I want to change the variable_scope by the value of some tensors. For an easy example, I defined a very simple code like this:
import tensorflow as tf
def calculate_variable(scope):
with tf.variable_scope(scope or type(self).__name__, reuse=tf.AUTO_REUSE):
w = tf.get_variable('ww', shape=[5], initializer=tf.truncated_normal_initializer(mean=0.0, stddev=0.1))
return w
w = calculate_variable('in_first')
w1 = calculate_variable('in_second')
The function is very simple. It just returns value defined in a certain variable scope. Now, 'w' and 'w1' would have different values.
What I want to do is to select this variable scope by some condition of tensors. Assuming I have two tensors x, y, if their value is same, I want to get value from the function above with certain variable scope.
x = tf.constant(3)
y = tf.constant(3)
condi = tf.cond(tf.equal(x, y), lambda: 'in_first', lambda: 'in_second')
w_cond = calculate_variable(condi)
I tried many other methods and searched the Internet. However, whenever I want to determine variable_scope from condition of tensors in a similar way to this example, it shows an error.
TypeError: Using a `tf.Tensor` as a Python `bool` is not allowed. Use `if t is not None:` instead of `if t:` to test if a tensor is defined, and use TensorFlow ops such as tf.cond to execute subgraphs conditioned on the value of a tensor.
Is there any good workaround?
The way you stated it, this is not possible. variable_scope class explicitly checks that name_or_scope argument is either a string or a VariableScope instance:
...
if not isinstance(self._name_or_scope,
(VariableScope,) + six.string_types):
raise TypeError("VariableScope: name_or_scope must be a string or "
"VariableScope.")
It does not accept a Tensor. This is reasonable, because variable scope is part of graph definition, one can't define variables dynamically.
The closest supported expression is this:
x = tf.constant(3)
y = tf.constant(3)
w_cond = tf.cond(tf.equal(x, y),
lambda: calculate_variable('in_first'),
lambda: calculate_variable('in_second'))
... where you can select any of the two variables at runtime.
I have the following code which defines a nested tf.variable_scope().
def function(inputs)
with tf.variable_scope("1st") as scope:
#define some variables here using tf.get_variable()
with tf.variable_scope("2nd") as scope:
#define some variables here using tf.get_variable()
my_wanted_variable = tf.get_variable('my_wanted_variable',[dim0,
dim1], tf.float(32)，
tf.constant_initializer(0.0))
In another class, I want to get my_wanted_variable, I use
with tf.variable_scope("function/2nd", reuse=True):
got_my_wanted_variable = tf.get_variable("my_wanted_variable")
I was told that
ValueError: Variable function/2nd/my_wanted_variable does not exist,
or was not created with tf.get_variable(). Did you mean to set
reuse=None in VarScope?
If I set reuse=None when fetching my_wanted_variable then,
ValueError: Shape of a new variable (function/2nd/my_wanted_variable)
must be fully defined, but instead was .
So, how can I get a variable (or tensor) by name in a nested scope.
add debug info:
I used print(xxx.name) to see what is their name and scope indeed, I found that although their scope is right, e.g xxx/function/2nd. all variables which defined in scope 1st and 2nd are not named by their assigned name, for example, my_wanted_variable is xxx/function/2nd/sub:0.
The :0 is normal for every variable (it symbolizes the endpoint).
The name sub is not that weird it just shows that you did not name explicitely the variable so it gave the name of the operation you used (tf.sub() probably) to the tensor.
Use explicitely the argument called name="my_wanted_variable". Try without a scope first to be sure it is named appropriately. Then use print nn.name or inspect the nodes of the graph_def object to check.
Or we could check all the tensor in the debug mode with,
with tf.Session() as sess:
model = tf.train.import_meta_graph('./model.ckpt-30000.meta')
model.restore(sess, tf.train.latest_checkpoint('./'))
graph = tf.get_default_graph()
then, in debug mode,
graph._collections
This will enlist all the context_tensor, training_op, trainable_variables, variables.
or Even Better is:
[tensor.name for tensor in tf.get_default_graph().as_graph_def().node]
What's the differences between these functions?
tf.variable_op_scope(values, name, default_name, initializer=None)
Returns a context manager for defining an op that creates variables.
