Deep Learning Quick Reference

Measuring the performance of our model

Now that our MLP has been trained, we can start to understand how good it is. I'll make a prediction on our Train, Val, and Test datasets to do so. The code for the same is as follows:

print("Model Train MAE: " + str(mean_absolute_error(data["train_y"], model.predict(data["train_X"]))))print("Model Val MAE: " + str(mean_absolute_error(data["val_y"], model.predict(data["val_X"]))))print("Model Test MAE: " + str(mean_absolute_error(data["test_y"], model.predict(data["test_X"]))))

For our MLP, this is how well we did:

Model Train MAE: 0.190074701809Model Val MAE: 0.213255747475 Model Test MAE: 0.199885450841

Keep in mind that our data has been scaled to 0 mean and unit variance. The Train MAE is 0.19, and ...