It's a simple network with one convolution layer to classify cases with low or high risk of having breast cancer. Increase the Accuracy of Your CNN by Following These 5 Tips I Learned ... If I don't use loss_validation = torch.sqrt (F.mse_loss (model (factors_val), product_val)) the code works fine. python - reducing validation loss in CNN Model - Stack Overflow Validation loss value depends on the scale of the data. As mentioned about the peculiarity of the data set given, the performance of the model recorded 100% for all the training set, validation set and test set. Learning Curves in Machine Learning | Baeldung on Computer Science We will use the L2 vector norm also called weight decay with a regularization parameter (called alpha or lambda) of 0.001, chosen arbitrarily. About the changes in the loss and training accuracy, after 100 epochs, the training accuracy reaches to 99.9% and the loss comes to 0.28! In other words, our model would overfit to the training data. The key point to consider is that your loss for both validation and train is more than 1. As a result, you get a simpler model that will be forced to learn only the . What can I do if a validation error continuously increases? MixUp did not improve the accuracy or loss, the result was lower than using CutMix. In general, putting 80% of the data in the training set, 10% in the validation set, and 10% in the test set is a good split to start with. It returns a history of the training, useful for debugging & visualization. Mein CNN erzeugt einen volatilen Validation_loss und konvergiert nicht ... See an example showing validation and training cost (loss) curves: The cost (loss) function is high and doesn't decrease with the number of iterations, both for the validation and training curves; We could actually use just the training curve and check that the loss is high and that it doesn't decrease, to see that it's underfitting; 3.2. If your validation loss is lower than the training loss, it means you have not split the training data correctly. You should try to get more data, use more complex features or use a d. the . Dropout from anywhere between 0.5-0.8 after each CNN+dense+pooling layer Heavy data augmentation in "on the fly" in Keras Realising that perhaps I have too many free parameters: decreasing the network to only contain 2 CNN blocks + dense + output. I have done this twice (at the points marked . As part of the optimization algorithm, the error for the current state of the model must be estimated repeatedly. Let's dive into the three reasons now to answer the question, "Why is my validation loss lower than my training loss?". So we are doing as follows: Build temp_ds from cat images (usually have *.jpg) Add label (0) in train_ds. Use Early Stopping to Halt the Training of Neural Networks At the Right ...