A16 Artificial Neural Networks

Artificial neural networks are modeled after the way our brain works. Just like our brain, ANN’s most defining feature is its ability to learn through successive trainings. Training of an ANN is done by presenting to the network a set of input and target pairs. The network will then adapt its weights or strengths of connections between neurons so that the difference between the target and the network’s output will be minimized.

Among the applications of ANNs is pattern recognition. The training set will have the features-matrix as input and the classes as target.

Some of the network parameters that can be manipulated are

1. Number of hidden layers/ neurons in the hidden layer

- Bigger networks can generally solve more complicated problems. However care must also be taken to limit network size so that memorization can be avoided.

2. Epoch

- Time when all patterns in the data set have been presented to the network. The weights are adjusted after each epoch.

3. Learning rate

- May be set by trial and error.

We still use the features extracted from the two previous activities…

… and the code from Cole’s blog.

The results of the training and testing classification for different learning rates are listed below.

Correct classification would be 0 for strawberries and 1 for blueberries. Notice that for both training and testing classification the values that the neural network outputs are not exactly 0 or 1. The sample is just classified to the class that is nearer to its own value. As the learning rate is increased the classification goes closer to 0 and 1. The same also happens when we increase the number of epochs.

I give myself a grade of 10 because all the classifications were correct.