for spot in spot_list: name = spot.find("dt", class_="").find("a", class_="").text.strip() if spot.find("b", class_="hot_score_number"): hot_score = spot.find("b", class_="hot_score_number").text.strip() else: hot_score = 0

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PWM（Pulse Width Modulation）是一种常见的数字信号控制技术，广泛应用于各种电子设备，包括电机控制、电源管理、LED亮度调节以及本例中的点焊机。在这个“PWM.rar”压缩包中，包含的是一个用于控制点焊机的测试...

class ParkingSpot: def init(self, spot_id, status='Available'): self.spot_id = spot_id self.status = status def str(self): return f'Spot ID: {self.spot_id} Status: {self.status}' class ParkingLot: def init(self, num_spots): self.spots = [ParkingSpot(i) for i in range(1, num_spots+1)] def park_vehicle(self, spot_id): spot = self.spots[spot_id-1] if spot.status == 'Available': spot.status = 'Occupied' print(f'Spot {spot_id} is now occupied.') else: print(f'Spot {spot_id} is already occupied.') def release_spot(self, spot_id): spot = self.spots[spot_id-1] if spot.status == 'Occupied': spot.status = 'Available' print(f'Spot {spot_id} is now available.') else: print(f'Spot {spot_id} is already available.') def display_spots(self): for spot in self.spots: print(spot)具体解释一下这些代码运行作用

release_spot方法用于将车离开指定的停车位，其工作流程是检查指定停车位的状态是否为占用，如果是则将其状态设置为可用，并打印相关消息；否则打印停车位已经可用的相关消息。display_spots方法用于展示停车场所有...

对以下代码画类图# Create your models here. class OrderStatus(models.TextChoices): NOT_PAID = ('r', "ready to pay") PAID = ('p', "paid") class Order(models.Model): name = models.CharField(max_length=50) status = models.CharField(max_length=30, choices=OrderStatus.choices) purchase_time = models.DateField(auto_now_add=True) user = models.ForeignKey(User, related_name="orders", on_delete=models.CASCADE) def caculate_total_price(self): total_price = self.tickets.aggregate(total=Sum('scenic_spot__ticket_price')).get('total') return total_price class Ticket(models.Model): name = models.CharField(max_length=200) order = models.ForeignKey(Order, related_name="tickets", on_delete=models.CASCADE) scenic_spot = models.ForeignKey(ScenicSpot, related_name="tickets", on_delete=models.CASCADE)

weight_reset_spot = model_spot.get_weights()

这段代码的作用是获取模型 model_spot 的所有权重，并将其存储在变量 weight_reset_spot 中。通常情况下，我们可以使用 get_weights() 方法获取模型的所有权重，然后使用 set_weights() 方法来设置新的权重...

def train_test(X, y, X1, y1, X2, y2, dataset_name, emotion_class, groupsLabel, groupsLabel1, spot_multiple, final_subjects, final_emotions, final_samples, final_dataset_spotting, k, k_p, expression_type, epochs_spot=10, epochs_recog=100, spot_lr=0.0005, recog_lr=0.0005, batch_size=32, ratio=5, p=0.55, spot_attempt=1, recog_attempt=1, train=False): start = time.time() loso = LeaveOneGroupOut() subject_count = 0 total_gt_spot = 0 metric_final = MeanAveragePrecision2d(num_classes=1) adam_spot = keras.optimizers.Adam(lr=spot_lr) adam_recog = keras.optimizers.Adam(lr=recog_lr) model_spot = MEAN_Spot(adam_spot) weight_reset_spot = model_spot.get_weights() #Initial weights

