com.intel.analytics.zoo.pipeline.api.autograd
Expand variable to configured size
Expand variable to configured size
target variable sizes, dim whose size is -1 will be ignored
Select an index of the input in the given dim and return the subset part.
Select an index of the input in the given dim and return the subset part. The batch dimension needs to be unchanged. The selected dim would be remove after this operation. For example, if input is: 1 2 3 4 5 6 Select(1, 1) will give output [2 5] Select(1, -1) will give output [3 6]
The dimension to select. 0-based index. Cannot select the batch dimension. -1 means the last dimension of the input.
The index of the dimension to be selected. 0-based index. -1 means the last dimension of the input.
A method used to initialize the Parameter.
A method used to initialize the Parameter. The default value is RandomUniform(-0.05, 0.05)
The init value for the Parameter
Shape of this Parameter
Same as Narrow in torch.
Same as Narrow in torch. Slice the input with the number of dimensions not being reduced. The batch dimension needs to be unchanged. For example, if input is: 1 2 3 4 5 6 slice(1, 1, 2) will give output 2 3 5 6 slice(1, 2, -1) will give output 3 6
The dimension to narrow. 0-based index. Cannot narrow the batch dimension. -1 means the last dimension of the input.
Non-negative integer. The start index on the given dimension. 0-based index.
The length to be sliced. Default is 1.
Delete the singleton dimension(s).
Delete the singleton dimension(s). The batch dimension needs to be unchanged. For example, if input has size (2, 1, 3, 4, 1): Squeeze(dim = 1) will give output size (2, 3, 4, 1) Squeeze(dims = null) will give output size (2, 3, 4)
It's true by default, meaning the value would be updated by gradient.
Parameters is trainable Variable and it can be treated as a constant value if trainable is set to be False.