8.Dropout

import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data

#载入数据集
mnist = input_data.read_data_sets("MNIST_data",one_hot=True)

#每个批次的大小
batch_size = 64
#计算一共有多少个批次
n_batch = mnist.train.num_examples // batch_size

#定义三个placeholder
x = tf.placeholder(tf.float32,[None,784])
y = tf.placeholder(tf.float32,[None,10])
keep_prob=tf.placeholder(tf.float32)

# 784-1000-500-10
W1 = tf.Variable(tf.truncated_normal([784,1000],stddev=0.1))
b1 = tf.Variable(tf.zeros([1000])+0.1)
L1 = tf.nn.tanh(tf.matmul(x,W1)+b1)
L1_drop = tf.nn.dropout(L1,keep_prob) 

W2 = tf.Variable(tf.truncated_normal([1000,500],stddev=0.1))
b2 = tf.Variable(tf.zeros([500])+0.1)
L2 = tf.nn.tanh(tf.matmul(L1_drop,W2)+b2)
L2_drop = tf.nn.dropout(L2,keep_prob) 

W3 = tf.Variable(tf.truncated_normal([500,10],stddev=0.1))
b3 = tf.Variable(tf.zeros([10])+0.1)
prediction = tf.nn.softmax(tf.matmul(L2_drop,W3)+b3)

#交叉熵
loss = tf.losses.softmax_cross_entropy(y,prediction)
#使用梯度下降法
train_step = tf.train.GradientDescentOptimizer(0.5).minimize(loss)

#初始化变量
init = tf.global_variables_initializer()

#结果存放在一个布尔型列表中
correct_prediction = tf.equal(tf.argmax(y,1),tf.argmax(prediction,1))#argmax返回一维张量中最大的值所在的位置
#求准确率
accuracy = tf.reduce_mean(tf.cast(correct_prediction,tf.float32))

with tf.Session() as sess:
    sess.run(init)
    for epoch in range(31):
        for batch in range(n_batch):
            batch_xs,batch_ys =  mnist.train.next_batch(batch_size)
            sess.run(train_step,feed_dict={x:batch_xs,y:batch_ys,keep_prob:0.5})

        test_acc = sess.run(accuracy,feed_dict={x:mnist.test.images,y:mnist.test.labels,keep_prob:1.0})
        train_acc = sess.run(accuracy,feed_dict={x:mnist.train.images,y:mnist.train.labels,keep_prob:1.0})
        print("Iter " + str(epoch) + ",Testing Accuracy " + str(test_acc) +",Training Accuracy " + str(train_acc))

Extracting MNIST_data\train-images-idx3-ubyte.gz
Extracting MNIST_data\train-labels-idx1-ubyte.gz
Extracting MNIST_data\t10k-images-idx3-ubyte.gz
Extracting MNIST_data\t10k-labels-idx1-ubyte.gz
Iter 0,Testing Accuracy 0.9201,Training Accuracy 0.91234547
Iter 1,Testing Accuracy 0.9256,Training Accuracy 0.9229636
Iter 2,Testing Accuracy 0.9359,Training Accuracy 0.9328182
Iter 3,Testing Accuracy 0.9375,Training Accuracy 0.93716365
Iter 4,Testing Accuracy 0.9408,Training Accuracy 0.9411273
Iter 5,Testing Accuracy 0.9407,Training Accuracy 0.94365454
Iter 6,Testing Accuracy 0.9472,Training Accuracy 0.9484909
Iter 7,Testing Accuracy 0.9472,Training Accuracy 0.9502
Iter 8,Testing Accuracy 0.9516,Training Accuracy 0.95336366
Iter 9,Testing Accuracy 0.9522,Training Accuracy 0.95552725
Iter 10,Testing Accuracy 0.9525,Training Accuracy 0.95632726
Iter 11,Testing Accuracy 0.9566,Training Accuracy 0.9578909
Iter 12,Testing Accuracy 0.9574,Training Accuracy 0.9606182
Iter 13,Testing Accuracy 0.9573,Training Accuracy 0.96107274
Iter 14,Testing Accuracy 0.9587,Training Accuracy 0.9614546
Iter 15,Testing Accuracy 0.9581,Training Accuracy 0.9616727
Iter 16,Testing Accuracy 0.9599,Training Accuracy 0.96369094
Iter 17,Testing Accuracy 0.9601,Training Accuracy 0.96403635
Iter 18,Testing Accuracy 0.9618,Training Accuracy 0.9658909
Iter 19,Testing Accuracy 0.9608,Training Accuracy 0.9652
Iter 20,Testing Accuracy 0.9618,Training Accuracy 0.96607274
Iter 21,Testing Accuracy 0.9634,Training Accuracy 0.96794546
Iter 22,Testing Accuracy 0.9639,Training Accuracy 0.96836364
Iter 23,Testing Accuracy 0.964,Training Accuracy 0.96965456
Iter 24,Testing Accuracy 0.9644,Training Accuracy 0.9693091
Iter 25,Testing Accuracy 0.9647,Training Accuracy 0.9703818
Iter 26,Testing Accuracy 0.9639,Training Accuracy 0.9702
Iter 27,Testing Accuracy 0.9651,Training Accuracy 0.9708909
Iter 28,Testing Accuracy 0.9666,Training Accuracy 0.9711818
Iter 29,Testing Accuracy 0.9644,Training Accuracy 0.9710364
Iter 30,Testing Accuracy 0.9659,Training Accuracy 0.97205454