Cifar10ClassAnalysis.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Cifar10ClassAnalysis.ipynb",
      "version": "0.3.2",
      "provenance": [],
      "collapsed_sections": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/preritt/CS231n-/blob/master/Cifar10ClassAnalysis.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "metadata": {
        "id": "P_Ws1V_S6K_H",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "ccff5b89-90a9-4c08-c46f-a6293adeb03a"
      },
      "cell_type": "code",
      "source": [
        "import keras\n",
        "from matplotlib import pyplot as plt\n",
        "import numpy as np\n",
        "import gzip\n",
        "%matplotlib inline\n",
        "from keras.models import Model\n",
        "from keras.optimizers import RMSprop\n",
        "from keras.layers import Input,Dense,Flatten,Dropout,merge,Reshape,Conv2D,MaxPooling2D,UpSampling2D,Conv2DTranspose\n",
        "from keras.layers.normalization import BatchNormalization\n",
        "from keras.models import Model,Sequential\n",
        "from keras.callbacks import ModelCheckpoint\n",
        "from keras.optimizers import Adadelta, RMSprop,SGD,Adam\n",
        "from keras import regularizers\n",
        "from keras import backend as K\n",
        "from keras.utils import to_categorical\n",
        "from keras.datasets import cifar10"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Using TensorFlow backend.\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "metadata": {
        "id": "Iq3MOKQy6K_N",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "import keras\n",
        "from keras.models import Sequential\n",
        "from keras.utils import np_utils\n",
        "from keras.preprocessing.image import ImageDataGenerator\n",
        "from keras.layers import Dense, Activation, Flatten, Dropout, BatchNormalization\n",
        "from keras.layers import Conv2D, MaxPooling2D\n",
        "from keras.datasets import cifar10\n",
        "from keras import regularizers\n",
        "from keras.callbacks import LearningRateScheduler\n",
        "import numpy as np\n",
        "from sklearn.metrics import confusion_matrix"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "uB8F9zG96K_P",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 371
        },
        "outputId": "96a24e00-e82f-4e58-91a1-463ed99c35ff"
      },
      "cell_type": "code",
      "source": [
        "from matplotlib import pyplot\n",
        "from scipy.misc import toimage\n",
        "from keras.datasets import cifar10\n",
        "def show_imgs(X):\n",
        "    pyplot.figure(1)\n",
        "    k = 0\n",
        "    for i in range(0,4):\n",
        "        for j in range(0,4):\n",
        "            pyplot.subplot2grid((4,4),(i,j))\n",
        "            pyplot.imshow(toimage(X[k]))\n",
        "            k = k+1\n",
        "    # show the plot\n",
        "    pyplot.show()\n",
        " \n",
        "(x_train, y_train), (x_test, y_test) = cifar10.load_data()\n",
        "show_imgs(x_test[:16])\n"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Downloading data from https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz\n",
            "170500096/170498071 [==============================] - 19s 0us/step\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:10: DeprecationWarning: `toimage` is deprecated!\n",
            "`toimage` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n",
            "Use Pillow's ``Image.fromarray`` directly instead.\n",
            "  # Remove the CWD from sys.path while we load stuff.\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
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MYkmyuJpUjkgyw7791kJrHz/Kw/dZtqWXT53ni19+ceeFscNQklDSykW7EZ7U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\n/wHgOTlYMURaiwAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 16 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "_AR3CAGN6K_T",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "from keras.preprocessing.image import ImageDataGenerator\n",
        "datagen = ImageDataGenerator( rotation_range=90,\n",
        "                 width_shift_range=0.1, height_shift_range=0.1,\n",
        "                 horizontal_flip=True)\n",
        "datagen.fit(x_train)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "aFldQZl16K_V",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# generate images using flow\n",
        "from keras.preprocessing.image import ImageDataGenerator\n",
        "datagen = ImageDataGenerator( rotation_range=90,\n",
        "                 width_shift_range=0.1, height_shift_range=0.1,\n",
        "                 horizontal_flip=True)\n",
        "datagen.fit(x_train)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "dNz4QTZr6K_X",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "train_X,valid_X,train_ground,valid_ground = train_test_split(x_train,\n",
        "                                                             y_train,\n",
        "                                                             test_size=0.2,\n",
        "                                                             random_state=13)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "QmlGnst__sdP",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "10d63d19-2477-44b8-e64e-97949fcce2f7"
      },
      "cell_type": "code",
      "source": [
        "y_train.shape"
      ],
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(50000, 10)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 46
        }
      ]
    },
    {
      "metadata": {
        "id": "Mndo7ypu_GDP",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "y_test_ground = y_test"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "Txw0jSJ97BVl",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# train_ground = to_categorical(train_ground)\n",
        "# valid_ground = to_categorical(valid_ground)\n",
        "# y_test_ground = to_categorical(y_test)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "Vw7sJrQt6K_Z",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1411
        },
        "outputId": "9e835537-cd33-4458-b346-1ea6ae5da116"
      },
      "cell_type": "code",
      "source": [
        "def lr_schedule(epoch):\n",
        "    lrate = 0.001\n",
