yolo_implementation_FRONT_END.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "fFJkM6ADt6tC"
   },
   "source": [
    "# Yolo for lung opacity detection\n",
    "  \n",
    "\n",
    "### Brief\n",
    "\n",
    "*We attempt to predcit lung opacities in X-ray images using the YOLO netowork, on a dataset provided by the RSNA. The link to the dataset can be found [here](https://www.kaggle.com/c/rsna-pneumonia-detection-challenge/data). A description of the dataset, and details of the Exploratory data analysis can be found on the EDA file.*\n",
    "\n",
    "## Yolo:\n",
    "While predecessor of YOLO repurposed classifiers for detection, YOLO presented a completly new approch to object detection by framing object detection as a regression problem to spatially separated boundingboxes and associated class probabilities.[[1](#yolo_dest)]\n",
    "\n",
    "### We are choosing yolo for the following reasons:\n",
    "1. It is simple.[[1](#yolo_dest)]\n",
    "2. It is fast.[[1](#yolo_dest)]\n",
    "3. Yolo reasons globally when making predictions.[[1](#yolo_dest)]\n",
    "4. Yolo is very generalisable.[[1](#yolo_dest)]\n",
    "5. Requires comparitively cheaper computationaly.[[1](#yolo_dest)]\n",
    "6. Specalised in detecting objects and drawing bounding boxes. [[1](#yolo_dest)]\n",
    "\n",
    "### Limitations:\n",
    "1. Strong spatial constrains on bounding boxes. [[1](#yolo_dest)]\n",
    "2. Struggles to generalise to objects in new or unusual aspect ratios or combinations.[[1](#yolo_dest)]\n",
    "3. Struggles with small objects that appear in groups.[[1](#yolo_dest)]\n",
    "4. The main source of error is incorrect localization.[[1](#yolo_dest)]\n",
    "\n",
    "### Architecture  [[1](#yolo_dest)]\n",
    "The network constits of  24 Convolutional layers, followed by 2 fully connected layers. The inputs are an images of size ($224 \\times 224$). Each bounding box consists of 5 predictions: x, y, w, h, and conﬁdence. The (x,y) coordinates represent the center of the box relative to the bounds of the grid cell. The width andheightarepredictedrelativetothewholeimage. Finally the conﬁdence prediction represents the IOU between the predicted box and any ground truth box. The ﬁnal output of our network is the ($7 \\times 7 \\times 30$) tensor of predictions.\n",
    "\n",
    "![yoloarch.jpg](attachment:yoloarch.jpg)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "YK_LeWY-t6tD"
   },
   "outputs": [],
   "source": [
    "import datetime\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "import tensorflow as tf\n",
    "from tensorflow.keras.applications.mobilenet import preprocess_input\n",
    "from tensorflow.keras.layers import Concatenate, UpSampling2D, Conv2D, Reshape, Flatten, Dense, GlobalMaxPooling2D, Concatenate\n",
    "from tensorflow.keras.losses import MeanSquaredError\n",
    "from tensorflow.keras.metrics import MeanSquaredError\n",
    "from tensorflow.keras.callbacks import ModelCheckpoint, EarlyStopping, ReduceLROnPlateau\n",
    "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n",
    "from tensorflow.keras.losses import BinaryCrossentropy\n",
    "from tensorflow.keras.utils import plot_model\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "from PIL import Image\n",
    "\n",
    "from tqdm import tqdm\n",
    "\n",
    "from matplotlib import pyplot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "HHRAhvnht6tL"
   },
   "outputs": [],
   "source": [
    "data = np.load(r'/content/drive/My Drive/pneumonia yolo/train_set_undooped-pit-tgt-img-b_box_rescaled_tgtis1.npy', \n",
    "               allow_pickle = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "5SJEb5Itt6tT"
   },
   "outputs": [],
   "source": [
    "df = pd.DataFrame(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "pkn0JA_Pt6tY"
   },
   "outputs": [],
   "source": [
    "# pre-processing the input\n",
    "from tensorflow.keras.applications.mobilenet import preprocess_input\n",
    "\n",
    "df['imgs'] = df[2].map(lambda x: x/255.0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "Hk88Qgpdt6tc"
   },
   "outputs": [],
   "source": [
    "X = df['imgs'].map(lambda x: np.expand_dims(x, axis = 2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 437
    },
    "colab_type": "code",
    "id": "iwrWIQgMt6th",
    "outputId": "61c8bd47-c313-4a9b-ecfb-c3d1c4885884"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0.15686275 0.04313725 0.00784314 ... 0.00784314 0.00784314 0.00392157]\n",
      " [0.14901961 0.04313725 0.00784314 ... 0.00784314 0.00784314 0.00392157]\n",
      " [0.15294118 0.04313725 0.00784314 ... 0.00784314 0.00784314 0.00392157]\n",
      " ...\n",
      " [0.12156863 0.05882353 0.03921569 ... 0.01176471 0.02745098 0.04705882]\n",
      " [0.21176471 0.1372549  0.11372549 ... 0.02352941 0.05490196 0.08627451]\n",
      " [0.34509804 0.2745098  0.25098039 ... 0.0627451  0.09803922 0.14117647]]\n",
      "(224, 224)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7ff70cfae1d0>"
      ]
     },
     "execution_count": 7,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib import pyplot\n",
    "n = 1\n",
    "print(df['imgs'][n])\n",
    "print(df['imgs'][n].shape)\n",
    "pyplot.imshow(df['imgs'][n])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "_RQNgWJHt6tm"
   },
   "outputs": [],
   "source": [
    "X1 = np.zeros((int(df.shape[0]),224, 224,1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "IMOcvg_ft6tu"
   },
   "outputs": [],
   "source": [
    "for i in range(len(X)):\n",
    "    X1[i] = X[i]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "cKcgFodht6ty"
   },
   "outputs": [],
   "source": [
    "preds = np.zeros((int(df.shape[0]), 16))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 73
    },
    "colab_type": "code",
    "id": "fnhWAYTTt6t2",
    "outputId": "17b661e4-4662-43db-c942-047420898b62"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 57.75   ,  33.25   ,  46.59375,  46.59375, 122.9375 ,  33.25   ,\n",
       "        56.     ,  56.     ,   0.     ,   0.     ,   0.     ,   0.     ,\n",
       "         0.     ,   0.     ,   0.     ,   0.     ])"
      ]
     },
     "execution_count": 11,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "for i in range(len(df)):\n",
    "    for j in range(16):\n",
    "        preds[i][j] = df[3][i][j]\n",
    "        #print(type(y[i][j]))\n",
    "preds[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "32h7YKumt6t5"
   },
   "outputs": [],
   "source": [
    "preds_class = np.zeros((int(df.shape[0]), 1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 36
    },
    "colab_type": "code",
    "id": "6_DBib0Qt6t-",
    "outputId": "11e99fbe-c843-489d-eb44-483ff198599e"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1.])"
