Batch Normalization.ipynb

{
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  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "batch norm Deep_Learning_Project_All_Data.ipynb",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "fORmWVbsZtdv",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aiPyjmoImrLj",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 124
        },
        "outputId": "10585b5e-cc82-4ce4-b80f-688b2783b27f"
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "execution_count": null,
      "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&response_type=code&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\n",
            "\n",
            "Enter your authorization code:\n",
            "··········\n",
            "Mounted at /content/drive\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "J2iCpqjscRxu",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "5983a066-29e3-4f92-f7fc-b2e7acfb4a9a"
      },
      "source": [
        "!unzip -q \"/content/drive/My Drive/DL proj/3GroupsData.zip\" -d \"/content/\"\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "replace /content/3GroupsData/Hyderabad_1-1tile_0-11000.png? [y]es, [n]o, [A]ll, [N]one, [r]ename: N\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cNQUdwjXT8xm",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "9e07148e-fdc1-40e1-af9c-336df32f4ef9"
      },
      "source": [
        "import cv2\n",
        "import time\n",
        "import numpy as np \n",
        "import os\n",
        "import numpy as np\n",
        "from keras.models import *\n",
        "from keras.layers import *\n",
        "from keras.optimizers import *\n",
        "from keras.callbacks import ModelCheckpoint, LearningRateScheduler\n",
        "from keras import backend as keras\n",
        "import glob\n",
        "import matplotlib.pyplot as plt\n",
        "from keras import backend as K\n",
        "import keras\n",
        "import matplotlib.pyplot as plt\n",
        "import glob\n",
        "from keras.preprocessing import image"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Using TensorFlow backend.\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YdP-miZ5HpI1",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import tensorflow_addons as tfa"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rltqNHpg0jbT",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from keras.layers.normalization import BatchNormalization"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LzqwAtYk0jX8",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XP4iy1s4diXk",
        "colab_type": "text"
      },
      "source": [
        ""
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EF7nAdj8T8xs",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def unet(pretrained_weights = None,input_size = (256,256,3)):\n",
        "    inputs = Input(input_size)\n",
        "    conv1 = Conv2D(64, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(inputs)\n",
        "    conv1 = Conv2D(64, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(conv1)\n",
        "    \n",
        "    pool1 = MaxPooling2D(pool_size=(2, 2))(conv1)\n",
        "    conv2 = Conv2D(128, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(pool1)\n",
        "    conv2 = Conv2D(128, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(conv2)\n",
        "    \n",
        "    pool2 = MaxPooling2D(pool_size=(2, 2))(conv2)\n",
        "    conv3 = Conv2D(256, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(pool2)\n",
        "    conv3 = Conv2D(256, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(conv3)\n",
        "    pool3 = MaxPooling2D(pool_size=(2, 2))(conv3)\n",
        "    conv4 = Conv2D(512, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(pool3)\n",
        "    conv4 = Conv2D(512, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(conv4)\n",
        "    batch4=BatchNormalization()(conv4)\n",
        "    drop4 = Dropout(0.5)(batch4)\n",
        "    pool4 = MaxPooling2D(pool_size=(2, 2))(drop4)\n",
        "    conv5 = Conv2D(1024, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(pool4)\n",
        "    conv5 = Conv2D(1024, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(conv5)\n",
        "    drop5 = Dropout(0.5)(conv5)\n",
        "    pool5 = MaxPooling2D(pool_size=(2, 2))(drop5)\n",
        "    \n",
        "    F1=Flatten()(pool5)\n",
        "    d1=Dense(1024,activation='relu')(F1)    \n",
        "    d1_2=Dense(512,activation='relu')(d1)\n",
        "\n",
        "    d2=BatchNormalization()(d1_2)\n",
        "    \n",
        "    d2_1=Dense(256,activation='relu')(d2)\n",
        "    d2_1=Dense(64,activation='relu')(d2_1)\n",
        "    d2_1=Dense(1,activation='sigmoid')(d2_1)\n",
        "    \n",
        "    d2_2=Dense(256,activation='relu')(d2)\n",
        "    d2_2=Dense(64,activation='relu')(d2_2)\n",
        "    d2_2=Dense(1,activation='sigmoid')(d2_2)\n",
        "    \n",
        "    \n",
        "    d2_3=Dense(256,activation='relu')(d2)\n",
        "    d2_3=Dense(64,activation='relu')(d2_3)\n",
        "    d2_3=Dense(1,activation='sigmoid')(d2_3)\n",
        "    \n",
        "    d2_4=Dense(256,activation='relu')(d2)\n",
        "    d2_4=Dense(64,activation='relu')(d2_4)\n",
        "    d2_4=Dense(1,activation='sigmoid')(d2_4)\n",
        "    \n",
        "    d2_5=Dense(256,activation='relu')(d2)\n",
        "    d2_5=Dense(64,activation='relu')(d2_5)\n",
        "    d2_5=Dense(1,activation='sigmoid')(d2_5)\n",
        "    \n",
        "    d2_6=Dense(256,activation='relu')(d2)\n",
        "    d2_6=Dense(64,activation='relu')(d2_6)\n",
        "    d2_6=Dense(1,activation='sigmoid')(d2_6)\n",
        "    \n",
        "    d2_7=Dense(256,activation='relu')(d2)\n",
        "    d2_7=Dense(64,activation='relu')(d2_7)\n",
        "    d2_7=Dense(1,activation='sigmoid')(d2_7)\n",
        "    \n",
        "    d2_8=Dense(256,activation='relu')(d2)\n",
        "    d2_8=Dense(64,activation='relu')(d2_8)\n",
        "    d2_8=Dense(1,activation='sigmoid')(d2_8)\n",
        "    \n",
        "    d2_9=Dense(256,activation='relu')(d2)\n",
        "    d2_9=Dense(64,activation='relu')(d2_9)\n",
        "    d2_9=Dense(1,activation='sigmoid')(d2_9)\n",
        "    \n",
        "    d2_10=Dense(256,activation='relu')(d2)\n",
        "    d2_10=Dense(64,activation='relu')(d2_10)\n",
        "    d2_10=Dense(1,activation='sigmoid')(d2_10)\n",
        "    \n",
        "    d2_11=Dense(256,activation='relu')(d2)\n",
        "    d2_11=Dense(64,activation='relu')(d2_11)\n",
        "    d2_11=Dense(1,activation='sigmoid')(d2_11)\n",
        "    \n",
        "    d2_12=Dense(256,activation='relu')(d2)\n",
        "    d2_12=Dense(64,activation='relu')(d2_12)\n",
        "    d2_12=Dense(1,activation='sigmoid')(d2_12)\n",
        "    \n",
        "    model = Model(input = inputs, output = [d2_1,d2_2,d2_3,d2_4,d2_5,d2_6,d2_7,d2_8,d2_9,d2_10,d2_11,d2_12])\n",
        "    \n",
        "    # model.compile(optimizer = Adam(lr = 1e-4), loss = 'binary_crossentropy',metrics=['accuracy'])\n",
        "\n",
        "    model.compile(optimizer = Adam(lr = 1e-4), loss= tfa.losses.focal_loss.SigmoidFocalCrossEntropy(),metrics=['accuracy'])\n",
        "    \n",
        "    \n",
        "    if(pretrained_weights):\n",
        "        model.load_weights(pretrained_weights)\n",
        "        \n",
        "    return model"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "q99SN8YBNuC-",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pV0kAQnXT8xv",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 55
        },
        "outputId": "33b48ea6-d77d-40c0-da3b-8f416ad85935"
      },
      "source": [
        "model=unet()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:79: UserWarning: Update your `Model` call to the Keras 2 API: `Model(inputs=Tensor(\"in..., outputs=[<tf.Tenso...)`\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "o4Pp2hF3T8x1",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import glob\n",
        "    "
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6dy6Qra4T8x5",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import pandas as pd  \n",
        "\n",
        "data = pd.read_csv('/content/drive/My Drive/DL proj/AllLabels.csv')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BJN1iGuCT8x8",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "train_x,train_y =[],[]\n",
        "# rows[\"Peshawar_1-1tile_11000-10200.png\"]\n",
        "for filename in glob.glob('/content/3GroupsData' + '/*.png' ):\n",
        "    im=plt.imread(filename)\n",
        "    filenameExact = filename.split('/')[-1].replace('.tif','.png')\n",
        "    if len(data.loc[data[\"content\"] == filenameExact].values) is not 0:\n",
        "        #train_y.append(rows[filename.split('\\\\')[-1]])\n",
        "\n",
        "        train_y.append(data.loc[data[\"content\"] == filenameExact].values[0][1:])\n",
        "        train_x.append(im)\n",
        "    #train_y.append(rows[filename.split('\\\\')[-1]])\n",
        "    #print(filename)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ryN60v8wT8x_",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "7a9aaa9f-4b6c-4db9-829b-2a7b063255b0"
      },
      "source": [
