HTML5入门指南：Up and Running

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"HTML5_ Up and Running.pdf" 是一本由 Mark Pilgrim 撰写的关于 HTML5 的技术书籍，尚未有中文版，因此阅读英文原版可以更准确地理解其内容。书中涵盖了 HTML5 的核心概念、新特性以及实际应用。 HTML5 是超文本标记语言（HTML）的最新版本，它在 Web 开发领域引入了多项重大改进和创新。这本书详细介绍了这些变化，旨在帮助读者快速上手并深入理解 HTML5。 1. **新元素与语义化**：HTML5 强调网页内容的语义性，引入了如 `<header>`, `<footer>`, `<article>`, `<section>` 等新元素，使页面结构更加清晰，有利于搜索引擎优化和无障碍访问。 2. **多媒体支持**：HTML5 提供了内建的 `<audio>` 和 `<video>` 元素，使得网页可以直接嵌入音频和视频，不再依赖 Flash 或其他插件，提高了跨平台兼容性和性能。 3. **离线存储**：HTML5 的离线存储功能（如 AppCache 和 IndexedDB）允许网站在用户离线时也能提供部分服务，增强了 Web 应用的用户体验。 4. **Canvas 与 SVG**：`<canvas>` 元素提供了动态图形绘制能力，而 Scalable Vector Graphics (SVG) 则支持矢量图，两者结合可实现丰富的交互式图形和动画。 5. **Web Workers 和 Web Sockets**：Web Workers 提供了后台处理能力，让复杂的计算可以在后台进行，不阻塞用户界面。Web Sockets 则实现了双向实时通信，为构建实时应用程序提供了可能。 6. **表单控件增强**：HTML5 对表单元素进行了改进，新增了日期选择器、搜索框、滑块等控件，简化了表单验证，并引入了新的输入类型。 7. **Geolocation API**：通过 Geolocation API，网页可以获取用户的地理位置信息，为本地化服务和基于位置的应用提供了基础。 8. **WebRTC**：这项技术使得浏览器间可以直接进行音视频通信，无需借助第三方插件，推动了在线协作和通信的发展。 9. **CSS3**：虽然 CSS3 不是 HTML5 的一部分，但它们常被一起提及，因为 HTML5 的新元素往往需要 CSS3 来美化和布局。 10. **渐进增强与优雅降级**：HTML5 的设计原则之一是渐进增强，这意味着即使在不支持新特性的老旧浏览器中，网页仍能正常工作，而对新特性的支持则会提供更好的用户体验。 这本书《HTML5: Up and Running》通过实例和实践向读者展示了如何有效地利用这些新技术，无论是初学者还是经验丰富的开发者，都能从中受益。通过阅读此书，读者将能够熟练掌握 HTML5 的各项功能，提高开发现代 Web 应用的能力。

优化下面代码：def running_state_post_data(**kw): attrs = running_state_ctrl.RunningState._wsme_attributes running_state = db_utils.get_test_running_state(**kw) fields = [field.key for field in attrs] post_data = {k: v for k, v in running_state.items() if k in fields} post_data.update({k: v for k, v in kw.items() if k in fields}) return post_data

2023-07-13 上传
post_data = {k: v for k, v in running_state.items() if k in running_state_ctrl.RunningState._wsme_attributes} post_data.update({k: v for k, v in kw.items() if k in running_state_ctrl.RunningState._...

下面代码有什么问题attrs = running_state_ctrl.RunningState._wsme_attributes running_state = db_utils.get_test_running_state(**kw) fields = [field.key for field in attrs] post_data = {k: v for k, v in running_state.items() if k in fields} post_data.update({k: v for k, v in kw.items() if k in fields}) return post_data

2023-07-12 上传
2. 未定义`running_state_ctrl.RunningState`的属性`_wsme_attributes`：代码中通过`attrs = running_state_ctrl.RunningState._wsme_attributes`获取属性列表，但如果`_wsme_attributes`属性未在`RunningState`类中...

对这段代码进行优化attrs = running_state_ctrl.RunningState._wsme_attributes running_state = db_utils.get_test_running_state(**kw) fields = [field.key for field in attrs] post_data = {k: v for k, v in running_state.items() if k in fields} post_data.update({k: v for k, v in kw.items() if k in fields}) return post_data

2023-07-12 上传
post_data = {k: v for k, v in running_state.items() if k in fields or (k in kw and k in fields)} ``` 3. 使用`dict.get()`方法：如果存在某个键在`kw`中但不在`running_state`中，原始代码会报错。可以使用`...

下面代码怎么优化 def update_running_state_to_restore(self): # Get audits that should be stashed. need_stash_audits = self.get_need_rollback_audits() need_suspend_audits = [] need_rollback_audits = [] for audit in need_stash_audits: self.record_audit_init_state(audit) if audit.goal.name in ['saving_energy', 'power_limit']: need_rollback_audits.append(audit) else: need_suspend_audits.append(audit) LOG.info("Audits need suspend are: %s", need_suspend_audits) LOG.info("Audits need rollback are: %s", need_rollback_audits) # The drs audits should be stopped in a timely manner, # so need to handle the drs audits first. for audit in need_suspend_audits: if audit.state == objects.audit.State.ONGOING: LOG.info("Suspend non-dpm audit: %s", audit.uuid) audit.state = objects.audit.State.SUSPENDED audit.save() for audit in need_rollback_audits: LOG.info("Begin to roll back audit: %s", audit.uuid) self.dc_client.rollback_audit(self.context, audit.uuid) # Confirm that the audit is rolled back completely self.confirm_audit_rolled_back(audit) disabled_nodes = self.get_watcher_disabled_nodes() LOG.info("Audits have all been updated, disabled nodes by watcher: %s", disabled_nodes) self.running_state.stash_audits = self.stash_audits self.running_state.watcher_disabled_nodes = disabled_nodes

