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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 (β3 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 (β2 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()) | |