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+1. Classification_dataset: test image dataset and a labels file
+2. Small_subset_of_segmentation_dataset: a small subset of exact train, valid, and test dataset
+3. classification_model_extend_ai: classification approach to solve the task
+4. segmentation_model_extend_ai: segmentation approach to solve this task and I also provide the answers of the two questions. 
\ No newline at end of file
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diff --git a/classification_model_extend_AI.ipynb b/classification_model_extend_AI.ipynb
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+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "I want to complete the task in two ways:\n",
+    "1.\tAt first, I want to try a classification approach to tackle this problem.\n",
+    "2.\tI want to develop a segmentation approach to accomplish this task."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Classification\n",
+    "We can divide the classification into two sections:\n",
+    "1.\tData preparation\n",
+    "2.\tTrain and validate my custom made model\n",
+    "3.\tTest the dataset given by Extend AI\n",
+    "## Libraries selection\n",
+    "I can use PyTorch as I have good expertise in this deep learning library. However, to train the dataset, I used RAM-Net architecture which is built on Tensorflow and Keras. Therefore, I choose TensorFlow and Keras to tackle this problem.\n",
+    "Model Description\n",
+    "I developed this Residual Attention Mobilenet (RAM-Net) in the last year for medical image analysis, and this is a published work at ICMLA 2020 conference. This is a mobile-friendly computer vision network with fewer parameters and flops with the capabilities to achieve higher performance on image datasets. So, I decide to choose this model as I am confident enough about its performance.\n",
+    "## Dataset Preparation\n",
+    "I use the offline data augmentation technique to produce a total of 357 images (51*7 where 51 images from each image) from these real 7 images (which are provided by extend AI). I split this dataset by training and validation. Here I use 90% data for training and 10% data for validation. Furthermore, I use these real 7 images for testing.\n",
+    "Here, I try to perform a binary classification problem where I divide the dataset into two classes: non_problametic (those images which do not have any issues) and problematic (those images which have knots and spots)\n",
+    "In the test dataset, two images are regarded as non_problematic images, and five images are counted as problematic images. Therefore, in the augmented dataset, I have 102 images for the non_problematic class and the remaining 255 images for the problematic class.\n",
+    "Data Augmentation Functionality: rotation, width shift, height shift, shear, zoom, horizontal flip, and fill mode=nearest\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Using TensorFlow backend.\n"
+     ]
+    }
+   ],
+   "source": [
+    "#import libraries\n",
+    "import os\n",
+    "from glob import glob\n",
+    "from keras.initializers import glorot_uniform\n",
+    "from keras.layers import Input, Add, Dense, Activation, ZeroPadding2D, BatchNormalization, Flatten, Conv2D, AveragePooling2D, MaxPooling2D, GlobalMaxPooling2D\n",
+    "from sklearn.metrics import classification_report\n",
+    "import functools\n",
+    "import keras\n",
+    "from keras.layers.normalization import BatchNormalization\n",
+    "from sklearn.preprocessing import MinMaxScaler\n",
+    "from sklearn.metrics import roc_curve\n",
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import keras.backend as K\n",
+    "import tensorflow as tf\n",
+    "from sklearn.metrics import roc_auc_score\n",
+    "import numpy\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "from keras.utils.np_utils import to_categorical # convert to one-hot-encoding\n",
+    "from keras.callbacks import TensorBoard\n",
+    "from keras.preprocessing.image import ImageDataGenerator\n",
+    "from keras import layers\n",
+    "from keras import Model\n",
+    "from keras.applications.vgg16 import VGG16, preprocess_input\n",
+    "from keras.applications import MobileNet\n",
+    "from keras.optimizers import Adam\n",
+    "from keras.callbacks import ReduceLROnPlateau, EarlyStopping,ModelCheckpoint\n",
+    "import random\n",
+    "from keras.layers import Dense, Dropout, GlobalAveragePooling2D,GlobalMaxPooling2D,DepthwiseConv2D,Concatenate\n",
+    "import matplotlib.pyplot as plt\n",
+    "import tensorflow\n",
+    "from at import augmented_conv2d\n",
+    "from sklearn.metrics import confusion_matrix\n",
+    "tensorboard = TensorBoard(log_dir='./logs', histogram_freq=0,\n",
+    "                          write_graph=True, write_images=False)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Resize the whole dataset to 224X224, then normalize the dataset, and at last convert it to a numpy array."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "train set:  (321, 224, 224, 3) (321,)\n",
+      "validation set:  (36, 224, 224, 3) (36,)\n",
+      "(321, 2)\n",
+      "(36, 2)\n"
+     ]
+    }
+   ],
+   "source": [
+    "\n",
+    "\n",
+    "X_train = np.load(\"D:/polynomial/covid19/anomaly dataset/new_224_224_train.npy\")\n",
+    "y_train = np.load(\"D:/polynomial/covid19/anomaly dataset/new_train_labels.npy\")\n",
+    "X_val = np.load(\"D:/polynomial/covid19/anomaly dataset/new_224_224_val.npy\")\n",
+    "\n",
+    "y_val = np.load(\"D:/polynomial/covid19/anomaly dataset/new_val_labels.npy\")\n",
+    "\n",
+    "print('train set: ', X_train.shape, y_train.shape )\n",
+    "print('validation set: ',X_val.shape, y_val.shape)\n",
+    "\n",
+    "y_train = to_categorical(y_train)\n",
+    "y_val = to_categorical(y_val)\n",
+    "print(y_train.shape)\n",
+    "print(y_val.shape)\n",
+    "#print(y_val)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Used Pre-trained weight from ImageNet"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<function augmented_conv2d at 0x00000251FFB17EA0> <class 'function'>\n"
+     ]
+    }
+   ],
+   "source": [
+    "f=augmented_conv2d #attention augmented convolution\n",
+    "print(f,type(f))\n",
+    "\n",
+    "#take pretrained weight of imagenet for thr last few classifier layers\n",
+    "pre_trained_model = MobileNet(input_shape=(224,224, 3), include_top=False, weights=\"imagenet\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "for layer in pre_trained_model.layers:\n",
+    "    #print(layer.name)\n",
+    "    layer.trainable = False"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#print(len(pre_trained_model.layers))\n",
+    "last_layer = pre_trained_model.get_layer('conv_pw_13_relu')\n",
+    "#print('last layer output shape:', last_layer.output_shape)\n",
+    "last_output = last_layer.output\n",
+    "x=GlobalAveragePooling2D()(last_output)\n",
+    "#x=Dense(512,activation='relu')(x) #we add dense layers so that the model can learn more complex functions and classify for better results.\n",
+    "#x = Dropout(0.5)(x)\n",
+    "x = layers.Dense(512, activation='relu')(x)\n",
+    "# Add a dropout rate of 0.7\n",
+    "x = layers.Dropout(0.5)(x)\n",
+    "\n",
+    "x = layers.Dense(256, activation='relu')(x)\n",
+    "# Add a dropout rate of 0.7\n",
+    "x = layers.Dropout(0.5)(x)\n",
+    "\n",
+    "\n",
+    "#x = layers.Dense(128, activation='relu')(x)\n",
+    "# Add a dropout rate of 0.7\n",
+    "#x = layers.Dropout(0.5)(x)\n",
+    "\n",
+    "\n",
+    "# Add a final sigmoid layer for classification\n",
+    "x = layers.Dense(2, activation='softmax',name='visualized_layer')(x)\n",
+    "\n",
+    "model=Model(pre_trained_model.input, x)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Hyperparameters and Online Data Augmentations\n",
+    "\n",
+    "Loss function: Binary cross-entropy \n",
+    "\n",
+    "Optimizer: Adam\n",
+    "\n",
+    "In addition, I also use online data augmentation for the training.\n",
+    "\n",
+    "Online data augmentation function: rotation, width shift, height shift, shear, zoom, and fill mode=nearest\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "_________________________________________________________________\n",
+      "Layer (type)                 Output Shape              Param #   \n",
+      "=================================================================\n",
+      "input_1 (InputLayer)         (None, 224, 224, 3)       0         \n",
+      "_________________________________________________________________\n",
+      "conv1_pad (ZeroPadding2D)    (None, 225, 225, 3)       0         \n",
+      "_________________________________________________________________\n",
+      "conv1 (Conv2D)               (None, 112, 112, 32)      864       \n",
+      "_________________________________________________________________\n",
+      "conv1_bn (BatchNormalization (None, 112, 112, 32)      128       \n",
+      "_________________________________________________________________\n",
+      "conv1_relu (ReLU)            (None, 112, 112, 32)      0         \n",
+      "_________________________________________________________________\n",
+      "conv_dw_1 (DepthwiseConv2D)  (None, 112, 112, 32)      288       \n",
+      "_________________________________________________________________\n",
+      "conv_dw_1_bn (BatchNormaliza (None, 112, 112, 32)      128       \n",
+      "_________________________________________________________________\n",
+      "conv_dw_1_relu (ReLU)        (None, 112, 112, 32)      0         \n",
+      "_________________________________________________________________\n",
+      "conv_pw_1 (Conv2D)           (None, 112, 112, 64)      2048      \n",
+      "_________________________________________________________________\n",
+      "conv_pw_1_bn (BatchNormaliza (None, 112, 112, 64)      256       \n",
+      "_________________________________________________________________\n",
+      "conv_pw_1_relu (ReLU)        (None, 112, 112, 64)      0         \n",
+      "_________________________________________________________________\n",
+      "conv_pad_2 (ZeroPadding2D)   (None, 113, 113, 64)      0         \n",
+      "_________________________________________________________________\n",
+      "conv_dw_2 (DepthwiseConv2D)  (None, 56, 56, 64)        576       \n",
+      "_________________________________________________________________\n",
+      "conv_dw_2_bn (BatchNormaliza (None, 56, 56, 64)        256       \n",
+      "_________________________________________________________________\n",
+      "conv_dw_2_relu (ReLU)        (None, 56, 56, 64)        0         \n",
+      "_________________________________________________________________\n",
+      "conv_pw_2 (Conv2D)           (None, 56, 56, 128)       8192      \n",
+      "_________________________________________________________________\n",
+      "conv_pw_2_bn (BatchNormaliza (None, 56, 56, 128)       512       \n",
+      "_________________________________________________________________\n",
+      "conv_pw_2_relu (ReLU)        (None, 56, 56, 128)       0         \n",
+      "_________________________________________________________________\n",
+      "conv_dw_3 (DepthwiseConv2D)  (None, 56, 56, 128)       1152      \n",
+      "_________________________________________________________________\n",
+      "conv_dw_3_bn (BatchNormaliza (None, 56, 56, 128)       512       \n",
+      "_________________________________________________________________\n",
+      "conv_dw_3_relu (ReLU)        (None, 56, 56, 128)       0         \n",
+      "_________________________________________________________________\n",
+      "conv_pw_3 (Conv2D)           (None, 56, 56, 128)       16384     \n",
+      "_________________________________________________________________\n",
+      "conv_pw_3_bn (BatchNormaliza (None, 56, 56, 128)       512       \n",
+      "_________________________________________________________________\n",
+      "conv_pw_3_relu (ReLU)        (None, 56, 56, 128)       0         \n",
+      "_________________________________________________________________\n",
+      "conv_pad_4 (ZeroPadding2D)   (None, 57, 57, 128)       0         \n",
+      "_________________________________________________________________\n",
+      "conv_dw_4 (DepthwiseConv2D)  (None, 28, 28, 128)       1152      \n",
+      "_________________________________________________________________\n",
+      "conv_dw_4_bn (BatchNormaliza (None, 28, 28, 128)       512       \n",
+      "_________________________________________________________________\n",
+      "conv_dw_4_relu (ReLU)        (None, 28, 28, 128)       0         \n",
+      "_________________________________________________________________\n",
+      "conv_pw_4 (Conv2D)           (None, 28, 28, 256)       32768     \n",
+      "_________________________________________________________________\n",
+      "conv_pw_4_bn (BatchNormaliza (None, 28, 28, 256)       1024      \n",
+      "_________________________________________________________________\n",
+      "conv_pw_4_relu (ReLU)        (None, 28, 28, 256)       0         \n",
+      "_________________________________________________________________\n",
+      "conv_dw_5 (DepthwiseConv2D)  (None, 28, 28, 256)       2304      \n",
+      "_________________________________________________________________\n",
+      "conv_dw_5_bn (BatchNormaliza (None, 28, 28, 256)       1024      \n",
+      "_________________________________________________________________\n",
+      "conv_dw_5_relu (ReLU)        (None, 28, 28, 256)       0         \n",
+      "_________________________________________________________________\n",
+      "conv_pw_5 (Conv2D)           (None, 28, 28, 256)       65536     \n",
+      "_________________________________________________________________\n",
+      "conv_pw_5_bn (BatchNormaliza (None, 28, 28, 256)       1024      \n",
+      "_________________________________________________________________\n",
+      "conv_pw_5_relu (ReLU)        (None, 28, 28, 256)       0         \n",
+      "_________________________________________________________________\n",
+      "conv_pad_6 (ZeroPadding2D)   (None, 29, 29, 256)       0         \n",
+      "_________________________________________________________________\n",
+      "conv_dw_6 (DepthwiseConv2D)  (None, 14, 14, 256)       2304      \n",
+      "_________________________________________________________________\n",
+      "conv_dw_6_bn (BatchNormaliza (None, 14, 14, 256)       1024      \n",
+      "_________________________________________________________________\n",
+      "conv_dw_6_relu (ReLU)        (None, 14, 14, 256)       0         \n",
+      "_________________________________________________________________\n",
+      "conv_pw_6 (Conv2D)           (None, 14, 14, 512)       131072    \n",
+      "_________________________________________________________________\n",
+      "conv_pw_6_bn (BatchNormaliza (None, 14, 14, 512)       2048      \n",
+      "_________________________________________________________________\n",
+      "conv_pw_6_relu (ReLU)        (None, 14, 14, 512)       0         \n",
+      "_________________________________________________________________\n",
+      "conv_dw_7 (DepthwiseConv2D)  (None, 14, 14, 512)       4608      \n",
+      "_________________________________________________________________\n",
+      "conv_dw_7_bn (BatchNormaliza (None, 14, 14, 512)       2048      \n",
+      "_________________________________________________________________\n",
+      "conv_dw_7_relu (ReLU)        (None, 14, 14, 512)       0         \n",
+      "_________________________________________________________________\n",
+      "conv_pw_7 (Conv2D)           (None, 14, 14, 512)       262144    \n",
