validation loss increasing after first epoch

Print the validation loss in each epoch in PyTorch - STACKOOM 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! Difference between Loss, Accuracy, Validation loss, Validation accuracy (Getting increasing loss and stable accuracy could also be caused by good predictions being classified a little worse, but I find it less likely because of this loss … Assuming the goal of a training is to minimize the loss. 3. Finding Good Learning Rate and The One Cycle Policy. In the example from the previous section, a default batch size of 32 across 500 examples results in 16 updates per epoch and 3,200 updates across the 200 epochs. But with val_loss (keras validation loss) and val_acc (keras … … This means that we need to pass the current epoch’s … In both of the previous examples—classifying text and predicting fuel efficiency—the accuracy of models on the validation data would peak after training for a number of epochs … But validation loss and validation acc decrease straight after the 2nd epoch itself. Initial model learning curve (starting from epoch 10) Our first model turned out to be quite a failure, we have horrendous overfitting on Training data and our Validation Loss is actually increasing after epoch 100. However, I am stuck in a bit weird situation. but the validation accuracy remains 17% and the validation loss becomes 4.5%. Loss Training loss not decrease after certain epochs. But validation loss and validation acc decrease straight after the 2nd epoch itself. The overall testing after training gives an accuracy around 60s. I've already cleaned, shuffled, down-sampled (all classes have 42427 number of data samples) and split the data properly to training (70%) / validation (10%) / testing (20%). you can use more data, Data augmentation techniques could help.

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