TrainingFacenetRemontada (1).ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "8kc6LRT3n1zs"
   },
   "source": [
    "## RE-TRAINING THE FACENET MODEL USING BLACK-FACES"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "4kGn24yZZfd7"
   },
   "source": [
    "## The Arcface loss class"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from collections import defaultdict\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "#from sklearn.cross_validation import train_test_split\n",
    "#from sklearn.preprocessing import normalize\n",
    "from scipy.stats import logistic\n",
    "from os.path import join\n",
    "from tqdm import tqdm\n",
    "from PIL import Image\n",
    "from keras import backend as K\n",
    "from keras.models import Model\n",
    "from keras.optimizers import Adam\n",
    "from keras.layers import Input, Dense, Dropout, Lambda, Convolution2D, MaxPooling2D, Flatten\n",
    "from keras.losses import categorical_crossentropy\n",
    "from keras.callbacks import ModelCheckpoint, EarlyStopping"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "early = EarlyStopping(monitor=\"val_loss\", mode=\"min\", patience=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "id": "_7G46HMXZeuF"
   },
   "outputs": [],
   "source": [
    "\n",
    "from tensorflow import keras \n",
    "\n",
    "\n",
    "from keras import regularizers\n",
    "\n",
    "\n",
    "\n",
    "# Original paper: https://arxiv.org/pdf/1801.07698.pdf\n",
    "# Original implementation: https://github.com/deepinsight/insightface\n",
    "# Adapted from tensorflow implementation: https://github.com/luckycallor/InsightFace-tensorflow\n",
    "\n",
    "from keras import backend as K\n",
    "from keras.layers import Layer\n",
    "from keras.metrics import categorical_accuracy\n",
    "\n",
    "import tensorflow as tf\n",
    "import math as m\n",
    "\n",
    "\n",
    "class ArcFace(Layer):\n",
    "    '''Custom Keras layer implementing ArcFace including:\n",
    "    1. Generation of embeddings\n",
    "    2. Loss function\n",
    "    3. Accuracy function\n",
    "    '''\n",
    "\n",
    "    def __init__(self, output_dim, class_num, margin=0.5, scale=64., **kwargs):\n",
    "        self.output_dim = output_dim\n",
    "        self.class_num = class_num\n",
    "        self.margin = margin\n",
    "        self.s = scale\n",
    "\n",
    "        self.cos_m = tf.math.cos(margin)\n",
    "        self.sin_m = tf.math.sin(margin)\n",
    "        self.mm = self.sin_m * margin\n",
    "        self.threshold = tf.math.cos(tf.constant(m.pi) - margin)\n",
    "        super(ArcFace, self).__init__(**kwargs)\n",
    "\n",
    "\n",
    "    def build(self, input_shape):\n",
    "        # Create a trainable weight variable for this layer.\n",
    "        self.kernel = self.add_weight(name='kernel', \n",
    "                                      shape=(input_shape[1], self.class_num),\n",
    "                                      initializer='glorot_normal',\n",
    "                                      trainable=True)\n",
    "        super(ArcFace, self).build(input_shape)  # Be sure to call this at the end\n",
    "\n",
    "\n",
    "    def call(self, x):\n",
    "        embeddings = tf.nn.l2_normalize(x, axis=1, name='normed_embeddings')\n",
    "        weights = tf.nn.l2_normalize(self.kernel, axis=0, name='normed_weights')\n",
    "        cos_t = tf.matmul(embeddings, weights, name='cos_t')\n",
    "        return cos_t\n",
    "\n",
    "\n",
    "    def get_logits(self, labels, y_pred):\n",
    "        cos_t = y_pred\n",
    "        cos_t2 = tf.square(cos_t, name='cos_2')\n",
    "        sin_t2 = tf.subtract(1., cos_t2, name='sin_2')\n",
    "        sin_t = tf.sqrt(sin_t2, name='sin_t')\n",
    "        cos_mt = self.s * tf.subtract(tf.multiply(cos_t, self.cos_m), tf.multiply(sin_t, self.sin_m), name='cos_mt')\n",
    "        cond_v = cos_t - self.threshold\n",
    "        cond = tf.cast(tf.nn.relu(cond_v, name='if_else'), dtype=tf.bool)\n",
    "        keep_val = self.s*(cos_t - self.mm)\n",
    "        cos_mt_temp = tf.where(cond, cos_mt, keep_val)\n",
    "        mask = tf.one_hot(labels, depth=self.class_num, name='one_hot_mask')\n",
    "        inv_mask = tf.subtract(1., mask, name='inverse_mask')\n",
    "        s_cos_t = tf.multiply(self.s, cos_t, name='scalar_cos_t')\n",
    "        output = tf.add(tf.multiply(s_cos_t, inv_mask), tf.multiply(cos_mt_temp, mask), name='arcface_logits')\n",
    "        return output\n",
    "\n",
    "\n",
    "    def loss(self, y_true, y_pred):\n",
    "        labels = K.argmax(y_true, axis=-1)\n",
    "        logits = self.get_logits(labels, y_pred)\n",
    "        loss = tf.losses.sparse_softmax_cross_entropy(labels=labels, logits=logits)\n",
    "        return loss\n",
    "\n",
    "\n",
    "    def accuracy(self, y_true, y_pred):\n",
    "        labels = K.argmax(y_true, axis=-1)\n",
    "        logits = self.get_logits(labels, y_pred)\n",
    "        accuracy = categorical_accuracy(y_true=labels, y_pred=logits)\n",
    "        return accuracy\n",
    "    \n",
    "\n",
    "    def compute_output_shape(self, input_shape):\n",
    "        return (input_shape[0], self.output_dim)\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "uc5CotoFIHcg"
   },
   "source": [
    "### Alternative implementation of arcface"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "id": "cRqNl77CISg7"
   },
   "outputs": [],
   "source": [
    "from keras import backend as K\n",
    "from keras.layers import Layer\n",
    "from keras import regularizers\n",
    "\n",
    "import tensorflow as tf\n",
    "\n",
    "\n",
    "class ArcFace(Layer):\n",
    "    def __init__(self, n_classes=10, s=30.0, m=0.50, regularizer=None, **kwargs):\n",
    "        super(ArcFace, self).__init__(**kwargs)\n",
    "        self.n_classes = n_classes\n",
    "        self.s = s\n",
    "        self.m = m\n",
    "        self.regularizer = regularizers.get(regularizer)\n",
    "\n",
    "    def build(self, input_shape):\n",
    "        super(ArcFace, self).build(input_shape[0])\n",
    "        self.W = self.add_weight(name='W',\n",
    "                                shape=(input_shape[0][-1], self.n_classes),\n",
    "                                initializer='glorot_uniform',\n",
    "                                trainable=True,\n",
    "                                regularizer=self.regularizer)\n",
    "\n",
    "    def call(self, inputs):\n",
    "        x, y = inputs\n",
    "        c = K.shape(x)[-1]\n",
    "        # normalize feature\n",
    "        x = tf.nn.l2_normalize(x, axis=1)\n",
    "        # normalize weights\n",
    "        W = tf.nn.l2_normalize(self.W, axis=0)\n",
    "        # dot product\n",
    "        logits = x @ W\n",
    "        # add margin\n",
    "        # clip logits to prevent zero division when backward\n",
    "        theta = tf.acos(K.clip(logits, -1.0 + K.epsilon(), 1.0 - K.epsilon()))\n",
    "        target_logits = tf.cos(theta + self.m)\n",
    "        # sin = tf.sqrt(1 - logits**2)\n",
    "        # cos_m = tf.cos(logits)\n",
    "        # sin_m = tf.sin(logits)\n",
    "        # target_logits = logits * cos_m - sin * sin_m\n",
    "        #\n",
    "        logits = logits * (1 - y) + target_logits * y\n",
    "        # feature re-scale\n",
    "        logits *= self.s\n",
    "        out = tf.nn.softmax(logits)\n",
    "\n",
    "        return out\n",
    "\n",
    "    def compute_output_shape(self, input_shape):\n",
    "        return (None, self.n_classes)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ky5pDC8BoEko"
   },
   "source": [
    "#### Importing the necessary packages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "id": "wKKv6QdxOp63"
   },
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "from tensorflow import keras \n",
    "from keras.preprocessing import image \n",
    "from keras.preprocessing.image import ImageDataGenerator\n",
    "from keras.optimizers import RMSprop\n",
    "from keras.models import load_model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 876,
     "status": "ok",
     "timestamp": 1619467972408,
     "user": {
      "displayName": "blaise mavin",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GgUhbcpHWNXczan3ui3BGJe8CM_G7bUv9e9RG5m9A=s64",
      "userId": "14168894997160457371"
     },
     "user_tz": -180
    },
    "id": "wPpKDHgpzMWU",
    "outputId": "1616682f-d39c-40ba-9bea-853b388a4edd"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Name:  Apass count:  31\n",
      "Name:  Becky count:  39\n",
      "Name:  Canary count:  39\n",
      "Name:  Cindy count:  52\n",
      "Name:  Gamzi count:  30\n",
      "Name:  Kabuura count:  23\n",
      "Name:  Katatumba count:  62\n",
      "Name:  KenMugabi count:  59\n",
      "Name:  Lucky count:  57\n",
      "Name:  Nabata count:  48\n",
      "Name:  Raymond count:  21\n",
      "Name:  Renal count:  26\n",
      "Name:  Ruth count:  20\n",
      "Name:  Samson count:  68\n",
      "Name:  Vanny count:  38\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "from os import listdir\n",
    "\n",
    "path=\"C:/Users/Diana/Desktop/Extracted/Training/\"\n",
    "count=0\n",
    "for f in listdir(path):\n",
    "  count=0\n",
    "  for nome in listdir(path+f):\n",
    "    count+=1\n",
    "  print(\"Name: \",f,\"count: \",count)\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 1319,
     "status": "ok",
     "timestamp": 1619468019375,
     "user": {
      "displayName": "blaise mavin",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GgUhbcpHWNXczan3ui3BGJe8CM_G7bUv9e9RG5m9A=s64",
      "userId": "14168894997160457371"
     },
     "user_tz": -180
    },
    "id": "K4HiJlgE0JUU",
    "outputId": "7235bb91-919a-429b-ff31-ac72133faf0c"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Name:  Apass count:  9\n",
      "Name:  Becky count:  10\n",
      "Name:  Canary count:  17\n",
      "Name:  Cindy count:  17\n",
      "Name:  Gamzi count:  9\n",
      "Name:  Kabuura count:  8\n",
      "Name:  Katatumba count:  20\n",
      "Name:  KenMugabi count:  14\n",
      "Name:  Lucky count:  16\n",
      "Name:  Nabata count:  22\n",
      "Name:  Raymond count:  6\n",
      "Name:  Renal count:  8\n",
      "Name:  Ruth count:  9\n",
      "Name:  Samson count:  22\n",
      "Name:  Vanny count:  14\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "from os import listdir\n",
    "\n",
    "path=\"C:/Users/Diana/Desktop/Extracted/Validation/\"\n",
    "count=0\n",
    "for f in listdir(path):\n",
    "  count=0\n",
    "  for nome in listdir(path+f):\n",
    "    count+=1\n",
    "  print(\"Name: \",f,\"count: \",count)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Min14FmgoLZ7"
   },
   "source": [
    "#### Setting up the Image Data Generator API"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 954,
     "status": "ok",
     "timestamp": 1619472869982,
     "user": {
      "displayName": "blaise mavin",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GgUhbcpHWNXczan3ui3BGJe8CM_G7bUv9e9RG5m9A=s64",
      "userId": "14168894997160457371"
     },
     "user_tz": -180
    },
    "id": "_oP9GPv9eyP_",
    "outputId": "1b96c986-8820-42ac-a1a6-5a8868014413"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 613 images belonging to 15 classes.\n",
      "Found 201 images belonging to 15 classes.\n"
     ]
    }
   ],
   "source": [
    "#Import shutil first, this package deletes ipnb_checkpoints files that create a ghost class\n",
    "import shutil\n",
    "#The next step is to delete every ipynb_checkpoints file created by colab\n",
    "#shutil.rmtree(\"/tmp/training/.ipynb_checkpoints\") #be careful with shutil.rmtree() because it deletes every tree in that path. In other words, do not make mistakes.\n",
    "#shutil.rmtree(\"/tmp/testing/.ipynb_checkpoints\")\n",
    "\n",
    "#specify both the training and validation directories\n",
    "TRAINING_DIR=\"C:/Users/Diana/Desktop/Extracted/Training/\"\n",
    "VALIDATION_DIR=\"C:/Users/Diana/Desktop/Extracted/Validation/\"\n",
    "\n",
    "#Initialize Image Data Generator objects, and rescale the image \n",
    "training_datagen=ImageDataGenerator(rescale=1/255)\n",
    "validation_datagen=ImageDataGenerator(rescale=1/255)\n",
    "\n",
    "#Create the image generators that create the create the classes for all images uploaded\n",
    "training_generator=training_datagen.flow_from_directory(TRAINING_DIR,class_mode='categorical',target_size=(160,160))\n",
    "validation_generator=validation_datagen.flow_from_directory(VALIDATION_DIR,class_mode='categorical',target_size=(160,160))\n",
    "\n",
    "#Load the facenet model architecture\n",
    "#model=load_model('/tmp/facenet/facenet_keras.h5')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Xni0h7FSQ7oq"
   },
   "source": [
    "## Loading the facenet Model architecture"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 3577,
     "status": "ok",
     "timestamp": 1619472882494,
     "user": {
      "displayName": "blaise mavin",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GgUhbcpHWNXczan3ui3BGJe8CM_G7bUv9e9RG5m9A=s64",
      "userId": "14168894997160457371"
     },
     "user_tz": -180
    },
    "id": "IAs1PSgVLNP0",
    "outputId": "0a3393f5-a56d-4737-c9d4-9b2b6458a4d2"
   },
   "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",
      "Model: \"inception_resnet_v1\"\n",
      "__________________________________________________________________________________________________\n",
      "Layer (type)                    Output Shape         Param #     Connected to                     \n",
      "==================================================================================================\n",
      "input_1 (InputLayer)            [(None, 160, 160, 3) 0                                            \n",
      "__________________________________________________________________________________________________\n",
      "Conv2d_1a_3x3 (Conv2D)          (None, 79, 79, 32)   864         input_1[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "Conv2d_1a_3x3_BatchNorm (BatchN (None, 79, 79, 32)   96          Conv2d_1a_3x3[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "Conv2d_1a_3x3_Activation (Activ (None, 79, 79, 32)   0           Conv2d_1a_3x3_BatchNorm[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "Conv2d_2a_3x3 (Conv2D)          (None, 77, 77, 32)   9216        Conv2d_1a_3x3_Activation[0][0]   \n",
      "__________________________________________________________________________________________________\n",
      "Conv2d_2a_3x3_BatchNorm (BatchN (None, 77, 77, 32)   96          Conv2d_2a_3x3[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "Conv2d_2a_3x3_Activation (Activ (None, 77, 77, 32)   0           Conv2d_2a_3x3_BatchNorm[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "Conv2d_2b_3x3 (Conv2D)          (None, 77, 77, 64)   18432       Conv2d_2a_3x3_Activation[0][0]   \n",
      "__________________________________________________________________________________________________\n",
      "Conv2d_2b_3x3_BatchNorm (BatchN (None, 77, 77, 64)   192         Conv2d_2b_3x3[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "Conv2d_2b_3x3_Activation (Activ (None, 77, 77, 64)   0           Conv2d_2b_3x3_BatchNorm[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "MaxPool_3a_3x3 (MaxPooling2D)   (None, 38, 38, 64)   0           Conv2d_2b_3x3_Activation[0][0]   \n",
      "__________________________________________________________________________________________________\n",
      "Conv2d_3b_1x1 (Conv2D)          (None, 38, 38, 80)   5120        MaxPool_3a_3x3[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "Conv2d_3b_1x1_BatchNorm (BatchN (None, 38, 38, 80)   240         Conv2d_3b_1x1[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "Conv2d_3b_1x1_Activation (Activ (None, 38, 38, 80)   0           Conv2d_3b_1x1_BatchNorm[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "Conv2d_4a_3x3 (Conv2D)          (None, 36, 36, 192)  138240      Conv2d_3b_1x1_Activation[0][0]   \n",
      "__________________________________________________________________________________________________\n",
      "Conv2d_4a_3x3_BatchNorm (BatchN (None, 36, 36, 192)  576         Conv2d_4a_3x3[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "Conv2d_4a_3x3_Activation (Activ (None, 36, 36, 192)  0           Conv2d_4a_3x3_BatchNorm[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "Conv2d_4b_3x3 (Conv2D)          (None, 17, 17, 256)  442368      Conv2d_4a_3x3_Activation[0][0]   \n",
      "__________________________________________________________________________________________________\n",
      "Conv2d_4b_3x3_BatchNorm (BatchN (None, 17, 17, 256)  768         Conv2d_4b_3x3[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "Conv2d_4b_3x3_Activation (Activ (None, 17, 17, 256)  0           Conv2d_4b_3x3_BatchNorm[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "Block35_1_Branch_2_Conv2d_0a_1x (None, 17, 17, 32)   8192        Conv2d_4b_3x3_Activation[0][0]   \n",
      "__________________________________________________________________________________________________\n",
      "Block35_1_Branch_2_Conv2d_0a_1x (None, 17, 17, 32)   96          Block35_1_Branch_2_Conv2d_0a_1x1[\n",
      "__________________________________________________________________________________________________\n",
      "Block35_1_Branch_2_Conv2d_0a_1x (None, 17, 17, 32)   0           Block35_1_Branch_2_Conv2d_0a_1x1_\n",
      "__________________________________________________________________________________________________\n",
      "Block35_1_Branch_1_Conv2d_0a_1x (None, 17, 17, 32)   8192        Conv2d_4b_3x3_Activation[0][0]   \n",
      "__________________________________________________________________________________________________\n",
      "Block35_1_Branch_2_Conv2d_0b_3x (None, 17, 17, 32)   9216        Block35_1_Branch_2_Conv2d_0a_1x1_\n",
      "__________________________________________________________________________________________________\n",
      "Block35_1_Branch_1_Conv2d_0a_1x (None, 17, 17, 32)   96          Block35_1_Branch_1_Conv2d_0a_1x1[\n",
      "__________________________________________________________________________________________________\n",
      "Block35_1_Branch_2_Conv2d_0b_3x (None, 17, 17, 32)   96          Block35_1_Branch_2_Conv2d_0b_3x3[\n",
