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All rights reserved. from NeuralNetwork import NeuralNetwork from MySingletons import MyDevice import numpy as np import torch class AutoEncoder(NeuralNetwork): _greedy_layer_bias = None _greedy_layer_output_bias = None def __init__(self, layers=[]): NeuralNetwork.__init__(self, layers) for i in range(self.number_hidden_layers): self.activation_function[i] = self.ACTIVATION_FUNCTION_SIGMOID self.output_activation_function = self.ACTIVATION_FUNCTION_SIGMOID self.loss_function = self.LOSS_FUNCTION_MSE def train(self, x: torch.tensor, is_tied_weight: bool = False, noise_ratio: float = 0.0, weight_number: int = None, y: torch.tensor = None): if is_tied_weight: for i in range(int(self.number_hidden_layers/2)): if i == 0: self.output_weight = self.weight[i].T else: self.weight[-i] = self.weight[i].T if y is None: y = x NeuralNetwork.train(self, x=self.masking_noise(x=x, noise_ratio=noise_ratio), y=y, weight_no=weight_number) def test(self, x: torch.tensor, is_beta_updatable: bool = False, y: torch.tensor = None): if y is None: y = x NeuralNetwork.test(self, x=x, y=y, is_beta_updatable=is_beta_updatable) def grow_node(self, layer_number): NeuralNetwork.grow_node(self, layer_number) self.grow_greedy_layer_bias(layer_number) def prune_node(self, layer_number, node_number): NeuralNetwork.prune_node(self, layer_number, node_number) self.prune_greedy_layer_bias(layer_number, node_number) def grow_greedy_layer_bias(self, layer_number): b = layer_number if b is self.number_hidden_layers: [n_out, n_in] = self._greedy_layer_output_bias.shape self._greedy_layer_output_bias = torch.cat((self._greedy_layer_output_bias, self.xavier_weight_initialization(1, 1)), axis=1) else: [n_out, n_in] = self._greedy_layer_bias[b].shape n_in = n_in + 1 self._greedy_layer_bias[b] = np.append(self._greedy_layer_bias[b], self.xavier_weight_initialization(n_out, n_in, shape=(n_out, 1))) def prune_greedy_layer_bias(self, layer_number, node_number): def remove_nth_element(greedy_bias_tensor, n): bias_tensor = torch.cat([greedy_bias_tensor[0][:n], greedy_bias_tensor[0][n + 1:]]) return bias_tensor.view(1, bias_tensor.shape[0]) b = layer_number # readability n = node_number # readability if b is self.number_hidden_layers: self._greedy_layer_output_bias = remove_nth_element(self._greedy_layer_output_bias, n) else: self._greedy_layer_bias[b] = remove_nth_element(self._greedy_layer_bias[b], n) def greedy_layer_wise_pretrain(self, x: torch.tensor, number_epochs: int = 1, is_tied_weight: bool = False, noise_ratio: float = 0.0): for i in range(len(self.layers) - 1): if i > self.number_hidden_layers: nn = NeuralNetwork([self.layers[i], self.layers[-1], self.layers[i]]) else: nn = NeuralNetwork([self.layers[i], self.layers[i + 1], self.layers[i]]) nn.output_activation_function = self.ACTIVATION_FUNCTION_SIGMOID nn.loss_function = self.LOSS_FUNCTION_MSE nn.momentum_rate = 0 if i >= self.number_hidden_layers: nn.weight[0] = self.output_weight.clone() nn.bias[0] = self.output_bias.clone() nn.output_weight = self.output_weight.T.clone() if self._greedy_layer_output_bias is None: nodes_before = nn.layers[-2] nodes_after = nn.layers[-1] self._greedy_layer_output_bias = self.xavier_weight_initialization(1, nodes_after) nn.output_bias = self._greedy_layer_output_bias.clone() else: nn.weight[0] = self.weight[i].clone() nn.bias[0] = self.bias[i].clone() nn.output_weight = self.weight[i].T.clone() try: nn.output_bias = self._greedy_layer_bias[i].clone() except (TypeError, IndexError): nodes_before = nn.layers[-2] nodes_after = nn.layers[-1] if self._greedy_layer_bias is None: self._greedy_layer_bias = [] self._greedy_layer_bias.append(self.xavier_weight_initialization(1, nodes_after)) nn.output_bias = self._greedy_layer_bias[i].clone() for j in range(0, number_epochs): training_x = self.forward_pass(x=x).layer_value[i] nn.train(self.masking_noise(x=training_x, noise_ratio=noise_ratio), training_x) if i >= self.number_hidden_layers: self.output_weight = nn.weight[0].clone() self.output_bias = nn.bias[0].clone() else: self.weight[i] = nn.weight[0].clone() self.bias[i] = nn.bias[0].clone() def update_weights_kullback_leibler(self, Xs, Xt, gamma=0.0001): loss = NeuralNetwork.update_weights_kullback_leibler(self, Xs, Xs, Xt, Xt, gamma) return loss def compute_bias(self, y): return torch.mean((self.Ey.T - y) ** 2) @property def network_variance(self): return torch.mean(self.Ey2 - self.Ey ** 2) class DenoisingAutoEncoder(AutoEncoder): def __init__(self, layers=[]): AutoEncoder.__init__(self, layers) def train(self, x: torch.tensor, noise_ratio: float = 0.0, is_tied_weight: bool = False, weight_number: int = None, y: torch.tensor = None): AutoEncoder.train(self, x=x, noise_ratio=noise_ratio, is_tied_weight=is_tied_weight, weight_number=weight_number, y=y) def greedy_layer_wise_pretrain(self, x: torch.tensor, number_epochs: int = 1, is_tied_weight: bool = False, noise_ratio: float = 0.0, y: torch.tensor = None): AutoEncoder.greedy_layer_wise_pretrain(self, x=x, number_epochs=number_epochs, is_tied_weight=is_tied_weight, noise_ratio=noise_ratio)