我已经构建了一个模型，它以时间序列的3张图像和5个数值信息作为输入，并生成时间序列的接下来三张图像。我通过以下步骤实现了这一点：
- 为处理图像构建一个ConvLSTM2D模型（与Keras文档中列出的示例非常相似，在此）。输入大小=(3x128x128x3)
- 为表格数据构建一个简单的模型，包含几个Dense层。输入大小=(1,5)
- 将这两个模型连接起来
- 使用一个Conv3D模型生成接下来的3张图像
LSTM模型的输出大小为393216（3x128x128x8）。现在我不得不将表格模型的输出设置为49,152，这样我就可以在下一层中得到442368（3x128x128x9）的大小输入。因此，这种对表格模型Dense层的无谓膨胀使得原本高效的LSTM模型表现得非常糟糕。
有没有更好的方法来连接这两个模型？我能否只让表格模型的Dense层的输出为10？
模型如下：
x_input = Input(shape=(None, 128, 128, 3))x = ConvLSTM2D(32, 3, strides = 1, padding='same', dilation_rate = 2,return_sequences=True)(x_input)x = BatchNormalization()(x)x = ConvLSTM2D(16, 3, strides = 1, padding='same', dilation_rate = 2,return_sequences=True)(x)x = BatchNormalization()(x)x = ConvLSTM2D(8, 3, strides = 1, padding='same', dilation_rate = 2,return_sequences=True)(x)x = BatchNormalization()(x)x = Flatten()(x)# x = MaxPooling3D()(x)x_tab_input = Input(shape=(5))x_tab = Dense(100, activation="relu")(x_tab_input)x_tab = Dense(49152, activation="relu")(x_tab)x_tab = Flatten()(x_tab)concat = Concatenate()([x, x_tab])output = Reshape((3,128,128,9))(concat)output = Conv3D(filters=3, kernel_size=(3, 3, 3), activation='relu', padding="same")(output)model = Model([x_input, x_tab_input], output)model.compile(loss='mae', optimizer='rmsprop')模型摘要：
Model: "functional_3"______________________________________________________________________________________________________________________________________________________Layer (type) Output Shape Param # Connected to ======================================================================================================================================================input_4 (InputLayer) [(None, None, 128, 128, 3)] 0 ______________________________________________________________________________________________________________________________________________________conv_lst_m2d_9 (ConvLSTM2D) (None, None, 128, 128, 32) 40448 input_4[0][0] ______________________________________________________________________________________________________________________________________________________batch_normalization_9 (BatchNormalization) (None, None, 128, 128, 32) 128 conv_lst_m2d_9[0][0] ______________________________________________________________________________________________________________________________________________________conv_lst_m2d_10 (ConvLSTM2D) (None, None, 128, 128, 16) 27712 batch_normalization_9[0][0] ______________________________________________________________________________________________________________________________________________________batch_normalization_10 (BatchNormalization) (None, None, 128, 128, 16) 64 conv_lst_m2d_10[0][0] ______________________________________________________________________________________________________________________________________________________input_5 (InputLayer) [(None, 5)] 0 ______________________________________________________________________________________________________________________________________________________conv_lst_m2d_11 (ConvLSTM2D) (None, None, 128, 128, 8) 6944 batch_normalization_10[0][0] ______________________________________________________________________________________________________________________________________________________dense (Dense) (None, 100) 600 input_5[0][0] ______________________________________________________________________________________________________________________________________________________batch_normalization_11 (BatchNormalization) (None, None, 128, 128, 8) 32 conv_lst_m2d_11[0][0] ______________________________________________________________________________________________________________________________________________________dense_1 (Dense) (None, 49152) 4964352 dense[0][0] ______________________________________________________________________________________________________________________________________________________flatten_3 (Flatten) (None, None) 0 batch_normalization_11[0][0] ______________________________________________________________________________________________________________________________________________________flatten_4 (Flatten) (None, 49152) 0 dense_1[0][0] ______________________________________________________________________________________________________________________________________________________concatenate (Concatenate) (None, None) 0 flatten_3[0][0] flatten_4[0][0] ______________________________________________________________________________________________________________________________________________________reshape_2 (Reshape) (None, 3, 128, 128, 9) 0 concatenate[0][0] ______________________________________________________________________________________________________________________________________________________conv3d_2 (Conv3D) (None, 3, 128, 128, 3) 732 reshape_2[0][0] ======================================================================================================================================================Total params: 5,041,012Trainable params: 5,040,900Non-trainable params: 112______________________________________________________________________________________________________________________________________________________回答：
我同意你的观点，巨大的Dense层（拥有数百万参数）可能会阻碍模型的性能。与其用Dense层来“膨胀”表格数据，你可以选择以下两种方法之一。
选项1： 平铺x_tab张量，使其匹配你所需的形状。这可以通过以下步骤实现：
首先，无需展平ConvLSTM2D的编码张量：
x_input = Input(shape=(3, 128, 128, 3))x = ConvLSTM2D(32, 3, strides = 1, padding='same', dilation_rate = 2,return_sequences=True)(x_input)x = BatchNormalization()(x)x = ConvLSTM2D(16, 3, strides = 1, padding='same', dilation_rate = 2,return_sequences=True)(x)x = BatchNormalization()(x)x = ConvLSTM2D(8, 3, strides = 1, padding='same', dilation_rate = 2,return_sequences=True)(x)x = BatchNormalization()(x) # Shape=(None, None, 128, 128, 8) # 注释掉：x = Flatten()(x)其次，你可以用一个或多个Dense层处理你的表格数据。例如：
dim = 10x_tab_input = Input(shape=(5))x_tab = Dense(100, activation="relu")(x_tab_input)x_tab = Dense(dim, activation="relu")(x_tab)# x_tab = Flatten()(x_tab) # 注意：展平2D张量不会改变张量第三，我们将tensorflow操作tf.tile包装在Lambda层中，有效地创建x_tab张量的副本，使其匹配所需的形状：
def repeat_tabular(x_tab): h = x_tab[:, None, None, None, :] # Shape=(bs, 1, 1, 1, dim) h = tf.tile(h, [1, 3, 128, 128, 1]) # Shape=(bs, 3, 128, 128, dim) return hx_tab = Lambda(repeat_tabular)(x_tab)最后，我们沿最后一个轴连接x和平铺的x_tab张量（你也可以考虑沿第一个轴连接，对应于通道维度）
concat = Concatenate(axis=-1)([x, x_tab]) # Shape=(3,128,128,8+dim)output = concatoutput = Conv3D(filters=3, kernel_size=(3, 3, 3), activation='relu', padding="same")(output)# ...请注意，这个解决方案可能有点简单，因为模型没有将输入的图像序列编码成低维表示，限制了网络的感受野，可能会导致性能下降。
选项2： 类似于自动编码器和U-Net，将你的图像序列编码成低维表示可能是有利的，这样可以丢弃不需要的变化（例如噪声），同时保留有意义的信号（例如推断序列的接下来的3张图像所需的信号）。这可以通过以下方式实现：
首先，将输入的图像序列编码成低维的2维张量。例如，类似于以下内容：
x_input = Input(shape=(None, 128, 128, 3))x = ConvLSTM2D(32, 3, strides = 1, padding='same', dilation_rate = 2,return_sequences=True)(x_input)x = BatchNormalization()(x)x = ConvLSTM2D(16, 3, strides = 1, padding='same', dilation_rate = 2,return_sequences=True)(x)x = BatchNormalization()(x)x = ConvLSTM2D(8, 3, strides = 1, padding='same', dilation_rate = 2, return_sequences=False)(x)x = BatchNormalization()(x)x = Flatten()(x)x = Dense(64, activation='relu')(x)请注意，最后一个ConvLSTM2D不返回序列。你可能想探索不同的编码器来达到这一点（例如，你也可以在这里使用池化层）。
其次，用Dense层处理你的表格数据。例如：
dim = 10x_tab_input = Input(shape=(5))x_tab = Dense(100, activation="relu")(x_tab_input)x_tab = Dense(dim, activation="relu")(x_tab)第三，连接来自前两个流的数据：
concat = Concatenate(axis=-1)([x, x_tab])第四，使用Dense + Reshape层将连接的向量投影成一系列低分辨率图像：
h = Dense(3 * 32 * 32 * 3)(concat)output = Reshape((3, 32, 32, 3))(h)output的形状允许将图像上采样到(128, 128, 3)的形状，但它本身是任意的（例如，你可能也想在这里进行实验）。
最后，应用一个或多个Conv3DTranspose层以获得所需的输出（例如，3张(128, 128, 3)形状的图像）。
output = tf.keras.layers.Conv3DTranspose(filters=50, kernel_size=(3, 3, 3), strides=(1, 2, 2), padding='same', activation='relu')(output)output = tf.keras.layers.Conv3DTranspose(filters=3, kernel_size=(3, 3, 3), strides=(1, 2, 2), padding='same', activation='relu')(output) # Shape=(None, 3, 128, 128, 3)转置卷积层的原理在这里讨论。本质上，Conv3DTranspose层与普通卷积的方向相反——它允许将你的低分辨率图像上采样成高分辨率图像。
