Save Tensorflow graph for viewing in Tensorboard without summary operations

Question

I have a rather complicated Tensorflow graph that I\'d like to visualize for optimization purposes. Is there a function that I can call that will simply save the graph for v

Answer 1

You can also dump the graph as a GraphDef protobuf and load that directly in TensorBoard. You can do this without starting a session or running the model.

## ... create graph ...
>>> graph_def = tf.get_default_graph().as_graph_def()
>>> graphpb_txt = str(graph_def)
>>> with open('graphpb.txt', 'w') as f: f.write(graphpb_txt)

This will output a file that looks something like this, depending on the specifics of your model.

node {
  name: "W"
  op: "Const"
  attr {
    key: "dtype"
    value {
      type: DT_FLOAT
    }
  }
...
version 1

In TensorBoard you can then use the "Upload" button to load it from disk.

Answer 2

For all clarity, this is how I used the .flush() method and resolved the issue:

Initialize the writer with:

writer = tf.train.SummaryWriter("/home/rob/Dropbox/ConvNets/tf/log_tb", sess.graph_def)

and use the writer with:

writer.add_summary(summary_str, i)
    writer.flush()

Answer 3

This worked for me:

graph = tf.Graph()
with graph.as_default():
    ... build graph (without annotations) ...
writer = tf.summary.FileWriter(logdir='logdir', graph=graph)
writer.flush()

The graph is loaded automatically when launching tensorboard with "--logdir=logdir/". No "upload" button needed.

Answer 4

For efficiency, the tf.train.SummaryWriter logs asynchronously to disk. To ensure that the graph appears in the log, you must call close() or flush() on the writer before the program exits.

Answer 5

Nothing worked for me except this

# Helper for Converting Frozen graph from Disk to TF serving compatible Model
def get_graph_def_from_file(graph_filepath):
  tf.reset_default_graph()
  with ops.Graph().as_default():
    with tf.gfile.GFile(graph_filepath, 'rb') as f:
      graph_def = tf.GraphDef()
      graph_def.ParseFromString(f.read())
      return graph_def

#let us get the output nodes from the graph
graph_def =get_graph_def_from_file('/coding/ssd_inception_v2_coco_2018_01_28/frozen_inference_graph.pb')

with tf.Session(graph=tf.Graph()) as session:
    tf.import_graph_def(graph_def, name='')
    writer = tf.summary.FileWriter(logdir='/coding/log_tb/1', graph=session.graph)
    writer.flush()

Then using TB worked

#ssh -L 6006:127.0.0.1:6006 root@<remoteip> # for tensor board - in your local machine type 127.0.0.1
!tensorboard --logdir '/coding/log_tb/1'