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All rights reserved. from MyUtil import MyUtil as MyUtil from ElasticNodes import ElasticNodes from MySingletons import MyDevice import AGMM import numpy as np import torch class NeuralNetwork(ElasticNodes): layers = None layer_value = None output_layer_value = None weight = None bias = None momentum = None bias_momentum = None output_weight = None output_bias = None output_momentum = None output_bias_momentum = None activation_function = None output_activation_function = None loss_function = None learning_rate = 0.01 momentum_rate = 0.95 ### error_value = None loss_value = None classification_rate = None ### ### output_beta = None output_beta_decreasing_factor = None ### __Eh = None __Eh2 = None ### ### is_agmm_able = False agmm = None ### @property def number_hidden_layers(self): return len(self.layers) - 2 @property def input_size(self): return self.layers[0] @property def output_size(self): return self.layers[-1] @property def output(self): return self.output_layer_value @property def raw_output(self): return torch.max(self.output, axis=1) @property def outputed_classes(self): return torch.argmax(self.output, axis=1) @property def residual_error(self): return 1 - self.raw_output.values ACTIVATION_FUNCTION_AFFINE = 1 ACTIVATION_FUNCTION_SIGMOID = ACTIVATION_FUNCTION_AFFINE + 1 ACTIVATION_FUNCTION_TANH = ACTIVATION_FUNCTION_SIGMOID + 1 ACTIVATION_FUNCTION_RELU = ACTIVATION_FUNCTION_TANH + 1 ACTIVATION_FUNCTION_LINEAR = ACTIVATION_FUNCTION_RELU + 1 ACTIVATION_FUNCTION_SOFTMAX = ACTIVATION_FUNCTION_LINEAR + 1 LOSS_FUNCTION_MSE = ACTIVATION_FUNCTION_SOFTMAX + 1 LOSS_FUNCTION_CROSS_ENTROPY = LOSS_FUNCTION_MSE + 1 PRUNE_NODE_STRATEGY_SINGLE = LOSS_FUNCTION_CROSS_ENTROPY + 1 PRUNE_NODE_STRATEGY_MULTIPLE = PRUNE_NODE_STRATEGY_SINGLE + 1 def __init__(self, layers: list): self.layers = layers self.weight = [] self.bias = [] self.momentum = [] self.bias_momentum = [] self.activation_function = [] for i in range(self.number_hidden_layers): nodes_before = layers[i] nodes_after = layers[i + 1] self.weight.append(self.xavier_weight_initialization(nodes_after, nodes_before)) self.bias.append(self.xavier_weight_initialization(1, nodes_after)) self.momentum.append(torch.zeros(self.weight[i].shape, dtype=torch.float, device=MyDevice().get())) self.bias_momentum.append(torch.zeros(self.bias[i].shape, dtype=torch.float, device=MyDevice().get())) self.activation_function.append(self.ACTIVATION_FUNCTION_SIGMOID) nodes_before = layers[-2] nodes_after = layers[-1] self.output_weight = self.xavier_weight_initialization(nodes_after, nodes_before) self.output_bias = self.xavier_weight_initialization(1, nodes_after) self.output_momentum = torch.zeros(self.output_weight.shape, dtype=torch.float, device=MyDevice().get()) self.output_bias_momentum = torch.zeros(self.output_bias.shape, dtype=torch.float, device=MyDevice().get()) self.output_activation_function = self.ACTIVATION_FUNCTION_SOFTMAX self.loss_function = self.LOSS_FUNCTION_CROSS_ENTROPY ElasticNodes.__init__(self, len(self.layers)) ##### Weight initializations ##### def xavier_weight_initialization(self, n_out: int, n_in: int, uniform: bool = False): if uniform: return torch.nn.init.xavier_uniform(tensor=torch.zeros(int(n_out), int(n_in), dtype=torch.float, requires_grad=True, device=MyDevice().get())) return torch.nn.init.xavier_normal_(tensor=torch.zeros(int(n_out), int(n_in), dtype=torch.float, requires_grad=True, device=MyDevice().get())) def he_weight_initialization(self, n_out, n_in, shape=None): #TODO mean = 0.0 sigma = np.sqrt(2 / n_in) if shape is None: shape = (n_out, n_in) return np.random.normal(mean, sigma, shape) ##### Noise ##### def masking_noise(self, x: torch.tensor, noise_ratio=0.0): return x.clone().masked_fill(torch.rand(x.shape, device=MyDevice().get()) <= noise_ratio, 0) ##### Activation functions ##### @staticmethod def sigmoid(z: torch.tensor): return torch.sigmoid(z) @staticmethod