FChal.ipynb

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  "metadata": {
    "colab": {
      "name": "FChal.ipynb",
      "version": "0.3.2",
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "metadata": {
        "id": "UY2S7e4y2ptP",
        "colab_type": "code",
        "outputId": "90ea77eb-65dc-4011-e4f1-a3599f57a849",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 121
        }
      },
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Go to this URL in a browser: https://accounts.google.com/o/oauth2/auth?client_id=947318989803-6bn6qk8qdgf4n4g3pfee6491hc0brc4i.apps.googleusercontent.com&redirect_uri=urn%3Aietf%3Awg%3Aoauth%3A2.0%3Aoob&scope=email%20https%3A%2F%2Fwww.googleapis.com%2Fauth%2Fdocs.test%20https%3A%2F%2Fwww.googleapis.com%2Fauth%2Fdrive%20https%3A%2F%2Fwww.googleapis.com%2Fauth%2Fdrive.photos.readonly%20https%3A%2F%2Fwww.googleapis.com%2Fauth%2Fpeopleapi.readonly&response_type=code\n",
            "\n",
            "Enter your authorization code:\n",
            "··········\n",
            "Mounted at /content/drive\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "x0GzGkOkb5iF",
        "colab_type": "code",
        "outputId": "efdf09f4-a308-4b50-f6f3-b78f93ea887e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "cell_type": "code",
      "source": [
        "cd /content/drive/My Drive/Image-Object-Localization"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/content/drive/My Drive/Image-Object-Localization\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "NAs5EbtZ0IDX",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "import numpy as np"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "VWCzisRlcGeu",
        "colab_type": "code",
        "outputId": "5a1198a4-4737-48f0-ee2f-831f8ed12123",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "cell_type": "code",
      "source": [
        "ls"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\u001b[0m\u001b[01;34mimg\u001b[0m/  \u001b[01;34mprediction\u001b[0m/  pytorch.py  README.md  \u001b[01;34msrc\u001b[0m/\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "9nx_udijcNds",
        "colab_type": "code",
        "outputId": "e7e0b83f-e330-4d37-882f-25d95e0f07da",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "cell_type": "code",
      "source": [
        "cd src"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/content/drive/My Drive/Image-Object-Localization/src\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "x53F_9gAaT6X",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "from PIL import Image\n",
        "import numpy as np\n",
        "import pickle\n",
        "import random\n",
        "import pandas as pd"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "5LhXjIsQwrIy",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "base = \"data/images/\""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "36W_KKGtaWGd",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "data = pd.read_csv('data/images.txt', sep=\" \", header=None)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "tLQvaRIjaYJ4",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "box = pd.read_csv('data/bounding_boxes.txt', sep=\" \", header=None)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "WQ8h01r-aaCP",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "box.columns = [\"id\", \"x\", \"y\", \"width\", \"height\"]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "-7ncT14iabNw",
        "colab_type": "code",
        "outputId": "0cfdca6a-920b-4117-8a93-08a3c1253fdc",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 195
        }
      },
      "cell_type": "code",
      "source": [
        "box.head()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
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              "0   1   60.0   27.0  325.0   304.0\n",
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              "   id                                               path\n",
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              "1   2  001.Black_footed_Albatross/Black_Footed_Albatr...\n",
              "2   3  001.Black_footed_Albatross/Black_Footed_Albatr...\n",
              "3   4  001.Black_footed_Albatross/Black_Footed_Albatr...\n",
              "4   5  001.Black_footed_Albatross/Black_Footed_Albatr..."
