Softmax issue 46 #56
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Softmax issue 46 #56
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This should allow to implement axis!=1
By now, only the simple example is passing the tests.
The problem is related to the comment in the ONNX definition:
Input does not need to explicitly be a 2D vector; rather, it will be coerced into one. For an arbitrary n-dimensional tensor input \in [a_0, a_1, ..., a_{k-1}, a_k, ..., a_{n-1}] and k is the axis provided, then input will be coerced into a 2-dimensional tensor with dimensions [a_0 * ... * a_{k-1}, a_k * ... * a_{n-1}]. For the default case where axis=1, this means the input tensor will be coerced into a 2D tensor of dimensions [a_0, a_1 * ... * a_{n-1}], where a_0 is often the batch size. In this situation, we must have a_0 = N and a_1 * ... * a_{n-1} = D. Each of these dimensions must be matched correctly, or else the operator will throw errors.
owulveryck
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Sep 29, 2019
* feat: skeleton for softmax
* wip: Softmax is roughly a copy/paste from Gorgonia
This should allow implementing axis!=1
By now, only the simple example is passing the tests.
The problem is related to the comment in the ONNX definition:
Input does not need to explicitly be a 2D vector; rather, it will be coerced into one. For an arbitrary n-dimensional tensor input \in [a_0, a_1, ..., a_{k-1}, a_k, ..., a_{n-1}] and k is the axis provided, then input will be coerced into a 2-dimensional tensor with dimensions [a_0 * ... * a_{k-1}, a_k * ... * a_{n-1}]. For the default case where axis=1, this means the input tensor will be coerced into a 2D tensor of dimensions [a_0, a_1 * ... * a_{n-1}], where a_0 is often the batch size. In this situation, we must have a_0 = N and a_1 * ... * a_{n-1} = D. Each of these dimensions must be matched correctly, or else the operator will throw errors.
* chore: replaced div with hadamarddiv
* test: this is a stable version of the Softmax but it still fails
* fix: if the root is nil, gracefully return an error
* fix: return an error is the node is not a tensor nor an operation
* feat: add a local test for the softmax operator
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