        "    if epoch > 75:\n",
        "        lrate = 0.0005\n",
        "    if epoch > 100:\n",
        "        lrate = 0.0003\n",
        "    return lrate\n",
        "ep = 10 \n",
        "(x_train, y_train), (x_test, y_test) = cifar10.load_data()\n",
        "x_train = x_train.astype('float32')\n",
        "x_test = x_test.astype('float32')\n",
        " \n",
        "#z-score\n",
        "mean = np.mean(x_train,axis=(0,1,2,3))\n",
        "std = np.std(x_train,axis=(0,1,2,3))\n",
        "x_train = (x_train-mean)/(std+1e-7)\n",
        "x_test = (x_test-mean)/(std+1e-7)\n",
        " \n",
        "num_classes = 10\n",
        "y_train = np_utils.to_categorical(y_train,num_classes)\n",
        "y_test = np_utils.to_categorical(y_test,num_classes)\n",
        " \n",
        "weight_decay = 1e-4\n",
        "model = Sequential()\n",
        "model.add(Conv2D(32, (3,3), padding='same', kernel_regularizer=regularizers.l2(weight_decay), input_shape=x_train.shape[1:]))\n",
        "model.add(Activation('elu'))\n",
        "model.add(BatchNormalization())\n",
        "model.add(Conv2D(32, (3,3), padding='same', kernel_regularizer=regularizers.l2(weight_decay)))\n",
        "model.add(Activation('elu'))\n",
        "model.add(BatchNormalization())\n",
        "model.add(MaxPooling2D(pool_size=(2,2)))\n",
        "model.add(Dropout(0.2))\n",
        " \n",
        "model.add(Conv2D(64, (3,3), padding='same', kernel_regularizer=regularizers.l2(weight_decay)))\n",
        "model.add(Activation('elu'))\n",
        "model.add(BatchNormalization())\n",
        "model.add(Conv2D(64, (3,3), padding='same', kernel_regularizer=regularizers.l2(weight_decay)))\n",
        "model.add(Activation('elu'))\n",
        "model.add(BatchNormalization())\n",
        "model.add(MaxPooling2D(pool_size=(2,2)))\n",
        "model.add(Dropout(0.3))\n",
        " \n",
        "model.add(Conv2D(128, (3,3), padding='same', kernel_regularizer=regularizers.l2(weight_decay)))\n",
        "model.add(Activation('elu'))\n",
        "model.add(BatchNormalization())\n",
        "model.add(Conv2D(128, (3,3), padding='same', kernel_regularizer=regularizers.l2(weight_decay)))\n",
        "model.add(Activation('elu'))\n",
        "model.add(BatchNormalization())\n",
        "model.add(MaxPooling2D(pool_size=(2,2)))\n",
        "model.add(Dropout(0.4))\n",
        " \n",
        "model.add(Flatten())\n",
        "model.add(Dense(num_classes, activation='softmax'))\n",
        " \n",
        "model.summary()\n",
        " \n",
        "#data augmentation\n",
        "datagen = ImageDataGenerator(\n",
        "    rotation_range=15,\n",
        "    width_shift_range=0.1,\n",
        "    height_shift_range=0.1,\n",
        "    horizontal_flip=True,\n",
        "    )\n",
        "datagen.fit(x_train)\n",
        " \n",
        "#training\n",
        "batch_size = 64\n",
        " \n",
        "opt_rms = keras.optimizers.rmsprop(lr=0.001,decay=1e-6)\n",
        "model.compile(loss='categorical_crossentropy', optimizer=opt_rms, metrics=['accuracy'])\n",
        "model.fit_generator(datagen.flow(train_X, train_ground, batch_size=batch_size),\\\n",
        "                    steps_per_epoch=x_train.shape[0] // batch_size,epochs=ep,\\\n",
        "                    verbose=1,validation_data=(valid_X,valid_ground),callbacks=[LearningRateScheduler(lr_schedule)])\n",
        "#save to disk\n",
        "model_json = model.to_json()\n",
        "with open('model.json', 'w') as json_file:\n",
        "    json_file.write(model_json)\n",
        "model.save_weights('model.h5') \n",
        " \n",
        "#testing\n",
        "scores = model.evaluate(x_test, y_test, batch_size=128, verbose=1)\n",
        "print('\\nTest result: %.3f loss: %.3f' % (scores[1]*100,scores[0]))"
      ],
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "conv2d_25 (Conv2D)           (None, 32, 32, 32)        896       \n",
            "_________________________________________________________________\n",
            "activation_25 (Activation)   (None, 32, 32, 32)        0         \n",
            "_________________________________________________________________\n",
            "batch_normalization_25 (Batc (None, 32, 32, 32)        128       \n",
            "_________________________________________________________________\n",
            "conv2d_26 (Conv2D)           (None, 32, 32, 32)        9248      \n",
            "_________________________________________________________________\n",
            "activation_26 (Activation)   (None, 32, 32, 32)        0         \n",
            "_________________________________________________________________\n",
            "batch_normalization_26 (Batc (None, 32, 32, 32)        128       \n",
            "_________________________________________________________________\n",
            "max_pooling2d_13 (MaxPooling (None, 16, 16, 32)        0         \n",
            "_________________________________________________________________\n",
            "dropout_13 (Dropout)         (None, 16, 16, 32)        0         \n",
            "_________________________________________________________________\n",
            "conv2d_27 (Conv2D)           (None, 16, 16, 64)        18496     \n",
            "_________________________________________________________________\n",
            "activation_27 (Activation)   (None, 16, 16, 64)        0         \n",
            "_________________________________________________________________\n",
            "batch_normalization_27 (Batc (None, 16, 16, 64)        256       \n",
            "_________________________________________________________________\n",
            "conv2d_28 (Conv2D)           (None, 16, 16, 64)        36928     \n",
            "_________________________________________________________________\n",
            "activation_28 (Activation)   (None, 16, 16, 64)        0         \n",
            "_________________________________________________________________\n",
            "batch_normalization_28 (Batc (None, 16, 16, 64)        256       \n",
            "_________________________________________________________________\n",
            "max_pooling2d_14 (MaxPooling (None, 8, 8, 64)          0         \n",
