Comments (3)
I have tried all tricks raised in your previous two papers, such as spiking max pooling, spiking softmax, analog input and normalization scale. They all achieved good conversion loss reduction. But once I add biases in network, the accuracy of SNN will be very bad. The classification result will converge to the output neuron with biggest bias. Also, I find the biases in fully-connected layer rather than convolutional layer cause this problem, as you talk in your paper.
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Thank you very much, I'll try these methods you provide and make more exploration.
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Related Issues (20)
- TypeError: can't multiply sequence by non-int of type 'float' HOT 4
- IndexError: only integers, slices (`:`), ellipsis (`...`), numpy.newaxis (`None`) and integer or boolean arrays are valid indices
- SpinnmanIOException: IO Error: Failed to communicate with the machine HOT 7
- Query regarding INI simulator HOT 6
- Conv1D Conversion Normalization Issue HOT 2
- ONNX model could not be ported to Keras.Mismatched elements 100% HOT 1
- Code required for a research paper HOT 2
- ModuleNotFoundError: No module named 'keras_rewiring' HOT 2
- Which neuromorphic hardware does SNNtoolbox simulate ? HOT 3
- Error happened while building parsed model HOT 2
- Key Error HOT 1
- index -1 is out of bounds for axis 1 with size 0 HOT 2
- Membrane Potential Values after spike conv layer. HOT 1
- Loading a a converted SNN .h5 model using 'load_model' HOT 1
- Energy and runtime estimation for running the SNN on neuromorphic simulator HOT 3
- TTTFS dyn thresh and TTFS corrective not working HOT 2
- Poisson Rate Encoding HOT 4
- Quantization HOT 6
- TTFS HOT 1
- Cannot import name 'literal' from typing. HOT 1
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