Update README.md

README.md

@@ -64,14 +64,14 @@ As can be seen from the graph, the decreasing trend in the graph is due to the f
    
   - `mlp.png`: refers to the MLP neural network, it represents the plot of the training accuracies evolution along 100 epochs 
 
-The accuracy returned by the model has an increasing trend starting at about 10 %, which is understandable given the presence of 10 classes and thus the network let's say that at the beginning it tries to guess the class, while as the epochs advance and the layer weights and biases are updated step by step, we notice an improvement up to 18 % of the 100th epoch, which given the low complexity of our network is an acceptable result.
+  The accuracy returned by the model has an increasing trend starting at about 10 %, which is understandable given the presence of 10 classes and thus the network let's say that at the beginning it tries to guess the class, while as the epochs advance and the layer weights and biases are updated step by step, we notice an improvement up to 18 % of the 100th epoch, which given the low complexity of our network is an acceptable result.
 <div style="text-align:center;">
   <img src="results/mlp.png" alt="mlp" width="300" height="200">
 </div>
   
 - `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)
 
-Another way to see if our network is training is to look at the trend of the loss, which having a decreasing trend confirms what we said before.
+  Another way to see if our network is training is to look at the trend of the loss, which having a decreasing trend confirms what we said before.
 <div style="text-align:center;">
   <img src="results/loss.png" alt="loss" width="300" height="200">
 </div>
@@ -84,4 +84,4 @@ Another way to see if our network is training is to look at the trend of the los
     ```
 2. Create a folder named data in which you move the downloaded cifar-10-batches-py folder. 
 3. run the desired model KNN or MLP NN by running the respective files `knn.py` et `mlp.py`.
-   - if you want to modify the hyperparameters just go for both files in the `main()` function and modify them as desired.
\ No newline at end of file
+   - if you want to modify the hyperparameters just go for both files in the `main()` function and modify them as desired.