所以我在尝试使用Tensorflow和CNN类型来创建一个面部识别系统，我只为我的数据提供了15张我的面部图片（这是一个简单的项目），我希望它能预测这是否是我的面部，像是真或假，问题是我不明白我的代码中哪里出了错误。这就是

ValueError: logits and labels must have the same shape ((None, 2) vs (None, 1))

这是我的代码

# Face ID project, using CNN tensorflowfrom tensorflow.keras.preprocessing.image import img_to_arrayfrom tensorflow.keras.optimizers import Adam from tensorflow.keras.models import Sequentialfrom tensorflow.keras.layers import Dense, Dropout, Flatten, Conv2D, MaxPooling2D, BatchNormalization, Activationfrom tensorflow.keras import backend as Kimport numpy as npimport cv2  import glob# Preparing the data and parametersepochs = 10lr = 1e-3batch_size = 64 img_dims = (96,96,3)data = []labels = []image_files = glob.glob("C:/Users/berna/Desktop/Programming/AI_ML_DL/Projects/FaceID/Data/*")for img in image_files:    image = cv2.imread(img)    image = cv2.resize(image, (img_dims[0], img_dims[1]))    image = img_to_array(image)    data.append(image)    if img == img:        label = 1    else:        label = 0        labels.append([label])    # Preproccesing the data (convert arrays)data = np.array(data, dtype="float32") / 255.0labels = np.array(labels)X = data y = labelsdef build(width, height, depth, classes):    model = Sequential()    inputShape = height, width, depth     chanDim = -1    if K.image_data_format() == "channels_first":        inputShape = depth, height, width         chanDim = 1        # Creating the model    model.add(Conv2D(32, (3,3), padding="same", input_shape=inputShape))    model.add(Activation("relu"))    model.add(BatchNormalization(axis=chanDim))    model.add(MaxPooling2D(pool_size=(3,3)))    model.add(Dropout(0.25))    model.add(Conv2D(64, (3,3), padding="same"))    model.add(Activation("relu"))    model.add(BatchNormalization(axis=chanDim))    model.add(Conv2D(64, (3,3), padding="same"))    model.add(Activation("relu"))    model.add(BatchNormalization(axis=chanDim))    model.add(MaxPooling2D(pool_size=(2,2)))    model.add(Dropout(0.25))    model.add(Conv2D(128, (3,3), padding="same"))    model.add(Activation("relu"))    model.add(BatchNormalization(axis=chanDim))    model.add(Conv2D(128, (3,3), padding="same"))    model.add(Activation("relu"))    model.add(BatchNormalization(axis=chanDim))    model.add(MaxPooling2D(pool_size=(2,2)))    model.add(Dropout(0.25))    model.add(Flatten())    model.add(Dense(1024))    model.add(Activation("relu"))    model.add(BatchNormalization())    model.add(Dropout(0.5))    model.add(Dense(classes))    model.add(Activation("sigmoid"))    return model# Build the model call model = build(width=img_dims[0], height=img_dims[1], depth=img_dims[2], classes=2)# compile the modelopt = Adam(lr=lr, decay=lr/epochs)model.compile(loss="binary_crossentropy",            optimizer=opt,            metrics=['accuracy'])# fitting the modelH = model.fit(X, y, batch_size=batch_size,                    epochs=epochs, verbose=1)model.save('faceid.model')

回答：

正如@Frightera所说，更换:

model.add(Dense(classes)) 为 model.add(Dense(1)) 应该可以解决问题。

你的标签是一个整数，但你的最后一层输出的是一个二维数组。