cifar103ClassAccuracyHigh.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "cifar103ClassAccuracyHigh.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/cifar103ClassAccuracyHigh.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "metadata": {
        "id": "FgwWyMb6iCkO",
        "colab_type": "code",
        "outputId": "4c409972-4258-480b-8e15-0fedae6f8f1e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "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\n",
        "from sklearn import preprocessing"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Using TensorFlow backend.\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "metadata": {
        "id": "PYdWJtFsiF1a",
        "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\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.utils import shuffle"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "9m3ehGh2iIBA",
        "colab_type": "code",
        "outputId": "87ee815d-d995-4bb4-a80e-75ac16272dc3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 342
        }
      },
      "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": [
            "/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": {
            "image/png": 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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": "hVAjMdeSrloa",
        "colab_type": "code",
        "outputId": "fd51bba7-3fa5-4c81-c994-1bcfe96c24f9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "cell_type": "code",
      "source": [
        "x_test.shape"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(10000, 32, 32, 3)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 4
        }
      ]
    },
    {
      "metadata": {
        "id": "RCa5Jm4NiQav",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# # This function takes the entire traaining data and test data and corresponding categorical label and returns the corresponding data in class indices\n",
        "# def getThreeClassesTrainTest(class_indices,train_data,train_labels,test_data,test_labels):\n",
        "#   all_train_data = []\n",
        "#   all_train_data_label = []\n",
        "#   all_test_data = []\n",
        "#   all_test_data_label = []\n",
        "#   for ind in class_indices:\n",
        "#     this_class_location_train = train_labels[train_labels == ind]\n",
        "#     this_class_data_train = train_data[this_class_location_train,:]\n",
        "#     this_class_data_train_label = train_labels[this_class_location_train]\n",
        "    \n",
        "#     this_class_location_test = test_labels[test_labels == ind]\n",
        "#     this_class_data_test = test_data[this_class_location_test,:]\n",
        "#     this_class_data_test_label = test_labels[this_class_data_test]\n",
        "    \n",
        "#     all_train_data.append(this_class_data_train)\n",
        "#     all_test_data.append(this_class_data_test)\n",
        "#     all_train_data_label.append(this_class_data_train_label)\n",
        "#     all_test_data_label.append(this_class_data_test_label)\n",
        "    \n",
        "#   train_data_three = all_train_data[0]\n",
        "#   train_label_three = np.zeros((train_data_three.shape[0],))\n",
        "#   test_data_three = all_test_data[0]\n",
        "#   test_label_three = np.zeros((test_data_three.shape[0],))\n",
        "  \n",
        "#   for j in range(0,len(class_indices)):\n",
        "#     if(j>0):\n",
        "#       train_data_three = np.concatenate((train_data_three,all_train_data[j]))\n",
        "#       this_train_label = np.zeros((all_train_data[j].shape[0])) + j\n",
        "#       train_label_three = np.concatenate((train_label_three,this_train_label))\n",
        "      \n",
        "#       test_data_three = np.concatenate((test_data_three,all_test_data[j]))s\n",
        "#       this_test_label = np.zeros((all_test_data[j].shape[0])) + j\n",
        "#       test_label_three = np.concatenate((test_label_three,this_test_label))\n",
        "#   return train_data_three, train_label_three, test_data_three,test_label_three"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "ngfK-2Qewhtq",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# This function takes the entire traaining data and test data and corresponding categorical label and returns the corresponding data in class indices\n",
        "def getThreeClassesTrainTest(class_indices,train_data,train_labels,test_data,test_labels):\n",
        "  all_train_data = []\n",
        "  all_train_data_label = []\n",
        "  all_test_data = []\n",
        "  all_test_data_label = []\n",
        "  for ind in class_indices:\n",
        "    this_class_location_train =  np.where(train_labels==ind)[0] # train_labels[train_labels == ind]\n",
        "    this_class_data_train = train_data[this_class_location_train,:]\n",
        "    this_class_data_train_label = train_labels[this_class_location_train]\n",
        "    print(this_class_location_train)\n",
        "    \n",