def tanh(z): return torch.tanh(z) @staticmethod def relu(z): return torch.nn.functional.relu(z) @staticmethod def linear(layer_value: torch.tensor, weight: torch.tensor, bias: torch.tensor): return torch.nn.functional.linear(layer_value, weight, bias) @staticmethod def softmax(z, axis: int = 1): return torch.nn.functional.softmax(z, dim=axis) def reset_grad(self): for i in range(self.number_hidden_layers): self.weight[i] = self.weight[i].detach() self.bias[i] = self.bias[i].detach() self.weight[i].requires_grad = True self.bias[i].requires_grad = True self.output_weight = self.output_weight.detach() self.output_bias = self.output_bias.detach() self.output_weight.requires_grad = True self.output_bias.requires_grad = True def feedforward(self, x: torch.Tensor, y: torch.Tensor, train: bool = False): return self.forward_pass(x, train=train).calculate_error(y) def backpropagate(self): self.loss_value.backward() return self def test(self, x: torch.Tensor, y: torch.Tensor, is_beta_updatable: bool = False): self.feedforward(x=x, y=y) m = y.shape[0] true_classes = torch.argmax(y, axis=1) misclassified = torch.sum(torch.ne(self.outputed_classes, true_classes)).item() self.classification_rate = 1 - misclassified / m if is_beta_updatable: class_label = self.output_layer_value.max(axis=2) for i in range(m): if self.true_classes[i] == class_label[i]: self.output_beta = np.max(self.output_beta * self.output_beta_decreasing_factor, 0) self.output_beta_decreasing_factor = np.max(self.output_beta_decreasing_factor - 0.01, 0) else: self.output_beta = max(self.output_beta * (1 + self.output_beta_decreasing_factor), 1) self.output_beta_decreasing_factor = max(self.output_beta_decreasing_factor + 0.01, 1) def train(self, x: torch.Tensor, y: torch.Tensor, weight_no: int = None): self.feedforward(x=x, y=y, train=True).backpropagate() if weight_no is None: for i in range(self.number_hidden_layers, -1, -1): self.update_weight(i) else: self.update_weight(weight_no) def update_weight(self, weight_no: int): if weight_no >= self.number_hidden_layers: dW: torch.Tensor = self.learning_rate * self.output_weight.grad db: torch.Tensor = self.learning_rate * self.output_bias.grad if self.momentum_rate > 0: self.output_momentum: torch.Tensor = self.momentum_rate * self.output_momentum + dW self.output_bias_momentum: torch.Tensor = self.momentum_rate * self.output_bias_momentum + db dW: torch.Tensor = self.output_momentum db: torch.Tensor = self.output_bias_momentum self.output_weight: torch.Tensor = self.output_weight - dW self.output_bias: torch.Tensor = self.output_bias - db else: dW: torch.Tensor = self.learning_rate * self.weight[weight_no].grad db: torch.Tensor = self.learning_rate * self.bias[weight_no].grad if self.momentum_rate > 0: self.momentum[weight_no]: torch.Tensor = self.momentum_rate * self.momentum[weight_no] + dW self.bias_momentum[weight_no]: torch.Tensor = self.momentum_rate * self.bias_momentum[weight_no] + db dW: torch.Tensor = self.momentum[weight_no] db: torch.Tensor = self.bias_momentum[weight_no] self.weight[weight_no]: torch.Tensor = self.weight[weight_no] - dW self.bias[weight_no]: torch.Tensor = self.bias[weight_no] - db def forward_pass(self, x: torch.Tensor, train: bool = False): if train: self.reset_grad() self.layer_value = [] self.layer_value.append(x) for i in range(self.number_hidden_layers): if self.activation_function[i] == self.ACTIVATION_FUNCTION_AFFINE: self.layer_value.append(self.linear(self.layer_value[i], self.weight[i], self.bias[i])) elif self.activation_function[i] == self.ACTIVATION_FUNCTION_SIGMOID: self.layer_value.append(self.sigmoid(self.linear(self.layer_value[i], self.weight[i], self.bias[i]))) elif self.activation_function[i] == self.ACTIVATION_FUNCTION_TANH: self.layer_value.append(self.tanh(self.linear(self.layer_value[i], self.weight[i], self.bias[i]))) elif self.activation_function[i] == self.ACTIVATION_FUNCTION_RELU: