import numpy as np import pandas as pd import matplotlib.pyplot as plt import os import tensorflow as tf import numpy as np from keras import layers from keras.layers import Input, Add, Dense, Activation, ZeroPadding2D, BatchNormalization, Flatten, Conv2D, AveragePooling2D, MaxPooling2D, GlobalMaxPooling2D from keras.models import Model, load_model from keras.preprocessing import image from keras.applications.imagenet_utils import preprocess_input from keras.utils import plot_model from matplotlib.pyplot import imshow from keras.layers import Input, Add, Dense, Activation, ZeroPadding2D, BatchNormalization, Flatten, Conv2D, AveragePooling2D, MaxPooling2D, GlobalMaxPooling2D # x is input, y=F(x) # identity block simply means input should be equal to output. # y = x + F(x) the layers in a traditional network are learning the true output H(x) # F(x) = y - x the layers in a residual network are learning the residual F(x) # Hence, the name: Residual Block. def identity_block(X, f, filters, stage, block): """ Arguments: X -- input of shape (m, height, width, channel) f -- shape of the middle CONV's window for the main path filters -- python list of integers, defining the number of filters in the CONV layers of the main path stage -- integer, used to name the layers, depending on their position in the network block -- string/character, used to name the layers, depending on their position in the network Returns: X -- output of the identity block, tensor of shape (n_H, n_W, n_C) """ # defining name basis conv_name_base = 'res' + str(stage) + block + '_branch' bn_name_base = 'bn' + str(stage) + block + '_branch' # Retrieve Filters F1, F2, F3 = filters # Saving the input value.we need this later to add to the output. X_shortcut = X # First component of main path X = Conv2D(filters = F1, kernel_size = (1, 1), strides = (1,1), padding = 'valid', name = conv_name_base + '2a')(X) X = BatchNormalization(axis = 3, name = bn_name_base + '2a')(X) X = Activation('relu')(X) # Second component of main path (≈3 lines) X = Conv2D(filters = F2, kernel_size = (f, f), strides = (1,1), padding = 'same', name = conv_name_base + '2b')(X) X = BatchNormalization(axis = 3, name = bn_name_base + '2b')(X) X = Activation('relu')(X) # Third component of main path (≈2 lines) X = Conv2D(filters = F3, kernel_size = (1, 1), strides = (1,1), padding = 'valid', name = conv_name_base + '2c')(X) X = BatchNormalization(axis = 3, name = bn_name_base + '2c')(X) # Final step: Add shortcut value to main path, and pass it through a RELU activation X = Add()([X, X_shortcut]) X = Activation('relu')(X) return X # CONVOLUTIONAL BLOCK def convolutional_block(X, f, filters, stage, block, s = 2): # defining name basis conv_name_base = 'res' + str(stage) + block + '_branch' bn_name_base = 'bn' + str(stage) + block + '_branch' # Retrieve Filters F1, F2, F3 = filters # Save the input value X_shortcut = X # First layer X = Conv2D(F1, (1, 1), strides = (s,s), name = conv_name_base + '2a')(X) # 1,1 is filter size X = BatchNormalization(axis = 3, name = bn_name_base + '2a')(X) # normalization on channels X = Activation('relu')(X) # Second layer (f,f)=3*3 filter by default X = Conv2D(filters = F2, kernel_size = (f, f), strides = (1,1), padding = 'same', name = conv_name_base + '2b')(X) X = BatchNormalization(axis = 3, name = bn_name_base + '2b')(X) X = Activation('relu')(X) # Third layer X = Conv2D(filters = F3, kernel_size = (1, 1), strides = (1,1), padding = 'valid', name = conv_name_base + '2c')(X) X = BatchNormalization(axis = 3, name = bn_name_base + '2c')(X) ##### SHORTCUT PATH #### X_shortcut = Conv2D(filters = F3, kernel_size = (1, 1), strides = (s,s), padding = 'valid', name = conv_name_base + '1')(X_shortcut) X_shortcut = BatchNormalization(axis = 3, name = bn_name_base + '1')(X_shortcut) # Final step: Add shortcut value here, and pass it through a RELU activation X = Add()([X, X_shortcut]) X = Activation('relu')(X) return X # CREATING RESNET50 #Each ResNet block is either 2 layer deep def ResNet50(input_shape=(224, 224, 3)): """ Implementation of the ResNet50 architecture: CONV2D -> BATCHNORM -> RELU -> MAXPOOL -> CONVBLOCK -> IDBLOCK*2 -> CONVBLOCK -> IDBLOCK*3 -> CONVBLOCK -> IDBLOCK*5 -> CONVBLOCK -> IDBLOCK*2 -> AVGPOOL -> TOPLAYER """ # Define the input as a tensor with shape input_shape X_input = Input(input_shape) # Zero-Padding X = ZeroPadding2D((3, 3))(X_input) #3,3 padding # Stage 1 X = Conv2D(64, (7, 7), strides=(2, 2), name='conv1')(X) #64 filters of 7*7 X = BatchNormalization(axis=3, name='bn_conv1')(X) #batchnorm applied on channels X = Activation('relu')(X) X = MaxPooling2D((3, 3), strides=(2, 2))(X) #window size is 3*3 # Stage 2 X = convolutional_block(X, f=3, filters=[64, 64, 256], stage=2, block='a', s=1) X = identity_block(X, 3, [64, 64, 256], stage=2, block='b') X = identity_block(X, 3, [64, 64, 256], stage=2, block='c') ### START CODE HERE ### # Stage 3 X = convolutional_block(X, f = 3, filters = [128, 128, 512], stage = 3, block='a', s = 2) X = identity_block(X, 3, [128, 128, 512], stage=3, block='b') X = identity_block(X, 3, [128, 128, 512], stage=3, block='c') X = identity_block(X, 3, [128, 128, 512], stage=3, block='d') # Stage 4 X = convolutional_block(X, f = 3, filters = [256, 256, 1024], stage = 4, block='a', s = 2) X = identity_block(X, 3, [256, 256, 1024], stage=4, block='b') X = identity_block(X, 3, [256, 256, 1024], stage=4, block='c') X = identity_block(X, 3, [256, 256, 1024], stage=4, block='d') X = identity_block(X, 3, [256, 256, 1024], stage=4, block='e') X = identity_block(X, 3, [256, 256, 1024], stage=4, block='f') # Stage 5 X = convolutional_block(X, f = 3, filters = [512, 512, 2048], stage = 5, block='a', s = 2) X = identity_block(X, 3, [512, 512, 2048], stage=5, block='b') X = identity_block(X, 3, [512, 512, 2048], stage=5, block='c') # AVGPOOL X = AveragePooling2D((2,2), name="avg_pool")(X) ### END CODE HERE ### # output layer X = Flatten()(X) X = Dense(1,activation='relu', name='output')(X) # Create model model = Model(inputs = X_input, outputs = X, name='ResNet50') return model model_resnet = ResNet50() print(model_resnet.summary())