Things on this page are fragmentary and immature notes/thoughts of the author. Please read with your own judgement!
The PyTorch doc Reproducibility has very detailed instructions on how to make your model training reproducible. Basically, you need the following code.
torch.manual_seed(args.seed)
np.random.seed(args.seed …Things on this page are fragmentary and immature notes/thoughts of the author. Please read with your own judgement!
https://en.wikipedia.org/wiki/Cosine_similarity
https://en.wikipedia.org/wiki/Jaccard_index
Things on this page are fragmentary and immature notes/thoughts of the author. Please read with your own judgement!
The split-by-leaf mode (grow_policy="lossguide") is not supported in distributed training,
which makes XGBoost4J on Spark much slower than LightGBM on Spark.
https://towardsdatascience.com/build-xgboost-lightgbm-models-on-large-datasets-what-are-the-possible-solutions-bf882da2c27d
https://xgboost …
There are multiple ways to convert a Tensor to a numpy array in PyTorch.
First,
you can call the method Tensor.numpy.
my_tensor.numpy()
Second,
you can use the function numpy.array.
import numpy as np
np.array(my_tensor)
It is suggested that you use the function numpy.array
to convert a Tensor to a numpy array.
The reason is that numpy.array is more generic.
You can also use it to convert other objects (e.g., PIL.Image)
to numpy arrays
while those objects might not have a method named numpy