构建Roc-rk3399-pc嵌入式Linux系统：配置工具链与下载源码

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def adversarial(x, model, loss_func, c=1e-4, kappa=0, num_iter=100, lr=0.01): """ Create adversarial examples using CW algorithm Args: - x: input image - model: the neural network model - loss_func: the loss function to use - c: the weight for the L2 regularization term (default=1e-4) - kappa: the confidence parameter (default=0) - num_iter: number of iterations for the algorithm (default=100) - lr: learning rate for the optimization (default=0.01) Returns: - x_adv: adversarial example """ x_adv = x.clone().detach().requires_grad_(True) for i in range(num_iter): output = model(x_adv) loss = loss_func(output, torch.tensor([kappa]), x, x_adv, c) model.zero_grad() loss.backward() with torch.no_grad(): x_adv += lr * x_adv.grad x_adv = torch.max(torch.min(x_adv, x + 0.35), x - 0.35) x_adv = torch.clamp(x_adv, 0, 1) x_adv.requires_grad_() return x_adv.detach()上述代码出现TypeError: ce_loss() takes 2 positional arguments but 5 were given错误，请改正

- lr: learning rate for the optimization (default=0.01) Returns: - x_adv: adversarial example """ x_adv = x.clone().detach().requires_grad_(True) for i in range(num_iter): output = model(x_adv)...

ModuleNotFoundError: No module named 'tensorflow_model_optimization'

根据您提供的引用内容，出现“ModuleNotFoundError: No module named 'tensorflow_model_optimization'”错误提示，说明您的Python环境中没有安装名为“tensorflow_model_optimization”的模块。您需要先安装该模块...

详细介绍《Deep Reinforcement Learning for Multiobjective Optimization》

《Deep Reinforcement Learning for Multiobjective Optimization》是一篇学术论文，主要研究了如何使用深度强化学习来解决多目标优化问题。多目标优化是一类常见的优化问题，其中需要同时考虑多个目标。例如，在...

train with base lr in the first 100 epochs # and half the lr in the last 100 epochs To train with a base learning rate for the first 100 epochs and half the learning rate for the last 100 epochs, you can use a learning rate scheduler in PyTorch. Here's an example of how you can modify the training loop in your code: import torch import torch.nn as nn import torch.optim as optim from torch.optim.lr_scheduler import MultiStepLR # Define your model, criterion, and optimizer model = YourModel() criterion = nn.CrossEntropyLoss() optimizer = optim.SGD(model.parameters(), lr=0.01) # Define the number of epochs and the milestone epochs num_epochs = 200 milestones = [100] # Create a learning rate scheduler scheduler = MultiStepLR(optimizer, milestones=milestones, gamma=0.5) # Train the model for epoch in range(num_epochs): # Train with base lr for the first 100 epochs, and half the lr for the last 100 epochs if epoch >= milestones[0]: scheduler.step() for inputs, labels in train_loader: # Forward pass outputs = model(inputs) loss = criterion(outputs, labels) # Backward pass and optimization optimizer.zero_grad() loss.backward() optimizer.step() # Perform validation or testing after each epoch with torch.no_grad(): # Validation or testing code # Print training information print(f"Epoch [{epoch+1}/{num_epochs}], Loss: {loss.item()}, LR: {scheduler.get_last_lr()[0]}") # Save the model or perform other operations after training In this code snippet, we create a MultiStepLR scheduler and specify the milestones as [100] and gamma as 0.5. The learning rate is halved at the specified milestone epochs. Inside the training loop, we check if the current epoch is greater than or equal to the milestone epoch, and if so, we call scheduler.step() to update the learning rate. Remember to adjust the num_epochs and other hyperparameters according to your specific requirements. 翻译成中文

为了在前100个epochs中使用基本学习率训练，并在最后100个epochs中将学习率减半，你可以在PyTorch中使用学习率调度器。这是一个示例，展示了如何修改代码中的训练循环： ```python import torch import torch.nn as...

