resnet50.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "resnet50.ipynb",
      "version": "0.3.2",
      "provenance": [],
      "collapsed_sections": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "TPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/harsh204016/Game-of-Deep-Learning/blob/master/resnet50.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KJLhq7_qsJS4",
        "colab_type": "code",
        "outputId": "ff579ee7-fded-4786-9a05-ada52ffd0719",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        }
      },
      "source": [
        "!pip install -U -q PyDrive\n",
        "import os\n",
        "from pydrive.auth import GoogleAuth\n",
        "from pydrive.drive import GoogleDrive\n",
        "from google.colab import auth\n",
        "from oauth2client.client import GoogleCredentials"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
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            "\u001b[?25h  Building wheel for PyDrive (setup.py) ... \u001b[?25l\u001b[?25hdone\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "deHP8Gk1wBuL",
        "colab_type": "code",
        "outputId": "7151f7ba-80dd-48a4-c352-9d9896112a44",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 136
        }
      },
      "source": [
        "auth.authenticate_user()\n",
        "gauth = GoogleAuth()\n",
        "gauth.credentials = GoogleCredentials.get_application_default()\n",
        "drive = GoogleDrive(gauth)\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\n",
            "WARNING: The TensorFlow contrib module will not be included in TensorFlow 2.0.\n",
            "For more information, please see:\n",
            "  * https://github.com/tensorflow/community/blob/master/rfcs/20180907-contrib-sunset.md\n",
            "  * https://github.com/tensorflow/addons\n",
            "If you depend on functionality not listed there, please file an issue.\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "smZRhg1QwSLg",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from google.colab import drive"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "D8S2cj2lzP3M",
        "colab_type": "code",
        "outputId": "3910a7c9-717c-465c-d3ed-772ea1c149f2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 122
        }
      },
      "source": [
        "drive.mount('/content/drive')"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Go to this URL in a browser: https://accounts.google.com/o/oauth2/auth?client_id=947318989803-6bn6qk8qdgf4n4g3pfee6491hc0brc4i.apps.googleusercontent.com&redirect_uri=urn%3Aietf%3Awg%3Aoauth%3A2.0%3Aoob&scope=email%20https%3A%2F%2Fwww.googleapis.com%2Fauth%2Fdocs.test%20https%3A%2F%2Fwww.googleapis.com%2Fauth%2Fdrive%20https%3A%2F%2Fwww.googleapis.com%2Fauth%2Fdrive.photos.readonly%20https%3A%2F%2Fwww.googleapis.com%2Fauth%2Fpeopleapi.readonly&response_type=code\n",
            "\n",
            "Enter your authorization code:\n",
            "··········\n",
            "Mounted at /content/drive\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YWl_jQwO4wp2",
        "colab_type": "code",
        "outputId": "58f1ea7a-c9a9-4f7f-f1bc-e8632e70669b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        }
      },
      "source": [
        "!ls \"/content/drive/My Drive/ML Competitions/Game of Deep learning\""
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "code1.ipynb  data1.csv\tmodel2_with10epochs.h5\toutput128_3.csv   test.csv\n",
            "code2.ipynb  model2.h5\tmodel.h5\t\tsubmit1_1283.csv  train.csv\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AOBBPiRxvrF1",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import os \n",
        "os.chdir('/content/drive/My Drive/ML Competitions/Game of Deep learning')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "203ZtcHJ7ko2",
        "colab_type": "code",
        "outputId": "b79dcb55-d589-4632-b791-da65c96debf8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "import datetime\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "import keras\n",
        "from keras.layers import Dense,GlobalAveragePooling2D\n",
        "from keras.layers.core import Dropout\n",
        "from keras.models import Model\n",
        "from keras.preprocessing.image import ImageDataGenerator\n",
        "from keras.applications import resnet50\n",
        "from sklearn.preprocessing import LabelEncoder\n",
