6.Cifar10 morelayers added.ipynb

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  "metadata": {
    "kernelspec": {
      "display_name": "Python [conda env:tensorflow_p36]",
      "language": "python",
      "name": "conda-env-tensorflow_p36-py"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.6.4"
    },
    "colab": {
      "name": "6.+Cifar10_morelayer_notebook.ipynb",
      "provenance": []
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "8LX_CaM4awUJ",
        "colab_type": "code",
        "colab": {},
        "outputId": "983a981d-7ed7-40ca-b5e6-82f537b089a8"
      },
      "source": [
        "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.normalization import BatchNormalization\n",
        "from keras.layers import Conv2D, MaxPooling2D\n",
        "from keras.regularizers import l2\n",
        "import numpy as np\n",
        "import os\n",
        "import matplotlib.pyplot as plt\n",
        "%matplotlib inline"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/home/ubuntu/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/h5py/__init__.py:36: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.\n",
            "  from ._conv import register_converters as _register_converters\n",
            "Using TensorFlow backend.\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "As1xRIc6awUa",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Defining the parameters\n",
        "batch_size = 32\n",
        "num_classes = 10\n",
        "epochs = 50"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gh5JaC1UawUm",
        "colab_type": "code",
        "colab": {},
        "outputId": "0f3ee16d-1fcf-4762-bf09-4a10c72413e9"
      },
      "source": [
        "# Splitting the data between train and test\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": [
            "x_train shape: (50000, 32, 32, 3)\n",
            "50000 train samples\n",
            "10000 test samples\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pGn34e0dawUy",
        "colab_type": "code",
        "colab": {},
        "outputId": "15a3cd30-ac80-45d8-ac3c-fce567b58327"
      },
      "source": [
        "# plotting some random 10 images\n",
        "\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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\n",
            "text/plain": [
              "<Figure size 576x216 with 10 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "03fndYJvawU6",
        "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": "P3Z-gOUPawVA",
        "colab_type": "code",
        "colab": {},
        "outputId": "4dc2e1f8-e0a4-4ddc-e1da-386107a699cb"
      },
      "source": [
        "# Printing sample data\n",
        "print(y_train[:10])"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[[0. 0. 0. 0. 0. 0. 1. 0. 0. 0.]\n",
            " [0. 0. 0. 0. 0. 0. 0. 0. 0. 1.]\n",
            " [0. 0. 0. 0. 0. 0. 0. 0. 0. 1.]\n",
            " [0. 0. 0. 0. 1. 0. 0. 0. 0. 0.]\n",
            " [0. 1. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
            " [0. 1. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
            " [0. 0. 1. 0. 0. 0. 0. 0. 0. 0.]\n",
            " [0. 0. 0. 0. 0. 0. 0. 1. 0. 0.]\n",
            " [0. 0. 0. 0. 0. 0. 0. 0. 1. 0.]\n",
            " [0. 0. 0. 1. 0. 0. 0. 0. 0. 0.]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JDSn5En2awVF",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "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(BatchNormalization())\n",
        "model.add(Conv2D(32, (3, 3)))\n",
        "model.add(Activation('relu'))\n",
        "model.add(BatchNormalization())\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(BatchNormalization())\n",
        "model.add(Conv2D(64, (3, 3)))\n",
        "model.add(Activation('relu'))\n",
        "model.add(BatchNormalization())\n",
        "model.add(MaxPooling2D(pool_size=(2, 2)))\n",
        "model.add(Dropout(0.25))\n",
        "\n",
