Xlabel and ylabel are not shown
plt.figure(1)
plt.plot(history.history["loss"], "b", label="Mean Square Error of training")
plt.plot(history.history["val_loss"], "g", label="Mean Square Error of
validation")
plt.legend()
plt.xlabel("Epoche")
plt.ylabel("Mean Square Error")
plt.xlim(0,200)
plt.show()
plt.savefig(r"C:\Users\aheidari\Dropbox\Dissertation\primary hot water use
prediction\Diagrams\UnivariateTrainingerror.png", dpi=1200)
--
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Using the same data for both validation and prediction in Keras
I was reading a tutorial for time series prediction by Neural Networks. I found that this code have used the same test data in the following code for validation, and later also for prediction. history = model.fit(train_X, train_y, epochs=50, batch_size=72, validation_data=(test_X, test_y), verbose=2, shuffle=False) Does it mean that the validation and test data are the same, or there is a default percentage to split the data into validation and prediction? -- https://mail.python.org/mailman/listinfo/python-list
How Bidirectional Neural Networks could be used to predict future data while they need future data?!
In the following link I see that Bidirectional LSTM is used to predict the future data in time series. I know that a Bidirectional recurrent neural network use both past and future data, and therefore for predicting future data we need future data1! Can anyone explain me how they work when in a time series we are going to predict the future? https://machinelearningmastery.com/how-to-develop-lstm-models-for-time-series-forecasting/ -- https://mail.python.org/mailman/listinfo/python-list