+      "_________________________________________________________________\n",
+      "conv_pw_7_bn (BatchNormaliza (None, 14, 14, 512)       2048      \n",
+      "_________________________________________________________________\n",
+      "conv_pw_7_relu (ReLU)        (None, 14, 14, 512)       0         \n",
+      "_________________________________________________________________\n",
+      "conv_dw_8 (DepthwiseConv2D)  (None, 14, 14, 512)       4608      \n",
+      "_________________________________________________________________\n",
+      "conv_dw_8_bn (BatchNormaliza (None, 14, 14, 512)       2048      \n",
+      "_________________________________________________________________\n",
+      "conv_dw_8_relu (ReLU)        (None, 14, 14, 512)       0         \n",
+      "_________________________________________________________________\n",
+      "conv_pw_8 (Conv2D)           (None, 14, 14, 512)       262144    \n",
+      "_________________________________________________________________\n",
+      "conv_pw_8_bn (BatchNormaliza (None, 14, 14, 512)       2048      \n",
+      "_________________________________________________________________\n",
+      "conv_pw_8_relu (ReLU)        (None, 14, 14, 512)       0         \n",
+      "_________________________________________________________________\n",
+      "conv_dw_9 (DepthwiseConv2D)  (None, 14, 14, 512)       4608      \n",
+      "_________________________________________________________________\n",
+      "conv_dw_9_bn (BatchNormaliza (None, 14, 14, 512)       2048      \n",
+      "_________________________________________________________________\n",
+      "conv_dw_9_relu (ReLU)        (None, 14, 14, 512)       0         \n",
+      "_________________________________________________________________\n",
+      "conv_pw_9 (Conv2D)           (None, 14, 14, 512)       262144    \n",
+      "_________________________________________________________________\n",
+      "conv_pw_9_bn (BatchNormaliza (None, 14, 14, 512)       2048      \n",
+      "_________________________________________________________________\n",
+      "conv_pw_9_relu (ReLU)        (None, 14, 14, 512)       0         \n",
+      "_________________________________________________________________\n",
+      "conv_dw_10 (DepthwiseConv2D) (None, 14, 14, 512)       4608      \n",
+      "_________________________________________________________________\n",
+      "conv_dw_10_bn (BatchNormaliz (None, 14, 14, 512)       2048      \n",
+      "_________________________________________________________________\n",
+      "conv_dw_10_relu (ReLU)       (None, 14, 14, 512)       0         \n",
+      "_________________________________________________________________\n",
+      "conv_pw_10 (Conv2D)          (None, 14, 14, 512)       262144    \n",
+      "_________________________________________________________________\n",
+      "conv_pw_10_bn (BatchNormaliz (None, 14, 14, 512)       2048      \n",
+      "_________________________________________________________________\n",
+      "conv_pw_10_relu (ReLU)       (None, 14, 14, 512)       0         \n",
+      "_________________________________________________________________\n",
+      "conv_dw_11 (DepthwiseConv2D) (None, 14, 14, 512)       4608      \n",
+      "_________________________________________________________________\n",
+      "conv_dw_11_bn (BatchNormaliz (None, 14, 14, 512)       2048      \n",
+      "_________________________________________________________________\n",
+      "conv_dw_11_relu (ReLU)       (None, 14, 14, 512)       0         \n",
+      "_________________________________________________________________\n",
+      "conv_pw_11 (Conv2D)          (None, 14, 14, 512)       262144    \n",
+      "_________________________________________________________________\n",
+      "conv_pw_11_bn (BatchNormaliz (None, 14, 14, 512)       2048      \n",
+      "_________________________________________________________________\n",
+      "conv_pw_11_relu (ReLU)       (None, 14, 14, 512)       0         \n",
+      "_________________________________________________________________\n",
+      "conv_pad_12 (ZeroPadding2D)  (None, 15, 15, 512)       0         \n",
+      "_________________________________________________________________\n",
+      "conv_dw_12 (DepthwiseConv2D) (None, 7, 7, 512)         4608      \n",
+      "_________________________________________________________________\n",
+      "conv_dw_12_bn (BatchNormaliz (None, 7, 7, 512)         2048      \n",
+      "_________________________________________________________________\n",
+      "conv_dw_12_relu (ReLU)       (None, 7, 7, 512)         0         \n",
+      "_________________________________________________________________\n",
+      "conv_pw_12 (Conv2D)          (None, 7, 7, 1024)        524288    \n",
+      "_________________________________________________________________\n",
+      "conv_pw_12_bn (BatchNormaliz (None, 7, 7, 1024)        4096      \n",
+      "_________________________________________________________________\n",
+      "conv_pw_12_relu (ReLU)       (None, 7, 7, 1024)        0         \n",
+      "_________________________________________________________________\n",
+      "conv_dw_13 (DepthwiseConv2D) (None, 7, 7, 1024)        9216      \n",
+      "_________________________________________________________________\n",
+      "conv_dw_13_bn (BatchNormaliz (None, 7, 7, 1024)        4096      \n",
+      "_________________________________________________________________\n",
+      "conv_dw_13_relu (ReLU)       (None, 7, 7, 1024)        0         \n",
+      "_________________________________________________________________\n",
+      "conv_pw_13 (Conv2D)          (None, 7, 7, 1024)        1048576   \n",
+      "_________________________________________________________________\n",
+      "conv_pw_13_bn (BatchNormaliz (None, 7, 7, 1024)        4096      \n",
+      "_________________________________________________________________\n",
+      "conv_pw_13_relu (ReLU)       (None, 7, 7, 1024)        0         \n",
+      "_________________________________________________________________\n",
+      "global_average_pooling2d_1 ( (None, 1024)              0         \n",
+      "_________________________________________________________________\n",
+      "dense_1 (Dense)              (None, 512)               524800    \n",
+      "_________________________________________________________________\n",
+      "dropout_1 (Dropout)          (None, 512)               0         \n",
+      "_________________________________________________________________\n",
+      "dense_2 (Dense)              (None, 256)               131328    \n",
+      "_________________________________________________________________\n",
+      "dropout_2 (Dropout)          (None, 256)               0         \n",
+      "_________________________________________________________________\n",
+      "visualized_layer (Dense)     (None, 2)                 514       \n",
+      "=================================================================\n",
+      "Total params: 3,885,506\n",
+      "Trainable params: 656,642\n",
+      "Non-trainable params: 3,228,864\n",
+      "_________________________________________________________________\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 1/3\n",
+      "10/10 [==============================] - 5s 498ms/step - loss: 0.9783 - acc: 0.5844 - top_k_categorical_accuracy: 1.0000 - val_loss: 0.5643 - val_acc: 0.7812 - val_top_k_categorical_accuracy: 1.0000\n",
+      "Epoch 2/3\n",
+      "10/10 [==============================] - 3s 331ms/step - loss: 0.8359 - acc: 0.6626 - top_k_categorical_accuracy: 1.0000 - val_loss: 1.8075 - val_acc: 0.2500 - val_top_k_categorical_accuracy: 1.0000\n",
+      "Epoch 3/3\n",
+      "10/10 [==============================] - 4s 353ms/step - loss: 0.5797 - acc: 0.7570 - top_k_categorical_accuracy: 1.0000 - val_loss: 0.6817 - val_acc: 0.7188 - val_top_k_categorical_accuracy: 1.0000\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Configure and compile the model\n",
+    "'''\n",
+    "smooth = 1.\n",
+    "def dice_coef(y_true, y_pred):\n",
+    "    y_true_f = K.flatten(y_true)\n",
+    "    y_pred_f = K.flatten(y_pred)\n",
+    "    intersection = K.sum(y_true_f * y_pred_f)\n",
+    "    return (2. * intersection + smooth) / (K.sum(y_true_f) + K.sum(y_pred_f) + smooth)\n",
+    "\n",
+    "\n",
+    "def dice_coef_loss(y_true, y_pred):\n",
+    "    return 1-dice_coef(y_true, y_pred)\n",
+    "'''\n",
+    "#top3_acc = functools.partial(keras.metrics.top_k_categorical_accuracy, k=3)\n",
+    "\n",
+    "#top3_acc.__name__ = 'top3_acc'\n",
+    "\n",
+    "optimizer = Adam(lr=0.0001, beta_1=0.9, beta_2=0.999, epsilon=None, decay=0.0, amsgrad=True)\n",
+    "model.compile(loss='binary_crossentropy',\n",
+    "              optimizer=optimizer,\n",
+    "              metrics=['accuracy','top_k_categorical_accuracy'])\n",
+    "\n",
+    "model.summary()\n",
+    "\n",
+    "\n",
+    "train_datagen = ImageDataGenerator(rotation_range=60, width_shift_range=0.2, height_shift_range=0.2,\n",
+    "                                   shear_range=0.2, zoom_range=0.2, fill_mode='nearest')\n",
+    "\n",
+    "train_datagen.fit(X_train)\n",
+    "\n",
+    "val_datagen = ImageDataGenerator()\n",
+    "val_datagen.fit(X_val)\n",
+    "\n",
+    "batch_size = 32\n",
+    "epochs = 3\n",
+    "history = model.fit_generator(train_datagen.flow(X_train,y_train, batch_size=batch_size),\n",
+    "                              epochs = epochs, validation_data = val_datagen.flow(X_val, y_val),\n",
+    "                              verbose = 1, steps_per_epoch=(X_train.shape[0] // batch_size),\n",
+    "                              validation_steps=(X_val.shape[0] // batch_size))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def identity_block(X, f, filters):\n",
+    "    \"\"\"\n",
+    "    Implementation of the identity block as defined in Figure 3\n",
+    "\n",
+    "    Arguments:\n",
+    "    X -- input tensor of shape (m, n_H_prev, n_W_prev, n_C_prev)\n",
+    "    f -- integer, specifying the shape of the middle CONV's window for the main path\n",
+    "    filters -- python list of integers, defining the number of filters in the CONV layers of the main path\n",
+    "    stage -- integer, used to name the layers, depending on their position in the network\n",
+    "    block -- string/character, used to name the layers, depending on their position in the network\n",
+    "\n",
+    "    Returns:\n",
+    "    X -- output of the identity block, tensor of shape (n_H, n_W, n_C)\n",
+    "    \"\"\"\n",
+    "\n",
+    "    # defining name basis\n",
+    "    #conv_name_base = 'res' + str(stage) + block + '_branch'\n",
+    "    #bn_name_base = 'bn' + str(stage) + block + '_branch'\n",
+    "\n",
+    "    # Retrieve Filters\n",
+    "    F1, F2, F3 = filters\n",
+    "\n",
+    "    # Save the input value. You'll need this later to add back to the main path.\n",
+    "    X_shortcut = X\n",
+    "    #print(X_shortcut)\n",
+    "\n",
+    "    # First component of main path\n",
+    "    X = Conv2D(filters=F1, kernel_size=(1, 1), strides=(1, 1), padding='valid',\n",
+    "               kernel_initializer=glorot_uniform(seed=0))(X)\n",
+    "    X = BatchNormalization(axis=3)(X)\n",
+    "    X = Activation('relu')(X)\n",
+    "    #extra\n",
+    "\n",
+    "\n",
+    "    # Second component of main path (≈3 lines)\n",
+    "    X = Conv2D(filters=F2, kernel_size=(f, f), strides=(1, 1), padding='same',\n",
+    "               kernel_initializer=glorot_uniform(seed=0))(X)\n",
+    "    X = BatchNormalization(axis=3)(X)\n",
+    "    X = Activation('relu')(X)\n",
+    "\n",
+    "\n",
+    "\n",
+    "    # Third component of main path (≈2 lines)\n",
+    "    X = Conv2D(filters=F3, kernel_size=(1, 1), strides=(1, 1), padding='valid',\n",
+    "               kernel_initializer=glorot_uniform(seed=0))(X)\n",
+    "    X = BatchNormalization(axis=3)(X)\n",
+    "\n",
+    "    # Final step: Add shortcut value to main path, and pass it through a RELU activation (≈2 lines)\n",
+    "    X = Add()([X, X_shortcut])\n",
+    "    X = Activation('relu')(X)\n",
+    "\n",
+    "    return X\n",
+    "\n",
+    "\n",
+    "def convolutional_block(X, f, filters, s=2):\n",
+    "    \"\"\"\n",
+    "    Implementation of the convolutional block as defined in Figure 4\n",
+    "\n",
+    "    Arguments:\n",
+    "    X -- input tensor of shape (m, n_H_prev, n_W_prev, n_C_prev)\n",
+    "    f -- integer, specifying the shape of the middle CONV's window for the main path\n",
+    "    filters -- python list of integers, defining the number of filters in the CONV layers of the main path\n",
+    "    stage -- integer, used to name the layers, depending on their position in the network\n",
+    "    block -- string/character, used to name the layers, depending on their position in the network\n",
+    "    s -- Integer, specifying the stride to be used\n",
+    "\n",
+    "    Returns:\n",
+    "    X -- output of the convolutional block, tensor of shape (n_H, n_W, n_C)\n",
+    "    \"\"\"\n",
+    "\n",
+    "    # defining name basis\n",
+    "    #conv_name_base = 'res' + str(stage) + block + '_branch'\n",
+    "    #bn_name_base = 'bn' + str(stage) + block + '_branch'\n",
+    "\n",
+    "    # Retrieve Filters\n",
+    "    F1, F2, F3 = filters\n",
+    "\n",
+    "    # Save the input value\n",
+    "    X_shortcut = X\n",
+    "\n",
+    "    ##### MAIN PATH #####\n",
+    "    # First component of main path\n",
+    "    X = Conv2D(F1, (1, 1), strides=(s, s),  kernel_initializer=glorot_uniform(seed=0))(X)\n",
+    "    X = BatchNormalization(axis=3)(X)\n",
+    "    X = Activation('relu')(X)\n",
+    "\n",
+    "    # Second component of main path (≈3 lines)\n",
+    "    X = Conv2D(filters=F2, kernel_size=(f, f), strides=(1, 1), padding='same',\n",
+    "               kernel_initializer=glorot_uniform(seed=0))(X)\n",
+    "    X = BatchNormalization(axis=3)(X)\n",
+    "    X = Activation('relu')(X)\n",
+    "\n",
+    "\n",
+    "\n",
+    "    # Third component of main path (≈2 lines)\n",
+    "    X = Conv2D(filters=F3, kernel_size=(1, 1), strides=(1, 1), padding='valid',\n",
+    "               kernel_initializer=glorot_uniform(seed=0))(X)\n",
+    "    X = BatchNormalization(axis=3)(X)\n",
+    "\n",
+    "    ##### SHORTCUT PATH #### (≈2 lines)\n",
+    "    X_shortcut = Conv2D(filters=F3, kernel_size=(1, 1), strides=(s, s), padding='valid',\n",
+    "                        kernel_initializer=glorot_uniform(seed=0))(X_shortcut)\n",
+    "    X_shortcut = BatchNormalization(axis=3)(X_shortcut)\n",
+    "\n",
+    "    # Final step: Add shortcut value to main path, and pass it through a RELU activation (≈2 lines)\n",
+    "    X = Add()([X, X_shortcut])\n",
+    "    X = Activation('relu')(X)\n",
+    "\n",
+    "    return X\n",
+    "'''for layer in model.layers[:20]:\n",
+    "    layer.trainable = False\n",
+    "\n",
+    "for layer in model.layers[20:]:\n",
+    "    layer.trainable = True'''\n",
+    "for layer in model.layers[:12]:\n",
+    "    layer.trainable=True\n",
+    "for layer in model.layers[12:13]:\n",
+    "    layer.trainable=DepthwiseConv2D(64,strides=(2, 2),dilation_rate=(1,1))\n",
+    "for layer in model.layers[13:25]:\n",
+    "    layer.trainable=True\n",
+    "for layer in model.layers[25:26]:\n",
+    "    layer.trainable=DepthwiseConv2D(128,strides=(2, 2),dilation_rate=(1,1))\n",
+    "for layer in model.layers[26:38]:\n",
+    "    layer.trainable=True\n",
+    "for layer in model.layers[38:39]:\n",
+    "    layer.trainable=DepthwiseConv2D(256,strides=(2, 2),dilation_rate=(2,2))\n",
+    "\n",
+    "for layer in model.layers[39:75]:\n",
+    "    layer.trainable=True\n",
