diff --git a/NeuralJAXwork/loss.py b/NeuralJAXwork/loss.py
index b0fab4b..9fc582d 100644
--- a/NeuralJAXwork/loss.py
+++ b/NeuralJAXwork/loss.py
@@ -1,6 +1,6 @@
 # Import jit from JAX
 from jax import jit
-from NeuralJAXwork import Errors
+from .errors import Errors
 
 class Loss:
     """
@@ -37,9 +37,9 @@ def __init__(self, loss, loss_prime):
         # If it fails, switch to regular Python interpreator
         try:
             self.loss_prime = jit(loss_prime)
-        except:
+        except Exception:
             print(Errors.jit_error)
-            self.loss = loss_prime
+            self.loss_prime = loss_prime
 
     def loss(self, y_true, y_pred):
         """
diff --git a/NeuralJAXwork/model.py b/NeuralJAXwork/model.py
index 09e1593..c426741 100644
--- a/NeuralJAXwork/model.py
+++ b/NeuralJAXwork/model.py
@@ -10,9 +10,18 @@ def forward(self, x):
             x = layer.forward(x)
         return x
 
-    def backward(self, grad):
+    def backward(self, grad, learning_rate):
+        """Propagate the gradient through the network.
+
+        Parameters
+        ----------
+        grad: jax.numpy.ndarray
+            Gradient of the loss with respect to the network output.
+        learning_rate: float
+            Learning rate used to update the parameters of each layer.
+        """
         for layer in reversed(self.layers):
-            grad = layer.backward(grad)
+            grad = layer.backward(grad, learning_rate)
         return grad
     
     def train(self, x_train, y_train, epochs = 1000, learning_rate = 0.01, verbose = True):
@@ -27,7 +36,7 @@ def train(self, x_train, y_train, epochs = 1000, learning_rate = 0.01, verbose =
 
                 # backward
                 grad = self.loss.prime(y, output)
-                self.backward(grad)
+                self.backward(grad, learning_rate)
 
             error /= len(x_train)
 
@@ -38,4 +47,5 @@ def __call__(self, x):
         return self.forward(x)
 
     def __repr__(self):
-        return f'SequentialModel({zip(enumerate(self.layers))})'
\ No newline at end of file
+        layers_repr = ", ".join(layer.__class__.__name__ for layer in self.layers)
+        return f"SequentialModel([{layers_repr}])"