# Machine Learning: Feedforward backpropagation neural networks

If we take a logistic function as in logistic regression, and feed the outputs of many logistic regressions into another logistic regression, and do this for several levels, we end up with a neural network architecture. This works nicely to increase the number of parameters as well as the number of features from the basic set you have, since a neural network's hidden layers act as new features.

Each non-input neuron in a layer gets its inputs from every neuron from the previous layer, including a fixed bias neuron which acts as the x0 = 1 term we always have.

Rather than θ, we now call the parameters weights, and the outputs are now called activations. The equation for the output (activation) of neuron p in layer l is:

\begin{align*} z^{(l)}_q &= \sum_{p=0}^{s_{l-1}} w_{pq}^{(l-1)}a_p^{(l-1)}\\ a^{(l)}_q &= g\left (z^{(l)}_q \right ) \end{align*}

Breaking it down:

• w(l-1)pq is the weight from neuron p in layer l-1 to neuron q in layer l
• a(l-1)pq is the activation of neuron p in layer l-1, and of course when p=0, the activation is by definition 1.
• z(l)is the usual sum, specifically for neuron q in layer l.
• g is some function, which we can take to be the logistic function.

So we see that the output of any given neuron is a logistic function of its inputs.

We will define the cost function for the entire output, for a single data point, to be as follows:

$J^{(i)} = \frac{1}{2}\sum_{p=1}^{s_L} \left ( y_p^{(i)} - a_p^{(i, L)} \right )^2$

Note that we are using the linear regression cost, because we will want the output to be an actual output rather than a classification. The cost can be defined using the logistic cost function if the output is a classification.

Now, the algorithm proceeds as follows:

1. Compute all the activations for a single data point
2. For each output neuron q, compute:

$\delta^{(L)}_q = -(y_q^{(i)} - a_q^{(i, L)})(1 - a_q^{(i, L)})a_q^{(i, L)}$

3. For each non-output neuron p, working backwards in layers from layer L-1 to layer 1, compute:

$\delta^{(l)}_p = (1 - a_p^{(i, l)})a_p^{(i, l)}\sum_{q=1}^{s_{l+1}} w^{(l)}_{pq}\delta^{(l+1)}_q$

4. Compute the weight updates as follows:

$w^{(l)}_{pq} \leftarrow w^{(l)}_{pq} + \alpha a^{(l)}_p \delta^{(l+1)}_q$

The last step can, in fact, be delayed. Simply present multiple data points, or even the entire training set, adding up the changes to the weights, and then only update the weights afterwards.

Because it is extraordinarily easy to get the implementation wrong, I highly suggest the use of a neural network library such as the impressively expansive Encog as opposed to implementing it yourself. Also, many neural network libraries include training algorithms other than backpropagation.

### The Concrete Example

I used Encog to train a neural network on the concrete data from the earlier post. I first took the log of the output, since that seemed to represent the data better and led to less network error. Then I normalized the data, except I used the range 0-1 for both the days input and the strength output, since that seemed to make sense, and also led to less network error.

Here's the Java code I used. Compile it with the Encog core library in the classpath. The only argument to it is the path to the Concrete_Data.csv file.

The network I chose, after some experimentation checking for under- and overfitting, was an 8:20:10:1 network. I used this network to train against different sizes of training sets to see the learning curves. Each set of data was presented to the network for 10,000 iterations of an algorithm called Resilient Backpropagation, which has various advantages over backpropagation, namely that the learning rate generally doesn't have to be set.

As before, the blue line is the training cost, the mean squared error against the training set, and the red line is the cross-validation cost, the mean squared error against the cross-validation set. This is generally what I would expect for an algorithm that is neither underfitting nor overfitting. Overfitting would show a large gap between training and cross-validation, while underfitting would show high errors for both.

If we saw underfitting, then we would have to increase the parameter space, which would mean increase the number of neurons in the hidden layers. If we saw overfitting, then decreasing the parameters space would be appropriate, so decreasing the number of neurons in the hidden layers would help.

Since the range of the output is 0-1, over the entire training set we get an MSE (training) of 0.0003, which means the average error per data point is 0.017. This doesn't quite tell the whole story, because if an output is supposed to be, say, 0.01, and error of 0.017 means the output wasn't very well-fit. Instead, let's just look at the entire data set, ordered by value, after denormalization:

The majority of errors fall under 10%, which is probably good enough. If I were concerned with the data points whose error was above 10%, I might be tempted treat those data points as "difficult", try to train a classifier to train data points as "difficult" or "not difficult", and then train different regression networks on each class.

The problem with that is that I could end up overfitting my data again, this time manually. If I manually divide my points into "difficult" and "not difficult" points, then what is the difference between that and having more than two classes? How about as many classes as there are data points?

What would be nice is if I could have an automatic way to determine if there is more than one cluster in my data set. One clustering algorithm will be the subject of the next post.