      ]
     },
     "execution_count": 13,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "for i in range(len(df)):\n",
    "    for j in range(16):\n",
    "        preds_class[i] = df[1][i]\n",
    "        #print(type(y[i][j]))\n",
    "preds_class[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "ANrbWIfyt6uB"
   },
   "outputs": [],
   "source": [
    "df = None"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "fJPbPJHCt6uF"
   },
   "outputs": [],
   "source": [
    "preds = tf.constant(preds, shape = (6012,16), dtype = tf.float64)\n",
    "preds_class = tf.constant(preds_class, shape = (6012,1), dtype = tf.float64)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "vt7v08JHt6uI"
   },
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "from tensorflow import keras\n",
    "from tensorflow.keras.applications.mobilenet import MobileNet\n",
    "from tensorflow.keras.layers import Concatenate, UpSampling2D, Conv2D, Reshape, Flatten, Dense, GlobalAveragePooling2D, Input\n",
    "from tensorflow.keras.models import Model\n",
    "from tensorflow.keras.losses import MeanSquaredError\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 56
    },
    "colab_type": "code",
    "id": "czEuLt6Nt6uM",
    "outputId": "f2ee8942-58b0-492f-82b9-a8ae074088fa"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:No training configuration found in the save file, so the model was *not* compiled. Compile it manually.\n"
     ]
    }
   ],
   "source": [
    "yolo_model = keras.models.load_model('/content/drive/My Drive/pneumonia yolo/yolo_model11.h5')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "colab_type": "code",
    "id": "I3IFSDBJt6uQ",
    "outputId": "2662534f-9e3c-4245-f25d-b099ea525999"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"model_1\"\n",
      "__________________________________________________________________________________________________\n",
      "Layer (type)                    Output Shape         Param #     Connected to                     \n",
      "==================================================================================================\n",
      "input_1 (InputLayer)            [(None, None, None,  0                                            \n",
      "__________________________________________________________________________________________________\n",
      "conv_0 (Conv2D)                 (None, None, None, 3 864         input_1[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "bnorm_0 (BatchNormalization)    (None, None, None, 3 128         conv_0[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "leaky_0 (LeakyReLU)             (None, None, None, 3 0           bnorm_0[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "zero_padding2d_1 (ZeroPadding2D (None, None, None, 3 0           leaky_0[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "conv_1 (Conv2D)                 (None, None, None, 6 18432       zero_padding2d_1[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "bnorm_1 (BatchNormalization)    (None, None, None, 6 256         conv_1[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "leaky_1 (LeakyReLU)             (None, None, None, 6 0           bnorm_1[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "conv_2 (Conv2D)                 (None, None, None, 3 2048        leaky_1[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "bnorm_2 (BatchNormalization)    (None, None, None, 3 128         conv_2[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "leaky_2 (LeakyReLU)             (None, None, None, 3 0           bnorm_2[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "conv_3 (Conv2D)                 (None, None, None, 6 18432       leaky_2[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "bnorm_3 (BatchNormalization)    (None, None, None, 6 256         conv_3[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "leaky_3 (LeakyReLU)             (None, None, None, 6 0           bnorm_3[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "add_1 (Add)                     (None, None, None, 6 0           leaky_1[0][0]                    \n",
      "                                                                 leaky_3[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "zero_padding2d_2 (ZeroPadding2D (None, None, None, 6 0           add_1[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "conv_5 (Conv2D)                 (None, None, None, 1 73728       zero_padding2d_2[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "bnorm_5 (BatchNormalization)    (None, None, None, 1 512         conv_5[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "leaky_5 (LeakyReLU)             (None, None, None, 1 0           bnorm_5[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "conv_6 (Conv2D)                 (None, None, None, 6 8192        leaky_5[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "bnorm_6 (BatchNormalization)    (None, None, None, 6 256         conv_6[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "leaky_6 (LeakyReLU)             (None, None, None, 6 0           bnorm_6[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "conv_7 (Conv2D)                 (None, None, None, 1 73728       leaky_6[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "bnorm_7 (BatchNormalization)    (None, None, None, 1 512         conv_7[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "leaky_7 (LeakyReLU)             (None, None, None, 1 0           bnorm_7[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "add_2 (Add)                     (None, None, None, 1 0           leaky_5[0][0]                    \n",
      "                                                                 leaky_7[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "conv_9 (Conv2D)                 (None, None, None, 6 8192        add_2[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "bnorm_9 (BatchNormalization)    (None, None, None, 6 256         conv_9[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "leaky_9 (LeakyReLU)             (None, None, None, 6 0           bnorm_9[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "conv_10 (Conv2D)                (None, None, None, 1 73728       leaky_9[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "bnorm_10 (BatchNormalization)   (None, None, None, 1 512         conv_10[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "leaky_10 (LeakyReLU)            (None, None, None, 1 0           bnorm_10[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "add_3 (Add)                     (None, None, None, 1 0           add_2[0][0]                      \n",
      "                                                                 leaky_10[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "zero_padding2d_3 (ZeroPadding2D (None, None, None, 1 0           add_3[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "conv_12 (Conv2D)                (None, None, None, 2 294912      zero_padding2d_3[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "bnorm_12 (BatchNormalization)   (None, None, None, 2 1024        conv_12[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "leaky_12 (LeakyReLU)            (None, None, None, 2 0           bnorm_12[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "conv_13 (Conv2D)                (None, None, None, 1 32768       leaky_12[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "bnorm_13 (BatchNormalization)   (None, None, None, 1 512         conv_13[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "leaky_13 (LeakyReLU)            (None, None, None, 1 0           bnorm_13[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "conv_14 (Conv2D)                (None, None, None, 2 294912      leaky_13[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "bnorm_14 (BatchNormalization)   (None, None, None, 2 1024        conv_14[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "leaky_14 (LeakyReLU)            (None, None, None, 2 0           bnorm_14[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "add_4 (Add)                     (None, None, None, 2 0           leaky_12[0][0]                   \n",
      "                                                                 leaky_14[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "conv_16 (Conv2D)                (None, None, None, 1 32768       add_4[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "bnorm_16 (BatchNormalization)   (None, None, None, 1 512         conv_16[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "leaky_16 (LeakyReLU)            (None, None, None, 1 0           bnorm_16[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "conv_17 (Conv2D)                (None, None, None, 2 294912      leaky_16[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "bnorm_17 (BatchNormalization)   (None, None, None, 2 1024        conv_17[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "leaky_17 (LeakyReLU)            (None, None, None, 2 0           bnorm_17[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "add_5 (Add)                     (None, None, None, 2 0           add_4[0][0]                      \n",
      "                                                                 leaky_17[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "conv_19 (Conv2D)                (None, None, None, 1 32768       add_5[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "bnorm_19 (BatchNormalization)   (None, None, None, 1 512         conv_19[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "leaky_19 (LeakyReLU)            (None, None, None, 1 0           bnorm_19[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "conv_20 (Conv2D)                (None, None, None, 2 294912      leaky_19[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "bnorm_20 (BatchNormalization)   (None, None, None, 2 1024        conv_20[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "leaky_20 (LeakyReLU)            (None, None, None, 2 0           bnorm_20[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "add_6 (Add)                     (None, None, None, 2 0           add_5[0][0]                      \n",
      "                                                                 leaky_20[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "conv_22 (Conv2D)                (None, None, None, 1 32768       add_6[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "bnorm_22 (BatchNormalization)   (None, None, None, 1 512         conv_22[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "leaky_22 (LeakyReLU)            (None, None, None, 1 0           bnorm_22[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "conv_23 (Conv2D)                (None, None, None, 2 294912      leaky_22[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "bnorm_23 (BatchNormalization)   (None, None, None, 2 1024        conv_23[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "leaky_23 (LeakyReLU)            (None, None, None, 2 0           bnorm_23[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "add_7 (Add)                     (None, None, None, 2 0           add_6[0][0]                      \n",
      "                                                                 leaky_23[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "conv_25 (Conv2D)                (None, None, None, 1 32768       add_7[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "bnorm_25 (BatchNormalization)   (None, None, None, 1 512         conv_25[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "leaky_25 (LeakyReLU)            (None, None, None, 1 0           bnorm_25[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "conv_26 (Conv2D)                (None, None, None, 2 294912      leaky_25[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "bnorm_26 (BatchNormalization)   (None, None, None, 2 1024        conv_26[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "leaky_26 (LeakyReLU)            (None, None, None, 2 0           bnorm_26[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "add_8 (Add)                     (None, None, None, 2 0           add_7[0][0]                      \n",