      "__________________________________________________________________________________________________\n",
      "Block35_1_Branch_1_Conv2d_0a_1x (None, 17, 17, 32)   0           Block35_1_Branch_1_Conv2d_0a_1x1_\n",
      "__________________________________________________________________________________________________\n",
      "Block35_1_Branch_2_Conv2d_0b_3x (None, 17, 17, 32)   0           Block35_1_Branch_2_Conv2d_0b_3x3_\n",
      "__________________________________________________________________________________________________\n",
      "Block35_1_Branch_0_Conv2d_1x1 ( (None, 17, 17, 32)   8192        Conv2d_4b_3x3_Activation[0][0]   \n",
      "__________________________________________________________________________________________________\n",
      "Block35_1_Branch_1_Conv2d_0b_3x (None, 17, 17, 32)   9216        Block35_1_Branch_1_Conv2d_0a_1x1_\n",
      "__________________________________________________________________________________________________\n",
      "Block35_1_Branch_2_Conv2d_0c_3x (None, 17, 17, 32)   9216        Block35_1_Branch_2_Conv2d_0b_3x3_\n",
      "__________________________________________________________________________________________________\n",
      "Block35_1_Branch_0_Conv2d_1x1_B (None, 17, 17, 32)   96          Block35_1_Branch_0_Conv2d_1x1[0][\n",
      "__________________________________________________________________________________________________\n",
      "Block35_1_Branch_1_Conv2d_0b_3x (None, 17, 17, 32)   96          Block35_1_Branch_1_Conv2d_0b_3x3[\n",
      "__________________________________________________________________________________________________\n",
      "Block35_1_Branch_2_Conv2d_0c_3x (None, 17, 17, 32)   96          Block35_1_Branch_2_Conv2d_0c_3x3[\n",
      "__________________________________________________________________________________________________\n",
      "Block35_1_Branch_0_Conv2d_1x1_A (None, 17, 17, 32)   0           Block35_1_Branch_0_Conv2d_1x1_Bat\n",
      "__________________________________________________________________________________________________\n",
      "Block35_1_Branch_1_Conv2d_0b_3x (None, 17, 17, 32)   0           Block35_1_Branch_1_Conv2d_0b_3x3_\n",
      "__________________________________________________________________________________________________\n",
      "Block35_1_Branch_2_Conv2d_0c_3x (None, 17, 17, 32)   0           Block35_1_Branch_2_Conv2d_0c_3x3_\n",
      "__________________________________________________________________________________________________\n",
      "Block35_1_Concatenate (Concaten (None, 17, 17, 96)   0           Block35_1_Branch_0_Conv2d_1x1_Act\n",
      "                                                                 Block35_1_Branch_1_Conv2d_0b_3x3_\n",
      "                                                                 Block35_1_Branch_2_Conv2d_0c_3x3_\n",
      "__________________________________________________________________________________________________\n",
      "Block35_1_Conv2d_1x1 (Conv2D)   (None, 17, 17, 256)  24832       Block35_1_Concatenate[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "Block35_1_ScaleSum (Lambda)     (None, 17, 17, 256)  0           Conv2d_4b_3x3_Activation[0][0]   \n",
      "                                                                 Block35_1_Conv2d_1x1[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "Block35_1_Activation (Activatio (None, 17, 17, 256)  0           Block35_1_ScaleSum[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "Block35_2_Branch_2_Conv2d_0a_1x (None, 17, 17, 32)   8192        Block35_1_Activation[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "Block35_2_Branch_2_Conv2d_0a_1x (None, 17, 17, 32)   96          Block35_2_Branch_2_Conv2d_0a_1x1[\n",
      "__________________________________________________________________________________________________\n",
      "Block35_2_Branch_2_Conv2d_0a_1x (None, 17, 17, 32)   0           Block35_2_Branch_2_Conv2d_0a_1x1_\n",
      "__________________________________________________________________________________________________\n",
      "Block35_2_Branch_1_Conv2d_0a_1x (None, 17, 17, 32)   8192        Block35_1_Activation[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "Block35_2_Branch_2_Conv2d_0b_3x (None, 17, 17, 32)   9216        Block35_2_Branch_2_Conv2d_0a_1x1_\n",
      "__________________________________________________________________________________________________\n",
      "Block35_2_Branch_1_Conv2d_0a_1x (None, 17, 17, 32)   96          Block35_2_Branch_1_Conv2d_0a_1x1[\n",
      "__________________________________________________________________________________________________\n",
      "Block35_2_Branch_2_Conv2d_0b_3x (None, 17, 17, 32)   96          Block35_2_Branch_2_Conv2d_0b_3x3[\n",
      "__________________________________________________________________________________________________\n",
      "Block35_2_Branch_1_Conv2d_0a_1x (None, 17, 17, 32)   0           Block35_2_Branch_1_Conv2d_0a_1x1_\n",
      "__________________________________________________________________________________________________\n",
      "Block35_2_Branch_2_Conv2d_0b_3x (None, 17, 17, 32)   0           Block35_2_Branch_2_Conv2d_0b_3x3_\n",
      "__________________________________________________________________________________________________\n",
      "Block35_2_Branch_0_Conv2d_1x1 ( (None, 17, 17, 32)   8192        Block35_1_Activation[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "Block35_2_Branch_1_Conv2d_0b_3x (None, 17, 17, 32)   9216        Block35_2_Branch_1_Conv2d_0a_1x1_\n",
      "__________________________________________________________________________________________________\n",
      "Block35_2_Branch_2_Conv2d_0c_3x (None, 17, 17, 32)   9216        Block35_2_Branch_2_Conv2d_0b_3x3_\n",
      "__________________________________________________________________________________________________\n",
      "Block35_2_Branch_0_Conv2d_1x1_B (None, 17, 17, 32)   96          Block35_2_Branch_0_Conv2d_1x1[0][\n",
      "__________________________________________________________________________________________________\n",
      "Block35_2_Branch_1_Conv2d_0b_3x (None, 17, 17, 32)   96          Block35_2_Branch_1_Conv2d_0b_3x3[\n",
      "__________________________________________________________________________________________________\n",
      "Block35_2_Branch_2_Conv2d_0c_3x (None, 17, 17, 32)   96          Block35_2_Branch_2_Conv2d_0c_3x3[\n",
      "__________________________________________________________________________________________________\n",
      "Block35_2_Branch_0_Conv2d_1x1_A (None, 17, 17, 32)   0           Block35_2_Branch_0_Conv2d_1x1_Bat\n",
      "__________________________________________________________________________________________________\n",
      "Block35_2_Branch_1_Conv2d_0b_3x (None, 17, 17, 32)   0           Block35_2_Branch_1_Conv2d_0b_3x3_\n",
      "__________________________________________________________________________________________________\n",
      "Block35_2_Branch_2_Conv2d_0c_3x (None, 17, 17, 32)   0           Block35_2_Branch_2_Conv2d_0c_3x3_\n",
      "__________________________________________________________________________________________________\n",
      "Block35_2_Concatenate (Concaten (None, 17, 17, 96)   0           Block35_2_Branch_0_Conv2d_1x1_Act\n",
      "                                                                 Block35_2_Branch_1_Conv2d_0b_3x3_\n",
      "                                                                 Block35_2_Branch_2_Conv2d_0c_3x3_\n",
      "__________________________________________________________________________________________________\n",
      "Block35_2_Conv2d_1x1 (Conv2D)   (None, 17, 17, 256)  24832       Block35_2_Concatenate[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "Block35_2_ScaleSum (Lambda)     (None, 17, 17, 256)  0           Block35_1_Activation[0][0]       \n",
      "                                                                 Block35_2_Conv2d_1x1[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "Block35_2_Activation (Activatio (None, 17, 17, 256)  0           Block35_2_ScaleSum[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "Block35_3_Branch_2_Conv2d_0a_1x (None, 17, 17, 32)   8192        Block35_2_Activation[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "Block35_3_Branch_2_Conv2d_0a_1x (None, 17, 17, 32)   96          Block35_3_Branch_2_Conv2d_0a_1x1[\n",
      "__________________________________________________________________________________________________\n",
      "Block35_3_Branch_2_Conv2d_0a_1x (None, 17, 17, 32)   0           Block35_3_Branch_2_Conv2d_0a_1x1_\n",
      "__________________________________________________________________________________________________\n",
      "Block35_3_Branch_1_Conv2d_0a_1x (None, 17, 17, 32)   8192        Block35_2_Activation[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "Block35_3_Branch_2_Conv2d_0b_3x (None, 17, 17, 32)   9216        Block35_3_Branch_2_Conv2d_0a_1x1_\n",
      "__________________________________________________________________________________________________\n",
      "Block35_3_Branch_1_Conv2d_0a_1x (None, 17, 17, 32)   96          Block35_3_Branch_1_Conv2d_0a_1x1[\n",
      "__________________________________________________________________________________________________\n",
      "Block35_3_Branch_2_Conv2d_0b_3x (None, 17, 17, 32)   96          Block35_3_Branch_2_Conv2d_0b_3x3[\n",
      "__________________________________________________________________________________________________\n",
      "Block35_3_Branch_1_Conv2d_0a_1x (None, 17, 17, 32)   0           Block35_3_Branch_1_Conv2d_0a_1x1_\n",
      "__________________________________________________________________________________________________\n",
      "Block35_3_Branch_2_Conv2d_0b_3x (None, 17, 17, 32)   0           Block35_3_Branch_2_Conv2d_0b_3x3_\n",
      "__________________________________________________________________________________________________\n",
      "Block35_3_Branch_0_Conv2d_1x1 ( (None, 17, 17, 32)   8192        Block35_2_Activation[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "Block35_3_Branch_1_Conv2d_0b_3x (None, 17, 17, 32)   9216        Block35_3_Branch_1_Conv2d_0a_1x1_\n",
      "__________________________________________________________________________________________________\n",
      "Block35_3_Branch_2_Conv2d_0c_3x (None, 17, 17, 32)   9216        Block35_3_Branch_2_Conv2d_0b_3x3_\n",
      "__________________________________________________________________________________________________\n",
      "Block35_3_Branch_0_Conv2d_1x1_B (None, 17, 17, 32)   96          Block35_3_Branch_0_Conv2d_1x1[0][\n",
      "__________________________________________________________________________________________________\n",
      "Block35_3_Branch_1_Conv2d_0b_3x (None, 17, 17, 32)   96          Block35_3_Branch_1_Conv2d_0b_3x3[\n",
      "__________________________________________________________________________________________________\n",
      "Block35_3_Branch_2_Conv2d_0c_3x (None, 17, 17, 32)   96          Block35_3_Branch_2_Conv2d_0c_3x3[\n",
      "__________________________________________________________________________________________________\n",
      "Block35_3_Branch_0_Conv2d_1x1_A (None, 17, 17, 32)   0           Block35_3_Branch_0_Conv2d_1x1_Bat\n",
      "__________________________________________________________________________________________________\n",
      "Block35_3_Branch_1_Conv2d_0b_3x (None, 17, 17, 32)   0           Block35_3_Branch_1_Conv2d_0b_3x3_\n",
      "__________________________________________________________________________________________________\n",
      "Block35_3_Branch_2_Conv2d_0c_3x (None, 17, 17, 32)   0           Block35_3_Branch_2_Conv2d_0c_3x3_\n",
      "__________________________________________________________________________________________________\n",
      "Block35_3_Concatenate (Concaten (None, 17, 17, 96)   0           Block35_3_Branch_0_Conv2d_1x1_Act\n",
      "                                                                 Block35_3_Branch_1_Conv2d_0b_3x3_\n",
      "                                                                 Block35_3_Branch_2_Conv2d_0c_3x3_\n",
      "__________________________________________________________________________________________________\n",
      "Block35_3_Conv2d_1x1 (Conv2D)   (None, 17, 17, 256)  24832       Block35_3_Concatenate[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "Block35_3_ScaleSum (Lambda)     (None, 17, 17, 256)  0           Block35_2_Activation[0][0]       \n",
      "                                                                 Block35_3_Conv2d_1x1[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "Block35_3_Activation (Activatio (None, 17, 17, 256)  0           Block35_3_ScaleSum[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "Block35_4_Branch_2_Conv2d_0a_1x (None, 17, 17, 32)   8192        Block35_3_Activation[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "Block35_4_Branch_2_Conv2d_0a_1x (None, 17, 17, 32)   96          Block35_4_Branch_2_Conv2d_0a_1x1[\n",
      "__________________________________________________________________________________________________\n",
      "Block35_4_Branch_2_Conv2d_0a_1x (None, 17, 17, 32)   0           Block35_4_Branch_2_Conv2d_0a_1x1_\n",
      "__________________________________________________________________________________________________\n",
      "Block35_4_Branch_1_Conv2d_0a_1x (None, 17, 17, 32)   8192        Block35_3_Activation[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "Block35_4_Branch_2_Conv2d_0b_3x (None, 17, 17, 32)   9216        Block35_4_Branch_2_Conv2d_0a_1x1_\n",
      "__________________________________________________________________________________________________\n",
      "Block35_4_Branch_1_Conv2d_0a_1x (None, 17, 17, 32)   96          Block35_4_Branch_1_Conv2d_0a_1x1[\n",
      "__________________________________________________________________________________________________\n",
      "Block35_4_Branch_2_Conv2d_0b_3x (None, 17, 17, 32)   96          Block35_4_Branch_2_Conv2d_0b_3x3[\n",
      "__________________________________________________________________________________________________\n",
      "Block35_4_Branch_1_Conv2d_0a_1x (None, 17, 17, 32)   0           Block35_4_Branch_1_Conv2d_0a_1x1_\n",
      "__________________________________________________________________________________________________\n",
      "Block35_4_Branch_2_Conv2d_0b_3x (None, 17, 17, 32)   0           Block35_4_Branch_2_Conv2d_0b_3x3_\n",
      "__________________________________________________________________________________________________\n",
      "Block35_4_Branch_0_Conv2d_1x1 ( (None, 17, 17, 32)   8192        Block35_3_Activation[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "Block35_4_Branch_1_Conv2d_0b_3x (None, 17, 17, 32)   9216        Block35_4_Branch_1_Conv2d_0a_1x1_\n",
      "__________________________________________________________________________________________________\n",
      "Block35_4_Branch_2_Conv2d_0c_3x (None, 17, 17, 32)   9216        Block35_4_Branch_2_Conv2d_0b_3x3_\n",
      "__________________________________________________________________________________________________\n",
      "Block35_4_Branch_0_Conv2d_1x1_B (None, 17, 17, 32)   96          Block35_4_Branch_0_Conv2d_1x1[0][\n",
      "__________________________________________________________________________________________________\n",
      "Block35_4_Branch_1_Conv2d_0b_3x (None, 17, 17, 32)   96          Block35_4_Branch_1_Conv2d_0b_3x3[\n",
      "__________________________________________________________________________________________________\n",
      "Block35_4_Branch_2_Conv2d_0c_3x (None, 17, 17, 32)   96          Block35_4_Branch_2_Conv2d_0c_3x3[\n",
      "__________________________________________________________________________________________________\n",
      "Block35_4_Branch_0_Conv2d_1x1_A (None, 17, 17, 32)   0           Block35_4_Branch_0_Conv2d_1x1_Bat\n",
      "__________________________________________________________________________________________________\n",
      "Block35_4_Branch_1_Conv2d_0b_3x (None, 17, 17, 32)   0           Block35_4_Branch_1_Conv2d_0b_3x3_\n",
      "__________________________________________________________________________________________________\n",
      "Block35_4_Branch_2_Conv2d_0c_3x (None, 17, 17, 32)   0           Block35_4_Branch_2_Conv2d_0c_3x3_\n",
      "__________________________________________________________________________________________________\n",
      "Block35_4_Concatenate (Concaten (None, 17, 17, 96)   0           Block35_4_Branch_0_Conv2d_1x1_Act\n",
      "                                                                 Block35_4_Branch_1_Conv2d_0b_3x3_\n",
      "                                                                 Block35_4_Branch_2_Conv2d_0c_3x3_\n",
      "__________________________________________________________________________________________________\n",
      "Block35_4_Conv2d_1x1 (Conv2D)   (None, 17, 17, 256)  24832       Block35_4_Concatenate[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "Block35_4_ScaleSum (Lambda)     (None, 17, 17, 256)  0           Block35_3_Activation[0][0]       \n",
      "                                                                 Block35_4_Conv2d_1x1[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "Block35_4_Activation (Activatio (None, 17, 17, 256)  0           Block35_4_ScaleSum[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "Block35_5_Branch_2_Conv2d_0a_1x (None, 17, 17, 32)   8192        Block35_4_Activation[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "Block35_5_Branch_2_Conv2d_0a_1x (None, 17, 17, 32)   96          Block35_5_Branch_2_Conv2d_0a_1x1[\n",
      "__________________________________________________________________________________________________\n",
      "Block35_5_Branch_2_Conv2d_0a_1x (None, 17, 17, 32)   0           Block35_5_Branch_2_Conv2d_0a_1x1_\n",
      "__________________________________________________________________________________________________\n",
      "Block35_5_Branch_1_Conv2d_0a_1x (None, 17, 17, 32)   8192        Block35_4_Activation[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "Block35_5_Branch_2_Conv2d_0b_3x (None, 17, 17, 32)   9216        Block35_5_Branch_2_Conv2d_0a_1x1_\n",
      "__________________________________________________________________________________________________\n",
      "Block35_5_Branch_1_Conv2d_0a_1x (None, 17, 17, 32)   96          Block35_5_Branch_1_Conv2d_0a_1x1[\n",
