keras_utils.py 文件源码

def reset(self):
        """ Resets the state of the generator"""
        self.step = 0
        Y = np.argmax(self.Y,1)
        labels = np.unique(Y)
        idx = []
        smallest = len(Y)
        for i,label in enumerate(labels):
            where = np.where(Y==label)[0]
            if smallest > len(where): 
                self.slabel = i
                smallest = len(where)
            idx.append(where)
        self.idx = idx
        self.labels = labels
        self.n_per_class = int(self.batch_size // len(labels))
        self.n_batches = int(np.ceil((smallest//self.n_per_class)))+1
        self.update_probabilities()