Casola, V., & Castiglione, A. (2020). Secure and Trustworthy Big Data Storage. Springer. Corriveau, D., Gerrish, B., & Wu, Z. (2020). End-to-end Encryption on the Server: The Why and the How. arXiv preprint arXiv:2010.01403. Dowsley, R., Nascimento, A. C. A., & Nita, D. M. (2021). Private database access using homomorphic encryption. Journal of Network and Computer Applications, 181, 103055. Hossain, M. A., Fotouhi, R., & Hasan, R. (2019). Towards a big data storage security framework for the cloud. In Proceedings of the 9th Annual Computing and Communication Workshop and Conference (CCWC), Las Vegas, USA (pp. 402-408). Rughani, R. (2019). Analysis of Security Issues and Their Solutions in Cloud Storage Environment. International Journal of Computer Trends and Technology (IJCTT), 67(6), 37-42. van Esbroeck, A. (2019). Zero-Knowledge Proofs in the Age of Cryptography: Preventing Fraud Without Compromising Privacy. Chicago-Kent Journal of Intellectual Property, 19, 374. Berman, L. (2021). Watch out for hidden cloud costs. CFO Dive. Retrieved from https://www.cfodive.com/news/watch-out-for-hidden-cloud-costs/603921/ Bradley, T. (2021). Cloud storage costs continue to trend downward. Forbes. Retrieved from https://www.forbes.com/sites/tonybradley/2021/08/27/cloud-storage-costs-continue-to-trend-downward/?sh=6f9d6ade7978 Cisco. (2019). Cost optimization in the multicloud. Cisco. Retrieved from https://www.cisco.com/c/dam/en/us/solutions/collateral/data-center-virtualization/cloud-cost-optimization/cost-optimization_in_multicloud.pdf IBM. (2020). Storage efficiency solutions. IBM. Retrieved from https://www.ibm.com/blogs/systems/storage-efficiency-solutions/ Microsoft Azure. (n.d.). Azure Blob storage tiers. Microsoft Azure. Retrieved from https://azure.microsoft.com/en-us/services/storage/blobs/#pricing Nawrocki, M. (2019). The benefits of a hybrid cloud strategy for businesses. DataCenterNews. Retrieved from https://datacenternews.asia/story/the-benefits-of-a-hybrid-cloud-strategy-for，请把这一段reference list改为标准哈佛格式

(2019) 'Towards a big data storage security framework for the cloud', in Proceedings of the 9th Annual Computing and Communication Workshop and Conference (CCWC), Las Vegas, USA, pp. 402-408. ...

dcc-garch model code

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Multi-objective Evolutionary Federated Learning

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This error message suggests that you are trying to run an MLIR-based tool or application without any optimization passes enabled. MLIR is a compiler infrastructure that provides a framework for ...

php 请详细说明一下以下的php编译参数，包含其使用场景、有与没有的区别，越详细越好。 '--prefix=/www/server/php/74' '--with-config-file-path=/www/server/php/74/etc' '--enable-fpm' '--with-fpm-user=www' '--with-fpm-group=www' '--enable-mysqlnd' '--with-mysqli=mysqlnd' '--with-pdo-mysql=mysqlnd' '--with-iconv-dir' '--with-freetype' '--with-jpeg' '--with-zlib' '--with-libxml-dir=/usr' '--enable-xml' '--disable-rpath' '--enable-bcmath' '--enable-shmop' '--enable-sysvsem' '--enable-inline-optimization' '--with-curl' '--enable-mbregex' '--enable-mbstring' '--enable-intl' '--enable-ftp' '--enable-gd' '--with-openssl' '--with-mhash' '--enable-pcntl' '--enable-sockets' '--with-xmlrpc' '--enable-soap' '--with-gettext' '--disable-fileinfo' '--enable-opcache' '--with-sodium=/usr/local/libsodium' '--with-webp' '--enable-mysqlnd-compression-support' '--with-png-dir' '--with-libmbfl' '--enable-zip' '--with-pear '--enable-maintainer-zts' '--without-gdbm'

