MNIST handwriting classifier
Logistic regression neural network classifier for MNIST handwriting data
Sample results
With
- Network (single hidden layer): 784 nodes -> 100 nodes -> 10 nodes
- Training data: 60,000 labelled samples (with 10 training epochs)
- Bias: 1
- Learning rate: 0.01
- Node activation function: sigmoid logistic (1 / (1 + e-x))
Accuracy over training data: 98.04%
Accuracy over test data: 96.56%
Network confusion matrix:
[[5883 1 31 18 5 14 37 12 29 16] [ 2 6704 20 12 17 8 11 15 43 10] [ 2 10 5820 41 9 7 11 34 9 2] [ 1 2 25 5922 1 23 0 6 23 16] [ 4 7 7 5 5713 1 5 6 7 14] [ 2 1 5 39 2 5314 17 4 25 9] [ 6 0 7 3 12 16 5811 0 7 0] [ 2 7 12 20 15 1 1 6127 4 21] [ 19 5 24 34 7 20 23 13 5680 14] [ 2 5 7 37 61 17 2 48 24 5847]]
Sample classified output:
