Final commit

README.md

@@ -22,7 +22,7 @@ The two models are tested on the image database CIFAR-10 which consists of 60 00
     - `read_cifar(path)`: takes the path of the directory containing the six batches and returns a matrix `data` and a vector `labels`.
 
     - `split_dataset(data, labels, split_factor)`: randomly splits the dataset into a training set and a test set with a specified split factor.
-
+m
 ### K-Nearest Neighbors
 
 3. The Python file named `knn.py` is composed of:
@@ -55,9 +55,18 @@ The two models are tested on the image database CIFAR-10 which consists of 60 00
 
 5. In the `results` folder there are three plot images:
    - `knn.png`: refers to the knn algorithm, it represents the plot of the accuracy evolution along increasing value of 'k' (from 1 to 20)
+   <div style="text-align:center;">
+      <img src="results/knn.png" alt="knn" width="300" height="200">
+   </div>
+   
    - `mlp.png`: refers to the MLP neural network, it represents the plot of the training accuracies evolution along 100 epochs 
    - `loss.png`: refers to the MLP neural network, it represents the plot of the loss evolution along 100 epochs (further proof that the network works)
 
+<div style="text-align:center;">
+  <img src="results/mlp.png" alt="mlp" width="300" height="200">
+  <img src="results/loss.png" alt="loss" width="300" height="200">
+</div>
+
 ## Usage
 
 1. Clone the repository.