM/WvkzzavDrDOxbLUgrsqtXxuVT6w0CrS2+8eEhbAd1hrHG7g7Udre1ZsOmDsoPynQG92WAIaKOVjjj+m7YzWSWReurCOtg6aLXff52o/R8qeptW4qAKzaGbykav4/FWNVFbLiI+KZnPk3OJu5ZB3PV0Yq9rrzvOXOXyzczbYlCgtxOfDG5++/OD4nVgSllA5eVsx4CWWnrjG48El0shtkq1a6ZW9KzN88Ydb+sA3V+H36fA6fXQsJRdxUoK6IU6TYJz4fDnW1C7c5kF5CqCBHVhPVo8rZUzWPO6tuDIiDK7lkBRFGr6eFDTBygh8SUrN4+EG5nEXkvnUnIm3Rr63p177+KnbRByfpvBKp/Pdw5kaVQcS6NieaFg5alFunkZjizTSgE4ehh/XqvnYPsX2tx6g4fvHhQYCuigrel4Ypa2leGy0fDiJqPXFJQXmX4R1kNvo20ekp+rPRgsqzaKlbC30RPk40y3hr481a4W1dxLCSJBXbVglJtd6rWaBVhJeuPeH7RaRR3GmXaejZ32je/SoYLtD4spK6AXcvWHgbPgylEte+k+k6AurEvjx7X/s3WebNFTLxUiqBvkpEPCPoOHPd/ZwtMbczK0cgqN+hqd03+bFkPBpyGEf3RrlypjA3qhBg9r+e97voOTa01vwz0oM6grijJHUZQriqIcMXBMD0VRDiiKclRRFCMfHQtRARRF24yi7ajKbknVUycUUMrc4q53UwtPbzy0RCs813GCeefr9NDjH5B4Qqtbb2pAL/TQu9q3xVUT7usG4MaM1OcBpdYaVRTFA5gBPK6qalPgyfJpmhCiXDl5aUGmjKBua8npjaqqPSD1b27UxuilavKEdo3wj8wL6AA29jBkrlb7fsXY8tmb1ghlBnVVVbcChrLpnwFWqKoaU3C8hX5nE+IBENRNWzVZxh6vlpre6Hn9ACQe1xak3cv0m06n1dG/ft68gF7IpwH0/VTLxtrxhfntMUF5zKk3BDwVRdmsKEqUoigjyuGaQoiKENQd8rLKXA5fPL3Rkjanrhm3WlvZaU4AvlPDPlqm0NBF93a91s9ppZI3faht1FHBlFI3Fyh+kKIEAr+rqnrXSg5FUb4G2gK9AEdgJ/Coqqp3FbhWFGUsMBbA398/5Oeffzar0ampqbi4lFH/2sJYW5+srT9gHX3S56bTZfuzxNQezGG/Jwz250JKHu9EZPJ0Izv6BFX9nHWntFja753E+cBnuBBoYEPuSmCTk0rbyCmoikJk28/Js3E26ryS/s2FhYVFqarattR73VtTAYgDklRVTQPSFEXZCrQE7grqqqp+B3wH0LZtW7VHjx5m3XDz5s2Ye25VZW19srb+gBX1KTqEOmoM511cyuzPHwk72ZGYwYfPd6/S9XMA+O0V8hVbgp78D0HOPpXdmrs18oW5femavBIGfW/U9JA5/+bKY/rlV6CLoig2iqI4AR2A4+VwXSFERQjqBvFR6HPTyzy0ML1x4/HL96Fh9yA1EQ4u5lK1HlAVAzpA7Q5aVs3hpXDQvFkKYxiT0rgYbUqlkaIocYqijFEUZZyiKOMAVFU9DvwJHAL2AD+oqlpq+qMQopIFdoX8XNyTyx579W7qT4CHI19tOkN+VV6MtOdbyM0irmYVX0Xc9VVtrl5XcYv5y7yyqqrDjDjmM+CzcmmREKJi1eoAejs8bhwu81BbvY5XH27Ia0sP8tuhBAa0CrgPDTRRVirs+R6CHyXduWZlt8YwnR6G/VyhC+NkRakQDxo7J6jZHs/rh4w6/InWATSu7sZn606SlXt/cq1Nsm8+ZN4wWNOmSqnglc4S1IV4EAV1wyX1nFErHfU6hbf6BRN3PYMFOy/ch8aZIDcbdn4DdbpAzVITQh4oEtSFeBC1eApQtIBohK4NfOnawIevNp2pWnnrh5dq2/x1eaWyW1JlSFAX4kHkFcQVvy7aZtZGbr/2j76NScnMYcbmMxXbNmPl52ullv2bQf2HKrs1VYYEdSEeUDG1B0N2qlam1ghNargxqHVN5kZEE3e97HTICnfqT7h6EkJf4VB8Mg9N28L3h7JIzSp5y74HhQR1IR5QaS6B0OARrQBWdppR57zWuyEA//vrrrWF95eqwvbPUT1qszC1DUNm7uRGeg4RCbk89tV2jiYkV277KpEEdSEeZF1fhYxrsO9How6v4eHI6NAgVu6P50h8JQbOmJ0Qt4fl9k/w9uoTdK7vzfop3fh7ewfSs3MZOCOCBTujMaYMirWRoC7Eg6x2R20vzYivDe6IVNyEsHp4Otny8drjlRY00zZN5Ybixr9iWvPaww2Z83w7PJ3tCPbSs2ZyVzrX8+Zfvx5l4k/7SM4w/8FuZk6exe0AJUFdiAddlymQEqdlkhjBzcGWl3o2YMeZJLacSqzgxt1ty7bNOF/YyE/05fvR3XipVwN0xerSeLvYM+f5dvyjbzB/Hb1M/6+2cTD2htHXz8nLZ8Oxy4xbEEXzd9fR7sMNvLrkAH8cukhKZhXK/CmFbDwtxIOuwcPahhA7voCWw7Ra4mV4rmMd5kVE88naE3Rt4KsV+7p+AU6ugXYvgL78qzpm5+bz0ZrjNN/7GRk2Dgwa9y7V/Euu86LTKfxf93q0DfRi8uL9DJkVwd/7BDOmSxBKKYt/jl9MYVlUHL8eiOdqajbeznY80742yRk5bDx+hRX74rHRKbQP8qJnsB89g/2o61v1qnZKUBfiQacoWp738jFw4ndo8niZp9jZ6HijTyMm/bSf5fvieKqFF/w0VNug4uwmeHK+tnK1nCTcyGDiT/u4EnOafzvsRG03lmr+Nco8L6SOJ39M7sIbyw7xwR/H2Xk2ialPtsTT2Q6ApNQsfj2QwLKoOI5dTMFWr9Az2I8hIbXo0cgXW732Cy43L5/9sTfYePwKm05c5oM/jvPBH8cJ8nGmZ7AfvYL9aBvohZ1N5U9+SFAXQmjbt236ALZPg8aPGbWU/dHm1fm+5jmmrTvJoOgl2CSegHYvaimSCwdpNU4cPe65adtOJ/LyzwfIysnj96aR6M4rEDrJ6PM9nOz4dngI8yOi+WjNCfpN38aEsPpsPZVI+Ikr5OarNA9w593HmvB4qwC8CgJ+cTZ6He0CvWgX6MWbfYOJvZZO+MkrbDx+hQW7LjB7+3l8XOz4eFALHm7if899vheV/2tFCFH59DYQ+jIk7Ifzxu0drygKb/VrTI/0tdgc+UUrK/voVHhyLsRFwrz+kGr+7paqqvJN+BlGzNmDj4sdv7/YhKCY5dD8KXA3rXCXoiiMDA1i+fjO2Op1/GvVEfbH3GB0lyDWvdKN317qwsjQoBIDeklqeTkxolMg80e358C/H+a74SH4uTrw4o+R/HPlYTKyK69GjozUhRCalsNg88ewbRrU7WHUKR0cYmljN58d+S0JDpmMN2hbt9m7wS/PwZxHYPgq8KxjUlMyc/J4c/khVh1I4LGWNfjv4OY4RUyFnHTtl4+Zmtd0Z83LXTlzJZVmG8CXPgAACGBJREFUNdyw0d/7uNbJzobeTavRvZEv//vrFN9tPceuc0l8+XRrmgW43/P1TSUjdSGExtYBOk3URurxUWUfn3EdlowAJx9ezh7PV+Hnbn1WvxeM+BXSk2BOH7hywuhmXEnJ5OnvdrHqQAJ/692Q6U+3woks2P0tNOwLfsFmdO4WF3sbWtXyKJeAXpy9jZ63+jVm4ZgOpGblMnDGDr7beva+16GXoC6EuKXtaHBw10brhqgqrJoAKfHYPv0jD7drxsJdF4i+Wmxlaq32MGotqHkwt69RvyiOxCcz4JsdnLx0k1nPhTCpZwMtW2XfAm2RlAWU1+3SwIc/X+5GWCM/PlpzguFzdnMpOfO+3V+CuhDiFntXaD9Wy4JJPFn6cRHTtfTF3h9ArfZMebgBdjY6PlpznJy8/FvH+TeF0X9q153/OJwrfb5+zeGLDJkVgQIsG9+JPs2qaR/k5cDOr6F2J21LOAvg6aw9nP14UHP2XbhBny+38ueRS/fl3hLUhRC36zAObBy1CogluRABG96DJgO0YwE/VwfGda/HX8cu0+njTXyy9sStUbtXXRi9Djxqw6IhcPz32y6nqipfbjjNhEX7aFLdjV8ndaFpjWJz0UdWQHIshFpWeV1FURjWvja/T+5CLU8nxi2M4h8rDpGeXbEFxySoCyFu5+wDbUbAoV/gRuztn6VegaWjwDMQHv/6ttTHSWH1mf18W1rV8uD7befoMXUzw77bxa8H4sl09IORf0D1lrBkOOxfBEBGdh6TFu/n8w2nGNQmgMVjO+Lran/rfqqqLYryawINet+Hzpe/er4uLB/fmXHd6/Hz3lj6T9/O4biKq5sj2S9CiLt1fgkiZ2vTHn3/q72XnwfLRmtbxz23HBzcbjtFp1Po1difXo39uZySydLIWH6JjOXlnw/g4WTLwNYBPNNnIQ02jYNfJ5ATMYP4axmMzVF5x9cR32RHlHkKoICi035h5GXDlWMw8FujVrpWVXY2Ot7sG0y3hj68+stB/jp2ieY1KyYzRoK6EOJuHrW0fPCo+dDtdW30Hv4RRG+DATOgWjODp/u7OTCpZwMm9KhPxNkkFu+NYeGuC8zdEU27Wq/wdu3aJMceJT/fjmYBrvg42wEqqPna6FzN137W2UDzJ6HZ4PvS7YrWuZ4Pf77SFWf7igu9EtSFECULfRkO/qSlEtZsB9umQuvh0PpZoy+h0yl0aeBDlwY+XEvLZsW+OBbviWFA7CPU9HyC2c+3w6eaawV2ourxcDJugZO5JKgLIUrmFwzB/WHPt7D3e63oV7/PzL6cl7MdL3Sty5guQZy6nEoNDwdcHcq/8NeDznInqYQQFa/Lq5CZrM2nPzUfbB3v+ZKKotComqsE9AoiI3UhROlqhkDvD6FGK/CuV9mtEUaQoC6EMKyz8RURReWT6RchhLAiEtSFEMKKSFAXQggrIkFdCCGsiAR1IYSwIhLUhRDCikhQF0IIKyJBXQghrIiiqvd3/7yiGytKInDBzNN9gKvl2JyqwNr6ZG39Aevrk7X1B6yvTyX1p46qqr6lnVBpQf1eKIoSqapq28puR3mytj5ZW3/A+vpkbf0B6+uTOf2R6RchhLAiEtSFEMKKWGpQ/66yG1ABrK1P1tYfsL4+WVt/wPr6ZHJ/LHJOXQghRMksdaQuhBCiBBLUhRDCilhcUFcUpY+iKCcVRTmj/H975xNiVR3F8c+X0SimQIUQUaM/BC0iLCIIJCQoqI0FIQmBrWpRYLRJ2mhBEFHiziASDKpJ0splLoRqY6aNaQ70j4EcxpmFSM2mKL8t7hl4DN43vGbgdl7nA4/7u+fedzmHL+/77j2/++6TdnWdz3IgaVLSWUnjkr7pOp9BkXRA0qykcz2xNZKOSfoxlqu7zHFQWmraI2kqdBqX9GiXOQ6CpI2Sjks6L+l7STsjnlKnPvVk1uhaSV9LOhM1vRLxWySdCM/7SFLff65O1VOXNAL8ADwEXABOAtttn+80sSUiaRK413bKH01IegCYA96zfWfE3gAu2X49vnxX236pyzwHoaWmPcCc7Te7zO3fIGkdsM72aUk3AKeAx4CnSahTn3q2kVcjAaO25yStBL4CdgIvAkdsj0l6Gzhje3/bcbKdqd8H/GT7F9t/AmPA1o5z+t9j+wvg0oLwVuBgjA/SfODS0FJTWmxP2z4d49+BCWA9SXXqU09a3DAXqyvjZeBB4OOIL6pRNlNfD/zas36B5EIGBj6XdErSM10ns0ystT0d44vA2i6TWUael/RdtGdStCoWIulm4G7gBEOg04J6ILFGkkYkjQOzwDHgZ+Cy7b9il0U9L5upDyubbd8DPAI8F5f+Q4ObHl+ePl87+4HbgE3ANPBWt+kMjqTrgcPAC7Z/692WUaer1JNaI9t/294EbKDpTNwx6DGymfoUsLFnfUPEUmN7KpazwCc0YmZnJvqe8/3P2Y7zWTK2Z+JDdwV4h2Q6RZ/2MPC+7SMRTqvT1erJrtE8ti8Dx4H7gVWSVsSmRT0vm6mfBG6P2eBrgCeBox3ntCQkjcZED5JGgYeBc/3flYKjwI4Y7wA+6zCXZWHe/ILHSaRTTMK9C0zY3tuzKaVObfUk1+hGSatifB3NDSETNOb+ROy2qEap7n4BiFuU9gEjwAHbr3Wc0pKQdCvN2TnACuCDbDVJ+hDYQvOY0BlgN/ApcAi4ieYRy9tsp5l4bKlpC81lvYFJ4NmefvR/GkmbgS+Bs8CVCL9M04dOp1OferaTV6O7aCZCR2hOuA/ZfjU8YgxYA3wLPGX7j9bjZDP1oiiKop1s7ZeiKIqiD2XqRVEUQ0SZelEUxRBRpl4URTFElKkXRVEMEWXqRVEUQ0SZelEUxRDxD3cxjwANDP9YAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "id": "ojK0hAS8xWtU",
        "outputId": "9be12f55-6e87-4dc2-fa65-100e683c8968"
      },
      "source": [
        "df = pd.DataFrame()\n",
        "df['accuracy'] = m_h3.history['accuracy']\n",
        "df.plot(grid=True)"
      ],
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fee30623dd8>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 39
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 762
        },
        "id": "wxCwnMFvxWtV",
        "outputId": "32767c11-e48d-4dad-bd1f-029cf5305a13"
      },
      "source": [
        "df = pd.DataFrame()\n",
        "for i in range(0,len(weights)):\n",
        "        w = weights[i]\n",
        "        W = final_w[i]\n",
        "        learning_speed = [0]\n",
        "        cnt=0\n",
        "        for i in range(1,len(w)):\n",
        "            wold = w[i-1]\n",
        "            wnew = w[i]\n",
        "            diff = wold-wnew\n",
        "            d = wnew\n",
        "            m = np.median(d[d>0])\n",
        "            d[d==0] = m\n",
        "            diff = abs(diff)/d\n",
        "            if diff.size:\n",
        "                avg = abs(np.mean(diff))\n",
        "            else:\n",
        "                avg = 0\n",
        "            learning_speed.append(avg)\n",
        "        df['learning speed'] = learning_speed\n",
        "        df.plot(grid=True)"
      ],
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bTd5oaMZxWtV",
        "outputId": "ef22b12f-af21-46bf-fbdf-dbbf144d2df5"
      },
      "source": [
        "print(model3.summary())"
      ],
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"sequential_2\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "dense_5 (Dense)              (None, 30)                23550     \n",
            "_________________________________________________________________\n",
            "dense_6 (Dense)              (None, 30)                930       \n",
            "_________________________________________________________________\n",
            "dense_7 (Dense)              (None, 10)                310       \n",
            "=================================================================\n",
            "Total params: 24,790\n",
            "Trainable params: 24,790\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n",
            "None\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xZ8XzWxzxWtV"
      },
      "source": [
        "# For 1 Hidden Layers with regularization"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EmHpAdc8xWtV",
        "outputId": "212b3ff0-0de4-449c-b895-982b7aa61912"
      },
      "source": [
        "from keras import regularizers\n",
        "model_r = Sequential()\n",
        "start_time = time.time()\n",
        "model_r.add(Dense(30, activation= \"sigmoid\",kernel_regularizer=regularizers.l2(5)))\n",
        "model_r.add(Dense(10, activation='softmax'))\n",
        "weights = []\n",
        "save_weights = LambdaCallback(on_epoch_end=lambda batch, logs: weights.append(model_r.layers[0].get_weights()[0]))\n",