        "len(train_x)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "4678"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "N53hh-CWT8yD",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "train_y"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eR5yqavkT8yG",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# train_y = [[0,0,1,1,0,0,1,0,0,1,1,1],\n",
        "# [0,1,0,0,1,1,0,0,1,1,0,1]]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PjMtlTPil-2x",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "validation_x = train_x[:800] \n",
        "validation_y = train_y[:800]\n",
        "train_x = train_x[800:] \n",
        "train_y = train_y[800:]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EnERkVlBT8yJ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "train_x= np.array(train_x)\n",
        "train_y =list(map(list, zip(*train_y)))\n",
        "\n",
        "validation_x= np.array(validation_x)\n",
        "validation_y =list(map(list, zip(*validation_y)))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Y02OGaVrT8yL",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "len(validation_y)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2dags8xuMqkU",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "35844e20-e74c-4570-c704-2a72569f3f42"
      },
      "source": [
        "list(map(list, zip(*validation_y)))[0]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[1, 0, 0, 1, 0, 0, 0, 0, 1, 1, 0, 1]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rlnqnSnlT8yR",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 349
        },
        "outputId": "42a36e5d-efcf-4ab3-e389-ca8c9e60799d"
      },
      "source": [
        "model.fit(train_x,train_y,validation_data=(validation_x,validation_y),epochs=8, batch_size=32, verbose=1)\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Train on 3878 samples, validate on 800 samples\n",
            "Epoch 1/8\n",
            "3878/3878 [==============================] - 91s 24ms/step - loss: 0.7166 - dense_5_loss: 0.0540 - dense_8_loss: 0.0569 - dense_11_loss: 0.0522 - dense_14_loss: 0.0667 - dense_17_loss: 0.0704 - dense_20_loss: 0.0375 - dense_23_loss: 0.0704 - dense_26_loss: 0.0609 - dense_29_loss: 0.0698 - dense_32_loss: 0.0561 - dense_35_loss: 0.0547 - dense_38_loss: 0.0671 - dense_5_accuracy: 0.8056 - dense_8_accuracy: 0.6919 - dense_11_accuracy: 0.7653 - dense_14_accuracy: 0.6723 - dense_17_accuracy: 0.4982 - dense_20_accuracy: 0.8752 - dense_23_accuracy: 0.5812 - dense_26_accuracy: 0.7839 - dense_29_accuracy: 0.5542 - dense_32_accuracy: 0.6952 - dense_35_accuracy: 0.7744 - dense_38_accuracy: 0.6024 - val_loss: 0.8505 - val_dense_5_loss: 0.0495 - val_dense_8_loss: 0.1084 - val_dense_11_loss: 0.0826 - val_dense_14_loss: 0.0625 - val_dense_17_loss: 0.0872 - val_dense_20_loss: 0.0351 - val_dense_23_loss: 0.0909 - val_dense_26_loss: 0.0766 - val_dense_29_loss: 0.0752 - val_dense_32_loss: 0.0682 - val_dense_35_loss: 0.0589 - val_dense_38_loss: 0.0555 - val_dense_5_accuracy: 0.8200 - val_dense_8_accuracy: 0.6850 - val_dense_11_accuracy: 0.7025 - val_dense_14_accuracy: 0.6725 - val_dense_17_accuracy: 0.3425 - val_dense_20_accuracy: 0.8750 - val_dense_23_accuracy: 0.5650 - val_dense_26_accuracy: 0.7675 - val_dense_29_accuracy: 0.6612 - val_dense_32_accuracy: 0.1925 - val_dense_35_accuracy: 0.8025 - val_dense_38_accuracy: 0.7538\n",
            "Epoch 2/8\n",
            "3878/3878 [==============================] - 73s 19ms/step - loss: 0.5835 - dense_5_loss: 0.0277 - dense_8_loss: 0.0486 - dense_11_loss: 0.0398 - dense_14_loss: 0.0594 - dense_17_loss: 0.0653 - dense_20_loss: 0.0290 - dense_23_loss: 0.0616 - dense_26_loss: 0.0368 - dense_29_loss: 0.0619 - dense_32_loss: 0.0503 - dense_35_loss: 0.0490 - dense_38_loss: 0.0541 - dense_5_accuracy: 0.8809 - dense_8_accuracy: 0.7089 - dense_11_accuracy: 0.8179 - dense_14_accuracy: 0.7027 - dense_17_accuracy: 0.5665 - dense_20_accuracy: 0.8806 - dense_23_accuracy: 0.6269 - dense_26_accuracy: 0.8574 - dense_29_accuracy: 0.6258 - dense_32_accuracy: 0.7329 - dense_35_accuracy: 0.7811 - dense_38_accuracy: 0.7362 - val_loss: 0.8953 - val_dense_5_loss: 0.1132 - val_dense_8_loss: 0.0828 - val_dense_11_loss: 0.0643 - val_dense_14_loss: 0.0717 - val_dense_17_loss: 0.0737 - val_dense_20_loss: 0.0368 - val_dense_23_loss: 0.1310 - val_dense_26_loss: 0.0583 - val_dense_29_loss: 0.0648 - val_dense_32_loss: 0.0533 - val_dense_35_loss: 0.0682 - val_dense_38_loss: 0.0769 - val_dense_5_accuracy: 0.8200 - val_dense_8_accuracy: 0.6850 - val_dense_11_accuracy: 0.8450 - val_dense_14_accuracy: 0.6725 - val_dense_17_accuracy: 0.3425 - val_dense_20_accuracy: 0.8763 - val_dense_23_accuracy: 0.5650 - val_dense_26_accuracy: 0.7800 - val_dense_29_accuracy: 0.5638 - val_dense_32_accuracy: 0.7987 - val_dense_35_accuracy: 0.8025 - val_dense_38_accuracy: 0.2587\n",
            "Epoch 3/8\n",
            "3878/3878 [==============================] - 74s 19ms/step - loss: 0.5559 - dense_5_loss: 0.0249 - dense_8_loss: 0.0465 - dense_11_loss: 0.0372 - dense_14_loss: 0.0553 - dense_17_loss: 0.0622 - dense_20_loss: 0.0265 - dense_23_loss: 0.0603 - dense_26_loss: 0.0356 - dense_29_loss: 0.0609 - dense_32_loss: 0.0485 - dense_35_loss: 0.0466 - dense_38_loss: 0.0514 - dense_5_accuracy: 0.8901 - dense_8_accuracy: 0.7127 - dense_11_accuracy: 0.8234 - dense_14_accuracy: 0.7027 - dense_17_accuracy: 0.6166 - dense_20_accuracy: 0.8860 - dense_23_accuracy: 0.6400 - dense_26_accuracy: 0.8610 - dense_29_accuracy: 0.6374 - dense_32_accuracy: 0.7646 - dense_35_accuracy: 0.7811 - dense_38_accuracy: 0.7437 - val_loss: 1.1144 - val_dense_5_loss: 0.0560 - val_dense_8_loss: 0.1873 - val_dense_11_loss: 0.0516 - val_dense_14_loss: 0.1181 - val_dense_17_loss: 0.0892 - val_dense_20_loss: 0.0288 - val_dense_23_loss: 0.1145 - val_dense_26_loss: 0.0603 - val_dense_29_loss: 0.1542 - val_dense_32_loss: 0.0501 - val_dense_35_loss: 0.0913 - val_dense_38_loss: 0.1130 - val_dense_5_accuracy: 0.8200 - val_dense_8_accuracy: 0.6850 - val_dense_11_accuracy: 0.7638 - val_dense_14_accuracy: 0.6725 - val_dense_17_accuracy: 0.4725 - val_dense_20_accuracy: 0.8750 - val_dense_23_accuracy: 0.5650 - val_dense_26_accuracy: 0.7800 - val_dense_29_accuracy: 0.6637 - val_dense_32_accuracy: 0.7775 - val_dense_35_accuracy: 0.8025 - val_dense_38_accuracy: 0.2475\n",
            "Epoch 4/8\n",
            "3878/3878 [==============================] - 75s 19ms/step - loss: 0.5365 - dense_5_loss: 0.0248 - dense_8_loss: 0.0451 - dense_11_loss: 0.0358 - dense_14_loss: 0.0527 - dense_17_loss: 0.0600 - dense_20_loss: 0.0250 - dense_23_loss: 0.0582 - dense_26_loss: 0.0337 - dense_29_loss: 0.0594 - dense_32_loss: 0.0462 - dense_35_loss: 0.0461 - dense_38_loss: 0.0496 - dense_5_accuracy: 0.8842 - dense_8_accuracy: 0.7158 - dense_11_accuracy: 0.8319 - dense_14_accuracy: 0.7246 - dense_17_accuracy: 0.6403 - dense_20_accuracy: 0.8881 - dense_23_accuracy: 0.6612 - dense_26_accuracy: 0.8672 - dense_29_accuracy: 0.6542 - dense_32_accuracy: 0.7919 - dense_35_accuracy: 0.7808 - dense_38_accuracy: 0.7589 - val_loss: 1.4572 - val_dense_5_loss: 0.1233 - val_dense_8_loss: 0.2080 - val_dense_11_loss: 0.1707 - val_dense_14_loss: 0.1753 - val_dense_17_loss: 0.0792 - val_dense_20_loss: 0.1135 - val_dense_23_loss: 0.0950 - val_dense_26_loss: 0.0566 - val_dense_29_loss: 0.0935 - val_dense_32_loss: 0.1491 - val_dense_35_loss: 0.1102 - val_dense_38_loss: 0.0828 - val_dense_5_accuracy: 0.8200 - val_dense_8_accuracy: 0.6850 - val_dense_11_accuracy: 0.5462 - val_dense_14_accuracy: 0.6725 - val_dense_17_accuracy: 0.4650 - val_dense_20_accuracy: 0.8750 - val_dense_23_accuracy: 0.5650 - val_dense_26_accuracy: 0.7800 - val_dense_29_accuracy: 0.4000 - val_dense_32_accuracy: 0.1875 - val_dense_35_accuracy: 0.8025 - val_dense_38_accuracy: 0.7625\n",
            "Epoch 5/8\n",
            "3878/3878 [==============================] - 75s 19ms/step - loss: 0.5217 - dense_5_loss: 0.0230 - dense_8_loss: 0.0446 - dense_11_loss: 0.0347 - dense_14_loss: 0.0514 - dense_17_loss: 0.0586 - dense_20_loss: 0.0243 - dense_23_loss: 0.0567 - dense_26_loss: 0.0315 - dense_29_loss: 0.0581 - dense_32_loss: 0.0446 - dense_35_loss: 0.0449 - dense_38_loss: 0.0493 - dense_5_accuracy: 0.8971 - dense_8_accuracy: 0.7287 - dense_11_accuracy: 0.8368 - dense_14_accuracy: 0.7303 - dense_17_accuracy: 0.6423 - dense_20_accuracy: 0.8920 - dense_23_accuracy: 0.6697 - dense_26_accuracy: 0.8739 - dense_29_accuracy: 0.6666 - dense_32_accuracy: 0.7862 - dense_35_accuracy: 0.7808 - dense_38_accuracy: 0.7602 - val_loss: 1.5882 - val_dense_5_loss: 0.1156 - val_dense_8_loss: 0.1848 - val_dense_11_loss: 0.1905 - val_dense_14_loss: 0.1630 - val_dense_17_loss: 0.1327 - val_dense_20_loss: 0.1959 - val_dense_23_loss: 0.1057 - val_dense_26_loss: 0.0595 - val_dense_29_loss: 0.0922 - val_dense_32_loss: 0.1570 - val_dense_35_loss: 0.0777 - val_dense_38_loss: 0.1137 - val_dense_5_accuracy: 0.8200 - val_dense_8_accuracy: 0.6850 - val_dense_11_accuracy: 0.5025 - val_dense_14_accuracy: 0.6725 - val_dense_17_accuracy: 0.4100 - val_dense_20_accuracy: 0.7850 - val_dense_23_accuracy: 0.5650 - val_dense_26_accuracy: 0.7750 - val_dense_29_accuracy: 0.6450 - val_dense_32_accuracy: 0.6837 - val_dense_35_accuracy: 0.8025 - val_dense_38_accuracy: 0.3000\n",
            "Epoch 6/8\n",
