remove unused ema code

train.py

@@ -17,7 +17,7 @@
 from datasets import rocstories
 from analysis import rocstories as rocstories_analysis
 from text_utils import TextEncoder
-from utils import encode_dataset, flatten, iter_data, find_trainable_variables, get_ema_vars, convert_gradient_to_tensor, shape_list, ResultLogger, assign_to_gpu, average_grads, make_path
+from utils import encode_dataset, flatten, iter_data, find_trainable_variables, convert_gradient_to_tensor, shape_list, ResultLogger, assign_to_gpu, average_grads, make_path
 
 def gelu(x):
     return 0.5*x*(1+tf.tanh(math.sqrt(2/math.pi)*(x+0.044715*tf.pow(x, 3))))
@@ -54,7 +54,6 @@ def norm(x, scope, axis=[-1]):
         n_state = shape_list(x)[-1]
         g = tf.get_variable("g", [n_state], initializer=tf.constant_initializer(1))
         b = tf.get_variable("b", [n_state], initializer=tf.constant_initializer(0))
-        g, b = get_ema_vars(g, b)
         return _norm(x, g, b, axis=axis)
 
 def dropout(x, pdrop, train):

utils.py

@@ -114,23 +114,6 @@ def iter_data(*datas, n_batch=128, truncate=False, verbose=False, max_batches=fl
             yield (d[i:i+n_batch] for d in datas)
         n_batches += 1
 
-def get_ema_if_exists(v, gvs):
-    name = v.name.split(':')[0]
-    ema_name = name+'/ExponentialMovingAverage:0'
-    ema_v = [v for v in gvs if v.name == ema_name]
-    if len(ema_v) == 0:
-        ema_v = [v]
-    return ema_v[0]
-
-def get_ema_vars(*vs):
-    if tf.get_variable_scope().reuse:
-        gvs = tf.global_variables()
-        vs = [get_ema_if_exists(v, gvs) for v in vs]
-    if len(vs) == 1:
-        return vs[0]
-    else:
-        return vs
-
 @function.Defun(
     python_grad_func=lambda x, dy: tf.convert_to_tensor(dy),
     shape_func=lambda op: [op.inputs[0].get_shape()])