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What is loss and Val loss?
Your “loss” is the value of your loss function (unknown as you do not show your code) Your “acc” is the value of your metrics (in this case accuracy) The val_* simply means that the value corresponds to your validation data.
What is Val loss?
val_loss is the value of cost function for your cross-validation data and loss is the value of cost function for your training data.
Why validation loss is lower than training loss?
Reason #2: Training loss is measured during each epoch while validation loss is measured after each epoch. The second reason you may see validation loss lower than training loss is due to how the loss value are measured and reported: Training loss is measured during each epoch.
What is difference between loss and validation loss?
One of the most widely used metrics combinations is training loss + validation loss over time. The training loss indicates how well the model is fitting the training data, while the validation loss indicates how well the model fits new data.
What is the difference between Val _ loss and loss?
val_lossis the value of cost function for your cross-validation data and loss is the value of cost function for your training data. On validation data, neurons using drop out do not drop random neurons. The reason is that during training we use drop out in order to add some noise for avoiding over-fitting.
How to avoid over fitting in Val _ loss?
Namely, I just run 100 epoch, and then select smallest val_loss or biggest val_acc using following command: Over-fitting is inevitable once your model is still too powerful. This is a trade-off problem: Using less powerful model and easy to prevent over-fitting, however, you might get worse performance.
Why does Val _ loss don’t converge after 20 epoch?
The accuracy is very good, but I found a interesting thing: although val_acc is improving, val_loss don’t converge, more badly, after 20 epoch, the val_loss is larger than val_loss at first epoch. I have attached my training log, please check! Could you tell me the reason why this happen? Thanks in advance! It’s due to over-fitting.
Why are keras loss and Val loss decreasing?
I am trying to train a LSTM model, but the problem is that the loss and val_loss are decreasing from 12 and 5 to less than 0.01, but the training set acc = 0.024 and validation set acc = 0.0000e+00 and they remain constant during the training. Why this happening and how can I fix it?