- Implementation of Single Layer Perceptron (SLP)
Classify the IRIS dataset by using the single layer neural network.
- Download iris data from UCI web repository. Click her e
- Read the data: input features in one variable and class labels into another in a vector form suitable for a neural network class label representation (2 marks)
- Randomly select training and the test set: x% (begin with x=10) data from each class for training and all the rest for testing (2 marks)
- Compute training and testing accuracy using SLP for 10 independent simulations and store the results from the individual simulations programmatically in an excel sheet (2 marks)
- Compute training and testing accuracy by varying accuracy x (from 10% to 60%) as the following and report overall training and testing accuracy (average over 10 simulations): (2 marks)
| 1. | Amount of randomly
selected training data |
2. Training accuracy (Average over 10 simulations) | 3. Testing accuracy (Average over 10 simulations) |
| 10% | |||
| 20% | |||
| 30% | |||
| 40% | |||
| 50% | |||
| 60% |
- Plot a graph keeping column 1 at x-axis and column 2 and 3 (at the same figure) at the y-axis. Use curves of different colors to denote curves for column 2 and 3