      "                                                                 leaky_26[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "conv_28 (Conv2D)                (None, None, None, 1 32768       add_8[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "bnorm_28 (BatchNormalization)   (None, None, None, 1 512         conv_28[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "leaky_28 (LeakyReLU)            (None, None, None, 1 0           bnorm_28[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "conv_29 (Conv2D)                (None, None, None, 2 294912      leaky_28[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "bnorm_29 (BatchNormalization)   (None, None, None, 2 1024        conv_29[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "leaky_29 (LeakyReLU)            (None, None, None, 2 0           bnorm_29[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "add_9 (Add)                     (None, None, None, 2 0           add_8[0][0]                      \n",
      "                                                                 leaky_29[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "conv_31 (Conv2D)                (None, None, None, 1 32768       add_9[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "bnorm_31 (BatchNormalization)   (None, None, None, 1 512         conv_31[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "leaky_31 (LeakyReLU)            (None, None, None, 1 0           bnorm_31[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "conv_32 (Conv2D)                (None, None, None, 2 294912      leaky_31[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "bnorm_32 (BatchNormalization)   (None, None, None, 2 1024        conv_32[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "leaky_32 (LeakyReLU)            (None, None, None, 2 0           bnorm_32[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "add_10 (Add)                    (None, None, None, 2 0           add_9[0][0]                      \n",
      "                                                                 leaky_32[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "conv_34 (Conv2D)                (None, None, None, 1 32768       add_10[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "bnorm_34 (BatchNormalization)   (None, None, None, 1 512         conv_34[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "leaky_34 (LeakyReLU)            (None, None, None, 1 0           bnorm_34[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "conv_35 (Conv2D)                (None, None, None, 2 294912      leaky_34[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "bnorm_35 (BatchNormalization)   (None, None, None, 2 1024        conv_35[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "leaky_35 (LeakyReLU)            (None, None, None, 2 0           bnorm_35[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "add_11 (Add)                    (None, None, None, 2 0           add_10[0][0]                     \n",
      "                                                                 leaky_35[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "zero_padding2d_4 (ZeroPadding2D (None, None, None, 2 0           add_11[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv_37 (Conv2D)                (None, None, None, 5 1179648     zero_padding2d_4[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "bnorm_37 (BatchNormalization)   (None, None, None, 5 2048        conv_37[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "leaky_37 (LeakyReLU)            (None, None, None, 5 0           bnorm_37[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "conv_38 (Conv2D)                (None, None, None, 2 131072      leaky_37[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "bnorm_38 (BatchNormalization)   (None, None, None, 2 1024        conv_38[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "leaky_38 (LeakyReLU)            (None, None, None, 2 0           bnorm_38[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "conv_39 (Conv2D)                (None, None, None, 5 1179648     leaky_38[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "bnorm_39 (BatchNormalization)   (None, None, None, 5 2048        conv_39[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "leaky_39 (LeakyReLU)            (None, None, None, 5 0           bnorm_39[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "add_12 (Add)                    (None, None, None, 5 0           leaky_37[0][0]                   \n",
      "                                                                 leaky_39[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "conv_41 (Conv2D)                (None, None, None, 2 131072      add_12[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "bnorm_41 (BatchNormalization)   (None, None, None, 2 1024        conv_41[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "leaky_41 (LeakyReLU)            (None, None, None, 2 0           bnorm_41[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "conv_42 (Conv2D)                (None, None, None, 5 1179648     leaky_41[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "bnorm_42 (BatchNormalization)   (None, None, None, 5 2048        conv_42[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "leaky_42 (LeakyReLU)            (None, None, None, 5 0           bnorm_42[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "add_13 (Add)                    (None, None, None, 5 0           add_12[0][0]                     \n",
      "                                                                 leaky_42[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "conv_44 (Conv2D)                (None, None, None, 2 131072      add_13[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "bnorm_44 (BatchNormalization)   (None, None, None, 2 1024        conv_44[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "leaky_44 (LeakyReLU)            (None, None, None, 2 0           bnorm_44[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "conv_45 (Conv2D)                (None, None, None, 5 1179648     leaky_44[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "bnorm_45 (BatchNormalization)   (None, None, None, 5 2048        conv_45[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "leaky_45 (LeakyReLU)            (None, None, None, 5 0           bnorm_45[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "add_14 (Add)                    (None, None, None, 5 0           add_13[0][0]                     \n",
      "                                                                 leaky_45[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "conv_47 (Conv2D)                (None, None, None, 2 131072      add_14[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "bnorm_47 (BatchNormalization)   (None, None, None, 2 1024        conv_47[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "leaky_47 (LeakyReLU)            (None, None, None, 2 0           bnorm_47[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "conv_48 (Conv2D)                (None, None, None, 5 1179648     leaky_47[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "bnorm_48 (BatchNormalization)   (None, None, None, 5 2048        conv_48[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "leaky_48 (LeakyReLU)            (None, None, None, 5 0           bnorm_48[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "add_15 (Add)                    (None, None, None, 5 0           add_14[0][0]                     \n",
      "                                                                 leaky_48[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "conv_50 (Conv2D)                (None, None, None, 2 131072      add_15[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "bnorm_50 (BatchNormalization)   (None, None, None, 2 1024        conv_50[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "leaky_50 (LeakyReLU)            (None, None, None, 2 0           bnorm_50[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "conv_51 (Conv2D)                (None, None, None, 5 1179648     leaky_50[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "bnorm_51 (BatchNormalization)   (None, None, None, 5 2048        conv_51[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "leaky_51 (LeakyReLU)            (None, None, None, 5 0           bnorm_51[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "add_16 (Add)                    (None, None, None, 5 0           add_15[0][0]                     \n",
      "                                                                 leaky_51[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "conv_53 (Conv2D)                (None, None, None, 2 131072      add_16[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "bnorm_53 (BatchNormalization)   (None, None, None, 2 1024        conv_53[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "leaky_53 (LeakyReLU)            (None, None, None, 2 0           bnorm_53[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "conv_54 (Conv2D)                (None, None, None, 5 1179648     leaky_53[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "bnorm_54 (BatchNormalization)   (None, None, None, 5 2048        conv_54[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "leaky_54 (LeakyReLU)            (None, None, None, 5 0           bnorm_54[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "add_17 (Add)                    (None, None, None, 5 0           add_16[0][0]                     \n",
      "                                                                 leaky_54[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "conv_56 (Conv2D)                (None, None, None, 2 131072      add_17[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "bnorm_56 (BatchNormalization)   (None, None, None, 2 1024        conv_56[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "leaky_56 (LeakyReLU)            (None, None, None, 2 0           bnorm_56[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "conv_57 (Conv2D)                (None, None, None, 5 1179648     leaky_56[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "bnorm_57 (BatchNormalization)   (None, None, None, 5 2048        conv_57[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "leaky_57 (LeakyReLU)            (None, None, None, 5 0           bnorm_57[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "add_18 (Add)                    (None, None, None, 5 0           add_17[0][0]                     \n",
      "                                                                 leaky_57[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "conv_59 (Conv2D)                (None, None, None, 2 131072      add_18[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "bnorm_59 (BatchNormalization)   (None, None, None, 2 1024        conv_59[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "leaky_59 (LeakyReLU)            (None, None, None, 2 0           bnorm_59[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "conv_60 (Conv2D)                (None, None, None, 5 1179648     leaky_59[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "bnorm_60 (BatchNormalization)   (None, None, None, 5 2048        conv_60[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "leaky_60 (LeakyReLU)            (None, None, None, 5 0           bnorm_60[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "add_19 (Add)                    (None, None, None, 5 0           add_18[0][0]                     \n",
