[argonaut]

Two major errors: 1) This doesn't seem to be controlling overfitting on the right validation set. 2) There isn't a test set at all (separate from validation).

Using Keras' "validation_split" parameter will just randomly select a validation set. This is not the right thing to do when your data is image sequences, because you will get essentially identical data in training and validation.

Because of this, the numbers/plot here might as well be training accuracy numbers.

[mhanus]

Keras uses the end of the data set as validation, and only randomizes it if the "shuffle" argument is set to True [1].

[1]: https://keras.io/getting-started/faq/#how-is-the-validation-...

[argonaut]

Except the second half of the data is the flipped of the first half (X = np.concatenate([X, X_flipped]))

[wroscoe]

Well shit. Thanks for pointing that out. I'll revise.