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話不多說，干就完了。
變量重命名的用處？
簡單定義：簡單來說就是將模型A中的參數parameter_A賦給模型B中的parameter_B
使用場景：當需要使用已經訓練好的模型參數，尤其是使用別人訓練好的模型參數時，往往別人模型中的參數命名方式與自己當前的命名方式不同，所以在加載模型參數時需要對參數進行重命名，使得代碼更簡潔易懂。
實現方法：
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）、模型加載（變量名稱保持不變）
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）、模型加載（變量重命名）
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)
總結：
# 之前模型中叫 'weights'的變量賦值給當前的conv1_w變量
# 之前模型中叫 'biases' 的變量賦值給當前的conv1_b變量
# 之前模型中叫 'weights_2'的變量賦值給當前的conv2_w變量
saver = tf.train.Saver({
"weights": conv1_w,
"biases": conv1_b,
"weights_2": conv2_w
})
以上這篇tensorflow模型保存、加載之變量重命名實例就是小編分享給大家的全部內容了，希望能給大家一個參考，也希望大家多多支持億速云。
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