      "                                                                 leaky_60[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "zero_padding2d_5 (ZeroPadding2D (None, None, None, 5 0           add_19[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv_62 (Conv2D)                (None, None, None, 1 4718592     zero_padding2d_5[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "bnorm_62 (BatchNormalization)   (None, None, None, 1 4096        conv_62[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "leaky_62 (LeakyReLU)            (None, None, None, 1 0           bnorm_62[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "conv_63 (Conv2D)                (None, None, None, 5 524288      leaky_62[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "bnorm_63 (BatchNormalization)   (None, None, None, 5 2048        conv_63[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "leaky_63 (LeakyReLU)            (None, None, None, 5 0           bnorm_63[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "conv_64 (Conv2D)                (None, None, None, 1 4718592     leaky_63[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "bnorm_64 (BatchNormalization)   (None, None, None, 1 4096        conv_64[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "leaky_64 (LeakyReLU)            (None, None, None, 1 0           bnorm_64[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "add_20 (Add)                    (None, None, None, 1 0           leaky_62[0][0]                   \n",
      "                                                                 leaky_64[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "conv_66 (Conv2D)                (None, None, None, 5 524288      add_20[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "bnorm_66 (BatchNormalization)   (None, None, None, 5 2048        conv_66[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "leaky_66 (LeakyReLU)            (None, None, None, 5 0           bnorm_66[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "conv_67 (Conv2D)                (None, None, None, 1 4718592     leaky_66[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "bnorm_67 (BatchNormalization)   (None, None, None, 1 4096        conv_67[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "leaky_67 (LeakyReLU)            (None, None, None, 1 0           bnorm_67[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "add_21 (Add)                    (None, None, None, 1 0           add_20[0][0]                     \n",
      "                                                                 leaky_67[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "conv_69 (Conv2D)                (None, None, None, 5 524288      add_21[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "bnorm_69 (BatchNormalization)   (None, None, None, 5 2048        conv_69[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "leaky_69 (LeakyReLU)            (None, None, None, 5 0           bnorm_69[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "conv_70 (Conv2D)                (None, None, None, 1 4718592     leaky_69[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "bnorm_70 (BatchNormalization)   (None, None, None, 1 4096        conv_70[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "leaky_70 (LeakyReLU)            (None, None, None, 1 0           bnorm_70[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "add_22 (Add)                    (None, None, None, 1 0           add_21[0][0]                     \n",
      "                                                                 leaky_70[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "conv_72 (Conv2D)                (None, None, None, 5 524288      add_22[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "bnorm_72 (BatchNormalization)   (None, None, None, 5 2048        conv_72[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "leaky_72 (LeakyReLU)            (None, None, None, 5 0           bnorm_72[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "conv_73 (Conv2D)                (None, None, None, 1 4718592     leaky_72[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "bnorm_73 (BatchNormalization)   (None, None, None, 1 4096        conv_73[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "leaky_73 (LeakyReLU)            (None, None, None, 1 0           bnorm_73[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "add_23 (Add)                    (None, None, None, 1 0           add_22[0][0]                     \n",
      "                                                                 leaky_73[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "conv_75 (Conv2D)                (None, None, None, 5 524288      add_23[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "bnorm_75 (BatchNormalization)   (None, None, None, 5 2048        conv_75[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "leaky_75 (LeakyReLU)            (None, None, None, 5 0           bnorm_75[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "conv_76 (Conv2D)                (None, None, None, 1 4718592     leaky_75[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "bnorm_76 (BatchNormalization)   (None, None, None, 1 4096        conv_76[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "leaky_76 (LeakyReLU)            (None, None, None, 1 0           bnorm_76[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "conv_77 (Conv2D)                (None, None, None, 5 524288      leaky_76[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "bnorm_77 (BatchNormalization)   (None, None, None, 5 2048        conv_77[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "leaky_77 (LeakyReLU)            (None, None, None, 5 0           bnorm_77[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "conv_78 (Conv2D)                (None, None, None, 1 4718592     leaky_77[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "bnorm_78 (BatchNormalization)   (None, None, None, 1 4096        conv_78[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "leaky_78 (LeakyReLU)            (None, None, None, 1 0           bnorm_78[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "conv_79 (Conv2D)                (None, None, None, 5 524288      leaky_78[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "bnorm_79 (BatchNormalization)   (None, None, None, 5 2048        conv_79[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "leaky_79 (LeakyReLU)            (None, None, None, 5 0           bnorm_79[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "conv_84 (Conv2D)                (None, None, None, 2 131072      leaky_79[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "bnorm_84 (BatchNormalization)   (None, None, None, 2 1024        conv_84[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "leaky_84 (LeakyReLU)            (None, None, None, 2 0           bnorm_84[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "up_sampling2d_1 (UpSampling2D)  (None, None, None, 2 0           leaky_84[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "concatenate_1 (Concatenate)     (None, None, None, 7 0           up_sampling2d_1[0][0]            \n",
      "                                                                 add_19[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv_87 (Conv2D)                (None, None, None, 2 196608      concatenate_1[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bnorm_87 (BatchNormalization)   (None, None, None, 2 1024        conv_87[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "leaky_87 (LeakyReLU)            (None, None, None, 2 0           bnorm_87[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "conv_88 (Conv2D)                (None, None, None, 5 1179648     leaky_87[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "bnorm_88 (BatchNormalization)   (None, None, None, 5 2048        conv_88[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "leaky_88 (LeakyReLU)            (None, None, None, 5 0           bnorm_88[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "conv_89 (Conv2D)                (None, None, None, 2 131072      leaky_88[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "bnorm_89 (BatchNormalization)   (None, None, None, 2 1024        conv_89[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "leaky_89 (LeakyReLU)            (None, None, None, 2 0           bnorm_89[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "conv_90 (Conv2D)                (None, None, None, 5 1179648     leaky_89[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "bnorm_90 (BatchNormalization)   (None, None, None, 5 2048        conv_90[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "leaky_90 (LeakyReLU)            (None, None, None, 5 0           bnorm_90[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "conv_91 (Conv2D)                (None, None, None, 2 131072      leaky_90[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "bnorm_91 (BatchNormalization)   (None, None, None, 2 1024        conv_91[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "leaky_91 (LeakyReLU)            (None, None, None, 2 0           bnorm_91[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "conv_96 (Conv2D)                (None, None, None, 1 32768       leaky_91[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "bnorm_96 (BatchNormalization)   (None, None, None, 1 512         conv_96[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "leaky_96 (LeakyReLU)            (None, None, None, 1 0           bnorm_96[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "up_sampling2d_2 (UpSampling2D)  (None, None, None, 1 0           leaky_96[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "concatenate_2 (Concatenate)     (None, None, None, 3 0           up_sampling2d_2[0][0]            \n",
      "                                                                 add_11[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv_99 (Conv2D)                (None, None, None, 1 49152       concatenate_2[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bnorm_99 (BatchNormalization)   (None, None, None, 1 512         conv_99[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "leaky_99 (LeakyReLU)            (None, None, None, 1 0           bnorm_99[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "conv_100 (Conv2D)               (None, None, None, 2 294912      leaky_99[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "bnorm_100 (BatchNormalization)  (None, None, None, 2 1024        conv_100[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "leaky_100 (LeakyReLU)           (None, None, None, 2 0           bnorm_100[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "conv_101 (Conv2D)               (None, None, None, 1 32768       leaky_100[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "bnorm_101 (BatchNormalization)  (None, None, None, 1 512         conv_101[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "leaky_101 (LeakyReLU)           (None, None, None, 1 0           bnorm_101[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "conv_102 (Conv2D)               (None, None, None, 2 294912      leaky_101[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "bnorm_102 (BatchNormalization)  (None, None, None, 2 1024        conv_102[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "leaky_102 (LeakyReLU)           (None, None, None, 2 0           bnorm_102[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "conv_103 (Conv2D)               (None, None, None, 1 32768       leaky_102[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "bnorm_103 (BatchNormalization)  (None, None, None, 1 512         conv_103[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "leaky_103 (LeakyReLU)           (None, None, None, 1 0           bnorm_103[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "conv_80 (Conv2D)                (None, None, None, 1 4718592     leaky_79[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "conv_92 (Conv2D)                (None, None, None, 5 1179648     leaky_91[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "conv_104 (Conv2D)               (None, None, None, 2 294912      leaky_103[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "bnorm_80 (BatchNormalization)   (None, None, None, 1 4096        conv_80[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "bnorm_92 (BatchNormalization)   (None, None, None, 5 2048        conv_92[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "bnorm_104 (BatchNormalization)  (None, None, None, 2 1024        conv_104[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "leaky_80 (LeakyReLU)            (None, None, None, 1 0           bnorm_80[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "leaky_92 (LeakyReLU)            (None, None, None, 5 0           bnorm_92[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "leaky_104 (LeakyReLU)           (None, None, None, 2 0           bnorm_104[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "conv_81 (Conv2D)                (None, None, None, 2 261375      leaky_80[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "conv_93 (Conv2D)                (None, None, None, 2 130815      leaky_92[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "conv_105 (Conv2D)               (None, None, None, 2 65535       leaky_104[0][0]                  \n",