这段代码是一个用 Keras 训练模型的函数。其中，它的参数包括输入数据 X 和标签 y，测试数据 X1 和标签 y1...最后，函数返回了模型的训练时间、总共正确识别的样本数以及 MEAN_Spot 模型的初始权重 weight_reset_spot。

import jittor as jt import jrender as jr jt.flags.use_cuda = 1 # 开启GPU加速 import os import tqdm import numpy as np import imageio import argparse # 获取当前文件所在目录路径和数据目录路径 current_dir = os.path.dirname(os.path.realpath(file)) data_dir = os.path.join(current_dir, 'data') def main(): # 创建命令行参数解析器 parser = argparse.ArgumentParser() parser.add_argument('-i', '--filename-input', type=str, default=os.path.join(data_dir, 'obj/spot/spot_triangulated.obj')) parser.add_argument('-o', '--output-dir', type=str, default=os.path.join(data_dir, 'results/output_render')) args = parser.parse_args() # other settings camera_distance = 2.732 elevation = 30 azimuth = 0 # load from Wavefront .obj file mesh = jr.Mesh.from_obj(args.filename_input, load_texture=True, texture_res=5, texture_type='surface', dr_type='softras') # create renderer with SoftRas renderer = jr.Renderer(dr_type='softras') os.makedirs(args.output_dir, exist_ok=True) # draw object from different view loop = tqdm.tqdm(list(range(0, 360, 4))) writer = imageio.get_writer(os.path.join(args.output_dir, 'rotation.gif'), mode='I') imgs = [] from PIL import Image for num, azimuth in enumerate(loop): # rest mesh to initial state mesh.reset_() loop.set_description('Drawing rotation') renderer.transform.set_eyes_from_angles(camera_distance, elevation, azimuth) rgb = renderer.render_mesh(mesh, mode='rgb') image = rgb.numpy()[0].transpose((1, 2, 0)) writer.append_data((255image).astype(np.uint8)) writer.close() # draw object from different sigma and gamma loop = tqdm.tqdm(list(np.arange(-4, -2, 0.2))) renderer.transform.set_eyes_from_angles(camera_distance, elevation, 45) writer = imageio.get_writer(os.path.join(args.output_dir, 'bluring.gif'), mode='I') for num, gamma_pow in enumerate(loop): # rest mesh to initial state mesh.reset_() renderer.set_gamma(10gamma_pow) renderer.set_sigma(10(gamma_pow - 1)) loop.set_description('Drawing blurring') images = renderer.render_mesh(mesh, mode='rgb') image = images.numpy()[0].transpose((1, 2, 0)) # [image_size, image_size, RGB] writer.append_data((255image).astype(np.uint8)) writer.close() # save to textured obj mesh.reset_() mesh.save_obj(os.path.join(args.output_dir, 'saved_spot.obj')) if name == 'main': main()在每行代码后添加注释

# 引入所需的库 import jittor as jt import jrender as jr jt.flags.use_cuda = 1 # 开启GPU... mesh.save_obj(os.path.join(args.output_dir, 'saved_spot.obj')) # 保存模型 if __name__ == '__main__': main()

如何打印输出def MEAN_Recog_TL(model_spot, opt, emotion_class): for layer in model_spot.layers: layer.trainable = False # Until last convolutional later merged = model_spot.layers[-6].output merged_conv = layers.Conv2D(8, (5,5), padding='same', activation='relu', kernel_regularizer=l2(0.1))(merged) merged_pool = layers.MaxPooling2D(pool_size=(2, 2), padding='same', strides=(2,2))(merged_conv) flat = layers.Flatten()(merged_pool) flat_do = layers.Dropout(0.2)(flat) outputs = layers.Dense(emotion_class, activation = "softmax", name='recog')(flat_do) model = keras.models.Model(inputs = model_spot.input, outputs = outputs) model.compile( loss={'recog':'categorical_crossentropy'}, optimizer=opt, metrics={'recog':tf.keras.metrics.CategoricalAccuracy()} ) return model

model = MEAN_Recog_TL(model_spot, opt, emotion_class) print(model) 这将打印出 MEAN_Recog_TL() 函数的返回值 model。请注意，这将仅打印出该模型的结构和参数，并不会显示每个层的输出或其他详细信息...

def get_scenic_spots(): for i in range(1, 45): url = f"https://you.ctrip.com/sight/shandong100/s0-p{i}.html" r = requests.get(url, headers=headers) soup = BeautifulSoup(r.text, "html.parser") spot_list = soup.find_all("div", class_="list_mod2") # print(spot_list)

