Transfer Learning with TFLite-Copy1.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "import tensorflow as tf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'2.0.0'"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tf.__version__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "from tensorflow.keras.models import Sequential\n",
    "from tensorflow.keras.layers import Dense, Conv2D, Flatten, Dropout, MaxPooling2D\n",
    "from tensorflow.keras.preprocessing.image import ImageDataGenerator, img_to_array, load_img\n",
    "\n",
    "import os\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import shutil\n",
    "from  shutil import copyfile\n",
    "import random\n",
    "import zipfile"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "try:\n",
    "    shutil.rmtree('Data\\\\dogs-vs-cats\\\\')\n",
    "except:\n",
    "    print(\"Directory Does not exists\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "# This code block unzips the full Cats-v-Dogs dataset to /tmp\n",
    "# which will create a tmp/PetImages directory containing subdirectories\n",
    "# called 'Cat' and 'Dog' (that's how the original researchers structured it)\n",
    "path_cats_and_dogs ='Data\\\\dogs-vs-cats.zip'\n",
    "\n",
    "\n",
    "local_zip = path_cats_and_dogs\n",
    "zip_ref = zipfile.ZipFile(local_zip, 'r')\n",
    "zip_ref.extractall('Data\\\\dogs-vs-cats\\\\')\n",
    "zip_ref.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "test_path_cats_and_dogs ='Data\\\\dogs-vs-cats\\\\test1.zip'\n",
    "\n",
    "\n",
    "local_zip = test_path_cats_and_dogs\n",
    "zip_ref = zipfile.ZipFile(local_zip, 'r')\n",
    "zip_ref.extractall('Data\\\\dogs-vs-cats\\\\')\n",
    "zip_ref.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "train_path_cats_and_dogs ='Data\\\\dogs-vs-cats\\\\train.zip'\n",
    "\n",
    "\n",
    "local_zip = train_path_cats_and_dogs\n",
    "zip_ref = zipfile.ZipFile(local_zip, 'r')\n",
    "zip_ref.extractall('Data\\\\dogs-vs-cats\\\\')\n",
    "zip_ref.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "BASE_PATH = 'Data\\\\dogs-vs-cats\\\\train\\\\'\n",
    "TRAIN_PATH='Data\\\\dogs-vs-cats\\\\train_data\\\\'\n",
    "VAL_PATH='Data\\\\dogs-vs-cats\\\\validation_data\\\\'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "\n",
    "try:\n",
    "    shutil.rmtree(TRAIN_PATH)\n",
    "    shutil.rmtree(VAL_PATH)\n",
    "except:\n",
    "    print(\"Directory does not exists\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "C:\\Data\\CV\\dogs-vs-cats\\train_data\\cat\n"
     ]
    }
   ],
   "source": [
    "os.mkdir(TRAIN_PATH)\n",
    "os.mkdir(VAL_PATH)\n",
    "train_dog=os.path.join(TRAIN_PATH , 'dog')\n",
    "train_cat=os.path.join(TRAIN_PATH , 'cat')\n",
    "val_dog=os.path.join(VAL_PATH , 'dog')\n",
    "val_cat=os.path.join(VAL_PATH , 'cat')\n",
    "print(train_cat)\n",
    "os.mkdir(train_dog)\n",
    "os.mkdir(train_cat)\n",
    "os.mkdir(val_dog)\n",
    "os.mkdir(val_cat)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "#segregating cats and dog files for training and validation\n",
    "cat_file_list=[]\n",
    "dog_file_list=[]\n",
    "for file in os.listdir(BASE_PATH):\n",
    "    if file[:3]=='cat':\n",
    "        cat_file_list.append(file)\n",
    "    elif file[:3]=='dog':\n",
    "        dog_file_list.append(file)\n",
    "#shuffling the list\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total training cat files 10000\n",
      "Training files dog 10000\n",
      "Total filess 25000\n",
      "Total val Dog files 2500\n"
     ]
    }
   ],
   "source": [
    "train_split=0.8\n",
    "\n",
    "random.sample(cat_file_list, len(cat_file_list))\n",
    "random.sample(dog_file_list, len(dog_file_list))\n",
    "num_of_training_files_cat= int(len(cat_file_list)* train_split)\n",
    "num_of_training_files_dog= int(len(dog_file_list)* train_split)\n",
    "print(\"Total training cat files\", num_of_training_files_cat)\n",
    "print(\"Training files dog\", num_of_training_files_dog)\n",
    "print(\"Total filess\", len(cat_file_list) + len(dog_file_list) )\n",
    "print(\"Total val Dog files\", len(dog_file_list) - num_of_training_files_dog)\n",
    "print(\"Total val Cat files\", len(dog_file_list) - num_of_training_files_cat)\n",
    "for file in cat_file_list[:num_of_training_files_cat]:\n",
    "   copyfile(os.path.join(BASE_PATH, file), os.path.join(train_cat, file))\n",
    "   \n",
    "for file in cat_file_list[num_of_training_files_cat:]:\n",
    "   copyfile(os.path.join(BASE_PATH, file), os.path.join(val_cat, file))\n",
    "\n",
    "for file in dog_file_list[:num_of_training_files_dog]:\n",
    "   copyfile(os.path.join(BASE_PATH, file), os.path.join(train_dog, file))\n",
    "   \n",
    "for file in dog_file_list[num_of_training_files_dog:]:\n",
    "   copyfile(os.path.join(BASE_PATH, file), os.path.join(val_dog, file))\n",
    "    \n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "print(\"Total training cat files\", num_of_training_files_cat)\n",
    "print(\"Training files dog\", num_of_training_files_dog)\n",
    "print(\"Total filess\", len(cat_file_list) + len(dog_file_list) )\n",
    "print(\"Total val Dog files\", len(dog_file_list) - num_of_training_files_dog)\n",
    "print(\"Total val Cat files\", len(dog_file_list) - num_of_training_files_cat)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total val Dog files 2500\n"
     ]
    }
   ],
   "source": [
    "print(\"Total val Dog files\", len(dog_file_list) - num_of_training_files_dog)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "batch_size = 32 #128\n",
    "epochs = 15\n",
    "IMG_HEIGHT = 150\n",
    "IMG_WIDTH = 150"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "train_image_generator = ImageDataGenerator(\n",
    "                                                rescale=1./255,\n",
    "                                                rotation_range=45,\n",
    "                                                width_shift_range=.15,\n",
    "                                                height_shift_range=.15,\n",
    "                                                horizontal_flip=True,\n",
    "                                                vertical_flip=True,\n",
    "                                                zoom_range=0.3                         \n",
    "\n",
    "                                            ) # Generator for our training data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "validation_image_generator = ImageDataGenerator(rescale=1./255) # Generator for our validation data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 20000 images belonging to 2 classes.\n",
      "Found 5000 images belonging to 2 classes.\n"
     ]
    }
   ],
   "source": [
    "train_data_gen = train_image_generator.flow_from_directory(batch_size=batch_size,\n",
    "                                                     directory=TRAIN_PATH,\n",
    "                                                     shuffle=True,\n",
    "                                                     target_size=(IMG_HEIGHT, IMG_WIDTH),\n",
    "                                                     class_mode='categorical')\n",
    "val_data_gen = validation_image_generator.flow_from_directory(batch_size=batch_size,\n",
    "                                                              directory=VAL_PATH,\n",
    "                                                              target_size=(IMG_HEIGHT, IMG_WIDTH),\n",
    "                                                              class_mode='categorical')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "# This function will plot images in the form of a grid with 1 row and 5 columns where images are placed in each column.\n",
    "def plotImages(images_arr):\n",
    "    fig, axes = plt.subplots(1, 5, figsize=(20,20))\n",
    "    axes = axes.flatten()\n",
    "    for img, ax in zip( images_arr, axes):\n",
    "        ax.imshow(img)\n",
    "        ax.axis('off')\n",
    "    plt.tight_layout()\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1440x1440 with 5 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "augmented_images = [train_data_gen[0][0][0] for i in range(5)]\n",
    "plotImages(augmented_images)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "sample_training_images, _ = next(train_data_gen)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x1440 with 5 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plotImages(sample_training_images[:5])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "#Build the model\n",
    "model = Sequential([\n",
    "    Conv2D(16, 3, padding='same', activation='relu', \n",
    "           input_shape=(IMG_HEIGHT, IMG_WIDTH ,3)),\n",
    "    MaxPooling2D(),\n",
    "    Dropout(0.2),\n",
    "    Conv2D(32, 3, padding='same', activation='relu'),\n",
    "    MaxPooling2D(),\n",
    "    Conv2D(64, 3, padding='same', activation='relu'),\n",
    "    MaxPooling2D(),\n",
    "    Conv2D(128, 3, padding='same', activation='relu'),\n",
    "    MaxPooling2D(),\n",
    "    Dropout(0.2),\n",
    "    Flatten(),\n",
    "    Dense(512, activation='relu'),\n",
    "    Dense(2, activation='softmax')\n",
    "])\n",
    "# Compile the model\n",
    "model.compile(optimizer='adam', loss=tf.keras.losses.CategoricalCrossentropy(from_logits=True),              metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/50\n",
      "1000/1000 [==============================] - 271s 271ms/step - loss: 0.7039 - accuracy: 0.4984 - val_loss: 0.6932 - val_accuracy: 0.5003\n",
      "Epoch 2/50\n",
      "1000/1000 [==============================] - 255s 255ms/step - loss: 0.6932 - accuracy: 0.4993 - val_loss: 0.7110 - val_accuracy: 0.5003\n",
      "Epoch 3/50\n",
      "1000/1000 [==============================] - 254s 254ms/step - loss: 0.6885 - accuracy: 0.5295 - val_loss: 0.6772 - val_accuracy: 0.5765\n",
      "Epoch 4/50\n",
      "1000/1000 [==============================] - 254s 254ms/step - loss: 0.6564 - accuracy: 0.6120 - val_loss: 0.5983 - val_accuracy: 0.6933\n",
      "Epoch 5/50\n",
      "1000/1000 [==============================] - 252s 252ms/step - loss: 0.6255 - accuracy: 0.6596 - val_loss: 0.5769 - val_accuracy: 0.7175\n",
      "Epoch 6/50\n",
      "1000/1000 [==============================] - 250s 250ms/step - loss: 0.5922 - accuracy: 0.6960 - val_loss: 0.5670 - val_accuracy: 0.7234\n",
      "Epoch 7/50\n",
      "1000/1000 [==============================] - 254s 254ms/step - loss: 0.5842 - accuracy: 0.7065 - val_loss: 0.5796 - val_accuracy: 0.7052\n",
      "Epoch 8/50\n",
      "1000/1000 [==============================] - 255s 255ms/step - loss: 0.5770 - accuracy: 0.7157 - val_loss: 0.5520 - val_accuracy: 0.7428\n",
      "Epoch 9/50\n",
      "1000/1000 [==============================] - 255s 255ms/step - loss: 0.5653 - accuracy: 0.7279 - val_loss: 0.5408 - val_accuracy: 0.7583\n",
      "Epoch 10/50\n",
      "1000/1000 [==============================] - 253s 253ms/step - loss: 0.5605 - accuracy: 0.7328 - val_loss: 0.5603 - val_accuracy: 0.7367\n",
      "Epoch 11/50\n",
      "1000/1000 [==============================] - 258s 258ms/step - loss: 0.5561 - accuracy: 0.7402 - val_loss: 0.5226 - val_accuracy: 0.7753\n",
      "Epoch 12/50\n",
      "1000/1000 [==============================] - 263s 263ms/step - loss: 0.5498 - accuracy: 0.7463 - val_loss: 0.5152 - val_accuracy: 0.7810\n",
      "Epoch 13/50\n",
      "1000/1000 [==============================] - 266s 266ms/step - loss: 0.5461 - accuracy: 0.7524 - val_loss: 0.5139 - val_accuracy: 0.7875\n",
      "Epoch 14/50\n",
      "1000/1000 [==============================] - 262s 262ms/step - loss: 0.5460 - accuracy: 0.7513 - val_loss: 0.5438 - val_accuracy: 0.7572\n",
      "Epoch 15/50\n",
      "1000/1000 [==============================] - 254s 254ms/step - loss: 0.5389 - accuracy: 0.7580 - val_loss: 0.5012 - val_accuracy: 0.7993\n",
      "Epoch 16/50\n",
      "1000/1000 [==============================] - 253s 253ms/step - loss: 0.5357 - accuracy: 0.7628 - val_loss: 0.5121 - val_accuracy: 0.7885\n",
      "Epoch 17/50\n",
      "1000/1000 [==============================] - 254s 254ms/step - loss: 0.5344 - accuracy: 0.7660 - val_loss: 0.5277 - val_accuracy: 0.7718\n",
      "Epoch 18/50\n",
      "1000/1000 [==============================] - 253s 253ms/step - loss: 0.5314 - accuracy: 0.7669 - val_loss: 0.5119 - val_accuracy: 0.7870\n",
      "Epoch 19/50\n",
      "1000/1000 [==============================] - 252s 252ms/step - loss: 0.5313 - accuracy: 0.7688 - val_loss: 0.4942 - val_accuracy: 0.8106\n",
      "Epoch 20/50\n",
      "1000/1000 [==============================] - 251s 251ms/step - loss: 0.5271 - accuracy: 0.7729 - val_loss: 0.5016 - val_accuracy: 0.7975\n",
      "Epoch 21/50\n",
      "1000/1000 [==============================] - 250s 250ms/step - loss: 0.5256 - accuracy: 0.7742 - val_loss: 0.4913 - val_accuracy: 0.8103\n",
      "Epoch 22/50\n",
      "1000/1000 [==============================] - 250s 250ms/step - loss: 0.5241 - accuracy: 0.7768 - val_loss: 0.4900 - val_accuracy: 0.8093\n",
      "Epoch 23/50\n",
      "1000/1000 [==============================] - 249s 249ms/step - loss: 0.5193 - accuracy: 0.7801 - val_loss: 0.4878 - val_accuracy: 0.8185\n",
      "Epoch 24/50\n",
      "1000/1000 [==============================] - 249s 249ms/step - loss: 0.5206 - accuracy: 0.7791 - val_loss: 0.4904 - val_accuracy: 0.8125\n",
      "Epoch 25/50\n",
      "1000/1000 [==============================] - 249s 249ms/step - loss: 0.5175 - accuracy: 0.7835 - val_loss: 0.4942 - val_accuracy: 0.8059\n",
      "Epoch 26/50\n",
      "1000/1000 [==============================] - 250s 250ms/step - loss: 0.5133 - accuracy: 0.7880 - val_loss: 0.4933 - val_accuracy: 0.8088\n",
      "Epoch 27/50\n",
      "1000/1000 [==============================] - 252s 252ms/step - loss: 0.5146 - accuracy: 0.7850 - val_loss: 0.5112 - val_accuracy: 0.7902\n",
      "Epoch 28/50\n",
      "1000/1000 [==============================] - 250s 250ms/step - loss: 0.5114 - accuracy: 0.7894 - val_loss: 0.4873 - val_accuracy: 0.8171\n",
      "Epoch 29/50\n",
      "1000/1000 [==============================] - 251s 251ms/step - loss: 0.5073 - accuracy: 0.7950 - val_loss: 0.4997 - val_accuracy: 0.8036\n",
      "Epoch 30/50\n",
      "1000/1000 [==============================] - 251s 251ms/step - loss: 0.5098 - accuracy: 0.7913 - val_loss: 0.4998 - val_accuracy: 0.8026\n",
      "Epoch 31/50\n",
      "1000/1000 [==============================] - 251s 251ms/step - loss: 0.5055 - accuracy: 0.7962 - val_loss: 0.4844 - val_accuracy: 0.8179\n",
      "Epoch 32/50\n",
      "1000/1000 [==============================] - 252s 252ms/step - loss: 0.5081 - accuracy: 0.7934 - val_loss: 0.4725 - val_accuracy: 0.8293\n",
      "Epoch 33/50\n",
      "1000/1000 [==============================] - 252s 252ms/step - loss: 0.5053 - accuracy: 0.7966 - val_loss: 0.5104 - val_accuracy: 0.7895\n",
      "Epoch 34/50\n",
      "1000/1000 [==============================] - 251s 251ms/step - loss: 0.5015 - accuracy: 0.8002 - val_loss: 0.4729 - val_accuracy: 0.8314\n",
      "Epoch 35/50\n",
      "1000/1000 [==============================] - 250s 250ms/step - loss: 0.5013 - accuracy: 0.8013 - val_loss: 0.4691 - val_accuracy: 0.8364\n",
      "Epoch 36/50\n",
      "1000/1000 [==============================] - 251s 251ms/step - loss: 0.5013 - accuracy: 0.8019 - val_loss: 0.4689 - val_accuracy: 0.8365\n",
      "Epoch 37/50\n",
      "1000/1000 [==============================] - 254s 254ms/step - loss: 0.4962 - accuracy: 0.8061 - val_loss: 0.4652 - val_accuracy: 0.8375\n",
      "Epoch 38/50\n",
      "1000/1000 [==============================] - 252s 252ms/step - loss: 0.4971 - accuracy: 0.8045 - val_loss: 0.4685 - val_accuracy: 0.8340\n",
      "Epoch 39/50\n",
      "1000/1000 [==============================] - 250s 250ms/step - loss: 0.4968 - accuracy: 0.8064 - val_loss: 0.4817 - val_accuracy: 0.8234\n",
      "Epoch 40/50\n",
      "1000/1000 [==============================] - 252s 252ms/step - loss: 0.4937 - accuracy: 0.8086 - val_loss: 0.4840 - val_accuracy: 0.8244\n",
      "Epoch 41/50\n",
      "1000/1000 [==============================] - 250s 250ms/step - loss: 0.4947 - accuracy: 0.8078 - val_loss: 0.4648 - val_accuracy: 0.8404\n",
      "Epoch 42/50\n",
      "1000/1000 [==============================] - 250s 250ms/step - loss: 0.4937 - accuracy: 0.8102 - val_loss: 0.4657 - val_accuracy: 0.8393\n",
      "Epoch 43/50\n",
      "1000/1000 [==============================] - 252s 252ms/step - loss: 0.4966 - accuracy: 0.8060 - val_loss: 0.4692 - val_accuracy: 0.8382\n",
      "Epoch 44/50\n",
      "1000/1000 [==============================] - 250s 250ms/step - loss: 0.4910 - accuracy: 0.8119 - val_loss: 0.4621 - val_accuracy: 0.8424\n",
      "Epoch 45/50\n",
      "1000/1000 [==============================] - 252s 252ms/step - loss: 0.4905 - accuracy: 0.8126 - val_loss: 0.4631 - val_accuracy: 0.8403\n",
      "Epoch 46/50\n",
      "1000/1000 [==============================] - 251s 251ms/step - loss: 0.4919 - accuracy: 0.8108 - val_loss: 0.4645 - val_accuracy: 0.8418\n",
      "Epoch 47/50\n",
      "1000/1000 [==============================] - 250s 250ms/step - loss: 0.4898 - accuracy: 0.8125 - val_loss: 0.4707 - val_accuracy: 0.8364\n",
      "Epoch 48/50\n",
      "1000/1000 [==============================] - 248s 248ms/step - loss: 0.4923 - accuracy: 0.8107 - val_loss: 0.4667 - val_accuracy: 0.8371\n",
      "Epoch 49/50\n",
      "1000/1000 [==============================] - 250s 250ms/step - loss: 0.4861 - accuracy: 0.8181 - val_loss: 0.4619 - val_accuracy: 0.8450\n",
      "Epoch 50/50\n",
      "1000/1000 [==============================] - 252s 252ms/step - loss: 0.4886 - accuracy: 0.8141 - val_loss: 0.4716 - val_accuracy: 0.8342\n"
     ]
    }
   ],
   "source": [
    "epochs=50\n",
    "history = model.fit_generator(\n",
    "    train_data_gen,\n",
    "    steps_per_epoch=1000,\n",
    "    epochs=epochs,\n",
    "    validation_data=val_data_gen,\n",
    "    validation_steps=1000\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Tansfer learning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "from tensorflow.python.keras.applications import imagenet_utils\n",
    "from tensorflow.python.keras.layers import Dense,GlobalAveragePooling2D\n",
    "from tensorflow.keras.applications import   DenseNet121\n",
    "from tensorflow.python.keras.applications.densenet import preprocess_input\n",
    "# Create the base model from the pre-trained model MobileNet V2\n",
    "base_model = tf.keras.applications.DenseNet121(input_shape=(IMG_WIDTH, IMG_HEIGHT,3),\n",
    "                                               include_top=False,\n",
    "                                               weights='imagenet')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "base_model.trainable = False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"densenet121\"\n",
      "__________________________________________________________________________________________________\n",
      "Layer (type)                    Output Shape         Param #     Connected to                     \n",
      "==================================================================================================\n",
      "input_2 (InputLayer)            [(None, 150, 150, 3) 0                                            \n",
      "__________________________________________________________________________________________________\n",
      "zero_padding2d_2 (ZeroPadding2D (None, 156, 156, 3)  0           input_2[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "conv1/conv (Conv2D)             (None, 75, 75, 64)   9408        zero_padding2d_2[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "conv1/bn (BatchNormalization)   (None, 75, 75, 64)   256         conv1/conv[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "conv1/relu (Activation)         (None, 75, 75, 64)   0           conv1/bn[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "zero_padding2d_3 (ZeroPadding2D (None, 77, 77, 64)   0           conv1/relu[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "pool1 (MaxPooling2D)            (None, 38, 38, 64)   0           zero_padding2d_3[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "conv2_block1_0_bn (BatchNormali (None, 38, 38, 64)   256         pool1[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "conv2_block1_0_relu (Activation (None, 38, 38, 64)   0           conv2_block1_0_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv2_block1_1_conv (Conv2D)    (None, 38, 38, 128)  8192        conv2_block1_0_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv2_block1_1_bn (BatchNormali (None, 38, 38, 128)  512         conv2_block1_1_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv2_block1_1_relu (Activation (None, 38, 38, 128)  0           conv2_block1_1_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv2_block1_2_conv (Conv2D)    (None, 38, 38, 32)   36864       conv2_block1_1_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv2_block1_concat (Concatenat (None, 38, 38, 96)   0           pool1[0][0]                      \n",
      "                                                                 conv2_block1_2_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv2_block2_0_bn (BatchNormali (None, 38, 38, 96)   384         conv2_block1_concat[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv2_block2_0_relu (Activation (None, 38, 38, 96)   0           conv2_block2_0_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv2_block2_1_conv (Conv2D)    (None, 38, 38, 128)  12288       conv2_block2_0_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv2_block2_1_bn (BatchNormali (None, 38, 38, 128)  512         conv2_block2_1_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv2_block2_1_relu (Activation (None, 38, 38, 128)  0           conv2_block2_1_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv2_block2_2_conv (Conv2D)    (None, 38, 38, 32)   36864       conv2_block2_1_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv2_block2_concat (Concatenat (None, 38, 38, 128)  0           conv2_block1_concat[0][0]        \n",
      "                                                                 conv2_block2_2_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv2_block3_0_bn (BatchNormali (None, 38, 38, 128)  512         conv2_block2_concat[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv2_block3_0_relu (Activation (None, 38, 38, 128)  0           conv2_block3_0_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv2_block3_1_conv (Conv2D)    (None, 38, 38, 128)  16384       conv2_block3_0_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv2_block3_1_bn (BatchNormali (None, 38, 38, 128)  512         conv2_block3_1_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv2_block3_1_relu (Activation (None, 38, 38, 128)  0           conv2_block3_1_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv2_block3_2_conv (Conv2D)    (None, 38, 38, 32)   36864       conv2_block3_1_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv2_block3_concat (Concatenat (None, 38, 38, 160)  0           conv2_block2_concat[0][0]        \n",
      "                                                                 conv2_block3_2_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv2_block4_0_bn (BatchNormali (None, 38, 38, 160)  640         conv2_block3_concat[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv2_block4_0_relu (Activation (None, 38, 38, 160)  0           conv2_block4_0_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv2_block4_1_conv (Conv2D)    (None, 38, 38, 128)  20480       conv2_block4_0_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv2_block4_1_bn (BatchNormali (None, 38, 38, 128)  512         conv2_block4_1_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv2_block4_1_relu (Activation (None, 38, 38, 128)  0           conv2_block4_1_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv2_block4_2_conv (Conv2D)    (None, 38, 38, 32)   36864       conv2_block4_1_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv2_block4_concat (Concatenat (None, 38, 38, 192)  0           conv2_block3_concat[0][0]        \n",
      "                                                                 conv2_block4_2_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv2_block5_0_bn (BatchNormali (None, 38, 38, 192)  768         conv2_block4_concat[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv2_block5_0_relu (Activation (None, 38, 38, 192)  0           conv2_block5_0_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv2_block5_1_conv (Conv2D)    (None, 38, 38, 128)  24576       conv2_block5_0_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv2_block5_1_bn (BatchNormali (None, 38, 38, 128)  512         conv2_block5_1_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv2_block5_1_relu (Activation (None, 38, 38, 128)  0           conv2_block5_1_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv2_block5_2_conv (Conv2D)    (None, 38, 38, 32)   36864       conv2_block5_1_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv2_block5_concat (Concatenat (None, 38, 38, 224)  0           conv2_block4_concat[0][0]        \n",
      "                                                                 conv2_block5_2_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv2_block6_0_bn (BatchNormali (None, 38, 38, 224)  896         conv2_block5_concat[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv2_block6_0_relu (Activation (None, 38, 38, 224)  0           conv2_block6_0_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv2_block6_1_conv (Conv2D)    (None, 38, 38, 128)  28672       conv2_block6_0_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv2_block6_1_bn (BatchNormali (None, 38, 38, 128)  512         conv2_block6_1_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv2_block6_1_relu (Activation (None, 38, 38, 128)  0           conv2_block6_1_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv2_block6_2_conv (Conv2D)    (None, 38, 38, 32)   36864       conv2_block6_1_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv2_block6_concat (Concatenat (None, 38, 38, 256)  0           conv2_block5_concat[0][0]        \n",
