Source code for epynn.rnn.models

# EpyNN/epynn/rnn/
# Local application/library specific imports
from epynn.commons.models import Layer
from epynn.commons.maths import (
from epynn.rnn.forward import rnn_forward
from epynn.rnn.backward import rnn_backward
from epynn.rnn.parameters import (

[docs]class RNN(Layer): """ Definition of a RNN layer prototype. :param units: Number of unit cells in RNN layer, defaults to 1. :type units: int, optional :param activate: Non-linear activation of hidden state, defaults to `tanh`. :type activate: function, optional :param initialization: Weight initialization function for RNN layer, defaults to `xavier`. :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, initialization=xavier, clip_gradients=True, sequences=False, se_hPars=None): """Initialize instance variable attributes. """ super().__init__(se_hPars) self.d['u'] = unit_cells self.activate = activate self.initialization = initialization self.clip_gradients = clip_gradients self.sequences = sequences self.activation = { 'activate': self.activate.__name__ } self.trainable = True return None
[docs] def compute_shapes(self, A): """Wrapper for :func:`epynn.rnn.parameters.rnn_compute_shapes()`. :param A: Output of forward propagation from previous layer. :type A: :class:`numpy.ndarray` """ rnn_compute_shapes(self, A) return None
[docs] def initialize_parameters(self): """Wrapper for :func:`epynn.rnn.parameters.rnn_initialize_parameters()`. """ rnn_initialize_parameters(self) return None
[docs] def forward(self, A): """Wrapper for :func:`epynn.rnn.forward.rnn_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'] = rnn_forward(self, A) self.update_shapes(self.fc, self.fs) return A
[docs] def backward(self, dX): """Wrapper for :func:`epynn.rnn.backward.rnn_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 = rnn_backward(self, dX) self.update_shapes(self.bc, return dX
[docs] def compute_gradients(self): """Wrapper for :func:`epynn.rnn.parameters.rnn_compute_gradients()`. """ rnn_compute_gradients(self) if self.clip_gradients: clip_gradient(self) return None
[docs] def update_parameters(self): """Wrapper for :func:`epynn.rnn.parameters.rnn_update_parameters()`. """ if self.trainable: rnn_update_parameters(self) return None