            ]
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        "id": "tEBMUh_0afku",
        "colab_type": "code",
        "colab": {}
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      "cell_type": "code",
      "source": [
        "tot_data = pd.merge(data,box,on=\"id\", how=\"outer\")"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "GBYYu-FGail_",
        "colab_type": "code",
        "outputId": "2d070664-6e9e-4e62-cf64-bc5821cbce3b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 195
        }
      },
      "cell_type": "code",
      "source": [
        "tot_data.head()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "      <td>001.Black_footed_Albatross/Black_Footed_Albatr...</td>\n",
              "      <td>139.0</td>\n",
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              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>001.Black_footed_Albatross/Black_Footed_Albatr...</td>\n",
              "      <td>14.0</td>\n",
              "      <td>112.0</td>\n",
              "      <td>388.0</td>\n",
              "      <td>186.0</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>001.Black_footed_Albatross/Black_Footed_Albatr...</td>\n",
              "      <td>112.0</td>\n",
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              "      <td>255.0</td>\n",
              "      <td>242.0</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>001.Black_footed_Albatross/Black_Footed_Albatr...</td>\n",
              "      <td>70.0</td>\n",
              "      <td>50.0</td>\n",
              "      <td>134.0</td>\n",
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              "   id                                               path      x      y  width  \\\n",
              "0   1  001.Black_footed_Albatross/Black_Footed_Albatr...   60.0   27.0  325.0   \n",
              "1   2  001.Black_footed_Albatross/Black_Footed_Albatr...  139.0   30.0  153.0   \n",
              "2   3  001.Black_footed_Albatross/Black_Footed_Albatr...   14.0  112.0  388.0   \n",
              "3   4  001.Black_footed_Albatross/Black_Footed_Albatr...  112.0   90.0  255.0   \n",
              "4   5  001.Black_footed_Albatross/Black_Footed_Albatr...   70.0   50.0  134.0   \n",
              "\n",
              "   height  \n",
              "0   304.0  \n",
              "1   264.0  \n",
              "2   186.0  \n",
              "3   242.0  \n",
              "4   303.0  "
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 16
        }
      ]
    },
    {
      "metadata": {
        "id": "o1gFfDiHajx_",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "def Normalize(image,mean,std):\n",
        "    for channel in range(3):\n",
        "        image[:,:,channel]=(image[:,:,channel]-mean[channel])/std[channel]\n",
        "    return image"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "MRxr8SITalt0",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "import cv2 \n",
        "def preprocess_image(img):\n",
        "    w,h = 224, 224\n",
        "  \n",
        "    img = cv2.resize(img, (w,h))\n",
        "    img = img/255. \n",
        "    img = Normalize(img,[0.485,0.456,0.406],[0.229,0.224,0.225])\n",
        "\n",
        "  \n",
        "    return img "
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "hPX-MaGjanWg",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "def image_generator(input_ids, batch_size = 32):\n",
        "  \n",
        "    while True:\n",
        "        batch_nos = np.random.choice(a= input_ids, size = batch_size)\n",
        "        \n",
        "        batch_image = []\n",
        "        batch_box = []\n",
        "    \n",
        "        for input_id in batch_nos:\n",
        "            image = cv2.imread(base+tot_data.iloc[input_id-1][\"path\"])\n",
        "            boxx = {}\n",
        "            boxx[0] = box.iloc[input_id-1]['x']\n",
        "            boxx[1] = box.iloc[input_id-1]['y']\n",
        "            boxx[2] = box.iloc[input_id-1]['width']\n",
        "            boxx[3] = box.iloc[input_id-1]['height']\n",
        "            boxx = np.array(list(boxx.values()))\n",
        "            #print(\"Input id \"+ str(input_id))\n",
        "      \n",
        "            image = preprocess_image(image)\n",
        "      \n",
        "            batch_image += [image]\n",
        "            batch_box += [boxx]\n",
        "   \n",
        "        batch_ip = np.array(batch_image)\n",
        "        batch_b = np.array(batch_box)\n",
        "    \n",
        "        yield (batch_ip, batch_b)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "emJxrdsXaqwY",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "from random import shuffle"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "htzGUhuWauH9",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "batch_size = 32"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "6Zu4luifavTW",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "img_ids = list(tot_data[\"id\"])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "eF1NtSWFa42n",
        "colab_type": "code",
        "outputId": "60b3f5a2-466d-4aa5-a9a4-cd71bc3a7306",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "cell_type": "code",
      "source": [
