New answers tagged loss-function
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The problem was I didn't one-hot encode my labels, so my model did not train correctly.
I.E I've given my model the label [4] instead of [0,0,0,0,1,0,0] (because there are 7 classes and the label is the fifth)
I found out when I switched from using a generator to numpy array, then tensorflow prompt me with an error.
I don't understand why didn't tensorflow ...
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One possibility is that DNNRegressor is a TensorFlow Estimator. TensorFlow Estimators have been deprecated because they "can behave unexpectedly". It might be better to explicitly define a model.
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One option is to create a custom score function that calculates the loss and groups by day.
Here is a rough start:
import numpy as np
from sklearn.metrics import make_scorer
from sklearn.model_selection import GridSearchCV
def custom_loss_function(model, X, y):
y_pred = clf.predict(X)
y_true = y
difference = y_pred-y_true
...
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Apparently the reason I was getting worse performance was because I was using cross validation during HP tuning but not when I built the base model. Hence the issue.
Another mistake was not scaling my data!
Typical noob mistakes!
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