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(shù)parameter_A賦給模型B中的parameter_B
使用場景：當(dāng)需要使用已經(jīng)訓(xùn)練好的模型參數(shù)，尤其是使用別人訓(xùn)練好的模型參數(shù)時，往往別人模型中的參數(shù)命名方式與自己當(dāng)前的命名方式不同，所以在加載模型參數(shù)時需要對參數(shù)進(jìn)行重命名，使得代碼更簡潔易懂。
實(shí)現(xiàn)方法：
1）、模型保存
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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）、模型加載（變量名稱保持不變）
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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）、模型加載（變量重命名）
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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) |
總結(jié)：
# 之前模型中叫 'weights'的變量賦值給當(dāng)前的conv1_w變量
# 之前模型中叫 'biases' 的變量賦值給當(dāng)前的conv1_b變量
# 之前模型中叫 'weights_2'的變量賦值給當(dāng)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(shí)例就是小編分享給大家的全部內(nèi)容了，希望能給大家一個參考，也希望大家多多支持服務(wù)器之家。
原文鏈接：https://blog.csdn.net/cxx654/article/details/88927962