      "==================================================================================================\n",
      "Total params: 62,001,757\n",
      "Trainable params: 61,949,149\n",
      "Non-trainable params: 52,608\n",
      "__________________________________________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "yolo_model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "D_y6yg4mt6uS"
   },
   "outputs": [],
   "source": [
    "input_tensor = Input(shape=(224,224,1))\n",
    "x = Conv2D(3,(3,3),padding='same')(input_tensor)    # x has a dimension of (IMG_SIZE,IMG_SIZE,3)\n",
    "out = yolo_model (x) \n",
    "\n",
    "model = Model(inputs=input_tensor,outputs=out)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 279
    },
    "colab_type": "code",
    "id": "kZ08r9KKt6uV",
    "outputId": "a8b84e38-8351-4a15-882c-2dbbc5200e54"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"model\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "input_1 (InputLayer)         [(None, 224, 224, 1)]     0         \n",
      "_________________________________________________________________\n",
      "conv2d (Conv2D)              (None, 224, 224, 3)       30        \n",
      "_________________________________________________________________\n",
      "model_1 (Model)              multiple                  62001757  \n",
      "=================================================================\n",
      "Total params: 62,001,787\n",
      "Trainable params: 61,949,179\n",
      "Non-trainable params: 52,608\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "colab_type": "code",
    "id": "D1Yhvl6Qt6uY",
    "outputId": "36704652-abd8-45e8-d245-2998b23f4c07"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"model_1\"\n",
      "__________________________________________________________________________________________________\n",
      "Layer (type)                    Output Shape         Param #     Connected to                     \n",
      "==================================================================================================\n",
      "input_1 (InputLayer)            [(None, None, None,  0                                            \n",
      "__________________________________________________________________________________________________\n",
      "conv_0 (Conv2D)                 multiple             864         input_1[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "bnorm_0 (BatchNormalization)    multiple             128         conv_0[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "leaky_0 (LeakyReLU)             multiple             0           bnorm_0[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "zero_padding2d_1 (ZeroPadding2D multiple             0           leaky_0[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "conv_1 (Conv2D)                 multiple             18432       zero_padding2d_1[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "bnorm_1 (BatchNormalization)    multiple             256         conv_1[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "leaky_1 (LeakyReLU)             multiple             0           bnorm_1[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "conv_2 (Conv2D)                 multiple             2048        leaky_1[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "bnorm_2 (BatchNormalization)    multiple             128         conv_2[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "leaky_2 (LeakyReLU)             multiple             0           bnorm_2[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "conv_3 (Conv2D)                 multiple             18432       leaky_2[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "bnorm_3 (BatchNormalization)    multiple             256         conv_3[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "leaky_3 (LeakyReLU)             multiple             0           bnorm_3[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "add_1 (Add)                     multiple             0           leaky_1[0][0]                    \n",
      "                                                                 leaky_3[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "zero_padding2d_2 (ZeroPadding2D multiple             0           add_1[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "conv_5 (Conv2D)                 multiple             73728       zero_padding2d_2[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "bnorm_5 (BatchNormalization)    multiple             512         conv_5[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "leaky_5 (LeakyReLU)             multiple             0           bnorm_5[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "conv_6 (Conv2D)                 multiple             8192        leaky_5[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "bnorm_6 (BatchNormalization)    multiple             256         conv_6[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "leaky_6 (LeakyReLU)             multiple             0           bnorm_6[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "conv_7 (Conv2D)                 multiple             73728       leaky_6[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "bnorm_7 (BatchNormalization)    multiple             512         conv_7[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "leaky_7 (LeakyReLU)             multiple             0           bnorm_7[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "add_2 (Add)                     multiple             0           leaky_5[0][0]                    \n",
      "                                                                 leaky_7[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "conv_9 (Conv2D)                 multiple             8192        add_2[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "bnorm_9 (BatchNormalization)    multiple             256         conv_9[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "leaky_9 (LeakyReLU)             multiple             0           bnorm_9[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "conv_10 (Conv2D)                multiple             73728       leaky_9[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "bnorm_10 (BatchNormalization)   multiple             512         conv_10[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "leaky_10 (LeakyReLU)            multiple             0           bnorm_10[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "add_3 (Add)                     multiple             0           add_2[0][0]                      \n",
      "                                                                 leaky_10[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "zero_padding2d_3 (ZeroPadding2D multiple             0           add_3[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "conv_12 (Conv2D)                multiple             294912      zero_padding2d_3[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "bnorm_12 (BatchNormalization)   multiple             1024        conv_12[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "leaky_12 (LeakyReLU)            multiple             0           bnorm_12[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "conv_13 (Conv2D)                multiple             32768       leaky_12[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "bnorm_13 (BatchNormalization)   multiple             512         conv_13[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "leaky_13 (LeakyReLU)            multiple             0           bnorm_13[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "conv_14 (Conv2D)                multiple             294912      leaky_13[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "bnorm_14 (BatchNormalization)   multiple             1024        conv_14[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "leaky_14 (LeakyReLU)            multiple             0           bnorm_14[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "add_4 (Add)                     multiple             0           leaky_12[0][0]                   \n",
      "                                                                 leaky_14[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "conv_16 (Conv2D)                multiple             32768       add_4[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "bnorm_16 (BatchNormalization)   multiple             512         conv_16[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "leaky_16 (LeakyReLU)            multiple             0           bnorm_16[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "conv_17 (Conv2D)                multiple             294912      leaky_16[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "bnorm_17 (BatchNormalization)   multiple             1024        conv_17[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "leaky_17 (LeakyReLU)            multiple             0           bnorm_17[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "add_5 (Add)                     multiple             0           add_4[0][0]                      \n",
      "                                                                 leaky_17[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "conv_19 (Conv2D)                multiple             32768       add_5[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "bnorm_19 (BatchNormalization)   multiple             512         conv_19[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "leaky_19 (LeakyReLU)            multiple             0           bnorm_19[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "conv_20 (Conv2D)                multiple             294912      leaky_19[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "bnorm_20 (BatchNormalization)   multiple             1024        conv_20[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "leaky_20 (LeakyReLU)            multiple             0           bnorm_20[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "add_6 (Add)                     multiple             0           add_5[0][0]                      \n",
      "                                                                 leaky_20[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "conv_22 (Conv2D)                multiple             32768       add_6[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "bnorm_22 (BatchNormalization)   multiple             512         conv_22[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "leaky_22 (LeakyReLU)            multiple             0           bnorm_22[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "conv_23 (Conv2D)                multiple             294912      leaky_22[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "bnorm_23 (BatchNormalization)   multiple             1024        conv_23[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "leaky_23 (LeakyReLU)            multiple             0           bnorm_23[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "add_7 (Add)                     multiple             0           add_6[0][0]                      \n",
