arrayiterator.py 文件源码

def __iter__(self):
        """
        Returns a new minibatch of data with each call.
        Yields:
            dictionary: The next minibatch
                samples[key]: numpy array with shape (batch_size, seq_len, feature_dim)
        """

        while self.current_iter < self.total_iterations:
            for batch_idx in range(self.batch_size):
                if self.shuffle:
                    strt_idx = self.start + (self.current_iter * self.stride)
                    seq_start = strt_idx + (batch_idx * self.nbatches * self.seq_len)
                else:
                    strt_idx = self.start + (self.current_iter * self.batch_size * self.stride)
                    seq_start = strt_idx + (batch_idx * self.stride)

                idcs = np.arange(seq_start, seq_start + self.seq_len) % self.ndata
                for key in self.data_arrays.keys():
                    self.samples[key][batch_idx] = self.data_arrays[key][idcs]

            self.current_iter += 1

            if self.reverse_target:
                self.samples[self.tgt_key][:] = self.samples[self.tgt_key][:, ::-1]

            if self.get_prev_target:
                self.samples['prev_tgt'] = np.roll(self.samples[self.tgt_key], shift=1, axis=1)

            if self.include_iteration is True:
                self.samples['iteration'] = self.index
            yield self.samples