Easy convolution neural network framework.
small, clean, easy to understand!
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blog about EasyCNN in Chinese.
port tensorflow model to easycnn model
- All in one: without any dependency, pure c++ implemented.
- Basic layer: data layer, convolution layer, pooling layer, full connect layer, softmax layer, activation layers(sigmod, tanh, RELU)
- Loss function: Cross Entropy, MSE.
- Optimize method: SGD, SGDWithMomentum.
- Multi-thread parallel optimized.
- Tensorflow model support (traditional CNN only now).(link)
- mnist demo, with ConvNet and MLP net, examples/mnist/mnist_train_test.cpp
fix train error when batch > 1 issue.add load & save model function.- add more layer, such as batch normalization layer, dropout layer, etc.
- add weight regular.
- port to other platforms, such as linux, mac, android, iOS, etc.
- optimize network train/test speed, use cuBLAS/OpenBLAS etc.
- add more optimize method.
- add unit test.
add license.
Use github issues please.
Pull request is welcome.
This project is released under the WTFPL LICENSE.