1.Cifar 10 with dropout without BN.ipynb

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      "language": "python",
      "name": "rnn_venv"
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      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.6.5"
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    "colab": {
      "name": "1.+Cifar_10_with_dropout_without_BN.ipynb",
      "provenance": []
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "MOb9RlgVMxN6",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# import libraries\n",
        "from __future__ import print_function\n",
        "import keras\n",
        "from keras.datasets import cifar10\n",
        "from keras.preprocessing.image import ImageDataGenerator\n",
        "from keras.models import Sequential\n",
        "from keras.layers import Dense, Dropout, Activation, Flatten\n",
        "from keras.layers import Conv2D, MaxPooling2D\n",
        "import numpy as np\n",
        "import os\n",
        "import matplotlib.pyplot as plt\n",
        "%matplotlib inline"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9xMp4mXgOKNL",
        "colab_type": "code",
        "outputId": "b830cf99-47f8-4f3c-ab7f-e76e0b1f39dd",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "import tensorflow as tf\n",
        "\n",
        "tf.__version__"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'1.15.0'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_BnbWDycMxOO",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# batch, classes, epochs\n",
        "batch_size = 32\n",
        "num_classes = 10\n",
        "epochs = 50"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6qzVe1boMxOX",
        "colab_type": "code",
        "outputId": "e5b14e44-a6f2-4da5-e1a4-23246623fce4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 104
        }
      },
      "source": [
        "# The data, split between train and test sets:\n",
        "(x_train, y_train), (x_test, y_test) = cifar10.load_data()\n",
        "print('x_train shape:', x_train.shape)\n",
        "print(x_train.shape[0], 'train samples')\n",
        "print(x_test.shape[0], 'test samples')"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Downloading data from https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz\n",
            "170500096/170498071 [==============================] - 13s 0us/step\n",
            "x_train shape: (50000, 32, 32, 3)\n",
            "50000 train samples\n",
            "10000 test samples\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6YA2Y5PNMxOh",
        "colab_type": "code",
        "outputId": "0fe28d6e-817e-434f-e7b1-97f0cb13de39",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 213
        }
      },
      "source": [
        "# plotting some random 10 images\n",
        "class_names = ['airplane','automobile','bird','cat','deer',\n",
        "               'dog','frog','horse','ship','truck']\n",
        "\n",
        "fig = plt.figure(figsize=(8,3))\n",
        "for i in range(num_classes):\n",
        "    ax = fig.add_subplot(2, 5, 1 + i, xticks=[], yticks=[])\n",
        "    idx = np.where(y_train[:]==i)[0]\n",
        "    features_idx = x_train[idx,::]\n",
        "    img_num = np.random.randint(features_idx.shape[0])\n",
        "    im = (features_idx[img_num,::])\n",
        "    ax.set_title(class_names[i])\n",
        "    plt.imshow(im)\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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BqHFrdX52v2/aJfM2knXKmt+yjM9LEl5nShWvroZzIzbOzQiSZkBAQEBAwC6xJ0mTwF5G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\n0NDQmBP6R1NDQ0NDQ2NO/F9IO585TSvJDQAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 576x216 with 10 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HrPt-VtHMxOm",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Convert class vectors to binary class matrices.\n",
        "y_train = keras.utils.to_categorical(y_train, num_classes)\n",