        "model_r.compile(optimizer=tf.keras.optimizers.Adam(learning_rate=0.1),\n",
        "              loss='sparse_categorical_crossentropy',\n",
        "              metrics=['accuracy'])\n",
        "m_h_r = model_r.fit(train_x, train_y,validation_data = (x_val,y_val), epochs=30, batch_size=10,callbacks = [save_weights])\n",
        "final_w = [model_r.layers[0].get_weights()[0]]\n",
        "weights = [weights]\n",
        "total_time = (time.time()-start_time)\n",
        "print(\"--- %s seconds ---\" % total_time)"
      ],
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/30\n",
            "100/100 [==============================] - 1s 9ms/step - loss: 86.7496 - accuracy: 0.0897 - val_loss: 2.3684 - val_accuracy: 0.0961\n",
            "Epoch 2/30\n",
            "100/100 [==============================] - 1s 8ms/step - loss: 2.3676 - accuracy: 0.0800 - val_loss: 2.3469 - val_accuracy: 0.1009\n",
            "Epoch 3/30\n",
            "100/100 [==============================] - 1s 8ms/step - loss: 2.3441 - accuracy: 0.1008 - val_loss: 2.3327 - val_accuracy: 0.0983\n",
            "Epoch 4/30\n",
            "100/100 [==============================] - 1s 8ms/step - loss: 2.3430 - accuracy: 0.1030 - val_loss: 2.3508 - val_accuracy: 0.0961\n",
            "Epoch 5/30\n",
            "100/100 [==============================] - 1s 8ms/step - loss: 2.3438 - accuracy: 0.0956 - val_loss: 2.3217 - val_accuracy: 0.1064\n",
            "Epoch 6/30\n",
            "100/100 [==============================] - 1s 8ms/step - loss: 2.3307 - accuracy: 0.0878 - val_loss: 2.3190 - val_accuracy: 0.0983\n",
            "Epoch 7/30\n",
            "100/100 [==============================] - 1s 8ms/step - loss: 2.3300 - accuracy: 0.0780 - val_loss: 2.3343 - val_accuracy: 0.0967\n",
            "Epoch 8/30\n",
            "100/100 [==============================] - 1s 8ms/step - loss: 2.3334 - accuracy: 0.0956 - val_loss: 2.3250 - val_accuracy: 0.0967\n",
            "Epoch 9/30\n",
            "100/100 [==============================] - 1s 8ms/step - loss: 2.3297 - accuracy: 0.0791 - val_loss: 2.3202 - val_accuracy: 0.0990\n",
            "Epoch 10/30\n",
            "100/100 [==============================] - 1s 8ms/step - loss: 2.3301 - accuracy: 0.0904 - val_loss: 2.3217 - val_accuracy: 0.0983\n",
            "Epoch 11/30\n",
            "100/100 [==============================] - 1s 8ms/step - loss: 2.3269 - accuracy: 0.0756 - val_loss: 2.3319 - val_accuracy: 0.0915\n",
            "Epoch 12/30\n",
            "100/100 [==============================] - 1s 8ms/step - loss: 2.3410 - accuracy: 0.0714 - val_loss: 2.4053 - val_accuracy: 0.0990\n",
            "Epoch 13/30\n",
            "100/100 [==============================] - 1s 8ms/step - loss: 2.5293 - accuracy: 0.0917 - val_loss: 2.5329 - val_accuracy: 0.1064\n",
            "Epoch 14/30\n",
            "100/100 [==============================] - 1s 8ms/step - loss: 2.9222 - accuracy: 0.1076 - val_loss: 3.0625 - val_accuracy: 0.0967\n",
            "Epoch 15/30\n",
            "100/100 [==============================] - 1s 8ms/step - loss: 3.0953 - accuracy: 0.0798 - val_loss: 3.0465 - val_accuracy: 0.0983\n",
            "Epoch 16/30\n",
            "100/100 [==============================] - 1s 8ms/step - loss: 3.0945 - accuracy: 0.1142 - val_loss: 3.1090 - val_accuracy: 0.0961\n",
            "Epoch 17/30\n",
            "100/100 [==============================] - 1s 8ms/step - loss: 3.1511 - accuracy: 0.0744 - val_loss: 3.2549 - val_accuracy: 0.0990\n",
            "Epoch 18/30\n",
            "100/100 [==============================] - 1s 8ms/step - loss: 3.2634 - accuracy: 0.1022 - val_loss: 3.2842 - val_accuracy: 0.0991\n",
            "Epoch 19/30\n",
            "100/100 [==============================] - 1s 8ms/step - loss: 3.2860 - accuracy: 0.0813 - val_loss: 3.3529 - val_accuracy: 0.0991\n",
            "Epoch 20/30\n",
            "100/100 [==============================] - 1s 8ms/step - loss: 3.2443 - accuracy: 0.0820 - val_loss: 3.0841 - val_accuracy: 0.1090\n",
            "Epoch 21/30\n",
            "100/100 [==============================] - 1s 8ms/step - loss: 3.1220 - accuracy: 0.0952 - val_loss: 3.1256 - val_accuracy: 0.1064\n",
            "Epoch 22/30\n",
            "100/100 [==============================] - 1s 8ms/step - loss: 3.1309 - accuracy: 0.0967 - val_loss: 2.9763 - val_accuracy: 0.0967\n",
            "Epoch 23/30\n",
            "100/100 [==============================] - 1s 8ms/step - loss: 3.0744 - accuracy: 0.1048 - val_loss: 3.3604 - val_accuracy: 0.1090\n",
            "Epoch 24/30\n",
            "100/100 [==============================] - 1s 8ms/step - loss: 3.3047 - accuracy: 0.1031 - val_loss: 3.2068 - val_accuracy: 0.0983\n",
            "Epoch 25/30\n",
            "100/100 [==============================] - 1s 9ms/step - loss: 3.1898 - accuracy: 0.0804 - val_loss: 3.1527 - val_accuracy: 0.1009\n",
            "Epoch 26/30\n",
            "100/100 [==============================] - 1s 8ms/step - loss: 3.1666 - accuracy: 0.0817 - val_loss: 3.1173 - val_accuracy: 0.0990\n",
            "Epoch 27/30\n",
            "100/100 [==============================] - 1s 8ms/step - loss: 3.1584 - accuracy: 0.0910 - val_loss: 3.2081 - val_accuracy: 0.0990\n",
            "Epoch 28/30\n",
            "100/100 [==============================] - 1s 8ms/step - loss: 3.1648 - accuracy: 0.0928 - val_loss: 3.1459 - val_accuracy: 0.0983\n",
            "Epoch 29/30\n",
            "100/100 [==============================] - 1s 8ms/step - loss: 3.1989 - accuracy: 0.0912 - val_loss: 3.1682 - val_accuracy: 0.0915\n",
            "Epoch 30/30\n",
            "100/100 [==============================] - 1s 8ms/step - loss: 3.1919 - accuracy: 0.1100 - val_loss: 3.4319 - val_accuracy: 0.1030\n",
            "--- 24.994810819625854 seconds ---\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sCzGBRshxWtV",
        "outputId": "dfeee6ad-d0a7-49bc-8886-f966d5d627a1"
      },
      "source": [
        "predict_train = model_r.predict_classes(train_x)\n",
        "acc_train = np.where(train_y == predict_train)[0].shape[0]/train_x.shape[0]\n",
        "print('Training accuracy for 1 hidden layer with regularization = {}'.format(acc_train))\n",
        "predict_test = model_r.predict_classes(x_test)\n",
        "acc_test = np.where(y_test == predict_test)[0].shape[0]/x_test.shape[0]\n",
        "print('Testing accuracy for 1 hidden layer with regularization = {}'.format(acc_test))\n",
        "print('Total run time = {} seconds'.format(total_time))"
      ],
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/sequential.py:450: UserWarning: `model.predict_classes()` is deprecated and will be removed after 2021-01-01. Please use instead:* `np.argmax(model.predict(x), axis=-1)`,   if your model does multi-class classification   (e.g. if it uses a `softmax` last-layer activation).* `(model.predict(x) > 0.5).astype(\"int32\")`,   if your model does binary classification   (e.g. if it uses a `sigmoid` last-layer activation).\n",
            "  warnings.warn('`model.predict_classes()` is deprecated and '\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Training accuracy for 1 hidden layer with regularization = 0.1\n",
            "Testing accuracy for 1 hidden layer with regularization = 0.101\n",
            "Total run time = 24.994810819625854 seconds\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "id": "qkKS0zK0xWtW",
        "outputId": "fa03e913-01ff-43a6-9d30-0cdf062773c3"
      },
      "source": [
        "df = pd.DataFrame()\n",
        "df['training_loss'] = m_h_r.history['loss']\n",
        "#df['accuracy'] = model_history.history['accuracy']\n",
        "df['validation_loss'] = m_h_r.history['val_loss']\n",
        "df.plot(grid=True)"
      ],
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fee305a45f8>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 44
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "id": "MSPwbrQJxWtW",
        "outputId": "1a8c391e-1834-4ffe-da88-d9e559b2294f"
      },
      "source": [
        "df = pd.DataFrame()\n",
        "df['accuracy'] = m_h_r.history['accuracy']\n",
        "df.plot(grid=True)"
      ],
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fee30afa550>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 45
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 276
        },
        "id": "jCyPaad0xWtW",
        "outputId": "4c003ea7-4865-485d-9068-f6ca2b27c1cd"
      },
      "source": [
        "df = pd.DataFrame()\n",
        "for i in range(0,len(weights)):\n",
        "        w = weights[i]\n",
        "        W = final_w[i]\n",
        "        learning_speed = [0]\n",
        "        cnt=0\n",
        "        for i in range(1,len(w)):\n",
        "            wold = w[i-1]\n",
        "            wnew = w[i]\n",
        "            diff = wold-wnew\n",
        "            d = wnew\n",
        "            m = np.median(d[d>0])\n",
        "            d[d==0] = m\n",
        "            diff = abs(diff)/d\n",
        "            if diff.size:\n",
        "                avg = abs(np.mean(diff))\n",
        "            else:\n",
        "                avg = 0\n",
        "            learning_speed.append(avg)\n",
        "        df['learning speed'] = learning_speed\n",
        "        df.plot(grid=True)"
      ],
