jonbruner
Error possibly linked to definition of variable scope
Error:
ValueError: Variable d_w1/Adam/ does not exist, or was not created with tf.get_variable(). Did you mean to set reuse=None in VarScope?
at line : with tf.variable_scope(tf.get_variable_scope(), reuse=False) as scope:
Have you tried restarting the notebook and running from the first cell? Many of these scope errors come from repeated definitions that you'd get from running an individual cell multiple times without re-initializing the graph.
I have tried restarting and running from the first cell. If it was repeatedly defined, then it should be already exist but does not exist.
This is the whole error info. I got.
ValueError Traceback (most recent call last)
<ipython-input-4-f781a98cf3e0> in <module>()
31 # Increasing from 0.001 in GitHub version
32 with tf.variable_scope(tf.get_variable_scope(), reuse=False) as scope:
---> 33 d_trainer_fake = tf.train.AdamOptimizer(0.0001).minimize(d_loss_fake, var_list=d_vars)
34 d_trainer_real = tf.train.AdamOptimizer(0.0001).minimize(d_loss_real, var_list=d_vars)
35
~/workspace/jupyter/env3/lib/python3.5/site-packages/tensorflow/python/training/optimizer.py 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,
~/workspace/jupyter/env3/lib/python3.5/site-packages/tensorflow/python/training/optimizer.py 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:
~/workspace/jupyter/env3/lib/python3.5/site-packages/tensorflow/python/training/adam.py in _create_slots(self, var_list)
130 # Create slots for the first and second moments.
131 for v in var_list:
--> 132 self._zeros_slot(v, "m", self._name)
133 self._zeros_slot(v, "v", self._name)
134
~/workspace/jupyter/env3/lib/python3.5/site-packages/tensorflow/python/training/optimizer.py 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)]
~/workspace/jupyter/env3/lib/python3.5/site-packages/tensorflow/python/training/slot_creator.py 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)
~/workspace/jupyter/env3/lib/python3.5/site-packages/tensorflow/python/training/slot_creator.py 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,
~/workspace/jupyter/env3/lib/python3.5/site-packages/tensorflow/python/training/slot_creator.py 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
~/workspace/jupyter/env3/lib/python3.5/site-packages/tensorflow/python/ops/variable_scope.py 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
~/workspace/jupyter/env3/lib/python3.5/site-packages/tensorflow/python/ops/variable_scope.py 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,
~/workspace/jupyter/env3/lib/python3.5/site-packages/tensorflow/python/ops/variable_scope.py 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(
~/workspace/jupyter/env3/lib/python3.5/site-packages/tensorflow/python/ops/variable_scope.py 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:
~/workspace/jupyter/env3/lib/python3.5/site-packages/tensorflow/python/ops/variable_scope.py 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 d_w1/Adam/ does not exist, or was not created with tf.get_variable(). Did you mean to set reuse=None in VarScope?
it seems to me, the reason might be:
in
Dx = discriminator(x_placeholder)
# Dx hold the discriminator's prediction probabilities
# for real MNIST images
Dg = discriminator(Gz, reuse=True)
# Dg holds discriminator prediction probabilities for generated images
you set reuse as Dg = discriminator(Gz, reuse=True)
therefore, in the code `def discriminator(x_image, reuse=False): if (reuse): tf.get_variable_scope().reuse_variables()
# First convolutional and pool layers
# These search for 32 different 5 x 5 pixel features
d_w1 = tf.get_variable('d_w1', [5, 5, 1, 32], initializer=tf.truncated_normal_initializer(stddev=0.02))
d_b1 = tf.get_variable('d_b1', [32], initializer=tf.constant_initializer(0))
d1 = tf.nn.conv2d(input=x_image, filter=d_w1, strides=[1, 1, 1, 1], padding='SAME')
d1 = d1 + d_b1
d1 = tf.nn.relu(d1)
d1 = tf.nn.avg_pool(d1, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME')
when you tried to get variable d_w1, since the reuse has been set to True, it gives you the error, d_w1/adam does not exist..
I am not very sure about that....do you think that might be it?
Thanks for pointing this out; I think this has to do with a TensorFlow version update that came along after I published this. I'll try to find time to update the code, but in the meantime you might have better luck with this similar project that's more up-to-date and should be compatible with TF 1+: https://github.com/jonbruner/generative-adversarial-networks