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

Question

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 usi

Answer 1

You can do

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

Also, if you are prototyping in an IPython notebook, you can show the graph directly in notebook, see show_graph function in Alexander's Deep Dream notebook

Answer 2

I'll try to summarize the answers:

To get all nodes: (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: (type tensorflow.python.framework.ops.Operation)

all_ops = tf.get_default_graph().get_operations()

To get all variables: (type tensorflow.python.ops.resource_variable_ops.ResourceVariable)

all_vars = tf.global_variables()

To get all tensors: (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 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

Answer 3

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))

Answer 4

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']