def conv2_block(input_layer: "Layer",
k: int = 1,
dropout: float = 0.0) -> "Layer":
"""
:param input_layer:
:param k:
:param dropout:
:return:
"""
layer = BatchNormalization(momentum=0.1,
epsilon=1e-5,
gamma_initializer='uniform')(input_layer)
layer = Activation('relu')(layer)
layer = create_cnn_layer(layer,
32 * k,
dropout=dropout,
kernel_init='he_normal',
kernel_regularizer=l2(weight_decay))
layer = create_cnn_layer(layer,
32 * k,
kernel_init='he_normal',
kernel_regularizer=l2(weight_decay))
layer_after_addition = Add()([input_layer, layer])
return layer_after_addition
def conv1(input_layer: "Layer", filters: int) -> "Layer":
"""
:param input_layer:
:param filters:
:return:
"""
layer = create_cnn_layer(input_layer, filters, kernel_size=1)
return layer
def conv3_downsample(input_layer: "Layer", filters: int):
"""
:param input_layer:
:param filters:
:return:
"""
layer = create_cnn_layer(input_layer, filters)
return layer
def initial_conv(input_layer: "Layer") -> "Layer":
"""
:return:
"""
layer = create_cnn_layer(input_layer=input_layer,
num_filter=16,
kernel_init='he_normal',
kernel_regularizer=l2(weight_decay))
return layer
def expand_conv(input_layer: "Layer",
base: int or Tuple[int, int],
k: int,
strides: int or Tuple[int, int] = (1, 1)) -> "Layer":
"""
:param input_layer:
:param base:
:param k:
:param strides:
:return:
"""
layer = create_cnn_layer(input_layer,
base * k,
strides=strides,
kernel_init='he_normal',
kernel_regularizer=l2(weight_decay))
layer = create_cnn_layer(layer,
base * k,
strides=strides,
kernel_init='he_normal',
kernel_regularizer=l2(weight_decay),
batch_normalization=False,
activation=False)
skip = create_cnn_layer(input_layer,
base * k,
kernel_size=(1, 1),
strides=strides,
kernel_init='he_normal',
kernel_regularizer=l2(weight_decay))
layer_after_addition = Add()([layer, skip])
return layer_after_addition
def _pre_res_blocks(input_layer: "Layer") -> "Layer":
layer = create_cnn_layer(input_layer, 64, 3)
return layer