            "3878/3878 [==============================] - 75s 19ms/step - loss: 0.5137 - dense_5_loss: 0.0222 - dense_8_loss: 0.0436 - dense_11_loss: 0.0338 - dense_14_loss: 0.0509 - dense_17_loss: 0.0575 - dense_20_loss: 0.0244 - dense_23_loss: 0.0568 - dense_26_loss: 0.0314 - dense_29_loss: 0.0582 - dense_32_loss: 0.0436 - dense_35_loss: 0.0441 - dense_38_loss: 0.0471 - dense_5_accuracy: 0.8945 - dense_8_accuracy: 0.7295 - dense_11_accuracy: 0.8458 - dense_14_accuracy: 0.7354 - dense_17_accuracy: 0.6661 - dense_20_accuracy: 0.8876 - dense_23_accuracy: 0.6828 - dense_26_accuracy: 0.8729 - dense_29_accuracy: 0.6689 - dense_32_accuracy: 0.7883 - dense_35_accuracy: 0.7821 - dense_38_accuracy: 0.7633 - val_loss: 2.5981 - val_dense_5_loss: 0.0309 - val_dense_8_loss: 0.2537 - val_dense_11_loss: 0.1778 - val_dense_14_loss: 0.1891 - val_dense_17_loss: 0.5239 - val_dense_20_loss: 0.1011 - val_dense_23_loss: 0.4312 - val_dense_26_loss: 0.0506 - val_dense_29_loss: 0.1331 - val_dense_32_loss: 0.2368 - val_dense_35_loss: 0.2659 - val_dense_38_loss: 0.2040 - val_dense_5_accuracy: 0.8512 - val_dense_8_accuracy: 0.6850 - val_dense_11_accuracy: 0.5163 - val_dense_14_accuracy: 0.6725 - val_dense_17_accuracy: 0.6575 - val_dense_20_accuracy: 0.8750 - val_dense_23_accuracy: 0.5650 - val_dense_26_accuracy: 0.7800 - val_dense_29_accuracy: 0.6062 - val_dense_32_accuracy: 0.8138 - val_dense_35_accuracy: 0.8025 - val_dense_38_accuracy: 0.2562\n",
            "Epoch 7/8\n",
            "3878/3878 [==============================] - 75s 19ms/step - loss: 0.5251 - dense_5_loss: 0.0238 - dense_8_loss: 0.0448 - dense_11_loss: 0.0348 - dense_14_loss: 0.0520 - dense_17_loss: 0.0592 - dense_20_loss: 0.0260 - dense_23_loss: 0.0573 - dense_26_loss: 0.0320 - dense_29_loss: 0.0585 - dense_32_loss: 0.0438 - dense_35_loss: 0.0444 - dense_38_loss: 0.0485 - dense_5_accuracy: 0.8868 - dense_8_accuracy: 0.7231 - dense_11_accuracy: 0.8386 - dense_14_accuracy: 0.7228 - dense_17_accuracy: 0.6359 - dense_20_accuracy: 0.8865 - dense_23_accuracy: 0.6733 - dense_26_accuracy: 0.8708 - dense_29_accuracy: 0.6686 - dense_32_accuracy: 0.7842 - dense_35_accuracy: 0.7821 - dense_38_accuracy: 0.7625 - val_loss: 5.4619 - val_dense_5_loss: 0.7235 - val_dense_8_loss: 0.4266 - val_dense_11_loss: 0.1925 - val_dense_14_loss: 0.4983 - val_dense_17_loss: 0.4284 - val_dense_20_loss: 0.4248 - val_dense_23_loss: 0.5267 - val_dense_26_loss: 0.3730 - val_dense_29_loss: 1.0956 - val_dense_32_loss: 0.2664 - val_dense_35_loss: 0.2128 - val_dense_38_loss: 0.2934 - val_dense_5_accuracy: 0.7300 - val_dense_8_accuracy: 0.6850 - val_dense_11_accuracy: 0.5612 - val_dense_14_accuracy: 0.6725 - val_dense_17_accuracy: 0.6125 - val_dense_20_accuracy: 0.8313 - val_dense_23_accuracy: 0.5650 - val_dense_26_accuracy: 0.7462 - val_dense_29_accuracy: 0.6637 - val_dense_32_accuracy: 0.8263 - val_dense_35_accuracy: 0.7987 - val_dense_38_accuracy: 0.2550\n",
            "Epoch 8/8\n",
            "3878/3878 [==============================] - 75s 19ms/step - loss: 0.5104 - dense_5_loss: 0.0219 - dense_8_loss: 0.0421 - dense_11_loss: 0.0336 - dense_14_loss: 0.0514 - dense_17_loss: 0.0586 - dense_20_loss: 0.0240 - dense_23_loss: 0.0558 - dense_26_loss: 0.0312 - dense_29_loss: 0.0574 - dense_32_loss: 0.0430 - dense_35_loss: 0.0447 - dense_38_loss: 0.0467 - dense_5_accuracy: 0.8979 - dense_8_accuracy: 0.7452 - dense_11_accuracy: 0.8424 - dense_14_accuracy: 0.7323 - dense_17_accuracy: 0.6506 - dense_20_accuracy: 0.8883 - dense_23_accuracy: 0.6813 - dense_26_accuracy: 0.8711 - dense_29_accuracy: 0.6697 - dense_32_accuracy: 0.7901 - dense_35_accuracy: 0.7816 - dense_38_accuracy: 0.7684 - val_loss: 0.9813 - val_dense_5_loss: 0.0273 - val_dense_8_loss: 0.1336 - val_dense_11_loss: 0.1259 - val_dense_14_loss: 0.0790 - val_dense_17_loss: 0.0925 - val_dense_20_loss: 0.0796 - val_dense_23_loss: 0.1102 - val_dense_26_loss: 0.0637 - val_dense_29_loss: 0.0913 - val_dense_32_loss: 0.0577 - val_dense_35_loss: 0.0493 - val_dense_38_loss: 0.0713 - val_dense_5_accuracy: 0.8375 - val_dense_8_accuracy: 0.6850 - val_dense_11_accuracy: 0.7887 - val_dense_14_accuracy: 0.6725 - val_dense_17_accuracy: 0.5512 - val_dense_20_accuracy: 0.8800 - val_dense_23_accuracy: 0.5650 - val_dense_26_accuracy: 0.8025 - val_dense_29_accuracy: 0.3913 - val_dense_32_accuracy: 0.5063 - val_dense_35_accuracy: 0.8025 - val_dense_38_accuracy: 0.7150\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.callbacks.callbacks.History at 0x7f553c6e5f28>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SLYx1C8dT8yV",
        "colab_type": "code",
        "colab": {},
        "outputId": "1d8a6ea0-adae-4715-b42b-44e42cab7d6d"
      },
      "source": [
        "len(train_x[1:20])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "19"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 53
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "A1T8GuIwlize",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "labels = model.predict(validation_x)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8H6yoZCUlm4f",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "d8af1385-7f24-4882-dbeb-7fa3bc19806a"
      },
      "source": [
        "preducted_y = []\n",
        "\n",
        "for row in np.array(labels).T[0]:\n",
        "  building_density_labels = row[:3]\n",
        "  regular_labels = row[3:5]\n",
        "  grenary_labels = row[5:8]\n",
        "\n",
        "  max_building_desnsity = max(building_density_labels)\n",
        "  max_regular = max(regular_labels)\n",
        "  max_grenary = max(grenary_labels)\n",
        "\n",
        "  building_index = 0\n",
        "  regular_index = 0\n",
        "  greenary_index = 0\n",
        "\n",
        "  for index in range(len(building_density_labels)):\n",
        "    if(building_density_labels[index] == max_building_desnsity):\n",
        "      print(building_density_labels[index],index)\n",
        "      building_index = index\n",
        "\n",
        "  for index in range(len(regular_labels)):\n",
        "    if(regular_labels[index] == max_regular):\n",
        "      print(regular_labels[index],index+3)\n",
        "      regular_index = index+3\n",
        "\n",
        "  for index in range(len(grenary_labels)):\n",
        "    if(grenary_labels[index] == max_grenary):\n",
        "      print(grenary_labels[index],index+5)\n",
        "      greenary_index = index+5\n",
        "\n",
        "  list = [0,0,0,0,0,0,0,0,0,0,0,0]\n",
        "\n",
        "  list[building_index] = 1\n",
        "  list[regular_index] = 1\n",
        "  list[greenary_index] = 1\n",
        "  list[8] = 1 if row[8] >= .5 else 0\n",
        "  list[9] = 1 if row[9] >= .5 else 0\n",
        "  list[10] = 1 if row[10] >= .5 else 0\n",
        "  list[11] = 1 if row[11] >= .5 else 0\n",
        "\n",
        "  preducted_y.append(list)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
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          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ygLd3acXlnFg",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import sklearn.metrics as skm\n",
        "import numpy as np"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3DCP7ZSmlnDV",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 624
        },
        "outputId": "143a21fd-a131-4428-bad6-4b4d68558180"
      },
      "source": [
        "cm = skm.multilabel_confusion_matrix(np.array(validation_y).T, preducted_y,labels=[0,1,2,3,4,5,6,7,8,9,10,11])\n",
        "print(cm)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[[[612  44]\n",
            "  [ 68  76]]\n",
            "\n",
            " [[548   0]\n",
            "  [252   0]]\n",
            "\n",
            " [[102 299]\n",
            "  [ 18 381]]\n",
            "\n",
            " [[538   0]\n",
            "  [261   1]]\n",
            "\n",
            " [[  1 273]\n",
            "  [  0 526]]\n",
            "\n",
            " [[618  82]\n",
            "  [ 48  52]]\n",
            "\n",
            " [[397  55]\n",
            "  [301  47]]\n",
            "\n",
            " [[223 401]\n",
            "  [ 13 163]]\n",
            "\n",
            " [[263   6]\n",
            "  [481  50]]\n",
            "\n",
            " [[130  20]\n",
            "  [375 275]]\n",
            "\n",
            " [[642   0]\n",
            "  [158   0]]\n",
            "\n",
            " [[166  32]\n",
            "  [196 406]]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JxQgv1LRl4Af",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 419
        },
        "outputId": "a2eed753-9c77-4508-a705-6d63ba7347b0"
      },
      "source": [
        "print(skm.classification_report(np.array(validation_y).T,preducted_y))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.63      0.53      0.58       144\n",
            "           1       0.00      0.00      0.00       252\n",
            "           2       0.56      0.95      0.71       399\n",
            "           3       1.00      0.00      0.01       262\n",
            "           4       0.66      1.00      0.79       526\n",
            "           5       0.39      0.52      0.44       100\n",
            "           6       0.46      0.14      0.21       348\n",
            "           7       0.29      0.93      0.44       176\n",
            "           8       0.89      0.09      0.17       531\n",
            "           9       0.93      0.42      0.58       650\n",
            "          10       0.00      0.00      0.00       158\n",
            "          11       0.93      0.67      0.78       602\n",
            "\n",
            "   micro avg       0.62      0.48      0.54      4148\n",
