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训练卷积神经网络，并将训练得到的模型保存。

import tensorflow as tffrom tensorflow.examples.tutorials.mnist import input_datamnist = input_data.read_data_sets('MNIST_data', one_hot=True)sess = tf.Session()def compute_accuracy(v_xs, v_ys):    global prediction    y_pre = sess.run(prediction, feed_dict={
   xs: v_xs, keep_prob: 1})            #(10000,10)    correct_prediction = tf.equal(tf.argmax(y_pre, 1), tf.argmax(v_ys, 1))      #比较是否相等，返回bool    accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))          #将比较结果转换成tf.float32，并计算平均值    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):    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')keep_prob = tf.placeholder(tf.float32)with tf.name_scope('inputs'):    xs = tf.placeholder(tf.float32, [None, 784], name='x_input')  #28x28    ys = tf.placeholder(tf.float32, [None, 10], name='y_input')x_image = tf.reshape(xs, [-1, 28, 28, 1])#print("n_samples:", x_image.shape)#conv1 layerW_conv1 = weight_variable([5, 5, 1, 32])  #patch 5x5 in size=1, out size 32b_conv1 = bias_variable([32])h_conv1 = tf.nn.relu(conv2d(x_image, W_conv1) + b_conv1) # output size 28x28x32h_pool1 = max_pool_2x2(h_conv1)                          # size 14x14x32#conv2 layerW_conv2 = weight_variable([5, 5, 32, 64])  #patch 5x5 in size=32, out size 64b_conv2 = bias_variable([64])h_conv2 = tf.nn.relu(conv2d(h_pool1, W_conv2) + b_conv2) # output size 14x14x64h_pool2 = max_pool_2x2(h_conv2)                          # size 7x7x64#func1 layerW_fc1 = weight_variable([7*7*64, 1024])b_fc1 = bias_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)h_fc1_drop = tf.nn.dropout(h_fc1, keep_prob)#func2 layerW_fc2 = weight_variable([1024, 10])b_fc2 = bias_variable([10])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]))       #losstf.summary.scalar('loss', cross_entropy)with tf.name_scope('train'):    train_step = tf.train.AdamOptimizer(1e-4).minimize(cross_entropy)init = tf.global_variables_initializer()#summary writer goes in heremerged = tf.summary.merge_all()train_writer = tf.summary.FileWriter('D:/deng/logs/train', sess.graph)test_writer = tf.summary.FileWriter('D:/deng/logs/test', sess.graph)sess.run(init)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, mnist.test.labels        ))saver = tf.train.Saver()model_path = "D:/deng/model/model.ckpt"saver.save(sess, model_path)

训练结果：

保存模型：

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