add of color in the code

README.md

@@ -6,14 +6,14 @@ Corentin MASSALA
 All the code can be found on the python file read_cifar.py
 
 #### 2-
-```
+```rb
 def read_cifar_batch(file):
     with open(file, 'rb') as fo:
         dict = pickle.load(fo, encoding='bytes')
     return (np.array(dict[b'data']).astype('float32'), np.array(dict[b'labels']).astype('int64') )
 ```
 #### 3- 
-```
+```rb
 def read_cifar(path):
     data = []
     labels = []
@@ -39,7 +39,7 @@ def read_cifar(path):
 
 To split the dataset we use the split function from  the sklearn library
 
-```
+```rb
 def split_dataset(data, labels, split):
     X_train, X_test, y_train, y_test = train_test_split(
     data, labels, test_size=(1 - split), random_state=0)
@@ -52,7 +52,7 @@ def split_dataset(data, labels, split):
 All the code can be found on the python file knn.py
 #### 1-
 
-```
+```rb
 def distance_matrix(matrix1, matrix2):
     #X_test then X_train in this order
     sum_of_squares_matrix1 = np.sum(np.square(matrix1), axis=1, keepdims=True) #A^2
@@ -66,7 +66,7 @@ def distance_matrix(matrix1, matrix2):
 
 #### 2-
 
-```
+```rb
 def knn_predict(dists, labels_train, k):
     output = []
     # Loop on all the images_test
@@ -86,7 +86,7 @@ def knn_predict(dists, labels_train, k):
 
 #### 3-
 
-``` 
+``` rb
 def evaluate_knn(data_train, labels_train, data_test, labels_tests, k):
     dist = distance_matrix(data_test, data_train)
     result_test = knn_predict(dist, labels_train, k)
@@ -98,7 +98,7 @@ def evaluate_knn(data_train, labels_train, data_test, labels_tests, k):
 ```
 
 #### 4-
-```
+```rb
 def bench_knn():
 
     k_indices = [i for i in range(20) if i % 2 != 0]
@@ -165,7 +165,7 @@ Here are all the answer for the theory of the backpropagation.
 ### Coding part
 All the code can be found on the file mlp.py
 
-```
+```rb
 def learn_once_mse(w1, b1, w2, b2, data, targets, learning_rate):
 
     N_out = len(targets) #number of training examples
@@ -217,7 +217,7 @@ def learn_once_mse(w1, b1, w2, b2, data, targets, learning_rate):
 ```
 
 #### 11-
-```
+```rb
 def one_hot(labels):
     #num_classes = np.max(labels) + 1 on va le hardcoder ici
     num_classes = 10
@@ -227,14 +227,14 @@ def one_hot(labels):
 
 #### 12-
 The cross_entropy_loss is :
-```
+```rb
 def cross_entropy_loss(y_pred, y_true):
     loss = -np.sum(y_true * np.log(y_pred)) / len(y_pred)
     return loss
 ```
 The new learning function is : 
 
-```
+```rb
 def learn_once_cross_entropy(w1, b1, w2, b2, data, labels_train, learning_rate):
 
     N_out = len(labels_train) #number of training examples
@@ -275,7 +275,7 @@ def learn_once_cross_entropy(w1, b1, w2, b2, data, labels_train, learning_rate):
 ```
 
 #### 13-
-```
+```rb
 def forward(w1, b1, w2, b2, data):
     # Forward pass
     a0 = data # the data are the input of the first layer
@@ -286,7 +286,7 @@ def forward(w1, b1, w2, b2, data):
     predictions = a2  # the predicted values are the outputs of the output layer
     return(predictions)
 ```
-```
+```rb
 def train_mlp(w1, b1, w2, b2, data_train, labels_train, learning_rate, num_epoch):
     train_accuracies = []
     for epoch in range(num_epoch):
@@ -306,7 +306,8 @@ def train_mlp(w1, b1, w2, b2, data_train, labels_train, learning_rate, num_epoch
     return w1, b1, w2, b2, train_accuracies
 ```
 #### 14-
-```def test_mlp(w1, b1, w2, b2, data_test, labels_test):
+```rb
+def test_mlp(w1, b1, w2, b2, data_test, labels_test):
  
     # Compute accuracy
     predictions = forward(w1, b1, w2, b2, data_test)
@@ -319,7 +320,7 @@ def train_mlp(w1, b1, w2, b2, data_train, labels_train, learning_rate, num_epoch
 
 #### 15-
 
-```
+```rb
 def run_mlp_training(data_train, labels_train, data_test, labels_test, d_h,learning_rate, num_epoch):
 
     d_in = data_train.shape[1]