      "__________________________________________________________________________________________________\n",
      "Block35_5_Branch_2_Conv2d_0b_3x (None, 17, 17, 32)   96          Block35_5_Branch_2_Conv2d_0b_3x3[\n",
      "__________________________________________________________________________________________________\n",
      "Block35_5_Branch_1_Conv2d_0a_1x (None, 17, 17, 32)   0           Block35_5_Branch_1_Conv2d_0a_1x1_\n",
      "__________________________________________________________________________________________________\n",
      "Block35_5_Branch_2_Conv2d_0b_3x (None, 17, 17, 32)   0           Block35_5_Branch_2_Conv2d_0b_3x3_\n",
      "__________________________________________________________________________________________________\n",
      "Block35_5_Branch_0_Conv2d_1x1 ( (None, 17, 17, 32)   8192        Block35_4_Activation[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "Block35_5_Branch_1_Conv2d_0b_3x (None, 17, 17, 32)   9216        Block35_5_Branch_1_Conv2d_0a_1x1_\n",
      "__________________________________________________________________________________________________\n",
      "Block35_5_Branch_2_Conv2d_0c_3x (None, 17, 17, 32)   9216        Block35_5_Branch_2_Conv2d_0b_3x3_\n",
      "__________________________________________________________________________________________________\n",
      "Block35_5_Branch_0_Conv2d_1x1_B (None, 17, 17, 32)   96          Block35_5_Branch_0_Conv2d_1x1[0][\n",
      "__________________________________________________________________________________________________\n",
      "Block35_5_Branch_1_Conv2d_0b_3x (None, 17, 17, 32)   96          Block35_5_Branch_1_Conv2d_0b_3x3[\n",
      "__________________________________________________________________________________________________\n",
      "Block35_5_Branch_2_Conv2d_0c_3x (None, 17, 17, 32)   96          Block35_5_Branch_2_Conv2d_0c_3x3[\n",
      "__________________________________________________________________________________________________\n",
      "Block35_5_Branch_0_Conv2d_1x1_A (None, 17, 17, 32)   0           Block35_5_Branch_0_Conv2d_1x1_Bat\n",
      "__________________________________________________________________________________________________\n",
      "Block35_5_Branch_1_Conv2d_0b_3x (None, 17, 17, 32)   0           Block35_5_Branch_1_Conv2d_0b_3x3_\n",
      "__________________________________________________________________________________________________\n",
      "Block35_5_Branch_2_Conv2d_0c_3x (None, 17, 17, 32)   0           Block35_5_Branch_2_Conv2d_0c_3x3_\n",
      "__________________________________________________________________________________________________\n",
      "Block35_5_Concatenate (Concaten (None, 17, 17, 96)   0           Block35_5_Branch_0_Conv2d_1x1_Act\n",
      "                                                                 Block35_5_Branch_1_Conv2d_0b_3x3_\n",
      "                                                                 Block35_5_Branch_2_Conv2d_0c_3x3_\n",
      "__________________________________________________________________________________________________\n",
      "Block35_5_Conv2d_1x1 (Conv2D)   (None, 17, 17, 256)  24832       Block35_5_Concatenate[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "Block35_5_ScaleSum (Lambda)     (None, 17, 17, 256)  0           Block35_4_Activation[0][0]       \n",
      "                                                                 Block35_5_Conv2d_1x1[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "Block35_5_Activation (Activatio (None, 17, 17, 256)  0           Block35_5_ScaleSum[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "Mixed_6a_Branch_1_Conv2d_0a_1x1 (None, 17, 17, 192)  49152       Block35_5_Activation[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "Mixed_6a_Branch_1_Conv2d_0a_1x1 (None, 17, 17, 192)  576         Mixed_6a_Branch_1_Conv2d_0a_1x1[0\n",
      "__________________________________________________________________________________________________\n",
      "Mixed_6a_Branch_1_Conv2d_0a_1x1 (None, 17, 17, 192)  0           Mixed_6a_Branch_1_Conv2d_0a_1x1_B\n",
      "__________________________________________________________________________________________________\n",
      "Mixed_6a_Branch_1_Conv2d_0b_3x3 (None, 17, 17, 192)  331776      Mixed_6a_Branch_1_Conv2d_0a_1x1_A\n",
      "__________________________________________________________________________________________________\n",
      "Mixed_6a_Branch_1_Conv2d_0b_3x3 (None, 17, 17, 192)  576         Mixed_6a_Branch_1_Conv2d_0b_3x3[0\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "__________________________________________________________________________________________________\n",
      "Mixed_6a_Branch_1_Conv2d_0b_3x3 (None, 17, 17, 192)  0           Mixed_6a_Branch_1_Conv2d_0b_3x3_B\n",
      "__________________________________________________________________________________________________\n",
      "Mixed_6a_Branch_0_Conv2d_1a_3x3 (None, 8, 8, 384)    884736      Block35_5_Activation[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "Mixed_6a_Branch_1_Conv2d_1a_3x3 (None, 8, 8, 256)    442368      Mixed_6a_Branch_1_Conv2d_0b_3x3_A\n",
      "__________________________________________________________________________________________________\n",
      "Mixed_6a_Branch_0_Conv2d_1a_3x3 (None, 8, 8, 384)    1152        Mixed_6a_Branch_0_Conv2d_1a_3x3[0\n",
      "__________________________________________________________________________________________________\n",
      "Mixed_6a_Branch_1_Conv2d_1a_3x3 (None, 8, 8, 256)    768         Mixed_6a_Branch_1_Conv2d_1a_3x3[0\n",
      "__________________________________________________________________________________________________\n",
      "Mixed_6a_Branch_0_Conv2d_1a_3x3 (None, 8, 8, 384)    0           Mixed_6a_Branch_0_Conv2d_1a_3x3_B\n",
      "__________________________________________________________________________________________________\n",
      "Mixed_6a_Branch_1_Conv2d_1a_3x3 (None, 8, 8, 256)    0           Mixed_6a_Branch_1_Conv2d_1a_3x3_B\n",
      "__________________________________________________________________________________________________\n",
      "Mixed_6a_Branch_2_MaxPool_1a_3x (None, 8, 8, 256)    0           Block35_5_Activation[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "Mixed_6a (Concatenate)          (None, 8, 8, 896)    0           Mixed_6a_Branch_0_Conv2d_1a_3x3_A\n",
      "                                                                 Mixed_6a_Branch_1_Conv2d_1a_3x3_A\n",
      "                                                                 Mixed_6a_Branch_2_MaxPool_1a_3x3[\n",
      "__________________________________________________________________________________________________\n",
      "Block17_1_Branch_1_Conv2d_0a_1x (None, 8, 8, 128)    114688      Mixed_6a[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "Block17_1_Branch_1_Conv2d_0a_1x (None, 8, 8, 128)    384         Block17_1_Branch_1_Conv2d_0a_1x1[\n",
      "__________________________________________________________________________________________________\n",
      "Block17_1_Branch_1_Conv2d_0a_1x (None, 8, 8, 128)    0           Block17_1_Branch_1_Conv2d_0a_1x1_\n",
      "__________________________________________________________________________________________________\n",
      "Block17_1_Branch_1_Conv2d_0b_1x (None, 8, 8, 128)    114688      Block17_1_Branch_1_Conv2d_0a_1x1_\n",
      "__________________________________________________________________________________________________\n",
      "Block17_1_Branch_1_Conv2d_0b_1x (None, 8, 8, 128)    384         Block17_1_Branch_1_Conv2d_0b_1x7[\n",
      "__________________________________________________________________________________________________\n",
      "Block17_1_Branch_1_Conv2d_0b_1x (None, 8, 8, 128)    0           Block17_1_Branch_1_Conv2d_0b_1x7_\n",
      "__________________________________________________________________________________________________\n",
      "Block17_1_Branch_0_Conv2d_1x1 ( (None, 8, 8, 128)    114688      Mixed_6a[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "Block17_1_Branch_1_Conv2d_0c_7x (None, 8, 8, 128)    114688      Block17_1_Branch_1_Conv2d_0b_1x7_\n",
      "__________________________________________________________________________________________________\n",
      "Block17_1_Branch_0_Conv2d_1x1_B (None, 8, 8, 128)    384         Block17_1_Branch_0_Conv2d_1x1[0][\n",
      "__________________________________________________________________________________________________\n",
      "Block17_1_Branch_1_Conv2d_0c_7x (None, 8, 8, 128)    384         Block17_1_Branch_1_Conv2d_0c_7x1[\n",
      "__________________________________________________________________________________________________\n",
      "Block17_1_Branch_0_Conv2d_1x1_A (None, 8, 8, 128)    0           Block17_1_Branch_0_Conv2d_1x1_Bat\n",
      "__________________________________________________________________________________________________\n",
      "Block17_1_Branch_1_Conv2d_0c_7x (None, 8, 8, 128)    0           Block17_1_Branch_1_Conv2d_0c_7x1_\n",
      "__________________________________________________________________________________________________\n",
      "Block17_1_Concatenate (Concaten (None, 8, 8, 256)    0           Block17_1_Branch_0_Conv2d_1x1_Act\n",
      "                                                                 Block17_1_Branch_1_Conv2d_0c_7x1_\n",
      "__________________________________________________________________________________________________\n",
      "Block17_1_Conv2d_1x1 (Conv2D)   (None, 8, 8, 896)    230272      Block17_1_Concatenate[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "Block17_1_ScaleSum (Lambda)     (None, 8, 8, 896)    0           Mixed_6a[0][0]                   \n",
      "                                                                 Block17_1_Conv2d_1x1[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "Block17_1_Activation (Activatio (None, 8, 8, 896)    0           Block17_1_ScaleSum[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "Block17_2_Branch_1_Conv2d_0a_1x (None, 8, 8, 128)    114688      Block17_1_Activation[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "Block17_2_Branch_1_Conv2d_0a_1x (None, 8, 8, 128)    384         Block17_2_Branch_1_Conv2d_0a_1x1[\n",
      "__________________________________________________________________________________________________\n",
      "Block17_2_Branch_1_Conv2d_0a_1x (None, 8, 8, 128)    0           Block17_2_Branch_1_Conv2d_0a_1x1_\n",
      "__________________________________________________________________________________________________\n",
      "Block17_2_Branch_1_Conv2d_0b_1x (None, 8, 8, 128)    114688      Block17_2_Branch_1_Conv2d_0a_1x1_\n",
      "__________________________________________________________________________________________________\n",
      "Block17_2_Branch_1_Conv2d_0b_1x (None, 8, 8, 128)    384         Block17_2_Branch_1_Conv2d_0b_1x7[\n",
      "__________________________________________________________________________________________________\n",
      "Block17_2_Branch_1_Conv2d_0b_1x (None, 8, 8, 128)    0           Block17_2_Branch_1_Conv2d_0b_1x7_\n",
      "__________________________________________________________________________________________________\n",
      "Block17_2_Branch_0_Conv2d_1x1 ( (None, 8, 8, 128)    114688      Block17_1_Activation[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "Block17_2_Branch_1_Conv2d_0c_7x (None, 8, 8, 128)    114688      Block17_2_Branch_1_Conv2d_0b_1x7_\n",
      "__________________________________________________________________________________________________\n",
      "Block17_2_Branch_0_Conv2d_1x1_B (None, 8, 8, 128)    384         Block17_2_Branch_0_Conv2d_1x1[0][\n",
      "__________________________________________________________________________________________________\n",
      "Block17_2_Branch_1_Conv2d_0c_7x (None, 8, 8, 128)    384         Block17_2_Branch_1_Conv2d_0c_7x1[\n",
      "__________________________________________________________________________________________________\n",
      "Block17_2_Branch_0_Conv2d_1x1_A (None, 8, 8, 128)    0           Block17_2_Branch_0_Conv2d_1x1_Bat\n",
      "__________________________________________________________________________________________________\n",
      "Block17_2_Branch_1_Conv2d_0c_7x (None, 8, 8, 128)    0           Block17_2_Branch_1_Conv2d_0c_7x1_\n",
      "__________________________________________________________________________________________________\n",
      "Block17_2_Concatenate (Concaten (None, 8, 8, 256)    0           Block17_2_Branch_0_Conv2d_1x1_Act\n",
      "                                                                 Block17_2_Branch_1_Conv2d_0c_7x1_\n",
      "__________________________________________________________________________________________________\n",
      "Block17_2_Conv2d_1x1 (Conv2D)   (None, 8, 8, 896)    230272      Block17_2_Concatenate[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "Block17_2_ScaleSum (Lambda)     (None, 8, 8, 896)    0           Block17_1_Activation[0][0]       \n",
      "                                                                 Block17_2_Conv2d_1x1[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "Block17_2_Activation (Activatio (None, 8, 8, 896)    0           Block17_2_ScaleSum[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "Block17_3_Branch_1_Conv2d_0a_1x (None, 8, 8, 128)    114688      Block17_2_Activation[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "Block17_3_Branch_1_Conv2d_0a_1x (None, 8, 8, 128)    384         Block17_3_Branch_1_Conv2d_0a_1x1[\n",
      "__________________________________________________________________________________________________\n",
      "Block17_3_Branch_1_Conv2d_0a_1x (None, 8, 8, 128)    0           Block17_3_Branch_1_Conv2d_0a_1x1_\n",
      "__________________________________________________________________________________________________\n",
      "Block17_3_Branch_1_Conv2d_0b_1x (None, 8, 8, 128)    114688      Block17_3_Branch_1_Conv2d_0a_1x1_\n",
      "__________________________________________________________________________________________________\n",
      "Block17_3_Branch_1_Conv2d_0b_1x (None, 8, 8, 128)    384         Block17_3_Branch_1_Conv2d_0b_1x7[\n",
      "__________________________________________________________________________________________________\n",
      "Block17_3_Branch_1_Conv2d_0b_1x (None, 8, 8, 128)    0           Block17_3_Branch_1_Conv2d_0b_1x7_\n",
      "__________________________________________________________________________________________________\n",
      "Block17_3_Branch_0_Conv2d_1x1 ( (None, 8, 8, 128)    114688      Block17_2_Activation[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "Block17_3_Branch_1_Conv2d_0c_7x (None, 8, 8, 128)    114688      Block17_3_Branch_1_Conv2d_0b_1x7_\n",
      "__________________________________________________________________________________________________\n",
      "Block17_3_Branch_0_Conv2d_1x1_B (None, 8, 8, 128)    384         Block17_3_Branch_0_Conv2d_1x1[0][\n",
      "__________________________________________________________________________________________________\n",
      "Block17_3_Branch_1_Conv2d_0c_7x (None, 8, 8, 128)    384         Block17_3_Branch_1_Conv2d_0c_7x1[\n",
      "__________________________________________________________________________________________________\n",
      "Block17_3_Branch_0_Conv2d_1x1_A (None, 8, 8, 128)    0           Block17_3_Branch_0_Conv2d_1x1_Bat\n",
      "__________________________________________________________________________________________________\n",
      "Block17_3_Branch_1_Conv2d_0c_7x (None, 8, 8, 128)    0           Block17_3_Branch_1_Conv2d_0c_7x1_\n",
      "__________________________________________________________________________________________________\n",
      "Block17_3_Concatenate (Concaten (None, 8, 8, 256)    0           Block17_3_Branch_0_Conv2d_1x1_Act\n",
      "                                                                 Block17_3_Branch_1_Conv2d_0c_7x1_\n",
      "__________________________________________________________________________________________________\n",
      "Block17_3_Conv2d_1x1 (Conv2D)   (None, 8, 8, 896)    230272      Block17_3_Concatenate[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "Block17_3_ScaleSum (Lambda)     (None, 8, 8, 896)    0           Block17_2_Activation[0][0]       \n",
      "                                                                 Block17_3_Conv2d_1x1[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "Block17_3_Activation (Activatio (None, 8, 8, 896)    0           Block17_3_ScaleSum[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "Block17_4_Branch_1_Conv2d_0a_1x (None, 8, 8, 128)    114688      Block17_3_Activation[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "Block17_4_Branch_1_Conv2d_0a_1x (None, 8, 8, 128)    384         Block17_4_Branch_1_Conv2d_0a_1x1[\n",
      "__________________________________________________________________________________________________\n",
      "Block17_4_Branch_1_Conv2d_0a_1x (None, 8, 8, 128)    0           Block17_4_Branch_1_Conv2d_0a_1x1_\n",
      "__________________________________________________________________________________________________\n",
      "Block17_4_Branch_1_Conv2d_0b_1x (None, 8, 8, 128)    114688      Block17_4_Branch_1_Conv2d_0a_1x1_\n",
      "__________________________________________________________________________________________________\n",
      "Block17_4_Branch_1_Conv2d_0b_1x (None, 8, 8, 128)    384         Block17_4_Branch_1_Conv2d_0b_1x7[\n",
      "__________________________________________________________________________________________________\n",
      "Block17_4_Branch_1_Conv2d_0b_1x (None, 8, 8, 128)    0           Block17_4_Branch_1_Conv2d_0b_1x7_\n",
      "__________________________________________________________________________________________________\n",
      "Block17_4_Branch_0_Conv2d_1x1 ( (None, 8, 8, 128)    114688      Block17_3_Activation[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "Block17_4_Branch_1_Conv2d_0c_7x (None, 8, 8, 128)    114688      Block17_4_Branch_1_Conv2d_0b_1x7_\n",
      "__________________________________________________________________________________________________\n",
      "Block17_4_Branch_0_Conv2d_1x1_B (None, 8, 8, 128)    384         Block17_4_Branch_0_Conv2d_1x1[0][\n",
      "__________________________________________________________________________________________________\n",
