So I have a GRU model that predict output power. For the training data I have a csv file which has data from 2018, while for my testting data it is a different csv file which has data from 2019.
I just had to short questions.
Since I'm using 2 different csv files one for testing and one for training, I do not need to train_test_split
?
When it comes to model.fit, I really don't know the difference between Validation_data
and Validation_split
and which one should I use?
I have tested these 3 lines seperately, the 2nd and 3rd line give me the same exact results , while the first gives me way lower val_loss
.
Thank you.
history=model.fit(X_train, y_train, batch_size=256, epochs=25, validation_split=0.1, verbose=1, callbacks=[TensorBoardColabCallback(tbc)])
history=model.fit(X_train, y_train, batch_size=256, epochs=25, validation_data=(X_test, y_test), verbose=1, callbacks=[TensorBoardColabCallback(tbc)])
history=model.fit(X_train, y_train, batch_size=256, epochs=25, validation_data=(X_test, y_test), validation_split=0.1, verbose=1, callbacks=[TensorBoardColabCallback(tbc)])
train_test_split
if you wish. However, I would recommend you to merge them as you have data from different periods of time, there may be differences.validation_data
means you are providing the training set and validation set yourself, whereas using validation_split
means you only provide a training set and keras splits it into a training set and a validation set (with the validation set being validation_split
times the size of the training set)
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