In Tensorflow, get the names of all the Tensors in a graph

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.

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))

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.

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()]

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']

This worked for me:

for n in tf.get_default_graph().as_graph_def().node:
    print('n',n)

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

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.