      "Block17_4_Branch_1_Conv2d_0c_7x (None, 8, 8, 128)    384         Block17_4_Branch_1_Conv2d_0c_7x1[\n",
      "__________________________________________________________________________________________________\n",
      "Block17_4_Branch_0_Conv2d_1x1_A (None, 8, 8, 128)    0           Block17_4_Branch_0_Conv2d_1x1_Bat\n",
      "__________________________________________________________________________________________________\n",
      "Block17_4_Branch_1_Conv2d_0c_7x (None, 8, 8, 128)    0           Block17_4_Branch_1_Conv2d_0c_7x1_\n",
      "__________________________________________________________________________________________________\n",
      "Block17_4_Concatenate (Concaten (None, 8, 8, 256)    0           Block17_4_Branch_0_Conv2d_1x1_Act\n",
      "                                                                 Block17_4_Branch_1_Conv2d_0c_7x1_\n",
      "__________________________________________________________________________________________________\n",
      "Block17_4_Conv2d_1x1 (Conv2D)   (None, 8, 8, 896)    230272      Block17_4_Concatenate[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "Block17_4_ScaleSum (Lambda)     (None, 8, 8, 896)    0           Block17_3_Activation[0][0]       \n",
      "                                                                 Block17_4_Conv2d_1x1[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "Block17_4_Activation (Activatio (None, 8, 8, 896)    0           Block17_4_ScaleSum[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "Block17_5_Branch_1_Conv2d_0a_1x (None, 8, 8, 128)    114688      Block17_4_Activation[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "Block17_5_Branch_1_Conv2d_0a_1x (None, 8, 8, 128)    384         Block17_5_Branch_1_Conv2d_0a_1x1[\n",
      "__________________________________________________________________________________________________\n",
      "Block17_5_Branch_1_Conv2d_0a_1x (None, 8, 8, 128)    0           Block17_5_Branch_1_Conv2d_0a_1x1_\n",
      "__________________________________________________________________________________________________\n",
      "Block17_5_Branch_1_Conv2d_0b_1x (None, 8, 8, 128)    114688      Block17_5_Branch_1_Conv2d_0a_1x1_\n",
      "__________________________________________________________________________________________________\n",
      "Block17_5_Branch_1_Conv2d_0b_1x (None, 8, 8, 128)    384         Block17_5_Branch_1_Conv2d_0b_1x7[\n",
      "__________________________________________________________________________________________________\n",
      "Block17_5_Branch_1_Conv2d_0b_1x (None, 8, 8, 128)    0           Block17_5_Branch_1_Conv2d_0b_1x7_\n",
      "__________________________________________________________________________________________________\n",
      "Block17_5_Branch_0_Conv2d_1x1 ( (None, 8, 8, 128)    114688      Block17_4_Activation[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "Block17_5_Branch_1_Conv2d_0c_7x (None, 8, 8, 128)    114688      Block17_5_Branch_1_Conv2d_0b_1x7_\n",
      "__________________________________________________________________________________________________\n",
      "Block17_5_Branch_0_Conv2d_1x1_B (None, 8, 8, 128)    384         Block17_5_Branch_0_Conv2d_1x1[0][\n",
      "__________________________________________________________________________________________________\n",
      "Block17_5_Branch_1_Conv2d_0c_7x (None, 8, 8, 128)    384         Block17_5_Branch_1_Conv2d_0c_7x1[\n",
      "__________________________________________________________________________________________________\n",
      "Block17_5_Branch_0_Conv2d_1x1_A (None, 8, 8, 128)    0           Block17_5_Branch_0_Conv2d_1x1_Bat\n",
      "__________________________________________________________________________________________________\n",
      "Block17_5_Branch_1_Conv2d_0c_7x (None, 8, 8, 128)    0           Block17_5_Branch_1_Conv2d_0c_7x1_\n",
      "__________________________________________________________________________________________________\n",
      "Block17_5_Concatenate (Concaten (None, 8, 8, 256)    0           Block17_5_Branch_0_Conv2d_1x1_Act\n",
      "                                                                 Block17_5_Branch_1_Conv2d_0c_7x1_\n",
      "__________________________________________________________________________________________________\n",
      "Block17_5_Conv2d_1x1 (Conv2D)   (None, 8, 8, 896)    230272      Block17_5_Concatenate[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "Block17_5_ScaleSum (Lambda)     (None, 8, 8, 896)    0           Block17_4_Activation[0][0]       \n",
      "                                                                 Block17_5_Conv2d_1x1[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "Block17_5_Activation (Activatio (None, 8, 8, 896)    0           Block17_5_ScaleSum[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "Block17_6_Branch_1_Conv2d_0a_1x (None, 8, 8, 128)    114688      Block17_5_Activation[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "Block17_6_Branch_1_Conv2d_0a_1x (None, 8, 8, 128)    384         Block17_6_Branch_1_Conv2d_0a_1x1[\n",
      "__________________________________________________________________________________________________\n",
      "Block17_6_Branch_1_Conv2d_0a_1x (None, 8, 8, 128)    0           Block17_6_Branch_1_Conv2d_0a_1x1_\n",
      "__________________________________________________________________________________________________\n",
      "Block17_6_Branch_1_Conv2d_0b_1x (None, 8, 8, 128)    114688      Block17_6_Branch_1_Conv2d_0a_1x1_\n",
      "__________________________________________________________________________________________________\n",
      "Block17_6_Branch_1_Conv2d_0b_1x (None, 8, 8, 128)    384         Block17_6_Branch_1_Conv2d_0b_1x7[\n",
      "__________________________________________________________________________________________________\n",
      "Block17_6_Branch_1_Conv2d_0b_1x (None, 8, 8, 128)    0           Block17_6_Branch_1_Conv2d_0b_1x7_\n",
      "__________________________________________________________________________________________________\n",
      "Block17_6_Branch_0_Conv2d_1x1 ( (None, 8, 8, 128)    114688      Block17_5_Activation[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "Block17_6_Branch_1_Conv2d_0c_7x (None, 8, 8, 128)    114688      Block17_6_Branch_1_Conv2d_0b_1x7_\n",
      "__________________________________________________________________________________________________\n",
      "Block17_6_Branch_0_Conv2d_1x1_B (None, 8, 8, 128)    384         Block17_6_Branch_0_Conv2d_1x1[0][\n",
      "__________________________________________________________________________________________________\n",
      "Block17_6_Branch_1_Conv2d_0c_7x (None, 8, 8, 128)    384         Block17_6_Branch_1_Conv2d_0c_7x1[\n",
      "__________________________________________________________________________________________________\n",
      "Block17_6_Branch_0_Conv2d_1x1_A (None, 8, 8, 128)    0           Block17_6_Branch_0_Conv2d_1x1_Bat\n",
      "__________________________________________________________________________________________________\n",
      "Block17_6_Branch_1_Conv2d_0c_7x (None, 8, 8, 128)    0           Block17_6_Branch_1_Conv2d_0c_7x1_\n",
      "__________________________________________________________________________________________________\n",
      "Block17_6_Concatenate (Concaten (None, 8, 8, 256)    0           Block17_6_Branch_0_Conv2d_1x1_Act\n",
      "                                                                 Block17_6_Branch_1_Conv2d_0c_7x1_\n",
      "__________________________________________________________________________________________________\n",
      "Block17_6_Conv2d_1x1 (Conv2D)   (None, 8, 8, 896)    230272      Block17_6_Concatenate[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "Block17_6_ScaleSum (Lambda)     (None, 8, 8, 896)    0           Block17_5_Activation[0][0]       \n",
      "                                                                 Block17_6_Conv2d_1x1[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "Block17_6_Activation (Activatio (None, 8, 8, 896)    0           Block17_6_ScaleSum[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "Block17_7_Branch_1_Conv2d_0a_1x (None, 8, 8, 128)    114688      Block17_6_Activation[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "Block17_7_Branch_1_Conv2d_0a_1x (None, 8, 8, 128)    384         Block17_7_Branch_1_Conv2d_0a_1x1[\n",
      "__________________________________________________________________________________________________\n",
      "Block17_7_Branch_1_Conv2d_0a_1x (None, 8, 8, 128)    0           Block17_7_Branch_1_Conv2d_0a_1x1_\n",
      "__________________________________________________________________________________________________\n",
      "Block17_7_Branch_1_Conv2d_0b_1x (None, 8, 8, 128)    114688      Block17_7_Branch_1_Conv2d_0a_1x1_\n",
      "__________________________________________________________________________________________________\n",
      "Block17_7_Branch_1_Conv2d_0b_1x (None, 8, 8, 128)    384         Block17_7_Branch_1_Conv2d_0b_1x7[\n",
      "__________________________________________________________________________________________________\n",
      "Block17_7_Branch_1_Conv2d_0b_1x (None, 8, 8, 128)    0           Block17_7_Branch_1_Conv2d_0b_1x7_\n",
      "__________________________________________________________________________________________________\n",
      "Block17_7_Branch_0_Conv2d_1x1 ( (None, 8, 8, 128)    114688      Block17_6_Activation[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "Block17_7_Branch_1_Conv2d_0c_7x (None, 8, 8, 128)    114688      Block17_7_Branch_1_Conv2d_0b_1x7_\n",
      "__________________________________________________________________________________________________\n",
      "Block17_7_Branch_0_Conv2d_1x1_B (None, 8, 8, 128)    384         Block17_7_Branch_0_Conv2d_1x1[0][\n",
      "__________________________________________________________________________________________________\n",
      "Block17_7_Branch_1_Conv2d_0c_7x (None, 8, 8, 128)    384         Block17_7_Branch_1_Conv2d_0c_7x1[\n",
      "__________________________________________________________________________________________________\n",
      "Block17_7_Branch_0_Conv2d_1x1_A (None, 8, 8, 128)    0           Block17_7_Branch_0_Conv2d_1x1_Bat\n",
      "__________________________________________________________________________________________________\n",
      "Block17_7_Branch_1_Conv2d_0c_7x (None, 8, 8, 128)    0           Block17_7_Branch_1_Conv2d_0c_7x1_\n",
      "__________________________________________________________________________________________________\n",
      "Block17_7_Concatenate (Concaten (None, 8, 8, 256)    0           Block17_7_Branch_0_Conv2d_1x1_Act\n",
      "                                                                 Block17_7_Branch_1_Conv2d_0c_7x1_\n",
      "__________________________________________________________________________________________________\n",
      "Block17_7_Conv2d_1x1 (Conv2D)   (None, 8, 8, 896)    230272      Block17_7_Concatenate[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "Block17_7_ScaleSum (Lambda)     (None, 8, 8, 896)    0           Block17_6_Activation[0][0]       \n",
      "                                                                 Block17_7_Conv2d_1x1[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "Block17_7_Activation (Activatio (None, 8, 8, 896)    0           Block17_7_ScaleSum[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "Block17_8_Branch_1_Conv2d_0a_1x (None, 8, 8, 128)    114688      Block17_7_Activation[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "Block17_8_Branch_1_Conv2d_0a_1x (None, 8, 8, 128)    384         Block17_8_Branch_1_Conv2d_0a_1x1[\n",
      "__________________________________________________________________________________________________\n",
      "Block17_8_Branch_1_Conv2d_0a_1x (None, 8, 8, 128)    0           Block17_8_Branch_1_Conv2d_0a_1x1_\n",
      "__________________________________________________________________________________________________\n",
      "Block17_8_Branch_1_Conv2d_0b_1x (None, 8, 8, 128)    114688      Block17_8_Branch_1_Conv2d_0a_1x1_\n",
      "__________________________________________________________________________________________________\n",
      "Block17_8_Branch_1_Conv2d_0b_1x (None, 8, 8, 128)    384         Block17_8_Branch_1_Conv2d_0b_1x7[\n",
      "__________________________________________________________________________________________________\n",
      "Block17_8_Branch_1_Conv2d_0b_1x (None, 8, 8, 128)    0           Block17_8_Branch_1_Conv2d_0b_1x7_\n",
      "__________________________________________________________________________________________________\n",
      "Block17_8_Branch_0_Conv2d_1x1 ( (None, 8, 8, 128)    114688      Block17_7_Activation[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "Block17_8_Branch_1_Conv2d_0c_7x (None, 8, 8, 128)    114688      Block17_8_Branch_1_Conv2d_0b_1x7_\n",
      "__________________________________________________________________________________________________\n",
      "Block17_8_Branch_0_Conv2d_1x1_B (None, 8, 8, 128)    384         Block17_8_Branch_0_Conv2d_1x1[0][\n",
      "__________________________________________________________________________________________________\n",
      "Block17_8_Branch_1_Conv2d_0c_7x (None, 8, 8, 128)    384         Block17_8_Branch_1_Conv2d_0c_7x1[\n",
      "__________________________________________________________________________________________________\n",
      "Block17_8_Branch_0_Conv2d_1x1_A (None, 8, 8, 128)    0           Block17_8_Branch_0_Conv2d_1x1_Bat\n",
      "__________________________________________________________________________________________________\n",
      "Block17_8_Branch_1_Conv2d_0c_7x (None, 8, 8, 128)    0           Block17_8_Branch_1_Conv2d_0c_7x1_\n",
      "__________________________________________________________________________________________________\n",
      "Block17_8_Concatenate (Concaten (None, 8, 8, 256)    0           Block17_8_Branch_0_Conv2d_1x1_Act\n",
      "                                                                 Block17_8_Branch_1_Conv2d_0c_7x1_\n",
      "__________________________________________________________________________________________________\n",
      "Block17_8_Conv2d_1x1 (Conv2D)   (None, 8, 8, 896)    230272      Block17_8_Concatenate[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "Block17_8_ScaleSum (Lambda)     (None, 8, 8, 896)    0           Block17_7_Activation[0][0]       \n",
      "                                                                 Block17_8_Conv2d_1x1[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "Block17_8_Activation (Activatio (None, 8, 8, 896)    0           Block17_8_ScaleSum[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "Block17_9_Branch_1_Conv2d_0a_1x (None, 8, 8, 128)    114688      Block17_8_Activation[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "Block17_9_Branch_1_Conv2d_0a_1x (None, 8, 8, 128)    384         Block17_9_Branch_1_Conv2d_0a_1x1[\n",
      "__________________________________________________________________________________________________\n",
      "Block17_9_Branch_1_Conv2d_0a_1x (None, 8, 8, 128)    0           Block17_9_Branch_1_Conv2d_0a_1x1_\n",
      "__________________________________________________________________________________________________\n",
      "Block17_9_Branch_1_Conv2d_0b_1x (None, 8, 8, 128)    114688      Block17_9_Branch_1_Conv2d_0a_1x1_\n",
      "__________________________________________________________________________________________________\n",
      "Block17_9_Branch_1_Conv2d_0b_1x (None, 8, 8, 128)    384         Block17_9_Branch_1_Conv2d_0b_1x7[\n",
      "__________________________________________________________________________________________________\n",
      "Block17_9_Branch_1_Conv2d_0b_1x (None, 8, 8, 128)    0           Block17_9_Branch_1_Conv2d_0b_1x7_\n",
      "__________________________________________________________________________________________________\n",
      "Block17_9_Branch_0_Conv2d_1x1 ( (None, 8, 8, 128)    114688      Block17_8_Activation[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "Block17_9_Branch_1_Conv2d_0c_7x (None, 8, 8, 128)    114688      Block17_9_Branch_1_Conv2d_0b_1x7_\n",
      "__________________________________________________________________________________________________\n",
      "Block17_9_Branch_0_Conv2d_1x1_B (None, 8, 8, 128)    384         Block17_9_Branch_0_Conv2d_1x1[0][\n",
      "__________________________________________________________________________________________________\n",
      "Block17_9_Branch_1_Conv2d_0c_7x (None, 8, 8, 128)    384         Block17_9_Branch_1_Conv2d_0c_7x1[\n",
      "__________________________________________________________________________________________________\n",
      "Block17_9_Branch_0_Conv2d_1x1_A (None, 8, 8, 128)    0           Block17_9_Branch_0_Conv2d_1x1_Bat\n",
      "__________________________________________________________________________________________________\n",
      "Block17_9_Branch_1_Conv2d_0c_7x (None, 8, 8, 128)    0           Block17_9_Branch_1_Conv2d_0c_7x1_\n",
      "__________________________________________________________________________________________________\n",
      "Block17_9_Concatenate (Concaten (None, 8, 8, 256)    0           Block17_9_Branch_0_Conv2d_1x1_Act\n",
      "                                                                 Block17_9_Branch_1_Conv2d_0c_7x1_\n",
      "__________________________________________________________________________________________________\n",
      "Block17_9_Conv2d_1x1 (Conv2D)   (None, 8, 8, 896)    230272      Block17_9_Concatenate[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "Block17_9_ScaleSum (Lambda)     (None, 8, 8, 896)    0           Block17_8_Activation[0][0]       \n",
      "                                                                 Block17_9_Conv2d_1x1[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "Block17_9_Activation (Activatio (None, 8, 8, 896)    0           Block17_9_ScaleSum[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "Block17_10_Branch_1_Conv2d_0a_1 (None, 8, 8, 128)    114688      Block17_9_Activation[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "Block17_10_Branch_1_Conv2d_0a_1 (None, 8, 8, 128)    384         Block17_10_Branch_1_Conv2d_0a_1x1\n",
      "__________________________________________________________________________________________________\n",
