https://github.com/fomorians-oss/pyoneer
Tensor utilities, reinforcement learning, and more!
https://github.com/fomorians-oss/pyoneer
deep-learning eager-execution gym machine-learning reinforcement-learning tensorflow tensorflow-probability
Last synced: 3 months ago
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Tensor utilities, reinforcement learning, and more!
- Host: GitHub
- URL: https://github.com/fomorians-oss/pyoneer
- Owner: fomorians-oss
- License: other
- Created: 2018-11-18T17:14:30.000Z (over 6 years ago)
- Default Branch: master
- Last Pushed: 2020-06-25T20:37:36.000Z (almost 5 years ago)
- Last Synced: 2025-01-31T03:13:37.182Z (4 months ago)
- Topics: deep-learning, eager-execution, gym, machine-learning, reinforcement-learning, tensorflow, tensorflow-probability
- Language: Python
- Homepage: https://pypi.org/project/fomoro-pyoneer/
- Size: 512 KB
- Stars: 9
- Watchers: 8
- Forks: 2
- Open Issues: 13
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# pyoneer
Tensor utilities, reinforcement learning, and more! Designed to make research easier with low-level abstractions for common operations.
## Usage
For the top-level utilities, import like so:
import pyoneer as pynr
pynr.math.rescale(...)
For the larger sub-modules, such as reinforcement learning, we recommend:
import pyoneer.rl as pyrl
loss_fn = pyrl.losses.PolicyGradient(...)
In general, the Pyoneer API tries to adhere to the TensorFlow 2.0 API.
### Examples
- [Proximal Policy Optimization with Pyoneer and TF 2.0](https://github.com/fomorians/ppo)
## API
### Activations ([`pynr.activations`](pyoneer/activations))
- `pynr.activations.swish`
### Debugging ([`pynr.debugging`](pyoneer/debugging))
- `pynr.debugging.Stopwatch`
### Distributions ([`pynr.distributions`](pyoneer/distributions))
- `pynr.distributions.MultiCategorical`
### Initializers ([`pynr.initializers`](pyoneer/initializers))
- `pynr.initializers.SoftplusInverse`
### Layers ([`pynr.layers`](pyoneer/layers))
- `pynr.layers.Swish`
- `pynr.layers.OneHotEncoder`
- `pynr.layers.AngleEncoder`
- `pynr.layers.Nest`
- `pynr.layers.ConcreteDropout`
### Tensor Manipulation ([`pynr.manip`](pyoneer/manip))
- `pynr.manip.flatten`
- `pynr.manip.batched_index`
- `pynr.manip.pad_or_truncate`
- `pynr.manip.shift`
### Math ([`pynr.math`](pyoneer/math))
- `pynr.math.to_radians`
- `pynr.math.to_degrees`
- `pynr.math.to_cartesian`
- `pynr.math.to_polar`
- `pynr.math.RADIANS_TO_DEGREES`
- `pynr.math.DEGREES_TO_RADIANS`
- `pynr.math.isclose`
- `pynr.math.safe_divide`
- `pynr.math.rescale`
- `pynr.math.normalize`
- `pynr.math.denormalize`
- `pynr.math.sigmoid_inverse`
### Metrics ([`pynr.metrics`](pyoneer/metrics))
- `pynr.metrics.mape`
- `pynr.metrics.smape`
- `pynr.metrics.MAPE`
- `pynr.metrics.SMAPE`
### Moments ([`pynr.moments`](pyoneer/moments))
- `pynr.moments.range_moments`
- `pynr.moments.StaticMoments`
- `pynr.moments.StreamingMoments`
- `pynr.moments.ExponentialMovingMoments`
### Learning Rate Schedules ([`pynr.schedules`](pyoneer/schedules))
- `pynr.schedules.CyclicSchedule`
### Regularizers ([`pynr.regularizers`](pyoneer/regularizers))
- `pynr.regularizers.DropoutL2`
### Reinforcement Learning ([`pynr.rl`](pyoneer/rl))
Utilities for reinforcement learning.
#### Losses ([`pynr.rl.losses`](pyoneer/rl/losses))
- `pynr.rl.losses.policy_gradient`
- `pynr.rl.losses.policy_entropy`
- `pynr.rl.losses.clipped_policy_gradient`
- `pynr.rl.losses.PolicyGradient`
- `pynr.rl.losses.PolicyEntropy`
- `pynr.rl.losses.ClippedPolicyGradient`
#### Targets ([`pynr.rl.targets`](pyoneer/rl/targets))
- `pynr.rl.targets.DiscountedReturns`
- `pynr.rl.targets.GeneralizedAdvantages`
#### Strategies ([`pynr.rl.strategies`](pyoneer/rl/strategies))
- `pynr.rl.strategies.EpsilonGreedy`
- `pynr.rl.strategies.Mode`
- `pynr.rl.strategies.Sample`
#### Wrappers ([`pynr.rl.wrappers`](pyoneer/rl/wrappers))
- `pynr.rl.wrappers.ObservationCoordinates`
- `pynr.rl.wrappers.ObservationNormalization`
- `pynr.rl.wrappers.Batch`
- `pynr.rl.wrappers.Process`
## Installation
There are a few options for installation:
1. (Recommended) Install with `pipenv`:
pipenv install fomoro-pyoneer
2. Install locally for development with `pipenv`:
git clone https://github.com/fomorians/pyoneer.git
cd pyoneer
pipenv install
pipenv shell
## Testing
There are a few options for testing:
1. Run all tests:
python -m unittest discover -bfp '*_test.py'
2. Run specific tests:
python -m pyoneer.math.logical_ops_test
## Contributing
File an issue following the `ISSUE_TEMPLATE`. If the issue discussion warrants implementation, then submit a pull request from a branch describing the feature. This will eventually get merged into `master` after a few rounds of code review.