            "   macro avg       0.56      0.44      0.39      4148\n",
            "weighted avg       0.68      0.48      0.46      4148\n",
            " samples avg       0.62      0.50      0.54      4148\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/sklearn/metrics/_classification.py:1272: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Y6JcSEI2l_Es",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "fields = ['High Building Density (>80%)', 'Moderate Building Density (50%-80%)' , 'Low Building Density(<50%)' , 'Regular Layout', 'Irregular Layout', 'Dense Greenery(>50%)', 'Sparse Greenery (10-50%)', 'Very Sparse Greenery(<5%)', 'Grass', 'Trees', 'Large Buildings', 'Exposed Soil']  "
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QX_SdzsymAKv",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 72
        },
        "outputId": "85a4677f-8f6e-43a9-d50e-61a89298e21d"
      },
      "source": [
        "import seaborn as sns"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/statsmodels/tools/_testing.py:19: FutureWarning: pandas.util.testing is deprecated. Use the functions in the public API at pandas.testing instead.\n",
            "  import pandas.util.testing as tm\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eSXZzwEgmAcE",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "31651958-e13c-47e9-fee5-b6a7d9b11d81"
      },
      "source": [
        "def printConfusion_Matrix(c , classes_info , title):\n",
        "  plt.pause(0.01)\n",
        "  plt.figure()\n",
        "  ax= plt.subplot()\n",
        "  sns_plot = sns.heatmap(c, annot=True, ax = ax  ,cmap='Greens' ,fmt='g');\n",
        "\n",
        "  # labels, title and ticks\n",
        "  ax.set_xlabel('Predicted labels');ax.set_ylabel('True labels'); \n",
        "  ax.set_title(title); \n",
        "  ax.xaxis.set_ticklabels(classes_info); ax.yaxis.set_ticklabels(classes_info);\n",
        "  sns_plot.get_figure().savefig(title+\".png\")\n",
        "\n",
        "\n",
        "\n",
        "printConfusion_Matrix(cm[0],['-ive','+ive'] , fields[0])\n",
        "printConfusion_Matrix(cm[1],['-ive','+ive'] , fields[1])\n",
        "printConfusion_Matrix(cm[2],['-ive','+ive'] , fields[2])\n",
        "printConfusion_Matrix(cm[3],['-ive','+ive'] , fields[3])\n",
        "printConfusion_Matrix(cm[4],['-ive','+ive'] , fields[4])\n",
        "printConfusion_Matrix(cm[5],['-ive','+ive'] , fields[5])\n",
        "printConfusion_Matrix(cm[6],['-ive','+ive'] , fields[6])\n",
        "printConfusion_Matrix(cm[7],['-ive','+ive'] , fields[7])\n",
        "printConfusion_Matrix(cm[8],['-ive','+ive'] , fields[8])\n",
        "printConfusion_Matrix(cm[9],['-ive','+ive'] , fields[9])\n",
        "printConfusion_Matrix(cm[10],['-ive','+ive'] , fields[10])\n",
        "printConfusion_Matrix(cm[11],['-ive','+ive'] , fields[11])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "83mO0jHwmAZx",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bY5YQFOamAXa",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Dc0w6GcPmAT-",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0GIF11YHKbVW",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "1bdf9f3d-9b82-4822-9b38-9e25d34f04cb"
      },
      "source": [
        "model.summary()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"model_4\"\n",
            "__________________________________________________________________________________________________\n",
            "Layer (type)                    Output Shape         Param #     Connected to                     \n",
            "==================================================================================================\n",
            "input_4 (InputLayer)            (None, 256, 256, 3)  0                                            \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_31 (Conv2D)              (None, 256, 256, 64) 1792        input_4[0][0]                    \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_32 (Conv2D)              (None, 256, 256, 64) 36928       conv2d_31[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "max_pooling2d_16 (MaxPooling2D) (None, 128, 128, 64) 0           conv2d_32[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_33 (Conv2D)              (None, 128, 128, 128 73856       max_pooling2d_16[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_34 (Conv2D)              (None, 128, 128, 128 147584      conv2d_33[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "max_pooling2d_17 (MaxPooling2D) (None, 64, 64, 128)  0           conv2d_34[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_35 (Conv2D)              (None, 64, 64, 256)  295168      max_pooling2d_17[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_36 (Conv2D)              (None, 64, 64, 256)  590080      conv2d_35[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "max_pooling2d_18 (MaxPooling2D) (None, 32, 32, 256)  0           conv2d_36[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_37 (Conv2D)              (None, 32, 32, 512)  1180160     max_pooling2d_18[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_38 (Conv2D)              (None, 32, 32, 512)  2359808     conv2d_37[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "dropout_7 (Dropout)             (None, 32, 32, 512)  0           conv2d_38[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "max_pooling2d_19 (MaxPooling2D) (None, 16, 16, 512)  0           dropout_7[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_39 (Conv2D)              (None, 16, 16, 1024) 4719616     max_pooling2d_19[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_40 (Conv2D)              (None, 16, 16, 1024) 9438208     conv2d_39[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "dropout_8 (Dropout)             (None, 16, 16, 1024) 0           conv2d_40[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "max_pooling2d_20 (MaxPooling2D) (None, 8, 8, 1024)   0           dropout_8[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "flatten_4 (Flatten)             (None, 65536)        0           max_pooling2d_20[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "dense_115 (Dense)               (None, 1024)         67109888    flatten_4[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "dense_116 (Dense)               (None, 512)          524800      dense_115[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "dense_117 (Dense)               (None, 256)          131328      dense_116[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "dense_120 (Dense)               (None, 256)          131328      dense_116[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "dense_123 (Dense)               (None, 256)          131328      dense_116[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "dense_126 (Dense)               (None, 256)          131328      dense_116[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "dense_129 (Dense)               (None, 256)          131328      dense_116[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "dense_132 (Dense)               (None, 256)          131328      dense_116[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "dense_135 (Dense)               (None, 256)          131328      dense_116[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "dense_138 (Dense)               (None, 256)          131328      dense_116[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "dense_141 (Dense)               (None, 256)          131328      dense_116[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "dense_144 (Dense)               (None, 256)          131328      dense_116[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "dense_147 (Dense)               (None, 256)          131328      dense_116[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "dense_150 (Dense)               (None, 256)          131328      dense_116[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "dense_118 (Dense)               (None, 64)           16448       dense_117[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "dense_121 (Dense)               (None, 64)           16448       dense_120[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "dense_124 (Dense)               (None, 64)           16448       dense_123[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "dense_127 (Dense)               (None, 64)           16448       dense_126[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "dense_130 (Dense)               (None, 64)           16448       dense_129[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "dense_133 (Dense)               (None, 64)           16448       dense_132[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "dense_136 (Dense)               (None, 64)           16448       dense_135[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "dense_139 (Dense)               (None, 64)           16448       dense_138[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "dense_142 (Dense)               (None, 64)           16448       dense_141[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "dense_145 (Dense)               (None, 64)           16448       dense_144[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "dense_148 (Dense)               (None, 64)           16448       dense_147[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "dense_151 (Dense)               (None, 64)           16448       dense_150[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "dense_119 (Dense)               (None, 1)            65          dense_118[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "dense_122 (Dense)               (None, 1)            65          dense_121[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "dense_125 (Dense)               (None, 1)            65          dense_124[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "dense_128 (Dense)               (None, 1)            65          dense_127[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "dense_131 (Dense)               (None, 1)            65          dense_130[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "dense_134 (Dense)               (None, 1)            65          dense_133[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "dense_137 (Dense)               (None, 1)            65          dense_136[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "dense_140 (Dense)               (None, 1)            65          dense_139[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "dense_143 (Dense)               (None, 1)            65          dense_142[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "dense_146 (Dense)               (None, 1)            65          dense_145[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "dense_149 (Dense)               (None, 1)            65          dense_148[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "dense_152 (Dense)               (None, 1)            65          dense_151[0][0]                  \n",
            "==================================================================================================\n",
            "Total params: 88,251,980\n",
            "Trainable params: 88,251,980\n",
            "Non-trainable params: 0\n",
            "__________________________________________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rVrTFY2K3Uiq",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "model.save(\"/content/drive/My Drive/DL proj/focalLossModel.h5\")"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1dHs_3KZTK1G",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "results  = model.evaluate(validation_x,validation_y,batch_size=32,verbose=0)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Z0H1MeT8Ptwk",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "572c8a9e-6309-435e-eb2d-c842cc565058"
      },
      "source": [
        "# New Section\n",
        "sum(results[-12:])/len(results[-12:])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.758500004808108"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 43
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5qLrwcivMWxP",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 221
        },
        "outputId": "3f613c5c-d03f-4ca0-8042-b5b6b0d137b0"
      },
      "source": [
        "img_pred= image.load_img(\"/content/2GroupsData/Peshawar_1-1tile_0-11000.png\",target_size=(256,256))\n",
        "img_pred= image.img_to_array(img_pred)\n",
        "img_pred= np.expand_dims(img_pred,axis=0)\n",
        "model.predict(img_pred)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[array([[0.]], dtype=float32),\n",
              " array([[1.7956413e-36]], dtype=float32),\n",
              " array([[1.]], dtype=float32),\n",
              " array([[0.]], dtype=float32),\n",
              " array([[1.]], dtype=float32),\n",
              " array([[0.]], dtype=float32),\n",
              " array([[0.9982382]], dtype=float32),\n",
              " array([[0.]], dtype=float32),\n",
              " array([[1.]], dtype=float32),\n",
              " array([[1.]], dtype=float32),\n",
              " array([[1.8678796e-33]], dtype=float32),\n",
              " array([[1.]], dtype=float32)]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 38
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zi5tgYOVPXl7",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 221
        },
        "outputId": "203638ad-56c5-4995-8221-92364ddddaa3"
      },
      "source": [
        "img_pred= image.load_img(\"/content/2GroupsData/imageNo_1617.png\",target_size=(256,256))\n",
        "img_pred= image.img_to_array(img_pred)\n",
        "img_pred= np.expand_dims(img_pred,axis=0)\n",
        "model.predict(img_pred)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[array([[1.]], dtype=float32),\n",
              " array([[7.6461017e-28]], dtype=float32),\n",
              " array([[0.]], dtype=float32),\n",
              " array([[0.]], dtype=float32),\n",
              " array([[1.]], dtype=float32),\n",
              " array([[0.]], dtype=float32),\n",
              " array([[1.0397109e-33]], dtype=float32),\n",
              " array([[1.]], dtype=float32),\n",
              " array([[3.2209335e-09]], dtype=float32),\n",
              " array([[1.]], dtype=float32),\n",
              " array([[6.578986e-30]], dtype=float32),\n",
              " array([[2.583601e-23]], dtype=float32)]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 39
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3E8PWkMLTKPo",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "model.get_weights()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gxAsEvYyuzD4",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "new_model = load_model(\"/content/drive/My Drive/e32GData.h5\")"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "q44b0X5svHK7",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 88
        },
        "outputId": "1471bb1c-847c-4579-8d93-818361ececac"
      },
      "source": [
        "response = new_model.fit(train_x,train_y,validation_data=(validation_x,validation_y),epochs=1, batch_size=32, verbose=1)\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Train on 2701 samples, validate on 500 samples\n",
            "Epoch 1/1\n",
            "2701/2701 [==============================] - 5982s 2s/step - loss: 5.0136 - dense_5_loss: 0.2885 - dense_8_loss: 0.5311 - dense_11_loss: 0.4012 - dense_14_loss: 0.4887 - dense_17_loss: 0.5097 - dense_20_loss: 0.2223 - dense_23_loss: 0.5277 - dense_26_loss: 0.2900 - dense_29_loss: 0.4889 - dense_32_loss: 0.2644 - dense_35_loss: 0.5216 - dense_38_loss: 0.4780 - dense_5_accuracy: 0.8719 - dense_8_accuracy: 0.7238 - dense_11_accuracy: 0.8245 - dense_14_accuracy: 0.7605 - dense_17_accuracy: 0.7427 - dense_20_accuracy: 0.9137 - dense_23_accuracy: 0.7279 - dense_26_accuracy: 0.8808 - dense_29_accuracy: 0.7656 - dense_32_accuracy: 0.9126 - dense_35_accuracy: 0.7453 - dense_38_accuracy: 0.7497 - val_loss: 6.1079 - val_dense_5_loss: 0.3854 - val_dense_8_loss: 0.5964 - val_dense_11_loss: 0.5126 - val_dense_14_loss: 0.5470 - val_dense_17_loss: 0.5583 - val_dense_20_loss: 0.3435 - val_dense_23_loss: 0.5784 - val_dense_26_loss: 0.4202 - val_dense_29_loss: 0.5735 - val_dense_32_loss: 0.4279 - val_dense_35_loss: 0.6138 - val_dense_38_loss: 0.5439 - val_dense_5_accuracy: 0.8920 - val_dense_8_accuracy: 0.7480 - val_dense_11_accuracy: 0.8460 - val_dense_14_accuracy: 0.8000 - val_dense_17_accuracy: 0.7940 - val_dense_20_accuracy: 0.9400 - val_dense_23_accuracy: 0.7540 - val_dense_26_accuracy: 0.8680 - val_dense_29_accuracy: 0.7740 - val_dense_32_accuracy: 0.9020 - val_dense_35_accuracy: 0.7560 - val_dense_38_accuracy: 0.7620\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gZBOigPy_p3w",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "new_model.save(\"/content/e32GData_1e.h5\")"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lbcQKapHvqJ2",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#NEW MODEL PHASE 2"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "a9pw7Uidd1Kq",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "new_model2 = load_model(\"/content/drive/My Drive/e32GData_1e.h5\")"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4ADDxffZT--W",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 88
        },
        "outputId": "9520d0f6-57be-4abd-8dcf-a36dec513735"
      },
      "source": [
        "response2 = new_model2.fit(train_x,train_y,validation_data=(validation_x,validation_y),epochs=1, batch_size=32, verbose=1)\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Train on 2701 samples, validate on 500 samples\n",
            "Epoch 1/1\n",
            "2701/2701 [==============================] - 5955s 2s/step - loss: 4.8778 - dense_5_loss: 0.2738 - dense_8_loss: 0.5159 - dense_11_loss: 0.3930 - dense_14_loss: 0.4833 - dense_17_loss: 0.5044 - dense_20_loss: 0.2095 - dense_23_loss: 0.4996 - dense_26_loss: 0.2708 - dense_29_loss: 0.4812 - dense_32_loss: 0.2611 - dense_35_loss: 0.5168 - dense_38_loss: 0.4687 - dense_5_accuracy: 0.8800 - dense_8_accuracy: 0.7320 - dense_11_accuracy: 0.8197 - dense_14_accuracy: 0.7705 - dense_17_accuracy: 0.7516 - dense_20_accuracy: 0.9211 - dense_23_accuracy: 0.7527 - dense_26_accuracy: 0.8860 - dense_29_accuracy: 0.7690 - dense_32_accuracy: 0.9130 - dense_35_accuracy: 0.7549 - dense_38_accuracy: 0.7579 - val_loss: 5.8247 - val_dense_5_loss: 0.3586 - val_dense_8_loss: 0.5978 - val_dense_11_loss: 0.4900 - val_dense_14_loss: 0.5564 - val_dense_17_loss: 0.5732 - val_dense_20_loss: 0.3192 - val_dense_23_loss: 0.5912 - val_dense_26_loss: 0.3661 - val_dense_29_loss: 0.5341 - val_dense_32_loss: 0.3180 - val_dense_35_loss: 0.5550 - val_dense_38_loss: 0.5599 - val_dense_5_accuracy: 0.8920 - val_dense_8_accuracy: 0.7580 - val_dense_11_accuracy: 0.8400 - val_dense_14_accuracy: 0.7520 - val_dense_17_accuracy: 0.7380 - val_dense_20_accuracy: 0.9180 - val_dense_23_accuracy: 0.7400 - val_dense_26_accuracy: 0.8500 - val_dense_29_accuracy: 0.7480 - val_dense_32_accuracy: 0.9020 - val_dense_35_accuracy: 0.7580 - val_dense_38_accuracy: 0.7540\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0Cyy2iery19F",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "new_model2.save(\"/content/e32GData_1e_1e.h5\")"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-z-iC_9sUyMO",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "new_model_all = load_model(\"/content/drive/My Drive/e32GData_1e_1e.h5\")"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jrN1sWLuarTS",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 88
