def build(self, input_shape):
if self.data_format == 'channels_first':
input_dim, input_length = input_shape[1], input_shape[2]
else:
input_dim, input_length = input_shape[2], input_shape[1]
if input_dim is None:
raise ValueError(
'Axis 2 of input should be fully-defined. '
'Found shape:', input_shape)
self.output_length = conv_utils.conv_output_length(
input_length, self.kernel_size[0], self.padding, self.strides[0])
if self.output_length <= 0:
raise ValueError(
f'One of the dimensions in the output is <= 0 '
f'due to downsampling in {self.name}. Consider '
f'increasing the input size. '
f'Received input shape {input_shape} which would produce '
f'output shape with a zero or negative value in a '
f'dimension.')
if self.implementation == 1:
self.kernel_shape = (self.output_length,
self.kernel_size[0] * input_dim, self.filters)
self.kernel = self.add_weight(shape=self.kernel_shape,
initializer=self.kernel_initializer,
name='kernel',
regularizer=self.kernel_regularizer,
constraint=self.kernel_constraint)
elif self.implementation == 2:
if self.data_format == 'channels_first':
self.kernel_shape = (input_dim, input_length, self.filters,
self.output_length)
else:
self.kernel_shape = (input_length, input_dim,
self.output_length, self.filters)
self.kernel = self.add_weight(shape=self.kernel_shape,
initializer=self.kernel_initializer,
name='kernel',
regularizer=self.kernel_regularizer,
constraint=self.kernel_constraint)
self.kernel_mask = locally_connected_utils.get_locallyconnected_mask(
input_shape=(input_length, ),
kernel_shape=self.kernel_size,
strides=self.strides,
padding=self.padding,
data_format=self.data_format,
)
elif self.implementation == 3:
self.kernel_shape = (self.output_length * self.filters,
input_length * input_dim)
self.kernel_idxs = sorted(
conv_utils.conv_kernel_idxs(input_shape=(input_length, ),
kernel_shape=self.kernel_size,
strides=self.strides,
padding=self.padding,
filters_in=input_dim,
filters_out=self.filters,
data_format=self.data_format))
self.kernel = self.add_weight(shape=(len(self.kernel_idxs), ),
initializer=self.kernel_initializer,
name='kernel',
regularizer=self.kernel_regularizer,
constraint=self.kernel_constraint)
else:
raise ValueError('Unrecognized implementation mode: %d.' %
self.implementation)
if self.use_bias:
self.bias = self.add_weight(shape=(self.output_length,
self.filters),
initializer=self.bias_initializer,
name='bias',
regularizer=self.bias_regularizer,
constraint=self.bias_constraint)
else:
self.bias = None
if self.data_format == 'channels_first':
self.input_spec = InputSpec(ndim=3, axes={1: input_dim})
else:
self.input_spec = InputSpec(ndim=3, axes={-1: input_dim})
self.built = True
def build(self, input_shape):
if self.data_format == 'channels_first':
input_dim, input_length = input_shape[1], input_shape[2]
else:
input_dim, input_length = input_shape[2], input_shape[1]
if input_dim is None:
raise ValueError(
'Axis 2 of input should be fully-defined. '
'Found shape:', input_shape)
self.output_length = conv_utils.conv_output_length(
input_length, self.kernel_size[0], self.padding, self.strides[0])
if self.implementation == 1:
self.kernel_shape = (self.output_length,
self.kernel_size[0] * input_dim, self.filters)
self.kernel = self.add_weight(shape=self.kernel_shape,
initializer=self.kernel_initializer,
name='kernel',
regularizer=self.kernel_regularizer,
constraint=self.kernel_constraint)
elif self.implementation == 2:
if self.data_format == 'channels_first':
self.kernel_shape = (input_dim, input_length, self.filters,
self.output_length)
else:
self.kernel_shape = (input_length, input_dim,
self.output_length, self.filters)
