diff --git a/ecnet/models/mlp.py b/ecnet/models/mlp.py
index c33b287..ee44557 100644
--- a/ecnet/models/mlp.py
+++ b/ecnet/models/mlp.py
@@ -19,6 +19,7 @@
 stderr = sys.stderr
 sys.stderr = open(devnull, 'w')
 from keras.backend import clear_session, reset_uids
+from keras.callbacks import EarlyStopping
 from keras.layers import Dense
 from keras.losses import mean_squared_error
 from keras.metrics import mae
@@ -109,24 +110,20 @@ def fit(self, l_x: array, l_y: array, v_x: array=None, v_y: array=None,
         )
 
         if v_x is not None and v_y is not None:
-            valid_mae_lowest = self._model.evaluate(v_x, v_y, verbose=v)[1]
-            steps = int(epochs / 250)
-            for e in range(steps):
-                h = self._model.fit(
-                    l_x,
-                    l_y,
-                    validation_data=(v_x, v_y),
-                    epochs=250,
-                    verbose=v
-                )
-                valid_mae = h.history['val_mean_absolute_error'][-1]
-                if valid_mae < valid_mae_lowest:
-                    valid_mae_lowest = valid_mae
-                elif valid_mae > (valid_mae_lowest + 0.05 * valid_mae_lowest):
-                    logger.log('debug', 'Validation cutoff after {} epochs'
-                               .format(e * 250), call_loc='MLP')
-                    return
-
+            self._model.fit(
+                l_x,
+                l_y,
+                validation_data=(v_x, v_y),
+                callbacks=[EarlyStopping(
+                    monitor='val_loss',
+                    patience=250,
+                    verbose=v,
+                    mode='min',
+                    restore_best_weights=True
+                )],
+                epochs=epochs,
+                verbose=v
+            )
         else:
             self._model.fit(
                 l_x,
@@ -134,6 +131,7 @@ def fit(self, l_x: array, l_y: array, v_x: array=None, v_y: array=None,
                 epochs=epochs,
                 verbose=v
             )
+
         logger.log('debug', 'Training complete after {} epochs'.format(epochs),
                    call_loc='MLP')