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https://github.com/gosha20777/keras2cpp
it's a small library for running trained Keras 2 models from a native C++ code.
https://github.com/gosha20777/keras2cpp
c-plus-plus c-plus-plus-17 cpp keras keras2cpp machine-learning neural-networks python
Last synced: about 1 month ago
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it's a small library for running trained Keras 2 models from a native C++ code.
- Host: GitHub
- URL: https://github.com/gosha20777/keras2cpp
- Owner: gosha20777
- License: mit
- Created: 2018-11-19T10:43:27.000Z (about 6 years ago)
- Default Branch: master
- Last Pushed: 2022-11-21T07:02:03.000Z (about 2 years ago)
- Last Synced: 2023-11-07T17:36:51.310Z (about 1 year ago)
- Topics: c-plus-plus, c-plus-plus-17, cpp, keras, keras2cpp, machine-learning, neural-networks, python
- Language: C++
- Size: 90.8 KB
- Stars: 180
- Watchers: 20
- Forks: 54
- Open Issues: 19
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# Keras2cpp   [](https://travis-ci.org/gosha20777/keras2cpp)
Keras2cpp is a small library for running trained Keras models from a C++ application without any dependences.
Design goals:
- Compatibility with networks generated by Keras using TensorFlow backend.
- CPU only, no GPU.
- No external dependencies, standard library, C++17.
- Model stored on disk in binary format and can be quickly read.
- Model stored in memory in contiguous block for better cache performance.
*Not not layer and activation types are supported yet. Work in progress*
Supported Keras layers:
- [x] Dense
- [x] Convolution1D
- [x] Convolution2D
- [ ] Convolution3D
- [x] Flatten
- [x] ELU
- [x] Activation
- [x] MaxPooling2D
- [x] Embedding
- [x] LocallyConnected1D
- [x] LocallyConnected2D
- [x] LSTM
- [ ] GRU
- [ ] CNN
- [X] BatchNormalization
Supported activation:
- [x] linear
- [x] relu
- [x] softplus
- [x] tanh
- [x] sigmoid
- [x] hard_sigmoid
- [x] elu
- [x] softsign
- [x] softmax
Other tasks:
- [x] Create unit tests
- [x] Create Makefile
- [x] Code refactoring *(in progress)*
The project is compatible with Keras 2.x (all versions) and Python 3.x
# Example
python_model.py:
```python
import numpy as np
from keras import Sequential
from keras.layers import Dense
#create random data
test_x = np.random.rand(10, 10).astype('f')
test_y = np.random.rand(10).astype('f')
model = Sequential([
Dense(1, input_dim=10)
])
model.compile(loss='mse', optimizer='adam')
#train model by 1 iteration
model.fit(test_x, test_y, epochs=1, verbose=False)
#predict
data = np.array([[0, 1, 2, 3, 4, 5, 6, 7, 8, 9]])
prediction = model.predict(data)
print(prediction)
#save model
from keras2cpp import export_model
export_model(model, 'example.model')
```
cpp_model.cc:
```c++
#include "src/model.h"
using keras2cpp::Model;
using keras2cpp::Tensor;
int main() {
// Initialize model.
auto model = Model::load("example.model");
// Create a 1D Tensor on length 10 for input data.
Tensor in{10};
in.data_ = {0, 1, 2, 3, 4, 5, 6, 7, 8, 9};
// Run prediction.
Tensor out = model(in);
out.print();
return 0;
}
```
# How to build and run
*Tested with Keras 2.2.1, Python 3.6*
```bash
$ git clone https://github.com/gosha20777/keras2cpp.git
$ cd keras2cpp
$ mkdir build && cd build
$ python3 ../python_model.py
[[-1.85735667]]
$ cmake ..
$ cmake --build .
$ ./keras2cpp
[ -1.857357 ]
```
# License
MIT
# Similar projects
I found another similar projects on Github:
- ;
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But It works only with Keras 1 and didn’t work for me.
That's why I wrote my own implementation.