neural-network-in-cuda-sgupta38

Fully Connected Neural Network

This project achieves a 90% accuracy for given images. Expensive functions such as 'forward', 'backprop' and 'update_weights' are executed on GPU device. 'mnist' images has been used for training and testing purpose.

Following are some of the impotant files.

• main.cu --> File which has entry point function and this is where we configure our layers & adjust our 'configurations' such as Number of neurons, dropout rate etc.
• cuda_functions.cu --> GPU API's for forwarding, backpropogation and updating weights.
• CFullyConnectedLayer.cu --> Hidden layer which calls CUDA API's

How to RUN?

> ./fnn

Architecture:

Layers

Input Layer

The input layer is a 28×28×1 grayscale image.

Fully Connected Hidden Layer

This is just a fully connected layer with ReLU activation. It consists of 1024 neurons with dropout rate of 0.4. This means that during training, any given neuron has a 0.4 probability of “dropping out”, which means that it is set to 0, regardless of the inputs.

Output Layer

This is the final classification layer. It is a fully connected layer consisting of 10 neurons, one for each class. It will compute a softmax