self.kernel = self.add_weight(shape=self.kernel_shape,
initializer=self.kernel_initializer,
name='kernel',
regularizer=self.kernel_regularizer,
constraint=self.kernel_constraint)
self.kernel_mask = get_locallyconnected_mask(
input_shape=(input_length, ),
kernel_shape=self.kernel_size,
strides=self.strides,
padding=self.padding,
data_format=self.data_format,
)
elif self.implementation == 3:
self.kernel_shape = (self.output_length * self.filters,
input_length * input_dim)
self.kernel_idxs = sorted(
conv_utils.conv_kernel_idxs(input_shape=(input_length, ),
kernel_shape=self.kernel_size,
strides=self.strides,
padding=self.padding,
filters_in=input_dim,
filters_out=self.filters,
data_format=self.data_format))
self.kernel = self.add_weight(shape=(len(self.kernel_idxs), ),
initializer=self.kernel_initializer,
name='kernel',
regularizer=self.kernel_regularizer,
constraint=self.kernel_constraint)
else:
raise ValueError('Unrecognized implementation mode: %d.' %
self.implementation)
if self.use_bias:
self.bias = self.add_weight(shape=(self.output_length,
self.filters),
initializer=self.bias_initializer,
name='bias',
regularizer=self.bias_regularizer,
constraint=self.bias_constraint)
else:
self.bias = None
if self.data_format == 'channels_first':
self.input_spec = InputSpec(ndim=3, axes={1: input_dim})
else:
self.input_spec = InputSpec(ndim=3, axes={-1: input_dim})
self.built = True
def build(self, input_shape):
if self.data_format == 'channels_last':
input_row, input_col = input_shape[1:-1]
input_filter = input_shape[3]
else:
input_row, input_col = input_shape[2:]
input_filter = input_shape[1]
if input_row is None or input_col is None:
raise ValueError('The spatial dimensions of the inputs to '
' a LocallyConnected2D layer '
'should be fully-defined, but layer received '
'the inputs shape ' + str(input_shape))
output_row = conv_utils.conv_output_length(input_row,
self.kernel_size[0],
self.padding,
self.strides[0])
output_col = conv_utils.conv_output_length(input_col,
self.kernel_size[1],
self.padding,
self.strides[1])
self.output_row = output_row
self.output_col = output_col
if self.implementation == 1:
self.kernel_shape = (output_row * output_col, self.kernel_size[0] *
self.kernel_size[1] * input_filter,
self.filters)
self.kernel = self.add_weight(shape=self.kernel_shape,
initializer=self.kernel_initializer,
name='kernel',
regularizer=self.kernel_regularizer,
constraint=self.kernel_constraint)
elif self.implementation == 2:
if self.data_format == 'channels_first':
self.kernel_shape = (input_filter, input_row, input_col,
self.filters, self.output_row,
self.output_col)
else:
self.kernel_shape = (input_row, input_col, input_filter,
self.output_row, self.output_col,
self.filters)
self.kernel = self.add_weight(shape=self.kernel_shape,
initializer=self.kernel_initializer,
name='kernel',
regularizer=self.kernel_regularizer,
constraint=self.kernel_constraint)
self.kernel_mask = get_locallyconnected_mask(
input_shape=(input_row, input_col),
kernel_shape=self.kernel_size,
strides=self.strides,
padding=self.padding,
data_format=self.data_format,
)
elif self.implementation == 3:
self.kernel_shape = (self.output_row * self.output_col *
self.filters,
input_row * input_col * input_filter)
self.kernel_idxs = sorted(
conv_utils.conv_kernel_idxs(input_shape=(input_row, input_col),
kernel_shape=self.kernel_size,
strides=self.strides,
padding=self.padding,
filters_in=input_filter,
filters_out=self.filters,
data_format=self.data_format))
self.kernel = self.add_weight(shape=(len(self.kernel_idxs), ),
initializer=self.kernel_initializer,
name='kernel',
regularizer=self.kernel_regularizer,
constraint=self.kernel_constraint)
