tf.nn.dropout(x, keep_prob, noise_shape=None, seed=None, name=None)

Type: function

Docstring: Computes 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 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].

example:

import tensorflow as tf
import numpy as np
# 神经元输入值
neuros = np.array([1, 1, 1, 1],dtype=np.float32)
# 接入dropout层
neuros_drop = tf.nn.dropout(neuros, keep_prob=0.5)
with tf.Session() as sess:
    neuros_drop_res = sess.run(neuros_drop)
    print(neuros_drop_res)

[ 0.  2.  2.  2.]