diff --git a/code/methods.py b/code/methods.py
index 2aa0c2f..2490fe8 100644
--- a/code/methods.py
+++ b/code/methods.py
@@ -3,6 +3,10 @@ from keras.models import Sequential, Model
 from keras.layers import SimpleRNN, Dense
 from keras.layers import Input, TimeDistributed
 from tqdm import tqdm
+import time
+import os
+import dill
+
 
 import itertools
 
@@ -168,16 +172,47 @@ class FeedForwardNetwork(_BaseNetwork):
                 bar.update()
         return losses
 
+class Experiment:
+    
+    @staticmethod
+    def from_dill(path):
+        with open(path) as dill_file:
+            return dill.load(dill_file)
+    
+    def __init__(self, name=None, id=None):
+        self.experiment_id = id or time.time()
+        this_file = os.path.realpath(__file__)
+        self.experiment_name = name or os.path.basename(this_file)
+        self.base_dir = os.path.realpath((os.path.dirname(this_file) + "/..")) + "/"
+        self.next_iteration = 0
+    
+    def __enter__(self):
+        self.dir = self.base_dir + "experiments/exp-" + str(self.experiment_name) + "-" + str(self.experiment_id) + "-" + str(self.next_iteration) + "/"
+        os.mkdir(self.dir)
+        print("** created " + str(self.dir))
+    
+    def __exit__(self, exc_type, exc_value, traceback):
+        self.save(experiment=self)
+        self.next_iteration += 1
+    
+    def save(self, **kwargs):
+        for name,value in kwargs.items():
+            with open(self.dir + "/" + str(name) + ".dill", "wb") as dill_file:
+                dill.dump(value, dill_file)
+        
+    
+
 
 if __name__ == '__main__':
-    features, cells, layers = 2, 2, 2
-    use_recurrent = False
-    if use_recurrent:
-        network = Network(features, cells, layers, recurrent=use_recurrent)
-        r = RecurrentNetwork(network)
-        loss = r.fit(epochs=10)
-    else:
-        network = Network(features, cells, layers, recurrent=use_recurrent)
-        ff = FeedForwardNetwork(network)
-        loss = ff.fit(epochs=10)
-    print(loss)
+    with Experiment() as exp:
+        features, cells, layers = 2, 2, 2
+        use_recurrent = False
+        if use_recurrent:
+            network = Network(features, cells, layers, recurrent=use_recurrent)
+            r = RecurrentNetwork(network)
+            loss = r.fit(epochs=10)
+        else:
+            network = Network(features, cells, layers, recurrent=use_recurrent)
+            ff = FeedForwardNetwork(network)
+            loss = ff.fit(epochs=10)
+        print(loss)