GitHub - bhendi-boi/ann-snn

ANN

got around 98% accuracy for Arrhythmia_classifier
ANN and SNN networks can be calculated now. Download spikes.ipynb file and run it with the biases and weights you got after running the Arrhythmia_classifier model from ann.ipynb .

Download the snn.ipynb and 4vectors.csv file to replicate my work.
You need these libraries:
1. snntorch
2. pytorch
3. sklearn
4. imbalencelearn
5. matplotlib
6. pandas
7. numpy
Additionally, you can run the same model on google colab.
If you have an Nvidia GPU, install CUDA to take advantage of your GPU which highly reduces the time taken to train the model.

Input feature vectors include rr_interval, qrs_interval, st_interval, qt_interval.

If you want to update the flowchart, open flowchart.excalidraw on excalidraw

parameters you can change include beta for the LIF neurons, the sampling strategy, learning_rate specified in the ADAM optimizer.
beta is the decay rate of membrane potential, clipped between 0 and 1 during the forward-pass.
To know more about LIF neurons, visit this website.
sampling strategy is used to over sample classes whose count is very less. Whatever the number you assign to a particular key, that will be the number of elements in the final dataset before splitting for training and testing.
The over sampling method used is SMOTE.

Name		Name	Last commit message	Last commit date
Latest commit History 55 Commits
.gitignore		.gitignore
284-snn.png		284-snn.png
484.png		484.png
485.png		485.png
485snn.png		485snn.png
4vectors.csv		4vectors.csv
LeakyRELU-223.png		LeakyRELU-223.png
RELU-223.png		RELU-223.png
ReLU()-Sigmoid()-[2, 7, 5, 4]-223.png		ReLU()-Sigmoid()-[2, 7, 5, 4]-223.png
ReLU()-Sigmoid()-[2, 8, 8, 4]-223.png		ReLU()-Sigmoid()-[2, 8, 8, 4]-223.png
Tanh[2,8,8,4]-200.png		Tanh[2,8,8,4]-200.png
Tanh[2,8,8,4]-50.png		Tanh[2,8,8,4]-50.png
Tanh[2,8,8,4]-batch-10.png		Tanh[2,8,8,4]-batch-10.png
Tanh[2,8,8,4].png		Tanh[2,8,8,4].png
[2,8,8,4].png		[2,8,8,4].png
ann.ipynb		ann.ipynb
ann.pdf		ann.pdf
ann_sample.csv		ann_sample.csv
ann_sample.xlsx		ann_sample.xlsx
ann_train.csv		ann_train.csv
classifier_result.jpg		classifier_result.jpg
flowchart.excalidraw		flowchart.excalidraw
flowchart.png		flowchart.png
full_data_set.png		full_data_set.png
full_data_set_loss.png		full_data_set_loss.png
kaggle.csv		kaggle.csv
kaggle.ipynb		kaggle.ipynb
readme.md		readme.md
sigmoid-223-SMOTE.png		sigmoid-223-SMOTE.png
sigmoid-223.png		sigmoid-223.png
smote-weights-rmsprop.png		smote-weights-rmsprop.png
smote-weights.png		smote-weights.png
snn.excalidraw		snn.excalidraw
snn.ipynb		snn.ipynb
softmax-223.png		softmax-223.png
spikes.ipynb		spikes.ipynb
without-smote.png		without-smote.png