def basic_block(filters, init_strides=(1, 1), is_first_block_of_first_layer=False):
"""Basic 3 X 3 convolution blocks for use on resnets with layers <= 34.
Follows improved proposed scheme in http://arxiv.org/pdf/1603.05027v2.pdf
"""
def f(input):
if is_first_block_of_first_layer:
# don't repeat bn->relu since we just did bn->relu->maxpool
conv1 = Conv2D(filters=filters, kernel_size=(3, 3),
strides=init_strides,
padding="same",
kernel_initializer="he_normal",
kernel_regularizer=l2(1e-4))(input)
else:
conv1 = _bn_relu_conv(filters=filters, kernel_size=(3, 3),
strides=init_strides)(input)
residual = _bn_relu_conv(filters=filters, kernel_size=(3, 3))(conv1)
return _shortcut(input, residual)
return f
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