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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USXvvIPV1MWpjB/6za0hkRuoDCnUpnkJdpEzaeweZNbPuoO0NmdWPdAeMhEGhLlIm7b2DB5VeIKf8opq6hEChLlIm7b0DNM04+Hm/0fKLQl1CoFAXKZPxJvOC9BOloPKLhEOhLlImE4Z6XXbxaYW6FE+hLlImE10orakxGhJxhbqEQqEuUgZ9g8P0DY6MO1KHzKReKr9ICBTqImXQMcFkXlkNiThduk9dQqBQFymD9gkm88pqSMQ1UpdQKNRFymCiybyyGrRQhoREoS5SBhMtkJGlC6USFoW6SBlMVX6pT8Q1oZeEQqEuUgZThXpjIq4JvSQUCnWRMphoKbus+syFUq3XLsVSqIuUQXvvII3JOLEaG/f7Dck4QyNO/9BImVsmUaNQFymDiaYIyGpIaKoACYdCXaQM8g51XSyVIinURcpAI3UpF4W6SBmkJ/NSqEvpKdRFyqCtZ4qRemZJOz1VKsVSqIuUmLvTMcFSdln1GqlLSBTqIiXWNzjCwPDE0+5C+uEjUKhL8RTqIiW2fzKvgxfIyMqWX3T3ixRLoS5SYlNNEQAwozZGjWmkLsXLK9TN7Bwze87MWs3s2gn2udDMNpvZs2b283CbKVK92noGgMlD3czSUwUo1KVI8al2MLMYcAvwfmAX8JiZrXH3zTn7LAG+CCx39zfM7MhSNVik2uQzUgctlCHhyGekvgxodfft7j4ArAYuGLPPp4Bb3P0NAHffE24zRapXIaGumRqlWFOO1IH5wM6cr3cBp4/Z5z8AmNnDQAy43t1/O/aNzOwK4AqAefPmkUqlAjQZurq6Ah87XUWtT1HrDwTv08Yd6VB/euOf2F47/oReACP9vbz8Sm/Zfm46R9NfkP7kE+r5vs8SoAVYADxkZu9w97bcndx9FbAKoLm52VtaWgJ9WCqVIuix01XU+hS1/kDwPm1Y9xz2XCvnvreFmglmaQT48Qt/oqt/iJaW5UW0Mn86R9NfkP7kU37ZDSzM+XpBZluuXcAadx909xeB50mHvMghr713kMOStZMGOmTKL7pQKkXKJ9QfA5aY2WIzqwMuBtaM2ede0qN0zGwO6XLM9hDbKVK1pprMK6teF0olBFOGursPAVcD9wFbgDvd/Vkzu8HMVmZ2uw/YZ2abgQeBf3L3faVqtEg1mWoyrywtPi1hyKum7u5rgbVjtn0l57UDn8v8EZEcU03mldWYTIe6u2M2ealGZCJ6olSkxKaazCurPhFnxKF3cLgMrZKoUqiLlFi+NXXNqS5hUKiLlJC7Fx7qulgqRVCoi5RQz8AwQyPOrAJCvbtf5RcJTqEuUkJteU4RAPun3+3sHyxpmyTaFOoiJdTeU0Coq/wiIVCoi5RQvpN5QU75RZN6SREU6iIlNBrqeTx8VK+RuoRAoS5SQu29Uy+QkdWYXdJOF0qlCAp1kRIqpPySiNcQrzG6dKFUiqBQFymh9t5BYjU2Wi+fzOiSdiq/SBEU6iIllH3wKN+5XNKTeqn8IsEp1EVKqL13KK/SS1Y61FV+keAU6iIl1NYzkNdkXlkNybieKJWiKNRFSqgjz3lfsuoTcTo1oZcUQaEuUkL5TuaV1agl7aRICnWREmrvHcxrMq+sBt39IkVSqIuUyMhI/tPuZtVrSTspkkJdpES6BoYY8fwePMpqSMbpHhhiZMRL2DKJMoW6SIkUMkNjVkMihjv0aEk7CUihLlIihUzmldWQSO+ri6USlEJdpEQKmfcla3ShDF0slYAU6iIlEijUEzFAi09LcAp1kRIJFuoqv0hxFOoiJRIk1OszI3WVXyQohbpIibT3DlIbM2bWxfI+plEjdSmSQl2kRNp6Cpt2F/ZfKFVNXYJSqIuUSEfvYEEzNML+8otCXYJSqIuUSKFTBAAk4jHqYjUKdQlMoS5SIoVO5pVVn4hpUi8JLK9QN7NzzOw5M2s1s2sn2e/DZuZm1hxeE0WqU1vvQMEjdcgulKFQl2CmDHUziwG3AOcCxwOXmNnx4+zXCFwD/CnsRopUo/aewssvkL5XXQtlSFD5jNSXAa3uvt3dB4DVwAXj7Pc/gK8BfSG2T6QqjYw4nf2FrU+a1aDyixQhnsc+84GdOV/vAk7P3cHMlgIL3f3XZvZPE72RmV0BXAEwb948UqlUwQ0G6OrqCnzsdBW1PkWtP1BYn7oHHXd4/ZWXSaVeLehz+rv72NvvJf/5HernqBoE6U8+oT4pM6sBvgl8fKp93X0VsAqgubnZW1paAn1mKpUi6LHTVdT6FLX+QGF9enlfDzzwIKe+4220nLqgoM+5+5WNbH6lo+Q/v0P9HFWDIP3Jp/yyG1iY8/WCzLasRuAEIGVmO4AzgDW6WCqHsrbeAaCwKQKyGpNa/UiCyyfUHwOWmNliM6sDLgbWZL/p7u3uPsfdF7n7IuBRYKW7P16SFotUgSDzvmQ1aEk7KcKUoe7uQ8DVwH3AFuBOd3/WzG4ws5WlbqBINSom1OsTcXoGhhnWknYSQF41dXdfC6wds+0rE+zbUnyzRKpbNtRnFbDqUVZDIv3PsntgiMOShR8vhzY9USpSAm0B1ifNyoa6bmuUIBTqIiXQ0TtIXbyGZG3+0+5mZWdq1FOlEoRCXaQEgkzmlZUdqeupUglCoS5SAkEn84KcmrpCXQJQqIuUQFvAeV8gZ6EM1dQlAIW6SAkUU36pr1P5RYJTqIuUQDGh3qgLpVIEhbpICQRZyi6rXrc0ShEU6iIhGxoeobN/KNCDRwC1sRoS8Rq6BhTqUjiFukjIOjIj7KDlF8hM6qWRugSgUBcJWTHzvmTVa1IvCUihLhKyMEK9IaF1SiUYhbpIyIqZzCurPhGnU+UXCUChLhKytp7gC2RkNSbidOtCqQSgUBcJWUdmpB70lkZIP1WqC6UShEJdJGS6UCqVpFAXCVl77yAzamMk4oVPu5vVqFCXgBTqIiErZoqArPpEnL7BEYaGR0JqlRwqFOoiIStmhsas/dPvDofRJDmEKNRFQhbGSD07/W5n/2AYTZJDiEJdJGTtRUzmlaWRugSlUBcJWUfvYFEPHkHO4tMaqUuBFOoiIWsL6UIpoKdKpWAKdZEQDQ6P0DMwXHSo718oQ+UXKYxCXSREYTx4BCq/SHAKdZEQhTGZF+SsfqSRuhRIoS4Sorae4ud9gZyRumrqUiCFukiIOkIqv8RqjBm1MZVfpGAKdZEQhVVTh8xMjSq/SIEU6iIhCjPUNamXBKFQFwlRmKFeryXtJIC8Qt3MzjGz58ys1cyuHef7nzOzzWa2ycweMLNjwm+qyPT34t5u5jTUURsrfrzUkNBCGVK4Kf/mmVkMuAU4FzgeuMTMjh+z2xNAs7ufCNwNfD3shopMd+7Ow617OfPYOaG8X30iTqdG6lKgfIYTy4BWd9/u7gPAauCC3B3c/UF378l8+SiwINxmikx/2/Z0saezn3e9ZXYo79eYVPlFChfPY5/5wM6cr3cBp0+y/+XAb8b7hpldAVwBMG/ePFKpVH6tHKOrqyvwsdNV1PoUtf7A1H1atyNdT4/tbSWV2l7057Xv6+eNzqGS/RwPxXNUbYL0J59Qz5uZfQxoBt493vfdfRWwCqC5udlbWloCfU4qlSLosdNV1PoUtf7A1H366W2PsWh2Fx8596xQPu/R3q2sf+XFkv0cD8VzVG2C9Cef8stuYGHO1wsy2w5gZu8DvgSsdPf+glohUuUGh0d4dPs+lr8lnHo6pMsvA0Mj9A/pXnXJXz6h/hiwxMwWm1kdcDGwJncHMzsF+BHpQN8TfjNFprendrbRPTDMu0IM9fq69MLVmqlRCjFlqLv7EHA1cB+wBbjT3Z81sxvMbGVmt5uABuAuM3vSzNZM8HYikbS+dS9mcOax4VwkBWhIpu9118VSKUReNXV3XwusHbPtKzmv3xdyu0SqysOte3nH/CZmzawL7T0bEumRuhbKkELoiVKRInX1D/HEy22h1tMBGhKZkfqAQl3yp1AXKdKfX9zH0IiHWk+H9IReoOl3pTAKdZEird+2j0S8hlOPOTzU982WXzSplxRCoS5SpIdb97Js8REka2Ohvm+2/KJQl0Io1EWKsKezj+de6wy9ng5Qnx2pq/wiBVCoixThj637AEKvpwPU12XXKVWoS/4U6iJFWN+6l1kzazn+qMNCf++aGktPv6tQlwIo1EUCcnfWb9vL8mPnUFNjJfmM+kRMDx9JQRTqIgG98Ho3f+noK0k9PatBc6pLgRTqIgE93LoXKE09PUurH0mhFOoiAa1v3cvCI2Zw9OyZJfuMBi2UIQVSqIsEMDQ8wqMv7CvpKB3QhVIpmEJdJIBNu9vp7B8qaT0d0uuUKtSlEAp1kQAe3paup78zpEWmJ9KoUJcCKdRFAljfupe3v+kwjqgPb6rd8dRnLpS6e0k/R6JDoS5SoJ6BITa+/EbJ6+mQvlA6NOL0D42U/LMkGhTqIgX684t/ZXDYS15Ph3T5BTRVgORPoS5SoIdb91IXr2HZ4iNK/ln1mVDXbY2SL4W6SIHWt+6j+ZjDQ59qdzwNmVDXknaSL4W6SAH2dvWz5dWOspReYH+oq/wi+VKoixTgjy+Ubqrd8WSXtFP5RfKlUBcpwMPb9nJYMs4J85vK8nkaqUuhFOoieXJ31rfu5Z3HziFWoql2x1KoS6EU6iJ5eq3H2d3Wy/Il5Sm9wP7yi2ZqlHwp1EXytHnfMFC+ejrAjNoYNaaauuRPoS6Sp2f3DTN/1gwWlXCq3bHMjHotlCEFUKiL5GF4xNmyb5jlb5mNWXnq6VmNWihDCqBQF8nDM7vb6RmibPen56pPxOkeUKhLfhTqInlY31qeqXbH05CM64lSyZtCXSQPD7fuZWFjDXMbE2X/7IaElrST/MXz2cnMzgG+A8SA/+Xu/zLm+wngduBUYB9wkbvvCLepIuUxNDzC86918dSuNp58uY2ndrWx9S+dnL0or38uoWtIxHmto68iny3VZ8q/pWYWA24B3g/sAh4zszXuvjlnt8uBN9z9LWZ2MfA14KJSNFgkTO7pe8+f3NnGUzvbeGpnO0/vbqd3MH374qyZtZy8cBZnv/1vePPI7oq0sV4XSqUA+Qw9lgGt7r4dwMxWAxcAuaF+AXB95vXdwPfNzLwEy7Xc+dhOvr2+h/qN/y/st66o7p5o9ala+vNGzwB7uwYAqIvXcMKbDuPiZQs5eeEsTl44i6OPmDl6t0sq9UpF2tiQiPOXjj7e/81wf57Vco4KUS19+ux7l/B3J72pJO+dT6jPB3bmfL0LOH2ifdx9yMzagdnA3tydzOwK4AqAefPmkUqlCm7wrteGmJcYIWa9BR87nTVErE/V0p+/aTIWH13Hm5tqWNBYQ7xmEHgd2l7nxTZ4MWffrq6uQH9ni3X0yDDN82KMeLg/z2o5R4Wolj69tG0zqTeen3K/IH/nylokdPdVwCqA5uZmb2lpKfg9WoClqRRBjp3OUhHrU9T6A5XrUwvwiRK8r87R9BekP/nc/bIbWJjz9YLMtnH3MbM40ET6gqmIiJRRPqH+GLDEzBabWR1wMbBmzD5rgEszrz8C/L4U9XQREZnclOWXTI38auA+0rc0/sTdnzWzG4DH3X0N8GPgp2bWCvyVdPCLiEiZ5VVTd/e1wNox276S87oP+PtwmyYiIoXSE6UiIhGiUBcRiRCFuohIhCjURUQixCp156GZvQ68FPDwOYx5WjUCotanqPUHotenqPUHoten8fpzjLvPneiAioV6MczscXdvrnQ7whS1PkWtPxC9PkWtPxC9PgXpj8ovIiIRolAXEYmQag31VZVuQAlErU9R6w9Er09R6w9Er08F96cqa+oiIjK+ah2pi4jIOBTqIiIRUnWhbmbnmNlzZtZqZtdWuj3FMrMdZva0mT1pZo9Xuj1BmNlPzGyPmT2Ts+0IM/udmW3L/PfwSraxEBP053oz2505T0+a2XmVbGOhzGyhmT1oZpvN7FkzuyazvSrP0yT9qdrzZGZJM/uzmT2V6dM/Z7YvNrM/ZTLvf2emQJ/4faqppp5ZBPt5chbBBi4Zswh2VTGzHUCzu1ftAxNmtgLoAm539xMy274O/NXd/yXzP9/D3f0LlWxnviboz/VAl7t/o5JtC8rMjgKOcveNZtYIbAD+E/BxqnZUmawAAAJuSURBVPA8TdKfC6nS82TpxXDr3b3LzGqB9cA1wOeAX7r7ajO7FXjK3X840ftU20h9dBFsdx8AsotgSwW5+0Ok59HPdQHwb5nX/0b6H1xVmKA/Vc3dX3X3jZnXncAW0msLV+V5mqQ/VcvTujJf1mb+OPAe4O7M9inPUbWF+niLYFf1iSR90taZ2YbMwtxRMc/dX828/gswr5KNCcnVZrYpU56pijLFeMxsEXAK8CcicJ7G9Aeq+DyZWczMngT2AL8DXgDa3H0os8uUmVdtoR5F73L3pcC5wD9kfvWPlMzShtVT5xvfD4FjgZOBV4GbK9ucYMysAfgF8F/dvSP3e9V4nsbpT1WfJ3cfdveTSa8FvQx4a6HvUW2hns8i2FXF3Xdn/rsHuIf0iYyC1zJ1z2z9c0+F21MUd38t8w9uBPhXqvA8Zeq0vwD+3d1/mdlctedpvP5E4TwBuHsb8CBwJjDLzLKr1E2ZedUW6vksgl01zKw+c5EHM6sHPgA8M/lRVSN3MfJLgf9TwbYULRt8GR+kys5T5iLcj4Et7v7NnG9V5XmaqD/VfJ7MbK6Zzcq8nkH6hpAtpMP9I5ndpjxHVXX3C0DmFqVvs38R7Bsr3KTAzOzNpEfnkF4v9ufV2B8zuwNoIT1N6GvAdcC9wJ3A0aSnWL7Q3avi4uME/Wkh/Su9AzuAT+fUoqc9M3sX8AfgaWAks/m/k65DV915mqQ/l1Cl58nMTiR9ITRGesB9p7vfkMmJ1cARwBPAx9y9f8L3qbZQFxGRiVVb+UVERCahUBcRiRCFuohIhCjURUQiRKEuIhIhCnURkQhRqIuIRMj/ByF/I1YH2vraAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZW514g9qxWtW",
        "outputId": "0a99859b-485d-4ba6-9884-36560823cead"
      },
      "source": [
        "print(model_r.summary())"
      ],
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"sequential_3\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "dense_8 (Dense)              (None, 30)                23550     \n",
            "_________________________________________________________________\n",
            "dense_9 (Dense)              (None, 10)                310       \n",
            "=================================================================\n",
            "Total params: 23,860\n",
            "Trainable params: 23,860\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n",
            "None\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NBTBn3pcxWtW"
      },
      "source": [
        "# For 2 Hidden Layers with regularization"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OyXK3ulVxWtW",
        "outputId": "4c17fbdb-7e1f-4a38-fbf1-fe5b2ef28af6"
      },
      "source": [
        "model_r2 = Sequential()\n",
        "start_time = time.time()\n",
        "model_r2.add(Dense(30, activation= \"sigmoid\",kernel_regularizer=regularizers.l2(5)))\n",
        "model_r2.add(Dense(30, activation= \"sigmoid\",kernel_regularizer=regularizers.l2(5)))\n",
        "model_r2.add(Dense(10, activation='softmax'))\n",
        "weights1 = []\n",
        "weights2 = []\n",
        "save_weights1 = LambdaCallback(on_epoch_end=lambda batch, logs: weights1.append(model_r2.layers[0].get_weights()[0]))\n",
        "save_weights2 = LambdaCallback(on_epoch_end=lambda batch, logs: weights2.append(model_r2.layers[1].get_weights()[0]))\n",
        "model_r2.compile(optimizer=tf.keras.optimizers.Adam(learning_rate=0.1),\n",
        "              loss='sparse_categorical_crossentropy',\n",
        "              metrics=['accuracy'])\n",
        "m_h_r2 = model_r2.fit(train_x, train_y,validation_data = (x_val,y_val), epochs=30, batch_size=10,callbacks = [save_weights1,save_weights2])\n",
        "final_w = [model_r2.layers[0].get_weights()[0],model_r2.layers[1].get_weights()[0]]\n",
        "weights = [weights1,weights2]\n",
        "total_time = (time.time()-start_time)\n",
        "print(\"--- %s seconds ---\" % total_time)"
      ],
      "execution_count": 48,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/30\n",
            "100/100 [==============================] - 1s 10ms/step - loss: 105.7942 - accuracy: 0.1209 - val_loss: 2.3719 - val_accuracy: 0.0990\n",
