ValueError：检查目标时出错：预期dense_3的形状为（1，）但得到的数组形状为（2，）keras

Question

我在尝试从 pyimagesearch 学习 CNN 时发现了这个错误愚蠢和问题，也许同样的问题，但我已经做了我能做的事

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"))

Answer 1

在此行testY = to_categorical(testY, 2) 之后添加此行trainY = to_categorical(trainY, 2)。并将您的最后一层更改为model.add(Dense(2, activation="softmax"))，因为它应该与您的目标一样匹配二维矩阵。另外，确保你的损失函数是categorical_crossentropy，如果不是的话。

Answer 2

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 是您正在使用的类数。