使用pytorch实现with open('X_cal.pkl', 'rb') as f: (test_images) = pickle.load(f) with open('y_cal.pkl', 'rb') as f: (test_labels) = pickle.load(f)

你可以使用以下代码来实现：

import pickle

with open('X_cal.pkl', 'rb') as f:
    test_images = pickle.load(f)

with open('y_cal.pkl', 'rb') as f:
    test_labels = pickle.load(f)

首先，需要导入Python标准库中的pickle模块。然后，使用`with open`语句打开pickle文件，将读取到的数据存储到相应的变量中。在这里，`rb`表示以二进制模式读取文件。如果文件是以文本模式保存的，可以使用`r`代替`rb`。

相关问题

使用pytorch实现这串代码from sklearn.model_selection import train_test_split ts = 0.3 # Percentage of images that we want to use for testing. X_train, X_test1, y_train, y_test1 = train_test_split(X, y, test_size=ts, random_state=42) X_test, X_cal, y_test, y_cal = train_test_split(X_test1, y_test1, test_size=ts, random_state=4并生成pkl文件

你可以使用以下代码来实现：

import pickle
from sklearn.model_selection import train_test_split

ts = 0.3 # Percentage of images that we want to use for testing.

X_train, X_test1, y_train, y_test1 = train_test_split(X, y, test_size=ts, random_state=42)
X_test, X_cal, y_test, y_cal = train_test_split(X_test1, y_test1, test_size=ts, random_state=4)

# Save the datasets as pickle files
with open('X_train.pkl', 'wb') as f:
    pickle.dump(X_train, f)

with open('y_train.pkl', 'wb') as f:
    pickle.dump(y_train, f)

with open('X_test.pkl', 'wb') as f:
    pickle.dump(X_test, f)

with open('y_test.pkl', 'wb') as f:
    pickle.dump(y_test, f)

with open('X_cal.pkl', 'wb') as f:
    pickle.dump(X_cal, f)

with open('y_cal.pkl', 'wb') as f:
    pickle.dump(y_cal, f)

首先，导入pickle模块和train_test_split函数。然后，设置测试集的比例`ts`。接着，使用train_test_split函数将数据集分成训练集和第一份测试集。然后，使用train_test_split函数将第一份测试集分成第二份测试集和验证集。最后，将训练集、测试集和验证集分别保存成pickle文件。注意，'wb'表示以二进制模式写入文件。