annarailton

# View graphs with (Linux): $ tensorboard --logdir=/tmp/my_tf_model

import os
import tempfile
import tensorflow as tf
import numpy as np
from tensorboard import summary as summary_lib
from tensorboard.plugins.custom_scalar import layout_pb2

annarailton

"""Simple example of various types of Tensorflow profiling.

Adapted from:

    https://towardsdatascience.com/howto-profile-tensorflow-1a49fb18073d
    https://www.tensorflow.org/api_docs/python/tf/profiler/Profiler

"""
from __future__ import absolute_import
from __future__ import division

annarailton

# Tensorflow 1.8.0
import tensorflow as tf
import numpy as np


def make_iterators(train_dataset, test_dataset):
    """Creates the dataset iterators needed in train()."""
    handle = tf.placeholder(tf.string, shape=[])
    tf.add_to_collection('handle', handle)

annarailton

# Tensorflow 1.8.0
import tensorflow as tf
import numpy as np

def save(dataset):
    """
    Create graph with an Dataset and Iterator and save the model.

    There is some op that is applied to the data from the iterator.
    """

annarailton

"""MWE of a tensorboard setup with

    - a dummy training and validation op
    - loss for training and validation shown on the same graph
    - histograms showing weights changing with training

To run tensorboard after running this script

    $ tensorboard --log_dir=/tmp/tensorboard_demo