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update
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2
train.py
2
train.py
@@ -104,7 +104,7 @@ train, test = supervised_values[0:-12], supervised_values[-12:]
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scaler, train_scaled, test_scaled = scale(train, test)
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# fit the model
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lstm_model = fit_lstm(train_scaled, 1, 300, 4)
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lstm_model = fit_lstm(train_scaled, 1, 30, 4)
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# forecast the entire training dataset to build up state for forecasting
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train_reshaped = train_scaled[:, 0].reshape(len(train_scaled), 1, 1)
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lstm_model.predict(train_reshaped, batch_size=1)
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