sklearn Logistic回归" ValueError:找到的数组为暗3.估计器预期< == 2. [英] sklearn Logistic Regression "ValueError: Found array with dim 3. Estimator expected <= 2."
问题描述
我尝试在此笔记本中解决此问题6.问题是通过使用来自sklearn.linear_model的LogisticRegression模型，使用50、100、1000和5000个训练样本在此数据上训练一个简单的模型. https://github.com/tensorflow/examples/blob/master /courses/udacity_deep_learning/1_notmnist.ipynb
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
lr = LogisticRegression()
lr.fit(train_dataset,train_labels)
这是我尝试执行的代码，它给了我错误.
This is the code i trying to do and it give me the error.
ValueError:找到的数组为暗3.估计值应为< = 2.
ValueError: Found array with dim 3. Estimator expected <= 2.
有什么主意吗?
更新1:更新指向Jupyter Notebook的链接.
UPDATE 1: Update the link to the Jupyter Notebook.
推荐答案
scikit-learn expects 2d num arrays for the training dataset for a fit function. The dataset you are passing in is a 3d array you need to reshape the array into a 2d.
nsamples, nx, ny = train_dataset.shape
d2_train_dataset = train_dataset.reshape((nsamples,nx*ny))
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