please I'm trying to build an NLP classifier on top of BERT but I'm struggling with data imbalance. I'm looking for an implementation of weighted CategoricalCro
I am using XGBoost in order to do a sales forecasting. I need a custom objective function, as the value of the prediction depends on the sales price of an item.
I am building a multi-class Vision Transformer Network. When passing my values through my loss function, it always returns zero. My output layer consisits of 37
I'm trying to use a modified version of this custom loss and I'm getting the error below InvalidArgumentError: The second input must be a scalar, but it has sh
I want to implement Pytorch Faster-RCNN module on a custom dataset that I curated and labelled. The implementation detail looks straightforward, there was a dem
I am dealing with a CNN and I get the following error on the line loss = criterion(outputs, data_y): Here is the relevant code snippet: def run(model, X_train,
I try to pass 2 loss functions to a model as Keras allows that. loss: String (name of objective function) or objective function or Loss instance. See losses. I
Is there a way to get the loss of the model, with it's current weights, without running evaluate, or fit, on it? model = keras.Sequential([ keras.layers.In
I want to predict the center of the pupil from an image. so I used a CNN with 3 Dence layer. so the input is an image and the output is a coordinate (X,Y). my m
I am relatively new to PyTorch and Huggingface-transformers and experimented with DistillBertForSequenceClassification on this Kaggle-Dataset. from transformers