diff --git a/examples/addition_rnn.py b/examples/addition_rnn.py
index eb91ca25a..1fb24684e 100644
--- a/examples/addition_rnn.py
+++ b/examples/addition_rnn.py
@@ -78,7 +78,7 @@ INVERT = True
 
 # Maximum length of input is 'int + int' (e.g., '345+678'). Maximum length of
 # int is DIGITS.
-MAxLEN = DIGITS + 1 + DIGITS
+MAXLEN = DIGITS + 1 + DIGITS
 
 # All the numbers, plus sign and space for padding.
 chars = '0123456789+ '
@@ -98,9 +98,9 @@ while len(questions) < TRAINING_SIZE:
     if key in seen:
         continue
     seen.add(key)
-    # Pad the data with spaces such that it is always MAxLEN.
+    # Pad the data with spaces such that it is always MAXLEN.
     q = '{}+{}'.format(a, b)
-    query = q + ' ' * (MAxLEN - len(q))
+    query = q + ' ' * (MAXLEN - len(q))
     ans = str(a + b)
     # Answers can be of maximum size DIGITS + 1.
     ans += ' ' * (DIGITS + 1 - len(ans))
@@ -113,10 +113,10 @@ while len(questions) < TRAINING_SIZE:
 print('Total addition questions:', len(questions))
 
 print('Vectorization...')
-x = np.zeros((len(questions), MAxLEN, len(chars)), dtype=np.bool)
+x = np.zeros((len(questions), MAXLEN, len(chars)), dtype=np.bool)
 y = np.zeros((len(questions), DIGITS + 1, len(chars)), dtype=np.bool)
 for i, sentence in enumerate(questions):
-    x[i] = ctable.encode(sentence, MAxLEN)
+    x[i] = ctable.encode(sentence, MAXLEN)
 for i, sentence in enumerate(expected):
     y[i] = ctable.encode(sentence, DIGITS + 1)
 
@@ -151,7 +151,7 @@ model = Sequential()
 # "Encode" the input sequence using an RNN, producing an output of HIDDEN_SIZE.
 # Note: In a situation where your input sequences have a variable length,
 # use input_shape=(None, num_feature).
-model.add(RNN(HIDDEN_SIZE, input_shape=(MAxLEN, len(chars))))
+model.add(RNN(HIDDEN_SIZE, input_shape=(MAXLEN, len(chars))))
 # As the decoder RNN's input, repeatedly provide with the last hidden state of
 # RNN for each time step. Repeat 'DIGITS + 1' times as that's the maximum
 # length of output, e.g., when DIGITS=3, max output is 999+999=1998.