2023-07-12 上传
LOG.info("Audits have all been updated, disabled nodes by watcher: %s", self.running_state.watcher_disabled_nodes) ``` 通过这些优化，可以使代码更简洁、可读性更高，并且提高执行效率。

attrs = running_state_ctrl.RunningState._wsme_attributes running_state = db_utils.get_test_running_state(**kw) fields = [field.key for field in attrs] post_data = {k: v for k, v in running_state.items() if k in fields} post_data.update({k: v for k, v in kw.items() if k in fields}) return post_data

2023-07-12 上传
首先，`attrs`是一个包含`running_state_ctrl.RunningState`对象的属性列表。`running_state_ctrl.RunningState`是一个类，`_wsme_attributes`属性包含了该类的所有属性。 接下来，通过调用`db_utils.get_test_...

解释这段代码 def update_activation(i, pruned_model, activation, CBL_idx): next_idx = i + 1 if pruned_model.module_defs[next_idx]['type'] == 'convolutional': next_conv = pruned_model.module_list[next_idx][0] conv_sum = next_conv.weight.data.sum(dim=(2, 3)) offset = conv_sum.matmul(activation.reshape(-1, 1)).reshape(-1) if next_idx in CBL_idx: next_bn = pruned_model.module_list[next_idx][1] next_bn.running_mean.data.sub_(offset) else: next_conv.bias.data.add_(offset)

2023-07-08 上传
通过 `next_bn = pruned_model.module_list[next_idx][1]` 获取下一层的 Batch Normalization 操作符，然后使用 `next_bn.running_mean.data.sub_(offset)` 减去偏移量来更新其 running_mean。 如果下一层不在需要...

To do multiple steps, we implement a function the_loop that iterates over a dataloader. It should do a training step per batch for epochs. After one epoch, the loss on the validation set should be calculated.帮我根据这个要求补充完整下面代码中。。。的部分。 def the_loop(net, optimizer, train_loader, val_loader=None, epochs=None, swa_model=None, swa_start=5): if epochs is None: raise Exception("a training duration must be given: set epochs") log_iterval = 1 running_mean = 0. loss = Tensor([0.]).cuda() losses = [] val_losses = [] states = [] i, j = 0, 0 pbar = tqdm(train_loader, desc=f"epoch {i}", postfix={"loss": loss.item(), "step": j}) for i in range(epochs): running_mean = 0. j = 0 pbar.set_description(f"epoch {i}") pbar.refresh() pbar.reset() for j, batch in enumerate(train_loader): # implement training step by # - appending the current states to `states` # - doing a training_step # - appending the current loss to the `losses` list # - update the running_mean for logging ... if j % log_iterval == 0 and j != 0: pbar.set_postfix({"loss": running_mean.item(), "step": j}) running_mean = 0. pbar.update() if i > swa_start and swa_model is not None: swa_model.update_parameters(net) if val_loader is not None: # evaluate the current net on the validation data loader and # collect all losses in the ´val_loss´ list ... pbar.refresh() if val_loader is not None: return losses, states, val_losses return losses, states

2023-06-12 上传
running_mean = (running_mean * j + batch_loss.item()) / (j + 1) if j % log_iterval == 0 and j != 0: pbar.set_postfix({"loss": running_mean, "step": j}) running_mean = 0. pbar.update() if i...

_AppMixIn.app_list_running() missing 1 required positional argument: 'self' File "D:\work\open.py", line 4, in <module> connected_devices = u2.Device.app_list_running() # 获取已连接的设备列表 TypeError: _AppMixIn.app_list_running() missing 1 required positional argument: 'self'

2023-05-10 上传
这个错误提示表明你的代码中调用了 _AppMixIn.app_list_running() 方法，但是缺少了一个必需的参数 self。 通常情况下，self 参数是指对象本身，也就是类的实例。因此，你需要确认你的代码中是否创建了类的实例，并...

优化程序，将这段程序放到子线程里，避免拥堵。def rotate(self): count = 0 self._sem.release() while self._running: v = self._dgl.qpin(ROT) if len(v) > 0: count = count + sum(v) if count > self._inspect_step: self._sem.release() count = count % self._inspect_step

2023-05-26 上传
self._running = True self._count = 0 def run(self): self._sem.release() while self._running: v = self._dgl.qpin(ROT) if len(v) > 0: with threading.Lock(): self._count += sum(v) if self._...

def time_slice_scheduling(processes, time_slice): #时间片轮法 current_time = 0 ready_queue = processes.copy() while any(p.state != State.TERMINATED for p in ready_queue): processes_in_queue = len(ready_queue) current_process = ready_queue.pop(0) if current_process.state == State.TERMINATED: continue if current_process.state == State.NEW: current_process.state = State.RUNNING current_process.runtime = 0 current_process.start_time = current_time current_process.run(time_slice) current_time += time_slice if current_process.state == State.TERMINATED: current_process.end_time = current_time if current_process.state == State.RUNNING and current_process.rest_of_time > 0: ready_queue.append(current_process) if len(ready_queue) == processes_in_queue and current_process.rest_of_time > 0: ready_queue.append(current_process) return [p.end_time - p.start_time for p in processes],[(p.end_time - p.start_time) / p.cpu_time for p in processes],time_slice

2023-06-03 上传
5. `current_process = ready_queue.pop(0)` 用于从队列中取出下一个进程，需要确认代码中是否正确地维护了队列的顺序。 6. `current_process.run(time_slice)` 用于模拟进程运行，需要确认 `run()` 方法的实现是否...
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