Comments (1)
Sorry for the late reply!
Per the error message (which I assume is triggered here due to train_features
having 1772 rows)
predict_fn = predict.gradient_descent_mse_ensemble(kernel_fn, process_features(train_features, norm=True, conv1d=True),
train_labels.reshape((-1,1)), learning_rate=learning_rate)
our current implementation only supports batching when the batch size divides the number of training (and test/val) points.
I think the easiest workaround would be to
- Use the
nt.predict.gp_inferece
API which performs equivalent computation but acceptsk_train_train/test/val
covariance matrices as inputs. - Compute these input
k_train_train
(k_train_test
,k_train_val
) matrices by callingnt.batch(kernel_fn, batch_size=10)
on pairs oftrain, train
(train, test
,train, val
), where eachtrain
/test
/val
are first padded with dummy rows so that their sizes are divisible by 10, and the resulting matricesk_train_train
(k_train_test
,k_train_val
) are then truncated to remove the dummy covariance entries.
Sorry for the inconvenience, hope this helps!
from neural-tangents.
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