tensorflow模型保存、加载之变量重命名实例

技术  /  管理员 发布于 7年前   196

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

})

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