        "from sklearn.model_selection import train_test_split\n",
        "from keras.utils.np_utils import to_categorical\n",
        "from csv import DictReader\n",
        "import keras.backend as K\n",
        "from keras import optimizers"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Using TensorFlow backend.\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Nj4w6Owi7sLQ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#pixels=pd.read_csv(\"output128_3.csv\")\n",
        "#labels=pd.read_csv('train.csv')\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vnw1fK5hw0q2",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#data1=pd.merge(pixels,labels,on='image')\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gglvQRgwOcz0",
        "colab_type": "code",
        "outputId": "960c9c0a-aa33-4874-9cc8-1929ab54c1a5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "#data1.to_csv(\"data1.csv\",sep=',',index=False)\n",
        "a=datetime.datetime.now()\n",
        "data1=pd.read_csv(\"data1.csv\")\n",
        "b=datetime.datetime.now()\n",
        "print(b-a)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0:05:14.565679\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dejOo0vdxmxC",
        "colab_type": "code",
        "outputId": "03b90b9b-a23b-4064-bee9-63cb93fb3047",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "data1.shape"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(6252, 49154)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NWlKF_fOGVz2",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "Y=data1[\"category\"]\n",
        "X=data1.drop(labels = [\"category\",\"image\"],axis = 1)\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "itqjs_qt1Iqa",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "X=X/255"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zK3mvxUSGXa3",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "X=X.values.reshape(-1,128,128,3)\n",
        "\n",
        "le=LabelEncoder()\n",
        "Y=le.fit_transform(Y)\n",
        "Y = to_categorical(Y,5)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sc6msOi9DYID",
        "colab_type": "code",
        "outputId": "00636157-a9e8-4e2b-a316-3f07b132cfdc",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "x_train,x_test,y_train,y_test=train_test_split(X, Y, test_size = 0.2, random_state = 2)\n",
        "x_train.shape"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(5001, 128, 128, 3)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XQ5UjIR5ETYj",
        "colab_type": "code",
        "outputId": "bab91e5a-b4c9-4c50-a83c-7560665079cc",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 286
        }
      },
      "source": [
        "\n",
        "plt.imshow(x_train[0][:,:,0])"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7fde3ba540b8>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 14
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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LA6oIuUVbrCMBWJI52OutaHIFJmRTMB58qUvTuGU9bdN3PMQgnsQJVZrrcQ5a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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "if2ElGoMEW0t",
        "colab_type": "code",
        "outputId": "24c07017-c232-47cd-a3a8-f8aeec4dea3a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 122
        }
      },
      "source": [
        "\n",
        "base_model=resnet50.ResNet50(input_shape=(128,128,3),weights='imagenet',include_top=False)\n",
        " #imports the mobilenet model and discards the last 1000 neuron layer.\n",
        "  \n",
        "x=base_model.output\n",
        "x=GlobalAveragePooling2D()(x)\n",
        "x=Dense(1024,activation='relu')(x) #we add dense layers so that the model can learn more complex functions and classify for better results.\n",
        "x=Dropout(0.25)(x)\n",
        "x=Dense(512,activation='relu')(x) #dense layer 3\n",
        "x=Dropout(0.25)(x)\n",
        "preds=Dense(5,activation='softmax')(x) #final layer with softmax activation\n",
        "model=Model(inputs=base_model.input,outputs=preds)\n",
        "\n",
        "#K.set_learning_phase(1)\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/keras_applications/resnet50.py:265: UserWarning: The output shape of `ResNet50(include_top=False)` has been changed since Keras 2.2.0.\n",
