重写 Tensorflow 2.0 中的 tf.contrib.layers.batch_norm

Question

Could somebody help me rewrite the following block of code in Tf2.0?
I'm aware batch_norm is equivalent to keras.layers.BatchNormalization but the documentation doesn't give clear solution as to what 'decay' and 'epsilon' correspond to. Thanks!

def batch_norm(opts, _input, is_train, reuse, scope, scale=True):
    """Batch normalization based on tf.contrib.layers.

    """
    return tf.contrib.layers.batch_norm(
        _input, center=True, scale=scale,
        epsilon=opts['batch_norm_eps'], decay=opts['batch_norm_decay'],
        is_training=is_train, reuse=True, updates_collections=None,
        scope=scope, fused=False)

Answer 1

In this case decay corresponds to momentum of tf.keras.layers.BatchNormalization and epsilon is still epsilon .