在训练完模型之后，就要把模型保存起来，方便以后使用。
保存模型 save()：
saver = tf.train.Saver() with tf.Session() as sess: sess.run(...) saver.save(sess, savePath/fileName)
载入模型 restore() ：
saver = tf.train.Saver() with tf.Session() as sess1: sess.run(...) saver.restore(sess1, savePath/fileName)
保存模型时也可以指定变量名字与变量的对应关系：
1）saver = tf.train.Saver({key1: valve, key2: value})
例: saver = tf.train.Saver({'weights': w, biases: b})
2)saver = tf.train.Saver([w, b]) # 放到list中
3)saver = tf.train.Saver(v.op.name: v for v in [w, b]) # 将op的名字当做key
打印模型内容：
print_tensors_in_checkpoint_file(save_dir+'linerModel.cpkt', None, True)
下面是一个保存及载入模型的完整例子：
import numpy as np import tensorflow as tf from tensorflow.python.tools.inspect_checkpoint import print_tensors_in_checkpoint_file # 构建实验数据 train_x = np.linspace(-1, 1, 100) # y = 2 * x + b train_y = 2. * train_x + np.random.randn(*train_x.shape) * 0.3 # 创建模型 # 占位符 X = tf.placeholder(tf.float32) Y = tf.placeholder(tf.float32) # 模型参数 weights = tf.Variable(tf.random_normal([1]), name='weights') biases = tf.Variable(tf.zeros([1]), name='biases') z = tf.multiply(X, weights) + biases # 构建损失函数 loss = tf.reduce_mean(tf.square(Y - z)) # 定义学习率 learning_rate = 0.01 # 构建优化函数 optimizer = tf.train.GradientDescentOptimizer(learning_rate) # 最小化损失函数 train = optimizer.minimize(loss) # 初始化所有变量 init = tf.global_variables_initializer() # 定义 epochs training_epochs = 20 # 每隔两步显示一次中间值 display_step = 2 # 存放批次值和损失值 plot_data = {'batchsize': [], 'loss': []} # 定义保存模型对象 saver = tf.train.Saver() save_dir = 'logs/' # 生成模型的路径 # 启动Session with tf.Session() as sess: # 初始化全局变量 sess.run(init) # 向模型中 feed 数据 for epoch in range(training_epochs): for (x, y) in zip(train_x, train_y): feed_dict = {X: x, Y: y} sess.run(train, feed_dict=feed_dict) # 显示训练中的数据 if epoch % display_step == 0: loss_ = sess.run(loss, feed_dict={X: train_x, Y: train_y}) print('epoch:', epoch + 1, 'loss = ', loss_, 'weights=', sess.run(weights), 'biases=', sess.run(biases)) print('Finished...') # 保存模型 saver.save(sess, save_dir+'linerModel.cpkt') # 如果指定的文件夹不存在会自动创建 print('loss=', sess.run(loss, feed_dict={X: train_x, Y: train_y}), 'weights=', sess.run(weights), 'biases=', sess.run(biases)) # 使用模型 with tf.Session() as sess_2: saver.restore(sess_2, save_dir+'linerModel.cpkt') print('下面是模型载入结果： ') print('x=0.2, z=', sess_2.run(z, feed_dict={X: 0.2})) # 打印文件内容 print_tensors_in_checkpoint_file(save_dir+'linerModel.cpkt', None, True)
添加检查点 Checkpoint
在训练之中，难免会出现中断的情况，这时就设置一个检查点。saver = tf.train.Saver(max_to_keep=1) # 生成saver with tf.Session() as sess1: sess.run(...) saver.save(sess1, savePath/fileName, global_step=epoch)
max_to_keep 参数指定最多生成多少个检查点文件
载入检查点
load_epoch = 18 # 只是文件的一个后缀，可以根据需要修改 with tf.Session() as sess_2: saver.restore(sess_2, save_dir+'linearModel.cpkt-' + str(load_epoch))
另一种添加检查点的方式： trainMonitored
tf.reset_default_graph() global_step = tf.train.get_or_create_global_step() step = tf.assign_add(global_step, 1) with tf.train.MonitoredTrainingSession(checkpoint_dir='logs/ckpt', save_checkpoint_secs=2) as sess: print(sess.run([global_step])) while not sess.should_stop(): # 启用死循环，session不停止就不结束 i = sess.run(step) print(i)
如果不设置 save_checkpoint_secs 参数，默认时间是10mins, 该方法必须定义global_step,不然报错
下面是添加及载入检查点的完整例子：
import numpy as np import tensorflow as tf # 构建实验数据 train_x = np.linspace(-1, 1, 100) # y = 2 * x + b train_y = 2. * train_x + np.random.randn(*train_x.shape) * 0.3 # 创建模型 # 占位符 X = tf.placeholder(tf.float32) Y = tf.placeholder(tf.float32) # 模型参数 weights = tf.Variable(tf.random_normal([1]), name='weights') biases = tf.Variable(tf.zeros([1]), name='biases') z = tf.multiply(X, weights) + biases # 构建损失函数 loss = tf.reduce_mean(tf.square(Y - z)) # 定义学习率 learning_rate = 0.01 # 构建优化函数 optimizer = tf.train.GradientDescentOptimizer(learning_rate) # 最小化损失函数 train = optimizer.minimize(loss) # 初始化所有变量 init = tf.global_variables_initializer() # 定义 epochs training_epochs = 20 # 每隔两步显示一次中间值 display_step = 2 # 存放批次值和损失值 plot_data = {'batchsize': [], 'loss': []} # 定义保存模型对象 saver = tf.train.Saver(max_to_keep=2) save_dir = 'logs/' # 生成模型的路径 # 启动Session with tf.Session() as sess: # 初始化全局变量 sess.run(init) # 向模型中 feed 数据 for epoch in range(training_epochs): for (x, y) in zip(train_x, train_y): feed_dict = {X: x, Y: y} sess.run(train, feed_dict=feed_dict) # 显示训练中的数据 if epoch % display_step == 0: loss_ = sess.run(loss, feed_dict={X: train_x, Y: train_y}) print('epoch:', epoch + 1, 'loss = ', loss_, 'weights=', sess.run(weights), 'biases=', sess.run(biases)) # 保存检查点 saver.save(sess, save_dir + 'linearModel.cpkt', global_step=epoch) print('Finished...') # 保存模型 saver.save(sess, save_dir+'linerModel.cpkt') # 如果指定的文件夹不存在会自动创建 print('loss=', sess.run(loss, feed_dict={X: train_x, Y: train_y}), 'weights=', sess.run(weights), 'biases=', sess.run(biases)) # 载入检查点 load_epoch = 18 with tf.Session() as sess_2: saver.restore(sess_2, save_dir+'linearModel.cpkt-' + str(load_epoch)) print('下面是检查点的结果： ') print('x=0.2, z=', sess_2.run(z, feed_dict={X: 0.2})) # trainMonitored tf.reset_default_graph() global_step = tf.train.get_or_create_global_step() step = tf.assign_add(global_step, 1) with tf.train.MonitoredTrainingSession(checkpoint_dir='logs/ckpt', save_checkpoint_secs=2) as sess: print(sess.run([global_step])) while not sess.should_stop(): # 启用死循环，session不停止就不结束 i = sess.run(step) print(i)