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What is the difference between predict and predict_class functions in keras?
Why does Model object don't have predict_class function?
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Keras model predicts is the method of function provided in Keras that helps in the predictions of output depending on the specified samples of input to the model.
We can predict the class for new data instances using our finalized classification model in Keras using the predict_classes() function. Note that this function is only available on Sequential models, not those models developed using the functional API.
To train, we will use the 'fit()' function on our model with the following five parameters: training data (train_X), target data (train_y), validation split, the number of epochs and callbacks.
predict will return the scores of the regression and predict_class will return the class of your prediction. Although it seems similar, there are some differences:
Imagine you are trying to predict if the picture is a dog or a cat (you have a classifier):
predict will return you: 0.6 cat and 0.4 dog (for example).predict_class will return the index of the class having maximum value. For example, if cat is 0.6 and dog is 0.4, it will return 0 if the class cat is at index 0)Now, imagine you are trying to predict house prices (you have a regressor):
predict will return the predicted pricepredict_class will not make sense here since you do not have a classifierTL:DR: use predict_class for classifiers (outputs are labels) and use predict for regressions (outputs are non-discrete)
Hope it helps!
For your second question, the answer is here
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