This contains Feedforward and Recurrent neural networks
Features
Feedforward and Recurrent neural nets
Contains logistic, tanh, ReLU, softmax neurons
Standard loss functions (squared error, cross entropy, sparsity constraints), and support for custom loss functions
Different methods to initialize weights
Customizable connectivity between layers
Inputs can be generated dynamically by some other system (like a simulation)
Quick start
Install the package via:
pip install gaunnRequirements
python 2.7 or 3.5
numpy 1.9.2
matplotlib 1.3.1
1 maintainer
Metadata
1 maintainer
Release files for gaunn 1.0.0
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Source distribution (sdist)
| File | Size | Uploaded | Provenance | Actions |
|---|---|---|---|---|
| gaunn-1.0.0.tar.gz | 32.2 kB | Without provenance | Details for gaunn-1.0.0.tar.gz |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Actions Reset |
|---|---|---|---|---|
| gaunn-1.0.0-py2-none-any.whl | Python 2 | none | any | Details for gaunn-1.0.0-py2-none-any.whl |
Total release size: 69.6 kB
Release files / gaunn-1.0.0.tar.gz
| Download URL | gaunn-1.0.0.tar.gz |
|---|---|
| Size | 32.2 kB |
| Tags | Source |
|
SHA-256 checksum How to use checksums |
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Release files / gaunn-1.0.0-py2-none-any.whl
| Download URL | gaunn-1.0.0-py2-none-any.whl |
|---|---|
| Size | 37.4 kB |
| Tags | Python 2 |
|
SHA-256 checksum How to use checksums |
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Security for gaunn 1.0.0
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