tensorflow模型保存、加载之变量重命名实例

话不多说，干就完了。

变量重命名的用处？

简单定义：简单来说就是将模型A中的参数parameter_A赋给模型B中的parameter_B

使用场景：当需要使用已经训练好的模型参数，尤其是使用别人训练好的模型参数时，往往别人模型中的参数命名方式与自己当前的命名方式不同，所以在加载模型参数时需要对参数进行重命名，使得代码更简洁易懂。

实现方法：

1）、模型保存

import os
import tensorflow as tf
 
weights = tf.Variable(initial_value=tf.truncated_normal(shape=[1024, 2],
                            mean=0.0,
                            stddev=0.1),
           dtype=tf.float32,
           name="weights")
biases = tf.Variable(initial_value=tf.zeros(shape=[2]),
           dtype=tf.float32,
           name="biases")
 
weights_2 = tf.Variable(initial_value=weights.initialized_value(),
            dtype=tf.float32,
            name="weights_2")
 
# saver checkpoint
if os.path.exists("checkpoints") is False:
  os.makedirs("checkpoints")
 
saver = tf.train.Saver()
with tf.Session() as sess:
  init_op = [tf.global_variables_initializer()]
  sess.run(init_op)
  saver.save(sess=sess, save_path="checkpoints/variable.ckpt")

2）、模型加载（变量名称保持不变）

import tensorflow as tf
from matplotlib import pyplot as plt
import os
 
current_path = os.path.dirname(os.path.abspath(__file__))
 
def restore_variable(sess):
  # need not initilize variable, but need to define the same variable like checkpoint
  weights = tf.Variable(initial_value=tf.truncated_normal(shape=[1024, 2],
                              mean=0.0,
                              stddev=0.1),
             dtype=tf.float32,
             name="weights")
  biases = tf.Variable(initial_value=tf.zeros(shape=[2]),
             dtype=tf.float32,
             name="biases")
 
  weights_2 = tf.Variable(initial_value=weights.initialized_value(),
              dtype=tf.float32,
              name="weights_2")
 
  saver = tf.train.Saver()
 
  ckpt_path = os.path.join(current_path, "checkpoints", "variable.ckpt")
  saver.restore(sess=sess, save_path=ckpt_path)
 
  weights_val, weights_2_val = sess.run(
    [
      tf.reshape(weights, shape=[2048]),
      tf.reshape(weights_2, shape=[2048])
    ]
  )
 
  plt.subplot(1, 2, 1)
  plt.scatter([i for i in range(len(weights_val))], weights_val)
  plt.subplot(1, 2, 2)
  plt.scatter([i for i in range(len(weights_2_val))], weights_2_val)
  plt.show()
 
 
if __name__ == '__main__':
  with tf.Session() as sess:
    restore_variable(sess)

3）、模型加载（变量重命名）

import tensorflow as tf
from matplotlib import pyplot as plt
import os
 
current_path = os.path.dirname(os.path.abspath(__file__))
 
 
def restore_variable_renamed(sess):
  conv1_w = tf.Variable(initial_value=tf.truncated_normal(shape=[1024, 2],
                              mean=0.0,
                              stddev=0.1),
             dtype=tf.float32,
             name="conv1_w")
  conv1_b = tf.Variable(initial_value=tf.zeros(shape=[2]),
             dtype=tf.float32,
             name="conv1_b")
 
  conv2_w = tf.Variable(initial_value=conv1_w.initialized_value(),
             dtype=tf.float32,
             name="conv2_w")
 
  # variable named 'weights' in ckpt assigned to current variable conv1_w
  # variable named 'biases' in ckpt assigned to current variable conv1_b
  # variable named 'weights_2' in ckpt assigned to current variable conv2_w
  saver = tf.train.Saver({
    "weights": conv1_w,
    "biases": conv1_b,
    "weights_2": conv2_w
  })
 
  ckpt_path = os.path.join(current_path, "checkpoints", "variable.ckpt")
  saver.restore(sess=sess, save_path=ckpt_path)
 
  conv1_w__val, conv2_w__val = sess.run(
    [
      tf.reshape(conv1_w, shape=[2048]),
      tf.reshape(conv2_w, shape=[2048])
    ]
  )
 
  plt.subplot(1, 2, 1)
  plt.scatter([i for i in range(len(conv1_w__val))], conv1_w__val)
  plt.subplot(1, 2, 2)
  plt.scatter([i for i in range(len(conv2_w__val))], conv2_w__val)
  plt.show()
 
 
if __name__ == '__main__':
  with tf.Session() as sess:
    restore_variable_renamed(sess)

总结：

# 之前模型中叫 'weights'的变量赋值给当前的conv1_w变量

# 之前模型中叫 'biases' 的变量赋值给当前的conv1_b变量

# 之前模型中叫 'weights_2'的变量赋值给当前的conv2_w变量

saver = tf.train.Saver({

"weights": conv1_w,

"biases": conv1_b,

"weights_2": conv2_w

})

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