(self, n_samples)
return samples
Generate samples.

        Parameters
        ----------
        n_samples : int
            Number of samples to generate.

        Returns samples.
Generate samples.
Generate samples .
def sample(self, n_samples):
        """Generate samples.

        Parameters
        ----------
        n_samples : int
            Number of samples to generate.

        Returns samples.
        """
        samples = np.empty(shape=(n_samples, self.net_arch['n_input']))
        for i in range(n_samples):
            # Note: The dimensionality of z_mu is fixed, so we cannot generate
            # `n_samples` samples directly. Instead, we can take the first
            # sample or a random sample and repeat. Alternatively, we could
            # save the graph variables and reinitialize the graph with z_mu of
            # size `n_samples`.
            #samples[i] = self.generate()[0]
            samples[i] = self.generate()[self.random_state.randint(
                self.batch_size, size=1)]
        return samples
def sample ( self , n_samples ) : samples = np . empty ( shape = ( n_samples , self . net_arch [ 'n_input' ] ) ) for i in range ( n_samples ) : # Note: The dimensionality of z_mu is fixed, so we cannot generate # `n_samples` samples directly. Instead, we can take the first # sample or a random sample and repeat. Alternatively, we could # save the graph variables and reinitialize the graph with z_mu of # size `n_samples`. #samples[i] = self.generate()[0] samples [ i ] = self . generate ( ) [ self . random_state . randint ( self . batch_size , size = 1 ) ] return samples