        "    this_class_location_test = np.where(test_labels==ind)[0] #test_labels[test_labels == ind]\n",
        "    this_class_data_test = test_data[this_class_location_test,:]\n",
        "    this_class_data_test_label = test_labels[this_class_location_test]\n",
        "    \n",
        "    all_train_data.append(this_class_data_train)\n",
        "    all_test_data.append(this_class_data_test)\n",
        "    all_train_data_label.append(this_class_data_train_label)\n",
        "    all_test_data_label.append(this_class_data_test_label)\n",
        "    \n",
        "  train_data_three = all_train_data[0]\n",
        "  train_label_three = all_train_data_label[0]\n",
        "  test_data_three = all_test_data[0]\n",
        "  test_label_three = all_test_data_label[0]\n",
        "  \n",
        "  for j in range(1,len(class_indices)):\n",
        "    train_data_three = np.concatenate((train_data_three,all_train_data[j]))\n",
        "    train_label_three = np.concatenate((train_label_three,all_train_data_label[j]))\n",
        "\n",
        "    test_data_three = np.concatenate((test_data_three,all_test_data[j]))\n",
        "    test_label_three = np.concatenate((test_label_three,all_test_data_label[j]))\n",
        "  print(train_label_three.shape)\n",
        "  train_label_three = np.reshape(train_label_three,newshape=(-1,))\n",
        "  test_label_three = np.reshape(test_label_three,newshape=(-1,))\n",
        "  return train_data_three, train_label_three, test_data_three,test_label_three"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "3ZJV35fkrj3O",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "2ekn-CkLikYJ",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "### Get only three classes of data and train the model on it"
      ]
    },
    {
      "metadata": {
        "id": "VPYv1L-uigzL",
        "colab_type": "code",
        "outputId": "10559c80-be45-4ea7-f643-aabbff8ccd8e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 90
        }
      },
      "cell_type": "code",
      "source": [
        "train_three_data,train_three_label,test_three_data,test_three_label = getThreeClassesTrainTest([1,8,9],x_train,y_train,x_test,y_test)"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[    4     5    32 ... 49993 49998 49999]\n",
            "[    8    62    69 ... 49968 49976 49985]\n",
            "[    1     2    14 ... 49963 49971 49997]\n",
            "(15000, 1)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "9luxTYeM3aZO",
        "colab_type": "code",
        "outputId": "985e488a-a398-460a-b58e-32d1ab5584e9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 338
        }
      },
      "cell_type": "code",
      "source": [
        "plt.hist(train_three_label)"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(array([5000.,    0.,    0.,    0.,    0.,    0.,    0.,    0., 5000.,\n",
              "        5000.]),\n",
              " array([1. , 1.8, 2.6, 3.4, 4.2, 5. , 5.8, 6.6, 7.4, 8.2, 9. ]),\n",
              " <a list of 10 Patch objects>)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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gVWB1Zt5fc0lExFXAZU1Nv5uZs+qq55CImAV8GegHfg1Yk5lb6q0KIuINwOeBU4D9wNWZ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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "Dni1oqIcyPai",
        "colab_type": "code",
        "outputId": "24d8da5a-975b-498f-8e22-c16985595919",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 338
        }
      },
      "cell_type": "code",
      "source": [
        "# test_three_label.shape\n",
        "plt.hist(test_three_label)"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(array([1000.,    0.,    0.,    0.,    0.,    0.,    0.,    0., 1000.,\n",
              "        1000.]),\n",
              " array([1. , 1.8, 2.6, 3.4, 4.2, 5. , 5.8, 6.6, 7.4, 8.2, 9. ]),\n",
              " <a list of 10 Patch objects>)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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DwNvo4dlDl64HLgXObzrIGI6JiHXAbFo/8n+t6UDAkcD0KtcgcHVm9s1PtRFxPPD9zHyu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\npMJZ9JJUOItekgr3fwVhKOFEeJQdAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "fuM8CeuRsDzW",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "train_three_data, train_three_label = shuffle(train_three_data, train_three_label )\n",
        "test_three_data,test_three_label = shuffle(test_three_data,test_three_label )"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "mbK7iwsZx8QW",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "le = preprocessing.LabelEncoder()\n",
        "le.fit_transform(train_three_label)\n",
        "train_three_label_cat = le.transform(train_three_label)\n",
        "test_three_label_cat = le.transform(test_three_label)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "jYJt52bU0ooK",
        "colab_type": "code",
        "outputId": "8a7ae4bf-be19-4eba-c83a-d69f7eadd6ee",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "cell_type": "code",