      "Block17_10_Branch_1_Conv2d_0a_1 (None, 8, 8, 128)    0           Block17_10_Branch_1_Conv2d_0a_1x1\n",
      "__________________________________________________________________________________________________\n",
      "Block17_10_Branch_1_Conv2d_0b_1 (None, 8, 8, 128)    114688      Block17_10_Branch_1_Conv2d_0a_1x1\n",
      "__________________________________________________________________________________________________\n",
      "Block17_10_Branch_1_Conv2d_0b_1 (None, 8, 8, 128)    384         Block17_10_Branch_1_Conv2d_0b_1x7\n",
      "__________________________________________________________________________________________________\n",
      "Block17_10_Branch_1_Conv2d_0b_1 (None, 8, 8, 128)    0           Block17_10_Branch_1_Conv2d_0b_1x7\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "__________________________________________________________________________________________________\n",
      "Block17_10_Branch_0_Conv2d_1x1  (None, 8, 8, 128)    114688      Block17_9_Activation[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "Block17_10_Branch_1_Conv2d_0c_7 (None, 8, 8, 128)    114688      Block17_10_Branch_1_Conv2d_0b_1x7\n",
      "__________________________________________________________________________________________________\n",
      "Block17_10_Branch_0_Conv2d_1x1_ (None, 8, 8, 128)    384         Block17_10_Branch_0_Conv2d_1x1[0]\n",
      "__________________________________________________________________________________________________\n",
      "Block17_10_Branch_1_Conv2d_0c_7 (None, 8, 8, 128)    384         Block17_10_Branch_1_Conv2d_0c_7x1\n",
      "__________________________________________________________________________________________________\n",
      "Block17_10_Branch_0_Conv2d_1x1_ (None, 8, 8, 128)    0           Block17_10_Branch_0_Conv2d_1x1_Ba\n",
      "__________________________________________________________________________________________________\n",
      "Block17_10_Branch_1_Conv2d_0c_7 (None, 8, 8, 128)    0           Block17_10_Branch_1_Conv2d_0c_7x1\n",
      "__________________________________________________________________________________________________\n",
      "Block17_10_Concatenate (Concate (None, 8, 8, 256)    0           Block17_10_Branch_0_Conv2d_1x1_Ac\n",
      "                                                                 Block17_10_Branch_1_Conv2d_0c_7x1\n",
      "__________________________________________________________________________________________________\n",
      "Block17_10_Conv2d_1x1 (Conv2D)  (None, 8, 8, 896)    230272      Block17_10_Concatenate[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "Block17_10_ScaleSum (Lambda)    (None, 8, 8, 896)    0           Block17_9_Activation[0][0]       \n",
      "                                                                 Block17_10_Conv2d_1x1[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "Block17_10_Activation (Activati (None, 8, 8, 896)    0           Block17_10_ScaleSum[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "Mixed_7a_Branch_2_Conv2d_0a_1x1 (None, 8, 8, 256)    229376      Block17_10_Activation[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "Mixed_7a_Branch_2_Conv2d_0a_1x1 (None, 8, 8, 256)    768         Mixed_7a_Branch_2_Conv2d_0a_1x1[0\n",
      "__________________________________________________________________________________________________\n",
      "Mixed_7a_Branch_2_Conv2d_0a_1x1 (None, 8, 8, 256)    0           Mixed_7a_Branch_2_Conv2d_0a_1x1_B\n",
      "__________________________________________________________________________________________________\n",
      "Mixed_7a_Branch_0_Conv2d_0a_1x1 (None, 8, 8, 256)    229376      Block17_10_Activation[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "Mixed_7a_Branch_1_Conv2d_0a_1x1 (None, 8, 8, 256)    229376      Block17_10_Activation[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "Mixed_7a_Branch_2_Conv2d_0b_3x3 (None, 8, 8, 256)    589824      Mixed_7a_Branch_2_Conv2d_0a_1x1_A\n",
      "__________________________________________________________________________________________________\n",
      "Mixed_7a_Branch_0_Conv2d_0a_1x1 (None, 8, 8, 256)    768         Mixed_7a_Branch_0_Conv2d_0a_1x1[0\n",
      "__________________________________________________________________________________________________\n",
      "Mixed_7a_Branch_1_Conv2d_0a_1x1 (None, 8, 8, 256)    768         Mixed_7a_Branch_1_Conv2d_0a_1x1[0\n",
      "__________________________________________________________________________________________________\n",
      "Mixed_7a_Branch_2_Conv2d_0b_3x3 (None, 8, 8, 256)    768         Mixed_7a_Branch_2_Conv2d_0b_3x3[0\n",
      "__________________________________________________________________________________________________\n",
      "Mixed_7a_Branch_0_Conv2d_0a_1x1 (None, 8, 8, 256)    0           Mixed_7a_Branch_0_Conv2d_0a_1x1_B\n",
      "__________________________________________________________________________________________________\n",
      "Mixed_7a_Branch_1_Conv2d_0a_1x1 (None, 8, 8, 256)    0           Mixed_7a_Branch_1_Conv2d_0a_1x1_B\n",
      "__________________________________________________________________________________________________\n",
      "Mixed_7a_Branch_2_Conv2d_0b_3x3 (None, 8, 8, 256)    0           Mixed_7a_Branch_2_Conv2d_0b_3x3_B\n",
      "__________________________________________________________________________________________________\n",
      "Mixed_7a_Branch_0_Conv2d_1a_3x3 (None, 3, 3, 384)    884736      Mixed_7a_Branch_0_Conv2d_0a_1x1_A\n",
      "__________________________________________________________________________________________________\n",
      "Mixed_7a_Branch_1_Conv2d_1a_3x3 (None, 3, 3, 256)    589824      Mixed_7a_Branch_1_Conv2d_0a_1x1_A\n",
      "__________________________________________________________________________________________________\n",
      "Mixed_7a_Branch_2_Conv2d_1a_3x3 (None, 3, 3, 256)    589824      Mixed_7a_Branch_2_Conv2d_0b_3x3_A\n",
      "__________________________________________________________________________________________________\n",
      "Mixed_7a_Branch_0_Conv2d_1a_3x3 (None, 3, 3, 384)    1152        Mixed_7a_Branch_0_Conv2d_1a_3x3[0\n",
      "__________________________________________________________________________________________________\n",
      "Mixed_7a_Branch_1_Conv2d_1a_3x3 (None, 3, 3, 256)    768         Mixed_7a_Branch_1_Conv2d_1a_3x3[0\n",
      "__________________________________________________________________________________________________\n",
      "Mixed_7a_Branch_2_Conv2d_1a_3x3 (None, 3, 3, 256)    768         Mixed_7a_Branch_2_Conv2d_1a_3x3[0\n",
      "__________________________________________________________________________________________________\n",
      "Mixed_7a_Branch_0_Conv2d_1a_3x3 (None, 3, 3, 384)    0           Mixed_7a_Branch_0_Conv2d_1a_3x3_B\n",
      "__________________________________________________________________________________________________\n",
      "Mixed_7a_Branch_1_Conv2d_1a_3x3 (None, 3, 3, 256)    0           Mixed_7a_Branch_1_Conv2d_1a_3x3_B\n",
      "__________________________________________________________________________________________________\n",
      "Mixed_7a_Branch_2_Conv2d_1a_3x3 (None, 3, 3, 256)    0           Mixed_7a_Branch_2_Conv2d_1a_3x3_B\n",
      "__________________________________________________________________________________________________\n",
      "Mixed_7a_Branch_3_MaxPool_1a_3x (None, 3, 3, 896)    0           Block17_10_Activation[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "Mixed_7a (Concatenate)          (None, 3, 3, 1792)   0           Mixed_7a_Branch_0_Conv2d_1a_3x3_A\n",
      "                                                                 Mixed_7a_Branch_1_Conv2d_1a_3x3_A\n",
      "                                                                 Mixed_7a_Branch_2_Conv2d_1a_3x3_A\n",
      "                                                                 Mixed_7a_Branch_3_MaxPool_1a_3x3[\n",
      "__________________________________________________________________________________________________\n",
      "Block8_1_Branch_1_Conv2d_0a_1x1 (None, 3, 3, 192)    344064      Mixed_7a[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "Block8_1_Branch_1_Conv2d_0a_1x1 (None, 3, 3, 192)    576         Block8_1_Branch_1_Conv2d_0a_1x1[0\n",
      "__________________________________________________________________________________________________\n",
      "Block8_1_Branch_1_Conv2d_0a_1x1 (None, 3, 3, 192)    0           Block8_1_Branch_1_Conv2d_0a_1x1_B\n",
      "__________________________________________________________________________________________________\n",
      "Block8_1_Branch_1_Conv2d_0b_1x3 (None, 3, 3, 192)    110592      Block8_1_Branch_1_Conv2d_0a_1x1_A\n",
      "__________________________________________________________________________________________________\n",
      "Block8_1_Branch_1_Conv2d_0b_1x3 (None, 3, 3, 192)    576         Block8_1_Branch_1_Conv2d_0b_1x3[0\n",
      "__________________________________________________________________________________________________\n",
      "Block8_1_Branch_1_Conv2d_0b_1x3 (None, 3, 3, 192)    0           Block8_1_Branch_1_Conv2d_0b_1x3_B\n",
      "__________________________________________________________________________________________________\n",
      "Block8_1_Branch_0_Conv2d_1x1 (C (None, 3, 3, 192)    344064      Mixed_7a[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "Block8_1_Branch_1_Conv2d_0c_3x1 (None, 3, 3, 192)    110592      Block8_1_Branch_1_Conv2d_0b_1x3_A\n",
      "__________________________________________________________________________________________________\n",
      "Block8_1_Branch_0_Conv2d_1x1_Ba (None, 3, 3, 192)    576         Block8_1_Branch_0_Conv2d_1x1[0][0\n",
      "__________________________________________________________________________________________________\n",
      "Block8_1_Branch_1_Conv2d_0c_3x1 (None, 3, 3, 192)    576         Block8_1_Branch_1_Conv2d_0c_3x1[0\n",
      "__________________________________________________________________________________________________\n",
      "Block8_1_Branch_0_Conv2d_1x1_Ac (None, 3, 3, 192)    0           Block8_1_Branch_0_Conv2d_1x1_Batc\n",
      "__________________________________________________________________________________________________\n",
      "Block8_1_Branch_1_Conv2d_0c_3x1 (None, 3, 3, 192)    0           Block8_1_Branch_1_Conv2d_0c_3x1_B\n",
      "__________________________________________________________________________________________________\n",
      "Block8_1_Concatenate (Concatena (None, 3, 3, 384)    0           Block8_1_Branch_0_Conv2d_1x1_Acti\n",
      "                                                                 Block8_1_Branch_1_Conv2d_0c_3x1_A\n",
      "__________________________________________________________________________________________________\n",
      "Block8_1_Conv2d_1x1 (Conv2D)    (None, 3, 3, 1792)   689920      Block8_1_Concatenate[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "Block8_1_ScaleSum (Lambda)      (None, 3, 3, 1792)   0           Mixed_7a[0][0]                   \n",
      "                                                                 Block8_1_Conv2d_1x1[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "Block8_1_Activation (Activation (None, 3, 3, 1792)   0           Block8_1_ScaleSum[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "Block8_2_Branch_1_Conv2d_0a_1x1 (None, 3, 3, 192)    344064      Block8_1_Activation[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "Block8_2_Branch_1_Conv2d_0a_1x1 (None, 3, 3, 192)    576         Block8_2_Branch_1_Conv2d_0a_1x1[0\n",
      "__________________________________________________________________________________________________\n",
      "Block8_2_Branch_1_Conv2d_0a_1x1 (None, 3, 3, 192)    0           Block8_2_Branch_1_Conv2d_0a_1x1_B\n",
      "__________________________________________________________________________________________________\n",
      "Block8_2_Branch_1_Conv2d_0b_1x3 (None, 3, 3, 192)    110592      Block8_2_Branch_1_Conv2d_0a_1x1_A\n",
      "__________________________________________________________________________________________________\n",
      "Block8_2_Branch_1_Conv2d_0b_1x3 (None, 3, 3, 192)    576         Block8_2_Branch_1_Conv2d_0b_1x3[0\n",
      "__________________________________________________________________________________________________\n",
      "Block8_2_Branch_1_Conv2d_0b_1x3 (None, 3, 3, 192)    0           Block8_2_Branch_1_Conv2d_0b_1x3_B\n",
      "__________________________________________________________________________________________________\n",
      "Block8_2_Branch_0_Conv2d_1x1 (C (None, 3, 3, 192)    344064      Block8_1_Activation[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "Block8_2_Branch_1_Conv2d_0c_3x1 (None, 3, 3, 192)    110592      Block8_2_Branch_1_Conv2d_0b_1x3_A\n",
      "__________________________________________________________________________________________________\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Block8_2_Branch_0_Conv2d_1x1_Ba (None, 3, 3, 192)    576         Block8_2_Branch_0_Conv2d_1x1[0][0\n",
      "__________________________________________________________________________________________________\n",
      "Block8_2_Branch_1_Conv2d_0c_3x1 (None, 3, 3, 192)    576         Block8_2_Branch_1_Conv2d_0c_3x1[0\n",
      "__________________________________________________________________________________________________\n",
      "Block8_2_Branch_0_Conv2d_1x1_Ac (None, 3, 3, 192)    0           Block8_2_Branch_0_Conv2d_1x1_Batc\n",
      "__________________________________________________________________________________________________\n",
      "Block8_2_Branch_1_Conv2d_0c_3x1 (None, 3, 3, 192)    0           Block8_2_Branch_1_Conv2d_0c_3x1_B\n",
      "__________________________________________________________________________________________________\n",
      "Block8_2_Concatenate (Concatena (None, 3, 3, 384)    0           Block8_2_Branch_0_Conv2d_1x1_Acti\n",
      "                                                                 Block8_2_Branch_1_Conv2d_0c_3x1_A\n",
      "__________________________________________________________________________________________________\n",
      "Block8_2_Conv2d_1x1 (Conv2D)    (None, 3, 3, 1792)   689920      Block8_2_Concatenate[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "Block8_2_ScaleSum (Lambda)      (None, 3, 3, 1792)   0           Block8_1_Activation[0][0]        \n",
      "                                                                 Block8_2_Conv2d_1x1[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "Block8_2_Activation (Activation (None, 3, 3, 1792)   0           Block8_2_ScaleSum[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "Block8_3_Branch_1_Conv2d_0a_1x1 (None, 3, 3, 192)    344064      Block8_2_Activation[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "Block8_3_Branch_1_Conv2d_0a_1x1 (None, 3, 3, 192)    576         Block8_3_Branch_1_Conv2d_0a_1x1[0\n",
      "__________________________________________________________________________________________________\n",
      "Block8_3_Branch_1_Conv2d_0a_1x1 (None, 3, 3, 192)    0           Block8_3_Branch_1_Conv2d_0a_1x1_B\n",
      "__________________________________________________________________________________________________\n",
      "Block8_3_Branch_1_Conv2d_0b_1x3 (None, 3, 3, 192)    110592      Block8_3_Branch_1_Conv2d_0a_1x1_A\n",
      "__________________________________________________________________________________________________\n",
      "Block8_3_Branch_1_Conv2d_0b_1x3 (None, 3, 3, 192)    576         Block8_3_Branch_1_Conv2d_0b_1x3[0\n",
      "__________________________________________________________________________________________________\n",
      "Block8_3_Branch_1_Conv2d_0b_1x3 (None, 3, 3, 192)    0           Block8_3_Branch_1_Conv2d_0b_1x3_B\n",
      "__________________________________________________________________________________________________\n",
      "Block8_3_Branch_0_Conv2d_1x1 (C (None, 3, 3, 192)    344064      Block8_2_Activation[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "Block8_3_Branch_1_Conv2d_0c_3x1 (None, 3, 3, 192)    110592      Block8_3_Branch_1_Conv2d_0b_1x3_A\n",
      "__________________________________________________________________________________________________\n",
      "Block8_3_Branch_0_Conv2d_1x1_Ba (None, 3, 3, 192)    576         Block8_3_Branch_0_Conv2d_1x1[0][0\n",
      "__________________________________________________________________________________________________\n",
      "Block8_3_Branch_1_Conv2d_0c_3x1 (None, 3, 3, 192)    576         Block8_3_Branch_1_Conv2d_0c_3x1[0\n",
      "__________________________________________________________________________________________________\n",
      "Block8_3_Branch_0_Conv2d_1x1_Ac (None, 3, 3, 192)    0           Block8_3_Branch_0_Conv2d_1x1_Batc\n",
      "__________________________________________________________________________________________________\n",
      "Block8_3_Branch_1_Conv2d_0c_3x1 (None, 3, 3, 192)    0           Block8_3_Branch_1_Conv2d_0c_3x1_B\n",
      "__________________________________________________________________________________________________\n",
      "Block8_3_Concatenate (Concatena (None, 3, 3, 384)    0           Block8_3_Branch_0_Conv2d_1x1_Acti\n",
      "                                                                 Block8_3_Branch_1_Conv2d_0c_3x1_A\n",
      "__________________________________________________________________________________________________\n",
      "Block8_3_Conv2d_1x1 (Conv2D)    (None, 3, 3, 1792)   689920      Block8_3_Concatenate[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "Block8_3_ScaleSum (Lambda)      (None, 3, 3, 1792)   0           Block8_2_Activation[0][0]        \n",
      "                                                                 Block8_3_Conv2d_1x1[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "Block8_3_Activation (Activation (None, 3, 3, 1792)   0           Block8_3_ScaleSum[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "Block8_4_Branch_1_Conv2d_0a_1x1 (None, 3, 3, 192)    344064      Block8_3_Activation[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "Block8_4_Branch_1_Conv2d_0a_1x1 (None, 3, 3, 192)    576         Block8_4_Branch_1_Conv2d_0a_1x1[0\n",
      "__________________________________________________________________________________________________\n",
