layers.py 文件源码

def leaky_relu(x, alpha=0.01, name='leaky_relu', outputs_collections=None, **unused):
    """
    Computes leaky relu

    Args:
        x: a `Tensor` with type `float`, `double`, `int32`, `int64`, `uint8`, int16`, or `int8`.
        aplha: the conatant fro scalling the activation
        name: a optional scope/name of the layer
        outputs_collections: The collections to which the outputs are added.

    Returns:
        A `Tensor` representing the results of the activation operation.
    """
    _check_unused(unused, name)
    with tf.name_scope(name):
        try:
            output = tf.nn.relu(x) + tf.mul(alpha, (x - tf.abs(x))) * 0.5
        except Exception:
            output = tf.nn.relu(x) + tf.multiply(alpha, (x - tf.abs(x))) * 0.5
        return _collect_named_outputs(outputs_collections, name, output)