SECTION 1

Introduction, Research & Terminology

SECTION 2

Our Project
 

SECTION 3

The Data

SECTION 4

Hardware

Software

REQUIRED imports:

  1. matplotlib

     : A scientific plotting package, Python. On Line 3 matplotlib IS SET to use the 

    “Agg”

     backend to save training plots to disk.

  2. tensorflow.keras

     : We’ll be taking advantage of the 

    ImageDataGenerator

     , 

    LearningRateScheduler

     , 

    Adagrad

     optimizer, and 

    utils

     .

  3. sklearn

     : From scikit-learn we’ll need its implementation of a 

    classification_report

     and a 

    confusion_matrix

     .

  4. pyimagesearch

     : We’re going to be putting our newly defined CancerNet to use (training and evaluating it). We’ll also need our config to grab the paths to our three data splits. This module is not pip-installable; it is included the “Downloads” section of today’s post.

  5. imutils

     : I’ve made my convenience functions publicly available as a pip-installable package. We’ll be using the 

    paths

     module to grab paths to each of our images.

  6. numpy

     : The typical tool used by data scientists for numerical processing with Python.

  7. Python: Both
    argparse

     and 

    os

     are built into Python installations. We’ll use argparse to parse a command line argument.

SECTION 5

Example

SECTION 6

 

Conclusion and Next steps