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• keras2go uses go code to re-implement the functionality of keras2c
• keras2go is a library for deploying keras neural networks in pure go, using only standard libraries. It is designed to be as simple as possible for real time applications.

After cloning the repo, install the necessary packages with pip install -r requirements.txt.

1. Clone the repo
2. install the necessary packages pip install -r requirements.txt
3. Run the conversion tool to convert the .h5 model to the implementation of the go code, then run go test

    cd conv_tool
    python -m keras2go --num_tests 15 --model_path ./model.h5 --function_name Example --package_name example
    go fmt *.go
    go test -v .

keras2go can be used from the command line:

    python -m keras2go [-h] [--num_tests] 10 --model_path ./model.h5 --function_name Example2 --package_name example

    A library for converting the forward pass (inference) part of a keras model to a go function
    arguments:
      -t, --num_tests       Number of tests to generate. Default is 10
      -m, --model_path      File path to saved keras .h5 model file
      -f, --function_name   What to name the resulting go function
      -p, --package_name    What to name the resulting go package
      -h, --help            show this help message and exit      

• Core Layers: Dense, Activation, Dropout, Flatten, Input, Reshape, Permute, RepeatVector, ActivityRegularization, SpatialDropout1D, SpatialDropout2D, SpatialDropout3D
• Convolution Layers: Conv1D, Conv2D, Conv3D, Cropping1D, Cropping2D, Cropping3D, UpSampling1D, UpSampling2D, UpSampling3D, ZeroPadding1D, ZeroPadding2D, ZeroPadding3D
• Pooling Layers: MaxPooling1D, MaxPooling2D, AveragePooling1D, AveragePooling2D, GlobalMaxPooling1D, GlobalAveragePooling1D, GlobalMaxPooling2D, GlobalAveragePooling2D, GlobalMaxPooling3D,GlobalAveragePooling3D
• Recurrent Layers: SimpleRNN, GRU, LSTM, SimpleRNNCell, GRUCell, LSTMCell
• Embedding Layers: Embedding
• Merge Layers: Add, Subtract, Multiply, Average, Maximum, Minimum, Concatenate, Dot
• Advanced Activation Layers: LeakyReLU, PReLU, ELU, ThresholdedReLU, Softmax, ReLU
• Normalization Layers: BatchNormalization
• Noise Layers: GaussianNoise, GaussianDropout, AlphaDropout
• Layer Wrappers: TimeDistributed, Bidirectional

• test code
• Core Layers: Lambda, Masking
• Convolution Layers: SeparableConv1D, SeparableConv2D, DepthwiseConv2D, Conv2DTranspose, Conv3DTranspose
• Pooling Layers: MaxPooling3D, AveragePooling3D
• Locally Connected Layers: LocallyConnected1D, LocallyConnected2D
• Recurrent Layers: ConvLSTM2D, ConvLSTM2DCell
• Merge Layers: Broadcasting merge between different sizes
• Misc: models made from submodels

MIT

I found another similar projects on Github:

• https://github.com/gosha20777/keras2cpp
• https://github.com/pplonski/keras2cpp
• https://github.com/moof2k/kerasify
• https://github.com/Dobiasd/frugally-deep