      "                                                                 conv2_block6_2_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "pool2_bn (BatchNormalization)   (None, 38, 38, 256)  1024        conv2_block6_concat[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "pool2_relu (Activation)         (None, 38, 38, 256)  0           pool2_bn[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "pool2_conv (Conv2D)             (None, 38, 38, 128)  32768       pool2_relu[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "pool2_pool (AveragePooling2D)   (None, 19, 19, 128)  0           pool2_conv[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block1_0_bn (BatchNormali (None, 19, 19, 128)  512         pool2_pool[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block1_0_relu (Activation (None, 19, 19, 128)  0           conv3_block1_0_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block1_1_conv (Conv2D)    (None, 19, 19, 128)  16384       conv3_block1_0_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block1_1_bn (BatchNormali (None, 19, 19, 128)  512         conv3_block1_1_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block1_1_relu (Activation (None, 19, 19, 128)  0           conv3_block1_1_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block1_2_conv (Conv2D)    (None, 19, 19, 32)   36864       conv3_block1_1_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block1_concat (Concatenat (None, 19, 19, 160)  0           pool2_pool[0][0]                 \n",
      "                                                                 conv3_block1_2_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block2_0_bn (BatchNormali (None, 19, 19, 160)  640         conv3_block1_concat[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block2_0_relu (Activation (None, 19, 19, 160)  0           conv3_block2_0_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block2_1_conv (Conv2D)    (None, 19, 19, 128)  20480       conv3_block2_0_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block2_1_bn (BatchNormali (None, 19, 19, 128)  512         conv3_block2_1_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block2_1_relu (Activation (None, 19, 19, 128)  0           conv3_block2_1_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block2_2_conv (Conv2D)    (None, 19, 19, 32)   36864       conv3_block2_1_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block2_concat (Concatenat (None, 19, 19, 192)  0           conv3_block1_concat[0][0]        \n",
      "                                                                 conv3_block2_2_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block3_0_bn (BatchNormali (None, 19, 19, 192)  768         conv3_block2_concat[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block3_0_relu (Activation (None, 19, 19, 192)  0           conv3_block3_0_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block3_1_conv (Conv2D)    (None, 19, 19, 128)  24576       conv3_block3_0_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block3_1_bn (BatchNormali (None, 19, 19, 128)  512         conv3_block3_1_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block3_1_relu (Activation (None, 19, 19, 128)  0           conv3_block3_1_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block3_2_conv (Conv2D)    (None, 19, 19, 32)   36864       conv3_block3_1_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block3_concat (Concatenat (None, 19, 19, 224)  0           conv3_block2_concat[0][0]        \n",
      "                                                                 conv3_block3_2_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block4_0_bn (BatchNormali (None, 19, 19, 224)  896         conv3_block3_concat[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block4_0_relu (Activation (None, 19, 19, 224)  0           conv3_block4_0_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block4_1_conv (Conv2D)    (None, 19, 19, 128)  28672       conv3_block4_0_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block4_1_bn (BatchNormali (None, 19, 19, 128)  512         conv3_block4_1_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block4_1_relu (Activation (None, 19, 19, 128)  0           conv3_block4_1_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block4_2_conv (Conv2D)    (None, 19, 19, 32)   36864       conv3_block4_1_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block4_concat (Concatenat (None, 19, 19, 256)  0           conv3_block3_concat[0][0]        \n",
      "                                                                 conv3_block4_2_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block5_0_bn (BatchNormali (None, 19, 19, 256)  1024        conv3_block4_concat[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block5_0_relu (Activation (None, 19, 19, 256)  0           conv3_block5_0_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block5_1_conv (Conv2D)    (None, 19, 19, 128)  32768       conv3_block5_0_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block5_1_bn (BatchNormali (None, 19, 19, 128)  512         conv3_block5_1_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block5_1_relu (Activation (None, 19, 19, 128)  0           conv3_block5_1_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block5_2_conv (Conv2D)    (None, 19, 19, 32)   36864       conv3_block5_1_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block5_concat (Concatenat (None, 19, 19, 288)  0           conv3_block4_concat[0][0]        \n",
      "                                                                 conv3_block5_2_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block6_0_bn (BatchNormali (None, 19, 19, 288)  1152        conv3_block5_concat[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block6_0_relu (Activation (None, 19, 19, 288)  0           conv3_block6_0_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block6_1_conv (Conv2D)    (None, 19, 19, 128)  36864       conv3_block6_0_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block6_1_bn (BatchNormali (None, 19, 19, 128)  512         conv3_block6_1_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block6_1_relu (Activation (None, 19, 19, 128)  0           conv3_block6_1_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block6_2_conv (Conv2D)    (None, 19, 19, 32)   36864       conv3_block6_1_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block6_concat (Concatenat (None, 19, 19, 320)  0           conv3_block5_concat[0][0]        \n",
      "                                                                 conv3_block6_2_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block7_0_bn (BatchNormali (None, 19, 19, 320)  1280        conv3_block6_concat[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block7_0_relu (Activation (None, 19, 19, 320)  0           conv3_block7_0_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block7_1_conv (Conv2D)    (None, 19, 19, 128)  40960       conv3_block7_0_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block7_1_bn (BatchNormali (None, 19, 19, 128)  512         conv3_block7_1_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block7_1_relu (Activation (None, 19, 19, 128)  0           conv3_block7_1_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block7_2_conv (Conv2D)    (None, 19, 19, 32)   36864       conv3_block7_1_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block7_concat (Concatenat (None, 19, 19, 352)  0           conv3_block6_concat[0][0]        \n",
      "                                                                 conv3_block7_2_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block8_0_bn (BatchNormali (None, 19, 19, 352)  1408        conv3_block7_concat[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block8_0_relu (Activation (None, 19, 19, 352)  0           conv3_block8_0_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block8_1_conv (Conv2D)    (None, 19, 19, 128)  45056       conv3_block8_0_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block8_1_bn (BatchNormali (None, 19, 19, 128)  512         conv3_block8_1_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block8_1_relu (Activation (None, 19, 19, 128)  0           conv3_block8_1_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block8_2_conv (Conv2D)    (None, 19, 19, 32)   36864       conv3_block8_1_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block8_concat (Concatenat (None, 19, 19, 384)  0           conv3_block7_concat[0][0]        \n",
      "                                                                 conv3_block8_2_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block9_0_bn (BatchNormali (None, 19, 19, 384)  1536        conv3_block8_concat[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block9_0_relu (Activation (None, 19, 19, 384)  0           conv3_block9_0_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block9_1_conv (Conv2D)    (None, 19, 19, 128)  49152       conv3_block9_0_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block9_1_bn (BatchNormali (None, 19, 19, 128)  512         conv3_block9_1_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block9_1_relu (Activation (None, 19, 19, 128)  0           conv3_block9_1_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block9_2_conv (Conv2D)    (None, 19, 19, 32)   36864       conv3_block9_1_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block9_concat (Concatenat (None, 19, 19, 416)  0           conv3_block8_concat[0][0]        \n",
      "                                                                 conv3_block9_2_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block10_0_bn (BatchNormal (None, 19, 19, 416)  1664        conv3_block9_concat[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block10_0_relu (Activatio (None, 19, 19, 416)  0           conv3_block10_0_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block10_1_conv (Conv2D)   (None, 19, 19, 128)  53248       conv3_block10_0_relu[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block10_1_bn (BatchNormal (None, 19, 19, 128)  512         conv3_block10_1_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block10_1_relu (Activatio (None, 19, 19, 128)  0           conv3_block10_1_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block10_2_conv (Conv2D)   (None, 19, 19, 32)   36864       conv3_block10_1_relu[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block10_concat (Concatena (None, 19, 19, 448)  0           conv3_block9_concat[0][0]        \n",
      "                                                                 conv3_block10_2_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block11_0_bn (BatchNormal (None, 19, 19, 448)  1792        conv3_block10_concat[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block11_0_relu (Activatio (None, 19, 19, 448)  0           conv3_block11_0_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block11_1_conv (Conv2D)   (None, 19, 19, 128)  57344       conv3_block11_0_relu[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block11_1_bn (BatchNormal (None, 19, 19, 128)  512         conv3_block11_1_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block11_1_relu (Activatio (None, 19, 19, 128)  0           conv3_block11_1_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block11_2_conv (Conv2D)   (None, 19, 19, 32)   36864       conv3_block11_1_relu[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block11_concat (Concatena (None, 19, 19, 480)  0           conv3_block10_concat[0][0]       \n",
      "                                                                 conv3_block11_2_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block12_0_bn (BatchNormal (None, 19, 19, 480)  1920        conv3_block11_concat[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block12_0_relu (Activatio (None, 19, 19, 480)  0           conv3_block12_0_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block12_1_conv (Conv2D)   (None, 19, 19, 128)  61440       conv3_block12_0_relu[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block12_1_bn (BatchNormal (None, 19, 19, 128)  512         conv3_block12_1_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block12_1_relu (Activatio (None, 19, 19, 128)  0           conv3_block12_1_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block12_2_conv (Conv2D)   (None, 19, 19, 32)   36864       conv3_block12_1_relu[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block12_concat (Concatena (None, 19, 19, 512)  0           conv3_block11_concat[0][0]       \n",
      "                                                                 conv3_block12_2_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "pool3_bn (BatchNormalization)   (None, 19, 19, 512)  2048        conv3_block12_concat[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "pool3_relu (Activation)         (None, 19, 19, 512)  0           pool3_bn[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "pool3_conv (Conv2D)             (None, 19, 19, 256)  131072      pool3_relu[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "pool3_pool (AveragePooling2D)   (None, 9, 9, 256)    0           pool3_conv[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block1_0_bn (BatchNormali (None, 9, 9, 256)    1024        pool3_pool[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block1_0_relu (Activation (None, 9, 9, 256)    0           conv4_block1_0_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block1_1_conv (Conv2D)    (None, 9, 9, 128)    32768       conv4_block1_0_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block1_1_bn (BatchNormali (None, 9, 9, 128)    512         conv4_block1_1_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block1_1_relu (Activation (None, 9, 9, 128)    0           conv4_block1_1_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block1_2_conv (Conv2D)    (None, 9, 9, 32)     36864       conv4_block1_1_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block1_concat (Concatenat (None, 9, 9, 288)    0           pool3_pool[0][0]                 \n",
      "                                                                 conv4_block1_2_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block2_0_bn (BatchNormali (None, 9, 9, 288)    1152        conv4_block1_concat[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block2_0_relu (Activation (None, 9, 9, 288)    0           conv4_block2_0_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block2_1_conv (Conv2D)    (None, 9, 9, 128)    36864       conv4_block2_0_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block2_1_bn (BatchNormali (None, 9, 9, 128)    512         conv4_block2_1_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block2_1_relu (Activation (None, 9, 9, 128)    0           conv4_block2_1_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block2_2_conv (Conv2D)    (None, 9, 9, 32)     36864       conv4_block2_1_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block2_concat (Concatenat (None, 9, 9, 320)    0           conv4_block1_concat[0][0]        \n",
      "                                                                 conv4_block2_2_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block3_0_bn (BatchNormali (None, 9, 9, 320)    1280        conv4_block2_concat[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block3_0_relu (Activation (None, 9, 9, 320)    0           conv4_block3_0_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block3_1_conv (Conv2D)    (None, 9, 9, 128)    40960       conv4_block3_0_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block3_1_bn (BatchNormali (None, 9, 9, 128)    512         conv4_block3_1_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block3_1_relu (Activation (None, 9, 9, 128)    0           conv4_block3_1_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block3_2_conv (Conv2D)    (None, 9, 9, 32)     36864       conv4_block3_1_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block3_concat (Concatenat (None, 9, 9, 352)    0           conv4_block2_concat[0][0]        \n",
      "                                                                 conv4_block3_2_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block4_0_bn (BatchNormali (None, 9, 9, 352)    1408        conv4_block3_concat[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block4_0_relu (Activation (None, 9, 9, 352)    0           conv4_block4_0_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block4_1_conv (Conv2D)    (None, 9, 9, 128)    45056       conv4_block4_0_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block4_1_bn (BatchNormali (None, 9, 9, 128)    512         conv4_block4_1_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block4_1_relu (Activation (None, 9, 9, 128)    0           conv4_block4_1_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block4_2_conv (Conv2D)    (None, 9, 9, 32)     36864       conv4_block4_1_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block4_concat (Concatenat (None, 9, 9, 384)    0           conv4_block3_concat[0][0]        \n",
      "                                                                 conv4_block4_2_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block5_0_bn (BatchNormali (None, 9, 9, 384)    1536        conv4_block4_concat[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block5_0_relu (Activation (None, 9, 9, 384)    0           conv4_block5_0_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block5_1_conv (Conv2D)    (None, 9, 9, 128)    49152       conv4_block5_0_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block5_1_bn (BatchNormali (None, 9, 9, 128)    512         conv4_block5_1_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block5_1_relu (Activation (None, 9, 9, 128)    0           conv4_block5_1_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block5_2_conv (Conv2D)    (None, 9, 9, 32)     36864       conv4_block5_1_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block5_concat (Concatenat (None, 9, 9, 416)    0           conv4_block4_concat[0][0]        \n",
      "                                                                 conv4_block5_2_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block6_0_bn (BatchNormali (None, 9, 9, 416)    1664        conv4_block5_concat[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block6_0_relu (Activation (None, 9, 9, 416)    0           conv4_block6_0_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block6_1_conv (Conv2D)    (None, 9, 9, 128)    53248       conv4_block6_0_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block6_1_bn (BatchNormali (None, 9, 9, 128)    512         conv4_block6_1_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block6_1_relu (Activation (None, 9, 9, 128)    0           conv4_block6_1_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block6_2_conv (Conv2D)    (None, 9, 9, 32)     36864       conv4_block6_1_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block6_concat (Concatenat (None, 9, 9, 448)    0           conv4_block5_concat[0][0]        \n",
      "                                                                 conv4_block6_2_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block7_0_bn (BatchNormali (None, 9, 9, 448)    1792        conv4_block6_concat[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block7_0_relu (Activation (None, 9, 9, 448)    0           conv4_block7_0_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block7_1_conv (Conv2D)    (None, 9, 9, 128)    57344       conv4_block7_0_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block7_1_bn (BatchNormali (None, 9, 9, 128)    512         conv4_block7_1_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block7_1_relu (Activation (None, 9, 9, 128)    0           conv4_block7_1_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block7_2_conv (Conv2D)    (None, 9, 9, 32)     36864       conv4_block7_1_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block7_concat (Concatenat (None, 9, 9, 480)    0           conv4_block6_concat[0][0]        \n",
      "                                                                 conv4_block7_2_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block8_0_bn (BatchNormali (None, 9, 9, 480)    1920        conv4_block7_concat[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block8_0_relu (Activation (None, 9, 9, 480)    0           conv4_block8_0_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block8_1_conv (Conv2D)    (None, 9, 9, 128)    61440       conv4_block8_0_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block8_1_bn (BatchNormali (None, 9, 9, 128)    512         conv4_block8_1_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block8_1_relu (Activation (None, 9, 9, 128)    0           conv4_block8_1_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block8_2_conv (Conv2D)    (None, 9, 9, 32)     36864       conv4_block8_1_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block8_concat (Concatenat (None, 9, 9, 512)    0           conv4_block7_concat[0][0]        \n",
      "                                                                 conv4_block8_2_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block9_0_bn (BatchNormali (None, 9, 9, 512)    2048        conv4_block8_concat[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block9_0_relu (Activation (None, 9, 9, 512)    0           conv4_block9_0_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block9_1_conv (Conv2D)    (None, 9, 9, 128)    65536       conv4_block9_0_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block9_1_bn (BatchNormali (None, 9, 9, 128)    512         conv4_block9_1_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block9_1_relu (Activation (None, 9, 9, 128)    0           conv4_block9_1_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block9_2_conv (Conv2D)    (None, 9, 9, 32)     36864       conv4_block9_1_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block9_concat (Concatenat (None, 9, 9, 544)    0           conv4_block8_concat[0][0]        \n",
      "                                                                 conv4_block9_2_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block10_0_bn (BatchNormal (None, 9, 9, 544)    2176        conv4_block9_concat[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block10_0_relu (Activatio (None, 9, 9, 544)    0           conv4_block10_0_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block10_1_conv (Conv2D)   (None, 9, 9, 128)    69632       conv4_block10_0_relu[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block10_1_bn (BatchNormal (None, 9, 9, 128)    512         conv4_block10_1_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block10_1_relu (Activatio (None, 9, 9, 128)    0           conv4_block10_1_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block10_2_conv (Conv2D)   (None, 9, 9, 32)     36864       conv4_block10_1_relu[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block10_concat (Concatena (None, 9, 9, 576)    0           conv4_block9_concat[0][0]        \n",
      "                                                                 conv4_block10_2_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block11_0_bn (BatchNormal (None, 9, 9, 576)    2304        conv4_block10_concat[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block11_0_relu (Activatio (None, 9, 9, 576)    0           conv4_block11_0_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block11_1_conv (Conv2D)   (None, 9, 9, 128)    73728       conv4_block11_0_relu[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block11_1_bn (BatchNormal (None, 9, 9, 128)    512         conv4_block11_1_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block11_1_relu (Activatio (None, 9, 9, 128)    0           conv4_block11_1_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block11_2_conv (Conv2D)   (None, 9, 9, 32)     36864       conv4_block11_1_relu[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block11_concat (Concatena (None, 9, 9, 608)    0           conv4_block10_concat[0][0]       \n",
      "                                                                 conv4_block11_2_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block12_0_bn (BatchNormal (None, 9, 9, 608)    2432        conv4_block11_concat[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block12_0_relu (Activatio (None, 9, 9, 608)    0           conv4_block12_0_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block12_1_conv (Conv2D)   (None, 9, 9, 128)    77824       conv4_block12_0_relu[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block12_1_bn (BatchNormal (None, 9, 9, 128)    512         conv4_block12_1_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block12_1_relu (Activatio (None, 9, 9, 128)    0           conv4_block12_1_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block12_2_conv (Conv2D)   (None, 9, 9, 32)     36864       conv4_block12_1_relu[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block12_concat (Concatena (None, 9, 9, 640)    0           conv4_block11_concat[0][0]       \n",
      "                                                                 conv4_block12_2_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block13_0_bn (BatchNormal (None, 9, 9, 640)    2560        conv4_block12_concat[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block13_0_relu (Activatio (None, 9, 9, 640)    0           conv4_block13_0_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block13_1_conv (Conv2D)   (None, 9, 9, 128)    81920       conv4_block13_0_relu[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block13_1_bn (BatchNormal (None, 9, 9, 128)    512         conv4_block13_1_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block13_1_relu (Activatio (None, 9, 9, 128)    0           conv4_block13_1_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block13_2_conv (Conv2D)   (None, 9, 9, 32)     36864       conv4_block13_1_relu[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block13_concat (Concatena (None, 9, 9, 672)    0           conv4_block12_concat[0][0]       \n",
      "                                                                 conv4_block13_2_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block14_0_bn (BatchNormal (None, 9, 9, 672)    2688        conv4_block13_concat[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block14_0_relu (Activatio (None, 9, 9, 672)    0           conv4_block14_0_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block14_1_conv (Conv2D)   (None, 9, 9, 128)    86016       conv4_block14_0_relu[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block14_1_bn (BatchNormal (None, 9, 9, 128)    512         conv4_block14_1_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block14_1_relu (Activatio (None, 9, 9, 128)    0           conv4_block14_1_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block14_2_conv (Conv2D)   (None, 9, 9, 32)     36864       conv4_block14_1_relu[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block14_concat (Concatena (None, 9, 9, 704)    0           conv4_block13_concat[0][0]       \n",
      "                                                                 conv4_block14_2_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block15_0_bn (BatchNormal (None, 9, 9, 704)    2816        conv4_block14_concat[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block15_0_relu (Activatio (None, 9, 9, 704)    0           conv4_block15_0_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block15_1_conv (Conv2D)   (None, 9, 9, 128)    90112       conv4_block15_0_relu[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block15_1_bn (BatchNormal (None, 9, 9, 128)    512         conv4_block15_1_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block15_1_relu (Activatio (None, 9, 9, 128)    0           conv4_block15_1_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block15_2_conv (Conv2D)   (None, 9, 9, 32)     36864       conv4_block15_1_relu[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block15_concat (Concatena (None, 9, 9, 736)    0           conv4_block14_concat[0][0]       \n",
