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.)
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] out: (<tf.Tensor 'conv1/weights:0' shape=(4, 4, 3, 32) dtype=float32_ref>,)
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")
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
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()]
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