            "_________________________________________________________________\n",
            "dropout_14 (Dropout)         (None, 8, 8, 64)          0         \n",
            "_________________________________________________________________\n",
            "conv2d_29 (Conv2D)           (None, 8, 8, 128)         73856     \n",
            "_________________________________________________________________\n",
            "activation_29 (Activation)   (None, 8, 8, 128)         0         \n",
            "_________________________________________________________________\n",
            "batch_normalization_29 (Batc (None, 8, 8, 128)         512       \n",
            "_________________________________________________________________\n",
            "conv2d_30 (Conv2D)           (None, 8, 8, 128)         147584    \n",
            "_________________________________________________________________\n",
            "activation_30 (Activation)   (None, 8, 8, 128)         0         \n",
            "_________________________________________________________________\n",
            "batch_normalization_30 (Batc (None, 8, 8, 128)         512       \n",
            "_________________________________________________________________\n",
            "max_pooling2d_15 (MaxPooling (None, 4, 4, 128)         0         \n",
            "_________________________________________________________________\n",
            "dropout_15 (Dropout)         (None, 4, 4, 128)         0         \n",
            "_________________________________________________________________\n",
            "flatten_5 (Flatten)          (None, 2048)              0         \n",
            "_________________________________________________________________\n",
            "dense_5 (Dense)              (None, 10)                20490     \n",
            "=================================================================\n",
            "Total params: 309,290\n",
            "Trainable params: 308,394\n",
            "Non-trainable params: 896\n",
            "_________________________________________________________________\n",
            "Epoch 1/10\n",
            "781/781 [==============================] - 47s 61ms/step - loss: 1.9023 - acc: 0.4254 - val_loss: 1.5098 - val_acc: 0.5501\n",
            "Epoch 2/10\n",
            "781/781 [==============================] - 44s 56ms/step - loss: 1.2937 - acc: 0.5909 - val_loss: 1.2917 - val_acc: 0.6297\n",
            "Epoch 3/10\n",
            "781/781 [==============================] - 42s 54ms/step - loss: 1.0950 - acc: 0.6535 - val_loss: 0.9715 - val_acc: 0.7081\n",
            "Epoch 4/10\n",
            "781/781 [==============================] - 41s 52ms/step - loss: 0.9900 - acc: 0.6898 - val_loss: 0.9627 - val_acc: 0.7115\n",
            "Epoch 5/10\n",
            "781/781 [==============================] - 40s 51ms/step - loss: 0.9111 - acc: 0.7201 - val_loss: 0.8385 - val_acc: 0.7477\n",
            "Epoch 6/10\n",
            "781/781 [==============================] - 39s 50ms/step - loss: 0.8743 - acc: 0.7322 - val_loss: 0.8364 - val_acc: 0.7471\n",
            "Epoch 7/10\n",
            "781/781 [==============================] - 39s 50ms/step - loss: 0.8424 - acc: 0.7469 - val_loss: 0.7751 - val_acc: 0.7732\n",
            "Epoch 8/10\n",
            "781/781 [==============================] - 43s 55ms/step - loss: 0.8088 - acc: 0.7609 - val_loss: 0.7126 - val_acc: 0.7975\n",
            "Epoch 9/10\n",
            "781/781 [==============================] - 42s 54ms/step - loss: 0.7839 - acc: 0.7706 - val_loss: 0.7792 - val_acc: 0.7834\n",
            "Epoch 10/10\n",
            "781/781 [==============================] - 41s 52ms/step - loss: 0.7640 - acc: 0.7776 - val_loss: 0.7833 - val_acc: 0.7817\n",
            "10000/10000 [==============================] - 1s 118us/step\n",
            "\n",
            "Test result: 77.280 loss: 0.801\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "0vuUWEBs9uCd",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 714
        },
        "outputId": "ef974bc9-43a5-43ff-bee4-79a7c3eefec6"
      },
      "cell_type": "code",
      "source": [
        "model.history.history"
      ],
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'acc': [0.4253961267605634,\n",
              "  0.5909090909090909,\n",
              "  0.6535091229193342,\n",
              "  0.6898407490396927,\n",
              "  0.7200504161331626,\n",
              "  0.7322343149807938,\n",
              "  0.7469390204865557,\n",
              "  0.7608834827144686,\n",
              "  0.7705865877080665,\n",
              "  0.7776088348271447],\n",
              " 'loss': [1.9023341457913994,\n",
              "  1.293692758767156,\n",
              "  1.0950409921885453,\n",
              "  0.9899658419563889,\n",
              "  0.9111330136752159,\n",
              "  0.874347757819978,\n",
              "  0.8423592844326884,\n",
              "  0.8087905877759911,\n",
              "  0.7838797084271679,\n",
              "  0.7640381100922632],\n",
              " 'lr': [0.001, 0.001, 0.001, 0.001, 0.001, 0.001, 0.001, 0.001, 0.001, 0.001],\n",
              " 'val_acc': [0.5501,\n",
              "  0.6297,\n",
              "  0.7081,\n",
              "  0.7115,\n",
              "  0.7477,\n",
              "  0.7471,\n",
              "  0.7732,\n",
              "  0.7975,\n",
              "  0.7834,\n",
              "  0.7817],\n",
              " 'val_loss': [1.5098114976882935,\n",
              "  1.2916825669288636,\n",
              "  0.971472981262207,\n",
              "  0.9627156707763672,\n",
              "  0.8384797581672668,\n",
              "  0.8363616612911224,\n",
              "  0.7751194616317749,\n",
              "  0.7125618876457215,\n",
              "  0.7791899686813355,\n",
              "  0.7832756629943848]}"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 35
        }
      ]
    },
    {
      "metadata": {
        "id": "6co9NUbg6K_i",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 280
        },
        "outputId": "da2a3f29-2d2b-4fd9-b1cc-6a3fd286af30"
      },
      "cell_type": "code",
      "source": [
        "loss = model.history.history['loss']\n",
        "val_loss = model.history.history['val_loss']\n",