        },
        "outputId": "4757255d-288c-438a-e5fd-ac54e6dee01d"
      },
      "source": [
        "response2 = new_model_all.fit(train_x,train_y,validation_data=(validation_x,validation_y),epochs=1, batch_size=32, verbose=1)\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Train on 3878 samples, validate on 800 samples\n",
            "Epoch 1/1\n",
            "3878/3878 [==============================] - 8573s 2s/step - loss: 5.0010 - dense_5_loss: 0.2240 - dense_8_loss: 0.4442 - dense_11_loss: 0.3367 - dense_14_loss: 0.5401 - dense_17_loss: 0.5539 - dense_20_loss: 0.2605 - dense_23_loss: 0.5400 - dense_26_loss: 0.3084 - dense_29_loss: 0.5189 - dense_32_loss: 0.3810 - dense_35_loss: 0.4686 - dense_38_loss: 0.4258 - dense_5_accuracy: 0.9020 - dense_8_accuracy: 0.7775 - dense_11_accuracy: 0.8584 - dense_14_accuracy: 0.7339 - dense_17_accuracy: 0.7148 - dense_20_accuracy: 0.8889 - dense_23_accuracy: 0.7120 - dense_26_accuracy: 0.8871 - dense_29_accuracy: 0.7367 - dense_32_accuracy: 0.8345 - dense_35_accuracy: 0.7824 - dense_38_accuracy: 0.8020 - val_loss: 5.9974 - val_dense_5_loss: 0.3074 - val_dense_8_loss: 0.5264 - val_dense_11_loss: 0.4443 - val_dense_14_loss: 0.5918 - val_dense_17_loss: 0.6041 - val_dense_20_loss: 0.3457 - val_dense_23_loss: 0.5899 - val_dense_26_loss: 0.4177 - val_dense_29_loss: 0.5748 - val_dense_32_loss: 0.4867 - val_dense_35_loss: 0.5956 - val_dense_38_loss: 0.5130 - val_dense_5_accuracy: 0.9075 - val_dense_8_accuracy: 0.7862 - val_dense_11_accuracy: 0.8763 - val_dense_14_accuracy: 0.7550 - val_dense_17_accuracy: 0.7275 - val_dense_20_accuracy: 0.9125 - val_dense_23_accuracy: 0.7337 - val_dense_26_accuracy: 0.8650 - val_dense_29_accuracy: 0.7088 - val_dense_32_accuracy: 0.8363 - val_dense_35_accuracy: 0.7425 - val_dense_38_accuracy: 0.7837\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "g0Qrvzf2a-vA",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "new_model_all.save(\"/content/e32GData_1e_1e_AD1e.h5\")"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qsFw0A3g7Unk",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 89
        },
        "outputId": "f1ad56d0-d7be-4af0-826c-7d3236adfeb5"
      },
      "source": [
        "results = model.evaluate(validation_x,validation_y)\n",
        "print(results)\n",
        "print(sum(results[-12:])/len(results[-12:]))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "800/800 [==============================] - 4s 6ms/step\n",
            "[1.7467338609695435, 0.2670135200023651, 0.1825803518295288, 0.309762179851532, 0.075632244348526, 0.13917727768421173, 0.11095365881919861, 0.09509576857089996, 0.09525390714406967, 0.156975656747818, 0.12301097810268402, 0.08907577395439148, 0.10220254212617874, 0.8199999928474426, 0.6850000023841858, 0.4325000047683716, 0.65625, 0.5299999713897705, 0.8762500286102295, 0.5649999976158142, 0.7799999713897705, 0.6825000047683716, 0.23000000417232513, 0.8025000095367432, 0.7049999833106995]\n",
            "0.647083330899477\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fj1OTg8W8G2j",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 88
        },
        "outputId": "9ebd2e40-72e2-4189-a6ac-cd9b015ebe79"
      },
      "source": [
        "response2 = new_model_all.fit(train_x,train_y,validation_data=(validation_x,validation_y),epochs=1, batch_size=32, verbose=1)\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Train on 3878 samples, validate on 800 samples\n",
            "Epoch 1/1\n",
            "3878/3878 [==============================] - 8782s 2s/step - loss: 4.8467 - dense_5_loss: 0.2238 - dense_8_loss: 0.4314 - dense_11_loss: 0.3275 - dense_14_loss: 0.5184 - dense_17_loss: 0.5350 - dense_20_loss: 0.2498 - dense_23_loss: 0.5201 - dense_26_loss: 0.2870 - dense_29_loss: 0.5107 - dense_32_loss: 0.3628 - dense_35_loss: 0.4635 - dense_38_loss: 0.4097 - dense_5_accuracy: 0.9036 - dense_8_accuracy: 0.7816 - dense_11_accuracy: 0.8556 - dense_14_accuracy: 0.7450 - dense_17_accuracy: 0.7267 - dense_20_accuracy: 0.8943 - dense_23_accuracy: 0.7357 - dense_26_accuracy: 0.8860 - dense_29_accuracy: 0.7468 - dense_32_accuracy: 0.8476 - dense_35_accuracy: 0.7875 - dense_38_accuracy: 0.8022 - val_loss: 5.5625 - val_dense_5_loss: 0.2640 - val_dense_8_loss: 0.4876 - val_dense_11_loss: 0.3909 - val_dense_14_loss: 0.5865 - val_dense_17_loss: 0.6068 - val_dense_20_loss: 0.3163 - val_dense_23_loss: 0.5735 - val_dense_26_loss: 0.3693 - val_dense_29_loss: 0.5482 - val_dense_32_loss: 0.4017 - val_dense_35_loss: 0.5491 - val_dense_38_loss: 0.4685 - val_dense_5_accuracy: 0.9175 - val_dense_8_accuracy: 0.7912 - val_dense_11_accuracy: 0.8725 - val_dense_14_accuracy: 0.7287 - val_dense_17_accuracy: 0.7038 - val_dense_20_accuracy: 0.9062 - val_dense_23_accuracy: 0.7013 - val_dense_26_accuracy: 0.8587 - val_dense_29_accuracy: 0.7362 - val_dense_32_accuracy: 0.8512 - val_dense_35_accuracy: 0.7912 - val_dense_38_accuracy: 0.8062\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "unOVTd9Z8MWd",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "new_model_all.save(\"/content/e32GData_1e_1e_AD1e_1e.h5\")"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HWqXP3Wo_tdK",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 88
        },
        "outputId": "91aa833a-b436-4daf-8001-d92ef39837a8"
      },
      "source": [
        "response2 = new_model_all.fit(train_x,train_y,validation_data=(validation_x,validation_y),epochs=1, batch_size=32, verbose=1)\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Train on 3878 samples, validate on 800 samples\n",
            "Epoch 1/1\n",
            "3878/3878 [==============================] - 8764s 2s/step - loss: 4.6255 - dense_5_loss: 0.2080 - dense_8_loss: 0.4051 - dense_11_loss: 0.3024 - dense_14_loss: 0.4928 - dense_17_loss: 0.5134 - dense_20_loss: 0.2363 - dense_23_loss: 0.4998 - dense_26_loss: 0.2786 - dense_29_loss: 0.4907 - dense_32_loss: 0.3514 - dense_35_loss: 0.4529 - dense_38_loss: 0.3947 - dense_5_accuracy: 0.9095 - dense_8_accuracy: 0.7916 - dense_11_accuracy: 0.8664 - dense_14_accuracy: 0.7540 - dense_17_accuracy: 0.7398 - dense_20_accuracy: 0.9023 - dense_23_accuracy: 0.7527 - dense_26_accuracy: 0.8938 - dense_29_accuracy: 0.7705 - dense_32_accuracy: 0.8504 - dense_35_accuracy: 0.7811 - dense_38_accuracy: 0.8159 - val_loss: 5.7262 - val_dense_5_loss: 0.2849 - val_dense_8_loss: 0.5029 - val_dense_11_loss: 0.4357 - val_dense_14_loss: 0.5788 - val_dense_17_loss: 0.5836 - val_dense_20_loss: 0.3670 - val_dense_23_loss: 0.5878 - val_dense_26_loss: 0.3845 - val_dense_29_loss: 0.5712 - val_dense_32_loss: 0.4001 - val_dense_35_loss: 0.5388 - val_dense_38_loss: 0.4909 - val_dense_5_accuracy: 0.8850 - val_dense_8_accuracy: 0.7763 - val_dense_11_accuracy: 0.8725 - val_dense_14_accuracy: 0.7300 - val_dense_17_accuracy: 0.7200 - val_dense_20_accuracy: 0.9162 - val_dense_23_accuracy: 0.7025 - val_dense_26_accuracy: 0.8562 - val_dense_29_accuracy: 0.7425 - val_dense_32_accuracy: 0.8525 - val_dense_35_accuracy: 0.7887 - val_dense_38_accuracy: 0.8188\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uAy8pxZI_xba",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "new_model_all.save(\"/content/e32GData_1e_1e_AD1e_1e_1e.h5\")"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pVJsOKFP_zlL",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "new_model_all2 = load_model(\"/content/e32GData_1e_1e_AD1e_1e_1e.h5\")"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8nPyjV-dVHSj",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 377
        },
        "outputId": "6b169fa8-1464-4080-8cb9-fbeb6b46738b"
      },
      "source": [
        "response2 = new_model_all2.fit(train_x,train_y,validation_data=(validation_x,validation_y),epochs=1, batch_size=32, verbose=1)\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Train on 3878 samples, validate on 800 samples\n",
            "Epoch 1/1\n",
            "2272/3878 [================>.............] - ETA: 57:18 - loss: 4.4526 - dense_5_loss: 0.1973 - dense_8_loss: 0.3650 - dense_11_loss: 0.2764 - dense_14_loss: 0.4739 - dense_17_loss: 0.4883 - dense_20_loss: 0.2274 - dense_23_loss: 0.4841 - dense_26_loss: 0.2717 - dense_29_loss: 0.4948 - dense_32_loss: 0.3434 - dense_35_loss: 0.4402 - dense_38_loss: 0.3902 - dense_5_accuracy: 0.9142 - dense_8_accuracy: 0.8319 - dense_11_accuracy: 0.8908 - dense_14_accuracy: 0.7742 - dense_17_accuracy: 0.7579 - dense_20_accuracy: 0.8992 - dense_23_accuracy: 0.7575 - dense_26_accuracy: 0.8864 - dense_29_accuracy: 0.7566 - dense_32_accuracy: 0.8442 - dense_35_accuracy: 0.7918 - dense_38_accuracy: 0.8226"
          ],
          "name": "stdout"
        },
        {
          "output_type": "error",
