error model_rgb.train()

[jinjinyuan]

ValueError Traceback (most recent call last)
in ()
----> 1 model_rgb.train()

/home/jyuan/software/S2VT-master/model_rgb.py in train()
288 with tf.variable_scope(tf.get_variable_scope(), reuse=False):
289 saver = tf.train.Saver(max_to_keep=100, write_version=1)
--> 290 train_op = tf.train.AdamOptimizer(learning_rate).minimize(tf_loss)
291 tf.global_variables_initializer().run()
292

/usr/local/lib/python2.7/dist-packages/tensorflow/python/training/optimizer.pyc in minimize(self, loss, global_step, var_list, gate_gradients, aggregation_method, colocate_gradients_with_ops, name, grad_loss)
323
324 return self.apply_gradients(grads_and_vars, global_step=global_step,
--> 325 name=name)
326
327 def compute_gradients(self, loss, var_list=None,

/usr/local/lib/python2.7/dist-packages/tensorflow/python/training/optimizer.pyc in apply_gradients(self, grads_and_vars, global_step, name)
444 ([str(v) for _, _, v in converted_grads_and_vars],))
445 with ops.control_dependencies(None):
--> 446 self._create_slots([_get_variable_for(v) for v in var_list])
447 update_ops = []
448 with ops.name_scope(name, self._name) as name:

/usr/local/lib/python2.7/dist-packages/tensorflow/python/training/adam.pyc in _create_slots(self, var_list)
126 # Create slots for the first and second moments.
127 for v in var_list:
--> 128 self._zeros_slot(v, "m", self._name)
129 self._zeros_slot(v, "v", self._name)
130

/usr/local/lib/python2.7/dist-packages/tensorflow/python/training/optimizer.pyc in _zeros_slot(self, var, slot_name, op_name)
764 named_slots = self._slot_dict(slot_name)
765 if _var_key(var) not in named_slots:
--> 766 named_slots[_var_key(var)] = slot_creator.create_zeros_slot(var, op_name)
767 return named_slots[_var_key(var)]

/usr/local/lib/python2.7/dist-packages/tensorflow/python/training/slot_creator.pyc in create_zeros_slot(primary, name, dtype, colocate_with_primary)
172 return create_slot_with_initializer(
173 primary, initializer, slot_shape, dtype, name,
--> 174 colocate_with_primary=colocate_with_primary)
175 else:
176 val = array_ops.zeros(slot_shape, dtype=dtype)

/usr/local/lib/python2.7/dist-packages/tensorflow/python/training/slot_creator.pyc in create_slot_with_initializer(primary, initializer, shape, dtype, name, colocate_with_primary)
144 with ops.colocate_with(primary):
145 return _create_slot_var(primary, initializer, "", validate_shape, shape,
--> 146 dtype)
147 else:
148 return _create_slot_var(primary, initializer, "", validate_shape, shape,

/usr/local/lib/python2.7/dist-packages/tensorflow/python/training/slot_creator.pyc in _create_slot_var(primary, val, scope, validate_shape, shape, dtype)
64 use_resource=_is_resource(primary),
65 shape=shape, dtype=dtype,
---> 66 validate_shape=validate_shape)
67 variable_scope.get_variable_scope().set_partitioner(current_partitioner)
68

/usr/local/lib/python2.7/dist-packages/tensorflow/python/ops/variable_scope.pyc in get_variable(name, shape, dtype, initializer, regularizer, trainable, collections, caching_device, partitioner, validate_shape, use_resource, custom_getter)
1063 collections=collections, caching_device=caching_device,
1064 partitioner=partitioner, validate_shape=validate_shape,
-> 1065 use_resource=use_resource, custom_getter=custom_getter)
1066 get_variable_or_local_docstring = (
1067 """%s

/usr/local/lib/python2.7/dist-packages/tensorflow/python/ops/variable_scope.pyc in get_variable(self, var_store, name, shape, dtype, initializer, regularizer, reuse, trainable, collections, caching_device, partitioner, validate_shape, use_resource, custom_getter)
960 collections=collections, caching_device=caching_device,
961 partitioner=partitioner, validate_shape=validate_shape,
--> 962 use_resource=use_resource, custom_getter=custom_getter)
963
964 def _get_partitioned_variable(self,