            "  warnings.warn('The output shape of `ResNet50(include_top=False)` '\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "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"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "J65yQ85FEXAu",
        "colab_type": "code",
        "outputId": "d193a101-f02d-4cfb-8a80-99610cdc20f1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 3094
        }
      },
      "source": [
        "for i,layer in enumerate(model.layers):\n",
        "  print(i,layer.name)\n",
        "\n",
        "for layer in model.layers[:20]:\n",
        "    layer.trainable=False\n",
        "for layer in model.layers[20:]:\n",
        "    layer.trainable=True\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0 input_2\n",
            "1 conv1_pad\n",
            "2 conv1\n",
            "3 bn_conv1\n",
            "4 activation_50\n",
            "5 pool1_pad\n",
            "6 max_pooling2d_2\n",
            "7 res2a_branch2a\n",
            "8 bn2a_branch2a\n",
            "9 activation_51\n",
            "10 res2a_branch2b\n",
            "11 bn2a_branch2b\n",
            "12 activation_52\n",
            "13 res2a_branch2c\n",
            "14 res2a_branch1\n",
            "15 bn2a_branch2c\n",
            "16 bn2a_branch1\n",
            "17 add_17\n",
            "18 activation_53\n",
            "19 res2b_branch2a\n",
            "20 bn2b_branch2a\n",
            "21 activation_54\n",
            "22 res2b_branch2b\n",
            "23 bn2b_branch2b\n",
            "24 activation_55\n",
            "25 res2b_branch2c\n",
            "26 bn2b_branch2c\n",
            "27 add_18\n",
            "28 activation_56\n",
            "29 res2c_branch2a\n",
            "30 bn2c_branch2a\n",
            "31 activation_57\n",
            "32 res2c_branch2b\n",
            "33 bn2c_branch2b\n",
            "34 activation_58\n",
            "35 res2c_branch2c\n",
            "36 bn2c_branch2c\n",
            "37 add_19\n",
            "38 activation_59\n",
            "39 res3a_branch2a\n",
            "40 bn3a_branch2a\n",
            "41 activation_60\n",
            "42 res3a_branch2b\n",
            "43 bn3a_branch2b\n",
            "44 activation_61\n",
            "45 res3a_branch2c\n",
            "46 res3a_branch1\n",
            "47 bn3a_branch2c\n",
            "48 bn3a_branch1\n",
            "49 add_20\n",
            "50 activation_62\n",
            "51 res3b_branch2a\n",
            "52 bn3b_branch2a\n",
            "53 activation_63\n",
            "54 res3b_branch2b\n",
            "55 bn3b_branch2b\n",
            "56 activation_64\n",
            "57 res3b_branch2c\n",
            "58 bn3b_branch2c\n",
            "59 add_21\n",
            "60 activation_65\n",
            "61 res3c_branch2a\n",
            "62 bn3c_branch2a\n",
            "63 activation_66\n",
            "64 res3c_branch2b\n",
            "65 bn3c_branch2b\n",
            "66 activation_67\n",
            "67 res3c_branch2c\n",
            "68 bn3c_branch2c\n",
            "69 add_22\n",
            "70 activation_68\n",
            "71 res3d_branch2a\n",
            "72 bn3d_branch2a\n",
            "73 activation_69\n",
            "74 res3d_branch2b\n",
            "75 bn3d_branch2b\n",
            "76 activation_70\n",
            "77 res3d_branch2c\n",
            "78 bn3d_branch2c\n",
            "79 add_23\n",
            "80 activation_71\n",
            "81 res4a_branch2a\n",
            "82 bn4a_branch2a\n",
            "83 activation_72\n",
            "84 res4a_branch2b\n",
            "85 bn4a_branch2b\n",
            "86 activation_73\n",
            "87 res4a_branch2c\n",
            "88 res4a_branch1\n",
            "89 bn4a_branch2c\n",
            "90 bn4a_branch1\n",
            "91 add_24\n",
            "92 activation_74\n",
            "93 res4b_branch2a\n",
            "94 bn4b_branch2a\n",
            "95 activation_75\n",
            "96 res4b_branch2b\n",
            "97 bn4b_branch2b\n",
            "98 activation_76\n",
            "99 res4b_branch2c\n",
            "100 bn4b_branch2c\n",
            "101 add_25\n",
            "102 activation_77\n",
            "103 res4c_branch2a\n",
            "104 bn4c_branch2a\n",
            "105 activation_78\n",
            "106 res4c_branch2b\n",
            "107 bn4c_branch2b\n",
            "108 activation_79\n",
            "109 res4c_branch2c\n",
            "110 bn4c_branch2c\n",
