While things might seem very different at this point, training an LSTM is actually not any different than training a deep neural network on a typical cross-sectional problem:
LAGS=10df = read_data()df_train = select_dates(df, start="2017-01-01", end="2017-05-31")df_test = select_dates(df, start="2017-06-01", end="2017-06-30")X_train, X_test, y_train, y_test = prep_data(df_train, df_test, lags=LAGS)model = build_network(sequence_length=LAGS)callbacks = create_callbacks("lstm_100_100")model.fit(x=X_train, y=y_train, batch_size=100, epochs=10, callbacks=callbacks)model.save("lstm_model.h5")
After preparing our data, we instantiate a network with the architecture we've walked through and then call fit on it as expected.
Here I'm using ...