          "ename": "KeyboardInterrupt",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-38-c8338212f57b>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mresponse2\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnew_model_all2\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtrain_x\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mtrain_y\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mvalidation_data\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalidation_x\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mvalidation_y\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mepochs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbatch_size\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m32\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mverbose\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/keras/engine/training.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, validation_freq, max_queue_size, workers, use_multiprocessing, **kwargs)\u001b[0m\n\u001b[1;32m   1237\u001b[0m                                         \u001b[0msteps_per_epoch\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msteps_per_epoch\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1238\u001b[0m                                         \u001b[0mvalidation_steps\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mvalidation_steps\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1239\u001b[0;31m                                         validation_freq=validation_freq)\n\u001b[0m\u001b[1;32m   1240\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1241\u001b[0m     def evaluate(self,\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/keras/engine/training_arrays.py\u001b[0m in \u001b[0;36mfit_loop\u001b[0;34m(model, fit_function, fit_inputs, out_labels, batch_size, epochs, verbose, callbacks, val_function, val_inputs, shuffle, initial_epoch, steps_per_epoch, validation_steps, validation_freq)\u001b[0m\n\u001b[1;32m    194\u001b[0m                     \u001b[0mins_batch\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mins_batch\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtoarray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    195\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 196\u001b[0;31m                 \u001b[0mouts\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfit_function\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mins_batch\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    197\u001b[0m                 \u001b[0mouts\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mto_list\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mouts\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    198\u001b[0m                 \u001b[0;32mfor\u001b[0m \u001b[0ml\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mo\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mzip\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mout_labels\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mouts\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/backend.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, inputs)\u001b[0m\n\u001b[1;32m   3790\u001b[0m         \u001b[0mvalue\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmath_ops\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcast\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtensor\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3791\u001b[0m       \u001b[0mconverted_inputs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3792\u001b[0;31m     \u001b[0moutputs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_graph_fn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mconverted_inputs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3793\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3794\u001b[0m     \u001b[0;31m# EagerTensor.numpy() will often make a copy to ensure memory safety.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/tensorflow/python/eager/function.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1603\u001b[0m       \u001b[0mTypeError\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mFor\u001b[0m \u001b[0minvalid\u001b[0m \u001b[0mpositional\u001b[0m\u001b[0;34m/\u001b[0m\u001b[0mkeyword\u001b[0m \u001b[0margument\u001b[0m \u001b[0mcombinations\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1604\u001b[0m     \"\"\"\n\u001b[0;32m-> 1605\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_call_impl\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1606\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1607\u001b[0m   \u001b[0;32mdef\u001b[0m \u001b[0m_call_impl\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcancellation_manager\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/tensorflow/python/eager/function.py\u001b[0m in \u001b[0;36m_call_impl\u001b[0;34m(self, args, kwargs, cancellation_manager)\u001b[0m\n\u001b[1;32m   1643\u001b[0m       raise TypeError(\"Keyword arguments {} unknown. Expected {}.\".format(\n\u001b[1;32m   1644\u001b[0m           list(kwargs.keys()), list(self._arg_keywords)))\n\u001b[0;32m-> 1645\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_call_flat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcaptured_inputs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcancellation_manager\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1646\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1647\u001b[0m   \u001b[0;32mdef\u001b[0m \u001b[0m_filtered_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/tensorflow/python/eager/function.py\u001b[0m in \u001b[0;36m_call_flat\u001b[0;34m(self, args, captured_inputs, cancellation_manager)\u001b[0m\n\u001b[1;32m   1744\u001b[0m       \u001b[0;31m# No tape is watching; skip to running the function.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1745\u001b[0m       return self._build_call_outputs(self._inference_function.call(\n\u001b[0;32m-> 1746\u001b[0;31m           ctx, args, cancellation_manager=cancellation_manager))\n\u001b[0m\u001b[1;32m   1747\u001b[0m     forward_backward = self._select_forward_and_backward_functions(\n\u001b[1;32m   1748\u001b[0m         \u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/tensorflow/python/eager/function.py\u001b[0m in \u001b[0;36mcall\u001b[0;34m(self, ctx, args, cancellation_manager)\u001b[0m\n\u001b[1;32m    596\u001b[0m               \u001b[0minputs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    597\u001b[0m               \u001b[0mattrs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mattrs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 598\u001b[0;31m               ctx=ctx)\n\u001b[0m\u001b[1;32m    599\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    600\u001b[0m           outputs = execute.execute_with_cancellation(\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/tensorflow/python/eager/execute.py\u001b[0m in \u001b[0;36mquick_execute\u001b[0;34m(op_name, num_outputs, inputs, attrs, ctx, name)\u001b[0m\n\u001b[1;32m     58\u001b[0m     \u001b[0mctx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mensure_initialized\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     59\u001b[0m     tensors = pywrap_tfe.TFE_Py_Execute(ctx._handle, device_name, op_name,\n\u001b[0;32m---> 60\u001b[0;31m                                         inputs, attrs, num_outputs)\n\u001b[0m\u001b[1;32m     61\u001b[0m   \u001b[0;32mexcept\u001b[0m \u001b[0mcore\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_NotOkStatusException\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     62\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mname\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MBEOBPd4VkKw",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "new_model_all2.save(\"/content/e32GData_1e_1e_AD1e_1e_1e_1e.h5\")"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9078SE8ZVoVm",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 88
        },
        "outputId": "9e74baeb-7890-4c28-a2ba-f2858ec61949"
      },
      "source": [
        "new_model_all2 = load_model(\"/content/e32GData_1e_1e_AD1e_1e_1e.h5\")\n",
        "results = new_model_all2.evaluate(validation_x,validation_y)\n",
        "print(results)\n",
        "print(sum(results[-12:])/len(results[-12:]))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "800/800 [==============================] - 447s 559ms/step\n",
            "[5.7262163162231445, 0.28490516543388367, 0.502893328666687, 0.43569064140319824, 0.5788265466690063, 0.5835719704627991, 0.367043673992157, 0.5877869725227356, 0.3845367431640625, 0.5712087750434875, 0.40008077025413513, 0.5388109087944031, 0.4908607602119446, 0.8849999904632568, 0.7762500047683716, 0.8725000023841858, 0.7300000190734863, 0.7200000286102295, 0.9162499904632568, 0.7024999856948853, 0.856249988079071, 0.7425000071525574, 0.8525000214576721, 0.7887499928474426, 0.8187500238418579]\n",
            "0.8051041712363561\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vUtQRiZT5NE6",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "9d91b3f9-f4de-4ab6-94ee-a3d78ba1e4e2"
      },
      "source": [
        "sum(results[-12:])/12"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.8051041712363561"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 63
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jKrmt7fcp0sP",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "predicted_y = new_model_all2.predict(validation_x)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "j4dVmI_fsYWv",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 221
