(self, inputs, inference=False, beta=0.0)
return x
TODO:  Handle final layers that don't own their own outputs (bias, activation)

        Arguments:
            inputs:
            inference:  (Default value = False)
            beta:  (Default value = 0.0)

        Returns:
TODO:  Handle final layers that don't own their own outputs (bias, activation)
TODO : Handle final layers that don t own their own outputs ( bias activation )
def fprop(self, inputs, inference=False, beta=0.0):
        """
        TODO:  Handle final layers that don't own their own outputs (bias, activation)

        Arguments:
            inputs:
            inference:  (Default value = False)
            beta:  (Default value = 0.0)

        Returns:

        """
        x = inputs

        for l in self.layers:
            altered_tensor = l.be.distribute_data(x, l.parallelism)
            l.revert_list = [altered_tensor] if altered_tensor else []

            # try to convert to mkl
            l.be.convert_data(x, l.get_is_mklop())

            if l is self.layers[-1] and beta != 0:
                x = l.fprop(x, inference=inference, beta=beta)
            else:
                x = l.fprop(x, inference=inference)

        if inference:
            self.revert_tensors()

        return x
def fprop ( self , inputs , inference = False , beta = 0.0 ) : x = inputs for l in self . layers : altered_tensor = l . be . distribute_data ( x , l . parallelism ) l . revert_list = [ altered_tensor ] if altered_tensor else [ ] # try to convert to mkl l . be . convert_data ( x , l . get_is_mklop ( ) ) if l is self . layers [ - 1 ] and beta != 0 : x = l . fprop ( x , inference = inference , beta = beta ) else : x = l . fprop ( x , inference = inference ) if inference : self . revert_tensors ( ) return x