Comments (3)
attractivechaos
commented on September 4, 2024
building a NN without extra overhead
What overhead? It is a bad idea to print floating point numbers in plain text. KANN provides functions to save/load models and parameters. Please use those.
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james34602
commented on September 4, 2024
What my thoughts is user have chance to explore their model easier, or I would say print the coefficients with just one function call. Something like get_weights() in Keras.
Nothing bad to debug their model via weights monitoring.
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Levalicious
commented on September 4, 2024
I would be in favor of the addition of this feature, if possible. It would be nice to be able to make networks 'exportable' in some format other than the kann format, so that small networks can be put directly into code, or for other programs that support GPU training.
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Related Issues (20)
- Is quantization supported in Kann? HOT 1
- More examples (classify text) HOT 1
- Can KANN train an embedding layer?
- Support for batch normalisation HOT 1
- Batch processing of large dataset
- Feature request: model conversion utility. HOT 1
- Convolutional recurrent neural network
- mnist-cnn example fail assert on training
- kann_class_error() is broken for KANN_C_CEB HOT 3
- VAE error
- How to extract outputs from a hidden layer of autoencoder? HOT 4
- A time series data example for LSTM HOT 1
- Why accuracy does not increase anymore at specific epoch? HOT 4
- Getting Started example not working on macOS 11.3, Apple M1 chip
- Transfrom script for dataset
- Exporting weights? HOT 2
- Can we define the neuron number for each layer?
- resnet
- Image classification of cats, dogs, and pandas
- Custom loss function example using kad_op functions HOT 6
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