      "                                                                 conv4_block15_2_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block16_0_bn (BatchNormal (None, 9, 9, 736)    2944        conv4_block15_concat[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block16_0_relu (Activatio (None, 9, 9, 736)    0           conv4_block16_0_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block16_1_conv (Conv2D)   (None, 9, 9, 128)    94208       conv4_block16_0_relu[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block16_1_bn (BatchNormal (None, 9, 9, 128)    512         conv4_block16_1_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block16_1_relu (Activatio (None, 9, 9, 128)    0           conv4_block16_1_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block16_2_conv (Conv2D)   (None, 9, 9, 32)     36864       conv4_block16_1_relu[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block16_concat (Concatena (None, 9, 9, 768)    0           conv4_block15_concat[0][0]       \n",
      "                                                                 conv4_block16_2_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block17_0_bn (BatchNormal (None, 9, 9, 768)    3072        conv4_block16_concat[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block17_0_relu (Activatio (None, 9, 9, 768)    0           conv4_block17_0_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block17_1_conv (Conv2D)   (None, 9, 9, 128)    98304       conv4_block17_0_relu[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block17_1_bn (BatchNormal (None, 9, 9, 128)    512         conv4_block17_1_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block17_1_relu (Activatio (None, 9, 9, 128)    0           conv4_block17_1_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block17_2_conv (Conv2D)   (None, 9, 9, 32)     36864       conv4_block17_1_relu[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block17_concat (Concatena (None, 9, 9, 800)    0           conv4_block16_concat[0][0]       \n",
      "                                                                 conv4_block17_2_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block18_0_bn (BatchNormal (None, 9, 9, 800)    3200        conv4_block17_concat[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block18_0_relu (Activatio (None, 9, 9, 800)    0           conv4_block18_0_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block18_1_conv (Conv2D)   (None, 9, 9, 128)    102400      conv4_block18_0_relu[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block18_1_bn (BatchNormal (None, 9, 9, 128)    512         conv4_block18_1_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block18_1_relu (Activatio (None, 9, 9, 128)    0           conv4_block18_1_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block18_2_conv (Conv2D)   (None, 9, 9, 32)     36864       conv4_block18_1_relu[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block18_concat (Concatena (None, 9, 9, 832)    0           conv4_block17_concat[0][0]       \n",
      "                                                                 conv4_block18_2_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block19_0_bn (BatchNormal (None, 9, 9, 832)    3328        conv4_block18_concat[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block19_0_relu (Activatio (None, 9, 9, 832)    0           conv4_block19_0_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block19_1_conv (Conv2D)   (None, 9, 9, 128)    106496      conv4_block19_0_relu[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block19_1_bn (BatchNormal (None, 9, 9, 128)    512         conv4_block19_1_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block19_1_relu (Activatio (None, 9, 9, 128)    0           conv4_block19_1_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block19_2_conv (Conv2D)   (None, 9, 9, 32)     36864       conv4_block19_1_relu[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block19_concat (Concatena (None, 9, 9, 864)    0           conv4_block18_concat[0][0]       \n",
      "                                                                 conv4_block19_2_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block20_0_bn (BatchNormal (None, 9, 9, 864)    3456        conv4_block19_concat[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block20_0_relu (Activatio (None, 9, 9, 864)    0           conv4_block20_0_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block20_1_conv (Conv2D)   (None, 9, 9, 128)    110592      conv4_block20_0_relu[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block20_1_bn (BatchNormal (None, 9, 9, 128)    512         conv4_block20_1_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block20_1_relu (Activatio (None, 9, 9, 128)    0           conv4_block20_1_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block20_2_conv (Conv2D)   (None, 9, 9, 32)     36864       conv4_block20_1_relu[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block20_concat (Concatena (None, 9, 9, 896)    0           conv4_block19_concat[0][0]       \n",
      "                                                                 conv4_block20_2_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block21_0_bn (BatchNormal (None, 9, 9, 896)    3584        conv4_block20_concat[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block21_0_relu (Activatio (None, 9, 9, 896)    0           conv4_block21_0_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block21_1_conv (Conv2D)   (None, 9, 9, 128)    114688      conv4_block21_0_relu[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block21_1_bn (BatchNormal (None, 9, 9, 128)    512         conv4_block21_1_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block21_1_relu (Activatio (None, 9, 9, 128)    0           conv4_block21_1_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block21_2_conv (Conv2D)   (None, 9, 9, 32)     36864       conv4_block21_1_relu[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block21_concat (Concatena (None, 9, 9, 928)    0           conv4_block20_concat[0][0]       \n",
      "                                                                 conv4_block21_2_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block22_0_bn (BatchNormal (None, 9, 9, 928)    3712        conv4_block21_concat[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block22_0_relu (Activatio (None, 9, 9, 928)    0           conv4_block22_0_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block22_1_conv (Conv2D)   (None, 9, 9, 128)    118784      conv4_block22_0_relu[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block22_1_bn (BatchNormal (None, 9, 9, 128)    512         conv4_block22_1_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block22_1_relu (Activatio (None, 9, 9, 128)    0           conv4_block22_1_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block22_2_conv (Conv2D)   (None, 9, 9, 32)     36864       conv4_block22_1_relu[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block22_concat (Concatena (None, 9, 9, 960)    0           conv4_block21_concat[0][0]       \n",
      "                                                                 conv4_block22_2_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block23_0_bn (BatchNormal (None, 9, 9, 960)    3840        conv4_block22_concat[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block23_0_relu (Activatio (None, 9, 9, 960)    0           conv4_block23_0_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block23_1_conv (Conv2D)   (None, 9, 9, 128)    122880      conv4_block23_0_relu[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block23_1_bn (BatchNormal (None, 9, 9, 128)    512         conv4_block23_1_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block23_1_relu (Activatio (None, 9, 9, 128)    0           conv4_block23_1_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block23_2_conv (Conv2D)   (None, 9, 9, 32)     36864       conv4_block23_1_relu[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block23_concat (Concatena (None, 9, 9, 992)    0           conv4_block22_concat[0][0]       \n",
      "                                                                 conv4_block23_2_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block24_0_bn (BatchNormal (None, 9, 9, 992)    3968        conv4_block23_concat[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block24_0_relu (Activatio (None, 9, 9, 992)    0           conv4_block24_0_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block24_1_conv (Conv2D)   (None, 9, 9, 128)    126976      conv4_block24_0_relu[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block24_1_bn (BatchNormal (None, 9, 9, 128)    512         conv4_block24_1_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block24_1_relu (Activatio (None, 9, 9, 128)    0           conv4_block24_1_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block24_2_conv (Conv2D)   (None, 9, 9, 32)     36864       conv4_block24_1_relu[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block24_concat (Concatena (None, 9, 9, 1024)   0           conv4_block23_concat[0][0]       \n",
      "                                                                 conv4_block24_2_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "pool4_bn (BatchNormalization)   (None, 9, 9, 1024)   4096        conv4_block24_concat[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "pool4_relu (Activation)         (None, 9, 9, 1024)   0           pool4_bn[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "pool4_conv (Conv2D)             (None, 9, 9, 512)    524288      pool4_relu[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "pool4_pool (AveragePooling2D)   (None, 4, 4, 512)    0           pool4_conv[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block1_0_bn (BatchNormali (None, 4, 4, 512)    2048        pool4_pool[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block1_0_relu (Activation (None, 4, 4, 512)    0           conv5_block1_0_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block1_1_conv (Conv2D)    (None, 4, 4, 128)    65536       conv5_block1_0_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block1_1_bn (BatchNormali (None, 4, 4, 128)    512         conv5_block1_1_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block1_1_relu (Activation (None, 4, 4, 128)    0           conv5_block1_1_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block1_2_conv (Conv2D)    (None, 4, 4, 32)     36864       conv5_block1_1_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block1_concat (Concatenat (None, 4, 4, 544)    0           pool4_pool[0][0]                 \n",
      "                                                                 conv5_block1_2_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block2_0_bn (BatchNormali (None, 4, 4, 544)    2176        conv5_block1_concat[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block2_0_relu (Activation (None, 4, 4, 544)    0           conv5_block2_0_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block2_1_conv (Conv2D)    (None, 4, 4, 128)    69632       conv5_block2_0_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block2_1_bn (BatchNormali (None, 4, 4, 128)    512         conv5_block2_1_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block2_1_relu (Activation (None, 4, 4, 128)    0           conv5_block2_1_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block2_2_conv (Conv2D)    (None, 4, 4, 32)     36864       conv5_block2_1_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block2_concat (Concatenat (None, 4, 4, 576)    0           conv5_block1_concat[0][0]        \n",
      "                                                                 conv5_block2_2_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block3_0_bn (BatchNormali (None, 4, 4, 576)    2304        conv5_block2_concat[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block3_0_relu (Activation (None, 4, 4, 576)    0           conv5_block3_0_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block3_1_conv (Conv2D)    (None, 4, 4, 128)    73728       conv5_block3_0_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block3_1_bn (BatchNormali (None, 4, 4, 128)    512         conv5_block3_1_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block3_1_relu (Activation (None, 4, 4, 128)    0           conv5_block3_1_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block3_2_conv (Conv2D)    (None, 4, 4, 32)     36864       conv5_block3_1_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block3_concat (Concatenat (None, 4, 4, 608)    0           conv5_block2_concat[0][0]        \n",
      "                                                                 conv5_block3_2_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block4_0_bn (BatchNormali (None, 4, 4, 608)    2432        conv5_block3_concat[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block4_0_relu (Activation (None, 4, 4, 608)    0           conv5_block4_0_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block4_1_conv (Conv2D)    (None, 4, 4, 128)    77824       conv5_block4_0_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block4_1_bn (BatchNormali (None, 4, 4, 128)    512         conv5_block4_1_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block4_1_relu (Activation (None, 4, 4, 128)    0           conv5_block4_1_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block4_2_conv (Conv2D)    (None, 4, 4, 32)     36864       conv5_block4_1_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block4_concat (Concatenat (None, 4, 4, 640)    0           conv5_block3_concat[0][0]        \n",
      "                                                                 conv5_block4_2_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block5_0_bn (BatchNormali (None, 4, 4, 640)    2560        conv5_block4_concat[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block5_0_relu (Activation (None, 4, 4, 640)    0           conv5_block5_0_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block5_1_conv (Conv2D)    (None, 4, 4, 128)    81920       conv5_block5_0_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block5_1_bn (BatchNormali (None, 4, 4, 128)    512         conv5_block5_1_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block5_1_relu (Activation (None, 4, 4, 128)    0           conv5_block5_1_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block5_2_conv (Conv2D)    (None, 4, 4, 32)     36864       conv5_block5_1_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block5_concat (Concatenat (None, 4, 4, 672)    0           conv5_block4_concat[0][0]        \n",
      "                                                                 conv5_block5_2_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block6_0_bn (BatchNormali (None, 4, 4, 672)    2688        conv5_block5_concat[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block6_0_relu (Activation (None, 4, 4, 672)    0           conv5_block6_0_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block6_1_conv (Conv2D)    (None, 4, 4, 128)    86016       conv5_block6_0_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block6_1_bn (BatchNormali (None, 4, 4, 128)    512         conv5_block6_1_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block6_1_relu (Activation (None, 4, 4, 128)    0           conv5_block6_1_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block6_2_conv (Conv2D)    (None, 4, 4, 32)     36864       conv5_block6_1_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block6_concat (Concatenat (None, 4, 4, 704)    0           conv5_block5_concat[0][0]        \n",
      "                                                                 conv5_block6_2_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block7_0_bn (BatchNormali (None, 4, 4, 704)    2816        conv5_block6_concat[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block7_0_relu (Activation (None, 4, 4, 704)    0           conv5_block7_0_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block7_1_conv (Conv2D)    (None, 4, 4, 128)    90112       conv5_block7_0_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block7_1_bn (BatchNormali (None, 4, 4, 128)    512         conv5_block7_1_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block7_1_relu (Activation (None, 4, 4, 128)    0           conv5_block7_1_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block7_2_conv (Conv2D)    (None, 4, 4, 32)     36864       conv5_block7_1_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block7_concat (Concatenat (None, 4, 4, 736)    0           conv5_block6_concat[0][0]        \n",
      "                                                                 conv5_block7_2_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block8_0_bn (BatchNormali (None, 4, 4, 736)    2944        conv5_block7_concat[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block8_0_relu (Activation (None, 4, 4, 736)    0           conv5_block8_0_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block8_1_conv (Conv2D)    (None, 4, 4, 128)    94208       conv5_block8_0_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block8_1_bn (BatchNormali (None, 4, 4, 128)    512         conv5_block8_1_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block8_1_relu (Activation (None, 4, 4, 128)    0           conv5_block8_1_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block8_2_conv (Conv2D)    (None, 4, 4, 32)     36864       conv5_block8_1_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block8_concat (Concatenat (None, 4, 4, 768)    0           conv5_block7_concat[0][0]        \n",
      "                                                                 conv5_block8_2_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block9_0_bn (BatchNormali (None, 4, 4, 768)    3072        conv5_block8_concat[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block9_0_relu (Activation (None, 4, 4, 768)    0           conv5_block9_0_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block9_1_conv (Conv2D)    (None, 4, 4, 128)    98304       conv5_block9_0_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block9_1_bn (BatchNormali (None, 4, 4, 128)    512         conv5_block9_1_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block9_1_relu (Activation (None, 4, 4, 128)    0           conv5_block9_1_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block9_2_conv (Conv2D)    (None, 4, 4, 32)     36864       conv5_block9_1_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block9_concat (Concatenat (None, 4, 4, 800)    0           conv5_block8_concat[0][0]        \n",
      "                                                                 conv5_block9_2_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block10_0_bn (BatchNormal (None, 4, 4, 800)    3200        conv5_block9_concat[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block10_0_relu (Activatio (None, 4, 4, 800)    0           conv5_block10_0_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block10_1_conv (Conv2D)   (None, 4, 4, 128)    102400      conv5_block10_0_relu[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block10_1_bn (BatchNormal (None, 4, 4, 128)    512         conv5_block10_1_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block10_1_relu (Activatio (None, 4, 4, 128)    0           conv5_block10_1_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block10_2_conv (Conv2D)   (None, 4, 4, 32)     36864       conv5_block10_1_relu[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block10_concat (Concatena (None, 4, 4, 832)    0           conv5_block9_concat[0][0]        \n",
      "                                                                 conv5_block10_2_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block11_0_bn (BatchNormal (None, 4, 4, 832)    3328        conv5_block10_concat[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block11_0_relu (Activatio (None, 4, 4, 832)    0           conv5_block11_0_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block11_1_conv (Conv2D)   (None, 4, 4, 128)    106496      conv5_block11_0_relu[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block11_1_bn (BatchNormal (None, 4, 4, 128)    512         conv5_block11_1_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block11_1_relu (Activatio (None, 4, 4, 128)    0           conv5_block11_1_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block11_2_conv (Conv2D)   (None, 4, 4, 32)     36864       conv5_block11_1_relu[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block11_concat (Concatena (None, 4, 4, 864)    0           conv5_block10_concat[0][0]       \n",
      "                                                                 conv5_block11_2_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block12_0_bn (BatchNormal (None, 4, 4, 864)    3456        conv5_block11_concat[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block12_0_relu (Activatio (None, 4, 4, 864)    0           conv5_block12_0_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block12_1_conv (Conv2D)   (None, 4, 4, 128)    110592      conv5_block12_0_relu[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block12_1_bn (BatchNormal (None, 4, 4, 128)    512         conv5_block12_1_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block12_1_relu (Activatio (None, 4, 4, 128)    0           conv5_block12_1_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block12_2_conv (Conv2D)   (None, 4, 4, 32)     36864       conv5_block12_1_relu[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block12_concat (Concatena (None, 4, 4, 896)    0           conv5_block11_concat[0][0]       \n",
      "                                                                 conv5_block12_2_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block13_0_bn (BatchNormal (None, 4, 4, 896)    3584        conv5_block12_concat[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block13_0_relu (Activatio (None, 4, 4, 896)    0           conv5_block13_0_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block13_1_conv (Conv2D)   (None, 4, 4, 128)    114688      conv5_block13_0_relu[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block13_1_bn (BatchNormal (None, 4, 4, 128)    512         conv5_block13_1_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block13_1_relu (Activatio (None, 4, 4, 128)    0           conv5_block13_1_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block13_2_conv (Conv2D)   (None, 4, 4, 32)     36864       conv5_block13_1_relu[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block13_concat (Concatena (None, 4, 4, 928)    0           conv5_block12_concat[0][0]       \n",
      "                                                                 conv5_block13_2_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block14_0_bn (BatchNormal (None, 4, 4, 928)    3712        conv5_block13_concat[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block14_0_relu (Activatio (None, 4, 4, 928)    0           conv5_block14_0_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block14_1_conv (Conv2D)   (None, 4, 4, 128)    118784      conv5_block14_0_relu[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block14_1_bn (BatchNormal (None, 4, 4, 128)    512         conv5_block14_1_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block14_1_relu (Activatio (None, 4, 4, 128)    0           conv5_block14_1_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block14_2_conv (Conv2D)   (None, 4, 4, 32)     36864       conv5_block14_1_relu[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block14_concat (Concatena (None, 4, 4, 960)    0           conv5_block13_concat[0][0]       \n",
      "                                                                 conv5_block14_2_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block15_0_bn (BatchNormal (None, 4, 4, 960)    3840        conv5_block14_concat[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block15_0_relu (Activatio (None, 4, 4, 960)    0           conv5_block15_0_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block15_1_conv (Conv2D)   (None, 4, 4, 128)    122880      conv5_block15_0_relu[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block15_1_bn (BatchNormal (None, 4, 4, 128)    512         conv5_block15_1_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block15_1_relu (Activatio (None, 4, 4, 128)    0           conv5_block15_1_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block15_2_conv (Conv2D)   (None, 4, 4, 32)     36864       conv5_block15_1_relu[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block15_concat (Concatena (None, 4, 4, 992)    0           conv5_block14_concat[0][0]       \n",
      "                                                                 conv5_block15_2_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block16_0_bn (BatchNormal (None, 4, 4, 992)    3968        conv5_block15_concat[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block16_0_relu (Activatio (None, 4, 4, 992)    0           conv5_block16_0_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block16_1_conv (Conv2D)   (None, 4, 4, 128)    126976      conv5_block16_0_relu[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block16_1_bn (BatchNormal (None, 4, 4, 128)    512         conv5_block16_1_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block16_1_relu (Activatio (None, 4, 4, 128)    0           conv5_block16_1_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block16_2_conv (Conv2D)   (None, 4, 4, 32)     36864       conv5_block16_1_relu[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block16_concat (Concatena (None, 4, 4, 1024)   0           conv5_block15_concat[0][0]       \n",
      "                                                                 conv5_block16_2_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "bn (BatchNormalization)         (None, 4, 4, 1024)   4096        conv5_block16_concat[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "relu (Activation)               (None, 4, 4, 1024)   0           bn[0][0]                         \n",
      "==================================================================================================\n",
      "Total params: 7,037,504\n",
      "Trainable params: 0\n",
      "Non-trainable params: 7,037,504\n",
      "__________________________________________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "base_model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "427\n"
     ]
    }
   ],
   "source": [
    "print(len(base_model.layers))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "\n",
