(self,
                   layer_input,
                   layer_dim,
                   output_dim,
                   batch_norm=True,
                   stddev=0.5)
return tf.nn.tanh(linear(layer_input,
                                 output_dim,
                                 scope='G_final',
                                 stddev=stddev))
Define the generator network.

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

        Returns the output of the generator network.
Define the generator network.
Define the generator network .
def _generator(self,
                   layer_input,
                   layer_dim,
                   output_dim,
                   batch_norm=True,
                   stddev=0.5):
        """Define the generator network.

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

        Returns the output of the generator network.
        """
        for layer_i, n_output in enumerate(layer_dim):
            if batch_norm:
                layer_input = tf.contrib.layers.batch_norm(
                    layer_input, scope='G_{0}'.format(layer_i))
            output = self.g_transfer_fct(linear(layer_input,
                                                n_output,
                                                scope='G_{0}'.format(layer_i),
                                                stddev=stddev))
            layer_input = output
        return tf.nn.tanh(linear(layer_input,
                                 output_dim,
                                 scope='G_final',
                                 stddev=stddev))
def _generator ( self , layer_input , layer_dim , output_dim , batch_norm = True , stddev = 0.5 ) : for layer_i , n_output in enumerate ( layer_dim ) : if batch_norm : layer_input = tf . contrib . layers . batch_norm ( layer_input , scope = 'G_{0}' . format ( layer_i ) ) output = self . g_transfer_fct ( linear ( layer_input , n_output , scope = 'G_{0}' . format ( layer_i ) , stddev = stddev ) ) layer_input = output return tf . nn . tanh ( linear ( layer_input , output_dim , scope = 'G_final' , stddev = stddev ) )