def resnet(n=18, nr_classes=10):
input = Input(shape=(img_channels, img_rows, img_cols))
conv1 = conv_bn_relu(
input,
nb_filter=16,
nb_row=3,
nb_col=3,
W_regularizer=l2(weight_decay)) # Filters, filter_size
# Build residual blocks..
block_fn = _bottleneck
block1 = residual_block(conv1,
block_fn,
nb_filters=16,
repetations=n,
is_first_layer=True)
block2 = residual_block(block1, block_fn, nb_filters=32, repetations=n)
block3 = residual_block(block2,
block_fn,
nb_filters=64,
repetations=n,
subsample=True)
# Classifier block
pool2 = AveragePooling2D(pool_size=(8, 8))(block3)
flatten1 = Flatten()(pool2)
final = Dense(units=nr_classes,
kernel_initializer="he_normal",
activation="softmax",
kernel_regularizer=l2(weight_decay))(flatten1)
model = Model(inputs=input, outputs=final)
return model
def resnet():
input = Input(shape=(img_channels, img_rows, img_cols))
conv1 = conv_bn_relu(input, nb_filter=16, nb_row=3, nb_col=3, W_regularizer=l2(weight_decay))
# Build residual blocks..
block_fn = _bottleneck
block1 = residual_block(conv1, block_fn, nb_filters=16, repetations=18, is_first_layer=True)
block2 = residual_block(block1, block_fn, nb_filters=32, repetations=18)
block3 = residual_block(block2, block_fn, nb_filters=64, repetations=18, subsample=True)
# Classifier block
pool2 = AveragePooling2D(pool_size=(8, 8))(block3)
flatten1 = Flatten()(pool2)
final = Dense(output_dim=10, init="he_normal", activation="softmax", W_regularizer=l2(weight_decay))(flatten1)
model = Model(input=input, output=final)
return model