+    "    #layer.trainable=identity_block(layer.output,3,[64, 64, 256], stage=1, block='a')\n",
+    "for layer in model.layers[75:76]:\n",
+    "    layer.trainable=DepthwiseConv2D(512,strides=(2, 2),dilation_rate=(2,2))\n",
+    "'''\n",
+    "for layer in model.layers[76:81]:\n",
+    "    layer.trainable=identity_block(layer.output,3,[128, 128, 512], stage=2, block='a')\n",
+    "    #layer.trainable=True'''\n",
+    "for layer in model.layers[76:78]:\n",
+    "    ca1=convolutional_block(layer.output, f = 3, filters = [64, 64, 256],s=1)\n",
+    "    a1=identity_block(ca1, 3, [64, 64, 256])\n",
+    "    b1=identity_block(a1,3,[64, 64, 256])\n",
+    "    #ba1=BatchNormalization()(b1)\n",
+    "    ac1= augmented_conv2d(b1, 512)\n",
+    "    ca2 = convolutional_block(ac1, f=3, filters=[128, 128, 512], s=1)\n",
+    "    a2 = identity_block(ca2, 3, [128, 128, 512])\n",
+    "    b2 = identity_block(a2, 3, [128, 128, 512])\n",
+    "    c2 = identity_block(b2, 3, [128, 128, 512])\n",
+    "    #ba2=BatchNormalization()(c2)\n",
+    "    ac2=augmented_conv2d(c2,512)\n",
+    "    ca3= convolutional_block(ac2, f=3, filters=[256, 256, 1024], s=1)\n",
+    "    a3 = identity_block(ca3, 3, [1256, 256, 1024])\n",
+    "    b3 = identity_block(a3, 3, [256, 256, 1024])\n",
+    "    c3 = identity_block(b3, 3, [256, 256, 1024])\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n",
+    "    #layer.trainable=Concatenate([ca,a,b])\n",
+    "\n",
+    "    layer.trainable=c3\n",
+    "    #print('man')\n",
+    "    layer.trainable=True\n",
+    "for layer in model.layers[78:81]:\n",
+    "    layer.trainable=True\n",
+    "for layer in model.layers[81:82]:\n",
+    "\n",
+    "    #res2=identity_block(res1,3,[128, 128, 512], stage=3, block='b')\n",
+    "    #res3=identity_block(res2,3,[128, 128, 512], stage=3, block='c')\n",
+    "\n",
+    "    a=DepthwiseConv2D(1024, strides=(2, 2),dilation_rate=(4,4))\n",
+    "    b=DepthwiseConv2D(1024, strides=(2, 2),dilation_rate=(8,8))\n",
+    "    c=DepthwiseConv2D(1024, strides=(2, 2),dilation_rate=(16,16))\n",
+    "    d=Concatenate([a,b])\n",
+    "    layer.trainable=Concatenate([d,c])\n",
+    "for layer in model.layers[82:]:\n",
+    "    layer.trainable=True"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Hyperparameter\n",
+    "\n",
+    "epoch= 40 \n",
+    "batch size=32\n",
+    "learning rate=0.0001"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "_________________________________________________________________\n",
+      "Layer (type)                 Output Shape              Param #   \n",
+      "=================================================================\n",
+      "input_1 (InputLayer)         (None, 224, 224, 3)       0         \n",
+      "_________________________________________________________________\n",
+      "conv1_pad (ZeroPadding2D)    (None, 225, 225, 3)       0         \n",
+      "_________________________________________________________________\n",
+      "conv1 (Conv2D)               (None, 112, 112, 32)      864       \n",
+      "_________________________________________________________________\n",
+      "conv1_bn (BatchNormalization (None, 112, 112, 32)      128       \n",
+      "_________________________________________________________________\n",
+      "conv1_relu (ReLU)            (None, 112, 112, 32)      0         \n",
+      "_________________________________________________________________\n",
+      "conv_dw_1 (DepthwiseConv2D)  (None, 112, 112, 32)      288       \n",
+      "_________________________________________________________________\n",
+      "conv_dw_1_bn (BatchNormaliza (None, 112, 112, 32)      128       \n",
+      "_________________________________________________________________\n",
+      "conv_dw_1_relu (ReLU)        (None, 112, 112, 32)      0         \n",
+      "_________________________________________________________________\n",
+      "conv_pw_1 (Conv2D)           (None, 112, 112, 64)      2048      \n",
+      "_________________________________________________________________\n",
+      "conv_pw_1_bn (BatchNormaliza (None, 112, 112, 64)      256       \n",
+      "_________________________________________________________________\n",
+      "conv_pw_1_relu (ReLU)        (None, 112, 112, 64)      0         \n",
+      "_________________________________________________________________\n",
+      "conv_pad_2 (ZeroPadding2D)   (None, 113, 113, 64)      0         \n",
+      "_________________________________________________________________\n",
+      "conv_dw_2 (DepthwiseConv2D)  (None, 56, 56, 64)        576       \n",
+      "_________________________________________________________________\n",
+      "conv_dw_2_bn (BatchNormaliza (None, 56, 56, 64)        256       \n",
+      "_________________________________________________________________\n",
+      "conv_dw_2_relu (ReLU)        (None, 56, 56, 64)        0         \n",
+      "_________________________________________________________________\n",
+      "conv_pw_2 (Conv2D)           (None, 56, 56, 128)       8192      \n",
+      "_________________________________________________________________\n",
+      "conv_pw_2_bn (BatchNormaliza (None, 56, 56, 128)       512       \n",
+      "_________________________________________________________________\n",
+      "conv_pw_2_relu (ReLU)        (None, 56, 56, 128)       0         \n",
+      "_________________________________________________________________\n",
+      "conv_dw_3 (DepthwiseConv2D)  (None, 56, 56, 128)       1152      \n",
+      "_________________________________________________________________\n",
+      "conv_dw_3_bn (BatchNormaliza (None, 56, 56, 128)       512       \n",
+      "_________________________________________________________________\n",
+      "conv_dw_3_relu (ReLU)        (None, 56, 56, 128)       0         \n",
+      "_________________________________________________________________\n",
+      "conv_pw_3 (Conv2D)           (None, 56, 56, 128)       16384     \n",
+      "_________________________________________________________________\n",
+      "conv_pw_3_bn (BatchNormaliza (None, 56, 56, 128)       512       \n",
+      "_________________________________________________________________\n",
+      "conv_pw_3_relu (ReLU)        (None, 56, 56, 128)       0         \n",
+      "_________________________________________________________________\n",
+      "conv_pad_4 (ZeroPadding2D)   (None, 57, 57, 128)       0         \n",
+      "_________________________________________________________________\n",
+      "conv_dw_4 (DepthwiseConv2D)  (None, 28, 28, 128)       1152      \n",
+      "_________________________________________________________________\n",
+      "conv_dw_4_bn (BatchNormaliza (None, 28, 28, 128)       512       \n",
+      "_________________________________________________________________\n",
+      "conv_dw_4_relu (ReLU)        (None, 28, 28, 128)       0         \n",
+      "_________________________________________________________________\n",
+      "conv_pw_4 (Conv2D)           (None, 28, 28, 256)       32768     \n",
+      "_________________________________________________________________\n",
+      "conv_pw_4_bn (BatchNormaliza (None, 28, 28, 256)       1024      \n",
+      "_________________________________________________________________\n",
+      "conv_pw_4_relu (ReLU)        (None, 28, 28, 256)       0         \n",
+      "_________________________________________________________________\n",
+      "conv_dw_5 (DepthwiseConv2D)  (None, 28, 28, 256)       2304      \n",
+      "_________________________________________________________________\n",
+      "conv_dw_5_bn (BatchNormaliza (None, 28, 28, 256)       1024      \n",
+      "_________________________________________________________________\n",
+      "conv_dw_5_relu (ReLU)        (None, 28, 28, 256)       0         \n",
+      "_________________________________________________________________\n",
+      "conv_pw_5 (Conv2D)           (None, 28, 28, 256)       65536     \n",
+      "_________________________________________________________________\n",
+      "conv_pw_5_bn (BatchNormaliza (None, 28, 28, 256)       1024      \n",
+      "_________________________________________________________________\n",
+      "conv_pw_5_relu (ReLU)        (None, 28, 28, 256)       0         \n",
+      "_________________________________________________________________\n",
+      "conv_pad_6 (ZeroPadding2D)   (None, 29, 29, 256)       0         \n",
+      "_________________________________________________________________\n",
+      "conv_dw_6 (DepthwiseConv2D)  (None, 14, 14, 256)       2304      \n",
+      "_________________________________________________________________\n",
+      "conv_dw_6_bn (BatchNormaliza (None, 14, 14, 256)       1024      \n",
+      "_________________________________________________________________\n",
+      "conv_dw_6_relu (ReLU)        (None, 14, 14, 256)       0         \n",
+      "_________________________________________________________________\n",
+      "conv_pw_6 (Conv2D)           (None, 14, 14, 512)       131072    \n",
+      "_________________________________________________________________\n",
+      "conv_pw_6_bn (BatchNormaliza (None, 14, 14, 512)       2048      \n",
+      "_________________________________________________________________\n",
+      "conv_pw_6_relu (ReLU)        (None, 14, 14, 512)       0         \n",
+      "_________________________________________________________________\n",
+      "conv_dw_7 (DepthwiseConv2D)  (None, 14, 14, 512)       4608      \n",
+      "_________________________________________________________________\n",
+      "conv_dw_7_bn (BatchNormaliza (None, 14, 14, 512)       2048      \n",
+      "_________________________________________________________________\n",
+      "conv_dw_7_relu (ReLU)        (None, 14, 14, 512)       0         \n",
+      "_________________________________________________________________\n",
+      "conv_pw_7 (Conv2D)           (None, 14, 14, 512)       262144    \n",
+      "_________________________________________________________________\n",
+      "conv_pw_7_bn (BatchNormaliza (None, 14, 14, 512)       2048      \n",
+      "_________________________________________________________________\n",
+      "conv_pw_7_relu (ReLU)        (None, 14, 14, 512)       0         \n",
+      "_________________________________________________________________\n",
+      "conv_dw_8 (DepthwiseConv2D)  (None, 14, 14, 512)       4608      \n",
+      "_________________________________________________________________\n",
+      "conv_dw_8_bn (BatchNormaliza (None, 14, 14, 512)       2048      \n",
+      "_________________________________________________________________\n",
+      "conv_dw_8_relu (ReLU)        (None, 14, 14, 512)       0         \n",
+      "_________________________________________________________________\n",
+      "conv_pw_8 (Conv2D)           (None, 14, 14, 512)       262144    \n",
+      "_________________________________________________________________\n",
+      "conv_pw_8_bn (BatchNormaliza (None, 14, 14, 512)       2048      \n",
+      "_________________________________________________________________\n",
+      "conv_pw_8_relu (ReLU)        (None, 14, 14, 512)       0         \n",
+      "_________________________________________________________________\n",
+      "conv_dw_9 (DepthwiseConv2D)  (None, 14, 14, 512)       4608      \n",
+      "_________________________________________________________________\n",
+      "conv_dw_9_bn (BatchNormaliza (None, 14, 14, 512)       2048      \n",
+      "_________________________________________________________________\n",
+      "conv_dw_9_relu (ReLU)        (None, 14, 14, 512)       0         \n",
+      "_________________________________________________________________\n",
+      "conv_pw_9 (Conv2D)           (None, 14, 14, 512)       262144    \n",
+      "_________________________________________________________________\n",
+      "conv_pw_9_bn (BatchNormaliza (None, 14, 14, 512)       2048      \n",
+      "_________________________________________________________________\n",
+      "conv_pw_9_relu (ReLU)        (None, 14, 14, 512)       0         \n",
+      "_________________________________________________________________\n",
+      "conv_dw_10 (DepthwiseConv2D) (None, 14, 14, 512)       4608      \n",
+      "_________________________________________________________________\n",
+      "conv_dw_10_bn (BatchNormaliz (None, 14, 14, 512)       2048      \n",
+      "_________________________________________________________________\n",
+      "conv_dw_10_relu (ReLU)       (None, 14, 14, 512)       0         \n",
+      "_________________________________________________________________\n",
+      "conv_pw_10 (Conv2D)          (None, 14, 14, 512)       262144    \n",
+      "_________________________________________________________________\n",
+      "conv_pw_10_bn (BatchNormaliz (None, 14, 14, 512)       2048      \n",
+      "_________________________________________________________________\n",
+      "conv_pw_10_relu (ReLU)       (None, 14, 14, 512)       0         \n",
+      "_________________________________________________________________\n",
+      "conv_dw_11 (DepthwiseConv2D) (None, 14, 14, 512)       4608      \n",
+      "_________________________________________________________________\n",
+      "conv_dw_11_bn (BatchNormaliz (None, 14, 14, 512)       2048      \n",
+      "_________________________________________________________________\n",
+      "conv_dw_11_relu (ReLU)       (None, 14, 14, 512)       0         \n",
+      "_________________________________________________________________\n",
+      "conv_pw_11 (Conv2D)          (None, 14, 14, 512)       262144    \n",
+      "_________________________________________________________________\n",
+      "conv_pw_11_bn (BatchNormaliz (None, 14, 14, 512)       2048      \n",
+      "_________________________________________________________________\n",
+      "conv_pw_11_relu (ReLU)       (None, 14, 14, 512)       0         \n",
+      "_________________________________________________________________\n",
+      "conv_pad_12 (ZeroPadding2D)  (None, 15, 15, 512)       0         \n",
+      "_________________________________________________________________\n",
+      "conv_dw_12 (DepthwiseConv2D) (None, 7, 7, 512)         4608      \n",
+      "_________________________________________________________________\n",
+      "conv_dw_12_bn (BatchNormaliz (None, 7, 7, 512)         2048      \n",
+      "_________________________________________________________________\n",
+      "conv_dw_12_relu (ReLU)       (None, 7, 7, 512)         0         \n",
+      "_________________________________________________________________\n",
+      "conv_pw_12 (Conv2D)          (None, 7, 7, 1024)        524288    \n",
+      "_________________________________________________________________\n",
+      "conv_pw_12_bn (BatchNormaliz (None, 7, 7, 1024)        4096      \n",
+      "_________________________________________________________________\n",
+      "conv_pw_12_relu (ReLU)       (None, 7, 7, 1024)        0         \n",
+      "_________________________________________________________________\n",
+      "conv_dw_13 (DepthwiseConv2D) (None, 7, 7, 1024)        9216      \n",
+      "_________________________________________________________________\n",
+      "conv_dw_13_bn (BatchNormaliz (None, 7, 7, 1024)        4096      \n",
+      "_________________________________________________________________\n",
+      "conv_dw_13_relu (ReLU)       (None, 7, 7, 1024)        0         \n",
+      "_________________________________________________________________\n",
+      "conv_pw_13 (Conv2D)          (None, 7, 7, 1024)        1048576   \n",
+      "_________________________________________________________________\n",
