def call(self, inputs):
output = K.local_conv2d(inputs, self.kernel, self.kernel_size, self.strides,
(self.output_row, self.output_col),
self.data_format)
if self.use_bias:
output = K.bias_add(output, self.bias, data_format=self.data_format)
output = self.activation(output)
return output
def call(self, inputs):
frep_parts = tf.split(inputs, self.splitaxis, -1)
convs = []
for i, frep_part in enumerate(frep_parts):
individual_channels = tf.split(frep_part, frep_part.shape[-1], -1)
for ind_ch in individual_channels:
conv = K.local_conv2d(ind_ch, self.kernels[i],
self.kernel_size, self.strides,
(self.output_row, self.output_col),
self.data_format)
convs.append(conv)
outputs = tf.concat(convs, -1)
if self.use_bias:
outputs = K.bias_add(outputs,
self.bias,
data_format=self.data_format)
outputs = self.activation(outputs)
return outputs