self.layer_value.append(self.relu(self.linear(self.layer_value[i], self.weight[i], self.bias[i]))) elif self.activation_function[i] == self.ACTIVATION_FUNCTION_LINEAR: raise TypeError('Not implemented') elif self.activation_function[i] == self.ACTIVATION_FUNCTION_SOFTMAX: self.layer_value.append(self.softmax(self.linear(self.layer_value[i], self.weight[i], self.bias[i]))) raise TypeError('Not implemented') if self.output_activation_function == self.ACTIVATION_FUNCTION_AFFINE: self.output_layer_value = self.linear(self.layer_value[-1], self.output_weight, self.output_bias) elif self.output_activation_function == self.ACTIVATION_FUNCTION_SIGMOID: self.output_layer_value = self.sigmoid(self.linear(self.layer_value[-1], self.output_weight, self.output_bias)) elif self.output_activation_function == self.ACTIVATION_FUNCTION_TANH: self.output_layer_value = self.tanh(self.linear(self.layer_value[-1], self.output_weight, self.output_bias)) elif self.output_activation_function == self.ACTIVATION_FUNCTION_RELU: self.output_layer_value = self.relu(self.linear(self.layer_value[-1], self.output_weight, self.output_bias)) elif self.output_activation_function == self.ACTIVATION_FUNCTION_SOFTMAX: self.output_layer_value = self.softmax(self.linear(self.layer_value[-1], self.output_weight, self.output_bias), axis=1) return self def calculate_error(self, y: torch.tensor): self.error_value = y - self.output_layer_value if self.loss_function == self.LOSS_FUNCTION_MSE: self.loss_value = torch.nn.functional.mse_loss(self.output_layer_value, y) elif self.loss_function == self.LOSS_FUNCTION_CROSS_ENTROPY: self.loss_value = torch.nn.functional.cross_entropy(self.output_layer_value, torch.argmax(y, 1)) return self def compute_expected_values(self, in_place: bool = False): if in_place: data_mean, data_var, data_std = self.data_mean, self.data_variance, self.data_standard_deviation self.number_samples_feed += 1 self.data_mean, self.data_variance, self.data_standard_deviation = \ MyUtil.recursive_mean_standard_deviation(self.layer_value[0], self.data_mean, self.data_variance, self.number_samples_feed) if self.is_agmm_able and self.agmm.M() > 0: self.Eh, self.Eh2 = 0, 0 for gmm in self.agmm.gmm_array: tempEh, tempEh2 = self.compute_inbound_expected_values(gmm=gmm) self.Eh, self.Eh2 = self.Eh + tempEh, self.Eh2 + tempEh2 else: self.Eh, self.Eh2 = self.compute_inbound_expected_values() if in_place: self.Eh, self.Eh2 = self.compute_inbound_expected_values() self.data_mean, self.data_variance, self.data_standard_deviation = data_mean, data_var, data_std self.number_samples_feed -= 1 def compute_inbound_expected_values(self, number_hidden_layer: int = None, gmm: AGMM.GMM = None): nhl = number_hidden_layer # readability if nhl is None: nhl = self.number_hidden_layers - 1 if nhl == 0: inference, center, std = (1, self.data_mean, self.data_standard_deviation) if gmm is None else (gmm.weight, gmm.center, gmm.std) py = MyUtil.probit(center, std) Eh = inference * self.sigmoid(self.linear(self.weight[0], py, self.bias[0].T)) else: Eh, _ = self.compute_inbound_expected_values(number_hidden_layer=nhl - 1, gmm=gmm) weight, bias = (self.weight[nhl], self.bias[nhl]) if nhl < self.number_hidden_layers + 1 else (self.output_weight, self.output_bias) Eh = self.sigmoid(self.linear(weight, Eh.T, bias.T)) return Eh, Eh ** 2 @property def Eh(self): return self.__Eh @Eh.setter def Eh(self, value: torch.tensor): self.__Eh = value @property def Eh2(self): return self.__Eh2 @Eh2.setter def Eh2(self, value: torch.tensor): self.