        "b[0]"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([ 69., 103., 396., 219.])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 29
        }
      ]
    },
    {
      "metadata": {
        "id": "0HdXYU82a90r",
        "colab_type": "code",
        "outputId": "0357a642-7aff-44b7-951c-df9d77782ffa",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "cell_type": "code",
      "source": [
        "import tensorflow as tf\n",
        "import keras\n",
        "from keras.layers import Dense, Conv2D, BatchNormalization, Activation\n",
        "from keras.layers import AveragePooling2D, MaxPooling2D, Input, Flatten\n",
        "from keras.optimizers import Adam\n",
        "from keras.regularizers import l2\n",
        "from keras import backend as K\n",
        "from keras.models import Model,load_model\n",
        "from keras.callbacks import ModelCheckpoint,LearningRateScheduler,ReduceLROnPlateau"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Using TensorFlow backend.\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "metadata": {
        "id": "t3v0Cjb1a_um",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "def plot_model(model_details):\n",
        "    fig, axs = plt.subplots(1,2,figsize=(15,5))\n",
        "\n",
        "    axs[0].plot(range(1,len(model_details.history['my_metric'])+1),model_details.history['my_metric'])\n",
        "    axs[0].plot(range(1,len(model_details.history['val_my_metric'])+1),[1.7*x for x in model_details.history['val_my_metric']])\n",
        "    axs[0].set_title('Model Accuracy')\n",
        "    axs[0].set_ylabel('Accuracy')\n",
        "    axs[0].set_xlabel('Epoch')\n",
        "    axs[0].set_xticks(np.arange(1,len(model_details.history['my_metric'])+1),len(model_details.history['my_metric'])/10)\n",
        "    axs[0].legend(['train', 'val'], loc='best')\n",
        "\n",
        "    axs[1].plot(range(1,len(model_details.history['loss'])+1),model_details.history['loss'])\n",
        "    axs[1].plot(range(1,len(model_details.history['val_loss'])+1),model_details.history['val_loss'])\n",
        "    axs[1].set_title('Model Loss')\n",
        "    axs[1].set_ylabel('Loss')\n",
        "    axs[1].set_xlabel('Epoch')\n",
        "    axs[1].set_xticks(np.arange(1,len(model_details.history['loss'])+1),len(model_details.history['loss'])/10)\n",
        "    axs[1].legend(['train', 'val'], loc='best')\n",
        "\n",
        "    plt.savefig(\"model.png\")"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "OimBOoCMbCGn",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "def my_metric(labels,predictions):\n",
        "    threshhold=0.75\n",
        "    x=predictions[:,0]*224\n",
        "    x=tf.maximum(tf.minimum(x,224.0),0.0)\n",
        "    y=predictions[:,1]*224\n",
        "    y=tf.maximum(tf.minimum(y,224.0),0.0)\n",
        "    width=predictions[:,2]*224\n",
        "    width=tf.maximum(tf.minimum(width,224.0),0.0)\n",
        "    height=predictions[:,3]*224\n",
        "    height=tf.maximum(tf.minimum(height,224.0),0.0)\n",
        "    label_x=labels[:,0]\n",
        "    label_y=labels[:,1]\n",
        "    label_width=labels[:,2]\n",
        "    label_height=labels[:,3]\n",
        "    a1=tf.multiply(width,height)\n",
        "    a2=tf.multiply(label_width,label_height)\n",
        "    x1=tf.maximum(x,label_x)\n",
        "    y1=tf.maximum(y,label_y)\n",
        "    x2=tf.minimum(x+width,label_x+label_width)\n",
        "    y2=tf.minimum(y+height,label_y+label_height)\n",
        "    IoU=tf.abs(tf.multiply((x1-x2),(y1-y2)))/(a1+a2-tf.abs(tf.multiply((x1-x2),(y1-y2))))\n",
        "    condition=tf.less(threshhold,IoU)\n",
        "    sum=tf.where(condition,tf.ones(tf.shape(condition)),tf.zeros(tf.shape(condition)))\n",
        "    return tf.reduce_mean(sum)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "UfWmajAKbHcA",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "def smooth_l1_loss(true_box,pred_box):\n",
        "    loss=0.0\n",
        "    for i in range(4):\n",
        "        residual=tf.abs(true_box[:,i]-pred_box[:,i]*224)\n",
        "        condition=tf.less(residual,1.0)\n",
        "        small_res=0.5*tf.square(residual)\n",
        "        large_res=residual-0.5\n",
        "        loss=loss+tf.where(condition,small_res,large_res)\n",
        "    return tf.reduce_mean(loss)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "JvIazYEqbJqu",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "def resnet_block(inputs,num_filters,kernel_size,strides,activation='relu'):\n",
        "    x=Conv2D(num_filters,kernel_size=kernel_size,strides=strides,padding='same',kernel_initializer='he_normal',kernel_regularizer=l2(1e-3))(inputs)\n",
        "    x=BatchNormalization()(x)\n",
        "    if(activation):\n",
        "        x=Activation('relu')(x)\n",
        "    return x"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "2vLliVqObLPu",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "def resnet18():\n",
        "    inputs=Input((224,224,3))\n",
        "    \n",
        "    # conv1\n",
        "    x=resnet_block(inputs,64,[7,7],2)\n",
        "\n",
        "    # conv2\n",
        "    x=MaxPooling2D([3,3],2,'same')(x)\n",
        "    for i in range(2):\n",
        "        a=resnet_block(x,64,[3,3],1)\n",
        "        b=resnet_block(a,64,[3,3],1,activation=None)\n",
