图像分类器“ValueError:找到带有dim 3的数组。估计器期望<= 2。

Question

当我试图使用dataset的3个文件夹运行此代码时，出现了此问题。以前，这段代码在dataset文件夹中只运行了两个文件夹，一个叫做normal，另一个叫做covid。但在本例中，我添加了另一个名为pneumonia的分类器，使其成为一个3类图像分类器。我是机器学习方面的新手，所以我研究了很多关于如何解决这个问题的方法，但是每个解决方案都是不同的，而且代码也不一样。我试过了，但都没用，所以我才找你帮忙。

这个密码不属于我，是禤浩焯罗斯布罗克的代码，所有的功劳都归于他。这是关于在COVID或正常情况下对X射线图像进行分类，但是改进这个代码的想法增加了一个新的类别来对正常(非COVID)肺炎的图像进行分类。这就是为什么我在dataset中添加了一个新文件夹。希望你能帮我，谢谢！

# USAGE
python train.py --dataset dataset

# import the necessary packages

from sklearn.preprocessing import LabelBinarizer
from sklearn.model_selection import train_test_split
from sklearn.metrics import classification_report
from sklearn.metrics import confusion_matrix
from imutils import paths

import numpy as np
import argparse
import cv2
import os

# construct the argument parser and parse the arguments

ap = argparse.ArgumentParser()
ap.add_argument("-d", "--dataset", required=True,
    help="path to input dataset")
ap.add_argument("-p", "--plot", type=str, default="plot.png",
    help="path to output loss/accuracy plot")
ap.add_argument("-m", "--model", type=str, default="covid19.model",
    help="path to output loss/accuracy plot")
args = vars(ap.parse_args())

# initialize the initial learning rate, number of epochs to train for,
# and batch size

INIT_LR = 1e-3
EPOCHS = 1
BS = 8

# grab the list of images in our dataset directory, then initialize
# the list of data (i.e., images) and class images

print("[INFO] loading images...")
imagePaths = list(paths.list_images(args["dataset"]))
data = []
labels = []

# loop over the image paths

for imagePath in imagePaths:
    # extract the class label from the filename
    label = imagePath.split(os.path.sep)[-2]

    # load the image, swap color channels, and resize it to be a fixed
    # 224x224 pixels while ignoring aspect ratio
    image = cv2.imread(imagePath)
    image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
    image = cv2.resize(image,(224, 224))

    # update the data and labels lists, respectively
    data.append(image)
    labels.append(label)

# convert the data and labels to NumPy arrays while scaling the pixel
# intensities to the range [0, 255]

data = np.array(data) / 255.0
labels = np.array(labels)

# perform one-hot encoding on the labels

lb = LabelBinarizer()
labels = lb.fit_transform(labels)
labels = to_categorical(labels)

# partition the data into training and testing splits using 80% of
# the data for training and the remaining 20% for testing

(trainX, testX, trainY, testY) = train_test_split(data, labels,
    test_size=0.20, stratify=labels, random_state=42)

这是错误消息：

[INFO] loading images...
Traceback (most recent call last):
  File "train_covid19.py", line 77, in <module>
    test_size=0.20, stratify=labels, random_state=42)
  File "C:\Users\KQ\anaconda3\lib\site-packages\sklearn\model_selection\_split.py", line 2143, in train_test_split
    train, test = next(cv.split(X=arrays[0], y=stratify))
  File "C:\Users\KQ\anaconda3\lib\site-packages\sklearn\model_selection\_split.py", line 1737, in split
    y = check_array(y, ensure_2d=False, dtype=None)
  File "C:\Users\KQ\anaconda3\lib\site-packages\sklearn\utils\validation.py", line 574, in check_array
    % (array.ndim, estimator_name))
ValueError: Found array with dim 3. Estimator expected <= 2.

Answer 1

我认为你不需要LabelBinarizer和to_categorical。他们做同样的事，所以你只需要一个或另一个。尝试删除对to_categorical的调用。

lb = LabelBinarizer()
labels = lb.fit_transform(labels)
labels = to_categorical(labels)  # Remove this line.

您还需要更新模型中的类别数。

# Change the size from 2 to 3.
headModel = Dense(3, activation="softmax")(headModel)

为了避免每次添加或删除类别时都需要更改，您可以计算唯一的标签。

n_labels = len(set(labels))
headModel = Dense(n_labels, activation="softmax")(headModel)

更新

还请注意，to_categorical将只在整数标签上工作。这使得它更像OneHotEncoder而不是LabelBinarizer。

这是所谓的一切。

labels = [0, 1, 0, 2]                                                                                                                                                                                                               

lb = LabelBinarizer() 
binarized = lb.fit_transform(labels)                                                                                                                                                                                                
binarized                                                                                                                                                                                                                           
# array([[1, 0, 0],
#        [0, 1, 0],
#        [1, 0, 0],
#        [0, 0, 1]])

to_categorical(labels)                                                                                                                                                                                                              
# array([[1., 0., 0.],
#        [0., 1., 0.],
#        [1., 0., 0.],
#        [0., 0., 1.]], dtype=float32)

to_categorical(binarized)                                                                                                                                                                                                           
# array([[[0., 1.],
#         [1., 0.],
#         [1., 0.]],
# 
#        [[1., 0.],
#         [0., 1.],
#         [1., 0.]],
# 
#        [[0., 1.],
#         [1., 0.],
#         [1., 0.]],
# 
#        [[1., 0.],
#         [1., 0.],
#         [0., 1.]]], dtype=float32)

注意标签的三维输出，因为它尝试对一个热编码数据的三个部分进行一个热编码，添加一个train_test_split不知道如何处理的额外维度。

这就是为什么你有一个ValueError: Found array with dim 3.