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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 (≈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())
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