diff --git a/AGMM.py b/AGMM.py new file mode 100644 index 0000000..6dc7623 --- /dev/null +++ b/AGMM.py @@ -0,0 +1,335 @@ +# Marcus Vinicius Sousa Leite de Carvalho +# marcus.decarvalho@ntu.edu.sg +# ivsucram@gmail.com +# +# NANYANG TECHNOLOGICAL UNIVERSITY - NTUITIVE PTE LTD Dual License Agreement +# Non-Commercial Use Only +# This NTUITIVE License Agreement, including all exhibits ("NTUITIVE-LA") is a legal agreement between you and NTUITIVE (or “we”) located at 71 Nanyang Drive, NTU Innovation Centre, #01-109, Singapore 637722, a wholly owned subsidiary of Nanyang Technological University (“NTU”) for the software or data identified above, which may include source code, and any associated materials, text or speech files, associated media and "online" or electronic documentation and any updates we provide in our discretion (together, the "Software"). +# +# By installing, copying, or otherwise using this Software, found at https://github.com/Ivsucram/ATL_Matlab, you agree to be bound by the terms of this NTUITIVE-LA. If you do not agree, do not install copy or use the Software. The Software is protected by copyright and other intellectual property laws and is licensed, not sold. If you wish to obtain a commercial royalty bearing license to this software please contact us at marcus.decarvalho@ntu.edu.sg. +# +# SCOPE OF RIGHTS: +# You may use, copy, reproduce, and distribute this Software for any non-commercial purpose, subject to the restrictions in this NTUITIVE-LA. Some purposes which can be non-commercial are teaching, academic research, public demonstrations and personal experimentation. You may also distribute this Software with books or other teaching materials, or publish the Software on websites, that are intended to teach the use of the Software for academic or other non-commercial purposes. +# You may not use or distribute this Software or any derivative works in any form for commercial purposes. Examples of commercial purposes would be running business operations, licensing, leasing, or selling the Software, distributing the Software for use with commercial products, using the Software in the creation or use of commercial products or any other activity which purpose is to procure a commercial gain to you or others. +# If the Software includes source code or data, you may create derivative works of such portions of the Software and distribute the modified Software for non-commercial purposes, as provided herein. +# If you distribute the Software or any derivative works of the Software, you will distribute them under the same terms and conditions as in this license, and you will not grant other rights to the Software or derivative works that are different from those provided by this NTUITIVE-LA. +# If you have created derivative works of the Software, and distribute such derivative works, you will cause the modified files to carry prominent notices so that recipients know that they are not receiving the original Software. Such notices must state: (i) that you have changed the Software; and (ii) the date of any changes. +# +# You may not distribute this Software or any derivative works. +# In return, we simply require that you agree: +# 1. That you will not remove any copyright or other notices from the Software. +# 2. That if any of the Software is in binary format, you will not attempt to modify such portions of the Software, or to reverse engineer or decompile them, except and only to the extent authorized by applicable law. +# 3. That NTUITIVE is granted back, without any restrictions or limitations, a non-exclusive, perpetual, irrevocable, royalty-free, assignable and sub-licensable license, to reproduce, publicly perform or display, install, use, modify, post, distribute, make and have made, sell and transfer your modifications to and/or derivative works of the Software source code or data, for any purpose. +# 4. That any feedback about the Software provided by you to us is voluntarily given, and NTUITIVE shall be free to use the feedback as it sees fit without obligation or restriction of any kind, even if the feedback is designated by you as confidential. +# 5. THAT THE SOFTWARE COMES "AS IS", WITH NO WARRANTIES. THIS MEANS NO EXPRESS, IMPLIED OR STATUTORY WARRANTY, INCLUDING WITHOUT LIMITATION, WARRANTIES OF MERCHANTABILITY OR FITNESS FOR A PARTICULAR PURPOSE, ANY WARRANTY AGAINST INTERFERENCE WITH YOUR ENJOYMENT OF THE SOFTWARE OR ANY WARRANTY OF TITLE OR NON-INFRINGEMENT. THERE IS NO WARRANTY THAT THIS SOFTWARE WILL FULFILL ANY OF YOUR PARTICULAR PURPOSES OR NEEDS. ALSO, YOU MUST PASS THIS DISCLAIMER ON WHENEVER YOU DISTRIBUTE THE SOFTWARE OR DERIVATIVE WORKS. +# 6. THAT NEITHER NTUITIVE NOR NTU NOR ANY CONTRIBUTOR TO THE SOFTWARE WILL BE LIABLE FOR ANY DAMAGES RELATED TO THE SOFTWARE OR THIS NTUITIVE-LA, INCLUDING DIRECT, INDIRECT, SPECIAL, CONSEQUENTIAL OR INCIDENTAL DAMAGES, TO THE MAXIMUM EXTENT THE LAW PERMITS, NO MATTER WHAT LEGAL THEORY IT IS BASED ON. ALSO, YOU MUST PASS THIS LIMITATION OF LIABILITY ON WHENEVER YOU DISTRIBUTE THE SOFTWARE OR DERIVATIVE WORKS. +# 7. That we have no duty of reasonable care or lack of negligence, and we are not obligated to (and will not) provide technical support for the Software. +# 8. That if you breach this NTUITIVE-LA or if you sue anyone over patents that you think may apply to or read on the Software or anyone's use of the Software, this NTUITIVE-LA (and your license and rights obtained herein) terminate automatically. Upon any such termination, you shall destroy all of your copies of the Software immediately. Sections 3, 4, 5, 6, 7, 8, 11 and 12 of this NTUITIVE-LA shall survive any termination of this NTUITIVE-LA. +# 9. That the patent rights, if any, granted to you in this NTUITIVE-LA only apply to the Software, not to any derivative works you make. +# 10. That the Software may be subject to U.S. export jurisdiction at the time it is licensed to you, and it may be subject to additional export or import laws in other places. You agree to comply with all such laws and regulations that may apply to the Software after delivery of the software to you. +# 11. That all rights not expressly granted to you in this NTUITIVE-LA are reserved. +# 12. That this NTUITIVE-LA shall be construed and controlled by the laws of the Republic of Singapore without regard to conflicts of law. If any provision of this NTUITIVE-LA shall be deemed unenforceable or contrary to law, the rest of this NTUITIVE-LA shall remain in full effect and interpreted in an enforceable manner that most nearly captures the intent of the original language. +# +# Copyright (c) NTUITIVE. All rights reserved. +# +from MyUtil import MyUtil +from MySingletons import MyDevice + +import numpy as np +import torch +import math + + +class GMM: + weight = 1 + center = None + variance = None + win_counter = 1 + inference_sum = 0 + survive_counter = 0 + y_count = None + dist = None + + _number_features = None + + @property + def inference(self): + if self.dist is not None: + return torch.min(self.dist) + return torch.tensor(0) + + @property + def hyper_volume(self): + if self.dist is not None: + return self.variance[0][torch.argmin(self.dist)] + return torch.tensor([[0.01]]) + + @property + def standard_deviation(self): + return torch.sqrt(self.variance) + + @property + def std(self): + return self.standard_deviation + + @property + def precision(self): + return 1/self.variance + + @property + def first_order_moment(self): + return self.center + + @property + def second_order_moment(self): + return self.center ** 2 + self.variance + + @property + def mode(self): + return self.center + + @property + def probX_J(self): + denumerator = torch.sqrt(2 * math.pi * self.hyper_volume) + return denumerator * self.inference + + def __init__(self, x): + self._number_features = x.shape[1] + self.center = x + self.variance = 0.01 + torch.zeros(int(self._number_features), dtype=torch.float, device=MyDevice().get()) + self.variance = self.variance.view(1, self.variance.shape[0]) + + def compute_inference(self, x, y=None): + if y is not None: + if self.y_count is None: + self.y_count = y + else: + self.y_count += y + + c = self.center + + dist = ((x - c) ** 2) / (- 2 * self.variance) + self.dist = torch.exp(dist) + + +class AGMM: + gmm_array = None + number_samples_feed = 0 + rho = 0.1 + number_features = None + + @property + def hyper_volume(self): + hyper_volume = torch.tensor(0, dtype=torch.float, device=MyDevice().get()) + for i in range(len(self.gmm_array)): + hyper_volume += self.gmm_array[i].hyper_volume + return hyper_volume + + @property + def weight_sum(self): + weight_sum = torch.tensor(0, dtype=torch.float, device=MyDevice().get()) + for i in range(self.M()): + weight_sum += self.gmm_array[i].weight + return weight_sum + + def run(self, x, bias2): + self.number_samples_feed += 1 + if self.gmm_array is None: + self.gmm_array = [GMM(x)] + self.number_features = x.shape[1] + else: + self.compute_inference(x) + gmm_winner_idx = np.argmax(self.update_weights()) + + if self.M() > 1: + self.compute_overlap_degree(gmm_winner_idx, 3, 3) + + denominator = 1.25 * torch.exp(-bias2) + 0.75 * self.number_features + numerator = 4 - 2 * torch.exp(torch.tensor(-self.number_features / 2.0, dtype=torch.float, device=MyDevice().get())) + threshold = torch.exp(- denominator / numerator) + + condition1 = self.gmm_array[gmm_winner_idx].inference < threshold + condition2 = self.gmm_array[gmm_winner_idx].hyper_volume > self.rho * (self.hyper_volume - self.gmm_array[gmm_winner_idx].hyper_volume) + condition3 = self.number_samples_feed > 10 + if condition1 and condition2 and condition3: + self.create_cluster(x) + self.gmm_array[-1].variance = (x - self.gmm_array[gmm_winner_idx].center) ** 2 + else: + self.update_cluster(x, self.gmm_array[gmm_winner_idx]) + + def create_cluster(self, x): + self.gmm_array.append(GMM(x)) + + weight_sum = self.weight_sum + for gmm in self.gmm_array: + gmm.weight = gmm.weight / weight_sum + + def update_cluster(self, x, gmm): + gmm.win_counter += 1 + gmm.center += (x - gmm.center) / gmm.win_counter + gmm.variance += ((x - gmm.center) ** 2 - gmm.variance) / gmm.win_counter + + def delete_cluster(self): + if self.M() <= 1: + return + + accumulated_inference = [] + for gmm in self.gmm_array: + if gmm.survive_counter > 0: + accumulated_inference.append(gmm.inference_sum / gmm.survive_counter) + + accumulated_inference = torch.stack(accumulated_inference)[torch.isnan(torch.stack(accumulated_inference)) == False] + + delete_list = torch.where(accumulated_inference <= (torch.mean(accumulated_inference) - 0.5 * torch.std(accumulated_inference)))[0] + if len(delete_list) == len(self.gmm_array): + raise TypeError('problem') # FIXME if this happen, it means you have a great problem at your code + + if len(delete_list): + self.gmm_array = np.delete(self.gmm_array, delete_list.cpu().numpy()).tolist() + accumulated_inference = torch.tensor(np.delete(accumulated_inference.cpu().numpy(), delete_list.cpu().numpy()).tolist(), dtype=torch.float, device=MyDevice().get()) + + sum_weight = 0 + for gmm in self.gmm_array: + sum_weight += gmm.weight + + if sum_weight == 0: + max_index = torch.argmax(accumulated_inference) + self.gmm_array[max_index].weight += 1 + sum_weight = 0 + for gmm in self.gmm_array: + sum_weight += gmm.weight + + for gmm in self.gmm_array: + gmm.weight = gmm.weight / sum_weight + + def compute_inference(self, x, y=None): + for gmm in self.gmm_array: + gmm.compute_inference(x, y) + + def update_weights(self): + probX_JprobJ = torch.zeros(len(self.gmm_array)) + weights = torch.zeros(len(self.gmm_array)) + + sum_winner_counter = 0 + max_inference = 0 + max_inference_index = 0 + for i in range(self.M()): + sum_winner_counter += self.gmm_array[i].win_counter + if self.gmm_array[i].inference > max_inference: + max_inference = self.gmm_array[i].inference + max_inference_index = i + + for i in range(self.M()): + self.gmm_array[i].inference_sum += self.gmm_array[i].inference + self.gmm_array[i].survive_counter += 1 + + probJ = self.gmm_array[i].win_counter / sum_winner_counter + probX_JprobJ[i] = self.gmm_array[i].probX_J * probJ + + if torch.sum(probX_JprobJ) == 0: + probX_JprobJ[max_inference_index] += 1 + + for i in range(self.M()): + self.gmm_array[i].weight = float(probX_JprobJ[i] / torch.sum(probX_JprobJ)) + weights[i] = self.gmm_array[i].weight + + return weights + + def compute_overlap_degree(self, gmm_winner_idx, maximum_limit=None, minimum_limit=None): + if maximum_limit is None: + maximum_limit = minimum_limit = 3 + elif minimum_limit is None: + minimum_limit = maximum_limit + + overlap_coefficient = torch.tensor(1 / self.M(), dtype=torch.float, device=MyDevice().get()) + + sigma_maximum_winner = maximum_limit * torch.sqrt(self.gmm_array[gmm_winner_idx].variance) + sigma_minimum_winner = minimum_limit * torch.sqrt(self.gmm_array[gmm_winner_idx].variance) + + winner_center = self.gmm_array[gmm_winner_idx].center + + if maximum_limit == minimum_limit: + mean_positive_sigma_winner = winner_center + sigma_maximum_winner + mean_negative_sigma_winner = winner_center - sigma_minimum_winner + else: + # FIXME This seems wrong + mean_positive_sigma_winner = winner_center + sigma_minimum_winner + sigma_maximum_winner + mean_negative_sigma_winner = winner_center - sigma_minimum_winner - sigma_maximum_winner + + mean_positive_sigma = torch.zeros(int(self.M()), int(self.number_features), dtype=torch.float, device=MyDevice().get()) + mean_negative_sigma = torch.zeros(int(self.M()), int(self.number_features), dtype=torch.float, device=MyDevice().get()) + overlap_mins_mins = torch.zeros(int(self.M()), int(self.number_features), dtype=torch.float, device=MyDevice().get()) + overlap_mins_plus = torch.zeros(int(self.M()), int(self.number_features), dtype=torch.float, device=MyDevice().get()) + overlap_plus_mins = torch.zeros(int(self.M()), int(self.number_features), dtype=torch.float, device=MyDevice().get()) + overlap_plus_plus = torch.zeros(int(self.M()), int(self.number_features), dtype=torch.float, device=MyDevice().get()) + + overlap_score = [] + + for i in range(self.M()): + sigma_maximum = maximum_limit * torch.sqrt(self.gmm_array[i].variance) + sigma_minimum = minimum_limit * torch.sqrt(self.gmm_array[i].variance) + + if maximum_limit == minimum_limit: + mean_positive_sigma[i] = self.gmm_array[i].center + sigma_maximum + mean_negative_sigma[i] = self.gmm_array[i].center - sigma_maximum + else: + #FIXME This seems wrong + mean_positive_sigma[i] = sigma_maximum + sigma_minimum + mean_negative_sigma[i] = -sigma_minimum - sigma_maximum + + overlap_mins_mins[i] = torch.mean(mean_negative_sigma[i] - mean_negative_sigma_winner) + overlap_mins_plus[i] = torch.mean(mean_positive_sigma[i] - mean_negative_sigma_winner) + overlap_plus_mins[i] = torch.mean(mean_negative_sigma[i] - mean_positive_sigma_winner) + overlap_plus_plus[i] = torch.mean(mean_positive_sigma[i] - mean_positive_sigma_winner) + + condition1 = (overlap_mins_mins[i] >= 0).all() \ + and (overlap_mins_plus[i] >= 0).all() \ + and (overlap_plus_mins[i] <= 0).all() \ + and (overlap_plus_plus[i] <= 0).all() + condition2 = (overlap_mins_mins[i] <= 0).all() \ + and (overlap_mins_plus[i] >= 0).all() \ + and (overlap_plus_mins[i] <= 0).all() \ + and (overlap_plus_plus[i] >= 0).all() + condition3 = (overlap_mins_mins[i] > 0).all() \ + and (overlap_mins_plus[i] > 0).all() \ + and (overlap_plus_mins[i] < 0).all() \ + and (overlap_plus_plus[i] > 0).all() + condition4 = (overlap_mins_mins[i] < 0).all() \ + and (overlap_mins_plus[i] > 0).all() \ + and (overlap_plus_mins[i] < 0).all() \ + and (overlap_plus_plus[i] < 0).all() + + if condition1 or condition2: + # full overlap, the cluster is inside the winning cluster + # the score is full score + overlap_score.append(overlap_coefficient) + elif condition3 or condition4: + # partial overlap, the score is the full score multiplied by the overlap degree + reward = MyUtil.norm_2(self.gmm_array[i].center - self.gmm_array[gmm_winner_idx].center) \ + / MyUtil.norm_2(self.gmm_array[i].center + self.gmm_array[gmm_winner_idx].center) \ + + MyUtil.norm_2(self.gmm_array[i].center - torch.sqrt(self.gmm_array[gmm_winner_idx].variance)) \ + / MyUtil.norm_2(self.gmm_array[i].center + torch.sqrt(self.gmm_array[gmm_winner_idx].variance)) + overlap_score.append(overlap_coefficient * reward) + else: + # No overlap, then the score is 0 + overlap_score.append(torch.zeros(1)) + + overlap_score.pop(gmm_winner_idx) + self.rho = torch.sum(torch.stack(overlap_score)) + self.rho = torch.min(self.rho, torch.ones_like(self.rho)) + self.rho = torch.max(self.rho, torch.ones_like(self.rho) * 0.1) # Do not let rho = zero + + def compute_rho_vigilance_test(self, x, gmm_winner_idx): + pass + + def compute_rho_containing_rule(self, gmm_winner_idx, maximum_limit, minimum_limit): + pass + + def compute_number_of_gmms(self): + if self.gmm_array is None: + return 0 + else: + return len(self.gmm_array) + + def M(self): + return self.compute_number_of_gmms() \ No newline at end of file diff --git a/ATL.py b/ATL.py new file mode 100644 index 0000000..dbd9db1 --- /dev/null +++ b/ATL.py @@ -0,0 +1,489 @@ +# Marcus Vinicius Sousa Leite de Carvalho +# marcus.decarvalho@ntu.edu.sg +# ivsucram@gmail.com +# +# NANYANG TECHNOLOGICAL UNIVERSITY - NTUITIVE PTE LTD Dual License Agreement +# Non-Commercial Use Only +# This NTUITIVE License Agreement, including all exhibits ("NTUITIVE-LA") is a legal agreement between you and NTUITIVE (or “we”) located at 71 Nanyang Drive, NTU Innovation Centre, #01-109, Singapore 637722, a wholly owned subsidiary of Nanyang Technological University (“NTU”) for the software or data identified above, which may include source code, and any associated materials, text or speech files, associated media and "online" or electronic documentation and any updates we provide in our discretion (together, the "Software"). +# +# By installing, copying, or otherwise using this Software, found at https://github.com/Ivsucram/ATL_Matlab, you agree to be bound by the terms of this NTUITIVE-LA. If you do not agree, do not install copy or use the Software. The Software is protected by copyright and other intellectual property laws and is licensed, not sold. If you wish to obtain a commercial royalty bearing license to this software please contact us at marcus.decarvalho@ntu.edu.sg. +# +# SCOPE OF RIGHTS: +# You may use, copy, reproduce, and distribute this Software for any non-commercial purpose, subject to the restrictions in this NTUITIVE-LA. Some purposes which can be non-commercial are teaching, academic research, public demonstrations and personal experimentation. You may also distribute this Software with books or other teaching materials, or publish the Software on websites, that are intended to teach the use of the Software for academic or other non-commercial purposes. +# You may not use or distribute this Software or any derivative works in any form for commercial purposes. Examples of commercial purposes would be running business operations, licensing, leasing, or selling the Software, distributing the Software for use with commercial products, using the Software in the creation or use of commercial products or any other activity which purpose is to procure a commercial gain to you or others. +# If the Software includes source code or data, you may create derivative works of such portions of the Software and distribute the modified Software for non-commercial purposes, as provided herein. +# If you distribute the Software or any derivative works of the Software, you will distribute them under the same terms and conditions as in this license, and you will not grant other rights to the Software or derivative works that are different from those provided by this NTUITIVE-LA. +# If you have created derivative works of the Software, and distribute such derivative works, you will cause the modified files to carry prominent notices so that recipients know that they are not receiving the original Software. Such notices must state: (i) that you have changed the Software; and (ii) the date of any changes. +# +# You may not distribute this Software or any derivative works. +# In return, we simply require that you agree: +# 1. That you will not remove any copyright or other notices from the Software. +# 2. That if any of the Software is in binary format, you will not attempt to modify such portions of the Software, or to reverse engineer or decompile them, except and only to the extent authorized by applicable law. +# 3. That NTUITIVE is granted back, without any restrictions or limitations, a non-exclusive, perpetual, irrevocable, royalty-free, assignable and sub-licensable license, to reproduce, publicly perform or display, install, use, modify, post, distribute, make and have made, sell and transfer your modifications to and/or derivative works of the Software source code or data, for any purpose. +# 4. That any feedback about the Software provided by you to us is voluntarily given, and NTUITIVE shall be free to use the feedback as it sees fit without obligation or restriction of any kind, even if the feedback is designated by you as confidential. +# 5. THAT THE SOFTWARE COMES "AS IS", WITH NO WARRANTIES. THIS MEANS NO EXPRESS, IMPLIED OR STATUTORY WARRANTY, INCLUDING WITHOUT LIMITATION, WARRANTIES OF MERCHANTABILITY OR FITNESS FOR A PARTICULAR PURPOSE, ANY WARRANTY AGAINST INTERFERENCE WITH YOUR ENJOYMENT OF THE SOFTWARE OR ANY WARRANTY OF TITLE OR NON-INFRINGEMENT. THERE IS NO WARRANTY THAT THIS SOFTWARE WILL FULFILL ANY OF YOUR PARTICULAR PURPOSES OR NEEDS. ALSO, YOU MUST PASS THIS DISCLAIMER ON WHENEVER YOU DISTRIBUTE THE SOFTWARE OR DERIVATIVE WORKS. +# 6. THAT NEITHER NTUITIVE NOR NTU NOR ANY CONTRIBUTOR TO THE SOFTWARE WILL BE LIABLE FOR ANY DAMAGES RELATED TO THE SOFTWARE OR THIS NTUITIVE-LA, INCLUDING DIRECT, INDIRECT, SPECIAL, CONSEQUENTIAL OR INCIDENTAL DAMAGES, TO THE MAXIMUM EXTENT THE LAW PERMITS, NO MATTER WHAT LEGAL THEORY IT IS BASED ON. ALSO, YOU MUST PASS THIS LIMITATION OF LIABILITY ON WHENEVER YOU DISTRIBUTE THE SOFTWARE OR DERIVATIVE WORKS. +# 7. That we have no duty of reasonable care or lack of negligence, and we are not obligated to (and will not) provide technical support for the Software. +# 8. That if you breach this NTUITIVE-LA or if you sue anyone over patents that you think may apply to or read on the Software or anyone's use of the Software, this NTUITIVE-LA (and your license and rights obtained herein) terminate automatically. Upon any such termination, you shall destroy all of your copies of the Software immediately. Sections 3, 4, 5, 6, 7, 8, 11 and 12 of this NTUITIVE-LA shall survive any termination of this NTUITIVE-LA. +# 9. That the patent rights, if any, granted to you in this NTUITIVE-LA only apply to the Software, not to any derivative works you make. +# 10. That the Software may be subject to U.S. export jurisdiction at the time it is licensed to you, and it may be subject to additional export or import laws in other places. You agree to comply with all such laws and regulations that may apply to the Software after delivery of the software to you. +# 11. That all rights not expressly granted to you in this NTUITIVE-LA are reserved. +# 12. That this NTUITIVE-LA shall be construed and controlled by the laws of the Republic of Singapore without regard to conflicts of law. If any provision of this NTUITIVE-LA shall be deemed unenforceable or contrary to law, the rest of this NTUITIVE-LA shall remain in full effect and