Model 9 Resnet101.ipynb

{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"provenance":[{"file_id":"1UGmL8SYu54x6GpTLKwMB-dynvf4rDw81","timestamp":1676794685662},{"file_id":"1m02gqjW0qHHfnEmAXqX-sEPJzK41ksio","timestamp":1676268414351},{"file_id":"1_Q3rO4S1E94krtEiMXtilBddJ7nV7ojI","timestamp":1676268113893},{"file_id":"1UFMSkfoaQXtcPvA4qjSluird48rYXh29","timestamp":1676190619166}]},"kernelspec":{"name":"python3","display_name":"Python 3"},"language_info":{"name":"python"},"accelerator":"GPU","gpuClass":"standard"},"cells":[{"cell_type":"code","source":["from google.colab import drive\n","drive.mount('/content/drive')"],"metadata":{"id":"qzPWNwpIpe2C","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1676795856000,"user_tz":-420,"elapsed":58463,"user":{"displayName":"Trang Phạm","userId":"09872832977707306821"}},"outputId":"ccb2af90-1c8d-4d48-d62c-4e863c983716"},"execution_count":2,"outputs":[{"output_type":"stream","name":"stdout","text":["Mounted at /content/drive\n"]}]},{"cell_type":"code","execution_count":3,"metadata":{"id":"l3Mi4rbxczEK","executionInfo":{"status":"ok","timestamp":1676795859945,"user_tz":-420,"elapsed":974,"user":{"displayName":"Trang Phạm","userId":"09872832977707306821"}}},"outputs":[],"source":["import numpy as np # linear algebra\n","import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n","import os\n","import cv2\n","import matplotlib.pyplot as plt\n","\n","%matplotlib inline"]},{"cell_type":"code","source":["!pip install unrar\n","%cd /content/drive/MyDrive/dataincrease\n","!unrar x /content/drive/MyDrive/dataincrease/images.rar "],"metadata":{"id":"0CTZ2lN6ett3"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["DATASET_PATH = \"/content/drive/MyDrive/dataincrease/\"\n","print(os.listdir(DATASET_PATH))"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"rIDfNzEAeaCC","executionInfo":{"status":"ok","timestamp":1676796641709,"user_tz":-420,"elapsed":4,"user":{"displayName":"Trang Phạm","userId":"09872832977707306821"}},"outputId":"88c343fd-cff0-432b-a9d6-7131333d3cb1"},"execution_count":5,"outputs":[{"output_type":"stream","name":"stdout","text":["['images.rar', 'styles.csv', 'images']\n"]}]},{"cell_type":"code","source":["data_path = \"/content/drive/MyDrive/dataincrease/images/\"\n","print(len(os.listdir(data_path)))"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"T_fnb55FedcQ","executionInfo":{"status":"ok","timestamp":1676796644408,"user_tz":-420,"elapsed":558,"user":{"displayName":"Trang Phạm","userId":"09872832977707306821"}},"outputId":"686cf7c9-92e2-4b15-c792-34c6dc2b52ee"},"execution_count":6,"outputs":[{"output_type":"stream","name":"stdout","text":["79596\n"]}]},{"cell_type":"code","execution_count":7,"metadata":{"execution":{"iopub.execute_input":"2020-11-14T19:37:55.683578Z","iopub.status.busy":"2020-11-14T19:37:55.682899Z","iopub.status.idle":"2020-11-14T19:38:00.393150Z","shell.execute_reply":"2020-11-14T19:38:00.393687Z"},"papermill":{"duration":4.818566,"end_time":"2020-11-14T19:38:00.393849","exception":false,"start_time":"2020-11-14T19:37:55.575283","status":"completed"},"tags":[],"colab":{"base_uri":"https://localhost:8080/","height":656},"id":"n0pvaEBYvXvr","executionInfo":{"status":"ok","timestamp":1676796820919,"user_tz":-420,"elapsed":173317,"user":{"displayName":"Trang Phạm","userId":"09872832977707306821"}},"outputId":"064e7836-b433-4441-8d5a-e86555982b73"},"outputs":[{"output_type":"stream","name":"stdout","text":["['images.rar', 'styles.csv', 'images']\n","10000.jpg\n","10001.jpg\n","10002.jpg\n","10003.jpg\n","10004.jpg\n","10005.jpg\n","10006.jpg\n","10007.jpg\n","10008.jpg\n","10009.jpg\n"]},{"output_type":"display_data","data":{"text/plain":["<Figure size 1080x1080 with 10 Axes>"],"image/png":"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\n"},"metadata":{"needs_background":"light"}}],"source":["# what is inside the main directory\n","list_directory = os.listdir(DATASET_PATH)\n","print(list_directory)\n","\n","# each time directory files gets reordered. That's why needed to find where images folder is.\n","index=0\n","for i in range (len(list_directory)):\n","    if list_directory[i]==\"images\":\n","        index=i\n","        break\n","\n","# what is in the image directory\n","imageIndex = os.listdir(DATASET_PATH+list_directory[index])\n","# print(type(imageIndex))\n","head = 10\n","\n","# collecting some samples in list\n","sampleImages = []\n","\n","# showing indices\n","for i in range (head):\n","    sampleImages.append(imageIndex[i])\n","    print(sampleImages[i])\n","\n","# choosing some samples to observe\n","fig=plt.figure()\n","fig.set_figheight(15)\n","fig.set_figwidth(15)\n","axis=[]\n","row = len(sampleImages)/2\n","col = row+1\n","\n","for i in range (len(sampleImages)):\n","    Image_path=DATASET_PATH+list_directory[index]+\"/\"+sampleImages[i]\n","    src = cv2.imread(Image_path)\n","    image = cv2.cvtColor(src, cv2.COLOR_BGR2RGB) \n","    axis.append(fig.add_subplot(row, col, i+1))\n","    subplot_title=sampleImages[i]\n","    axis[-1].set_title(subplot_title)  \n","    plt.imshow(image)\n","\n","fig.tight_layout()\n","plt.show()"]},{"cell_type":"code","source":["df = pd.read_csv(DATASET_PATH + \"styles.csv\", nrows=100000, error_bad_lines=False)\n","df.drop(df.columns[0], axis=1, inplace=True)\n","df.head(10)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":456},"id":"IpYo5Y9zefhA","executionInfo":{"status":"ok","timestamp":1676796891450,"user_tz":-420,"elapsed":4224,"user":{"displayName":"Trang Phạm","userId":"09872832977707306821"}},"outputId":"3e2dcce8-eca2-4a31-fda2-e08a01b68f08"},"execution_count":9,"outputs":[{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.8/dist-packages/IPython/core/interactiveshell.py:3326: FutureWarning: The error_bad_lines argument has been deprecated and will be removed in a future version.\n","\n","\n","  exec(code_obj, self.user_global_ns, self.user_ns)\n"]},{"output_type":"execute_result","data":{"text/plain":["      id masterCategory      image\n","0  15970        Apparel  15970.jpg\n","1  39386        Apparel  39386.jpg\n","2  59263    Accessories  59263.jpg\n","3  21379        Apparel  21379.jpg\n","4  53759        Apparel  53759.jpg\n","5   1855        Apparel   1855.jpg\n","6  30805        Apparel  30805.jpg\n","7  26960        Apparel  26960.jpg\n","8  29114    Accessories  29114.jpg\n","9  30039    Accessories  30039.jpg"],"text/html":["\n","  <div id=\"df-310836fe-9a5f-4284-8382-2fa60a76c15d\">\n","    <div class=\"colab-df-container\">\n","      <div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>id</th>\n","      <th>masterCategory</th>\n","      <th>image</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>15970</td>\n","      <td>Apparel</td>\n","      <td>15970.jpg</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>39386</td>\n","      <td>Apparel</td>\n","      <td>39386.jpg</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>59263</td>\n","      <td>Accessories</td>\n","      <td>59263.jpg</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>21379</td>\n","      <td>Apparel</td>\n","      <td>21379.jpg</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>53759</td>\n","      <td>Apparel</td>\n","      <td>53759.jpg</td>\n","    </tr>\n","    <tr>\n","      <th>5</th>\n","      <td>1855</td>\n","      <td>Apparel</td>\n","      <td>1855.jpg</td>\n","    </tr>\n","    <tr>\n","      <th>6</th>\n","      <td>30805</td>\n","      <td>Apparel</td>\n","      <td>30805.jpg</td>\n","    </tr>\n","    <tr>\n","      <th>7</th>\n","      <td>26960</td>\n","      <td>Apparel</td>\n","      <td>26960.jpg</td>\n","    </tr>\n","    <tr>\n","      <th>8</th>\n","      <td>29114</td>\n","      <td>Accessories</td>\n","      <td>29114.jpg</td>\n","    </tr>\n","    <tr>\n","      <th>9</th>\n","      <td>30039</td>\n","      <td>Accessories</td>\n","      <td>30039.jpg</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-310836fe-9a5f-4284-8382-2fa60a76c15d')\"\n","              title=\"Convert this dataframe to an interactive table.\"\n","              style=\"display:none;\">\n","        \n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n","       width=\"24px\">\n","    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n","    <path d=\"M18.56 5.44l.94 2.06.94-2.06 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model.\n","training_generator = image_generator.flow_from_dataframe(\n","    dataframe = df,\n","    directory= \"/content/drive/MyDrive/dataincrease/images/\",\n","    x_col=\"image\",\n","    y_col=\"masterCategory\",\n","    target_size=(227,227),\n","    batch_size=32,\n","    subset=\"training\"\n","\n",")\n","\n","#create a flow of images for validating(testing) the trained model.\n","validation_generator = image_generator.flow_from_dataframe(\n","    dataframe = df,\n","    directory=\"/content/drive/MyDrive/dataincrease/images/\",\n","    x_col=\"image\",\n","    y_col=\"masterCategory\",\n","    target_size=(227,227),\n","    batch_size=32,\n","    subset=\"validation\"\n",")\n","\n"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"XJmkIVTTgIMI","executionInfo":{"status":"ok","timestamp":1676796923573,"user_tz":-420,"elapsed":29137,"user":{"displayName":"Trang Phạm","userId":"09872832977707306821"}},"outputId":"d8c21864-ec1a-48b4-fe62-6c26d47409d2"},"execution_count":10,"outputs":[{"output_type":"stream","name":"stdout","text":["Found 63677 validated image filenames belonging to 3 classes.\n","Found 15919 validated image filenames belonging to 3 classes.\n"]}]},{"cell_type":"code","metadata":{"id":"y4Udt7xj1dHK","executionInfo":{"status":"ok","timestamp":1676796957440,"user_tz":-420,"elapsed":580,"user":{"displayName":"Trang Phạm","userId":"09872832977707306821"}}},"source":["from tensorflow.keras.applications import ResNet101\n","from tensorflow.keras.layers import Input,Dense,Flatten,Dropout\n","from tensorflow.keras.preprocessing.image import ImageDataGenerator,load_img\n","from tensorflow.keras.models import Model\n"],"execution_count":11,"outputs":[]},{"cell_type":"code","metadata":{"id":"l_51nGy31oTO","executionInfo":{"status":"ok","timestamp":1676797748490,"user_tz":-420,"elapsed":3373,"user":{"displayName":"Trang Phạm","userId":"09872832977707306821"}}},"source":["image_size=[227,227]\n","model=ResNet101(input_shape=image_size+[3],include_top=False,weights=\"imagenet\")"],"execution_count":18,"outputs":[]},{"cell_type":"code","source":["for layers in model.layers:\n","  layers.trainable=False"],"metadata":{"id":"5dn1MHW3ZCT6","executionInfo":{"status":"ok","timestamp":1676797750303,"user_tz":-420,"elapsed":3,"user":{"displayName":"Trang Phạm","userId":"09872832977707306821"}}},"execution_count":19,"outputs":[]},{"cell_type":"code","source":["final_model=Model(inputs=model.input,outputs=Dense(3,activation=\"softmax\")(Flatten()(model.output)))"],"metadata":{"id":"rVK_dd93ZHu8","executionInfo":{"status":"ok","timestamp":1676797752028,"user_tz":-420,"elapsed":2,"user":{"displayName":"Trang Phạm","userId":"09872832977707306821"}}},"execution_count":20,"outputs":[]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"Ca2HfGJw2h-D","executionInfo":{"status":"ok","timestamp":1676797757831,"user_tz":-420,"elapsed":4056,"user":{"displayName":"Trang Phạm","userId":"09872832977707306821"}},"outputId":"61c64c98-76e1-405c-f5af-35076c04edad"},"source":["final_model.summary()"],"execution_count":21,"outputs":[{"output_type":"stream","name":"stdout","text":["Model: \"model_1\"\n","__________________________________________________________________________________________________\n"," Layer (type)                   Output Shape         Param #     Connected to                     \n","==================================================================================================\n"," input_2 (InputLayer)           [(None, 227, 227, 3  0           []                               \n","                                )]                                                                \n","                                                                                                  \n"," conv1_pad (ZeroPadding2D)      (None, 233, 233, 3)  0           ['input_2[0][0]']                \n","                                                                                                  \n"," conv1_conv (Conv2D)            (None, 114, 114, 64  9472        ['conv1_pad[0][0]']              \n","                                )                                                                 \n","                                                                                                  \n"," conv1_bn (BatchNormalization)  (None, 114, 114, 64  256         ['conv1_conv[0][0]']             \n","                                )                                                                 \n","                                                                                                  \n"," conv1_relu (Activation)        (None, 114, 114, 64  0           ['conv1_bn[0][0]']               \n","                                )                                                                 \n","                                                                                                  \n"," pool1_pad (ZeroPadding2D)      (None, 116, 116, 64  0           ['conv1_relu[0][0]']             \n","                                )                                                                 \n","                                                                                                  \n"," pool1_pool (MaxPooling2D)      (None, 57, 57, 64)   0           ['pool1_pad[0][0]']              \n","                                                                                                  \n"," conv2_block1_1_conv (Conv2D)   (None, 57, 57, 64)   4160        ['pool1_pool[0][0]']             \n","                                                                                                  \n"," conv2_block1_1_bn (BatchNormal  (None, 57, 57, 64)  256         ['conv2_block1_1_conv[0][0]']    \n"," ization)                                                                                         \n","                                                                                                  \n"," conv2_block1_1_relu (Activatio  (None, 57, 57, 64)  0           ['conv2_block1_1_bn[0][0]']      \n"," n)                                                                                               \n","                                                                                                  \n"," conv2_block1_2_conv (Conv2D)   (None, 57, 57, 64)   36928       ['conv2_block1_1_relu[0][0]']    \n","                                                                                                  \n"," conv2_block1_2_bn (BatchNormal  (None, 57, 57, 64)  256         ['conv2_block1_2_conv[0][0]']    \n"," ization)                                                                                         \n","                                                                                                  \n"," conv2_block1_2_relu (Activatio  (None, 57, 57, 64)  0           ['conv2_block1_2_bn[0][0]']      \n"," n)                                                                                               \n","                                                                                                  \n"," conv2_block1_0_conv (Conv2D)   (None, 57, 57, 256)  16640       ['pool1_pool[0][0]']             \n","                                                                                                  \n"," conv2_block1_3_conv (Conv2D)   (None, 57, 57, 256)  16640       ['conv2_block1_2_relu[0][0]']    \n","                                                                                                  \n"," conv2_block1_0_bn (BatchNormal  (None, 57, 57, 256)  1024       ['conv2_block1_0_conv[0][0]']    \n"," ization)                                                                                         \n","                                                                                                  \n"," conv2_block1_3_bn (BatchNormal  (None, 57, 57, 256)  1024       ['conv2_block1_3_conv[0][0]']    \n"," ization)                                                                                         \n","                                                                                                  \n"," conv2_block1_add (Add)         (None, 57, 57, 256)  0           ['conv2_block1_0_bn[0][0]',      \n","                                                                  'conv2_block1_3_bn[0][0]']      \n","                                                                                                  \n"," conv2_block1_out (Activation)  (None, 57, 57, 256)  0           ['conv2_block1_add[0][0]']       \n","                                                                                                  \n"," conv2_block2_1_conv (Conv2D)   (None, 57, 57, 64)   16448       ['conv2_block1_out[0][0]']       \n","                                                                                                  \n"," conv2_block2_1_bn (BatchNormal  (None, 57, 57, 64)  256         ['conv2_block2_1_conv[0][0]']    \n"," ization)                                                                                         \n","                                                                                                  \n"," conv2_block2_1_relu (Activatio  (None, 57, 57, 64)  0           ['conv2_block2_1_bn[0][0]']      \n"," n)                                                                                               \n","                                                                                                  \n"," conv2_block2_2_conv (Conv2D)   (None, 57, 57, 64)   36928       ['conv2_block2_1_relu[0][0]']    \n","                                                                                                  \n"," conv2_block2_2_bn (BatchNormal  (None, 57, 57, 64)  256         ['conv2_block2_2_conv[0][0]']    \n"," ization)                                                                                         \n","                                                                                                  \n"," conv2_block2_2_relu (Activatio  (None, 57, 57, 64)  0           ['conv2_block2_2_bn[0][0]']      \n"," n)                                                                                               \n","                                                                                                  \n"," conv2_block2_3_conv (Conv2D)   (None, 57, 57, 256)  16640       ['conv2_block2_2_relu[0][0]']    \n","                                                                                                  \n"," conv2_block2_3_bn (BatchNormal  (None, 57, 57, 256)  1024       ['conv2_block2_3_conv[0][0]']    \n"," ization)                                                                                         \n","                                                                                                  \n"," conv2_block2_add (Add)         (None, 57, 57, 256)  0           ['conv2_block1_out[0][0]',       \n","                                                                  'conv2_block2_3_bn[0][0]']      \n","                                                                                                  \n"," conv2_block2_out (Activation)  (None, 57, 57, 256)  0           ['conv2_block2_add[0][0]']       \n","                                                                                                  \n"," conv2_block3_1_conv (Conv2D)   (None, 57, 57, 64)   16448       ['conv2_block2_out[0][0]']       \n","                                                                                                  \n"," conv2_block3_1_bn (BatchNormal  (None, 57, 57, 64)  256         ['conv2_block3_1_conv[0][0]']    \n"," ization)                                                                                         \n","                                                                                                  \n"," conv2_block3_1_relu (Activatio  (None, 57, 57, 64)  0           ['conv2_block3_1_bn[0][0]']      \n"," n)                                                                                               \n","                                                                                                  \n"," conv2_block3_2_conv (Conv2D)   (None, 57, 57, 64)   36928       ['conv2_block3_1_relu[0][0]']    \n","                                                                                                  \n"," conv2_block3_2_bn (BatchNormal  (None, 57, 57, 64)  256         ['conv2_block3_2_conv[0][0]']    \n"," ization)                                                                                         \n","                                                                                                  \n"," conv2_block3_2_relu (Activatio  (None, 57, 57, 64)  0           ['conv2_block3_2_bn[0][0]']      \n"," n)                                                                                               \n","                                                                                                  \n"," conv2_block3_3_conv (Conv2D)   (None, 57, 57, 256)  16640       ['conv2_block3_2_relu[0][0]']    \n","                                                                                                  \n"," conv2_block3_3_bn (BatchNormal  (None, 57, 57, 256)  1024       ['conv2_block3_3_conv[0][0]']    \n"," ization)                                                                                         \n","                                                                                                  \n"," conv2_block3_add (Add)         (None, 57, 57, 256)  0           ['conv2_block2_out[0][0]',       \n","                                                                  'conv2_block3_3_bn[0][0]']      \n","                                                                                                  \n"," conv2_block3_out (Activation)  (None, 57, 57, 256)  0           ['conv2_block3_add[0][0]']       \n","                                                                                                  \n"," conv3_block1_1_conv (Conv2D)   (None, 29, 29, 128)  32896       ['conv2_block3_out[0][0]']       \n","                                                                                                  \n"," conv3_block1_1_bn (BatchNormal  (None, 29, 29, 128)  512        ['conv3_block1_1_conv[0][0]']    \n"," ization)                                                                                         \n","                                                                                                  \n"," conv3_block1_1_relu (Activatio  (None, 29, 29, 128)  0          ['conv3_block1_1_bn[0][0]']      \n"," n)                                                                                               \n","                                                                                                  \n"," conv3_block1_2_conv (Conv2D)   (None, 29, 29, 128)  147584      ['conv3_block1_1_relu[0][0]']    \n","                                                                                                  \n"," conv3_block1_2_bn (BatchNormal  (None, 29, 29, 128)  512        ['conv3_block1_2_conv[0][0]']    \n"," ization)                                                                                         \n","                                                                                                  \n"," conv3_block1_2_relu (Activatio  (None, 29, 29, 128)  0          ['conv3_block1_2_bn[0][0]']      \n"," n)                                                                                               \n","                                                                                                  \n"," conv3_block1_0_conv (Conv2D)   (None, 29, 29, 512)  131584      ['conv2_block3_out[0][0]']       \n","                                                                                                  \n"," conv3_block1_3_conv (Conv2D)   (None, 29, 29, 512)  66048       ['conv3_block1_2_relu[0][0]']    \n","                                                                                                  \n"," conv3_block1_0_bn (BatchNormal  (None, 29, 29, 512)  2048       ['conv3_block1_0_conv[0][0]']    \n"," ization)                                                                                         \n","                                                                                                  \n"," conv3_block1_3_bn (BatchNormal  (None, 29, 29, 512)  2048       ['conv3_block1_3_conv[0][0]']    \n"," ization)                                                                                         \n","                                                                                                  \n"," conv3_block1_add (Add)         (None, 29, 29, 512)  0           ['conv3_block1_0_bn[0][0]',      \n","                                                                  'conv3_block1_3_bn[0][0]']      \n","                                                                                                  \n"," conv3_block1_out (Activation)  (None, 29, 29, 512)  0           ['conv3_block1_add[0][0]']       \n","                                                                                                  \n"," conv3_block2_1_conv (Conv2D)   (None, 29, 29, 128)  65664       ['conv3_block1_out[0][0]']       \n","                                                                                                  \n"," conv3_block2_1_bn (BatchNormal  (None, 29, 29, 128)  512        ['conv3_block2_1_conv[0][0]']    \n"," ization)                                                                                         \n","                                                                                                  \n"," conv3_block2_1_relu (Activatio  (None, 29, 29, 128)  0          ['conv3_block2_1_bn[0][0]']      \n"," n)                                                                                               \n","                                                                                                  \n"," conv3_block2_2_conv (Conv2D)   (None, 29, 29, 128)  147584      ['conv3_block2_1_relu[0][0]']    \n","                                                                                                  \n"," conv3_block2_2_bn (BatchNormal  (None, 29, 29, 128)  512        ['conv3_block2_2_conv[0][0]']    \n"," ization)                                                                                         \n","                                                                                                  \n"," conv3_block2_2_relu (Activatio  (None, 29, 29, 128)  0          ['conv3_block2_2_bn[0][0]']      \n"," n)                                                                                               \n","                                                                                                  \n"," conv3_block2_3_conv (Conv2D)   (None, 29, 29, 512)  66048       ['conv3_block2_2_relu[0][0]']    \n","                                                                                                  \n"," conv3_block2_3_bn (BatchNormal  (None, 29, 29, 512)  2048       ['conv3_block2_3_conv[0][0]']    \n"," ization)                                                                                         \n","                                                                                                  \n"," conv3_block2_add (Add)         (None, 29, 29, 512)  0           ['conv3_block1_out[0][0]',       \n","                                                                  'conv3_block2_3_bn[0][0]']      \n","                                                                                                  \n"," conv3_block2_out (Activation)  (None, 29, 29, 512)  0           ['conv3_block2_add[0][0]']       \n","                                                                                                  \n"," conv3_block3_1_conv (Conv2D)   (None, 29, 29, 128)  65664       ['conv3_block2_out[0][0]']       \n","                                                                                                  \n"," conv3_block3_1_bn (BatchNormal  (None, 29, 29, 128)  512        ['conv3_block3_1_conv[0][0]']    \n"," ization)                                                                                         \n","                                                                                                  \n"," conv3_block3_1_relu (Activatio  (None, 29, 29, 128)  0          ['conv3_block3_1_bn[0][0]']      \n"," n)                                                                                               \n","                                                                                                  \n"," conv3_block3_2_conv (Conv2D)   (None, 29, 29, 128)  147584      ['conv3_block3_1_relu[0][0]']    \n","                                                                                                  \n"," conv3_block3_2_bn (BatchNormal  (None, 29, 29, 128)  512        ['conv3_block3_2_conv[0][0]']    \n"," ization)                                                                                         \n","                                                                                                  \n"," conv3_block3_2_relu (Activatio  (None, 29, 29, 128)  0          ['conv3_block3_2_bn[0][0]']      \n"," n)                                                                                               \n","                                                                                                  \n"," conv3_block3_3_conv (Conv2D)   (None, 29, 29, 512)  66048       ['conv3_block3_2_relu[0][0]']    \n","                                                                                                  \n"," conv3_block3_3_bn (BatchNormal  (None, 29, 29, 512)  2048       ['conv3_block3_3_conv[0][0]']    \n"," ization)                                                                                         \n","                                                                                                  \n"," conv3_block3_add (Add)         (None, 29, 29, 512)  0           ['conv3_block2_out[0][0]',       \n","                                                                  'conv3_block3_3_bn[0][0]']      \n","                                                                                                  \n"," conv3_block3_out (Activation)  (None, 29, 29, 512)  0           ['conv3_block3_add[0][0]']       \n","                                                                                                  \n"," conv3_block4_1_conv (Conv2D)   (None, 29, 29, 128)  65664       ['conv3_block3_out[0][0]']       \n","                                                                                                  \n"," conv3_block4_1_bn (BatchNormal  (None, 29, 29, 128)  512        ['conv3_block4_1_conv[0][0]']    \n"," ization)                                                                                         \n","                                                                                                  \n"," conv3_block4_1_relu (Activatio  (None, 29, 29, 128)  0          ['conv3_block4_1_bn[0][0]']      \n"," n)                                                                                               \n","                                                                                                  \n"," conv3_block4_2_conv (Conv2D)   (None, 29, 29, 128)  147584      ['conv3_block4_1_relu[0][0]']    \n","                                                                                                  \n"," conv3_block4_2_bn (BatchNormal  (None, 29, 29, 128)  512        ['conv3_block4_2_conv[0][0]']    \n"," ization)                                                                                         \n","                                                                                                  \n"," conv3_block4_2_relu (Activatio  (None, 29, 29, 128)  0          ['conv3_block4_2_bn[0][0]']      \n"," n)                                                                                               \n","                                                                                                  \n"," conv3_block4_3_conv (Conv2D)   (None, 29, 29, 512)  66048       ['conv3_block4_2_relu[0][0]']    \n","                                                                                                  \n"," conv3_block4_3_bn (BatchNormal  (None, 29, 29, 512)  2048       ['conv3_block4_3_conv[0][0]']    \n"," ization)                                                                                         \n","                                                                                                  \n"," conv3_block4_add (Add)         (None, 29, 29, 512)  0           ['conv3_block3_out[0][0]',       \n","                                                                  'conv3_block4_3_bn[0][0]']      \n","                                                                                                  \n"," conv3_block4_out (Activation)  (None, 29, 29, 512)  0           ['conv3_block4_add[0][0]']       \n","                                                                                                  \n"," conv4_block1_1_conv (Conv2D)   (None, 15, 15, 256)  131328      ['conv3_block4_out[0][0]']       \n","                                                                                                  \n"," conv4_block1_1_bn (BatchNormal  (None, 15, 15, 256)  1024       ['conv4_block1_1_conv[0][0]']    \n"," ization)                                                                                         \n","                                                                                                  \n"," conv4_block1_1_relu (Activatio  (None, 15, 15, 256)  0          ['conv4_block1_1_bn[0][0]']      \n"," n)                                                                                               \n","                                                                                                  \n"," conv4_block1_2_conv (Conv2D)   (None, 15, 15, 256)  590080      ['conv4_block1_1_relu[0][0]']    \n","                                                                                                  \n"," conv4_block1_2_bn (BatchNormal  (None, 15, 15, 256)  1024       ['conv4_block1_2_conv[0][0]']    \n"," ization)                                                                                         \n","                                                                                                  \n"," conv4_block1_2_relu (Activatio  (None, 15, 15, 256)  0          ['conv4_block1_2_bn[0][0]']      \n"," n)                                                                                               \n","                                                                                                  \n"," conv4_block1_0_conv (Conv2D)   (None, 15, 15, 1024  525312      ['conv3_block4_out[0][0]']       \n","                                )                                                                 \n","                                                                                                  \n"," conv4_block1_3_conv (Conv2D)   (None, 15, 15, 1024  263168      ['conv4_block1_2_relu[0][0]']    \n","                                )                                                                 \n","                                                                                                  \n"," conv4_block1_0_bn (BatchNormal  (None, 15, 15, 1024  4096       ['conv4_block1_0_conv[0][0]']    \n"," ization)                       )                                                                 \n","                                                                                                  \n"," conv4_block1_3_bn (BatchNormal  (None, 15, 15, 1024  4096       ['conv4_block1_3_conv[0][0]']    \n"," ization)                       )                                                                 \n","                                                                                                  \n"," conv4_block1_add (Add)         (None, 15, 15, 1024  0           ['conv4_block1_0_bn[0][0]',      \n","                                )                                 'conv4_block1_3_bn[0][0]']      \n","                                                                                                  \n"," conv4_block1_out (Activation)  (None, 15, 15, 1024  0           ['conv4_block1_add[0][0]']       \n","                                )                                                                 \n","                                                                                                  \n"," conv4_block2_1_conv (Conv2D)   (None, 15, 15, 256)  262400      ['conv4_block1_out[0][0]']       \n","                                                                                                  \n"," conv4_block2_1_bn (BatchNormal  (None, 15, 15, 256)  1024       ['conv4_block2_1_conv[0][0]']    \n"," ization)                                                                                         \n","                                                                                                  \n"," conv4_block2_1_relu (Activatio  (None, 15, 15, 256)  0          ['conv4_block2_1_bn[0][0]']      \n"," n)                                                                                               \n","                                                                                                  \n"," conv4_block2_2_conv (Conv2D)   (None, 15, 15, 256)  590080      ['conv4_block2_1_relu[0][0]']    \n","                                                                                                  \n"," conv4_block2_2_bn (BatchNormal  (None, 15, 15, 256)  1024       ['conv4_block2_2_conv[0][0]']    \n"," ization)                                                                                         \n","                                                                                                  \n"," conv4_block2_2_relu (Activatio  (None, 15, 15, 256)  0          ['conv4_block2_2_bn[0][0]']      \n"," n)                                                                                               \n","                                                                                                  \n"," conv4_block2_3_conv (Conv2D)   (None, 15, 15, 1024  263168      ['conv4_block2_2_relu[0][0]']    \n","                                )                                                                 \n","                                                                                                  \n"," conv4_block2_3_bn (BatchNormal  (None, 15, 15, 1024  4096       ['conv4_block2_3_conv[0][0]']    \n"," ization)                       )                                                                 \n","                                                                                                  \n"," conv4_block2_add (Add)         (None, 15, 15, 1024  0           ['conv4_block1_out[0][0]',       \n","                                )                                 'conv4_block2_3_bn[0][0]']      \n","                                                                                                  \n"," conv4_block2_out (Activation)  (None, 15, 15, 1024  0           ['conv4_block2_add[0][0]']       \n","                                )                                                                 \n","                                                                                                  \n"," conv4_block3_1_conv (Conv2D)   (None, 15, 15, 256)  262400      ['conv4_block2_out[0][0]']       \n","                                                                                                  \n"," conv4_block3_1_bn (BatchNormal  (None, 15, 15, 256)  1024       ['conv4_block3_1_conv[0][0]']    \n"," ization)                                                                                         \n","                                                                                                  \n"," conv4_block3_1_relu (Activatio  (None, 15, 15, 256)  0          ['conv4_block3_1_bn[0][0]']      \n"," n)                                                                                               \n","                                                                                                  \n"," conv4_block3_2_conv (Conv2D)   (None, 15, 15, 256)  590080      ['conv4_block3_1_relu[0][0]']    \n","                                                                                                  \n"," conv4_block3_2_bn (BatchNormal  (None, 15, 15, 256)  1024       ['conv4_block3_2_conv[0][0]']    \n"," ization)                                                                                         \n","                                                                                                  \n"," conv4_block3_2_relu (Activatio  (None, 15, 15, 256)  0          ['conv4_block3_2_bn[0][0]']      \n"," n)                                                                                               \n","                                                                                                  \n"," conv4_block3_3_conv (Conv2D)   (None, 15, 15, 1024  263168      ['conv4_block3_2_relu[0][0]']    \n","                                )                                                                 \n","                                                                                                  \n"," conv4_block3_3_bn (BatchNormal  (None, 15, 15, 1024  4096       ['conv4_block3_3_conv[0][0]']    \n"," ization)                       )                                                                 \n","                                                                                                  \n"," conv4_block3_add (Add)         (None, 15, 15, 1024  0           ['conv4_block2_out[0][0]',       \n","                                )                                 'conv4_block3_3_bn[0][0]']      \n","                                                                                                  \n"," conv4_block3_out (Activation)  (None, 15, 15, 1024  0           ['conv4_block3_add[0][0]']       \n","                                )                                                                 \n","                                                                                                  \n"," conv4_block4_1_conv (Conv2D)   (None, 15, 15, 256)  262400      ['conv4_block3_out[0][0]']       \n","                                                                                                  \n"," conv4_block4_1_bn (BatchNormal  (None, 15, 15, 256)  1024       ['conv4_block4_1_conv[0][0]']    \n"," ization)                                                                                         \n","                                                                                                  \n"," conv4_block4_1_relu (Activatio  (None, 15, 15, 256)  0          ['conv4_block4_1_bn[0][0]']      \n"," n)                                                                                               \n","                                                                                                  \n"," conv4_block4_2_conv (Conv2D)   (None, 15, 15, 256)  590080      ['conv4_block4_1_relu[0][0]']    \n","                                                                                                  \n"," conv4_block4_2_bn (BatchNormal  (None, 15, 15, 256)  1024       ['conv4_block4_2_conv[0][0]']    \n"," ization)                                                                                         \n","                                                                                                  \n"," conv4_block4_2_relu (Activatio  (None, 15, 15, 256)  0          ['conv4_block4_2_bn[0][0]']      \n"," n)                                                                                               \n","                                                                                                  \n"," conv4_block4_3_conv (Conv2D)   (None, 15, 15, 1024  263168      ['conv4_block4_2_relu[0][0]']    \n","                                )                                                                 \n","                                                                                                  \n"," conv4_block4_3_bn (BatchNormal  (None, 15, 15, 1024  4096       ['conv4_block4_3_conv[0][0]']    \n"," ization)                       )                                                                 \n","                                                                                                  \n"," conv4_block4_add (Add)         (None, 15, 15, 1024  0           ['conv4_block3_out[0][0]',       \n","                                )                                 'conv4_block4_3_bn[0][0]']      \n","                                                                                                  \n"," conv4_block4_out (Activation)  (None, 15, 15, 1024  0           ['conv4_block4_add[0][0]']       \n","                                )                                                                 \n","                                                                                                  \n"," conv4_block5_1_conv (Conv2D)   (None, 15, 15, 256)  262400      ['conv4_block4_out[0][0]']       \n","                                                                                                  \n"," conv4_block5_1_bn (BatchNormal  (None, 15, 15, 256)  1024       ['conv4_block5_1_conv[0][0]']    \n"," ization)                                                                                         \n","                                                                                                  \n"," conv4_block5_1_relu (Activatio  (None, 15, 15, 256)  0          ['conv4_block5_1_bn[0][0]']      \n"," n)                                                                                               \n","                                                                                                  \n"," conv4_block5_2_conv (Conv2D)   (None, 15, 15, 256)  590080      ['conv4_block5_1_relu[0][0]']    \n","                                                                                                  \n"," conv4_block5_2_bn (BatchNormal  (None, 15, 15, 256)  1024       ['conv4_block5_2_conv[0][0]']    \n"," ization)                                                                                         \n","                                                                                                  \n"," conv4_block5_2_relu (Activatio  (None, 15, 15, 256)  0          ['conv4_block5_2_bn[0][0]']      \n"," n)                                                                                               \n","                                                                                                  \n"," conv4_block5_3_conv (Conv2D)   (None, 15, 15, 1024  263168      ['conv4_block5_2_relu[0][0]']    \n","                                )                                                                 \n","                                                                                                  \n"," conv4_block5_3_bn (BatchNormal  (None, 15, 15, 1024  4096       ['conv4_block5_3_conv[0][0]']    \n"," ization)                       )                                                                 \n","                                                                                                  \n"," conv4_block5_add (Add)         (None, 15, 15, 1024  0           ['conv4_block4_out[0][0]',       \n","                                )                                 'conv4_block5_3_bn[0][0]']      \n","                                                                                                  \n"," conv4_block5_out (Activation)  (None, 15, 15, 1024  0           ['conv4_block5_add[0][0]']       \n","                                )                                                                 \n","                                                                                                  \n"," conv4_block6_1_conv (Conv2D)   (None, 15, 15, 256)  262400      ['conv4_block5_out[0][0]']       \n","                                                                                                  \n"," conv4_block6_1_bn (BatchNormal  (None, 15, 15, 256)  1024       ['conv4_block6_1_conv[0][0]']    \n"," ization)                                                                                         \n","                                                                                                  \n"," conv4_block6_1_relu (Activatio  (None, 15, 15, 256)  0          ['conv4_block6_1_bn[0][0]']      \n"," n)                                                                                               \n","                                                                                                  \n"," conv4_block6_2_conv (Conv2D)   (None, 15, 15, 256)  590080      ['conv4_block6_1_relu[0][0]']    \n","                                                                                                  \n"," conv4_block6_2_bn (BatchNormal  (None, 15, 15, 256)  1024       ['conv4_block6_2_conv[0][0]']    \n"," ization)                                                                                         \n","                                                                                                  \n"," conv4_block6_2_relu (Activatio  (None, 15, 15, 256)  0          ['conv4_block6_2_bn[0][0]']      \n"," n)                                                                                               \n","                                                                                                  \n"," conv4_block6_3_conv (Conv2D)   (None, 15, 15, 1024  263168      ['conv4_block6_2_relu[0][0]']    \n","                                )                                                                 \n","                                                                                                  \n"," conv4_block6_3_bn (BatchNormal  (None, 15, 15, 1024  4096       ['conv4_block6_3_conv[0][0]']    \n"," ization)                       )                                                                 \n","                                                                                                  \n"," conv4_block6_add (Add)         (None, 15, 15, 1024  0           ['conv4_block5_out[0][0]',       \n","                                )                                 'conv4_block6_3_bn[0][0]']      \n","                                                                                                  \n"," conv4_block6_out (Activation)  (None, 15, 15, 1024  0           ['conv4_block6_add[0][0]']       \n","                                )                                                                 \n","                                                                                                  \n"," conv4_block7_1_conv (Conv2D)   (None, 15, 15, 256)  262400      ['conv4_block6_out[0][0]']       \n","                                                                                                  \n"," conv4_block7_1_bn (BatchNormal  (None, 15, 15, 256)  1024       ['conv4_block7_1_conv[0][0]']    \n"," ization)                                                                                         \n","                                                                                                  \n"," conv4_block7_1_relu (Activatio  (None, 15, 15, 256)  0          ['conv4_block7_1_bn[0][0]']      \n"," n)                                                                                               \n","                                                                                                  \n"," conv4_block7_2_conv (Conv2D)   (None, 15, 15, 256)  590080      ['conv4_block7_1_relu[0][0]']    \n","                                                                                                  \n"," conv4_block7_2_bn (BatchNormal  (None, 15, 15, 256)  1024       ['conv4_block7_2_conv[0][0]']    \n"," ization)                                                                                         \n","                                                                                                  \n"," conv4_block7_2_relu (Activatio  (None, 15, 15, 256)  0          ['conv4_block7_2_bn[0][0]']      \n"," n)                                                                                               \n","                                                                                                  \n"," conv4_block7_3_conv (Conv2D)   (None, 15, 15, 1024  263168      ['conv4_block7_2_relu[0][0]']    \n","                                )                                                                 \n","                                                                                                  \n"," conv4_block7_3_bn (BatchNormal  (None, 15, 15, 1024  4096       ['conv4_block7_3_conv[0][0]']    \n"," ization)                       )                                                                 \n","                                                                                                  \n"," conv4_block7_add (Add)         (None, 15, 15, 1024  0           ['conv4_block6_out[0][0]',       \n","                                )                                 'conv4_block7_3_bn[0][0]']      \n","                                                                                                  \n"," conv4_block7_out (Activation)  (None, 15, 15, 1024  0           ['conv4_block7_add[0][0]']       \n","                                )                                                                 \n","                                                                                                  \n"," conv4_block8_1_conv (Conv2D)   (None, 15, 15, 256)  262400      ['conv4_block7_out[0][0]']       \n","                                                                                                  \n"," conv4_block8_1_bn (BatchNormal  (None, 15, 15, 256)  1024       ['conv4_block8_1_conv[0][0]']    \n"," ization)                                                                                         \n","                                                                                                  \n"," conv4_block8_1_relu (Activatio  (None, 15, 15, 256)  0          ['conv4_block8_1_bn[0][0]']      \n"," n)                                                                                               \n","                                                                                                  \n"," conv4_block8_2_conv (Conv2D)   (None, 15, 15, 256)  590080      ['conv4_block8_1_relu[0][0]']    \n","                                                                                                  \n"," conv4_block8_2_bn (BatchNormal  (None, 15, 15, 256)  1024       ['conv4_block8_2_conv[0][0]']    \n"," ization)                                                                                         \n","                                                                                                  \n"," conv4_block8_2_relu (Activatio  (None, 15, 15, 256)  0          ['conv4_block8_2_bn[0][0]']      \n"," n)                                                                                               \n","                                                                                                  \n"," conv4_block8_3_conv (Conv2D)   (None, 15, 15, 1024  263168      ['conv4_block8_2_relu[0][0]']    \n","                                )                                                                 \n","                                                                                                  \n"," conv4_block8_3_bn (BatchNormal  (None, 15, 15, 1024  4096       ['conv4_block8_3_conv[0][0]']    \n"," ization)                       )                                                                 \n","                                                                                                  \n"," conv4_block8_add (Add)         (None, 15, 15, 1024  0           ['conv4_block7_out[0][0]',       \n","                                )                                 'conv4_block8_3_bn[0][0]']      \n","                                                                                                  \n"," conv4_block8_out (Activation)  (None, 15, 15, 1024  0           ['conv4_block8_add[0][0]']       \n","                                )                                                                 \n","                                                                                                  \n"," conv4_block9_1_conv (Conv2D)   (None, 15, 15, 256)  262400      ['conv4_block8_out[0][0]']       \n","                                                                                                  \n"," conv4_block9_1_bn (BatchNormal  (None, 15, 15, 256)  1024       ['conv4_block9_1_conv[0][0]']    \n"," ization)                                                                                         \n","                                                                                                  \n"," conv4_block9_1_relu (Activatio  (None, 15, 15, 256)  0          ['conv4_block9_1_bn[0][0]']      \n"," n)                                                                                               \n","                                                                                                  \n"," conv4_block9_2_conv (Conv2D)   (None, 15, 15, 256)  590080      ['conv4_block9_1_relu[0][0]']    \n","                                                                                                  \n"," conv4_block9_2_bn (BatchNormal  (None, 15, 15, 256)  1024       ['conv4_block9_2_conv[0][0]']    \n"," ization)                                                                                         \n","                                                                                                  \n"," conv4_block9_2_relu (Activatio  (None, 15, 15, 256)  0          ['conv4_block9_2_bn[0][0]']      \n"," n)                                                                                               \n","                                                                                                  \n"," conv4_block9_3_conv (Conv2D)   (None, 15, 15, 1024  263168      ['conv4_block9_2_relu[0][0]']    \n","                                )                                                                 \n","                                                                                                  \n"," conv4_block9_3_bn (BatchNormal  (None, 15, 15, 1024  4096       ['conv4_block9_3_conv[0][0]']    \n"," ization)                       )                                                                 \n","                                                                                                  \n"," conv4_block9_add (Add)         (None, 15, 15, 1024  0           ['conv4_block8_out[0][0]',       \n","                                )                                 'conv4_block9_3_bn[0][0]']      \n","                                                                                                  \n"," conv4_block9_out (Activation)  (None, 15, 15, 1024  0           ['conv4_block9_add[0][0]']       \n","                                )                                                                 \n","                                                                                                  \n"," conv4_block10_1_conv (Conv2D)  (None, 15, 15, 256)  262400      ['conv4_block9_out[0][0]']       \n","                                                                                                  \n"," conv4_block10_1_bn (BatchNorma  (None, 15, 15, 256)  1024       ['conv4_block10_1_conv[0][0]']   \n"," lization)                                                                                        \n","                                                                                                  \n"," conv4_block10_1_relu (Activati  (None, 15, 15, 256)  0          ['conv4_block10_1_bn[0][0]']     \n"," on)                                                                                              \n","                                                                                                  \n"," conv4_block10_2_conv (Conv2D)  (None, 15, 15, 256)  590080      ['conv4_block10_1_relu[0][0]']   \n","                                                                                                  \n"," conv4_block10_2_bn (BatchNorma  (None, 15, 15, 256)  1024       ['conv4_block10_2_conv[0][0]']   \n"," lization)                                                                                        \n","                                                                                                  \n"," conv4_block10_2_relu (Activati  (None, 15, 15, 256)  0          ['conv4_block10_2_bn[0][0]']     \n"," on)                                                                                              \n","                                                                                                  \n"," conv4_block10_3_conv (Conv2D)  (None, 15, 15, 1024  263168      ['conv4_block10_2_relu[0][0]']   \n","                                )                                                                 \n","                                                                                                  \n"," conv4_block10_3_bn (BatchNorma  (None, 15, 15, 1024  4096       ['conv4_block10_3_conv[0][0]']   \n"," lization)                      )                                                                 \n","                                                                                                  \n"," conv4_block10_add (Add)        (None, 15, 15, 1024  0           ['conv4_block9_out[0][0]',       \n","                                )                                 'conv4_block10_3_bn[0][0]']     \n","                                                                                                  \n"," conv4_block10_out (Activation)  (None, 15, 15, 1024  0          ['conv4_block10_add[0][0]']      \n","                                )                                                                 \n","                                                                                                  \n"," conv4_block11_1_conv (Conv2D)  (None, 15, 15, 256)  262400      ['conv4_block10_out[0][0]']      \n","                                                                                                  \n"," conv4_block11_1_bn (BatchNorma  (None, 15, 15, 256)  1024       ['conv4_block11_1_conv[0][0]']   \n"," lization)                                                                                        \n","                                                                                                  \n"," conv4_block11_1_relu (Activati  (None, 15, 15, 256)  0          ['conv4_block11_1_bn[0][0]']     \n"," on)                                                                                              \n","                                                                                                  \n"," conv4_block11_2_conv (Conv2D)  (None, 15, 15, 256)  590080      ['conv4_block11_1_relu[0][0]']   \n","                                                                                                  \n"," conv4_block11_2_bn (BatchNorma  (None, 15, 15, 256)  1024       ['conv4_block11_2_conv[0][0]']   \n"," lization)                                                                                        \n","                                                                                                  \n"," conv4_block11_2_relu (Activati  (None, 15, 15, 256)  0          ['conv4_block11_2_bn[0][0]']     \n"," on)                                                                                              \n","                                                                                                  \n"," conv4_block11_3_conv (Conv2D)  (None, 15, 15, 1024  263168      ['conv4_block11_2_relu[0][0]']   \n","                                )                                                                 \n","                                                                                                  \n"," conv4_block11_3_bn (BatchNorma  (None, 15, 15, 1024  4096       ['conv4_block11_3_conv[0][0]']   \n"," lization)                      )                                                                 \n","                                                                                                  \n"," conv4_block11_add (Add)        (None, 15, 15, 1024  0           ['conv4_block10_out[0][0]',      \n","                                )                                 'conv4_block11_3_bn[0][0]']     \n","                                                                                                  \n"," conv4_block11_out (Activation)  (None, 15, 15, 1024  0          ['conv4_block11_add[0][0]']      \n","                                )                                                                 \n","                                                                                                  \n"," conv4_block12_1_conv (Conv2D)  (None, 15, 15, 256)  262400      ['conv4_block11_out[0][0]']      \n","                                                                                                  \n"," conv4_block12_1_bn (BatchNorma  (None, 15, 15, 256)  1024       ['conv4_block12_1_conv[0][0]']   \n"," lization)                                                                                        \n","                                                                                                  \n"," conv4_block12_1_relu (Activati  (None, 15, 15, 256)  0          ['conv4_block12_1_bn[0][0]']     \n"," on)                                                                                              \n","                                                                                                  \n"," conv4_block12_2_conv (Conv2D)  (None, 15, 15, 256)  590080      ['conv4_block12_1_relu[0][0]']   \n","                                                                                                  \n"," conv4_block12_2_bn (BatchNorma  (None, 15, 15, 256)  1024       ['conv4_block12_2_conv[0][0]']   \n"," lization)                                                                                        \n","                                                                                                  \n"," conv4_block12_2_relu (Activati  (None, 15, 15, 256)  0          ['conv4_block12_2_bn[0][0]']     \n"," on)                                                                                              \n","                                                                                                  \n"," conv4_block12_3_conv (Conv2D)  (None, 15, 15, 1024  263168      ['conv4_block12_2_relu[0][0]']   \n","                                )                                                                 \n","                                                                                                  \n"," conv4_block12_3_bn (BatchNorma  (None, 15, 15, 1024  4096       ['conv4_block12_3_conv[0][0]']   \n"," lization)                      )                                                                 \n","                                                                                                  \n"," conv4_block12_add (Add)        (None, 15, 15, 1024  0           ['conv4_block11_out[0][0]',      \n","                                )                                 'conv4_block12_3_bn[0][0]']     \n","                                                                                                  \n"," conv4_block12_out (Activation)  (None, 15, 15, 1024  0          ['conv4_block12_add[0][0]']      \n","                                )                                                                 \n","                                                                                                  \n"," conv4_block13_1_conv (Conv2D)  (None, 15, 15, 256)  262400      ['conv4_block12_out[0][0]']      \n","                                                                                                  \n"," conv4_block13_1_bn (BatchNorma  (None, 15, 15, 256)  1024       ['conv4_block13_1_conv[0][0]']   \n"," lization)                                                                                        \n","                                                                                                  \n"," conv4_block13_1_relu (Activati  (None, 15, 15, 256)  0          ['conv4_block13_1_bn[0][0]']     \n"," on)                                                                                              \n","                                                                                                  \n"," conv4_block13_2_conv (Conv2D)  (None, 15, 15, 256)  590080      ['conv4_block13_1_relu[0][0]']   \n","                                                                                                  \n"," conv4_block13_2_bn (BatchNorma  (None, 15, 15, 256)  1024       ['conv4_block13_2_conv[0][0]']   \n"," lization)                                                                                        \n","                                                                                                  \n"," conv4_block13_2_relu (Activati  (None, 15, 15, 256)  0          ['conv4_block13_2_bn[0][0]']     \n"," on)                                                                                              \n","                                                                                                  \n"," conv4_block13_3_conv (Conv2D)  (None, 15, 15, 1024  263168      ['conv4_block13_2_relu[0][0]']   \n","                                )                                                                 \n","                                                                                                  \n"," conv4_block13_3_bn (BatchNorma  (None, 15, 15, 1024  4096       ['conv4_block13_3_conv[0][0]']   \n"," lization)                      )                                                                 \n","                                                                                                  \n"," conv4_block13_add (Add)        (None, 15, 15, 1024  0           ['conv4_block12_out[0][0]',      \n","                                )                                 'conv4_block13_3_bn[0][0]']     \n","                                                                                                  \n"," conv4_block13_out (Activation)  (None, 15, 15, 1024  0          ['conv4_block13_add[0][0]']      \n","                                )                                                                 \n","                                                                                                  \n"," conv4_block14_1_conv (Conv2D)  (None, 15, 15, 256)  262400      ['conv4_block13_out[0][0]']      \n","                                                                                                  \n"," conv4_block14_1_bn (BatchNorma  (None, 15, 15, 256)  1024       ['conv4_block14_1_conv[0][0]']   \n"," lization)                                                                                        \n","                                                                                                  \n"," conv4_block14_1_relu (Activati  (None, 15, 15, 256)  0          ['conv4_block14_1_bn[0][0]']     \n"," on)                                                                                              \n","                                                                                                  \n"," conv4_block14_2_conv (Conv2D)  (None, 15, 15, 256)  590080      ['conv4_block14_1_relu[0][0]']   \n","                                                                                                  \n"," conv4_block14_2_bn (BatchNorma  (None, 15, 15, 256)  1024       ['conv4_block14_2_conv[0][0]']   \n"," lization)                                                                                        \n","                                                                                                  \n"," conv4_block14_2_relu (Activati  (None, 15, 15, 256)  0          ['conv4_block14_2_bn[0][0]']     \n"," on)                                                                                              \n","                                                                                                  \n"," conv4_block14_3_conv (Conv2D)  (None, 15, 15, 1024  263168      ['conv4_block14_2_relu[0][0]']   \n","                                )                                                                 \n","                                                                                                  \n"," conv4_block14_3_bn (BatchNorma  (None, 15, 15, 1024  4096       ['conv4_block14_3_conv[0][0]']   \n"," lization)                      )                                                                 \n","                                                                                                  \n"," conv4_block14_add (Add)        (None, 15, 15, 1024  0           ['conv4_block13_out[0][0]',      \n","                                )                                 'conv4_block14_3_bn[0][0]']     \n","                                                                                                  \n"," conv4_block14_out (Activation)  (None, 15, 15, 1024  0          ['conv4_block14_add[0][0]']      \n","                                )                                                                 \n","                                                                                                  \n"," conv4_block15_1_conv (Conv2D)  (None, 15, 15, 256)  262400      ['conv4_block14_out[0][0]']      \n","                                                                                                  \n"," conv4_block15_1_bn (BatchNorma  (None, 15, 15, 256)  1024       ['conv4_block15_1_conv[0][0]']   \n"," lization)                                                                                        \n","                                                                                                  \n"," conv4_block15_1_relu (Activati  (None, 15, 15, 256)  0          ['conv4_block15_1_bn[0][0]']     \n"," on)                                                                                              \n","                                                                                                  \n"," conv4_block15_2_conv (Conv2D)  (None, 15, 15, 256)  590080      ['conv4_block15_1_relu[0][0]']   \n","                                                                                                  \n"," conv4_block15_2_bn (BatchNorma  (None, 15, 15, 256)  1024       ['conv4_block15_2_conv[0][0]']   \n"," lization)                                                                                        \n","                                                                                                  \n"," conv4_block15_2_relu (Activati  (None, 15, 15, 256)  0          ['conv4_block15_2_bn[0][0]']     \n"," on)                                                                                              \n","                                                                                                  \n"," conv4_block15_3_conv (Conv2D)  (None, 15, 15, 1024  263168      ['conv4_block15_2_relu[0][0]']   \n","                                )                                                                 \n","                                                                                                  \n"," conv4_block15_3_bn (BatchNorma  (None, 15, 15, 1024  4096       ['conv4_block15_3_conv[0][0]']   \n"," lization)                      )                                                                 \n","                                                                                                  \n"," conv4_block15_add (Add)        (None, 15, 15, 1024  0           ['conv4_block14_out[0][0]',      \n","                                )                                 'conv4_block15_3_bn[0][0]']     \n","                                                                                                  \n"," conv4_block15_out (Activation)  (None, 15, 15, 1024  0          ['conv4_block15_add[0][0]']      \n","                                )                                                                 \n","                                                                                                  \n"," conv4_block16_1_conv (Conv2D)  (None, 15, 15, 256)  262400      ['conv4_block15_out[0][0]']      \n","                                                                                                  \n"," conv4_block16_1_bn (BatchNorma  (None, 15, 15, 256)  1024       ['conv4_block16_1_conv[0][0]']   \n"," lization)                                                                                        \n","                                                                                                  \n"," conv4_block16_1_relu (Activati  (None, 15, 15, 256)  0          ['conv4_block16_1_bn[0][0]']     \n"," on)                                                                                              \n","                                                                                                  \n"," conv4_block16_2_conv (Conv2D)  (None, 15, 15, 256)  590080      ['conv4_block16_1_relu[0][0]']   \n","                                                                                                  \n"," conv4_block16_2_bn (BatchNorma  (None, 15, 15, 256)  1024       ['conv4_block16_2_conv[0][0]']   \n"," lization)                                                                                        \n","                                                                                                  \n"," conv4_block16_2_relu (Activati  (None, 15, 15, 256)  0          ['conv4_block16_2_bn[0][0]']     \n"," on)                                                                                              \n","                                                                                                  \n"," conv4_block16_3_conv (Conv2D)  (None, 15, 15, 1024  263168      ['conv4_block16_2_relu[0][0]']   \n","                                )                                                                 \n","                                                                                                  \n"," conv4_block16_3_bn (BatchNorma  (None, 15, 15, 1024  4096       ['conv4_block16_3_conv[0][0]']   \n"," lization)                      )                                                                 \n","                                                                                                  \n"," conv4_block16_add (Add)        (None, 15, 15, 1024  0           ['conv4_block15_out[0][0]',      \n","                                )                                 'conv4_block16_3_bn[0][0]']     \n","                                                                                                  \n"," conv4_block16_out (Activation)  (None, 15, 15, 1024  0          ['conv4_block16_add[0][0]']      \n","                                )                                                                 \n","                                                                                                  \n"," conv4_block17_1_conv (Conv2D)  (None, 15, 15, 256)  262400      ['conv4_block16_out[0][0]']      \n","                                                                                                  \n"," conv4_block17_1_bn (BatchNorma  (None, 15, 15, 256)  1024       ['conv4_block17_1_conv[0][0]']   \n"," lization)                                                                                        \n","                                                                                                  \n"," conv4_block17_1_relu (Activati  (None, 15, 15, 256)  0          ['conv4_block17_1_bn[0][0]']     \n"," on)                                                                                              \n","                                                                                                  \n"," conv4_block17_2_conv (Conv2D)  (None, 15, 15, 256)  590080      ['conv4_block17_1_relu[0][0]']   \n","                                                                                                  \n"," conv4_block17_2_bn (BatchNorma  (None, 15, 15, 256)  1024       ['conv4_block17_2_conv[0][0]']   \n"," lization)                                                                                        \n","                                                                                                  \n"," conv4_block17_2_relu (Activati  (None, 15, 15, 256)  0          ['conv4_block17_2_bn[0][0]']     \n"," on)                                                                                              \n","                                                                                                  \n"," conv4_block17_3_conv (Conv2D)  (None, 15, 15, 1024  263168      ['conv4_block17_2_relu[0][0]']   \n","                                )                                                                 \n","                                                                                                  \n"," conv4_block17_3_bn (BatchNorma  (None, 15, 15, 1024  4096       ['conv4_block17_3_conv[0][0]']   \n"," lization)                      )                                                                 \n","                                                                                                  \n"," conv4_block17_add (Add)        (None, 15, 15, 1024  0           ['conv4_block16_out[0][0]',      \n","                                )                                 'conv4_block17_3_bn[0][0]']     \n","                                                                                                  \n"," conv4_block17_out (Activation)  (None, 15, 15, 1024  0          ['conv4_block17_add[0][0]']      \n","                                )                                                                 \n","                                                                                                  \n"," conv4_block18_1_conv (Conv2D)  (None, 15, 15, 256)  262400      ['conv4_block17_out[0][0]']      \n","                                                                                                  \n"," conv4_block18_1_bn (BatchNorma  (None, 15, 15, 256)  1024       ['conv4_block18_1_conv[0][0]']   \n"," lization)                                                                                        \n","                                                                                                  \n"," conv4_block18_1_relu (Activati  (None, 15, 15, 256)  0          ['conv4_block18_1_bn[0][0]']     \n"," on)                                                                                              \n","                                                                                                  \n"," conv4_block18_2_conv (Conv2D)  (None, 15, 15, 256)  590080      ['conv4_block18_1_relu[0][0]']   \n","                                                                                                  \n"," conv4_block18_2_bn (BatchNorma  (None, 15, 15, 256)  1024       ['conv4_block18_2_conv[0][0]']   \n"," lization)                                                                                        \n","                                                                                                  \n"," conv4_block18_2_relu (Activati  (None, 15, 15, 256)  0          ['conv4_block18_2_bn[0][0]']     \n"," on)                                                                                              \n","                                                                                                  \n"," conv4_block18_3_conv (Conv2D)  (None, 15, 15, 1024  263168      ['conv4_block18_2_relu[0][0]']   \n","                                )                                                                 \n","                                                                                                  \n"," conv4_block18_3_bn (BatchNorma  (None, 15, 15, 1024  4096       ['conv4_block18_3_conv[0][0]']   \n"," lization)                      )                                                                 \n","                                                                                                  \n"," conv4_block18_add (Add)        (None, 15, 15, 1024  0           ['conv4_block17_out[0][0]',      \n","                                )                                 'conv4_block18_3_bn[0][0]']     \n","                                                                                                  \n"," conv4_block18_out (Activation)  (None, 15, 15, 1024  0          ['conv4_block18_add[0][0]']      \n","                                )                                                                 \n","                                                                                                  \n"," conv4_block19_1_conv (Conv2D)  (None, 15, 15, 256)  262400      ['conv4_block18_out[0][0]']      \n","                                                                                                  \n"," conv4_block19_1_bn (BatchNorma  (None, 15, 15, 256)  1024       ['conv4_block19_1_conv[0][0]']   \n"," lization)                                                                                        \n","                                                                                                  \n"," conv4_block19_1_relu (Activati  (None, 15, 15, 256)  0          ['conv4_block19_1_bn[0][0]']     \n"," on)                                                                                              \n","                                                                                                  \n"," conv4_block19_2_conv (Conv2D)  (None, 15, 15, 256)  590080      ['conv4_block19_1_relu[0][0]']   \n","                                                                                                  \n"," conv4_block19_2_bn (BatchNorma  (None, 15, 15, 256)  1024       ['conv4_block19_2_conv[0][0]']   \n"," lization)                                                                                        \n","                                                                                                  \n"," conv4_block19_2_relu (Activati  (None, 15, 15, 256)  0          ['conv4_block19_2_bn[0][0]']     \n"," on)                                                                                              \n","                                                                                                  \n"," conv4_block19_3_conv (Conv2D)  (None, 15, 15, 1024  263168      ['conv4_block19_2_relu[0][0]']   \n","                                )                                                                 \n","                                                                                                  \n"," conv4_block19_3_bn (BatchNorma  (None, 15, 15, 1024  4096       ['conv4_block19_3_conv[0][0]']   \n"," lization)                      )                                                                 \n","                                                                                                  \n"," conv4_block19_add (Add)        (None, 15, 15, 1024  0           ['conv4_block18_out[0][0]',      \n","                                )                                 'conv4_block19_3_bn[0][0]']     \n","                                                                                                  \n"," conv4_block19_out (Activation)  (None, 15, 15, 1024  0          ['conv4_block19_add[0][0]']      \n","                                )                                                                 \n","                                                                                                  \n"," conv4_block20_1_conv (Conv2D)  (None, 15, 15, 256)  262400      ['conv4_block19_out[0][0]']      \n","                                                                                                  \n"," conv4_block20_1_bn (BatchNorma  (None, 15, 15, 256)  1024       ['conv4_block20_1_conv[0][0]']   \n"," lization)                                                                                        \n","                                                                                                  \n"," conv4_block20_1_relu (Activati  (None, 15, 15, 256)  0          ['conv4_block20_1_bn[0][0]']     \n"," on)                                                                                              \n","                                                                                                  \n"," conv4_block20_2_conv (Conv2D)  (None, 15, 15, 256)  590080      ['conv4_block20_1_relu[0][0]']   \n","                                                                                                  \n"," conv4_block20_2_bn (BatchNorma  (None, 15, 15, 256)  1024       ['conv4_block20_2_conv[0][0]']   \n"," lization)                                                                                        \n","                                                                                                  \n"," conv4_block20_2_relu (Activati  (None, 15, 15, 256)  0          ['conv4_block20_2_bn[0][0]']     \n"," on)                                                                                              \n","                                                                                                  \n"," conv4_block20_3_conv (Conv2D)  (None, 15, 15, 1024  263168      ['conv4_block20_2_relu[0][0]']   \n","                                )                                                                 \n","                                                                                                  \n"," conv4_block20_3_bn (BatchNorma  (None, 15, 15, 1024  4096       ['conv4_block20_3_conv[0][0]']   \n"," lization)                      )                                                                 \n","                                                                                                  \n"," conv4_block20_add (Add)        (None, 15, 15, 1024  0           ['conv4_block19_out[0][0]',      \n","                                )                                 'conv4_block20_3_bn[0][0]']     \n","                                                                                                  \n"," conv4_block20_out (Activation)  (None, 15, 15, 1024  0          ['conv4_block20_add[0][0]']      \n","                                )                                                                 \n","                                                                                                  \n"," conv4_block21_1_conv (Conv2D)  (None, 15, 15, 256)  262400      ['conv4_block20_out[0][0]']      \n","                                                                                                  \n"," conv4_block21_1_bn (BatchNorma  (None, 15, 15, 256)  1024       ['conv4_block21_1_conv[0][0]']   \n"," lization)                                                                                        \n","                                                                                                  \n"," conv4_block21_1_relu (Activati  (None, 15, 15, 256)  0          ['conv4_block21_1_bn[0][0]']     \n"," on)                                                                                              \n","                                                                                                  \n"," conv4_block21_2_conv (Conv2D)  (None, 15, 15, 256)  590080      ['conv4_block21_1_relu[0][0]']   \n","                                                                                                  \n"," conv4_block21_2_bn (BatchNorma  (None, 15, 15, 256)  1024       ['conv4_block21_2_conv[0][0]']   \n"," lization)                                                                                        \n","                                                                                                  \n"," conv4_block21_2_relu (Activati  (None, 15, 15, 256)  0          ['conv4_block21_2_bn[0][0]']     \n"," on)                                                                                              \n","                                                                                                  \n"," conv4_block21_3_conv (Conv2D)  (None, 15, 15, 1024  263168      ['conv4_block21_2_relu[0][0]']   \n","                                )                                                                 \n","                                                                                                  \n"," conv4_block21_3_bn (BatchNorma  (None, 15, 15, 1024  4096       ['conv4_block21_3_conv[0][0]']   \n"," lization)                      )                                                                 \n","                                                                                                  \n"," conv4_block21_add (Add)        (None, 15, 15, 1024  0           ['conv4_block20_out[0][0]',      \n","                                )                                 'conv4_block21_3_bn[0][0]']     \n","                                                                                                  \n"," conv4_block21_out (Activation)  (None, 15, 15, 1024  0          ['conv4_block21_add[0][0]']      \n","                                )                                                                 \n","                                                                                                  \n"," conv4_block22_1_conv (Conv2D)  (None, 15, 15, 256)  262400      ['conv4_block21_out[0][0]']      \n","                                                                                                  \n"," conv4_block22_1_bn (BatchNorma  (None, 15, 15, 256)  1024       ['conv4_block22_1_conv[0][0]']   \n"," lization)                                                                                        \n","                                                                                                  \n"," conv4_block22_1_relu (Activati  (None, 15, 15, 256)  0          ['conv4_block22_1_bn[0][0]']     \n"," on)                                                                                              \n","                                                                                                  \n"," conv4_block22_2_conv (Conv2D)  (None, 15, 15, 256)  590080      ['conv4_block22_1_relu[0][0]']   \n","                                                                                                  \n"," conv4_block22_2_bn (BatchNorma  (None, 15, 15, 256)  1024       ['conv4_block22_2_conv[0][0]']   \n"," lization)                                                                                        \n","                                                                                                  \n"," conv4_block22_2_relu (Activati  (None, 15, 15, 256)  0          ['conv4_block22_2_bn[0][0]']     \n"," on)                                                                                              \n","                                                                                                  \n"," conv4_block22_3_conv (Conv2D)  (None, 15, 15, 1024  263168      ['conv4_block22_2_relu[0][0]']   \n","                                )                                                                 \n","                                                                                                  \n"," conv4_block22_3_bn (BatchNorma  (None, 15, 15, 1024  4096       ['conv4_block22_3_conv[0][0]']   \n"," lization)                      )                                                                 \n","                                                                                                  \n"," conv4_block22_add (Add)        (None, 15, 15, 1024  0           ['conv4_block21_out[0][0]',      \n","                                )                                 'conv4_block22_3_bn[0][0]']     \n","                                                                                                  \n"," conv4_block22_out (Activation)  (None, 15, 15, 1024  0          ['conv4_block22_add[0][0]']      \n","                                )                                                                 \n","                                                                                                  \n"," conv4_block23_1_conv (Conv2D)  (None, 15, 15, 256)  262400      ['conv4_block22_out[0][0]']      \n","                                                                                                  \n"," conv4_block23_1_bn (BatchNorma  (None, 15, 15, 256)  1024       ['conv4_block23_1_conv[0][0]']   \n"," lization)                                                                                        \n","                                                                                                  \n"," conv4_block23_1_relu (Activati  (None, 15, 15, 256)  0          ['conv4_block23_1_bn[0][0]']     \n"," on)                                                                                              \n","                                                                                                  \n"," conv4_block23_2_conv (Conv2D)  (None, 15, 15, 256)  590080      ['conv4_block23_1_relu[0][0]']   \n","                                                                                                  \n"," conv4_block23_2_bn (BatchNorma  (None, 15, 15, 256)  1024       ['conv4_block23_2_conv[0][0]']   \n"," lization)                                                                                        \n","                                                                                                  \n"," conv4_block23_2_relu (Activati  (None, 15, 15, 256)  0          ['conv4_block23_2_bn[0][0]']     \n"," on)                                                                                              \n","                                                                                                  \n"," conv4_block23_3_conv (Conv2D)  (None, 15, 15, 1024  263168      ['conv4_block23_2_relu[0][0]']   \n","                                )                                                                 \n","                                                                                                  \n"," conv4_block23_3_bn (BatchNorma  (None, 15, 15, 1024  4096       ['conv4_block23_3_conv[0][0]']   \n"," lization)                      )                                                                 \n","                                                                                                  \n"," conv4_block23_add (Add)        (None, 15, 15, 1024  0           ['conv4_block22_out[0][0]',      \n","                                )                                 'conv4_block23_3_bn[0][0]']     \n","                                                                                                  \n"," conv4_block23_out (Activation)  (None, 15, 15, 1024  0          ['conv4_block23_add[0][0]']      \n","                                )                                                                 \n","                                                                                                  \n"," conv5_block1_1_conv (Conv2D)   (None, 8, 8, 512)    524800      ['conv4_block23_out[0][0]']      \n","                                                                                                  \n"," conv5_block1_1_bn (BatchNormal  (None, 8, 8, 512)   2048        ['conv5_block1_1_conv[0][0]']    \n"," ization)                                                                                         \n","                                                                                                  \n"," conv5_block1_1_relu (Activatio  (None, 8, 8, 512)   0           ['conv5_block1_1_bn[0][0]']      \n"," n)                                                                                               \n","                                                                                                  \n"," conv5_block1_2_conv (Conv2D)   (None, 8, 8, 512)    2359808     ['conv5_block1_1_relu[0][0]']    \n","                                                                                                  \n"," conv5_block1_2_bn (BatchNormal  (None, 8, 8, 512)   2048        ['conv5_block1_2_conv[0][0]']    \n"," ization)                                                                                         \n","                                                                                                  \n"," conv5_block1_2_relu (Activatio  (None, 8, 8, 512)   0           ['conv5_block1_2_bn[0][0]']      \n"," n)                                                                                               \n","                                                                                                  \n"," conv5_block1_0_conv (Conv2D)   (None, 8, 8, 2048)   2099200     ['conv4_block23_out[0][0]']      \n","                                                                                                  \n"," conv5_block1_3_conv (Conv2D)   (None, 8, 8, 2048)   1050624     ['conv5_block1_2_relu[0][0]']    \n","                                                                                                  \n"," conv5_block1_0_bn (BatchNormal  (None, 8, 8, 2048)  8192        ['conv5_block1_0_conv[0][0]']    \n"," ization)                                                                                         \n","                                                                                                  \n"," conv5_block1_3_bn (BatchNormal  (None, 8, 8, 2048)  8192        ['conv5_block1_3_conv[0][0]']    \n"," ization)                                                                                         \n","                                                                                                  \n"," conv5_block1_add (Add)         (None, 8, 8, 2048)   0           ['conv5_block1_0_bn[0][0]',      \n","                                                                  'conv5_block1_3_bn[0][0]']      \n","                                                                                                  \n"," conv5_block1_out (Activation)  (None, 8, 8, 2048)   0           ['conv5_block1_add[0][0]']       \n","                                                                                                  \n"," conv5_block2_1_conv (Conv2D)   (None, 8, 8, 512)    1049088     ['conv5_block1_out[0][0]']       \n","                                                                                                  \n"," conv5_block2_1_bn (BatchNormal  (None, 8, 8, 512)   2048        ['conv5_block2_1_conv[0][0]']    \n"," ization)                                                                                         \n","                                                                                                  \n"," conv5_block2_1_relu (Activatio  (None, 8, 8, 512)   0           ['conv5_block2_1_bn[0][0]']      \n"," n)                                                                                               \n","                                                                                                  \n"," conv5_block2_2_conv (Conv2D)   (None, 8, 8, 512)    2359808     ['conv5_block2_1_relu[0][0]']    \n","                                                                                                  \n"," conv5_block2_2_bn (BatchNormal  (None, 8, 8, 512)   2048        ['conv5_block2_2_conv[0][0]']    \n"," ization)                                                                                         \n","                                                                                                  \n"," conv5_block2_2_relu (Activatio  (None, 8, 8, 512)   0           ['conv5_block2_2_bn[0][0]']      \n"," n)                                                                                               \n","                                                                                                  \n"," conv5_block2_3_conv (Conv2D)   (None, 8, 8, 2048)   1050624     ['conv5_block2_2_relu[0][0]']    \n","                                                                                                  \n"," conv5_block2_3_bn (BatchNormal  (None, 8, 8, 2048)  8192        ['conv5_block2_3_conv[0][0]']    \n"," ization)                                                                                         \n","                                                                                                  \n"," conv5_block2_add (Add)         (None, 8, 8, 2048)   0           ['conv5_block1_out[0][0]',       \n","                                                                  'conv5_block2_3_bn[0][0]']      \n","                                                                                                  \n"," conv5_block2_out (Activation)  (None, 8, 8, 2048)   0           ['conv5_block2_add[0][0]']       \n","                                                                                                  \n"," conv5_block3_1_conv (Conv2D)   (None, 8, 8, 512)    1049088     ['conv5_block2_out[0][0]']       \n","                                                                                                  \n"," conv5_block3_1_bn (BatchNormal  (None, 8, 8, 512)   2048        ['conv5_block3_1_conv[0][0]']    \n"," ization)                                                                                         \n","                                                                                                  \n"," conv5_block3_1_relu (Activatio  (None, 8, 8, 512)   0           ['conv5_block3_1_bn[0][0]']      \n"," n)                                                                                               \n","                                                                                                  \n"," conv5_block3_2_conv (Conv2D)   (None, 8, 8, 512)    2359808     ['conv5_block3_1_relu[0][0]']    \n","                                                                                                  \n"," conv5_block3_2_bn (BatchNormal  (None, 8, 8, 512)   2048        ['conv5_block3_2_conv[0][0]']    \n"," ization)                                                                                         \n","                                                                                                  \n"," conv5_block3_2_relu (Activatio  (None, 8, 8, 512)   0           ['conv5_block3_2_bn[0][0]']      \n"," n)                                                                                               \n","                                                                                                  \n"," conv5_block3_3_conv (Conv2D)   (None, 8, 8, 2048)   1050624     ['conv5_block3_2_relu[0][0]']    \n","                                                                                                  \n"," conv5_block3_3_bn (BatchNormal  (None, 8, 8, 2048)  8192        ['conv5_block3_3_conv[0][0]']    \n"," ization)                                                                                         \n","                                                                                                  \n"," conv5_block3_add (Add)         (None, 8, 8, 2048)   0           ['conv5_block2_out[0][0]',       \n","                                                                  'conv5_block3_3_bn[0][0]']      \n","                                                                                                  \n"," conv5_block3_out (Activation)  (None, 8, 8, 2048)   0           ['conv5_block3_add[0][0]']       \n","                                                                                                  \n"," flatten_1 (Flatten)            (None, 131072)       0           ['conv5_block3_out[0][0]']       \n","                                                                                                  \n"," dense_1 (Dense)                (None, 3)            393219      ['flatten_1[0][0]']              \n","                                                                                                  \n","==================================================================================================\n","Total params: 43,051,395\n","Trainable params: 393,219\n","Non-trainable params: 42,658,176\n","__________________________________________________________________________________________________\n"]}]},{"cell_type":"code","metadata":{"id":"fzBk0U_s2o7H","executionInfo":{"status":"ok","timestamp":1676797763130,"user_tz":-420,"elapsed":376,"user":{"displayName":"Trang Phạm","userId":"09872832977707306821"}}},"source":["#from tensorflow.keras.optimizers import RMSprop\n","final_model.compile(loss=\"binary_crossentropy\",optimizer=\"adam\",metrics=['accuracy'])"],"execution_count":22,"outputs":[]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"rfjfmxKb84eX","outputId":"c7560c5d-b63e-40ae-dd2a-77507b79bfa2","executionInfo":{"status":"ok","timestamp":1676805502279,"user_tz":-420,"elapsed":2894875,"user":{"displayName":"Trang Phạm","userId":"09872832977707306821"}}},"source":["resnet = final_model.fit(training_generator,epochs=30,steps_per_epoch=20,validation_data=validation_generator)"],"execution_count":23,"outputs":[{"output_type":"stream","name":"stdout","text":["Epoch 1/30\n","20/20 [==============================] - 281s 14s/step - loss: 3.8859 - accuracy: 0.5312 - val_loss: 1.6623 - val_accuracy: 0.3284\n","Epoch 2/30\n","20/20 [==============================] - 273s 14s/step - loss: 1.0775 - accuracy: 0.5891 - val_loss: 0.7286 - val_accuracy: 0.5855\n","Epoch 3/30\n","20/20 [==============================] - 243s 13s/step - loss: 0.5006 - accuracy: 0.7234 - val_loss: 0.3846 - val_accuracy: 0.7894\n","Epoch 4/30\n","20/20 [==============================] - 237s 12s/step - loss: 0.5371 - accuracy: 0.7141 - val_loss: 0.6950 - val_accuracy: 0.6392\n","Epoch 5/30\n","20/20 [==============================] - 237s 12s/step - loss: 0.3584 - accuracy: 0.8031 - val_loss: 0.3879 - val_accuracy: 0.7862\n","Epoch 6/30\n","20/20 [==============================] - 248s 13s/step - loss: 0.3792 - accuracy: 0.7922 - val_loss: 0.3446 - val_accuracy: 0.8127\n","Epoch 7/30\n","20/20 [==============================] - 244s 13s/step - loss: 0.3686 - accuracy: 0.7953 - val_loss: 0.3277 - val_accuracy: 0.8021\n","Epoch 8/30\n","20/20 [==============================] - 237s 12s/step - loss: 0.3438 - accuracy: 0.8156 - val_loss: 0.2744 - val_accuracy: 0.8341\n","Epoch 9/30\n","20/20 [==============================] - 242s 13s/step - loss: 0.3364 - accuracy: 0.8078 - val_loss: 0.3857 - val_accuracy: 0.7696\n","Epoch 10/30\n","20/20 [==============================] - 246s 13s/step - loss: 0.3300 - accuracy: 0.8188 - val_loss: 0.5137 - val_accuracy: 0.7313\n","Epoch 11/30\n","20/20 [==============================] - 237s 12s/step - loss: 0.3291 - accuracy: 0.8266 - val_loss: 0.4892 - val_accuracy: 0.7068\n","Epoch 12/30\n","20/20 [==============================] - 236s 12s/step - loss: 0.3549 - accuracy: 0.7906 - val_loss: 0.3254 - val_accuracy: 0.8036\n","Epoch 13/30\n","20/20 [==============================] - 235s 12s/step - loss: 0.4090 - accuracy: 0.8000 - val_loss: 0.5066 - val_accuracy: 0.7222\n","Epoch 14/30\n","20/20 [==============================] - 237s 12s/step - loss: 0.4855 - accuracy: 0.7531 - val_loss: 0.4726 - val_accuracy: 0.7907\n","Epoch 15/30\n","20/20 [==============================] - 236s 12s/step - loss: 0.5800 - accuracy: 0.7828 - val_loss: 0.4437 - val_accuracy: 0.7683\n","Epoch 16/30\n","20/20 [==============================] - 235s 12s/step - loss: 0.3663 - accuracy: 0.8234 - val_loss: 0.3632 - val_accuracy: 0.8116\n","Epoch 17/30\n","20/20 [==============================] - 234s 12s/step - loss: 0.3484 - accuracy: 0.8156 - val_loss: 0.7538 - val_accuracy: 0.6115\n","Epoch 18/30\n","20/20 [==============================] - 234s 12s/step - loss: 0.5036 - accuracy: 0.7953 - val_loss: 0.2791 - val_accuracy: 0.8676\n","Epoch 19/30\n","20/20 [==============================] - 234s 12s/step - loss: 0.3390 - accuracy: 0.8453 - val_loss: 0.2488 - val_accuracy: 0.8586\n","Epoch 20/30\n","20/20 [==============================] - 234s 12s/step - loss: 0.3054 - accuracy: 0.8359 - val_loss: 0.3329 - val_accuracy: 0.8233\n","Epoch 21/30\n","20/20 [==============================] - 235s 12s/step - loss: 0.3073 - accuracy: 0.8313 - val_loss: 0.2699 - val_accuracy: 0.8396\n","Epoch 22/30\n","20/20 [==============================] - 233s 12s/step - loss: 0.2924 - accuracy: 0.8453 - val_loss: 0.3624 - val_accuracy: 0.8147\n","Epoch 23/30\n","20/20 [==============================] - 230s 12s/step - loss: 0.3061 - accuracy: 0.8281 - val_loss: 0.2947 - val_accuracy: 0.8303\n","Epoch 24/30\n","20/20 [==============================] - 234s 12s/step - loss: 0.3631 - accuracy: 0.8203 - val_loss: 0.7312 - val_accuracy: 0.6187\n","Epoch 25/30\n","20/20 [==============================] - 228s 12s/step - loss: 0.3325 - accuracy: 0.8328 - val_loss: 0.4750 - val_accuracy: 0.7319\n","Epoch 26/30\n","20/20 [==============================] - 229s 12s/step - loss: 0.3164 - accuracy: 0.8359 - val_loss: 0.2188 - val_accuracy: 0.8800\n","Epoch 27/30\n","20/20 [==============================] - 231s 12s/step - loss: 0.2466 - accuracy: 0.8687 - val_loss: 0.2486 - val_accuracy: 0.8575\n","Epoch 28/30\n","20/20 [==============================] - 229s 12s/step - loss: 0.2614 - accuracy: 0.8766 - val_loss: 0.2880 - val_accuracy: 0.8737\n","Epoch 29/30\n","20/20 [==============================] - 228s 12s/step - loss: 0.2710 - accuracy: 0.8625 - val_loss: 0.3617 - val_accuracy: 0.7952\n","Epoch 30/30\n","20/20 [==============================] - 273s 14s/step - loss: 0.3221 - accuracy: 0.8203 - val_loss: 0.2810 - val_accuracy: 0.8329\n"]}]},{"cell_type":"code","source":["plt.figure(figsize=(20, 16))\n","\n","plt.subplot(2, 2, 1)\n","plt.plot(final_model.history.history['loss'], label='Loss')\n","plt.plot(final_model.history.history['val_loss'], label='val_Loss')\n","plt.legend()\n","plt.grid()\n","plt.title('Loss evolution')\n","\n","plt.subplot(2, 2, 2)\n","plt.plot(final_model.history.history['accuracy'], label='accuracy')\n","plt.plot(final_model.history.history['val_accuracy'], label='val_accuracy')\n","plt.legend()\n","plt.grid()\n","plt.title('Accuracy evolution')"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":477},"id":"P6bB5B1Ggmbp","executionInfo":{"status":"ok","timestamp":1676805583655,"user_tz":-420,"elapsed":1806,"user":{"displayName":"Trang Phạm","userId":"09872832977707306821"}},"outputId":"171614c0-9619-4082-af20-60d46fef7ee1"},"execution_count":24,"outputs":[{"output_type":"execute_result","data":{"text/plain":["Text(0.5, 1.0, 'Accuracy evolution')"]},"metadata":{},"execution_count":24},{"output_type":"display_data","data":{"text/plain":["<Figure size 1440x1152 with 2 Axes>"],"image/png":"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\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"code","source":["model.save('Resnet101_increase.h5')"],"metadata":{"id":"QnwSXpKxkJ1e","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1676805619929,"user_tz":-420,"elapsed":4092,"user":{"displayName":"Trang Phạm","userId":"09872832977707306821"}},"outputId":"040d8d49-6d6b-4e31-8c62-c368ffdddd48"},"execution_count":25,"outputs":[{"output_type":"stream","name":"stderr","text":["WARNING:tensorflow:Compiled the loaded model, but the compiled metrics have yet to be built. `model.compile_metrics` will be empty until you train or evaluate the model.\n"]}]},{"cell_type":"code","source":["from keras import models\n","model = models.load_model('Resnet101_increase.h5')"],"metadata":{"id":"M4bUp2FTkVuo","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1676805631556,"user_tz":-420,"elapsed":5140,"user":{"displayName":"Trang Phạm","userId":"09872832977707306821"}},"outputId":"d619ea39-5686-41cc-ea97-7ab1078a1787"},"execution_count":26,"outputs":[{"output_type":"stream","name":"stderr","text":["WARNING:tensorflow:No training configuration found in the save file, so the model was *not* compiled. Compile it manually.\n"]}]},{"cell_type":"code","source":["batch_size = 32\n","#!pip install tqdm\n","import tqdm\n","validation_generator.reset()\n","X_test, y_test = next(validation_generator)\n","for i in tqdm.tqdm(range(int(validation_generator.n/batch_size)-400)): \n","  img, label = next(validation_generator)\n","  X_test = np.append(X_test, img, axis=0 )\n","  y_test = np.append(y_test, label, axis=0)\n","print(X_test.shape, y_test.shape)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"ZdHu7j0xnKpf","executionInfo":{"status":"ok","timestamp":1676805823678,"user_tz":-420,"elapsed":101079,"user":{"displayName":"Trang Phạm","userId":"09872832977707306821"}},"outputId":"5970ad90-aea8-4c52-ed2d-730bbea88536"},"execution_count":28,"outputs":[{"output_type":"stream","name":"stderr","text":["100%|██████████| 97/97 [01:40<00:00,  1.03s/it]"]},{"output_type":"stream","name":"stdout","text":["(3136, 227, 227, 3) (3136, 3)\n"]},{"output_type":"stream","name":"stderr","text":["\n"]}]},{"cell_type":"code","execution_count":29,"metadata":{"execution":{"iopub.execute_input":"2020-11-14T20:34:16.551443Z","iopub.status.busy":"2020-11-14T20:34:16.550753Z","iopub.status.idle":"2020-11-14T20:34:17.288221Z","shell.execute_reply":"2020-11-14T20:34:17.287474Z"},"papermill":{"duration":11.452942,"end_time":"2020-11-14T20:34:17.288341","exception":false,"start_time":"2020-11-14T20:34:05.835399","status":"completed"},"tags":[],"colab":{"base_uri":"https://localhost:8080/"},"id":"xPj14NBFvXv2","executionInfo":{"status":"ok","timestamp":1676805854483,"user_tz":-420,"elapsed":24042,"user":{"displayName":"Trang Phạm","userId":"09872832977707306821"}},"outputId":"a31c05d3-9b25-48b4-83b2-977de284d3d4"},"outputs":[{"output_type":"stream","name":"stdout","text":["98/98 [==============================] - 19s 178ms/step\n","[1 0 1 ... 0 0 0]\n"]}],"source":["#predicted_classes = model.predict_classes(X_test)\n","\n","y_predict=final_model.predict(X_test) \n","predicted_classes=np.argmax(y_predict,axis=1)\n","print(predicted_classes)"]},{"cell_type":"code","source":["y_test = np.argmax(y_test, axis = 1)"],"metadata":{"id":"CQKFYyhJm_wh","executionInfo":{"status":"ok","timestamp":1676805871367,"user_tz":-420,"elapsed":416,"user":{"displayName":"Trang Phạm","userId":"09872832977707306821"}}},"execution_count":30,"outputs":[]},{"cell_type":"code","source":["import seaborn as sns\n","from sklearn.metrics import confusion_matrix\n","cm = confusion_matrix(y_test, predicted_classes)\n","plt.figure(figsize = (10,10))\n","sns.heatmap(cm, annot=True, cmap = 'Pastel1', fmt='g')"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":610},"id":"GamoAqtIuKIJ","executionInfo":{"status":"ok","timestamp":1676805874937,"user_tz":-420,"elapsed":1951,"user":{"displayName":"Trang Phạm","userId":"09872832977707306821"}},"outputId":"56980e12-7a4d-4c9a-a4df-7d20ead6810b"},"execution_count":31,"outputs":[{"output_type":"execute_result","data":{"text/plain":["<matplotlib.axes._subplots.AxesSubplot at 0x7f6de9541790>"]},"metadata":{},"execution_count":31},{"output_type":"display_data","data":{"text/plain":["<Figure size 720x720 with 2 Axes>"],"image/png":"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= pd.DataFrame(cm)\n","cm_df.columns = [ 'Predicted Accessories','Predicted Apparel', 'Predicted Footwear']\n","cm_final_df = cm_df.rename(index={0: 'Actually Accessories', 1: 'Actually Apparel', 2: 'Actually Footwear'})\n","cm_final_df"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":143},"id":"hZpUhcfxulXT","executionInfo":{"status":"ok","timestamp":1676805881993,"user_tz":-420,"elapsed":571,"user":{"displayName":"Trang Phạm","userId":"09872832977707306821"}},"outputId":"16be3cc8-7485-44bd-e111-c519dab2a536"},"execution_count":32,"outputs":[{"output_type":"execute_result","data":{"text/plain":["                      Predicted Accessories  Predicted Apparel  \\\n","Actually Accessories                    780                 36   \n","Actually Apparel                        308               1303   \n","Actually Footwear                       135                  2   \n","\n","                      Predicted Footwear  \n","Actually Accessories                  34  \n","Actually Apparel                      14  \n","Actually Footwear                    524  "],"text/html":["\n","  <div id=\"df-a8b9020c-f7f7-4511-b355-754bd830bfcc\">\n","    <div class=\"colab-df-container\">\n","      <div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>Predicted Accessories</th>\n","      <th>Predicted Apparel</th>\n","      <th>Predicted Footwear</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>Actually Accessories</th>\n","      <td>780</td>\n","      <td>36</td>\n","      <td>34</td>\n","    </tr>\n","    <tr>\n","      <th>Actually Apparel</th>\n","      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