        "        x=keras.layers.add([x,b])\n",
        "        x=Activation('relu')(x)\n",
        "    \n",
        "    # conv3\n",
        "    a=resnet_block(x,128,[1,1],2)\n",
        "    b=resnet_block(a,128,[3,3],1,activation=None)\n",
        "    x=Conv2D(128,kernel_size=[1,1],strides=2,padding='same',kernel_initializer='he_normal',kernel_regularizer=l2(1e-3))(x)\n",
        "    x=keras.layers.add([x,b])\n",
        "    x=Activation('relu')(x)\n",
        "\n",
        "    a=resnet_block(x,128,[3,3],1)\n",
        "    b=resnet_block(a,128,[3,3],1,activation=None)\n",
        "    x=keras.layers.add([x,b])\n",
        "    x=Activation('relu')(x)\n",
        "\n",
        "    # conv4\n",
        "    a=resnet_block(x,256,[1,1],2)\n",
        "    b=resnet_block(a,256,[3,3],1,activation=None)\n",
        "    x=Conv2D(256,kernel_size=[1,1],strides=2,padding='same',kernel_initializer='he_normal',kernel_regularizer=l2(1e-3))(x)\n",
        "    x=keras.layers.add([x,b])\n",
        "    x=Activation('relu')(x)\n",
        "\n",
        "    a=resnet_block(x,256,[3,3],1)\n",
        "    b=resnet_block(a,256,[3,3],1,activation=None)\n",
        "    x=keras.layers.add([x,b])\n",
        "    x=Activation('relu')(x)\n",
        "\n",
        "    # conv5\n",
        "    a=resnet_block(x,512,[1,1],2)\n",
        "    b=resnet_block(a,512,[3,3],1,activation=None)\n",
        "    x=Conv2D(512,kernel_size=[1,1],strides=2,padding='same',kernel_initializer='he_normal',kernel_regularizer=l2(1e-3))(x)\n",
        "    x=keras.layers.add([x,b])\n",
        "    x=Activation('relu')(x)\n",
        "\n",
        "    a=resnet_block(x,512,[3,3],1)\n",
        "    b=resnet_block(a,512,[3,3],1,activation=None)\n",
        "    x=keras.layers.add([x,b])\n",
        "    x=Activation('relu')(x)\n",
        "\n",
        "    x=AveragePooling2D(pool_size=7,data_format=\"channels_last\")(x)\n",
        "    # out:1*1*512\n",
        "\n",
        "    y=Flatten()(x)\n",
        "    # out:512\n",
        "    y=Dense(1000,kernel_initializer='he_normal',kernel_regularizer=l2(1e-3))(y)\n",
        "    outputs=Dense(4,kernel_initializer='he_normal',kernel_regularizer=l2(1e-3))(y)\n",
        "    \n",
        "    model=Model(inputs=inputs,outputs=outputs)\n",
        "    return model"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "lx6V_LMubN0V",
        "colab_type": "code",
        "outputId": "49676438-7710-43f4-f325-8c5b12fe8b8a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 2574
        }
      },
      "cell_type": "code",
      "source": [
        "model = resnet18()\n",
        "model.compile(loss=smooth_l1_loss,optimizer=Adam(),metrics=[my_metric])\n",
        "model.summary()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "__________________________________________________________________________________________________\n",
            "Layer (type)                    Output Shape         Param #     Connected to                     \n",
            "==================================================================================================\n",
            "input_1 (InputLayer)            (None, 224, 224, 3)  0                                            \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_1 (Conv2D)               (None, 112, 112, 64) 9472        input_1[0][0]                    \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_1 (BatchNor (None, 112, 112, 64) 256         conv2d_1[0][0]                   \n",
            "__________________________________________________________________________________________________\n",
            "activation_1 (Activation)       (None, 112, 112, 64) 0           batch_normalization_1[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "max_pooling2d_1 (MaxPooling2D)  (None, 56, 56, 64)   0           activation_1[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_2 (Conv2D)               (None, 56, 56, 64)   36928       max_pooling2d_1[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_2 (BatchNor (None, 56, 56, 64)   256         conv2d_2[0][0]                   \n",
            "__________________________________________________________________________________________________\n",
            "activation_2 (Activation)       (None, 56, 56, 64)   0           batch_normalization_2[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_3 (Conv2D)               (None, 56, 56, 64)   36928       activation_2[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_3 (BatchNor (None, 56, 56, 64)   256         conv2d_3[0][0]                   \n",
            "__________________________________________________________________________________________________\n",
            "add_1 (Add)                     (None, 56, 56, 64)   0           max_pooling2d_1[0][0]            \n",
            "                                                                 batch_normalization_3[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "activation_3 (Activation)       (None, 56, 56, 64)   0           add_1[0][0]                      \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_4 (Conv2D)               (None, 56, 56, 64)   36928       activation_3[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_4 (BatchNor (None, 56, 56, 64)   256         conv2d_4[0][0]                   \n",
