diff --git a/mhcflurry/class1_affinity_prediction/class1_neural_network.py b/mhcflurry/class1_affinity_prediction/class1_neural_network.py
index df8e767c46060fbef92a77cecb2b4d3847972529..a6d93d2eb6c5dcf7dededc521a1299da59a29df2 100644
--- a/mhcflurry/class1_affinity_prediction/class1_neural_network.py
+++ b/mhcflurry/class1_affinity_prediction/class1_neural_network.py
@@ -73,13 +73,12 @@ class Class1NeuralNetwork(object):
         self.hyperparameters = self.hyperparameter_defaults.with_defaults(
             hyperparameters)
         self.network = None
-        self.fit_history = None
+        self.loss_history = None
         self.fit_seconds = None
 
     def __getstate__(self):
         result = dict(self.__dict__)
         del result['network']
-        result['fit_history'] = None
         result['network_json'] = self.network.to_json()
         result['network_weights'] = self.get_weights()
         return result
@@ -204,7 +203,7 @@ class Class1NeuralNetwork(object):
         min_val_loss_iteration = None
         min_val_loss = None
 
-        self.fit_history = collections.defaultdict(list)
+        self.loss_history = collections.defaultdict(list)
         start = time.time()
         for i in range(self.hyperparameters['max_epochs']):
             random_negative_peptides_list = []
@@ -243,17 +242,17 @@ class Class1NeuralNetwork(object):
                 sample_weight=sample_weights)
 
             for (key, value) in fit_history.history.items():
-                self.fit_history[key].extend(value)
+                self.loss_history[key].extend(value)
 
             logging.info(
                 "Epoch %3d / %3d: loss=%g. Min val loss at epoch %s" % (
                     i,
                     self.hyperparameters['max_epochs'],
-                    self.fit_history['loss'][-1],
+                    self.loss_history['loss'][-1],
                     min_val_loss_iteration))
 
             if self.hyperparameters['validation_split']:
-                val_loss = fit_history.history['val_loss'][-1]
+                val_loss = self.loss_history['val_loss'][-1]
                 val_losses.append(val_loss)
 
                 if min_val_loss is None or val_loss <= min_val_loss:
diff --git a/mhcflurry/class1_affinity_prediction/train_allele_specific_models_command.py b/mhcflurry/class1_affinity_prediction/train_allele_specific_models_command.py
index 4b4201915e8700b6fe633f94db3d2b711edd0d5c..e3d7f71543a0b8731eda2e10af275a3d1a6d2d50 100644
--- a/mhcflurry/class1_affinity_prediction/train_allele_specific_models_command.py
+++ b/mhcflurry/class1_affinity_prediction/train_allele_specific_models_command.py
@@ -16,6 +16,7 @@ import mhcnames
 from .class1_neural_network import Class1NeuralNetwork
 from ..common import configure_logging
 
+
 def normalize_allele_name(s):
     try:
         return mhcnames.normalize_allele_name(s)