a81022ad75c4d35a928a19aad31df8829c3bc966,basenji/rnn.py,RNN,predict,#RNN#Any#Any#Any#,411

Before Change

                preds_batch = preds_batch[:,:,target_indexes]

            // accumulate predictions
            preds.append(preds_batch[:Nb])

            // next batch
            Xb, _, _, Nb = batcher.next()

        // reset batcher
        batcher.reset()

        // accumulate predictions
        preds = np.vstack(preds)

        return preds

After Change

        if target_indexes is not None:
            num_targets = len(target_indexes)

        preds = np.zeros((batcher.num_seqs, buf_len, num_targets), dtype="float16")

        si = 0

        // get first batch
        Xb, _, _, Nb = batcher.next()

        while Xb is not None:
            // update feed dict
            fd[self.inputs] = Xb

            // compute predictions
            preds_batch = sess.run(self.preds_op, feed_dict=fd)

            // filter for specific targets
            if target_indexes is not None:
                preds_batch = preds_batch[:,:,target_indexes]

            // accumulate predictions
            preds[si:si+Nb,:,:] = preds_batch[:Nb,:,:]

            // update sequence index
            si += Nb

Instances