A functional example for save and load model from Tensorflow

Many examples from the Internet, while this example can work well.

import tensorflow as tffrom tensorflow.examples.tutorials.mnist import input_dataimport osdef get_data():    mnist = input_data.read_data_sets('../MNIST_data', reshape=False, one_hot=True)    data, label = mnist.train.images[0:100, :,:,:], mnist.train.labels[0:100, :]    return data,labeldef train():    batch_size = 100    X = tf.placeholder(dtype=tf.float32, shape=[batch_size, 28, 28, 1], name='Input')    Y = tf.placeholder(dtype=tf.float32, shape=[batch_size, 10], name='TrueLabel')    #the first layer    with tf.variable_scope('layer1'):        W = tf.get_variable(name='W', shape=[5,5,1, 5], dtype=tf.float32, initializer=tf.truncated_normal_initializer())        b = tf.get_variable(name='b', dtype=tf.float32, initializer=tf.constant(0.1, shape=[5]))        H1 = tf.nn.relu(tf.nn.conv2d(X, W, strides=[1,1,1,1], padding='SAME')) +  b    #the second layer    with tf.variable_scope('layer2'):        H2 = tf.nn.max_pool(H1, ksize=[1,2,2,1], strides=[1,2,2,1], padding='SAME')    #the third layer    with tf.variable_scope('layer3'):        D= tf.reshape(H2, shape=[batch_size, -1])        input_dim = D.get_shape()[1].value        W = tf.get_variable('W', shape=[input_dim, 10], dtype=tf.float32, initializer=tf.truncated_normal_initializer())        b = tf.get_variable('b', dtype=tf.float32, shape=[10], initializer=tf.constant_initializer(0.1, dtype=tf.float32))        H3 = tf.nn.relu(tf.matmul(D, W) + b)    with tf.name_scope('loss'):        loss = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(labels=Y, logits=H3), name="loss_op")    with tf.name_scope('train'):        train_op = tf.train.AdadeltaOptimizer(1e-3).minimize(loss)    with tf.name_scope('accuracy'):        accuracy = tf.reduce_mean(tf.cast(tf.equal(tf.argmax(H3,1), tf.argmax(Y, 1)), dtype=tf.float32), name="accuracy_op")    train_data, train_label = get_data()    with tf.Session() as sess:        sess.run(tf.global_variables_initializer())        writer = tf.summary.FileWriter('./g', sess.graph)        for _ in range(20):            sess.run(train_op, {X:train_data, Y:train_label})        saver0 = tf.train.Saver()        saver0.save(sess, './save/model')        saver0.export_meta_graph('./save/model.meta')        for _ in range(5):            loss_str, accuracy_str = sess.run([loss, accuracy], {X:train_data, Y:train_label})            print('loss:{}, accuracy:{}'.format(loss_str, accuracy_str))def load():    train_data, train_label = get_data()    with tf.Session() as sess:        new_saver = tf.train.import_meta_graph('./save/model.meta')        new_saver.restore(sess, './save/model')        graph = sess.graph        X = graph.get_tensor_by_name("Input:0")        Y = graph.get_tensor_by_name('TrueLabel:0')        loss = graph.get_tensor_by_name('loss/loss_op:0')        accuracy = graph.get_tensor_by_name('accuracy/accuracy_op:0')        for _ in range(5):            loss_str, accuracy_str = sess.run([loss, accuracy], {X:train_data, Y:train_label})            print('loss:{}, accuracy:{}'.format(loss_str, accuracy_str))#train()load()