调用时无法从loss函数运行打印语句模型.拟合在喀拉斯

Question

我创建了一个名为

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

现在我调用compile asmodel.compile(optimizer='Adamax', loss = customLoss)

编辑：我试过了tf.打印这是我的结果。在

    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)

输出是

^{pr2}$

打印语句仍未打印。我是否遗漏了什么-我对tf.Print的输入不正确吗？在

Answer 1

这并不是因为Keras丢弃缓冲区或是施展魔法，它只是不调用它们！一次调用损失函数来构造计算图，然后返回表示损失值的符号张量。Tensorflow用它来计算损耗、梯度等

相反，您可能会对tf.Print感兴趣，这是一个空操作，其副作用是打印传递的参数。由于tf.Print是计算图的一部分，因此在训练时也会运行它。根据文件：

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