python - Keras, output of model predict_proba

Question

In the docs, the predict_proba(self, x, batch_size=32, verbose=1) is

Generates class probability predictions for the input samples batch by batch.

and returns

A Numpy array of probability predictions.

Suppose my model is binary classification model, does the output is [a, b], for a is probability of class_0, and b is the probability of class_1?

Answer 1

Here the situation is different and somehow misleading, especially when you are comparing predict_proba method to sklearn methods with the same name. In Keras (not sklearn wrappers) a method predict_proba is exactly the same as a predict method. You can even check it here:

def predict_proba(self, x, batch_size=32, verbose=1):
        """Generates class probability predictions for the input samples
        batch by batch.
        # Arguments
            x: input data, as a Numpy array or list of Numpy arrays
                (if the model has multiple inputs).
            batch_size: integer.
            verbose: verbosity mode, 0 or 1.
        # Returns
            A Numpy array of probability predictions.
        """
        preds = self.predict(x, batch_size, verbose)
        if preds.min() < 0. or preds.max() > 1.:
            warnings.warn('Network returning invalid probability values. '
                          'The last layer might not normalize predictions '
                          'into probabilities '
                          '(like softmax or sigmoid would).')
        return preds

So - in a binary classification case - the output which you get depends on the design of your network:

• if the final output of your network is obtained by a single sigmoid output - then the output of predict_proba is simply a probability assigned to class 1.
• if the final output of your network is obtained by a two dimensional output to which you are applying a softmax function - then the output of predict_proba is a pair where [a, b] where a = P(class(x) = 0) and b = P(class(x) = 1).

This second method is rarely used and there are some theorethical advantages of using the first method - but I wanted to inform you - just in case.