How to understand loss acc val_loss val_acc in Keras model fitting

Question

I'm new on Keras and have some questions on how to understanding my model results. Here is my result:(for your convenience, I only paste the loss acc val_loss val_acc after each epoch here)

Train on 4160 samples, validate on 1040 samples as below:

Epoch 1/20
4160/4160 - loss: 3.3455 - acc: 0.1560 - val_loss: 1.6047 - val_acc: 0.4721

Epoch 2/20
4160/4160 - loss: 1.7639 - acc: 0.4274 - val_loss: 0.7060 - val_acc: 0.8019

Epoch 3/20
4160/4160 - loss: 1.0887 - acc: 0.5978 - val_loss: 0.3707 - val_acc: 0.9087

Epoch 4/20
4160/4160 - loss: 0.7736 - acc: 0.7067 - val_loss: 0.2619 - val_acc: 0.9442

Epoch 5/20
4160/4160 - loss: 0.5784 - acc: 0.7690 - val_loss: 0.2058 - val_acc: 0.9433

Epoch 6/20
4160/4160 - loss: 0.5000 - acc: 0.8065 - val_loss: 0.1557 - val_acc: 0.9750

Epoch 7/20
4160/4160 - loss: 0.4179 - acc: 0.8296 - val_loss: 0.1523 - val_acc: 0.9606

Epoch 8/20
4160/4160 - loss: 0.3758 - acc: 0.8495 - val_loss: 0.1063 - val_acc: 0.9712

Epoch 9/20
4160/4160 - loss: 0.3202 - acc: 0.8740 - val_loss: 0.1019 - val_acc: 0.9798

Epoch 10/20
4160/4160 - loss: 0.3028 - acc: 0.8788 - val_loss: 0.1074 - val_acc: 0.9644

Epoch 11/20
4160/4160 - loss: 0.2696 - acc: 0.8923 - val_loss: 0.0581 - val_acc: 0.9856

Epoch 12/20
4160/4160 - loss: 0.2738 - acc: 0.8894 - val_loss: 0.0713 - val_acc: 0.9837

Epoch 13/20
4160/4160 - loss: 0.2609 - acc: 0.8913 - val_loss: 0.0679 - val_acc: 0.9740

Epoch 14/20
4160/4160 - loss: 0.2556 - acc: 0.9022 - val_loss: 0.0599 - val_acc: 0.9769

Epoch 15/20
4160/4160 - loss: 0.2384 - acc: 0.9053 - val_loss: 0.0560 - val_acc: 0.9846

Epoch 16/20
4160/4160 - loss: 0.2305 - acc: 0.9079 - val_loss: 0.0502 - val_acc: 0.9865

Epoch 17/20
4160/4160 - loss: 0.2145 - acc: 0.9185 - val_loss: 0.0461 - val_acc: 0.9913

Epoch 18/20
4160/4160 - loss: 0.2046 - acc: 0.9183 - val_loss: 0.0524 - val_acc: 0.9750

Epoch 19/20
4160/4160 - loss: 0.2055 - acc: 0.9120 - val_loss: 0.0440 - val_acc: 0.9885

Epoch 20/20
4160/4160 - loss: 0.1890 - acc: 0.9236 - val_loss: 0.0501 - val_acc: 0.9827

Here are my understandings:

1. The two losses (both loss and val_loss) are decreasing and the tow acc (acc and val_acc) are increasing. So this indicates the modeling is trained in a good way.

2. The val_acc is the measure of how good the predictions of your model are. So for my case, it looks like the model was trained pretty well after 6 epochs, and the rest training is not necessary.

My Questions are:

1. The acc (the acc on training set) is always smaller, actually much smaller, than val_acc. Is this normal? Why this happens?In my mind, acc should usually similar to better than val_acc.

2. After 20 epochs, the acc is still increasing. So should I use more epochs and stop when acc stops increasing? Or I should stop where val_acc stops increasing, regardless of the trends of acc?

3. Is there any other thoughts on my results?

Thanks!

Answer 1

1. The training loss is that the average of the losses over every batch of training knowledge. Because your model is changing over time, the loss over the first batches of an epoch is generally higher than over the last batches. On the other hand, the testing loss for an epoch is computed using the model because it is at the tip of the epoch, leading to a lower loss.
2. Training should be stopped when val_acc stops increasing, otherwise, your model will probably overfit. You can use an early stopping callback to stop training.
3. Your model seems to achieve very good results.

This could be understood in a wider depth when studies types of machine learning.