how to decrease validation loss in cnn

2- the model you are using is not suitable (try two layers NN and more hidden units) 3- Also you may want to use less . I could notice that the training and validation accuracy started to converge towards . Reduce network complexity 2. Shuffle the dataset. How did the Deep Learning model achieve 100% accuracy? Why is my validation loss lower than my training loss? Of course these mild oscillations will naturally occur (that's a different discussion point). What does that signify? The training loss is very smooth. After the final iteration it displays a validation accuracy of above 80% but then suddenly it dropped to 73% without an iteration. Add BatchNormalization ( model.add (BatchNormalization ())) after each layer. Validation of Convolutional Neural Network Model - javatpoint You can investigate these graphs as I created them using Tensorboard. %set training dataset folder. Generally speaking that's a much bigger problem than having an accuracy of 0.37 (which of course is also a problem as it implies a model that does worse than a simple coin toss). Validation loss value depends on the scale of the data. Try the following tips- 1. To address overfitting, we can apply weight regularization to the model. How to Choose Loss Functions When Training Deep Learning Neural Networks The training loss will always tend to improve as training continues up until the model's capacity to learn has been saturated. After reading several other discourse posts the general solution seemed to be that I should reduce the learning rate. To check, you can see how is your validation loss defined and how is the scale of your input and think if that makes sense. Training loss is decreasing while validation loss is NaN So we need to extract folder name as an label and add it into the data pipeline. The curve of loss are shown in the following figure: It also seems that the validation loss will keep going up if I train the model for more epochs. Cite 2 Recommendations. Mein CNN erzeugt einen volatilen Validation_loss und konvergiert nicht ... Maybe your solution could be helpful for me too. Try data generators for training and validation sets to reduce the loss and increase accuracy. how to decrease validation loss in cnn - marearesort.com MixUp did not improve the accuracy or loss, the result was lower than using CutMix.

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