      "source": [
        "le.classes_"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([1, 8, 9], dtype=uint8)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        }
      ]
    },
    {
      "metadata": {
        "id": "r3Ydm3cykUFn",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "train_three_label_one_hot = to_categorical(train_three_label_cat)\n",
        "test_three_label_one_hot = to_categorical(test_three_label_cat)\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "_O4_VLoJ6vSF",
        "colab_type": "code",
        "outputId": "56312dcf-0b66-4766-ea99-00e811acdeb3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "cell_type": "code",
      "source": [
        "test_three_label_one_hot.shape"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(3000, 3)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 14
        }
      ]
    },
    {
      "metadata": {
        "id": "UIrWUPz_lGvF",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# split train data and to create validation data\n",
        "train_X,valid_X,train_ground,valid_ground = train_test_split(train_three_data,\n",
        "                                                             train_three_label_one_hot,\n",
        "                                                             test_size=0.2,\n",
        "                                                             random_state=13)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "H6AkQaz-kRh0",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "num_classes = test_three_label_one_hot.shape[1]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "duFF_pGbj2i1",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "x, y = 32, 32\n",
        "inChannel = 3\n",
        "input_img = Input(shape = (x, y, inChannel))\n",
        "ep = 10 \n",
        "weight_decay = 1e-4"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "Pt1coy0mkFrj",
        "colab_type": "code",
        "colab": {}
      },
      "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"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "4IbtMUdhiscG",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "def cifar10Classifier(input_img):\n",
        "  \n",
        "  conv1 = (Conv2D(32, (3,3), padding='same', kernel_regularizer=regularizers.l2(weight_decay), input_shape=x_train.shape[1:], activation='elu'))(input_img)\n",
        "  conv1 = BatchNormalization()(conv1)\n",
        "  conv1 = (Conv2D(32, (3,3), padding='same', activation= 'elu',kernel_regularizer=regularizers.l2(weight_decay)))(conv1)\n",
        "  conv1 = BatchNormalization()(conv1)\n",
        "  pool1 = MaxPooling2D(pool_size=(2, 2))(conv1) #14 x 14 x 32\n",
        "  drop1= Dropout(.2)(pool1)\n",
        "\n",
        "  \n",
        "  \n",
        "  conv2 = (Conv2D(64, (3,3), padding='same', kernel_regularizer=regularizers.l2(weight_decay), activation='elu'))(drop1)\n",
        "  conv2 = BatchNormalization()(conv2)\n",
        "  conv2 = (Conv2D(64, (3,3), padding='same', activation= 'elu',kernel_regularizer=regularizers.l2(weight_decay)))(conv2)\n",
        "  conv2 = BatchNormalization()(conv2)\n",
        "  pool2 = MaxPooling2D(pool_size=(2, 2))(conv2) #14 x 14 x 32\n",
        "  drop2= Dropout(.3)(pool2)\n",
        "\n",
        "  conv3 = (Conv2D(128, (3,3), padding='same', kernel_regularizer=regularizers.l2(weight_decay), activation='elu'))(drop2)\n",
        "  conv3 = BatchNormalization()(conv3)\n",
        "  conv3 = (Conv2D(128, (3,3), padding='same', activation= 'elu',kernel_regularizer=regularizers.l2(weight_decay)))(conv3)\n",
        "  conv3 = BatchNormalization()(conv3)\n",
        "  pool3 = MaxPooling2D(pool_size=(2, 2))(conv3) #14 x 14 x 32\n",
        "  drop3= Dropout(.4)(pool3)\n",
        "\n",
        "  flat = Flatten()(drop3)\n",
        "  out = Dense(num_classes, activation='softmax')(flat)\n",
        "  \n",
        "  return out"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "-VPIvSX1jytM",
        "colab_type": "code",
        "outputId": "0beebf7a-1ccd-4596-bd27-e89627c5f0fa",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1037
        }
      },
      "cell_type": "code",
      "source": [
        "model = Model(input_img, (cifar10Classifier(input_img)))\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(train_three_data)\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",
        "\n",
        "model.summary()\n",
        " "
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/op_def_library.py:263: colocate_with (from tensorflow.python.framework.ops) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Colocations handled automatically by placer.\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:3445: 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",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "input_1 (InputLayer)         (None, 32, 32, 3)         0         \n",
            "_________________________________________________________________\n",
