TensorFlow - Importing data from a TensorBoard TFEvent file?

Question

I\'ve run several training sessions with different graphs in TensorFlow. The summaries I set up show interesting results in the training and validation. Now, I\'d like to ta

Answer 1

Here is a complete example for obtaining values from a scalar. You can see the message specification for the Event protobuf message here

import tensorflow as tf


for event in tf.train.summary_iterator('runs/easy_name/events.out.tfevents.1521590363.DESKTOP-43A62TM'):
    for value in event.summary.value:
        print(value.tag)
        if value.HasField('simple_value'):
            print(value.simple_value)

Answer 2

I've been using this. It assumes that you only want to see tags you've logged more than once whose values are floats and returns the results as a pd.DataFrame. Just call metrics_df = parse_events_file(path).

from collections import defaultdict
import pandas as pd
import tensorflow as tf

def is_interesting_tag(tag):
    if 'val' in tag or 'train' in tag:
        return True
    else:
        return False


def parse_events_file(path: str) -> pd.DataFrame:
    metrics = defaultdict(list)
    for e in tf.train.summary_iterator(path):
        for v in e.summary.value:

            if isinstance(v.simple_value, float) and is_interesting_tag(v.tag):
                metrics[v.tag].append(v.simple_value)
            if v.tag == 'loss' or v.tag == 'accuracy':
                print(v.simple_value)
    metrics_df = pd.DataFrame({k: v for k,v in metrics.items() if len(v) > 1})
    return metrics_df

Answer 3

You can simply use:

tensorboard --inspect --event_file=myevents.out

or if you want to filter a specific subset of events of the graph:

tensorboard --inspect --event_file=myevents.out --tag=loss

If you want to create something more custom you can dig into the

/tensorflow/python/summary/event_file_inspector.py 

to understand how to parse the event files.

Answer 4

You can use the script serialize_tensorboard, which will take in a logdir and write out all the data in json format.

You can also use an EventAccumulator for a convenient Python API (this is the same API that TensorBoard uses).

Answer 5

As Fabrizio says, TensorBoard is a great tool for visualizing the contents of your summary logs. However, if you want to perform a custom analysis, you can use tf.train.summary_iterator() function to loop over all of the tf.Event and tf.Summary protocol buffers in the log:

for summary in tf.train.summary_iterator("/path/to/log/file"):
    # Perform custom processing in here.

UPDATE for tf2:

from tensorflow.python.summary.summary_iterator import summary_iterator

You need to import it, that module level is not currently imported by default. On 2.0.0-rc2

Answer 6

Following works as of tensorflow version 2.0.0-beta1:

import os

import tensorflow as tf
from tensorflow.python.framework import tensor_util

summary_dir = 'tmp/summaries'
summary_writer = tf.summary.create_file_writer('tmp/summaries')

with summary_writer.as_default():
  tf.summary.scalar('loss', 0.1, step=42)
  tf.summary.scalar('loss', 0.2, step=43)
  tf.summary.scalar('loss', 0.3, step=44)
  tf.summary.scalar('loss', 0.4, step=45)


from tensorflow.core.util import event_pb2
from tensorflow.python.lib.io import tf_record

def my_summary_iterator(path):
    for r in tf_record.tf_record_iterator(path):
        yield event_pb2.Event.FromString(r)

for filename in os.listdir(summary_dir):
    path = os.path.join(summary_dir, filename)
    for event in my_summary_iterator(path):
        for value in event.summary.value:
            t = tensor_util.MakeNdarray(value.tensor)
            print(value.tag, event.step, t, type(t))

the code for my_summary_iterator is copied from tensorflow.python.summary.summary_iterator.py - there was no way to import it at runtime.