        "model.add(Conv2D(128, (3, 3), padding='same'))\n",
        "model.add(Activation('relu'))\n",
        "model.add(BatchNormalization())\n",
        "model.add(Conv2D(128, (3, 3)))\n",
        "model.add(Activation('relu'))\n",
        "model.add(BatchNormalization())\n",
        "model.add(MaxPooling2D(pool_size=(2, 2)))\n",
        "model.add(Dropout(0.25))\n",
        "\n",
        "model.add(Flatten())\n",
        "model.add(Dense(512,kernel_regularizer=l2(0.01)))\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": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HQHGRfzqawVJ",
        "colab_type": "code",
        "colab": {},
        "outputId": "64bc524e-af67-4691-eff6-81620f674332"
      },
      "source": [
        "# summary of the model\n",
        "print(model.summary())"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "_________________________________________________________________\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",
            "batch_normalization_1 (Batch (None, 32, 32, 32)        128       \n",
            "_________________________________________________________________\n",
            "conv2d_2 (Conv2D)            (None, 30, 30, 32)        9248      \n",
            "_________________________________________________________________\n",
            "activation_2 (Activation)    (None, 30, 30, 32)        0         \n",
            "_________________________________________________________________\n",
            "batch_normalization_2 (Batch (None, 30, 30, 32)        128       \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",
            "batch_normalization_3 (Batch (None, 15, 15, 64)        256       \n",
            "_________________________________________________________________\n",
            "conv2d_4 (Conv2D)            (None, 13, 13, 64)        36928     \n",
            "_________________________________________________________________\n",
            "activation_4 (Activation)    (None, 13, 13, 64)        0         \n",
            "_________________________________________________________________\n",
            "batch_normalization_4 (Batch (None, 13, 13, 64)        256       \n",
            "_________________________________________________________________\n",
            "max_pooling2d_2 (MaxPooling2 (None, 6, 6, 64)          0         \n",
            "_________________________________________________________________\n",
            "dropout_2 (Dropout)          (None, 6, 6, 64)          0         \n",
            "_________________________________________________________________\n",
            "conv2d_5 (Conv2D)            (None, 6, 6, 128)         73856     \n",
            "_________________________________________________________________\n",
            "activation_5 (Activation)    (None, 6, 6, 128)         0         \n",
            "_________________________________________________________________\n",
            "batch_normalization_5 (Batch (None, 6, 6, 128)         512       \n",
            "_________________________________________________________________\n",
            "conv2d_6 (Conv2D)            (None, 4, 4, 128)         147584    \n",
            "_________________________________________________________________\n",
            "activation_6 (Activation)    (None, 4, 4, 128)         0         \n",
            "_________________________________________________________________\n",
            "batch_normalization_6 (Batch (None, 4, 4, 128)         512       \n",
            "_________________________________________________________________\n",
            "max_pooling2d_3 (MaxPooling2 (None, 2, 2, 128)         0         \n",
            "_________________________________________________________________\n",
            "dropout_3 (Dropout)          (None, 2, 2, 128)         0         \n",
            "_________________________________________________________________\n",
