14.06.2020 · this is a workaround to pass additional arguments to a custom loss function, in your case an array of weights. the trick consists in using fake inputs which are useful to build and use the loss in the correct ways. don't forget that keras handles fixed batch dimension I provide a dummy example in a regression problem
This answer is not useful. Show activity on this post. I think the best solution is: add the weights to the second column of y_true and then: def custom_loss (y_true, y_pred) weights = y_true [:,1] y_true = y_true [:,0] That way it's sure to be assigned to the correct sample when they are shuffled. Note that the metric functions will need to be ...
This answer is not useful. Show activity on this post. I think the best solution is: add the weights to the second column of y_true and then: def custom_loss (y_true, y_pred) weights = y_true [:,1] y_true = y_true [:,0] That way it's sure to be assigned to the correct sample when they are shuffled. Note that the metric functions will need to be ...
Jul 20, 2019 · this is a workaround to pass additional arguments to a custom loss function, in your case an array of weights. the trick consists in using fake inputs which are useful to build and use the loss in the correct ways. don't forget that keras handles fix...
01.12.2021 · Use of Keras loss weights During the training process, one can weigh the loss function by observations or samples. The weights can be arbitrary but a typical choice are class weights (distribution of labels).
K.pow can take a sequence of exponents as argument. So you can compute the exponents first, as a tensor ([num_examples - 1, num_examples - 2, ..., 0]), ...
I provide this generator to the fit_generator function when training a model with Keras. For this model I have a custom cosine contrastive loss function,
I would like to set up a custom loss function in Keras that assigns a weight function depending on the predicted sign. If the predicted sign is positive, a sigmoid weight function should scale prediction errors between 1 (for the most negative prediction error) and 2 (most positive prediction error).
And gradients are used to update the weights. This is how a Neural Net is trained. Keras has many inbuilt loss functions, which I have covered in one of my ...
Here you can see the performance of our model using 2 metrics. The first one is Loss and the second one is accuracy. It can be seen that our loss function (which was cross-entropy in this example) has a value of 0.4474 which is difficult to interpret whether it is a good loss or not, but it can be seen from the accuracy that currently it has an accuracy of 80%.
15.09.2017 · def weighted_mse(yTrue,yPred): ones = K.ones_like(yTrue[0,:]) #a simple vector with ones shaped as (60,) idx = K.cumsum(ones) #similar to a 'range(1,61)' return K.mean((1/idx)*K.square(yTrue-yPred)) The use of ones_like with cumsum allows you to use this loss function to any kind of (samples,classes) outputs.
Related Questions · Defining a callable "loss" function · How do I get Tensorflow to calculate the gradients w.r.t. my loss function? · Custom Loss ...
20.07.2019 · this is a workaround to pass additional arguments to a custom loss function, in your case an array of weights. the trick consists in using fake inputs which are useful to build and use the loss in the correct ways. don't forget that keras handles fixed batch dimension I provide a dummy example in a regression problem xxxxxxxxxx 1
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 …
This can be achieved by updating the weights of a machine learning model using some algorithm such as Gradient Descent. Here you can see the weight that is ...
Jun 15, 2020 · this is a workaround to pass additional arguments to a custom loss function, in your case an array of weights. the trick consists in using fake inputs which are useful to build and use the loss in the correct ways. don't forget that keras handles fixed batch dimension. I provide a dummy example in a regression problem.