TensorFlow手写识别

#coding:utf8from tensorflow.examples.tutorials.mnist import input_dataimport tensorflow as tfimport numpy as npmnist = input_data.read_data_sets('MNIST_data',one_hot=True)sess = tf.InteractiveSession()x = tf.placeholder('float',shape = [None,784])y_true = tf.placeholder('float',shape=[None,10])#W = tf.Variable(tf.zeros([784,10]))#b = tf.Variable(tf.zeros([10]))#初始化权重def weight_variable(shape):    initial = tf.truncated_normal(shape,stddev=0.1)    return tf.Variable(initial)#初始化偏值def biase_variable(shape):    initial = tf.constant(0.1,shape=shape)    return tf.Variable(initial)#二维卷积def conv2d(x,W):    return tf.nn.conv2d(x,W,strides=[1,1,1,1],padding='SAME')#最大池化def max_pool_2x2(x):    return tf.nn.max_pool(x,ksize=[1,2,2,1],strides=[1,2,2,1],padding='SAME')#第一层卷积W_conv1 = weight_variable([5,5,1,32])b_conv1 = biase_variable([32])x_image = tf.reshape(x,[-1,28,28,1])h_conv1 = tf.nn.softmax(conv2d(x_image,W_conv1) + b_conv1)h_pool1 = max_pool_2x2(h_conv1)#第二层卷积W_conv2 = weight_variable([5,5,32,64])b_conv2 = weight_variable([64])h_conv2 = tf.nn.relu(conv2d(h_pool1,W_conv2) + b_conv2)h_pool2 = max_pool_2x2(h_conv2)#密集全连接层W_fc1 = weight_variable([7*7*64,1024])b_fc1 = biase_variable([1024])h_pool2_flat = tf.reshape(h_pool2,[-1,7*7*64])h_fc1 = tf.nn.relu(tf.matmul(h_pool2_flat,W_fc1) + b_fc1)#Dropout减少过拟合keep_prob = tf.placeholder('float')h_fc1_drop = tf.nn.dropout(h_fc1,keep_prob)#输出层W_fc2 = weight_variable([1024,10])b_fc2 = biase_variable([10])y_conv = tf.nn.softmax(tf.matmul(h_fc1_drop,W_fc2) + b_fc2)#交叉熵计算误差cross_entropy = -tf.reduce_mean(y_true*tf.log(y_conv))#最小化交叉熵（优化误差）train_step = tf.train.AdamOptimizer(1e-4).minimize(cross_entropy)#计算真实值与预测值之间的误差correct_prediction = tf.equal(tf.argmax(y_conv,1),tf.argmax(y_true,1))#正确率accuracy = tf.reduce_mean(tf.cast(correct_prediction,'float'))#执行图sess.run(tf.global_variables_initializer())for i in range(20000):    batch = mnist.train.next_batch(50)    if i % 100 == 0:        train_accuracy = accuracy.eval(feed_dict = {            x:batch[0],y_true:batch[1],keep_prob:1.0        })        print("step %d training accuracy %g"%(i,train_accuracy))    train_step.run(feed_dict = {        x:batch[0],y_true:batch[1],keep_prob:0.5    })print ("test accuracy %g"%accuracy.eval(feed_dict={    x:mnist.test.images,y_true:mnist.test.labels,keep_prob:1.0}))