如何通过更新变量来改变张量的输出? [英] How do I change the output from a tensor by updating a variable?

问题描述

我有一个依赖于某些变量的损失函数，然后我想更改该变量并获取更新的损失.损失是作为函数的输入给我的，所以我不能简单地将该行移到 tf.control_dependencies 下(这确实给了我想要的东西).那么我该如何在以后更新变量和损失呢?

I have a loss function which is dependant on some variable, then I want to change that variable and get the updated loss. The loss is given to me as an input to a function, so I can't simply move that line under tf.control_dependencies (which does give me what I want). So how do I go about updating the variable and loss afterwards?

X = tf.Variable(1.0)                     #Dummy parameters of a neural network
loss = (X+1.0)**2                        #Dummy Loss function given to me as input

add = tf.assign(X,X+1.0)                 #Me changing the parameters of the network
with tf.control_dependencies( [ add ] ):
    updated_loss = loss                  #Me wanting the updated loss
print(K.eval(updated_loss ))             #Me not getting the updated loss :(

推荐答案

使损失变量具有初始值时，并不能使函数每次调用时都重新评估损失

When you make the loss variable, you are giving it an initial value, this doesn't make it a function that will reevaluate the loss every time it is called.

我们可以创建一个损失函数，每次调用它来计算损失

We can make a loss function that is called to calculate the loss each time

我更改了下面的代码，以将值重新分配给loss tensorflow变量，而不是返回新变量.

I changed the code below to reassign a value to the loss tensorflow variable rather than returning a new variable.

import tensorflow as tf
import tensorflow.keras.backend as K

# Function that updates the loss
@tf.function
def update_loss(X, loss):
    loss.assign((X+1.0)**2)

def test():
    # Initialise a value of loss
    loss = tf.Variable(0.0)
    print(K.eval(loss))

    # Initialise variable
    X = tf.Variable(2.0)

    # Update loss
    update_loss(X, loss)
    print(K.eval(loss))

    # Change network by adding 1.0
    X.assign_add(1.0)

    # Update loss
    update_loss(X, loss)
    print(K.eval(loss))

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