解释下列代码：class MyDataset(Dataset): def __init__(self, path,window_width): self.width = window_width

class MyDataset(Dataset): def init(self, num_samples): super(MyDataset, self).init() self.num_samples = num_samples # 在 init 中定义数据增强方法，此处为调整图像大小 self.transform = Compose([Resize(size=32)])

这段代码定义了一个自定义数据集MyDataset，继承自paddle.io.Dataset。num_samples表示数据集中的样本数量。在__init__方法中，通过调用父类的__init__方法初始化实例，并将num_samples赋值给实例变量...

from torch.utils.data import Dataset from torch.utils.data.dataset import T_co import os import cv2 as cv def read_label(path): file = open(path, "r", encoding='utf-8') label = file.readline() file.close() return label class MyDataset(Dataset): def _init__(self, train_path): self.train_path = train_path self.image_path = os.path.join(train_path, 'image') self.label_path = os.path.join(train_path, "label") self.image_path_list = os.listdir(self.image_path) def getitem(self, index) -> T_co: image_name = self.image_path_list[index] image_path = os.path.join(self.image_path, image_name) img = cv.imread(image_path) label_name = 'txt'.join(image_name.rsplit(image_name.split('. ')[-1], 1)) label_path = os.path.join(self.label_path, label_name) label = read_label(label_path) return img,label def len(self): return Len(self.image_path_list) my_dataset = MyDataset("dataset/train") data_index = 100 img, label = my_dataset[data_index] cv.imshow(label+ str(data_index)+str(Len(my_dataset)) + ')', img) cv.waitKey(0)详解这段代码

这段代码定义了一个自定义的 PyTorch 数据集类 MyDataset，用于加载训练数据集中的图像和标签。其中： - _init__ 方法初始化了数据集的路径和图像文件名列表； - __getitem__ 方法按照给定的索引 index，读取...

class myDataset(Dataset): def init(self, csv_file, txt_file, root_dir, other_file): self.csv_data = pd.read_csv(csv_file) with open(txt_file, 'r') as f: data_list = f.readlines() self.txt_data = data_list self.root_dir = root_dir def ien(self): return len(self.csv_data) def getitem(self, idx): data = (self.csv_data[idx], self.txt_data[idx]) return data

class myDataset(Dataset): def __init__(self, csv_file, txt_file, root_dir, other_file): self.csv_data = pd.read_csv(csv_file) with open(txt_file, 'r') as f: data_list = f.readlines() self.txt_...

def init(self, enc_inputs, dec_inputs, dec_outputs): super(MyDataSet, self).init() self.enc_inputs = enc_inputs self.dec_inputs = dec_inputs self.dec_outputs = dec_outputs def len(self): return self.enc_inputs.shape[0] def getitem(self, idx): return self.enc_inputs[idx], self.dec_inputs[idx], self.dec_outputs[idx]

&\text{class MyDataSet(Dataset):} \\ &\quad \text{def \_\_init\_\_(self, enc\_inputs, dec\_inputs, dec\_outputs):} \\ &\quad \quad \text{super().\_\_init\_\_()} \\ &\quad \quad \text{self.enc\_inputs ...

class MYLSTM: def init(self, seed): self.seed=seed self.valid_ratio=valid_ratio self.mydataset=MyDataset(Dataset)

class MyDataset(Dataset): def __init__(self, data, labels): self.data = data self.labels = labels def __len__(self): return len(self.data) def __getitem__(self, idx): return self.data[idx],...

class myDataset(Dataset): def _init_(self, csv_file, txt_file, root_dir, other_file): self.csv_data = pd.read_csv(csv_file) with open(txt_file, 'r') as f: data_list = f.readlines() self.txt_data = data_list self.root_dir = root_dir def _ien_(self): return len(self.csv_data) def _getitem_(self, idx): data = (self.csv_data[idx], self.txt_data[idx]) return data

class myDataset(Dataset): def __init__(self, csv_file, txt_file, root_dir, other_file): self.csv_data = pd.read_csv(csv_file) with open(txt_file, 'r') as f: data_list = f.readlines() self.txt_...

