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Multilayer Perceptron from Scratch

Here I implement the multilayer perceptron from "scratch" using numpy. See report.pdf for details on the neural network and project results. The implementation is derived solely from the readings and sources in the References section of the report.pdf . I compare my implementation with a reference tensorflow implementation and make use of N, K-fold cross validation with the construction of 99% confidence intervals to validate my models. The report.pdf also contains explanations on the foundational concepts of neural networks for personal elucidation.

Reproducing Results

The numpy, scikit-learn, scipy, matplotlib, and tensorflow packages are needed for this repository. I recommend using a conda environment and running the following commands:

conda create --name mlp-from-scratch
conda activate mlp-from-scratch
conda install numpy scipy scikit-learn
conda install -c conda-forge tensorflow matplotlib

You can copy and paste the first or second line of experiments.txt into the terminal to reproduce my results.

The script demo.py was used to run experiments as well as debug the multilayer perceptron. The list of arguments to demo.py can be shown by running python demo.py -h, though I list the arguments below as well. Note that the random-classification task only supports binary classification for now.

$ python demo.py -h
usage: demo.py [-h] [--debug {True,False}] [--inspect-model-history {True,False}] [--random-seed RANDOM_SEED] [--verbose {True,False}] [--m-examples M_EXAMPLES]
               [--n-features N_FEATURES] [--t-targets T_TARGETS] [--c-categories C_CATEGORIES] [--num-layers NUM_LAYERS] [--num-hidden-units NUM_HIDDEN_UNITS]
               [--test-size TEST_SIZE] [--batch-size BATCH_SIZE] [--num-epochs NUM_EPOCHS] [--learning-rate LEARNING_RATE] [--confidence-level CONFIDENCE_LEVEL]
               [--n-kfold-iterations N_KFOLD_ITERATIONS] [--bar-chart-path BAR_CHART_PATH] [--bar-plot-title BAR_PLOT_TITLE] [--bar-width BAR_WIDTH]
               {breast-cancer-classification,diabetes-regression,random-regression,random-classification}

script for testing MLP from scratch.

positional arguments:
  {breast-cancer-classification,diabetes-regression,random-regression,random-classification}
                        specify supervised learning task.

optional arguments:
  -h, --help            show this help message and exit
  --debug {True,False}  bool to debug
  --inspect-model-history {True,False}
                        bool to print model history at the end of N, KFold CV
  --random-seed RANDOM_SEED
                        random seed for reproducibility. (default: 0)
  --verbose {True,False}
                        whether to print model fitting output. (default: False)

random-data-params:
  parameters for random dataset tasks

  --m-examples M_EXAMPLES
                        number of training examples. (default: 32)
  --n-features N_FEATURES
                        number of features. (default: 1)
  --t-targets T_TARGETS
                        number of targets for regression. (default: 1)

hyperparameters:
  hyperparametesr for MLP.

  --num-layers NUM_LAYERS
                        number of hidden layers. (default: 1)
  --num-hidden-units NUM_HIDDEN_UNITS
                        number of hidden units in hidden layers. (default: 2)
  --test-size TEST_SIZE
                        percent of data to devote to testing. (default: 0.2)
  --batch-size BATCH_SIZE
                        batch size for training. (default: 4)
  --num-epochs NUM_EPOCHS
                        number of epochs to fit model. (default: 2)
  --learning-rate LEARNING_RATE
                        learning rate for gradient descent. (default: 1e-2)

stats:
  parameters for report statistics.

  --confidence-level CONFIDENCE_LEVEL
                        confidence level for intervals (aka alpha). (default: 0.95)
  --n-kfold-iterations N_KFOLD_ITERATIONS
                        number of times to conduct k-fold cross validation. Also changes random state for shuffling n times. (default: 1)

figures:
  parameters for figures.

  --bar-chart-path BAR_CHART_PATH
                        path to save bar chart. (default: ./tex/figures/bar_chart.svg)
  --bar-plot-title BAR_PLOT_TITLE
                        title for bar plot. (default: N, K-Fold CV Model Performance Comparison)
  --bar-width BAR_WIDTH
                        width of bars in bar charts. (default: 0.25)