CS1340: Course Project
Soham De, Tanvi Roy
This is an implementation of suggested prompt 2 (as follows):
Implementing your favourite Machine Learning training over LAN (client provides data, learning happens at server)
In our case, we are training a simple CNN on MNIST dataset. The CNN architecture used is:
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
conv2d (Conv2D) (None, 26, 26, 32) 320
max_pooling2d (MaxPooling2D (None, 13, 13, 32) 0
)
conv2d_1 (Conv2D) (None, 11, 11, 64) 18496
max_pooling2d_1 (MaxPooling (None, 5, 5, 64) 0
2D)
flatten (Flatten) (None, 1600) 0
dropout (Dropout) (None, 1600) 0
dense (Dense) (None, 10) 16010
=================================================================
Total params: 34,826
Trainable params: 34,826
Non-trainable params: 0
_________________________________________________________________
For testing, we are only using 10000 samples from the original MNIST (60000 samples), but this framework can natively support datasets of any size.
To run, open 2 tabs and run the following (in sequence)
python3 server.py
python3 client.py