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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.
- Clone the repo
- install the necessary packages
pip install -r requirements.txt - 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
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