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

巴特西

8.Dropout

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