      "Block8_4_Branch_1_Conv2d_0a_1x1 (None, 3, 3, 192)    0           Block8_4_Branch_1_Conv2d_0a_1x1_B\n",
      "__________________________________________________________________________________________________\n",
      "Block8_4_Branch_1_Conv2d_0b_1x3 (None, 3, 3, 192)    110592      Block8_4_Branch_1_Conv2d_0a_1x1_A\n",
      "__________________________________________________________________________________________________\n",
      "Block8_4_Branch_1_Conv2d_0b_1x3 (None, 3, 3, 192)    576         Block8_4_Branch_1_Conv2d_0b_1x3[0\n",
      "__________________________________________________________________________________________________\n",
      "Block8_4_Branch_1_Conv2d_0b_1x3 (None, 3, 3, 192)    0           Block8_4_Branch_1_Conv2d_0b_1x3_B\n",
      "__________________________________________________________________________________________________\n",
      "Block8_4_Branch_0_Conv2d_1x1 (C (None, 3, 3, 192)    344064      Block8_3_Activation[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "Block8_4_Branch_1_Conv2d_0c_3x1 (None, 3, 3, 192)    110592      Block8_4_Branch_1_Conv2d_0b_1x3_A\n",
      "__________________________________________________________________________________________________\n",
      "Block8_4_Branch_0_Conv2d_1x1_Ba (None, 3, 3, 192)    576         Block8_4_Branch_0_Conv2d_1x1[0][0\n",
      "__________________________________________________________________________________________________\n",
      "Block8_4_Branch_1_Conv2d_0c_3x1 (None, 3, 3, 192)    576         Block8_4_Branch_1_Conv2d_0c_3x1[0\n",
      "__________________________________________________________________________________________________\n",
      "Block8_4_Branch_0_Conv2d_1x1_Ac (None, 3, 3, 192)    0           Block8_4_Branch_0_Conv2d_1x1_Batc\n",
      "__________________________________________________________________________________________________\n",
      "Block8_4_Branch_1_Conv2d_0c_3x1 (None, 3, 3, 192)    0           Block8_4_Branch_1_Conv2d_0c_3x1_B\n",
      "__________________________________________________________________________________________________\n",
      "Block8_4_Concatenate (Concatena (None, 3, 3, 384)    0           Block8_4_Branch_0_Conv2d_1x1_Acti\n",
      "                                                                 Block8_4_Branch_1_Conv2d_0c_3x1_A\n",
      "__________________________________________________________________________________________________\n",
      "Block8_4_Conv2d_1x1 (Conv2D)    (None, 3, 3, 1792)   689920      Block8_4_Concatenate[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "Block8_4_ScaleSum (Lambda)      (None, 3, 3, 1792)   0           Block8_3_Activation[0][0]        \n",
      "                                                                 Block8_4_Conv2d_1x1[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "Block8_4_Activation (Activation (None, 3, 3, 1792)   0           Block8_4_ScaleSum[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "Block8_5_Branch_1_Conv2d_0a_1x1 (None, 3, 3, 192)    344064      Block8_4_Activation[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "Block8_5_Branch_1_Conv2d_0a_1x1 (None, 3, 3, 192)    576         Block8_5_Branch_1_Conv2d_0a_1x1[0\n",
      "__________________________________________________________________________________________________\n",
      "Block8_5_Branch_1_Conv2d_0a_1x1 (None, 3, 3, 192)    0           Block8_5_Branch_1_Conv2d_0a_1x1_B\n",
      "__________________________________________________________________________________________________\n",
      "Block8_5_Branch_1_Conv2d_0b_1x3 (None, 3, 3, 192)    110592      Block8_5_Branch_1_Conv2d_0a_1x1_A\n",
      "__________________________________________________________________________________________________\n",
      "Block8_5_Branch_1_Conv2d_0b_1x3 (None, 3, 3, 192)    576         Block8_5_Branch_1_Conv2d_0b_1x3[0\n",
      "__________________________________________________________________________________________________\n",
      "Block8_5_Branch_1_Conv2d_0b_1x3 (None, 3, 3, 192)    0           Block8_5_Branch_1_Conv2d_0b_1x3_B\n",
      "__________________________________________________________________________________________________\n",
      "Block8_5_Branch_0_Conv2d_1x1 (C (None, 3, 3, 192)    344064      Block8_4_Activation[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "Block8_5_Branch_1_Conv2d_0c_3x1 (None, 3, 3, 192)    110592      Block8_5_Branch_1_Conv2d_0b_1x3_A\n",
      "__________________________________________________________________________________________________\n",
      "Block8_5_Branch_0_Conv2d_1x1_Ba (None, 3, 3, 192)    576         Block8_5_Branch_0_Conv2d_1x1[0][0\n",
      "__________________________________________________________________________________________________\n",
      "Block8_5_Branch_1_Conv2d_0c_3x1 (None, 3, 3, 192)    576         Block8_5_Branch_1_Conv2d_0c_3x1[0\n",
      "__________________________________________________________________________________________________\n",
      "Block8_5_Branch_0_Conv2d_1x1_Ac (None, 3, 3, 192)    0           Block8_5_Branch_0_Conv2d_1x1_Batc\n",
      "__________________________________________________________________________________________________\n",
      "Block8_5_Branch_1_Conv2d_0c_3x1 (None, 3, 3, 192)    0           Block8_5_Branch_1_Conv2d_0c_3x1_B\n",
      "__________________________________________________________________________________________________\n",
      "Block8_5_Concatenate (Concatena (None, 3, 3, 384)    0           Block8_5_Branch_0_Conv2d_1x1_Acti\n",
      "                                                                 Block8_5_Branch_1_Conv2d_0c_3x1_A\n",
      "__________________________________________________________________________________________________\n",
      "Block8_5_Conv2d_1x1 (Conv2D)    (None, 3, 3, 1792)   689920      Block8_5_Concatenate[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "Block8_5_ScaleSum (Lambda)      (None, 3, 3, 1792)   0           Block8_4_Activation[0][0]        \n",
      "                                                                 Block8_5_Conv2d_1x1[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "Block8_5_Activation (Activation (None, 3, 3, 1792)   0           Block8_5_ScaleSum[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "Block8_6_Branch_1_Conv2d_0a_1x1 (None, 3, 3, 192)    344064      Block8_5_Activation[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "Block8_6_Branch_1_Conv2d_0a_1x1 (None, 3, 3, 192)    576         Block8_6_Branch_1_Conv2d_0a_1x1[0\n",
      "__________________________________________________________________________________________________\n",
      "Block8_6_Branch_1_Conv2d_0a_1x1 (None, 3, 3, 192)    0           Block8_6_Branch_1_Conv2d_0a_1x1_B\n",
      "__________________________________________________________________________________________________\n",
      "Block8_6_Branch_1_Conv2d_0b_1x3 (None, 3, 3, 192)    110592      Block8_6_Branch_1_Conv2d_0a_1x1_A\n",
      "__________________________________________________________________________________________________\n",
      "Block8_6_Branch_1_Conv2d_0b_1x3 (None, 3, 3, 192)    576         Block8_6_Branch_1_Conv2d_0b_1x3[0\n",
      "__________________________________________________________________________________________________\n",
      "Block8_6_Branch_1_Conv2d_0b_1x3 (None, 3, 3, 192)    0           Block8_6_Branch_1_Conv2d_0b_1x3_B\n",
      "__________________________________________________________________________________________________\n",
      "Block8_6_Branch_0_Conv2d_1x1 (C (None, 3, 3, 192)    344064      Block8_5_Activation[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "Block8_6_Branch_1_Conv2d_0c_3x1 (None, 3, 3, 192)    110592      Block8_6_Branch_1_Conv2d_0b_1x3_A\n",
      "__________________________________________________________________________________________________\n",
      "Block8_6_Branch_0_Conv2d_1x1_Ba (None, 3, 3, 192)    576         Block8_6_Branch_0_Conv2d_1x1[0][0\n",
      "__________________________________________________________________________________________________\n",
      "Block8_6_Branch_1_Conv2d_0c_3x1 (None, 3, 3, 192)    576         Block8_6_Branch_1_Conv2d_0c_3x1[0\n",
      "__________________________________________________________________________________________________\n",
      "Block8_6_Branch_0_Conv2d_1x1_Ac (None, 3, 3, 192)    0           Block8_6_Branch_0_Conv2d_1x1_Batc\n",
      "__________________________________________________________________________________________________\n",
      "Block8_6_Branch_1_Conv2d_0c_3x1 (None, 3, 3, 192)    0           Block8_6_Branch_1_Conv2d_0c_3x1_B\n",
      "__________________________________________________________________________________________________\n",
      "Block8_6_Concatenate (Concatena (None, 3, 3, 384)    0           Block8_6_Branch_0_Conv2d_1x1_Acti\n",
      "                                                                 Block8_6_Branch_1_Conv2d_0c_3x1_A\n",
      "__________________________________________________________________________________________________\n",
      "Block8_6_Conv2d_1x1 (Conv2D)    (None, 3, 3, 1792)   689920      Block8_6_Concatenate[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "Block8_6_ScaleSum (Lambda)      (None, 3, 3, 1792)   0           Block8_5_Activation[0][0]        \n",
      "                                                                 Block8_6_Conv2d_1x1[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "AvgPool (GlobalAveragePooling2D (None, 1792)         0           Block8_6_ScaleSum[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "Dropout (Dropout)               (None, 1792)         0           AvgPool[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "Bottleneck (Dense)              (None, 128)          229376      Dropout[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "Bottleneck_BatchNorm (BatchNorm (None, 128)          384         Bottleneck[0][0]                 \n",
      "==================================================================================================\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total params: 22,808,144\n",
      "Trainable params: 22,779,312\n",
      "Non-trainable params: 28,832\n",
      "__________________________________________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model=load_model('C:/Users/Diana/Desktop/facenet_keras.h5')\n",
    "#A summary of the model architecture\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 1308,
     "status": "ok",
     "timestamp": 1619472211576,
     "user": {
      "displayName": "blaise mavin",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GgUhbcpHWNXczan3ui3BGJe8CM_G7bUv9e9RG5m9A=s64",
      "userId": "14168894997160457371"
     },
     "user_tz": -180
    },
    "id": "rH7-R2Iz6LQy",
    "outputId": "ff62f6f8-db9a-442c-847a-f962ec81c84a"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of layers in the base model:  426\n"
     ]
    }
   ],
   "source": [
    "print(\"Number of layers in the base model: \", len(model.layers))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "id": "HWNkmU4SEBEm"
   },
   "outputs": [],
   "source": [
    "local_weights_file='C:/Users/Diana/Desktop/facenet_keras_weights.h5'\n",
    "model.load_weights(local_weights_file)\n",
    "\n",
    "for layer in model.layers:\n",
    "  layer.trainable=False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "id": "yjnsrv9Vhpau"
   },
   "outputs": [],
   "source": [
    "from tensorflow.keras import layers\n",
    "from tensorflow.keras import Model \n",
    "\n",
    "#Specify the last layer from the architecture, that you actually want\n",
    "last_layer=model.get_layer('Bottleneck')\n",
    "last_output=last_layer.output"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "1Kr5tbk2mHEi"
   },
   "source": [
    "### **Temporary Alternative to the above code cell**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: tensorflow_addons in c:\\users\\diana\\anaconda3\\lib\\site-packages (0.13.0)\n",
      "Requirement already satisfied: typeguard>=2.7 in c:\\users\\diana\\anaconda3\\lib\\site-packages (from tensorflow_addons) (2.12.0)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING: You are using pip version 21.0.1; however, version 21.1.2 is available.\n",
      "You should consider upgrading via the 'c:\\users\\diana\\anaconda3\\python.exe -m pip install --upgrade pip' command.\n"
     ]
    }
   ],
   "source": [
    "!pip install tensorflow_addons"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'tensorflow_addons' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-13-48e9bf582b0e>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtensorflow_addons\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__version__\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m: name 'tensorflow_addons' is not defined"
     ]
    }
   ],
   "source": [
    "print(tensorflow_addons.__version__)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "id": "NSQETUqLmN1n"
   },
   "outputs": [],
   "source": [
    "#Code from arcface repo\n",
    "from keras.layers import Input\n",
    "from keras.layers import BatchNormalization\n",
    "from keras.layers import Dropout\n",
    "import tensorflow_addons as tfa\n",
    "\n",
    "\n",
    "#customizable arcface layer\n",
    "af_layer = ArcFace(output_dim=128, class_num=128, margin=0.5, scale=64.)\n",
    "\n",
    "arcface_output = af_layer(last_output)\n",
    "x=layers.Flatten()(arcface_output)\n",
    "#print(arcface_output)\n",
    "x = Dropout(rate=0.3)(x)\n",
    "\n",
    "x=layers.Dense(1024,activation='relu')(arcface_output)\n",
    "\n",
    "x=layers.Dense(512,activation='relu')(x)\n",
    "x = Dropout(rate=0.5)(x)\n",
    "x=layers.Dense(128,activation='relu')(x)\n",
    "x=layers.Dense(15,activation='softmax')(arcface_output)\n",
    "\n",
    "model=Model(model.input,x)\n",
    "model.compile(optimizer=RMSprop(lr=0.0001),loss='categorical_crossentropy',metrics=['accuracy',tf.keras.metrics.AUC(multi_label = True) , tfa.metrics.F1Score(num_classes=15), tfa.metrics.MultiLabelConfusionMatrix(num_classes=15)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 259827,
     "status": "ok",
     "timestamp": 1619473169049,
     "user": {
      "displayName": "blaise mavin",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GgUhbcpHWNXczan3ui3BGJe8CM_G7bUv9e9RG5m9A=s64",
      "userId": "14168894997160457371"
     },
     "user_tz": -180
    },
    "id": "qje4VW0jl8d5",
    "outputId": "a501bd4f-51fc-4d8c-bc1f-b2c14918dc44"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/100\n",
      "20/20 - 275s - loss: 2.7164 - accuracy: 0.0685 - auc_2: 0.5293 - f1_score: 0.0739 - Multilabel_confusion_matrix: 153.2500 - val_loss: 2.6885 - val_accuracy: 0.0945 - val_auc_2: 0.5970 - val_f1_score: 0.0695 - val_Multilabel_confusion_matrix: 50.2500\n",
      "Epoch 2/100\n",
      "20/20 - 202s - loss: 2.6681 - accuracy: 0.1403 - auc_2: 0.6603 - f1_score: 0.1315 - Multilabel_confusion_matrix: 153.2500 - val_loss: 2.6510 - val_accuracy: 0.1592 - val_auc_2: 0.6883 - val_f1_score: 0.1425 - val_Multilabel_confusion_matrix: 50.2500\n",
      "Epoch 3/100\n",
      "20/20 - 199s - loss: 2.6258 - accuracy: 0.2235 - auc_2: 0.7510 - f1_score: 0.1873 - Multilabel_confusion_matrix: 153.2500 - val_loss: 2.6150 - val_accuracy: 0.2786 - val_auc_2: 0.7409 - val_f1_score: 0.2342 - val_Multilabel_confusion_matrix: 50.2500\n",
      "Epoch 4/100\n",
      "20/20 - 195s - loss: 2.5867 - accuracy: 0.3622 - auc_2: 0.8141 - f1_score: 0.2892 - Multilabel_confusion_matrix: 153.2500 - val_loss: 2.5788 - val_accuracy: 0.3980 - val_auc_2: 0.7920 - val_f1_score: 0.3198 - val_Multilabel_confusion_matrix: 50.2500\n",
      "Epoch 5/100\n",
      "20/20 - 216s - loss: 2.5465 - accuracy: 0.4486 - auc_2: 0.8562 - f1_score: 0.3560 - Multilabel_confusion_matrix: 153.2500 - val_loss: 2.5434 - val_accuracy: 0.4527 - val_auc_2: 0.8193 - val_f1_score: 0.3572 - val_Multilabel_confusion_matrix: 50.2500\n",
      "Epoch 6/100\n",
      "20/20 - 195s - loss: 2.5068 - accuracy: 0.5334 - auc_2: 0.8882 - f1_score: 0.4218 - Multilabel_confusion_matrix: 153.2500 - val_loss: 2.5082 - val_accuracy: 0.5075 - val_auc_2: 0.8505 - val_f1_score: 0.4012 - val_Multilabel_confusion_matrix: 50.2500\n",
      "Epoch 7/100\n",
      "20/20 - 103s - loss: 2.4661 - accuracy: 0.5840 - auc_2: 0.9080 - f1_score: 0.4621 - Multilabel_confusion_matrix: 153.2500 - val_loss: 2.4718 - val_accuracy: 0.5473 - val_auc_2: 0.8620 - val_f1_score: 0.4246 - val_Multilabel_confusion_matrix: 50.2500\n",
      "Epoch 8/100\n",
      "20/20 - 106s - loss: 2.4252 - accuracy: 0.6346 - auc_2: 0.9255 - f1_score: 0.5038 - Multilabel_confusion_matrix: 153.2500 - val_loss: 2.4362 - val_accuracy: 0.5871 - val_auc_2: 0.8753 - val_f1_score: 0.4507 - val_Multilabel_confusion_matrix: 50.2500\n",
      "Epoch 9/100\n",
      "20/20 - 102s - loss: 2.3860 - accuracy: 0.6623 - auc_2: 0.9359 - f1_score: 0.5250 - Multilabel_confusion_matrix: 153.2500 - val_loss: 2.4011 - val_accuracy: 0.5821 - val_auc_2: 0.8865 - val_f1_score: 0.4351 - val_Multilabel_confusion_matrix: 50.2500\n",
      "Epoch 10/100\n",
      "20/20 - 102s - loss: 2.3464 - accuracy: 0.6770 - auc_2: 0.9452 - f1_score: 0.5341 - Multilabel_confusion_matrix: 153.2500 - val_loss: 2.3647 - val_accuracy: 0.5920 - val_auc_2: 0.8995 - val_f1_score: 0.4524 - val_Multilabel_confusion_matrix: 50.2500\n",
      "Epoch 11/100\n",
      "20/20 - 102s - loss: 2.3056 - accuracy: 0.6721 - auc_2: 0.9502 - f1_score: 0.5282 - Multilabel_confusion_matrix: 153.2500 - val_loss: 2.3302 - val_accuracy: 0.5970 - val_auc_2: 0.9037 - val_f1_score: 0.4507 - val_Multilabel_confusion_matrix: 50.2500\n",
      "Epoch 12/100\n",
      "20/20 - 98s - loss: 2.2658 - accuracy: 0.6770 - auc_2: 0.9579 - f1_score: 0.5345 - Multilabel_confusion_matrix: 153.2500 - val_loss: 2.2949 - val_accuracy: 0.6119 - val_auc_2: 0.9105 - val_f1_score: 0.4667 - val_Multilabel_confusion_matrix: 50.2500\n",
      "Epoch 13/100\n",
      "20/20 - 97s - loss: 2.2268 - accuracy: 0.6737 - auc_2: 0.9606 - f1_score: 0.5255 - Multilabel_confusion_matrix: 153.2500 - val_loss: 2.2611 - val_accuracy: 0.6169 - val_auc_2: 0.9164 - val_f1_score: 0.4566 - val_Multilabel_confusion_matrix: 50.2500\n",
      "Epoch 14/100\n",
      "20/20 - 97s - loss: 2.1896 - accuracy: 0.6852 - auc_2: 0.9639 - f1_score: 0.5377 - Multilabel_confusion_matrix: 153.2500 - val_loss: 2.2272 - val_accuracy: 0.6119 - val_auc_2: 0.9182 - val_f1_score: 0.4515 - val_Multilabel_confusion_matrix: 50.2500\n",
