tf.nn.dropout：函数 官网说明：
tf.nn.dropout( x, keep_prob, noise_shape=None, seed=None, name=None)Defined in tensorflow/python/ops/nn_ops.py.See the guides: Layers (contrib) > Higher level ops for building neural network layers, Neural Network > Activation FunctionsComputes dropout.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.By default, each element is kept or dropped independently. If noise_shape is specified, it must be broadcastable to the shape of x, and only dimensions with noise_shape[i] == shape(x)[i] will make independent decisions. For example, if shape(x) = [k, l, m, n] and noise_shape = [k, 1, 1, n], each batch and channel component will be kept independently and each row and column will be kept or not kept together.Args:x: A floating point tensor.keep_prob: A scalar Tensor with the same type as x. The probability that each element is kept.noise_shape: A 1-D Tensor of type int32, representing the shape for randomly generated keep/drop flags.seed: A Python integer. Used to create random seeds. See tf.set_random_seed for behavior.name: A name for this operation (optional).Returns:A Tensor of the same shape of x.Raises:ValueError: If keep_prob is not in (0, 1] or if x is not a floating point tensor.
使用说明：
参数 keep_prob: 表示的是保留的比例，假设为0.8 则 20% 的数据变为0，然后其他的数据乘以 1/keep_prob；keep_prob 越大，保留的越多；
参数 noise_shape：干扰形状。 此字段默认是None，表示第一个元素的操作都是独立，但是也不一定。比例：数据的形状是shape(x)=[k, l, m, n]，而noise_shape=[k, 1, 1, n]，则第1和4列是独立保留或删除，第2和3列是要么全部保留，要么全部删除。
代码举例：
import osimport numpy as npimport tensorflow as tfx = tf.Variable(tf.ones([10, 10]))inputs = tf.nn.dropout(x, 0.8)init = tf.initialize_all_variables() with tf.Session() as sess: sess.run(init) print (x.eval()) print (inputs.eval())
输出结果：
[[1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]][[1.25 1.25 0. 1.25 1.25 1.25 1.25 1.25 1.25 0. ] [0. 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25] [1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25] [1.25 1.25 1.25 1.25 1.25 0. 1.25 1.25 1.25 1.25] [1.25 1.25 1.25 1.25 1.25 0. 1.25 1.25 0. 1.25] [1.25 1.25 1.25 1.25 1.25 0. 0. 1.25 1.25 1.25] [0. 1.25 1.25 0. 1.25 1.25 1.25 0. 1.25 0. ] [1.25 0. 0. 1.25 1.25 1.25 1.25 1.25 1.25 1.25] [1.25 1.25 1.25 0. 1.25 1.25 1.25 0. 0. 0. ] [1.25 1.25 0. 0. 0. 0. 1.25 1.25 1.25 1.25]]
加入 noise：
import osimport numpy as npimport tensorflow as tfx = tf.Variable(tf.ones([3,3,3]))inputs = tf.nn.dropout(x, 0.5,[3,1,3]) init = tf.initialize_all_variables()with tf.Session() as sess: sess.run(init) print (x.eval()) print (inputs.eval())
输出：
[[[1. 1. 1.] [1. 1. 1.] [1. 1. 1.]] [[1. 1. 1.] [1. 1. 1.] [1. 1. 1.]] [[1. 1. 1.] [1. 1. 1.] [1. 1. 1.]]][[[0. 2. 2.] [0. 2. 2.] [0. 2. 2.]] [[2. 2. 2.] [2. 2. 2.] [2. 2. 2.]] [[0. 2. 2.] [0. 2. 2.] [0. 2. 2.]]]