interpreted in an enforceable manner that most nearly captures the intent of the original language. +# +# Copyright (c) NTUITIVE. All rights reserved. + +from DataManipulator import DataManipulator +from NeuralNetwork import NeuralNetwork +from AutoEncoder import DenoisingAutoEncoder +from AGMM import AGMM +from MySingletons import MyDevice, TorchDevice +from colorama import Fore, Back, Style +from itertools import cycle + +import numpy as np +import matplotlib.pylab as plt + +import math +import torch +import time + + +def copy_weights(source: NeuralNetwork, target: NeuralNetwork, layer_numbers=[1], copy_moment: bool = True): + for layer_number in layer_numbers: + layer_number -= 1 + if layer_number >= source.number_hidden_layers: + target.output_weight = source.output_weight + target.output_bias = source.output_bias + if copy_moment: + target.output_momentum = source.output_momentum + target.output_bias_momentum = source.output_bias_momentum + else: + target.weight[layer_number] = source.weight[layer_number] + target.bias[layer_number] = source.bias[layer_number] + if copy_moment: + target.momentum[layer_number] = source.momentum[layer_number] + target.bias_momentum[layer_number] = source.bias_momentum[layer_number] + + +def grow_nodes(*networks): + origin = networks[0] + if origin.growable[origin.number_hidden_layers]: + if origin.get_agmm() is None: + nodes = 1 + else: + nodes = origin.get_agmm().M() + for i in range(nodes): + for network in networks: + network.grow_node(origin.number_hidden_layers) + return True + else: + return False + + +def prune_nodes(*networks): + origin = networks[0] + if origin.prunable[origin.number_hidden_layers][0] >= 0: + nodes_to_prune = origin.prunable[origin.number_hidden_layers].tolist() + for network in networks: + for node_to_prune in nodes_to_prune[::-1]: + network.prune_node(origin.number_hidden_layers, node_to_prune) + + +def width_evolution(network: NeuralNetwork, x: torch.tensor, y: torch.tensor = None, agmm: AGMM = None, train_agmm: bool = False): + if y is None: + y = x + + if agmm is not None: + network.set_agmm(agmm) + if train_agmm: + network.forward_pass(x) + network.run_agmm(x, y) + + + network.feedforward(x, y) + network.width_adaptation_stepwise(y) + + +def discriminative(network: NeuralNetwork, x: torch.tensor, y: torch.tensor = None, agmm: AGMM = None): + if agmm is not None: + network.set_agmm(agmm) + if y is None: + y = x + + network.train(x, y) + + +def generative(network: NeuralNetwork, x: torch.tensor, y: torch.tensor = None, agmm: AGMM = None, is_tied_weight=False, noise_ratio=0.1, glw_epochs: int = 1): + if agmm is not None: + network.set_agmm(agmm) + if y is None: + y = x + + network.greedy_layer_wise_pretrain(x=x, number_epochs=glw_epochs, noise_ratio=0.0) + network.train(x=x, y=y, noise_ratio=noise_ratio, is_tied_weight=is_tied_weight) + + +def test(network: NeuralNetwork, x: torch.tensor, y: torch.tensor = None, is_source: bool = False, is_discriminative: bool = False, metrics=None): + with torch.no_grad(): + if y is None: + y = x + network.test(x=x, y=y) + + if is_source: + if is_discriminative: + metrics['classification_rate_source'].append(network.classification_rate) + metrics['classification_source_loss'].append(float(network.loss_value)) + else: + metrics['reconstruction_source_loss'].append(float(network.loss_value)) + else: + if is_discriminative: + metrics['classification_rate_target'].append(network.classification_rate) + metrics['classification_target_loss'].append(float(network.loss_value)) + else: + metrics['reconstruction_target_loss'].append(float(network.loss_value)) + + +def force_same_size(a_tensor, b_tensor, shuffle=True, strategy='max'): + common = np.min([a_tensor.shape[0], b_tensor.shape[0]]) + + if shuffle: + a_tensor = a_tensor[torch.randperm(a_tensor.shape[0])] + b_tensor = b_tensor[torch.randperm(b_tensor.shape[0])] + + if strategy == 'max': + if math.ceil(a_tensor.shape[0] / common) <= math.ceil(b_tensor.shape[0] / common): + b_tensor = torch.stack(list(target for target, source in zip(b_tensor[torch.randperm(b_tensor.shape[0])], cycle(a_tensor[torch.randperm(a_tensor.shape[0])])))) + a_tensor = torch.stack(list(source for target, source in zip(b_tensor[torch.randperm(b_tensor.shape[0])], cycle(a_tensor[torch.randperm(a_tensor.shape[0])])))) + else: + b_tensor = torch.stack(list(target for target, source in zip(cycle(b_tensor[torch.randperm(b_tensor.shape[0])]), a_tensor[torch.randperm(a_tensor.shape[0])]))) + a_tensor = torch.stack(list(source for target, source in zip(cycle(b_tensor[torch.randperm(b_tensor.shape[0])]), a_tensor[torch.randperm(a_tensor.shape[0])]))) + + elif strategy == 'min': + a_tensor = a_tensor[:common] + b_tensor = b_tensor[:common] + + if shuffle: + a_tensor = a_tensor[torch.randperm(a_tensor.shape[0])] + b_tensor = b_tensor[torch.randperm(b_tensor.shape[0])] + + return a_tensor, b_tensor + + +def kl(ae: NeuralNetwork, x_source: torch.tensor, x_target: torch.tensor): + x_source, x_target = force_same_size(x_source, x_target) + + ae.reset_grad() + kl_loss = torch.nn.functional.kl_div(ae.forward_pass(x_target).layer_value[1], + ae.forward_pass(x_source).layer_value[1], reduction='batchmean') + + kl_loss.backward() + ae.weight[0] = ae.weight[0] - ae.learning_rate * ae.weight[0].grad + ae.bias[0] = ae.bias[0] - ae.learning_rate * ae.bias[0].grad + + return kl_loss.detach().cpu().numpy() + + +def print_annotation(lst): + def custom_range(xx): + return range(0, len(xx), int(len(xx) * 0.25) - 1) + + for idx in custom_range(lst): + pos = lst[idx] if isinstance(lst[idx], (int, float)) else lst[idx][0] + plt.annotate(format(pos, '.2f'), (idx, pos)) + pos = lst[-1] if isinstance(lst[-1], (int, float)) else lst[-1][0] + plt.annotate(format(pos, '.2f'), (len(lst), pos)) + + +def plot_time(train, test, annotation=True): + plt.title('Processing time') + plt.ylabel('Seconds') + plt.xlabel('Minibatches') + + plt.plot(train, linewidth=1, label=('Train time Mean | Accumulative %f | %f' % (np.mean(train), np.sum(train)))) + plt.plot(test, linewidth=1, label=('Test time Mean | Accumulative %f | %f' % (np.mean(test), np.sum(test)))) + plt.legend() + + if annotation: + print_annotation(train) + print_annotation(test) + + plt.tight_layout() + plt.show() + + +def plot_agmm(agmm_source, agmm_target, annotation=True): + plt.title('AGMM evolution') + plt.ylabel('GMMs') + plt.xlabel('Samples') + + plt.plot(agmm_source, linewidth=1, label=('AGMM Source Discriminative Mean: %f' % (np.mean(agmm_source)))) + plt.plot(agmm_target, linewidth=1, label=('AGMM Target Generative Mean: %f' % (np.mean(agmm_target)))) + plt.legend() + + if annotation: + print_annotation(agmm_source) + print_annotation(agmm_target) + + plt.tight_layout() + plt.show() + + +def plot_node_evolution(nodes, annotation=True): + plt.title('Node evolution') + plt.ylabel('Nodes') + plt.xlabel('Minibatches') + + plt.plot(nodes, linewidth=1, + label=('Hidden Layer Mean | Final: %f | %d' % (np.mean(nodes), nodes[-1]))) + plt.legend() + + if annotation: + print_annotation(nodes) + + plt.tight_layout() + plt.show() + + +def plot_losses(classification_source_loss, classification_target_loss, reconstruction_source_loss, + reconstruction_target_loss, annotation=True): + plt.title('Losses evolution') + plt.ylabel('Loss value') + plt.xlabel('Minibatches') + + plt.plot(classification_source_loss, linewidth=1, + label=('Classification Source Loss mean: %f' % (np.mean(classification_source_loss)))) + plt.plot(classification_target_loss, linewidth=1, + label=('Classification Target Loss mean: %f' % (np.mean(classification_target_loss)))) + plt.plot(reconstruction_source_loss, linewidth=1, + label=('Reconstruction Source Loss mean: %f' % (np.mean(reconstruction_source_loss)))) + plt.plot(reconstruction_target_loss, linewidth=1, + label=('Reconstruction Target Loss mean: %f' % (np.mean(reconstruction_target_loss)))) + plt.legend() + + if annotation: + print_annotation(classification_source_loss) + print_annotation(classification_target_loss) + print_annotation(reconstruction_source_loss) + print_annotation(reconstruction_target_loss) + + plt.tight_layout() + plt.show() + + +def plot_classification_rates(source_rate, target_rate, annotation=True): + plt.title('Source and Target Classification Rates') + plt.ylabel('Classification Rate') + plt.xlabel('Minibatches') + + plt.plot(source_rate, linewidth=1, label=('Source CR mean: %f' % (np.mean(source_rate)))) + plt.plot(target_rate, linewidth=1, label=('Target CR mean: %f' % (np.mean(target_rate)))) + + if annotation: + print_annotation(source_rate) + print_annotation(target_rate) + + plt.legend() + + plt.tight_layout() + plt.show() + + +def plot_ns(bias, var, ns, annotation=True): + plt.plot(bias, linewidth=1, label=('Bias2 mean: %f' % (np.mean(bias)))) + plt.plot(var, linewidth=1, label=('Variance mean: %f' % (np.mean(var)))) + plt.plot(ns, linewidth=1, label=('Network Significance mean: %f' % (np.mean(ns)))) + plt.legend() + + if annotation: + print_annotation(bias) + print_annotation(var) + print_annotation(ns) + + plt.tight_layout() + plt.show() + + +def plot_discriminative_network_significance(bias, var, annotation=True): + plt.title('Discriminative Source BIAS2, VAR, NS') + plt.ylabel('Value') + plt.xlabel('Sample') + + plot_ns(bias, var, (np.array(bias) + np.array(var)).tolist(), annotation) + + +def plot_generative_network_significance(bias, var, annotation=True, is_source=True): + if is_source: + plt.title('Generative Source BIAS2, VAR, NS') + else: + plt.title('Generative Target BIAS2, VAR, NS') + plt.ylabel('Value') + plt.xlabel('Sample') + + plot_ns(bias, var, (np.array(bias) + np.array(var)).tolist(), annotation) + + +def ATL(epochs: int = 1, n_batch: int = 1000, device='cpu'): + def print_metrics(minibatch, metrics, nn, ae, Xs, Xt): + print('Minibatch: %d | Execution time (dataset load/pre-processing + model run): %f' % (minibatch, time.time() - metrics['start_execution_time'])) + if minibatch > 1: + string_max = '' + Fore.GREEN + 'Max' + Style.RESET_ALL + string_mean = '' + Fore.YELLOW + 'Mean' + Style.RESET_ALL + string_min = '' + Fore.RED + 'Min' + Style.RESET_ALL + string_now = '' + Fore.BLUE + 'Now' + Style.RESET_ALL + string_accu = '' + Fore.MAGENTA + 'Accu' + Style.RESET_ALL + + print(('Total of samples:' + Fore.BLUE + ' %d Source' + Style.RESET_ALL +' |' + Fore.RED +' %d Target' + Style.RESET_ALL) % (Xs.shape[0], Xt.shape[0])) + print(('%s %s %s %s %s Training time:' + Fore.GREEN + ' %f' + Fore.YELLOW + ' %f' + Fore.RED + ' %f' + Fore.BLUE + ' %f' + Fore.MAGENTA + ' %f' + Style.RESET_ALL) % ( + string_max, string_mean, string_min, string_now, string_accu, + np.max(metrics['train_time']), + np.mean(metrics['train_time']), + np.min(metrics['train_time']), + metrics['train_time'][-1], + np.sum(metrics['train_time']))) + print(('%s %s %s %s %s Testing time:' + Fore.GREEN + ' %f' + Fore.YELLOW + ' %f' + Fore.RED + ' %f' + Fore.BLUE + ' %f' + Fore.MAGENTA + ' %f' + Style.RESET_ALL) % ( + string_max, string_mean, string_min, string_now, string_accu, + np.max(metrics['test_time']), + np.mean(metrics['test_time']), + np.min(metrics['test_time']), + metrics['test_time'][-1], + np.sum(metrics['test_time']))) + print(('%s %s %s %s CR Source:' + Fore.GREEN + ' %f%% ' + Back.BLUE + Fore.YELLOW + Style.BRIGHT + '%f%%' + Style.RESET_ALL + Fore.RED + ' %f%%' + Fore.BLUE + ' %f%%' + Style.RESET_ALL) % ( + string_max, string_mean, string_min, string_now, + np.max(metrics['classification_rate_source']) * 100, + np.mean(metrics['classification_rate_source']) * 100, + np.min(metrics['classification_rate_source']) * 100, + metrics['classification_rate_source'][-1] * 100)) + print(('%s %s %s %s CR Target:' + Fore.GREEN + ' %f%% ' + Back.RED + Fore.YELLOW + Style.BRIGHT + '%f%%' + Style.RESET_ALL + Fore.RED + ' %f%%' + Fore.BLUE + ' %f%%' + Style.RESET_ALL) % ( + string_max, string_mean, string_min, string_now, + np.max(metrics['classification_rate_target']) * 100, + np.mean(metrics['classification_rate_target']) * 100, + np.min(metrics['classification_rate_target']) * 100, + metrics['classification_rate_target'][-1] * 100)) + print(('%s %s %s %s Classification Source Loss:' + Fore.GREEN + ' %f' + Fore.YELLOW + ' %f' + Fore.RED + ' %f' + Fore.BLUE + ' %f' + Style.RESET_ALL) % ( + string_max, string_mean, string_min, string_now, + np.max(metrics['classification_source_loss']), + np.mean(metrics['classification_source_loss']), + np.min(metrics['classification_source_loss']), + metrics['classification_source_loss'][-1])) + print(('%s %s %s %s Classification Target Loss:' + Fore.GREEN + ' %f' + Fore.YELLOW + ' %f' + Fore.RED + ' %f' + Fore.BLUE + ' %f' + Style.RESET_ALL) % ( + string_max, string_mean, string_min, string_now, + np.max(metrics['classification_target_loss']), + np.mean(metrics['classification_target_loss']), + np.min(metrics['classification_target_loss']), + metrics['classification_target_loss'][-1])) + print(('%s %s %s %s Reconstruction Target Loss:' + Fore.GREEN + ' %f' + Fore.YELLOW + ' %f' + Fore.RED + ' %f' + Fore.BLUE + ' %f' + Style.RESET_ALL) % ( + string_max, string_mean, string_min, string_now, + np.max(metrics['reconstruction_target_loss']), + np.mean(metrics['reconstruction_target_loss']), + np.min(metrics['reconstruction_target_loss']), + metrics['reconstruction_target_loss'][-1])) + print(('%s %s %s %s Kullback-Leibler loss 1:' + Fore.GREEN + ' %f' + Fore.YELLOW + ' %f' + Fore.RED + ' %f' + Fore.BLUE + ' %f' + Style.RESET_ALL) % ( + string_max, string_mean, string_min, string_now, + np.max(metrics['kl_loss']), + np.mean(metrics['kl_loss']), + np.min(metrics['kl_loss']), + metrics['kl_loss'][-1])) + print(('%s %s %s %s Nodes:' + Fore.GREEN + ' %d' + Fore.YELLOW + ' %f' + Fore.RED + ' %d' + Fore.BLUE + ' %d' + Style.RESET_ALL) % ( + string_max, string_mean, string_min, string_now, + np.max(metrics['node_evolution']), + np.mean(metrics['node_evolution']), + np.min(metrics['node_evolution']), + metrics['node_evolution'][-1])) + print(('Network structure:' + Fore.BLUE + ' %s (Discriminative) %s (Generative)' + Style.RESET_ALL) % ( + " ".join(map(str, nn.layers)), + " ".join(map(str, ae.layers)))) + print(Style.RESET_ALL) + + metrics = {'classification_rate_source': [], + 'classification_rate_target': [], + 'train_time': [], + 'test_time': [], + 'node_evolution': [], + 'classification_target_loss': [], + 'classification_source_loss': [], + 'reconstruction_source_loss': [], + 'reconstruction_target_loss': [], + 'kl_loss': [], + 'agmm_target_size_by_batch': [], + 'agmm_source_size_by_batch': [], + 'start_execution_time': time.time()} + + TorchDevice.instance().device = device + + dm = DataManipulator('') + dm.load_custom_csv() + dm.normalize() + + dm.split_as_source_target_streams(n_batch, 'dallas_2', 0.5) + + nn = NeuralNetwork([dm.number_features, 1, dm.number_classes]) + ae = DenoisingAutoEncoder([nn.layers[0], nn.layers[1], nn.layers[0]]) + + # I am building the greedy_layer_bias + x = dm.get_Xs(0) + x = torch.tensor(np.atleast_2d(x), dtype=torch.float, device=MyDevice().get()) + ae.greedy_layer_wise_pretrain(x=x, number_epochs=0) + # I am building the greedy_layer_bias + + agmm_source_discriminative = AGMM() + agmm_target_generative = AGMM() + + for i in range(dm.number_minibatches): + Xs = torch.tensor(dm.get_Xs(i), dtype=torch.float, device=MyDevice().get()) + ys = torch.tensor(dm.get_ys(i), dtype=torch.float, device=MyDevice().get()) + Xt = torch.tensor(dm.get_Xt(i), dtype=torch.float, device=MyDevice().get()) + yt = torch.tensor(dm.get_yt(i), dtype=torch.float, device=MyDevice().get()) + + if i > 0: + metrics['test_time'].append(time.time()) + test(nn, Xt, yt, is_source=False, is_discriminative=True, metrics=metrics) + metrics['test_time'][-1] = time.time() - metrics['test_time'][-1] + + test(nn, Xs, ys, is_source=True, is_discriminative=True, metrics=metrics) + test(ae, Xt, is_source=False, is_discriminative=False, metrics=metrics) + + metrics['train_time'].append(time.time()) + for epoch in range(epochs): + for x, y in [(x.view(1, x.shape[0]), y.view(1, y.shape[0])) for x, y in zip(Xs, ys)]: + width_evolution(network=nn, x=x, y=y, agmm=agmm_source_discriminative, train_agmm=True if epoch == 1 else False) + if not grow_nodes(nn, ae): prune_nodes(nn, ae) + discriminative(network=nn, x=x, y=y, agmm=agmm_source_discriminative) + + copy_weights(source=nn, target=ae, layer_numbers=[1]) + + for x in [x.view(1, x.shape[0]) for x in Xt]: + width_evolution(network=ae, x=x, agmm=agmm_target_generative, train_agmm=True if epoch == 1 else False) + if not grow_nodes(ae, nn): prune_nodes(ae, nn) + generative(network=ae, x=x, agmm=agmm_target_generative) + + metrics['kl_loss'].append(kl(ae=ae, x_source=Xs, x_target=Xt)) + copy_weights(source=ae, target=nn, layer_numbers=[1]) + + if agmm_target_generative.M() > 1: agmm_target_generative.delete_cluster() + if agmm_source_discriminative.M() > 1: agmm_source_discriminative.delete_cluster() + + metrics['agmm_target_size_by_batch'].append(agmm_target_generative.M()) + metrics['agmm_source_size_by_batch'].append(agmm_source_discriminative.M()) + metrics['train_time'][-1] = time.time() - metrics['train_time'][-1] + metrics['node_evolution'].append(nn.layers[1]) + print_metrics(i + 1, metrics, nn, ae, Xs, Xt) + + result = '%f (T) ''| %f (S) \t %f | %d \t %f | %f' % ( + np.mean(metrics['classification_rate_target']), + np.mean(metrics['classification_rate_source']), + np.mean(metrics['node_evolution']), + metrics['node_evolution'][-1], + np.mean(metrics['train_time']), + np.sum(metrics['train_time'])) + + print(result) + + plot_time(metrics['train_time'], metrics['test_time']) + plot_node_evolution(metrics['node_evolution']) + plot_classification_rates(metrics['classification_rate_source'], metrics['classification_rate_target']) + + return result + +atl = ATL(epochs = 1, device='cpu') +print(atl) + diff --git a/AutoEncoder.py b/AutoEncoder.py new file mode 100644 index 0000000..2c4f83a --- /dev/null +++ b/AutoEncoder.py @@ -0,0 +1,168 @@ +# Marcus Vinicius Sousa Leite de Carvalho +# marcus.decarvalho@ntu.edu.sg +# ivsucram@gmail.com +# +# NANYANG TECHNOLOGICAL UNIVERSITY - NTUITIVE PTE LTD Dual License Agreement +# Non-Commercial Use Only +# This NTUITIVE License Agreement, including all exhibits ("NTUITIVE-LA") is a legal agreement between you and NTUITIVE (or “we”) located at 71 Nanyang Drive, NTU Innovation Centre, #01-109, Singapore 637722, a wholly owned subsidiary of Nanyang Technological University (“NTU”) for the software or data identified above, which may include source code, and any associated materials, text or speech files, associated media and "online" or electronic documentation and any updates we provide in our discretion (together, the "Software"). +# +# By installing, copying, or otherwise using this Software, found at https://github.com/Ivsucram/ATL_Matlab, you agree to be bound by the terms of this NTUITIVE-LA. If you do not agree, do not install copy or use the Software. The Software is protected by copyright and other intellectual property laws and is licensed, not sold. If you wish to obtain a commercial royalty bearing license to this software please contact us at marcus.decarvalho@ntu.edu.sg. +# +# SCOPE OF RIGHTS: +# You may use, copy, reproduce, and distribute this Software for any non-commercial purpose, subject to the restrictions in this NTUITIVE-LA. Some purposes which can be non-commercial are teaching, academic research, public demonstrations and personal experimentation. You may also distribute this Software with books or other teaching materials, or publish the Software on websites, that are intended to teach the use of the Software for academic or other non-commercial purposes. +# You may not use or distribute this Software or any derivative works in any form for commercial purposes. Examples of commercial purposes would be running business operations, licensing, leasing, or selling the Software, distributing the Software for use with commercial products, using the Software in the creation or use of commercial products or any other activity which purpose is to procure a commercial gain to you or others. +# If the Software includes source code or data, you may create derivative works of such portions of the Software and distribute the modified Software for non-commercial purposes, as provided herein. +# If you distribute the Software or any derivative works of the Software, you will distribute them under the same terms and conditions as in this license, and you will not grant other rights to the Software or derivative works that are different from those provided by this NTUITIVE-LA. +# If you have created derivative works of the Software, and distribute such derivative works, you will cause the modified files to carry prominent notices so that recipients know that they are not receiving the original Software. Such notices must state: (i) that you have changed the Software; and (ii) the date of any changes. +# +# You may not distribute this Software or any derivative works. +# In return, we simply require that you agree: +# 1. That you will not remove any copyright or other notices from the Software. +# 2. That if any of the Software is in binary format, you will not attempt to modify such portions of the Software, or to reverse engineer or decompile them, except and only to the extent authorized by applicable law. +# 3. That NTUITIVE is granted back, without any restrictions or limitations, a non-exclusive, perpetual, irrevocable, royalty-free, assignable and sub-licensable license, to reproduce, publicly perform or display, install, use, modify, post, distribute, make and have made, sell and transfer your modifications to and/or derivative works of the Software source code or data, for any purpose. +# 4. That any feedback about the Software provided by you to us is voluntarily given, and NTUITIVE shall be free to use the feedback as it sees fit without obligation or restriction of any kind, even if the feedback is designated by you as confidential. +# 5. THAT THE SOFTWARE COMES "AS IS", WITH NO WARRANTIES. THIS MEANS NO EXPRESS, IMPLIED OR STATUTORY WARRANTY, INCLUDING WITHOUT LIMITATION, WARRANTIES OF MERCHANTABILITY OR FITNESS FOR A PARTICULAR PURPOSE, ANY WARRANTY AGAINST INTERFERENCE WITH YOUR ENJOYMENT OF THE SOFTWARE OR ANY WARRANTY OF TITLE OR NON-INFRINGEMENT. THERE IS NO WARRANTY THAT THIS SOFTWARE WILL FULFILL ANY OF YOUR PARTICULAR PURPOSES OR NEEDS. ALSO, YOU MUST PASS THIS DISCLAIMER ON WHENEVER YOU DISTRIBUTE THE SOFTWARE OR DERIVATIVE WORKS. +# 6. THAT NEITHER NTUITIVE NOR NTU NOR ANY CONTRIBUTOR TO THE SOFTWARE WILL BE LIABLE FOR ANY DAMAGES RELATED TO THE SOFTWARE OR THIS NTUITIVE-LA, INCLUDING DIRECT, INDIRECT, SPECIAL, CONSEQUENTIAL OR INCIDENTAL DAMAGES, TO THE MAXIMUM EXTENT THE LAW PERMITS, NO MATTER WHAT LEGAL THEORY IT IS BASED ON. ALSO, YOU MUST PASS THIS LIMITATION OF LIABILITY ON WHENEVER YOU DISTRIBUTE THE SOFTWARE OR DERIVATIVE WORKS. +# 7. That we have no duty of reasonable care or lack of negligence, and we are not obligated to (and will not) provide technical support for the Software. +# 8. That if you breach this NTUITIVE-LA or if you sue anyone over patents that you think may apply to or read on the Software or anyone's use of the Software, this NTUITIVE-LA (and your license and rights obtained herein) terminate automatically. Upon any such termination, you shall destroy all of your copies of the Software immediately. Sections 3, 4, 5, 6, 7, 8, 11 and 12 of this NTUITIVE-LA shall survive any termination of this NTUITIVE-LA. +# 9. That the patent rights, if any, granted to you in this NTUITIVE-LA only apply to the Software, not to any derivative works you make. +# 10. That the Software may be subject to U.S. export jurisdiction at the time it is licensed to you, and it may be subject to additional export or import laws in other places. You agree to comply with all such laws and regulations that may apply to the Software after delivery of the software to you. +# 11. That all rights not expressly granted to you in this NTUITIVE-LA are reserved. +# 12. That this NTUITIVE-LA shall be construed and controlled by the laws of the Republic of Singapore without regard to conflicts of law. If any provision of this NTUITIVE-LA shall be deemed unenforceable or contrary to law, the rest of this NTUITIVE-LA shall remain in full effect and interpreted in an enforceable manner that most nearly captures the intent of the original language. +# +# Copyright (c) NTUITIVE. 