supervised_trainer.py 文件源码

def supervised_update(self):
        # array backend
        xp = cuda.cupy if self.gpu >= 0 else numpy

        # read data
        data = self.train_data_queues[0].get()
        vx = tuple([chainer.Variable(xp.asarray(data[k]))
                    for k in data.keys() if 'data' in k])
        vt = tuple([chainer.Variable(xp.asarray(data[k]))
                    for k in data.keys() if 'target' in k])

        # forward and update
        self.optimizer.update(self.optimizer.target, vx, vt)

        # get result
        res = {'loss': float(self.optimizer.target.loss.data)}
        if self.optimizer.target.accuracy is not None:
            res['accuracy'] = float(self.optimizer.target.accuracy.data)
        return res