这段代码是用于获取山东省旅游景点的信息，使用了...spot_list是通过find_all方法查找网页中所有class属性为"list_mod2"的div标签。具体获取到的景点信息在代码中并未展示，可能需要在后续的代码中进行处理和展示。

def MEAN_Recog_TL(model_spot, opt, emotion_class): for layer in model_spot.layers: layer.trainable = False # Until last convolutional later merged = model_spot.layers[-6].output merged_conv = layers.Conv2D(8, (5,5), padding='same', activation='relu', kernel_regularizer=l2(0.1))(merged) merged_pool = layers.MaxPooling2D(pool_size=(2, 2), padding='same', strides=(2,2))(merged_conv) flat = layers.Flatten()(merged_pool) flat_do = layers.Dropout(0.2)(flat) outputs = layers.Dense(emotion_class, activation = "softmax", name='recog')(flat_do) model = keras.models.Model(inputs = model_spot.input, outputs = outputs) model.compile( loss={'recog':'categorical_crossentropy'}, optimizer=opt, metrics={'recog':tf.keras.metrics.CategoricalAccuracy()} ) return model

这段代码定义了一个基于迁移学习的情感分类模型，其中 model_spot 是一个预训练的模型，opt 是优化器，emotion_class 是情感分类的类别数。在这个模型中，首先将 model_spot 中的所有层都设置为不可训练，...

from requests_html import HTMLSession import os class Spider: def init(self): self.base_url = 'https://s3-ap-northeast-1.amazonaws.com/data.binance.vision/data/spot/daily/klines' self.pair = '1INCHBTC' self.interval = '1d' self.session = HTMLSession() def get_urls(self): urls = [] # 首页 response = self.session.get(f'{self.base_url}/{self.pair}/{self.interval}/') if response.status_code == 200: for link in response.html.links: if link.endswith('.zip'): urls.append(link) # 分页 while True: response = self.session.get(response.html.links[-1]) if response.status_code != 200: # 请求失败 break for link in response.html.links: if link.endswith('.zip'): urls.append(link) if 'CHECKSUM' in response.html.links[-1]: break return urls def download_files(self): urls = self.get_urls() if not urls: print('下载失败') return if not os.path.exists('download_files'): os.mkdir('download_files') for url in urls: file_name = url.split('/')[-1] file_path = f'/Users/huyang/Desktop/2023/Grach/{file_name}' if os.path.exists(file_path): # 文件已存在 print(f'{file_name} 已存在') continue response = self.session.get(url) if response.status_code != 200: # 请求失败 print(f'{file_name} 下载失败') continue with open(file_path, 'wb') as f: f.write(response.content) print(f'{file_name} 下载成功') def run(self): self.download_files()，将对应的下载数据的网站地址改为'http://www.greenfinance.org.cn/'

for url in urls: file_name = url.split('/')[-1] file_path = f'/Users/huyang/Desktop/2023/Grach/{file_name}' if os.path.exists(file_path): print(f'{file_name} 已存在') continue response = self....

def MEAN_Spot(opt): inputs1 = layers.Input(shape=(42, 42, 1)) inputs2 = layers.Input(shape=(42, 42, 1)) inputs3 = layers.Input(shape=(42, 42, 1)) inputs = layers.Concatenate()([inputs1, inputs2, inputs3]) conv1 = layers.Conv2D(3, (7,7), padding='same', activation='relu', kernel_regularizer=l2(0.001))(inputs) ba1 = BasicBlock(3, 16)(conv1) ba2 = BasicBlock(16, 32, stride=2)(ba1) att = BasicBlock1(32, 64, stride=2)(ba2) # interpretation 1 merged_conv = layers.Conv2D(8, (5,5), padding='same', activation='relu', kernel_regularizer=l2(0.1))(att) merged_pool = layers.MaxPooling2D(pool_size=(2, 2), padding='same', strides=(2,2))(merged_conv) flat = layers.Flatten()(merged_pool) flat_do = layers.Dropout(0.2)(flat) # outputs outputs = layers.Dense(1, activation='linear', name='spot')(flat_do) #Takes input u, v, os model = keras.models.Model(inputs=[inputs1, inputs2, inputs3], outputs=[outputs]) model.compile( loss={'spot':'mse'}, optimizer=opt, metrics={'spot':tf.keras.metrics.MeanAbsoluteError()}, ) return model 如何加入多头自注意力

outputs = layers.Dense(1, activation='linear', name='spot')(flat_do) #Takes input u, v, os model = keras.models.Model(inputs=[inputs1, inputs2, inputs3], outputs=[outputs]) model.compile( ...

