Diff for "PyTorchCheatSheet" - AD Teaching Wiki

Differences between revisions 1 and 4 (spanning 3 versions)

A short overview over the PyTorch operations most relevant for this course. The full documentation can be accessed here.

Operation	Description
torch.empty	Create a tensor of a specific shape with uninitialized data
torch.zeros	Create a tensor of a specific shape and fill it with zeros
torch.ones	Create a tensor of a specific shape and fill it with ones
torch.tensor	Create a tensor with the given data
torch.sparse_coo_tensor	Create a sparse tensor with the given indices and values
torch.matmul	Perform matrix multiplication
torch.nonzero	Find the indices where a tensor contains non-zero values
torch.sort	Return the sorted values and sorting permutation of a tensor
torch.stack	Concatenate a list of tensors along a new dimension
torch.cat	Concatenate a list of tensors along an existing dimension
torch.sum	Sum a tensor along one or more dimensions
torch.mean	Average a tensor along one or more dimensions
torch.norm	Calculate the norm of tensor along the given dimensions
torch.where	Return a new tensor containing values from one of two sources depending on a condition
torch.topk	Get top k largest or smallest values and their indices from a tensor
torch.sigmoid	Apply the sigmoid function to all values of a tensor
torch.softmax	Perform softmax normalization
torch.index_select	Select indices from a tensor along a specific dimension
torch.arange	Create a tensor from a range
torch.multinomial	Sample from a multinomial distribution

-  ⇤ ← Revision 1 as of 2023-12-21 14:51:09 → 
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+   ← Revision 4 as of 2024-01-30 10:53:54 → ⇥
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-|| [[https://pytorch.org/docs/stable/generated/torch.matmul.html#torch-matmul|torch.matmul]] || Matrix multiplication ||
+|| [[https://pytorch.org/docs/stable/generated/torch.empty.html|torch.empty]] || Create a tensor of a specific shape with uninitialized data ||
|| [[https://pytorch.org/docs/stable/generated/torch.zeros.html|torch.zeros]] || Create a tensor of a specific shape and fill it with zeros ||
|| [[https://pytorch.org/docs/stable/generated/torch.ones.html|torch.ones]] || Create a tensor of a specific shape and fill it with ones ||
|| [[https://pytorch.org/docs/stable/generated/torch.tensor.html|torch.tensor]] || Create a tensor with the given data ||
|| [[https://pytorch.org/docs/stable/generated/torch.sparse_coo_tensor.html|torch.sparse_coo_tensor]] || Create a sparse tensor with the given indices and values ||
|| [[https://pytorch.org/docs/stable/generated/torch.matmul.html|torch.matmul]] || Perform matrix multiplication ||
|| [[https://pytorch.org/docs/stable/generated/torch.nonzero.html|torch.nonzero]] || Find the indices where a tensor contains non-zero values ||
|| [[https://pytorch.org/docs/stable/generated/torch.sort.html|torch.sort]] || Return the sorted values and sorting permutation of a tensor ||
|| [[https://pytorch.org/docs/stable/generated/torch.stack.html|torch.stack]] || Concatenate a list of tensors along a new dimension ||
|| [[https://pytorch.org/docs/stable/generated/torch.cat.html|torch.cat]] || Concatenate a list of tensors along an existing dimension ||
|| [[https://pytorch.org/docs/stable/generated/torch.sum.html|torch.sum]] || Sum a tensor along one or more dimensions ||
|| [[https://pytorch.org/docs/stable/generated/torch.mean.html|torch.mean]] || Average a tensor along one or more dimensions ||
|| [[https://pytorch.org/docs/stable/generated/torch.norm.html|torch.norm]] || Calculate the norm of tensor along the given dimensions ||
|| [[https://pytorch.org/docs/stable/generated/torch.where.html|torch.where]] || Return a new tensor containing values from one of two sources depending on a condition ||
|| [[https://pytorch.org/docs/stable/generated/torch.topk.html|torch.topk]] || Get top k largest or smallest values and their indices from a tensor ||
|| [[https://pytorch.org/docs/stable/generated/torch.sigmoid.html|torch.sigmoid]] || Apply the sigmoid function to all values of a tensor ||
|| [[https://pytorch.org/docs/stable/generated/torch.softmax.html|torch.softmax]] || Perform softmax normalization ||
|| [[https://pytorch.org/docs/stable/generated/torch.index_select.html|torch.index_select]] || Select indices from a tensor along a specific dimension ||
|| [[https://pytorch.org/docs/stable/generated/torch.arange.html|torch.arange]] || Create a tensor from a range ||
|| [[https://pytorch.org/docs/stable/generated/torch.multinomial.html|torch.multinomial]] || Sample from a multinomial distribution ||