Neural network implementation for learning purpose
- Including :
- Neural activation
- Loss calculating
- Forward activation propagation
- Backward adjustement propagation
- Predictions
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)
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)
Created with python 3.10.6
python -m pip install -r requirements.txt
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