def getitem(self, idx): i = np.random.randint(0, len(self.data) - (self.ctx_len + 1)) # cheat: pick a random spot in dataset chunk = self.data[i:i+self.ctx_len+1] dix = [self.stoi[s] for s in chunk] x = torch.tensor(dix[:-1], dtype=torch.long) y = torch.tensor(dix[1:], dtype=torch.long) return x, y

这段代码是Dataset类的__getitem__方法。该方法用于实现索引操作，通过索引获取数据集中的一个样本。首先，代码使用np.random.randint(0, len(self.data) - (self.ctx_len + 1))随机生成一个索引i，该索引...

解释这段代码import jittor as jt import jrender as jr jt.flags.use_cuda = 1 import os import tqdm import numpy as np import imageio import argparse current_dir = os.path.dirname(os.path.realpath(file)) data_dir = os.path.join(current_dir, 'data') def main(): parser = argparse.ArgumentParser() parser.add_argument('-i', '--filename-input', type=str, default=os.path.join(data_dir, 'obj/spot/spot_triangulated.obj')) parser.add_argument('-o', '--output-dir', type=str, default=os.path.join(data_dir, 'results/output_render')) args = parser.parse_args() # other settings camera_distance = 2.732 elevation = 30 azimuth = 0 # load from Wavefront .obj file mesh = jr.Mesh.from_obj(args.filename_input, load_texture=True, texture_res=5, texture_type='surface', dr_type='softras') # create renderer with SoftRas renderer = jr.Renderer(dr_type='softras') os.makedirs(args.output_dir, exist_ok=True) # draw object from different view loop = tqdm.tqdm(list(range(0, 360, 4))) writer = imageio.get_writer(os.path.join(args.output_dir, 'rotation.gif'), mode='I') imgs = [] from PIL import Image for num, azimuth in enumerate(loop): # rest mesh to initial state mesh.reset_() loop.set_description('Drawing rotation') renderer.transform.set_eyes_from_angles(camera_distance, elevation, azimuth) rgb = renderer.render_mesh(mesh, mode='rgb') image = rgb.numpy()[0].transpose((1, 2, 0)) writer.append_data((255image).astype(np.uint8)) writer.close() # draw object from different sigma and gamma loop = tqdm.tqdm(list(np.arange(-4, -2, 0.2))) renderer.transform.set_eyes_from_angles(camera_distance, elevation, 45) writer = imageio.get_writer(os.path.join(args.output_dir, 'bluring.gif'), mode='I') for num, gamma_pow in enumerate(loop): # rest mesh to initial state mesh.reset_() renderer.set_gamma(10gamma_pow) renderer.set_sigma(10(gamma_pow - 1)) loop.set_description('Drawing blurring') images = renderer.render_mesh(mesh, mode='rgb') image = images.numpy()[0].transpose((1, 2, 0)) # [image_size, image_size, RGB] writer.append_data((255image).astype(np.uint8)) writer.close() # save to textured obj mesh.reset_() mesh.save_obj(os.path.join(args.output_dir, 'saved_spot.obj')) if name == 'main': main()

这段代码是一个用 Jittor 和 Jrender 库实现的 3D 渲染程序。程序的主要功能是从不同的视角渲染给定的 3D 模型，并保存渲染结果。具体过程如下： 1. 导入必要的库，包括 Jittor、Jrender、os、tqdm、numpy、imageio...

