Update README

Author: pijner

README.md

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 # MLE-Image-Classification
 Meta-learning ensemble framework for image classification with convolutional neural networks
+
+
+## Purpose
+This project creates an ensemble of CNN models for the MINST, Fashion-MNIST, and CIFAR10 dataset. The predictions from the ensemble are evaluated by a CNN super-learner and a weight matrix optimized by a genetic algorithm.
+
+
+A special case is used for binary classification where a MLP detects errors in the aggregation of prediction matrices and flips the final predictions accordingly.
+
+All base learners in the ensemble are saved to disk along with the prediction matrices and final predictions from the GA and CNN meta learners.
+
+---
+
+## Set-up
+### Set up a virtual environment ([source](https://docs.python.org/3/tutorial/venv.html#virtual-environments-and-packages))
+*A virtual environment, a self-contained directory tree that contains a Python installation for a particular version of Python, plus a number of additional packages.*
+
+**Unix/MacOS**
+* `python3 -m pip install --user virtualenv` - if you don't have virtualenv installed
+* `python3 -m venv venv` - Create a virtual environment
+* `source venv/bin/activate` - Activate virtual environment
+* `python3 -m pip install -r requirements.txt ` - Install required libraries for project
+
+**Windows**
+* `py -m pip install --user virtualenv` - if you don't have virtualenv installed
+* `py -m venv venv` - Create a virtual environment
+* `.\venv\Scripts\activate` - Activate virtual environment
+* `py -m pip install -r requirements.txt ` - Install required libraries for project
+
+---
+
+## Config settings
+The project uses `dafault.ini` to set configurations for dataset, ensemble and GA prediction. The following table shows the allowed values for the configurations:
+
+| Section  | Setting              | Allowed values                                                              | Description                                                                                                                        |
+|----------|----------------------|-----------------------------------------------------------------------------|------------------------------------------------------------------------------------------------------------------------------------|
+| General  | Dataset              | any one of MNIST, MNIST_BIN, F_MNIST, F_MNIST_BIN, CIFAR10, CIFAR10_BIN     | Dataset to use                                                                                                                     |
+| General  | ValidationProportion | decimal number in range [0.0, 1.0]. Recommended 0.2                         | Proportion of training data to use to train meta-learners                                                                          |
+| Ensemble | Type                 | full or stacked                                                             | full: no meta-learner and full training set is used to train ensemble; stacked: validation data is left for training meta-learners |
+| Ensemble | NumBaseLearners      | Any positive integer > 2. Recommended a value above 30                      | Number of base learners in ensemble                                                                                                |
+| Ensemble | UseDynamicLoss       | True or False                                                               | Boolean value specifying whether or not to use dynamic loss to train base learners in the ensemble                                 |
+| Ensemble | DynamicLossThreshold | decimal number in range [0.0, 1.0]. Recommended a value between 0.6 and 0.9 | Threshold to use for dynamic loss                                                                                                  |
+| GA       | GAGenerations        | Any positive integer. Recommended a value above 150                         | Number of generations to run GA meta-learner for                                                                                   |
+| GA       | GAPopulation         | Any positive integer. Recommended a value above 100                         | Population to maintain in GA optimizer                                                                                             |