diff --git a/network2.py b/network2.py
index 9ecaa5b..09cf373 100644
--- a/network2.py
+++ b/network2.py
@@ -334,7 +334,8 @@ def load(filename):
     f = open(filename, "r")
     data = json.load(f)
     f.close()
-    cost = getattr(sys.modules[__name__], data["cost"])
+    cost = getattr(
+        [__name__], data["cost"])
     net = Network(data["sizes"], cost=cost)
     net.weights = [np.array(w) for w in data["weights"]]
     net.biases = [np.array(b) for b in data["biases"]]
diff --git a/test.py b/test.py
index d1f4996..c412580 100644
--- a/test.py
+++ b/test.py
@@ -17,19 +17,19 @@
 
 # ----------------------
 # - read the input data:
-'''
+
 import mnist_loader
 training_data, validation_data, test_data = mnist_loader.load_data_wrapper()
 training_data = list(training_data)
-'''
+
 # ---------------------
 # - network.py example:
-#import network
+import network
+
 
-'''
 net = network.Network([784, 30, 10])
 net.SGD(training_data, 30, 10, 3.0, test_data=test_data)
-'''
+
 
 # ----------------------
 # - network2.py example:
@@ -124,29 +124,30 @@
 
 
 """
-def testTheano():
-    from theano import function, config, shared, sandbox
-    import theano.tensor as T
-    import numpy
-    import time
-    print("Testing Theano library...")
-    vlen = 10 * 30 * 768  # 10 x #cores x # threads per core
-    iters = 1000
-
-    rng = numpy.random.RandomState(22)
-    x = shared(numpy.asarray(rng.rand(vlen), config.floatX))
-    f = function([], T.exp(x))
-    print(f.maker.fgraph.toposort())
-    t0 = time.time()
-    for i in range(iters):
-        r = f()
-    t1 = time.time()
-    print("Looping %d times took %f seconds" % (iters, t1 - t0))
-    print("Result is %s" % (r,))
-    if numpy.any([isinstance(x.op, T.Elemwise) for x in f.maker.fgraph.toposort()]):
-        print('Used the cpu')
-    else:
-        print('Used the gpu')
+# def testTheano():
+#     from theano import function, config, shared, sandbox
+#     import theano.tensor as T
+#     import numpy
+#     import time
+#     print("Testing Theano library...")
+#     vlen = 10 * 30 * 768  # 10 x #cores x # threads per core
+#     iters = 1000
+#
+#     rng = numpy.random.RandomState(22)
+#     x = shared(numpy.asarray(rng.rand(vlen), config.floatX))
+#     f = function([], T.exp(x))
+#     print(f.maker.fgraph.toposort())
+#     t0 = time.time()
+#     for i in range(iters):
+#         r = f()
+#     t1 = time.time()
+#     print("Looping %d times took %f seconds" % (iters, t1 - t0))
+#     print("Result is %s" % (r,))
+#     if numpy.any([isinstance(x.op, T.Elemwise) for x in f.maker.fgraph.toposort()]):
+#         print('Used the cpu')
+#     else:
+#         print('Used the gpu')
+#
 # Perform check:
 #testTheano()
 
@@ -195,16 +196,16 @@ def testTheano():
 '''
 
 # chapter 6 -  rectified linear units and some l2 regularization (lmbda=0.1) => even better accuracy
-from network3 import ReLU
-net = Network([
-    ConvPoolLayer(image_shape=(mini_batch_size, 1, 28, 28),
-                  filter_shape=(20, 1, 5, 5),
-                  poolsize=(2, 2),
-                  activation_fn=ReLU),
-    ConvPoolLayer(image_shape=(mini_batch_size, 20, 12, 12),
-                  filter_shape=(40, 20, 5, 5),
-                  poolsize=(2, 2),
-                  activation_fn=ReLU),
-    FullyConnectedLayer(n_in=40*4*4, n_out=100, activation_fn=ReLU),
-    SoftmaxLayer(n_in=100, n_out=10)], mini_batch_size)
-net.SGD(training_data, 60, mini_batch_size, 0.03, validation_data, test_data, lmbda=0.1)
+# from network3 import ReLU
+# net = Network([
+#     ConvPoolLayer(image_shape=(mini_batch_size, 1, 28, 28),
+#                   filter_shape=(20, 1, 5, 5),
+#                   poolsize=(2, 2),
+#                   activation_fn=ReLU),
+#     ConvPoolLayer(image_shape=(mini_batch_size, 20, 12, 12),
+#                   filter_shape=(40, 20, 5, 5),
+#                   poolsize=(2, 2),
+#                   activation_fn=ReLU),
+#     FullyConnectedLayer(n_in=40*4*4, n_out=100, activation_fn=ReLU),
+#     SoftmaxLayer(n_in=100, n_out=10)], mini_batch_size)
+# net.SGD(training_data, 60, mini_batch_size, 0.03, validation_data, test_data, lmbda=0.1)