    "x=base_model.output\n",
    "x=Flatten()(x)\n",
    "x=Dense(512,activation='relu')(x) #dense layer 3\n",
    "output=Dense(2,activation='softmax')(x) #final layer with softmax activation\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "from tensorflow.keras.models import Model\n",
    "model=Model(inputs=base_model.input,outputs=output)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"model_1\"\n",
      "__________________________________________________________________________________________________\n",
      "Layer (type)                    Output Shape         Param #     Connected to                     \n",
      "==================================================================================================\n",
      "input_2 (InputLayer)            [(None, 150, 150, 3) 0                                            \n",
      "__________________________________________________________________________________________________\n",
      "zero_padding2d_2 (ZeroPadding2D (None, 156, 156, 3)  0           input_2[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "conv1/conv (Conv2D)             (None, 75, 75, 64)   9408        zero_padding2d_2[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "conv1/bn (BatchNormalization)   (None, 75, 75, 64)   256         conv1/conv[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "conv1/relu (Activation)         (None, 75, 75, 64)   0           conv1/bn[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "zero_padding2d_3 (ZeroPadding2D (None, 77, 77, 64)   0           conv1/relu[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "pool1 (MaxPooling2D)            (None, 38, 38, 64)   0           zero_padding2d_3[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "conv2_block1_0_bn (BatchNormali (None, 38, 38, 64)   256         pool1[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "conv2_block1_0_relu (Activation (None, 38, 38, 64)   0           conv2_block1_0_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv2_block1_1_conv (Conv2D)    (None, 38, 38, 128)  8192        conv2_block1_0_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv2_block1_1_bn (BatchNormali (None, 38, 38, 128)  512         conv2_block1_1_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv2_block1_1_relu (Activation (None, 38, 38, 128)  0           conv2_block1_1_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv2_block1_2_conv (Conv2D)    (None, 38, 38, 32)   36864       conv2_block1_1_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv2_block1_concat (Concatenat (None, 38, 38, 96)   0           pool1[0][0]                      \n",
      "                                                                 conv2_block1_2_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv2_block2_0_bn (BatchNormali (None, 38, 38, 96)   384         conv2_block1_concat[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv2_block2_0_relu (Activation (None, 38, 38, 96)   0           conv2_block2_0_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv2_block2_1_conv (Conv2D)    (None, 38, 38, 128)  12288       conv2_block2_0_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv2_block2_1_bn (BatchNormali (None, 38, 38, 128)  512         conv2_block2_1_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv2_block2_1_relu (Activation (None, 38, 38, 128)  0           conv2_block2_1_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv2_block2_2_conv (Conv2D)    (None, 38, 38, 32)   36864       conv2_block2_1_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv2_block2_concat (Concatenat (None, 38, 38, 128)  0           conv2_block1_concat[0][0]        \n",
      "                                                                 conv2_block2_2_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv2_block3_0_bn (BatchNormali (None, 38, 38, 128)  512         conv2_block2_concat[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv2_block3_0_relu (Activation (None, 38, 38, 128)  0           conv2_block3_0_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv2_block3_1_conv (Conv2D)    (None, 38, 38, 128)  16384       conv2_block3_0_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv2_block3_1_bn (BatchNormali (None, 38, 38, 128)  512         conv2_block3_1_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv2_block3_1_relu (Activation (None, 38, 38, 128)  0           conv2_block3_1_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv2_block3_2_conv (Conv2D)    (None, 38, 38, 32)   36864       conv2_block3_1_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv2_block3_concat (Concatenat (None, 38, 38, 160)  0           conv2_block2_concat[0][0]        \n",
      "                                                                 conv2_block3_2_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv2_block4_0_bn (BatchNormali (None, 38, 38, 160)  640         conv2_block3_concat[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv2_block4_0_relu (Activation (None, 38, 38, 160)  0           conv2_block4_0_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv2_block4_1_conv (Conv2D)    (None, 38, 38, 128)  20480       conv2_block4_0_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv2_block4_1_bn (BatchNormali (None, 38, 38, 128)  512         conv2_block4_1_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv2_block4_1_relu (Activation (None, 38, 38, 128)  0           conv2_block4_1_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv2_block4_2_conv (Conv2D)    (None, 38, 38, 32)   36864       conv2_block4_1_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv2_block4_concat (Concatenat (None, 38, 38, 192)  0           conv2_block3_concat[0][0]        \n",
      "                                                                 conv2_block4_2_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv2_block5_0_bn (BatchNormali (None, 38, 38, 192)  768         conv2_block4_concat[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv2_block5_0_relu (Activation (None, 38, 38, 192)  0           conv2_block5_0_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv2_block5_1_conv (Conv2D)    (None, 38, 38, 128)  24576       conv2_block5_0_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv2_block5_1_bn (BatchNormali (None, 38, 38, 128)  512         conv2_block5_1_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv2_block5_1_relu (Activation (None, 38, 38, 128)  0           conv2_block5_1_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv2_block5_2_conv (Conv2D)    (None, 38, 38, 32)   36864       conv2_block5_1_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv2_block5_concat (Concatenat (None, 38, 38, 224)  0           conv2_block4_concat[0][0]        \n",
      "                                                                 conv2_block5_2_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv2_block6_0_bn (BatchNormali (None, 38, 38, 224)  896         conv2_block5_concat[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv2_block6_0_relu (Activation (None, 38, 38, 224)  0           conv2_block6_0_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv2_block6_1_conv (Conv2D)    (None, 38, 38, 128)  28672       conv2_block6_0_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv2_block6_1_bn (BatchNormali (None, 38, 38, 128)  512         conv2_block6_1_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv2_block6_1_relu (Activation (None, 38, 38, 128)  0           conv2_block6_1_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv2_block6_2_conv (Conv2D)    (None, 38, 38, 32)   36864       conv2_block6_1_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv2_block6_concat (Concatenat (None, 38, 38, 256)  0           conv2_block5_concat[0][0]        \n",
      "                                                                 conv2_block6_2_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "pool2_bn (BatchNormalization)   (None, 38, 38, 256)  1024        conv2_block6_concat[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "pool2_relu (Activation)         (None, 38, 38, 256)  0           pool2_bn[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "pool2_conv (Conv2D)             (None, 38, 38, 128)  32768       pool2_relu[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "pool2_pool (AveragePooling2D)   (None, 19, 19, 128)  0           pool2_conv[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block1_0_bn (BatchNormali (None, 19, 19, 128)  512         pool2_pool[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block1_0_relu (Activation (None, 19, 19, 128)  0           conv3_block1_0_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block1_1_conv (Conv2D)    (None, 19, 19, 128)  16384       conv3_block1_0_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block1_1_bn (BatchNormali (None, 19, 19, 128)  512         conv3_block1_1_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block1_1_relu (Activation (None, 19, 19, 128)  0           conv3_block1_1_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block1_2_conv (Conv2D)    (None, 19, 19, 32)   36864       conv3_block1_1_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block1_concat (Concatenat (None, 19, 19, 160)  0           pool2_pool[0][0]                 \n",
      "                                                                 conv3_block1_2_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block2_0_bn (BatchNormali (None, 19, 19, 160)  640         conv3_block1_concat[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block2_0_relu (Activation (None, 19, 19, 160)  0           conv3_block2_0_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block2_1_conv (Conv2D)    (None, 19, 19, 128)  20480       conv3_block2_0_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block2_1_bn (BatchNormali (None, 19, 19, 128)  512         conv3_block2_1_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block2_1_relu (Activation (None, 19, 19, 128)  0           conv3_block2_1_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block2_2_conv (Conv2D)    (None, 19, 19, 32)   36864       conv3_block2_1_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block2_concat (Concatenat (None, 19, 19, 192)  0           conv3_block1_concat[0][0]        \n",
      "                                                                 conv3_block2_2_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block3_0_bn (BatchNormali (None, 19, 19, 192)  768         conv3_block2_concat[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block3_0_relu (Activation (None, 19, 19, 192)  0           conv3_block3_0_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block3_1_conv (Conv2D)    (None, 19, 19, 128)  24576       conv3_block3_0_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block3_1_bn (BatchNormali (None, 19, 19, 128)  512         conv3_block3_1_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block3_1_relu (Activation (None, 19, 19, 128)  0           conv3_block3_1_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block3_2_conv (Conv2D)    (None, 19, 19, 32)   36864       conv3_block3_1_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block3_concat (Concatenat (None, 19, 19, 224)  0           conv3_block2_concat[0][0]        \n",
      "                                                                 conv3_block3_2_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block4_0_bn (BatchNormali (None, 19, 19, 224)  896         conv3_block3_concat[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block4_0_relu (Activation (None, 19, 19, 224)  0           conv3_block4_0_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block4_1_conv (Conv2D)    (None, 19, 19, 128)  28672       conv3_block4_0_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block4_1_bn (BatchNormali (None, 19, 19, 128)  512         conv3_block4_1_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block4_1_relu (Activation (None, 19, 19, 128)  0           conv3_block4_1_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block4_2_conv (Conv2D)    (None, 19, 19, 32)   36864       conv3_block4_1_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block4_concat (Concatenat (None, 19, 19, 256)  0           conv3_block3_concat[0][0]        \n",
      "                                                                 conv3_block4_2_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block5_0_bn (BatchNormali (None, 19, 19, 256)  1024        conv3_block4_concat[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block5_0_relu (Activation (None, 19, 19, 256)  0           conv3_block5_0_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block5_1_conv (Conv2D)    (None, 19, 19, 128)  32768       conv3_block5_0_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block5_1_bn (BatchNormali (None, 19, 19, 128)  512         conv3_block5_1_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block5_1_relu (Activation (None, 19, 19, 128)  0           conv3_block5_1_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block5_2_conv (Conv2D)    (None, 19, 19, 32)   36864       conv3_block5_1_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block5_concat (Concatenat (None, 19, 19, 288)  0           conv3_block4_concat[0][0]        \n",
      "                                                                 conv3_block5_2_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block6_0_bn (BatchNormali (None, 19, 19, 288)  1152        conv3_block5_concat[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block6_0_relu (Activation (None, 19, 19, 288)  0           conv3_block6_0_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block6_1_conv (Conv2D)    (None, 19, 19, 128)  36864       conv3_block6_0_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block6_1_bn (BatchNormali (None, 19, 19, 128)  512         conv3_block6_1_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block6_1_relu (Activation (None, 19, 19, 128)  0           conv3_block6_1_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block6_2_conv (Conv2D)    (None, 19, 19, 32)   36864       conv3_block6_1_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block6_concat (Concatenat (None, 19, 19, 320)  0           conv3_block5_concat[0][0]        \n",
      "                                                                 conv3_block6_2_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block7_0_bn (BatchNormali (None, 19, 19, 320)  1280        conv3_block6_concat[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block7_0_relu (Activation (None, 19, 19, 320)  0           conv3_block7_0_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block7_1_conv (Conv2D)    (None, 19, 19, 128)  40960       conv3_block7_0_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block7_1_bn (BatchNormali (None, 19, 19, 128)  512         conv3_block7_1_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block7_1_relu (Activation (None, 19, 19, 128)  0           conv3_block7_1_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block7_2_conv (Conv2D)    (None, 19, 19, 32)   36864       conv3_block7_1_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block7_concat (Concatenat (None, 19, 19, 352)  0           conv3_block6_concat[0][0]        \n",
      "                                                                 conv3_block7_2_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block8_0_bn (BatchNormali (None, 19, 19, 352)  1408        conv3_block7_concat[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block8_0_relu (Activation (None, 19, 19, 352)  0           conv3_block8_0_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block8_1_conv (Conv2D)    (None, 19, 19, 128)  45056       conv3_block8_0_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block8_1_bn (BatchNormali (None, 19, 19, 128)  512         conv3_block8_1_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block8_1_relu (Activation (None, 19, 19, 128)  0           conv3_block8_1_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block8_2_conv (Conv2D)    (None, 19, 19, 32)   36864       conv3_block8_1_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block8_concat (Concatenat (None, 19, 19, 384)  0           conv3_block7_concat[0][0]        \n",
      "                                                                 conv3_block8_2_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block9_0_bn (BatchNormali (None, 19, 19, 384)  1536        conv3_block8_concat[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block9_0_relu (Activation (None, 19, 19, 384)  0           conv3_block9_0_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block9_1_conv (Conv2D)    (None, 19, 19, 128)  49152       conv3_block9_0_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block9_1_bn (BatchNormali (None, 19, 19, 128)  512         conv3_block9_1_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block9_1_relu (Activation (None, 19, 19, 128)  0           conv3_block9_1_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block9_2_conv (Conv2D)    (None, 19, 19, 32)   36864       conv3_block9_1_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block9_concat (Concatenat (None, 19, 19, 416)  0           conv3_block8_concat[0][0]        \n",
      "                                                                 conv3_block9_2_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block10_0_bn (BatchNormal (None, 19, 19, 416)  1664        conv3_block9_concat[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block10_0_relu (Activatio (None, 19, 19, 416)  0           conv3_block10_0_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block10_1_conv (Conv2D)   (None, 19, 19, 128)  53248       conv3_block10_0_relu[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block10_1_bn (BatchNormal (None, 19, 19, 128)  512         conv3_block10_1_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block10_1_relu (Activatio (None, 19, 19, 128)  0           conv3_block10_1_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block10_2_conv (Conv2D)   (None, 19, 19, 32)   36864       conv3_block10_1_relu[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block10_concat (Concatena (None, 19, 19, 448)  0           conv3_block9_concat[0][0]        \n",
      "                                                                 conv3_block10_2_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block11_0_bn (BatchNormal (None, 19, 19, 448)  1792        conv3_block10_concat[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block11_0_relu (Activatio (None, 19, 19, 448)  0           conv3_block11_0_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block11_1_conv (Conv2D)   (None, 19, 19, 128)  57344       conv3_block11_0_relu[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block11_1_bn (BatchNormal (None, 19, 19, 128)  512         conv3_block11_1_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block11_1_relu (Activatio (None, 19, 19, 128)  0           conv3_block11_1_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block11_2_conv (Conv2D)   (None, 19, 19, 32)   36864       conv3_block11_1_relu[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block11_concat (Concatena (None, 19, 19, 480)  0           conv3_block10_concat[0][0]       \n",
      "                                                                 conv3_block11_2_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block12_0_bn (BatchNormal (None, 19, 19, 480)  1920        conv3_block11_concat[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block12_0_relu (Activatio (None, 19, 19, 480)  0           conv3_block12_0_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block12_1_conv (Conv2D)   (None, 19, 19, 128)  61440       conv3_block12_0_relu[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block12_1_bn (BatchNormal (None, 19, 19, 128)  512         conv3_block12_1_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block12_1_relu (Activatio (None, 19, 19, 128)  0           conv3_block12_1_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block12_2_conv (Conv2D)   (None, 19, 19, 32)   36864       conv3_block12_1_relu[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block12_concat (Concatena (None, 19, 19, 512)  0           conv3_block11_concat[0][0]       \n",
      "                                                                 conv3_block12_2_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "pool3_bn (BatchNormalization)   (None, 19, 19, 512)  2048        conv3_block12_concat[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "pool3_relu (Activation)         (None, 19, 19, 512)  0           pool3_bn[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "pool3_conv (Conv2D)             (None, 19, 19, 256)  131072      pool3_relu[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "pool3_pool (AveragePooling2D)   (None, 9, 9, 256)    0           pool3_conv[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block1_0_bn (BatchNormali (None, 9, 9, 256)    1024        pool3_pool[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block1_0_relu (Activation (None, 9, 9, 256)    0           conv4_block1_0_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block1_1_conv (Conv2D)    (None, 9, 9, 128)    32768       conv4_block1_0_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block1_1_bn (BatchNormali (None, 9, 9, 128)    512         conv4_block1_1_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block1_1_relu (Activation (None, 9, 9, 128)    0           conv4_block1_1_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block1_2_conv (Conv2D)    (None, 9, 9, 32)     36864       conv4_block1_1_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block1_concat (Concatenat (None, 9, 9, 288)    0           pool3_pool[0][0]                 \n",
      "                                                                 conv4_block1_2_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block2_0_bn (BatchNormali (None, 9, 9, 288)    1152        conv4_block1_concat[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block2_0_relu (Activation (None, 9, 9, 288)    0           conv4_block2_0_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block2_1_conv (Conv2D)    (None, 9, 9, 128)    36864       conv4_block2_0_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block2_1_bn (BatchNormali (None, 9, 9, 128)    512         conv4_block2_1_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block2_1_relu (Activation (None, 9, 9, 128)    0           conv4_block2_1_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block2_2_conv (Conv2D)    (None, 9, 9, 32)     36864       conv4_block2_1_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block2_concat (Concatenat (None, 9, 9, 320)    0           conv4_block1_concat[0][0]        \n",
      "                                                                 conv4_block2_2_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block3_0_bn (BatchNormali (None, 9, 9, 320)    1280        conv4_block2_concat[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block3_0_relu (Activation (None, 9, 9, 320)    0           conv4_block3_0_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block3_1_conv (Conv2D)    (None, 9, 9, 128)    40960       conv4_block3_0_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block3_1_bn (BatchNormali (None, 9, 9, 128)    512         conv4_block3_1_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block3_1_relu (Activation (None, 9, 9, 128)    0           conv4_block3_1_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block3_2_conv (Conv2D)    (None, 9, 9, 32)     36864       conv4_block3_1_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block3_concat (Concatenat (None, 9, 9, 352)    0           conv4_block2_concat[0][0]        \n",
      "                                                                 conv4_block3_2_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block4_0_bn (BatchNormali (None, 9, 9, 352)    1408        conv4_block3_concat[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block4_0_relu (Activation (None, 9, 9, 352)    0           conv4_block4_0_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block4_1_conv (Conv2D)    (None, 9, 9, 128)    45056       conv4_block4_0_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block4_1_bn (BatchNormali (None, 9, 9, 128)    512         conv4_block4_1_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block4_1_relu (Activation (None, 9, 9, 128)    0           conv4_block4_1_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block4_2_conv (Conv2D)    (None, 9, 9, 32)     36864       conv4_block4_1_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block4_concat (Concatenat (None, 9, 9, 384)    0           conv4_block3_concat[0][0]        \n",
      "                                                                 conv4_block4_2_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block5_0_bn (BatchNormali (None, 9, 9, 384)    1536        conv4_block4_concat[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block5_0_relu (Activation (None, 9, 9, 384)    0           conv4_block5_0_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block5_1_conv (Conv2D)    (None, 9, 9, 128)    49152       conv4_block5_0_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block5_1_bn (BatchNormali (None, 9, 9, 128)    512         conv4_block5_1_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block5_1_relu (Activation (None, 9, 9, 128)    0           conv4_block5_1_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block5_2_conv (Conv2D)    (None, 9, 9, 32)     36864       conv4_block5_1_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block5_concat (Concatenat (None, 9, 9, 416)    0           conv4_block4_concat[0][0]        \n",
      "                                                                 conv4_block5_2_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block6_0_bn (BatchNormali (None, 9, 9, 416)    1664        conv4_block5_concat[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block6_0_relu (Activation (None, 9, 9, 416)    0           conv4_block6_0_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block6_1_conv (Conv2D)    (None, 9, 9, 128)    53248       conv4_block6_0_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block6_1_bn (BatchNormali (None, 9, 9, 128)    512         conv4_block6_1_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block6_1_relu (Activation (None, 9, 9, 128)    0           conv4_block6_1_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block6_2_conv (Conv2D)    (None, 9, 9, 32)     36864       conv4_block6_1_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block6_concat (Concatenat (None, 9, 9, 448)    0           conv4_block5_concat[0][0]        \n",
