如何列出节点依赖的所有 Tensorflow 变量? [英] How can I list all Tensorflow variables a node depends on?

问题描述

如何列出节点依赖的所有 Tensorflow 变量/常量/占位符?

How can I list all Tensorflow variables/constants/placeholders a node depends on?

示例 1(添加常量):

Example 1 (addition of constants):

import tensorflow as tf

a = tf.constant(1, name = 'a')
b = tf.constant(3, name = 'b')
c = tf.constant(9, name = 'c')
d = tf.add(a, b, name='d')
e = tf.add(d, c, name='e')

sess = tf.Session()
print(sess.run([d, e]))

我想要一个函数 list_dependencies() 如:

I would like to have a function list_dependencies() such as:

• list_dependencies(d) 返回 ['a', 'b']
• list_dependencies(e) 返回 ['a', 'b', 'c']

• list_dependencies(d) returns ['a', 'b']
• list_dependencies(e) returns ['a', 'b', 'c']

示例 2(占位符和权重矩阵之间的矩阵乘法，然后添加偏置向量):

Example 2 (matrix multiplication between a placeholder and a weight matrix, followed by the addition of a bias vector):

tf.set_random_seed(1)
input_size  = 5
output_size = 3
input       = tf.placeholder(tf.float32, shape=[1, input_size], name='input')
W           = tf.get_variable(
                "W",
                shape=[input_size, output_size],
                initializer=tf.contrib.layers.xavier_initializer())
b           = tf.get_variable(
                "b",
                shape=[output_size],
                initializer=tf.constant_initializer(2))
output      = tf.matmul(input, W, name="output")
output_bias = tf.nn.xw_plus_b(input, W, b, name="output_bias")

sess = tf.Session()
sess.run(tf.global_variables_initializer())
print(sess.run([output,output_bias], feed_dict={input: [[2]*input_size]}))

我想要一个函数 list_dependencies() 如:

I would like to have a function list_dependencies() such as:

• list_dependencies(output) 返回 ['W', 'input']
• list_dependencies(output_bias) 返回 ['W', 'b', 'input']

• list_dependencies(output) returns ['W', 'input']
• list_dependencies(output_bias) returns ['W', 'b', 'input']

推荐答案

以下是我为此使用的实用程序(来自 https://github.com/yaroslavvb/stuff/blob/master/linearize/linearize.py)

Here are utilities I use for this (from https://github.com/yaroslavvb/stuff/blob/master/linearize/linearize.py)

# computation flows from parents to children

def parents(op):
  return set(input.op for input in op.inputs)

def children(op):
  return set(op for out in op.outputs for op in out.consumers())

def get_graph():
  """Creates dictionary {node: {child1, child2, ..},..} for current
  TensorFlow graph. Result is compatible with networkx/toposort"""

  ops = tf.get_default_graph().get_operations()
  return {op: children(op) for op in ops}


def print_tf_graph(graph):
  """Prints tensorflow graph in dictionary form."""
  for node in graph:
    for child in graph[node]:
      print("%s -> %s" % (node.name, child.name))

这些函数适用于 ops.要获得生成张量 t 的操作，请使用 t.op.要获得 op op 生成的张量，请使用 op.outputs

These functions work on ops. To get an op that produces tensor t, use t.op. To get tensors produced by op op, use op.outputs

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