我正在尝试使用sklearn中的test_train_split来分割我的数据。我的数据包括用于图像和面部点的numpy.ndarray。然而，我发现它们具有不同的形状，图像的形状是((2811, 250, 250, 3))，面部点的形状是(2811, 68, 2)。我不知道如何重新调整它们的大小，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信息
---------------------------------------------------------------------------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.回答：
你可以将你的特征和标签重塑为二维或更低维度。
N = img.shape[0]img = np.reshape(img, (N, -1)) # 将图像展平为适当维度的向量points = np.reshape(points, (N, -1)) # 将目标展平x_train, x_test, y_train, y_test = train_test_split(img, points, test_size=0.2)reg.fit(x_train,y_train)