Created
April 2, 2019 18:07
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def ResNet50(input_shape = (64, 64, 3), classes = 6): | |
# Define the input as a tensor with shape input_shape | |
X_input = Input(input_shape) | |
# Zero-Padding | |
X = ZeroPadding2D((3, 3))(X_input) | |
# Stage 1 | |
X = Conv2D(64, (7, 7), strides = (2, 2), name = 'conv1', kernel_initializer = glorot_uniform(seed=0))(X) | |
X = BatchNormalization(axis = 3, name = 'bn_conv1')(X) | |
X = Activation('relu')(X) | |
X = MaxPooling2D((3, 3), strides=(2, 2))(X) | |
# 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') | |
# 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(pool_size=(2,2), padding='same')(X) | |
# Output layer | |
X = Flatten()(X) | |
X = Dense(classes, activation='softmax', name='fc' + str(classes), kernel_initializer = glorot_uniform(seed=0))(X) | |
# Create model | |
model = Model(inputs = X_input, outputs = X, name='ResNet50') | |
return model |
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