这是一组 PHP 编译参数，下面逐一解释： ... ... ...7. --with-mysqli=mysqlnd：指定 MySQLi 扩展使用的驱动为 mysqlnd，同样用于连接 MySQL 数据库。...没有这些编译参数，则可能会导致某些功能无法使用或者性能较差。

oss = self._train_model(input_fn, hooks, saving_listeners) File "E:\pycharm-workspace\BERT\BERT-BiLSTM-CRF-NER-master\venv\lib\site-packages\tensorflow_estimator\python\estimator\estimator.py", line 1161, in _train_model return self._train_model_default(input_fn, hooks, saving_listeners) File "E:\pycharm-workspace\BERT\BERT-BiLSTM-CRF-NER-master\venv\lib\site-packages\tensorflow_estimator\python\estimator\estimator.py", line 1191, in _train_model_default features, labels, ModeKeys.TRAIN, self.config) File "E:\pycharm-workspace\BERT\BERT-BiLSTM-CRF-NER-master\venv\lib\site-packages\tensorflow_estimator\python\estimator\estimator.py", line 1149, in _call_model_fn model_fn_results = self._model_fn(features=features, **kwargs) File "E:\pycharm-workspace\BERT\BERT-BiLSTM-CRF-NER-master\bert_base\train\bert_lstm_ner.py", line 405, in model_fn total_loss, learning_rate, num_train_steps, num_warmup_steps, False) File "E:\pycharm-workspace\BERT\BERT-BiLSTM-CRF-NER-master\bert_base\bert\optimization.py", line 27, in create_optimizer global_step = tf.train.get_or_create_global_step() UnboundLocalError: local variable 'tf' referenced before assignment 进程已结束,退出代码1

import tensorflow as tf ``` 如果你已经正确导入了 TensorFlow，但仍然遇到此错误，请检查你的代码中是否存在命名冲突或变量重复定义的情况。另外，请确保你的代码中使用的 TensorFlow 版本与 transformers 库...

File "E:\pycharm-workspace\BERT\BERT-BiLSTM-CRF-NER-master\venv\lib\site-packages\tensorflow_estimator\python\estimator\estimator.py", line 1161, in _train_model return self._train_model_default(input_fn, hooks, saving_listeners) File "E:\pycharm-workspace\BERT\BERT-BiLSTM-CRF-NER-master\venv\lib\site-packages\tensorflow_estimator\python\estimator\estimator.py", line 1191, in _train_model_default features, labels, ModeKeys.TRAIN, self.config) File "E:\pycharm-workspace\BERT\BERT-BiLSTM-CRF-NER-master\venv\lib\site-packages\tensorflow_estimator\python\estimator\estimator.py", line 1149, in _call_model_fn model_fn_results = self._model_fn(features=features, **kwargs) File "E:\pycharm-workspace\BERT\BERT-BiLSTM-CRF-NER-master\bert_base\train\bert_lstm_ner.py", line 405, in model_fn total_loss, learning_rate, num_train_steps, num_warmup_steps, False) File "E:\pycharm-workspace\BERT\BERT-BiLSTM-CRF-NER-master\bert_base\bert\optimization.py", line 59, in create_optimizer from transformers import AdamWeightDecay ImportError: cannot import name 'AdamWeightDecay' from 'transformers' (E:\pycharm-workspace\BERT\BERT-BiLSTM-CRF-NER-master\venv\lib\site-packages\transformers\init.py)

如果你已经使用了最新版本的 transformers 库，但仍然遇到导入错误，请检查你的环境配置并确保没有其他命名冲突或导入问题。另外，也请确保你的代码和依赖项正确配置，并且 transformers 库的文件路径是正确的。 ...

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构建Roc-rk3399-pc嵌入式Linux系统：配置工具链与下载源码

元学习论文​​Optimization as a model for few-shot learning的简单理解_详细注释

Optimization Methods for Large-Scale Machine Learning

深度学习论文集二

ModuleNotFoundError: No module named 'tensorflow_model_optimization'

详细介绍《Deep Reinforcement Learning for Multiobjective Optimization》

dcc-garch model code

Multi-objective Evolutionary Federated Learning

linear algebra and optimization for machine learning" csdn

energy-based model

Genetic Algorithms in Search, Optimization and Machine Learning

Deep Learning Toolbox

None of the MLIR Optimization Passes are enabled (registered 2)

Closed-loop Rescheduling using Deep Reinforcement Learning

交叉编译 opencv O3

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元学习论文Optimization as a model for few-shot learning的简单理解_详细注释