Set Tensor values during training in Tensorflow.
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.
- Python >= 3
- tensorflow >= 1.0
Install the package with pip:
pip install tfset
Or clone and install from github:
git clone https://github.com/ondrejba/tfset.git cd tfset python setup.py install
Check MNIST_demo.ipynb for a demostration of the usage of tfset in a simple training script.
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.
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
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.
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