What happens to the loss after an epoch?
But I noted that the Loss, Val_loss, Mean absolute value and Val_Mean absolute value are not changed after some epochs. My loss was at 0.05 but after some epoch it went up to 15 , even with a raw SGD. High epoch dint effect with Adam but only with SGD optimiser.
How can I test my LSTM without validation?
You would agree to test your data: first compute the Bayes error rate using a KNN (use the trick regression in case you need), in this way you can check whether the input data contain all the information you need. Then try the LSTM without the validation or dropout to verify that it has the ability to achieve the result for you necessary.
How does loss increase after some epochs in keras?
My loss was at 0.05 but after some epoch it went up to 15 , even with a raw SGD. High epoch dint effect with Adam but only with SGD optimiser. Hi thank you for your explanation.
How many epochs does it take to reach 100% accuracy?
And here are the loss&accuracy during the training: (Note that the accuracy actually does reach 100% eventually, but it takes around 800 epochs.) I thought that these fluctuations occur because of Dropout layers / changes in the learning rate (I used rmsprop/adam), so I made a simpler model:
Why does cross entropy loss increase with time?
I am using cross entropy loss and my learning rate is 0.0002. Update: It turned out that the learning rate was too high. With low a low enough learning rate I dont observe this behaviour. However I still find this peculiar. Any good explanations are welcome as to why this happens
When does loss increase in a CIFAR10 network?
The network starts out training well and decreases the loss but after sometime the loss just starts to increase. I have shown an example below: I have tried this on different cifar10 architectures I have found on githubs.