我正在尝试使用sklearn中的test_train_split拆分数据。我的数据由图像和面部点的numpy.ndarray组成。然而，我意识到它们都是不同的形状，图像是((2811， 250， 250， 3(，面部点是(2811， 68， 2((。我不确定如何重新缩放它们以使它们具有相同的大小。有什么建议吗？
reg= linear_model.LinearRegression()
x_train, x_test, y_train, y_test = train_test_split(img, points, test_size=0.2)
reg.fit(x_train,y_train)
以下是我收到的错误消息
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-55-ae4bf08b45bb> in <module>()
----> 1 reg.fit(x_train,y_train)
2 frames
/usr/local/lib/python3.6/dist-packages/sklearn/linear_model/_base.py in fit(self, X, y, sample_weight)
490 n_jobs_ = self.n_jobs
491 X, y = check_X_y(X, y, accept_sparse=['csr', 'csc', 'coo'],
--> 492 y_numeric=True, multi_output=True)
493
494 if sample_weight is not None:
/usr/local/lib/python3.6/dist-packages/sklearn/utils/validation.py in check_X_y(X, y, accept_sparse, accept_large_sparse, dtype, order, copy, force_all_finite, ensure_2d, allow_nd, multi_output, ensure_min_samples, ensure_min_features, y_numeric, warn_on_dtype, estimator)
753 ensure_min_features=ensure_min_features,
754 warn_on_dtype=warn_on_dtype,
--> 755 estimator=estimator)
756 if multi_output:
757 y = check_array(y, 'csr', force_all_finite=True, ensure_2d=False,
/usr/local/lib/python3.6/dist-packages/sklearn/utils/validation.py in check_array(array, accept_sparse, accept_large_sparse, dtype, order, copy, force_all_finite, ensure_2d, allow_nd, ensure_min_samples, ensure_min_features, warn_on_dtype, estimator)
572 if not allow_nd and array.ndim >= 3:
573 raise ValueError("Found array with dim %d. %s expected <= 2."
--> 574 % (array.ndim, estimator_name))
575
576 if force_all_finite:
ValueError: Found array with dim 4. Estimator expected <= 2.
您可以将要素和标注的形状调整为 <= 2D。
N = img.shape[0]
img = np.reshape(img, (N, -1)) # flattens the image to a vector of appropriate dimension
points = np.reshape(points, (N, -1)) # flattens the target
x_train, x_test, y_train, y_test = train_test_split(img, points, test_size=0.2)
reg.fit(x_train,y_train)