DL_book_Nikolenko/DL_chapter_2_keras.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "DL_chapter_2_keras.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/LenaVolzhina/playing-with-neural-networks/blob/dl-book-chapter-2/DL_chapter_2_keras.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "z0RuClV0cDoH",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import numpy as np\n",
        "from keras.models import Sequential\n",
        "from keras.layers import Input, Dense, Activation"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2QMEWsVEcM83",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 224
        },
        "outputId": "721a8780-251e-4351-9769-6dbfdec8cb09"
      },
      "source": [
        "# create model layer-by-layer\n",
        "logr = Sequential()\n",
        "\n",
        "# add dense layer with sigmoid as output activation\n",
        "logr.add(Dense(1, input_dim=2, activation='sigmoid'))\n",
        "\n",
        "# compile model with certain loss, optimizer and metrics\n",
        "logr.compile(loss='binary_crossentropy', optimizer='sgd', metrics=['accuracy'])\n",
        "\n"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:541: The name tf.placeholder is deprecated. Please use tf.compat.v1.placeholder instead.\n",
            "\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:4432: The name tf.random_uniform is deprecated. Please use tf.random.uniform instead.\n",
            "\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/optimizers.py:793: The name tf.train.Optimizer is deprecated. Please use tf.compat.v1.train.Optimizer instead.\n",
            "\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:3657: The name tf.log is deprecated. Please use tf.math.log instead.\n",
            "\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow_core/python/ops/nn_impl.py:183: where (from tensorflow.python.ops.array_ops) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Use tf.where in 2.0, which has the same broadcast rule as np.where\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VEg0-mFrc8Vj",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def sampler(n, x, y):\n",
        "  # creates array of n rows like [x + noise, y + noise]\n",
        "  return np.random.normal(size=[n, 2]) + [x, y]\n",
        "\n",
        "def sample_data(n=1000, p0=(-1., -1.), p1=(1., 1.)):\n",
        "  # create labels\n",
        "  zeros, ones = np.zeros((n, 1)), np.ones((n, 1))\n",
        "  labels = np.vstack([zeros, ones])\n",
        "\n",
        "  # create input data\n",
        "  z_sample = sampler(n, x=p0[0], y=p0[1])\n",
        "  o_sample = sampler(n, x=p1[0], y=p1[1])\n",
        "\n",
        "  return np.vstack([z_sample, o_sample]), labels\n",
        "\n",
        "X_train, Y_train = sample_data()\n",
        "X_test, Y_test = sample_data(100)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HHHg-R8peB77",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "eadbd8b1-506a-411d-f9de-09d09f60ad29"
      },
      "source": [
        "logr.fit(\n",
        "    X_train, Y_train,\n",
        "    batch_size=16,\n",
        "    nb_epoch=100,\n",
        "    verbose=1,\n",
        "    validation_data=(X_test, Y_test)\n",
        ")"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:6: UserWarning: The `nb_epoch` argument in `fit` has been renamed `epochs`.\n",
            "  \n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:1033: The name tf.assign_add is deprecated. Please use tf.compat.v1.assign_add instead.\n",
            "\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:1020: The name tf.assign is deprecated. Please use tf.compat.v1.assign instead.\n",
