(self, X, Z)
return (cost_d, cost_g)
Train model based on mini-batch of input data.

        Parameters
        ----------
        X : ndarray, shape (n_samples, n_features)
            Matrix containing the data to be learned for the discriminator.
        Z : ndarray, shape (n_samples, n_features)
            Matrix containing the data to be learned for the generator.
        Returns cost of mini-batch.
Train model based on mini-batch of input data.
Train model based on mini - batch of input data .
def partial_fit(self, X, Z):
        """Train model based on mini-batch of input data.

        Parameters
        ----------
        X : ndarray, shape (n_samples, n_features)
            Matrix containing the data to be learned for the discriminator.
        Z : ndarray, shape (n_samples, n_features)
            Matrix containing the data to be learned for the generator.
        Returns cost of mini-batch.
        """
        # Update discriminator.
        opt_d, cost_d = self.sess.run((self.opt_d, self.loss_d),
                                      feed_dict={self.z: Z, self.x: X})
        # Update generator.
        opt_g, cost_g = self.sess.run((self.opt_g, self.loss_g),
                                      feed_dict={self.z: Z})

        return (cost_d, cost_g)
def partial_fit ( self , X , Z ) : # Update discriminator. opt_d , cost_d = self . sess . run ( ( self . opt_d , self . loss_d ) , feed_dict = { self . z : Z , self . x : X } ) # Update generator. opt_g , cost_g = self . sess . run ( ( self . opt_g , self . loss_g ) , feed_dict = { self . z : Z } ) return ( cost_d , cost_g )