BMEN4460-Notebook1

Simple Cell Segmentation with a Single Layered Perceptron.

Nanyan "Rosalie" Zhu and Chen "Raphael" Liu

Correction

More appropriately, we should have use the word "sigmoid activation unit" instead of "perceptron" throughout this entire repository. Strictly speaking, a "perceptron" shall only be called a "perceptron" if the activation function is a step function, instead of the sigmoid function that we are using here. You may explore on your own what might happen if you swap out the activation function and make the "perceptrons" real "perceptrons".

Overview

This repository is a child repository of RnR-2018/Deep-learning-with-PyTorch-and-GCP. This serves a primary purpose of facilitating the course BMEN4460 instructed by Dr. Andrew Laine and Dr. Jia Guo at Columbia University. However, it can also be used as a general beginner-level tutorial to implementing deep learning algorithms with PyTorch on Google Cloud Platform.

This repository, Simple Cell Segmentation with a Single Layered Perceptron, presents three simple examples of the least complicated neural networks.

For students in BMEN4460 (or who follow the instructions Step00 through Step02 in the parent repository), please create a Projects folder within your GCP VM and download this repository into that folder.

On the GCP VM Terminal:

cd /home/[username]/
mkdir BMEN4460 # This is only necessary if you have not done this yet
mkdir BMEN4460/Perceptron # This is only necessary if you have not done this yet
cd BMEN4460/Perceptron
git clone https://github.com/RnR-2018/BMEN4460-NB1-simple_cell_segmentation_with_a_single_layered_perceptron/

If it says "fatal: could not create work tree dir ...", you may as well try it again with super user permission

sudo git clone https://github.com/RnR-2018/BMEN4460-NB1-simple_cell_segmentation_with_a_single_layered_perceptron/

You shall then see the following hierarchy of files and folders, hopefully, which matches the hierarchy of the current repository.

BMEN4460-NB1-simple_cell_segmentation_with_a_single_layered_perceptron
    ├── BMEN4460_NB1_simple_cell_segmentation_with_single_layered_perceptrons.ipynb
    ├── helper.py
    └── data
        └── sample_cell_image.tif
        └── perceptron_single_input.PNG
        └── perceptron_multi_input.PNG
        └── perceptron_CNN.PNG

One thing to note: we are not sure why, but it seems that the markdown support at GitHub is a little different from that in jupyter lab. The notations in our '.ipynb' notebook went crazy if you look at them on GitHub. The issue will most likely go away once you download it and open it in jupyter lab.

Again, the concepts covered are quite rudimentary. Hope you enjoy this.

Figures we created to illustrate the three perceptron candidates.

Candidate 1: Perceptron, single input.

Candidate 2: Perceptron, multi input.

Candidate 3: Perceptron, convolutional neural network.