            "Epoch 2/30\n",
            "100/100 [==============================] - 1s 8ms/step - loss: 2.3128 - accuracy: 0.1229 - val_loss: 2.3333 - val_accuracy: 0.0983\n",
            "Epoch 3/30\n",
            "100/100 [==============================] - 1s 8ms/step - loss: 2.3347 - accuracy: 0.0959 - val_loss: 2.3266 - val_accuracy: 0.0983\n",
            "Epoch 4/30\n",
            "100/100 [==============================] - 1s 8ms/step - loss: 2.3313 - accuracy: 0.1102 - val_loss: 2.3229 - val_accuracy: 0.1064\n",
            "Epoch 5/30\n",
            "100/100 [==============================] - 1s 8ms/step - loss: 2.3399 - accuracy: 0.0801 - val_loss: 2.3192 - val_accuracy: 0.0961\n",
            "Epoch 6/30\n",
            "100/100 [==============================] - 1s 8ms/step - loss: 2.3470 - accuracy: 0.0728 - val_loss: 2.3229 - val_accuracy: 0.1009\n",
            "Epoch 7/30\n",
            "100/100 [==============================] - 1s 8ms/step - loss: 2.3375 - accuracy: 0.0941 - val_loss: 2.3145 - val_accuracy: 0.1090\n",
            "Epoch 8/30\n",
            "100/100 [==============================] - 1s 8ms/step - loss: 2.3260 - accuracy: 0.1048 - val_loss: 2.3478 - val_accuracy: 0.1064\n",
            "Epoch 9/30\n",
            "100/100 [==============================] - 1s 8ms/step - loss: 2.3346 - accuracy: 0.0812 - val_loss: 2.3179 - val_accuracy: 0.0990\n",
            "Epoch 10/30\n",
            "100/100 [==============================] - 1s 8ms/step - loss: 2.3368 - accuracy: 0.0565 - val_loss: 2.3152 - val_accuracy: 0.1030\n",
            "Epoch 11/30\n",
            "100/100 [==============================] - 1s 8ms/step - loss: 2.3281 - accuracy: 0.0881 - val_loss: 2.3188 - val_accuracy: 0.1030\n",
            "Epoch 12/30\n",
            "100/100 [==============================] - 1s 8ms/step - loss: 2.3264 - accuracy: 0.0867 - val_loss: 2.3140 - val_accuracy: 0.0961\n",
            "Epoch 13/30\n",
            "100/100 [==============================] - 1s 8ms/step - loss: 2.6250 - accuracy: 0.0648 - val_loss: 2.7321 - val_accuracy: 0.0961\n",
            "Epoch 14/30\n",
            "100/100 [==============================] - 1s 8ms/step - loss: 3.0416 - accuracy: 0.0916 - val_loss: 3.1534 - val_accuracy: 0.0915\n",
            "Epoch 15/30\n",
            "100/100 [==============================] - 1s 8ms/step - loss: 3.0445 - accuracy: 0.0947 - val_loss: 3.0153 - val_accuracy: 0.0967\n",
            "Epoch 16/30\n",
            "100/100 [==============================] - 1s 8ms/step - loss: 3.1864 - accuracy: 0.0820 - val_loss: 3.3570 - val_accuracy: 0.1064\n",
            "Epoch 17/30\n",
            "100/100 [==============================] - 1s 8ms/step - loss: 3.3110 - accuracy: 0.1067 - val_loss: 3.2382 - val_accuracy: 0.1090\n",
            "Epoch 18/30\n",
            "100/100 [==============================] - 1s 8ms/step - loss: 3.2926 - accuracy: 0.0888 - val_loss: 3.2361 - val_accuracy: 0.1090\n",
            "Epoch 19/30\n",
            "100/100 [==============================] - 1s 8ms/step - loss: 3.3175 - accuracy: 0.1002 - val_loss: 3.3527 - val_accuracy: 0.1064\n",
            "Epoch 20/30\n",
            "100/100 [==============================] - 1s 8ms/step - loss: 3.3163 - accuracy: 0.0849 - val_loss: 3.2410 - val_accuracy: 0.0967\n",
            "Epoch 21/30\n",
            "100/100 [==============================] - 1s 8ms/step - loss: 3.2120 - accuracy: 0.1058 - val_loss: 3.1713 - val_accuracy: 0.0983\n",
            "Epoch 22/30\n",
            "100/100 [==============================] - 1s 8ms/step - loss: 3.1811 - accuracy: 0.0843 - val_loss: 3.0483 - val_accuracy: 0.0991\n",
            "Epoch 23/30\n",
            "100/100 [==============================] - 1s 8ms/step - loss: 3.1255 - accuracy: 0.0829 - val_loss: 3.3381 - val_accuracy: 0.0983\n",
            "Epoch 24/30\n",
            "100/100 [==============================] - 1s 8ms/step - loss: 3.3533 - accuracy: 0.0907 - val_loss: 3.2545 - val_accuracy: 0.0915\n",
            "Epoch 25/30\n",
            "100/100 [==============================] - 1s 8ms/step - loss: 3.2516 - accuracy: 0.0962 - val_loss: 3.1847 - val_accuracy: 0.0991\n",
            "Epoch 26/30\n",
            "100/100 [==============================] - 1s 8ms/step - loss: 3.1998 - accuracy: 0.0951 - val_loss: 3.2127 - val_accuracy: 0.0915\n",
            "Epoch 27/30\n",
            "100/100 [==============================] - 1s 8ms/step - loss: 3.2235 - accuracy: 0.0749 - val_loss: 3.1652 - val_accuracy: 0.0990\n",
            "Epoch 28/30\n",
            "100/100 [==============================] - 1s 8ms/step - loss: 3.2049 - accuracy: 0.0908 - val_loss: 3.1596 - val_accuracy: 0.1009\n",
            "Epoch 29/30\n",
            "100/100 [==============================] - 1s 8ms/step - loss: 3.2463 - accuracy: 0.0740 - val_loss: 3.2220 - val_accuracy: 0.0915\n",
            "Epoch 30/30\n",
            "100/100 [==============================] - 1s 8ms/step - loss: 3.2903 - accuracy: 0.0964 - val_loss: 3.4666 - val_accuracy: 0.0990\n",
            "--- 25.050969123840332 seconds ---\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3eq4WdNlxWtX",
        "outputId": "f2305d59-b658-4682-db39-945cb97bbf9f"
      },
      "source": [
        "predict_train = model_r2.predict_classes(train_x)\n",
        "acc_train = np.where(train_y == predict_train)[0].shape[0]/train_x.shape[0]\n",
        "print('Training accuracy for 2 hidden layer with regularization = {}'.format(acc_train))\n",
        "predict_test = model_r2.predict_classes(x_test)\n",
        "acc_test = np.where(y_test == predict_test)[0].shape[0]/x_test.shape[0]\n",
        "print('Testing accuracy for 2 hidden layer with regularization = {}'.format(acc_test))\n",
        "print('Total run time = {} seconds'.format(total_time))"
      ],
      "execution_count": 54,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/sequential.py:450: UserWarning: `model.predict_classes()` is deprecated and will be removed after 2021-01-01. Please use instead:* `np.argmax(model.predict(x), axis=-1)`,   if your model does multi-class classification   (e.g. if it uses a `softmax` last-layer activation).* `(model.predict(x) > 0.5).astype(\"int32\")`,   if your model does binary classification   (e.g. if it uses a `sigmoid` last-layer activation).\n",
            "  warnings.warn('`model.predict_classes()` is deprecated and '\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Training accuracy for 2 hidden layer with regularization = 0.1\n",
            "Testing accuracy for 2 hidden layer with regularization = 0.1032\n",
            "Total run time = 25.050969123840332 seconds\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "id": "qtEs2saixWtX",
        "outputId": "79b2cc77-5339-4a37-ad81-c7994b8ca236"
      },
      "source": [
        "df = pd.DataFrame()\n",
        "df['training_loss'] = m_h_r2.history['loss']\n",
        "#df['accuracy'] = model_history.history['accuracy']\n",
        "df['validation_loss'] = m_h_r2.history['val_loss']\n",
        "df.plot(grid=True)"
      ],
      "execution_count": 55,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fee3042d6d8>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 55
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "id": "e7wvqJNsxWtX",
        "outputId": "741c17c2-305c-4b01-d7c4-6dcddf12ecdc"
      },
      "source": [
        "df = pd.DataFrame()\n",
        "df['accuracy'] = m_h_r2.history['accuracy']\n",
        "df.plot(grid=True)"
      ],
      "execution_count": 56,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fee3051f828>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 56
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 524
        },
        "id": "hs6yOsdGxWtX",
        "outputId": "11dc1dae-0318-4e3f-d10d-9f182072b084"
      },
      "source": [
        "df = pd.DataFrame()\n",
        "for i in range(0,len(weights)):\n",
        "        w = weights[i]\n",
        "        W = final_w[i]\n",
        "        learning_speed = [0]\n",
        "        cnt=0\n",
        "        for i in range(1,len(w)):\n",
        "            wold = w[i-1]\n",
        "            wnew = w[i]\n",
        "            diff = wold-wnew\n",
        "            d = wnew\n",
        "            m = np.median(d[d>0])\n",
        "            d[d==0] = m\n",
        "            diff = abs(diff)/d\n",
        "            if diff.size:\n",
        "                avg = abs(np.mean(diff))\n",
        "            else:\n",
        "                avg = 0\n",
        "            learning_speed.append(avg)\n",
        "        df['learning speed'] = learning_speed\n",
        "        df.plot(grid=True)"
      ],
      "execution_count": 57,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "juVmKxiixWtX",
        "outputId": "af6c86ba-9109-4282-f2b7-489884fc0d49"
      },
      "source": [
        "print(model_r2.summary())"
      ],
      "execution_count": 58,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"sequential_4\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "dense_10 (Dense)             (None, 30)                23550     \n",
            "_________________________________________________________________\n",
            "dense_11 (Dense)             (None, 30)                930       \n",
            "_________________________________________________________________\n",