            "\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:3005: The name tf.Session is deprecated. Please use tf.compat.v1.Session instead.\n",
            "\n",
            "Train on 2000 samples, validate on 200 samples\n",
            "Epoch 1/100\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:190: The name tf.get_default_session is deprecated. Please use tf.compat.v1.get_default_session instead.\n",
            "\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:197: The name tf.ConfigProto is deprecated. Please use tf.compat.v1.ConfigProto instead.\n",
            "\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:207: The name tf.global_variables is deprecated. Please use tf.compat.v1.global_variables instead.\n",
            "\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:216: The name tf.is_variable_initialized is deprecated. Please use tf.compat.v1.is_variable_initialized instead.\n",
            "\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:223: The name tf.variables_initializer is deprecated. Please use tf.compat.v1.variables_initializer instead.\n",
            "\n",
            "2000/2000 [==============================] - 1s 442us/step - loss: 1.2392 - acc: 0.0905 - val_loss: 0.6835 - val_acc: 0.7850\n",
            "Epoch 2/100\n",
            "2000/2000 [==============================] - 0s 79us/step - loss: 0.5042 - acc: 0.9220 - val_loss: 0.4102 - val_acc: 0.9100\n",
            "Epoch 3/100\n",
            "2000/2000 [==============================] - 0s 75us/step - loss: 0.3501 - acc: 0.9245 - val_loss: 0.3313 - val_acc: 0.9100\n",
            "Epoch 4/100\n",
            "2000/2000 [==============================] - 0s 74us/step - loss: 0.2959 - acc: 0.9250 - val_loss: 0.2950 - val_acc: 0.9100\n",
            "Epoch 5/100\n",
            "2000/2000 [==============================] - 0s 73us/step - loss: 0.2686 - acc: 0.9260 - val_loss: 0.2742 - val_acc: 0.9100\n",
            "Epoch 6/100\n",
            "2000/2000 [==============================] - 0s 96us/step - loss: 0.2523 - acc: 0.9250 - val_loss: 0.2608 - val_acc: 0.9100\n",
            "Epoch 7/100\n",
            "2000/2000 [==============================] - 0s 86us/step - loss: 0.2414 - acc: 0.9255 - val_loss: 0.2513 - val_acc: 0.9100\n",
            "Epoch 8/100\n",
            "2000/2000 [==============================] - 0s 88us/step - loss: 0.2337 - acc: 0.9255 - val_loss: 0.2444 - val_acc: 0.9100\n",
            "Epoch 9/100\n",
            "2000/2000 [==============================] - 0s 83us/step - loss: 0.2280 - acc: 0.9255 - val_loss: 0.2391 - val_acc: 0.9100\n",
            "Epoch 10/100\n",
            "2000/2000 [==============================] - 0s 78us/step - loss: 0.2237 - acc: 0.9255 - val_loss: 0.2349 - val_acc: 0.9100\n",
            "Epoch 11/100\n",
            "2000/2000 [==============================] - 0s 74us/step - loss: 0.2203 - acc: 0.9255 - val_loss: 0.2315 - val_acc: 0.9100\n",
            "Epoch 12/100\n",
            "2000/2000 [==============================] - 0s 86us/step - loss: 0.2175 - acc: 0.9255 - val_loss: 0.2288 - val_acc: 0.9100\n",
            "Epoch 13/100\n",
            "2000/2000 [==============================] - 0s 77us/step - loss: 0.2153 - acc: 0.9255 - val_loss: 0.2265 - val_acc: 0.9100\n",
            "Epoch 14/100\n",
            "2000/2000 [==============================] - 0s 76us/step - loss: 0.2134 - acc: 0.9255 - val_loss: 0.2245 - val_acc: 0.9100\n",
            "Epoch 15/100\n",
            "2000/2000 [==============================] - 0s 74us/step - loss: 0.2119 - acc: 0.9255 - val_loss: 0.2229 - val_acc: 0.9100\n",
