dc56b7e531a727b1fbaacdffccb01fbe7475dee1,tflearn/layers/normalization.py,,batch_normalization,#Any#Any#Any#Any#Any#Any#Any#Any#Any#,11

Before Change

            tf.add_to_collection(tf.GraphKeys.EXCL_RESTORE_VARS, gamma)

        ema = tf.train.ExponentialMovingAverage(decay=decay)
        axis = [i for i in range(input_ndim - 1)]
        if len(axis) < 1: axis = [0]
        batch_mean, batch_var = tf.nn.moments(incoming, axis,
                                              name="moments")
        ema_apply_op = ema.apply([batch_mean, batch_var])
        ema_mean, ema_var = ema.average(batch_mean), ema.average(
            batch_var)

After Change

                incoming, mean, var, beta, gamma, epsilon,
                scale_after_normalization=True,
            )
            inference.set_shape(input_shape)

    // Add attributes for easy access
    inference.scope = scope
    inference.beta = beta

Instances