Introduction to Neural Networks: Playground Exercises

A First Neural Network

In this exercise, we will train our first little neural net. Neural nets will give us a way to learn nonlinear models without the use of explicit feature crosses.

Task 1: The model as given combines our two input features into a single neuron. Will this model learn any nonlinearities? Run it to confirm your guess.

Task 2: Try increasing the number of neurons in the hidden layer from 1 to 2, and also try changing from a Linear activation to a nonlinear activation like ReLU. Can you create a model that can learn nonlinearities?

Task 3: Continue experimenting by adding or removing hidden layers and neurons per layer. Also feel free to change learning rates, regularization, and other learning settings. What is the smallest number of nodes and layers you can use that gives test loss of 0.177 or lower?

(Answers appear just below the exercise.)



Neural Net Initialization

This exercise uses the XOR data again, but looks at the repeatability of training Neural Nets and the importance of initialization.

Task 1: Run the model as given four or five times. Before each trial, hit the Reset the network button to get a new random initialization. (The Reset the network button is the circular reset arrow just to the left of the Play button.) Let each trial run for at least 500 steps to ensure convergence. What shape does each model output converge to? What does this say about the role of initialization in non-convex optimization?

Task 2: Try making the model slightly more complex by adding a layer and a couple of extra nodes. Repeat the trials from Task 1. Does this add any additional stability to the results?

(Answers appear just below the exercise.)



Neural Net Spiral

This data set is a noisy spiral. Obviously, a linear model will fail here, but even manually defined feature crosses may be hard to construct.

Task 1: Train the best model you can, using just X1 and X2. Feel free to add or remove layers and neurons, change learning settings like learning rate, regularization rate, and batch size. What is the best test loss you can get? How smooth is the model output surface?

Task 2: Even with Neural Nets, some amount of feature engineering is often needed to achieve best performance. Try adding in additional cross product features or other transformations like sin(X1) and sin(X2). Do you get a better model? Is the model output surface any smoother?

(Answers appear just below the exercise.)



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