            "dense_12 (Dense)             (None, 10)                310       \n",
            "=================================================================\n",
            "Total params: 24,790\n",
            "Trainable params: 24,790\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n",
            "None\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Pk75c1P5xWtX"
      },
      "source": [
        "# For 3 Hidden Layers with regularization"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Yu-fnx8ZxWtY",
        "outputId": "9a66e5ea-fea7-430e-be26-25755afd23c7"
      },
      "source": [
        "model_r3 = Sequential()\n",
        "start_time = time.time()\n",
        "model_r3.add(Dense(30, activation= \"sigmoid\",kernel_regularizer=regularizers.l2(5)))\n",
        "\n",
        "model_r3.add(Dense(30, activation= \"sigmoid\",kernel_regularizer=regularizers.l2(5)))\n",
        "model_r3.add(Dense(30, activation= \"sigmoid\",kernel_regularizer=regularizers.l2(5)))\n",
        "model_r3.add(Dense(10, activation='softmax'))\n",
        "weights1 = []\n",
        "weights2 = []\n",
        "weights3 = []\n",
        "save_weights1 = LambdaCallback(on_epoch_end=lambda batch, logs: weights1.append(model_r3.layers[0].get_weights()[0]))\n",
        "save_weights2 = LambdaCallback(on_epoch_end=lambda batch, logs: weights2.append(model_r3.layers[1].get_weights()[0]))\n",
        "save_weights3 = LambdaCallback(on_epoch_end=lambda batch, logs: weights3.append(model_r3.layers[1].get_weights()[0]))\n",
        "model_r3.compile(optimizer=tf.keras.optimizers.Adam(learning_rate=0.1),\n",
        "              loss='sparse_categorical_crossentropy',\n",
        "              metrics=['accuracy'])\n",
        "m_h_r3 = model_r3.fit(train_x, train_y,validation_data = (x_val,y_val), epochs=30, batch_size=10,callbacks = [save_weights1,save_weights2,save_weights3])\n",
        "final_w = [model_r3.layers[0].get_weights()[0],model_r3.layers[1].get_weights()[0],model_r3.layers[2].get_weights()[0]]\n",
        "weights = [weights1,weights2,weights3]\n",
        "total_time = (time.time()-start_time)\n",
        "print(\"--- %s seconds ---\" % total_time)"
      ],
      "execution_count": 59,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/30\n",
            "100/100 [==============================] - 2s 15ms/step - loss: 122.6703 - accuracy: 0.1033 - val_loss: 2.3688 - val_accuracy: 0.0983\n",
            "Epoch 2/30\n",
            "100/100 [==============================] - 1s 13ms/step - loss: 2.3541 - accuracy: 0.0797 - val_loss: 2.3378 - val_accuracy: 0.1090\n",
            "Epoch 3/30\n",
            "100/100 [==============================] - 1s 10ms/step - loss: 2.3494 - accuracy: 0.0887 - val_loss: 2.3098 - val_accuracy: 0.0967\n",
            "Epoch 4/30\n",
            "100/100 [==============================] - 1s 8ms/step - loss: 2.3278 - accuracy: 0.0838 - val_loss: 2.3499 - val_accuracy: 0.0991\n",
            "Epoch 5/30\n",
            "100/100 [==============================] - 1s 9ms/step - loss: 2.3443 - accuracy: 0.0918 - val_loss: 2.3079 - val_accuracy: 0.1009\n",
            "Epoch 6/30\n",
            "100/100 [==============================] - 1s 8ms/step - loss: 2.3199 - accuracy: 0.0932 - val_loss: 2.3196 - val_accuracy: 0.1030\n",
            "Epoch 7/30\n",
            "100/100 [==============================] - 1s 8ms/step - loss: 2.3185 - accuracy: 0.1204 - val_loss: 2.3384 - val_accuracy: 0.1064\n",
            "Epoch 8/30\n",
            "100/100 [==============================] - 1s 8ms/step - loss: 2.3569 - accuracy: 0.0742 - val_loss: 2.3159 - val_accuracy: 0.1090\n",
            "Epoch 9/30\n",
            "100/100 [==============================] - 1s 8ms/step - loss: 2.3288 - accuracy: 0.0838 - val_loss: 2.3065 - val_accuracy: 0.1064\n",
            "Epoch 10/30\n",
            "100/100 [==============================] - 1s 9ms/step - loss: 2.3206 - accuracy: 0.0929 - val_loss: 2.3160 - val_accuracy: 0.0961\n",
            "Epoch 11/30\n",
            "100/100 [==============================] - 1s 8ms/step - loss: 2.3309 - accuracy: 0.0919 - val_loss: 2.3236 - val_accuracy: 0.1090\n",
            "Epoch 12/30\n",
            "100/100 [==============================] - 1s 8ms/step - loss: 2.3453 - accuracy: 0.0706 - val_loss: 2.3206 - val_accuracy: 0.0915\n",
            "Epoch 13/30\n",
            "100/100 [==============================] - 1s 8ms/step - loss: 2.4579 - accuracy: 0.0877 - val_loss: 4.0036 - val_accuracy: 0.0961\n",
            "Epoch 14/30\n",
            "100/100 [==============================] - 1s 8ms/step - loss: 3.6932 - accuracy: 0.0956 - val_loss: 3.2100 - val_accuracy: 0.0983\n",
            "Epoch 15/30\n",
            "100/100 [==============================] - 1s 9ms/step - loss: 3.1769 - accuracy: 0.1044 - val_loss: 3.1301 - val_accuracy: 0.0983\n",
            "Epoch 16/30\n",
            "100/100 [==============================] - 1s 8ms/step - loss: 3.1191 - accuracy: 0.0993 - val_loss: 3.0987 - val_accuracy: 0.1009\n",
            "Epoch 17/30\n",
            "100/100 [==============================] - 1s 8ms/step - loss: 3.1236 - accuracy: 0.0817 - val_loss: 3.0757 - val_accuracy: 0.1009\n",
            "Epoch 18/30\n",
            "100/100 [==============================] - 1s 8ms/step - loss: 3.3221 - accuracy: 0.0799 - val_loss: 3.5613 - val_accuracy: 0.1009\n",
            "Epoch 19/30\n",
            "100/100 [==============================] - 1s 9ms/step - loss: 3.6709 - accuracy: 0.1045 - val_loss: 3.6513 - val_accuracy: 0.0990\n",
            "Epoch 20/30\n",
            "100/100 [==============================] - 1s 9ms/step - loss: 3.3836 - accuracy: 0.0882 - val_loss: 3.1244 - val_accuracy: 0.1009\n",
            "Epoch 21/30\n",
            "100/100 [==============================] - 1s 9ms/step - loss: 3.1421 - accuracy: 0.0866 - val_loss: 2.9606 - val_accuracy: 0.0961\n",
            "Epoch 22/30\n",
            "100/100 [==============================] - 1s 9ms/step - loss: 3.0103 - accuracy: 0.1081 - val_loss: 3.0714 - val_accuracy: 0.0991\n",
            "Epoch 23/30\n",
            "100/100 [==============================] - 1s 9ms/step - loss: 3.2581 - accuracy: 0.0952 - val_loss: 3.6469 - val_accuracy: 0.0915\n",
            "Epoch 24/30\n",
            "100/100 [==============================] - 1s 9ms/step - loss: 3.5379 - accuracy: 0.0827 - val_loss: 3.2353 - val_accuracy: 0.1064\n",
            "Epoch 25/30\n",
            "100/100 [==============================] - 1s 9ms/step - loss: 3.2074 - accuracy: 0.0815 - val_loss: 3.1308 - val_accuracy: 0.1030\n",
            "Epoch 26/30\n",
            "100/100 [==============================] - 1s 9ms/step - loss: 3.1273 - accuracy: 0.0894 - val_loss: 3.1742 - val_accuracy: 0.0991\n",
            "Epoch 27/30\n",
            "100/100 [==============================] - 1s 9ms/step - loss: 3.2595 - accuracy: 0.1141 - val_loss: 3.3614 - val_accuracy: 0.1090\n",
            "Epoch 28/30\n",
            "100/100 [==============================] - 1s 9ms/step - loss: 3.3891 - accuracy: 0.0919 - val_loss: 3.4048 - val_accuracy: 0.0990\n",
            "Epoch 29/30\n",
            "100/100 [==============================] - 1s 9ms/step - loss: 3.3169 - accuracy: 0.0951 - val_loss: 3.1968 - val_accuracy: 0.1009\n",
            "Epoch 30/30\n",
            "100/100 [==============================] - 1s 9ms/step - loss: 3.2214 - accuracy: 0.0869 - val_loss: 3.4591 - val_accuracy: 0.0990\n",
            "--- 27.823678493499756 seconds ---\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Dz1jbymlxWtY",
        "outputId": "59f8b7c6-e6c0-4ff3-f10f-b00161ff2f4f"
      },
      "source": [
        "predict_train = model_r3.predict_classes(train_x)\n",
        "acc_train = np.where(train_y == predict_train)[0].shape[0]/train_x.shape[0]\n",
        "print('Training accuracy for 3 hidden layer with regularization = {}'.format(acc_train))\n",
        "predict_test = model_r3.predict_classes(x_test)\n",
        "acc_test = np.where(y_test == predict_test)[0].shape[0]/x_test.shape[0]\n",
        "print('Testing accuracy for 3 hidden layer with regularization = {}'.format(acc_test))\n",
        "print('Total run time = {} seconds'.format(total_time))"
      ],
      "execution_count": 60,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Training accuracy for 3 hidden layer with regularization = 0.1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/sequential.py:450: UserWarning: `model.predict_classes()` is deprecated and will be removed after 2021-01-01. Please use instead:* `np.argmax(model.predict(x), axis=-1)`,   if your model does multi-class classification   (e.g. if it uses a `softmax` last-layer activation).* `(model.predict(x) > 0.5).astype(\"int32\")`,   if your model does binary classification   (e.g. if it uses a `sigmoid` last-layer activation).\n",
            "  warnings.warn('`model.predict_classes()` is deprecated and '\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Testing accuracy for 3 hidden layer with regularization = 0.1032\n",
            "Total run time = 27.823678493499756 seconds\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "id": "ekgKWqTcxWtY",
        "outputId": "550ba576-6cdc-401f-bfc3-840f5256c550"
      },
      "source": [
        "df = pd.DataFrame()\n",
        "df['training_loss'] = m_h_r3.history['loss']\n",
        "#df['accuracy'] = model_history.history['accuracy']\n",