            "111 add_26\n",
            "112 activation_80\n",
            "113 res4d_branch2a\n",
            "114 bn4d_branch2a\n",
            "115 activation_81\n",
            "116 res4d_branch2b\n",
            "117 bn4d_branch2b\n",
            "118 activation_82\n",
            "119 res4d_branch2c\n",
            "120 bn4d_branch2c\n",
            "121 add_27\n",
            "122 activation_83\n",
            "123 res4e_branch2a\n",
            "124 bn4e_branch2a\n",
            "125 activation_84\n",
            "126 res4e_branch2b\n",
            "127 bn4e_branch2b\n",
            "128 activation_85\n",
            "129 res4e_branch2c\n",
            "130 bn4e_branch2c\n",
            "131 add_28\n",
            "132 activation_86\n",
            "133 res4f_branch2a\n",
            "134 bn4f_branch2a\n",
            "135 activation_87\n",
            "136 res4f_branch2b\n",
            "137 bn4f_branch2b\n",
            "138 activation_88\n",
            "139 res4f_branch2c\n",
            "140 bn4f_branch2c\n",
            "141 add_29\n",
            "142 activation_89\n",
            "143 res5a_branch2a\n",
            "144 bn5a_branch2a\n",
            "145 activation_90\n",
            "146 res5a_branch2b\n",
            "147 bn5a_branch2b\n",
            "148 activation_91\n",
            "149 res5a_branch2c\n",
            "150 res5a_branch1\n",
            "151 bn5a_branch2c\n",
            "152 bn5a_branch1\n",
            "153 add_30\n",
            "154 activation_92\n",
            "155 res5b_branch2a\n",
            "156 bn5b_branch2a\n",
            "157 activation_93\n",
            "158 res5b_branch2b\n",
            "159 bn5b_branch2b\n",
            "160 activation_94\n",
            "161 res5b_branch2c\n",
            "162 bn5b_branch2c\n",
            "163 add_31\n",
            "164 activation_95\n",
            "165 res5c_branch2a\n",
            "166 bn5c_branch2a\n",
            "167 activation_96\n",
            "168 res5c_branch2b\n",
            "169 bn5c_branch2b\n",
            "170 activation_97\n",
            "171 res5c_branch2c\n",
            "172 bn5c_branch2c\n",
            "173 add_32\n",
            "174 activation_98\n",
            "175 global_average_pooling2d_3\n",
            "176 dense_1\n",
            "177 dropout_1\n",
            "178 dense_2\n",
            "179 dropout_2\n",
            "180 dense_3\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Qz5gZiDJZ9v8",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Setting the Learning rate multipliers\n",
        "#LR_mult_dict = {}\n",
        "#LR_mult_dict['dense_5']=0.01\n",
        "#LR_mult_dict['dense_6']=0.0001\n",
        "\n",
        "# Setting up optimizer\n",
        "optimizer = optimizers.Adam(lr=0.001, decay=1e-6)\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BcTnHPJUEXOa",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#datagen=ImageDataGenerator(rotation_range=15,width_shift_range=0.1,zoom_range=[0.9, 1.25], horizontal_flip=True,) #included in our dependencies\n",
        "datagen = ImageDataGenerator(preprocessing_function=keras.applications.resnet50.preprocess_input,horizontal_flip=True,rotation_range=15)\n",
        "\n",
        "model.compile(optimizer=optimizer,loss='categorical_crossentropy',metrics=['accuracy'])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zNLLqhL8q5XI",
        "colab_type": "code",
        "outputId": "66f387c9-91db-4404-c6d1-f2f0d5232ccc",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "keras.callbacks.EarlyStopping(monitor='val_loss',\n",
        "                              min_delta=0,\n",
        "                              patience=0,\n",
        "                              verbose=0, mode='auto')"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.callbacks.EarlyStopping at 0x7fde3a3653c8>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aRe1ypemKnJF",
        "colab_type": "code",
        "outputId": "1c65576f-f687-4490-ee36-d42073cafce1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 445
        }
      },
      "source": [
        "a=datetime.datetime.now()\n",
        "h=model.fit_generator(datagen.flow(x_train,y_train, batch_size=64),\n",
        "                      validation_data=(x_test,y_test),\n",
        "                   steps_per_epoch=x_train.shape[0] // 64,\n",
