In Tensorflow, get the names of all the Tensors in

问题:

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?

回答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

回答2:

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='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) 

回答3:

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.

回答4:

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()]

回答5:

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.

回答6:

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']

回答7:

This worked for me:

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