      "                                                                 leaky_23[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "conv_25 (Conv2D)                multiple             32768       add_7[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "bnorm_25 (BatchNormalization)   multiple             512         conv_25[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "leaky_25 (LeakyReLU)            multiple             0           bnorm_25[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "conv_26 (Conv2D)                multiple             294912      leaky_25[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "bnorm_26 (BatchNormalization)   multiple             1024        conv_26[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "leaky_26 (LeakyReLU)            multiple             0           bnorm_26[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "add_8 (Add)                     multiple             0           add_7[0][0]                      \n",
      "                                                                 leaky_26[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "conv_28 (Conv2D)                multiple             32768       add_8[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "bnorm_28 (BatchNormalization)   multiple             512         conv_28[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "leaky_28 (LeakyReLU)            multiple             0           bnorm_28[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "conv_29 (Conv2D)                multiple             294912      leaky_28[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "bnorm_29 (BatchNormalization)   multiple             1024        conv_29[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "leaky_29 (LeakyReLU)            multiple             0           bnorm_29[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "add_9 (Add)                     multiple             0           add_8[0][0]                      \n",
      "                                                                 leaky_29[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "conv_31 (Conv2D)                multiple             32768       add_9[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "bnorm_31 (BatchNormalization)   multiple             512         conv_31[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "leaky_31 (LeakyReLU)            multiple             0           bnorm_31[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "conv_32 (Conv2D)                multiple             294912      leaky_31[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "bnorm_32 (BatchNormalization)   multiple             1024        conv_32[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "leaky_32 (LeakyReLU)            multiple             0           bnorm_32[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "add_10 (Add)                    multiple             0           add_9[0][0]                      \n",
      "                                                                 leaky_32[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "conv_34 (Conv2D)                multiple             32768       add_10[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "bnorm_34 (BatchNormalization)   multiple             512         conv_34[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "leaky_34 (LeakyReLU)            multiple             0           bnorm_34[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "conv_35 (Conv2D)                multiple             294912      leaky_34[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "bnorm_35 (BatchNormalization)   multiple             1024        conv_35[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "leaky_35 (LeakyReLU)            multiple             0           bnorm_35[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "add_11 (Add)                    multiple             0           add_10[0][0]                     \n",
      "                                                                 leaky_35[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "zero_padding2d_4 (ZeroPadding2D multiple             0           add_11[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv_37 (Conv2D)                multiple             1179648     zero_padding2d_4[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "bnorm_37 (BatchNormalization)   multiple             2048        conv_37[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "leaky_37 (LeakyReLU)            multiple             0           bnorm_37[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "conv_38 (Conv2D)                multiple             131072      leaky_37[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "bnorm_38 (BatchNormalization)   multiple             1024        conv_38[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "leaky_38 (LeakyReLU)            multiple             0           bnorm_38[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "conv_39 (Conv2D)                multiple             1179648     leaky_38[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "bnorm_39 (BatchNormalization)   multiple             2048        conv_39[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "leaky_39 (LeakyReLU)            multiple             0           bnorm_39[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "add_12 (Add)                    multiple             0           leaky_37[0][0]                   \n",
      "                                                                 leaky_39[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "conv_41 (Conv2D)                multiple             131072      add_12[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "bnorm_41 (BatchNormalization)   multiple             1024        conv_41[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "leaky_41 (LeakyReLU)            multiple             0           bnorm_41[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "conv_42 (Conv2D)                multiple             1179648     leaky_41[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "bnorm_42 (BatchNormalization)   multiple             2048        conv_42[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "leaky_42 (LeakyReLU)            multiple             0           bnorm_42[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "add_13 (Add)                    multiple             0           add_12[0][0]                     \n",
      "                                                                 leaky_42[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "conv_44 (Conv2D)                multiple             131072      add_13[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "bnorm_44 (BatchNormalization)   multiple             1024        conv_44[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "leaky_44 (LeakyReLU)            multiple             0           bnorm_44[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "conv_45 (Conv2D)                multiple             1179648     leaky_44[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "bnorm_45 (BatchNormalization)   multiple             2048        conv_45[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "leaky_45 (LeakyReLU)            multiple             0           bnorm_45[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "add_14 (Add)                    multiple             0           add_13[0][0]                     \n",
      "                                                                 leaky_45[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "conv_47 (Conv2D)                multiple             131072      add_14[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "bnorm_47 (BatchNormalization)   multiple             1024        conv_47[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "leaky_47 (LeakyReLU)            multiple             0           bnorm_47[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "conv_48 (Conv2D)                multiple             1179648     leaky_47[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "bnorm_48 (BatchNormalization)   multiple             2048        conv_48[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "leaky_48 (LeakyReLU)            multiple             0           bnorm_48[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "add_15 (Add)                    multiple             0           add_14[0][0]                     \n",
      "                                                                 leaky_48[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "conv_50 (Conv2D)                multiple             131072      add_15[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "bnorm_50 (BatchNormalization)   multiple             1024        conv_50[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "leaky_50 (LeakyReLU)            multiple             0           bnorm_50[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "conv_51 (Conv2D)                multiple             1179648     leaky_50[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "bnorm_51 (BatchNormalization)   multiple             2048        conv_51[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "leaky_51 (LeakyReLU)            multiple             0           bnorm_51[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "add_16 (Add)                    multiple             0           add_15[0][0]                     \n",
      "                                                                 leaky_51[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "conv_53 (Conv2D)                multiple             131072      add_16[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "bnorm_53 (BatchNormalization)   multiple             1024        conv_53[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "leaky_53 (LeakyReLU)            multiple             0           bnorm_53[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "conv_54 (Conv2D)                multiple             1179648     leaky_53[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "bnorm_54 (BatchNormalization)   multiple             2048        conv_54[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "leaky_54 (LeakyReLU)            multiple             0           bnorm_54[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "add_17 (Add)                    multiple             0           add_16[0][0]                     \n",
      "                                                                 leaky_54[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "conv_56 (Conv2D)                multiple             131072      add_17[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "bnorm_56 (BatchNormalization)   multiple             1024        conv_56[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "leaky_56 (LeakyReLU)            multiple             0           bnorm_56[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "conv_57 (Conv2D)                multiple             1179648     leaky_56[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "bnorm_57 (BatchNormalization)   multiple             2048        conv_57[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "leaky_57 (LeakyReLU)            multiple             0           bnorm_57[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "add_18 (Add)                    multiple             0           add_17[0][0]                     \n",