      "                                                                 conv4_block6_2_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block7_0_bn (BatchNormali (None, 9, 9, 448)    1792        conv4_block6_concat[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block7_0_relu (Activation (None, 9, 9, 448)    0           conv4_block7_0_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block7_1_conv (Conv2D)    (None, 9, 9, 128)    57344       conv4_block7_0_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block7_1_bn (BatchNormali (None, 9, 9, 128)    512         conv4_block7_1_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block7_1_relu (Activation (None, 9, 9, 128)    0           conv4_block7_1_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block7_2_conv (Conv2D)    (None, 9, 9, 32)     36864       conv4_block7_1_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block7_concat (Concatenat (None, 9, 9, 480)    0           conv4_block6_concat[0][0]        \n",
      "                                                                 conv4_block7_2_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block8_0_bn (BatchNormali (None, 9, 9, 480)    1920        conv4_block7_concat[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block8_0_relu (Activation (None, 9, 9, 480)    0           conv4_block8_0_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block8_1_conv (Conv2D)    (None, 9, 9, 128)    61440       conv4_block8_0_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block8_1_bn (BatchNormali (None, 9, 9, 128)    512         conv4_block8_1_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block8_1_relu (Activation (None, 9, 9, 128)    0           conv4_block8_1_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block8_2_conv (Conv2D)    (None, 9, 9, 32)     36864       conv4_block8_1_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block8_concat (Concatenat (None, 9, 9, 512)    0           conv4_block7_concat[0][0]        \n",
      "                                                                 conv4_block8_2_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block9_0_bn (BatchNormali (None, 9, 9, 512)    2048        conv4_block8_concat[0][0]        \n",
      "__________________________________________________________________________________________________\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "conv4_block9_0_relu (Activation (None, 9, 9, 512)    0           conv4_block9_0_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block9_1_conv (Conv2D)    (None, 9, 9, 128)    65536       conv4_block9_0_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block9_1_bn (BatchNormali (None, 9, 9, 128)    512         conv4_block9_1_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block9_1_relu (Activation (None, 9, 9, 128)    0           conv4_block9_1_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block9_2_conv (Conv2D)    (None, 9, 9, 32)     36864       conv4_block9_1_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block9_concat (Concatenat (None, 9, 9, 544)    0           conv4_block8_concat[0][0]        \n",
      "                                                                 conv4_block9_2_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block10_0_bn (BatchNormal (None, 9, 9, 544)    2176        conv4_block9_concat[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block10_0_relu (Activatio (None, 9, 9, 544)    0           conv4_block10_0_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block10_1_conv (Conv2D)   (None, 9, 9, 128)    69632       conv4_block10_0_relu[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block10_1_bn (BatchNormal (None, 9, 9, 128)    512         conv4_block10_1_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block10_1_relu (Activatio (None, 9, 9, 128)    0           conv4_block10_1_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block10_2_conv (Conv2D)   (None, 9, 9, 32)     36864       conv4_block10_1_relu[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block10_concat (Concatena (None, 9, 9, 576)    0           conv4_block9_concat[0][0]        \n",
      "                                                                 conv4_block10_2_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block11_0_bn (BatchNormal (None, 9, 9, 576)    2304        conv4_block10_concat[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block11_0_relu (Activatio (None, 9, 9, 576)    0           conv4_block11_0_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block11_1_conv (Conv2D)   (None, 9, 9, 128)    73728       conv4_block11_0_relu[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block11_1_bn (BatchNormal (None, 9, 9, 128)    512         conv4_block11_1_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block11_1_relu (Activatio (None, 9, 9, 128)    0           conv4_block11_1_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block11_2_conv (Conv2D)   (None, 9, 9, 32)     36864       conv4_block11_1_relu[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block11_concat (Concatena (None, 9, 9, 608)    0           conv4_block10_concat[0][0]       \n",
      "                                                                 conv4_block11_2_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block12_0_bn (BatchNormal (None, 9, 9, 608)    2432        conv4_block11_concat[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block12_0_relu (Activatio (None, 9, 9, 608)    0           conv4_block12_0_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block12_1_conv (Conv2D)   (None, 9, 9, 128)    77824       conv4_block12_0_relu[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block12_1_bn (BatchNormal (None, 9, 9, 128)    512         conv4_block12_1_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block12_1_relu (Activatio (None, 9, 9, 128)    0           conv4_block12_1_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block12_2_conv (Conv2D)   (None, 9, 9, 32)     36864       conv4_block12_1_relu[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block12_concat (Concatena (None, 9, 9, 640)    0           conv4_block11_concat[0][0]       \n",
      "                                                                 conv4_block12_2_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block13_0_bn (BatchNormal (None, 9, 9, 640)    2560        conv4_block12_concat[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block13_0_relu (Activatio (None, 9, 9, 640)    0           conv4_block13_0_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block13_1_conv (Conv2D)   (None, 9, 9, 128)    81920       conv4_block13_0_relu[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block13_1_bn (BatchNormal (None, 9, 9, 128)    512         conv4_block13_1_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block13_1_relu (Activatio (None, 9, 9, 128)    0           conv4_block13_1_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block13_2_conv (Conv2D)   (None, 9, 9, 32)     36864       conv4_block13_1_relu[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block13_concat (Concatena (None, 9, 9, 672)    0           conv4_block12_concat[0][0]       \n",
      "                                                                 conv4_block13_2_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block14_0_bn (BatchNormal (None, 9, 9, 672)    2688        conv4_block13_concat[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block14_0_relu (Activatio (None, 9, 9, 672)    0           conv4_block14_0_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block14_1_conv (Conv2D)   (None, 9, 9, 128)    86016       conv4_block14_0_relu[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block14_1_bn (BatchNormal (None, 9, 9, 128)    512         conv4_block14_1_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block14_1_relu (Activatio (None, 9, 9, 128)    0           conv4_block14_1_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block14_2_conv (Conv2D)   (None, 9, 9, 32)     36864       conv4_block14_1_relu[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block14_concat (Concatena (None, 9, 9, 704)    0           conv4_block13_concat[0][0]       \n",
      "                                                                 conv4_block14_2_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block15_0_bn (BatchNormal (None, 9, 9, 704)    2816        conv4_block14_concat[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block15_0_relu (Activatio (None, 9, 9, 704)    0           conv4_block15_0_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block15_1_conv (Conv2D)   (None, 9, 9, 128)    90112       conv4_block15_0_relu[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block15_1_bn (BatchNormal (None, 9, 9, 128)    512         conv4_block15_1_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block15_1_relu (Activatio (None, 9, 9, 128)    0           conv4_block15_1_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block15_2_conv (Conv2D)   (None, 9, 9, 32)     36864       conv4_block15_1_relu[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block15_concat (Concatena (None, 9, 9, 736)    0           conv4_block14_concat[0][0]       \n",
      "                                                                 conv4_block15_2_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block16_0_bn (BatchNormal (None, 9, 9, 736)    2944        conv4_block15_concat[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block16_0_relu (Activatio (None, 9, 9, 736)    0           conv4_block16_0_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block16_1_conv (Conv2D)   (None, 9, 9, 128)    94208       conv4_block16_0_relu[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block16_1_bn (BatchNormal (None, 9, 9, 128)    512         conv4_block16_1_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block16_1_relu (Activatio (None, 9, 9, 128)    0           conv4_block16_1_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block16_2_conv (Conv2D)   (None, 9, 9, 32)     36864       conv4_block16_1_relu[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block16_concat (Concatena (None, 9, 9, 768)    0           conv4_block15_concat[0][0]       \n",
      "                                                                 conv4_block16_2_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block17_0_bn (BatchNormal (None, 9, 9, 768)    3072        conv4_block16_concat[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block17_0_relu (Activatio (None, 9, 9, 768)    0           conv4_block17_0_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block17_1_conv (Conv2D)   (None, 9, 9, 128)    98304       conv4_block17_0_relu[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block17_1_bn (BatchNormal (None, 9, 9, 128)    512         conv4_block17_1_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block17_1_relu (Activatio (None, 9, 9, 128)    0           conv4_block17_1_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block17_2_conv (Conv2D)   (None, 9, 9, 32)     36864       conv4_block17_1_relu[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block17_concat (Concatena (None, 9, 9, 800)    0           conv4_block16_concat[0][0]       \n",
      "                                                                 conv4_block17_2_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block18_0_bn (BatchNormal (None, 9, 9, 800)    3200        conv4_block17_concat[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block18_0_relu (Activatio (None, 9, 9, 800)    0           conv4_block18_0_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block18_1_conv (Conv2D)   (None, 9, 9, 128)    102400      conv4_block18_0_relu[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block18_1_bn (BatchNormal (None, 9, 9, 128)    512         conv4_block18_1_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block18_1_relu (Activatio (None, 9, 9, 128)    0           conv4_block18_1_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block18_2_conv (Conv2D)   (None, 9, 9, 32)     36864       conv4_block18_1_relu[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block18_concat (Concatena (None, 9, 9, 832)    0           conv4_block17_concat[0][0]       \n",
      "                                                                 conv4_block18_2_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block19_0_bn (BatchNormal (None, 9, 9, 832)    3328        conv4_block18_concat[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block19_0_relu (Activatio (None, 9, 9, 832)    0           conv4_block19_0_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block19_1_conv (Conv2D)   (None, 9, 9, 128)    106496      conv4_block19_0_relu[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block19_1_bn (BatchNormal (None, 9, 9, 128)    512         conv4_block19_1_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block19_1_relu (Activatio (None, 9, 9, 128)    0           conv4_block19_1_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block19_2_conv (Conv2D)   (None, 9, 9, 32)     36864       conv4_block19_1_relu[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block19_concat (Concatena (None, 9, 9, 864)    0           conv4_block18_concat[0][0]       \n",
      "                                                                 conv4_block19_2_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block20_0_bn (BatchNormal (None, 9, 9, 864)    3456        conv4_block19_concat[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block20_0_relu (Activatio (None, 9, 9, 864)    0           conv4_block20_0_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block20_1_conv (Conv2D)   (None, 9, 9, 128)    110592      conv4_block20_0_relu[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block20_1_bn (BatchNormal (None, 9, 9, 128)    512         conv4_block20_1_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block20_1_relu (Activatio (None, 9, 9, 128)    0           conv4_block20_1_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block20_2_conv (Conv2D)   (None, 9, 9, 32)     36864       conv4_block20_1_relu[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block20_concat (Concatena (None, 9, 9, 896)    0           conv4_block19_concat[0][0]       \n",
      "                                                                 conv4_block20_2_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block21_0_bn (BatchNormal (None, 9, 9, 896)    3584        conv4_block20_concat[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block21_0_relu (Activatio (None, 9, 9, 896)    0           conv4_block21_0_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block21_1_conv (Conv2D)   (None, 9, 9, 128)    114688      conv4_block21_0_relu[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block21_1_bn (BatchNormal (None, 9, 9, 128)    512         conv4_block21_1_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block21_1_relu (Activatio (None, 9, 9, 128)    0           conv4_block21_1_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block21_2_conv (Conv2D)   (None, 9, 9, 32)     36864       conv4_block21_1_relu[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block21_concat (Concatena (None, 9, 9, 928)    0           conv4_block20_concat[0][0]       \n",
      "                                                                 conv4_block21_2_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block22_0_bn (BatchNormal (None, 9, 9, 928)    3712        conv4_block21_concat[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block22_0_relu (Activatio (None, 9, 9, 928)    0           conv4_block22_0_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block22_1_conv (Conv2D)   (None, 9, 9, 128)    118784      conv4_block22_0_relu[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block22_1_bn (BatchNormal (None, 9, 9, 128)    512         conv4_block22_1_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block22_1_relu (Activatio (None, 9, 9, 128)    0           conv4_block22_1_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block22_2_conv (Conv2D)   (None, 9, 9, 32)     36864       conv4_block22_1_relu[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block22_concat (Concatena (None, 9, 9, 960)    0           conv4_block21_concat[0][0]       \n",
      "                                                                 conv4_block22_2_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block23_0_bn (BatchNormal (None, 9, 9, 960)    3840        conv4_block22_concat[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block23_0_relu (Activatio (None, 9, 9, 960)    0           conv4_block23_0_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block23_1_conv (Conv2D)   (None, 9, 9, 128)    122880      conv4_block23_0_relu[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block23_1_bn (BatchNormal (None, 9, 9, 128)    512         conv4_block23_1_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block23_1_relu (Activatio (None, 9, 9, 128)    0           conv4_block23_1_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block23_2_conv (Conv2D)   (None, 9, 9, 32)     36864       conv4_block23_1_relu[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block23_concat (Concatena (None, 9, 9, 992)    0           conv4_block22_concat[0][0]       \n",
      "                                                                 conv4_block23_2_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block24_0_bn (BatchNormal (None, 9, 9, 992)    3968        conv4_block23_concat[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block24_0_relu (Activatio (None, 9, 9, 992)    0           conv4_block24_0_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block24_1_conv (Conv2D)   (None, 9, 9, 128)    126976      conv4_block24_0_relu[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block24_1_bn (BatchNormal (None, 9, 9, 128)    512         conv4_block24_1_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block24_1_relu (Activatio (None, 9, 9, 128)    0           conv4_block24_1_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block24_2_conv (Conv2D)   (None, 9, 9, 32)     36864       conv4_block24_1_relu[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block24_concat (Concatena (None, 9, 9, 1024)   0           conv4_block23_concat[0][0]       \n",
      "                                                                 conv4_block24_2_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "pool4_bn (BatchNormalization)   (None, 9, 9, 1024)   4096        conv4_block24_concat[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "pool4_relu (Activation)         (None, 9, 9, 1024)   0           pool4_bn[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "pool4_conv (Conv2D)             (None, 9, 9, 512)    524288      pool4_relu[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "pool4_pool (AveragePooling2D)   (None, 4, 4, 512)    0           pool4_conv[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block1_0_bn (BatchNormali (None, 4, 4, 512)    2048        pool4_pool[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block1_0_relu (Activation (None, 4, 4, 512)    0           conv5_block1_0_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block1_1_conv (Conv2D)    (None, 4, 4, 128)    65536       conv5_block1_0_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block1_1_bn (BatchNormali (None, 4, 4, 128)    512         conv5_block1_1_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block1_1_relu (Activation (None, 4, 4, 128)    0           conv5_block1_1_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block1_2_conv (Conv2D)    (None, 4, 4, 32)     36864       conv5_block1_1_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block1_concat (Concatenat (None, 4, 4, 544)    0           pool4_pool[0][0]                 \n",
      "                                                                 conv5_block1_2_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block2_0_bn (BatchNormali (None, 4, 4, 544)    2176        conv5_block1_concat[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block2_0_relu (Activation (None, 4, 4, 544)    0           conv5_block2_0_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block2_1_conv (Conv2D)    (None, 4, 4, 128)    69632       conv5_block2_0_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block2_1_bn (BatchNormali (None, 4, 4, 128)    512         conv5_block2_1_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block2_1_relu (Activation (None, 4, 4, 128)    0           conv5_block2_1_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block2_2_conv (Conv2D)    (None, 4, 4, 32)     36864       conv5_block2_1_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block2_concat (Concatenat (None, 4, 4, 576)    0           conv5_block1_concat[0][0]        \n",
      "                                                                 conv5_block2_2_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block3_0_bn (BatchNormali (None, 4, 4, 576)    2304        conv5_block2_concat[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block3_0_relu (Activation (None, 4, 4, 576)    0           conv5_block3_0_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block3_1_conv (Conv2D)    (None, 4, 4, 128)    73728       conv5_block3_0_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block3_1_bn (BatchNormali (None, 4, 4, 128)    512         conv5_block3_1_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block3_1_relu (Activation (None, 4, 4, 128)    0           conv5_block3_1_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block3_2_conv (Conv2D)    (None, 4, 4, 32)     36864       conv5_block3_1_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block3_concat (Concatenat (None, 4, 4, 608)    0           conv5_block2_concat[0][0]        \n",
      "                                                                 conv5_block3_2_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block4_0_bn (BatchNormali (None, 4, 4, 608)    2432        conv5_block3_concat[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block4_0_relu (Activation (None, 4, 4, 608)    0           conv5_block4_0_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block4_1_conv (Conv2D)    (None, 4, 4, 128)    77824       conv5_block4_0_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block4_1_bn (BatchNormali (None, 4, 4, 128)    512         conv5_block4_1_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block4_1_relu (Activation (None, 4, 4, 128)    0           conv5_block4_1_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block4_2_conv (Conv2D)    (None, 4, 4, 32)     36864       conv5_block4_1_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block4_concat (Concatenat (None, 4, 4, 640)    0           conv5_block3_concat[0][0]        \n",
      "                                                                 conv5_block4_2_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block5_0_bn (BatchNormali (None, 4, 4, 640)    2560        conv5_block4_concat[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block5_0_relu (Activation (None, 4, 4, 640)    0           conv5_block5_0_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block5_1_conv (Conv2D)    (None, 4, 4, 128)    81920       conv5_block5_0_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block5_1_bn (BatchNormali (None, 4, 4, 128)    512         conv5_block5_1_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block5_1_relu (Activation (None, 4, 4, 128)    0           conv5_block5_1_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block5_2_conv (Conv2D)    (None, 4, 4, 32)     36864       conv5_block5_1_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block5_concat (Concatenat (None, 4, 4, 672)    0           conv5_block4_concat[0][0]        \n",
      "                                                                 conv5_block5_2_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block6_0_bn (BatchNormali (None, 4, 4, 672)    2688        conv5_block5_concat[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block6_0_relu (Activation (None, 4, 4, 672)    0           conv5_block6_0_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block6_1_conv (Conv2D)    (None, 4, 4, 128)    86016       conv5_block6_0_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block6_1_bn (BatchNormali (None, 4, 4, 128)    512         conv5_block6_1_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block6_1_relu (Activation (None, 4, 4, 128)    0           conv5_block6_1_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block6_2_conv (Conv2D)    (None, 4, 4, 32)     36864       conv5_block6_1_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block6_concat (Concatenat (None, 4, 4, 704)    0           conv5_block5_concat[0][0]        \n",
      "                                                                 conv5_block6_2_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block7_0_bn (BatchNormali (None, 4, 4, 704)    2816        conv5_block6_concat[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block7_0_relu (Activation (None, 4, 4, 704)    0           conv5_block7_0_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block7_1_conv (Conv2D)    (None, 4, 4, 128)    90112       conv5_block7_0_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block7_1_bn (BatchNormali (None, 4, 4, 128)    512         conv5_block7_1_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block7_1_relu (Activation (None, 4, 4, 128)    0           conv5_block7_1_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block7_2_conv (Conv2D)    (None, 4, 4, 32)     36864       conv5_block7_1_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block7_concat (Concatenat (None, 4, 4, 736)    0           conv5_block6_concat[0][0]        \n",
      "                                                                 conv5_block7_2_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block8_0_bn (BatchNormali (None, 4, 4, 736)    2944        conv5_block7_concat[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block8_0_relu (Activation (None, 4, 4, 736)    0           conv5_block8_0_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block8_1_conv (Conv2D)    (None, 4, 4, 128)    94208       conv5_block8_0_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block8_1_bn (BatchNormali (None, 4, 4, 128)    512         conv5_block8_1_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block8_1_relu (Activation (None, 4, 4, 128)    0           conv5_block8_1_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block8_2_conv (Conv2D)    (None, 4, 4, 32)     36864       conv5_block8_1_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block8_concat (Concatenat (None, 4, 4, 768)    0           conv5_block7_concat[0][0]        \n",
