srch

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 ...

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 …

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.

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 …

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 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.

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.

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

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, ...

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?

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 ...

[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 ...

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 ...

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

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 .