Hardmax - 1 vs 11#

Next section compares an older to a newer version of the same operator after both definition are converted into markdown text. Green means an addition to the newer version, red means a deletion. Anything else is unchanged.

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  1. Hardmax1 → Hardmax11 +6 -7
Hardmax1 → Hardmax11 RENAMED
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  The operator computes the hardmax (1 for the first maximum value, and 0 for all others) values for each layer in the batch
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+ of the given input. The input is a 2-D tensor (Tensor<float>) of size
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+ (batch_size x input_feature_dimensions). The output tensor has the same shape
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- of the given input.
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+ and contains the hardmax values of the corresponding input.
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- The input does not need to explicitly be a 2D vector; rather, it will be
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+ Input does not need to explicitly be a 2D vector; rather, it will be
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  coerced into one. For an arbitrary n-dimensional tensor
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  input in [a_0, a_1, ..., a_{k-1}, a_k, ..., a_{n-1}] and k is
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  the axis provided, then input will be coerced into a 2-dimensional tensor with
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  dimensions [a_0 * ... * a_{k-1}, a_k * ... * a_{n-1}]. For the default
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  case where axis=1, this means the input tensor will be coerced into a 2D tensor
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  of dimensions [a_0, a_1 * ... * a_{n-1}], where a_0 is often the batch size.
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  In this situation, we must have a_0 = N and a_1 * ... * a_{n-1} = D.
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  Each of these dimensions must be matched correctly, or else the operator
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+ will throw errors.
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- will throw errors. The output tensor has the same shape
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- and contains the hardmax values of the corresponding input.
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  **Attributes**
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  * **axis**:
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  Describes the axis of the inputs when coerced to 2D; defaults to one
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- because the 0th axis most likely describes the batch_size. Negative
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+ because the 0th axis most likely describes the batch_size
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- value means counting dimensions from the back. Accepted range is
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- [-r, r-1] where r = rank(input).
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  **Inputs**
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  * **input** (heterogeneous) - **T**:
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  The input tensor that's coerced into a 2D matrix of size (NxD) as
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  described above.
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  **Outputs**
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  * **output** (heterogeneous) - **T**:
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  The output values with the same shape as input tensor (the original
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  size without coercion).
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  **Type Constraints**
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  * **T** in (
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  tensor(double),
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  tensor(float),
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  tensor(float16)
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  ):
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  Constrain input and output types to float tensors.