tf.nn.dropout

tf.nn.dropout函数

首先看官方函数定义：

def dropout(x, keep_prob, noise_shape=None, seed=None, name=None)

输入是：

• x，你自己的训练、测试数据等
• keep_prob，dropout概率
• ……，其它参数不咋用，不介绍了

输出是：

• A Tensor of the same shape of x

然后我们看看官方API是怎么说这个函数的：

With probability keep_prob, outputs the input element scaled up by 1 / keep_prob, otherwise outputs 0. The scaling is so that the expected sum is unchanged.

注意，输出的非0元素是原来的 “1/keep_prob” 倍！说了这么多，下面给一个程序例子：

import tensorflow as tf

dropout = tf.placeholder(tf.float32)
x = tf.Variable(tf.ones([10, 10]))
y = tf.nn.dropout(x, dropout)

init = tf.initialize_all_variables()
sess = tf.Session()
sess.run(init)

print sess.run(y, feed_dict = {dropout: 0.4})

运行的结果如下：

[[ 0.   0.   2.5  2.5  0.   0.   2.5  2.5  2.5  2.5]
 [ 0.   2.5  2.5  2.5