      "Epoch 15/100\n",
      "20/20 - 98s - loss: 2.1521 - accuracy: 0.6754 - auc_2: 0.9657 - f1_score: 0.5238 - Multilabel_confusion_matrix: 153.2500 - val_loss: 2.1938 - val_accuracy: 0.6119 - val_auc_2: 0.9191 - val_f1_score: 0.4512 - val_Multilabel_confusion_matrix: 50.2500\n",
      "Epoch 16/100\n",
      "20/20 - 95s - loss: 2.1147 - accuracy: 0.6737 - auc_2: 0.9690 - f1_score: 0.5256 - Multilabel_confusion_matrix: 153.2500 - val_loss: 2.1608 - val_accuracy: 0.6070 - val_auc_2: 0.9214 - val_f1_score: 0.4467 - val_Multilabel_confusion_matrix: 50.2500\n",
      "Epoch 17/100\n",
      "20/20 - 99s - loss: 2.0777 - accuracy: 0.6786 - auc_2: 0.9706 - f1_score: 0.5241 - Multilabel_confusion_matrix: 153.2500 - val_loss: 2.1299 - val_accuracy: 0.6070 - val_auc_2: 0.9249 - val_f1_score: 0.4467 - val_Multilabel_confusion_matrix: 50.2500\n",
      "Epoch 18/100\n",
      "20/20 - 96s - loss: 2.0422 - accuracy: 0.6786 - auc_2: 0.9731 - f1_score: 0.5307 - Multilabel_confusion_matrix: 153.2500 - val_loss: 2.0981 - val_accuracy: 0.6119 - val_auc_2: 0.9285 - val_f1_score: 0.4488 - val_Multilabel_confusion_matrix: 50.2500\n",
      "Epoch 19/100\n",
      "20/20 - 95s - loss: 2.0058 - accuracy: 0.6852 - auc_2: 0.9727 - f1_score: 0.5354 - Multilabel_confusion_matrix: 153.2500 - val_loss: 2.0658 - val_accuracy: 0.6070 - val_auc_2: 0.9338 - val_f1_score: 0.4432 - val_Multilabel_confusion_matrix: 50.2500\n",
      "Epoch 20/100\n",
      "20/20 - 94s - loss: 1.9712 - accuracy: 0.6900 - auc_2: 0.9739 - f1_score: 0.5397 - Multilabel_confusion_matrix: 153.2500 - val_loss: 2.0345 - val_accuracy: 0.6020 - val_auc_2: 0.9350 - val_f1_score: 0.4377 - val_Multilabel_confusion_matrix: 50.2500\n",
      "Epoch 21/100\n",
      "20/20 - 96s - loss: 1.9339 - accuracy: 0.6852 - auc_2: 0.9755 - f1_score: 0.5312 - Multilabel_confusion_matrix: 153.2500 - val_loss: 2.0041 - val_accuracy: 0.6020 - val_auc_2: 0.9349 - val_f1_score: 0.4377 - val_Multilabel_confusion_matrix: 50.2500\n",
      "Epoch 22/100\n",
      "20/20 - 93s - loss: 1.8990 - accuracy: 0.6835 - auc_2: 0.9761 - f1_score: 0.5300 - Multilabel_confusion_matrix: 153.2500 - val_loss: 1.9739 - val_accuracy: 0.5970 - val_auc_2: 0.9390 - val_f1_score: 0.4309 - val_Multilabel_confusion_matrix: 50.2500\n",
      "Epoch 23/100\n",
      "20/20 - 93s - loss: 1.8657 - accuracy: 0.6868 - auc_2: 0.9764 - f1_score: 0.5314 - Multilabel_confusion_matrix: 153.2500 - val_loss: 1.9448 - val_accuracy: 0.5920 - val_auc_2: 0.9407 - val_f1_score: 0.4246 - val_Multilabel_confusion_matrix: 50.2500\n",
      "Epoch 24/100\n",
      "20/20 - 95s - loss: 1.8335 - accuracy: 0.6900 - auc_2: 0.9777 - f1_score: 0.5384 - Multilabel_confusion_matrix: 153.2500 - val_loss: 1.9154 - val_accuracy: 0.5920 - val_auc_2: 0.9418 - val_f1_score: 0.4249 - val_Multilabel_confusion_matrix: 50.2500\n",
      "Epoch 25/100\n",
      "20/20 - 93s - loss: 1.7990 - accuracy: 0.6949 - auc_2: 0.9789 - f1_score: 0.5464 - Multilabel_confusion_matrix: 153.2500 - val_loss: 1.8867 - val_accuracy: 0.6020 - val_auc_2: 0.9422 - val_f1_score: 0.4358 - val_Multilabel_confusion_matrix: 50.2500\n",
      "Epoch 26/100\n",
      "20/20 - 94s - loss: 1.7683 - accuracy: 0.6982 - auc_2: 0.9788 - f1_score: 0.5497 - Multilabel_confusion_matrix: 153.2500 - val_loss: 1.8585 - val_accuracy: 0.6020 - val_auc_2: 0.9441 - val_f1_score: 0.4362 - val_Multilabel_confusion_matrix: 50.2500\n",
      "Epoch 27/100\n",
      "20/20 - 96s - loss: 1.7368 - accuracy: 0.6998 - auc_2: 0.9805 - f1_score: 0.5523 - Multilabel_confusion_matrix: 153.2500 - val_loss: 1.8308 - val_accuracy: 0.6020 - val_auc_2: 0.9453 - val_f1_score: 0.4353 - val_Multilabel_confusion_matrix: 50.2500\n",
      "Epoch 28/100\n",
      "20/20 - 94s - loss: 1.7060 - accuracy: 0.6966 - auc_2: 0.9803 - f1_score: 0.5493 - Multilabel_confusion_matrix: 153.2500 - val_loss: 1.8049 - val_accuracy: 0.6070 - val_auc_2: 0.9470 - val_f1_score: 0.4491 - val_Multilabel_confusion_matrix: 50.2500\n",
      "Epoch 29/100\n",
      "20/20 - 95s - loss: 1.6748 - accuracy: 0.7096 - auc_2: 0.9810 - f1_score: 0.5628 - Multilabel_confusion_matrix: 153.2500 - val_loss: 1.7787 - val_accuracy: 0.6119 - val_auc_2: 0.9477 - val_f1_score: 0.4533 - val_Multilabel_confusion_matrix: 50.2500\n",
      "Epoch 30/100\n",
      "20/20 - 95s - loss: 1.6454 - accuracy: 0.7129 - auc_2: 0.9814 - f1_score: 0.5630 - Multilabel_confusion_matrix: 153.2500 - val_loss: 1.7525 - val_accuracy: 0.6070 - val_auc_2: 0.9495 - val_f1_score: 0.4459 - val_Multilabel_confusion_matrix: 50.2500\n",
      "Epoch 31/100\n",
      "20/20 - 97s - loss: 1.6148 - accuracy: 0.7047 - auc_2: 0.9818 - f1_score: 0.5596 - Multilabel_confusion_matrix: 153.2500 - val_loss: 1.7270 - val_accuracy: 0.6169 - val_auc_2: 0.9482 - val_f1_score: 0.4599 - val_Multilabel_confusion_matrix: 50.2500\n",
      "Epoch 32/100\n",
      "20/20 - 99s - loss: 1.5876 - accuracy: 0.7162 - auc_2: 0.9827 - f1_score: 0.5653 - Multilabel_confusion_matrix: 153.2500 - val_loss: 1.7030 - val_accuracy: 0.6219 - val_auc_2: 0.9502 - val_f1_score: 0.4643 - val_Multilabel_confusion_matrix: 50.2500\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 33/100\n",
      "20/20 - 102s - loss: 1.5593 - accuracy: 0.7243 - auc_2: 0.9830 - f1_score: 0.5759 - Multilabel_confusion_matrix: 153.2500 - val_loss: 1.6793 - val_accuracy: 0.6269 - val_auc_2: 0.9513 - val_f1_score: 0.4685 - val_Multilabel_confusion_matrix: 50.2500\n",
      "Epoch 34/100\n",
      "20/20 - 98s - loss: 1.5320 - accuracy: 0.7210 - auc_2: 0.9838 - f1_score: 0.5731 - Multilabel_confusion_matrix: 153.2500 - val_loss: 1.6572 - val_accuracy: 0.6269 - val_auc_2: 0.9517 - val_f1_score: 0.4682 - val_Multilabel_confusion_matrix: 50.2500\n",
      "Epoch 35/100\n",
      "20/20 - 103s - loss: 1.5061 - accuracy: 0.7259 - auc_2: 0.9838 - f1_score: 0.5768 - Multilabel_confusion_matrix: 153.2500 - val_loss: 1.6346 - val_accuracy: 0.6318 - val_auc_2: 0.9536 - val_f1_score: 0.4801 - val_Multilabel_confusion_matrix: 50.2500\n",
      "Epoch 36/100\n",
      "20/20 - 110s - loss: 1.4803 - accuracy: 0.7243 - auc_2: 0.9842 - f1_score: 0.5819 - Multilabel_confusion_matrix: 153.2500 - val_loss: 1.6118 - val_accuracy: 0.6368 - val_auc_2: 0.9539 - val_f1_score: 0.4866 - val_Multilabel_confusion_matrix: 50.2500\n",
      "Epoch 37/100\n",
      "20/20 - 114s - loss: 1.4539 - accuracy: 0.7308 - auc_2: 0.9846 - f1_score: 0.5836 - Multilabel_confusion_matrix: 153.2500 - val_loss: 1.5903 - val_accuracy: 0.6418 - val_auc_2: 0.9549 - val_f1_score: 0.4951 - val_Multilabel_confusion_matrix: 50.2500\n",
      "Epoch 38/100\n",
      "20/20 - 112s - loss: 1.4273 - accuracy: 0.7325 - auc_2: 0.9852 - f1_score: 0.5838 - Multilabel_confusion_matrix: 153.2500 - val_loss: 1.5686 - val_accuracy: 0.6418 - val_auc_2: 0.9551 - val_f1_score: 0.4954 - val_Multilabel_confusion_matrix: 50.2500\n",
      "Epoch 39/100\n",
      "20/20 - 120s - loss: 1.4048 - accuracy: 0.7390 - auc_2: 0.9856 - f1_score: 0.6036 - Multilabel_confusion_matrix: 153.2500 - val_loss: 1.5481 - val_accuracy: 0.6468 - val_auc_2: 0.9555 - val_f1_score: 0.5067 - val_Multilabel_confusion_matrix: 50.2500\n",
      "Epoch 40/100\n",
      "20/20 - 126s - loss: 1.3791 - accuracy: 0.7455 - auc_2: 0.9861 - f1_score: 0.6043 - Multilabel_confusion_matrix: 153.2500 - val_loss: 1.5275 - val_accuracy: 0.6468 - val_auc_2: 0.9564 - val_f1_score: 0.5057 - val_Multilabel_confusion_matrix: 50.2500\n",
      "Epoch 41/100\n",
      "20/20 - 114s - loss: 1.3558 - accuracy: 0.7455 - auc_2: 0.9864 - f1_score: 0.6066 - Multilabel_confusion_matrix: 153.2500 - val_loss: 1.5079 - val_accuracy: 0.6517 - val_auc_2: 0.9570 - val_f1_score: 0.5170 - val_Multilabel_confusion_matrix: 50.2500\n",
      "Epoch 42/100\n",
      "20/20 - 116s - loss: 1.3329 - accuracy: 0.7537 - auc_2: 0.9864 - f1_score: 0.6182 - Multilabel_confusion_matrix: 153.2500 - val_loss: 1.4885 - val_accuracy: 0.6567 - val_auc_2: 0.9583 - val_f1_score: 0.5211 - val_Multilabel_confusion_matrix: 50.2500\n",
      "Epoch 43/100\n",
      "20/20 - 109s - loss: 1.3134 - accuracy: 0.7455 - auc_2: 0.9866 - f1_score: 0.6109 - Multilabel_confusion_matrix: 153.2500 - val_loss: 1.4698 - val_accuracy: 0.6567 - val_auc_2: 0.9583 - val_f1_score: 0.5211 - val_Multilabel_confusion_matrix: 50.2500\n",
      "Epoch 44/100\n",
      "20/20 - 124s - loss: 1.2901 - accuracy: 0.7520 - auc_2: 0.9869 - f1_score: 0.6152 - Multilabel_confusion_matrix: 153.2500 - val_loss: 1.4515 - val_accuracy: 0.6667 - val_auc_2: 0.9593 - val_f1_score: 0.5309 - val_Multilabel_confusion_matrix: 50.2500\n",
      "Epoch 45/100\n",
      "20/20 - 112s - loss: 1.2690 - accuracy: 0.7618 - auc_2: 0.9870 - f1_score: 0.6364 - Multilabel_confusion_matrix: 153.2500 - val_loss: 1.4335 - val_accuracy: 0.6667 - val_auc_2: 0.9597 - val_f1_score: 0.5314 - val_Multilabel_confusion_matrix: 50.2500\n",
      "Epoch 46/100\n",
      "20/20 - 123s - loss: 1.2467 - accuracy: 0.7684 - auc_2: 0.9875 - f1_score: 0.6332 - Multilabel_confusion_matrix: 153.2500 - val_loss: 1.4160 - val_accuracy: 0.6716 - val_auc_2: 0.9604 - val_f1_score: 0.5398 - val_Multilabel_confusion_matrix: 50.2500\n",
      "Epoch 47/100\n",
      "20/20 - 119s - loss: 1.2281 - accuracy: 0.7765 - auc_2: 0.9876 - f1_score: 0.6536 - Multilabel_confusion_matrix: 153.2500 - val_loss: 1.3996 - val_accuracy: 0.6766 - val_auc_2: 0.9609 - val_f1_score: 0.5458 - val_Multilabel_confusion_matrix: 50.2500\n",
      "Epoch 48/100\n",
      "20/20 - 106s - loss: 1.2096 - accuracy: 0.7667 - auc_2: 0.9880 - f1_score: 0.6417 - Multilabel_confusion_matrix: 153.2500 - val_loss: 1.3834 - val_accuracy: 0.6866 - val_auc_2: 0.9623 - val_f1_score: 0.5695 - val_Multilabel_confusion_matrix: 50.2500\n",
      "Epoch 49/100\n",
      "20/20 - 121s - loss: 1.1911 - accuracy: 0.7765 - auc_2: 0.9882 - f1_score: 0.6460 - Multilabel_confusion_matrix: 153.2500 - val_loss: 1.3676 - val_accuracy: 0.6866 - val_auc_2: 0.9623 - val_f1_score: 0.5693 - val_Multilabel_confusion_matrix: 50.2500\n",
      "Epoch 50/100\n",
      "20/20 - 108s - loss: 1.1709 - accuracy: 0.7814 - auc_2: 0.9884 - f1_score: 0.6584 - Multilabel_confusion_matrix: 153.2500 - val_loss: 1.3517 - val_accuracy: 0.6866 - val_auc_2: 0.9619 - val_f1_score: 0.5693 - val_Multilabel_confusion_matrix: 50.2500\n",
      "Epoch 51/100\n",
      "20/20 - 106s - loss: 1.1547 - accuracy: 0.7798 - auc_2: 0.9884 - f1_score: 0.6529 - Multilabel_confusion_matrix: 153.2500 - val_loss: 1.3370 - val_accuracy: 0.6866 - val_auc_2: 0.9624 - val_f1_score: 0.5693 - val_Multilabel_confusion_matrix: 50.2500\n",
      "Epoch 52/100\n",
      "20/20 - 105s - loss: 1.1357 - accuracy: 0.7863 - auc_2: 0.9888 - f1_score: 0.6631 - Multilabel_confusion_matrix: 153.2500 - val_loss: 1.3217 - val_accuracy: 0.6866 - val_auc_2: 0.9625 - val_f1_score: 0.5693 - val_Multilabel_confusion_matrix: 50.2500\n",
      "Epoch 53/100\n",
      "20/20 - 108s - loss: 1.1199 - accuracy: 0.7863 - auc_2: 0.9888 - f1_score: 0.6731 - Multilabel_confusion_matrix: 153.2500 - val_loss: 1.3078 - val_accuracy: 0.6866 - val_auc_2: 0.9632 - val_f1_score: 0.5693 - val_Multilabel_confusion_matrix: 50.2500\n",
      "Epoch 54/100\n",
      "20/20 - 117s - loss: 1.1030 - accuracy: 0.7912 - auc_2: 0.9892 - f1_score: 0.6827 - Multilabel_confusion_matrix: 153.2500 - val_loss: 1.2937 - val_accuracy: 0.6866 - val_auc_2: 0.9638 - val_f1_score: 0.5683 - val_Multilabel_confusion_matrix: 50.2500\n",
      "Epoch 55/100\n",
      "20/20 - 122s - loss: 1.0867 - accuracy: 0.7961 - auc_2: 0.9893 - f1_score: 0.6859 - Multilabel_confusion_matrix: 153.2500 - val_loss: 1.2807 - val_accuracy: 0.6915 - val_auc_2: 0.9639 - val_f1_score: 0.5717 - val_Multilabel_confusion_matrix: 50.2500\n",
      "Epoch 56/100\n",
      "20/20 - 111s - loss: 1.0700 - accuracy: 0.7977 - auc_2: 0.9894 - f1_score: 0.6968 - Multilabel_confusion_matrix: 153.2500 - val_loss: 1.2673 - val_accuracy: 0.6915 - val_auc_2: 0.9643 - val_f1_score: 0.5717 - val_Multilabel_confusion_matrix: 50.2500\n",
      "Epoch 57/100\n",
      "20/20 - 112s - loss: 1.0548 - accuracy: 0.7945 - auc_2: 0.9897 - f1_score: 0.6886 - Multilabel_confusion_matrix: 153.2500 - val_loss: 1.2537 - val_accuracy: 0.6965 - val_auc_2: 0.9646 - val_f1_score: 0.5872 - val_Multilabel_confusion_matrix: 50.2500\n",
      "Epoch 58/100\n",
      "20/20 - 120s - loss: 1.0378 - accuracy: 0.7993 - auc_2: 0.9900 - f1_score: 0.6944 - Multilabel_confusion_matrix: 153.2500 - val_loss: 1.2406 - val_accuracy: 0.6965 - val_auc_2: 0.9651 - val_f1_score: 0.5872 - val_Multilabel_confusion_matrix: 50.2500\n",
      "Epoch 59/100\n",
      "20/20 - 128s - loss: 1.0228 - accuracy: 0.8108 - auc_2: 0.9900 - f1_score: 0.7188 - Multilabel_confusion_matrix: 153.2500 - val_loss: 1.2284 - val_accuracy: 0.6965 - val_auc_2: 0.9655 - val_f1_score: 0.5870 - val_Multilabel_confusion_matrix: 50.2500\n",
      "Epoch 60/100\n",
      "20/20 - 126s - loss: 1.0093 - accuracy: 0.8173 - auc_2: 0.9903 - f1_score: 0.7369 - Multilabel_confusion_matrix: 153.2500 - val_loss: 1.2161 - val_accuracy: 0.7065 - val_auc_2: 0.9656 - val_f1_score: 0.5990 - val_Multilabel_confusion_matrix: 50.2500\n",
      "Epoch 61/100\n",
      "20/20 - 118s - loss: 0.9935 - accuracy: 0.8140 - auc_2: 0.9904 - f1_score: 0.7227 - Multilabel_confusion_matrix: 153.2500 - val_loss: 1.2042 - val_accuracy: 0.7164 - val_auc_2: 0.9664 - val_f1_score: 0.6157 - val_Multilabel_confusion_matrix: 50.2500\n",
      "Epoch 62/100\n",
      "20/20 - 118s - loss: 0.9802 - accuracy: 0.8189 - auc_2: 0.9905 - f1_score: 0.7312 - Multilabel_confusion_matrix: 153.2500 - val_loss: 1.1928 - val_accuracy: 0.7164 - val_auc_2: 0.9667 - val_f1_score: 0.6157 - val_Multilabel_confusion_matrix: 50.2500\n",
      "Epoch 63/100\n",
      "20/20 - 116s - loss: 0.9673 - accuracy: 0.8287 - auc_2: 0.9907 - f1_score: 0.7485 - Multilabel_confusion_matrix: 153.2500 - val_loss: 1.1810 - val_accuracy: 0.7164 - val_auc_2: 0.9665 - val_f1_score: 0.6167 - val_Multilabel_confusion_matrix: 50.2500\n",
      "Epoch 64/100\n",
      "20/20 - 115s - loss: 0.9542 - accuracy: 0.8238 - auc_2: 0.9908 - f1_score: 0.7415 - Multilabel_confusion_matrix: 153.2500 - val_loss: 1.1701 - val_accuracy: 0.7214 - val_auc_2: 0.9662 - val_f1_score: 0.6233 - val_Multilabel_confusion_matrix: 50.2500\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 65/100\n",
      "20/20 - 124s - loss: 0.9395 - accuracy: 0.8320 - auc_2: 0.9910 - f1_score: 0.7642 - Multilabel_confusion_matrix: 153.2500 - val_loss: 1.1592 - val_accuracy: 0.7214 - val_auc_2: 0.9667 - val_f1_score: 0.6237 - val_Multilabel_confusion_matrix: 50.2500\n",
      "Epoch 66/100\n",
      "20/20 - 119s - loss: 0.9272 - accuracy: 0.8336 - auc_2: 0.9913 - f1_score: 0.7561 - Multilabel_confusion_matrix: 153.2500 - val_loss: 1.1487 - val_accuracy: 0.7214 - val_auc_2: 0.9670 - val_f1_score: 0.6237 - val_Multilabel_confusion_matrix: 50.2500\n",
      "Epoch 67/100\n",
      "20/20 - 122s - loss: 0.9138 - accuracy: 0.8467 - auc_2: 0.9912 - f1_score: 0.7757 - Multilabel_confusion_matrix: 153.2500 - val_loss: 1.1383 - val_accuracy: 0.7313 - val_auc_2: 0.9673 - val_f1_score: 0.6412 - val_Multilabel_confusion_matrix: 50.2500\n",
      "Epoch 68/100\n",
      "20/20 - 125s - loss: 0.9003 - accuracy: 0.8499 - auc_2: 0.9914 - f1_score: 0.7879 - Multilabel_confusion_matrix: 153.2500 - val_loss: 1.1286 - val_accuracy: 0.7264 - val_auc_2: 0.9672 - val_f1_score: 0.6383 - val_Multilabel_confusion_matrix: 50.2500\n",
      "Epoch 69/100\n",
      "20/20 - 128s - loss: 0.8897 - accuracy: 0.8515 - auc_2: 0.9916 - f1_score: 0.7859 - Multilabel_confusion_matrix: 153.2500 - val_loss: 1.1193 - val_accuracy: 0.7313 - val_auc_2: 0.9672 - val_f1_score: 0.6404 - val_Multilabel_confusion_matrix: 50.2500\n",
      "Epoch 70/100\n",
      "20/20 - 141s - loss: 0.8797 - accuracy: 0.8597 - auc_2: 0.9916 - f1_score: 0.8084 - Multilabel_confusion_matrix: 153.2500 - val_loss: 1.1099 - val_accuracy: 0.7313 - val_auc_2: 0.9674 - val_f1_score: 0.6480 - val_Multilabel_confusion_matrix: 50.2500\n",
      "Epoch 71/100\n",
      "20/20 - 142s - loss: 0.8693 - accuracy: 0.8613 - auc_2: 0.9917 - f1_score: 0.8087 - Multilabel_confusion_matrix: 153.2500 - val_loss: 1.1003 - val_accuracy: 0.7413 - val_auc_2: 0.9681 - val_f1_score: 0.6596 - val_Multilabel_confusion_matrix: 50.2500\n",
      "Epoch 72/100\n",
      "20/20 - 146s - loss: 0.8574 - accuracy: 0.8679 - auc_2: 0.9917 - f1_score: 0.8219 - Multilabel_confusion_matrix: 153.2500 - val_loss: 1.0910 - val_accuracy: 0.7413 - val_auc_2: 0.9684 - val_f1_score: 0.6596 - val_Multilabel_confusion_matrix: 50.2500\n",
      "Epoch 73/100\n",
      "20/20 - 151s - loss: 0.8441 - accuracy: 0.8744 - auc_2: 0.9919 - f1_score: 0.8243 - Multilabel_confusion_matrix: 153.2500 - val_loss: 1.0814 - val_accuracy: 0.7512 - val_auc_2: 0.9685 - val_f1_score: 0.6766 - val_Multilabel_confusion_matrix: 50.2500\n",
      "Epoch 74/100\n",
      "20/20 - 151s - loss: 0.8347 - accuracy: 0.8711 - auc_2: 0.9922 - f1_score: 0.8315 - Multilabel_confusion_matrix: 153.2500 - val_loss: 1.0727 - val_accuracy: 0.7562 - val_auc_2: 0.9686 - val_f1_score: 0.6808 - val_Multilabel_confusion_matrix: 50.2500\n",
      "Epoch 75/100\n",
      "20/20 - 120s - loss: 0.8271 - accuracy: 0.8728 - auc_2: 0.9922 - f1_score: 0.8283 - Multilabel_confusion_matrix: 153.2500 - val_loss: 1.0640 - val_accuracy: 0.7562 - val_auc_2: 0.9693 - val_f1_score: 0.6785 - val_Multilabel_confusion_matrix: 50.2500\n",
      "Epoch 76/100\n",
      "20/20 - 118s - loss: 0.8142 - accuracy: 0.8809 - auc_2: 0.9924 - f1_score: 0.8424 - Multilabel_confusion_matrix: 153.2500 - val_loss: 1.0556 - val_accuracy: 0.7612 - val_auc_2: 0.9692 - val_f1_score: 0.6849 - val_Multilabel_confusion_matrix: 50.2500\n",
      "Epoch 77/100\n",
      "20/20 - 128s - loss: 0.8003 - accuracy: 0.8874 - auc_2: 0.9928 - f1_score: 0.8512 - Multilabel_confusion_matrix: 153.2500 - val_loss: 1.0476 - val_accuracy: 0.7612 - val_auc_2: 0.9690 - val_f1_score: 0.6847 - val_Multilabel_confusion_matrix: 50.2500\n",
      "Epoch 78/100\n",
      "20/20 - 157s - loss: 0.7921 - accuracy: 0.8825 - auc_2: 0.9924 - f1_score: 0.8448 - Multilabel_confusion_matrix: 153.2500 - val_loss: 1.0393 - val_accuracy: 0.7612 - val_auc_2: 0.9693 - val_f1_score: 0.6847 - val_Multilabel_confusion_matrix: 50.2500\n",
      "Epoch 79/100\n",
      "20/20 - 121s - loss: 0.7814 - accuracy: 0.8940 - auc_2: 0.9927 - f1_score: 0.8605 - Multilabel_confusion_matrix: 153.2500 - val_loss: 1.0314 - val_accuracy: 0.7612 - val_auc_2: 0.9696 - val_f1_score: 0.6847 - val_Multilabel_confusion_matrix: 50.2500\n",