+      "conv_pw_13_bn (BatchNormaliz (None, 7, 7, 1024)        4096      \n",
+      "_________________________________________________________________\n",
+      "conv_pw_13_relu (ReLU)       (None, 7, 7, 1024)        0         \n",
+      "_________________________________________________________________\n",
+      "global_average_pooling2d_1 ( (None, 1024)              0         \n",
+      "_________________________________________________________________\n",
+      "dense_1 (Dense)              (None, 512)               524800    \n",
+      "_________________________________________________________________\n",
+      "dropout_1 (Dropout)          (None, 512)               0         \n",
+      "_________________________________________________________________\n",
+      "dense_2 (Dense)              (None, 256)               131328    \n",
+      "_________________________________________________________________\n",
+      "dropout_2 (Dropout)          (None, 256)               0         \n",
+      "_________________________________________________________________\n",
+      "visualized_layer (Dense)     (None, 2)                 514       \n",
+      "=================================================================\n",
+      "Total params: 3,885,506\n",
+      "Trainable params: 3,863,618\n",
+      "Non-trainable params: 21,888\n",
+      "_________________________________________________________________\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 1/40\n",
+      "10/10 [==============================] - 8s 772ms/step - loss: 0.2429 - acc: 0.8906 - top_k_categorical_accuracy: 1.0000 - val_loss: 0.0200 - val_acc: 1.0000 - val_top_k_categorical_accuracy: 1.0000\n",
+      "\n",
+      "Epoch 00001: val_acc improved from -inf to 1.00000, saving model to D:/polynomial/covid19/anomaly dataset/RAM_Net.h5\n",
+      "Epoch 2/40\n",
+      "10/10 [==============================] - 4s 376ms/step - loss: 0.3146 - acc: 0.8520 - top_k_categorical_accuracy: 1.0000 - val_loss: 0.0533 - val_acc: 1.0000 - val_top_k_categorical_accuracy: 1.0000\n",
+      "\n",
+      "Epoch 00002: val_acc did not improve from 1.00000\n",
+      "Epoch 3/40\n",
+      "10/10 [==============================] - 4s 357ms/step - loss: 0.1054 - acc: 0.9653 - top_k_categorical_accuracy: 1.0000 - val_loss: 0.0123 - val_acc: 1.0000 - val_top_k_categorical_accuracy: 1.0000\n",
+      "\n",
+      "Epoch 00003: val_acc did not improve from 1.00000\n",
+      "Epoch 4/40\n",
+      "10/10 [==============================] - 4s 355ms/step - loss: 0.0772 - acc: 0.9811 - top_k_categorical_accuracy: 1.0000 - val_loss: 0.0017 - val_acc: 1.0000 - val_top_k_categorical_accuracy: 1.0000\n",
+      "\n",
+      "Epoch 00004: val_acc did not improve from 1.00000\n",
+      "Epoch 5/40\n",
+      "10/10 [==============================] - 4s 366ms/step - loss: 0.0878 - acc: 0.9684 - top_k_categorical_accuracy: 1.0000 - val_loss: 0.0035 - val_acc: 1.0000 - val_top_k_categorical_accuracy: 1.0000\n",
+      "\n",
+      "Epoch 00005: val_acc did not improve from 1.00000\n",
+      "Epoch 6/40\n",
+      "10/10 [==============================] - 4s 366ms/step - loss: 0.1706 - acc: 0.9056 - top_k_categorical_accuracy: 1.0000 - val_loss: 4.8714e-04 - val_acc: 1.0000 - val_top_k_categorical_accuracy: 1.0000\n",
+      "\n",
+      "Epoch 00006: ReduceLROnPlateau reducing learning rate to 3.1622775802825264e-05.\n",
+      "\n",
+      "Epoch 00006: val_acc did not improve from 1.00000\n",
+      "Epoch 7/40\n",
+      "10/10 [==============================] - 4s 380ms/step - loss: 0.0538 - acc: 0.9842 - top_k_categorical_accuracy: 1.0000 - val_loss: 4.5762e-04 - val_acc: 1.0000 - val_top_k_categorical_accuracy: 1.0000\n",
+      "\n",
+      "Epoch 00007: val_acc did not improve from 1.00000\n",
+      "Epoch 8/40\n",
+      "10/10 [==============================] - 4s 381ms/step - loss: 0.0774 - acc: 0.9874 - top_k_categorical_accuracy: 1.0000 - val_loss: 3.7320e-05 - val_acc: 1.0000 - val_top_k_categorical_accuracy: 1.0000\n",
+      "\n",
+      "Epoch 00008: val_acc did not improve from 1.00000\n",
+      "Epoch 9/40\n",
+      "10/10 [==============================] - 4s 397ms/step - loss: 0.0282 - acc: 0.9905 - top_k_categorical_accuracy: 1.0000 - val_loss: 2.5864e-04 - val_acc: 1.0000 - val_top_k_categorical_accuracy: 1.0000\n",
+      "\n",
+      "Epoch 00009: val_acc did not improve from 1.00000\n",
+      "Epoch 10/40\n",
+      "10/10 [==============================] - 4s 410ms/step - loss: 0.0418 - acc: 0.9842 - top_k_categorical_accuracy: 1.0000 - val_loss: 2.4340e-05 - val_acc: 1.0000 - val_top_k_categorical_accuracy: 1.0000\n",
+      "\n",
+      "Epoch 00010: val_acc did not improve from 1.00000\n",
+      "Epoch 11/40\n",
+      "10/10 [==============================] - 4s 421ms/step - loss: 0.0407 - acc: 0.9905 - top_k_categorical_accuracy: 1.0000 - val_loss: 2.0831e-04 - val_acc: 1.0000 - val_top_k_categorical_accuracy: 1.0000\n",
+      "\n",
+      "Epoch 00011: ReduceLROnPlateau reducing learning rate to 9.999999259090306e-06.\n",
+      "\n",
+      "Epoch 00011: val_acc did not improve from 1.00000\n",
+      "Epoch 12/40\n",
+      "10/10 [==============================] - 5s 477ms/step - loss: 0.0198 - acc: 0.9969 - top_k_categorical_accuracy: 1.0000 - val_loss: 1.1782e-05 - val_acc: 1.0000 - val_top_k_categorical_accuracy: 1.0000\n",
+      "\n",
+      "Epoch 00012: val_acc did not improve from 1.00000\n",
+      "Epoch 13/40\n",
+      "10/10 [==============================] - 5s 462ms/step - loss: 0.0454 - acc: 0.9842 - top_k_categorical_accuracy: 1.0000 - val_loss: 1.9259e-04 - val_acc: 1.0000 - val_top_k_categorical_accuracy: 1.0000\n",
+      "\n",
+      "Epoch 00013: val_acc did not improve from 1.00000\n",
+      "Epoch 14/40\n",
+      "10/10 [==============================] - 5s 494ms/step - loss: 0.1096 - acc: 0.8962 - top_k_categorical_accuracy: 1.0000 - val_loss: 1.5636e-04 - val_acc: 1.0000 - val_top_k_categorical_accuracy: 1.0000\n",
+      "\n",
+      "Epoch 00014: val_acc did not improve from 1.00000\n",
+      "Epoch 15/40\n",
+      "10/10 [==============================] - 6s 556ms/step - loss: 0.3343 - acc: 0.9056 - top_k_categorical_accuracy: 1.0000 - val_loss: 1.7151e-04 - val_acc: 1.0000 - val_top_k_categorical_accuracy: 1.0000\n",
+      "\n",
+      "Epoch 00015: val_acc did not improve from 1.00000\n",
+      "Epoch 16/40\n",
+      "10/10 [==============================] - 6s 640ms/step - loss: 0.1562 - acc: 0.9025 - top_k_categorical_accuracy: 1.0000 - val_loss: 3.7321e-04 - val_acc: 1.0000 - val_top_k_categorical_accuracy: 1.0000\n",
+      "\n",
+      "Epoch 00016: ReduceLROnPlateau reducing learning rate to 3.162277292675049e-06.\n",
+      "\n",
+      "Epoch 00016: val_acc did not improve from 1.00000\n",
+      "Epoch 17/40\n",
+      "10/10 [==============================] - 7s 675ms/step - loss: 0.4544 - acc: 0.9025 - top_k_categorical_accuracy: 1.0000 - val_loss: 2.2542e-04 - val_acc: 1.0000 - val_top_k_categorical_accuracy: 1.0000\n",
+      "\n",
+      "Epoch 00017: val_acc did not improve from 1.00000\n",
+      "Epoch 18/40\n",
+      "10/10 [==============================] - 9s 859ms/step - loss: 0.0487 - acc: 0.9968 - top_k_categorical_accuracy: 1.0000 - val_loss: 9.1457e-05 - val_acc: 1.0000 - val_top_k_categorical_accuracy: 1.0000\n",
+      "\n",
+      "Epoch 00018: val_acc did not improve from 1.00000\n",
+      "Epoch 19/40\n",
+      "10/10 [==============================] - 8s 768ms/step - loss: 0.0261 - acc: 0.9968 - top_k_categorical_accuracy: 1.0000 - val_loss: 2.0283e-04 - val_acc: 1.0000 - val_top_k_categorical_accuracy: 1.0000\n",
+      "\n",
+      "Epoch 00019: val_acc did not improve from 1.00000\n",
+      "Epoch 20/40\n",
+      "10/10 [==============================] - 10s 978ms/step - loss: 0.0601 - acc: 0.9905 - top_k_categorical_accuracy: 1.0000 - val_loss: 5.0729e-04 - val_acc: 1.0000 - val_top_k_categorical_accuracy: 1.0000\n",
+      "\n",
+      "Epoch 00020: val_acc did not improve from 1.00000\n",
+      "Epoch 21/40\n",
+      "10/10 [==============================] - 10s 1s/step - loss: 0.0156 - acc: 0.9968 - top_k_categorical_accuracy: 1.0000 - val_loss: 2.6926e-04 - val_acc: 1.0000 - val_top_k_categorical_accuracy: 1.0000\n",
+      "\n",
+      "Epoch 00021: ReduceLROnPlateau reducing learning rate to 9.999999115286567e-07.\n",
+      "\n",
+      "Epoch 00021: val_acc did not improve from 1.00000\n",
+      "Epoch 22/40\n",
+      "10/10 [==============================] - 10s 988ms/step - loss: 0.0224 - acc: 0.9968 - top_k_categorical_accuracy: 1.0000 - val_loss: 1.8895e-04 - val_acc: 1.0000 - val_top_k_categorical_accuracy: 1.0000\n",
+      "\n",
+      "Epoch 00022: val_acc did not improve from 1.00000\n",
+      "Epoch 23/40\n",
+      "10/10 [==============================] - 11s 1s/step - loss: 0.0227 - acc: 0.9938 - top_k_categorical_accuracy: 1.0000 - val_loss: 2.9605e-04 - val_acc: 1.0000 - val_top_k_categorical_accuracy: 1.0000\n",
+      "\n",
+      "Epoch 00023: val_acc did not improve from 1.00000\n",
+      "Epoch 24/40\n",
+      "10/10 [==============================] - 13s 1s/step - loss: 0.0245 - acc: 0.9937 - top_k_categorical_accuracy: 1.0000 - val_loss: 8.7585e-05 - val_acc: 1.0000 - val_top_k_categorical_accuracy: 1.0000\n",
+      "\n",
+      "Epoch 00024: val_acc did not improve from 1.00000\n",
+      "Epoch 25/40\n",
+      "10/10 [==============================] - 21s 2s/step - loss: 0.2920 - acc: 0.9056 - top_k_categorical_accuracy: 1.0000 - val_loss: 2.4203e-04 - val_acc: 1.0000 - val_top_k_categorical_accuracy: 1.0000\n",
+      "\n",
+      "Epoch 00025: val_acc did not improve from 1.00000\n",
+      "Epoch 26/40\n",
+      "10/10 [==============================] - 21s 2s/step - loss: 0.0551 - acc: 0.9874 - top_k_categorical_accuracy: 1.0000 - val_loss: 4.9715e-04 - val_acc: 1.0000 - val_top_k_categorical_accuracy: 1.0000\n",
+      "\n",
+      "Epoch 00026: ReduceLROnPlateau reducing learning rate to 5e-07.\n",
+      "\n",
+      "Epoch 00026: val_acc did not improve from 1.00000\n",
+      "Epoch 27/40\n",
+      "10/10 [==============================] - 21s 2s/step - loss: 0.0162 - acc: 0.9968 - top_k_categorical_accuracy: 1.0000 - val_loss: 2.8434e-04 - val_acc: 1.0000 - val_top_k_categorical_accuracy: 1.0000\n",
+      "\n",
+      "Epoch 00027: val_acc did not improve from 1.00000\n",
+      "Epoch 28/40\n",
+      "10/10 [==============================] - 21s 2s/step - loss: 0.0210 - acc: 0.9937 - top_k_categorical_accuracy: 1.0000 - val_loss: 2.9756e-04 - val_acc: 1.0000 - val_top_k_categorical_accuracy: 1.0000\n",
+      "\n",
+      "Epoch 00028: val_acc did not improve from 1.00000\n",
+      "Epoch 29/40\n",
+      "10/10 [==============================] - 21s 2s/step - loss: 0.0282 - acc: 0.9937 - top_k_categorical_accuracy: 1.0000 - val_loss: 3.0934e-04 - val_acc: 1.0000 - val_top_k_categorical_accuracy: 1.0000\n",
+      "\n",
+      "Epoch 00029: val_acc did not improve from 1.00000\n",
+      "Epoch 30/40\n",
+      "10/10 [==============================] - 21s 2s/step - loss: 0.0242 - acc: 0.9937 - top_k_categorical_accuracy: 1.0000 - val_loss: 8.1572e-05 - val_acc: 1.0000 - val_top_k_categorical_accuracy: 1.0000\n",
+      "\n",
+      "Epoch 00030: val_acc did not improve from 1.00000\n",
+      "Epoch 31/40\n",
+      "10/10 [==============================] - 21s 2s/step - loss: 0.0314 - acc: 0.9905 - top_k_categorical_accuracy: 1.0000 - val_loss: 3.2502e-04 - val_acc: 1.0000 - val_top_k_categorical_accuracy: 1.0000\n",
+      "\n",
+      "Epoch 00031: val_acc did not improve from 1.00000\n",
+      "Epoch 32/40\n",
+      "10/10 [==============================] - 21s 2s/step - loss: 0.0234 - acc: 0.9937 - top_k_categorical_accuracy: 1.0000 - val_loss: 2.5417e-04 - val_acc: 1.0000 - val_top_k_categorical_accuracy: 1.0000\n",
+      "\n",
+      "Epoch 00032: val_acc did not improve from 1.00000\n",
+      "Epoch 33/40\n",
+      "10/10 [==============================] - 21s 2s/step - loss: 0.0367 - acc: 0.9874 - top_k_categorical_accuracy: 1.0000 - val_loss: 2.4584e-04 - val_acc: 1.0000 - val_top_k_categorical_accuracy: 1.0000\n",
+      "\n",
+      "Epoch 00033: val_acc did not improve from 1.00000\n",
+      "Epoch 34/40\n",
+      "10/10 [==============================] - 23s 2s/step - loss: 0.0112 - acc: 1.0000 - top_k_categorical_accuracy: 1.0000 - val_loss: 9.9977e-04 - val_acc: 1.0000 - val_top_k_categorical_accuracy: 1.0000\n",
+      "\n",
+      "Epoch 00034: val_acc did not improve from 1.00000\n",
+      "Epoch 35/40\n",
+      "10/10 [==============================] - 21s 2s/step - loss: 0.0211 - acc: 0.9968 - top_k_categorical_accuracy: 1.0000 - val_loss: 3.1936e-04 - val_acc: 1.0000 - val_top_k_categorical_accuracy: 1.0000\n",
+      "\n",
+      "Epoch 00035: val_acc did not improve from 1.00000\n",
+      "Epoch 36/40\n",
+      "10/10 [==============================] - 21s 2s/step - loss: 0.1917 - acc: 0.9056 - top_k_categorical_accuracy: 1.0000 - val_loss: 5.0530e-04 - val_acc: 1.0000 - val_top_k_categorical_accuracy: 1.0000\n",
+      "\n",
+      "Epoch 00036: val_acc did not improve from 1.00000\n",
+      "Epoch 37/40\n",
+      "10/10 [==============================] - 21s 2s/step - loss: 0.3056 - acc: 0.9025 - top_k_categorical_accuracy: 1.0000 - val_loss: 2.7431e-04 - val_acc: 1.0000 - val_top_k_categorical_accuracy: 1.0000\n",
+      "\n",
+      "Epoch 00037: val_acc did not improve from 1.00000\n",
+      "Epoch 38/40\n",
+      "10/10 [==============================] - 21s 2s/step - loss: 0.0117 - acc: 1.0000 - top_k_categorical_accuracy: 1.0000 - val_loss: 6.9746e-04 - val_acc: 1.0000 - val_top_k_categorical_accuracy: 1.0000\n",
+      "\n",
+      "Epoch 00038: val_acc did not improve from 1.00000\n",
+      "Epoch 39/40\n",
+      "10/10 [==============================] - 21s 2s/step - loss: 0.0225 - acc: 0.9968 - top_k_categorical_accuracy: 1.0000 - val_loss: 3.6515e-04 - val_acc: 1.0000 - val_top_k_categorical_accuracy: 1.0000\n",
+      "\n",
+      "Epoch 00039: val_acc did not improve from 1.00000\n",
+      "Epoch 40/40\n",
+      "10/10 [==============================] - 21s 2s/step - loss: 0.1231 - acc: 0.9056 - top_k_categorical_accuracy: 1.0000 - val_loss: 1.7276e-04 - val_acc: 1.0000 - val_top_k_categorical_accuracy: 1.0000\n",
+      "\n",
+      "Epoch 00040: val_acc did not improve from 1.00000\n"
+     ]
+    }
+   ],
+   "source": [
+    "def auroc(y_true, y_pred):\n",
+    "    return tf.py_func(roc_auc_score, (y_true, y_pred), tf.double)\n",
+    "\n",
+    "\n",
+    "optimizer = Adam(lr=0.0001, beta_1=0.9, beta_2=0.999, epsilon=None, decay=0.0, amsgrad=False)\n",
+    "model.compile(loss='binary_crossentropy',\n",
+    "              optimizer=optimizer,\n",
+    "              metrics=['acc','top_k_categorical_accuracy'])\n",
+    "\n",
+    "#save_best = ModelCheckpoint(filepath='D:/polynomial/covid19/inception_dl4_val_new/checkpoint-{val_acc:.4f}.h5',monitor='val_acc',mode='auto',save_best_only=True)\n",
+    "save_best=ModelCheckpoint('D:/polynomial/covid19/anomaly dataset/RAM_Net.h5', verbose=1,monitor='val_acc',save_best_only=True, save_weights_only=True)\n",
+    "learning_rate_reduction = ReduceLROnPlateau(monitor='val_acc', patience=5, verbose=1, factor=np.sqrt(0.1),\n",
+    "                                            min_lr=0.5e-6, cooldown=0,mode='auto')\n",
+    "callbacks=[learning_rate_reduction,tensorboard,save_best]\n",
+    "\n",
+    "model.summary()\n",
+    "\n",
+    "batch_size = 32\n",
+    "epochs = 40\n",
+    "history = model.fit_generator(train_datagen.flow(X_train,y_train, batch_size=batch_size),\n",
+    "                              epochs = epochs, validation_data = val_datagen.flow(X_val, y_val),\n",
