lenet-5-on-mnist

The goal is to apply LeNet-5 on minist data and predict the handwritten digits. The preview of data follows:

Prerequisites:

Download the following :

• Keras
• tensorflow
• mnist_data

Quick start:

• clone or download the repository and run le-net5.py
  le-net5.py

LeNet-5 Architecture:

LeNet-5 comprises of 7 layers, not counting the input layer as you can see in the graph above. MNIST images are 28x28 pixels which is smaller than what LeNet-5 expects 32x32 pixels. An easy solution to that is just to pad the images with zeros to bring the MNIST images size up to 32x32 pixels.

LeNet-5 layers:

1. Convolution #1. Input = 32x32x1. Output = 28x28x6 conv2d
2. SubSampling #1. Input = 28x28x6. Output = 14x14x6. SubSampling is simply Average Pooling so we use avg_pool
3. Convolution #2. Input = 14x14x6. Output = 10x10x16 conv2d
4. SubSampling #2. Input = 10x10x16. Output = 5x5x16 avg_pool
5. Fully Connected #1. Input = 5x5x16. Output = 120
6. Fully Connected #2. Input = 120. Output = 84
7. Output 10

Results:

• Train accuracy: 0.9979
• Test accuracy: 0.9876