我在使用随机梯度下降和 MNIST 数据库训练代码时遇到问题。
from sklearn.datasets import fetch_mldata
from sklearn.linear_model import SGDClassifier
mnist = fetch_mldata('MNIST original')
X, y = mnist["data"], mnist["target"]
some_digit = X[36000]
some_digit_image = some_digit.reshape(28, 28)
X_train, X_train, y_train, y_test = X[:60000], X[60000:], y[:60000], y[60000:]
y_train_5 = (y_train == 5)
y_test_5 = (y_test == 5)
sgd_clf = SGDClassifier(random_state=42)
sgd_clf.fit(X_train, y_train_5)
进程结束时出错(我认为最后一段代码很糟糕):
ValueError: Found input variables with inconsistent numbers of samples: [10000, 60000]
最佳答案
这是您这边的拼写错误，您分配给 X_train 两次:
X_train, X_train, y_train, y_test = X[:60000], X[60000:], y[:60000], y[60000:]
正确答案是:
X_train, X_test, y_train, y_test = X[:60000], X[60000:], y[:60000], y[60000:]
fetch_mldata 很快就会被弃用，使用它会是一个更好的主意:
from sklearn.datasets import fetch_openml
X, y = fetch_openml("mnist_784", version=1, return_X_y=True)
关于python - 如何修复 "ValueError: Found input variables with inconsistent numbers of samples: [10000, 60000]"？，我们在Stack Overflow上找到一个类似的问题： https://stackoverflow.com/questions/54259778/