Is there something like “keras.utils.to_categorical” in pytorch
def to_categorical(y, num_classes): """ 1-hot encodes a tensor """ return np.eye(num_classes, dtype='uint8')[y]
Thnx, it works great.
Is this the expected solution in pytorch to build categorical tensors?
Better yet, you can use the code directly from Keras: https://github.com/keras-team/keras/blob/master/keras/utils/np_utils.py#L9-L37
Dude, your code is awesome! How do you think up that.
amazing! I am still learning the advanced indexing.
How did that work? What kind of sorcery is that? How is that kind of indexing possible. Could you link me to a useful resource?
This code works! y is a 1D NumPy array holding the class number of the samples.
temp_outs = numpy.zeros((y.shape, numpy.unique(y).size), dtype=numpy.uint8) temp_outs[numpy.arange(y.shape), numpy.uint8(y)] = 1 y = temp_outs
import torch import torch.nn.functional as F x = torch.tensor([1, 0]) F.one_hot(x, num_classes=2)