58f4620699ac8e8eecbc0746dfcc8a497f01d6ba,tensorlayer/activation.py,,leaky_relu,#Any#Any#Any#,42

Before Change

    // m_x = tf.nn.relu(-x)
    // x -= alpha * m_x
    x = tf.maximum(x, alpha * x, name=name)
    return x


def swish(x, name="swish"):
    Swish function.

After Change

    if not (0 < alpha <= 1):
        raise ValueError("`alpha` value must be in [0, 1]`")

    with tf.name_scope(name, "leaky_relu") as name_scope:
        x = tf.convert_to_tensor(x, name="features")
        return tf.maximum(x, alpha * x, name=name_scope)


def leaky_relu6(x, alpha=0.2, name="leaky_relu6"):
    :func:`leaky_relu6` can be used through its shortcut: :func:`tl.act.lrelu6`.

    This activation function is a modified version :func:`leaky_relu` introduced by the following paper:

Instances