import tensorflow as tffrom tensorflow.examples.tutorials.mnist import input_datamnist = input_data.read_data_sets('MNIST_data',one_hot=True)def compute_accuracy(v_xs, v_ys):    global prediction    y_pre = sess.run(prediction, feed_dict={xs: v_xs, keep_prob: 1})    correct_prediction = tf.equal(tf.argmax(y_pre,1), tf.argmax(v_ys,1))    accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))    result = sess.run(accuracy, feed_dict={xs: v_xs, ys: v_ys, keep_prob: 1})    return resultdef weight_variable(shape):    initial = tf.truncated_normal(shape,stddev=0.1)    return tf.Variable(initial)def bias_variable(shape):    initial = tf.constant(0.1,shape=shape)    return tf.Variable(initial)def conv2d(x,W):    #stride:[1,x_movement,y_movement,1]    return tf.nn.conv2d(x,W,strides=[1,1,1,1],padding='SAME')def max_pool_2x2(x):    return tf.nn.max_pool(x,ksize=[1,2,2,1],strides=[1,2,2,1],padding="SAME")#image with and height#result lengthwh = 28rl = 10xs = tf.placeholder(tf.float32,[None,wh*wh])ys = tf.placeholder(tf.float32,[None,rl])keep_prob = tf.placeholder(tf.float32)x_image=tf.reshape(xs,[-1,wh,wh,1])#patch 5*5 in size 1 ,out size 32W_conv1 = weight_variable([5,5,1,32])b_conv1 = bias_variable([32])h_conv1 = tf.nn.relu(conv2d(x_image,W_conv1)+b_conv1)h_pool1 = max_pool_2x2(h_conv1)W_conv2 = weight_variable([5,5,32,64])b_conv2 = bias_variable([64])h_conv2 = tf.nn.relu(conv2d(h_pool1,W_conv2)+b_conv2)h_pool2 = max_pool_2x2(h_conv2)W_fc1 = weight_variable([7*7*64,1024])B_fc1 = bias_variable([1024])#[7,7,64]=>[7*7*64]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)h_fc1_drop = tf.nn.dropout(h_fc1,keep_prob)W_fc2 = weight_variable([1024,rl])B_fc2 = bias_variable([rl])prediction = tf.nn.softmax(tf.matmul(h_fc1_drop,W_fc2)+B_fc2)cross_entropy = tf.reduce_mean(-tf.reduce_sum(ys*tf.log(prediction),reduction_indices=[1]))#1e-4=0.0001train_step = tf.train.AdamOptimizer(1e-4).minimize(cross_entropy)sess = tf.Session()sess.run(tf.global_variables_initializer())for i in range(1000):    batch_xs, batch_ys = mnist.train.next_batch(100)    sess.run(train_step, feed_dict={xs: batch_xs, ys: batch_ys, keep_prob: 0.5})    if i % 50 == 0:        print(compute_accuracy(            mnist.test.images[:1000], mnist.test.labels[:1000]))sess.close()

这次运行代码计算时间非常长，而且跑到后面，电脑开始明显的发热。排风扇也开始响了。

最后准确率到达了0.964