The graph object in Tensorflow has a method called “get_tensor_by_name(name)”. Is there anyway to get a list of valid tensor names?

If not, does anyone know the valid names for the pretrained model inception-v3 from here? From their example, pool_3, is one valid tensor but a list of all of them would be nice. I looked at the paper referred to and some of the layers seems to correspond to the sizes in table 1 but not all of them.

The paper is not accurately reflecting the model. If you download the source from arxiv it has an accurate model description as model.txt, and the names in there correlate strongly with the names in the released model.

To answer your first question, `sess.graph.get_operations()`

gives you a list of operations. For an op, `op.name`

gives you the name and `op.values()`

gives you a list of tensors it produces (in the inception-v3 model, all tensor names are the op name with a “:0” appended to it, so `pool_3:0`

is the tensor produced by the final pooling op.)

### Answer：

To see the operations in the graph (You will see many, so to cut short I have given here only the first string).

```
sess = tf.Session()
op = sess.graph.get_operations()
[m.values() for m in op][1]
out:
(<tf.Tensor 'conv1/weights:0' shape=(4, 4, 3, 32) dtype=float32_ref>,)
```

### Answer：

The above answers are correct. I came across an easy to understand / simple code for the above task. So sharing it here :-

```
import tensorflow as tf
def printTensors(pb_file):
# read pb into graph_def
with tf.gfile.GFile(pb_file, "rb") as f:
graph_def = tf.GraphDef()
graph_def.ParseFromString(f.read())
# import graph_def
with tf.Graph().as_default() as graph:
tf.import_graph_def(graph_def)
# print operations
for op in graph.get_operations():
print(op.name)
printTensors("path-to-my-pbfile.pb")
```

### Answer：

You do not even have to create a session to see the names of all operation names in the graph. To do this you just need to grab a default graph `tf.get_default_graph()`

and extract all the operations: `.get_operations`

. Each operation has many fields, the one you need is name.

Here is the code:

```
import tensorflow as tf
a = tf.Variable(5)
b = tf.Variable(6)
c = tf.Variable(7)
d = (a + b) * c
for i in tf.get_default_graph().get_operations():
print i.name
```

### Answer：

As a nested list comprehension:

```
tensor_names = [t.name for op in tf.get_default_graph().get_operations() for t in op.values()]
```

Function to get names of Tensors in a graph (defaults to default graph):

```
def get_names(graph=tf.get_default_graph()):
return [t.name for op in graph.get_operations() for t in op.values()]
```

Function to get Tensors in a graph (defaults to default graph):

```
def get_tensors(graph=tf.get_default_graph()):
return [t for op in graph.get_operations() for t in op.values()]
```

### Answer：

`saved_model_cli`

is An alternative command line tool comes with TF that *might* be useful if your dealing with the “SavedModel” format. From the docs

```
!saved_model_cli show --dir /tmp/mobilenet/1 --tag_set serve --all
```

This output might be useful, something like:

```
MetaGraphDef with tag-set: 'serve' contains the following SignatureDefs:
signature_def['__saved_model_init_op']:
The given SavedModel SignatureDef contains the following input(s):
The given SavedModel SignatureDef contains the following output(s):
outputs['__saved_model_init_op'] tensor_info:
dtype: DT_INVALID
shape: unknown_rank
name: NoOp
Method name is:
signature_def['serving_default']:
The given SavedModel SignatureDef contains the following input(s):
inputs['dense_input'] tensor_info:
dtype: DT_FLOAT
shape: (-1, 1280)
name: serving_default_dense_input:0
The given SavedModel SignatureDef contains the following output(s):
outputs['dense_1'] tensor_info:
dtype: DT_FLOAT
shape: (-1, 1)
name: StatefulPartitionedCall:0
Method name is: tensorflow/serving/predict
```