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View Code? Open in Web Editor NEWA small, GPU enabled tensor library for C++.
License: GNU Lesser General Public License v3.0
A small, GPU enabled tensor library for C++.
License: GNU Lesser General Public License v3.0
The current code does not make use of processing batches of multiple operations while broadcasting, even if it is easy to do so.
A multi-dimensional tensor shall be transformed into a lower-dimensional tensor by "flattening" a range of dimensions.
For example a tensor of shape (3, 3, 3) may be transformed into a tensor of shape (3, 9) by flattening the last two dimensions.
A two-dimensional convolution layer with support for different kernel size, stride and padding configurations.
Implement a SIFT feature detector.
https://en.wikipedia.org/wiki/Scale-invariant_feature_transform
The sigmoid activation function.
A 2D pooling layer supporting maximum and average pooling.
All shape and stride members are currently some variation of an std::vector, which uses heap allocation. Because most tensors only have a relatively small number of dimensions (often smaller than ten), a small vector optimized for such a case may improve performance.
A Rectified Linear Unit implementation.
A fully connected layer of neurons.
Implement the Harris corner detector.
Load models from ONNX formatted files.
Some calls, especially OpenCL ones, need to be checked for correctness when the memory strides are shuffled, for example after a transpose or permute call.
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