+    "                              verbose = 1, steps_per_epoch=(X_train.shape[0] // batch_size),\n",
+    "                              validation_steps=(X_val.shape[0] // batch_size),\n",
+    "                              callbacks=callbacks)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Performance evaluation\n",
+    "\n",
+    "For the performance evaluation, I use several performance evaluation matrices:\n",
+    "1. Test accuracy\n",
+    "2. confusion matrix\n",
+    "3. Precision\n",
+    "4. Recall\n",
+    "5. F1 Score\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "36/36 [==============================] - 0s 3ms/step\n",
+      "Validation accuracy= 1.000000  ;  loss_v= 0.031549\n",
+      "True Label for the test set:  [1 0 1 1 1 1 0]\n",
+      "Predicted label for the test set: [1 0 1 1 1 1 0]\n",
+      "                 precision    recall  f1-score   support\n",
+      "\n",
+      "Non_Problematic       1.00      1.00      1.00         2\n",
+      "    Problematic       1.00      1.00      1.00         5\n",
+      "\n",
+      "       accuracy                           1.00         7\n",
+      "      macro avg       1.00      1.00      1.00         7\n",
+      "   weighted avg       1.00      1.00      1.00         7\n",
+      "\n",
+      "7/7 [==============================] - 0s 4ms/step\n",
+      "Test: accuracy = 1.000000  ;  loss = 0.088632\n",
+      "7/7 [==============================] - 0s 4ms/step\n",
+      "confusion matrix [[2 0]\n",
+      " [0 5]]\n"
+     ]
+    }
+   ],
+   "source": [
+    "model.load_weights('D:/polynomial/covid19/anomaly dataset/RAM_Net.h5')\n",
+    "loss_val, acc_val,top5 = model.evaluate(X_val, y_val, verbose=1)\n",
+    "print(\"Validation accuracy= %f  ;  loss_v= %f\" % (acc_val, loss_val))\n",
+    "#print('top5 validate accuracy: ',top5)\n",
+    "#print('top3 validate accuracy: ',top3)\n",
+    "\n",
+    "\n",
+    "X_test = np.load(\"D:/polynomial/covid19/anomaly dataset/test.npy\")\n",
+    "y_test = np.load(\"D:/polynomial/covid19/anomaly dataset/test_labels.npy\")\n",
+    "#print(\"1st ytest: \",y_test)\n",
+    "print(\"True Label for the test set: \",y_test)\n",
+    "target_names = ['Non_Problematic', 'Problematic']\n",
+    "#print(\"1st y_test\",y_test, y_test.shape)\n",
+    "y_pred = model.predict(X_test)\n",
+    "#print(\"Test Accuracy\",y_pred.astype(float),y_pred.shape,type(y_pred))\n",
+    "np.set_printoptions(precision=3, suppress=True)\n",
+    "#print(\"1: \",y_pred)\n",
+    "np.set_printoptions(formatter={'float': '{: 0.3f}'.format})\n",
+    "#print(\"2: \",y_pred)\n",
+    "\n",
+    "y_classes = y_pred.argmax(axis=-1)\n",
+    "print(\"Predicted label for the test set:\",y_classes)\n",
+    "\n",
+    "#print(\"5th y_pred\",y_classes,y_classes.shape)\n",
+    "print(classification_report(y_test, y_classes, target_names=target_names))\n",
+    "y_test = to_categorical(y_test)\n",
+    "#print(\"2nd y_test\",y_test)\n",
+    "\n",
+    "#print(\"2nd ytest: \",y_test)\n",
+    "\n",
+    "loss_test, acc_test,top5 = model.evaluate(X_test, y_test, verbose=1)\n",
+    "print(\"Test: accuracy = %f  ;  loss = %f\" % (acc_test, loss_test))\n",
+    "#print(\"top5 test accuracy: \",top5)\n",
+    "#print(\"top3 test accuracy: \",top3)\n",
+    "\n",
+    "\n",
+    "pre=model.predict(X_test,verbose=1)\n",
+    "#print(\"pre\",pre)\n",
+    "n_values=3\n",
+    "c = np.eye(n_values, dtype=int)[np.argmax(pre, axis=1)]\n",
+    "#print('one-hot-encoding',c)\n",
+    "cm = confusion_matrix(y_test.argmax(axis=1), c.argmax(axis=1))\n",
+    "print(\"confusion matrix\",cm)\n",
+    "cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\n",
+    "#print('new_cm',cm)\n",
+    "#print('acc',cm.diagonal())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### To sum up, all the evaluation functions prove that My algorithm (RAM-Net) perfectly detects the problematic and non_problematic images with 100% test accuracy.\n",
+    "\n",
+    "In the Segmentation_model_extend_ai.ipynb I provide the segmentation related solution for this same problem. \n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.5.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/segmentation_model_extend_ai.ipynb b/segmentation_model_extend_ai.ipynb
new file mode 100644
index 0000000..dc71822
--- /dev/null
+++ b/segmentation_model_extend_ai.ipynb
@@ -0,0 +1,1331 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In my classification-related jupyter notebook, I describe how I want to solve this problem by taking two different approaches. Previously I tackled this problem through the Classification approach after creating an augmented dataset by offline data augmentation method. In addition, I use my custom build network RAM-Net to tackle this issue. Moreover, the approach was successful as my network was able to classify the 7 real images (which are given by extend ai) correctly.\n",
+    "\n",
+    "# Segmentation Process\n",
+    "\n",
+    "Here, I try to solve this issue through segmentation. For the segmentation, I use the offline data augmentation method to create a paired augmented dataset (base image, mask). Next, I use the popular deep learning model U-Net to predict the segmentation mask.\n",
+    "\n",
+    "# Data Preparation\n",
+    "\n",
+    "For data preparation, I used offline data augmentation to create the paired train and validation set. \n",
+    "\n",
+    "At first, I took 7 real images which were provided by Extend AI, and I utilized CVAT (https://cvat.org/) computer vision annotation tools to create ground truth segmentation masks. \n",
+    "\n",
+    "Secondly, I used offline data augmentation to create paired (base image and segmentation mask) image train set and validation set. For the training, I provide a total of 350 paired images (7*50 means 50 images from each real image). For the validation, I created 56 paired images (7*8 means 8 images from each real image). \n",
+    "\n",
+    "Thirdly, for the testing, I used 7 real images and corresponding ground truth segmentation masks to perform the qualitative evaluation. \n",
+    "\n",
+    "Lastly, I perform normalization and convert the images to gray level to cut the computation and make a proficient training procedure.\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n",
+    "# Architecture\n",
+    "\n",
+    "I use popular U-Net architecture to tackle this task. As Input, I use the NumPy array and resize the dimension to (224 X 224 X 1). For the loss function, I used binary cross-entropy loss. I used several call-back functionalities such as ReduceLROnPlateau, EarlyStopping, and ModelCheckpoint. Furthermore, I used Adam optimizer as the optimization function, and the batch size is 8. \n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 228,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np \n",
+    "import tensorflow as tf\n",
+    "import pandas as pd\n",
+    "from tqdm import tqdm\n",
+    "import os\n",
+    "from cv2 import imread, createCLAHE \n",
+    "import cv2\n",
+    "from glob import glob\n",
+    "%matplotlib inline\n",
+    "import matplotlib.pyplot as plt\n",
+    "import matplotlib.cm as cm\n",
+    "import keras\n",
+    "from keras.models import *\n",
+    "from keras.layers import *\n",
+    "from keras.optimizers import *\n",
+    "from keras import backend as keras\n",
+    "from keras.preprocessing.image import ImageDataGenerator\n",
+    "from keras.callbacks import ModelCheckpoint, LearningRateScheduler,EarlyStopping, ReduceLROnPlateau\n",
+    "#print(tf.__version__)\n",
+    "os.environ['TF_FORCE_GPU_ALLOW_GROWTH'] = 'true'"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Take all the data is in a NumPy format."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 229,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1.0 0.0 0.95686275 0.0\n",
+      "(350, 224, 224) (350, 224, 224)\n"
+     ]
+    }
+   ],
+   "source": [
+    "#Train set\n",
+    "images=np.load('D:/aminur/anomaly dataset/npy_real.npy')\n",
+    "mask=np.load('D:/aminur/anomaly dataset/npy_mask.npy')\n",
+    "\n",
+    "#validation set\n",
+    "images_val=np.load('D:/aminur/anomaly dataset/npy_val_real.npy')\n",
+    "mask_val=np.load('D:/aminur/anomaly dataset/npy_val_mask.npy')\n",
+    "\n",
+    "print(mask.max(), mask.min(),images.max(),images.min())\n",
+    "print(images.shape,mask.shape)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Example of one image and corresponding segmentation map from the augmented dataset. I think the gray level would be enough to tackle this problem and find a good prediction."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 230,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "#display three examples\n",
+    "def display(x,y):\n",
+    "    fig = plt.figure()\n",
+    "    ax1 = fig.add_subplot(121)\n",
+    "    # Bilinear interpolation - this will look blurry\n",
+    "    ax1.imshow(x, interpolation='bilinear', cmap=cm.Greys_r)\n",
+    "\n",
+    "    ax2 = fig.add_subplot(122)\n",
+    "    # 'nearest' interpolation - faithful but blocky\n",
+    "    ax2.imshow(y, interpolation='nearest', cmap=cm.Greys_r)\n",
+    "\n",
+    "    plt.show()\n",
+    "display(images[100],mask[100])\n",
+    "display(images[200],mask[200])\n",
+    "display(images[250],mask[250])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "I think the dimension of the image 224X224 would be enough for this task to predict some good results. At the same time, it will minimize the Flops of the network."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 231,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dim=224\n",
+    "images = np.array(images).reshape(len(images),dim,dim,1)\n",
+    "mask = np.array(mask).reshape(len(mask),dim,dim,1)\n",
+    "images_val=np.array(images_val).reshape(len(images_val),dim,dim,1)\n",
+    "mask_val=np.array(mask_val).reshape(len(mask_val),dim,dim,1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 232,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Unet Network\n",
+    "def unet(input_size=(224,224,3)):\n",
+    "    inputs = Input(input_size)\n",
+    "    \n",
+    "    conv1 = Conv2D(32, (3, 3), activation='relu', padding='same')(inputs)\n",
+    "    conv1 = Conv2D(32, (3, 3), activation='relu', padding='same')(conv1)\n",
+    "    pool1 = MaxPooling2D(pool_size=(2, 2))(conv1)\n",
+    "\n",
+    "    conv2 = Conv2D(64, (3, 3), activation='relu', padding='same')(pool1)\n",
+    "    conv2 = Conv2D(64, (3, 3), activation='relu', padding='same')(conv2)\n",
+    "    pool2 = MaxPooling2D(pool_size=(2, 2))(conv2)\n",
+    "\n",
+    "    conv3 = Conv2D(128, (3, 3), activation='relu', padding='same')(pool2)\n",
+    "    conv3 = Conv2D(128, (3, 3), activation='relu', padding='same')(conv3)\n",
+    "    pool3 = MaxPooling2D(pool_size=(2, 2))(conv3)\n",
+    "\n",
+    "    conv4 = Conv2D(256, (3, 3), activation='relu', padding='same')(pool3)\n",
+    "    conv4 = Conv2D(256, (3, 3), activation='relu', padding='same')(conv4)\n",
+    "    pool4 = MaxPooling2D(pool_size=(2, 2))(conv4)\n",
+    "\n",
+    "    conv5 = Conv2D(512, (3, 3), activation='relu', padding='same')(pool4)\n",
+    "    conv5 = Conv2D(512, (3, 3), activation='relu', padding='same')(conv5)\n",
+    "\n",
+    "    up6 = concatenate([Conv2DTranspose(256, (2, 2), strides=(2, 2), padding='same')(conv5), conv4], axis=3)\n",
+    "    conv6 = Conv2D(256, (3, 3), activation='relu', padding='same')(up6)\n",
+    "    conv6 = Conv2D(256, (3, 3), activation='relu', padding='same')(conv6)\n",
+    "    \n",
+    "    up7 = concatenate([Conv2DTranspose(128, (2, 2), strides=(2, 2), padding='same')(conv6), conv3], axis=3)\n",
+    "    conv7 = Conv2D(128, (3, 3), activation='relu', padding='same')(up7)\n",
+    "    conv7 = Conv2D(128, (3, 3), activation='relu', padding='same')(conv7)\n",
+    "\n",
+    "    up8 = concatenate([Conv2DTranspose(64, (2, 2), strides=(2, 2), padding='same')(conv7), conv2], axis=3)\n",
+    "    conv8 = Conv2D(64, (3, 3), activation='relu', padding='same')(up8)\n",
+    "    conv8 = Conv2D(64, (3, 3), activation='relu', padding='same')(conv8)\n",
+    "\n",
+    "    up9 = concatenate([Conv2DTranspose(32, (2, 2), strides=(2, 2), padding='same')(conv8), conv1], axis=3)\n",
+    "    conv9 = Conv2D(32, (3, 3), activation='relu', padding='same')(up9)\n",
+    "    conv9 = Conv2D(32, (3, 3), activation='relu', padding='same')(conv9)\n",
+    "\n",
+    "    conv10 = Conv2D(1, (1, 1), activation='sigmoid',padding='same')(conv9)\n",
+    "\n",
+    "    return Model(inputs=[inputs], outputs=[conv10])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 233,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Model: \"model_21\"\n",
+      "__________________________________________________________________________________________________\n",
+      "Layer (type)                    Output Shape         Param #     Connected to                     \n",
+      "==================================================================================================\n",
+      "input_21 (InputLayer)           (None, 224, 224, 1)  0                                            \n",
+      "__________________________________________________________________________________________________\n",
+      "conv2d_381 (Conv2D)             (None, 224, 224, 32) 320         input_21[0][0]                   \n",
+      "__________________________________________________________________________________________________\n",
+      "conv2d_382 (Conv2D)             (None, 224, 224, 32) 9248        conv2d_381[0][0]                 \n",
+      "__________________________________________________________________________________________________\n",
+      "max_pooling2d_81 (MaxPooling2D) (None, 112, 112, 32) 0           conv2d_382[0][0]                 \n",
+      "__________________________________________________________________________________________________\n",
+      "conv2d_383 (Conv2D)             (None, 112, 112, 64) 18496       max_pooling2d_81[0][0]           \n",
+      "__________________________________________________________________________________________________\n",
+      "conv2d_384 (Conv2D)             (None, 112, 112, 64) 36928       conv2d_383[0][0]                 \n",
+      "__________________________________________________________________________________________________\n",
+      "max_pooling2d_82 (MaxPooling2D) (None, 56, 56, 64)   0           conv2d_384[0][0]                 \n",
+      "__________________________________________________________________________________________________\n",
+      "conv2d_385 (Conv2D)             (None, 56, 56, 128)  73856       max_pooling2d_82[0][0]           \n",
+      "__________________________________________________________________________________________________\n",
+      "conv2d_386 (Conv2D)             (None, 56, 56, 128)  147584      conv2d_385[0][0]                 \n",
+      "__________________________________________________________________________________________________\n",
+      "max_pooling2d_83 (MaxPooling2D) (None, 28, 28, 128)  0           conv2d_386[0][0]                 \n",
+      "__________________________________________________________________________________________________\n",
+      "conv2d_387 (Conv2D)             (None, 28, 28, 256)  295168      max_pooling2d_83[0][0]           \n",
+      "__________________________________________________________________________________________________\n",
+      "conv2d_388 (Conv2D)             (None, 28, 28, 256)  590080      conv2d_387[0][0]                 \n",
+      "__________________________________________________________________________________________________\n",
+      "max_pooling2d_84 (MaxPooling2D) (None, 14, 14, 256)  0           conv2d_388[0][0]                 \n",
