ValueError: Input 0 is incompatible with layer conv2d_42: expected ndim=4, found ndim=2 for a numeric dataset

Question

我正在尝试在具有 3100 个特征和 53953 行的数值数据集上运行卷积自动编码器。所以在编码器层，当我传递输入时，我得到ValueError: Input 0 is incompatible with layer conv2d_44: expected ndim=4, found ndim=2的错误。我找到了很多关于图像数据的解释。但是，找不到对数值数据集的任何解释。我的代码如下：

from keras.layers import Input, Dense, Conv2D, MaxPooling2D, UpSampling2D
from keras.models import Model

    from keras.layers import *
    inputTensor = Input(shape=(3100,))
    
    x = Conv2D(32, (3, 3), activation='relu', padding='same')(inputTensor)
    x = MaxPooling2D((2, 2), padding='same')(x)
    x = Conv2D(32, (3, 3), activation='relu', padding='same')(x)
    x = MaxPooling2D((2, 2), padding='same')(x)
    x = Conv2D(32, (3, 3), activation='relu', padding='same')(x)
    encoded = MaxPooling2D((2, 2), padding='same')(x)
    
    x = Conv2D(32, (3, 3), activation='relu', padding='same')(encoded)
    x = UpSampling2D((2, 2))(x)
    x = Conv2D(32, (3, 3), activation='relu', padding='same')(x)
    x = UpSampling2D((2, 2))(x)
    x = Conv2D(32, (3, 3), activation='relu')(x)
    x = UpSampling2D((2, 2))(x)
    decoded = Conv2D(1, (3, 3), activation='sigmoid', padding='same')(x)
    
    autoencoder = Model(inputTensor, decoded)
    autoencoder.compile(optimizer='adadelta', loss='mean_squared_error')
    autoencoder.fit(X, X, epochs=5, batch_size=1032)

有人可以解释一下我在这里遗漏了什么吗？或者如何在卷积层中传递数值数据？