            "flatten_1 (Flatten)          (None, 512)               0         \n",
            "_________________________________________________________________\n",
            "dense_1 (Dense)              (None, 512)               262656    \n",
            "_________________________________________________________________\n",
            "activation_7 (Activation)    (None, 512)               0         \n",
            "_________________________________________________________________\n",
            "dropout_4 (Dropout)          (None, 512)               0         \n",
            "_________________________________________________________________\n",
            "dense_2 (Dense)              (None, 10)                5130      \n",
            "_________________________________________________________________\n",
            "activation_8 (Activation)    (None, 10)                0         \n",
            "=================================================================\n",
            "Total params: 556,586\n",
            "Trainable params: 555,690\n",
            "Non-trainable params: 896\n",
            "_________________________________________________________________\n",
            "None\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SVVY5l4RawVN",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# compile\n",
        "model.compile(loss='categorical_crossentropy',\n",
        "              optimizer='sgd',\n",
        "              metrics=['accuracy'])\n",
        "\n",
        "x_train = x_train.astype('float32')\n",
        "x_test = x_test.astype('float32')\n",
        "\n",
        "# Normalizing the input image\n",
        "x_train /= 255\n",
        "x_test /= 255\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pq6lk-4EawVR",
        "colab_type": "code",
        "colab": {},
        "outputId": "46741d03-a91b-4899-c02e-9b47a0bd2c1d"
      },
      "source": [
        "# Training the model\n",
        "history = 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": [
            "Train on 50000 samples, validate on 10000 samples\n",
            "Epoch 1/50\n",
            "50000/50000 [==============================] - 28s 553us/step - loss: 5.5533 - acc: 0.3696 - val_loss: 4.7551 - val_acc: 0.3308\n",
            "Epoch 2/50\n",
            "50000/50000 [==============================] - 25s 509us/step - loss: 3.3812 - acc: 0.5089 - val_loss: 2.6966 - val_acc: 0.5636\n",
            "Epoch 3/50\n",
            "50000/50000 [==============================] - 25s 509us/step - loss: 2.2605 - acc: 0.5856 - val_loss: 1.8537 - val_acc: 0.6282\n",
            "Epoch 4/50\n",
            "50000/50000 [==============================] - 25s 509us/step - loss: 1.6526 - acc: 0.6317 - val_loss: 1.3915 - val_acc: 0.6678\n",
            "Epoch 5/50\n",
            "50000/50000 [==============================] - 25s 509us/step - loss: 1.2981 - acc: 0.6642 - val_loss: 1.1459 - val_acc: 0.6928\n",
            "Epoch 6/50\n",
            "50000/50000 [==============================] - 26s 512us/step - loss: 1.1031 - acc: 0.6859 - val_loss: 1.1166 - val_acc: 0.6677\n",
            "Epoch 7/50\n",
            "50000/50000 [==============================] - 25s 510us/step - loss: 0.9725 - acc: 0.7096 - val_loss: 0.9192 - val_acc: 0.7190\n",
            "Epoch 8/50\n",
            "50000/50000 [==============================] - 25s 509us/step - loss: 0.8997 - acc: 0.7209 - val_loss: 0.8376 - val_acc: 0.7375\n",
            "Epoch 9/50\n",
            "50000/50000 [==============================] - 25s 509us/step - loss: 0.8420 - acc: 0.7347 - val_loss: 0.7684 - val_acc: 0.7601\n",
            "Epoch 10/50\n",
            "50000/50000 [==============================] - 25s 509us/step - loss: 0.8074 - acc: 0.7455 - val_loss: 0.7664 - val_acc: 0.7557\n",
            "Epoch 11/50\n",
            "50000/50000 [==============================] - 25s 509us/step - loss: 0.7717 - acc: 0.7572 - val_loss: 0.7916 - val_acc: 0.7490\n",