+      "__________________________________________________________________________________________________\n",
+      "conv2d_389 (Conv2D)             (None, 14, 14, 512)  1180160     max_pooling2d_84[0][0]           \n",
+      "__________________________________________________________________________________________________\n",
+      "conv2d_390 (Conv2D)             (None, 14, 14, 512)  2359808     conv2d_389[0][0]                 \n",
+      "__________________________________________________________________________________________________\n",
+      "conv2d_transpose_81 (Conv2DTran (None, 28, 28, 256)  524544      conv2d_390[0][0]                 \n",
+      "__________________________________________________________________________________________________\n",
+      "concatenate_81 (Concatenate)    (None, 28, 28, 512)  0           conv2d_transpose_81[0][0]        \n",
+      "                                                                 conv2d_388[0][0]                 \n",
+      "__________________________________________________________________________________________________\n",
+      "conv2d_391 (Conv2D)             (None, 28, 28, 256)  1179904     concatenate_81[0][0]             \n",
+      "__________________________________________________________________________________________________\n",
+      "conv2d_392 (Conv2D)             (None, 28, 28, 256)  590080      conv2d_391[0][0]                 \n",
+      "__________________________________________________________________________________________________\n",
+      "conv2d_transpose_82 (Conv2DTran (None, 56, 56, 128)  131200      conv2d_392[0][0]                 \n",
+      "__________________________________________________________________________________________________\n",
+      "concatenate_82 (Concatenate)    (None, 56, 56, 256)  0           conv2d_transpose_82[0][0]        \n",
+      "                                                                 conv2d_386[0][0]                 \n",
+      "__________________________________________________________________________________________________\n",
+      "conv2d_393 (Conv2D)             (None, 56, 56, 128)  295040      concatenate_82[0][0]             \n",
+      "__________________________________________________________________________________________________\n",
+      "conv2d_394 (Conv2D)             (None, 56, 56, 128)  147584      conv2d_393[0][0]                 \n",
+      "__________________________________________________________________________________________________\n",
+      "conv2d_transpose_83 (Conv2DTran (None, 112, 112, 64) 32832       conv2d_394[0][0]                 \n",
+      "__________________________________________________________________________________________________\n",
+      "concatenate_83 (Concatenate)    (None, 112, 112, 128 0           conv2d_transpose_83[0][0]        \n",
+      "                                                                 conv2d_384[0][0]                 \n",
+      "__________________________________________________________________________________________________\n",
+      "conv2d_395 (Conv2D)             (None, 112, 112, 64) 73792       concatenate_83[0][0]             \n",
+      "__________________________________________________________________________________________________\n",
+      "conv2d_396 (Conv2D)             (None, 112, 112, 64) 36928       conv2d_395[0][0]                 \n",
+      "__________________________________________________________________________________________________\n",
+      "conv2d_transpose_84 (Conv2DTran (None, 224, 224, 32) 8224        conv2d_396[0][0]                 \n",
+      "__________________________________________________________________________________________________\n",
+      "concatenate_84 (Concatenate)    (None, 224, 224, 64) 0           conv2d_transpose_84[0][0]        \n",
+      "                                                                 conv2d_382[0][0]                 \n",
+      "__________________________________________________________________________________________________\n",
+      "conv2d_397 (Conv2D)             (None, 224, 224, 32) 18464       concatenate_84[0][0]             \n",
+      "__________________________________________________________________________________________________\n",
+      "conv2d_398 (Conv2D)             (None, 224, 224, 32) 9248        conv2d_397[0][0]                 \n",
+      "__________________________________________________________________________________________________\n",
+      "conv2d_399 (Conv2D)             (None, 224, 224, 1)  33          conv2d_398[0][0]                 \n",
+      "==================================================================================================\n",
+      "Total params: 7,759,521\n",
+      "Trainable params: 7,759,521\n",
+      "Non-trainable params: 0\n",
+      "__________________________________________________________________________________________________\n"
+     ]
+    }
+   ],
+   "source": [
+    "model = unet(input_size=(224,224,1))\n",
+    "#lr=1e-5\n",
+    "#model.compile(optimizer=Adam(lr=1e-5), loss=dice_coef_loss,\n",
+    "#                  metrics=[dice_coef, 'binary_accuracy'])\n",
+    "\n",
+    "model.summary()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Callback function: We need it to inspect the training procedure and based on the result, we can decide about our hyperparameters.  "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 234,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "D:\\Anaconda3\\envs\\deep\\lib\\site-packages\\keras\\callbacks\\callbacks.py:998: UserWarning: `epsilon` argument is deprecated and will be removed, use `min_delta` instead.\n",
+      "  warnings.warn('`epsilon` argument is deprecated and '\n"
+     ]
+    }
+   ],
+   "source": [
+    "weight_path=\"D:/aminur/anomaly dataset/{}_weights.best.hdf5\".format('anomaly_reg')\n",
+    "\n",
+    "#callback Functions\n",
+    "checkpoint = ModelCheckpoint('D:/aminur/anomaly dataset/model_Unet_anomly.h5', monitor='val_loss', verbose=1, \n",
+    "                             save_best_only=True, mode='min', save_weights_only = True)\n",
+    "\n",
+    "reduceLROnPlat = ReduceLROnPlateau(monitor='val_loss', factor=0.5, \n",
+    "                                   patience=15, \n",
+    "                                   verbose=1, mode='min', epsilon=0.0001, cooldown=2, min_lr=1e-6)\n",
+    "early = EarlyStopping(monitor=\"val_loss\", \n",
+    "                      mode=\"min\", \n",
+    "                      patience=15) # probably needs to be more patient, but kaggle time is limited\n",
+    "callbacks_list = [checkpoint,early, reduceLROnPlat]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Run this model for 100 epoch with batch size 8. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 262,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Train on 350 samples, validate on 56 samples\n",
+      "Epoch 1/100\n",
+      "350/350 [==============================] - 6s 17ms/step - loss: 0.0017 - accuracy: 0.9954 - val_loss: 0.0012 - val_accuracy: 0.9958\n",
+      "\n",
+      "Epoch 00001: val_loss did not improve from 0.00106\n",
+      "Epoch 2/100\n",
+      "350/350 [==============================] - 5s 15ms/step - loss: 0.0012 - accuracy: 0.9955 - val_loss: 0.0011 - val_accuracy: 0.9958\n",
+      "\n",
+      "Epoch 00002: val_loss did not improve from 0.00106\n",
+      "Epoch 3/100\n",
+      "350/350 [==============================] - 5s 15ms/step - loss: 0.0012 - accuracy: 0.9955 - val_loss: 0.0011 - val_accuracy: 0.9958\n",
+      "\n",
+      "Epoch 00003: val_loss did not improve from 0.00106\n",
+      "Epoch 4/100\n",
+      "350/350 [==============================] - 5s 15ms/step - loss: 0.0012 - accuracy: 0.9955 - val_loss: 0.0011 - val_accuracy: 0.9958\n",
+      "\n",
+      "Epoch 00004: val_loss did not improve from 0.00106\n",
+      "Epoch 5/100\n",
+      "350/350 [==============================] - 5s 15ms/step - loss: 0.0012 - accuracy: 0.9955 - val_loss: 0.0011 - val_accuracy: 0.9958\n",
+      "\n",
+      "Epoch 00005: val_loss did not improve from 0.00106\n",
+      "Epoch 6/100\n",
+      "350/350 [==============================] - 5s 15ms/step - loss: 0.0013 - accuracy: 0.9955 - val_loss: 0.0012 - val_accuracy: 0.9958\n",
+      "\n",
+      "Epoch 00006: val_loss did not improve from 0.00106\n",
+      "Epoch 7/100\n",
+      "350/350 [==============================] - 5s 15ms/step - loss: 0.0012 - accuracy: 0.9955 - val_loss: 0.0011 - val_accuracy: 0.9958\n",
+      "\n",
+      "Epoch 00007: val_loss did not improve from 0.00106\n",
+      "Epoch 8/100\n",
+      "350/350 [==============================] - 5s 15ms/step - loss: 0.0012 - accuracy: 0.9955 - val_loss: 0.0011 - val_accuracy: 0.9958\n",
+      "\n",
+      "Epoch 00008: val_loss did not improve from 0.00106\n",
+      "Epoch 9/100\n",
+      "350/350 [==============================] - 5s 15ms/step - loss: 0.0012 - accuracy: 0.9955 - val_loss: 0.0011 - val_accuracy: 0.9958\n",
+      "\n",
+      "Epoch 00009: val_loss did not improve from 0.00106\n",
+      "Epoch 10/100\n",
+      "350/350 [==============================] - 5s 15ms/step - loss: 0.0012 - accuracy: 0.9955 - val_loss: 0.0013 - val_accuracy: 0.9958\n",
+      "\n",
+      "Epoch 00010: val_loss did not improve from 0.00106\n",
+      "Epoch 11/100\n",
+      "350/350 [==============================] - 5s 15ms/step - loss: 0.0013 - accuracy: 0.9955 - val_loss: 0.0013 - val_accuracy: 0.9958\n",
+      "\n",
+      "Epoch 00011: val_loss did not improve from 0.00106\n",
+      "Epoch 12/100\n",
+      "350/350 [==============================] - 5s 15ms/step - loss: 0.0013 - accuracy: 0.9955 - val_loss: 0.0011 - val_accuracy: 0.9958\n",
+      "\n",
+      "Epoch 00012: val_loss did not improve from 0.00106\n",
+      "Epoch 13/100\n",
+      "350/350 [==============================] - 5s 15ms/step - loss: 0.0012 - accuracy: 0.9955 - val_loss: 0.0011 - val_accuracy: 0.9958\n",
+      "\n",
+      "Epoch 00013: val_loss did not improve from 0.00106\n",
+      "Epoch 14/100\n",
+      "350/350 [==============================] - 5s 15ms/step - loss: 0.0012 - accuracy: 0.9955 - val_loss: 0.0011 - val_accuracy: 0.9958\n",
+      "\n",
+      "Epoch 00014: val_loss did not improve from 0.00106\n",
+      "Epoch 15/100\n",
+      "350/350 [==============================] - 5s 15ms/step - loss: 0.0012 - accuracy: 0.9955 - val_loss: 0.0011 - val_accuracy: 0.9958\n",
+      "\n",
+      "Epoch 00015: val_loss did not improve from 0.00106\n",
+      "Epoch 16/100\n",
+      "350/350 [==============================] - 5s 15ms/step - loss: 0.0012 - accuracy: 0.9955 - val_loss: 0.0011 - val_accuracy: 0.9958\n",
+      "\n",
+      "Epoch 00016: val_loss did not improve from 0.00106\n",
+      "Epoch 17/100\n",
+      "350/350 [==============================] - 5s 15ms/step - loss: 0.0012 - accuracy: 0.9955 - val_loss: 0.0011 - val_accuracy: 0.9958\n",
+      "\n",
+      "Epoch 00017: val_loss did not improve from 0.00106\n",
+      "Epoch 18/100\n",
+      "350/350 [==============================] - 5s 15ms/step - loss: 0.0012 - accuracy: 0.9955 - val_loss: 0.0011 - val_accuracy: 0.9958\n",
+      "\n",
+      "Epoch 00018: val_loss did not improve from 0.00106\n",
+      "Epoch 19/100\n",
+      "350/350 [==============================] - 5s 15ms/step - loss: 0.0012 - accuracy: 0.9955 - val_loss: 0.0012 - val_accuracy: 0.9958\n",
+      "\n",
+      "Epoch 00019: val_loss did not improve from 0.00106\n",
+      "Epoch 20/100\n",
+      "350/350 [==============================] - 5s 15ms/step - loss: 0.0012 - accuracy: 0.9955 - val_loss: 0.0011 - val_accuracy: 0.9958\n",
+      "\n",
+      "Epoch 00020: val_loss did not improve from 0.00106\n",
+      "Epoch 21/100\n",
+      "350/350 [==============================] - 5s 15ms/step - loss: 0.0012 - accuracy: 0.9955 - val_loss: 0.0011 - val_accuracy: 0.9958\n",
+      "\n",
+      "Epoch 00021: val_loss did not improve from 0.00106\n",
+      "Epoch 22/100\n",
+      "350/350 [==============================] - 5s 15ms/step - loss: 0.0012 - accuracy: 0.9955 - val_loss: 0.0011 - val_accuracy: 0.9958\n",
+      "\n",
+      "Epoch 00022: val_loss did not improve from 0.00106\n",
+      "Epoch 23/100\n",
+      "350/350 [==============================] - 5s 15ms/step - loss: 0.0012 - accuracy: 0.9955 - val_loss: 0.0011 - val_accuracy: 0.9958\n",
+      "\n",
+      "Epoch 00023: val_loss did not improve from 0.00106\n",
+      "Epoch 24/100\n",
+      "350/350 [==============================] - 5s 15ms/step - loss: 0.0012 - accuracy: 0.9955 - val_loss: 0.0011 - val_accuracy: 0.9958\n",
+      "\n",
+      "Epoch 00024: val_loss did not improve from 0.00106\n",
+      "Epoch 25/100\n",
+      "350/350 [==============================] - 5s 15ms/step - loss: 0.0012 - accuracy: 0.9955 - val_loss: 0.0011 - val_accuracy: 0.9958\n",
+      "\n",
+      "Epoch 00025: val_loss did not improve from 0.00106\n",
+      "Epoch 26/100\n",
+      "350/350 [==============================] - 5s 15ms/step - loss: 0.0012 - accuracy: 0.9955 - val_loss: 0.0011 - val_accuracy: 0.9958\n",
+      "\n",
+      "Epoch 00026: val_loss did not improve from 0.00106\n",
+      "Epoch 27/100\n",
+      "350/350 [==============================] - 5s 15ms/step - loss: 0.0012 - accuracy: 0.9955 - val_loss: 0.0012 - val_accuracy: 0.9958\n",
+      "\n",
+      "Epoch 00027: val_loss did not improve from 0.00106\n",
+      "Epoch 28/100\n",
+      "350/350 [==============================] - 5s 15ms/step - loss: 0.0014 - accuracy: 0.9955 - val_loss: 0.0011 - val_accuracy: 0.9958\n",
+      "\n",
+      "Epoch 00028: val_loss did not improve from 0.00106\n",
+      "\n",
+      "Epoch 00028: ReduceLROnPlateau reducing learning rate to 4.999999873689376e-05.\n",
+      "Epoch 29/100\n",
+      "350/350 [==============================] - 5s 15ms/step - loss: 0.0012 - accuracy: 0.9955 - val_loss: 0.0011 - val_accuracy: 0.9958\n",
+      "\n",
+      "Epoch 00029: val_loss did not improve from 0.00106\n",
+      "Epoch 30/100\n",
+      "350/350 [==============================] - 5s 15ms/step - loss: 0.0012 - accuracy: 0.9955 - val_loss: 0.0011 - val_accuracy: 0.9958\n",
+      "\n",
+      "Epoch 00030: val_loss did not improve from 0.00106\n",
+      "Epoch 31/100\n",
+      "350/350 [==============================] - 5s 15ms/step - loss: 0.0011 - accuracy: 0.9955 - val_loss: 0.0011 - val_accuracy: 0.9958\n",
+      "\n",
+      "Epoch 00031: val_loss did not improve from 0.00106\n",
+      "Epoch 32/100\n",
+      "350/350 [==============================] - 5s 15ms/step - loss: 0.0011 - accuracy: 0.9955 - val_loss: 0.0011 - val_accuracy: 0.9958\n",
+      "\n",
+      "Epoch 00032: val_loss improved from 0.00106 to 0.00105, saving model to D:/aminur/anomaly dataset/model_Unet_anomly.h5\n",
+      "Epoch 33/100\n",
+      "350/350 [==============================] - 5s 15ms/step - loss: 0.0011 - accuracy: 0.9955 - val_loss: 0.0011 - val_accuracy: 0.9958\n",
+      "\n",
+      "Epoch 00033: val_loss improved from 0.00105 to 0.00105, saving model to D:/aminur/anomaly dataset/model_Unet_anomly.h5\n",
+      "Epoch 34/100\n",
+      "350/350 [==============================] - 5s 15ms/step - loss: 0.0011 - accuracy: 0.9955 - val_loss: 0.0011 - val_accuracy: 0.9958\n",
+      "\n",
+      "Epoch 00034: val_loss improved from 0.00105 to 0.00105, saving model to D:/aminur/anomaly dataset/model_Unet_anomly.h5\n",
+      "Epoch 35/100\n",
+      "350/350 [==============================] - 5s 15ms/step - loss: 0.0011 - accuracy: 0.9955 - val_loss: 0.0010 - val_accuracy: 0.9958\n",
+      "\n",
