Simple Neural Network

Neural network implementation for learning purpose

• Including :
  • Neural activation
  • Loss calculating
  • Forward activation propagation
  • Backward adjustement propagation
  • Predictions

Single Layer Model

A neuron on a layer to be able to understand the general functioning of a neural network.

X_train, y_train = Datasets.get_random_blobs_set(n_samples=10000)
X_test, y_test = Datasets.get_random_blobs_set(n_samples=200)

model = SingleLayerNeuralModel(X_train, y_train)
model.start_training(iteration=2000, learning_rate=0.05)
model.show_training_results()
preds = model.predict(X_test)

Multi Layer Model

Neural network on several layers to improve the accuracy of the predictions and general abilities of the model.

X_train, y_train = Datasets.get_random_circles_set(n_samples=1000)
X_test, y_test = Datasets.get_random_circles_set(n_samples=200)

# hidden_layers determinates the number of layers and neurons
model = MultiLayerNeuralModel(X_train, y_train, hidden_layers=(16, 16, 16))
model.start_training(iteration=10000, learning_rate=0.05)
model.show_training_results()
preds = model.predict(X_test)

Install Requirements

Created with python 3.10.6

python -m pip install -r requirements.txt