            "Epoch 16/100\n",
            "2000/2000 [==============================] - 0s 80us/step - loss: 0.2106 - acc: 0.9255 - val_loss: 0.2215 - val_acc: 0.9100\n",
            "Epoch 17/100\n",
            "2000/2000 [==============================] - 0s 72us/step - loss: 0.2094 - acc: 0.9255 - val_loss: 0.2203 - val_acc: 0.9100\n",
            "Epoch 18/100\n",
            "2000/2000 [==============================] - 0s 79us/step - loss: 0.2085 - acc: 0.9255 - val_loss: 0.2192 - val_acc: 0.9100\n",
            "Epoch 19/100\n",
            "2000/2000 [==============================] - 0s 78us/step - loss: 0.2077 - acc: 0.9255 - val_loss: 0.2183 - val_acc: 0.9100\n",
            "Epoch 20/100\n",
            "2000/2000 [==============================] - 0s 78us/step - loss: 0.2070 - acc: 0.9255 - val_loss: 0.2175 - val_acc: 0.9100\n",
            "Epoch 21/100\n",
            "2000/2000 [==============================] - 0s 86us/step - loss: 0.2063 - acc: 0.9255 - val_loss: 0.2167 - val_acc: 0.9100\n",
            "Epoch 22/100\n",
            "2000/2000 [==============================] - 0s 85us/step - loss: 0.2058 - acc: 0.9255 - val_loss: 0.2161 - val_acc: 0.9100\n",
            "Epoch 23/100\n",
            "2000/2000 [==============================] - 0s 79us/step - loss: 0.2053 - acc: 0.9255 - val_loss: 0.2155 - val_acc: 0.9100\n",
            "Epoch 24/100\n",
            "2000/2000 [==============================] - 0s 74us/step - loss: 0.2049 - acc: 0.9255 - val_loss: 0.2150 - val_acc: 0.9100\n",
            "Epoch 25/100\n",
            "2000/2000 [==============================] - 0s 77us/step - loss: 0.2045 - acc: 0.9255 - val_loss: 0.2146 - val_acc: 0.9100\n",
            "Epoch 26/100\n",
            "2000/2000 [==============================] - 0s 77us/step - loss: 0.2042 - acc: 0.9255 - val_loss: 0.2141 - val_acc: 0.9100\n",
            "Epoch 27/100\n",
            "2000/2000 [==============================] - 0s 86us/step - loss: 0.2039 - acc: 0.9255 - val_loss: 0.2138 - val_acc: 0.9100\n",
            "Epoch 28/100\n",
            "2000/2000 [==============================] - 0s 83us/step - loss: 0.2036 - acc: 0.9255 - val_loss: 0.2134 - val_acc: 0.9100\n",
            "Epoch 29/100\n",
            "2000/2000 [==============================] - 0s 77us/step - loss: 0.2034 - acc: 0.9255 - val_loss: 0.2131 - val_acc: 0.9100\n",
            "Epoch 30/100\n",
            "2000/2000 [==============================] - 0s 82us/step - loss: 0.2032 - acc: 0.9255 - val_loss: 0.2128 - val_acc: 0.9100\n",
            "Epoch 31/100\n",
            "2000/2000 [==============================] - 0s 77us/step - loss: 0.2030 - acc: 0.9255 - val_loss: 0.2125 - val_acc: 0.9100\n",
            "Epoch 32/100\n",
            "2000/2000 [==============================] - 0s 82us/step - loss: 0.2028 - acc: 0.9255 - val_loss: 0.2123 - val_acc: 0.9100\n",
            "Epoch 33/100\n",
            "2000/2000 [==============================] - 0s 86us/step - loss: 0.2026 - acc: 0.9255 - val_loss: 0.2121 - val_acc: 0.9100\n",
            "Epoch 34/100\n",
            "2000/2000 [==============================] - 0s 80us/step - loss: 0.2025 - acc: 0.9255 - val_loss: 0.2119 - val_acc: 0.9100\n",
            "Epoch 35/100\n",
            "2000/2000 [==============================] - 0s 82us/step - loss: 0.2024 - acc: 0.9255 - val_loss: 0.2117 - val_acc: 0.9100\n",
            "Epoch 36/100\n",
            "2000/2000 [==============================] - 0s 77us/step - loss: 0.2023 - acc: 0.9255 - val_loss: 0.2115 - val_acc: 0.9100\n",
            "Epoch 37/100\n",