+      "Epoch 00035: val_loss improved from 0.00105 to 0.00105, saving model to D:/aminur/anomaly dataset/model_Unet_anomly.h5\n",
+      "Epoch 36/100\n",
+      "350/350 [==============================] - 5s 15ms/step - loss: 0.0011 - accuracy: 0.9955 - val_loss: 0.0010 - val_accuracy: 0.9958\n",
+      "\n",
+      "Epoch 00036: val_loss improved from 0.00105 to 0.00105, saving model to D:/aminur/anomaly dataset/model_Unet_anomly.h5\n",
+      "Epoch 37/100\n",
+      "350/350 [==============================] - 5s 15ms/step - loss: 0.0011 - accuracy: 0.9955 - val_loss: 0.0010 - val_accuracy: 0.9958\n",
+      "\n",
+      "Epoch 00037: val_loss did not improve from 0.00105\n",
+      "Epoch 38/100\n",
+      "350/350 [==============================] - 5s 15ms/step - loss: 0.0011 - accuracy: 0.9955 - val_loss: 0.0010 - val_accuracy: 0.9958\n",
+      "\n",
+      "Epoch 00038: val_loss improved from 0.00105 to 0.00105, saving model to D:/aminur/anomaly dataset/model_Unet_anomly.h5\n",
+      "Epoch 39/100\n",
+      "350/350 [==============================] - 5s 15ms/step - loss: 0.0011 - accuracy: 0.9955 - val_loss: 0.0010 - val_accuracy: 0.9958\n",
+      "\n",
+      "Epoch 00039: val_loss improved from 0.00105 to 0.00105, saving model to D:/aminur/anomaly dataset/model_Unet_anomly.h5\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 40/100\n",
+      "350/350 [==============================] - 5s 15ms/step - loss: 0.0011 - accuracy: 0.9955 - val_loss: 0.0010 - val_accuracy: 0.9958\n",
+      "\n",
+      "Epoch 00040: val_loss improved from 0.00105 to 0.00104, saving model to D:/aminur/anomaly dataset/model_Unet_anomly.h5\n",
+      "Epoch 41/100\n",
+      "350/350 [==============================] - 5s 15ms/step - loss: 0.0011 - accuracy: 0.9955 - val_loss: 0.0010 - val_accuracy: 0.9958\n",
+      "\n",
+      "Epoch 00041: val_loss improved from 0.00104 to 0.00104, saving model to D:/aminur/anomaly dataset/model_Unet_anomly.h5\n",
+      "Epoch 42/100\n",
+      "350/350 [==============================] - 5s 15ms/step - loss: 0.0011 - accuracy: 0.9955 - val_loss: 0.0010 - val_accuracy: 0.9958\n",
+      "\n",
+      "Epoch 00042: val_loss improved from 0.00104 to 0.00104, saving model to D:/aminur/anomaly dataset/model_Unet_anomly.h5\n",
+      "Epoch 43/100\n",
+      "350/350 [==============================] - 5s 15ms/step - loss: 0.0011 - accuracy: 0.9955 - val_loss: 0.0010 - val_accuracy: 0.9958\n",
+      "\n",
+      "Epoch 00043: val_loss improved from 0.00104 to 0.00104, saving model to D:/aminur/anomaly dataset/model_Unet_anomly.h5\n",
+      "Epoch 44/100\n",
+      "350/350 [==============================] - 5s 15ms/step - loss: 0.0011 - accuracy: 0.9955 - val_loss: 0.0010 - val_accuracy: 0.9958\n",
+      "\n",
+      "Epoch 00044: val_loss improved from 0.00104 to 0.00104, saving model to D:/aminur/anomaly dataset/model_Unet_anomly.h5\n",
+      "\n",
+      "Epoch 00044: ReduceLROnPlateau reducing learning rate to 2.499999936844688e-05.\n",
+      "Epoch 45/100\n",
+      "350/350 [==============================] - 5s 15ms/step - loss: 0.0011 - accuracy: 0.9955 - val_loss: 0.0010 - val_accuracy: 0.9958\n",
+      "\n",
+      "Epoch 00045: val_loss improved from 0.00104 to 0.00104, saving model to D:/aminur/anomaly dataset/model_Unet_anomly.h5\n",
+      "Epoch 46/100\n",
+      "350/350 [==============================] - 5s 15ms/step - loss: 0.0011 - accuracy: 0.9955 - val_loss: 0.0010 - val_accuracy: 0.9958\n",
+      "\n",
+      "Epoch 00046: val_loss improved from 0.00104 to 0.00104, saving model to D:/aminur/anomaly dataset/model_Unet_anomly.h5\n",
+      "Epoch 47/100\n",
+      "350/350 [==============================] - 5s 15ms/step - loss: 0.0011 - accuracy: 0.9955 - val_loss: 0.0010 - val_accuracy: 0.9958\n",
+      "\n",
+      "Epoch 00047: val_loss improved from 0.00104 to 0.00104, saving model to D:/aminur/anomaly dataset/model_Unet_anomly.h5\n",
+      "Epoch 48/100\n",
+      "350/350 [==============================] - 5s 15ms/step - loss: 0.0011 - accuracy: 0.9955 - val_loss: 0.0010 - val_accuracy: 0.9958\n",
+      "\n",
+      "Epoch 00048: val_loss did not improve from 0.00104\n",
+      "Epoch 49/100\n",
+      "350/350 [==============================] - 5s 15ms/step - loss: 0.0011 - accuracy: 0.9955 - val_loss: 0.0010 - val_accuracy: 0.9958\n",
+      "\n",
+      "Epoch 00049: val_loss improved from 0.00104 to 0.00104, saving model to D:/aminur/anomaly dataset/model_Unet_anomly.h5\n",
+      "Epoch 50/100\n",
+      "350/350 [==============================] - 5s 15ms/step - loss: 0.0011 - accuracy: 0.9955 - val_loss: 0.0010 - val_accuracy: 0.9958\n",
+      "\n",
+      "Epoch 00050: val_loss improved from 0.00104 to 0.00104, saving model to D:/aminur/anomaly dataset/model_Unet_anomly.h5\n",
+      "Epoch 51/100\n",
+      "350/350 [==============================] - 5s 15ms/step - loss: 0.0011 - accuracy: 0.9955 - val_loss: 0.0010 - val_accuracy: 0.9958\n",
+      "\n",
+      "Epoch 00051: val_loss improved from 0.00104 to 0.00104, saving model to D:/aminur/anomaly dataset/model_Unet_anomly.h5\n",
+      "Epoch 52/100\n",
+      "350/350 [==============================] - 5s 15ms/step - loss: 0.0011 - accuracy: 0.9955 - val_loss: 0.0010 - val_accuracy: 0.9958\n",
+      "\n",
+      "Epoch 00052: val_loss improved from 0.00104 to 0.00104, saving model to D:/aminur/anomaly dataset/model_Unet_anomly.h5\n",
+      "Epoch 53/100\n",
+      "350/350 [==============================] - 5s 15ms/step - loss: 0.0011 - accuracy: 0.9955 - val_loss: 0.0010 - val_accuracy: 0.9958\n",
+      "\n",
+      "Epoch 00053: val_loss did not improve from 0.00104\n",
+      "Epoch 54/100\n",
+      "350/350 [==============================] - 5s 15ms/step - loss: 0.0011 - accuracy: 0.9955 - val_loss: 0.0010 - val_accuracy: 0.9958\n",
+      "\n",
+      "Epoch 00054: val_loss improved from 0.00104 to 0.00104, saving model to D:/aminur/anomaly dataset/model_Unet_anomly.h5\n",
+      "Epoch 55/100\n",
+      "350/350 [==============================] - 5s 15ms/step - loss: 0.0011 - accuracy: 0.9955 - val_loss: 0.0010 - val_accuracy: 0.9958\n",
+      "\n",
+      "Epoch 00055: val_loss improved from 0.00104 to 0.00104, saving model to D:/aminur/anomaly dataset/model_Unet_anomly.h5\n",
+      "Epoch 56/100\n",
+      "350/350 [==============================] - 5s 15ms/step - loss: 0.0011 - accuracy: 0.9955 - val_loss: 0.0010 - val_accuracy: 0.9958\n",
+      "\n",
+      "Epoch 00056: val_loss did not improve from 0.00104\n",
+      "Epoch 57/100\n",
+      "350/350 [==============================] - 5s 15ms/step - loss: 0.0011 - accuracy: 0.9955 - val_loss: 0.0010 - val_accuracy: 0.9958\n",
+      "\n",
+      "Epoch 00057: val_loss did not improve from 0.00104\n",
+      "Epoch 58/100\n",
+      "350/350 [==============================] - 5s 15ms/step - loss: 0.0011 - accuracy: 0.9955 - val_loss: 0.0010 - val_accuracy: 0.9958\n",
+      "\n",
+      "Epoch 00058: val_loss improved from 0.00104 to 0.00103, saving model to D:/aminur/anomaly dataset/model_Unet_anomly.h5\n",
+      "Epoch 59/100\n",
+      "350/350 [==============================] - 5s 15ms/step - loss: 0.0011 - accuracy: 0.9955 - val_loss: 0.0010 - val_accuracy: 0.9958\n",
+      "\n",
+      "Epoch 00059: val_loss improved from 0.00103 to 0.00103, saving model to D:/aminur/anomaly dataset/model_Unet_anomly.h5\n",
+      "Epoch 60/100\n",
+      "350/350 [==============================] - 5s 15ms/step - loss: 0.0011 - accuracy: 0.9955 - val_loss: 0.0010 - val_accuracy: 0.9958\n",
+      "\n",
+      "Epoch 00060: val_loss improved from 0.00103 to 0.00103, saving model to D:/aminur/anomaly dataset/model_Unet_anomly.h5\n",
+      "\n",
+      "Epoch 00060: ReduceLROnPlateau reducing learning rate to 1.249999968422344e-05.\n",
+      "Epoch 61/100\n",
+      "350/350 [==============================] - 5s 15ms/step - loss: 0.0011 - accuracy: 0.9955 - val_loss: 0.0010 - val_accuracy: 0.9958\n",
+      "\n",
+      "Epoch 00061: val_loss improved from 0.00103 to 0.00103, saving model to D:/aminur/anomaly dataset/model_Unet_anomly.h5\n",
+      "Epoch 62/100\n",
+      "350/350 [==============================] - 5s 15ms/step - loss: 0.0011 - accuracy: 0.9955 - val_loss: 0.0010 - val_accuracy: 0.9958\n",
+      "\n",
+      "Epoch 00062: val_loss improved from 0.00103 to 0.00103, saving model to D:/aminur/anomaly dataset/model_Unet_anomly.h5\n",
+      "Epoch 63/100\n",
+      "350/350 [==============================] - 5s 15ms/step - loss: 0.0011 - accuracy: 0.9955 - val_loss: 0.0010 - val_accuracy: 0.9958\n",
+      "\n",
+      "Epoch 00063: val_loss improved from 0.00103 to 0.00103, saving model to D:/aminur/anomaly dataset/model_Unet_anomly.h5\n",
+      "Epoch 64/100\n",
+      "350/350 [==============================] - 5s 15ms/step - loss: 0.0011 - accuracy: 0.9955 - val_loss: 0.0010 - val_accuracy: 0.9958\n",
+      "\n",
+      "Epoch 00064: val_loss improved from 0.00103 to 0.00103, saving model to D:/aminur/anomaly dataset/model_Unet_anomly.h5\n",
+      "Epoch 65/100\n",
+      "350/350 [==============================] - 5s 15ms/step - loss: 0.0011 - accuracy: 0.9955 - val_loss: 0.0010 - val_accuracy: 0.9958\n",
+      "\n",
+      "Epoch 00065: val_loss improved from 0.00103 to 0.00103, saving model to D:/aminur/anomaly dataset/model_Unet_anomly.h5\n",
+      "Epoch 66/100\n",
+      "350/350 [==============================] - 5s 15ms/step - loss: 0.0011 - accuracy: 0.9955 - val_loss: 0.0010 - val_accuracy: 0.9958\n",
+      "\n",
+      "Epoch 00066: val_loss did not improve from 0.00103\n",
+      "Epoch 67/100\n",
+      "350/350 [==============================] - 5s 15ms/step - loss: 0.0011 - accuracy: 0.9955 - val_loss: 0.0010 - val_accuracy: 0.9958\n",
+      "\n",
+      "Epoch 00067: val_loss improved from 0.00103 to 0.00103, saving model to D:/aminur/anomaly dataset/model_Unet_anomly.h5\n",
+      "Epoch 68/100\n",
+      "350/350 [==============================] - 5s 15ms/step - loss: 0.0011 - accuracy: 0.9955 - val_loss: 0.0010 - val_accuracy: 0.9958\n",
+      "\n",
+      "Epoch 00068: val_loss improved from 0.00103 to 0.00103, saving model to D:/aminur/anomaly dataset/model_Unet_anomly.h5\n",
+      "Epoch 69/100\n",
+      "350/350 [==============================] - 5s 15ms/step - loss: 0.0011 - accuracy: 0.9955 - val_loss: 0.0010 - val_accuracy: 0.9958\n",
+      "\n",
+      "Epoch 00069: val_loss improved from 0.00103 to 0.00103, saving model to D:/aminur/anomaly dataset/model_Unet_anomly.h5\n",
+      "Epoch 70/100\n",
+      "350/350 [==============================] - 5s 15ms/step - loss: 0.0011 - accuracy: 0.9955 - val_loss: 0.0010 - val_accuracy: 0.9958\n",
+      "\n",
+      "Epoch 00070: val_loss improved from 0.00103 to 0.00103, saving model to D:/aminur/anomaly dataset/model_Unet_anomly.h5\n",
+      "Epoch 71/100\n",
+      "350/350 [==============================] - 5s 15ms/step - loss: 0.0011 - accuracy: 0.9955 - val_loss: 0.0010 - val_accuracy: 0.9958\n",
+      "\n",
+      "Epoch 00071: val_loss improved from 0.00103 to 0.00103, saving model to D:/aminur/anomaly dataset/model_Unet_anomly.h5\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 72/100\n",
+      "350/350 [==============================] - 5s 15ms/step - loss: 0.0011 - accuracy: 0.9955 - val_loss: 0.0010 - val_accuracy: 0.9958\n",
+      "\n",
+      "Epoch 00072: val_loss improved from 0.00103 to 0.00103, saving model to D:/aminur/anomaly dataset/model_Unet_anomly.h5\n",
+      "Epoch 73/100\n",
+      "350/350 [==============================] - 5s 15ms/step - loss: 0.0011 - accuracy: 0.9955 - val_loss: 0.0010 - val_accuracy: 0.9958\n",
+      "\n",
+      "Epoch 00073: val_loss did not improve from 0.00103\n",
+      "Epoch 74/100\n",
+      "350/350 [==============================] - 5s 15ms/step - loss: 0.0011 - accuracy: 0.9955 - val_loss: 0.0010 - val_accuracy: 0.9958\n",
+      "\n",
+      "Epoch 00074: val_loss improved from 0.00103 to 0.00103, saving model to D:/aminur/anomaly dataset/model_Unet_anomly.h5\n",
+      "Epoch 75/100\n",
+      "350/350 [==============================] - 5s 15ms/step - loss: 0.0011 - accuracy: 0.9955 - val_loss: 0.0010 - val_accuracy: 0.9958\n",
+      "\n",
+      "Epoch 00075: val_loss improved from 0.00103 to 0.00103, saving model to D:/aminur/anomaly dataset/model_Unet_anomly.h5\n",
+      "Epoch 76/100\n",
+      "350/350 [==============================] - 5s 15ms/step - loss: 0.0011 - accuracy: 0.9955 - val_loss: 0.0010 - val_accuracy: 0.9958\n",
+      "\n",
+      "Epoch 00076: val_loss improved from 0.00103 to 0.00103, saving model to D:/aminur/anomaly dataset/model_Unet_anomly.h5\n",
+      "\n",
+      "Epoch 00076: ReduceLROnPlateau reducing learning rate to 6.24999984211172e-06.\n",
+      "Epoch 77/100\n",
+      "350/350 [==============================] - 5s 15ms/step - loss: 0.0011 - accuracy: 0.9955 - val_loss: 0.0010 - val_accuracy: 0.9958\n",
+      "\n",
+      "Epoch 00077: val_loss improved from 0.00103 to 0.00103, saving model to D:/aminur/anomaly dataset/model_Unet_anomly.h5\n",
+      "Epoch 78/100\n",
+      "350/350 [==============================] - 5s 15ms/step - loss: 0.0011 - accuracy: 0.9955 - val_loss: 0.0010 - val_accuracy: 0.9958\n",
+      "\n",
+      "Epoch 00078: val_loss improved from 0.00103 to 0.00103, saving model to D:/aminur/anomaly dataset/model_Unet_anomly.h5\n",
+      "Epoch 79/100\n",
+      "350/350 [==============================] - 5s 15ms/step - loss: 0.0011 - accuracy: 0.9955 - val_loss: 0.0010 - val_accuracy: 0.9958\n",
+      "\n",
+      "Epoch 00079: val_loss improved from 0.00103 to 0.00103, saving model to D:/aminur/anomaly dataset/model_Unet_anomly.h5\n",
+      "Epoch 80/100\n",
+      "350/350 [==============================] - 5s 15ms/step - loss: 0.0011 - accuracy: 0.9955 - val_loss: 0.0010 - val_accuracy: 0.9958\n",
+      "\n",
+      "Epoch 00080: val_loss improved from 0.00103 to 0.00103, saving model to D:/aminur/anomaly dataset/model_Unet_anomly.h5\n",
+      "Epoch 81/100\n",
+      "350/350 [==============================] - 5s 15ms/step - loss: 0.0011 - accuracy: 0.9955 - val_loss: 0.0010 - val_accuracy: 0.9958\n",
+      "\n",
+      "Epoch 00081: val_loss improved from 0.00103 to 0.00103, saving model to D:/aminur/anomaly dataset/model_Unet_anomly.h5\n",
+      "Epoch 82/100\n",
+      "350/350 [==============================] - 5s 15ms/step - loss: 0.0011 - accuracy: 0.9955 - val_loss: 0.0010 - val_accuracy: 0.9958\n",
+      "\n",
+      "Epoch 00082: val_loss did not improve from 0.00103\n",
+      "Epoch 83/100\n",
+      "350/350 [==============================] - 5s 15ms/step - loss: 0.0011 - accuracy: 0.9955 - val_loss: 0.0010 - val_accuracy: 0.9958\n",
+      "\n",
+      "Epoch 00083: val_loss improved from 0.00103 to 0.00103, saving model to D:/aminur/anomaly dataset/model_Unet_anomly.h5\n",
+      "Epoch 84/100\n",
+      "350/350 [==============================] - 5s 15ms/step - loss: 0.0011 - accuracy: 0.9955 - val_loss: 0.0010 - val_accuracy: 0.9958\n",
+      "\n",
+      "Epoch 00084: val_loss improved from 0.00103 to 0.00103, saving model to D:/aminur/anomaly dataset/model_Unet_anomly.h5\n",
+      "Epoch 85/100\n",
+      "350/350 [==============================] - 5s 15ms/step - loss: 0.0011 - accuracy: 0.9955 - val_loss: 0.0010 - val_accuracy: 0.9958\n",
+      "\n",
+      "Epoch 00085: val_loss improved from 0.00103 to 0.00103, saving model to D:/aminur/anomaly dataset/model_Unet_anomly.h5\n",
+      "Epoch 86/100\n",
+      "350/350 [==============================] - 5s 15ms/step - loss: 0.0011 - accuracy: 0.9955 - val_loss: 0.0010 - val_accuracy: 0.9958\n",
+      "\n",
+      "Epoch 00086: val_loss improved from 0.00103 to 0.00103, saving model to D:/aminur/anomaly dataset/model_Unet_anomly.h5\n",