        },
        "outputId": "3fa019d2-d5db-4c6f-b8e1-372b1b4a4c70"
      },
      "source": [
        "results[-12:]\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[0.8849999904632568,\n",
              " 0.7762500047683716,\n",
              " 0.8725000023841858,\n",
              " 0.7300000190734863,\n",
              " 0.7200000286102295,\n",
              " 0.9162499904632568,\n",
              " 0.7024999856948853,\n",
              " 0.856249988079071,\n",
              " 0.7425000071525574,\n",
              " 0.8525000214576721,\n",
              " 0.7887499928474426,\n",
              " 0.8187500238418579]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 66
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "l06kRgsBtWLM",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "56802f60-a27f-427e-f457-88baba5a02e0"
      },
      "source": [
        "len(validation_y[0])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "800"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 44
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "phsLF_byqbtA",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "validation_y_for_measure = np.array(validation_y).T"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tgUJoTmjosBI",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def precision(y_true, y_pred):\n",
        "    i = set(y_true).intersection(y_pred)\n",
        "    len1 = len(y_pred)\n",
        "    if len1 == 0:\n",
        "        return 0\n",
        "    else:\n",
        "        return len(i) / len1\n",
        "\n",
        "\n",
        "def recall(y_true, y_pred):\n",
        "    i = set(y_true).intersection(y_pred)\n",
        "    return len(i) / len(y_true)\n",
        "\n",
        "\n",
        "def f1(y_true, y_pred):\n",
        "    p = precision(y_true, y_pred)\n",
        "    r = recall(y_true, y_pred)\n",
        "    if p + r == 0:\n",
        "        return 0\n",
        "    else:\n",
        "        return 2 * (p * r) / (p + r)\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "q0ig5Ga-wJFD",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "adbba602-2c79-4c57-cb28-9cc9e911262d"
      },
      "source": [
        "predicted_y"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
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              "        [0.69793236],\n",
              "        [0.7179854 ]], dtype=float32)]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 59
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1seI4GIhwSwi",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 374
        },
        "outputId": "60eaf77d-9819-4fb6-9ec3-3ae6ec27ae3a"
      },
      "source": [
        "from sklearn.metrics import confusion_matrix\n",
        "import numpy as np\n",
        "\n",
        "labels = np.array(validation_y[0]).T\n",
        "predictions = np.array(predicted_y[0]).T\n",
        "\n",
        "cm = confusion_matrix(labels, predictions)\n",
        "recall = np.diag(cm) / np.sum(cm, axis = 1)\n",
        "precision = np.diag(cm) / np.sum(cm, axis = 0)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "error",
          "ename": "ValueError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-56-e53bea5bab5e>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0mpredictions\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpredicted_y\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mT\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      6\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 7\u001b[0;31m \u001b[0mcm\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mconfusion_matrix\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlabels\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpredictions\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      8\u001b[0m \u001b[0mrecall\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdiag\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcm\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m/\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msum\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcm\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      9\u001b[0m \u001b[0mprecision\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdiag\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcm\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m/\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msum\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcm\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/sklearn/metrics/_classification.py\u001b[0m in \u001b[0;36mconfusion_matrix\u001b[0;34m(y_true, y_pred, labels, sample_weight, normalize)\u001b[0m\n\u001b[1;32m    266\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    267\u001b[0m     \"\"\"\n\u001b[0;32m--> 268\u001b[0;31m     \u001b[0my_type\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_true\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_pred\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_check_targets\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_true\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_pred\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    269\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0my_type\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32min\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;34m\"binary\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"multiclass\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    270\u001b[0m         \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"%s is not supported\"\u001b[0m \u001b[0;34m%\u001b[0m \u001b[0my_type\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/sklearn/metrics/_classification.py\u001b[0m in \u001b[0;36m_check_targets\u001b[0;34m(y_true, y_pred)\u001b[0m\n\u001b[1;32m     78\u001b[0m     \u001b[0my_pred\u001b[0m \u001b[0;34m:\u001b[0m \u001b[0marray\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mindicator\u001b[0m \u001b[0mmatrix\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     79\u001b[0m     \"\"\"\n\u001b[0;32m---> 80\u001b[0;31m     \u001b[0mcheck_consistent_length\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_true\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_pred\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     81\u001b[0m     \u001b[0mtype_true\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtype_of_target\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_true\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     82\u001b[0m     \u001b[0mtype_pred\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtype_of_target\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_pred\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/sklearn/utils/validation.py\u001b[0m in \u001b[0;36mcheck_consistent_length\u001b[0;34m(*arrays)\u001b[0m\n\u001b[1;32m    210\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0muniques\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    211\u001b[0m         raise ValueError(\"Found input variables with inconsistent numbers of\"\n\u001b[0;32m--> 212\u001b[0;31m                          \" samples: %r\" % [int(l) for l in lengths])\n\u001b[0m\u001b[1;32m    213\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    214\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mValueError\u001b[0m: Found input variables with inconsistent numbers of samples: [800, 1]"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qkdcW6zQwv_h",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 646
        },
        "outputId": "e7060f4c-2d35-41d0-e564-701f76fe030f"
      },
      "source": [
        "labels"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
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            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 58
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "036vJ0TjxhHH",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 442
        },
        "outputId": "6d3d1549-6d61-417c-dc7b-31518361fff0"
      },
      "source": [
        "new_model_all2.metrics_names"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['loss',\n",
              " 'dense_5_loss',\n",
              " 'dense_8_loss',\n",
              " 'dense_11_loss',\n",
              " 'dense_14_loss',\n",
              " 'dense_17_loss',\n",
              " 'dense_20_loss',\n",
              " 'dense_23_loss',\n",
              " 'dense_26_loss',\n",
              " 'dense_29_loss',\n",
              " 'dense_32_loss',\n",
              " 'dense_35_loss',\n",
              " 'dense_38_loss',\n",
              " 'dense_5_accuracy',\n",
              " 'dense_8_accuracy',\n",
              " 'dense_11_accuracy',\n",
              " 'dense_14_accuracy',\n",
              " 'dense_17_accuracy',\n",
              " 'dense_20_accuracy',\n",
              " 'dense_23_accuracy',\n",
              " 'dense_26_accuracy',\n",
              " 'dense_29_accuracy',\n",
              " 'dense_32_accuracy',\n",
              " 'dense_35_accuracy',\n",
              " 'dense_38_accuracy']"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 60
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Bhpu1Nju3iGR",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}