ValueError: Error when checking target: expected dense_3 to have shape (1,) but got array with shape (2,) keras

ValueError: Error when checking target: expected dense_3 to have shape (1,) but got array with shape (2,) keras

我在尝试从 pyimagesearch 学习 CNN 时发现了这个错误我试图将最后一个密集从 3 更改为 1 但它没有解决我的问题我已经将它更改为 binnar_crossentroypy 但它仍然没有在这里工作我的代码很抱歉愚蠢和问题,也许是同样的问题,但我已经做了我能做的

ss# grab the image paths and randomly shuffle them
imagePaths = sorted(list(paths.list_images(args["dataset"])))
random.seed(42)
random.shuffle(imagePaths)


for imagePath in imagePaths:
# load the image, resize the image to be 32x32 pixels (ignoring
# aspect ratio), flatten the image into 32x32x3=3072 pixel image
# into a list, and store the image in the data list
image = cv2.imread(imagePath)
image = cv2.resize(image, (32, 32)).flatten()
data.append(image)

# extract the class label from the image path and update the
# labels list
label = imagePath.split(os.path.sep)[-2]
labels.append(label)

# scale the raw pixel intensities to the range [0, 1]
data = np.array(data, dtype="float") / 255.0
labels = np.array(labels)

 # partition the data into training and testing splits using 75% of
 # the data for training and the remaining 25% for testing
  (trainX, testX, trainY, testY) = train_test_split(data,
   labels, test_size=0.25, random_state=42)



 lb =  LabelEncoder()
 trainY = lb.fit_transform(trainY)
 testY = to_categorical(testY, 2)


 # define the 3072-1024-512-3 architecture using Keras
 model = Sequential()
 model.add(Dense(1024, input_shape=(3072,), activation="sigmoid"))
 model.add(Dense(512, activation="sigmoid"))
 model.add(Dense(1, activation="softmax"))

在这一行 testY = to_categorical(testY, 2) 之后添加这一行 trainY = to_categorical(trainY, 2)。并将你的最后一层更改为 model.add(Dense(2, activation="softmax")),因为它应该与你的目标一样匹配二维矩阵。另外,确保你的损失函数是 categorical_crossentropy,如果它还没有的话。

错误可能来自这一行:

model.add(Dense(1, activation="softmax"))

神经网络期望数组 y 只有一个值。这没有任何意义,因为 y 的维度有两个值。所以,你应该试试这个:

model.add(Dense(2, activation="softmax"))

如果 2 是您正在使用的 类 的数量。