y = data['血糖']data = data.drop(columns=['血糖'])# 归一化mean = train.mean(axis=0)std = train.std(axis=0)train = (train - mean) / stdtest = (test - mean) / std# 执行PCA降维pca = PCA(n_components=10)train = pca.fit_transform(train)test = pca.transform(test)input_dim = 10 # 降维后的输入特征维度time_steps = 60output_steps = 1 # 可以预测一步，或预测多步#target_index = data.columns.tolist().index('血糖') # 待预测变量是第几个特征class MyDataset(Dataset): def init(self, data, label, time_steps, output_steps, target_index): # 新增label参数 self.time_steps = time_steps self.output_steps = output_steps self.target_index = target_index self.data = self.transform(data.astype(np.float32)) self.label = label # 定义self.label def transform(self, data): output = [] for i in range(data.shape[0] - self.time_steps - self.output_steps): output.append(data[i: (i + self.time_steps + self.output_steps), :]) return np.stack(output) def getitem(self, index): data = self.data[index, 0:self.time_steps, :] label = self.label[index + self.time_steps] # 注意，这里改为预测一步之后的标签的运算过程

这段代码定义了一个自定义数据集 MyDataset，用于将数据转换成模型输入所需的格式。它接收包括所有特征在内的数据和标签，以及时间步数 time_steps 和输出步数 output_steps，用于将数据转换成模型需要的形式。在 __...

import torch import torch.nn as nn import torch.optim as optim from torch.utils.data import DataLoader, Dataset class ConvNet(nn.Module): def init(self): super(ConvNet, self).init() self.conv1 = nn.Conv2d(in_channels=1, out_channels=32, kernel_size=3, stride=1, padding=1) self.relu = nn.ReLU() self.pool = nn.MaxPool2d(kernel_size=2, stride=2) self.fc1 = nn.Linear(32 * 14 * 14, 128) self.fc2 = nn.Linear(128, 10) def forward(self, x): x = self.conv1(x) x = self.relu(x) x = self.pool(x) x = x.view(-1, 32 * 14 * 14) x = self.fc1(x) x = self.relu(x) x = self.fc2(x) return x class MyDataset(Dataset): def init(self, data, target): self.data = data self.target = target def getitem(self, index): x = self.data[index] y = self.target[index] return x, y def len(self): return len(self.data) # 定义一些超参数 batch_size = 32 learning_rate = 0.001 epochs = 10 # 加载数据集 train_data = torch.randn(1000, 1, 28, 28) print(train_data) train_target = torch.randint(0, 10, (1000,)) print(train_target) train_dataset = MyDataset(train_data, train_target) train_loader = DataLoader(train_dataset, batch_size=batch_size, shuffle=True) # 构建模型 model = ConvNet() # 定义损失函数和优化器 criterion = nn.CrossEntropyLoss() optimizer = optim.Adam(model.parameters(), lr=learning_rate) # 训练模型 for epoch in range(epochs): for batch_idx, (data, target) in enumerate(train_loader): optimizer.zero_grad() output = model(data) loss = criterion(output, target) loss.backward() optimizer.step() if batch_idx % 10 == 0: print('Train Epoch: {} [{}/{} ({:.0f}%)]\tLoss: {:.6f}'.format( epoch, batch_idx * len(data), len(train_loader.dataset), 100. * batch_idx / len(train_loader), loss.item())) # 保存模型 # torch.save(model.state_dict(), 'convnet.pth')

同时，代码还定义了一个MyDataset类来加载数据集，以及一个DataLoader类来管理数据的批量处理。训练过程中使用了交叉熵损失函数和Adam优化器，将模型在数据集上训练了10个epochs。最后，代码还注释了一行保存模型的...

class MyDataSet(Dataset): """自定义数据集""" def init(self, images_path: list, images_class: list, transform=None): self.images_path = images_path self.images_class = images_class self.transform = transform def len(self): return len(self.images_path) def getitem(self, item): img = Image.open(self.images_path[item]) # RGB为彩色图片，L为灰度图片 if img.mode != 'RGB': raise ValueError("image: {} isn't RGB mode.".format(self.images_path[item])) label = self.images_class[item] if self.transform is not None: img = self.transform(img) print(label) return img, label @staticmethod def collate_fn(batch): # 官方实现的default_collate可以参考 # https://github.com/pytorch/pytorch/blob/67b7e751e6b5931a9f45274653f4f653a4e6cdf6/torch/utils/data/_utils/collate.py images, labels = tuple(zip(*batch)) images = torch.stack(images, dim=0) labels = torch.as_tensor(np.array(labels[:, 0])) return images, labels