      "                                                                 leaky_57[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "conv_59 (Conv2D)                multiple             131072      add_18[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "bnorm_59 (BatchNormalization)   multiple             1024        conv_59[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "leaky_59 (LeakyReLU)            multiple             0           bnorm_59[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "conv_60 (Conv2D)                multiple             1179648     leaky_59[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "bnorm_60 (BatchNormalization)   multiple             2048        conv_60[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "leaky_60 (LeakyReLU)            multiple             0           bnorm_60[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "add_19 (Add)                    multiple             0           add_18[0][0]                     \n",
      "                                                                 leaky_60[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "zero_padding2d_5 (ZeroPadding2D multiple             0           add_19[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv_62 (Conv2D)                multiple             4718592     zero_padding2d_5[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "bnorm_62 (BatchNormalization)   multiple             4096        conv_62[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "leaky_62 (LeakyReLU)            multiple             0           bnorm_62[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "conv_63 (Conv2D)                multiple             524288      leaky_62[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "bnorm_63 (BatchNormalization)   multiple             2048        conv_63[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "leaky_63 (LeakyReLU)            multiple             0           bnorm_63[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "conv_64 (Conv2D)                multiple             4718592     leaky_63[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "bnorm_64 (BatchNormalization)   multiple             4096        conv_64[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "leaky_64 (LeakyReLU)            multiple             0           bnorm_64[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "add_20 (Add)                    multiple             0           leaky_62[0][0]                   \n",
      "                                                                 leaky_64[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "conv_66 (Conv2D)                multiple             524288      add_20[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "bnorm_66 (BatchNormalization)   multiple             2048        conv_66[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "leaky_66 (LeakyReLU)            multiple             0           bnorm_66[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "conv_67 (Conv2D)                multiple             4718592     leaky_66[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "bnorm_67 (BatchNormalization)   multiple             4096        conv_67[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "leaky_67 (LeakyReLU)            multiple             0           bnorm_67[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "add_21 (Add)                    multiple             0           add_20[0][0]                     \n",
      "                                                                 leaky_67[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "conv_69 (Conv2D)                multiple             524288      add_21[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "bnorm_69 (BatchNormalization)   multiple             2048        conv_69[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "leaky_69 (LeakyReLU)            multiple             0           bnorm_69[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "conv_70 (Conv2D)                multiple             4718592     leaky_69[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "bnorm_70 (BatchNormalization)   multiple             4096        conv_70[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "leaky_70 (LeakyReLU)            multiple             0           bnorm_70[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "add_22 (Add)                    multiple             0           add_21[0][0]                     \n",
      "                                                                 leaky_70[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "conv_72 (Conv2D)                multiple             524288      add_22[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "bnorm_72 (BatchNormalization)   multiple             2048        conv_72[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "leaky_72 (LeakyReLU)            multiple             0           bnorm_72[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "conv_73 (Conv2D)                multiple             4718592     leaky_72[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "bnorm_73 (BatchNormalization)   multiple             4096        conv_73[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "leaky_73 (LeakyReLU)            multiple             0           bnorm_73[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "add_23 (Add)                    multiple             0           add_22[0][0]                     \n",
      "                                                                 leaky_73[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "conv_75 (Conv2D)                multiple             524288      add_23[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "bnorm_75 (BatchNormalization)   multiple             2048        conv_75[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "leaky_75 (LeakyReLU)            multiple             0           bnorm_75[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "conv_76 (Conv2D)                multiple             4718592     leaky_75[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "bnorm_76 (BatchNormalization)   multiple             4096        conv_76[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "leaky_76 (LeakyReLU)            multiple             0           bnorm_76[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "conv_77 (Conv2D)                multiple             524288      leaky_76[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "bnorm_77 (BatchNormalization)   multiple             2048        conv_77[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "leaky_77 (LeakyReLU)            multiple             0           bnorm_77[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "conv_78 (Conv2D)                multiple             4718592     leaky_77[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "bnorm_78 (BatchNormalization)   multiple             4096        conv_78[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "leaky_78 (LeakyReLU)            multiple             0           bnorm_78[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "conv_79 (Conv2D)                multiple             524288      leaky_78[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "bnorm_79 (BatchNormalization)   multiple             2048        conv_79[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "leaky_79 (LeakyReLU)            multiple             0           bnorm_79[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "conv_84 (Conv2D)                multiple             131072      leaky_79[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "bnorm_84 (BatchNormalization)   multiple             1024        conv_84[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "leaky_84 (LeakyReLU)            multiple             0           bnorm_84[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "up_sampling2d_1 (UpSampling2D)  multiple             0           leaky_84[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "concatenate_1 (Concatenate)     multiple             0           up_sampling2d_1[0][0]            \n",
      "                                                                 add_19[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv_87 (Conv2D)                multiple             196608      concatenate_1[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bnorm_87 (BatchNormalization)   multiple             1024        conv_87[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "leaky_87 (LeakyReLU)            multiple             0           bnorm_87[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "conv_88 (Conv2D)                multiple             1179648     leaky_87[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "bnorm_88 (BatchNormalization)   multiple             2048        conv_88[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "leaky_88 (LeakyReLU)            multiple             0           bnorm_88[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "conv_89 (Conv2D)                multiple             131072      leaky_88[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "bnorm_89 (BatchNormalization)   multiple             1024        conv_89[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "leaky_89 (LeakyReLU)            multiple             0           bnorm_89[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "conv_90 (Conv2D)                multiple             1179648     leaky_89[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "bnorm_90 (BatchNormalization)   multiple             2048        conv_90[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "leaky_90 (LeakyReLU)            multiple             0           bnorm_90[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "conv_91 (Conv2D)                multiple             131072      leaky_90[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "bnorm_91 (BatchNormalization)   multiple             1024        conv_91[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "leaky_91 (LeakyReLU)            multiple             0           bnorm_91[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "conv_96 (Conv2D)                multiple             32768       leaky_91[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "bnorm_96 (BatchNormalization)   multiple             512         conv_96[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "leaky_96 (LeakyReLU)            multiple             0           bnorm_96[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "up_sampling2d_2 (UpSampling2D)  multiple             0           leaky_96[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "concatenate_2 (Concatenate)     multiple             0           up_sampling2d_2[0][0]            \n",
