https://github.com/deephealthproject/pyeddl
Python wrapper for the EDDL library.
https://github.com/deephealthproject/pyeddl
eddl python-wrapper
Last synced: 2 months ago
JSON representation
Python wrapper for the EDDL library.
- Host: GitHub
- URL: https://github.com/deephealthproject/pyeddl
- Owner: deephealthproject
- License: mit
- Created: 2019-06-19T11:25:49.000Z (almost 7 years ago)
- Default Branch: master
- Last Pushed: 2022-06-14T15:40:41.000Z (almost 4 years ago)
- Last Synced: 2025-09-25T14:17:05.164Z (6 months ago)
- Topics: eddl, python-wrapper
- Language: C++
- Homepage:
- Size: 39.4 MB
- Stars: 13
- Watchers: 22
- Forks: 2
- Open Issues: 4
-
Metadata Files:
- Readme: README.md
- Contributing: CONTRIBUTING.md
- License: LICENSE
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README
-----------------
**PyEDDL** is a Python wrapper for [EDDL](https://github.com/deephealthproject/eddl), the European Distributed Deep Learning library.
The documentation is available at https://deephealthproject.github.io/pyeddl.
As a preview, here is a simple neural network training example:
```python
import pyeddl.eddl as eddl
from pyeddl.tensor import Tensor
def main():
eddl.download_mnist()
epochs = 10
batch_size = 100
num_classes = 10
in_ = eddl.Input([784])
layer = in_
layer = eddl.LeakyReLu(eddl.Dense(layer, 1024))
layer = eddl.LeakyReLu(eddl.Dense(layer, 1024))
layer = eddl.LeakyReLu(eddl.Dense(layer, 1024))
out = eddl.Softmax(eddl.Dense(layer, num_classes))
net = eddl.Model([in_], [out])
eddl.build(
net,
eddl.rmsprop(0.01),
["soft_cross_entropy"],
["categorical_accuracy"],
eddl.CS_CPU()
)
x_train = Tensor.load("mnist_trX.bin")
y_train = Tensor.load("mnist_trY.bin")
x_test = Tensor.load("mnist_tsX.bin")
y_test = Tensor.load("mnist_tsY.bin")
x_train.div_(255.0)
x_test.div_(255.0)
eddl.fit(net, [x_train], [y_train], batch_size, epochs)
eddl.evaluate(net, [x_test], [y_test])
if __name__ == "__main__":
main()
```
If you're interested in contributing to the development, see the
[contributing](CONTRIBUTING.md) docs. They contain information on how to
generate the automated part of the bindings, build binary wheels, etc.