在Keras中调用model.fit时无法从损失函数运行打印语句

Question

I have created a custom loss function called

def customLoss(true, pred) //do_stuff //print(variables) return loss

Now I'm calling compile as model.compile(optimizer='Adamax', loss = customLoss)

EDIT: I tried tf.Print and this is my result.

    def customLoss(params):

        def lossFunc(true, pred):
            true = tf.Print(true, [true.shape],'loss-func') #obviously this won't work because the tensors aren't the same shape; however, this is what I want to do.
            #stuff
            return loss
        return lossFunc

    model = Model(inputs=[inputs], outputs=[outputs])
    parallel_model = multi_gpu_model(model, gpus=8)
    parallel_model.compile(opimizer='Adam', loss = customLoss(params), metrics = [mean_iou)
    history = parallel_model.fit(X_train, Y_train, validation_split=0.25, batch_size = 32, verbose=1)

and the output is

Epoch 1/10
1159/1159 [==============================] - 75s 65ms/step - loss: 0.1051 - mean_iou: 0.4942 - val_loss: 0.0924 - val_mean_iou: 0.6933
Epoch 2/10
1152/1159 [============================>.] - ETA: 0s - loss: 0.0408 - mean_iou: 0.7608

The print statements still aren't printing. Am I missing something - are my inputs into tf.Print not proper?

Answer 1

It's not because Keras dumps buffers or does magic, it simply doesn't call them! The loss function is called once to construct the computation graph and then the symbolic tensor that represents the loss value is returned. Tensorflow uses that to compute the loss, gradients etc.

You might instead be interested tf.Print that is null operation with a side effect that prints the arguments passed. Since tf.Print is part of the computation graph it will be run when training as well. From the documentation:

Prints a list of tensors. This is an identity op (behaves like tf.identity) with the side effect of printing data when evaluating.