        "                   epochs=10)\n",
        "b=datetime.datetime.now()\n",
        "print(b-a)"
      ],
      "execution_count": 0,
      "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 1/10\n",
            "78/78 [==============================] - 2184s 28s/step - loss: 1.6544 - acc: 0.3882 - val_loss: 10.6552 - val_acc: 0.3389\n",
            "Epoch 2/10\n",
            "78/78 [==============================] - 2159s 28s/step - loss: 1.3551 - acc: 0.5075 - val_loss: 2.0109 - val_acc: 0.1847\n",
            "Epoch 3/10\n",
            "78/78 [==============================] - 2137s 27s/step - loss: 1.2227 - acc: 0.5341 - val_loss: 2.8419 - val_acc: 0.1847\n",
            "Epoch 4/10\n",
            "78/78 [==============================] - 2166s 28s/step - loss: 0.9766 - acc: 0.6083 - val_loss: 1.7350 - val_acc: 0.1455\n",
            "Epoch 5/10\n",
            "78/78 [==============================] - 2173s 28s/step - loss: 0.8860 - acc: 0.6451 - val_loss: 2.5765 - val_acc: 0.1455\n",
            "Epoch 6/10\n",
            "78/78 [==============================] - 2146s 28s/step - loss: 0.7590 - acc: 0.7005 - val_loss: 2.7044 - val_acc: 0.1455\n",
            "Epoch 7/10\n",
            "78/78 [==============================] - 2109s 27s/step - loss: 0.7282 - acc: 0.7213 - val_loss: 3.0559 - val_acc: 0.1455\n",
            "Epoch 8/10\n",
            "78/78 [==============================] - 2131s 27s/step - loss: 0.6531 - acc: 0.7489 - val_loss: 2.6683 - val_acc: 0.3389\n",
            "Epoch 9/10\n",
            "78/78 [==============================] - 2117s 27s/step - loss: 0.5983 - acc: 0.7746 - val_loss: 2.0296 - val_acc: 0.3389\n",
            "Epoch 10/10\n",
            "78/78 [==============================] - 2127s 27s/step - loss: 0.5416 - acc: 0.7972 - val_loss: 2.2602 - val_acc: 0.1455\n",
            "5:57:41.841788\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EKNbI5PfKnSo",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "model.save(\"model_10epochs.h5\")"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "skmz6yF6fyO7",
        "colab_type": "code",
        "outputId": "2083ac5d-05eb-4def-85f4-9cdc0dc959d6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        }
      },
      "source": [
        "\n",
        "plt.plot(h.history['loss'])\n",
        "plt.plot(h.history['val_loss'])\n",
        "plt.title('Model Loss')\n",
        "plt.ylabel('Loss')\n",
        "plt.xlabel('Epochs')\n",
        "plt.legend(['train', 'test'])\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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N4r8F/h74F3d/zczmAr8Yxed+Frg1dcTQq6n3n9jUMBaRHDWsIHD31cBlAGZWBZS7+7+N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\nMacgEBGJuf8P+oISvN5IJcIAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QQBOUoGzgBI_",
        "colab_type": "code",
        "outputId": "d9b5565e-956d-4fd5-9d87-2360ba2ca498",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        }
      },
      "source": [
        "plt.plot(h.history['acc'])\n",
        "plt.plot(h.history['val_acc'])\n",
        "plt.title('Model Accuracy')\n",
        "plt.ylabel('Accuracy')\n",
        "plt.xlabel('Epochs')\n",
        "plt.legend(['train', 'test'])\n",
        "plt.show()\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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x6TaVtTEmaPlyRbONlB7DzJU7uOf9FQzplMRzl/e2ieyMMUHNlyuar6xuv6r+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\nVLKiYIwxptL/AyRqAC3UzXGBAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Z2b6PYoNkaK-",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import pandas as pd"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KfbtNbQtgJ1K",
        "colab_type": "code",
        "outputId": "d6efe386-1ea5-48fc-967a-96f919b40dfa",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "test=pd.read_csv(\"test.csv\")\n",
        "test.shape"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(2680, 1)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FjcdApOigRBz",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "pixels=pd.read_csv(\"output128_3.csv\")"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-TWSJk55gRJT",
        "colab_type": "code",
        "outputId": "4ff91910-0792-4350-e04d-ec445a5df860",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 139
        }
      },
      "source": [
