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The first dimension of arguments y_true and y_pred is always the same as batch size . Ex - If you are fitting data with a batch size of 32, and your neural net ...

The add_loss() API. Loss functions applied to the output of a model aren't the only way to create losses. When writing the call method of a custom layer or a subclassed model, you may want to compute scalar quantities that you want to minimize during training (e.g. regularization losses). You can use the add_loss() layer method to keep track of such loss terms.

The purpose of loss functions is to compute the quantity that a model should ... of size [batch_size] , then the total loss for each sample of the batch is ...

When implementing a custom loss function in Keras, I require a tf.Variable with the shape of the batch size of my input data (y_true, ...

how you can define your own custom loss function in Keras,; how to add sample weighing to create observation-sensitive losses,; how to avoid ...

I would say this highly depends on how you have your code set up. What type of model are you creating? Are you not setting batch-size as a global parameter?

The problem is that when compiling the model, you set x_true to be a static tensor, in the size of all the samples.

I am implementing a custom loss in keras, for example, a sum: def custom_loss(y_true, y_pred): K.sum(y_true, y_pred) Now, I want to normalize it by the batch size. Is it possible, to retrieve the batch size from y_true or y_pred?

This article should give you good foundations in dealing with loss functions, especially in Keras, implementing your own custom loss functions which you develop yourself or a researcher has already developed, and you are implementing that, their implementation using Keras a deep learning framework, avoiding silly errors such as repeating NaNs in your loss function, and how …

I smile a little, and decide to increase the batch size so that the training is more stable and it converges faster. BOOM! Loss function ...

09.11.2018 · While the input for keras loss functions are the y_true and y_pred, where each of them is of size [batch_size, :]. As I see it there are 2 options you can solve this, the first one is using a generator for creating the batches, in such a way that you will have control over which indices are evaluated each time, and at the loss function you could slice the x_true tensor to fit …

20.05.2020 · Now you can simply plug this loss function to your model. model.compile(loss=custom_mse, optimizer='adam') Note. I would advise you to use Keras backend functions instead of Numpy functions to avoid any misadventure. Keras backend functions work almost similar to Numpy functions.

05.11.2018 · Issue of batch sizes when using custom loss functions #11582. yuanzhangxiao opened this issue on Nov 5, 2018 · 1 comment. Labels. type:support. Comments. gabrieldemarmiesse added the type:support label on Nov 5, 2018. fchollet closed this on Jun 24. Sign up for free to join this conversation on GitHub .

loss calculation over different batch sizes in keras ... of the loss functions defined in losses.py file or it may be a user-defined custom loss function.

[10]. It seems that Keras is able to automatically adjust the size of the inputs to its own loss function base on the batch size, but cannot do ...