【深度学习图像识别课程】神经网络系列：（7）tensorflow中dropout应用

1、dropout原理

论文：Dropout: A Simple Way to Prevent Neural Networks fromOverfitting

防止过拟合的正则化技术

 

2、keras实现

tf.nn.dropout(hidden_layer, keep_prob)有2个参数：

（1）hidden_layer：应用dropout的tensor

（2）keep_prob：留存率。一般训练时初始留存率设置为0.5，测试时设为1.0，最大化模型的能力。

 

要求：用 ReLU 层和 dropout 层构建一个模型，keep_prob值设为 0.5。打印这个模型的 logits。

import tensorflow as tf

hidden_layer_weights = [
    [0.1, 0.2, 0.4],
    [0.4, 0.6, 0.6],
    [0.5, 0.9, 0.1],
    [0.8, 0.2, 0.8]]
out_weights = [
    [0.1, 0.6],
    [0.2, 0.1],
    [0.7, 0.9]]

# Weights and biases
weights = [
    tf.Variable(hidden_layer_weights),
    tf.Variable(out_weights)]
biases = [
    tf.Variable(tf.zeros(3)),
    tf.Variable(tf.zeros(2))]

# Input
features = tf.Variable([[0.0, 2.0, 3.0, 4.0], [0.1, 0.2, 0.3, 0.4], [11.0, 12.0, 13.0, 14.0]])

# TODO: Create Model with Dropout
keep_prob = tf.placeholder(tf.float32)
hidden_layer = tf.add(tf.matmul(features, weights[0]), biases[0])
hidden_layer = tf.nn.relu(hidden_layer)
hidden_layer = tf.nn.dropout(hidden_layer, keep_prob)

logits = tf.add(tf.matmul(hidden_layer, weights[1]), biases[1])
# TODO: Print logits from a session
with tf.Session() as sess:
    sess.run(tf.global_variables_initializer())
    print(sess.run(logits, feed_dict={keep_prob:0.5}))

3个输入，每个输入给出2个结果。