- image classification - Why training loss is decreasing down too fast . . .
$\begingroup$ So your model is getting slightly overfit, becuase train loss is lower than the val loss You can look into techniques to avoid overfitting And at the end, validation set is your target If your model is doing satisfactory performance of val set, then training is successful
- Having issues with neural network training. Loss not decreasing
My loss is not reducing and training accuracy doesn't fluctuate much I'm guessing I have something wrong with the model Any advice is much appreciated! I get at least 91% accuracy using random forest My classes are extremely unbalanced so I attempted to adjust training weights based on the proportion of classes within the training data
- Training and Validation Loss in Deep Learning - GeeksforGeeks
Early Stopping: Stopping the training when the validation loss starts increasing while training loss continues to decrease Tackling Underfitting: Increase Model Complexity: Use a deeper or wider model architecture Train for More Epochs: If the model hasn’t had enough time to learn, training longer might help it capture more patterns
- How to reduce both training and validation loss without causing . . .
Deal with one problem at a time First Underfitting --> Increase the number of epochs and or data size Then overfitting --> Tune the regularization parameters
- How to correct unstable loss and accuracy during training?
Quick fix - use ReLU activation as described below Additionally, neural network does not care about accuracy, only about minimizing the loss value (which it tries to do most of the time) Say it predicts probabilities: [0 55, 0 55, 0 55, 0 55, 0 45] for classes [1, 1, 1, 1, 0] so it's accuracy is 100% but it's pretty uncertain
- deep learning - How to improve loss and avoid overfitting - Data . . .
There are a few things you can do to reduce over-fitting Use Dropout increase its value and increase the number of training epochs; Increase Dataset by using Data augmentation; Tweak your CNN model by adding more training parameters Reduce Fully Connected Layers Change the whole Model; Use Transfer Learning (Pre-Trained Models)
- neural networks - Training loss is decreasing very slowly while . . .
Decrease increase learning rate (l_rate) value; Decrease increase momentum (and set it to 0) Replace sigmoid with relu; but there still is the problem
- Training loss decrease slowly - PyTorch Forums
Training loss decrease slowly with different learning rate Optimizer used is adam I tried with different scheduling scheme but it follow the same I started with small dataset If i need to converge loss i have to go with larger number of epochs but its time consuming
- What should I do when my neural network doesnt learn?
The only way the NN can learn now is by memorising the training set, which means that the training loss will decrease very slowly, while the test loss will increase very quickly In particular, you should reach the random chance loss on the test set
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