保存和读取权重

#保存和读取权重
tf.train.Saver.save(sess, save_path)
tf.train.Saver.restore(sess, save_path)

#定义模型
model = SkipGramModel(params)

#优化器
optimizer=tf.GradientDescentOptimizer(learning_rate=0.1)

#创建保持器
saver = tf.train.Saver()

with tf.Session() as sess:
	for step in range(training_steps): 
		sess.run([optimizer])
    
  saver.save(sess, 'model_name')

#四个文件
checkpoint  保留至最新
model.data-00000-of-00001 变量值
model.index 变量目录
model.meta  图结构

#只保存某几个变量

saver = tf.train.Saver({'v1': v1, 'v2': v2})
saver = tf.train.Saver([v1, v2])
saver = tf.train.Saver({v.op.name: v for v in [v1, v2]})


with tf.Session() as sess:
  saver.restore(sess, 'name_of_the_checkpoint')

  #检查路径是否有检查点
  ckpt = tf.train.get_checkpoint_state(os.path.dirname('checkpoints/checkpoint'))
  #读取最新的保存
  if ckpt and ckpt.model_checkpoint_path:
     saver.restore(sess, ckpt.model_checkpoint_path)

#全局步骤
global_step = tf.Variable(0,trainable=False, name='global_step')

optimizer.minimize(loss, global_step=global_step)

saver.save(sess, 'model_name',global_step=global_step)