This context manager validates that the given values are from the same graph, ensures that that graph is the default graph, and pushes a name scope and a variable scope.
tf.op_scope(values, name, default_name=None)
Returns a context manager for use when defining a Python op.
This context manager validates that the given values are from the same graph, ensures that that graph is the default graph, and pushes a name scope.
tf.name_scope(name)
Wrapper for Graph.name_scope() using the default graph.
See Graph.name_scope() for more details.
tf.variable_scope(name_or_scope, reuse=None, initializer=None)
Returns a context for variable scope.
Variable scope allows to create new variables and to share already created ones while providing checks to not create or share by accident. For details, see the Variable Scope How To, here we present only a few basic examples.
Let's begin by a short introduction to variable sharing. It is a mechanism in TensorFlow that allows for sharing variables accessed in different parts of the code without passing references to the variable around.
The method tf.get_variable can be used with the name of the variable as the argument to either create a new variable with such name or retrieve the one that was created before. This is different from using the tf.Variable constructor which will create a new variable every time it is called (and potentially add a suffix to the variable name if a variable with such name already exists).
It is for the purpose of the variable sharing mechanism that a separate type of scope (variable scope) was introduced.
As a result, we end up having two different types of scopes:
name scope, created using tf.name_scope
variable scope, created using tf.variable_scope
Both scopes have the same effect on all operations as well as variables created using tf.Variable, i.e., the scope will be added as a prefix to the operation or variable name.
However, name scope is ignored by tf.get_variable. We can see that in the following example:
with tf.name_scope("my_scope"):
v1 = tf.get_variable("var1", [1], dtype=tf.float32)
v2 = tf.Variable(1, name="var2", dtype=tf.float32)
a = tf.add(v1, v2)
print(v1.name) # var1:0
print(v2.name) # my_scope/var2:0
print(a.name) # my_scope/Add:0
The only way to place a variable accessed using tf.get_variable in a scope is to use a variable scope, as in the following example:
with tf.variable_scope("my_scope"):
v1 = tf.get_variable("var1", [1], dtype=tf.float32)
v2 = tf.Variable(1, name="var2", dtype=tf.float32)
a = tf.add(v1, v2)
print(v1.name) # my_scope/var1:0
print(v2.name) # my_scope/var2:0
print(a.name) # my_scope/Add:0
This allows us to easily share variables across different parts of the program, even within different name scopes:
with tf.name_scope("foo"):
with tf.variable_scope("var_scope"):
v = tf.get_variable("var", [1])
with tf.name_scope("bar"):
with tf.variable_scope("var_scope", reuse=True):
v1 = tf.get_variable("var", [1])
assert v1 == v
print(v.name) # var_scope/var:0
print(v1.name) # var_scope/var:0
UPDATE
As of version r0.11, op_scope and variable_op_scope are both deprecated and replaced by name_scope and variable_scope.
Both variable_op_scope and op_scope are now deprecated and should not be used at all.
Regarding the other two, I also had problems understanding the difference between variable_scope and name_scope (they looked almost the same) before I tried to visualize everything by creating a simple example:
import tensorflow as tf
def scoping(fn, scope1, scope2, vals):
with fn(scope1):
a = tf.Variable(vals[0], name='a')
b = tf.get_variable('b', initializer=vals[1])
c = tf.constant(vals[2], name='c')
with fn(scope2):
d = tf.add(a * b, c, name='res')
print '\n '.join([scope1, a.name, b.name, c.name, d.name]), '\n'
return d
d1 = scoping(tf.variable_scope, 'scope_vars', 'res', [1, 2, 3])
d2 = scoping(tf.name_scope, 'scope_name', 'res', [1, 2, 3])
with tf.Session() as sess:
writer = tf.summary.FileWriter('logs', sess.graph)
sess.run(tf.global_variables_initializer())
print sess.run([d1, d2])
writer.close()
Here I create a function that creates some variables and constants and groups them in scopes (depending on the type I provided). In this function, I also print the names of all the variables. After that, I executes the graph to get values of the resulting values and save event-files to investigate them in TensorBoard. If you run this, you will get the following:
scope_vars
scope_vars/a:0
scope_vars/b:0
scope_vars/c:0
scope_vars/res/res:0
scope_name
scope_name/a:0
b:0
scope_name/c:0
scope_name/res/res:0
You see the similar pattern if you open TensorBoard (as you see b is outside of scope_name rectangular):
This gives you the answer:
Now you see that tf.variable_scope() adds a prefix to the names of all variables (no matter how you create them), ops, constants. On the other hand tf.name_scope() ignores variables created with tf.get_variable() because it assumes that you know which variable and in which scope you wanted to use.