            "Epoch 12/50\n",
            "50000/50000 [==============================] - 25s 509us/step - loss: 0.7457 - acc: 0.7669 - val_loss: 0.7167 - val_acc: 0.7724\n",
            "Epoch 13/50\n",
            "50000/50000 [==============================] - 25s 510us/step - loss: 0.7173 - acc: 0.7747 - val_loss: 0.7588 - val_acc: 0.7626\n",
            "Epoch 14/50\n",
            "50000/50000 [==============================] - 25s 509us/step - loss: 0.6980 - acc: 0.7824 - val_loss: 0.7145 - val_acc: 0.7791\n",
            "Epoch 15/50\n",
            "50000/50000 [==============================] - 25s 509us/step - loss: 0.6754 - acc: 0.7898 - val_loss: 0.6995 - val_acc: 0.7817\n",
            "Epoch 16/50\n",
            "50000/50000 [==============================] - 25s 509us/step - loss: 0.6626 - acc: 0.7950 - val_loss: 0.7048 - val_acc: 0.7796\n",
            "Epoch 17/50\n",
            "50000/50000 [==============================] - 26s 510us/step - loss: 0.6480 - acc: 0.8022 - val_loss: 0.7580 - val_acc: 0.7683\n",
            "Epoch 18/50\n",
            "50000/50000 [==============================] - 26s 511us/step - loss: 0.6346 - acc: 0.8044 - val_loss: 0.6402 - val_acc: 0.8031\n",
            "Epoch 19/50\n",
            "50000/50000 [==============================] - 26s 515us/step - loss: 0.6179 - acc: 0.8095 - val_loss: 0.6295 - val_acc: 0.8093\n",
            "Epoch 20/50\n",
            "50000/50000 [==============================] - 25s 510us/step - loss: 0.6003 - acc: 0.8154 - val_loss: 0.6199 - val_acc: 0.8135\n",
            "Epoch 21/50\n",
            "50000/50000 [==============================] - 25s 509us/step - loss: 0.5919 - acc: 0.8208 - val_loss: 0.6201 - val_acc: 0.8129\n",
            "Epoch 22/50\n",
            "50000/50000 [==============================] - 25s 509us/step - loss: 0.5800 - acc: 0.8257 - val_loss: 0.6096 - val_acc: 0.8187\n",
            "Epoch 23/50\n",
            "50000/50000 [==============================] - 25s 510us/step - loss: 0.5654 - acc: 0.8323 - val_loss: 0.5983 - val_acc: 0.8198\n",
            "Epoch 24/50\n",
            "50000/50000 [==============================] - 26s 512us/step - loss: 0.5582 - acc: 0.8345 - val_loss: 0.6129 - val_acc: 0.8142\n",
            "Epoch 25/50\n",
            "50000/50000 [==============================] - 26s 515us/step - loss: 0.5507 - acc: 0.8354 - val_loss: 0.6212 - val_acc: 0.8144\n",
            "Epoch 26/50\n",
            "50000/50000 [==============================] - 26s 512us/step - loss: 0.5408 - acc: 0.8395 - val_loss: 0.6045 - val_acc: 0.8232\n",
            "Epoch 27/50\n",
            "50000/50000 [==============================] - 26s 511us/step - loss: 0.5246 - acc: 0.8460 - val_loss: 0.5952 - val_acc: 0.8258\n",
            "Epoch 28/50\n",
            "50000/50000 [==============================] - 26s 512us/step - loss: 0.5222 - acc: 0.8443 - val_loss: 0.5760 - val_acc: 0.8335\n",
            "Epoch 29/50\n",
            "50000/50000 [==============================] - 26s 514us/step - loss: 0.5185 - acc: 0.8466 - val_loss: 0.5955 - val_acc: 0.8220\n",
            "Epoch 30/50\n",
            "50000/50000 [==============================] - 26s 512us/step - loss: 0.5053 - acc: 0.8514 - val_loss: 0.6019 - val_acc: 0.8243\n",
            "Epoch 31/50\n",
            "50000/50000 [==============================] - 26s 510us/step - loss: 0.4950 - acc: 0.8560 - val_loss: 0.6259 - val_acc: 0.8191\n",
            "Epoch 32/50\n",
            "50000/50000 [==============================] - 26s 513us/step - loss: 0.4863 - acc: 0.8591 - val_loss: 0.5913 - val_acc: 0.8297\n",
            "Epoch 33/50\n",
            "50000/50000 [==============================] - 25s 510us/step - loss: 0.4847 - acc: 0.8584 - val_loss: 0.6331 - val_acc: 0.8187\n",
            "Epoch 34/50\n",
            "50000/50000 [==============================] - 25s 510us/step - loss: 0.4760 - acc: 0.8629 - val_loss: 0.5887 - val_acc: 0.8253\n",
