Usage of torch.squeeze () and torch.unsqueeze () in pytorch learning
The usage of squeeze is mainly to compress or decompress the dimensions of the data.
Look firsttorch.squeeze() This function mainly compresses the dimensions of the data and removes the dimension with a dimension of 1, such as a row or a column. One row and three columns (1, 3) of the number removes the first dimension with a dimension of one. Becomes line (3). Squeeze (a) is to delete all the dimensions of 1 in a. Dimensions other than 1 have no effect. a.squeeze (N) is to remove the dimension specified in a as one. Another form is b = torch.squeeze (a, N) a remove the specified dimension as a dimension.
Look againtorch.unsqueeze()This function is mainly to expand the data dimension. Add a dimension of one dimension to the specified position. For example, there are three rows of data (3). Adding one dimension to the position of 0 becomes one row and three columns (1, 3). a.squeeze (N) is to add a dimension with dimension 1 to the specified position N in a. Another form is b = torch.squeeze (a, N) a is a specified position N in a plus a dimension with a dimension of 1
Look at Liezi below:
Reference materials:
- Pytorch common function analysis (1)
- numpy.squeeze
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