A good documentation on Sharing variables tells you that
tf.variable_scope(): Manages namespaces for names passed to tf.get_variable().
The same documentation provides a more details how does Variable Scope work and when it is useful.
Namespaces is a way to organize names for variables and operators in hierarchical manner (e.g. "scopeA/scopeB/scopeC/op1")
tf.name_scope creates namespace for operators in the default graph.
tf.variable_scope creates namespace for both variables and operators in the default graph.
tf.op_scope same as tf.name_scope, but for the graph in which specified variables were created.
tf.variable_op_scope same as tf.variable_scope, but for the graph in which specified variables were created.
Links to the sources above help to disambiguate this documentation issue.
This example shows that all types of scopes define namespaces for both variables and operators with following differences:
scopes defined by tf.variable_op_scope or tf.variable_scope are compatible with tf.get_variable (it ignores two other scopes)
tf.op_scope and tf.variable_op_scope just select a graph from a list of specified variables to create a scope for. Other than than their behavior equal to tf.name_scope and tf.variable_scope accordingly
tf.variable_scope and variable_op_scope add specified or default initializer.
Let's make it simple: just use tf.variable_scope. Quoting a TF developer,:
Currently, we recommend everyone to use variable_scope and not use name_scope except for internal code and libraries.
Besides the fact that variable_scope's functionality basically extends those of name_scope, together they behave in a way that may surprises you:
with tf.name_scope('foo'):
with tf.variable_scope('bar'):
x = tf.get_variable('x', shape=())
x2 = tf.square(x**2, name='x2')
print(x.name)
# bar/x:0
print(x2.name)
# foo/bar/x2:0
This behavior has its use and justifies the coexistance of both scopes -- but unless you know what you are doing, sticking to variable_scope only will avoid you some headaches due to this.
As for API r0.11, op_scope and variable_op_scope are both deprecated.
name_scope and variable_scope can be nested:
with tf.name_scope('ns'):
with tf.variable_scope('vs'): #scope creation
v1 = tf.get_variable("v1",[1.0]) #v1.name = 'vs/v1:0'
v2 = tf.Variable([2.0],name = 'v2') #v2.name= 'ns/vs/v2:0'
v3 = v1 + v2 #v3.name = 'ns/vs/add:0'
You can think them as two groups: variable_op_scope and op_scope take a set of variables as input and are designed to create operations. The difference is in how they affect the creation of variables with tf.get_variable:
def mysum(a,b,name=None):
with tf.op_scope([a,b],name,"mysum") as scope:
v = tf.get_variable("v", 1)
v2 = tf.Variable([0], name="v2")
assert v.name == "v:0", v.name
assert v2.name == "mysum/v2:0", v2.name
return tf.add(a,b)
def mysum2(a,b,name=None):
with tf.variable_op_scope([a,b],name,"mysum2") as scope:
v = tf.get_variable("v", 1)
v2 = tf.Variable([0], name="v2")
assert v.name == "mysum2/v:0", v.name
assert v2.name == "mysum2/v2:0", v2.name
return tf.add(a,b)
with tf.Graph().as_default():
op = mysum(tf.Variable(1), tf.Variable(2))
op2 = mysum2(tf.Variable(1), tf.Variable(2))
assert op.name == 'mysum/Add:0', op.name
assert op2.name == 'mysum2/Add:0', op2.name
notice the name of the variable v in the two examples.
same for tf.name_scope and tf.variable_scope:
with tf.Graph().as_default():
with tf.name_scope("name_scope") as scope:
v = tf.get_variable("v", [1])
op = tf.add(v, v)
v2 = tf.Variable([0], name="v2")
assert v.name == "v:0", v.name
assert op.name == "name_scope/Add:0", op.name
assert v2.name == "name_scope/v2:0", v2.name
with tf.Graph().as_default():
with tf.variable_scope("name_scope") as scope:
v = tf.get_variable("v", [1])
op = tf.add(v, v)
v2 = tf.Variable([0], name="v2")
assert v.name == "name_scope/v:0", v.name
assert op.name == "name_scope/Add:0", op.name
assert v2.name == "name_scope/v2:0", v2.name
You can read more about variable scope in the tutorial.
