Touch-ups to addition RNN example

examples/addition_rnn.py

@@ -25,14 +25,21 @@
 http://papers.nips.cc/paper/5346-sequence-to-sequence-learning-with-neural-networks.pdf
 Theoretically it introduces shorter term dependencies between source and target.
 
+
 Two digits inverted:
-+ One layer JZS1 (128 HN) with 55 iterations = 99% train/test accuracy
++ One layer JZS1 (128 HN), 5k training examples = 99% train/test accuracy in 55 epochs
+
 Three digits inverted:
-+ One layer JZS1 (128 HN) with 19 iterations = 99% train/test accuracy
++ One layer JZS1 (128 HN), 50k training examples = 99% train/test accuracy in 100 epochs
+
+
 Four digits inverted:
-+ One layer JZS1 (128 HN) with 20 iterations = 99% train/test accuracy
++ One layer JZS1 (128 HN), 400k training examples = 99% train/test accuracy in 20 epochs
+
+
 Five digits inverted:
-+ One layer JZS1 (128 HN) with 28 iterations = 99% train/test accuracy
++ One layer JZS1 (128 HN), 550k training examples = 99% train/test accuracy in 30 epochs
+
 """
 
 
@@ -61,9 +68,14 @@ def decode(self, X, calc_argmax=True):
             X = X.argmax(axis=-1)
         return ''.join(self.indices_char[x] for x in X)
 
+
+class colors:
+    ok = '\033[92m'
+    fail = '\033[91m'
+    close = '\033[0m'
+
 # Parameters for the model and dataset
-# Note: Training size is number of queries to generate, not final number of unique queries
-TRAINING_SIZE = 800000
+TRAINING_SIZE = 50000
 DIGITS = 3
 INVERT = True
 # Try replacing JZS1 with LSTM, GRU, or SimpleRNN
@@ -80,7 +92,7 @@ def decode(self, X, calc_argmax=True):
 expected = []
 seen = set()
 print('Generating data...')
-for i in xrange(TRAINING_SIZE):
+while len(questions) < TRAINING_SIZE:
     f = lambda: int(''.join(np.random.choice(list('0123456789')) for i in xrange(np.random.randint(1, DIGITS + 1))))
     a, b = f(), f()
     # Skip any addition questions we've already seen
@@ -132,7 +144,7 @@ def decode(self, X, calc_argmax=True):
 model.compile(loss='categorical_crossentropy', optimizer='adam')
 
 # Train the model each generation and show predictions against the validation dataset
-for iteration in range(1, 60):
+for iteration in range(1, 200):
     print()
     print('-' * 50)
     print('Iteration', iteration)
@@ -148,5 +160,5 @@ def decode(self, X, calc_argmax=True):
         guess = ctable.decode(preds[0], calc_argmax=False)
         print('Q', q[::-1] if INVERT else q)
         print('T', correct)
-        print('☑' if correct == guess else '☒', guess)
+        print(colors.ok + '☑' + colors.close if correct == guess else colors.fail + '☒' + colors.close, guess)
         print('---')