- A simple image classification implementation, where description and classification are done together.
- Models used
- Linear single-layer perceptron.
- Non-linear single-layer perceptron.
- Multi-layer perceptron.
- A pipeline with a convolutional layer.
- All models, forward and backward propogation steps are implemented from scratch.
- This is unlike a shallow classification, where description and classification are done separately.
Problems in hw4.pdf
are solved.
- All source code is in
cnn.py
&main_functions.py
. Ironicallycnn.py
is the main file. main_functions.py
readsmnist_train.mat
&mnist_test.mat
.- Description of data format is given in
hw4.pdf
. comps
contains results of models for different hyper parameters. It mainly serves as an example of how NOT to do a hyper parameter search :)
- Code is the documentation of itself.
- Use
python3 cnn.py
to classify images and visualize results using above mentioned models. - A summary of the methods and corresponding results is given in
report.pdf
.
- Linear single-layer perceptron.
- Non-linear single-layer perceptron.
- Multi-layer perceptron.
- A pipeline with a convolutional layer.