Various way to adjust the model.
- Increase dense layer -> Training takes longer, but is more accurate.
- Remove Flatten() layer -> Get an error about the shape of the data.
- Revise the final output layer to be differed from classified category -> Get an error as soon as it finds an unexpected value.
- Add additional layers in the network -> No big impact due to simple data.
- More poches -> overfitting
- Don't normalize data
- Stop the training when reached a desired value
Stop the training when it reaches a certain condition, such as accuracy>0.5 or loss<0.5
- Implement a callback to stop training.
- Check the condition at the end of epoch.
- Because while data is not completed processed, the acc or loss may be up and down.
- Trigger the callback.
- Teach how to implement CNN & Pooling
- CNN concept:https://bit.ly/2UGa7uH.