tensorflow学习笔记（二十七）：leaky relu

def relu(x, alpha=0., max_value=None):
    '''ReLU.

    alpha: slope of negative section.
    '''
    negative_part = tf.nn.relu(-x)
    x = tf.nn.relu(x)
    if max_value is not None:
        x = tf.clip_by_value(x, tf.cast(0., dtype=_FLOATX),
                             tf.cast(max_value, dtype=_FLOATX))
    x -= tf.constant(alpha, dtype=_FLOATX) * negative_part
    return x

x = tf.maximum(alpha*x,x)

import tensorflow as tf
w1 = tf.Variable(0) #0或5时，打印出来1， 2时打印出来1,6时打印出来0，-1时打印出来0
g = tf.clip_by_value(w1, 0, 5)
grad = tf.gradients(g, [w1])

with tf.Session() as sess:
    tf.global_variables_initializer().run()
    print(sess.run(grad))