Contents
How does batch size affect loss?
higher batch sizes leads to lower asymptotic test accuracy. we can recover the lost test accuracy from a larger batch size by increasing the learning rate. starting with a large batch size doesn’t “get the model stuck” in some neighbourhood of bad local optimums.
What is epoch loss?
Epoch is the number of passes over the data. Loss is the error over the training set typically in terms of mean squared error (for regression) or log loss (for classification).
Why is Test loss lower than training loss?
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. While validation loss is measured after each epoch.
Why is training loss more than validation loss?
If your training loss is much lower than validation loss then this means the network might be overfitting . Solutions to this are to decrease your network size, or to increase dropout. For example you could try dropout of 0.5 and so on. If your training/validation loss are about equal then your model is underfitting.
Is it normal to have less validation loss than training loss?
In case I use mini-batch training losses fluctuate a lot, depending on the random choice of training data, and sometimes validation loss is less than training loss. Is this normal?
What is the effect of batch size on training dynamics?
Training loss and accuracy when the model is trained using different learning rates. Testing loss and accuracy when the model is trained using different learning rates. Orange curves: batch size 64, learning rate 0.01 (reference) Purple curves: batch size 1024, learning rate 0.01 (reference) Blue: batch size 1024, learning rate 0.1
Which is better training data or unknown data?
1) Your model performs better on the training data than on the unknown validation data. A bit of overfitting is normal, but higher amounts need to be regulated with techniques like dropout to ensure generalization. 2) Your model performs better on the validation data.
How is training loss calculated in Keras framework?
During training, frameworks like Keras will output the current training loss to the console. The loss is calculated as a moving average over all processed batches, meaning that in the early training stage when loss drops quickly the first batch of an epoch will have a much higher loss than the last.