      "                                                                 conv5_block8_2_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block9_0_bn (BatchNormali (None, 4, 4, 768)    3072        conv5_block8_concat[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block9_0_relu (Activation (None, 4, 4, 768)    0           conv5_block9_0_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block9_1_conv (Conv2D)    (None, 4, 4, 128)    98304       conv5_block9_0_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block9_1_bn (BatchNormali (None, 4, 4, 128)    512         conv5_block9_1_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block9_1_relu (Activation (None, 4, 4, 128)    0           conv5_block9_1_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block9_2_conv (Conv2D)    (None, 4, 4, 32)     36864       conv5_block9_1_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block9_concat (Concatenat (None, 4, 4, 800)    0           conv5_block8_concat[0][0]        \n",
      "                                                                 conv5_block9_2_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block10_0_bn (BatchNormal (None, 4, 4, 800)    3200        conv5_block9_concat[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block10_0_relu (Activatio (None, 4, 4, 800)    0           conv5_block10_0_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block10_1_conv (Conv2D)   (None, 4, 4, 128)    102400      conv5_block10_0_relu[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block10_1_bn (BatchNormal (None, 4, 4, 128)    512         conv5_block10_1_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block10_1_relu (Activatio (None, 4, 4, 128)    0           conv5_block10_1_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block10_2_conv (Conv2D)   (None, 4, 4, 32)     36864       conv5_block10_1_relu[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block10_concat (Concatena (None, 4, 4, 832)    0           conv5_block9_concat[0][0]        \n",
      "                                                                 conv5_block10_2_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block11_0_bn (BatchNormal (None, 4, 4, 832)    3328        conv5_block10_concat[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block11_0_relu (Activatio (None, 4, 4, 832)    0           conv5_block11_0_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block11_1_conv (Conv2D)   (None, 4, 4, 128)    106496      conv5_block11_0_relu[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block11_1_bn (BatchNormal (None, 4, 4, 128)    512         conv5_block11_1_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block11_1_relu (Activatio (None, 4, 4, 128)    0           conv5_block11_1_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block11_2_conv (Conv2D)   (None, 4, 4, 32)     36864       conv5_block11_1_relu[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block11_concat (Concatena (None, 4, 4, 864)    0           conv5_block10_concat[0][0]       \n",
      "                                                                 conv5_block11_2_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block12_0_bn (BatchNormal (None, 4, 4, 864)    3456        conv5_block11_concat[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block12_0_relu (Activatio (None, 4, 4, 864)    0           conv5_block12_0_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block12_1_conv (Conv2D)   (None, 4, 4, 128)    110592      conv5_block12_0_relu[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block12_1_bn (BatchNormal (None, 4, 4, 128)    512         conv5_block12_1_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block12_1_relu (Activatio (None, 4, 4, 128)    0           conv5_block12_1_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block12_2_conv (Conv2D)   (None, 4, 4, 32)     36864       conv5_block12_1_relu[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block12_concat (Concatena (None, 4, 4, 896)    0           conv5_block11_concat[0][0]       \n",
      "                                                                 conv5_block12_2_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block13_0_bn (BatchNormal (None, 4, 4, 896)    3584        conv5_block12_concat[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block13_0_relu (Activatio (None, 4, 4, 896)    0           conv5_block13_0_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block13_1_conv (Conv2D)   (None, 4, 4, 128)    114688      conv5_block13_0_relu[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block13_1_bn (BatchNormal (None, 4, 4, 128)    512         conv5_block13_1_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block13_1_relu (Activatio (None, 4, 4, 128)    0           conv5_block13_1_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block13_2_conv (Conv2D)   (None, 4, 4, 32)     36864       conv5_block13_1_relu[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block13_concat (Concatena (None, 4, 4, 928)    0           conv5_block12_concat[0][0]       \n",
      "                                                                 conv5_block13_2_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block14_0_bn (BatchNormal (None, 4, 4, 928)    3712        conv5_block13_concat[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block14_0_relu (Activatio (None, 4, 4, 928)    0           conv5_block14_0_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block14_1_conv (Conv2D)   (None, 4, 4, 128)    118784      conv5_block14_0_relu[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block14_1_bn (BatchNormal (None, 4, 4, 128)    512         conv5_block14_1_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block14_1_relu (Activatio (None, 4, 4, 128)    0           conv5_block14_1_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block14_2_conv (Conv2D)   (None, 4, 4, 32)     36864       conv5_block14_1_relu[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block14_concat (Concatena (None, 4, 4, 960)    0           conv5_block13_concat[0][0]       \n",
      "                                                                 conv5_block14_2_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block15_0_bn (BatchNormal (None, 4, 4, 960)    3840        conv5_block14_concat[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block15_0_relu (Activatio (None, 4, 4, 960)    0           conv5_block15_0_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block15_1_conv (Conv2D)   (None, 4, 4, 128)    122880      conv5_block15_0_relu[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block15_1_bn (BatchNormal (None, 4, 4, 128)    512         conv5_block15_1_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block15_1_relu (Activatio (None, 4, 4, 128)    0           conv5_block15_1_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block15_2_conv (Conv2D)   (None, 4, 4, 32)     36864       conv5_block15_1_relu[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block15_concat (Concatena (None, 4, 4, 992)    0           conv5_block14_concat[0][0]       \n",
      "                                                                 conv5_block15_2_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block16_0_bn (BatchNormal (None, 4, 4, 992)    3968        conv5_block15_concat[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block16_0_relu (Activatio (None, 4, 4, 992)    0           conv5_block16_0_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block16_1_conv (Conv2D)   (None, 4, 4, 128)    126976      conv5_block16_0_relu[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block16_1_bn (BatchNormal (None, 4, 4, 128)    512         conv5_block16_1_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block16_1_relu (Activatio (None, 4, 4, 128)    0           conv5_block16_1_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block16_2_conv (Conv2D)   (None, 4, 4, 32)     36864       conv5_block16_1_relu[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block16_concat (Concatena (None, 4, 4, 1024)   0           conv5_block15_concat[0][0]       \n",
      "                                                                 conv5_block16_2_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "bn (BatchNormalization)         (None, 4, 4, 1024)   4096        conv5_block16_concat[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "relu (Activation)               (None, 4, 4, 1024)   0           bn[0][0]                         \n",
      "__________________________________________________________________________________________________\n",
      "flatten_2 (Flatten)             (None, 16384)        0           relu[0][0]                       \n",
      "__________________________________________________________________________________________________\n",
      "dense_4 (Dense)                 (None, 512)          8389120     flatten_2[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "dense_5 (Dense)                 (None, 2)            1026        dense_4[0][0]                    \n",
      "==================================================================================================\n",
      "Total params: 15,427,650\n",
      "Trainable params: 8,390,146\n",
      "Non-trainable params: 7,037,504\n",
      "__________________________________________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "8\n"
     ]
    }
   ],
   "source": [
    "# or if we want to set the first 20 layers of the network to be non-trainable\n",
    "TRAINABLE_LAYERS= len(model.layers)-len(base_model.layers)+5\n",
    "print(TRAINABLE_LAYERS)\n",
    "for layer in model.layers[:-TRAINABLE_LAYERS]:\n",
    "    layer.trainable=False\n",
    "for layer in model.layers[-TRAINABLE_LAYERS:]:\n",
    "    layer.trainable=True"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"model_1\"\n",
      "__________________________________________________________________________________________________\n",
      "Layer (type)                    Output Shape         Param #     Connected to                     \n",
      "==================================================================================================\n",
      "input_2 (InputLayer)            [(None, 150, 150, 3) 0                                            \n",
      "__________________________________________________________________________________________________\n",
      "zero_padding2d_2 (ZeroPadding2D (None, 156, 156, 3)  0           input_2[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "conv1/conv (Conv2D)             (None, 75, 75, 64)   9408        zero_padding2d_2[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "conv1/bn (BatchNormalization)   (None, 75, 75, 64)   256         conv1/conv[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "conv1/relu (Activation)         (None, 75, 75, 64)   0           conv1/bn[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "zero_padding2d_3 (ZeroPadding2D (None, 77, 77, 64)   0           conv1/relu[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "pool1 (MaxPooling2D)            (None, 38, 38, 64)   0           zero_padding2d_3[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "conv2_block1_0_bn (BatchNormali (None, 38, 38, 64)   256         pool1[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "conv2_block1_0_relu (Activation (None, 38, 38, 64)   0           conv2_block1_0_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv2_block1_1_conv (Conv2D)    (None, 38, 38, 128)  8192        conv2_block1_0_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv2_block1_1_bn (BatchNormali (None, 38, 38, 128)  512         conv2_block1_1_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv2_block1_1_relu (Activation (None, 38, 38, 128)  0           conv2_block1_1_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv2_block1_2_conv (Conv2D)    (None, 38, 38, 32)   36864       conv2_block1_1_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv2_block1_concat (Concatenat (None, 38, 38, 96)   0           pool1[0][0]                      \n",
      "                                                                 conv2_block1_2_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv2_block2_0_bn (BatchNormali (None, 38, 38, 96)   384         conv2_block1_concat[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv2_block2_0_relu (Activation (None, 38, 38, 96)   0           conv2_block2_0_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv2_block2_1_conv (Conv2D)    (None, 38, 38, 128)  12288       conv2_block2_0_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv2_block2_1_bn (BatchNormali (None, 38, 38, 128)  512         conv2_block2_1_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv2_block2_1_relu (Activation (None, 38, 38, 128)  0           conv2_block2_1_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv2_block2_2_conv (Conv2D)    (None, 38, 38, 32)   36864       conv2_block2_1_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv2_block2_concat (Concatenat (None, 38, 38, 128)  0           conv2_block1_concat[0][0]        \n",
      "                                                                 conv2_block2_2_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv2_block3_0_bn (BatchNormali (None, 38, 38, 128)  512         conv2_block2_concat[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv2_block3_0_relu (Activation (None, 38, 38, 128)  0           conv2_block3_0_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv2_block3_1_conv (Conv2D)    (None, 38, 38, 128)  16384       conv2_block3_0_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv2_block3_1_bn (BatchNormali (None, 38, 38, 128)  512         conv2_block3_1_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv2_block3_1_relu (Activation (None, 38, 38, 128)  0           conv2_block3_1_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv2_block3_2_conv (Conv2D)    (None, 38, 38, 32)   36864       conv2_block3_1_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv2_block3_concat (Concatenat (None, 38, 38, 160)  0           conv2_block2_concat[0][0]        \n",
      "                                                                 conv2_block3_2_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv2_block4_0_bn (BatchNormali (None, 38, 38, 160)  640         conv2_block3_concat[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv2_block4_0_relu (Activation (None, 38, 38, 160)  0           conv2_block4_0_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv2_block4_1_conv (Conv2D)    (None, 38, 38, 128)  20480       conv2_block4_0_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv2_block4_1_bn (BatchNormali (None, 38, 38, 128)  512         conv2_block4_1_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv2_block4_1_relu (Activation (None, 38, 38, 128)  0           conv2_block4_1_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv2_block4_2_conv (Conv2D)    (None, 38, 38, 32)   36864       conv2_block4_1_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv2_block4_concat (Concatenat (None, 38, 38, 192)  0           conv2_block3_concat[0][0]        \n",
      "                                                                 conv2_block4_2_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv2_block5_0_bn (BatchNormali (None, 38, 38, 192)  768         conv2_block4_concat[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv2_block5_0_relu (Activation (None, 38, 38, 192)  0           conv2_block5_0_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv2_block5_1_conv (Conv2D)    (None, 38, 38, 128)  24576       conv2_block5_0_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv2_block5_1_bn (BatchNormali (None, 38, 38, 128)  512         conv2_block5_1_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv2_block5_1_relu (Activation (None, 38, 38, 128)  0           conv2_block5_1_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv2_block5_2_conv (Conv2D)    (None, 38, 38, 32)   36864       conv2_block5_1_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv2_block5_concat (Concatenat (None, 38, 38, 224)  0           conv2_block4_concat[0][0]        \n",
      "                                                                 conv2_block5_2_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv2_block6_0_bn (BatchNormali (None, 38, 38, 224)  896         conv2_block5_concat[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv2_block6_0_relu (Activation (None, 38, 38, 224)  0           conv2_block6_0_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv2_block6_1_conv (Conv2D)    (None, 38, 38, 128)  28672       conv2_block6_0_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv2_block6_1_bn (BatchNormali (None, 38, 38, 128)  512         conv2_block6_1_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv2_block6_1_relu (Activation (None, 38, 38, 128)  0           conv2_block6_1_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv2_block6_2_conv (Conv2D)    (None, 38, 38, 32)   36864       conv2_block6_1_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv2_block6_concat (Concatenat (None, 38, 38, 256)  0           conv2_block5_concat[0][0]        \n",
      "                                                                 conv2_block6_2_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "pool2_bn (BatchNormalization)   (None, 38, 38, 256)  1024        conv2_block6_concat[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "pool2_relu (Activation)         (None, 38, 38, 256)  0           pool2_bn[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "pool2_conv (Conv2D)             (None, 38, 38, 128)  32768       pool2_relu[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "pool2_pool (AveragePooling2D)   (None, 19, 19, 128)  0           pool2_conv[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block1_0_bn (BatchNormali (None, 19, 19, 128)  512         pool2_pool[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block1_0_relu (Activation (None, 19, 19, 128)  0           conv3_block1_0_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block1_1_conv (Conv2D)    (None, 19, 19, 128)  16384       conv3_block1_0_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block1_1_bn (BatchNormali (None, 19, 19, 128)  512         conv3_block1_1_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block1_1_relu (Activation (None, 19, 19, 128)  0           conv3_block1_1_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block1_2_conv (Conv2D)    (None, 19, 19, 32)   36864       conv3_block1_1_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block1_concat (Concatenat (None, 19, 19, 160)  0           pool2_pool[0][0]                 \n",
      "                                                                 conv3_block1_2_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block2_0_bn (BatchNormali (None, 19, 19, 160)  640         conv3_block1_concat[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block2_0_relu (Activation (None, 19, 19, 160)  0           conv3_block2_0_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block2_1_conv (Conv2D)    (None, 19, 19, 128)  20480       conv3_block2_0_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block2_1_bn (BatchNormali (None, 19, 19, 128)  512         conv3_block2_1_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block2_1_relu (Activation (None, 19, 19, 128)  0           conv3_block2_1_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block2_2_conv (Conv2D)    (None, 19, 19, 32)   36864       conv3_block2_1_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block2_concat (Concatenat (None, 19, 19, 192)  0           conv3_block1_concat[0][0]        \n",
      "                                                                 conv3_block2_2_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block3_0_bn (BatchNormali (None, 19, 19, 192)  768         conv3_block2_concat[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block3_0_relu (Activation (None, 19, 19, 192)  0           conv3_block3_0_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block3_1_conv (Conv2D)    (None, 19, 19, 128)  24576       conv3_block3_0_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block3_1_bn (BatchNormali (None, 19, 19, 128)  512         conv3_block3_1_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block3_1_relu (Activation (None, 19, 19, 128)  0           conv3_block3_1_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block3_2_conv (Conv2D)    (None, 19, 19, 32)   36864       conv3_block3_1_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block3_concat (Concatenat (None, 19, 19, 224)  0           conv3_block2_concat[0][0]        \n",
      "                                                                 conv3_block3_2_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block4_0_bn (BatchNormali (None, 19, 19, 224)  896         conv3_block3_concat[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block4_0_relu (Activation (None, 19, 19, 224)  0           conv3_block4_0_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block4_1_conv (Conv2D)    (None, 19, 19, 128)  28672       conv3_block4_0_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block4_1_bn (BatchNormali (None, 19, 19, 128)  512         conv3_block4_1_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block4_1_relu (Activation (None, 19, 19, 128)  0           conv3_block4_1_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block4_2_conv (Conv2D)    (None, 19, 19, 32)   36864       conv3_block4_1_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block4_concat (Concatenat (None, 19, 19, 256)  0           conv3_block3_concat[0][0]        \n",
      "                                                                 conv3_block4_2_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block5_0_bn (BatchNormali (None, 19, 19, 256)  1024        conv3_block4_concat[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block5_0_relu (Activation (None, 19, 19, 256)  0           conv3_block5_0_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block5_1_conv (Conv2D)    (None, 19, 19, 128)  32768       conv3_block5_0_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block5_1_bn (BatchNormali (None, 19, 19, 128)  512         conv3_block5_1_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block5_1_relu (Activation (None, 19, 19, 128)  0           conv3_block5_1_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block5_2_conv (Conv2D)    (None, 19, 19, 32)   36864       conv3_block5_1_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block5_concat (Concatenat (None, 19, 19, 288)  0           conv3_block4_concat[0][0]        \n",
      "                                                                 conv3_block5_2_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block6_0_bn (BatchNormali (None, 19, 19, 288)  1152        conv3_block5_concat[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block6_0_relu (Activation (None, 19, 19, 288)  0           conv3_block6_0_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block6_1_conv (Conv2D)    (None, 19, 19, 128)  36864       conv3_block6_0_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block6_1_bn (BatchNormali (None, 19, 19, 128)  512         conv3_block6_1_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block6_1_relu (Activation (None, 19, 19, 128)  0           conv3_block6_1_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block6_2_conv (Conv2D)    (None, 19, 19, 32)   36864       conv3_block6_1_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block6_concat (Concatenat (None, 19, 19, 320)  0           conv3_block5_concat[0][0]        \n",
      "                                                                 conv3_block6_2_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block7_0_bn (BatchNormali (None, 19, 19, 320)  1280        conv3_block6_concat[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block7_0_relu (Activation (None, 19, 19, 320)  0           conv3_block7_0_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block7_1_conv (Conv2D)    (None, 19, 19, 128)  40960       conv3_block7_0_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block7_1_bn (BatchNormali (None, 19, 19, 128)  512         conv3_block7_1_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block7_1_relu (Activation (None, 19, 19, 128)  0           conv3_block7_1_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block7_2_conv (Conv2D)    (None, 19, 19, 32)   36864       conv3_block7_1_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block7_concat (Concatenat (None, 19, 19, 352)  0           conv3_block6_concat[0][0]        \n",
      "                                                                 conv3_block7_2_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block8_0_bn (BatchNormali (None, 19, 19, 352)  1408        conv3_block7_concat[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block8_0_relu (Activation (None, 19, 19, 352)  0           conv3_block8_0_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block8_1_conv (Conv2D)    (None, 19, 19, 128)  45056       conv3_block8_0_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block8_1_bn (BatchNormali (None, 19, 19, 128)  512         conv3_block8_1_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block8_1_relu (Activation (None, 19, 19, 128)  0           conv3_block8_1_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block8_2_conv (Conv2D)    (None, 19, 19, 32)   36864       conv3_block8_1_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block8_concat (Concatenat (None, 19, 19, 384)  0           conv3_block7_concat[0][0]        \n",
      "                                                                 conv3_block8_2_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block9_0_bn (BatchNormali (None, 19, 19, 384)  1536        conv3_block8_concat[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block9_0_relu (Activation (None, 19, 19, 384)  0           conv3_block9_0_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block9_1_conv (Conv2D)    (None, 19, 19, 128)  49152       conv3_block9_0_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block9_1_bn (BatchNormali (None, 19, 19, 128)  512         conv3_block9_1_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block9_1_relu (Activation (None, 19, 19, 128)  0           conv3_block9_1_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block9_2_conv (Conv2D)    (None, 19, 19, 32)   36864       conv3_block9_1_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block9_concat (Concatenat (None, 19, 19, 416)  0           conv3_block8_concat[0][0]        \n",
      "                                                                 conv3_block9_2_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block10_0_bn (BatchNormal (None, 19, 19, 416)  1664        conv3_block9_concat[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block10_0_relu (Activatio (None, 19, 19, 416)  0           conv3_block10_0_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block10_1_conv (Conv2D)   (None, 19, 19, 128)  53248       conv3_block10_0_relu[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block10_1_bn (BatchNormal (None, 19, 19, 128)  512         conv3_block10_1_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block10_1_relu (Activatio (None, 19, 19, 128)  0           conv3_block10_1_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block10_2_conv (Conv2D)   (None, 19, 19, 32)   36864       conv3_block10_1_relu[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block10_concat (Concatena (None, 19, 19, 448)  0           conv3_block9_concat[0][0]        \n",
