I am creating neural nets with Tensorflow and skflow; for some reason I want to get the values of some inner tensors for a given input, so I am using myClassifier.get_layer_value(input, "tensorName"), myClassifier being a skflow.estimators.TensorFlowEstimator.
However, I find it difficult to find the correct syntax of the tensor name, even knowing its name (and I'm getting confused between operation and tensors), so I'm using tensorboard to plot the graph and look for the name.
Is there a way to enumerate all the tensors in a graph without using tensorboard?
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The accepted answer only gives you a list of strings with the names. I prefer a different approach, which gives you (almost) direct access to the tensors:
graph = tf.get_default_graph()
list_of_tuples = [op.values() for op in graph.get_operations()]
list_of_tuples now contains every tensor, each within a tuple. You could also adapt it to get the tensors directly:
graph = tf.get_default_graph()
list_of_tuples = [op.values()[0] for op in graph.get_operations()]
tf.all_variables() can get you the information you want.
Also, this commit made today in TensorFlow Learn that provides a function get_variable_names in estimator that you can use to retrieve all variable names easily.
The following solution works for me in TensorFlow 2.3 -
def load_pb(path_to_pb):
with tf.io.gfile.GFile(path_to_pb, 'rb') as f:
graph_def = tf.compat.v1.GraphDef()
graph_def.ParseFromString(f.read())
with tf.Graph().as_default() as graph:
tf.import_graph_def(graph_def, name='')
return graph
tf_graph = load_pb(MODEL_FILE)
sess = tf.compat.v1.Session(graph=tf_graph)
# Show tensor names in graph
for op in tf_graph.get_operations():
print(op.values())
where MODEL_FILE is the path to your frozen graph.
Taken from here.
There is a way to do it slightly faster than in Yaroslav's answer by using get_operations. Here is a quick example:
import tensorflow as tf
a = tf.constant(1.3, name='const_a')
b = tf.Variable(3.1, name='variable_b')
c = tf.add(a, b, name='addition')
d = tf.multiply(c, a, name='multiply')
for op in tf.get_default_graph().get_operations():
print(str(op.name))
Previous answers are good, I'd just like to share a utility function I wrote to select Tensors from a graph:
def get_graph_op(graph, and_conds=None, op='and', or_conds=None):
"""Selects nodes' names in the graph if:
- The name contains all items in and_conds
- OR/AND depending on op
- The name contains any item in or_conds
Condition starting with a "!" are negated.
Returns all ops if no optional arguments is given.
Args:
graph (tf.Graph): The graph containing sought tensors
and_conds (list(str)), optional): Defaults to None.
"and" conditions
op (str, optional): Defaults to 'and'.
How to link the and_conds and or_conds:
with an 'and' or an 'or'
or_conds (list(str), optional): Defaults to None.
"or conditions"
Returns:
list(str): list of relevant tensor names
"""
assert op in {'and', 'or'}
if and_conds is None:
and_conds = ['']
if or_conds is None:
or_conds = ['']
node_names = [n.name for n in graph.as_graph_def().node]
ands = {
n for n in node_names
if all(
cond in n if '!' not in cond
else cond[1:] not in n
for cond in and_conds
)}
ors = {
n for n in node_names
if any(
cond in n if '!' not in cond
else cond[1:] not in n
for cond in or_conds
)}
if op == 'and':
return [
n for n in node_names
if n in ands.intersection(ors)
]
elif op == 'or':
return [
n for n in node_names
if n in ands.union(ors)
]
So if you have a graph with ops:
['model/classifier/dense/kernel',
'model/classifier/dense/kernel/Assign',
'model/classifier/dense/kernel/read',
'model/classifier/dense/bias',
'model/classifier/dense/bias/Assign',
'model/classifier/dense/bias/read',
'model/classifier/dense/MatMul',
'model/classifier/dense/BiasAdd',
'model/classifier/ArgMax/dimension',
'model/classifier/ArgMax']
Then running
get_graph_op(tf.get_default_graph(), ['dense', '!kernel'], 'or', ['Assign'])
returns:
['model/classifier/dense/kernel/Assign',
'model/classifier/dense/bias',
'model/classifier/dense/bias/Assign',
'model/classifier/dense/bias/read',
'model/classifier/dense/MatMul',
'model/classifier/dense/BiasAdd']
Since the OP asked for the list of the tensors instead of the list of operations/nodes, the code should be slightly different:
graph = tf.get_default_graph()
tensors_per_node = [node.values() for node in graph.get_operations()]
tensor_names = [tensor.name for tensors in tensors_per_node for tensor in tensors]
I'll try to summarize the answers:
To get all nodes in the graph: (type tensorflow.core.framework.node_def_pb2.NodeDef)
all_nodes = [n for n in tf.get_default_graph().as_graph_def().node]
To get all ops in the graph: (type tensorflow.python.framework.ops.Operation)
all_ops = tf.get_default_graph().get_operations()
To get all variables in the graph: (type tensorflow.python.ops.resource_variable_ops.ResourceVariable)
all_vars = tf.global_variables()
To get all tensors in the graph: (type tensorflow.python.framework.ops.Tensor)
all_tensors = [tensor for op in tf.get_default_graph().get_operations() for tensor in op.values()]
To get all placeholders in the graph: (type tensorflow.python.framework.ops.Tensor)
all_placeholders = [placeholder for op in tf.get_default_graph().get_operations() if op.type=='Placeholder' for placeholder in op.values()]
Tensorflow 2
To get the graph in Tensorflow 2, instead of tf.get_default_graph() you need to instantiate a tf.function first and access the graph attribute, for example:
graph = func.get_concrete_function().graph
where func is a tf.function
I think this will do too:
print(tf.contrib.graph_editor.get_tensors(tf.get_default_graph()))
But compared with Salvado and Yaroslav's answers, I don't know which one is better.
This worked for me:
for n in tf.get_default_graph().as_graph_def().node:
print('\n',n)
Source: Stackoverflow.com