            "2000/2000 [==============================] - 0s 72us/step - loss: 0.2021 - acc: 0.9255 - val_loss: 0.2114 - val_acc: 0.9100\n",
            "Epoch 38/100\n",
            "2000/2000 [==============================] - 0s 74us/step - loss: 0.2020 - acc: 0.9255 - val_loss: 0.2112 - val_acc: 0.9100\n",
            "Epoch 39/100\n",
            "2000/2000 [==============================] - 0s 74us/step - loss: 0.2020 - acc: 0.9255 - val_loss: 0.2111 - val_acc: 0.9100\n",
            "Epoch 40/100\n",
            "2000/2000 [==============================] - 0s 75us/step - loss: 0.2019 - acc: 0.9255 - val_loss: 0.2110 - val_acc: 0.9100\n",
            "Epoch 41/100\n",
            "2000/2000 [==============================] - 0s 77us/step - loss: 0.2018 - acc: 0.9255 - val_loss: 0.2108 - val_acc: 0.9100\n",
            "Epoch 42/100\n",
            "2000/2000 [==============================] - 0s 72us/step - loss: 0.2018 - acc: 0.9255 - val_loss: 0.2107 - val_acc: 0.9100\n",
            "Epoch 43/100\n",
            "2000/2000 [==============================] - 0s 78us/step - loss: 0.2017 - acc: 0.9255 - val_loss: 0.2106 - val_acc: 0.9100\n",
            "Epoch 44/100\n",
            "2000/2000 [==============================] - 0s 74us/step - loss: 0.2016 - acc: 0.9255 - val_loss: 0.2105 - val_acc: 0.9100\n",
            "Epoch 45/100\n",
            "2000/2000 [==============================] - 0s 80us/step - loss: 0.2016 - acc: 0.9255 - val_loss: 0.2105 - val_acc: 0.9100\n",
            "Epoch 46/100\n",
            "2000/2000 [==============================] - 0s 76us/step - loss: 0.2015 - acc: 0.9255 - val_loss: 0.2104 - val_acc: 0.9100\n",
            "Epoch 47/100\n",
            "2000/2000 [==============================] - 0s 77us/step - loss: 0.2015 - acc: 0.9255 - val_loss: 0.2103 - val_acc: 0.9100\n",
            "Epoch 48/100\n",
            "2000/2000 [==============================] - 0s 77us/step - loss: 0.2015 - acc: 0.9255 - val_loss: 0.2102 - val_acc: 0.9100\n",
            "Epoch 49/100\n",
            "2000/2000 [==============================] - 0s 80us/step - loss: 0.2014 - acc: 0.9255 - val_loss: 0.2101 - val_acc: 0.9100\n",
            "Epoch 50/100\n",
            "2000/2000 [==============================] - 0s 73us/step - loss: 0.2014 - acc: 0.9255 - val_loss: 0.2101 - val_acc: 0.9100\n",
            "Epoch 51/100\n",
            "2000/2000 [==============================] - 0s 73us/step - loss: 0.2013 - acc: 0.9255 - val_loss: 0.2100 - val_acc: 0.9100\n",
            "Epoch 52/100\n",
            "2000/2000 [==============================] - 0s 78us/step - loss: 0.2013 - acc: 0.9255 - val_loss: 0.2100 - val_acc: 0.9100\n",
            "Epoch 53/100\n",
            "2000/2000 [==============================] - 0s 81us/step - loss: 0.2013 - acc: 0.9255 - val_loss: 0.2099 - val_acc: 0.9100\n",
            "Epoch 54/100\n",
            "2000/2000 [==============================] - 0s 78us/step - loss: 0.2013 - acc: 0.9255 - val_loss: 0.2099 - val_acc: 0.9100\n",
            "Epoch 55/100\n",
            "2000/2000 [==============================] - 0s 77us/step - loss: 0.2013 - acc: 0.9255 - val_loss: 0.2098 - val_acc: 0.9100\n",
            "Epoch 56/100\n",
            "2000/2000 [==============================] - 0s 76us/step - loss: 0.2012 - acc: 0.9255 - val_loss: 0.2098 - val_acc: 0.9100\n",
            "Epoch 57/100\n",
            "2000/2000 [==============================] - 0s 71us/step - loss: 0.2012 - acc: 0.9255 - val_loss: 0.2097 - val_acc: 0.9100\n",
            "Epoch 58/100\n",
            "2000/2000 [==============================] - 0s 74us/step - loss: 0.2012 - acc: 0.9255 - val_loss: 0.2097 - val_acc: 0.9100\n",
            "Epoch 59/100\n",
