图像分类器“ValueError：找到暗淡为 3 的数组。预计估计器 <= 2。”

Question

当我尝试使用 3 个数据集文件夹运行此代码时，我遇到了这个问题。以前，此代码在数据集文件夹内只有 2 个文件夹即可完美运行，一个名为 normal，另一个名为 covid。但在这种情况下，我添加了另一个名为 pneumonia 的分类器，使其成为 3 类图像分类器。我是机器学习的新手，所以我对如何解决这个问题进行了很多研究，但每个解决方案都不同，代码也不同。我尝试了它们，但它们不起作用，这就是我寻求您帮助的原因。

此代码不属于我，它是 Adrian Rosebrock 代码，所有功劳归于他。它是关于在 COVID 或正常情况下对 X 射线图像进行分类，但改进此代码的想法添加了一个新类别来对具有正常（非 COVID）肺炎的图像进行分类。这就是我在数据集中添加一个新文件夹的原因。希望您能帮帮我，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)

这是错误信息：

[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

我认为您不需要LabelBinarizer 和to_categorical。他们做同样的事情，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量。

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

为避免每次添加或删除类别时都需要更改此设置，您可以计算唯一标签。

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

更新

另请注意，to_categorical 仅适用于整数标签。这使它更像OneHotEncoder 而不是LabelBinarizer。

这就是所谓的一切。

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)

请注意标签的 3 维输出，因为它尝试对已单热编码数据的 3 个部分中的每一个进行单热编码，添加了一个 train_test_split 不知道如何处理的额外维度。

这就是你得到ValueError: Found array with dim 3.的原因