参考资料：
https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/4_Utils/save_restore_model.py

from tensorflow.examples.tutorials.mnist import input_data
import tensorflow as tf
mnist = input_data.read_data_sets("MNIST_data/", one_hot=True)

"""
将session 通过saver存入文件中
在新的session中可以通过saver载入已经训练好的model
新的session中定义的变量必须在载入的模型中存在，反之不必要
"""



learning_rate = 0.001
batch_size = 100
display_step = 1
model_path = "tmp/model.ckpt"

n_hidden_1 = 256
n_hidden_2 = 256
n_input = 784
n_classes = 10

x = tf.placeholder('float', [None, n_input])
y = tf.placeholder('float', [None, n_classes])

weights = {
    'h1': tf.Variable(tf.random_normal([n_input, n_hidden_1])),
    'h2': tf.Variable(tf.random_normal([n_hidden_1, n_hidden_2])),
    'out': tf.Variable(tf.random_normal([n_hidden_2, n_classes]))
}

biases = {
    'b1': tf.Variable(tf.random_normal([n_hidden_1])),
    'b2': tf.Variable(tf.random_normal([n_hidden_2])),
    'out': tf.Variable(tf.random_normal([n_classes]))
}

def multiplayer_perceptron(x, weights, biases):
    layer_1 = tf.add(tf.matmul(x, weights["h1"]), biases["b1"])
    layer_1 = tf.nn.relu(layer_1)

    layer_2 = tf.add(tf.matmul(layer_1, weights["h2"]), biases["b2"])
    layer_2 = tf.nn.relu(layer_2)

    out_layer = tf.add(tf.matmul(layer_2, weights['out']), biases['out'])
    return out_layer

#得到预测值
pred = multiplayer_perceptron(x, weights, biases)
#损失函数
cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=pred, labels=y))
optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate).minimize(cost)
init = tf.global_variables_initializer()

#创建saver
saver = tf.train.Saver()

print("Starting 1st Session")
with tf.Session() as sess:
    sess.run(init)
    for epoch in range(5):
        avg_cost = 0
        total_batch = int(mnist.train.num_examples / batch_size)
        for i in range(total_batch):
            batch_x, batch_y = mnist.train.next_batch(batch_size)
            _, c = sess.run([optimizer, cost], feed_dict={x:batch_x, y: batch_y})
            avg_cost += c / total_batch

        if epoch % display_step == 0:
            print("Epoch:", '%04d' % (epoch+1), "cost=", "{:.9f}".format(avg_cost))

    print("First Optimization Finished!")

    correct_prediction = tf.equal(tf.argmax(pred, 1), tf.argmax(y,1))

    accuracy = tf.reduce_mean(tf.cast(correct_prediction, 'float'))

    print("Accuracy:", accuracy.eval({x:mnist.test.images, y:mnist.test.labels}))

    #保存session, global_step可以标志一个checkpoint
    save_path = saver.save(sess, model_path, global_step=epoch+1)
    print("Model saved in file: %s" % save_path)

# 基于之前训练好的权重继续训练
exit()

print("Statring 2and session..")
with tf.Session() as sess:
    sess.run(init)
    # 从model_path中加载模型到当前的sess
    saver.restore(sess, model_path)
    print("Model restored from file : %s" % save_path)

    # 跟上面的训练过程相同
    for epoch in range(3):
        avg_cost = 0.
        total_batch = int(mnist.train.num_examples / batch_size)
        for i in range(total_batch):
            batch_x, batch_y = mnist.train.next_batch(batch_size)
            _, c = sess.run([optimizer, cost], feed_dict={x: batch_x,
                                                          y: batch_y})
            avg_cost += c / total_batch
        if epoch % display_step == 0:
            print("Epoch:", '%04d' % (epoch + 1), "cost=", "{:.9f}".format(avg_cost))
    print("Second Optimization Finished!")

    correct_prediction = tf.equal(tf.argmax(pred, 1), tf.argmax(y, 1))
    accuracy = tf.reduce_mean(tf.cast(correct_prediction, "float"))
    print("Accuracy:", accuracy.eval(
        {x: mnist.test.images, y: mnist.test.labels}))