            "2000/2000 [==============================] - 0s 75us/step - loss: 0.2012 - acc: 0.9255 - val_loss: 0.2097 - val_acc: 0.9100\n",
            "Epoch 60/100\n",
            "2000/2000 [==============================] - 0s 75us/step - loss: 0.2012 - acc: 0.9255 - val_loss: 0.2096 - val_acc: 0.9100\n",
            "Epoch 61/100\n",
            "2000/2000 [==============================] - 0s 76us/step - loss: 0.2012 - acc: 0.9255 - val_loss: 0.2096 - val_acc: 0.9100\n",
            "Epoch 62/100\n",
            "2000/2000 [==============================] - 0s 80us/step - loss: 0.2011 - acc: 0.9255 - val_loss: 0.2096 - val_acc: 0.9100\n",
            "Epoch 63/100\n",
            "2000/2000 [==============================] - 0s 74us/step - loss: 0.2011 - acc: 0.9250 - val_loss: 0.2095 - val_acc: 0.9100\n",
            "Epoch 64/100\n",
            "2000/2000 [==============================] - 0s 77us/step - loss: 0.2011 - acc: 0.9255 - val_loss: 0.2095 - val_acc: 0.9100\n",
            "Epoch 65/100\n",
            "2000/2000 [==============================] - 0s 75us/step - loss: 0.2011 - acc: 0.9255 - val_loss: 0.2095 - val_acc: 0.9100\n",
            "Epoch 66/100\n",
            "2000/2000 [==============================] - 0s 72us/step - loss: 0.2011 - acc: 0.9250 - val_loss: 0.2094 - val_acc: 0.9100\n",
            "Epoch 67/100\n",
            "2000/2000 [==============================] - 0s 73us/step - loss: 0.2011 - acc: 0.9255 - val_loss: 0.2094 - val_acc: 0.9100\n",
            "Epoch 68/100\n",
            "2000/2000 [==============================] - 0s 81us/step - loss: 0.2011 - acc: 0.9255 - val_loss: 0.2094 - val_acc: 0.9100\n",
            "Epoch 69/100\n",
            "2000/2000 [==============================] - 0s 76us/step - loss: 0.2011 - acc: 0.9255 - val_loss: 0.2094 - val_acc: 0.9100\n",
            "Epoch 70/100\n",
            "2000/2000 [==============================] - 0s 74us/step - loss: 0.2011 - acc: 0.9250 - val_loss: 0.2094 - val_acc: 0.9100\n",
            "Epoch 71/100\n",
            "2000/2000 [==============================] - 0s 91us/step - loss: 0.2011 - acc: 0.9255 - val_loss: 0.2094 - val_acc: 0.9100\n",
            "Epoch 72/100\n",
            "2000/2000 [==============================] - 0s 75us/step - loss: 0.2011 - acc: 0.9255 - val_loss: 0.2093 - val_acc: 0.9100\n",
            "Epoch 73/100\n",
            "2000/2000 [==============================] - 0s 77us/step - loss: 0.2011 - acc: 0.9250 - val_loss: 0.2093 - val_acc: 0.9100\n",
            "Epoch 74/100\n",
            "2000/2000 [==============================] - 0s 82us/step - loss: 0.2010 - acc: 0.9255 - val_loss: 0.2093 - val_acc: 0.9100\n",
            "Epoch 75/100\n",
            "2000/2000 [==============================] - 0s 74us/step - loss: 0.2010 - acc: 0.9255 - val_loss: 0.2093 - val_acc: 0.9100\n",
            "Epoch 76/100\n",
            "2000/2000 [==============================] - 0s 74us/step - loss: 0.2010 - acc: 0.9255 - val_loss: 0.2093 - val_acc: 0.9100\n",
            "Epoch 77/100\n",
            "2000/2000 [==============================] - 0s 78us/step - loss: 0.2010 - acc: 0.9255 - val_loss: 0.2093 - val_acc: 0.9100\n",
            "Epoch 78/100\n",
            "2000/2000 [==============================] - 0s 78us/step - loss: 0.2010 - acc: 0.9255 - val_loss: 0.2092 - val_acc: 0.9100\n",
            "Epoch 79/100\n",
            "2000/2000 [==============================] - 0s 74us/step - loss: 0.2010 - acc: 0.9255 - val_loss: 0.2092 - val_acc: 0.9100\n",
            "Epoch 80/100\n",
            "2000/2000 [==============================] - 0s 77us/step - loss: 0.2010 - acc: 0.9250 - val_loss: 0.2092 - val_acc: 0.9100\n",
