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Use the perceptron learning method to train a simple system (single layer perceptron) for the

recognition of handwritten digits (0, 1,...., 9).

Design a fully connected network structure of 784 input nodes and 10 output nodes.

The input to the single layer network architecture will be a set of binary pixels

representing a 28×28 image of handwritten digits. The output should indicate which of

the digits (0,....,9) is in the input image.

Use the MNIST database of handwritten digits available at:

for testing and training the system. This dataset is also provided in Blackboard - Folder Name:

Hand Written Digits Data Set.

Select a subset of the MNIST database consisting around 500 images of handwritten

digits (0,...,9) for training the system, and use another 100 images for testing the system.

Create binary or bipolar images of handwritten digits from gray scale images available

in MNIST by simple thresholding (indicate the threshold value you used).

Plot a learning curve that illustrates the mean square error versus iterations.

(One iteration: apply all the training inputs once to the network and compute the mean

square error).

Plot the percentage error in testing your handwritten digit recognition system as a bar


(Mean error occurred while testing each digit with the test data).

• Task #1: Repeat this experiment for different learning rate parameters (at least 3

experiments. Start with a large value and gradually decrease to a small value).

• Task #2: Repeat Task #1 with a large database. (5000 or more for training and 500 or more

for testing).

• Task #3: Repeat Task #2 with multilevel data (without thresholding the input data,

normalize the input data, use sigmoid function for output thresholding).