        "from keras.models import load_model\n",
        "new=load_model(\"model2_with10epochs.h5\")\n"
      ],
      "execution_count": 0,
      "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/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"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DNejVW-mgRME",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "test_data=pd.merge(test, pixels, on='image')\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2PUPzSS-gRPe",
        "colab_type": "code",
        "outputId": "5ec8f8d4-282a-47ec-839b-d756e802e651",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "test_data.shape"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(2680, 49153)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KBFwwDYMgRXg",
        "colab_type": "code",
        "outputId": "8700aee7-78b4-44e7-ded3-ce9f4c738975",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 102
        }
      },
      "source": [
        "test_data.columns"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['image', 'f1', 'f2', 'f3', 'f4', 'f5', 'f6', 'f7', 'f8', 'f9',\n",
              "       ...\n",
              "       'f49143', 'f49144', 'f49145', 'f49146', 'f49147', 'f49148', 'f49149',\n",
              "       'f49150', 'f49151', 'f49152'],\n",
              "      dtype='object', length=49153)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3RHIO1G4uSk9",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "x_test=test_data.drop(labels = [\"image\"],axis = 1)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1-WgJ9R9uduT",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "x_test1=x_test/5"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "W-N8lRUnujbI",
        "colab_type": "code",
        "outputId": "f2450d28-ffce-463e-ad33-957852cd05df",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "x_test1.shape"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(2680, 49152)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NM0EixHKvSys",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "x_test1=x_test1.values.reshape(-1,128,128,3)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "l3V2yHRovfEs",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "ypred_test=new.predict(x_test1)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BZ9EyGZJvjz6",
        "colab_type": "code",
        "outputId": "d26f16ba-a184-4813-d19f-ddc34fd1bddd",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "ypred_test.shape"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(2680, 5)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mTlJo8-tycEl",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import numpy as np\n",
        "prediction=np.argmax(ypred_test,axis=1)\n",
        "      \n",
        "    "
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qractOiO4B2j",
        "colab_type": "code",
        "outputId": "80dab213-9e59-4a6b-ad25-c3be89fc84f7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "prediction.shape"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(2680,)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cFzNuyfU4vRh",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "pred_list=prediction.tolist()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ocX8UFflC1_M",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "pred_list1=[]\n",
        "for i in pred_list:\n",
        "  pred_list1.append(i+1)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vx9HAuM35kR-",
        "colab_type": "code",
        "outputId": "9bd71be9-204b-451c-8bf4-c3bc99e413c9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 54
        }
      },
      "source": [
        "from csv import DictReader\n",
        "with open('test.csv') as f:\n",
        "    image1=[row['image'] for row in DictReader(f)]\n",
        "\n",
        "df=pd.DataFrame(data={\"image\":image1,\"category\":pred_list1})\n",
        "df.to_csv(\"submit1_1283.csv\",sep=',',index=False)\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'from csv import DictReader\\nwith open(\\'test.csv\\') as f:\\n    image1=[row[\\'image\\'] for row in DictReader(f)]\\n\\ndf=pd.DataFrame(data={\"image\":image1,\"category\":pred_list1})\\ndf.to_csv(\"submit1_1283.csv\",sep=\\',\\',index=False)\\n'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-xObi04LCKEt",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}