            "conv2d_1 (Conv2D)            (None, 32, 32, 32)        896       \n",
            "_________________________________________________________________\n",
            "batch_normalization_1 (Batch (None, 32, 32, 32)        128       \n",
            "_________________________________________________________________\n",
            "conv2d_2 (Conv2D)            (None, 32, 32, 32)        9248      \n",
            "_________________________________________________________________\n",
            "batch_normalization_2 (Batch (None, 32, 32, 32)        128       \n",
            "_________________________________________________________________\n",
            "max_pooling2d_1 (MaxPooling2 (None, 16, 16, 32)        0         \n",
            "_________________________________________________________________\n",
            "dropout_1 (Dropout)          (None, 16, 16, 32)        0         \n",
            "_________________________________________________________________\n",
            "conv2d_3 (Conv2D)            (None, 16, 16, 64)        18496     \n",
            "_________________________________________________________________\n",
            "batch_normalization_3 (Batch (None, 16, 16, 64)        256       \n",
            "_________________________________________________________________\n",
            "conv2d_4 (Conv2D)            (None, 16, 16, 64)        36928     \n",
            "_________________________________________________________________\n",
            "batch_normalization_4 (Batch (None, 16, 16, 64)        256       \n",
            "_________________________________________________________________\n",
            "max_pooling2d_2 (MaxPooling2 (None, 8, 8, 64)          0         \n",
            "_________________________________________________________________\n",
            "dropout_2 (Dropout)          (None, 8, 8, 64)          0         \n",
            "_________________________________________________________________\n",
            "conv2d_5 (Conv2D)            (None, 8, 8, 128)         73856     \n",
            "_________________________________________________________________\n",
            "batch_normalization_5 (Batch (None, 8, 8, 128)         512       \n",
            "_________________________________________________________________\n",
            "conv2d_6 (Conv2D)            (None, 8, 8, 128)         147584    \n",
            "_________________________________________________________________\n",
            "batch_normalization_6 (Batch (None, 8, 8, 128)         512       \n",
            "_________________________________________________________________\n",
            "max_pooling2d_3 (MaxPooling2 (None, 4, 4, 128)         0         \n",
            "_________________________________________________________________\n",
            "dropout_3 (Dropout)          (None, 4, 4, 128)         0         \n",
            "_________________________________________________________________\n",
            "flatten_1 (Flatten)          (None, 2048)              0         \n",
            "_________________________________________________________________\n",
            "dense_1 (Dense)              (None, 3)                 6147      \n",
            "=================================================================\n",
            "Total params: 294,947\n",
            "Trainable params: 294,051\n",
            "Non-trainable params: 896\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "2jabvkmcr1KR",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "yBK4UwK8lffO",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "train_X,valid_X,train_ground,valid_ground"
      ]
    },
    {
      "metadata": {
        "id": "0x-iDMcukCCT",
        "colab_type": "code",
        "outputId": "1799eb19-45c3-4946-b596-e7095254e7f5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 799
        }
      },
      "cell_type": "code",
      "source": [
        "model.fit_generator(datagen.flow(train_X, train_ground, batch_size=batch_size),\\\n",
        "                    steps_per_epoch=train_X.shape[0] // batch_size,epochs=20,\\\n",
        "                    verbose=1,validation_data=(valid_X,valid_ground),shuffle = True,callbacks=[LearningRateScheduler(lr_schedule)])\n",
        "#save to disk\n",
        "model_json = model.to_json()\n",
        "with open('model1.json', 'w') as json_file:\n",
        "    json_file.write(model_json)\n",
        "model.save_weights('model1.h5') \n",
        " \n"
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow/python/ops/math_ops.py:3066: to_int32 (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Use tf.cast instead.\n",
            "Epoch 1/20\n",
            "187/187 [==============================] - 22s 119ms/step - loss: 1.2126 - acc: 0.6354 - val_loss: 1.4920 - val_acc: 0.6313\n",
            "Epoch 2/20\n",
            "187/187 [==============================] - 17s 89ms/step - loss: 0.7624 - acc: 0.7445 - val_loss: 0.9294 - val_acc: 0.7473\n",
