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@@ -17,10 +17,9 @@ pip install continuation-jax | |
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```python | ||
import cjax | ||
help(cjax) | ||
``` | ||
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#### Simple Math on Pytrees | ||
#### Math operations on Pytrees | ||
```python | ||
>>> import cjax | ||
>>> from cjax.utils import math_trees | ||
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@@ -35,52 +34,78 @@ help(cjax) | |
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#### Examples: | ||
- Examples: https://github.com/harsh306/continuation-jax/tree/main/examples | ||
- Sample Runner: https://github.com/harsh306/continuation-jax/blob/main/run.py | ||
- Sample Runner: https://github.com/harsh306/continuation-jax/blob/main/model_simple_classifier/run.py | ||
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```python | ||
""" | ||
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 | ||
).get_continuation_method() | ||
continuation.run() | ||
""" | ||
from cjax.continuation.creator.continuation_creator import ContinuationCreator | ||
from examples.toy.vectror_pitchfork import SigmoidFold | ||
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 | ||
from cjax.utils.visualizer import bif_plot, pick_array | ||
import mlflow | ||
from cjax.utils.visualizer import pick_array, bif_plot | ||
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config.update("jax_debug_nans", True) | ||
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# TODO: use **kwargs to reduce params | ||
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if __name__ == "__main__": | ||
problem = SigmoidFold() | ||
problem = ModelContClassifier() | ||
problem = ProblemWraper(problem) | ||
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with open(problem.HPARAMS_PATH, "r") as hfile: | ||
hparams = json.load(hfile) | ||
start_time = datetime.now() | ||
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if hparams["n_perturbs"] > 1: | ||
for perturb in range(hparams["n_perturbs"]): | ||
print(f"Running perturb {perturb}") | ||
mlflow.set_tracking_uri(hparams['meta']["mlflow_uri"]) | ||
mlflow.set_experiment(hparams['meta']["name"]) | ||
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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 = datetime.now() | ||
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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 | ||
).get_continuation_method() | ||
continuation.run() | ||
else: | ||
continuation = ContinuationCreator( | ||
problem=problem, hparams=hparams, key=perturb | ||
problem=problem, hparams=hparams | ||
).get_continuation_method() | ||
continuation.run() | ||
else: | ||
continuation = ContinuationCreator( | ||
problem=problem, hparams=hparams | ||
).get_continuation_method() | ||
continuation.run() | ||
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end_time = datetime.now() | ||
print(f"Duration: {end_time-start_time}") | ||
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bif_plot(hparams['output_dir'], pick_array, hparams['n_perturbs']) | ||
end_time = datetime.now() | ||
print(f"Duration: {end_time-start_time}") | ||
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figure = bif_plot(hparams["meta"]["output_dir"], pick_array) | ||
mlflow.log_figure(figure, artifact_file="plots/fig.png") | ||
``` | ||
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#### Note on Hyperparameters | ||
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#### Papers: | ||
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#### Contact: | ||
`[email protected]` |
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