diff --git a/create_model_1.py b/create_model_1.py
index 28fb02f..ea8a795 100644
--- a/create_model_1.py
+++ b/create_model_1.py
@@ -41,7 +41,7 @@
 cross_entropy = -tf.reduce_sum(y_*tf.log(y))
 train_step = tf.train.GradientDescentOptimizer(0.01).minimize(cross_entropy)
 
-init_op = global_variables_initializer()
+init_op = tf.global_variables_initializer()
 saver = tf.train.Saver()
 
 
@@ -58,6 +58,6 @@
         batch_xs, batch_ys = mnist.train.next_batch(100)
         sess.run(train_step, feed_dict={x: batch_xs, y_: batch_ys})
         
-    save_path = saver.save(sess, "model.ckpt")
+    save_path = saver.save(sess, "./model.ckpt")
     print ("Model saved in file: ", save_path)
 
diff --git a/create_model_2.py b/create_model_2.py
index 4c3a965..f796f67 100644
--- a/create_model_2.py
+++ b/create_model_2.py
@@ -99,7 +99,7 @@ def max_pool_2x2(x):
 https://www.tensorflow.org/versions/master/how_tos/variables/index.html
 """
 saver = tf.train.Saver()
-sess.run(global_variables_initializer())
+sess.run(tf.global_variables_initializer())
 #with tf.Session() as sess:
     #sess.run(init_op)
 for i in range(20000):
@@ -110,7 +110,7 @@ def max_pool_2x2(x):
     print("step %d, training accuracy %g"%(i, train_accuracy))
   train_step.run(feed_dict={x: batch[0], y_: batch[1], keep_prob: 0.5})
 
-save_path = saver.save(sess, "model2.ckpt")
+save_path = saver.save(sess, "./model2.ckpt")
 print ("Model saved in file: ", save_path)
 
 print("test accuracy %g"%accuracy.eval(feed_dict={
diff --git a/predict_2.py b/predict_2.py
index 2ca339d..c5d9ed4 100644
--- a/predict_2.py
+++ b/predict_2.py
@@ -114,8 +114,8 @@ def imageprepare(argv):
     if width > height: #check which dimension is bigger
         #Width is bigger. Width becomes 20 pixels.
         nheight = int(round((20.0/width*height),0)) #resize height according to ratio width
-        if (nheigth == 0): #rare case but minimum is 1 pixel
-            nheigth = 1  
+        if (nheight == 0): #rare case but minimum is 1 pixel
+            nheight = 1  
         # resize and sharpen
         img = im.resize((20,nheight), Image.ANTIALIAS).filter(ImageFilter.SHARPEN)
         wtop = int(round(((28 - nheight)/2),0)) #caculate horizontal pozition