输入以下命令来安装datasets库。注意,虽然datasets库本身不在huggingface频道,但通常与transformers库一起使用,且transformers库在huggingface频道中可用。然而,为了直接安装datasets,你应该使用conda-forge频道: bash conda install -c conda-forge datasets 验证安装是否成功: 打开Python解释器(可以通过命令行输入python或python...
conda-forge/rasterio | 1.0a2 | conda | linux-64, win-32, win-64, osx-64 : Rasterio reads and writes geospatial raster datasets dharhas/rasterio | 0.23.0 | conda | win-64 : Rasterio reads and writes geospatial raster datasets. erdc/rasterio | 0.23.0 | conda | win-64 : Rasterio re...
# 导入必要库 import numpy as np from sklearn import datasets from sklearn.model_selection import train_test_split from sklearn.ensemble import RandomForestClassifier from sklearn.metrics import accuracy_score # 加载鸢尾花数据集 iris = datasets.load_iris() X = iris.data y = iris.target # 划...
安装完成后,使用Apple官方文档提供的脚本(代码如下)进行测试,如果看到程序运行中数据集能正常加载、模型编译无错误、训练过程中能看到进度条和指标正常更新,则表明tensorflow2已经成功安装。 importtensorflowastf# 加载 CIFAR-100 数据集cifar = tf.keras.datasets.cifar100 (x_train, y_train), (x_test, y_test)...
from sklearn import datasets from sklearn.model_selection import train_test_split data = datasets.load_iris() x = data.data, y = data.target x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.2, random_state=42) 以上就是使用conda安装sklearn的步骤。希望对你有...
安装datasets pip install datasets 安装accelerate库 pip install"accelerate>=0.26.0" 安装tensorboard库 pip install tensorboard 安装sentencepiece,用于调用llama.cpp做输出gguf模型格式使用 pip install sentencepiece 下载需要训练微调的基座模型,由于本地是消费显卡RTX3080TI,所以就选择一个最小的qwen0.5b模型吧。
from torchvision.datasets import ImageFolder from torch.utils.data import DataLoader # ROOT_TRAIN = 'D:/pycharm/AlexNet/data/train' ROOT_TEST = 'dataset' # 将图像的像素值归一化到[-1,1]之间 normalize = transforms.Normalize([0.5, 0.5, 0.5], [0.5, 0.5, 0.5]) ...
The modin toolkit is geared more toward data analytics. Modin is significantly faster and compatible replacement for pandas. The toolkit also features tuned scikit-learn and XGBoost. XGBoost is a high performancegradient boostinglibrary that can handle huge datasets for regression and classifi...
tensorflow-datasets 2.0.0 pypi_0 pypi tensorflow-estimator 2.1.0 pypi_0 pypi tensorflow-metadata 0.21.1 pypi_0 pypi termcolor 1.1.0 pypi_0 pypi terminado 0.8.2 py37_0 testpath 0.4.2 py37_0 theano 1.0.4 py37_0 tk 8.6.8 hfa6e2cd_0 ...
datasets.MNIST("/home/david", train=False, transform=transforms.Compose( [transforms.ToTensor(), transforms.Normalize((0.1307,), (0.3081,))] ), ), batch_size=128, shuffle=True, **kwargs ) model = Net().to(device) optimizer = optim.Adadelta(model.parameters(), lr=1.0) ...