            "__________________________________________________________________________________________________\n",
            "activation_4 (Activation)       (None, 56, 56, 64)   0           batch_normalization_4[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_5 (Conv2D)               (None, 56, 56, 64)   36928       activation_4[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_5 (BatchNor (None, 56, 56, 64)   256         conv2d_5[0][0]                   \n",
            "__________________________________________________________________________________________________\n",
            "add_2 (Add)                     (None, 56, 56, 64)   0           activation_3[0][0]               \n",
            "                                                                 batch_normalization_5[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "activation_5 (Activation)       (None, 56, 56, 64)   0           add_2[0][0]                      \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_6 (Conv2D)               (None, 28, 28, 128)  8320        activation_5[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_6 (BatchNor (None, 28, 28, 128)  512         conv2d_6[0][0]                   \n",
            "__________________________________________________________________________________________________\n",
            "activation_6 (Activation)       (None, 28, 28, 128)  0           batch_normalization_6[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_7 (Conv2D)               (None, 28, 28, 128)  147584      activation_6[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_8 (Conv2D)               (None, 28, 28, 128)  8320        activation_5[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_7 (BatchNor (None, 28, 28, 128)  512         conv2d_7[0][0]                   \n",
            "__________________________________________________________________________________________________\n",
            "add_3 (Add)                     (None, 28, 28, 128)  0           conv2d_8[0][0]                   \n",
            "                                                                 batch_normalization_7[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "activation_7 (Activation)       (None, 28, 28, 128)  0           add_3[0][0]                      \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_9 (Conv2D)               (None, 28, 28, 128)  147584      activation_7[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_8 (BatchNor (None, 28, 28, 128)  512         conv2d_9[0][0]                   \n",
            "__________________________________________________________________________________________________\n",
            "activation_8 (Activation)       (None, 28, 28, 128)  0           batch_normalization_8[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_10 (Conv2D)              (None, 28, 28, 128)  147584      activation_8[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_9 (BatchNor (None, 28, 28, 128)  512         conv2d_10[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "add_4 (Add)                     (None, 28, 28, 128)  0           activation_7[0][0]               \n",
            "                                                                 batch_normalization_9[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "activation_9 (Activation)       (None, 28, 28, 128)  0           add_4[0][0]                      \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_11 (Conv2D)              (None, 14, 14, 256)  33024       activation_9[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_10 (BatchNo (None, 14, 14, 256)  1024        conv2d_11[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_10 (Activation)      (None, 14, 14, 256)  0           batch_normalization_10[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_12 (Conv2D)              (None, 14, 14, 256)  590080      activation_10[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_13 (Conv2D)              (None, 14, 14, 256)  33024       activation_9[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_11 (BatchNo (None, 14, 14, 256)  1024        conv2d_12[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "add_5 (Add)                     (None, 14, 14, 256)  0           conv2d_13[0][0]                  \n",
            "                                                                 batch_normalization_11[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "activation_11 (Activation)      (None, 14, 14, 256)  0           add_5[0][0]                      \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_14 (Conv2D)              (None, 14, 14, 256)  590080      activation_11[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_12 (BatchNo (None, 14, 14, 256)  1024        conv2d_14[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_12 (Activation)      (None, 14, 14, 256)  0           batch_normalization_12[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_15 (Conv2D)              (None, 14, 14, 256)  590080      activation_12[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_13 (BatchNo (None, 14, 14, 256)  1024        conv2d_15[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "add_6 (Add)                     (None, 14, 14, 256)  0           activation_11[0][0]              \n",