      "                                                                 add_11[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv_99 (Conv2D)                multiple             49152       concatenate_2[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bnorm_99 (BatchNormalization)   multiple             512         conv_99[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "leaky_99 (LeakyReLU)            multiple             0           bnorm_99[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "conv_100 (Conv2D)               multiple             294912      leaky_99[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "bnorm_100 (BatchNormalization)  multiple             1024        conv_100[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "leaky_100 (LeakyReLU)           multiple             0           bnorm_100[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "conv_101 (Conv2D)               multiple             32768       leaky_100[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "bnorm_101 (BatchNormalization)  multiple             512         conv_101[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "leaky_101 (LeakyReLU)           multiple             0           bnorm_101[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "conv_102 (Conv2D)               multiple             294912      leaky_101[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "bnorm_102 (BatchNormalization)  multiple             1024        conv_102[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "leaky_102 (LeakyReLU)           multiple             0           bnorm_102[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "conv_103 (Conv2D)               multiple             32768       leaky_102[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "bnorm_103 (BatchNormalization)  multiple             512         conv_103[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "leaky_103 (LeakyReLU)           multiple             0           bnorm_103[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "conv_80 (Conv2D)                multiple             4718592     leaky_79[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "conv_92 (Conv2D)                multiple             1179648     leaky_91[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "conv_104 (Conv2D)               multiple             294912      leaky_103[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "bnorm_80 (BatchNormalization)   multiple             4096        conv_80[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "bnorm_92 (BatchNormalization)   multiple             2048        conv_92[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "bnorm_104 (BatchNormalization)  multiple             1024        conv_104[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "leaky_80 (LeakyReLU)            multiple             0           bnorm_80[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "leaky_92 (LeakyReLU)            multiple             0           bnorm_92[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "leaky_104 (LeakyReLU)           multiple             0           bnorm_104[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "conv_81 (Conv2D)                multiple             261375      leaky_80[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "conv_93 (Conv2D)                multiple             130815      leaky_92[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "conv_105 (Conv2D)               multiple             65535       leaky_104[0][0]                  \n",
      "==================================================================================================\n",
      "Total params: 62,001,757\n",
      "Trainable params: 61,949,149\n",
      "Non-trainable params: 52,608\n",
      "__________________________________________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "yolo_model.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "SwZpRPQRt6ug"
   },
   "source": [
    "Got inputs shapes: [(None, 7, 7, 255), (None, 14, 14, 255), (None, 28, 28, 255)]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "c8gLnFkrt6ug"
   },
   "outputs": [],
   "source": [
    "x0 = tf.keras.layers.UpSampling2D(size=(2, 2))(model.output[0])\n",
    "x0 = tf.keras.layers.UpSampling2D(size=(2, 2))(x0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "iZe-WWzit6uj"
   },
   "outputs": [],
   "source": [
    "x1 = tf.keras.layers.UpSampling2D(size=(2, 2))(model.output[1])\n",
    "x1 = tf.keras.layers.Concatenate(axis=1)([x0, x1])\n",
    "x1 = GlobalAveragePooling2D()(x1)\n",
    "x1 = Dense(588,  activation = 'relu')(x1)\n",
    "x1 = Dense(294, activation = 'relu')(x1)\n",
    "x1 = Dense(147, activation = 'relu')(x1)\n",
    "regr = Dense(16, activation = 'linear')(x1)\n",
    "model1 = Model(inputs = model.input, outputs = regr, name = 'regr_op')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "o-XdspS2t6um"
   },
   "source": [
    "[(None, 28, 28, 255), (None, 28, 28, 255), (None, 96)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "zHny6H3Vt6un"
   },
   "outputs": [],
   "source": [
    "import tensorflow"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "0lyfIKUet6up"
   },
   "outputs": [],
   "source": [
    "x = GlobalAveragePooling2D()(model.output[2])\n",
    "x = Dense(48,  activation = 'relu')(x)\n",
    "x = Dense(24, activation = 'relu')(x)\n",
    "clf = Dense(1, activation = 'sigmoid', name = 'clf')(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "Jy06SEdWt6ur"
   },
   "outputs": [],
   "source": [
    "modelCLF_REGR = Model(inputs = model1.input, outputs = [regr, clf])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "pXeR2ilMt6uv"
   },
   "outputs": [],
   "source": [
    "from tensorflow.keras.losses import BinaryCrossentropy\n",
    "from tensorflow.keras.metrics import BinaryAccuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "qdQp8vW6t6ux"
   },
   "outputs": [],
   "source": [
    "optimizer = 'adam'\n",
    "modelCLF_REGR.compile(optimizer = optimizer,\n",
    "                loss = {'dense_3' : MeanSquaredError(), 'clf' : BinaryCrossentropy()},  \n",
    "                metrics = {'dense_3' :MeanSquaredError(), 'clf' :BinaryAccuracy()})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "3EKiem6Rt6uz"
   },
   "outputs": [],
   "source": [
    "from tensorflow.keras.callbacks import ModelCheckpoint, EarlyStopping, ReduceLROnPlateau\n",
    "import datetime\n",
    "\n",
    "\n",
    "checkpoint = ModelCheckpoint(\"model-{loss:.2f}.h5\", monitor = \"val_loss\", save_best_only = True,\n",
    "                             save_weights_only = True, mode = \"auto\")\n",
    "\n",
    "\n",
    "stop = EarlyStopping(monitor = \"val_dense_3_mean_squared_error\", patience = 5, mode = \"min\", \n",
    "                     restore_best_weights = True)\n",
    "\n",
    "\n",
    "reduce_lr = ReduceLROnPlateau(monitor = \"val_dense_3_mean_squared_error\", factor = 0.2, patience = 2, \n",
    "                              min_lr = 1e-6, mode = \"auto\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 374
    },
    "colab_type": "code",
    "id": "90vjkkQMt6u2",
    "outputId": "3010dcec-a73d-4488-c234-cc8274e476df"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/100\n",
      "2405/2405 [==============================] - 190s 79ms/step - loss: 687.3893 - dense_3_loss: 687.3893 - clf_loss: 3.6261e-07 - dense_3_mean_squared_error: 687.4286 - clf_binary_accuracy: 1.0000 - val_loss: 725.4901 - val_dense_3_loss: 725.4901 - val_clf_loss: 8.5099e-15 - val_dense_3_mean_squared_error: 725.2105 - val_clf_binary_accuracy: 1.0000 - lr: 0.0010\n",
      "Epoch 2/100\n",
      "2405/2405 [==============================] - 185s 77ms/step - loss: 666.1327 - dense_3_loss: 666.1327 - clf_loss: 3.7918e-15 - dense_3_mean_squared_error: 666.0797 - clf_binary_accuracy: 1.0000 - val_loss: 668.9372 - val_dense_3_loss: 668.9372 - val_clf_loss: 4.1143e-15 - val_dense_3_mean_squared_error: 668.6957 - val_clf_binary_accuracy: 1.0000 - lr: 0.0010\n",
      "Epoch 3/100\n",
      "2405/2405 [==============================] - 184s 76ms/step - loss: 664.4577 - dense_3_loss: 664.4577 - clf_loss: 1.6436e-14 - dense_3_mean_squared_error: 664.3893 - clf_binary_accuracy: 1.0000 - val_loss: 705.7968 - val_dense_3_loss: 705.7968 - val_clf_loss: 2.1463e-14 - val_dense_3_mean_squared_error: 705.4922 - val_clf_binary_accuracy: 1.0000 - lr: 0.0010\n",
      "Epoch 4/100\n",
      "2405/2405 [==============================] - 186s 77ms/step - loss: 662.6936 - dense_3_loss: 662.6936 - clf_loss: 1.6225e-14 - dense_3_mean_squared_error: 662.7058 - clf_binary_accuracy: 1.0000 - val_loss: 662.7321 - val_dense_3_loss: 662.7321 - val_clf_loss: 2.1087e-14 - val_dense_3_mean_squared_error: 662.4015 - val_clf_binary_accuracy: 1.0000 - lr: 0.0010\n",
      "Epoch 5/100\n",
      "2405/2405 [==============================] - 184s 77ms/step - loss: 660.6407 - dense_3_loss: 660.6407 - clf_loss: 2.9360e-14 - dense_3_mean_squared_error: 660.6676 - clf_binary_accuracy: 1.0000 - val_loss: 702.1833 - val_dense_3_loss: 702.1833 - val_clf_loss: 7.1553e-13 - val_dense_3_mean_squared_error: 701.8027 - val_clf_binary_accuracy: 1.0000 - lr: 0.0010\n",
      "Epoch 6/100\n",
      "2405/2405 [==============================] - 184s 77ms/step - loss: 658.4894 - dense_3_loss: 658.4894 - clf_loss: 3.3783e-14 - dense_3_mean_squared_error: 658.4907 - clf_binary_accuracy: 1.0000 - val_loss: 663.1774 - val_dense_3_loss: 663.1774 - val_clf_loss: 1.3826e-14 - val_dense_3_mean_squared_error: 662.8790 - val_clf_binary_accuracy: 1.0000 - lr: 0.0010\n",
      "Epoch 7/100\n",
      "2405/2405 [==============================] - 183s 76ms/step - loss: 650.1186 - dense_3_loss: 650.1186 - clf_loss: 1.0411e-14 - dense_3_mean_squared_error: 650.2691 - clf_binary_accuracy: 1.0000 - val_loss: 859.4644 - val_dense_3_loss: 859.4644 - val_clf_loss: 1.2379e-14 - val_dense_3_mean_squared_error: 858.9280 - val_clf_binary_accuracy: 1.0000 - lr: 2.0000e-04\n",
      "Epoch 8/100\n",
      "2405/2405 [==============================] - 184s 76ms/step - loss: 649.4058 - dense_3_loss: 649.4058 - clf_loss: 1.4531e-14 - dense_3_mean_squared_error: 649.3763 - clf_binary_accuracy: 1.0000 - val_loss: 887.1245 - val_dense_3_loss: 887.1245 - val_clf_loss: 1.8110e-14 - val_dense_3_mean_squared_error: 886.5292 - val_clf_binary_accuracy: 1.0000 - lr: 2.0000e-04\n",
      "Epoch 9/100\n",
      "2405/2405 [==============================] - 184s 76ms/step - loss: 646.9044 - dense_3_loss: 646.9044 - clf_loss: 1.8518e-14 - dense_3_mean_squared_error: 646.9274 - clf_binary_accuracy: 1.0000 - val_loss: 806.9110 - val_dense_3_loss: 806.9110 - val_clf_loss: 1.9593e-14 - val_dense_3_mean_squared_error: 806.4429 - val_clf_binary_accuracy: 1.0000 - lr: 4.0000e-05\n"
     ]
    }
   ],
   "source": [
    "history = modelCLF_REGR.fit(X1, [preds, preds_class],\n",
    "                      validation_split = 0.2,\n",
    "                      batch_size = 2,\n",
    "                      epochs=100, \n",
    "                      callbacks=[checkpoint, reduce_lr, stop],\n",
    "                     )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 56
    },
    "colab_type": "code",
    "id": "eJnPgp3Ct6u5",
    "outputId": "12afbbb7-92e5-412c-e048-54bbd0f50bc6"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dict_keys(['loss', 'dense_3_loss', 'clf_loss', 'dense_3_mean_squared_error', 'clf_binary_accuracy', 'val_loss', 'val_dense_3_loss', 'val_clf_loss', 'val_dense_3_mean_squared_error', 'val_clf_binary_accuracy', 'lr'])\n"
     ]
    }
   ],
   "source": [
    "print(history.history.keys())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 295
    },
    "colab_type": "code",
    "id": "2QWpN72h3HyS",
    "outputId": "f70c1e3e-13cf-43f2-916e-c884cadb38bd"
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(history.history['dense_3_loss'])\n",
    "plt.plot(history.history['val_dense_3_loss'])\n",
    "plt.title('MSE by Epochs - BBox regression Loss')\n",
    "plt.ylabel('MSE')\n",
    "plt.xlabel('Epoch')\n",
    "plt.legend(['train', 'test'], loc='upper left')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 295
    },
    "colab_type": "code",
    "id": "PYLdUGLm3QVY",
    "outputId": "1c238b50-31fa-4454-93b3-919eab48d851"
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(history.history['clf_loss'])\n",
    "plt.plot(history.history['val_clf_loss'])\n",
    "plt.title('Binary Cross Entropy by Epochs')\n",
    "plt.ylabel('BinaryCrEntropy')\n",
    "plt.xlabel('Epoch')\n",
    "plt.legend(['train', 'test'], loc='upper left')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "4sUos0pxt6u7"
   },
   "outputs": [],
   "source": [
    "#mascar MCNN"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "iasvFnNEt6u9"
   },
   "source": [
    " fine tuning [deep lizard](https://www.youtube.com/watch?v=4Tcqw5oIfIg)\n",
    " yolo implemetation [mm](https://machinelearningmastery.com/how-to-perform-object-detection-with-yolov3-in-keras/)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "ltFqhMU5t6u-"
   },
   "source": [
    "<b>References</b>\n",
    "<a id='yolo_dest'></a>\n",
    "\n",
    ">**You Only Look Once: Unified, Real-Time Object Detection**\n",
    ">\n",
    ">Joseph Redmon, Santosh Divvala, Ross Girshick, Ali Farhadi.[[arXiv:1506.02640](https://arxiv.org/abs/1506.02640)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "8tld9NPWt6u_"
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "colab": {
   "name": "yolo implementation FRONT END.ipynb",
   "provenance": [],
   "toc_visible": true
  },
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}