X, y = mnist['data'],mnist['target'].astype(int)
时间: 2024-01-23 07:04:30 浏览: 18
As an AI language model, I am not able to access external resources or libraries. However, I can explain what this line of code would do if executed in Python.
This line of code would assign two variables, X and y, with the values of the 'data' and 'target' attributes of the MNIST dataset. MNIST is a commonly used dataset in machine learning for handwritten digit recognition. The 'data' attribute contains the pixel values of the images, while the 'target' attribute contains the corresponding labels (0-9) for each image. The 'astype(int)' method is used to convert the labels from float to integer type.
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解析代码:mnist = fetch_openml('mnist_784', version=1) X = mnist.data y = mnist.target.astype(np.int)
1. `fetch_openml('mnist_784', version=1)`:从OpenML中获取名为`mnist_784`的数据集,版本号为1。
2. `X = mnist.data`:将数据集中的样本数据赋值给变量`X`。
3. `y = mnist.target.astype(np.int)`:将数据集中的目标(标签)数据转换为整数类型,并赋值给变量`y`。
解析代码mnist = fetch_openml('mnist_784', version=1) X = mnist.data y = mnist.target.astype(np.int)如果我已经有测试集与训练集了的话,这里的代码应该改成什么比较合适
如果您已经有了自己的测试集和训练集,您可以使用以下代码将其加载到X_train, y_train, X_test, y_test变量中:
```
# 加载训练集和测试集
X_train = ... # 训练集特征
y_train = ... # 训练集标签
X_test = ... # 测试集特征
y_test = ... # 测试集标签
```
请使用您自己的数据集来替换省略号。注意,您的特征和标签应该是numpy数组或pandas DataFrame格式。如果您的标签是字符串格式,您可能需要使用`LabelEncoder`进行转换,以便在训练模型时能够正确处理标签。