base.py 文件源码

def _optim(self, xys):
        idx = np.arange(len(xys))
        self.batch_size = np.ceil(len(xys) / self.nbatches)
        batch_idx = np.arange(self.batch_size, len(xys), self.batch_size)

        for self.epoch in range(1, self.max_epochs + 1):
            # shuffle training examples
            self._pre_epoch()
            shuffle(idx)

            # store epoch for callback
            self.epoch_start = timeit.default_timer()

            # process mini-batches
            for batch in np.split(idx, batch_idx):
                # select indices for current batch
                bxys = [xys[z] for z in batch]
                self._process_batch(bxys)

            # check callback function, if false return
            for f in self.post_epoch:
                if not f(self):
                    break