tensorflow学习笔记（八）：dropout

def dropout(x, keep_prob, noise_shape=None, seed=None, name=None)
#x: 输入
#keep_prob: 名字代表的意思, keep_prob 参数可以为 tensor，意味着，训练时候 feed 为0.5，测试时候 feed 为 1.0 就 OK。 
#return：包装了dropout的x。训练的时候用，test的时候就不需要dropout了
#例：
w = tf.get_variable("w1",shape=[size, out_size])
x = tf.placeholder(tf.float32, shape=[batch_size, size])
x = tf.nn.dropout(x, keep_prob=0.5)
y = tf.matmul(x,w)

def rnn_cell.DropoutWrapper(rnn_cell, input_keep_prob=1.0, output_keep_prob=1.0):
#例
lstm_cell = tf.nn.rnn_cell.BasicLSTMCell(size, forget_bias=0.0, state_is_tuple=True)
lstm_cell = tf.nn.rnn_cell.DropoutWrapper(lstm_cell, output_keep_prob=0.5)
#经过dropout包装的lstm_cell就出来了