https://github.com/mrphys/keras-declarative
A Declarative Keras Interface
https://github.com/mrphys/keras-declarative
Last synced: 2 months ago
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A Declarative Keras Interface
- Host: GitHub
- URL: https://github.com/mrphys/keras-declarative
- Owner: mrphys
- License: apache-2.0
- Created: 2021-10-02T08:17:31.000Z (over 3 years ago)
- Default Branch: master
- Last Pushed: 2022-10-25T15:13:50.000Z (over 2 years ago)
- Last Synced: 2025-03-01T00:56:42.516Z (3 months ago)
- Language: Python
- Homepage:
- Size: 106 KB
- Stars: 1
- Watchers: 0
- Forks: 0
- Open Issues: 1
-
Metadata Files:
- Readme: README.rst
- License: LICENSE
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README
Keras Declarative
=================
This package is in alpha stage.
This README is a work in progress.
Creating Experiments
--------------------
There are two types of experiments: **training** experiments, which involve
training a new or existing model, and **testing** experiments, which involve
testing an existing model.
.. code-block:: console
keras.train --config_file path/to/config/file.yaml
keras.test --config_file path/to/config/file.yaml
Specifying Keras Objects
------------------------
Keras Declarative automatically discovers objects, such as layers and loss
functions, which are available in core TensorFlow, TensorFlow MRI and TF
Playground. This means these objects can be used in the configuration file
without explicit registration.
Serializable Keras objects, such as layers and loss functions, are specified by
a class name and a configuration dictionary. If no parameters should be passed
to the object, the configuration dictionary may be omitted.
.. code-block:: yaml
# class name and parameters
training:
optimizer:
class_name: Adam
config:
learning_rate: 0.001
# class name only (instantiated with default parameters)
training:
optimizer: Adam
External Modules
----------------
It is possible to use objects defined in external modules within Keras
Declarative, i.e., objects that are not part of either core TensorFlow,
TensorFlow MRI or TF Playground. This is particularly useful to define
preprocessing functions but can be used for any other purpose. External modules
can be specified with the ``$external`` directive.
Any external modules used during an experiment will be automatically saved to
the experiment folder to enable reproducibility.
.. code-block:: yaml
data:
transforms:
train:
- type: map
map:
map_func:
$external:
# Use a preprocessing function defined in an external module.
filename: /path/to/preprocessing_fn.py
object_name: preprocessing_fn
# Any parameters that should be passed to this object may be
# specified here.
args: null
kwargs: null
Anchors and Aliases
-------------------
If you need to repeat a node more than once, you can anchor it once with the
``&`` character and then alias it any number of times using the ``*`` character.
For example, to use the same list of data transforms for the training and
validation sets, you may use:
.. code-block:: yaml
data:
transforms:
train: &transforms
# Define the list of transforms here.
val: *transforms # Reusing training transforms here.
Hyperparameter Tuning
---------------------
Keras Declarative can configure the Keras Tuner to automatically tune one or
more parameters.
Most parameters can be set as tunable using the ``$tunable`` directive. For
example, to tune the kernel size of a U-Net model, you might use:
.. code-block:: yaml
model:
network:
class_name: UNet
config:
scales: 3
base_filters: 32
kernel_size:
$tunable:
type: int
int:
name: kernel_size
min_value: 3
max_value: 7
step: 2
Valid tunable types are `boolean`, `choice`, `fixed`, `float` and `int`. For
more details, see https://keras.io/api/keras_tuner/hyperparameters/.
The tuner type and options can be specified with ``tuning.tuner`` parameter:
.. code-block:: yaml
tuning:
tuner:
class_name: Hyperband
config:
objective:
name: val_ssim
direction: max
max_epochs: 100
Available tuners are ``RandomSearch``, ``BayesianOptimization`` and
``Hyperband``. For more details about these tuners and their options, see
https://keras.io/api/keras_tuner/tuners/.
Note that some parameters cannot be tuned. These include all parameters
under ``experiment`` and under ``data.sources``.