            "Epoch 81/100\n",
            "2000/2000 [==============================] - 0s 76us/step - loss: 0.2010 - acc: 0.9255 - val_loss: 0.2092 - val_acc: 0.9100\n",
            "Epoch 82/100\n",
            "2000/2000 [==============================] - 0s 72us/step - loss: 0.2010 - acc: 0.9255 - val_loss: 0.2092 - val_acc: 0.9100\n",
            "Epoch 83/100\n",
            "2000/2000 [==============================] - 0s 83us/step - loss: 0.2010 - acc: 0.9255 - val_loss: 0.2092 - val_acc: 0.9100\n",
            "Epoch 84/100\n",
            "2000/2000 [==============================] - 0s 75us/step - loss: 0.2010 - acc: 0.9255 - val_loss: 0.2092 - val_acc: 0.9100\n",
            "Epoch 85/100\n",
            "2000/2000 [==============================] - 0s 78us/step - loss: 0.2010 - acc: 0.9250 - val_loss: 0.2092 - val_acc: 0.9100\n",
            "Epoch 86/100\n",
            "2000/2000 [==============================] - 0s 75us/step - loss: 0.2010 - acc: 0.9255 - val_loss: 0.2092 - val_acc: 0.9100\n",
            "Epoch 87/100\n",
            "2000/2000 [==============================] - 0s 78us/step - loss: 0.2010 - acc: 0.9250 - val_loss: 0.2091 - val_acc: 0.9100\n",
            "Epoch 88/100\n",
            "2000/2000 [==============================] - 0s 70us/step - loss: 0.2010 - acc: 0.9255 - val_loss: 0.2091 - val_acc: 0.9100\n",
            "Epoch 89/100\n",
            "2000/2000 [==============================] - 0s 74us/step - loss: 0.2010 - acc: 0.9255 - val_loss: 0.2091 - val_acc: 0.9100\n",
            "Epoch 90/100\n",
            "2000/2000 [==============================] - 0s 84us/step - loss: 0.2010 - acc: 0.9250 - val_loss: 0.2091 - val_acc: 0.9100\n",
            "Epoch 91/100\n",
            "2000/2000 [==============================] - 0s 80us/step - loss: 0.2010 - acc: 0.9250 - val_loss: 0.2091 - val_acc: 0.9100\n",
            "Epoch 92/100\n",
            "2000/2000 [==============================] - 0s 74us/step - loss: 0.2010 - acc: 0.9255 - val_loss: 0.2091 - val_acc: 0.9100\n",
            "Epoch 93/100\n",
            "2000/2000 [==============================] - 0s 79us/step - loss: 0.2010 - acc: 0.9250 - val_loss: 0.2091 - val_acc: 0.9100\n",
            "Epoch 94/100\n",
            "2000/2000 [==============================] - 0s 75us/step - loss: 0.2010 - acc: 0.9255 - val_loss: 0.2091 - val_acc: 0.9100\n",
            "Epoch 95/100\n",
            "2000/2000 [==============================] - 0s 87us/step - loss: 0.2010 - acc: 0.9255 - val_loss: 0.2091 - val_acc: 0.9100\n",
            "Epoch 96/100\n",
            "2000/2000 [==============================] - 0s 87us/step - loss: 0.2010 - acc: 0.9250 - val_loss: 0.2091 - val_acc: 0.9100\n",
            "Epoch 97/100\n",
            "2000/2000 [==============================] - 0s 81us/step - loss: 0.2010 - acc: 0.9250 - val_loss: 0.2091 - val_acc: 0.9100\n",
            "Epoch 98/100\n",
            "2000/2000 [==============================] - 0s 87us/step - loss: 0.2010 - acc: 0.9255 - val_loss: 0.2091 - val_acc: 0.9100\n",
            "Epoch 99/100\n",
            "2000/2000 [==============================] - 0s 76us/step - loss: 0.2010 - acc: 0.9250 - val_loss: 0.2090 - val_acc: 0.9100\n",
            "Epoch 100/100\n",
            "2000/2000 [==============================] - 0s 77us/step - loss: 0.2010 - acc: 0.9255 - val_loss: 0.2090 - val_acc: 0.9100\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.callbacks.History at 0x7f7eec1c5550>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ukSA0FCQeL76",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}