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 osimport 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 checkpointif 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 tffrom matplotlib import pyplot as pltimport 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 tffrom matplotlib import pyplot as pltimport 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

})

以上这篇tensorflow模型保存、加载之变量重命名实例就是小编分享给大家的全部内容了，希望能给大家一个参考，也希望大家多多支持。