值更改后Tensorflow找不到变量？答案

【问题标题】：Tensorflow cannot find variables after values change?值更改后Tensorflow找不到变量？
【发布时间】：2021-09-23 17:08:22
【问题描述】：

我在 tensorflow 2.5.0 中构建了一个自定义模型，我正在尝试对超参数值进行网格搜索。模型训练正确，但是当我重新初始化网格搜索循环中的参数时会引发错误（它似乎与张量流图有关，但我不知道问题的确切根源是什么）。下面包含一个简单的线性回归模型的最小可重复示例。

import tensorflow as tf
import numpy as np

class Model:
    
    def __init__(self, X, y):
        
        self.X = X
        self.y = y
        
    # initialize model weights
    def initialize_model(self, lr, optimizer):
    
        self.lr = lr
        initializer = tf.keras.initializers.glorot_normal()
        self.weights = tf.Variable(initializer(shape = (X.shape[1],)), 
                                   name = 'weights')
        self.optimizer = optimizer(lr = lr)
        
    # loss function
    def sq_error_loss(self):
        
        preds = tf.linalg.matvec(self.X, self.weights)
        sq_error = tf.math.reduce_sum((self.y - preds)**2)
        
        return sq_error
    
    # one epoch of training
    @tf.function
    def train_step(self):
        
        with tf.GradientTape() as tape:
            loss = self.sq_error_loss()
        grads = tape.gradient(loss, [self.weights])
        self.optimizer.apply_gradients(zip(grads, [self.weights]))
        
        return loss
    
    # train the model for some number of epochs
    def train(self, num_epochs = 10):
    
        for e in range(num_epochs):
            loss = self.train_step()
            
        print('Training finished.')
                
    # grid search over different values of the learning rate hyperparameter
    def grid_search(self, lrs):
        
        for lr in lrs:
            self.initialize_model(lr, tf.keras.optimizers.Adam)
            self.train()

X = tf.Variable(np.random.normal(size = (1000, 10)).astype('float32'))
y = tf.Variable(np.random.normal(size = 1000).astype('float32'))

model = Model(X, y)

lrs = [i*0.01 for i in range(1, 11)]

model.grid_search(lrs)

这会引发以下错误：

FailedPreconditionError:  Could not find variable _AnonymousVar143. This could mean that the variable has been deleted. In TF1, it can also mean the variable is uninitialized. Debug info: container=localhost, status=Not found: Resource localhost/_AnonymousVar143/class tensorflow::Var does not exist.
     [[node Adam/Cast_2/ReadVariableOp (defined at <ipython-input-112-1e76ddb3815a>:34) ]] [Op:__inference_train_step_293348]

Function call stack:
train_step

当我从 train_step() 函数中删除 @tf.function 装饰器时，该错误已得到修复，但理想情况下，我希望使用 tensorflow 图形执行而不是急切执行，因为它可以显着提高我的代码速度。

任何想法我做错了什么？

【问题讨论】：

标签： python tensorflow tensorflow2.0

【解决方案1】：

嗯，这是因为你改变了优化器本身，而不是它的学习率。这会导致@tf.function 出现一些问题。

所以不要通过改变整个优化器来改变优化器的lr，你可以使用下面的代码来实现：

optimzer.lr = lr

希望对您有所帮助！

【讨论】：