(self, layer_input, layer_dim, stddev=0.5)
return lrelu(linear(layer_input, 1, scope='D_final', stddev=stddev))
Define the discriminator network.

        Parameters
        ----------
        layer_input : Tensor
            Input to the initial layer.
        layer_dim : list
            Number of neurons for each hidden layer of the discriminator network.

        Returns the output of the discriminator network. The output layer has
        one neuron for binary discrimination.
Define the discriminator network.
Define the discriminator network .
def _discriminator(self, layer_input, layer_dim, stddev=0.5):
        """Define the discriminator network.

        Parameters
        ----------
        layer_input : Tensor
            Input to the initial layer.
        layer_dim : list
            Number of neurons for each hidden layer of the discriminator network.

        Returns the output of the discriminator network. The output layer has
        one neuron for binary discrimination.
        """
        for layer_i, n_output in enumerate(layer_dim):
            output = self.d_transfer_fct(linear(layer_input,
                                                n_output,
                                                scope='D_{0}'.format(layer_i),
                                                stddev=stddev))
            layer_input = output
        # return tf.nn.sigmoid(linear(layer_input, 1, scope='D_final'))
        return lrelu(linear(layer_input, 1, scope='D_final', stddev=stddev))
def _discriminator ( self , layer_input , layer_dim , stddev = 0.5 ) : for layer_i , n_output in enumerate ( layer_dim ) : output = self . d_transfer_fct ( linear ( layer_input , n_output , scope = 'D_{0}' . format ( layer_i ) , stddev = stddev ) ) layer_input = output # return tf.nn.sigmoid(linear(layer_input, 1, scope='D_final')) return lrelu ( linear ( layer_input , 1 , scope = 'D_final' , stddev = stddev ) )