TensorBoard graph结构介绍 基于TensorBoard中graph模块图结构分析

node {
 name: "Input/X"
 op: "Placeholder"
 attr {
 key: "_output_shapes"
 value {
  list {
  shape {
   unknown_rank: true
  }
  }
 }
 }
 attr {
 key: "dtype"
 value {
  type: DT_FLOAT
 }
 }
 attr {
 key: "shape"
 value {
  shape {
  unknown_rank: true
  }
 }
 }
}
...

def get_plugin_apps(self):
 return {
  '/graph': self.graph_route,
  '/runs': self.runs_route,
  '/run_metadata': self.run_metadata_route,
  '/run_metadata_tags': self.run_metadata_tags_route,
 }

 @wrappers.Request.application
 def graph_route(self, request):
 """Given a single run, return the graph definition in protobuf format."""
 run = request.args.get('run')
 if run is None:
  return http_util.Respond(
   request, 'query parameter "run" is required', 'text/plain', 400)
 
 limit_attr_size = request.args.get('limit_attr_size', None)
 if limit_attr_size is not None:
  try:
  limit_attr_size = int(limit_attr_size)
  except ValueError:
  return http_util.Respond(
   request, 'query parameter `limit_attr_size` must be an integer',
   'text/plain', 400)
 
 large_attrs_key = request.args.get('large_attrs_key', None)
 
 try:
  result = self.graph_impl(run, limit_attr_size, large_attrs_key)
 except ValueError as e:
  return http_util.Respond(request, e.message, 'text/plain', code=400)
 else:
  if result is not None:
  (body, mime_type) = result # pylint: disable=unpacking-non-sequence
  return http_util.Respond(request, body, mime_type)
  else:
  return http_util.Respond(request, '404 Not Found', 'text/plain',
         code=404)

def graph_impl(self, run, limit_attr_size=None, large_attrs_key=None):
 """Result of the form `(body, mime_type)`, or `None` if no graph exists."""
 try:
  graph = self._multiplexer.Graph(run)
 except ValueError:
  return None
 # This next line might raise a ValueError if the limit parameters
 # are invalid (size is negative, size present but key absent, etc.).
 process_graph.prepare_graph_for_ui(graph, limit_attr_size, large_attrs_key)
 return (str(graph), 'text/x-protobuf') # pbtxt

 def __init__(self, context):
 """Instantiates GraphsPlugin via TensorBoard core.
 Args:
  context: A base_plugin.TBContext instance.
 """
 self._multiplexer = context.multiplexer

def Graph(self, run):
 """Retrieve the graph associated with the provided run.
 Args:
  run: A string name of a run to load the graph for.
 Raises:
  KeyError: If the run is not found.
  ValueError: If the run does not have an associated graph.
 Returns:
  The `GraphDef` protobuf data structure.
 """
 accumulator = self.GetAccumulator(run)
 return accumulator.Graph()
 
 def GetAccumulator(self, run):
 """Returns EventAccumulator for a given run.
 Args:
  run: String name of run.
 Returns:
  An EventAccumulator object.
 Raises:
  KeyError: If run does not exist.
 """
 with self._accumulators_mutex:
  return self._accumulators[run]

 def Graph(self):
 """Return the graph definition, if there is one.
 If the graph is stored directly, return that. If no graph is stored
 directly but a metagraph is stored containing a graph, return that.
 Raises:
  ValueError: If there is no graph for this run.
 Returns:
  The `graph_def` proto.
 """
 graph = tf.GraphDef()
 if self._graph is not None:
  graph.ParseFromString(self._graph)
  return graph
 raise ValueError('There is no graph in this EventAccumulator')

# 导入要用到的基本模块。为了在python2、python3 中可以使用E侣兼容的 print 函数
from __future__ import print_function
import numpy as np
import tensorflow as tf
 
# 创建图和Session
graph = tf.Graph()
sess = tf.InteractiveSession(graph=graph)
 
#日志路径
model_fn = '/log/events.out.tfevents.1535957014.ubuntu'
 
for e in tf.train.summary_iterator(model_fn):
 if e.HasField('graph_def'):
  graph = e.graph_def;
  graph_def = tf.GraphDef()
  graph_def.ParseFromString(graph)
  print(graph_def)