在 __init__ 方法中，该类接收两个列表 images_path 和 images_class，分别代表图片的路径和对应的类别。还可以传入一个可选的 transform 参数，用于对图片进行预处理。在 __getitem__ 方法中，该类会根据...

def load_data(file_name): df = pd.read_csv('data/new_data/' + file_name, encoding='gbk') columns = df.columns df.fillna(df.mean(), inplace=True) return df class MyDataset(Dataset): def init(self, data): self.data = data def getitem(self, item): return self.data[item] def len(self): return len(self.data) def nn_seq_us(B): print('data processing...') dataset = load_data() # split train = dataset[:int(len(dataset) * 0.6)] val = dataset[int(len(dataset) * 0.6):int(len(dataset) * 0.8)] test = dataset[int(len(dataset) * 0.8):len(dataset)] m, n = np.max(train[train.columns[1]]), np.min(train[train.columns[1]]) def process(data, batch_size): load = data[data.columns[1]] load = load.tolist() data = data.values.tolist() load = (load - n) / (m - n) seq = [] for i in range(len(data) - 24): train_seq = [] train_label = [] for j in range(i, i + 24): x = [load[j]] train_seq.append(x) # for c in range(2, 8): # train_seq.append(data[i + 24][c]) train_label.append(load[i + 24]) train_seq = torch.FloatTensor(train_seq) train_label = torch.FloatTensor(train_label).view(-1) seq.append((train_seq, train_label)) # print(seq[-1]) seq = MyDataset(seq) seq = DataLoader(dataset=seq, batch_size=batch_size, shuffle=False, num_workers=0, drop_last=True) return seq Dtr = process(train, B) Val = process(val, B) Dte = process(test, B) return Dtr, Val, Dte, m, n分别解释一下每行代码都是什么意思

这段代码是一个数据处理的程序，主要包括以下几个部分： 1. load_data(file_name) 函数用于读取指定文件名的 CSV 文件，并返回一个 Pandas DataFrame 对象。其中，文件路径是 "data/new_data/" 加上给定的文件名...

import pandas as pd import torch import numpy as np from gcnlstm import GCNLSTM import time import torch.serialization from torch.utils.data import DataLoader from torch.utils.data import Dataset import os seed_values = 2020 np.random.seed(seed_values) os.environ['PYTHONHASHSEED'] = str(seed_values) torch.manual_seed(seed_values) torch.cuda.manual_seed(seed_values) torch.cuda.manual_seed(seed_values) torch.backends.cuda.deterministic = True def map_minmax(data): """ 归一化 :param data: :return:normalized data_max data_min """ data_max = np.max(data, axis=0) data_min = np.min(data, axis=0) base = data_max - data_min normalized = (data - data_min) / base return normalized, data_max, data_min def re_normalize(normalized_data, data_max, data_min): base = data_max - data_min original_data = np.zeros_like(normalized_data) for i in range(np.size(normalized_data, axis=1)): original_data[:, i] = normalized_data[:, i] * base.iloc[i] + data_min.iloc[i] return original_data def add_window_horizon(data, window=3, horizon=1, single=False): """ :param data: shape [B, ...] :param window: :param horizon: :return: X is [B, W, ...], Y is [B, H, ...] """ length = len(data) end_index = length - horizon - window + 1 X = [] # windows Y = [] # horizon index = 0 if single: while index < end_index: X.append(data[index:index + window]) Y.append(data[index + window + horizon - 1:index + window + horizon]) index = index + 1 else: while index < end_index: X.append(data[index:index + window]) Y.append(data[index + window:index + window + horizon]) index = index + 1 X = np.array(X) Y = np.array(Y) return X, Y class MyDataset(Dataset): def init(self, data_tensor, target_tensor): self.data = data_tensor self.target = target_tensor

def __init__(self, X, y): self.X = torch.tensor(X, dtype=torch.float32) self.y = torch.tensor(y, dtype=torch.float32) def __getitem__(self, idx): return self.X[idx], self.y[idx] def __len__...

