pocket-tensor is an arquolo's Kerasify fork designed for running trained Keras models from a C++ application on embedded devices.

• Compatibility with sequential networks generated by Keras 2.x using Tensorflow backend.
• Multithread CPU support (no GPU support).
• Low RAM usage.
• Easy to build and run (no external dependencies).
• Fast build times.

• Thanks to the awesome libsimdpp library, tensor operations have been rewritten using SIMD instructions to improve prediction performance.
• Memory (re)usage has been improved in order to reduce memory allocations.
• Apart from float, double precision tensors are supported (see pt_tweakme.h file).
• Tensor dimensions are rigorously validated on each layer to avoid bad models usage.
• Besides GCC and Clang, Visual Studio compiler is properly supported.

Since there's no GPU support, by default pocket-tensor requires the following CPU SIMD instruction sets:

• ARM: NEON with floating point support.
• x86: AVX.

Required SIMD instruction sets are specified in the pt_tweakme.h file, so they can be modified with ease.

Since a copy of libsimdpp comes bundled with this library, there's no external dependencies required, so the only software requirements are a C++11-compatible compiler and CMake >= 3.4.

pocket-tensor has been tested with these compilers:

• GCC 4.9.
• MSVC 2017.
• Whatever Clang comes with Apple LLVM 9.1.0.

A CMakeLists.txt is provided with this library, so in order to use it you only need to include this file in your CMake project.

To build and run the unit tests, you need to generate them first:

python make_tests.py
mkdir tests_build
cd tests_build
cmake -DPT_BUILD_TESTS=ON -DCMAKE_BUILD_TYPE=Release ../..
make
./tests/pocket-tensor-tests

1. Use Keras to build (model.compile(...)) and train (model.fit(...)) your model as usual.

2. Now convert it to the Kerasify file format with kerasify.export_model(model, 'example.model').

3. Finally load it in C++ (pt::create("example.model")) and use model->predict(...) to perform a prediction with your data.

The following example shows the full workflow:

# make_model.py:

import numpy as np
from keras.models import Sequential
from keras.layers import Dense
from kerasify import export_model

test_x = np.random.rand(10, 10).astype('f')
test_y = np.random.rand(10).astype('f')

model = Sequential()
model.add(Dense(1, input_dim=10))

model.compile(loss='mean_squared_error', optimizer='adamax')
model.fit(test_x, test_y, epochs=1)

print model.predict(np.array([[0, 1, 2, 3, 4, 5, 6, 7, 8, 9]]))

export_model(model, 'example.model')

// main.cpp:

#include <iostream>
#include "pt_model.h"
#include "pt_tensor.h"

int main()
{
    // Initialize model:
    auto model = pt::create("example.model");
    // REQUIRE(model);

    // Create input tensor:
    pt::Tensor in(10);
    in.setData({0, 1, 2, 3, 4, 5, 6, 7, 8, 9});

    // Run prediction:
    pt::Tensor out;
    bool success = model->predict(std::move(in), out);
    // REQUIRE(success);
	
    // Print output:
    std::cout << out << std::endl;
    return 0;
}

The most common layer types used in image recognition and sequences prediction are supported, making many popular model architectures possible:

• Convolutions: Conv1D, Conv2D, LocallyConnected1D.
• Sequences related: LSTM, Embedding.
• Activations: Linear, ReLU, ELU, Softplus, Softsign, Tanh, Sigmoid, HardSigmoid, Softmax.
• Other: Dense, Flatten, MaxPooling2D, BatchNormalization, ELU.

The prediction time of the following models has been measured on a Raspberry Pi 3:

model = Sequential()
model.add(Conv2D(32, kernel_size=(3, 3),
                 activation='relu',
                 input_shape=input_shape))
model.add(Conv2D(64, (3, 3), activation='relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.25))
model.add(Flatten())
model.add(Dense(128, activation='relu'))
model.add(Dropout(0.5))
model.add(Dense(num_classes, activation='sigmoid'))

model = Sequential()
model.add(Embedding(max_features, 128))
model.add(LSTM(128, return_sequences=True, dropout=0.2, recurrent_dropout=0.2))
model.add(LSTM(128, return_sequences=False, dropout=0.2, recurrent_dropout=0.2))
model.add(Dense(1, activation='sigmoid'))