如何为 keras 模型使用 tensorflow 自定义损失? [英] How to use tensorflow custom loss for a keras model?

问题描述

我正在尝试通过使用中间层的表示来实现损失函数.据我所知，Keras 后端自定义损失函数只接受两个输入参数(y_ture 和 y-pred).如何使用 @tf.function 定义损失函数并将其用于已通过 Keras 定义的模型?任何帮助将不胜感激.

I'm trying to implement a loss function by using the representations of the intermediate layers. As far as I know, the Keras backend custom loss function only accepts two input arguments(y_ture, and y-pred). How can I define a loss function with @tf.function and use it for a model that has been defined via Keras? any help would be appreciated.

推荐答案

这是一个将额外变量传递给损失函数的简单解决方法.在我们的例子中，我们传递了其中一层 (x1) 的隐藏输出.这个输出可以用来在损失函数内部做一些事情(我做了一个虚拟操作)

this a simple workaround to pass additional variables to your loss function. in our case, we pass the hidden output of one of our layers (x1). this output can be used to do something inside the loss function (I do a dummy operation)

def mse(y_true, y_pred, hidden):
    error = y_true-y_pred
    return K.mean(K.square(error)) + K.mean(hidden)


X = np.random.uniform(0,1, (1000,10))
y = np.random.uniform(0,1, 1000)

inp = Input((10,))
true = Input((1,))
x1 = Dense(32, activation='relu')(inp)
x2 = Dense(16, activation='relu')(x1)
out = Dense(1)(x2)

m = Model([inp,true], out)
m.add_loss( mse( true, out, x1 ) )
m.compile(loss=None, optimizer='adam')
m.fit(x=[X, y], y=None, epochs=3)

## final fitted model to compute predictions
final_m = Model(inp, out)

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