            "Epoch 3/20\n",
            "187/187 [==============================] - 17s 92ms/step - loss: 0.6328 - acc: 0.8011 - val_loss: 1.0162 - val_acc: 0.6850\n",
            "Epoch 4/20\n",
            "187/187 [==============================] - 16s 87ms/step - loss: 0.5897 - acc: 0.8182 - val_loss: 0.6332 - val_acc: 0.8013\n",
            "Epoch 5/20\n",
            "187/187 [==============================] - 17s 90ms/step - loss: 0.5322 - acc: 0.8406 - val_loss: 0.4017 - val_acc: 0.8773\n",
            "Epoch 6/20\n",
            "187/187 [==============================] - 16s 85ms/step - loss: 0.5032 - acc: 0.8485 - val_loss: 0.6342 - val_acc: 0.7900\n",
            "Epoch 7/20\n",
            "187/187 [==============================] - 17s 91ms/step - loss: 0.4960 - acc: 0.8590 - val_loss: 0.4959 - val_acc: 0.8317\n",
            "Epoch 8/20\n",
            "187/187 [==============================] - 17s 91ms/step - loss: 0.4369 - acc: 0.8697 - val_loss: 0.3850 - val_acc: 0.8763\n",
            "Epoch 9/20\n",
            "187/187 [==============================] - 17s 89ms/step - loss: 0.3969 - acc: 0.8812 - val_loss: 0.3613 - val_acc: 0.8897\n",
            "Epoch 10/20\n",
            "187/187 [==============================] - 17s 93ms/step - loss: 0.3787 - acc: 0.8860 - val_loss: 0.2877 - val_acc: 0.9180\n",
            "Epoch 11/20\n",
            "187/187 [==============================] - 18s 98ms/step - loss: 0.3689 - acc: 0.8910 - val_loss: 0.2883 - val_acc: 0.9220\n",
            "Epoch 12/20\n",
            "187/187 [==============================] - 17s 90ms/step - loss: 0.3553 - acc: 0.8954 - val_loss: 0.2900 - val_acc: 0.9197\n",
            "Epoch 13/20\n",
            "187/187 [==============================] - 17s 93ms/step - loss: 0.3262 - acc: 0.9039 - val_loss: 0.2888 - val_acc: 0.9220\n",
            "Epoch 14/20\n",
            "187/187 [==============================] - 18s 95ms/step - loss: 0.3224 - acc: 0.9047 - val_loss: 0.3085 - val_acc: 0.9183\n",
            "Epoch 15/20\n",
            "187/187 [==============================] - 17s 90ms/step - loss: 0.3161 - acc: 0.9108 - val_loss: 0.3275 - val_acc: 0.9123\n",
            "Epoch 16/20\n",
            "187/187 [==============================] - 9s 49ms/step - loss: 0.3037 - acc: 0.9144 - val_loss: 0.5247 - val_acc: 0.8590\n",
            "Epoch 17/20\n",
            "187/187 [==============================] - 9s 49ms/step - loss: 0.2939 - acc: 0.9181 - val_loss: 0.3547 - val_acc: 0.8927\n",
            "Epoch 18/20\n",
            "187/187 [==============================] - 9s 48ms/step - loss: 0.2960 - acc: 0.9225 - val_loss: 0.2579 - val_acc: 0.9380\n",
            "Epoch 19/20\n",
            "187/187 [==============================] - 10s 55ms/step - loss: 0.2862 - acc: 0.9240 - val_loss: 0.2271 - val_acc: 0.9457\n",
            "Epoch 20/20\n",
            "187/187 [==============================] - 9s 46ms/step - loss: 0.2795 - acc: 0.9265 - val_loss: 0.3094 - val_acc: 0.9170\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "w4M2DfGjmT3I",
        "colab_type": "code",
        "outputId": "3ff64533-2d2e-421a-e56b-3102f3268110",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 54
        }
      },
      "cell_type": "code",
      "source": [
        "#testing\n",
        "test_eval = model.evaluate(test_three_data, test_three_label_one_hot, verbose=0)\n",
        "print('Test loss:', test_eval[0])\n",
        "print('Test accuracy:', test_eval[1])"
      ],
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Test loss: 0.3233321091334025\n",
            "Test accuracy: 0.9130000001589457\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "M1_boK_fprtB",
        "colab_type": "code",
        "outputId": "10b763c9-b878-4e36-f7cf-f7577ce44faf",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "cell_type": "code",
      "source": [
        "test_three_data.shape"
      ],
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(3000, 32, 32, 3)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 23
        }
      ]
    },
    {
      "metadata": {
        "id": "ZuH6igT7nbza",
        "colab_type": "code",
        "outputId": "22af3e45-13ce-44be-ccae-58fb3980ef63",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 342
        }
      },
      "cell_type": "code",
      "source": [
        "# show_imgs(test_three_data[:,1])\n",
        "show_imgs(test_three_data[:16])"
      ],
      "execution_count": 24,
      "outputs": [
        {
          "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": {
            "image/png": 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txjaepYDbNz5FQZpdtdHZ5NSLXwBgeWWRtnWpOp3U7chprJF2LlWm4PTJF8w5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\n",
            "text/plain": [
              "<Figure size 432x288 with 16 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "aYEcwdWr7BPP",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}