import torch from torchvision import transforms from torch.utils.data import Dataset, DataLoader from PIL import Image import torch.nn. functional as F def Myloader (path) : return Image.open(path).convert('RGB ') #得到一个包含所有图片文件名与标签的列表 def init_process (path, lens) : data = [] name = find_label(path) for i in range(lens[0],lens[1]): data.append([path % i, (name,)]) return data class MyDataset (Dataset) : def _init_(self, data, transform, loder) : self. data = data self. transform = transform self. loader = loder def _getitem_(self, item) : img, label = self. data [item] img = self. loader (img) img = self. transform (img) return img, label def _len_(self) : return len (self.data) def find_label (str) : first, last = 0, 0 for i in range (len(str)-1,-1,-1): if str[i] == '%' and str[ i - 1] == '.': last = i - 1 if(str[i] == 'c' or str[i] == 'd')and str[i - 1] == '/': first=i break name = str[first:last] if name == 'dog' : return 1 else: return 0 def load_data () : transform = transforms. Compose ([ transforms .RandomHorizontalFlip (p=0.3) , transforms. RandomVerticalFlip (p=0.3) , transforms. Resize ( (256, 256)), transforms. ToTensor () , transforms. Normalize (mean= (0.5, 0.5, 0.5), std= (0.5,0.5,0.5))]) path1 = 'G:/学校文件/大三下学期/cat and dog/train/&d.jpg' data1 = init_process (path1, [0, 500]) path2 = 'G:/学校文件/大三下学期/cat and dog/train/?d.jpg' data2 = init_process (path2, [0, 500]) path3 = 'G:/学校文件/大三下学期/cat and dog/test1/8d.jpg' data3 = init_process (path3, [1000, 1200]) path4 = 'G:/学校文件/大三下学期/cat and dog/test1/&d. jpg' data4 = init_process (path4, [1000, 1200]) # 1300个训练 train_data = data1 + data2 + data3[0: 150] + data[0: 150] train = MyDataset(train_

class MyDataset(Dataset): def _init_(self, data, transform, loder): # 错误 def _getitem_(self, item): # 错误 def _len_(self): # 错误 **修正**： PyTorch的Dataset子类需使用双下划线方法名： ...

优化代码（尤其是# 数据集读取#）import torch.utils.data import numpy as np import os, random, glob from torchvision import transforms from PIL import Image import matplotlib.pyplot as plt # 数据集读取 class DogCatDataSet(torch.utils.data.Dataset): def init(self, img_dir, transform=None): self.transform = transform dog_dir = os.path.join(img_dir, "dog") cat_dir = os.path.join(img_dir, "cat") imgsLib = [] imgsLib.extend(glob.glob(os.path.join(dog_dir, ".jpg"))) imgsLib.extend(glob.glob(os.path.join(cat_dir, ".jpg"))) random.shuffle(imgsLib) # 打乱数据集 self.imgsLib = imgsLib # 作为迭代器必须要有的 def getitem(self, index): img_path = self.imgsLib[index] label = 1 if 'dog' in img_path.split('/')[-1] else 0 # 狗的label设为1，猫的设为0 img = Image.open(img_path).convert("RGB") img = self.transform(img) return img, label def len(self): return len(self.imgsLib) # 读取数据 if name == "main": CLASSES = {0: "cat", 1: "dog"} img_dir = "D:\\深度学习1\\test" data_transform = transforms.Compose([ transforms.Resize(256), # resize到256 transforms.CenterCrop(224), # crop到224 transforms.ToTensor(), # 把一个取值范围是[0,255]的PIL.Image或者shape为(H,W,C)的numpy.ndarray，转换成形状为[C,H,W]，取值范围是[0,1.0]的torch.FloadTensor /255.操作 ]) dataSet = DogCatDataSet(img_dir=img_dir, transform=data_transform) dataLoader = torch.utils.data.DataLoader(dataSet, batch_size=8, shuffle=True, num_workers=4) image_batch, label_batch = next(iter(dataLoader)) for i in range(image_batch.data.shape[0]): label = np.array(label_batch.data[i]) ## tensor ==> numpy # print(label) img = np.array(image_batch.data[i] * 255, np.int32) print(CLASSES[int(label)]) plt.imshow(np.transpose(img, [1, 2, 0])) plt.show()

class MyDataset(Dataset): def __init__(self, paths, labels): self.images = [Image.open(path).convert('RGB') for path in paths] self.labels = labels 引用[1]提到初始化时传入路径和预处理方法可...

