--- a/optimise_hyperparameters.py	Wed Sep 25 06:42:40 2019 -0400
+++ b/optimise_hyperparameters.py	Fri Oct 11 18:24:54 2019 -0400
@@ -22,7 +22,7 @@
         """ Init method. """
 
     @classmethod
-    def train_model(self, config, reverse_dictionary, train_data, train_labels, test_data, test_labels, class_weights):
+    def train_model(self, config, reverse_dictionary, train_data, train_labels, class_weights):
         """
         Train a model and report accuracy
         """
@@ -46,7 +46,7 @@
         # get dimensions
         dimensions = len(reverse_dictionary) + 1
         best_model_params = dict()
-        early_stopping = EarlyStopping(monitor='val_loss', mode='min', min_delta=1e-4, verbose=1, patience=1)
+        early_stopping = EarlyStopping(monitor='val_loss', mode='min', verbose=1, min_delta=1e-4)
 
         # specify the search space for finding the best combination of parameters using Bayesian optimisation
         params = {
@@ -82,11 +82,12 @@
                 validation_split=validation_split,
                 callbacks=[early_stopping]
             )
-            return {'loss': model_fit.history["val_loss"][-1], 'status': STATUS_OK}
-        # minimize the objective function using the set of parameters above4
+            return {'loss': model_fit.history["val_loss"][-1], 'status': STATUS_OK, 'model': model}
+        # minimize the objective function using the set of parameters above
         trials = Trials()
         learned_params = fmin(create_model, params, trials=trials, algo=tpe.suggest, max_evals=int(config["max_evals"]))
-        print(learned_params)
+        best_model = trials.results[np.argmin([r['loss'] for r in trials.results])]['model']
+
         # set the best params with respective values
         for item in learned_params:
             item_val = learned_params[item]
@@ -96,4 +97,4 @@
                 best_model_params[item] = l_recurrent_activations[item_val]
             else:
                 best_model_params[item] = item_val
-        return best_model_params
+        return best_model_params, best_model