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) \ No newline at end of file diff --git a/DataManipulator.py b/DataManipulator.py new file mode 100644 index 0000000..0811d6f --- /dev/null +++ b/DataManipulator.py @@ -0,0 +1,177 @@ +# Marcus Vinicius Sousa Leite de Carvalho +# marcus.decarvalho@ntu.edu.sg +# ivsucram@gmail.com +# +# NANYANG TECHNOLOGICAL UNIVERSITY - NTUITIVE PTE LTD Dual License Agreement +# Non-Commercial Use Only +# This NTUITIVE License Agreement, including all exhibits ("NTUITIVE-LA") is a legal agreement between you and NTUITIVE (or “we”) located at 71 Nanyang Drive, NTU Innovation Centre, #01-109, Singapore 637722, a wholly owned subsidiary of Nanyang Technological University (“NTU”) for the software or data identified above, which may include source code, and any associated materials, text or speech files, associated media and "online" or electronic documentation and any updates we provide in our discretion (together, the "Software"). +# +# By installing, copying, or otherwise using this Software, found at https://github.com/Ivsucram/ATL_Matlab, you agree to be bound by the terms of this NTUITIVE-LA. If you do not agree, do not install copy or use the Software. The Software is protected by copyright and other intellectual property laws and is licensed, not sold. If you wish to obtain a commercial royalty bearing license to this software please contact us at marcus.decarvalho@ntu.edu.sg. +# +# SCOPE OF RIGHTS: +# You may use, copy, reproduce, and distribute this Software for any non-commercial purpose, subject to the restrictions in this NTUITIVE-LA. Some purposes which can be non-commercial are teaching, academic research, public demonstrations and personal experimentation. You may also distribute this Software with books or other teaching materials, or publish the Software on websites, that are intended to teach the use of the Software for academic or other non-commercial purposes. +# You may not use or distribute this Software or any derivative works in any form for commercial purposes. Examples of commercial purposes would be running business operations, licensing, leasing, or selling the Software, distributing the Software for use with commercial products, using the Software in the creation or use of commercial products or any other activity which purpose is to procure a commercial gain to you or others. +# If the Software includes source code or data, you may create derivative works of such portions of the Software and distribute the modified Software for non-commercial purposes, as provided herein. +# If you distribute the Software or any derivative works of the Software, you will distribute them under the same terms and conditions as in this license, and you will not grant other rights to the Software or derivative works that are different from those provided by this NTUITIVE-LA. +# If you have created derivative works of the Software, and distribute such derivative works, you will cause the modified files to carry prominent notices so that recipients know that they are not receiving the original Software. Such notices must state: (i) that you have changed the Software; and (ii) the date of any changes. +# +# You may not distribute this Software or any derivative works. +# In return, we simply require that you agree: +# 1. That you will not remove any copyright or other notices from the Software. +# 2. That if any of the Software is in binary format, you will not attempt to modify such portions of the Software, or to reverse engineer or decompile them, except and only to the extent authorized by applicable law. +# 3. That NTUITIVE is granted back, without any restrictions or limitations, a non-exclusive, perpetual, irrevocable, royalty-free, assignable and sub-licensable license, to reproduce, publicly perform or display, install, use, modify, post, distribute, make and have made, sell and transfer your modifications to and/or derivative works of the Software source code or data, for any purpose. +# 4. That any feedback about the Software provided by you to us is voluntarily given, and NTUITIVE shall be free to use the feedback as it sees fit without obligation or restriction of any kind, even if the feedback is designated by you as confidential. +# 5. THAT THE SOFTWARE COMES "AS IS", WITH NO WARRANTIES. THIS MEANS NO EXPRESS, IMPLIED OR STATUTORY WARRANTY, INCLUDING WITHOUT LIMITATION, WARRANTIES OF MERCHANTABILITY OR FITNESS FOR A PARTICULAR PURPOSE, ANY WARRANTY AGAINST INTERFERENCE WITH YOUR ENJOYMENT OF THE SOFTWARE OR ANY WARRANTY OF TITLE OR NON-INFRINGEMENT. THERE IS NO WARRANTY THAT THIS SOFTWARE WILL FULFILL ANY OF YOUR PARTICULAR PURPOSES OR NEEDS. ALSO, YOU MUST PASS THIS DISCLAIMER ON WHENEVER YOU DISTRIBUTE THE SOFTWARE OR DERIVATIVE WORKS. +# 6. THAT NEITHER NTUITIVE NOR NTU NOR ANY CONTRIBUTOR TO THE SOFTWARE WILL BE LIABLE FOR ANY DAMAGES RELATED TO THE SOFTWARE OR THIS NTUITIVE-LA, INCLUDING DIRECT, INDIRECT, SPECIAL, CONSEQUENTIAL OR INCIDENTAL DAMAGES, TO THE MAXIMUM EXTENT THE LAW PERMITS, NO MATTER WHAT LEGAL THEORY IT IS BASED ON. ALSO, YOU MUST PASS THIS LIMITATION OF LIABILITY ON WHENEVER YOU DISTRIBUTE THE SOFTWARE OR DERIVATIVE WORKS. +# 7. That we have no duty of reasonable care or lack of negligence, and we are not obligated to (and will not) provide technical support for the Software. +# 8. That if you breach this NTUITIVE-LA or if you sue anyone over patents that you think may apply to or read on the Software or anyone's use of the Software, this NTUITIVE-LA (and your license and rights obtained herein) terminate automatically. Upon any such termination, you shall destroy all of your copies of the Software immediately. Sections 3, 4, 5, 6, 7, 8, 11 and 12 of this NTUITIVE-LA shall survive any termination of this NTUITIVE-LA. +# 9. That the patent rights, if any, granted to you in this NTUITIVE-LA only apply to the Software, not to any derivative works you make. +# 10. That the Software may be subject to U.S. export jurisdiction at the time it is licensed to you, and it may be subject to additional export or import laws in other places. You agree to comply with all such laws and regulations that may apply to the Software after delivery of the software to you. +# 11. That all rights not expressly granted to you in this NTUITIVE-LA are reserved. +# 12. That this NTUITIVE-LA shall be construed and controlled by the laws of the Republic of Singapore without regard to conflicts of law. If any provision of this NTUITIVE-LA shall be deemed unenforceable or contrary to law, the rest of this NTUITIVE-LA shall remain in full effect and interpreted in an enforceable manner that most nearly captures the intent of the original language. +# +# Copyright (c) NTUITIVE. All rights reserved. + +import numpy as np +import pandas as pd + +class DataManipulator: + data = None + number_features = None + number_classes = None + + number_fold_elements = None + number_minibatches = None + + source_data = None + target_data = None + + __X = None + __y = None + __Xs = None + __ys = None + __Xt = None + __yt = None + + __permutedX = None + __permutedy = None + + __index_permutation = None + + __data_folder_path = None + + def __init__(self, data_folder_path): + self.__data_folder_path = data_folder_path + + def load_mnist(self): + raise TypeError('Not implemented') + + def load_custom_csv(self): + print('Loading data.csv') + self.data = pd.read_csv(filepath_or_buffer='data.csv', header=None) + self.check_dataset_is_even() + self.number_features = self.data.shape[1] - 1 + self.X = self.data.iloc[:, 0:self.number_features].add_prefix('feature_').astype(dtype=np.float64) + self.y = pd.get_dummies(self.data.iloc[:, self.number_features], prefix='class', dtype=np.float64) + self.number_classes = self.y.shape[1] + self.data = self.X.join(self.y) + + def normalize(self): + print('Normalizing data') + self.X = (self.X - self.X.min())/(self.X.max() - self.X.min()) + self.data = self.X.join(self.y) + + def normalize_image(self): + raise TypeError('Not implemented') + + def split_as_source_target_streams(self, number_fold_elements=0, method=None, sampling_ratio=0.5): + if number_fold_elements == 0: + self.number_fold_elements == self.data.shape[0] + else: + self.number_fold_elements = number_fold_elements + + if method == None or method == 'none' or method == 'None': + self.__split_as_source_target_streams_none(self.number_fold_elements, sampling_ratio) + elif method == 'dallas_1' or method == 'dallas1': + self.__split_as_source_target_streams_dallas_1(self.number_fold_elements, sampling_ratio) + elif method == 'dallas_2' or method == 'dallas2': + self.__split_as_source_target_streams_dallas_2(self.number_fold_elements, sampling_ratio) + + self.__create_Xs_ys_Xt_yt() + + def get_Xs(self, number_minibatch): + return self.Xs[number_minibatch].values + + def get_ys(self, number_minibatch): + return self.ys[number_minibatch].values + + def get_Xt(self, number_minibatch): + return self.Xt[number_minibatch].values + + def get_yt(self, number_minibatch): + return self.yt[number_minibatch].values + + def __split_as_source_target_streams_dallas_2(self, elements_per_fold=1000, sampling_ratio=0.5): + rows_number = self.data.shape[0] + + number_of_folds = round(rows_number / elements_per_fold) + chunk_size = round(rows_number / number_of_folds) + number_of_folds_rounded = round(rows_number / chunk_size) + if (rows_number / number_of_folds_rounded) % 2: + self.number_fold_elements = min(elements_per_fold, np.floor(rows_number / number_of_folds_rounded) - 1) + else: + self.number_fold_elements = min(elements_per_fold, np.floor(rows_number / number_of_folds_rounded)) + + if rows_number / number_of_folds_rounded > elements_per_fold: + number_of_folds = number_of_folds + 1 + + self.number_minibatches = number_of_folds + ck = self.number_fold_elements + + self.source = [] + self.target = [] + + def chunkify(pnds): + nfe = self.number_fold_elements # readability + nof = self.number_minibatches # readability + return [pnds[i * nfe: (i + 1) * nfe] for i in range(nof)] + + for x, data in zip(chunkify(self.X), chunkify(self.data)): + x_mean = np.mean(x, axis=0) + norm_1 = np.linalg.norm(x - x_mean) + norm_2 = np.linalg.norm(x - x_mean, axis=1) + numerator = norm_2 + denominator = 2 * (norm_1.std() ** 2) + probability = np.exp(-numerator / denominator) + idx = np.argsort(probability) + + m = data.shape[0] + self.source.append(data.iloc[idx[: round(m * sampling_ratio)]].sort_index()) + self.target.append(data.iloc[idx[round(m * sampling_ratio):]].sort_index()) + + def __create_Xs_ys_Xt_yt(self): + self.X, self.y = [], [] + self.Xs, self.ys = [], [] + self.Xt, self.yt = [], [] + self.__permutedX, self.__permutedy = [], [] + self.