'Generates class probability predictions for the input samples.
The input samples are processed 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.'

def predict_proba(self, x, batch_size=32, verbose=1):

    preds = self.predict(x, batch_size, verbose)
    if ((preds.min() < 0.0) or (preds.max() > 1.0)):
        warnings.warn('Network    returning    invalid    probability    values.    The    last    layer    might    not    normalize    predictions    into    probabilities    (like    softmax    or    sigmoid    would).')
    return preds
