def recurrent_conv(self, x, w): conv_out = K.conv2d(x, w, strides=(1, 1), padding='same', data_format=self.data_format) return conv_out
def input_conv(self, x, w, b=None, padding='valid'): conv_out = K.conv2d(x, w, strides=self.strides, padding=padding, data_format=self.data_format, dilation_rate=self.dilation_rate) if b is not None: conv_out = K.bias_add(conv_out, b, data_format=self.data_format) return conv_out
def call(self, inputs, training=None): outputs = K.conv2d(inputs, self.compute_spectral_normal(training), strides=self.strides, padding=self.padding, data_format=self.data_format, dilation_rate=self.dilation_rate) if self.bias is not None: outputs = K.bias_add(outputs, self.bias, data_format=self.data_format) if self.activation is not None: return self.activation(outputs) return outputs