        "y_test = keras.utils.to_categorical(y_test, num_classes)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mZxIe60GMxOs",
        "colab_type": "code",
        "outputId": "d917a6ad-a5b0-4eb8-d843-c7e2058b094d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 263
        }
      },
      "source": [
        "# model architecture\n",
        "model = Sequential()\n",
        "model.add(Conv2D(32, (3, 3), padding='same',\n",
        "                 input_shape=x_train.shape[1:]))\n",
        "model.add(Activation('relu'))\n",
        "model.add(Conv2D(32, (3, 3)))\n",
        "model.add(Activation('relu'))\n",
        "model.add(MaxPooling2D(pool_size=(2, 2)))\n",
        "model.add(Dropout(0.25))\n",
        "\n",
        "model.add(Conv2D(64, (3, 3), padding='same'))\n",
        "model.add(Activation('relu'))\n",
        "model.add(Conv2D(64, (3, 3)))\n",
        "model.add(Activation('relu'))\n",
        "model.add(MaxPooling2D(pool_size=(2, 2)))\n",
        "model.add(Dropout(0.25))\n",
        "\n",
        "model.add(Flatten())\n",
        "model.add(Dense(512))\n",
        "model.add(Activation('relu'))\n",
        "model.add(Dropout(0.5))\n",
        "model.add(Dense(num_classes))\n",
        "model.add(Activation('softmax'))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:66: The name tf.get_default_graph is deprecated. Please use tf.compat.v1.get_default_graph instead.\n",
            "\n",
            "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/backend/tensorflow_backend.py:4267: The name tf.nn.max_pool is deprecated. Please use tf.nn.max_pool2d instead.\n",
            "\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:148: The name tf.placeholder_with_default is deprecated. Please use tf.compat.v1.placeholder_with_default instead.\n",
            "\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:3733: calling dropout (from tensorflow.python.ops.nn_ops) with keep_prob is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Please use `rate` instead of `keep_prob`. Rate should be set to `rate = 1 - keep_prob`.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lAXokTPBMxOy",
        "colab_type": "code",
        "outputId": "a5826e88-5a63-4741-fec9-301a71b0a076",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 780
        }
      },
      "source": [
        "# summary\n",
        "model.summary()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"sequential_1\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "conv2d_1 (Conv2D)            (None, 32, 32, 32)        896       \n",
            "_________________________________________________________________\n",
            "activation_1 (Activation)    (None, 32, 32, 32)        0         \n",
            "_________________________________________________________________\n",
            "conv2d_2 (Conv2D)            (None, 30, 30, 32)        9248      \n",
            "_________________________________________________________________\n",
            "activation_2 (Activation)    (None, 30, 30, 32)        0         \n",
            "_________________________________________________________________\n",
            "max_pooling2d_1 (MaxPooling2 (None, 15, 15, 32)        0         \n",
            "_________________________________________________________________\n",
            "dropout_1 (Dropout)          (None, 15, 15, 32)        0         \n",
            "_________________________________________________________________\n",
            "conv2d_3 (Conv2D)            (None, 15, 15, 64)        18496     \n",
            "_________________________________________________________________\n",
            "activation_3 (Activation)    (None, 15, 15, 64)        0         \n",
            "_________________________________________________________________\n",
            "conv2d_4 (Conv2D)            (None, 13, 13, 64)        36928     \n",
            "_________________________________________________________________\n",
            "activation_4 (Activation)    (None, 13, 13, 64)        0         \n",
