How can I list all the Tensorflow variables that node depends on?

Question

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

Example 1 (adding 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]))

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

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

Example 2 (matrix multiplication between the filler and the weight matrix, followed by the addition of a displacement 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]}))

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

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

Answer 1

Here are the 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))

ops. op, t, t.op. , op op, op.outputs

Answer 2

, , get_graph() children():

import matplotlib.pyplot as plt
import networkx as nx
def plot_graph(G):
    '''Plot a DAG using NetworkX'''        
    def mapping(node):
        return node.name
    G = nx.DiGraph(G)
    nx.relabel_nodes(G, mapping, copy=False)
    nx.draw(G, cmap = plt.get_cmap('jet'), with_labels = True)
    plt.show()

plot_graph(get_graph())

1 :

import matplotlib.pyplot as plt
import networkx as nx
import tensorflow as tf

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

def plot_graph(G):
    '''Plot a DAG using NetworkX'''        
    def mapping(node):
        return node.name
    G = nx.DiGraph(G)
    nx.relabel_nodes(G, mapping, copy=False)
    nx.draw(G, cmap = plt.get_cmap('jet'), with_labels = True)
    plt.show()

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]))
plot_graph(get_graph())

:

2 :

Microsoft Windows, : Python (ValueError: _getfullpathname: -), matplotlib .

Answer 3

"" , "" , . ( ):

def findVars(atensor):
    allinputs=atensor.op.inputs
    if len(allinputs)==0:
        if atensor.op.type == 'VariableV2' or atensor.op.type == 'Variable':
            return set([atensor.op])
    a=set()
    for t in allinputs:
        a=a | findVars(t)
    return a

, , .

Answer 4

, , , .

tf.get_default_graph().as_graph_def()

, JSON, . OP .

import tensorflow as tf

a = tf.placeholder(tf.float32, name='placeholder_1')
b = tf.placeholder(tf.float32, name='placeholder_2')
c = a + b

tf.get_default_graph().as_graph_def()

Out[14]: 
node {
  name: "placeholder_1"
  op: "Placeholder"
  attr {
    key: "dtype"
    value {
      type: DT_FLOAT
    }
  }
  attr {
    key: "shape"
    value {
      shape {
        unknown_rank: true
      }
    }
  }
}
node {
  name: "placeholder_2"
  op: "Placeholder"
  attr {
    key: "dtype"
    value {
      type: DT_FLOAT
    }
  }
  attr {
    key: "shape"
    value {
      shape {
        unknown_rank: true
      }
    }
  }
}
node {
  name: "add"
  op: "Add"
  input: "placeholder_1"
  input: "placeholder_2"
  attr {
    key: "T"
    value {
      type: DT_FLOAT
    }
  }
}
versions {
  producer: 27
}