semvec/pypi-packages huggingface dataset:
gradient-optimizers 0.0.4
Python package for wrapping gradient optimizers for models in Theano
License: MIT
Keywords: Machine Learning,Gradient Descent,NLP,Optimization,Hessian Free optimization

# Gradient Optimizers

Optimize you Theano Models with [Adagrad](http://www.magicbroom.info/Papers/DuchiHaSi10.pdf), Hessian Free optimization, or linear updates.


    pip3 install gradient-optimizers


See example notebook (TBD) for tutorial.

Two classes **GradientModel**, and **GradientHFModel**, for optimizing gradient
based models (specifically built with indexed parameters in mind (e.g.
for language models))

## GradientModel

A gradient model for updating your model with
hessian free, adagrad, or linear decay updates.

You will need to define the following attributes,
and fill them as appropriate:
    
    # a forward method for getting errors:
    projection = self.projection_function(ivector <indices/>)

    # a cost function (that takes the result of projection function and labels as input)
    # and returns a symbolic differentiable theano variable
    self.cost_function(projection, ivector <label/>).sum()

    self.params = []
    self.indexed_params = set()

    self._l2_regularization = True / False

    self.store_max_updates = True / False

    # set this theano setting
    self.theano_mode = "FAST_RUN"

    # set this theano setting
    self.disconnected_inputs = 'ignore' / None

    # if L2 is true store this parameter:
    self._l2_regularization_parameter = theano.shared(np.float64(l2_regularization).astype(REAL), name='l2_regularization_parameter')

Upon initialization you must run:

    self._select_update_mechanism(update_method_name)

    # then to compile this mechanism:
    self.create_update_fun()

The update methods expect the input to be of the form:

    ivector <indices/>, ivector <labels/>

If this is not the case you can modify them as appropriate.

## GradientHFModel

Implements an symbolic one step of hessian-free [1]
optimization that approximates the curvature,
requires a _compute_cost method that takes an example
as input or a _compute_cost_gradients that returns
gradients for each example provided.

Model should have a params property containing symbolic
theano variables.

[[1] James Martens, ``Deep learning via Hessian-free optimization", ICML 2010](http://www.icml2010.org/papers/458.pdf)

Make sure the following parameters are not tampered with:

    self._additional_params

    self._num_updates