diff --git a/neural_network.py b/neural_network.py
index 7eabbc6..d045de8 100644
--- a/neural_network.py
+++ b/neural_network.py
@@ -81,7 +81,7 @@ def format_man_dist(elem):
 target = []
 
 for i in range(29):
-    filename = join('data', str(i) + '.bin')
+    filename = join('/pub/faculty_share/daugher/datafiles/data/' + str(i) + 'states.bin')
 
     # Debugging to print the current file from which states are being parsed.
     #print(i)
@@ -91,7 +91,7 @@ for i in range(29):
         data = f.read(8)
         counter = 0
 
-        while(data and counter < 1000):
+        while(data and counter < 2000):
             temp.append(format_man_dist(i))
 
             data = f.read(8)
@@ -123,7 +123,7 @@ model.compile(optimizer='sgd',
 
 
 for i in range(29):
-    filename = join('data', str(i) + '.bin')
+    filename = join('/pub/faculty_share/daugher/datafiles/data/' + str(i) + 'states.bin')
 
     # Debugging to print the current file from which states are being parsed.
     print(i)
@@ -133,7 +133,7 @@ for i in range(29):
         counter = 0
         training = []
 
-        while(data and counter < 1000):
+        while(data and counter < 2000):
             bin_data = reduce(format_input, list(data), [])
             bin_data.reverse()
             bin_data = bin_data[16:]
@@ -146,70 +146,74 @@ for i in range(29):
             #print(training[0])
     # Train the network.
 
-    model.fit(np.array(training), np.array(target[i]), epochs=5, batch_size=1000)
+    model.fit(np.array(training), np.array(target[i]), epochs=8, batch_size=2000)
     #model.train_on_batch(np.array(temp), np.array(target))
 
 # Used for testing data
 
-with open('data/18.bin', 'rb') as f:
+for i in range(11, 29):
+    filename = join('/pub/faculty_share/daugher/datafiles/data/', str(i) + '.bin')
 
-    for i in range(1000):
-        data = f.read(8)
+    print(i)
 
-    data = f.read(8)
+    with open('/pub/faculty_share/daugher/datafiles/data/18states.bin', 'rb') as f:
 
-    counter = 0
+    for i in range(2000):
+	data = f.read(8)
 
-    testing = []
+	data = f.read(8)
 
-    testing_target = []
+	counter = 0
 
-    while(data):
-        bin_data = reduce(format_input, list(data), [])
-        bin_data.reverse()
-        bin_data = bin_data[16:]
+	testing = []
 
-        testing.append(bin_data)
+	testing_target = []
 
-        pos_data = reduce(format_pos, enumerate(list(data)), [])
-        pos_data.reverse()
-        pos_data = pos_data[1:]
+	while(data):
+	    bin_data = reduce(format_input, list(data), [])
+	    bin_data.reverse()
+	    bin_data = bin_data[16:]
 
-        state_pos = []
+	    testing.append(bin_data)
 
-        for p in pos_data:
-            state_pos.append(p[1])
+	    pos_data = reduce(format_pos, enumerate(list(data)), [])
+	    pos_data.reverse()
+	    pos_data = pos_data[1:]
 
-        testing_target_pos = reduce(generate_pos, pos_data, [])
+	    state_pos = []
 
-        testing_target.append(format_man_dist(man_dist_state(state_pos, testing_target_pos)))
+	    for p in pos_data:
+		state_pos.append(p[1])
 
-        counter += 1
-        data = f.read(8)
+	    testing_target_pos = reduce(generate_pos, pos_data, [])
 
-#print(testing_target)
+	    testing_target.append(format_man_dist(man_dist_state(state_pos, testing_target_pos)))
 
-# Evaluate accuracy
+	    counter += 1
+	    data = f.read(8)
 
-loss_and_metrics = model.evaluate(np.array(testing),np.array(testing_target), batch_size=1000)
+        
+        # Evaluate accuracy
 
-# Generating predictions:
+        loss_and_metrics = model.evaluate(np.array(testing),np.array(testing_target), batch_size=1000)
 
-predictions = model.predict(np.array(testing), batch_size=1000)
+        # Generating predictions:
 
-output = []
+        predictions = model.predict(np.array(testing), batch_size=1000)
+        
+        output = []
 
-for p in range(len(predictions)):
-    if np.argmax(testing_target[p]) < 18:
-        output.append(100*((18 - (28 - np.argmax(predictions[p]))) / (18 - np.argmax(testing_target[p]))))
-    else:
-        output.append(0)
+        for p in range(len(predictions)):
+            if np.argmax(testing_target[p]) < 18:
+                output.append(100*((18 - (28 - np.argmax(predictions[p]))) / (18 - np.argmax(testing_target[p]))))
+            else:
+                output.append(0)
 
-#for i in range(len(output)):
-#    print(output[i])
+        #for i in range(len(output)):
+        #    print(output[i])
 
-print(np.array(output).mean())
+        print("Percentage possible improvement: ", np.array(output).mean())
 
-print(loss_and_metrics)
+        print(model.metrics_names[0], loss_and_metrics[0])
 
-print(model.metrics_names)
+        print(model.metrics_names[1], loss_and_metrics[1])