This is an architecture which was proposed in 1998 Proceedings of IEEE journal - Gradient-based learning applied to document recognition with about 37505 citations as of today. Even though this is a paper from 1998, it provides basic fundamentals of a neural network. By the development of network architecture and error rate reduction, we can grasp the importance of Deep Learning components to be added on to the network.

This is a simple implementation of the MNIST dataset on LeNet-5 Architecture using PyTorch(Handwritten Digits).

To predict the class labels of the dataset (here, Handwritten Digits Images) on new data with respect to classes accurately.

MNIST (Handwritten Digits)

The provided code will automatically extract the dataset from the module 'torchvision.datasets'.

Python >= 3.0

PyTorch Version >= 0.4.0

torchvision >= 0.2.1

Number of Image Channels = 1

Activation = ReLU

32×32 Input Image

Six 28×28 feature maps convolutional layer (5×5 size)

Max Pooling layers (2×2 size)

Sixteen 10×10 feature maps convolutional layer (5×5 size)

Max Pooling layers (2×2 size)

Flattened to 16x5x5 which is

Fully connected to 120 neurons

Fully connected to 84 neurons

Fully connected to 10 outputs

~99 %