        "df['validation_loss'] = m_h_r3.history['val_loss']\n",
        "df.plot(grid=True)"
      ],
      "execution_count": 67,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fee27404978>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 67
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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Q1VucrpUzSO+xzwNuVdXGNtadDcwGKC0tLSsvL+9yIwFisRi3vCucPtjP904Mdr6CC8RisZY7vnmB1+oB79XktXrAezW1Vc/UqVM7PGsFVc14AoqACmAMMBgQ0n3njwP/3tn6ZWVlmq2KigqddNc8van8/ay3caSpqKjo7SZ0K6/Vo+q9mrxWj6r3amqrHmCJdpCtXTr9UFV3O0E+TVW3OJ/RCPwJmNiVbWUjEvTb4BLGGHOAToNcRAaISJHzvAA4D/hQRAY78wS4BFjZkw2FdJDb6YfGGLO/TM5aGQw87vST5wHPqOpLIvI/IjKAdPfKMuCHPdhOACIhPztrm3r6Y4wxxlUyOWvlA2B8G/PP6ZEWdSAc9LO+uq7zBY0xJoe45hJ9gGjQb6cfGmPMAVwV5GELcmOMOYirgjwS9FPXlCRpw70ZY0wLVwV51LlOv7bJ9sqNMaaZq4I87IzVGbNb2RpjTAtXBXnzoMt2LrkxxuzjyiC3qzuNMWYfdwV5yPbIjTHmQO4KcusjN8aYg7gyyK1rxRhj9nFlkFvXijHG7OOqILfTD40x5mCuCvJ8fx75/jxidkGQMca0cFWQg3PjLNsjN8aYFq4LcrtxljHG7M91QW6jBBljzP5cGeQ11rVijDEtMhmzMyQi74nIchFZJSJznPkjReRdEflERJ4Wkfyeb2766k67+6ExxuyTyR55I3COqo4FxgHTROTLwH8Av1PVY4FdwLU918x9Inaw0xhj9tNpkGtazHkZcCYFzgGec+Y/DlzSIy08gB3sNMaY/Ylq56PtiIgPqASOBR4A7gbecfbGEZHhwCuqOqaNdWcDswFKS0vLysvLs2poLBYjEolQ/mET/7M+ziPnh7PazpGkuSav8Fo94L2avFYPeK+mtuqZOnVqpapOaHclVc14AoqACuBM4JNW84cDKztbv6ysTLNVUVGhqqr3vf6xHnXLSxpPJLPe1pGiuSav8Fo9qt6ryWv1qHqvprbqAZZoB9napbNWVHW3E+RnAEUi4nfeGgZs6sq2srXvVrbJw/FxxhhzxMvkrJUBIlLkPC8AzgOqSAf6dGexq4EXeqqRrUVb7oAYPxwfZ4wxRzx/54swGHjc6SfPA55R1ZdEZDVQLiJ3AO8Df+zBdrZouXGWHfA0xhgggyBX1Q+A8W3M/xSY2BON6oiNEmSMMftz4ZWdPgC7utMYYxwuDPIAYAc7jTGmmfuCPNTcR24HO40xBtwY5PnOWSvWtWKMMYALgzzs9JFb14oxxqS5Lsj9vjxCgTzrWjHGGIfrghzSBzxjtkdujDGAS4M8GrI7IBpjTDNXBnk46CPWYF0rxhgDLg3y9Lid1rVijDHg4iCvsa4VY4wBXBzkdq8VY4xJc2eQ28FOY4xp4cogD9sAzMYY08KVQR4N+mlKpmhM2AFPY4xxZZA3Dy5hZ64YY0xmQ70NF5EKEVktIqtE5EZn/m0isklEljnTBT3f3LRI0AaXMMaYZpkM9ZYAfqaqS0UkClSKyOvOe79T1Xt6rnlti4bsDojGGNMsk6HetgBbnOc1IlIFDO3phnXExu00xph9RFUzX1hkBLAAGAP8FJgJ7AWWkN5r39XGOrOB2QClpaVl5eXlWTU0FosRiUQAWLs7yf95p4GbyoKMHZDJHxVHptY1eYHX6gHv1eS1esB7NbVVz9SpUytVdUK7K6lqRhMQASqBbzmvSwEf6X72O4HHOttGWVmZZquioqLl+Zqte/WoW17SF5Ztynp7R4LWNXmB1+pR9V5NXqtH1Xs1tVUPsEQ7yNaMzloRkQDwN+BJVX3e+QWwVVWTqpoCHgUmdunXziEI28FOY4xpkclZKwL8EahS1XtbzR/carFLgZXd37y2NZ+1YhcFGWNMZmetTAK+C6wQkWXOvF8CV4jIOECBdcAPeqSFbQg3j9tpe+TGGJPRWSsLAWnjrZe7vzmZycsTwvk+61oxxhhcemUnODfOsq4VY4xxb5CHg3YHRGOMARcHedSC3BhjABcHue2RG2NMmmuD3EYJMsaYNPcGechvN80yxhjcHOTWtWKMMYDLg7y2MdF8HxhjjMlZrg3ycNBPIqU0JlK93RRjjOlVrg3y5sElrHvFGJPrXBvkduMsY4xJc22Q2yhBxhiT5togj1qQG2MM4OIgD1vXijHGAC4O8ohzsLO2yYLcGJPbXBvkzV0rdnWnMSbXuTbI7WCnMcakZTJm53ARqRCR1SKySkRudOb3E5HXRWSN81jc883dpzDfh4gNwGyMMZnskSeAn6nqicCXgetF5ETgVmCeqh4HzHNeHzYiQiTfbpxljDGdBrmqblHVpc7zGqAKGApcDDzuLPY4cElPNbI9kZDdytYYY6QrN50SkRHAAmAMsF5Vi5z5Auxqfn3AOrOB2QClpaVl5eXlWTU0FosRiUT2m/fLhXUMCefxo/GhrLbZ29qqyc28Vg94ryav1QPeq6mteqZOnVqpqhPaXUlVM5qACFAJfMt5vfuA93d1to2ysjLNVkVFxUHzLv7DQr3qv97Jepu9ra2a3Mxr9ah6ryav1aPqvZraqgdYoh1ka0ZnrYhIAPgb8KSqPu/M3ioig533BwPbuvBLp1tEQ3ZPcmOMyeSsFQH+CFSp6r2t3noRuNp5fjXwQvc3r2PhfL9d2WmMyXn+DJaZBHwXWCEiy5x5vwTuAp4RkWuBz4HLe6aJ7bODncYYk0GQq+pCQNp5+9zubU7XRIJ+aizIjTE5zrVXdoIN92aMMeD2IA/5SSnUx5O93RRjjOk1rg5yu5WtMca4PMhtcAljjHF5kEcsyI0xxt1Bbl0rxhjj8iCPhmyP3BhjXB3kNriEMca4PMib+8jt6k5jTC5zdZA3d63Y1Z3GmFzm6iAP+vPw5Ykd7DTG5DRXB7mItFymb4wxucrVQQ524yxjjPFEkNseuTEml7k/yG2UIGNMjnN9kIeDNkqQMSa3ZTLU22Misk1EVraad5uIbBKRZc50Qc82s33RoO2RG2NyWyZ75H8GprUx/3eqOs6ZXu7eZmUuHPRZkBtjclqnQa6qC4Cdh6EtWYkEA9Q22sASxpjcJZkMkyYiI4CXVHWM8/o2YCawF1gC/ExVd7Wz7mxgNkBpaWlZeXl5Vg2NxWJEIpGD5s9d08QLa+M89rVC8qS9oUWPTO3V5FZeqwe8V5PX6gHv1dRWPVOnTq1U1QntrqSqnU7ACGBlq9elgI/0Hv2dwGOZbKesrEyzVVFR0eb8h9/8RI+65SXdW9+U9bZ7S3s1uZXX6lH1Xk1eq0fVezW1VQ+wRDvI1qzOWlHVraqaVNUU8CgwMZvtdIdIMABg3SvGmJyVVZCLyOBWLy8FVra3bE+LtNyTPN5bTTDGmF7l72wBEfkrMAXoLyIbgV8DU0RkHKDAOuAHPdjGDkWCPgBitkdujMlRnQa5ql7Rxuw/9kBbstLctWIXBRljcpUHruxs3iO3rhVjTG5yfZBHm/fIrWvFGJOjXB/kLQc7G2yP3BiTm1wf5M1dK7VNtkdujMlNrg/yoN9Hvi+PGjvYaYzJUa4Pcmi+cZZ1rRhjcpMngjwS8tuVncaYnOWNIA8GrGvFGJOzPBLkPhu30xiTszwS5DZKkDEmd3kiyMMW5MaYHOaJII+GLMiNMbnLE0EeCfrtplnGmJzliSAPB/3Ux5MkU50PW2eMMV7jiSCPBJsHl7C9cmNM7rEgN8YYl/NGkDt3QLRzyY0xuajTIBeRx0Rkm4isbDWvn4i8LiJrnMfinm1mx5r3yO3qTmNMLspkj/zPwLQD5t0KzFPV44B5zute0xzktkdujMlFnQa5qi4Adh4w+2Lgcef548Al3dyuLmkZXMKC3BiTg0S181P2RGQE8JKqjnFe71bVIue5ALuaX7ex7mxgNkBpaWlZeXl5Vg2NxWJEIpE239tel+LmBfVcMyafs4YFstp+b+ioJjfyWj3gvZq8Vg94r6a26pk6dWqlqk5odyVV7XQCRgArW73efcD7uzLZTllZmWaroqKi3fd21TbqUbe8pH9869Ost98bOqrJjbxWj6r3avJaPareq6mteoAl2kG2ZnvWylYRGQzgPG7LcjvdImynHxpjcli2Qf4icLXz/Grghe5pTnYCvjyC/jw72GmMyUmZnH74V2ARcIKIbBSRa4G7gPNEZA3wVed1r4qG/NRYkBtjcpC/swVU9Yp23jq3m9tySMJ24yxjTI7yxJWdkD6X3LpWjDG5yFNBbl0rxphc5Kkgtz1yY0wu8k6Q2yhBxpgc5Zkgt4Odxphc5Zkgj9oAzMaYHOWZII8E/TQmUsSTqd5uijHGHFaeCfKw3crWGJOjPBPkzbeytcEljDG5xjtBbjfOMsbkKM8FuXWtGGNyjXeCvLlrxYLcGJNjvBPktkdujMlRngtyuyjIGJNrPBPkNkqQMSZXeSbI7awVY0yu6nRgiY6IyDqgBkgCCe1olOce5ssTCvN91rVijMk5hxTkjqmquqMbtnPIwkE/tU0W5MaY3OKZrhVI3zjLruw0xuQaUdXsVxb5DNgFKPCwqj7SxjKzgdkApaWlZeXl5Vl9ViwWIxKJdLjMbW/X0ydf+OmEUFafcbhlUpObeK0e8F5NXqsHvFdTW/VMnTq1ssOua1XNegKGOo8DgeXAWR0tX1ZWptmqqKjodJlvP7xIpz/4r6w/43DLpCY38Vo9qt6ryWv1qHqvprbqAZZoB9l6SF0rqrrJedwGzAUmHsr2DlUkZF0rxpjck3WQi0hYRKLNz4HzgZXd1bBsROxgpzEmBx3KWSulwFwRad7OU6r6z25pVZYiNtybt2xZDts/guOnQahPb7fGmCNW1kGuqp8CY7uxLYcsbMO9eUfln+G/fw6pOAQK4cRL4NTvwpfOgPTOg2c1xJN8Xl2HLw+OGRBBPF6vOXTdcR75ESMa8hNPKo2JJEG/r7ebY7KRjMM//zcsfhSOORfO/AmseA5WPg/Ln4J+x8D4q2DclRAdBEB1rJHKz3cxpKiAEwZFCfiO0LNq4w2wdRU07CLREGPX7t3s3LWL3Xv3EKvZQ32shsb6GmiqpYBG9miYPwdOJjViMqOPP54vH13CsQOPkGBPNML6RfDJPGjcC4PHwuBxUHoS+IO93bqc46kgb33jrGDEgtx1aqvh2ath3Vtwxo/gvNshzwcjz4Jpv4HVL8DSJ2DeHPR/7mBd8VcoT57NY9uOJ67p7z7oz+PEIX0YO6yIscP7csqwIkaWhMnL64Xwq99FfN077PlwAbLhHfruWoFf40D6P94AZ2rWSD5xXwGpwgIkP0yw/kOuSFTAZ/ezdu1g3k6dxH/lnwJHTWbM8UdzxjElh2+PXRV2fgqfvEHTR6/j+3whvmQ9cQI0SpCI/hmABH425Y9kfeh4NhWcwJbC0ewoPJq8QIh8fx6lfYIMLSpkWHEBQ4sLKAnn92z7Uymo34nGttJUt4dkUyOJeBOJRDz9PNFEMtFEKt5EMtmExuMkU0koLEGig/EXDcbfdwihcJTCfB8hv6/jf0uqULcTdq+DXZ/D7vWw+/P08/PmwKCTe6RMTwX5vnE7k5R457TS3LB1Ffz1Cqj5Ai55CMZdsd/bNal8FvqmUtHnJNb6l3Nuw+v8W/UC/re8xY2Rfuw57lK2B4aybk+KNbtTrFmc5KN3/DyuQXzBAo4aVMIxQwZywrCBfGlgP4LBfPIDAfL9PoIBH/m+PAI+yTpUUill8/o1VFctQNYvoqR6KYObPiOA0ld9rNCRLNWvsSl6CuF+g+jfr5iBJSUMHdif4YMGUNynD0Gfn/32ZVNJ+GIF+tkChqyZz7c3vE0g+QZ8di9Va4ezIHUSD+afAiPOpF/JAIoK8+kXzqfYeewXDlBcmE9RYT6+bH6RNdZQ/3EFe1b8k8L18+nTsAmATalS3kxNZkHqFLb2m0C/omL6NW3hS00fM6JxDUfH1zB275tM3vvfAMTxs4bhrE6NYFsqyioN8R4F1BKiKa+QUKQv4WgRfYuKKS7qR0lJP4oiYfJJEJAUARLkS4IAKfySwK8J/CQJkCQv2UDg80Ws+8d7JPZ+gca24avfQbBhB+H4TqLJXfhIIcCh/J2wVwvZrEVs1WJ2SDE780rY4yvB74OhbGNwaiuDdBulqW0UaP1+62ltyGUAAAn6SURBVMbyolQHBpPc9AVHW5B3rnmPvKYx3sstMV1S9RI8PxuCUZj1MgxLX/fw6fYY86q2UfHRNhav20k8qURDfs46fgylJ5yD77hi2LKAwqVPUFj1ZwZrklOat+lzpmZfONPS/T86oXkk8ZEgjwbSz1PiSz+Sx7HkseFNH4qQJI+UMx34vH+qmmGynWFATEOs9o3ivaKZNA2ZSPSY0zl22ECu7h/uWrdPng+GjEOGjKNg0g3pbqfNy9DP3mTEmjc5blMF/tQ/4dPfsmdtmJ0aYRdRqrUP6zTKUqLs1Ci7iNKYX0wyVEIinuCNZVVEqSdCPWHqCFNPodZTqHUUUkdBqo5IYjdfaqiigCQpDfJ2agzLgxeyd+hkhhx9EqcM68u/De1LNBRou+2qsGsdbFlGYPMyTtyyjNFbV0L9LiR1wHGsemfalvmPprVJAJ9Bk/rYQV92aBGbfcXUBUbQEO5PsmAAEh1AXkERPn8QXyCA+IP4/Pn4AgF8/nz8gXx8gXz8/iB+fx6pmh1ozRYk9gW+2q0E6raSX7+dkQ3bOKlxLZH4u/iTcUhCgxSwIzCI7b4hfOg7lW2+gXwhg9iSN5DNDGSvFpBMpbiz5GSOzq7ETnkqyKMhuye5q6jCgruh4k4Ycir67SdZsbeQV1/9kNdWbWXNthgAJ5RGuebMkZxzwkBOPap4/zDs83U44evQVAeNNRCvg0RD+jFe70zp5/GGWrZW76QmVksqmXCmOJpMoKlE+jGZIJVKQioBqTiNdbUUFgQRdWJbkwgp8pzXPk2RT4rd+UPYOngi4ePOZNio05hY0ANXF/sCMPw0ZPhpFJz183Q/9cYlsOEd+tZsJVK7gyE1O0jV7iCvfjP+hp34Uk3Oz5p0WALsPnjTSfKolULqKaCWAmokzKt9plM3fAr9T5zM+KMGcl6kC/u0ItBvZHo66dL0LEh/54lGaKqFphpojEFTzHmsobFuL3t376ShsZEEAZLiI0GAOD4S4iOuPuL4SaiPJvzE8bNpew2nTJxMcckA+kdDjCkIdENX2nEdv60K9bsACBUUM0yEYYf4iYfCU0He0rVi55If+Zpq4e/XweoX2DbyEh7ucwMvP1DFlj0N+PKEiSP6ceXpX+K8E0sZVlzY+fbyC9NTBwLQ5f9s8+fP59QpU7q41mHiD8KISemJg/8IQTX9c66rhrodULeTFcuXcnLZGem/foJ90o/5EXyBAvqI0Pokzx45JU0EAqH0FC456O0g+x83yMT8+fM5/aRjuqV5GROBwn6H9zM74I4gr9lKQd0m2PlZ+s/NPH+rad/rSEAApaYhQSqlKM4tCEj/m1aU5lvLND/m5UGeiDOxr49UNd1HmYqn987El94jyvN7/vS3ntawfR1N/28GkT0fc598l/urphH0b+Os4wfw0/OO56ujSykO5/d2M91PBIKR9FR8FADVmwLpg8fGU9wR5At+y+mL/wve63ixY4F1IUjMzSM+N93fmXT6MJMc8FrTf577JX3QxE8CPyn8zvN8Sbb7OXH1kSD9J16c9PMEfpo03a8qKIKSJ4oAec7Bln2PSh4pTkbZVaHOfHUOyqRf73ue3k5Cm/tk9/XVJp0tJVvNU+fTU84jcMBrSf9iQ1otve+dA1/ntSy9/zK0bImW98ai7Kpo/sx99rUjLUI9EOBHcguhUdN46KRSzjp+AIX57vjnaMyRxh3/c8Z9h9WxPpx4wvFO32UCNOnsMSdaTUlWrN9BXWNTOuY03afZ3LeZRwrRZPq5poO6TvykxE9C/C0HuhL4SIoT6eIjqT4ETce2pg+N+TSBT53XmmiZRJOoHByoLZM4Ua0Qq62jMNKn1fJ5pCQdzunlfM4j+DSV7pttnpzXos2h3zwvHbygiKbSPz/d15p0mqbf3y+aRVAVEGkV3ekYT/8Fsi/Kcea1vBYAoSYWIxqJ0hzZIi1Ltwp9SOXlk5pwLb8fN+HIPefbGBdxR5APPZVtpXs5cdyUThftmZN7esb8+fM57Ujtf83C/PnzKfNQPca4he0OGWOMy1mQG2OMy1mQG2OMy1mQG2OMy1mQG2OMy1mQG2OMy1mQG2OMy1mQG2OMy4mqdr5Ud32YyHbg8yxX7w/s6MbmHAm8VpPX6gHv1eS1esB7NbVVz1Gq2u79xA5rkB8KEVmiqhN6ux3dyWs1ea0e8F5NXqsHvFdTNvVY14oxxricBbkxxricm4L8kd5uQA/wWk1eqwe8V5PX6gHv1dTlelzTR26MMaZtbtojN8YY0wYLcmOMcTlXBLmITBORj0TkExG5tbfbc6hEZJ2IrBCRZSKypLfbkw0ReUxEtonIylbz+onI6yKyxnks7s02dkU79dwmIpuc72mZiFzQm23sKhEZLiIVIrJaRFaJyI3OfFd+Tx3U49rvSURCIvKeiCx3aprjzB8pIu86mfe0iHQ4iO0R30cuIj7gY+A8YCOwGLhCVVf3asMOgYisAyaoqmsvYhCRs4AY8BdVHePM+y2wU1Xvcn7hFqvqLb3Zzky1U89tQExV7+nNtmVLRAYDg1V1qYhEgUrgEmAmLvyeOqjnclz6PUl6tPewqsZEJAAsBG4Efgo8r6rlIvIQsFxVH2xvO27YI58IfKKqn6pqE1AOXNzLbcp5qroA2HnA7IuBx53nj5P+T+YK7dTjaqq6RVWXOs9rgCpgKC79njqox7U0Lea8DDiTAucAzznzO/2O3BDkQ4ENrV5vxOVfHukv6jURqRSR2b3dmG5UqqpbnOdfAKW92Zhu8iMR+cDpenFFF0RbRGQEMB54Fw98TwfUAy7+nkTEJyLLgG3A68BaYLeqJpxFOs08NwS5F52pqqcCXweud/6s9xRN99kd2f12nXsQOAYYB2wB/m/vNic7IhIB/gb8RFX3tn7Pjd9TG/W4+ntS1aSqjgOGke6BGNXVbbghyDcBw1u9HubMcy1V3eQ8bgPmkv7yvGCr04/Z3J+5rZfbc0hUdavznywFPIoLvyen3/VvwJOq+rwz27XfU1v1eOF7AlDV3UAFcAZQJCJ+561OM88NQb4YOM45ipsPfBt4sZfblDURCTsHahCRMHA+sLLjtVzjReBq5/nVwAu92JZD1hx2jktx2ffkHEj7I1Clqve2esuV31N79bj5exKRASJS5DwvIH1SRxXpQJ/uLNbpd3TEn7UC4JxOdB/gAx5T1Tt7uUlZE5GjSe+FA/iBp9xYj4j8FZhC+pabW4FfA38HngG+RPp2xZerqisOILZTzxTSf64rsA74Qau+5SOeiJwJvAWsAFLO7F+S7ld23ffUQT1X4NLvSUROIX0w00d6x/oZVb3dyYlyoB/wPnCVqja2ux03BLkxxpj2uaFrxRhjTAcsyI0xxuUsyI0xxuUsyI0xxuUsyI0xxuUsyI0xxuUsyI0xxuX+P8UVeFOtwiALAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "id": "oCZJ7Vh5xWtY",
