50

I'm having trouble recovering a tensor by name, I don't even know if it's possible.

I have a function that creates my graph:

def create_structure(tf, x, input_size,dropout):    
 with tf.variable_scope("scale_1") as scope:
  W_S1_conv1 = deep_dive.weight_variable_scaling([7,7,3,64], name='W_S1_conv1')
  b_S1_conv1 = deep_dive.bias_variable([64])
  S1_conv1 = tf.nn.relu(deep_dive.conv2d(x_image, W_S1_conv1,strides=[1, 2, 2, 1], padding='SAME') + b_S1_conv1, name="Scale1_first_relu")
.
.
.
return S3_conv1,regularizer

I want to access the variable S1_conv1 outside this function. I tried:

with tf.variable_scope('scale_1') as scope_conv: 
 tf.get_variable_scope().reuse_variables()
 ft=tf.get_variable('Scale1_first_relu')

But that is giving me an error:

ValueError: Under-sharing: Variable scale_1/Scale1_first_relu does not exist, disallowed. Did you mean to set reuse=None in VarScope?

But this works:

with tf.variable_scope('scale_1') as scope_conv: 
 tf.get_variable_scope().reuse_variables()
 ft=tf.get_variable('W_S1_conv1')

I can get around this with

return S3_conv1,regularizer, S1_conv1

but I don't want to do that.

I think my problem is that S1_conv1 is not really a variable, it's just a tensor. Is there a way to do what I want?

Improve this question

4 Answers 4

Reset to default
58

There is a function tf.Graph.get_tensor_by_name(). For instance:

import tensorflow as tf

c = tf.constant([[1.0, 2.0], [3.0, 4.0]])
d = tf.constant([[1.0, 1.0], [0.0, 1.0]])
e = tf.matmul(c, d, name='example')

with tf.Session() as sess:
    test =  sess.run(e)
    print e.name #example:0
    test = tf.get_default_graph().get_tensor_by_name("example:0")
    print test #Tensor("example:0", shape=(2, 2), dtype=float32)
Improve this answer
Sign up to request clarification or add additional context in comments.

5 Comments

For reference, if you need to get an OP instead of a tensor: stackoverflow.com/questions/42685994/…
@apfalz How to arrive at "example:0" for the tensor name?
Not sure if I get your question but, in the above example, if you print e.name you would know the name is example:0. Tensorflow adds the :0 to the name you specify.
Hi, thanks. Could you please tell me can I use get_tensor_by_name to get something defined by tf.layers.dense, e.g., means in this code sample here.
I don't know how defining tf.variable_scopes inside of python functions complicates matters, but you could try using tf.get_collection(key, scope=None). Alternatively you could try something like mean_vars = [i for i in tf.global_variables() if i.name.startswith('mean')] or potentially mean_vars = [i for i in tf.global_variables() if 'mean' in i.name] I haven't tested the second one so no promises. That would give you the full name as tensorflow understands it. From there tf.get_tensor_by_name() should work fine.
34

All tensors have string names which you can see as follows

[tensor.name for tensor in tf.get_default_graph().as_graph_def().node]

Once you know the name you can fetch the Tensor using <name>:0 (0 refers to endpoint which is somewhat redundant)

For instance if you do this

tf.constant(1)+tf.constant(2)

You have the following Tensor names

[u'Const', u'Const_1', u'add']

So you can fetch output of addition as

sess.run('add:0')

Note, this is part not part of public API. Automatically generated string tensor names are an implementation detail and may change.

Improve this answer

5 Comments

Thanks for the help, but that not really my problem. My tensor has been explicitly named ""Scale1_first_relu", but I can't get a reference to it outside the function where it has been declared. I can, however, get a reference to the variable "W_S1_conv1". Are tensors local? Do they exist outside the function where they are created?
Tensors exist in a Graph. If you use default Graph, it's shared between all functions in the same thread. The Scale1_first_relu is more of a "suggestion" rather than actual name. It''s true name name in a graph may have a prefix if it was created inside a scope or it may have a suffix automatically added for deduping. Just print the name of the tensors in the graph using recipe above and search for tensors containing Scale1_first_relu string
I've tried that already. The complete name is "scale_1\Scale1_first_relu:0". Searching for that still gives me an error, but searching for 'scale_1\W_S1_conv1' works. It's like the tensor no longer exists.
If the name is scale_1\Scale1_first_relu:0 then you can get its value with session.run('scale_1\Scale1_first_relu:0')
That's not exactly what I want and I'm not even sure this will work, but thanks for the effort. We have found a way around that, turns out returning S1_conv1 from the function is not much of a problem. I apologize if the question wasn't clear enough.
0

All you gotta do in this case is:

ft=tf.get_variable('scale1/Scale1_first_relu:0')
Improve this answer

Comments

0

Or simpler still, infer it from the corresponding .pbtxt file that comes with the model .pb file. Since it depends on the model, every case is different.

Improve this answer

Comments

Your Answer

By clicking “Post Your Answer”, you agree to our terms of service and acknowledge you have read our privacy policy.

Start asking to get answers

Find the answer to your question by asking.

Ask question

Explore related questions

See similar questions with these tags.