Comments (5)
LinearNode
merely performs the matrix multiplication. The activation part is done separately. This is similar to what other neural network libraries / frameworks are doing. From what I have looked at, this looks like a good design choice. I am not sure if changing LinearNode
is a good idea. If it is necessary for convenience, you can easily do this in user code.
For example FFNet
sort of does this, but is more generalized. You can have a specialization of FFNet
and restrict it to what you have mentioned here.
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The linear node was just an example. I can't have the activations be external to the node for an LSTM.
Some gates (eg: memory gate) are dependent on activations of the previous gates and thus cannot be refactored outside of the node level.
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Fair enough. I have not yet started looking into LSTMs. I am currently implementing some core functionality necessary for convolutional nets in ArrayFire. I will keep this in mind when looking at LSTMs.
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I'v already started writing a skeleton of LSTMs. Will submit a pull soon
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No longer relevant.
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Related Issues (20)
- Example: Generalized Linear Regression HOT 1
- k-means
- Base classifier class HOT 1
- TODO List for 0.1 release HOT 53
- Principle Component Analysis
- Linear Discriminant Analysis
- RNN Models HOT 12
- HMM and Viterbi
- OpenCL Error OSX Radeon HD6750 HOT 6
- Add Initializers HOT 3
- Remove bias from Weights.hpp HOT 3
- Autodiff HOT 1
- Is ML project dead? HOT 2
- Convolution Functions HOT 1
- Loss functions HOT 2
- Optimizers
- Indexing and assignment support in autograd
- Degraded performance for variable input size HOT 2
- Possible anachronism in CMake `target_include_directories`
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