        "outputId": "bcfb442a-f984-4cff-8e2f-582d6d1799ae"
      },
      "source": [
        "df = pd.DataFrame()\n",
        "df['accuracy'] = m_h_r3.history['accuracy']\n",
        "df.plot(grid=True)"
      ],
      "execution_count": 66,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fee28e403c8>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 66
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 772
        },
        "id": "3_MVp4raxWtY",
        "outputId": "a70c3202-e6b1-4f76-8710-7b087b083fa3"
      },
      "source": [
        "df = pd.DataFrame()\n",
        "for i in range(0,len(weights)):\n",
        "        w = weights[i]\n",
        "        W = final_w[i]\n",
        "        learning_speed = [0]\n",
        "        cnt=0\n",
        "        for i in range(1,len(w)):\n",
        "            wold = w[i-1]\n",
        "            wnew = w[i]\n",
        "            diff = wold-wnew\n",
        "            d = wnew\n",
        "            m = np.median(d[d>0])\n",
        "            d[d==0] = m\n",
        "            diff = abs(diff)/d\n",
        "            if diff.size:\n",
        "                avg = abs(np.mean(diff))\n",
        "            else:\n",
        "                avg = 0\n",
        "            learning_speed.append(avg)\n",
        "        df['learning speed'] = learning_speed\n",
        "        df.plot(grid=True)"
      ],
      "execution_count": 65,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KEjs0BSgxWtY",
        "outputId": "fcf04cd1-cb3d-46a7-ea0d-5bd7d2938dfe"
      },
      "source": [
        "print(model_r3.summary())"
      ],
      "execution_count": 64,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"sequential_5\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "dense_13 (Dense)             (None, 30)                23550     \n",
            "_________________________________________________________________\n",
            "dense_14 (Dense)             (None, 30)                930       \n",
            "_________________________________________________________________\n",
            "dense_15 (Dense)             (None, 30)                930       \n",
            "_________________________________________________________________\n",
            "dense_16 (Dense)             (None, 10)                310       \n",
            "=================================================================\n",
            "Total params: 25,720\n",
            "Trainable params: 25,720\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n",
            "None\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "a4DIMy5YxWtZ"
      },
      "source": [
        "# CNN"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2nk-nqhI01jM"
      },
      "source": [
        "import matplotlib.pyplot as pyplot"
      ],
      "execution_count": 71,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 268
        },
        "id": "tH0iVvEPxWtZ",
        "outputId": "fd48c1de-55ce-41b3-dec0-47d20a667803"
      },
      "source": [
        "##testing###\n",
        "\n",
        "from keras.preprocessing.image import ImageDataGenerator\n",
        "\n",
        "train_x = train_x.reshape((train_x.shape[0], 28, 28, 1))\n",
        "x_test = x_test.reshape((x_test.shape[0], 28, 28, 1))\n",
        "# convert from int to float\n",
        "train_x = train_x.astype('float32')\n",
        "x_test = x_test.astype('float32')\n",
        "# define data preparation\n",
        "datagen = ImageDataGenerator(rotation_range=90)\n",
        "# fit parameters from data\n",
        "datagen.fit(train_x)\n",
        "# configure batch size and retrieve one batch of images\n",
        "for X_batch, y_batch in datagen.flow(train_x, train_y, batch_size=9):\n",
        "\t# create a grid of 3x3 images\n",
        "\tfor i in range(0, 9):\n",
        "\t\tpyplot.subplot(330 + 1 + i)\n",
        "\t\tpyplot.imshow(X_batch[i].reshape(28, 28), cmap=pyplot.get_cmap('gray'))\n",
        "\t# plot\n",
        "\tpyplot.show()\n",
        "\tbreak\n"
      ],
      "execution_count": 72,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 9 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 268
        },
        "id": "YlxcH3ccxWtZ",
        "outputId": "8573d03e-b9c8-49b7-bdc5-f3ff181aff26"
      },
      "source": [
        "shift = 3\n",
        "datagen = ImageDataGenerator(width_shift_range=shift, height_shift_range=shift)\n",
        "# fit parameters from data\n",
        "datagen.fit(train_x)\n",
        "# configure batch size and retrieve one batch of images\n",
        "for X_batch, y_batch in datagen.flow(train_x, train_y, batch_size=9):\n",
        "\t# create a grid of 3x3 images\n",
        "\tfor i in range(0, 9):\n",
        "\t\tpyplot.subplot(330 + 1 + i)\n",
        "\t\tpyplot.imshow(X_batch[i].reshape(28, 28), cmap=pyplot.get_cmap('gray'))\n",
        "\t# show the plot\n",
        "\tpyplot.show()\n",
        "\tbreak"
      ],
      "execution_count": 73,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 9 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BsUAmepbxWtZ"
      },
      "source": [
        "datagen = ImageDataGenerator(\n",
        "        rotation_range=3,  # randomly rotate images in the range (degrees, 0 to 180)\n",
        "        width_shift_range=3,  # randomly shift images horizontally (fraction of total width)\n",
        "        height_shift_range=3)  # randomly flip images"
      ],
      "execution_count": 74,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3pFqP4VAxWtZ"
      },
      "source": [
        "x_val = x_val.reshape((x_val.shape[0], 28, 28, 1))"
      ],
      "execution_count": 75,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fM-V6Te3xWtZ",
        "outputId": "d55343e4-2b97-4e70-a667-744df9a8c219"
      },
      "source": [
        "x_val.shape, train_x.shape , x_test.shape"
      ],
      "execution_count": 76,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((10000, 28, 28, 1), (1000, 28, 28, 1), (10000, 28, 28, 1))"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 76
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NN9OyX85xWta",
        "outputId": "eaa57ea0-6229-49fa-a42b-fd6e3e1e1608"
      },
      "source": [
        "from keras.layers import Conv2D\n",
        "from keras.layers import MaxPooling2D\n",
        "from keras.layers import Flatten\n",
        "from keras.layers import Dense\n",
        "\n",
        "from keras.layers.core import Dropout\n",
        "model_cnn = Sequential()\n",
        "model_cnn.add(Conv2D(30, kernel_size=(5,5), strides=(1,1), activation='sigmoid', input_shape=(28,28,1)))\n",
        "model_cnn.add(MaxPooling2D(pool_size=(2,2), strides=(2,2)))\n",
        "model_cnn.add(Flatten())\n",
        "model_cnn.add(Dropout(0.25))\n",
        "model_cnn.add(Dense(10, activation='softmax'))\n",
        "weights = []\n",
        "save_weights = LambdaCallback(on_epoch_end=lambda batch, logs: weights.append(model_cnn.layers[0].get_weights()[0]))\n",
        "\n",
        "model_cnn.compile(optimizer=tf.keras.optimizers.Adam(learning_rate=0.1),loss='sparse_categorical_crossentropy',metrics=['accuracy'])\n",
        "history_cnn = model_cnn.fit(datagen.flow(train_x, train_y, batch_size=10), validation_data = (x_val,y_val), epochs=30,callbacks = [save_weights])\n",
        "final_w = [model_cnn.layers[0].get_weights()[0]]\n",
        "weights = [weights]\n",
        "total_time = (time.time()-start_time)\n",
        "print(\"--- %s seconds ---\" % total_time)"
      ],
      "execution_count": 77,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/30\n",
            "100/100 [==============================] - 3s 26ms/step - loss: 76.0765 - accuracy: 0.2150 - val_loss: 0.9779 - val_accuracy: 0.6966\n",
            "Epoch 2/30\n",
            "100/100 [==============================] - 2s 24ms/step - loss: 1.2849 - accuracy: 0.5909 - val_loss: 0.9579 - val_accuracy: 0.6835\n",
            "Epoch 3/30\n",
            "100/100 [==============================] - 2s 24ms/step - loss: 1.1902 - accuracy: 0.6320 - val_loss: 0.8400 - val_accuracy: 0.7315\n",
            "Epoch 4/30\n",
            "100/100 [==============================] - 2s 25ms/step - loss: 1.1615 - accuracy: 0.6360 - val_loss: 0.7316 - val_accuracy: 0.7453\n",
            "Epoch 5/30\n",
            "100/100 [==============================] - 2s 24ms/step - loss: 1.0736 - accuracy: 0.6427 - val_loss: 0.6726 - val_accuracy: 0.7659\n",
            "Epoch 6/30\n",
            "100/100 [==============================] - 2s 24ms/step - loss: 1.0098 - accuracy: 0.6700 - val_loss: 0.6971 - val_accuracy: 0.7740\n",
            "Epoch 7/30\n",
            "100/100 [==============================] - 2s 24ms/step - loss: 1.0551 - accuracy: 0.6709 - val_loss: 0.7880 - val_accuracy: 0.7354\n",
            "Epoch 8/30\n",
            "100/100 [==============================] - 2s 24ms/step - loss: 1.0274 - accuracy: 0.6806 - val_loss: 0.6992 - val_accuracy: 0.7635\n",
            "Epoch 9/30\n",
