Implementing a Neural Network from Scratch in Python – An Introduction

This article was written by Denny Britz.

In this post we will implement a simple 3-layer neural network from scratch. We won’t derive all the math that’s required, but I will try to give an intuitive explanation of what we are doing. I will also point to resources for you read up on the details.

Here I’m assuming that you are familiar with basic Calculus and Machine Learning concepts, e.g. you know what classification and regularization is. Ideally you also know a bit about how optimization techniques like gradient descent work. But even if you’re not familiar with any of the above this post could still turn out to be interesting 😉

But why implement a Neural Network from scratch at all? Even if you plan on using Neural Network libraries like PyBrain in the future, implementing a network from scratch at least once is an extremely valuable exercise. It helps you gain an understanding of how neural networks work, and that is essential for designing effective models.

One thing to note is that the code examples here aren’t terribly efficient. They are meant to be easy to understand. In an upcoming post I will explore how to write an efficient Neural Network implementation using Theano.

Generating a dataset

Let’s start by generating a dataset we can play with. Fortunately, scikit-learn has some useful dataset generators, so we don’t need to write the code ourselves. We will go with the make_moons function.

# Generate a dataset and plot it

np.random.seed(0)

X, y = sklearn.datasets.make_moons(200, noise=0.20)

plt.scatter(X[:,0], X[:,1], s=40, c=y, cmap=plt.cm.Spectral)

The dataset we generated has two classes, plotted as red and blue points. You can think of the blue dots as male patients and the red dots as female patients, with the x- and y- axis being medical measurements.

Our goal is to train a Machine Learning classifier that predicts the correct class (male of female) given the x- and y- coordinates. Note that the data is not linearly separable, we can’t draw a straight line that separates the two classes. This means that linear classifiers, such as Logistic Regression, won’t be able to fit the data unless you hand-engineer non-linear features (such as polynomials) that work well for the given dataset.

In fact, that’s one of the major advantages of Neural Networks. You don’t need to worry about feature engineering. The hidden layer of a neural network will learn features for you.

Logistic Regression:

To demonstrate the point let’s train a Logistic Regression classifier. It’s input will be the x- and y-values and the output the predicted class (0 or 1). To make our life easy we use the Logistic Regression class from scikit-learn.

# Train the logistic rgeression classifier
clf = sklearn.linear_model.LogisticRegressionCV()
clf.fit(X, y)
 
# Plot the decision boundary
plot_decision_boundary(lambda x: clf.predict(x))
plt.title("Logistic Regression")

The graph shows the decision boundary learned by our Logistic Regression classifier. It separates the data as good as it can using a straight line, but it’s unable to capture the “moon shape” of our data.

Training a Neural Network:

Let’s now build a 3-layer neural network with one input layer, one hidden layer, and one output layer. The number of nodes in the input layer is determined by the dimensionality of our data, 2. Similarly, the number of nodes in the output layer is determined by the number of classes we have, also 2. (Because we only have 2 classes we could actually get away with only one output node predicting 0 or 1, but having 2 makes it easier to extend the network to more classes later on). The input to the network will be x- and y- coordinates and its output will be two probabilities, one for class 0 (“female”) and one for class 1 (“male”). It looks something like this:

We can choose the dimensionality (the number of nodes) of the hidden layer. The more nodes we put into the hidden layer the more complex functions we will be able fit. But higher dimensionality comes at a cost. First, more computation is required to make predictions and learn the network parameters. A bigger number of parameters also means we become more prone to overfitting our data.

How to choose the size of the hidden layer? While there are some general guidelines and recommendations, it always depends on your specific problem and is more of an art than a science. We will play with the number of nodes in the hidden later later on and see how it affects our output.

We also need to pick an activation function for our hidden layer. The activation function transforms the inputs of the layer into its outputs. A nonlinear activation function is what allows us to fit nonlinear hypotheses. Common choices for activation functions are tanh, the sigmoid function, or ReLUs. We will use tanh, which performs quite well in many scenarios. A nice property of these functions is that their derivate can be computed using the original function value. For example, the derivative of $\tanh x$ is $1-\tanh^2 x$ . This is useful because it allows us to compute $\tanh x$ once and re-use its value later on to get the derivative.

Because we want our network to output probabilities the activation function for the output layer will be the softmax, which is simply a way to convert raw scores to probabilities. If you’re familiar with the logistic function you can think of softmax as its generalization to multiple classes.

How our network makes predictions:

Our network makes predictions using forward propagation, which is just a bunch of matrix multiplications and the application of the activation function(s) we defined above. If x is the 2-dimensional input to our network then we calculate our prediction $\hat{y}$ (also two-dimensional) as follows:

$\begin{aligned} z_1 & = xW_1 + b_1 \\ a_1 & = \tanh(z_1) \\ z_2 & = a_1W_2 + b_2 \\ a_2 & = \hat{y} = \mathrm{softmax}(z_2) \end{aligned}$

$z_i$ is the input of layer $i$ and $a_i$ is the output of layer $i$ after applying the activation function. $W_1, b_1, W_2, b_2$ are parameters of our network, which we need to learn from our training data. You can think of them as matrices transforming data between layers of the network. Looking at the matrix multiplications above we can figure out the dimensionality of these matrices. If we use 500 nodes for our hidden layer then $W_1 \in \mathbb{R}^{2\times500}$ , $b_1 \in \mathbb{R}^{500}$ , $W_2 \in \mathbb{R}^{500\times2}$ , $b_2 \in \mathbb{R}^{2}$ . Now you see why we have more parameters if we increase the size of the hidden layer.

Learning the Parameters

Learning the parameters for our network means finding parameters ( $W_1, b_1, W_2, b_2$ ) that minimize the error on our training data. But how do we define the error? We call the function that measures our error the loss function. A common choice with the softmax output is the categorical cross-entropy loss (also known as negative log likelihood). If we have $N$ training examples and $C$ classes then the loss for our prediction $\hat{y}$ with respect to the true labels $y$ is given by:

$\begin{aligned} L(y,\hat{y}) = - \frac{1}{N} \sum_{n \in N} \sum_{i \in C} y_{n,i} \log\hat{y}_{n,i} \end{aligned}$

The formula looks complicated, but all it really does is sum over our training examples and add to the loss if we predicted the incorrect class. The further away the two probability distributions $y$ (the correct labels) and $\hat{y}$ (our predictions) are, the greater our loss will be. By finding parameters that minimize the loss we maximize the likelihood of our training data.

We can use gradient descent to find the minimum and I will implement the most vanilla version of gradient descent, also called batch gradient descent with a fixed learning rate. Variations such as SGD (stochastic gradient descent) or minibatch gradient descent typically perform better in practice. So if you are serious you’ll want to use one of these, and ideally you would also decay the learning rate over time.

As an input, gradient descent needs the gradients (vector of derivatives) of the loss function with respect to our parameters: $\frac{\partial{L}}{\partial{W_1}}$ , $\frac{\partial{L}}{\partial{b_1}}$ , $\frac{\partial{L}}{\partial{W_2}}$ , $\frac{\partial{L}}{\partial{b_2}}$ . To calculate these gradients we use the famous backpropagation algorithm, which is a way to efficiently calculate the gradients starting from the output. I won’t go into detail how backpropagation works, but there are many excellent explanations floating around the web.

To read the full original article with source code, click here. For more neural network related articles on DSC click here.

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