SkyNet is a deep learning library designed for both efficiency and flexibility.
Linux/Windows |
License |
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the library is written from scratch in C++ (only STL + OpenBLAS for calculation), C-interface
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win / linux;
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network structure is set in JSON;
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base layers: fully connected, convolutional, pooling. Additional: resize, crop ..;
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basic chips: batchNorm, dropout, weight optimizers - adam, adagrad ..;
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for calculation on the CPU, OpenBLAS is used, for the video card - CUDA / cuDNN;
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for each layer there is an opportunity to separately set on what to count - CPU or GPU (and which one);
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the size of the input data is not rigidly specified, may vary in the process of work / training;
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interfaces for C++, C# and Python.
# create net
net = snNet.Net()
net.addNode('In', snOperator.Input(), 'C1') \
.addNode('C1', snOperator.Convolution(15, 0, snType.calcMode.CUDA), 'C2') \
.addNode('C2', snOperator.Convolution(25, 0, snType.calcMode.CUDA), 'P1') \
.addNode('P1', snOperator.Pooling(snType.calcMode.CUDA), 'F1') \
.addNode('F1', snOperator.FullyConnected(256, snType.calcMode.CUDA), 'F2') \
.addNode('F2', snOperator.FullyConnected(10, snType.calcMode.CUDA), 'LS') \
.addNode('LS', snOperator.LossFunction(snType.lossType.softMaxToCrossEntropy), 'Output')
.............
# cycle lern
for n in range(1000):
acc = [0]
net.training(lr, inLayer, outLayer, targLayer, acc)
# calc accurate
acc[0] = 0
for i in range(bsz):
if (np.argmax(outLayer[i][0][0]) == np.argmax(targLayer[i][0][0])):
acc[0] += 1
accuratSumm += acc[0]/bsz
print(datetime.datetime.now().strftime('%H:%M:%S'), n, "accurate", accuratSumm / (n + 1))-
pip install libskynet - CPU
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pip install libskynet-cu - CPU + CUDA9.2
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pip install libskynet-cudnn - CPU + cuDNN7.3.1
Licensed under an [MIT-2.0]-license.