        "epochs = range(10)\n",
        "plt.figure()\n",
        "plt.plot(epochs, loss, 'bo', label='Training loss')\n",
        "plt.plot(epochs, val_loss, 'b', label='Validation loss')\n",
        "plt.title('Training and validation loss')\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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Tp/+B0aNHsnbtu2zfHnkQqs2bN9O5cw4Axx9/IgCZmVmsX/9/DB06lLvumlzlugDWrueE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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "4opY8pp7-PF-",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 543
        },
        "outputId": "f972d740-5776-4327-a0ed-8ef9f4c00f76"
      },
      "cell_type": "code",
      "source": [
        "accuracy = model.history.history['acc']\n",
        "val_accuracy = model.history.history['val_acc']\n",
        "loss = model.history.history['loss']\n",
        "val_loss = model.history.history['val_loss']\n",
        "epochs = range(len(accuracy))\n",
        "plt.plot(epochs, accuracy, 'bo', label='Training accuracy')\n",
        "plt.plot(epochs, val_accuracy, 'b', label='Validation accuracy')\n",
        "plt.title('Training and validation accuracy')\n",
        "plt.legend()\n",
        "plt.figure()\n",
        "plt.plot(epochs, loss, 'bo', label='Training loss')\n",
        "plt.plot(epochs, val_loss, 'b', label='Validation loss')\n",
        "plt.title('Training and validation loss')\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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kDPCdqs4UkWNxWvEW2E2d2boVHn44lWnTkgmFfJxxRpB77y2kV69m5OZW/i3k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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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0LS+7U1H79s11pyKRGKrxk2et9QN+Y0yk2e2AggrPvweOqG57LVum4/PV/fLz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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "KEHDQn-N_nOO",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "7c1fd84b-081b-4e5a-a5c3-8ce17dd0d74e"
      },
      "cell_type": "code",
      "source": [
        "y_test_ground.shape"
      ],
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(10000, 10, 2)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 38
        }
      ]
    },
    {
      "metadata": {
        "id": "uq7TQvdQ-7A4",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "test_eval = model.evaluate(x_test, y_test, verbose=0)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "hmeoMKnE-7Ds",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        },
        "outputId": "579d6f8b-1edb-4d2d-e057-a9fc58335886"
      },
      "cell_type": "code",
      "source": [
        "print('Test loss:', test_eval[0])\n",
        "print('Test accuracy:', test_eval[1])"
      ],
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Test loss: 0.8012291809082032\n",
            "Test accuracy: 0.7728\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "OnhzuCNJ-7Gd",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "predicted_classes = model.predict(x_test)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "TN7iz5CVCZ4r",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "predicted_classes = np.argmax(np.round(predicted_classes),axis=1)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "WULn858zDJA2",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "orig_classes = np.argmax(np.round(y_test),axis=1)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "kMGsIgImEJ8X",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "\n",
        "\n",
        "def plot_confusion_matrix(cm,\n",
        "                          target_names,\n",
        "                          title='Confusion matrix',\n",
        "                          cmap=None,\n",
        "                          normalize=True):\n",
        "    \"\"\"\n",
        "    given a sklearn confusion matrix (cm), make a nice plot\n",
        "\n",
        "    Arguments\n",
        "    ---------\n",
        "    cm:           confusion matrix from sklearn.metrics.confusion_matrix\n",
        "\n",
        "    target_names: given classification classes such as [0, 1, 2]\n",
        "                  the class names, for example: ['high', 'medium', 'low']\n",
        "\n",
        "    title:        the text to display at the top of the matrix\n",
        "\n",
        "    cmap:         the gradient of the values displayed from matplotlib.pyplot.cm\n",
        "                  see http://matplotlib.org/examples/color/colormaps_reference.html\n",
        "                  plt.get_cmap('jet') or plt.cm.Blues\n",
        "\n",
        "    normalize:    If False, plot the raw numbers\n",
        "                  If True, plot the proportions\n",
        "\n",
        "    Usage\n",
        "    -----\n",
        "    plot_confusion_matrix(cm           = cm,                  # confusion matrix created by\n",
        "                                                              # sklearn.metrics.confusion_matrix\n",
        "                          normalize    = True,                # show proportions\n",
        "                          target_names = y_labels_vals,       # list of names of the classes\n",
        "                          title        = best_estimator_name) # title of graph\n",
        "\n",
        "    Citiation\n",
        "    ---------\n",
        "    http://scikit-learn.org/stable/auto_examples/model_selection/plot_confusion_matrix.html\n",
        "\n",
        "    \"\"\"\n",
        "    import matplotlib.pyplot as plt\n",
        "    import numpy as np\n",
        "    import itertools\n",
        "\n",
        "    accuracy = np.trace(cm) / float(np.sum(cm))\n",
        "    misclass = 1 - accuracy\n",
        "\n",
        "    if cmap is None:\n",
        "        cmap = plt.get_cmap('Blues')\n",
        "\n",
        "    plt.figure(figsize=(8, 6))\n",
        "    plt.imshow(cm, interpolation='nearest', cmap=cmap)\n",
        "    plt.title(title)\n",
        "    plt.colorbar()\n",
        "\n",
        "    if target_names is not None:\n",
        "        tick_marks = np.arange(len(target_names))\n",
        "        plt.xticks(tick_marks, target_names, rotation=45)\n",