            "100/100 [==============================] - 2s 24ms/step - loss: 0.9940 - accuracy: 0.6892 - val_loss: 0.6573 - val_accuracy: 0.7858\n",
            "Epoch 10/30\n",
            "100/100 [==============================] - 2s 24ms/step - loss: 1.0097 - accuracy: 0.6841 - val_loss: 0.7243 - val_accuracy: 0.7500\n",
            "Epoch 11/30\n",
            "100/100 [==============================] - 3s 27ms/step - loss: 1.0620 - accuracy: 0.6939 - val_loss: 0.6960 - val_accuracy: 0.7598\n",
            "Epoch 12/30\n",
            "100/100 [==============================] - 2s 25ms/step - loss: 0.9882 - accuracy: 0.7005 - val_loss: 0.6386 - val_accuracy: 0.7956\n",
            "Epoch 13/30\n",
            "100/100 [==============================] - 2s 24ms/step - loss: 1.0279 - accuracy: 0.6880 - val_loss: 0.6173 - val_accuracy: 0.8024\n",
            "Epoch 14/30\n",
            "100/100 [==============================] - 2s 24ms/step - loss: 0.9222 - accuracy: 0.7093 - val_loss: 0.6592 - val_accuracy: 0.7819\n",
            "Epoch 15/30\n",
            "100/100 [==============================] - 2s 24ms/step - loss: 1.0469 - accuracy: 0.7029 - val_loss: 0.5714 - val_accuracy: 0.8051\n",
            "Epoch 16/30\n",
            "100/100 [==============================] - 2s 24ms/step - loss: 0.8470 - accuracy: 0.7368 - val_loss: 0.4705 - val_accuracy: 0.8490\n",
            "Epoch 17/30\n",
            "100/100 [==============================] - 2s 24ms/step - loss: 0.8043 - accuracy: 0.7477 - val_loss: 0.4563 - val_accuracy: 0.8412\n",
            "Epoch 18/30\n",
            "100/100 [==============================] - 2s 24ms/step - loss: 0.6593 - accuracy: 0.7880 - val_loss: 0.4716 - val_accuracy: 0.8449\n",
            "Epoch 19/30\n",
            "100/100 [==============================] - 2s 25ms/step - loss: 0.5882 - accuracy: 0.8387 - val_loss: 0.4637 - val_accuracy: 0.8565\n",
            "Epoch 20/30\n",
            "100/100 [==============================] - 2s 24ms/step - loss: 0.6337 - accuracy: 0.8089 - val_loss: 0.4275 - val_accuracy: 0.8585\n",
            "Epoch 21/30\n",
            "100/100 [==============================] - 2s 24ms/step - loss: 0.6940 - accuracy: 0.7688 - val_loss: 0.3675 - val_accuracy: 0.8853\n",
            "Epoch 22/30\n",
            "100/100 [==============================] - 2s 24ms/step - loss: 0.6918 - accuracy: 0.7940 - val_loss: 0.4069 - val_accuracy: 0.8663\n",
            "Epoch 23/30\n",
            "100/100 [==============================] - 2s 25ms/step - loss: 0.5716 - accuracy: 0.8156 - val_loss: 0.4631 - val_accuracy: 0.8448\n",
            "Epoch 24/30\n",
            "100/100 [==============================] - 2s 24ms/step - loss: 0.6526 - accuracy: 0.7924 - val_loss: 0.3531 - val_accuracy: 0.8856\n",
            "Epoch 25/30\n",
            "100/100 [==============================] - 2s 24ms/step - loss: 0.5920 - accuracy: 0.8140 - val_loss: 0.3545 - val_accuracy: 0.8897\n",
            "Epoch 26/30\n",
            "100/100 [==============================] - 2s 24ms/step - loss: 0.5337 - accuracy: 0.8371 - val_loss: 0.3348 - val_accuracy: 0.8989\n",
            "Epoch 27/30\n",
            "100/100 [==============================] - 2s 24ms/step - loss: 0.4937 - accuracy: 0.8510 - val_loss: 0.3073 - val_accuracy: 0.9043\n",
            "Epoch 28/30\n",
            "100/100 [==============================] - 2s 24ms/step - loss: 0.4168 - accuracy: 0.8757 - val_loss: 0.3052 - val_accuracy: 0.9049\n",
            "Epoch 29/30\n",
            "100/100 [==============================] - 2s 25ms/step - loss: 0.4662 - accuracy: 0.8595 - val_loss: 0.3078 - val_accuracy: 0.9035\n",
            "Epoch 30/30\n",
            "100/100 [==============================] - 2s 24ms/step - loss: 0.5685 - accuracy: 0.8148 - val_loss: 0.3779 - val_accuracy: 0.8878\n",
            "--- 294.0643229484558 seconds ---\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HtWF_DLlxWta",
        "outputId": "f219b41d-2cd1-4f47-c951-47cec6d5f45e"
      },
      "source": [
        "predict_train = model_cnn.predict_classes(train_x)\n",
        "acc_train = np.where(train_y == predict_train)[0].shape[0]/train_x.shape[0]\n",
        "print('Training accuracy for CNN-MNIST= {}'.format(acc_train))\n",
        "predict_test = model_cnn.predict_classes(x_test)\n",
        "acc_test = np.where(y_test == predict_test)[0].shape[0]/x_test.shape[0]\n",
        "print('Testing accuracy for CNN-MNIST = {}'.format(acc_test))\n",
        "print('Total run time = {} seconds'.format(total_time))"
      ],
      "execution_count": 78,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/sequential.py:450: UserWarning: `model.predict_classes()` is deprecated and will be removed after 2021-01-01. Please use instead:* `np.argmax(model.predict(x), axis=-1)`,   if your model does multi-class classification   (e.g. if it uses a `softmax` last-layer activation).* `(model.predict(x) > 0.5).astype(\"int32\")`,   if your model does binary classification   (e.g. if it uses a `sigmoid` last-layer activation).\n",
            "  warnings.warn('`model.predict_classes()` is deprecated and '\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Training accuracy for CNN-MNIST= 0.941\n",
            "Testing accuracy for CNN-MNIST = 0.889\n",
            "Total run time = 294.0643229484558 seconds\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "id": "4Zn9-5uFxWta",
        "outputId": "b27b182e-ee59-44a0-df32-38125f428096"
      },
      "source": [
        "df = pd.DataFrame()\n",
        "df['training_loss'] = history_cnn.history['loss']\n",
        "df['validation_loss'] = history_cnn.history['val_loss']\n",
        "df.plot(grid=True)"
      ],
      "execution_count": 79,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fee267b03c8>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 79
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "id": "AjKZlc9mxWta",
        "outputId": "217882a0-a7b9-4286-e229-9670c717105f"
      },
      "source": [
        "df = pd.DataFrame()\n",
        "df['accuracy'] = history_cnn.history['accuracy']\n",
        "df.plot(grid=True)"
      ],
      "execution_count": 80,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fee267aa780>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 80
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 333
        },
        "id": "zi8vcAZFxWta",
        "outputId": "6c73af9c-bef7-427c-9b49-605fe868b6e8"
      },
      "source": [
        "df = pd.DataFrame()\n",
        "for i in range(0,len(weights)):\n",
        "        w = weights[i]\n",
        "        W = final_w[i]\n",
        "        learning_speed = [0]\n",
        "        cnt=0\n",
        "        for i in range(1,len(w)):\n",
        "            wold = w[i-1]\n",
        "            wnew = w[i]\n",
        "            diff = wold-wnew\n",
        "            d = wnew\n",
        "            m = np.median(d[d>0])\n",
        "            d[d==0] = m\n",
        "            diff = abs(diff)/d\n",
        "            if diff.size:\n",
        "                avg = abs(np.mean(diff))\n",
        "            else:\n",
        "                avg = 0\n",
        "            learning_speed.append(avg)\n",
        "        df['learning speed'] = learning_speed\n",
        "        df.plot(grid=True)"
      ],
      "execution_count": 81,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/numpy/core/fromnumeric.py:3373: RuntimeWarning: Mean of empty slice.\n",
            "  out=out, **kwargs)\n",
            "/usr/local/lib/python3.6/dist-packages/numpy/core/_methods.py:170: RuntimeWarning: invalid value encountered in true_divide\n",
            "  ret = ret.dtype.type(ret / rcount)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IMtEnSQZxWta",
        "outputId": "6ba18cc4-9de2-4535-e2e7-9a388fc19472"
      },
      "source": [
        "print(model_cnn.summary())"
      ],
      "execution_count": 82,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"sequential_6\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "conv2d (Conv2D)              (None, 24, 24, 30)        780       \n",
            "_________________________________________________________________\n",
            "max_pooling2d (MaxPooling2D) (None, 12, 12, 30)        0         \n",
            "_________________________________________________________________\n",
            "flatten (Flatten)            (None, 4320)              0         \n",
            "_________________________________________________________________\n",
            "dropout (Dropout)            (None, 4320)              0         \n",
            "_________________________________________________________________\n",
            "dense_17 (Dense)             (None, 10)                43210     \n",
            "=================================================================\n",
            "Total params: 43,990\n",
            "Trainable params: 43,990\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n",
            "None\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MzrMOkquxWtb"
      },
      "source": [
        "---------------------------------------------------------------------------------------------------------------"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0_MzQ0rWxWtb"
      },
      "source": [
        "https://machinelearningmastery.com/polyak-neural-network-model-weight-ensemble/\n",
        "#  END"
      ]
    }
  ]
}