Python sklearn Logistic Regression “ValueError: Found array with dim 3. Estimator expected <= 2.”
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sklearn Logistic Regression "ValueError: Found array with dim 3. Estimator expected <= 2."
提问by edwin
I attempt to solve this problem 6 in this notebook. The question is to train a simple model on this data using 50, 100, 1000 and 5000 training samples by using the LogisticRegression model from sklearn.linear_model. https://github.com/tensorflow/examples/blob/master/courses/udacity_deep_learning/1_notmnist.ipynb
我试图在这个笔记本中解决这个问题 6。问题是使用 sklearn.linear_model 中的 LogisticRegression 模型，使用 50、100、1000 和 5000 个训练样本对这些数据训练一个简单的模型。 https://github.com/tensorflow/examples/blob/master/courses/udacity_deep_learning/1_notmnist.ipynb
lr = LogisticRegression()
lr.fit(train_dataset,train_labels)
This is the code i trying to do and it give me the error.
这是我试图做的代码，它给了我错误。
ValueError: Found array with dim 3. Estimator expected <= 2.
ValueError: 找到带有暗淡 3 的数组。估计器预期 <= 2。
Any idea?
任何的想法？
UPDATE 1: Update the link to the Jupyter Notebook.
更新 1：更新 Jupyter Notebook 的链接。
采纳答案by Kristian K.
scikit-learn expects 2d num arrays for the training dataset for a fitfunction. The dataset you are passing in is a 3d array you need to reshape the array into a 2d.
scikit-learn 需要 2d num 数组作为拟合函数的训练数据集。您传入的数据集是一个 3d 数组，您需要将数组重塑为 2d。
nsamples, nx, ny = train_dataset.shape
d2_train_dataset = train_dataset.reshape((nsamples,nx*ny))