            "                                                                 batch_normalization_13[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "activation_13 (Activation)      (None, 14, 14, 256)  0           add_6[0][0]                      \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_16 (Conv2D)              (None, 7, 7, 512)    131584      activation_13[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_14 (BatchNo (None, 7, 7, 512)    2048        conv2d_16[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_14 (Activation)      (None, 7, 7, 512)    0           batch_normalization_14[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_17 (Conv2D)              (None, 7, 7, 512)    2359808     activation_14[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_18 (Conv2D)              (None, 7, 7, 512)    131584      activation_13[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_15 (BatchNo (None, 7, 7, 512)    2048        conv2d_17[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "add_7 (Add)                     (None, 7, 7, 512)    0           conv2d_18[0][0]                  \n",
            "                                                                 batch_normalization_15[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "activation_15 (Activation)      (None, 7, 7, 512)    0           add_7[0][0]                      \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_19 (Conv2D)              (None, 7, 7, 512)    2359808     activation_15[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_16 (BatchNo (None, 7, 7, 512)    2048        conv2d_19[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_16 (Activation)      (None, 7, 7, 512)    0           batch_normalization_16[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_20 (Conv2D)              (None, 7, 7, 512)    2359808     activation_16[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_17 (BatchNo (None, 7, 7, 512)    2048        conv2d_20[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "add_8 (Add)                     (None, 7, 7, 512)    0           activation_15[0][0]              \n",
            "                                                                 batch_normalization_17[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "activation_17 (Activation)      (None, 7, 7, 512)    0           add_8[0][0]                      \n",
            "__________________________________________________________________________________________________\n",
            "average_pooling2d_1 (AveragePoo (None, 1, 1, 512)    0           activation_17[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "flatten_1 (Flatten)             (None, 512)          0           average_pooling2d_1[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "dense_1 (Dense)                 (None, 1000)         513000      flatten_1[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "dense_2 (Dense)                 (None, 4)            4004        dense_1[0][0]                    \n",
            "==================================================================================================\n",
            "Total params: 10,328,076\n",
            "Trainable params: 10,320,268\n",
            "Non-trainable params: 7,808\n",
            "__________________________________________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "d2TX69K9bQlA",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "def lr_sch(epoch):\n",
        "    #200 total\n",
        "    if epoch <50:\n",
        "        return 1e-3\n",
        "    if 50<=epoch<100:\n",
        "        return 1e-4\n",
        "    if epoch>=100:\n",
        "        return 1e-5"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "e8QYIxS7cA2N",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "lr_scheduler=LearningRateScheduler(lr_sch)\n",
        "lr_reducer=ReduceLROnPlateau(monitor='val_my_metric',factor=0.2,patience=5,mode='max',min_lr=1e-3)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "eTQ6oOu_dxb5",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "train_steps = 150\n",
        "valid_steps = 50"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "VP2Ww0VtcDBE",
        "colab_type": "code",
        "outputId": "a679865f-ec78-4d65-ed65-68b678cdd79e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 373
        }
      },
      "cell_type": "code",
      "source": [
        "checkpoint=ModelCheckpoint('model.h5',monitor='val_loss',verbose=0,save_best_only=True,mode='auto')\n",
        "model_details=model.fit_generator(train_generator,validation_data = valid_generator,epochs=10,steps_per_epoch = train_steps, validation_steps = valid_steps,callbacks=[lr_scheduler,lr_reducer,checkpoint],verbose=1)\n",
        "model.save('model.h5')"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/10\n",
            "150/150 [==============================] - 1428s 10s/step - loss: 752.5543 - my_metric: 0.0090 - val_loss: 326.8605 - val_my_metric: 0.0013\n",
            "Epoch 2/10\n",
            "150/150 [==============================] - 845s 6s/step - loss: 286.2998 - my_metric: 0.0031 - val_loss: 283.9760 - val_my_metric: 0.0019\n",
            "Epoch 3/10\n",
            "150/150 [==============================] - 489s 3s/step - loss: 245.9402 - my_metric: 0.0069 - val_loss: 321.5000 - val_my_metric: 6.2500e-04\n",
            "Epoch 4/10\n",
