Source code for epynn.convolution.models

# EpyNN/epynn/convolution/
# Local application/library specific imports
from epynn.commons.models import Layer
from epynn.commons.maths import (
from epynn.convolution.forward import convolution_forward
from epynn.convolution.backward import convolution_backward
from epynn.convolution.parameters import (

[docs]class Convolution(Layer): """ Definition of a convolution layer prototype. :param unit_filters: Number of unit filters in convolution layer, defaults to 1. :type unit_filters: int, optional :param filter_size: Height and width for convolution window, defaults to `(3, 3)`. :type filter_size: int or tuple[int], optional :param strides: Height and width to shift the convolution window by, defaults to `None` which equals `filter_size`. :type strides: int or tuple[int], optional :param padding: Number of zeros to pad each features plane with, defaults to 0. :type padding: int, optional :param activate: Non-linear activation of unit filters, defaults to `relu`. :type activate: function, optional :param initialization: Weight initialization function for convolution layer, defaults to `xavier`. :type initialization: function, optional :param use_bias: Whether the layer uses bias, defaults to `True`. :type use_bias: 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_filters=1, filter_size=(3, 3), strides=None, padding=0, activate=relu, initialization=xavier, use_bias=True, se_hPars=None): """Initialize instance variable attributes. """ super().__init__() filter_size = filter_size if isinstance(filter_size, tuple) else (filter_size, filter_size) strides = strides if isinstance(strides, tuple) else filter_size self.d['u'] = unit_filters self.d['fh'], self.d['fw'] = filter_size self.d['sh'], self.d['sw'] = strides self.d['p'] = padding self.activate = activate self.initialization = initialization self.use_bias = use_bias self.activation = { 'activate': activate.__name__ } self.trainable = True return None
[docs] def compute_shapes(self, A): """Wrapper for :func:`epynn.convolution.parameters.convolution_compute_shapes()`. :param A: Output of forward propagation from previous layer. :type A: :class:`numpy.ndarray` """ convolution_compute_shapes(self, A) return None
[docs] def initialize_parameters(self): """Wrapper for :func:`epynn.convolution.parameters.convolution_initialize_parameters()`. """ convolution_initialize_parameters(self) return None
[docs] def forward(self, A): """Wrapper for :func:`epynn.convolution.forward.convolution_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` """ activation_tune(self.se_hPars) A = convolution_forward(self, A) self.update_shapes(self.fc, self.fs) return A
[docs] def backward(self, dX): """Wrapper for :func:`epynn.convolution.backward.convolution_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 = convolution_backward(self, dX) self.update_shapes(self.bc, return dX
[docs] def compute_gradients(self): """Wrapper for :func:`epynn.convolution.parameters.convolution_compute_gradients()`. """ convolution_compute_gradients(self) return None
[docs] def update_parameters(self): """Wrapper for :func:`epynn.convolution.parameters.convolution_update_parameters()`. """ if self.trainable: convolution_update_parameters(self) return None