Update mlp.py

mlp.py

@@ -58,7 +58,22 @@ def learn_once_cross_entropy(w1, b1, w2, b2, data, labels_train, learning_rate):
     targets_one_hot = one_hot(labels_train) # target as a one-hot encoding for the desired labels
     
     # cross-entropy loss
-    loss = 
+    loss = -np.sum(targets_one_hot * np.log(predictions)) / N
+    
+    # Backpropagation
+    d_z2 = a2 - targets_one_hot
+    d_w2 = np.dot(a1.T, d_z2) / N
+    d_b2 = d_z2 / N
+    d_a1 = np.dot(d_z2, w2.T)
+    d_z1 = d_a1 * z1 * (1 - a1)
+    d_w1 = np.dot(a0.T, d_z1) / N
+    d_b1 = d_z1 / N
+    
+    # Calculation of the updated weights and biases of the network with gradient descent method
+    w1 -= learning_rate * d_w1
+    w2 -= learning_rate * d_w2
+    b2 -= learning_rate * d_b2
+    b1 -= learning_rate * d_b1
     
     return w1, b1, w2, b2, loss