A similar question was asked before on Stack Overflow.
From the last section of this page of the tensorflow documentation: Names of ops in tf.variable_scope()
[...] when we do with tf.variable_scope("name"), this implicitly opens a tf.name_scope("name"). For example:
with tf.variable_scope("foo"):
x = 1.0 + tf.get_variable("v", [1])
assert x.op.name == "foo/add"
Name scopes can be opened in addition to a variable scope, and then they will only affect the names of the ops, but not of variables.
with tf.variable_scope("foo"):
with tf.name_scope("bar"):
v = tf.get_variable("v", [1])
x = 1.0 + v
assert v.name == "foo/v:0"
assert x.op.name == "foo/bar/add"
When opening a variable scope using a captured object instead of a string, we do not alter the current name scope for ops.
Tensorflow 2.0 Compatible Answer: The explanations of Andrzej Pronobis and Salvador Dali are very detailed about the Functions related to Scope.
Of the Scope Functions discussed above, which are active as of now (17th Feb 2020) are variable_scope and name_scope.
Specifying the 2.0 Compatible Calls for those functions, we discussed above, for the benefit of the community.
Function in 1.x:
tf.variable_scope
tf.name_scope
Respective Function in 2.x:
tf.compat.v1.variable_scope
tf.name_scope (tf.compat.v2.name_scope if migrated from 1.x to 2.x)
For more information about migration from 1.x to 2.x, please refer this Migration Guide.
Recently I have been trying to learn to use TensorFlow, and I do not understand how variable scopes work exactly. In particular, I have the following problem:
import tensorflow as tf
from tensorflow.models.rnn import rnn_cell
from tensorflow.models.rnn import rnn
inputs = [tf.placeholder(tf.float32,shape=[10,10]) for _ in range(5)]
cell = rnn_cell.BasicLSTMCell(10)
outpts, states = rnn.rnn(cell, inputs, dtype=tf.float32)
print outpts[2].name
# ==> u'RNN/BasicLSTMCell_2/mul_2:0'
Where does the '_2' in 'BasicLSTMCell_2' come from? How does it work when later using tf.get_variable(reuse=True) to get the same variable again?
edit: I think I find a related problem:
def creating(s):
with tf.variable_scope('test'):
with tf.variable_scope('inner'):
a=tf.get_variable(s,[1])
return a
def creating_mod(s):
with tf.variable_scope('test'):
with tf.variable_scope('inner'):
a=tf.Variable(0.0, name=s)
return a
tf.ops.reset_default_graph()
a=creating('a')
b=creating_mod('b')
c=creating('c')
d=creating_mod('d')
print a.name, '\n', b.name,'\n', c.name,'\n', d.name
The output is
test/inner/a:0
test_1/inner/b:0
test/inner/c:0
test_3/inner/d:0
I'm confused...
The answer above is somehow misguiding.
Let me answer why you got two different scope names, even though it looks like that you defined two identical functions: creating and creating_mod.
This is simply because you used tf.Variable(0.0, name=s) to create the variable in the function creating_mod.
ALWAYS use tf.get_variable, if you want your variable to be recognized by scope!
Check out this issue for more details.
Thanks!
