47c71c0524c7804863f2f4bcffdef5a873d84a55,txtgen/modules/connectors/connectors.py,StochasticConnector,_build,#StochasticConnector#,321

Before Change

        output = distribution.sample(batch_size)
        if len(output.get_shape()) == 1:
            output = tf.reshape(output, [batch_size, 1])

        // Disable gradients through samples
        output = tf.stop_gradient(output)

After Change

        if batch_size:
            output = dist_instance.sample(batch_size)
        else:
            output = dist_instance.sample()

        if dist_instance.event_shape == []:
            output = tf.reshape(output,
                                output.shape.concatenate(tf.TensorShape(1)))

        // Disable gradients through samples
        output = tf.stop_gradient(output)

Instances