from requests_html import HTMLSession import os class Spider: def init(self): self.base_url = 'https://s3-ap-northeast-1.amazonaws.com/data.binance.vision/data/spot/daily/klines' self.pair = '1INCHBTC' self.interval = '1d' self.session = HTMLSession() def get_urls(self): urls = [] # 首页 response = self.session.get(f'{self.base_url}/{self.pair}/{self.interval}/') if response.status_code == 200: for link in response.html.links: if link.endswith('.zip'): urls.append(link) # 分页 while True: response = self.session.get(response.html.links[-1]) if response.status_code != 200: # 请求失败 break for link in response.html.links: if link.endswith('.zip'): urls.append(link) if 'CHECKSUM' in response.html.links[-1]: break return urls def download_files(self): urls = self.get_urls() if not urls: print('下载失败') return if not os.path.exists('download_files'): os.mkdir('download_files') for url in urls: file_name = url.split('/')[-1] file_path = f'download_files/{file_name}' if os.path.exists(file_path): # 文件已存在 print(f'{file_name} 已存在') continue response = self.session.get(url) if response.status_code != 200: # 请求失败 print(f'{file_name} 下载失败') continue with open(file_path, 'wb') as f: f.write(response.content) print(f'{file_name} 下载成功') def run(self): self.download_files()

这是一个 Python 爬虫程序，用于从 Binance 网站下载 1INCHBTC 在 1 天内的 K 线数据。...方法 get_urls 用来获取数据文件的下载链接列表，方法 download_files 用来下载这些数据文件，如果文件已存在则不会重复下载。...

def MEAN_Spot(opt): # channel 1 inputs1 = layers.Input(shape=(42,42,1)) conv1 = layers.Conv2D(3, (5,5), padding='same', activation='relu', kernel_regularizer=l2(0.001))(inputs1) bn1 = layers.BatchNormalization()(conv1) pool1 = layers.MaxPooling2D(pool_size=(3, 3), padding='same', strides=(3,3))(bn1) do1 = layers.Dropout(0.3)(pool1) # channel 2 inputs2 = layers.Input(shape=(42,42,1)) conv2 = layers.Conv2D(3, (5,5), padding='same', activation='relu', kernel_regularizer=l2(0.001))(inputs2) bn2 = layers.BatchNormalization()(conv2) pool2 = layers.MaxPooling2D(pool_size=(3, 3), padding='same', strides=(3,3))(bn2) do2 = layers.Dropout(0.3)(pool2) # channel 3 inputs3 = layers.Input(shape=(42,42,1)) conv3 = layers.Conv2D(8, (5,5), padding='same', activation='relu', kernel_regularizer=l2(0.001))(inputs3) bn3 = layers.BatchNormalization()(conv3) pool3 = layers.MaxPooling2D(pool_size=(3, 3), padding='same', strides=(3,3))(bn3) do3 = layers.Dropout(0.3)(pool3) # merge 1 merged = layers.Concatenate()([do1, do2, do3]) # interpretation 1 merged_conv = layers.Conv2D(8, (5,5), padding='same', activation='relu', kernel_regularizer=l2(0.1))(merged) merged_pool = layers.MaxPooling2D(pool_size=(2, 2), padding='same', strides=(2,2))(merged_conv) flat = layers.Flatten()(merged_pool) flat_do = layers.Dropout(0.2)(flat) # outputs outputs = layers.Dense(1, activation='linear', name='spot')(flat_do) #Takes input u, v, os model = keras.models.Model(inputs=[inputs1, inputs2, inputs3], outputs=[outputs]) model.compile( loss={'spot':'mse'}, optimizer=opt, metrics={'spot':tf.keras.metrics.MeanAbsoluteError()}, ) return model 更改模型加入CBAM模块

outputs = layers.Dense(1, activation='linear', name='spot')(flat_do) #Takes input u, v, os model = keras.models.Model(inputs=[inputs1, inputs2, inputs3], outputs=[outputs]) model.compile( loss=...

for spot in spot_list: name = spot.find("dt", class_="").find("a", class_="").text.strip() if spot.find("b", class_="hot_score_number"): hot_score = spot.find("b", class_="hot_score_number").text.strip() else: hot_score = 0

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