The "_2" in "BasicLSTMCell_2" relates to the name scope in which the op outpts[2] was created. Every time you create a new name scope (with tf.name_scope()) or variable scope (with tf.variable_scope()) a unique suffix is added to the current name scope, based on the given string, possibly with an additional suffix to make it unique. The call to rnn.rnn(...) has the following pseudocode (simplified and using public API methods for clarity):
outputs = []
with tf.variable_scope("RNN"):
for timestep, input_t in enumerate(inputs):
if timestep > 0:
tf.get_variable_scope().reuse_variables()
with tf.variable_scope("BasicLSTMCell"):
outputs.append(...)
return outputs
If you look at the names of the tensors in outpts, you'll see that they are the following:
>>> print [o.name for o in outpts]
[u'RNN/BasicLSTMCell/mul_2:0',
u'RNN/BasicLSTMCell_1/mul_2:0',
u'RNN/BasicLSTMCell_2/mul_2:0',
u'RNN/BasicLSTMCell_3/mul_2:0',
u'RNN/BasicLSTMCell_4/mul_2:0']
When you enter a new name scope (by entering a with tf.name_scope("..."): or with tf.variable_scope("..."): block), TensorFlow creates a new, unique name for the scope. The first time the "BasicLSTMCell" scope is entered, TensorFlow uses that name verbatim, because it hasn't been used before (in the "RNN/" scope). The next time, TensorFlow appends "_1" to the scope to make it unique, and so on up to "RNN/BasicLSTMCell_4".
The main difference between variable scopes and name scopes is that a variable scope also has a set of name-to-tf.Variable bindings. By calling tf.get_variable_scope().reuse_variables(), we instruct TensorFlow to reuse rather than create variables for the "RNN/" scope (and its children), after timestep 0. This ensures that the weights are correctly shared between the multiple RNN cells.
In numpy I can create a copy of the variable with numpy.copy. Is there a similar method, that I can use to create a copy of a Tensor in TensorFlow?
You asked how to copy a variable in the title, but how to copy a tensor in the question. Let's look at the different possible answers.
(1) You want to create a tensor that has the same value that is currently stored in a variable that we'll call var.
tensor = tf.identity(var)
But remember, 'tensor' is a graph node that will have that value when evaluated, and any time you evaluate it, it will grab the current value of var. You can play around with control flow ops such as with_dependencies() to see the ordering of updates to the variable and the timing of the identity.
(2) You want to create another variable and set its value to the value currently stored in a variable:
import tensorflow as tf
var = tf.Variable(0.9)
var2 = tf.Variable(0.0)
copy_first_variable = var2.assign(var)
init = tf.initialize_all_variables()
sess = tf.Session()
sess.run(init)
print sess.run(var2)
sess.run(copy_first_variable)
print sess.run(var2)
(3) You want to define a variable and set its starting value to the same thing you already initialized a variable to (this is what nivwu.. above answered):
var2 = tf.Variable(var.initialized_value())
var2 will get initialized when you call tf.initialize_all_variables. You can't use this to copy var after you've already initialized the graph and started running things.
You can do this in a couple of ways.
this will create you a copy: v2 = tf.Variable(v1)
you can also use identity op: v2 = tf.identity(v1) (which I think is a proper way of doing it.
Here is a code example:
import tensorflow as tf
v1 = tf.Variable([[1, 2], [3, 4]])
v_copy1 = tf.Variable(v1)
v_copy2 = tf.identity(v1)
init = tf.initialize_all_variables()
sess = tf.Session()
sess.run(init)
a, b = sess.run([v_copy1, v_copy2])
sess.close()
print a
print b
Both of them would print the same tensors.
This performs a deep copy
copied_variable = tf.Variable(source_variable.initialized_value())
It also handles intialization properly, i.e.
tf.intialize_all_variables()
will properly initialize source_variable first and then copy that value to copied_variable
In TF2 :
tf.identity() will do the good deed for you. Recently I encountered some problems using the function in google colab. In case that's why you're here, this will be helping you.
Error : Failed copying input tensor from /job:localhost/replica:0/task:0/device:CPU:0 to /job:localhost/replica:0/task:0/device:GPU:0 in order to run Identity: No unary variant device copy function found for direction: 1 and Variant type_index: tensorflow::data::(anonymous namespace)::DatasetVariantWrapper [Op:Identity]
#Erroneous code
tensor1 = tf.data.Dataset.from_tensor_slices([[[1], [2]], [[3], [4]]])
tensor2 = tf.identity(tensor1)
#Correction
tensor1 = tf.data.Dataset.from_tensor_slices([[[1], [2]], [[3], [4]]])
with tf.device('CPU'): tensor2 = tf.identity(tensor1)