I have been trying to revisit my python code for prediction on neural network and i realized after running the code that model.predict_classes is deprecated since 1st Jan 2021.
Kindly can you support me to know what i can use instead for my code ?
The code line is:
y_pred_nn = model.predict_classes(X_test)
The issue:
NameError
Traceback (most recent call last)
<ipython-input-11-fc1ddbecb622> in <module>
----> 1 print(y_pred_nn)
NameError: name 'y_pred_nn' is not defined
The best explanation of how to handle this is given at:
https://androidkt.com/get-class-labels-from-predict-method-in-keras/
First use model.predict() to extract the class probabilities. Then depending on the number of classes do the following:
Use a threshold to select the probabilities that will determine class 0 or 1
np.where(y_pred > threshold, 1,0)
For example use a threshold of 0.5
Select the class with the highest probability
np.argmax(predictions, axis=1)
Where you can have multiple output classes per example, use a threshold to select which labels apply.
y_pred = model.predict(x, axis=1)
[i for i,prob in enumerate(y_pred) if prob > 0.5]
If your model performs multi-class classification (e.g. if it uses a softmax last-layer activation) use:
np.argmax(model.predict(x), axis=-1)
If your model performs binary classification (e.g. if it uses a sigmoid last-layer activation) use:
(model.predict(X_test) > 0.5).astype("int32")
model.predict_classes is deprecated, as mentioned by @Kaveh, use model.predict() function instead.
np.argmax(model.predict(x_test), axis=-1)
To know more about model.predict() check this link.
model.predict_classes() provides the output classes for the parameter.
It has been removed from Keras for some reasons.
Instead, if you use model.predict(), you will end up getting output predictions for the input samples. It is mainly used for batch processing of large number of inputs. Though they are not the same thing, you will get an idea what you are aiming for.