__index_permutation = [] + + for i in range(0, self.number_minibatches): + self.Xs.append(self.source[i].iloc[:, : -self.number_classes]) + self.ys.append(self.source[i].iloc[:, self.number_features:]) + self.Xt.append(self.target[i].iloc[:, : -self.number_classes]) + self.yt.append(self.target[i].iloc[:, self.number_features:]) + self.X.append(pd.concat([self.Xs[i], self.Xt[i]])) + self.y.append(pd.concat([self.ys[i], self.yt[i]])) + + x = self.X[i] + y = self.y[i] + + p = np.random.permutation(x.shape[0]) + self.__permutedX.append(x.iloc[p]) + self.__permutedy.append(y.iloc[p]) + self.__index_permutation.append(p) + + def check_dataset_is_even(self): + if self.data.shape[0] % 2: + self.data.drop(axis='index', index=np.random.randint(1, self.data.shape[0]), inplace=True) diff --git a/ElasticNodes.py b/ElasticNodes.py new file mode 100644 index 0000000..2121173 --- /dev/null +++ b/ElasticNodes.py @@ -0,0 +1,95 @@ +# Marcus Vinicius Sousa Leite de Carvalho +# marcus.decarvalho@ntu.edu.sg +# ivsucram@gmail.com +# +# NANYANG TECHNOLOGICAL UNIVERSITY - NTUITIVE PTE LTD Dual License Agreement +# Non-Commercial Use Only +# This NTUITIVE License Agreement, including all exhibits ("NTUITIVE-LA") is a legal agreement between you and NTUITIVE (or “we”) located at 71 Nanyang Drive, NTU Innovation Centre, #01-109, Singapore 637722, a wholly owned subsidiary of Nanyang Technological University (“NTU”) for the software or data identified above, which may include source code, and any associated materials, text or speech files, associated media and "online" or electronic documentation and any updates we provide in our discretion (together, the "Software"). +# +# By installing, copying, or otherwise using this Software, found at https://github.com/Ivsucram/ATL_Matlab, you agree to be bound by the terms of this NTUITIVE-LA. If you do not agree, do not install copy or use the Software. The Software is protected by copyright and other intellectual property laws and is licensed, not sold. If you wish to obtain a commercial royalty bearing license to this software please contact us at marcus.decarvalho@ntu.edu.sg. +# +# SCOPE OF RIGHTS: +# You may use, copy, reproduce, and distribute this Software for any non-commercial purpose, subject to the restrictions in this NTUITIVE-LA. Some purposes which can be non-commercial are teaching, academic research, public demonstrations and personal experimentation. You may also distribute this Software with books or other teaching materials, or publish the Software on websites, that are intended to teach the use of the Software for academic or other non-commercial purposes. +# You may not use or distribute this Software or any derivative works in any form for commercial purposes. Examples of commercial purposes would be running business operations, licensing, leasing, or selling the Software, distributing the Software for use with commercial products, using the Software in the creation or use of commercial products or any other activity which purpose is to procure a commercial gain to you or others. +# If the Software includes source code or data, you may create derivative works of such portions of the Software and distribute the modified Software for non-commercial purposes, as provided herein. +# If you distribute the Software or any derivative works of the Software, you will distribute them under the same terms and conditions as in this license, and you will not grant other rights to the Software or derivative works that are different from those provided by this NTUITIVE-LA. +# If you have created derivative works of the Software, and distribute such derivative works, you will cause the modified files to carry prominent notices so that recipients know that they are not receiving the original Software. Such notices must state: (i) that you have changed the Software; and (ii) the date of any changes. +# +# You may not distribute this Software or any derivative works. +# In return, we simply require that you agree: +# 1. That you will not remove any copyright or other notices from the Software. +# 2. That if any of the Software is in binary format, you will not attempt to modify such portions of the Software, or to reverse engineer or decompile them, except and only to the extent authorized by applicable law. +# 3. That NTUITIVE is granted back, without any restrictions or limitations, a non-exclusive, perpetual, irrevocable, royalty-free, assignable and sub-licensable license, to reproduce, publicly perform or display, install, use, modify, post, distribute, make and have made, sell and transfer your modifications to and/or derivative works of the Software source code or data, for any purpose. +# 4. That any feedback about the Software provided by you to us is voluntarily given, and NTUITIVE shall be free to use the feedback as it sees fit without obligation or restriction of any kind, even if the feedback is designated by you as confidential. +# 5. THAT THE SOFTWARE COMES "AS IS", WITH NO WARRANTIES. THIS MEANS NO EXPRESS, IMPLIED OR STATUTORY WARRANTY, INCLUDING WITHOUT LIMITATION, WARRANTIES OF MERCHANTABILITY OR FITNESS FOR A PARTICULAR PURPOSE, ANY WARRANTY AGAINST INTERFERENCE WITH YOUR ENJOYMENT OF THE SOFTWARE OR ANY WARRANTY OF TITLE OR NON-INFRINGEMENT. THERE IS NO WARRANTY THAT THIS SOFTWARE WILL FULFILL ANY OF YOUR PARTICULAR PURPOSES OR NEEDS. ALSO, YOU MUST PASS THIS DISCLAIMER ON WHENEVER YOU DISTRIBUTE THE SOFTWARE OR DERIVATIVE WORKS. +# 6. THAT NEITHER NTUITIVE NOR NTU NOR ANY CONTRIBUTOR TO THE SOFTWARE WILL BE LIABLE FOR ANY DAMAGES RELATED TO THE SOFTWARE OR THIS NTUITIVE-LA, INCLUDING DIRECT, INDIRECT, SPECIAL, CONSEQUENTIAL OR INCIDENTAL DAMAGES, TO THE MAXIMUM EXTENT THE LAW PERMITS, NO MATTER WHAT LEGAL THEORY IT IS BASED ON. ALSO, YOU MUST PASS THIS LIMITATION OF LIABILITY ON WHENEVER YOU DISTRIBUTE THE SOFTWARE OR DERIVATIVE WORKS. +# 7. That we have no duty of reasonable care or lack of negligence, and we are not obligated to (and will not) provide technical support for the Software. +# 8. That if you breach this NTUITIVE-LA or if you sue anyone over patents that you think may apply to or read on the Software or anyone's use of the Software, this NTUITIVE-LA (and your license and rights obtained herein) terminate automatically. Upon any such termination, you shall destroy all of your copies of the Software immediately. Sections 3, 4, 5, 6, 7, 8, 11 and 12 of this NTUITIVE-LA shall survive any termination of this NTUITIVE-LA. +# 9. That the patent rights, if any, granted to you in this NTUITIVE-LA only apply to the Software, not to any derivative works you make. +# 10. That the Software may be subject to U.S. export jurisdiction at the time it is licensed to you, and it may be subject to additional export or import laws in other places. You agree to comply with all such laws and regulations that may apply to the Software after delivery of the software to you. +# 11. That all rights not expressly granted to you in this NTUITIVE-LA are reserved. +# 12. That this NTUITIVE-LA shall be construed and controlled by the laws of the Republic of Singapore without regard to conflicts of law. If any provision of this NTUITIVE-LA shall be deemed unenforceable or contrary to law, the rest of this NTUITIVE-LA shall remain in full effect and interpreted in an enforceable manner that most nearly captures the intent of the original language. +# +# Copyright (c) NTUITIVE. All rights reserved. + +import numpy as np + +class ElasticNodes: + ###### Elastic Nodes ###### + growable = None + prunable = None + + data_mean = 0 + data_standard_deviation = 0 + data_variance = 0 + + number_samples_feed = 0 + number_samples_layer = None + + bias_mean = None + bias_variance = None + bias_standard_deviation = None + minimum_bias_mean = None + minimum_bias_standard_deviation = None + bias = None + + var_mean = None + var_variance = None + var_standard_deviation = None + minimum_var_mean = None + minimum_var_standard_deviation = None + var = None + + node_evolution = None + + bias_gradient = None + bias_mean_net = None + var_mean_net = None + + ###### + def __init__(self, number_hidden_layers=1): + nhl = number_hidden_layers #readability + + self.number_samples_layer = np.zeros(nhl) + self.bias_mean = np.zeros(nhl) + self.bias_variance = np.zeros(nhl) + self.bias_standard_deviation = np.zeros(nhl) + self.minimum_bias_mean = np.ones(nhl) * np.inf + self.minimum_bias_standard_deviation = np.ones(nhl) * np.inf + self.BIAS = [] + + self.var_mean = np.zeros(nhl) + self.var_variance = np.zeros(nhl) + self.var_standard_deviation = np.zeros(nhl) + self.minimum_var_mean = np.ones(nhl) * np.inf + self.minimum_var_standard_deviation = np.ones(nhl) * np.inf + self.VAR = [] + + self.growable = np.ones(nhl) * False + self.prunable = [] + + for i in range(nhl): + self.prunable.append([-1]) + + + diff --git a/MySingletons.py b/MySingletons.py new file mode 100644 index 0000000..0fe6e4d --- /dev/null +++ b/MySingletons.py @@ -0,0 +1,69 @@ +# Marcus Vinicius Sousa Leite de Carvalho +# marcus.decarvalho@ntu.edu.sg +# ivsucram@gmail.com +# +# NANYANG TECHNOLOGICAL UNIVERSITY - NTUITIVE PTE LTD Dual License Agreement +# Non-Commercial Use Only +# This NTUITIVE License Agreement, including all exhibits ("NTUITIVE-LA") is a legal agreement between you and NTUITIVE (or “we”) located at 71 Nanyang Drive, NTU Innovation Centre, #01-109, Singapore 637722, a wholly owned subsidiary of Nanyang Technological University (“NTU”) for the software or data identified above, which may include source code, and any associated materials, text or speech files, associated media and "online" or electronic documentation and any updates we provide in our discretion (together, the "Software"). +# +# By installing, copying, or otherwise using this Software, found at https://github.com/Ivsucram/ATL_Matlab, you agree to be bound by the terms of this NTUITIVE-LA. If you do not agree, do not install copy or use the Software. The Software is protected by copyright and other intellectual property laws and is licensed, not sold. If you wish to obtain a commercial royalty bearing license to this software please contact us at marcus.decarvalho@ntu.edu.sg. +# +# SCOPE OF RIGHTS: +# You may use, copy, reproduce, and distribute this Software for any non-commercial purpose, subject to the restrictions in this NTUITIVE-LA. Some purposes which can be non-commercial are teaching, academic research, public demonstrations and personal experimentation. You may also distribute this Software with books or other teaching materials, or publish the Software on websites, that are intended to teach the use of the Software for academic or other non-commercial purposes. +# You may not use or distribute this Software or any derivative works in any form for commercial purposes. Examples of commercial purposes would be running business operations, licensing, leasing, or selling the Software, distributing the Software for use with commercial products, using the Software in the creation or use of commercial products or any other activity which purpose is to procure a commercial gain to you or others. +# If the Software includes source code or data, you may create derivative works of such portions of the Software and distribute the modified Software for non-commercial purposes, as provided herein. +# If you distribute the Software or any derivative works of the Software, you will distribute them under the same terms and conditions as in this license, and you will not grant other rights to the Software or derivative works that are different from those provided by this NTUITIVE-LA. +# If you have created derivative works of the Software, and distribute such derivative works, you will cause the modified files to carry prominent notices so that recipients know that they are not receiving the