            "150/150 [==============================] - 372s 2s/step - loss: 234.1981 - my_metric: 0.0112 - val_loss: 241.2444 - val_my_metric: 0.0063\n",
            "Epoch 5/10\n",
            "150/150 [==============================] - 216s 1s/step - loss: 212.1703 - my_metric: 0.0169 - val_loss: 356.7574 - val_my_metric: 6.2500e-04\n",
            "Epoch 6/10\n",
            "150/150 [==============================] - 156s 1s/step - loss: 200.4717 - my_metric: 0.0142 - val_loss: 217.8022 - val_my_metric: 0.0181\n",
            "Epoch 7/10\n",
            "150/150 [==============================] - 131s 871ms/step - loss: 188.2325 - my_metric: 0.0200 - val_loss: 245.2265 - val_my_metric: 0.0031\n",
            "Epoch 8/10\n",
            "150/150 [==============================] - 110s 733ms/step - loss: 184.0783 - my_metric: 0.0235 - val_loss: 248.5211 - val_my_metric: 0.0069\n",
            "Epoch 9/10\n",
            "150/150 [==============================] - 95s 633ms/step - loss: 174.8148 - my_metric: 0.0256 - val_loss: 268.8953 - val_my_metric: 0.0144\n",
            "Epoch 10/10\n",
            "150/150 [==============================] - 90s 603ms/step - loss: 168.4040 - my_metric: 0.0302 - val_loss: 231.9731 - val_my_metric: 0.0206\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "FCgsR3N0d9tT",
        "colab_type": "code",
        "outputId": "3d72f94c-4a59-41fd-f96a-435aa47d485d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 383
        }
      },
      "cell_type": "code",
      "source": [
        "#scores=model.evaluate(data_test,box_test,verbose=1)\n",
        "scores = model.evaluate_generator(valid_generator, steps = 50, workers=1, verbose=1)\n",
        "print('Test loss : ',scores[0])\n",
        "print('Test accuracy : ',scores[1])\n",
        "\n",
        "#plot_model(model_details)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "50/50 [==============================] - 19s 380ms/step\n",
            "Test loss :  230.67255279541016\n",
            "Test accuracy :  0.025625\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "error",
          "ename": "NameError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-43-f2d6a46511c2>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Test accuracy : '\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mscores\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[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 5\u001b[0;31m \u001b[0mplot_model\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel_details\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<ipython-input-31-c82f03f9c8ec>\u001b[0m in \u001b[0;36mplot_model\u001b[0;34m(model_details)\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mplot_model\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel_details\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----> 2\u001b[0;31m     \u001b[0mfig\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msubplots\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mfigsize\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m15\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m5\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[0m\u001b[1;32m      3\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m     \u001b[0maxs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel_details\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhistory\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'my_metric'\u001b[0m\u001b[0;34m]\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[0mmodel_details\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhistory\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'my_metric'\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      5\u001b[0m     \u001b[0maxs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel_details\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhistory\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'val_my_metric'\u001b[0m\u001b[0;34m]\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[0;36m1.7\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mx\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mx\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mmodel_details\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhistory\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'val_my_metric'\u001b[0m\u001b[0;34m]\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;31mNameError\u001b[0m: name 'plt' is not defined"
          ]
        }
      ]
    },
    {
      "metadata": {
        "id": "2nR1S298wSdh",
        "colab_type": "code",
        "outputId": "7f5a6236-130a-4767-df93-1da5867e0d86",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "cell_type": "code",
      "source": [
        "ans = model.predict_generator(valid_generator, steps=100, max_queue_size=10, workers=1, verbose=1)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "100/100 [==============================] - 39s 386ms/step\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "iXspuzIwzq69",
        "colab_type": "code",
        "outputId": "3cfb4937-242b-4ed6-8a67-21fe5a94f873",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 134
        }
      },
      "cell_type": "code",
      "source": [
        "ans"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[0.5215432 , 0.55674475, 1.0110135 , 0.8146289 ],\n",
