Merge pull request #16 from AFAgarap/docs-cnn-svm

docs : add results documentation

README.md

@@ -7,6 +7,8 @@ An Architecture Combining Convolutional Neural Network (CNN) and Support Vector
 *This project was inspired by Y. Tang's [Deep Learning using Support Vector Machines](https://arxiv.org/abs/1306.0239)
 (2013).*
 
+## Abstract 
+
 Convolutional Neural Networks (CNNs) are similar to "ordinary" neural networks in the sense that they are made up of
 hidden layers consisting neurons with "learnable" parameters. These neurons receive inputs, performs a dot product, and
 then follows it with a non-linearity. The whole network expresses the mapping between raw image pixels and class scores.
@@ -20,8 +22,72 @@ inspired by [[Tang, 2017](https://arxiv.org/abs/1306.0239)].
 
 ## Usage
 
+First, clone the project.
+```bash
+git clone https://github.com/AFAgarap/cnn-svm.git/
+```
+
+Run the `setup.sh` to ensure that the pre-requisite libraries are installed in the environment.
+```bash
+sudo chmod +x
+./setup.sh
+```
+
+Program parameters.
+```bash
+usage: main.py [-h] -m MODEL -d DATASET [-p PENALTY_PARAMETER] -c
+               CHECKPOINT_PATH -l LOG_PATH
+
+CNN & CNN-SVM for Image Classification
+
+optional arguments:
+  -h, --help            show this help message and exit
+
+Arguments:
+  -m MODEL, --model MODEL
+                        [1] CNN-Softmax, [2] CNN-SVM
+  -d DATASET, --dataset DATASET
+                        path of the MNIST dataset
+  -p PENALTY_PARAMETER, --penalty_parameter PENALTY_PARAMETER
+                        the SVM C penalty parameter
+  -c CHECKPOINT_PATH, --checkpoint_path CHECKPOINT_PATH
+                        path where to save the trained model
+  -l LOG_PATH, --log_path LOG_PATH
+                        path where to save the TensorBoard logs
+```
+
+Then, go to the repository's directory, and run the `main.py` module as per the desired parameters.
+```bash
+cd cnn-svm
+python3 main.py --model 2 --dataset ./MNIST_data --penalty_parameter 1 --checkpoint_path ./checkpoint --log_path ./logs
+```
+
 ## Results
 
+The hyperparameters used in this project were manually assigned, and not through optimization.
+
+|Hyperparameters|CNN-Softmax|CNN-SVM|
+|---------------|-----------|-------|
+|Batch size|128|128|
+|Epochs|10000|10000|
+|Learning rate|1e-3|1e-3|
+|SVM C|N/A|1|
+
+The experiments were conducted on a laptop computer with Intel Core(TM) i5-6300HQ CPU @ 2.30GHz x 4, 16GB of DDR3 RAM,
+and NVIDIA GeForce GTX 960M 4GB DDR5 GPU.
+
+![](figures/accuracy-loss.png)
+
+**Figure 1. Training accuracy (left) and loss (right) of CNN-Softmax and CNN-SVM on image classification using
+[MNIST](http://yann.lecun.com/exdb/mnist/).**
+
+The orange plot refers to the training accuracy and loss of CNN-Softmax, with a test accuracy of 99.22999739646912%.
+On the other hand, the blue plot refers to the training accuracy and loss of CNN-SVM, with a test accuracy of
+99.04000163078308%. The results do not corroborate the findings of [Tang (2017)](https://arxiv.org/abs/1306.0239)
+for [MNIST handwritten digits](http://yann.lecun.com/exdb/mnist/) classification. This may be attributed to the fact
+that no data preprocessing nor dimensionality reduction was done on the dataset for this project.
+
+
 ## Citation
 ```
 ```