Unsqueeze - 1 vs 11

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  1. Unsqueeze1 → Unsqueeze11 +14 -7
Unsqueeze1 → Unsqueeze11 RENAMED
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- Insert single-dimensional entries to the shape of a tensor.
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+ Insert single-dimensional entries to the shape of an input tensor (data).
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- Takes one required argument axes, a list of dimensions that will be inserted.
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+ Takes one required argument axes - which contains a list of dimension indices and this operator will insert a dimension of value 1 into the corresponding index of the output tensor (expanded).
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+
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- Dimension indices in axes are as seen in the output tensor. For example:
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+ For example:
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- Given a tensor such that tensor with shape [3, 4, 5], then
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+ Given an input tensor (data) of shape [3, 4, 5], then
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- Unsqueeze(tensor, axes=[0, 4]) has shape [1, 3, 4, 5, 1]
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+ Unsqueeze(data, axes=[0, 4]) outputs a tensor (expanded) containing same data as data but with shape [1, 3, 4, 5, 1].
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+
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+ The attribute axes should not contain any duplicate entries. It is an error if it contains duplicates.
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+ The rank of the output tensor (output_rank) is the rank of the input tensor (data) plus the number of values in axes.
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+ Each value in axes should be within the (inclusive) range [-output_rank , output_rank - 1].
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+ The order of values in axes does not matter and can come in any order.
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  **Attributes**
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  * **axes** (required):
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- List of non-negative integers, indicate the dimensions to be
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+ List of integers indicating the dimensions to be inserted. Negative
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+ value means counting dimensions from the back. Accepted range is
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- inserted
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+ [-r, r-1] where r = rank(expanded).
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  **Inputs**
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  * **data** (heterogeneous) - **T**:
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  Original tensor
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  **Outputs**
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  * **expanded** (heterogeneous) - **T**:
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  Reshaped tensor with same data as input.
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  **Type Constraints**
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  * **T** in (
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  tensor(bool),
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  tensor(complex128),
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  tensor(complex64),
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  tensor(double),
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  tensor(float),
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  tensor(float16),
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  tensor(int16),
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  tensor(int32),
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  tensor(int64),
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  tensor(int8),
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  tensor(string),
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  tensor(uint16),
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  tensor(uint32),
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  tensor(uint64),
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  tensor(uint8)
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  ):
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  Constrain input and output types to all tensor types.