            "Epoch 35/50\n",
            "50000/50000 [==============================] - 25s 510us/step - loss: 0.4711 - acc: 0.8643 - val_loss: 0.5734 - val_acc: 0.8371\n",
            "Epoch 36/50\n",
            "50000/50000 [==============================] - 26s 511us/step - loss: 0.4578 - acc: 0.8696 - val_loss: 0.5539 - val_acc: 0.8415\n",
            "Epoch 37/50\n",
            "50000/50000 [==============================] - 26s 510us/step - loss: 0.4505 - acc: 0.8716 - val_loss: 0.6095 - val_acc: 0.8278\n",
            "Epoch 38/50\n",
            "50000/50000 [==============================] - 26s 513us/step - loss: 0.4501 - acc: 0.8721 - val_loss: 0.5835 - val_acc: 0.8334\n",
            "Epoch 39/50\n",
            "50000/50000 [==============================] - 25s 509us/step - loss: 0.4439 - acc: 0.8733 - val_loss: 0.5870 - val_acc: 0.8316\n",
            "Epoch 40/50\n",
            "50000/50000 [==============================] - 25s 510us/step - loss: 0.4363 - acc: 0.8765 - val_loss: 0.5555 - val_acc: 0.8411\n",
            "Epoch 41/50\n",
            "50000/50000 [==============================] - 26s 513us/step - loss: 0.4279 - acc: 0.8811 - val_loss: 0.5827 - val_acc: 0.8363\n",
            "Epoch 42/50\n",
            "50000/50000 [==============================] - 26s 519us/step - loss: 0.4272 - acc: 0.8804 - val_loss: 0.5687 - val_acc: 0.8416\n",
            "Epoch 43/50\n",
            "50000/50000 [==============================] - 26s 514us/step - loss: 0.4226 - acc: 0.8829 - val_loss: 0.5776 - val_acc: 0.8405\n",
            "Epoch 44/50\n",
            "50000/50000 [==============================] - 26s 511us/step - loss: 0.4168 - acc: 0.8836 - val_loss: 0.6035 - val_acc: 0.8352\n",
            "Epoch 45/50\n",
            "50000/50000 [==============================] - 26s 510us/step - loss: 0.4101 - acc: 0.8851 - val_loss: 0.5725 - val_acc: 0.8406\n",
            "Epoch 46/50\n",
            "50000/50000 [==============================] - 26s 513us/step - loss: 0.4038 - acc: 0.8888 - val_loss: 0.5856 - val_acc: 0.8409\n",
            "Epoch 47/50\n",
            "50000/50000 [==============================] - 26s 511us/step - loss: 0.4028 - acc: 0.8896 - val_loss: 0.5637 - val_acc: 0.8452\n",
            "Epoch 48/50\n",
            "50000/50000 [==============================] - 26s 510us/step - loss: 0.3965 - acc: 0.8900 - val_loss: 0.5774 - val_acc: 0.8388\n",
            "Epoch 49/50\n",
            "50000/50000 [==============================] - 26s 512us/step - loss: 0.3952 - acc: 0.8903 - val_loss: 0.6032 - val_acc: 0.8352\n",
            "Epoch 50/50\n",
            "50000/50000 [==============================] - 26s 511us/step - loss: 0.3853 - acc: 0.8957 - val_loss: 0.5861 - val_acc: 0.8418\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_AG098M-awVU",
        "colab_type": "code",
        "colab": {},
        "outputId": "223967dc-c6be-4825-feb9-306c8e586a29"
      },
      "source": [
        "# list all data in history\n",
        "print(history.history.keys())\n",
        "# summarize history for accuracy\n",
        "plt.plot(history.history['acc'])\n",
        "plt.plot(history.history['val_acc'])\n",
        "plt.title('model accuracy')\n",
        "plt.ylabel('accuracy')\n",
        "plt.xlabel('epoch')\n",
        "plt.legend(['train', 'test'], loc='upper left')\n",
        "plt.show()\n",
        "# summarize history for loss\n",
        "plt.plot(history.history['loss'])\n",
        "plt.plot(history.history['val_loss'])\n",
        "plt.title('model loss')\n",
        "plt.ylabel('loss')\n",
        "plt.xlabel('epoch')\n",
        "plt.legend(['train', 'test'], loc='upper left')\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "dict_keys(['val_loss', 'val_acc', 'loss', 'acc'])\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wujAx8O8awVZ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}