![]() Read the tutorial of clock driven for more details. This simple network with a Poisson encoder can achieve 92% accuracy on MNIST test dataset. LIFNode ( tau = tau, v_threshold = v_threshold, v_reset = v_reset ) ) def forward ( self, x ): return self. Linear ( 14 * 14, 10, bias = False ), neuron. LIFNode ( tau = tau, v_threshold = v_threshold, v_reset = v_reset ), nn. _init_ () # Network structure, a simple two-layer fully connected network, each layer is followed by LIF neurons self. Building SNN with SpikingJelly is as simple as building ANN in PyTorch: class Net ( nn. Install the latest developing version from the source codes: Install the last stable version (0.0.0.0.8) from PyPI: pip install spikingjelly The even version number is the stable version and available at PyPI. The odd version number is the developing version, which is updated with GitHub/OpenI repository. Please make sure that you have installed PyTorch before you install SpikingJelly. Note that SpikingJelly is based on PyTorch. Build SNN In An Unprecedented Simple Way.The documentation of SpikingJelly is written in both English and Chinese. ![]() SpikingJelly is an open-source deep learning framework for Spiking Neural Network (SNN) based on PyTorch.
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