network.py 文件源码

def hard_sigmoid(x, name=None):
    """Hard sigmoid implementation. This is a very rough approximation
       of a real sigmoid function, but is much faster to calculate.
    Parameters
    ----------
    x : Tensor
        The tensor to apply the nonlinearity to.
    name : str, optional
        Variable scope to use.
    Returns
    ----------
    x: Tensor
        Output of the nonlinearity.
    """
    with tf.op_scope([x], name, 'hard_sigmoid'):
        x = (0.2 * x) + 0.5
        x = tf.clip_by_value(x, tf.cast(0., dtype=tf.float32),
                            tf.cast(1., dtype=tf.float32))
        return x