            "_________________________________________________________________\n",
            "max_pooling2d_2 (MaxPooling2 (None, 6, 6, 64)          0         \n",
            "_________________________________________________________________\n",
            "dropout_2 (Dropout)          (None, 6, 6, 64)          0         \n",
            "_________________________________________________________________\n",
            "flatten_1 (Flatten)          (None, 2304)              0         \n",
            "_________________________________________________________________\n",
            "dense_1 (Dense)              (None, 512)               1180160   \n",
            "_________________________________________________________________\n",
            "activation_5 (Activation)    (None, 512)               0         \n",
            "_________________________________________________________________\n",
            "dropout_3 (Dropout)          (None, 512)               0         \n",
            "_________________________________________________________________\n",
            "dense_2 (Dense)              (None, 10)                5130      \n",
            "_________________________________________________________________\n",
            "activation_6 (Activation)    (None, 10)                0         \n",
            "=================================================================\n",
            "Total params: 1,250,858\n",
            "Trainable params: 1,250,858\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ohhjJDbtMxO5",
        "colab_type": "code",
        "outputId": "efc8c576-e8f8-4881-88f8-03bf02f0d65d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 107
        }
      },
      "source": [
        "# compile the model\n",
        "model.compile(loss='categorical_crossentropy',\n",
        "              optimizer='sgd',\n",
        "              metrics=['accuracy'])\n",
        "\n",
        "# convert to float, normalise the data\n",
        "x_train = x_train.astype('float32')\n",
        "x_test = x_test.astype('float32')\n",
        "x_train /= 255\n",
        "x_test /= 255\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "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:3576: The name tf.log is deprecated. Please use tf.math.log instead.\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yx9rkNrXMxO8",
        "colab_type": "code",
        "outputId": "7757c930-f8b4-4976-92b3-4cfb515c6626",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "# train \n",
        "model.fit(x_train, y_train,\n",
        "              batch_size=batch_size,\n",
        "              epochs=epochs,\n",
        "              validation_data=(x_test, y_test),\n",
        "              shuffle=True)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow_core/python/ops/math_grad.py:1424: 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",
            "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 50000 samples, validate on 10000 samples\n",
            "Epoch 1/50\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",
            "50000/50000 [==============================] - 25s 506us/step - loss: 2.0906 - acc: 0.2203 - val_loss: 1.8822 - val_acc: 0.3343\n",
            "Epoch 2/50\n",
            "50000/50000 [==============================] - 10s 203us/step - loss: 1.8041 - acc: 0.3458 - val_loss: 1.6894 - val_acc: 0.3839\n",
            "Epoch 3/50\n",
            "50000/50000 [==============================] - 10s 199us/step - loss: 1.6199 - acc: 0.4111 - val_loss: 1.4839 - val_acc: 0.4621\n",
            "Epoch 4/50\n",
            "50000/50000 [==============================] - 10s 198us/step - loss: 1.4882 - acc: 0.4569 - val_loss: 1.3969 - val_acc: 0.4988\n",
            "Epoch 5/50\n",
            "50000/50000 [==============================] - 10s 195us/step - loss: 1.3963 - acc: 0.4924 - val_loss: 1.2841 - val_acc: 0.5417\n",
            "Epoch 6/50\n",
            "50000/50000 [==============================] - 10s 198us/step - loss: 1.3268 - acc: 0.5220 - val_loss: 1.2186 - val_acc: 0.5645\n",
