# EpyNN/epynn/gru/models.py
# Local application/library specific imports
from epynn.commons.models import Layer
from epynn.commons.maths import (
tanh,
sigmoid,
orthogonal,
clip_gradient,
activation_tune,
)
from epynn.gru.forward import gru_forward
from epynn.gru.backward import gru_backward
from epynn.gru.parameters import (
gru_compute_shapes,
gru_initialize_parameters,
gru_compute_gradients,
gru_update_parameters,
)
[docs]class GRU(Layer):
"""
Definition of a GRU layer prototype.
:param units: Number of unit cells in GRU layer, defaults to 1.
:type units: int, optional
:param activate: Non-linear activation of hidden hat (hh) state, defaults to `tanh`.
:type activate: function, optional
:param activate_output: Non-linear activation of update gate, defaults to `sigmoid`.
:type activate_output: function, optional
:param activate_candidate: Non-linear activation of reset gate, defaults to `sigmoid`.
:type activate_candidate: function, optional
:param initialization: Weight initialization function for GRU layer, defaults to `orthogonal`.
:type initialization: function, optional
:param clip_gradients: May prevent exploding/vanishing gradients, defaults to `False`.
:type clip_gradients: bool, optional
:param sequences: Whether to return only the last hidden state or the full sequence, defaults to `False`.
:type sequences: bool, optional
:param se_hPars: Layer hyper-parameters, defaults to `None` and inherits from model.
:type se_hPars: dict[str, str or float] or NoneType, optional
"""
def __init__(self,
unit_cells=1,
activate=tanh,
activate_update=sigmoid,
activate_reset=sigmoid,
initialization=orthogonal,
clip_gradients=False,
sequences=False,
se_hPars=None):
"""Initialize instance variable attributes.
"""
super().__init__(se_hPars)
self.d['u'] = unit_cells
self.activate = activate
self.activate_update = activate_update
self.activate_reset = activate_reset
self.initialization = initialization
self.clip_gradients = clip_gradients
self.sequences = sequences
self.activation = {
'activate': self.activate.__name__,
'activate_update': self.activate_update.__name__,
'activate_reset': self.activate_reset.__name__,
}
self.trainable = True
return None
[docs] def compute_shapes(self, A):
"""Wrapper for :func:`epynn.gru.parameters.gru_compute_shapes()`.
:param A: Output of forward propagation from previous layer.
:type A: :class:`numpy.ndarray`
"""
gru_compute_shapes(self, A)
return None
[docs] def initialize_parameters(self):
"""Wrapper for :func:`epynn.gru.parameters.gru_initialize_parameters()`.
"""
gru_initialize_parameters(self)
return None
[docs] def forward(self, A):
"""Wrapper for :func:`epynn.gru.forward.gru_forward()`.
:param A: Output of forward propagation from previous layer.
:type A: :class:`numpy.ndarray`
:return: Output of forward propagation for current layer.
:rtype: :class:`numpy.ndarray`
"""
self.compute_shapes(A)
activation_tune(self.se_hPars)
A = self.fc['A'] = gru_forward(self, A)
self.update_shapes(self.fc, self.fs)
return A
[docs] def backward(self, dX):
"""Wrapper for :func:`epynn.gru.backward.gru_backward()`.
:param dX: Output of backward propagation from next layer.
:type dX: :class:`numpy.ndarray`
:return: Output of backward propagation for current layer.
:rtype: :class:`numpy.ndarray`
"""
activation_tune(self.se_hPars)
dX = gru_backward(self, dX)
self.update_shapes(self.bc, self.bs)
return dX
[docs] def compute_gradients(self):
"""Wrapper for :func:`epynn.gru.parameters.gru_compute_gradients()`.
"""
gru_compute_gradients(self)
if self.clip_gradients:
clip_gradient(self)
return None
[docs] def update_parameters(self):
"""Wrapper for :func:`epynn.gru.parameters.gru_update_parameters()`.
"""
if self.trainable:
gru_update_parameters(self)
return None