importtorchfromtorchvisionimportdatasets# 设置数据集路径data_path='./data/'# 定义数据集train_dataset=datasets.MNIST(data_path,train=True,download=True)test_dataset=datasets.MNIST(data_path,train=False,download=True)# 加载数据集train_loader=torch.utils.data.DataLoader(train_dataset,batch_size=64,shuff...
data = self.series_to_supervised(self.std_data, n_in, n_out) re_data = self.series_to_supervised(df['Temperature'].values.reshape(-1, 1), n_in, n_out) self.x = torch.from_numpy(data.iloc[:, :n_in].values).view(-1, n_in, 1) self.y = torch.from_numpy(re_data.iloc[:...
PyTorch map pytorch mapping op finds wide application in various domains, including but not limited to the following: Data preprocessing: Mapping ops can be used for transforming raw data into a format suitable for machine learning algorithms. For instance, applying standardization or normalization tech...
fromtorchtext.dataimportTabularDatasettv_datafields=[("id",None),# we won't be needing the id, so we pass in None as the field("comment_text",TEXT),("toxic",LABEL),("severe_toxic",LABEL),("threat",LABEL),("obscene",LABEL),("insult",LABEL),("identity_hate",LABEL)]trn,vld=Tabular...
utils.data import Dataset, DataLoader from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score, classification_report, confusion_matrix from sklearn.preprocessing import label_binarize from sklearn.metrics import roc_curve, auc 设置随机种子 设置随机种子总是需要的,它可以让...
scaler=sklearn.preprocessing.StandardScaler()x_train=scaler.fit_transform(x_train)x_test=scaler.fit_transform(x_test) 现在,在使用Logistic 模型之前,还有最后一个关键的数据处理步骤。在Pytorch 需要使用张量。因此,我们使用“torch.from_numpy()”方法将所有四个数据转换为张量。
在torchvision.transforms模块中提供了一般的图像数据变换操作类,可以用于实现数据预处理(data preprocessing)和数据增广(data argumentation)。这里列举一些常用的变换操作。 一般图像是一个具有 形状的张量。其中c(channel),h(height),w(width)。当然也可以使用batch的Tensor图像。形状为 ...
importtorchimporttorch.nnasnnimporttorch.optimasoptimimportnumpyasnpimportpandasaspdimportmatplotlib.pyplotaspltfromsklearn.model_selectionimporttrain_test_split, KFoldfromsklearn.metricsimportmean_squared_error, r2_scorefromsklearn.preprocessingimpor...
数据加载和预处理(Data Loading and Preprocessing)PyTorch提供了多种数据加载和预处理方法,例如使用torchvision.datasets加载图像数据集,使用torchvision.transforms定义数据预处理操作等。在使用数据时,需要将数据划分为训练集和测试集,并使用.train()和.test()方法分别加载训练集和测试集。例如,trainloader = torch.utils...
from torch.utils.data import DataLoader, Dataset from sklearn.model_selection import train_test_split from sklearn.feature_extraction.text import CountVectorizer from sklearn.preprocessing import LabelEncoder import pandas as pd 五、 创建数据集加载器 ...