            "Epoch 7/50\n",
            "50000/50000 [==============================] - 10s 201us/step - loss: 1.2623 - acc: 0.5468 - val_loss: 1.1857 - val_acc: 0.5758\n",
            "Epoch 8/50\n",
            "50000/50000 [==============================] - 10s 198us/step - loss: 1.2103 - acc: 0.5681 - val_loss: 1.1159 - val_acc: 0.6037\n",
            "Epoch 9/50\n",
            "50000/50000 [==============================] - 10s 195us/step - loss: 1.1629 - acc: 0.5832 - val_loss: 1.0448 - val_acc: 0.6337\n",
            "Epoch 10/50\n",
            "50000/50000 [==============================] - 10s 201us/step - loss: 1.1144 - acc: 0.6020 - val_loss: 1.0151 - val_acc: 0.6402\n",
            "Epoch 11/50\n",
            "50000/50000 [==============================] - 10s 199us/step - loss: 1.0756 - acc: 0.6192 - val_loss: 0.9739 - val_acc: 0.6573\n",
            "Epoch 12/50\n",
            "50000/50000 [==============================] - 10s 194us/step - loss: 1.0448 - acc: 0.6312 - val_loss: 0.9718 - val_acc: 0.6635\n",
            "Epoch 13/50\n",
            "50000/50000 [==============================] - 10s 203us/step - loss: 1.0036 - acc: 0.6420 - val_loss: 0.9246 - val_acc: 0.6741\n",
            "Epoch 14/50\n",
            "50000/50000 [==============================] - 10s 196us/step - loss: 0.9736 - acc: 0.6554 - val_loss: 0.8949 - val_acc: 0.6906\n",
            "Epoch 15/50\n",
            "50000/50000 [==============================] - 10s 199us/step - loss: 0.9415 - acc: 0.6670 - val_loss: 0.8989 - val_acc: 0.6798\n",
            "Epoch 16/50\n",
            "50000/50000 [==============================] - 10s 207us/step - loss: 0.9116 - acc: 0.6776 - val_loss: 0.8464 - val_acc: 0.7057\n",
            "Epoch 17/50\n",
            "50000/50000 [==============================] - 10s 195us/step - loss: 0.8829 - acc: 0.6897 - val_loss: 0.8052 - val_acc: 0.7189\n",
            "Epoch 18/50\n",
            "50000/50000 [==============================] - 10s 196us/step - loss: 0.8551 - acc: 0.7000 - val_loss: 0.8380 - val_acc: 0.7047\n",
            "Epoch 19/50\n",
            "50000/50000 [==============================] - 10s 196us/step - loss: 0.8354 - acc: 0.7058 - val_loss: 0.7928 - val_acc: 0.7267\n",
            "Epoch 20/50\n",
            "50000/50000 [==============================] - 10s 198us/step - loss: 0.8146 - acc: 0.7149 - val_loss: 0.7511 - val_acc: 0.7396\n",
            "Epoch 21/50\n",
            "50000/50000 [==============================] - 10s 195us/step - loss: 0.7891 - acc: 0.7202 - val_loss: 0.7476 - val_acc: 0.7430\n",
            "Epoch 22/50\n",
            "50000/50000 [==============================] - 10s 203us/step - loss: 0.7733 - acc: 0.7284 - val_loss: 0.7462 - val_acc: 0.7439\n",
            "Epoch 23/50\n",
            "50000/50000 [==============================] - 10s 197us/step - loss: 0.7507 - acc: 0.7347 - val_loss: 0.7149 - val_acc: 0.7518\n",
            "Epoch 24/50\n",
            "50000/50000 [==============================] - 10s 195us/step - loss: 0.7304 - acc: 0.7437 - val_loss: 0.7371 - val_acc: 0.7450\n",
            "Epoch 25/50\n",
            "50000/50000 [==============================] - 10s 197us/step - loss: 0.7155 - acc: 0.7467 - val_loss: 0.7067 - val_acc: 0.7530\n",
            "Epoch 26/50\n",
            "50000/50000 [==============================] - 10s 198us/step - loss: 0.6992 - acc: 0.7538 - val_loss: 0.6919 - val_acc: 0.7604\n",
            "Epoch 27/50\n",
            "50000/50000 [==============================] - 10s 196us/step - loss: 0.6849 - acc: 0.7597 - val_loss: 0.6872 - val_acc: 0.7651\n",
            "Epoch 28/50\n",
            "50000/50000 [==============================] - 10s 199us/step - loss: 0.6687 - acc: 0.7645 - val_loss: 0.6828 - val_acc: 0.7668\n",
            "Epoch 29/50\n",
            "50000/50000 [==============================] - 10s 198us/step - loss: 0.6529 - acc: 0.7710 - val_loss: 0.7324 - val_acc: 0.7468\n",