original Software. Such notices must state: (i) that you have changed the Software; and (ii) the date of any changes. +# +# You may not distribute this Software or any derivative works. +# In return, we simply require that you agree: +# 1. That you will not remove any copyright or other notices from the Software. +# 2. That if any of the Software is in binary format, you will not attempt to modify such portions of the Software, or to reverse engineer or decompile them, except and only to the extent authorized by applicable law. +# 3. That NTUITIVE is granted back, without any restrictions or limitations, a non-exclusive, perpetual, irrevocable, royalty-free, assignable and sub-licensable license, to reproduce, publicly perform or display, install, use, modify, post, distribute, make and have made, sell and transfer your modifications to and/or derivative works of the Software source code or data, for any purpose. +# 4. That any feedback about the Software provided by you to us is voluntarily given, and NTUITIVE shall be free to use the feedback as it sees fit without obligation or restriction of any kind, even if the feedback is designated by you as confidential. +# 5. THAT THE SOFTWARE COMES "AS IS", WITH NO WARRANTIES. THIS MEANS NO EXPRESS, IMPLIED OR STATUTORY WARRANTY, INCLUDING WITHOUT LIMITATION, WARRANTIES OF MERCHANTABILITY OR FITNESS FOR A PARTICULAR PURPOSE, ANY WARRANTY AGAINST INTERFERENCE WITH YOUR ENJOYMENT OF THE SOFTWARE OR ANY WARRANTY OF TITLE OR NON-INFRINGEMENT. THERE IS NO WARRANTY THAT THIS SOFTWARE WILL FULFILL ANY OF YOUR PARTICULAR PURPOSES OR NEEDS. ALSO, YOU MUST PASS THIS DISCLAIMER ON WHENEVER YOU DISTRIBUTE THE SOFTWARE OR DERIVATIVE WORKS. +# 6. THAT NEITHER NTUITIVE NOR NTU NOR ANY CONTRIBUTOR TO THE SOFTWARE WILL BE LIABLE FOR ANY DAMAGES RELATED TO THE SOFTWARE OR THIS NTUITIVE-LA, INCLUDING DIRECT, INDIRECT, SPECIAL, CONSEQUENTIAL OR INCIDENTAL DAMAGES, TO THE MAXIMUM EXTENT THE LAW PERMITS, NO MATTER WHAT LEGAL THEORY IT IS BASED ON. ALSO, YOU MUST PASS THIS LIMITATION OF LIABILITY ON WHENEVER YOU DISTRIBUTE THE SOFTWARE OR DERIVATIVE WORKS. +# 7. That we have no duty of reasonable care or lack of negligence, and we are not obligated to (and will not) provide technical support for the Software. +# 8. That if you breach this NTUITIVE-LA or if you sue anyone over patents that you think may apply to or read on the Software or anyone's use of the Software, this NTUITIVE-LA (and your license and rights obtained herein) terminate automatically. Upon any such termination, you shall destroy all of your copies of the Software immediately. Sections 3, 4, 5, 6, 7, 8, 11 and 12 of this NTUITIVE-LA shall survive any termination of this NTUITIVE-LA. +# 9. That the patent rights, if any, granted to you in this NTUITIVE-LA only apply to the Software, not to any derivative works you make. +# 10. That the Software may be subject to U.S. export jurisdiction at the time it is licensed to you, and it may be subject to additional export or import laws in other places. You agree to comply with all such laws and regulations that may apply to the Software after delivery of the software to you. +# 11. That all rights not expressly granted to you in this NTUITIVE-LA are reserved. +# 12. That this NTUITIVE-LA shall be construed and controlled by the laws of the Republic of Singapore without regard to conflicts of law. If any provision of this NTUITIVE-LA shall be deemed unenforceable or contrary to law, the rest of this NTUITIVE-LA shall remain in full effect and interpreted in an enforceable manner that most nearly captures the intent of the original language. +# +# Copyright (c) NTUITIVE. All rights reserved. + +import torch + +class MyDevice: + def get(self): + return TorchDevice.instance().device + +class TorchDevice: + class __TorchDevice: + def __init__(self, device: torch.device = None): + if device: + self.device = device + else: + self.device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu") + + def __str__(self): + return repr(self) + self.device + + _instance = None + __instance = None + + def __init__(self): + raise RuntimeError('Call instance() instead') + + @classmethod + def instance(cls, device: torch.device = None): + if cls._instance is None: + cls._instance = cls.__new__(cls) + if device is None: + cls.__instance = TorchDevice.__TorchDevice() + else: + cls.__instance = TorchDevice.__TorchDevice(device) + return cls._instance + + def __getattr__(self, name): + return getattr(self.__instance, name) \ No newline at end of file diff --git a/MyUtil.py b/MyUtil.py new file mode 100644 index 0000000..f349afe --- /dev/null +++ b/MyUtil.py @@ -0,0 +1,63 @@ +# Marcus Vinicius Sousa Leite de Carvalho +# marcus.decarvalho@ntu.edu.sg +# ivsucram@gmail.com +# +# NANYANG TECHNOLOGICAL UNIVERSITY - NTUITIVE PTE LTD Dual License Agreement +# Non-Commercial Use Only +# This NTUITIVE License Agreement, including all exhibits ("NTUITIVE-LA") is a legal agreement between you and NTUITIVE (or “we”) located at 71 Nanyang Drive, NTU Innovation Centre, #01-109, Singapore 637722, a wholly owned subsidiary of Nanyang Technological University (“NTU”) for the software or data identified above, which may include source code, and any associated materials, text or speech files, associated media and "online" or electronic documentation and any updates we provide in our discretion (together, the "Software"). +# +# By installing, copying, or otherwise using this Software, found at https://github.com/Ivsucram/ATL_Matlab, you agree to be bound by the terms of this NTUITIVE-LA. If you do not agree, do not install copy or use the Software. The Software is protected by copyright and other intellectual property laws and is licensed, not sold. If you wish to obtain a commercial royalty bearing license to this software please contact us at marcus.decarvalho@ntu.edu.sg. +# +# SCOPE OF RIGHTS: +# You may use, copy, reproduce, and distribute this Software for any non-commercial purpose, subject to the restrictions in this NTUITIVE-LA. Some purposes which can be non-commercial are teaching, academic research, public demonstrations and personal experimentation. You may also distribute this Software with books or other teaching materials, or publish the Software on websites, that are intended to teach the use of the Software for academic or other non-commercial purposes. +# You may not use or distribute this Software or any derivative works in any form for commercial purposes. Examples of commercial purposes would be running business operations, licensing, leasing, or selling the Software, distributing the Software for use with commercial products, using the Software in the creation or use of commercial products or any other activity which purpose is to procure a commercial gain to you or others. +# If the Software includes source code or data, you may create derivative works of such portions of the Software and distribute the modified Software for non-commercial purposes, as provided herein. +# If you distribute the Software or any derivative works of the Software, you will distribute them under the same terms and conditions as in this license, and you will not grant other rights to the Software or derivative works that are different from those provided by this NTUITIVE-LA. +# If you have created derivative works of the Software, and distribute such derivative works, you will cause the modified files to carry prominent notices so that recipients know that they are not receiving the original Software. Such notices must state: (i) that you have changed the Software; and (ii) the date of any changes. +# +# You may not distribute this Software or any derivative works. +# In return, we simply require that you agree: +# 1. That you will not remove any copyright or other notices from the Software. +# 2. That if any of the Software is in binary format, you will not attempt to modify such portions of the Software, or to reverse engineer or decompile them, except and only to the extent authorized by applicable law. +# 3. That NTUITIVE is granted back, without any restrictions or limitations, a non-exclusive, perpetual, irrevocable, royalty-free, assignable and sub-licensable license, to reproduce, publicly perform or display, install, use, modify, post, distribute, make and have made, sell and transfer your modifications to and/or derivative works of the Software source code or data, for any purpose. +# 4. That any feedback about the Software provided by you to us is voluntarily given, and NTUITIVE shall be free to use the feedback as it sees fit without obligation or restriction of any kind, even if the feedback is designated by you as confidential. +# 5. THAT THE SOFTWARE COMES "AS IS", WITH NO WARRANTIES. THIS MEANS NO EXPRESS, IMPLIED OR STATUTORY WARRANTY, INCLUDING WITHOUT LIMITATION, WARRANTIES OF MERCHANTABILITY OR FITNESS FOR A PARTICULAR PURPOSE, ANY WARRANTY AGAINST INTERFERENCE WITH YOUR ENJOYMENT OF THE SOFTWARE OR ANY WARRANTY OF TITLE OR NON-INFRINGEMENT. THERE IS NO WARRANTY THAT THIS SOFTWARE WILL FULFILL ANY OF YOUR PARTICULAR PURPOSES OR NEEDS. ALSO, YOU MUST PASS THIS DISCLAIMER ON WHENEVER YOU DISTRIBUTE THE SOFTWARE OR DERIVATIVE WORKS. +# 6. THAT NEITHER NTUITIVE NOR NTU NOR ANY CONTRIBUTOR TO THE SOFTWARE WILL BE LIABLE FOR ANY DAMAGES RELATED TO THE SOFTWARE OR THIS NTUITIVE-LA, INCLUDING DIRECT, INDIRECT, SPECIAL, CONSEQUENTIAL OR INCIDENTAL DAMAGES, TO THE MAXIMUM EXTENT THE LAW PERMITS, NO MATTER WHAT LEGAL THEORY IT IS BASED ON. ALSO, YOU MUST PASS THIS LIMITATION OF LIABILITY ON WHENEVER YOU DISTRIBUTE THE SOFTWARE OR DERIVATIVE WORKS. +# 7. That we have no duty of reasonable care or lack of negligence, and we are not obligated to (and will not) provide technical support for the Software. +# 8. That if you breach this NTUITIVE-LA or if you sue anyone over patents that you think may apply to or read on the Software or anyone's use of the Software, this NTUITIVE-LA (and your license and rights obtained herein) terminate automatically. Upon any such termination, you shall destroy all of your copies of the Software immediately. Sections 3, 4, 5, 6, 7, 8, 11 and 12 of this NTUITIVE-LA shall survive any termination of this NTUITIVE-LA. +# 9. That the patent rights, if any, granted to you in this NTUITIVE-LA only apply to the Software, not to any derivative works you make. +# 10. That the Software may be subject to U.S. export jurisdiction at the time it is licensed to you, and it may be subject to additional export or import laws in other places. You agree to comply with all such laws and regulations that may apply to the Software after delivery of the software to you. +# 11. That all rights not expressly granted to you in this NTUITIVE-LA are reserved. +# 12. That this NTUITIVE-LA shall be construed and controlled by the laws of the Republic of Singapore without regard to conflicts of law. If any provision of this NTUITIVE-LA shall be deemed unenforceable or contrary to law, the rest of this NTUITIVE-LA shall remain in full effect and interpreted in an enforceable manner that most nearly captures the intent of the original language. +# +# Copyright (c) NTUITIVE. All rights reserved. + +import math +import torch + +class MyUtil: + def __init__(self): + pass + + @staticmethod + def recursive_mean_standard_deviation(x, old_mean, old_variance, number_samples): + mean = old_mean + (x - old_mean) / number_samples + var = old_variance + (x - old_mean) * (x - mean) + return mean, var, torch.sqrt(var/number_samples) + + @staticmethod + def probit(mean, standard_deviation): + p = (1 + math.pi * (standard_deviation ** 2) / 8) + return mean / torch.sqrt(p) + + @staticmethod + def norm_1(x): + return torch.norm(x, 1) + + @staticmethod + def norm_2(x): + return