https://github.com/ondrejbiza/tfset

Interactive Tensorflow training.
https://github.com/ondrejbiza/tfset

deep-learning deep-neural-networks machine-learning tensorflow tensorflow-experiments

Last synced: 11 days ago
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Interactive Tensorflow training.

• Host: GitHub
• URL: https://github.com/ondrejbiza/tfset
• Owner: ondrejbiza
• License: mit
• Created: 2017-08-27T08:35:03.000Z (over 7 years ago)
• Default Branch: master
• Last Pushed: 2018-02-02T12:30:09.000Z (almost 7 years ago)
• Last Synced: 2024-11-30T05:06:54.358Z (about 1 month ago)
• Topics: deep-learning, deep-neural-networks, machine-learning, tensorflow, tensorflow-experiments
• Language: Python
• Homepage:
• Size: 10.9 MB
• Stars: 1
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.rst
  • License: LICENSE

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README

        
tfset

=====

.. figure:: tfset/images/validation_curve.png

   :alt: Validation Curve

**Change the hyper-parameters** of your Tensorflow training session on

the fly. The package allows you to schedule events that change the

values of arbitrary Tensors with a simple command.

Requirements

~~~~~~~~~~~~

-  Python >= 3

-  tensorflow >= 1.0

Set Up

~~~~~~

Install the package with pip:

``pip install tfset``

Or clone and install from github:

.. code-block:: bash

   git clone https://github.com/ondrejba/tfset.git

   cd tfset

   python setup.py install

Usage

~~~~~

tfset DEMO

^^^^^^^^^^^^^^^^^^^^^^^^^^^

Check

`MNIST\_demo.ipynb `__

for a demostration of the usage of tfset in a simple

training script.

Server

^^^^^^

Import tfset server.

::

    import tfset.server as server

Create Tensors for your hyper-parameters.

::

    learning_rate = tf.get_variable("learning_rate", initializer=tf.constant(0.1, dtype=tf.float32))

    dropout_prob = tf.get_variable("dropout_prob", initializer=tf.constant(0.9, dtype=tf.float32))

Create and start a Session Server.

::

    # "session" is a Tensorflow session

    s, thread = server.run_server([learning_rate, dropout_prob], session)

Periodically check for events.

::

    # "step" is the global step of your training procedure

    s.check_events(step)

Stop the server.

::

    s.shutdown()

    thread.join(timeout=10)

Client

^^^^^^

Get status.

``tfset -s``

Add an event (this event sets the learning rate to 0.01 at iteration

10000).

``tfset -a -n learning_rate:0 -i 10000 --value 0.01``

Remove an event (with index 0 in this case).

``tfset -r -e 0``

Events

^^^^^^

tfset schedules hyper-parameter changes based on

**events**. An event contains the following information:

-  **iteration**: when to execute the event

-  **Tensor name**: which Tensor to change

-  **value**: value to set the Tensor to

The reason for the use of events is that you might want to schedule

hyper-parameter changes in the future (e.g. lower learning rate to 10e-3

at 800k iteration). If two events targeting the same Tensor are

scheduled at the same iteration, the one that was scheduled later is

going to be executed.