+      "Epoch 87/100\n",
+      "350/350 [==============================] - 5s 15ms/step - loss: 0.0011 - accuracy: 0.9955 - val_loss: 0.0010 - val_accuracy: 0.9958\n",
+      "\n",
+      "Epoch 00087: val_loss improved from 0.00103 to 0.00103, saving model to D:/aminur/anomaly dataset/model_Unet_anomly.h5\n",
+      "Epoch 88/100\n",
+      "350/350 [==============================] - 5s 15ms/step - loss: 0.0011 - accuracy: 0.9955 - val_loss: 0.0010 - val_accuracy: 0.9958\n",
+      "\n",
+      "Epoch 00088: val_loss improved from 0.00103 to 0.00103, saving model to D:/aminur/anomaly dataset/model_Unet_anomly.h5\n",
+      "Epoch 89/100\n",
+      "350/350 [==============================] - 5s 15ms/step - loss: 0.0011 - accuracy: 0.9955 - val_loss: 0.0010 - val_accuracy: 0.9958\n",
+      "\n",
+      "Epoch 00089: val_loss improved from 0.00103 to 0.00103, saving model to D:/aminur/anomaly dataset/model_Unet_anomly.h5\n",
+      "Epoch 90/100\n",
+      "350/350 [==============================] - 5s 15ms/step - loss: 0.0011 - accuracy: 0.9955 - val_loss: 0.0010 - val_accuracy: 0.9958\n",
+      "\n",
+      "Epoch 00090: val_loss improved from 0.00103 to 0.00103, saving model to D:/aminur/anomaly dataset/model_Unet_anomly.h5\n",
+      "Epoch 91/100\n",
+      "350/350 [==============================] - 5s 15ms/step - loss: 0.0011 - accuracy: 0.9955 - val_loss: 0.0010 - val_accuracy: 0.9958\n",
+      "\n",
+      "Epoch 00091: val_loss improved from 0.00103 to 0.00103, saving model to D:/aminur/anomaly dataset/model_Unet_anomly.h5\n",
+      "Epoch 92/100\n",
+      "350/350 [==============================] - 5s 15ms/step - loss: 0.0011 - accuracy: 0.9955 - val_loss: 0.0010 - val_accuracy: 0.9958\n",
+      "\n",
+      "Epoch 00092: val_loss improved from 0.00103 to 0.00103, saving model to D:/aminur/anomaly dataset/model_Unet_anomly.h5\n",
+      "\n",
+      "Epoch 00092: ReduceLROnPlateau reducing learning rate to 3.12499992105586e-06.\n",
+      "Epoch 93/100\n",
+      "350/350 [==============================] - 5s 15ms/step - loss: 0.0011 - accuracy: 0.9955 - val_loss: 0.0010 - val_accuracy: 0.9958\n",
+      "\n",
+      "Epoch 00093: val_loss improved from 0.00103 to 0.00103, saving model to D:/aminur/anomaly dataset/model_Unet_anomly.h5\n",
+      "Epoch 94/100\n",
+      "350/350 [==============================] - 5s 15ms/step - loss: 0.0011 - accuracy: 0.9955 - val_loss: 0.0010 - val_accuracy: 0.9958\n",
+      "\n",
+      "Epoch 00094: val_loss improved from 0.00103 to 0.00103, saving model to D:/aminur/anomaly dataset/model_Unet_anomly.h5\n",
+      "Epoch 95/100\n",
+      "350/350 [==============================] - 5s 15ms/step - loss: 0.0011 - accuracy: 0.9955 - val_loss: 0.0010 - val_accuracy: 0.9958\n",
+      "\n",
+      "Epoch 00095: val_loss improved from 0.00103 to 0.00103, saving model to D:/aminur/anomaly dataset/model_Unet_anomly.h5\n",
+      "Epoch 96/100\n",
+      "350/350 [==============================] - 5s 15ms/step - loss: 0.0011 - accuracy: 0.9955 - val_loss: 0.0010 - val_accuracy: 0.9958\n",
+      "\n",
+      "Epoch 00096: val_loss improved from 0.00103 to 0.00103, saving model to D:/aminur/anomaly dataset/model_Unet_anomly.h5\n",
+      "Epoch 97/100\n",
+      "350/350 [==============================] - 5s 15ms/step - loss: 0.0011 - accuracy: 0.9955 - val_loss: 0.0010 - val_accuracy: 0.9958\n",
+      "\n",
+      "Epoch 00097: val_loss improved from 0.00103 to 0.00103, saving model to D:/aminur/anomaly dataset/model_Unet_anomly.h5\n",
+      "Epoch 98/100\n",
+      "350/350 [==============================] - 5s 15ms/step - loss: 0.0011 - accuracy: 0.9955 - val_loss: 0.0010 - val_accuracy: 0.9958\n",
+      "\n",
+      "Epoch 00098: val_loss improved from 0.00103 to 0.00103, saving model to D:/aminur/anomaly dataset/model_Unet_anomly.h5\n",
+      "Epoch 99/100\n",
+      "350/350 [==============================] - 5s 15ms/step - loss: 0.0011 - accuracy: 0.9955 - val_loss: 0.0010 - val_accuracy: 0.9958\n",
+      "\n",
+      "Epoch 00099: val_loss improved from 0.00103 to 0.00103, saving model to D:/aminur/anomaly dataset/model_Unet_anomly.h5\n",
+      "Epoch 100/100\n",
+      "350/350 [==============================] - 5s 15ms/step - loss: 0.0011 - accuracy: 0.9955 - val_loss: 0.0010 - val_accuracy: 0.9958\n",
+      "\n",
+      "Epoch 00100: val_loss improved from 0.00103 to 0.00103, saving model to D:/aminur/anomaly dataset/model_Unet_anomly.h5\n"
+     ]
+    }
+   ],
+   "source": [
+    "from IPython.display import clear_output\n",
+    "from keras.optimizers import Adam \n",
+    "from sklearn.model_selection import train_test_split\n",
+    "#lr=2e-4\n",
+    "'''model.compile(optimizer=Adam(lr=1e-5), \n",
+    "              loss=[dice_coef_loss], \n",
+    "           metrics = [dice_coef,'binary_accuracy'])'''\n",
+    "model.compile(Adam(lr = 1e-4), loss = 'binary_crossentropy', metrics = ['accuracy'])\n",
+    "\n",
+    "\n",
+    "#train_vol, test_vol, train_seg, test_seg = train_test_split(train_vol,train_seg, \n",
+    " #                                                           test_size = 0.1, \n",
+    "  #                                                          random_state = 2018)\n",
+    "#print(images.shape)\n",
+    "loss_history = model.fit(x = images,\n",
+    "                       y = mask,\n",
+    "                         batch_size = 8,\n",
+    "                  epochs = 100,\n",
+    "                  validation_data =(images_val,mask_val) ,\n",
+    "                  callbacks=callbacks_list)\n",
+    "\n",
+    "\n",
+    "#clear_output()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Inspect the training vs validation loss and training accuracy vs validation accuracy of our network. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 272,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 720x360 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, (ax1, ax2) = plt.subplots(1, 2, figsize = (10, 5))\n",
+    "ax1.plot(loss_history.history['loss'], '-', label = 'Loss')\n",
+    "ax1.plot(loss_history.history['val_loss'], '-', label = 'Validation Loss')\n",
+    "ax1.legend()\n",
+    "\n",
+    "ax2.plot(100*np.array(loss_history.history['accuracy']), '-', \n",
+    "         label = 'Accuracy')\n",
+    "ax2.plot(100*np.array(loss_history.history['val_accuracy']), '-',\n",
+    "         label = 'Validation Accuracy')\n",
+    "ax2.legend()\n",
+    "#ax2.set_ylim([80,100])\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Inspect the Qualitative analysis of our test set (7 real images from Extend AI). We check all 7 images and display the real RGB images with ground truth segmentation map and predicted segmentation map. \n",
+    "\n",
+    "In the following section: \n",
+    "1. real= real RGB image\n",
+    "2. mask= ground truth segmentation mask\n",
+    "3. predicted= predicted segmentation mask\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 264,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "7/7 [==============================] - 0s 17ms/step\n",
+      "<class 'numpy.ndarray'> (7, 224, 224)\n"
+     ]
+    },
+    {
+     "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"
+    }
+   ],
+   "source": [
+    "images_test=np.load('D:/aminur/anomaly dataset/npy_test_real.npy')\n",
+    "mask_test=np.load('D:/aminur/anomaly dataset/npy_test_mask.npy')\n",
+    "\n",
+    "images_test_re=np.array(images_test).reshape(len(images_test),dim,dim,1)\n",
+    "\n",
+    "test_real=np.load('D:/aminur/anomaly dataset/npy_test_RGB_real.npy')\n",
+    "\n",
+    "\n",
+    "preds = model.predict(images_test_re,batch_size=1,verbose=1)\n",
+    "\n",
+    "preds=np.array(preds).reshape(len(preds),dim,dim)\n",
+    "print(type(preds),preds.shape)\n",
+    "def display(x,name):\n",
+    "    fig = plt.figure()\n",
+    "    ax1 = fig.add_subplot()\n",
+    "    # Bilinear interpolation - this will look blurry\n",
+    "    ax1.imshow(x,cmap=cm.Greys_r)\n",
+    "\n",
+    "    #ax2 = fig.add_subplot(322)\n",
+    "    # 'nearest' interpolation - faithful but blocky\n",
+    "    #ax2.imshow(y,cmap=cm.Greys_r)\n",
+    "    \n",
+    "    #ax3 = fig.add_subplot(323)\n",
+    "    # 'nearest' interpolation - faithful but blocky\n",
+    "    #ax3.imshow(z,cmap=cm.Greys_r)\n",
+    "    ax1.title.set_text(name)\n",
+    "    #ax2.title.set_text('Second Plot')\n",
+    "    #ax3.title.set_text('Third Plot')\n",
+    "\n",
+    "    plt.show()\n",
+    "\n",
+    "display(test_real[0],name='real')\n",
+    "display(mask_test[0],name='mask')\n",
+    "display(preds[0],name='predicted')\n",
+    "#display(images[200],mask[200])\n",
+    "#display(images[250],mask[250])\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 265,
+   "metadata": {},
+   "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"
+    }
+   ],
+   "source": [
+    "display(test_real[1],name='real')\n",
+    "display(mask_test[1],name='mask')\n",
+    "display(preds[1],name='predicted')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 266,
+   "metadata": {},
+   "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"
+    }
+   ],
+   "source": [
+    "display(test_real[2],name='real')\n",
+    "display(mask_test[2],name='mask')\n",
+    "display(preds[2],name='predicted')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 267,
+   "metadata": {},
+   "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"
+    }
+   ],
+   "source": [
+    "display(test_real[3],name='real')\n",
+    "display(mask_test[3],name='mask')\n",
+    "display(preds[3],name='predicted')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 268,
+   "metadata": {},
+   "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"
+    }
+   ],
+   "source": [
+    "display(test_real[4],name='real')\n",
+    "display(mask_test[4],name='mask')\n",
+    "display(preds[4],name='predicted')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 269,
+   "metadata": {},
+   "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"
+    }
+   ],
+   "source": [
+    "display(test_real[5],name='real')\n",
+    "display(mask_test[5],name='mask')\n",
+    "display(preds[5],name='predicted')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 270,
+   "metadata": {},
+   "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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JYf+VeNQQkLSIorvxk4EBYG1EfF7SHIquxRYD24HXR8QjqQfizwOrgAPAJRFx1yjbOGF+ynbshtu9P5bly0FSqVQG//pXKhWmTp1Ka2srkmhtbWVgYICWlhYqlQpLly7l9NNP5/zzz+dpT3sa5513Hrt27eKmm27i1ltvZcOGDRw4cGAwDE6AS5jjDoFTgFMi4i5JM4H1wKuAS4CHI+ITkq4AOiLifZJWAe+iCIHnAp+PiOeOso1J/ZO1+it/8If+zlYDYCx/tat7HC0tLcyaNYu2tjba2tqICDo6Oujr62PGjBnMnz+fU089lfnz5zN37tzBqxe7d+9GEjt37qSrq4tt27bR09PD3r176enpOWLvoQn3IsYXAketIN1MMRjJlcCFEdGVguL/IuJsSVel6evS8puryz3BczbVT8ryUg2Y8h7F1KlTaWtrY9asWSxZsoRly5Zx8OBBzjnnHNrb2+np6WHPnj3MnDmTAwcOcODAAXbs2EFPTw+bNm2iu7ub/fv3D55zaJJAGDYEjunEYBqEZAXwa2BB9YOdgmB+WmwhsKO0WmdqOyIEJF0KXHos2zerh/JlyKq+vj4OHTpEd3c3O3bs4Pbbbx+cP2XKFCqVyuDhR6VSGfxuQ/ULT8NdwmxWYw4BSe3Ad4H3RMS+JziOG27GUT8JjztwfD3R7rQNrxwO5SsII12mLK8zmYwpBCRNpQiAb0bEjal5t6RTSocDD6b2TmBRafXTgF0TVbAdG0lMmzaNqVOnMmfOHCKC/fv309PTw+HDh4f9ss2xfAGneixeNhk/CMfqRHqNo4ZAOtv/VeC+iPhsadYtwBrgE+n+5lL75ZKupzgxuPeJzgdYfbW0tDBv3jxWrFjBa1/7Wl70ohfR1tbGvn37uOeee7j//vupVCpMnz6dFStW0Nvby2OPPcbGjRvZs2cPW7ZsYfr06fT399Pe3s7DDz/Mo48+ys6dO4Hist1jjz12xImxnp6eE+pDcqIby9WBFwI/BzZSXCIE+ADFeYFvA6cDfwZeFxEPp9C4EriI4hLhWyLiCccb9OFAfVWPXVtbW2lra6O1tZXe3t7BY9np06cjiblz5zJlyhQWLlxIb28vixYVO3QnnXQS3d3drFq1imc961mceuqpbNu2jZkzZzJz5kx27NjBD3/4QzZv3sztt9/O9u3bHQTNaWKuDtSDQ+D4KJ8FrzqW97+8XvlMevVLPdXniwiHQHOq/eqATW7lf8IZy3LDtQ83DdDb2zu4TpNcDrMxcghkZqwfzvF8iP3Bn5zcvZhZ5hwCZplzCJhlziFgljmHgFnmHAJmmXMImGXOIWCWOYeAWeYcAmaZcwiYZc4hYJY5h4BZ5hwCZplzCJhlrln6E3gIeCzdT1ZPZnLXD5P/NUz2+qG+r+EpwzU2RfdiAJLuHK7ro8listcPk/81TPb6oTGvwYcDZplzCJhlrplCYG2jC6jRZK8fJv9rmOz1QwNeQ9OcEzCzxmimPQEzawCHgFnmGh4Cki6StFnSVklXNLqesZK0XdJGSRsk3Zna5khaJ2lLuu9odJ1lkq6W9KCke0ptw9aswhfS+3K3pJWNq3yw1uHq/4iknel92CBpVWne+1P9myW9rDFVP07SIkk/kXSfpHslvTu1N/Y9qI4W04gbUAEeAM4AWoHfA+c0sqZjqH078OQhbZ8CrkjTVwCfbHSdQ+q7AFgJ3DNazcAq4AcUQ80/D/h1k9b/EeC9wyx7Tvp9mgYsSb9nlQbXfwqwMk3PBO5PdTb0PWj0nsBzgK0RsS0iDgPXA6sbXFMtVgPXpulrgVc1sJajRMTPgIeHNI9U82rg61G4A5idhqBvmBHqH8lq4PqI6ImIPwJbKX7fGiYiuiLirjT9KHAfsJAGvweNDoGFwI7S487UNhkE8CNJ6yVdmtoWRBqGPd3Pb1h1YzdSzZPpvbk87S5fXToEa+r6JS0GVlCM7t3Q96DRIXD0qJfFh2syeEFErAReDrxT0gWNLmiCTZb35kvAmcByoAv4TGpv2voltQPfBd4TEfueaNFh2ib8NTQ6BDqBRaXHpwG7GlTLMYmIXen+QeAmil3N3dXdtXT/YOMqHLORap4U701E7I6I/ogYAL7C47v8TVm/pKkUAfDNiLgxNTf0PWh0CPwWOEvSEkmtwMXALQ2uaVSSZkiaWZ0GXgrcQ1H7mrTYGuDmxlR4TEaq+RbgzekM9fOAvdVd1mYy5Bj51RTvAxT1XyxpmqQlwFnAb453fWUqxm7/KnBfRHy2NKux70Ejz5aWzoDeT3H29oONrmeMNZ9Bceb598C91bqBucBtwJZ0P6fRtQ6p+zqKXeZeir8ybxupZopd0S+m92UjcF6T1v+NVN/d6UNzSmn5D6b6NwMvb4L6X0ixO383sCHdVjX6PfDXhs0y1+jDATNrMIeAWeYcAmaZcwiYZc4hYJY5h4BZ5hwCZpn7f5/ZAAMKctgNAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "display(test_real[6],name='real')\n",
+    "display(mask_test[6],name='mask')\n",
+    "display(preds[6],name='predicted')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "To sum up, I can easily say that the segmentation result is pretty good, and I think my approach to performing this task is successful. However, there is always some room for improvement in this kind of issue."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# how you would improve the model in the near future? \n",
+    "\n",
+    "I can take several steps to improve this method:\n",
+    "1. At first, we can have more realistic images to make our dataset because right now, the model is trained and validated on augmented images. \n",
+    "2. I can try some other network or my custom build network, which performs better than U-Net. Some networks such as HR-Net or HR-Net lookalike custom-made networks would be perfect for this task.\n",
+    "3. Most importantly, I want to build a model where the network can perform segmentation, classification, and detection combinedly (something like Mask-R-CNN).\n",
+    "4. I performed the online augmentation during the training process in my classification network. Moreover, I want to perform the same procedure in my segmentation network as well. \n",
+    "5. For both classification and segmentation, I want to apply online data augmentation for the validation set as well\n",
+    "6. During the classification, I used the pre-trained weight of Imagenet in the backbone, but I did not take any pre-trained weight in the segmentation task. I think this pre-trained weight can enhance the segmentation result.\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# how you would transfer learnings to other types of surfaces (i.e. not wood)?\n",
+    "\n",
+    "Transfer Learning is a popular deep learning and machine learning method where we train our model on a particular task, and then we can apply that knowledge to a related second task. Through this approach, we can enhance the performance and prediction when modeling the second task. By this approach, we can improve the result where we have a small dataset to train with.  \n",
+    "\n",
+    "For example, here, we can shift the weight of the anomaly detection task of wood into a related second task. If we train our anomaly detection architecture with a large dataset, then we can easily transfer this knowledge. Generally, in computer vision, we detect the edges of images in the first few layers, in the middle layers mostly detect forms, and the last few layers detect the task-specific features such as anomaly for this task. So for the second task, we only have to retrain the last few layers, but the earlier layers and the middle layers are employed in transfer learning.\n",
+    "\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.5.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
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