torch.norm(x, 2) + + @staticmethod + def frobenius_norm(x): + return torch.norm(x, 'fro') \ No newline at end of file diff --git a/NeuralNetwork.py b/NeuralNetwork.py new file mode 100644 index 0000000..7d0cabe --- /dev/null +++ b/NeuralNetwork.py @@ -0,0 +1,563 @@ +# Marcus Vinicius Sousa Leite de Carvalho +# marcus.decarvalho@ntu.edu.sg +# ivsucram@gmail.com +# +# NANYANG TECHNOLOGICAL UNIVERSITY - NTUITIVE PTE LTD Dual License Agreement +# Non-Commercial Use Only +# This NTUITIVE License Agreement, including all exhibits ("NTUITIVE-LA") is a legal agreement between you and NTUITIVE (or “we”) located at 71 Nanyang Drive, NTU Innovation Centre, #01-109, Singapore 637722, a wholly owned subsidiary of Nanyang Technological University (“NTU”) for the software or data identified above, which may include source code, and any associated materials, text or speech files, associated media and "online" or electronic documentation and any updates we provide in our discretion (together, the "Software"). +# +# By installing, copying, or otherwise using this Software, found at https://github.com/Ivsucram/ATL_Matlab, you agree to be bound by the terms of this NTUITIVE-LA. If you do not agree, do not install copy or use the Software. The Software is protected by copyright and other intellectual property laws and is licensed, not sold. If you wish to obtain a commercial royalty bearing license to this software please contact us at marcus.decarvalho@ntu.edu.sg. +# +# SCOPE OF RIGHTS: +# You may use, copy, reproduce, and distribute this Software for any non-commercial purpose, subject to the restrictions in this NTUITIVE-LA. Some purposes which can be non-commercial are teaching, academic research, public demonstrations and personal experimentation. You may also distribute this Software with books or other teaching materials, or publish the Software on websites, that are intended to teach the use of the Software for academic or other non-commercial purposes. +# You may not use or distribute this Software or any derivative works in any form for commercial purposes. Examples of commercial purposes would be running business operations, licensing, leasing, or selling the Software, distributing the Software for use with commercial products, using the Software in the creation or use of commercial products or any other activity which purpose is to procure a commercial gain to you or others. +# If the Software includes source code or data, you may create derivative works of such portions of the Software and distribute the modified Software for non-commercial purposes, as provided herein. +# If you distribute the Software or any derivative works of the Software, you will distribute them under the same terms and conditions as in this license, and you will not grant other rights to the Software or derivative works that are different from those provided by this NTUITIVE-LA. +# If you have created derivative works of the Software, and distribute such derivative works, you will cause the modified files to carry prominent notices so that recipients know that they are not receiving the original Software. Such notices must state: (i) that you have changed the Software; and (ii) the date of any changes. +# +# You may not distribute this Software or any derivative works. +# In return, we simply require that you agree: +# 1. That you will not remove any copyright or other notices from the Software. +# 2. That if any of the Software is in binary format, you will not attempt to modify such portions of the Software, or to reverse engineer or decompile them, except and only to the extent authorized by applicable law. +# 3. That NTUITIVE is granted back, without any restrictions or limitations, a non-exclusive, perpetual, irrevocable, royalty-free, assignable and sub-licensable license, to reproduce, publicly perform or display, install, use, modify, post, distribute, make and have made, sell and transfer your modifications to and/or derivative works of the Software source code or data, for any purpose. +# 4. That any feedback about the Software provided by you to us is voluntarily given, and NTUITIVE shall be free to use the feedback as it sees fit without obligation or restriction of any kind, even if the feedback is designated by you as confidential. +# 5. THAT THE SOFTWARE COMES "AS IS", WITH NO WARRANTIES. THIS MEANS NO EXPRESS, IMPLIED OR STATUTORY WARRANTY, INCLUDING WITHOUT LIMITATION, WARRANTIES OF MERCHANTABILITY OR FITNESS FOR A PARTICULAR PURPOSE, ANY WARRANTY AGAINST INTERFERENCE WITH YOUR ENJOYMENT OF THE SOFTWARE OR ANY WARRANTY OF TITLE OR NON-INFRINGEMENT. THERE IS NO WARRANTY THAT THIS SOFTWARE WILL FULFILL ANY OF YOUR PARTICULAR PURPOSES OR NEEDS. ALSO, YOU MUST PASS THIS DISCLAIMER ON WHENEVER YOU DISTRIBUTE THE SOFTWARE OR DERIVATIVE WORKS. +# 6. THAT NEITHER NTUITIVE NOR NTU NOR ANY CONTRIBUTOR TO THE SOFTWARE WILL BE LIABLE FOR ANY DAMAGES RELATED TO THE SOFTWARE OR THIS NTUITIVE-LA, INCLUDING DIRECT, INDIRECT, SPECIAL, CONSEQUENTIAL OR INCIDENTAL DAMAGES, TO THE MAXIMUM EXTENT THE LAW PERMITS, NO MATTER WHAT LEGAL THEORY IT IS BASED ON. ALSO, YOU MUST PASS THIS LIMITATION OF LIABILITY ON WHENEVER YOU DISTRIBUTE THE SOFTWARE OR DERIVATIVE WORKS. +# 7. That we have no duty of reasonable care or lack of negligence, and we are not obligated to (and will not) provide technical support for the Software. +# 8. That if you breach this NTUITIVE-LA or if you sue anyone over patents that you think may apply to or read on the Software or anyone's use of the Software, this NTUITIVE-LA (and your license and rights obtained herein) terminate automatically. Upon any such termination, you shall destroy all of your copies of the Software immediately. Sections 3, 4, 5, 6, 7, 8, 11 and 12 of this NTUITIVE-LA shall survive any termination of this NTUITIVE-LA. +# 9. That the patent rights, if any, granted to you in this NTUITIVE-LA only apply to the Software, not to any derivative works you make. +# 10. That the Software may be subject to U.S. export jurisdiction at the time it is licensed to you, and it may be subject to additional export or import laws in other places. You agree to comply with all such laws and regulations that may apply to the Software after delivery of the software to you. +# 11. That all rights not expressly granted to you in this NTUITIVE-LA are reserved. +# 12. That this NTUITIVE-LA shall be construed and controlled by the laws of the Republic of Singapore without regard to conflicts of law. If any provision of this NTUITIVE-LA shall be deemed unenforceable or contrary to law, the rest of this NTUITIVE-LA shall remain in full effect and interpreted in an enforceable manner that most nearly captures the intent of the original language. +# +# Copyright (c) NTUITIVE. 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) \ No newline at end of file diff --git a/README.md b/README.md index 1945790..889374c 100644 --- a/README.md +++ b/README.md @@ -1,2 +1,79 @@ +# Reference + +[ArXiv -> ATL: Autonomous Knowledge Transfer from Many Streaming Processes](https://arxiv.org/abs/1910.03434) + +[ResearchGate -> ATL: Autonomous Knowledge Transfer from Many Streaming Processes](https://www.researchgate.net/publication/336361712_ATL_Autonomous_Knowledge_Transfer_from_Many_Streaming_Processes) + +# Notes + +If you want to see the original code used for this paper, access [ATL_Matlab](https://github.com/Ivsucram/ATL_Matlab) + +`ATL_Python` is a reconstruction of `ATL_Matlab` by the same author, but using Python 3.6 and PyTorch (with autograd enabled and GPU support). The code is still not one-to-one and some differences in results can be found (specially on the data split methods in `DataManipulator`, however the network structure is correct and can be used by whoever is interested on this work in order to understand the structure or to build comparative results with your own research work. + +Having said that, expect `ATL_Python` to be updated in the following weeks, including functions refactoring and functions documentation. + # ATL_Python - ATL code converted to Python 3 + +ATL: Autonomous Knowledge Transfer From Many Streaming Processes +ACM CIKM 2019 + +1. Clone `ATL_Python` git to your computer, or just download the files. + +2. Provide a dataset by replacing the file `data.csv` +The current `data.csv` holds [https://www.researchgate.net/publication/221653408_A_Streaming_Ensemble_Algorithm_SEA_for_Large-Scale_Classification](SEA) dataset. +`data.csv` must be prepared as following: + +``` +- Each row presents a new data sample +- Each column presents a data feature +- The last column presents the label for that sample. Don't use one-hot encoding. Use a format from 1 onwards +``` + +3. Open Matlab. The code was developed using Matlab 2018b, so if you use an older version, you might get some incompability errors. + +You can use Matlab 2018b or newer. +Matlab may prompt you to install some official add-ons, as: + +``` +- Deep Learning Toolbox +- Fuzzy Logic Toolbox +- Digital Processing Signal Toolbox +``` + +4. Inside Matlab, travel until the folder where you downloaded `ATL_Matlab`. + +5. On the Matlab terminal, just type `ATL`. This will execute ATL, which will read your data.csv and process it. + +ATL will automatically normalize your data and split your data into 2 streams (Source and Target data streams) with a bias between them, as described in the paper. + +Matlab will print ATL status at the end of every minibatch, where you will be able to follow useful information as: + +``` +- Training time (maximum, mean, minimum, current and accumulated) +- Testing time (maximum, mean, minimum, current and accumulated) +- The number of GMM clusters (maximum, mean, minimum and current) +- The target classification rate +- And a quick review of ATL structure (both discriminative and generative phases), where you can see how many automatically generated nodes were created. +``` + +At the end of the process, Matlab will plot 6 graphs: + +``` +- Network bias and Network variance w.r.t. the generative phase +- Network bias and Network variance w.r.t. the discriminative phase +- The target and source classification rate evolution, as well as the final mean accuracy of the network +- All losses over time, and how they influence the network learning +- The evolution of GMMs on Source and Taret AGMMs over time +- The processing time per mini-batch and the total processing time as well, both for training and testing +``` + +Thank you. + +# Download all datasets used on the paper + +As some datasets are too big, we can't upload them to GitHub. GitHub has a size limite of 35MB per file. Because of that, you can find all the datasets in a csv format on the anonymous link below. To test it, copy the desired dataset to the same foler as ATL and rename it to `data.csv`. + +- [https://drive.google.com/open?id=1emgVw6muSodzozQcuz7ks8XeZYPxGEZ7](https://drive.google.com/open?id=1emgVw6muSodzozQcuz7ks8XeZYPxGEZ7) + + + diff --git a/data.csv b/data.csv new file mode 100644 index 0000000..f53e843 --- /dev/null +++ b/data.csv @@ -0,0 +1,100000 @@ +0.94191,0.11647,0.44388,1 +0.058918,0.050822,0.046485,2 +0.2568,0.79758,0.47589,1 +0.31489,0.82813,0.3682,1 +0.49814,0.41386,0.94836,1 +0.15691,0.87121,0.99897,2 +0.92387,0.24614,0.73709,1 +0.18953,0.7878,0.60138,1 +0.6223,0.69138,0.45837,1 +0.3472,0.89583,0.34567,1 +0.99433,0.22415,0.34654,1 +0.76228,0.50179,0.43968,1 +0.23842,0.19532,0.98298,2 +0.0042418,0.41148,0.54694,1 +0.48818,0.42572,0.6498,1 +0.094715,0.69877,0.63481,2 +0.016261,0.38951,0.18116,2 +0.26161,0.27017,0.19319,2 +0.060672,0.53143,0.27664,2 +0.39312,0.70277,0.49455,1 +0.17679,0.36093,0.66187,2 +0.033244,0.096427,0.42492,2 +0.04103,0.25989,0.60337,2 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