keras_utils.py 文件源码

def next_normal(self):
        x_batch = self.X[self.step*self.batch_size:(self.step+1)*self.batch_size]
        y_batch = self.Y[self.step*self.batch_size:(self.step+1)*self.batch_size]

        diff = len(x_batch[0]) - self.cropsize
        if self.cropsize!=0 and not self.val:
            start = np.random.choice(np.arange(0,diff+5,5), len(x_batch))
            x_batch = [x[start[i]:start[i]+self.cropsize,:] for i,x in enumerate(x_batch)]
        elif self.cropsize !=0 and self.val:
            x_batch = [x[diff//2:diff//2+self.cropsize] for i,x in enumerate(x_batch)]

        x_batch = np.array(x_batch, dtype=np.float32)
        y_batch = np.array(y_batch, dtype=np.int32)
        self.step+=1
        if self.val:
            self.Y_last_epoch.extend(y_batch)
            return x_batch # for validation generator, save the new y_labels
        else:
            weights = np.ones(len(y_batch))
            for t in np.unique(np.argmax(y_batch,1)):
                weights[np.argmax(y_batch,1)==t] = self.c_weights[t]
            return (x_batch,y_batch)