      "                                                                 conv3_block10_2_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block11_0_bn (BatchNormal (None, 19, 19, 448)  1792        conv3_block10_concat[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block11_0_relu (Activatio (None, 19, 19, 448)  0           conv3_block11_0_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block11_1_conv (Conv2D)   (None, 19, 19, 128)  57344       conv3_block11_0_relu[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block11_1_bn (BatchNormal (None, 19, 19, 128)  512         conv3_block11_1_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block11_1_relu (Activatio (None, 19, 19, 128)  0           conv3_block11_1_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block11_2_conv (Conv2D)   (None, 19, 19, 32)   36864       conv3_block11_1_relu[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block11_concat (Concatena (None, 19, 19, 480)  0           conv3_block10_concat[0][0]       \n",
      "                                                                 conv3_block11_2_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block12_0_bn (BatchNormal (None, 19, 19, 480)  1920        conv3_block11_concat[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block12_0_relu (Activatio (None, 19, 19, 480)  0           conv3_block12_0_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block12_1_conv (Conv2D)   (None, 19, 19, 128)  61440       conv3_block12_0_relu[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block12_1_bn (BatchNormal (None, 19, 19, 128)  512         conv3_block12_1_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block12_1_relu (Activatio (None, 19, 19, 128)  0           conv3_block12_1_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block12_2_conv (Conv2D)   (None, 19, 19, 32)   36864       conv3_block12_1_relu[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block12_concat (Concatena (None, 19, 19, 512)  0           conv3_block11_concat[0][0]       \n",
      "                                                                 conv3_block12_2_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "pool3_bn (BatchNormalization)   (None, 19, 19, 512)  2048        conv3_block12_concat[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "pool3_relu (Activation)         (None, 19, 19, 512)  0           pool3_bn[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "pool3_conv (Conv2D)             (None, 19, 19, 256)  131072      pool3_relu[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "pool3_pool (AveragePooling2D)   (None, 9, 9, 256)    0           pool3_conv[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block1_0_bn (BatchNormali (None, 9, 9, 256)    1024        pool3_pool[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block1_0_relu (Activation (None, 9, 9, 256)    0           conv4_block1_0_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block1_1_conv (Conv2D)    (None, 9, 9, 128)    32768       conv4_block1_0_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block1_1_bn (BatchNormali (None, 9, 9, 128)    512         conv4_block1_1_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block1_1_relu (Activation (None, 9, 9, 128)    0           conv4_block1_1_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block1_2_conv (Conv2D)    (None, 9, 9, 32)     36864       conv4_block1_1_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block1_concat (Concatenat (None, 9, 9, 288)    0           pool3_pool[0][0]                 \n",
      "                                                                 conv4_block1_2_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block2_0_bn (BatchNormali (None, 9, 9, 288)    1152        conv4_block1_concat[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block2_0_relu (Activation (None, 9, 9, 288)    0           conv4_block2_0_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block2_1_conv (Conv2D)    (None, 9, 9, 128)    36864       conv4_block2_0_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block2_1_bn (BatchNormali (None, 9, 9, 128)    512         conv4_block2_1_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block2_1_relu (Activation (None, 9, 9, 128)    0           conv4_block2_1_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block2_2_conv (Conv2D)    (None, 9, 9, 32)     36864       conv4_block2_1_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block2_concat (Concatenat (None, 9, 9, 320)    0           conv4_block1_concat[0][0]        \n",
      "                                                                 conv4_block2_2_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block3_0_bn (BatchNormali (None, 9, 9, 320)    1280        conv4_block2_concat[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block3_0_relu (Activation (None, 9, 9, 320)    0           conv4_block3_0_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block3_1_conv (Conv2D)    (None, 9, 9, 128)    40960       conv4_block3_0_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block3_1_bn (BatchNormali (None, 9, 9, 128)    512         conv4_block3_1_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block3_1_relu (Activation (None, 9, 9, 128)    0           conv4_block3_1_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block3_2_conv (Conv2D)    (None, 9, 9, 32)     36864       conv4_block3_1_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block3_concat (Concatenat (None, 9, 9, 352)    0           conv4_block2_concat[0][0]        \n",
      "                                                                 conv4_block3_2_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block4_0_bn (BatchNormali (None, 9, 9, 352)    1408        conv4_block3_concat[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block4_0_relu (Activation (None, 9, 9, 352)    0           conv4_block4_0_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block4_1_conv (Conv2D)    (None, 9, 9, 128)    45056       conv4_block4_0_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block4_1_bn (BatchNormali (None, 9, 9, 128)    512         conv4_block4_1_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block4_1_relu (Activation (None, 9, 9, 128)    0           conv4_block4_1_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block4_2_conv (Conv2D)    (None, 9, 9, 32)     36864       conv4_block4_1_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block4_concat (Concatenat (None, 9, 9, 384)    0           conv4_block3_concat[0][0]        \n",
      "                                                                 conv4_block4_2_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block5_0_bn (BatchNormali (None, 9, 9, 384)    1536        conv4_block4_concat[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block5_0_relu (Activation (None, 9, 9, 384)    0           conv4_block5_0_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block5_1_conv (Conv2D)    (None, 9, 9, 128)    49152       conv4_block5_0_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block5_1_bn (BatchNormali (None, 9, 9, 128)    512         conv4_block5_1_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block5_1_relu (Activation (None, 9, 9, 128)    0           conv4_block5_1_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block5_2_conv (Conv2D)    (None, 9, 9, 32)     36864       conv4_block5_1_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block5_concat (Concatenat (None, 9, 9, 416)    0           conv4_block4_concat[0][0]        \n",
      "                                                                 conv4_block5_2_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block6_0_bn (BatchNormali (None, 9, 9, 416)    1664        conv4_block5_concat[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block6_0_relu (Activation (None, 9, 9, 416)    0           conv4_block6_0_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block6_1_conv (Conv2D)    (None, 9, 9, 128)    53248       conv4_block6_0_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block6_1_bn (BatchNormali (None, 9, 9, 128)    512         conv4_block6_1_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block6_1_relu (Activation (None, 9, 9, 128)    0           conv4_block6_1_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block6_2_conv (Conv2D)    (None, 9, 9, 32)     36864       conv4_block6_1_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block6_concat (Concatenat (None, 9, 9, 448)    0           conv4_block5_concat[0][0]        \n",
      "                                                                 conv4_block6_2_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block7_0_bn (BatchNormali (None, 9, 9, 448)    1792        conv4_block6_concat[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block7_0_relu (Activation (None, 9, 9, 448)    0           conv4_block7_0_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block7_1_conv (Conv2D)    (None, 9, 9, 128)    57344       conv4_block7_0_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block7_1_bn (BatchNormali (None, 9, 9, 128)    512         conv4_block7_1_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block7_1_relu (Activation (None, 9, 9, 128)    0           conv4_block7_1_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block7_2_conv (Conv2D)    (None, 9, 9, 32)     36864       conv4_block7_1_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block7_concat (Concatenat (None, 9, 9, 480)    0           conv4_block6_concat[0][0]        \n",
      "                                                                 conv4_block7_2_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block8_0_bn (BatchNormali (None, 9, 9, 480)    1920        conv4_block7_concat[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block8_0_relu (Activation (None, 9, 9, 480)    0           conv4_block8_0_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block8_1_conv (Conv2D)    (None, 9, 9, 128)    61440       conv4_block8_0_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block8_1_bn (BatchNormali (None, 9, 9, 128)    512         conv4_block8_1_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block8_1_relu (Activation (None, 9, 9, 128)    0           conv4_block8_1_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block8_2_conv (Conv2D)    (None, 9, 9, 32)     36864       conv4_block8_1_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block8_concat (Concatenat (None, 9, 9, 512)    0           conv4_block7_concat[0][0]        \n",
      "                                                                 conv4_block8_2_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block9_0_bn (BatchNormali (None, 9, 9, 512)    2048        conv4_block8_concat[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block9_0_relu (Activation (None, 9, 9, 512)    0           conv4_block9_0_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block9_1_conv (Conv2D)    (None, 9, 9, 128)    65536       conv4_block9_0_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block9_1_bn (BatchNormali (None, 9, 9, 128)    512         conv4_block9_1_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block9_1_relu (Activation (None, 9, 9, 128)    0           conv4_block9_1_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block9_2_conv (Conv2D)    (None, 9, 9, 32)     36864       conv4_block9_1_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block9_concat (Concatenat (None, 9, 9, 544)    0           conv4_block8_concat[0][0]        \n",
      "                                                                 conv4_block9_2_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block10_0_bn (BatchNormal (None, 9, 9, 544)    2176        conv4_block9_concat[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block10_0_relu (Activatio (None, 9, 9, 544)    0           conv4_block10_0_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block10_1_conv (Conv2D)   (None, 9, 9, 128)    69632       conv4_block10_0_relu[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block10_1_bn (BatchNormal (None, 9, 9, 128)    512         conv4_block10_1_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block10_1_relu (Activatio (None, 9, 9, 128)    0           conv4_block10_1_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block10_2_conv (Conv2D)   (None, 9, 9, 32)     36864       conv4_block10_1_relu[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block10_concat (Concatena (None, 9, 9, 576)    0           conv4_block9_concat[0][0]        \n",
      "                                                                 conv4_block10_2_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block11_0_bn (BatchNormal (None, 9, 9, 576)    2304        conv4_block10_concat[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block11_0_relu (Activatio (None, 9, 9, 576)    0           conv4_block11_0_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block11_1_conv (Conv2D)   (None, 9, 9, 128)    73728       conv4_block11_0_relu[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block11_1_bn (BatchNormal (None, 9, 9, 128)    512         conv4_block11_1_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block11_1_relu (Activatio (None, 9, 9, 128)    0           conv4_block11_1_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block11_2_conv (Conv2D)   (None, 9, 9, 32)     36864       conv4_block11_1_relu[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block11_concat (Concatena (None, 9, 9, 608)    0           conv4_block10_concat[0][0]       \n",
      "                                                                 conv4_block11_2_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block12_0_bn (BatchNormal (None, 9, 9, 608)    2432        conv4_block11_concat[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block12_0_relu (Activatio (None, 9, 9, 608)    0           conv4_block12_0_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block12_1_conv (Conv2D)   (None, 9, 9, 128)    77824       conv4_block12_0_relu[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block12_1_bn (BatchNormal (None, 9, 9, 128)    512         conv4_block12_1_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block12_1_relu (Activatio (None, 9, 9, 128)    0           conv4_block12_1_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block12_2_conv (Conv2D)   (None, 9, 9, 32)     36864       conv4_block12_1_relu[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block12_concat (Concatena (None, 9, 9, 640)    0           conv4_block11_concat[0][0]       \n",
      "                                                                 conv4_block12_2_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block13_0_bn (BatchNormal (None, 9, 9, 640)    2560        conv4_block12_concat[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block13_0_relu (Activatio (None, 9, 9, 640)    0           conv4_block13_0_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block13_1_conv (Conv2D)   (None, 9, 9, 128)    81920       conv4_block13_0_relu[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block13_1_bn (BatchNormal (None, 9, 9, 128)    512         conv4_block13_1_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block13_1_relu (Activatio (None, 9, 9, 128)    0           conv4_block13_1_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block13_2_conv (Conv2D)   (None, 9, 9, 32)     36864       conv4_block13_1_relu[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block13_concat (Concatena (None, 9, 9, 672)    0           conv4_block12_concat[0][0]       \n",
      "                                                                 conv4_block13_2_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block14_0_bn (BatchNormal (None, 9, 9, 672)    2688        conv4_block13_concat[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block14_0_relu (Activatio (None, 9, 9, 672)    0           conv4_block14_0_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block14_1_conv (Conv2D)   (None, 9, 9, 128)    86016       conv4_block14_0_relu[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block14_1_bn (BatchNormal (None, 9, 9, 128)    512         conv4_block14_1_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block14_1_relu (Activatio (None, 9, 9, 128)    0           conv4_block14_1_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block14_2_conv (Conv2D)   (None, 9, 9, 32)     36864       conv4_block14_1_relu[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block14_concat (Concatena (None, 9, 9, 704)    0           conv4_block13_concat[0][0]       \n",
      "                                                                 conv4_block14_2_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block15_0_bn (BatchNormal (None, 9, 9, 704)    2816        conv4_block14_concat[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block15_0_relu (Activatio (None, 9, 9, 704)    0           conv4_block15_0_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block15_1_conv (Conv2D)   (None, 9, 9, 128)    90112       conv4_block15_0_relu[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block15_1_bn (BatchNormal (None, 9, 9, 128)    512         conv4_block15_1_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block15_1_relu (Activatio (None, 9, 9, 128)    0           conv4_block15_1_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block15_2_conv (Conv2D)   (None, 9, 9, 32)     36864       conv4_block15_1_relu[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block15_concat (Concatena (None, 9, 9, 736)    0           conv4_block14_concat[0][0]       \n",
      "                                                                 conv4_block15_2_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block16_0_bn (BatchNormal (None, 9, 9, 736)    2944        conv4_block15_concat[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block16_0_relu (Activatio (None, 9, 9, 736)    0           conv4_block16_0_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block16_1_conv (Conv2D)   (None, 9, 9, 128)    94208       conv4_block16_0_relu[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block16_1_bn (BatchNormal (None, 9, 9, 128)    512         conv4_block16_1_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block16_1_relu (Activatio (None, 9, 9, 128)    0           conv4_block16_1_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block16_2_conv (Conv2D)   (None, 9, 9, 32)     36864       conv4_block16_1_relu[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block16_concat (Concatena (None, 9, 9, 768)    0           conv4_block15_concat[0][0]       \n",
      "                                                                 conv4_block16_2_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block17_0_bn (BatchNormal (None, 9, 9, 768)    3072        conv4_block16_concat[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block17_0_relu (Activatio (None, 9, 9, 768)    0           conv4_block17_0_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block17_1_conv (Conv2D)   (None, 9, 9, 128)    98304       conv4_block17_0_relu[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block17_1_bn (BatchNormal (None, 9, 9, 128)    512         conv4_block17_1_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block17_1_relu (Activatio (None, 9, 9, 128)    0           conv4_block17_1_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block17_2_conv (Conv2D)   (None, 9, 9, 32)     36864       conv4_block17_1_relu[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block17_concat (Concatena (None, 9, 9, 800)    0           conv4_block16_concat[0][0]       \n",
      "                                                                 conv4_block17_2_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block18_0_bn (BatchNormal (None, 9, 9, 800)    3200        conv4_block17_concat[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block18_0_relu (Activatio (None, 9, 9, 800)    0           conv4_block18_0_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block18_1_conv (Conv2D)   (None, 9, 9, 128)    102400      conv4_block18_0_relu[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block18_1_bn (BatchNormal (None, 9, 9, 128)    512         conv4_block18_1_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block18_1_relu (Activatio (None, 9, 9, 128)    0           conv4_block18_1_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block18_2_conv (Conv2D)   (None, 9, 9, 32)     36864       conv4_block18_1_relu[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block18_concat (Concatena (None, 9, 9, 832)    0           conv4_block17_concat[0][0]       \n",
      "                                                                 conv4_block18_2_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block19_0_bn (BatchNormal (None, 9, 9, 832)    3328        conv4_block18_concat[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block19_0_relu (Activatio (None, 9, 9, 832)    0           conv4_block19_0_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block19_1_conv (Conv2D)   (None, 9, 9, 128)    106496      conv4_block19_0_relu[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block19_1_bn (BatchNormal (None, 9, 9, 128)    512         conv4_block19_1_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block19_1_relu (Activatio (None, 9, 9, 128)    0           conv4_block19_1_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block19_2_conv (Conv2D)   (None, 9, 9, 32)     36864       conv4_block19_1_relu[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block19_concat (Concatena (None, 9, 9, 864)    0           conv4_block18_concat[0][0]       \n",
      "                                                                 conv4_block19_2_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block20_0_bn (BatchNormal (None, 9, 9, 864)    3456        conv4_block19_concat[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block20_0_relu (Activatio (None, 9, 9, 864)    0           conv4_block20_0_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block20_1_conv (Conv2D)   (None, 9, 9, 128)    110592      conv4_block20_0_relu[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block20_1_bn (BatchNormal (None, 9, 9, 128)    512         conv4_block20_1_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block20_1_relu (Activatio (None, 9, 9, 128)    0           conv4_block20_1_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block20_2_conv (Conv2D)   (None, 9, 9, 32)     36864       conv4_block20_1_relu[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block20_concat (Concatena (None, 9, 9, 896)    0           conv4_block19_concat[0][0]       \n",
      "                                                                 conv4_block20_2_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block21_0_bn (BatchNormal (None, 9, 9, 896)    3584        conv4_block20_concat[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block21_0_relu (Activatio (None, 9, 9, 896)    0           conv4_block21_0_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block21_1_conv (Conv2D)   (None, 9, 9, 128)    114688      conv4_block21_0_relu[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block21_1_bn (BatchNormal (None, 9, 9, 128)    512         conv4_block21_1_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block21_1_relu (Activatio (None, 9, 9, 128)    0           conv4_block21_1_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block21_2_conv (Conv2D)   (None, 9, 9, 32)     36864       conv4_block21_1_relu[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block21_concat (Concatena (None, 9, 9, 928)    0           conv4_block20_concat[0][0]       \n",
      "                                                                 conv4_block21_2_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block22_0_bn (BatchNormal (None, 9, 9, 928)    3712        conv4_block21_concat[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block22_0_relu (Activatio (None, 9, 9, 928)    0           conv4_block22_0_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block22_1_conv (Conv2D)   (None, 9, 9, 128)    118784      conv4_block22_0_relu[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block22_1_bn (BatchNormal (None, 9, 9, 128)    512         conv4_block22_1_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block22_1_relu (Activatio (None, 9, 9, 128)    0           conv4_block22_1_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block22_2_conv (Conv2D)   (None, 9, 9, 32)     36864       conv4_block22_1_relu[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block22_concat (Concatena (None, 9, 9, 960)    0           conv4_block21_concat[0][0]       \n",
      "                                                                 conv4_block22_2_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block23_0_bn (BatchNormal (None, 9, 9, 960)    3840        conv4_block22_concat[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block23_0_relu (Activatio (None, 9, 9, 960)    0           conv4_block23_0_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block23_1_conv (Conv2D)   (None, 9, 9, 128)    122880      conv4_block23_0_relu[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block23_1_bn (BatchNormal (None, 9, 9, 128)    512         conv4_block23_1_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block23_1_relu (Activatio (None, 9, 9, 128)    0           conv4_block23_1_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block23_2_conv (Conv2D)   (None, 9, 9, 32)     36864       conv4_block23_1_relu[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block23_concat (Concatena (None, 9, 9, 992)    0           conv4_block22_concat[0][0]       \n",
      "                                                                 conv4_block23_2_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block24_0_bn (BatchNormal (None, 9, 9, 992)    3968        conv4_block23_concat[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block24_0_relu (Activatio (None, 9, 9, 992)    0           conv4_block24_0_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block24_1_conv (Conv2D)   (None, 9, 9, 128)    126976      conv4_block24_0_relu[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block24_1_bn (BatchNormal (None, 9, 9, 128)    512         conv4_block24_1_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block24_1_relu (Activatio (None, 9, 9, 128)    0           conv4_block24_1_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block24_2_conv (Conv2D)   (None, 9, 9, 32)     36864       conv4_block24_1_relu[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block24_concat (Concatena (None, 9, 9, 1024)   0           conv4_block23_concat[0][0]       \n",
