Add of graphs

README.md

@@ -126,7 +126,7 @@ Here is the graph of the accuracy vs K for the whole Cifar dataset with a split
 
 ![Image](results/knn.png)
 
-Here we can conclude that the best K is 9, (if we don't use k = 1)  with a performace of 35% of accuracy.
+Here we can conclude that the best K is 9, (if we don't use k = 1)  with a performace of 35% of accuracy. (I tried to updtade the graph, but my kernel kept dying at each run so I kept the first version that could execute.)
 
 ## Artificial Neural Network
 
@@ -326,12 +326,11 @@ def run_mlp_training(data_train, labels_train, data_test, labels_test, d_h,learn
     d_in = data_train.shape[1]
     d_out = 10 #we can hard code it here or len(np.unique(label_train))
 
-    #Random initialisation of weights
-    w1 = np.random.randn(d_in, d_h)
-    b1 = np.random.randn(1, d_h)
-
-    w2 = np.random.randn(d_h, d_out)
-    b2 = np.random.randn(1, d_out)
+    #Random initialisation of weights (Xavier initialisation)
+    w1 = np.random.randn(d_in, d_h) / np.sqrt(d_in)
+    b1 = np.zeros((1, d_h))
+    w2 = np.random.randn(d_h, d_out) / np.sqrt(d_h)
+    b2 = np.zeros((1, d_out))
 
     # Train MLP
     w1, b1, w2, b2, train_accuracies = train_mlp(w1, b1, w2, b2, data_train, labels_train, learning_rate, num_epoch)
@@ -339,4 +338,29 @@ def run_mlp_training(data_train, labels_train, data_test, labels_test, d_h,learn
     # Test MLP
     test_accuracy = test_mlp(w1, b1, w2, b2, data_test, labels_test)
     return train_accuracies, test_accuracy
-```
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+```
+
+#### 16-
+```rb
+def plot_graph(data_train, labels_train, data_test, labels_test, d_h, learning_rate, num_epoch):
+    # Run MLP training
+    train_accuracies, test_accuracy = run_mlp_training(data_train, labels_train, data_test, labels_test, d_h, learning_rate, num_epoch)
+    
+    # Plot and save the learning accuracy graph
+    plt.figure(figsize=(8, 6))
+    epochs = np.arange(1, num_epoch + 1)
+    plt.plot(epochs, train_accuracies, marker='x', color='b', label='Train Accuracy')
+    plt.xlabel('Epochs')
+    plt.ylabel('Accuracy')
+    plt.title('MLP Train Accuracy')
+    plt.legend()
+    plt.grid(True)
+    plt.savefig('image-classification/results/mlp.png')
+    plt.show()
+```
+
+![Image](results/mlp.png)
+
+The accuracy is increasing with each epochs without converging, we could increase the learning rate to speed up the training and inscrease the numbers of epoch to see what would be our maximum accuracy.
+For 100 epochs and a learning rate of 0.1 we got a test accuracy of 0.13.
+For 300 epochs and a learning rate of 0.1 we increased the training accuracy to 0.15991 and we got a test accuracy of 0.155
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