Source code for epynn.embedding.models

# EpyNN/epynn/embedding/
# Related third party imports
import numpy as np

# Local application/library specific imports
from epynn.commons.models import Layer
from epynn.embedding.dataset import (
from epynn.embedding.forward import embedding_forward
from epynn.embedding.backward import embedding_backward
from epynn.embedding.parameters import (

[docs]class Embedding(Layer): """ Definition of an embedding layer prototype. :param X_data: Dataset containing samples features, defaults to `None` which returns an empty layer. :type X_data: list[list[float or str or list[float or str]]] or NoneType, optional :param Y_data: Dataset containing samples label, defaults to `None`. :type Y_data: list[int or list[int]] or NoneType, optional :param relative_size: For training, validation and testing sets. Defaults to `(2, 1, 1)` :type relative_size: tuple[int], optional :param batch_size: For training batches, defaults to None which makes a single batch out of the training data. :type batch_size: int or NoneType, optional :param X_encode: Set to True to one-hot encode features, default to `False`. :type encode: bool, optional :param Y_encode: Set to True to one-hot encode labels, default to `False`. :type encode: bool, optional :param X_scale: Normalize sample features within [0, 1], default to `False`. :type X_scale: bool, optional """ def __init__(self, X_data=None, Y_data=None, relative_size=(2, 1, 0), batch_size=None, X_encode=False, Y_encode=False, X_scale=False): """Initialize instance variable attributes. """ super().__init__() self.se_dataset = { 'dtrain_relative': relative_size[0], 'dval_relative': relative_size[1], 'dtest_relative': relative_size[2], 'batch_size': batch_size, 'X_scale': X_scale, 'X_encode': X_encode, 'Y_encode': Y_encode, } X_data, Y_data = embedding_check(X_data, Y_data, X_scale) X_data, Y_data = embedding_encode(self, X_data, Y_data, X_encode, Y_encode) embedded_data = embedding_prepare(self, X_data, Y_data) self.dtrain, self.dval, self.dtest = embedded_data # Keep non-empty datasets self.dsets = [self.dtrain, self.dval, self.dtest] self.dsets = [dset for dset in self.dsets if] self.trainable = False return None
[docs] def training_batches(self, init=False): """Wrapper for :func:`epynn.embedding.dataset.mini_batches()`. :param init: Wether to prepare a zip of X and Y data, defaults to False. :type init: bool, optional """ if init: self.dtrain_zip = list(zip(self.dtrain.X, self.dtrain.Y)) self.batch_dtrain = mini_batches(self) return None
[docs] def compute_shapes(self, A): """Wrapper for :func:`epynn.embedding.parameters.embedding_compute_shapes()`. :param A: Output of forward propagation from previous layer. :type A: :class:`numpy.ndarray` """ embedding_compute_shapes(self, A) return None
[docs] def initialize_parameters(self): """Wrapper for :func:`epynn.embedding.parameters.embedding_initialize_parameters()`. """ embedding_initialize_parameters(self) return None
[docs] def forward(self, A): """Wrapper for :func:`epynn.embedding.forward.embedding_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` """ A = embedding_forward(self, A) self.update_shapes(self.fc, self.fs) return A
[docs] def backward(self, dX): """Wrapper for :func:`epynn.embedding.backward.embedding_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` """ dX = embedding_backward(self, dX) self.update_shapes(self.bc, return dX
[docs] def compute_gradients(self): """Wrapper for :func:`epynn.embedding.parameters.embedding_compute_gradients()`. Dummy method, there are no gradients to compute in layer. """ embedding_compute_gradients(self) return None
[docs] def update_parameters(self): """Wrapper for :func:`epynn.embedding.parameters.embedding_update_parameters()`. Dummy method, there are no parameters to update in layer. """ if self.trainable: embedding_update_parameters(self) return None