      "                                                                 conv4_block24_2_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "pool4_bn (BatchNormalization)   (None, 9, 9, 1024)   4096        conv4_block24_concat[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "pool4_relu (Activation)         (None, 9, 9, 1024)   0           pool4_bn[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "pool4_conv (Conv2D)             (None, 9, 9, 512)    524288      pool4_relu[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "pool4_pool (AveragePooling2D)   (None, 4, 4, 512)    0           pool4_conv[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block1_0_bn (BatchNormali (None, 4, 4, 512)    2048        pool4_pool[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block1_0_relu (Activation (None, 4, 4, 512)    0           conv5_block1_0_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block1_1_conv (Conv2D)    (None, 4, 4, 128)    65536       conv5_block1_0_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block1_1_bn (BatchNormali (None, 4, 4, 128)    512         conv5_block1_1_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block1_1_relu (Activation (None, 4, 4, 128)    0           conv5_block1_1_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block1_2_conv (Conv2D)    (None, 4, 4, 32)     36864       conv5_block1_1_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block1_concat (Concatenat (None, 4, 4, 544)    0           pool4_pool[0][0]                 \n",
      "                                                                 conv5_block1_2_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block2_0_bn (BatchNormali (None, 4, 4, 544)    2176        conv5_block1_concat[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block2_0_relu (Activation (None, 4, 4, 544)    0           conv5_block2_0_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block2_1_conv (Conv2D)    (None, 4, 4, 128)    69632       conv5_block2_0_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block2_1_bn (BatchNormali (None, 4, 4, 128)    512         conv5_block2_1_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block2_1_relu (Activation (None, 4, 4, 128)    0           conv5_block2_1_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block2_2_conv (Conv2D)    (None, 4, 4, 32)     36864       conv5_block2_1_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block2_concat (Concatenat (None, 4, 4, 576)    0           conv5_block1_concat[0][0]        \n",
      "                                                                 conv5_block2_2_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block3_0_bn (BatchNormali (None, 4, 4, 576)    2304        conv5_block2_concat[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block3_0_relu (Activation (None, 4, 4, 576)    0           conv5_block3_0_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block3_1_conv (Conv2D)    (None, 4, 4, 128)    73728       conv5_block3_0_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block3_1_bn (BatchNormali (None, 4, 4, 128)    512         conv5_block3_1_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block3_1_relu (Activation (None, 4, 4, 128)    0           conv5_block3_1_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block3_2_conv (Conv2D)    (None, 4, 4, 32)     36864       conv5_block3_1_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block3_concat (Concatenat (None, 4, 4, 608)    0           conv5_block2_concat[0][0]        \n",
      "                                                                 conv5_block3_2_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block4_0_bn (BatchNormali (None, 4, 4, 608)    2432        conv5_block3_concat[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block4_0_relu (Activation (None, 4, 4, 608)    0           conv5_block4_0_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block4_1_conv (Conv2D)    (None, 4, 4, 128)    77824       conv5_block4_0_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block4_1_bn (BatchNormali (None, 4, 4, 128)    512         conv5_block4_1_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block4_1_relu (Activation (None, 4, 4, 128)    0           conv5_block4_1_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block4_2_conv (Conv2D)    (None, 4, 4, 32)     36864       conv5_block4_1_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block4_concat (Concatenat (None, 4, 4, 640)    0           conv5_block3_concat[0][0]        \n",
      "                                                                 conv5_block4_2_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block5_0_bn (BatchNormali (None, 4, 4, 640)    2560        conv5_block4_concat[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block5_0_relu (Activation (None, 4, 4, 640)    0           conv5_block5_0_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block5_1_conv (Conv2D)    (None, 4, 4, 128)    81920       conv5_block5_0_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block5_1_bn (BatchNormali (None, 4, 4, 128)    512         conv5_block5_1_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block5_1_relu (Activation (None, 4, 4, 128)    0           conv5_block5_1_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block5_2_conv (Conv2D)    (None, 4, 4, 32)     36864       conv5_block5_1_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block5_concat (Concatenat (None, 4, 4, 672)    0           conv5_block4_concat[0][0]        \n",
      "                                                                 conv5_block5_2_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block6_0_bn (BatchNormali (None, 4, 4, 672)    2688        conv5_block5_concat[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block6_0_relu (Activation (None, 4, 4, 672)    0           conv5_block6_0_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block6_1_conv (Conv2D)    (None, 4, 4, 128)    86016       conv5_block6_0_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block6_1_bn (BatchNormali (None, 4, 4, 128)    512         conv5_block6_1_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block6_1_relu (Activation (None, 4, 4, 128)    0           conv5_block6_1_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block6_2_conv (Conv2D)    (None, 4, 4, 32)     36864       conv5_block6_1_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block6_concat (Concatenat (None, 4, 4, 704)    0           conv5_block5_concat[0][0]        \n",
      "                                                                 conv5_block6_2_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block7_0_bn (BatchNormali (None, 4, 4, 704)    2816        conv5_block6_concat[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block7_0_relu (Activation (None, 4, 4, 704)    0           conv5_block7_0_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block7_1_conv (Conv2D)    (None, 4, 4, 128)    90112       conv5_block7_0_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block7_1_bn (BatchNormali (None, 4, 4, 128)    512         conv5_block7_1_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block7_1_relu (Activation (None, 4, 4, 128)    0           conv5_block7_1_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block7_2_conv (Conv2D)    (None, 4, 4, 32)     36864       conv5_block7_1_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block7_concat (Concatenat (None, 4, 4, 736)    0           conv5_block6_concat[0][0]        \n",
      "                                                                 conv5_block7_2_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block8_0_bn (BatchNormali (None, 4, 4, 736)    2944        conv5_block7_concat[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block8_0_relu (Activation (None, 4, 4, 736)    0           conv5_block8_0_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block8_1_conv (Conv2D)    (None, 4, 4, 128)    94208       conv5_block8_0_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block8_1_bn (BatchNormali (None, 4, 4, 128)    512         conv5_block8_1_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block8_1_relu (Activation (None, 4, 4, 128)    0           conv5_block8_1_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block8_2_conv (Conv2D)    (None, 4, 4, 32)     36864       conv5_block8_1_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block8_concat (Concatenat (None, 4, 4, 768)    0           conv5_block7_concat[0][0]        \n",
      "                                                                 conv5_block8_2_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block9_0_bn (BatchNormali (None, 4, 4, 768)    3072        conv5_block8_concat[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block9_0_relu (Activation (None, 4, 4, 768)    0           conv5_block9_0_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block9_1_conv (Conv2D)    (None, 4, 4, 128)    98304       conv5_block9_0_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block9_1_bn (BatchNormali (None, 4, 4, 128)    512         conv5_block9_1_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block9_1_relu (Activation (None, 4, 4, 128)    0           conv5_block9_1_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block9_2_conv (Conv2D)    (None, 4, 4, 32)     36864       conv5_block9_1_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block9_concat (Concatenat (None, 4, 4, 800)    0           conv5_block8_concat[0][0]        \n",
      "                                                                 conv5_block9_2_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block10_0_bn (BatchNormal (None, 4, 4, 800)    3200        conv5_block9_concat[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block10_0_relu (Activatio (None, 4, 4, 800)    0           conv5_block10_0_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block10_1_conv (Conv2D)   (None, 4, 4, 128)    102400      conv5_block10_0_relu[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block10_1_bn (BatchNormal (None, 4, 4, 128)    512         conv5_block10_1_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block10_1_relu (Activatio (None, 4, 4, 128)    0           conv5_block10_1_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block10_2_conv (Conv2D)   (None, 4, 4, 32)     36864       conv5_block10_1_relu[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block10_concat (Concatena (None, 4, 4, 832)    0           conv5_block9_concat[0][0]        \n",
      "                                                                 conv5_block10_2_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block11_0_bn (BatchNormal (None, 4, 4, 832)    3328        conv5_block10_concat[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block11_0_relu (Activatio (None, 4, 4, 832)    0           conv5_block11_0_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block11_1_conv (Conv2D)   (None, 4, 4, 128)    106496      conv5_block11_0_relu[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block11_1_bn (BatchNormal (None, 4, 4, 128)    512         conv5_block11_1_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block11_1_relu (Activatio (None, 4, 4, 128)    0           conv5_block11_1_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block11_2_conv (Conv2D)   (None, 4, 4, 32)     36864       conv5_block11_1_relu[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block11_concat (Concatena (None, 4, 4, 864)    0           conv5_block10_concat[0][0]       \n",
      "                                                                 conv5_block11_2_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block12_0_bn (BatchNormal (None, 4, 4, 864)    3456        conv5_block11_concat[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block12_0_relu (Activatio (None, 4, 4, 864)    0           conv5_block12_0_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block12_1_conv (Conv2D)   (None, 4, 4, 128)    110592      conv5_block12_0_relu[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block12_1_bn (BatchNormal (None, 4, 4, 128)    512         conv5_block12_1_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block12_1_relu (Activatio (None, 4, 4, 128)    0           conv5_block12_1_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block12_2_conv (Conv2D)   (None, 4, 4, 32)     36864       conv5_block12_1_relu[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block12_concat (Concatena (None, 4, 4, 896)    0           conv5_block11_concat[0][0]       \n",
      "                                                                 conv5_block12_2_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block13_0_bn (BatchNormal (None, 4, 4, 896)    3584        conv5_block12_concat[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block13_0_relu (Activatio (None, 4, 4, 896)    0           conv5_block13_0_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block13_1_conv (Conv2D)   (None, 4, 4, 128)    114688      conv5_block13_0_relu[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block13_1_bn (BatchNormal (None, 4, 4, 128)    512         conv5_block13_1_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block13_1_relu (Activatio (None, 4, 4, 128)    0           conv5_block13_1_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block13_2_conv (Conv2D)   (None, 4, 4, 32)     36864       conv5_block13_1_relu[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block13_concat (Concatena (None, 4, 4, 928)    0           conv5_block12_concat[0][0]       \n",
      "                                                                 conv5_block13_2_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block14_0_bn (BatchNormal (None, 4, 4, 928)    3712        conv5_block13_concat[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block14_0_relu (Activatio (None, 4, 4, 928)    0           conv5_block14_0_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block14_1_conv (Conv2D)   (None, 4, 4, 128)    118784      conv5_block14_0_relu[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block14_1_bn (BatchNormal (None, 4, 4, 128)    512         conv5_block14_1_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block14_1_relu (Activatio (None, 4, 4, 128)    0           conv5_block14_1_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block14_2_conv (Conv2D)   (None, 4, 4, 32)     36864       conv5_block14_1_relu[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block14_concat (Concatena (None, 4, 4, 960)    0           conv5_block13_concat[0][0]       \n",
      "                                                                 conv5_block14_2_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block15_0_bn (BatchNormal (None, 4, 4, 960)    3840        conv5_block14_concat[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block15_0_relu (Activatio (None, 4, 4, 960)    0           conv5_block15_0_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block15_1_conv (Conv2D)   (None, 4, 4, 128)    122880      conv5_block15_0_relu[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block15_1_bn (BatchNormal (None, 4, 4, 128)    512         conv5_block15_1_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block15_1_relu (Activatio (None, 4, 4, 128)    0           conv5_block15_1_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block15_2_conv (Conv2D)   (None, 4, 4, 32)     36864       conv5_block15_1_relu[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block15_concat (Concatena (None, 4, 4, 992)    0           conv5_block14_concat[0][0]       \n",
      "                                                                 conv5_block15_2_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block16_0_bn (BatchNormal (None, 4, 4, 992)    3968        conv5_block15_concat[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block16_0_relu (Activatio (None, 4, 4, 992)    0           conv5_block16_0_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block16_1_conv (Conv2D)   (None, 4, 4, 128)    126976      conv5_block16_0_relu[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block16_1_bn (BatchNormal (None, 4, 4, 128)    512         conv5_block16_1_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block16_1_relu (Activatio (None, 4, 4, 128)    0           conv5_block16_1_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block16_2_conv (Conv2D)   (None, 4, 4, 32)     36864       conv5_block16_1_relu[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block16_concat (Concatena (None, 4, 4, 1024)   0           conv5_block15_concat[0][0]       \n",
      "                                                                 conv5_block16_2_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "bn (BatchNormalization)         (None, 4, 4, 1024)   4096        conv5_block16_concat[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "relu (Activation)               (None, 4, 4, 1024)   0           bn[0][0]                         \n",
      "__________________________________________________________________________________________________\n",
      "flatten_2 (Flatten)             (None, 16384)        0           relu[0][0]                       \n",
      "__________________________________________________________________________________________________\n",
      "dense_4 (Dense)                 (None, 512)          8389120     flatten_2[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "dense_5 (Dense)                 (None, 2)            1026        dense_4[0][0]                    \n",
      "==================================================================================================\n",
      "Total params: 15,427,650\n",
      "Trainable params: 8,429,058\n",
      "Non-trainable params: 6,998,592\n",
      "__________________________________________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "model.compile(loss='categorical_crossentropy', optimizer=tf.keras.optimizers.RMSprop(lr=2e-5), metrics=['acc'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "625\n",
      "20000\n",
      "Epoch 1/20\n",
      "625/625 [==============================] - 178s 285ms/step - loss: 0.3223 - acc: 0.8633\n",
      "Epoch 2/20\n",
      "625/625 [==============================] - 179s 287ms/step - loss: 0.2790 - acc: 0.8794\n",
      "Epoch 3/20\n",
      "625/625 [==============================] - 178s 285ms/step - loss: 0.2676 - acc: 0.8885\n",
      "Epoch 4/20\n",
      "625/625 [==============================] - 178s 285ms/step - loss: 0.2569 - acc: 0.8884\n",
      "Epoch 5/20\n",
      "625/625 [==============================] - 177s 283ms/step - loss: 0.2403 - acc: 0.8984\n",
      "Epoch 6/20\n",
      "625/625 [==============================] - 176s 281ms/step - loss: 0.2348 - acc: 0.9003\n",
      "Epoch 7/20\n",
      "625/625 [==============================] - 175s 280ms/step - loss: 0.2295 - acc: 0.9011\n",
      "Epoch 8/20\n",
      "625/625 [==============================] - 176s 282ms/step - loss: 0.2239 - acc: 0.9046\n",
      "Epoch 9/20\n",
      "625/625 [==============================] - 180s 288ms/step - loss: 0.2191 - acc: 0.9089\n",
      "Epoch 10/20\n",
      "625/625 [==============================] - 177s 283ms/step - loss: 0.2078 - acc: 0.9145\n",
      "Epoch 11/20\n",
      "625/625 [==============================] - 177s 283ms/step - loss: 0.2063 - acc: 0.9133\n",
      "Epoch 12/20\n",
      "625/625 [==============================] - 177s 283ms/step - loss: 0.2045 - acc: 0.9161\n",
      "Epoch 13/20\n",
      "625/625 [==============================] - 178s 285ms/step - loss: 0.2037 - acc: 0.9158\n",
      "Epoch 14/20\n",
      "625/625 [==============================] - 177s 284ms/step - loss: 0.1966 - acc: 0.9186\n",
      "Epoch 15/20\n",
      "625/625 [==============================] - 177s 284ms/step - loss: 0.1953 - acc: 0.9189\n",
      "Epoch 16/20\n",
      "625/625 [==============================] - 177s 283ms/step - loss: 0.1904 - acc: 0.9219\n",
      "Epoch 17/20\n",
      "625/625 [==============================] - 179s 286ms/step - loss: 0.1883 - acc: 0.9215\n",
      "Epoch 18/20\n",
      "625/625 [==============================] - 179s 286ms/step - loss: 0.1850 - acc: 0.9238\n",
      "Epoch 19/20\n",
      "625/625 [==============================] - 178s 284ms/step - loss: 0.1769 - acc: 0.9258\n",
      "Epoch 20/20\n",
      "625/625 [==============================] - 176s 282ms/step - loss: 0.1848 - acc: 0.9261\n"
     ]
    }
   ],
   "source": [
    "#batch_size=32\n",
    "epochs=20\n",
    "step_size_train=train_data_gen.n//train_data_gen.batch_size\n",
    "print(step_size_train)\n",
    "print(train_data_gen.n)\n",
    "\n",
    "history =model.fit_generator(generator=train_data_gen,\n",
    "                   steps_per_epoch=step_size_train,\n",
    "                   epochs=epochs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "model.save('model_transfer_20.h5')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "model.load_weights('model_transfer_20.h5')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From C:\\Users\\khandelwalr\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\tensorflow_core\\python\\ops\\resource_variable_ops.py:1781: calling BaseResourceVariable.__init__ (from tensorflow.python.ops.resource_variable_ops) with constraint is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "If using Keras pass *_constraint arguments to layers.\n",
      "INFO:tensorflow:Assets written to: saved_model\\assets\n"
     ]
    }
   ],
   "source": [
    "#save your model in the SavedModel format\n",
    "export_dir = 'saved_model'\n",
    "tf.saved_model.save(model, export_dir)\n",
    "# Converting a SavedModel to a TensorFlow Lite model.\n",
    "converter = tf.lite.TFLiteConverter.from_saved_model(export_dir)\n",
    "tflite_model = converter.convert()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "optimize=\"Speed\"\n",
    "if optimize=='Speed':\n",
    "    converter.optimizations = [tf.lite.Optimize.OPTIMIZE_FOR_LATENCY]\n",
    "elif optimize=='Storage':\n",
    "     converter.optimizations = [tf.lite.Optimize.OPTIMIZE_FOR_SIZE]\n",
    "else:    \n",
    "    converter.optimizations = [tf.lite.Optimize.DEFAULT]\n",
    "#reduce the size of a floating point model by quantizing the weights to float16\n",
    "converter.target_spec.supported_types = [tf.float16]\n",
    "tflite_quant_model = converter.convert()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "30821348"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "open(\"model_tl.tflite\", \"wb\").write(tflite_model)\n",
    "open(\"model_quant_tl.tflite\", \"wb\").write(tflite_quant_model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# Load TFLite model and allocate tensors.\n",
    "interpreter = tf.lite.Interpreter(model_content=tflite_quant_model)\n",
    "interpreter.allocate_tensors()\n",
    "#get input and output tensors\n",
    "input_details = interpreter.get_input_details()\n",
    "output_details = interpreter.get_output_details()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "import cv2\n",
    "import tflite_runtime.interpreter as tflite\n",
    "# Read the image and decode to a tensor\n",
    "image_path='Data\\\\dogs-vs-cats\\\\test1\\\\151.jpg' \n",
    "img = cv2.imread(image_path)\n",
    "img = cv2.resize(img,(IMG_WIDTH,IMG_HEIGHT))\n",
    "#Preprocess the image to required size and cast\n",
    "input_shape = input_details[0]['shape']\n",
    "input_tensor= np.array(np.expand_dims(img,0), dtype=np.float32)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "#set the tensor to point to the input data to be inferred\n",
    "input_index = interpreter.get_input_details()[0][\"index\"]\n",
    "interpreter.set_tensor(input_index, input_tensor)\n",
    "#Run the inference\n",
    "interpreter.invoke()\n",
    "output_details = interpreter.get_output_details()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[{'name': 'Identity',\n",
       "  'index': 0,\n",
       "  'shape': array([1, 2]),\n",
       "  'dtype': numpy.float32,\n",
       "  'quantization': (0.0, 0)}]"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "output_details"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[3.38194556e-10 1.00000000e+00]\n"
     ]
    }
   ],
   "source": [
    "prediction = interpreter.get_tensor(output_details[0]['index'])[0]\n",
    "print((prediction).astype(float))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[3.3819456e-10 1.0000000e+00]\n",
      "[0 1]\n"
     ]
    }
   ],
   "source": [
    "output_data = interpreter.get_tensor(output_details[0]['index'])\n",
    "results = np.squeeze(output_data)\n",
    "\n",
    "top_k = results.argsort()\n",
    "\n",
    "print(results)\n",
    "top_k\n",
    "print(top_k)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 0])"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "_, top_k_indices=tf.math.top_k(output_data, k= 2)\n",
    "top_k_indices=np.array(top_k_indices)[0]\n",
    "top_k_indices"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dog 1 1\n"
     ]
    }
   ],
   "source": [
    "for label, idx in train_data_gen.class_indices.items():  # for name, age in dictionary.iteritems():  (for Python 2.x)\n",
    "    if top_k[idx]==1:\n",
    "        print(label, idx, top_k[idx])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'cat': 0, 'dog': 1}"
      ]
     },
     "execution_count": 114,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data_gen.class_indices"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[5.5706005e-22, 1.0000000e+00]], dtype=float32)"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from tensorflow.keras.preprocessing.image import ImageDataGenerator, img_to_array, load_img\n",
    "def load_image(img_path, show=False):\n",
    "\n",
    "    img = tf.keras.preprocessing.image.load_img(img_path, target_size=(150, 150))\n",
    "    img_tensor = tf.keras.preprocessing.image.img_to_array(img)                    # (height, width, channels)\n",
    "    img_tensor = np.expand_dims(img_tensor, axis=0)         # (1, height, width, channels), add a dimension because the model expects this shape: (batch_size, height, width, channels)\n",
    "    img_tensor /= 255.                                      # imshow expects values in the range [0, 1]\n",
    "\n",
    "    if show:\n",
    "        plt.imshow(img_tensor[0])                           \n",
    "        plt.axis('off')\n",
    "        plt.show()\n",
    "\n",
    "    return img_tensor\n",
    "  \n",
    "#img_path = 'C:/Users/Ferhat/Python Code/Workshop/Tensoorflow transfer learning/blue_tit.jpg'\n",
    "img_path='Data\\\\dogs-vs-cats\\\\test1\\\\137.jpg' \n",
    "new_image = load_image(img_path)\n",
    "\n",
    "pred = model.predict(new_image)\n",
    "pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[5.5706005e-22, 1.0000000e+00]], dtype=float32)"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": []
  }
 ],
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