      "Epoch 80/100\n",
      "20/20 - 131s - loss: 0.7729 - accuracy: 0.8825 - auc_2: 0.9928 - f1_score: 0.8477 - Multilabel_confusion_matrix: 153.2500 - val_loss: 1.0233 - val_accuracy: 0.7612 - val_auc_2: 0.9698 - val_f1_score: 0.6847 - val_Multilabel_confusion_matrix: 50.2500\n",
      "Epoch 81/100\n",
      "20/20 - 161s - loss: 0.7640 - accuracy: 0.8907 - auc_2: 0.9926 - f1_score: 0.8576 - Multilabel_confusion_matrix: 153.2500 - val_loss: 1.0158 - val_accuracy: 0.7612 - val_auc_2: 0.9700 - val_f1_score: 0.6863 - val_Multilabel_confusion_matrix: 50.2500\n",
      "Epoch 82/100\n",
      "20/20 - 114s - loss: 0.7559 - accuracy: 0.8989 - auc_2: 0.9928 - f1_score: 0.8680 - Multilabel_confusion_matrix: 153.2500 - val_loss: 1.0087 - val_accuracy: 0.7662 - val_auc_2: 0.9701 - val_f1_score: 0.6924 - val_Multilabel_confusion_matrix: 50.2500\n",
      "Epoch 83/100\n",
      "20/20 - 112s - loss: 0.7459 - accuracy: 0.8940 - auc_2: 0.9931 - f1_score: 0.8621 - Multilabel_confusion_matrix: 153.2500 - val_loss: 1.0012 - val_accuracy: 0.7711 - val_auc_2: 0.9704 - val_f1_score: 0.6973 - val_Multilabel_confusion_matrix: 50.2500\n",
      "Epoch 84/100\n",
      "20/20 - 112s - loss: 0.7391 - accuracy: 0.8891 - auc_2: 0.9931 - f1_score: 0.8578 - Multilabel_confusion_matrix: 153.2500 - val_loss: 0.9937 - val_accuracy: 0.7711 - val_auc_2: 0.9709 - val_f1_score: 0.6973 - val_Multilabel_confusion_matrix: 50.2500\n",
      "Epoch 85/100\n",
      "20/20 - 109s - loss: 0.7288 - accuracy: 0.8989 - auc_2: 0.9930 - f1_score: 0.8685 - Multilabel_confusion_matrix: 153.2500 - val_loss: 0.9864 - val_accuracy: 0.7711 - val_auc_2: 0.9711 - val_f1_score: 0.6973 - val_Multilabel_confusion_matrix: 50.2500\n",
      "Epoch 86/100\n",
      "20/20 - 121s - loss: 0.7228 - accuracy: 0.9021 - auc_2: 0.9935 - f1_score: 0.8703 - Multilabel_confusion_matrix: 153.2500 - val_loss: 0.9799 - val_accuracy: 0.7662 - val_auc_2: 0.9712 - val_f1_score: 0.6935 - val_Multilabel_confusion_matrix: 50.2500\n",
      "Epoch 87/100\n",
      "20/20 - 166s - loss: 0.7136 - accuracy: 0.8989 - auc_2: 0.9933 - f1_score: 0.8703 - Multilabel_confusion_matrix: 153.2500 - val_loss: 0.9735 - val_accuracy: 0.7711 - val_auc_2: 0.9717 - val_f1_score: 0.7069 - val_Multilabel_confusion_matrix: 50.2500\n",
      "Epoch 88/100\n",
      "20/20 - 115s - loss: 0.7056 - accuracy: 0.9005 - auc_2: 0.9934 - f1_score: 0.8707 - Multilabel_confusion_matrix: 153.2500 - val_loss: 0.9669 - val_accuracy: 0.7711 - val_auc_2: 0.9718 - val_f1_score: 0.7069 - val_Multilabel_confusion_matrix: 50.2500\n",
      "Epoch 89/100\n",
      "20/20 - 119s - loss: 0.6982 - accuracy: 0.8972 - auc_2: 0.9939 - f1_score: 0.8677 - Multilabel_confusion_matrix: 153.2500 - val_loss: 0.9608 - val_accuracy: 0.7711 - val_auc_2: 0.9718 - val_f1_score: 0.7058 - val_Multilabel_confusion_matrix: 50.2500\n",
      "Epoch 90/100\n",
      "20/20 - 120s - loss: 0.6909 - accuracy: 0.8972 - auc_2: 0.9935 - f1_score: 0.8690 - Multilabel_confusion_matrix: 153.2500 - val_loss: 0.9547 - val_accuracy: 0.7711 - val_auc_2: 0.9719 - val_f1_score: 0.7069 - val_Multilabel_confusion_matrix: 50.2500\n",
      "Epoch 91/100\n",
      "20/20 - 124s - loss: 0.6838 - accuracy: 0.9070 - auc_2: 0.9936 - f1_score: 0.8826 - Multilabel_confusion_matrix: 153.2500 - val_loss: 0.9486 - val_accuracy: 0.7711 - val_auc_2: 0.9720 - val_f1_score: 0.7058 - val_Multilabel_confusion_matrix: 50.2500\n",
      "Epoch 92/100\n",
      "20/20 - 123s - loss: 0.6769 - accuracy: 0.9070 - auc_2: 0.9937 - f1_score: 0.8817 - Multilabel_confusion_matrix: 153.2500 - val_loss: 0.9424 - val_accuracy: 0.7711 - val_auc_2: 0.9721 - val_f1_score: 0.7058 - val_Multilabel_confusion_matrix: 50.2500\n",
      "Epoch 93/100\n",
      "20/20 - 126s - loss: 0.6694 - accuracy: 0.9086 - auc_2: 0.9939 - f1_score: 0.8850 - Multilabel_confusion_matrix: 153.2500 - val_loss: 0.9367 - val_accuracy: 0.7711 - val_auc_2: 0.9720 - val_f1_score: 0.7058 - val_Multilabel_confusion_matrix: 50.2500\n",
      "Epoch 94/100\n",
      "20/20 - 129s - loss: 0.6602 - accuracy: 0.9135 - auc_2: 0.9938 - f1_score: 0.8902 - Multilabel_confusion_matrix: 153.2500 - val_loss: 0.9310 - val_accuracy: 0.7811 - val_auc_2: 0.9722 - val_f1_score: 0.7161 - val_Multilabel_confusion_matrix: 50.2500\n",
      "Epoch 95/100\n",
      "20/20 - 127s - loss: 0.6548 - accuracy: 0.9119 - auc_2: 0.9942 - f1_score: 0.8901 - Multilabel_confusion_matrix: 153.2500 - val_loss: 0.9253 - val_accuracy: 0.7811 - val_auc_2: 0.9721 - val_f1_score: 0.7161 - val_Multilabel_confusion_matrix: 50.2500\n",
      "Epoch 96/100\n",
      "20/20 - 131s - loss: 0.6465 - accuracy: 0.9086 - auc_2: 0.9940 - f1_score: 0.8867 - Multilabel_confusion_matrix: 153.2500 - val_loss: 0.9198 - val_accuracy: 0.7811 - val_auc_2: 0.9723 - val_f1_score: 0.7161 - val_Multilabel_confusion_matrix: 50.2500\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 97/100\n",
      "20/20 - 127s - loss: 0.6386 - accuracy: 0.9152 - auc_2: 0.9941 - f1_score: 0.8934 - Multilabel_confusion_matrix: 153.2500 - val_loss: 0.9144 - val_accuracy: 0.7861 - val_auc_2: 0.9725 - val_f1_score: 0.7275 - val_Multilabel_confusion_matrix: 50.2500\n",
      "Epoch 98/100\n",
      "20/20 - 130s - loss: 0.6322 - accuracy: 0.9119 - auc_2: 0.9943 - f1_score: 0.8924 - Multilabel_confusion_matrix: 153.2500 - val_loss: 0.9093 - val_accuracy: 0.7861 - val_auc_2: 0.9725 - val_f1_score: 0.7275 - val_Multilabel_confusion_matrix: 50.2500\n",
      "Epoch 99/100\n",
      "20/20 - 125s - loss: 0.6242 - accuracy: 0.9184 - auc_2: 0.9943 - f1_score: 0.8977 - Multilabel_confusion_matrix: 153.2500 - val_loss: 0.9040 - val_accuracy: 0.7861 - val_auc_2: 0.9727 - val_f1_score: 0.7275 - val_Multilabel_confusion_matrix: 50.2500\n",
      "Epoch 100/100\n",
      "20/20 - 128s - loss: 0.6195 - accuracy: 0.9168 - auc_2: 0.9943 - f1_score: 0.8959 - Multilabel_confusion_matrix: 153.2500 - val_loss: 0.8991 - val_accuracy: 0.7861 - val_auc_2: 0.9730 - val_f1_score: 0.7275 - val_Multilabel_confusion_matrix: 50.2500\n"
     ]
    }
   ],
   "source": [
    "#training for 100 epochs\n",
    "history=model.fit(training_generator,validation_data=validation_generator,epochs=100,verbose=2, callbacks=early)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "4F1yNvnnqpjc"
   },
   "source": [
    "### Lets visualize the output of the training phase"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "XYaRMnfWC-d2"
   },
   "source": [
    "## 413 from 426"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 762
    },
    "executionInfo": {
     "elapsed": 1117,
     "status": "ok",
     "timestamp": 1619471625557,
     "user": {
      "displayName": "blaise mavin",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GgUhbcpHWNXczan3ui3BGJe8CM_G7bUv9e9RG5m9A=s64",
      "userId": "14168894997160457371"
     },
     "user_tz": -180
    },
    "id": "uWpYratUmo8T",
    "outputId": "ab8c999d-7b5e-4b27-d578-26e00f3863ba"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "ename": "ValueError",
     "evalue": "x and y can be no greater than 2-D, but have shapes (100,) and (100, 15, 2, 2)",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-20-e6daac9fe330>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m     38\u001b[0m \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshow\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     39\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 40\u001b[1;33m \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mepochs\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mMultilabel_confusion_matrix\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;34m'b'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mlabel\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m\"Training Multilable CM\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     41\u001b[0m \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mepochs\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mval_Multilabel_confusion_matrix\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;34m'r'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mlabel\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m\"Validation Multilable CM\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     42\u001b[0m \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mlegend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\envs\\tf\\lib\\site-packages\\matplotlib\\pyplot.py\u001b[0m in \u001b[0;36mplot\u001b[1;34m(scalex, scaley, data, *args, **kwargs)\u001b[0m\n\u001b[0;32m   2840\u001b[0m     return gca().plot(\n\u001b[0;32m   2841\u001b[0m         \u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mscalex\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mscalex\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mscaley\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mscaley\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2842\u001b[1;33m         **({\"data\": data} if data is not None else {}), **kwargs)\n\u001b[0m\u001b[0;32m   2843\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2844\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\envs\\tf\\lib\\site-packages\\matplotlib\\axes\\_axes.py\u001b[0m in \u001b[0;36mplot\u001b[1;34m(self, scalex, scaley, data, *args, **kwargs)\u001b[0m\n\u001b[0;32m   1741\u001b[0m         \"\"\"\n\u001b[0;32m   1742\u001b[0m         \u001b[0mkwargs\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcbook\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mnormalize_kwargs\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmlines\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mLine2D\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1743\u001b[1;33m         \u001b[0mlines\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_get_lines\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1744\u001b[0m         \u001b[1;32mfor\u001b[0m \u001b[0mline\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mlines\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1745\u001b[0m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0madd_line\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mline\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\envs\\tf\\lib\\site-packages\\matplotlib\\axes\\_base.py\u001b[0m in \u001b[0;36m__call__\u001b[1;34m(self, data, *args, **kwargs)\u001b[0m\n\u001b[0;32m    271\u001b[0m                 \u001b[0mthis\u001b[0m \u001b[1;33m+=\u001b[0m \u001b[0margs\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    272\u001b[0m                 \u001b[0margs\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0margs\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 273\u001b[1;33m             \u001b[1;32myield\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_plot_args\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mthis\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    274\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    275\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mget_next_color\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\envs\\tf\\lib\\site-packages\\matplotlib\\axes\\_base.py\u001b[0m in \u001b[0;36m_plot_args\u001b[1;34m(self, tup, kwargs)\u001b[0m\n\u001b[0;32m    400\u001b[0m                              f\"have shapes {x.shape} and {y.shape}\")\n\u001b[0;32m    401\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mx\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mndim\u001b[0m \u001b[1;33m>\u001b[0m \u001b[1;36m2\u001b[0m \u001b[1;32mor\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mndim\u001b[0m \u001b[1;33m>\u001b[0m \u001b[1;36m2\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 402\u001b[1;33m             raise ValueError(f\"x and y can be no greater than 2-D, but have \"\n\u001b[0m\u001b[0;32m    403\u001b[0m                              f\"shapes {x.shape} and {y.shape}\")\n\u001b[0;32m    404\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mx\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mndim\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mValueError\u001b[0m: x and y can be no greater than 2-D, but have shapes (100,) and (100, 15, 2, 2)"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "auc=history.history['auc_2']\n",
    "val_auc=history.history['val_auc_2']\n",
    "\n",
    "acc=history.history['accuracy']\n",
    "val_acc=history.history['val_accuracy']\n",
    "loss=history.history['loss']\n",
    "val_loss=history.history['val_loss']\n",
    "\n",
    "f1_score=history.history['f1_score']\n",
    "val_f1_score=history.history['val_f1_score']\n",
    "\n",
    "Multilabel_confusion_matrix=history.history['Multilabel_confusion_matrix']\n",
    "val_Multilabel_confusion_matrix=history.history['val_Multilabel_confusion_matrix']\n",
    "\n",
    "epochs=range(len(acc))\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "plt.plot(epochs,loss,'bo',label=\"Training Loss\")\n",
    "plt.plot(epochs,val_loss,'r',label=\"Validation Loss\")\n",
    "plt.legend()\n",
    "plt.show()\n",
    "\n",
    "\n",
    "plt.plot(epochs,auc,'bo',label=\"Training AUC\")\n",
    "plt.plot(epochs,val_auc,'r',label=\"Validation AUC\")\n",
    "plt.legend()\n",
    "plt.figure()\n",
    "\n",
    "plt.plot(epochs,acc,'bo',label=\"Training Accuracy\")\n",
    "plt.plot(epochs,val_acc,'r',label=\"Validation Accuracy\")\n",
    "plt.legend()\n",
    "plt.show()\n",
    "\n",
    "plt.plot(epochs,f1_score,'b',label=\"Training F1SCORE\")\n",
    "plt.plot(epochs,val_f1_score,'r',label=\"Validation F1SCORE\")\n",
    "plt.legend()\n",
    "plt.show()\n",
    "\n",
    "plt.plot(epochs,Multilabel_confusion_matrix,'b',label=\"Training Multilable CM\")\n",
    "plt.plot(epochs,val_Multilabel_confusion_matrix,'r',label=\"Validation Multilable CM\")\n",
    "plt.legend()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 1562,
     "status": "ok",
     "timestamp": 1619472386251,
     "user": {
      "displayName": "blaise mavin",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GgUhbcpHWNXczan3ui3BGJe8CM_G7bUv9e9RG5m9A=s64",
      "userId": "14168894997160457371"
     },
     "user_tz": -180
    },
    "id": "2k8RzXSFvYjT",
    "outputId": "a21565a8-4579-4bdb-a2b2-da8341c5275c"
   },
   "outputs": [],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 1327,
     "status": "ok",
     "timestamp": 1619473249062,
     "user": {
      "displayName": "blaise mavin",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GgUhbcpHWNXczan3ui3BGJe8CM_G7bUv9e9RG5m9A=s64",
      "userId": "14168894997160457371"
     },
     "user_tz": -180
    },
    "id": "vpB6CtjbveC7",
    "outputId": "3a50fbdd-cf2c-4238-e368-3f1d1b344076"
   },
   "outputs": [],
   "source": [
    "model2=Model(model.input,model.layers[-3].output)\n",
    "model2.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "ILR3Wqmb7bwV"
   },
   "outputs": [],
   "source": [
    "import tensorflow.compat.v1 as tf\n",
    "tf.disable_v2_behavior()\n",
    "sess=tf.Session()\n",
    "from tensorflow.python.framework import graph_io\n",
    "frozen = tf.graph_util.convert_variables_to_constants(sess, sess.graph_def, [\"name_of_the_output_node\"])\n",
    "graph_io.write_graph(frozen, '/tmp/session-frozens', 'inference_graph.pb', as_text=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 89
    },
    "executionInfo": {
     "elapsed": 1781,
     "status": "ok",
     "timestamp": 1616953191959,
     "user": {
      "displayName": "blaise mavin",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GgUhbcpHWNXczan3ui3BGJe8CM_G7bUv9e9RG5m9A=s64",
      "userId": "14168894997160457371"
     },
     "user_tz": -180
    },
    "id": "Wci41p_z2LSO",
    "outputId": "1dcb8f36-ea34-4e23-bce6-af6f8c04c912"
   },
   "outputs": [],
   "source": [
    "import tensorflow.compat.v1 as tf\n",
    "tf.disable_v2_behavior()\n",
    "\n",
    "from keras import backend as K \n",
    "from keras.models import Sequential, Model\n",
    "\n",
    "\n",
    "sess=tf.Session()\n",
    "K.set_learning_phase(0)   # Set the learning phase to 0\n",
    "model = model2 \n",
    "config = model2.get_config()\n",
    "#weights = model2.get_weights()\n",
    "#model = Sequential.from_config(config)\n",
    "output_node = model2.output.name.split(':')[0]  # We need this in the next step\n",
    "graph_file = \"kerasFacenet.pb\" \n",
    "ckpt_file = \"kerasFacenet.ckpt\"\n",
    "saver = tf.train.Saver(sharded=True)\n",
    "tf.train.write_graph(sess.graph_def, '', graph_file)\n",
    "#saver.save(sess, ckpt_file) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "executionInfo": {
     "elapsed": 48568,
     "status": "ok",
     "timestamp": 1619473307385,
     "user": {
      "displayName": "blaise mavin",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GgUhbcpHWNXczan3ui3BGJe8CM_G7bUv9e9RG5m9A=s64",
      "userId": "14168894997160457371"
     },
     "user_tz": -180
    },
    "id": "kue-AGmHwiod",
    "outputId": "5beaef0a-adcf-4f50-9456-352c2bc2ed0e"
   },
   "outputs": [],
   "source": [
    "from tensorflow.python.framework.convert_to_constants import convert_variables_to_constants_v2\n",
    "\n",
    "tf.saved_model.save(model2, \"/tmp/saved-models\")\n",
    "\n",
    "# Convert Keras model to ConcreteFunction\n",
    "full_model = tf.function(lambda x: model2(x))\n",
    "full_model = full_model.get_concrete_function(\n",
    "    tf.TensorSpec(model.inputs[0].shape, model.inputs[0].dtype))\n",
    "\n",
    "# Get frozen ConcreteFunction\n",
    "frozen_func = convert_variables_to_constants_v2(full_model)\n",
    "frozen_func.graph.as_graph_def()\n",
    "\n",
    "layers = [op.name for op in frozen_func.graph.get_operations()]\n",
    "#print(\"-\" * 50)\n",
    "#print(\"Frozen model layers: \")\n",
    "for layer in layers:\n",
    "    print(layer)\n",
    "\n",
    "#print(\"-\" * 50)\n",
    "#print(\"Frozen model inputs: \")\n",
    "#print(frozen_func.inputs)\n",
    "#print(\"Frozen model outputs: \")\n",
    "#print(frozen_func.outputs)\n",
    "\n",
    "# Save frozen graph from frozen ConcreteFunction to hard drive\n",
    "tf.io.write_graph(graph_or_graph_def=frozen_func.graph,\n",
    "                  logdir=\"/tmp/saved-model\",\n",
    "                  name=\"facenet-Original-LastLayer.pb\",\n",
    "                  as_text=False)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "0NvKeRsc8Xgd"
   },
   "outputs": [],
   "source": []
  }
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