__Eh2 = value @property def Ey(self): return self.softmax(self.linear(self.output_weight, self.Eh.T, self.output_bias.T), axis=0) @property def Ey2(self): return self.softmax(self.linear(self.output_weight, self.Eh2.T, self.output_bias.T), axis=0) @property def network_variance(self): return MyUtil.frobenius_norm(self.Ey2 - self.Ey ** 2) def compute_bias(self, y): return MyUtil.frobenius_norm((self.Ey.T - y) ** 2) def set_agmm(self, agmm: AGMM.GMM): self.is_agmm_able = True self.agmm = agmm def get_agmm(self): return self.agmm def run_agmm(self, x: torch.tensor, y: torch.tensor): self.compute_expected_values(in_place=True) self.agmm.run(x, self.compute_bias(y)) return self.agmm def width_adaptation_stepwise(self, y, prune_strategy: int = None): if prune_strategy is None: prune_strategy = self.PRUNE_NODE_STRATEGY_MULTIPLE nhl: int = self.number_hidden_layers self.number_samples_feed = self.number_samples_feed + 1 self.number_samples_layer[nhl] = self.number_samples_layer[nhl] + 1 self.compute_expected_values() self.bias_mean[nhl], self.bias_variance[nhl], self.bias_standard_deviation[nhl] = \ MyUtil.recursive_mean_standard_deviation(self.compute_bias(y), self.bias_mean[nhl], self.bias_variance[nhl], self.number_samples_feed) self.var_mean[nhl], self.var_variance[nhl], self.var_standard_deviation[nhl] = \ MyUtil.recursive_mean_standard_deviation(self.network_variance, self.var_mean[nhl], self.var_variance[nhl], self.number_samples_feed) if self.number_samples_layer[nhl] <= 1 or self.growable[nhl]: self.minimum_bias_mean[nhl] = self.bias_mean[nhl] self.minimum_bias_standard_deviation[nhl] = self.bias_standard_deviation[nhl] else: self.minimum_bias_mean[nhl] = np.min([self.minimum_bias_mean[nhl], self.bias_mean[nhl]]) self.minimum_bias_standard_deviation[nhl] = np.min([self.minimum_bias_standard_deviation[nhl], self.bias_standard_deviation[nhl]]) if self.number_samples_layer[nhl] <= self.input_size + 1 or self.prunable[nhl][0] != -1: self.minimum_var_mean[nhl] = self.var_mean[nhl] self.minimum_var_standard_deviation[nhl] = self.var_standard_deviation[nhl] else: self.minimum_var_mean[nhl] = np.min([self.minimum_var_mean[nhl], self.var_mean[nhl]]) self.minimum_var_standard_deviation[nhl] = np.min([self.minimum_var_standard_deviation[nhl], self.var_standard_deviation[nhl]]) self.BIAS.append(self.bias_mean[nhl]) self.VAR.append(self.var_mean[nhl]) self.growable[nhl] = self.is_growable(self.compute_bias(y)) self.prunable[nhl] = self.is_prunable(prune_strategy) def is_growable(self, bias: torch.tensor, alpha_1: float = 1.25, alpha_2: float = 0.75): nhl = self.number_hidden_layers # readability current = self.bias_mean[nhl] + self.bias_standard_deviation[nhl] biased_min = self.minimum_bias_mean[nhl] \ + (alpha_1 * torch.exp(-bias) + alpha_2) * self.minimum_bias_standard_deviation[nhl] if self.number_samples_layer[nhl] > 1 and current >= biased_min: return True return False def is_prunable(self, prune_strategy: int = None, alpha_1: float = 2.5, alpha_2: float = 1.5): if prune_strategy is None: prune_strategy = self.PRUNE_NODE_STRATEGY_MULTIPLE nhl = self.number_hidden_layers # readability current = self.var_mean[nhl] + self.var_standard_deviation[nhl] biased_min = self.minimum_var_mean[nhl] \ + (alpha_1 * torch.exp(-self.network_variance) + alpha_2) * self.minimum_var_standard_deviation[nhl] if not self.growable[nhl] \ and self.layers[nhl] > 1 \ and self.number_samples_layer[nhl] > self.input_size + 1 \ and current >= biased_min: if prune_strategy == self.PRUNE_NODE_STRATEGY_SINGLE: return torch.argmin(self.Eh) elif prune_strategy == self.PRUNE_NODE_STRATEGY_MULTIPLE: nodes_to_prune = torch.where(self.Eh < torch.abs(torch.mean(self.Eh) - torch.var(self.Eh))) if len(nodes_to_prune[0]): return nodes_to_prune[0] else: return torch.argmin(self.Eh) return [-1] def grow_node(self, layer_number: int): self.layers[layer_number] += 1 if layer_number >= 0: self.grow_weight_row(layer_number - 1) self.grow_bias(layer_number - 1) if layer_number <= self.number_hidden_layers: self.grow_weight_column(layer_number) def grow_weight_row(self, layer_number: int): def add_element(tensor_data: torch.tensor, momentum_tensor_data: torch.tensor, n_out: int): tensor_data = torch.cat((tensor_data, self.xavier_weight_initialization(1, n_out)), axis=0) momentum_tensor_data = torch.cat((momentum_tensor_data, torch.zeros(1, n_out, dtype=torch.float, device=MyDevice().get())), axis=0) return