This project is an open-source custom deep learning framework - EduNN, inspired by Keras and PyTorch. In general, the goal was to implement an educational platform, using which anybody, mostly students, can learn and experiment with neural networks through the simple API. The complete framework code can be found here.
We hope that you will like our approach and that you will try out our framework. Any feedback is welcomed.
model = Model([[4], [2, "relu"], [3, "softmax"]], loss_function='cross_entropy', learning_rate=0.001)When creating model of neural network, user has to provide list of layers (input, hidden and output), including activation functions they want to choose, according to the problem. Next up is to pass loss function type and learning rate as other two parameters.
After the Model has been initialized, it's time to train it using model.train() function. There are 2 types of algorithms for training our network:
- Gradient Descent
- Genetic Algorithm
model.train("gradient_descent", x_train, y_train, epochs=5, shuffle_data=True, batch_size=10)model.train("genetic_algorithm", np.array(training_data), np.array(targets), epochs=5, shuffle_data=True, batch_size=10)After choosing the algorithm for training, required parameters are training data and targeting data. User needs to provide those two data sets, and then the net can be trained. Other parameters are provided by default, but user can change them and track the performance of it's net.
-Mean Squared Error
-Mean Asolute Error
-Cross Entropy
-Binary Cross Entropy
-Sigmoid
-ReLu
-Sinusoid
-SoftMax
-SoftPlus
-Gaussian
-Tahn