        "        plt.yticks(tick_marks, target_names)\n",
        "\n",
        "    if normalize:\n",
        "        cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\n",
        "\n",
        "\n",
        "    thresh = cm.max() / 1.5 if normalize else cm.max() / 2\n",
        "    for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\n",
        "        if normalize:\n",
        "            plt.text(j, i, \"{:0.4f}\".format(cm[i, j]),\n",
        "                     horizontalalignment=\"center\",\n",
        "                     color=\"white\" if cm[i, j] > thresh else \"black\")\n",
        "        else:\n",
        "            plt.text(j, i, \"{:,}\".format(cm[i, j]),\n",
        "                     horizontalalignment=\"center\",\n",
        "                     color=\"white\" if cm[i, j] > thresh else \"black\")\n",
        "\n",
        "\n",
        "    plt.tight_layout()\n",
        "    plt.ylabel('True label')\n",
        "    plt.xlabel('Predicted label\\naccuracy={:0.4f}; misclass={:0.4f}'.format(accuracy, misclass))\n",
        "    plt.show()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "RLqaNAoxCZ2K",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 467
        },
        "outputId": "5a6313f5-448b-4f71-9ef5-cd2e79887f99"
      },
      "cell_type": "code",
      "source": [
        "class_names = ['airplane','automobile','bird','cat','deer',\n",
        "               'dog','frog','horse','ship','truck']\n",
        "plot_confusion_matrix(cm           = confusion_matrix(predicted_classes, orig_classes), \n",
        "                      normalize    = False,\n",
        "                      target_names = class_names,\n",
        "                      title        = \"Confusion Matrix\")"
      ],
      "execution_count": 52,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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W1m3ewR+/H+LNnq9RotiTjBw9zi5xPJIc0rPyqHJ8i1xr3Tk7STynCar4BBv2nQLgj78v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\n",
            "text/plain": [
              "<Figure size 576x432 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "ncYA_S34CZzZ",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "Trq0JfNq6K_k",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 187
        },
        "outputId": "367aa5f0-a187-45bd-a70c-89474335afef"
      },
      "cell_type": "code",
      "source": [
        "confusion_matrix(predicted_classes, orig_classes)"
      ],
      "execution_count": 49,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[875,  28, 279, 209, 132, 258,  31, 115,  74,  34],\n",
              "       [  8, 867,   1,   8,   2,   2,   2,   1,  10,  20],\n",
              "       [  6,   0, 481,   6,  17,   6,   9,   1,   0,   1],\n",
              "       [  6,   0,  18, 534,  16, 126,   7,  11,   1,   2],\n",
              "       [  5,   0,  31,  26, 665,  17,   3,  21,   0,   0],\n",
              "       [  2,   0,  14,  28,   1, 463,   0,   2,   0,   0],\n",
              "       [  7,   4, 139, 141, 137,  83, 943,  28,   7,   2],\n",
              "       [  4,   0,  13,  12,  24,  27,   0, 799,   2,   0],\n",
              "       [ 49,   1,   6,  12,   3,   4,   3,   1, 883,   8],\n",
              "       [ 38, 100,  18,  24,   3,  14,   2,  21,  23, 933]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 49
        }
      ]
    },
    {
      "metadata": {
        "id": "urUVnfMq6K_m",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "f9khULnV6K_o",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "hGPtJdFD6K_r",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "3S8hPJ4v6K_s",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "## Old setup"
      ]
    },
    {
      "metadata": {
        "id": "42cOfz9p6K_t",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# Define function to load the data and conver to array format (#images,dim1,dim2)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "cinvZmO_6K_v",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "def extract_data(filename, num_images):\n",
        "    with gzip.open(filename) as bytestream:\n",
        "        bytestream.read(16)\n",
        "        buf = bytestream.read(28 * 28 * num_images)\n",
        "        data = np.frombuffer(buf, dtype=np.uint8).astype(np.float32)\n",
        "        data = data.reshape(num_images, 28,28)\n",
        "        return data"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "Yr9D9nEs6K_x",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# Extract images"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "CxbmS9Iy6K_0",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "train_data = extract_data('train-images-idx3-ubyte.gz', 60000)\n",
        "test_data = extract_data('t10k-images-idx3-ubyte.gz', 10000)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "8xfOpWrC6K_5",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# Extract labels"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "M4YX0-pf6LAB",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "def extract_labels(filename, num_images):\n",
        "    with gzip.open(filename) as bytestream:\n",
        "        bytestream.read(8)\n",
        "        buf = bytestream.read(1 * num_images)\n",
        "        labels = np.frombuffer(buf, dtype=np.uint8).astype(np.int64)\n",
        "        return labels"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "P3-9QNwK6LAM",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "train_labels = extract_labels('train-labels-idx1-ubyte.gz',60000)\n",
        "test_labels = extract_labels('t10k-labels-idx1-ubyte.gz',10000)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "E6-O-BDS6LAO",
        "colab_type": "code",
        "colab": {},
        "outputId": "b44e6fb2-2aef-47e4-b41c-9289a874cd2c"
      },
      "cell_type": "code",
      "source": [
        "# Shapes of training set\n",
        "print(\"Training set (images) shape: {shape}\".format(shape=train_data.shape))\n",
        "\n",
        "# Shapes of test set\n",
        "print(\"Test set (images) shape: {shape}\".format(shape=test_data.shape))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Training set (images) shape: (60000, 28, 28)\n",
