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:
    `Rectifier Nonlinearities Improve Neural Network Acoustic Models [A. L. Maas et al., 2013] <https://ai.stanford.edu/~amaas/papers/relu_hybrid_icml2013_final.pdf>`__

    This activation function also follows the behaviour of the activation function :func:`tf.nn.relu6` introduced by the following paper:
    `Convolutional Deep Belief Networks on CIFAR-10 [A. Krizhevsky, 2010] <http://www.cs.utoronto.ca/~kriz/conv-cifar10-aug2010.pdf>`__

    The function return the following results:
      - When x < 0: ``f(x) = alpha_low * x``.
      - When x in [0, 6]: ``f(x) = x``.
      - When x > 6: ``f(x) = 6``.

    Parameters
    ----------
    x : Tensor
        Support input type ``float``, ``double``, ``int32``, ``int64``, ``uint8``, ``int16``, or ``int8``.
    alpha : float
        Slope.
    name : str
        The function name (optional).

    Examples
    --------
    >>> import tensorlayer as tl
    >>> net = tl.layers.DenseLayer(net, 100, act=lambda x : tl.act.leaky_relu6(x, 0.2), name="dense")

    Returns
    -------
    Tensor
        A ``Tensor`` in the same type as ``x``.

    References
    ----------
    - `Rectifier Nonlinearities Improve Neural Network Acoustic Models [A. L. Maas et al., 2013] <https://ai.stanford.edu/~amaas/papers/relu_hybrid_icml2013_final.pdf>`__
    - `Convolutional Deep Belief Networks on CIFAR-10 [A. Krizhevsky, 2010] <http://www.cs.utoronto.ca/~kriz/conv-cifar10-aug2010.pdf>`__
    

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

    with tf.name_scope(name, "leaky_relu6") as name_scope:
        x = tf.convert_to_tensor(x, name="features")
        return tf.minimum(tf.maximum(x, alpha * x), 6, name=name_scope)


def leaky_twice_relu6(x, alpha_low=0.2, alpha_high=0.2, name="leaky_relu6"):
    :func:`leaky_twice_relu6` can be used through its shortcut: :func:`:func:`tl.act.ltrelu6`.

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

Instances