Logistic Regression

Model

The code is the implementation of Minibatch SGD to learn Logistic Regression model.

Requirements

I have coded Logistic Regression in Python 3. The requirements are:

• numpy>=1.19.4
• torchvision>=0.3.0
• scikit-learn>=0.24.2 Dependency can be installed using the following command:

pip install -r requirements.txt

Data

I use MNIST data as my test case. There are 25 pairs of even-odd pairs of digits that can be used for our binary classification. Only one pair is used to train and test the model. To select the pair, pair_idx argument should be used. Indices of pairs are as follows:

To be able to feed the data in Logistic Regression, the images are flattened. Moreover, PCA is used to reduce the size of the input data from 784 to 50.

Run the Model

To run the code, run the following command:

python -m LR_code --pair_idx=0 --batch_size=0 --n_epoch=25 --n_stepsize=10

where batch_size is the size of the batch in Minibatch SGD, n_epoch is the number of epochs, and n_stepsize is the number of stepsizes considered in hyperparameter tuning. Argument pair_idx is elaborated in Data subsection. Default value of batch_size is 0 which means the batch size is equal to size of the data. In other words, batch_size=0 means the algorithm is Gradient Descent.