else:
raise ValueError('Unrecognized implementation mode: %d.' %
self.implementation)
if self.use_bias:
self.bias = self.add_weight(shape=(output_row, output_col,
self.filters),
initializer=self.bias_initializer,
name='bias',
regularizer=self.bias_regularizer,
constraint=self.bias_constraint)
else:
self.bias = None
if self.data_format == 'channels_first':
self.input_spec = InputSpec(ndim=4, axes={1: input_filter})
else:
self.input_spec = InputSpec(ndim=4, axes={-1: input_filter})
self.built = True
def build(self, input_shape):
if self.data_format == "channels_last":
input_row, input_col = input_shape[1:-1]
input_filter = input_shape[3]
else:
input_row, input_col = input_shape[2:]
input_filter = input_shape[1]
if input_row is None or input_col is None:
raise ValueError("The spatial dimensions of the inputs to "
" a LocallyConnected2D layer "
"should be fully-defined, but layer received "
"the inputs shape " + str(input_shape))
output_row = conv_utils.conv_output_length(input_row,
self.kernel_size[0],
self.padding,
self.strides[0])
output_col = conv_utils.conv_output_length(input_col,
self.kernel_size[1],
self.padding,
self.strides[1])
self.output_row = output_row
self.output_col = output_col
if self.output_row <= 0 or self.output_col <= 0:
raise ValueError(
f"One of the dimensions in the output is <= 0 "
f"due to downsampling in {self.name}. Consider "
f"increasing the input size. "
f"Received input shape {input_shape} which would produce "
f"output shape with a zero or negative value in a "
f"dimension.")
if self.implementation == 1:
self.kernel_shape = (
output_row * output_col,
self.kernel_size[0] * self.kernel_size[1] * input_filter,
self.filters,
)
self.kernel = self.add_weight(
shape=self.kernel_shape,
initializer=self.kernel_initializer,
name="kernel",
regularizer=self.kernel_regularizer,
constraint=self.kernel_constraint,
)
elif self.implementation == 2:
if self.data_format == "channels_first":
self.kernel_shape = (
input_filter,
input_row,
input_col,
self.filters,
self.output_row,
self.output_col,
)
else:
self.kernel_shape = (
input_row,
input_col,
input_filter,
self.output_row,
self.output_col,
self.filters,
)
self.kernel = self.add_weight(
shape=self.kernel_shape,
initializer=self.kernel_initializer,
name="kernel",
regularizer=self.kernel_regularizer,
constraint=self.kernel_constraint,
)
self.kernel_mask = (
locally_connected_utils.get_locallyconnected_mask(
input_shape=(input_row, input_col),
kernel_shape=self.kernel_size,
strides=self.strides,
padding=self.padding,
data_format=self.data_format,
))
elif self.implementation == 3:
self.kernel_shape = (
self.output_row * self.output_col * self.filters,
input_row * input_col * input_filter,
)
self.kernel_idxs = sorted(
conv_utils.conv_kernel_idxs(
input_shape=(input_row, input_col),
kernel_shape=self.kernel_size,
strides=self.strides,
padding=self.padding,
filters_in=input_filter,
filters_out=self.filters,
data_format=self.data_format,
))
self.kernel = self.add_weight(
shape=(len(self.kernel_idxs), ),
initializer=self.kernel_initializer,
name="kernel",
regularizer=self.kernel_regularizer,
constraint=self.kernel_constraint,
)
else:
raise ValueError("Unrecognized implementation mode: %d." %
self.implementation)
if self.use_bias:
self.bias = self.add_weight(
shape=(output_row, output_col, self.filters),
initializer=self.bias_initializer,
name="bias",
regularizer=self.bias_regularizer,
constraint=self.bias_constraint,
)
else:
self.bias = None
if self.data_format == "channels_first":
self.input_spec = InputSpec(ndim=4, axes={1: input_filter})
else:
self.input_spec = InputSpec(ndim=4, axes={-1: input_filter})
self.built = True
