1. Artificial Neural Networks for Pattern Recognition
Jack Breese
Computer Systems
Quarter 4, Pd. 7

2. What is a Neural Network?
Interconnected neurons
Weights
Output

3. Uses of Neural Networks
Pattern Recognition
Face Recognition
OCR

4. Neurons Add up each weighted input
Use an activation function to determine output
Pass on output to next layer

5. Training Neural Networks
Large input set
Outputs are verified, weights adjusted along a gradient based on these results. For each neuron in the network: For each connection to the neuron: weight = random_value() Until desired accuracy is reached: For each example in the training data: actual_out = run_network(example) exp_out = calculate_expected(example) error = exp_out – actual out For each neuron in the network: calculate_delta_weights(error) adjust_weights(neuron)

6. Program Information Neural Network Library written in C
Currently capable of initializing a two-layer perceptron with working, weighted connections.
Capable of loading images and propagating data through the network.
Can load images up to 500x500 pixels in size.

7. Data Structure typedef struct _connection { float weight;
struct _neuron * from;
} connection;
typedef struct _neuron { //TODO: Implement a neuron which supports connections.
float d;
connection * cons;
}neuron;
neuron* mkneuron(int c) {
neuron* n = malloc(sizeof(neuron));
n->d = 0;
connection * a = malloc(c*sizeof(connection));;
n->cons = a;
return n; }

8. New Progress Load PGM Images Create TrainingInfo structs
Begin Training
Perform Backpropagation

9. Training and Propagation Algos.
Calculating Neuron Values
For each neuron in the previous layer: Sum += neuron_weight*neuron_value neuron_value = activation_function(sum) Training For each neuron in the network: For each connection to the neuron: weight = random_value() Until desired accuracy is reached: For each example in the training data: actual_out = run_network(example) exp_out = calculate_expected(example) error = exp_out – actual out For each neuron in the network: calculate_delta_weights(error) adjust_weights(neuron)‏

10. New Data Structures
TrainInfo
pImg

11. Testing Memory Usage was tested Training was attempted
Values for known images and random weights propagated through.

12. Problems Encountered Initially thought memory usage was low.
Forgot to reset counter in nested for loops to 0.
That was dumb.
Corrected problem, memory usage went up
Decided to scale back network size/interconnectedness
Issues with String arrays in C
Prevented progress with training.

13. Conclusion Works as a valid header file Many methods
Useful for further exploration