fix bug in temperature & async

tensorpack/models/softmax.py

@@ -15,7 +15,7 @@ def SoftMax(x, use_temperature=False, temperature_init=1.0):
     :param x: a 2D tensor
     """
     if use_temperature:
-        t = tf.get_variable('temp', [1],
+        t = tf.get_variable('invtemp', [],
                 initializer=tf.constant_initializer(1.0 / float(temperature_init)))
         x = x * t
     return tf.nn.softmax(x, name='output')

tensorpack/train/multigpu.py

@@ -92,7 +92,8 @@ class AsyncMultiGPUTrainer(MultiGPUTrainer):
         # sync have consistent effective learning rate
         def scale(grads):
             with tf.name_scope('async_scale_grad'):
-                return [(grad / self.config.nr_tower, var) for grad, var in grads]
+                return [(grad / self.config.nr_tower if grad is not None else None, var)
+                            for grad, var in grads]
         grad_list = map(scale, grad_list)
         grad_list = [self.process_grads(g) for g in grad_list]