# Path: util.py
# def numpy_floatX(data):
#     """
#         Returns the source as an appropriate float type.
#     """
#     return numpy.asarray(data, dtype=config.floatX)


import theano
from theano import tensor
from util import numpy_floatX
    ----------
    lr : Theano SharedVariable
        Initial learning rate
    tpramas: Theano SharedVariable
        Model parameters
    grads: Theano variable
        Gradients of cost w.r.t to parameres
    x: Theano variable
        Model inputs
    mask: Theano variable
        Sequence mask
    y: Theano variable
        Targets
    cost: Theano variable
        Objective fucntion to minimize

    Notes
    -----
    For more information, see [ADADELTA]_.

    .. [ADADELTA] Matthew D. Zeiler, *ADADELTA: An Adaptive Learning
       Rate Method*, arXiv:1212.5701.
    """

    zipped_grads = [theano.shared(p.get_value() * numpy_floatX(0.),
                                  name='%s_grad' % k)
                    for k, p in tparams.iteritems()]
    running_up2 = [theano.shared(p.get_value() * numpy_floatX(0.),
                                 name='%s_rup2' % k)
                   for k, p in tparams.iteritems()]

# Path: util.py
# def numpy_floatX(data):
#     """
#         Returns the source as an appropriate float type.
#     """
#     return numpy.asarray(data, dtype=config.floatX)


# Path: nn_optimizers.py
import theano
from theano import tensor
from util import numpy_floatX

    ----------
    lr : Theano SharedVariable
        Initial learning rate
    tpramas: Theano SharedVariable
        Model parameters
    grads: Theano variable
        Gradients of cost w.r.t to parameres
    x: Theano variable
        Model inputs
    mask: Theano variable
        Sequence mask
    y: Theano variable
        Targets
    cost: Theano variable
        Objective fucntion to minimize

    Notes
    -----
    For more information, see [ADADELTA]_.

    .. [ADADELTA] Matthew D. Zeiler, *ADADELTA: An Adaptive Learning
       Rate Method*, arXiv:1212.5701.
    """

    zipped_grads = [theano.shared(p.get_value() * numpy_floatX(0.),
                                  name='%s_grad' % k)
                    for k, p in tparams.iteritems()]
    running_up2 = [theano.shared(p.get_value() * numpy_floatX(0.),
                                 name='%s_rup2' % k)
                   for k, p in tparams.iteritems()]

    running_grads2 = [theano.shared(p.get_value() * numpy_floatX(0.),