list comprehension

Author: chiphuyen

assignments/chatbot/model.py

@@ -34,22 +34,16 @@ def __init__(self, forward_only, batch_size):
     def _create_placeholders(self):
         # Feeds for inputs. It's a list of placeholders
         print('Create placeholders')
-        self.encoder_inputs = []
-        self.decoder_inputs = []
-        self.decoder_masks = []
-        for i in xrange(config.BUCKETS[-1][0]):  # Last bucket is the biggest one.
-            self.encoder_inputs.append(tf.placeholder(tf.int32, shape=[None],
-                                                    name='encoder{}'.format(i)))
-        for i in xrange(config.BUCKETS[-1][1] + 1):
-            self.decoder_inputs.append(tf.placeholder(tf.int32, shape=[None],
-                                                    name='decoder{}'.format(i)))
-            self.decoder_masks.append(tf.placeholder(tf.float32, shape=[None],
-                                                    name='mask{}'.format(i)))
+        self.encoder_inputs = [tf.placeholder(tf.int32, shape=[None], name='encoder{}'.format(i))
+                               for i in xrange(config.BUCKETS[-1][0])]
+        self.decoder_inputs = [tf.placeholder(tf.int32, shape=[None], name='decoder{}'.format(i))
+                               for i in xrange(config.BUCKETS[-1][1] + 1)]
+        self.decoder_masks = [tf.placeholder(tf.float32, shape=[None], name='mask{}'.format(i))
+                              for i in xrange(config.BUCKETS[-1][1] + 1)]
 
         # Our targets are decoder inputs shifted by one (to ignore <s> symbol)
-        self.targets = [self.decoder_inputs[i + 1]
-                        for i in xrange(len(self.decoder_inputs) - 1)]
-
+        self.targets = self.decoder_inputs[1:]
+        
     def _inference(self):
         print('Create inference')
         # If we use sampled softmax, we need an output projection.

examples/11_char_rnn_gist.py

@@ -81,12 +81,12 @@ def training(vocab, seq, loss, optimizer, global_step, temp, sample, in_state, o
             batch_loss, _ = sess.run([loss, optimizer], {seq: batch})
             if (iteration + 1) % SKIP_STEP == 0:
                 print('Iter {}. \n    Loss {}. Time {}'.format(iteration, batch_loss, time.time() - start))
-                online_intference(sess, vocab, seq, sample, temp, in_state, out_state)
+                online_inference(sess, vocab, seq, sample, temp, in_state, out_state)
                 start = time.time()
                 saver.save(sess, 'checkpoints/arvix/char-rnn', iteration)
             iteration += 1
 
-def online_intference(sess, vocab, seq, sample, temp, in_state, out_state, seed='T'):
+def online_inference(sess, vocab, seq, sample, temp, in_state, out_state, seed='T'):
     """ Generate sequence one character at a time, based on the previous character
     """
     sentence = seed