tensor_data, momentum_tensor_data if layer_number >= len(self.weight): [_, n_out] = self.output_weight.shape self.output_weight, self.output_momentum = add_element(self.output_weight, self.output_momentum, n_out) else: [_, n_out] = self.weight[layer_number].shape self.weight[layer_number], self.momentum[layer_number] = add_element(self.weight[layer_number], self.momentum[layer_number], n_out) def grow_weight_column(self, layer_number: int): def add_element(tensor_data: torch.tensor, momentum_tensor_data: torch.tensor, n_out: int): tensor_data = torch.cat((tensor_data, self.xavier_weight_initialization(n_out, 1)), axis=1) momentum_tensor_data = torch.cat((momentum_tensor_data, torch.zeros(n_out, 1, dtype=torch.float, device=MyDevice().get())), axis=1) return tensor_data, momentum_tensor_data if layer_number >= len(self.weight): [n_out, _] = self.output_weight.shape self.output_weight, self.output_momentum = add_element(self.output_weight, self.output_momentum, n_out) else: [n_out, _] = self.weight[layer_number].shape self.weight[layer_number], self.momentum[layer_number] = add_element(self.weight[layer_number], self.momentum[layer_number], n_out) def grow_bias(self, layer_number): def add_element(tensor_data: torch.tensor, momentum_tensor_data: torch.tensor, n_out: int): tensor_data = torch.cat((tensor_data, self.xavier_weight_initialization(1, n_out)), axis=1) momentum_tensor_data = torch.cat((momentum_tensor_data, torch.zeros(1, n_out, dtype=torch.float, device=MyDevice().get())), axis=1) return tensor_data, momentum_tensor_data if layer_number >= len(self.bias): [n_out, _] = self.output_bias.shape self.output_bias, self.output_bias_momentum = add_element(self.output_bias, self.output_bias_momentum, n_out) else: [n_out, _] = self.bias[layer_number].shape self.bias[layer_number], self.bias_momentum[layer_number] = add_element(self.bias[layer_number], self.bias_momentum[layer_number], n_out) pass def prune_node(self, layer_number: int, node_number: int): self.layers[layer_number] -= 1 if layer_number >= 0: self.prune_weight_row(layer_number - 1, node_number) self.prune_bias(layer_number - 1, node_number) if layer_number <= self.number_hidden_layers: self.prune_weight_column(layer_number, node_number) def prune_weight_row(self, layer_number: int, node_number: int): def remove_nth_row(tensor_data: torch.tensor, n: int): return torch.cat([tensor_data[:n], tensor_data[n+1:]]) if layer_number >= len(self.weight): self.output_weight = remove_nth_row(self.output_weight, node_number) self.output_momentum = remove_nth_row(self.output_momentum, node_number) else: self.weight[layer_number] = remove_nth_row(self.weight[layer_number], node_number) self.momentum[layer_number] = remove_nth_row(self.momentum[layer_number], node_number) def prune_weight_column(self, layer_number: int, node_number: int): def remove_nth_column(weight_tensor: torch.tensor, n: int): return torch.cat([weight_tensor.T[:n], weight_tensor.T[n+1:]]).T if layer_number >= len(self.weight): self.output_weight = remove_nth_column(self.output_weight, node_number) self.output_momentum = remove_nth_column(self.output_momentum, node_number) else: self.weight[layer_number] = remove_nth_column(self.weight[layer_number], node_number) self.momentum[layer_number] = remove_nth_column(self.momentum[layer_number], node_number) def prune_bias(self, layer_number: int, node_number: int): def remove_nth_element(bias_tensor: torch.tensor, n: int): bias_tensor = torch.cat([bias_tensor[0][:n], bias_tensor[0][n+1:]]) return bias_tensor.view(1, bias_tensor.shape[0]) if layer_number >= len(self.bias): self.output_bias = remove_nth_element(self.output_bias, node_number) self.output_bias_momentum = remove_nth_element(self.output_bias_momentum, node_number) else: self.bias[layer_number] = remove_nth_element(self.bias[layer_number], node_number) self.bias_momentum[layer_number] = remove_nth_element(self.bias_momentum[layer_number], node_number)