In this repository, I will show you how to build a neural network from scratch (yes, by using plain python code with no framework involved) that trains by mini-batches using gradient descent. Check nn.py for the code.
In the related notebook Neural_Network_from_scratch_with_Numpy.ipynb we will test nn.py on a set of non-linear classification problems
- We'll train the neural network for some number of epochs and some hyperparameters
- Plot a live/interactive decision boundary
- Plot the train and validation metrics such as the loss and the accuracies
nn.py is a toy neural network that is meant for educational purposes only. So there's room for a lot of improvement if you want to pimp it. Here are some guidelines:
- Implement a different loss function such as the Binary Cross Entropy loss. For a classification problem, this loss works better than a Mean Square Error.
- Make the code generic regarding the activation functions so that we can choose any function we want: ReLU, Sigmoid, Tanh, etc.
- Try to code another optimizers: SGD is good but it has some limitations: sometimes it can be stuck in local minima. Look into Adam or RMSProp.
- Play with the hyperparameters and check the validation metrics
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