Skip to main content

Continuation Methods for Deep Neural Networks.

Project description

continuation-jax : Continuaion Framework for lambda

Continuation methods of Deep Neural Networks Tags: optimization, deep-learning, homotopy, bifurcation-analysis, continuation

Code style: black PyPI version License: MIT build

Install using pip:


pip install continuation-jax


import cjax

Math operations on Pytrees

>>> import cjax
>>> from cjax.utils import math_trees
>>> math_trees.pytree_element_mul([2,3,5], 2)
[4, 6, 10]
>>> math_trees.pytree_sub([2,3,5], [1,1,1])
[DeviceArray(1, dtype=int32), DeviceArray(2, dtype=int32), DeviceArray(4, dtype=int32)]
>>> math_trees.pytree_zeros_like({'a':12, 'b':45, 'c':[1,1]})
{'a': 0, 'b': 0, 'c': [0, 0]}


Main file to run contination on the user defined problem. Examples can be found in the examples/ directory.

Continuation is topological procedure to train a neural network. This module tracks all
the critical points or fixed points and dumps them to  output file provided in hparams.json file.

  Typical usage example:

  continuation = ContinuationCreator(
            problem=problem, hparams=hparams

from cjax.continuation.creator.continuation_creator import ContinuationCreator
from examples.model_simple_classifier.model_classifier import ModelContClassifier
from cjax.utils.abstract_problem import ProblemWraper
import json
from jax.config import config
from datetime import datetime
import mlflow
from cjax.utils.visualizer import pick_array, bif_plot

config.update("jax_debug_nans", True)

# TODO: use **kwargs to reduce params

if __name__ == "__main__":
    problem = ModelContClassifier()
    problem = ProblemWraper(problem)

    with open(problem.HPARAMS_PATH, "r") as hfile:
        hparams = json.load(hfile)

    with mlflow.start_run(run_name=hparams['meta']["method"]+"-"+hparams["meta"]["optimizer"]) as run:
        mlflow.log_dict(hparams, artifact_file="hparams/hparams.json")
        mlflow.log_text("", artifact_file="output/_touch.txt")
        artifact_uri = mlflow.get_artifact_uri("output/")
        hparams["meta"]["output_dir"] = artifact_uri
        print(f"URI: {artifact_uri}")
        start_time =

        if hparams["n_perturbs"] > 1:
            for perturb in range(hparams["n_perturbs"]):
                print(f"Running perturb {perturb}")
                continuation = ContinuationCreator(
                    problem=problem, hparams=hparams, key=perturb
            continuation = ContinuationCreator(
                problem=problem, hparams=hparams

        end_time =
        print(f"Duration: {end_time-start_time}")

        figure = bif_plot(hparams["meta"]["output_dir"], pick_array)
        mlflow.log_figure(figure, artifact_file="plots/fig.png")

Note on Hyperparameters



Project details

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

continuation_jax-0.0.7.tar.gz (57.4 kB view hashes)

Uploaded source

Built Distribution

continuation_jax-0.0.7-py3-none-any.whl (106.4 kB view hashes)

Uploaded py3

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page