            "Test set (images) shape: (10000, 28, 28)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "bFf6HlYd6LAQ",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# Create dictionary of target classes\n",
        "label_dict = {\n",
        " 0: 'A',\n",
        " 1: 'B',\n",
        " 2: 'C',\n",
        " 3: 'D',\n",
        " 4: 'E',\n",
        " 5: 'F',\n",
        " 6: 'G',\n",
        " 7: 'H',\n",
        " 8: 'I',\n",
        " 9: 'J',\n",
        "}"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "nSlC0AYK6LAS",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# Explore some images"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "7AomfuY16LAW",
        "colab_type": "code",
        "colab": {},
        "outputId": "ec9b9572-cbf9-431b-efdb-1b8f9dbfd2be"
      },
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=[5,5])\n",
        "\n",
        "# Display the first image in training data\n",
        "plt.subplot(121)\n",
        "curr_img = np.reshape(train_data[10], (28,28))\n",
        "curr_lbl = train_labels[10]\n",
        "plt.imshow(curr_img, cmap='gray')\n",
        "plt.title(\"(Label: \" + str(label_dict[curr_lbl]) + \")\")\n",
        "\n",
        "# Display the first image in testing data\n",
        "plt.subplot(122)\n",
        "curr_img = np.reshape(test_data[10], (28,28))\n",
        "curr_lbl = test_labels[10]\n",
        "plt.imshow(curr_img, cmap='gray')\n",
        "plt.title(\"(Label: \" + str(label_dict[curr_lbl]) + \")\")"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.text.Text at 0x27b969a9fd0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 17
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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N1zJrjFqPs46v9RKdQJwWS/vTWFWZY2TUqFGerav5AmGlYiupd+3atZ6tu9QBYfK7rrAN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\nRK5CYII=\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x27b9682ccf8>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "IrZtJAbM6LAa",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# convert all images to range 0 to 1 from 0 to 255\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "TVRLa-wj6LAc",
        "colab_type": "code",
        "colab": {},
        "outputId": "a17d8c40-8869-4409-f431-93cf8d5273cc"
      },
      "cell_type": "code",
      "source": [
        "train_data = train_data.reshape(-1, 28,28, 1)\n",
        "test_data = test_data.reshape(-1, 28,28, 1)\n",
        "train_data.shape, test_data.shape"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((60000, 28, 28, 1), (10000, 28, 28, 1))"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 19
        }
      ]
    },
    {
      "metadata": {
        "id": "y07S5G3C6LAf",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# make sure data is in float32 format"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "WgVi5Vvl6LAh",
        "colab_type": "code",
        "colab": {},
        "outputId": "3018f6cf-017a-4b14-a9b5-56fc47a5c971"
      },
      "cell_type": "code",
      "source": [
        "train_data.dtype, test_data.dtype"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(dtype('float32'), dtype('float32'))"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 21
        }
      ]
    },
    {
      "metadata": {
        "id": "OKDY138t6LAr",
        "colab_type": "code",
        "colab": {},
        "outputId": "3b9d8d8f-8607-4e95-e5cf-ba30a5c377b7"
      },
      "cell_type": "code",
      "source": [
        "# rescale data\n",
        "np.max(train_data), np.max(test_data)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(255.0, 255.0)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 22
        }
      ]
    },
    {
      "metadata": {
        "id": "kNpZv1_f6LAt",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "train_data = train_data / np.max(train_data)\n",
        "test_data = test_data / np.max(test_data)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "sp3i7GYT6LAv",
        "colab_type": "code",
        "colab": {},
        "outputId": "c64e2278-2f62-4b15-ab33-964e803c2a10"
      },
      "cell_type": "code",
      "source": [
        "np.max(train_data), np.max(test_data)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1.0, 1.0)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 24
        }
      ]
    },
    {
      "metadata": {
        "id": "XG4dOLSm6LAz",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# split the data into test and validation. Here the input and outpur are the same as we are using autoencoder\n",
        "from sklearn.model_selection import train_test_split\n",
        "train_X,valid_X,train_ground,valid_ground = train_test_split(train_data,\n",
        "                                                             train_data,\n",
        "                                                             test_size=0.2,\n",
        "                                                             random_state=13)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "_4boxuB56LA3",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# model parameters\n",
        "batch_size = 16\n",
        "epochs = 1\n",
        "inChannel = 1\n",
        "x, y = 28, 28\n",
        "input_img = Input(shape = (x, y, inChannel))\n",
        "num_classes = 10"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "UBNQ5k9P6LA6",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# define encoder and decoder\n",
        "def encoder(input_img):\n",
        "    #encoder\n",
        "    #input = 28 x 28 x 1 (wide and thin)\n",
        "    conv1 = Conv2D(32, (3, 3), activation='relu', padding='same')(input_img) #28 x 28 x 32\n",
        "    conv1 = BatchNormalization()(conv1)\n",
        "    conv1 = Conv2D(32, (3, 3), activation='relu', padding='same')(conv1)\n",
        "    conv1 = BatchNormalization()(conv1)\n",
        "    pool1 = MaxPooling2D(pool_size=(2, 2))(conv1) #14 x 14 x 32\n",
        "    conv2 = Conv2D(64, (3, 3), activation='relu', padding='same')(pool1) #14 x 14 x 64\n",
        "    conv2 = BatchNormalization()(conv2)\n",
        "    conv2 = Conv2D(64, (3, 3), activation='relu', padding='same')(conv2)\n",
        "    conv2 = BatchNormalization()(conv2)\n",
        "    pool2 = MaxPooling2D(pool_size=(2, 2))(conv2) #7 x 7 x 64\n",