            "Epoch 30/50\n",
            "50000/50000 [==============================] - 10s 194us/step - loss: 0.6361 - acc: 0.7759 - val_loss: 0.6686 - val_acc: 0.7687\n",
            "Epoch 31/50\n",
            "50000/50000 [==============================] - 10s 194us/step - loss: 0.6272 - acc: 0.7775 - val_loss: 0.6390 - val_acc: 0.7813\n",
            "Epoch 32/50\n",
            "50000/50000 [==============================] - 10s 201us/step - loss: 0.6194 - acc: 0.7814 - val_loss: 0.6460 - val_acc: 0.7760\n",
            "Epoch 33/50\n",
            "50000/50000 [==============================] - 10s 196us/step - loss: 0.6040 - acc: 0.7866 - val_loss: 0.6845 - val_acc: 0.7655\n",
            "Epoch 34/50\n",
            "50000/50000 [==============================] - 10s 204us/step - loss: 0.5894 - acc: 0.7907 - val_loss: 0.6756 - val_acc: 0.7645\n",
            "Epoch 35/50\n",
            "50000/50000 [==============================] - 10s 198us/step - loss: 0.5813 - acc: 0.7941 - val_loss: 0.6286 - val_acc: 0.7837\n",
            "Epoch 36/50\n",
            "50000/50000 [==============================] - 10s 194us/step - loss: 0.5681 - acc: 0.7991 - val_loss: 0.6314 - val_acc: 0.7837\n",
            "Epoch 37/50\n",
            "50000/50000 [==============================] - 10s 196us/step - loss: 0.5590 - acc: 0.8030 - val_loss: 0.6203 - val_acc: 0.7824\n",
            "Epoch 38/50\n",
            "50000/50000 [==============================] - 10s 197us/step - loss: 0.5477 - acc: 0.8069 - val_loss: 0.6157 - val_acc: 0.7874\n",
            "Epoch 39/50\n",
            "50000/50000 [==============================] - 10s 194us/step - loss: 0.5364 - acc: 0.8089 - val_loss: 0.6018 - val_acc: 0.7922\n",
            "Epoch 40/50\n",
            "50000/50000 [==============================] - 10s 196us/step - loss: 0.5226 - acc: 0.8145 - val_loss: 0.6332 - val_acc: 0.7841\n",
            "Epoch 41/50\n",
            "50000/50000 [==============================] - 10s 198us/step - loss: 0.5140 - acc: 0.8169 - val_loss: 0.6138 - val_acc: 0.7911\n",
            "Epoch 42/50\n",
            "50000/50000 [==============================] - 10s 193us/step - loss: 0.5082 - acc: 0.8193 - val_loss: 0.5939 - val_acc: 0.7955\n",
            "Epoch 43/50\n",
            "50000/50000 [==============================] - 10s 197us/step - loss: 0.4965 - acc: 0.8214 - val_loss: 0.5977 - val_acc: 0.7938\n",
            "Epoch 44/50\n",
            "50000/50000 [==============================] - 10s 200us/step - loss: 0.4831 - acc: 0.8277 - val_loss: 0.6061 - val_acc: 0.7966\n",
            "Epoch 45/50\n",
            "50000/50000 [==============================] - 10s 198us/step - loss: 0.4794 - acc: 0.8291 - val_loss: 0.6058 - val_acc: 0.7908\n",
            "Epoch 46/50\n",
            "50000/50000 [==============================] - 10s 193us/step - loss: 0.4698 - acc: 0.8320 - val_loss: 0.6051 - val_acc: 0.7957\n",
            "Epoch 47/50\n",
            "50000/50000 [==============================] - 10s 204us/step - loss: 0.4586 - acc: 0.8364 - val_loss: 0.6172 - val_acc: 0.7906\n",
            "Epoch 48/50\n",
            "50000/50000 [==============================] - 10s 195us/step - loss: 0.4515 - acc: 0.8377 - val_loss: 0.5991 - val_acc: 0.7946\n",
            "Epoch 49/50\n",
            "50000/50000 [==============================] - 10s 194us/step - loss: 0.4450 - acc: 0.8416 - val_loss: 0.5760 - val_acc: 0.8030\n",
            "Epoch 50/50\n",
            "50000/50000 [==============================] - 10s 196us/step - loss: 0.4342 - acc: 0.8444 - val_loss: 0.5963 - val_acc: 0.7982\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.callbacks.History at 0x7ff3639ffbe0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "G1qRbWtWMxPC",
        "colab_type": "text"
      },
      "source": [
        "Result: After 50 epochs, the difference between training and validation accuracy is low but the training is slow as compared to the model with batch normalization."
      ]
    }
  ]
}