Overview
Random Neural Network Simulator implemented in Python.
Setup
Requirements
- Python 3.6+
- NumPy
- Sklearn
Installation
Install this library directly into an activated virtual environment:
1 | $ pip install rnnsim |
or add it to your Poetry project:
1 | $ poetry add rnnsim |
Usage
After installation, the package can either be used as:
1 2 3 4 5 6 | from rnnsim.model import SequentialRNN sequential_model = SequentialRNN([2, 2, 1]) sequential_model.compile() sequential_model.fit(train_data=(X_train, y_train), epochs=50, metrics="acc") print(sequential_model.score((X_test, y_test))) |
or
1 2 3 4 5 6 7 8 9 10 11 | from rnnsim.RNN import RNN # define model connections conn_plus = { 1: [3, 4], 2: [3, 4], 3: [5], 4: [5], 5: []} conn_minus = { 1: [3, 4], 2: [3, 4], 3: [5], 4: [5], 5: []} model = RNN(n_total=5, input_neurons=2, output_neurons=1, conn_plus=conn_plus, conn_minus=conn_minus) model.fit(epochs=N_Iterations, train_data=(X, Y)) |
References
- E. Gelenbe, Random neural networks with negative and positive signals and product form solution," Neural Computation, vol. 1, no. 4, pp. 502-511, 1989.
- E. Gelenbe, Stability of the random neural network model," Neural Computation, vol. 2, no. 2, pp. 239-247, 1990.