Implement temperature

model.py

@@ -58,7 +58,7 @@ def loop(prev, _):
         optimizer = tf.train.AdamOptimizer(self.lr)
         self.train_op = optimizer.apply_gradients(zip(grads, tvars))
 
-    def sample(self, sess, chars, vocab, num=200, prime='The ', sampling_type=1):
+    def sample(self, sess, chars, vocab, num=200, prime='The ', sampling_type=1, temperature=1.):
         state = self.cell.zero_state(1, tf.float32).eval()
         for char in prime[:-1]:
             x = np.zeros((1, 1))
@@ -77,6 +77,7 @@ def weighted_pick(weights):
             x = np.zeros((1, 1))
             x[0, 0] = vocab[char]
             feed = {self.input_data: x, self.initial_state:state}
+            self.probs = tf.nn.softmax(tf.div(self.logits, temperature))
             [probs, state] = sess.run([self.probs, self.final_state], feed)
             p = probs[0]
 

sample.py

@@ -20,6 +20,8 @@ def main():
                        help='prime text')
     parser.add_argument('--sample', type=int, default=1,
                        help='0 to use max at each timestep, 1 to sample at each timestep, 2 to sample on spaces')
+    parser.add_argument('--temperature', type=float, default=1.,
+                       help='temperature for sampling, within the range of (0,1]')
 
     args = parser.parse_args()
     sample(args)
@@ -36,7 +38,7 @@ def sample(args):
         ckpt = tf.train.get_checkpoint_state(args.save_dir)
         if ckpt and ckpt.model_checkpoint_path:
             saver.restore(sess, ckpt.model_checkpoint_path)
-            print(model.sample(sess, chars, vocab, args.n, args.prime, args.sample))
+            print(model.sample(sess, chars, vocab, args.n, args.prime, args.sample, args.temperature))
 
 if __name__ == '__main__':
     main()

train.py

@@ -35,7 +35,7 @@ def main():
     parser.add_argument('--learning_rate', type=float, default=0.002,
                        help='learning rate')
     parser.add_argument('--decay_rate', type=float, default=0.97,
-                       help='decay rate for rmsprop')                       
+                       help='decay rate for rmsprop')
     parser.add_argument('--init_from', type=str, default=None,
                        help="""continue training from saved model at this path. Path must contain files saved by previous training process: 
                             'config.pkl'        : configuration;