/usr/local/lib/python2.7/dist-packages/tensorflow/python/ops/variable_scope.pyc in get_variable(self, name, shape, dtype, initializer, regularizer, reuse, trainable, collections, caching_device, partitioner, validate_shape, use_resource, custom_getter)
365 reuse=reuse, trainable=trainable, collections=collections,
366 caching_device=caching_device, partitioner=partitioner,
--> 367 validate_shape=validate_shape, use_resource=use_resource)
368
369 def _get_partitioned_variable(

/usr/local/lib/python2.7/dist-packages/tensorflow/python/ops/variable_scope.pyc in _true_getter(name, shape, dtype, initializer, regularizer, reuse, trainable, collections, caching_device, partitioner, validate_shape, use_resource)
350 trainable=trainable, collections=collections,
351 caching_device=caching_device, validate_shape=validate_shape,
--> 352 use_resource=use_resource)
353
354 if custom_getter is not None:

/usr/local/lib/python2.7/dist-packages/tensorflow/python/ops/variable_scope.pyc in _get_single_variable(self, name, shape, dtype, initializer, regularizer, partition_info, reuse, trainable, collections, caching_device, validate_shape, use_resource)
680 raise ValueError("Variable %s does not exist, or was not created with "
681 "tf.get_variable(). Did you mean to set reuse=None in "
--> 682 "VarScope?" % name)
683 if not shape.is_fully_defined() and not initializing_from_value:
684 raise ValueError("Shape of a new variable (%s) must be fully defined, "

ValueError: Variable Wemb/Adam/ does not exist, or was not created with tf.get_variable(). Did you mean to set reuse=None in VarScope?

[knwng]

I've removed all the tf.get_variable_scope().reuse_variables() and solve this problem, but I'm new to tf and not clear about the consequence.

[Tsingzao]

It seems adding a scope before model construction is feasible.

with tf.variable_scope(tf.get_variable_scope()) as scope:
    tf_loss, tf_video, tf_video_mask, tf_caption, tf_caption_mask, tf_probs = model.build_model()

[holibert]

def build_model(self): tf.variable_scope().reuse_variables() def build_generator(self): tf.variable_scope().reuse_variables()

change to

with tf.variable_scope("model") as scope: scope.reuse_variables() with tf.variable_scope("generator") as scope: scope.reuse_variables()

It works. I'm new in tensorflow. Now I can't tell what happened.

[zhengxinyu0825]

@Dateios Did you meet this error after adding a scope? I fix the previous error using your solution, however, i meet the following error.
/home2/xzhe3946/S2VT/model_RGB.py in build_model(self)
66 with tf.variable_scope("LSTM1"):
67 tf.reset_default_graph()
---> 68 output1, state1 = self.lstm1(image_emb[:,i,:], state1)
69
70 with tf.variable_scope("LSTM2"):

/usr/lib/python2.7/contextlib.pyc in exit(self, type, value, traceback)
33 value = type()
34 try:
---> 35 self.gen.throw(type, value, traceback)
36 raise RuntimeError("generator didn't stop after throw()")
37 except StopIteration, exc:

/usr/local/lib/python2.7/dist-packages/tensorflow/python/ops/variable_scope.pyc in variable_scope(name_or_scope, default_name, values, initializer, regularizer, caching_device, partitioner, custom_getter, reuse, dtype)
1461 old_name_scope=scope,
1462 dtype=dtype) as vs:
-> 1463 yield vs
1464
1465

/usr/lib/python2.7/contextlib.pyc in exit(self, type, value, traceback)
33 value = type()
34 try:
---> 35 self.gen.throw(type, value, traceback)
36 raise RuntimeError("generator didn't stop after throw()")
37 except StopIteration, exc:

/usr/local/lib/python2.7/dist-packages/tensorflow/python/framework/ops.pyc in get_controller(self, default)
3614 finally:
3615 if self._enforce_nesting:
-> 3616 if self.stack[-1] is not default:
3617 raise AssertionError(
3618 "Nesting violated for default stack of %s objects"

IndexError: list index out of range