        "    conv3 = Conv2D(128, (3, 3), activation='relu', padding='same')(pool2) #7 x 7 x 128 (small and thick)\n",
        "    conv3 = BatchNormalization()(conv3)\n",
        "    conv3 = Conv2D(128, (3, 3), activation='relu', padding='same')(conv3)\n",
        "    conv3 = BatchNormalization()(conv3)\n",
        "    conv4 = Conv2D(256, (3, 3), activation='relu', padding='same')(conv3) #7 x 7 x 256 (small and thick)\n",
        "    conv4 = BatchNormalization()(conv4)\n",
        "    conv4 = Conv2D(256, (3, 3), activation='relu', padding='same')(conv4)\n",
        "    conv4 = BatchNormalization()(conv4)\n",
        "    return conv4\n",
        "\n",
        "def decoder(conv4):    \n",
        "    #decoder\n",
        "    conv5 = Conv2D(128, (3, 3), activation='relu', padding='same')(conv4) #7 x 7 x 128\n",
        "    conv5 = BatchNormalization()(conv5)\n",
        "    conv5 = Conv2D(128, (3, 3), activation='relu', padding='same')(conv5)\n",
        "    conv5 = BatchNormalization()(conv5)\n",
        "    conv6 = Conv2D(64, (3, 3), activation='relu', padding='same')(conv5) #7 x 7 x 64\n",
        "    conv6 = BatchNormalization()(conv6)\n",
        "    conv6 = Conv2D(64, (3, 3), activation='relu', padding='same')(conv6)\n",
        "    conv6 = BatchNormalization()(conv6)\n",
        "    up1 = UpSampling2D((2,2))(conv6) #14 x 14 x 64\n",
        "    conv7 = Conv2D(32, (3, 3), activation='relu', padding='same')(up1) # 14 x 14 x 32\n",
        "    conv7 = BatchNormalization()(conv7)\n",
        "    conv7 = Conv2D(32, (3, 3), activation='relu', padding='same')(conv7)\n",
        "    conv7 = BatchNormalization()(conv7)\n",
        "    up2 = UpSampling2D((2,2))(conv7) # 28 x 28 x 32\n",
        "    decoded = Conv2D(1, (3, 3), activation='sigmoid', padding='same')(up2) # 28 x 28 x 1\n",
        "    return decoded"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "nIKCQBHu6LA8",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# compile the model\n",
        "autoencoder = Model(input_img, decoder(encoder(input_img)))\n",
        "autoencoder.compile(loss='mean_squared_error', optimizer = RMSprop())"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "scrolled": true,
        "id": "YVowY5F16LBB",
        "colab_type": "code",
        "colab": {},
        "outputId": "c5e9f6a4-18ca-4c98-cf58-1e3c8c7f051a"
      },
      "cell_type": "code",
      "source": [
        "autoencoder.summary()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "input_1 (InputLayer)         (None, 28, 28, 1)         0         \n",
            "_________________________________________________________________\n",
            "conv2d_1 (Conv2D)            (None, 28, 28, 32)        320       \n",
            "_________________________________________________________________\n",
            "batch_normalization_1 (Batch (None, 28, 28, 32)        128       \n",
            "_________________________________________________________________\n",
            "conv2d_2 (Conv2D)            (None, 28, 28, 32)        9248      \n",
            "_________________________________________________________________\n",
            "batch_normalization_2 (Batch (None, 28, 28, 32)        128       \n",
            "_________________________________________________________________\n",
            "max_pooling2d_1 (MaxPooling2 (None, 14, 14, 32)        0         \n",
            "_________________________________________________________________\n",
            "conv2d_3 (Conv2D)            (None, 14, 14, 64)        18496     \n",
            "_________________________________________________________________\n",
            "batch_normalization_3 (Batch (None, 14, 14, 64)        256       \n",
            "_________________________________________________________________\n",
            "conv2d_4 (Conv2D)            (None, 14, 14, 64)        36928     \n",
            "_________________________________________________________________\n",
            "batch_normalization_4 (Batch (None, 14, 14, 64)        256       \n",
            "_________________________________________________________________\n",
            "max_pooling2d_2 (MaxPooling2 (None, 7, 7, 64)          0         \n",
            "_________________________________________________________________\n",
            "conv2d_5 (Conv2D)            (None, 7, 7, 128)         73856     \n",
            "_________________________________________________________________\n",
            "batch_normalization_5 (Batch (None, 7, 7, 128)         512       \n",
            "_________________________________________________________________\n",
            "conv2d_6 (Conv2D)            (None, 7, 7, 128)         147584    \n",
            "_________________________________________________________________\n",
            "batch_normalization_6 (Batch (None, 7, 7, 128)         512       \n",
            "_________________________________________________________________\n",
            "conv2d_7 (Conv2D)            (None, 7, 7, 256)         295168    \n",
            "_________________________________________________________________\n",
            "batch_normalization_7 (Batch (None, 7, 7, 256)         1024      \n",
            "_________________________________________________________________\n",
            "conv2d_8 (Conv2D)            (None, 7, 7, 256)         590080    \n",
            "_________________________________________________________________\n",
            "batch_normalization_8 (Batch (None, 7, 7, 256)         1024      \n",
            "_________________________________________________________________\n",
            "conv2d_9 (Conv2D)            (None, 7, 7, 128)         295040    \n",
            "_________________________________________________________________\n",
            "batch_normalization_9 (Batch (None, 7, 7, 128)         512       \n",
            "_________________________________________________________________\n",
            "conv2d_10 (Conv2D)           (None, 7, 7, 128)         147584    \n",
            "_________________________________________________________________\n",
            "batch_normalization_10 (Batc (None, 7, 7, 128)         512       \n",
