Method Predict

src/model/feed_forward_net.cc:278–297

}

const Tensor FeedForwardNet::Predict(const Tensor& x, size_t batchsize) {
  CHECK_GE(x.shape(0), batchsize);
  int num_extra_samples = (int)(x.shape(0) % batchsize);
  const auto outshape = layers_.back()->GetOutputSampleShape();
  Tensor y(Shape{x.shape(0), Product(outshape)}, x.device());
  for (size_t b = 0; b < x.shape(0) / batchsize; b++) {
    int start = (int)(b * batchsize), end = (int)(start + batchsize);
    const Tensor bx = CopyRows(x, start, end);
    CopyDataToFrom(&y, PredictOnBatch(bx), batchsize * y.shape(1),
                   start * y.shape(1), 0);
  }
  if (num_extra_samples > 0) {
    int start = (int)(x.shape(0) - batchsize), end = (int)(x.shape(0));
    const Tensor bx = CopyRows(x, start, end);
    CopyDataToFrom(&y, PredictOnBatch(bx), num_extra_samples * y.shape(1),
                   (x.shape(0) - num_extra_samples) * y.shape(1),
                   (batchsize - num_extra_samples) * y.shape(1));
  }
  return y;
}

const Tensor FeedForwardNet::PredictOnBatch(const Tensor& x) {
  return Forward(kEval, x);