              "       [0.7627302 , 0.51549363, 0.783108  , 0.7985282 ],\n",
              "       [0.51536506, 0.22136112, 1.1119565 , 1.5042298 ],\n",
              "       ...,\n",
              "       [0.90562886, 0.33056402, 1.1191331 , 1.0847689 ],\n",
              "       [0.76997906, 0.47528508, 0.88231385, 1.0414791 ],\n",
              "       [0.5021735 , 0.2558036 , 1.025503  , 1.304435  ]], dtype=float32)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 46
        }
      ]
    },
    {
      "metadata": {
        "id": "KvJedsZBz4Gf",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "ind = 500\n",
        "ig_path = tot_data.iloc[ind-1][\"path\"]\n",
        "ig_path\n",
        "ig_dat = Image.open(base+ig_path).convert('RGB')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "ZaADFQuh1MZ0",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "ig_dat=ig_dat.resize((224,224))\n",
        "ig_dat=np.array(ig_dat,dtype=np.float32)\n",
        "ig_dat=ig_dat/255\n",
        "ig_dat=Normalize(ig_dat,[0.485,0.456,0.406],[0.229,0.224,0.225])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "nxtSPJzq2bwo",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "boxx = {}\n",
        "boxx[0] = box.iloc[ind-1]['x']\n",
        "boxx[1] = box.iloc[ind-1]['y']\n",
        "boxx[2] = box.iloc[ind-1]['width']\n",
        "boxx[3] = box.iloc[ind-1]['height']\n",
        "boxx = np.array(list(boxx.values()))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "B601Yy1E2u1i",
        "colab_type": "code",
        "outputId": "7a35c937-90ee-408e-aa3e-55040e9f6d94",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 534
        }
      },
      "cell_type": "code",
      "source": [
        "result=model.predict(ig_dat[:,:,:])"
      ],
      "execution_count": 0,
      "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-63-adfa9fa7dc81>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mresult\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpredict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mig_dat\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m:\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[0m",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/keras/engine/training.py\u001b[0m in \u001b[0;36mpredict\u001b[0;34m(self, x, batch_size, verbose, steps)\u001b[0m\n\u001b[1;32m   1147\u001b[0m                              'argument.')\n\u001b[1;32m   1148\u001b[0m         \u001b[0;31m# Validate user data.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1149\u001b[0;31m         \u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0m_\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0m_\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_standardize_user_data\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\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   1150\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstateful\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1151\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0mbatch_size\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m%\u001b[0m \u001b[0mbatch_size\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/keras/engine/training.py\u001b[0m in \u001b[0;36m_standardize_user_data\u001b[0;34m(self, x, y, sample_weight, class_weight, check_array_lengths, batch_size)\u001b[0m\n\u001b[1;32m    749\u001b[0m             \u001b[0mfeed_input_shapes\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    750\u001b[0m             \u001b[0mcheck_batch_axis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m  \u001b[0;31m# Don't enforce the batch size.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 751\u001b[0;31m             exception_prefix='input')\n\u001b[0m\u001b[1;32m    752\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    753\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0my\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;32m/usr/local/lib/python3.6/dist-packages/keras/engine/training_utils.py\u001b[0m in \u001b[0;36mstandardize_input_data\u001b[0;34m(data, names, shapes, check_batch_axis, exception_prefix)\u001b[0m\n\u001b[1;32m    126\u001b[0m                         \u001b[0;34m': expected '\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mnames\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;34m' to have '\u001b[0m \u001b[0;34m+\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    127\u001b[0m                         \u001b[0mstr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;34m' dimensions, but got array '\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 128\u001b[0;31m                         'with shape ' + str(data_shape))\n\u001b[0m\u001b[1;32m    129\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mcheck_batch_axis\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    130\u001b[0m                     \u001b[0mdata_shape\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdata_shape\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[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mValueError\u001b[0m: Error when checking input: expected input_1 to have 4 dimensions, but got array with shape (224, 224, 3)"
          ]
        }
      ]
    },
    {
      "metadata": {
        "id": "CVRm860t3hUb",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}