            "_________________________________________________________________\n",
            "conv2d_11 (Conv2D)           (None, 7, 7, 64)          73792     \n",
            "_________________________________________________________________\n",
            "batch_normalization_11 (Batc (None, 7, 7, 64)          256       \n",
            "_________________________________________________________________\n",
            "conv2d_12 (Conv2D)           (None, 7, 7, 64)          36928     \n",
            "_________________________________________________________________\n",
            "batch_normalization_12 (Batc (None, 7, 7, 64)          256       \n",
            "_________________________________________________________________\n",
            "up_sampling2d_1 (UpSampling2 (None, 14, 14, 64)        0         \n",
            "_________________________________________________________________\n",
            "conv2d_13 (Conv2D)           (None, 14, 14, 32)        18464     \n",
            "_________________________________________________________________\n",
            "batch_normalization_13 (Batc (None, 14, 14, 32)        128       \n",
            "_________________________________________________________________\n",
            "conv2d_14 (Conv2D)           (None, 14, 14, 32)        9248      \n",
            "_________________________________________________________________\n",
            "batch_normalization_14 (Batc (None, 14, 14, 32)        128       \n",
            "_________________________________________________________________\n",
            "up_sampling2d_2 (UpSampling2 (None, 28, 28, 32)        0         \n",
            "_________________________________________________________________\n",
            "conv2d_15 (Conv2D)           (None, 28, 28, 1)         289       \n",
            "=================================================================\n",
            "Total params: 1,758,657\n",
            "Trainable params: 1,755,841\n",
            "Non-trainable params: 2,816\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "GhBYIlF36LBD",
        "colab_type": "code",
        "colab": {},
        "outputId": "05822bba-f10b-4fcc-dda7-ae2b0cec4763"
      },
      "cell_type": "code",
      "source": [
        "# train the model\n",
        "autoencoder_train = autoencoder.fit(train_X, train_ground, batch_size=batch_size,epochs=epochs,verbose=1,validation_data=(valid_X, valid_ground))\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Train on 48000 samples, validate on 12000 samples\n",
            "Epoch 1/1\n",
            "10128/48000 [=====>........................] - ETA: 38:16 - loss: 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\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "uqyujb9c6LBJ",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# save autoencoder weights\n",
        "autoencoder.save_weights('autoencoder.h5')\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "IZ9Op31J6LBN",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# Change the labels from categorical to one-hot encoding\n",
        "train_Y_one_hot = to_categorical(train_labels)\n",
        "test_Y_one_hot = to_categorical(test_labels)\n",
        "\n",
        "# Display the change for category label using one-hot encoding\n",
        "print('Original label:', train_labels[0])\n",
        "print('After conversion to one-hot:', train_Y_one_hot[0])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "w-357tmQ6LBV",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "train_X,valid_X,train_label,valid_label = train_test_split(train_data,train_Y_one_hot,test_size=0.2,random_state=13)\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "VMbiJlDW6LBj",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "train_X.shape,valid_X.shape,train_label.shape,valid_label.shape\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "1ZrXpTkr6LBn",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# define classification model - encoder remains the same\n",
        "def encoder(input_img):\n",
        "    #encoder\n",
        "    #input = 28 x 28 x 1 (wide and thin)\n",
        "    conv1 = Conv2D(32, (3, 3), activation='relu', padding='same')(input_img) #28 x 28 x 32\n",
        "    conv1 = BatchNormalization()(conv1)\n",
        "    conv1 = Conv2D(32, (3, 3), activation='relu', padding='same')(conv1)\n",
        "    conv1 = BatchNormalization()(conv1)\n",
        "    pool1 = MaxPooling2D(pool_size=(2, 2))(conv1) #14 x 14 x 32\n",
        "    conv2 = Conv2D(64, (3, 3), activation='relu', padding='same')(pool1) #14 x 14 x 64\n",
        "    conv2 = BatchNormalization()(conv2)\n",
        "    conv2 = Conv2D(64, (3, 3), activation='relu', padding='same')(conv2)\n",
        "    conv2 = BatchNormalization()(conv2)\n",
        "    pool2 = MaxPooling2D(pool_size=(2, 2))(conv2) #7 x 7 x 64\n",
        "    conv3 = Conv2D(128, (3, 3), activation='relu', padding='same')(pool2) #7 x 7 x 128 (small and thick)\n",
        "    conv3 = BatchNormalization()(conv3)\n",
        "    conv3 = Conv2D(128, (3, 3), activation='relu', padding='same')(conv3)\n",
        "    conv3 = BatchNormalization()(conv3)\n",
        "    conv4 = Conv2D(256, (3, 3), activation='relu', padding='same')(conv3) #7 x 7 x 256 (small and thick)\n",
        "    conv4 = BatchNormalization()(conv4)\n",
        "    conv4 = Conv2D(256, (3, 3), activation='relu', padding='same')(conv4)\n",
        "    conv4 = BatchNormalization()(conv4)\n",
        "    return conv4"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "jtSGkoxI6LBq",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# fully connected layer on top of encoded values\n",
        "def fc(enco):\n",
        "    flat = Flatten()(enco)\n",
        "    den = Dense(128, activation='relu')(flat)\n",
        "    out = Dense(num_classes, activation='softmax')(den)\n",
        "    return out"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "qaBderY96LBs",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "encode = encoder(input_img)\n",
        "full_model = Model(input_img,fc(encode))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "Pq8gdy9l6LBt",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# "
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}