def load_data(file_name): df = pd.read_csv('data/new_data/' + file_name, encoding='gbk') columns = df.columns df.fillna(df.mean(), inplace=True) return df class MyDataset(Dataset): def init(self, data): self.data = data def getitem(self, item): return self.data[item] def len(self): return len(self.data) def nn_seq_us(B): print('data processing...') dataset = load_data() # split train = dataset[:int(len(dataset) * 0.6)] val = dataset[int(len(dataset) * 0.6):int(len(dataset) * 0.8)] test = dataset[int(len(dataset) * 0.8):len(dataset)] m, n = np.max(train[train.columns[1]]), np.min(train[train.columns[1]]) def process(data, batch_size): load = data[data.columns[1]] load = load.tolist() data = data.values.tolist() load = (load - n) / (m - n) seq = [] for i in range(len(data) - 24): train_seq = [] train_label = [] for j in range(i, i + 24): x = [load[j]] train_seq.append(x) # for c in range(2, 8): # train_seq.append(data[i + 24][c]) train_label.append(load[i + 24]) train_seq = torch.FloatTensor(train_seq) train_label = torch.FloatTensor(train_label).view(-1) seq.append((train_seq, train_label)) # print(seq[-1]) seq = MyDataset(seq) seq = DataLoader(dataset=seq, batch_size=batch_size, shuffle=False, num_workers=0, drop_last=True) return seq Dtr = process(train, B) Val = process(val, B) Dte = process(test, B) return Dtr, Val, Dte, m, n这写代码分别是什么意思

- MyDataset(Dataset)类：定义一个自定义数据集，包含__init__、__getitem__和__len__方法，用于返回数据集中的单个样本和样本数量。 - nn_seq_us(B)函数：主要用于数据处理，包括将数据集分成训练集、验证集和测试...

def load_data () : transform = transforms. Compose ([ transforms .RandomHorizontalFlip (p=0.3) , transforms. RandomVerticalFlip (p=0.3) , transforms. Resize ( (256, 256)), transforms. ToTensor () , transforms. Normalize (mean= (0.5, 0.5, 0.5), std= (0.5,0.5,0.5))]) path1 = '../data/training_data/cats/cat.&d.jpg' data1 = init_process (path1, [0, 500]) path2 = '../data/training_data/dogs/dog.?d.jpg' data2 = init_process (path2, [0, 500]) path3 = '../data/testing_data/cats/cat.8d.jpg' data3 = init_process (path3, [1000, 1200]) path4 = '/data/testing_data/dogs/dog.&d. jpg' data4 = init_process (path4, [1000, 1200]) # 1300个训练 train_data = data1 + data2 + data3[0: 150] + data4 [0: 150] train = MyDataset (train_data, transform=transform, loder=Myloader) #100个测试 test_data = data3 [150: 200] + data4 [150: 200] test = MyDataset (test_data, transform=transform, loder=Myloader) train_data = DataLoader(dataset=train, batch_size=10, shuffle=True,num_workers=0) test_data = DataLoader(dataset=test, batch_size=1, shuffle=True,num_workers=0) return train_data, test_data 如何调用

def __init__(self, data_path, transform=None): self.data = load_images(data_path) # 需自定义数据加载逻辑 self.transform = transform def __getitem__(self, index): image = self.data[index] if ...

解释下列代码：class MyDataset(Dataset): def init(self, path,window_width): self.width = window_width

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class MyDataset(Dataset): def __init__(self, num_samples): super(MyDataset, self).__init__() self.num_samples = num_samples # 在 __init__ 中定义数据增强方法，此处为调整图像大小 self.transform = Compose([Resize(size=32)])

class MYLSTM: def __init__(self, seed): self.seed=seed self.valid_ratio=valid_ratio self.mydataset=MyDataset(Dataset)

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解释下列代码：class MyDataset(Dataset): def init(self, path,window_width): self.width = window_width

class MyDataset(Dataset): def init(self, num_samples): super(MyDataset, self).init() self.num_samples = num_samples # 在 init 中定义数据增强方法，此处为调整图像大小 self.transform = Compose([Resize(size=32)])

class MYLSTM: def init(self, seed): self.seed=seed self.valid_ratio=valid_ratio self.mydataset=MyDataset(Dataset)