zenkai 0.0.2

A framework for flexibly developing beyond bakcpropagation.

Zenkai

Zenkai is a framework built on Pytorch for researchers to more easily explore a wider variety of machine architectures for deep learning (or just learning with hidden layers). It is fundamentally based on the concepts of target propagation, where a target is propagated backward. As backpropagation with gradient descent can be viewed as a form of target propagation, it extends what one can do with Pytorch to a much larger class of machines. It aims to allow for much more freedom and control over the learning process while minimizing the added complexity.

Installation

pip install zenkai

Brief Overview

Zenkai consists of several packages to more flexibly define and train deep learning machines beyond what is easy to do with Pytorch.

zenkai: The core package. It contains all modules necessary for defining a learning machine. zenkai.utils: Utils contains a variety of utility functions that are . For example, utilities for ensemble learning and getting retrieving model parameters. zenkai.kikai: Kikai contains different types of learning machines : Hill Climbing, Scikit-learn wrappers, Gradient based machines, etc. zenkai.tansaku: Package for adding more exploration to learning. Contains framework for defining and creating population-based optimizers.

Further documentation is available at https://zenkai.readthedocs.io

Usage

Zenkai's primary feature is the "LearningMachine" which aims to make defining learning machines flexible. The design is similar to Torch, in that there is a forward method, a parameter update method similar to accGradParameters(), and a backpropagation method similar to updateGradInputs(). So the primary usage will be to implement them.

Here is a (non-working) example

class MyLearner(zenkai.LearningMachine):
    """A LearningMachine couples the learning mechanics for the machine with its internal mechanics."""

    def __init__(
        self, module: nn.Module, step_theta: zenkai.StepTheta, 
        step_x: StepX, loss: zenkai.Loss
    ):
        super().__init__()
        self.module = module
        # step_theta is used to update the parameters of the
        # module
        self._step_theta = step_theta
        # step_x is used to update the inputs to the module
        self._step_x = step_x
        self.loss = loss

    def assess_y(
        self, y: IO, t: IO, reduction_override: str=None
    ) -> zenkai.AssessmentDict:
        # assess_y evaluates the output of the learning machine
        return self.loss.assess_dict(x, t, reduction_override)

    def step(
        self, x: IO, t: IO, state: State
    ):
        # use to update the parameters of the machine
        # x (IO): The input to update with
        # t (IO): the target to update
        # outputs for a connection of two machines
        return self._step_theta(x, t, state)

    def step_x(
        self, x: IO, t: IO, state: State
    ) -> IO:
        # use to update the target for the machine
        # step_x is analogous to updateGradInputs in Torch except
        # it calculates "new targets" for the incoming layer
        return self._step_x(x, t, state)

    def forward(self, x: zenkai.IO, state: State, release: bool=False) -> zenkai.IO:
        y = self.module(x.f)
        return y.out(release=release)


my_learner = MyLearner(...)

for x, t in dataloader:
    state = State()
    assessment = my_learner.learn(x, t, state=state)
    # outputs the logs stored by the learner
    print(state.logs)

Learning machines can be stacked by making use of step_x in the training process.

class MyMultilayerLearner(LearningMachine):
    """A LearningMachine couples the learning mechanics for the machine with its internal mechanics."""

    def __init__(
        self, layer1: LearningMachine, layer2: LearningMachine
    ):
        super().__init__()
        self.layer1 = layer1
        self.layer2 = layer2

        # use these hooks to indicate a dependency on another method
        self.add_step(StepXDep(self, 't1', use_x=True))
        self.add_step_x(ForwardDep(self, 'y1', use_x=True))

    def assess_y(
        self, y: IO, t: IO, reduction_override: str=None
    ) -> zenkai.AssessmentDict:
        # assess_y evaluates the output of the learning machine
        return self.layer2.assess_y(y, t)

    def step(
        self, x: IO, t: IO, state: State
    ):
        # use to update the parameters of the machine
        # x (IO): The input to update with
        # t (IO): the target to update
        # outputs for a connection of two machines
        my_state = state.mine((self, x))
        
        self.layer2.step(my_state['y2'], my_state['t1'])
        self.layer1.step(my_state['y1'], t1)

    def step_x(
        self, x: IO, t: IO, state: State
    ) -> IO:
        # use to update the target for the machine
        # it calculates "new targets" for the incoming layer
        my_state = state.mine((self, x))
        t1 = my_state['t1'] = self.layer2.step_x(my_state['y2'], t)
        return self.layer1.step_x(my_state['y1'], t1)

    def forward(self, x: zenkai.IO, state: State, release: bool=True) -> zenkai.IO:

        # define the state to be for the self, input pair
        my_state = state.mine((self, x))
        x = my_state['y1'] = self.layer1(x, state)
        x = my_state['y2'] = self.layer2(x, state, release=release)
        return x

my_learner = MyLearner(...)

for x, t in dataloader:
    state = State()
    assessment = my_learner.learn(x, t, state=state)
    # outputs the logs stored by the learner
    print(state.logs)

Contributing

To contribute to the project

1. Fork the project
2. Create your feature branch
3. Commit your changes
4. Push to the branch
5. Open a pull request

License

This project is licensed under the MIT License - see the LICENSE.md file for details.

Citing this Software

If you use this software in your research, we request you cite it. We have provided a CITATION.cff file in the root of the repository. Here is an example of how you might use it in BibTeX: