def call(self, inputs):
quantized_kernel = quantize(self.kernel, nb=self.nb)
inverse_kernel_lr_multiplier = 1. / self.kernel_lr_multiplier
inputs_qnn_gradient = (inputs - (1. - 1./inverse_kernel_lr_multiplier) * K.stop_gradient(inputs))\
* inverse_kernel_lr_multiplier
outputs_qnn_gradient = K.conv2d(inputs_qnn_gradient,
quantized_kernel,
strides=self.strides,
padding=self.padding,
data_format=self.data_format,
dilation_rate=self.dilation_rate)
outputs = (outputs_qnn_gradient - (1. - 1./self.kernel_lr_multiplier) * K.stop_gradient(outputs_qnn_gradient))\
* self.kernel_lr_multiplier
#outputs = outputs*K.mean(K.abs(self.kernel))
if self.use_bias:
outputs = K.bias_add(outputs,
self.bias,
data_format=self.data_format)
if self.activation is not None:
return self.activation(outputs)
return outputs
def call(self, inputs):
quantized_kernel = quantize(self.kernel, nb=self.nb)
output = K.dot(inputs, quantized_kernel)
if self.use_bias:
output = K.bias_add(output, self.bias)
if self.activation is not None:
output = self.activation(output)
return output
