我建立了一个模型,该模型将时间序列的 3 个图像以及 5 个数字信息作为输入,并生成时间序列的下三个图像。
我通过以下方式完成了这项工作:
LSTM 模型产生大小为 393216 (3x128x128x8) 的输出。现在我必须将表格模型的输出设置为 49,152,以便在下一层输入大小为 442368 (3x128x128x9)。因此,表格模型的 Dense 层的这种不必要的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两个模型?有没有办法在表格模型的 Dense 层中只输出 10?
该模型:
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')
型号概要: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: 5,041,012
Trainable params: 5,040,900
Non-trainable params: 112
______________________________________________________________________________________________________________________________________________________
最佳答案
我同意你说的巨大Dense
层(具有数百万个参数)可能会阻碍模型的性能。而不是用 Dense
来膨胀表格数据层,您宁愿选择以下两种方法之一。
选项 1:平铺x_tab
张量,使其与您想要的形状相匹配。这可以通过以下步骤来实现:
首先,没有必要把ConvLSTM2D
弄平。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)
# Commented: x = Flatten()(x)
其次,您可以使用一个或多个 Dense
处理表格数据。层。例如:dim = 10
x_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) # Note: Flattening a 2D tensor leaves the tensor unchanged
第三,我们包装tensorflow操作tf.tile在 Lambda层,有效地创建张量的副本 x_tab
以便它匹配所需的形状: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 h
x_tab = Lambda(repeat_tabular)(x_tab)
最后,我们连接 x
和瓷砖 x_tab
沿最后一个轴的张量(您也可以考虑沿第一个轴连接,对应于 channel 的维度)concat = Concatenate(axis=-1)([x, x_tab]) # Shape=(3,128,128,8+dim)
output = concat
output = 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幼稚,因为模型没有将图像的输入序列编码为低维表示,限制了网络的感受野,并可能导致性能下降。选项 2:类似于自动编码器和 U-Net ,可能需要将您的图像序列编码为低维表示,以丢弃不需要的变化(例如噪声),同时保留有意义的信号(例如推断序列的下 3 个图像所需)。这可以通过以下方式实现:
首先,将输入的图像序列编码为低维二维张量。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)
注意最后一个ConvLSTM2D
不返回序列。您可能想要探索不同的编码器以达到这一点(例如,您也可以在这里使用池化层)。其次,使用
Dense
处理您的表格数据层。例如:dim = 10
x_tab_input = Input(shape=(5))
x_tab = Dense(100, activation="relu")(x_tab_input)
x_tab = Dense(dim, activation="relu")(x_tab)
第三,连接前两个流中的数据:concat = Concatenate(axis=-1)([x, x_tab])
四、使用Dense
+ Reshape
层将连接的向量投影到一系列低分辨率图像中: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为 (128, 128, 3)
的形状,但它是任意的(例如,您可能还想在这里进行实验)。最后,申请一个或几个Conv3DTranspose层以获得所需的输出(例如 3 张形状为
(128, 128, 3)
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)
讨论了转置卷积层背后的基本原理 here .本质上,Conv3DTranspose
层与正常卷积相反 - 它允许将低分辨率图像上采样为高分辨率图像。
关于tensorflow - 连接 ConvLSTM2D 模型和表格模型的更好方法,我们在Stack Overflow上找到一个类似的问题: https://stackoverflow.com/questions/65963752/