If you look at the shape of the array before and after, you see that before it was (4,) and after it is (1, 4) (when second parameter is 0) and (4, 1) (when second parameter is 1). So a 1 was inserted in the shape of the array at axis 0 or 1, depending on the value of the second parameter. That is opposite of np.squeeze() (nomenclature borrowed from … Read more
nn.Embedding holds a Tensor of dimension (vocab_size, vector_size), i.e. of the size of the vocabulary x the dimension of each vector embedding, and a method that does the lookup. When you create an embedding layer, the Tensor is initialised randomly. It is only when you train it when this similarity between similar words should appear. Unless you … Read more
The view function is meant to reshape the tensor. Say you have a tensor a is a tensor that has 16 elements from 1 to 16(included). If you want to reshape this tensor to make it a 4 x 4 tensor then you can use Now a will be a 4 x 4 tensor. Note that after the reshape the total number of … Read more
In your example you are treating output [0, 0, 0, 1] as probabilities as required by the mathematical definition of cross entropy. But PyTorch treats them as outputs, that don’t need to sum to 1, and need to be first converted into probabilities for which it uses the softmax function. So H(p, q) becomes: Translating the output [0, 0, 0, 1] into … Read more
Stacking requires same number of dimensions. One way would be to unsqueeze and stack. For example:
stack Concatenates sequence of tensors along a new dimension. cat Concatenates the given sequence of seq tensors in the given dimension. So if A and B are of shape (3, 4), torch.cat([A, B], dim=0) will be of shape (6, 4) and torch.stack([A, B], dim=0) will be of shape (2, 3, 4).
The following worked for me. First install MKL: After this, install pytorch and torchvision: