Keras ConvLSTM2D: why use the averagepooling3d and how to to regression

Question

i have been studying Keras ConvLSTM2D: ValueError on output layer

i want to use the same code but i want to do regression ( single value ). I dont know how to do this. And i also dont understand the use of last layers of this post code. Why is averagepolling3d used?

the code from link is

model = Sequential()

model.add(ConvLSTM2D(
        filters=40,
        kernel_size=(3, 3),
        input_shape=(None, 135, 240, 1),
        padding='same',
        return_sequences=True))
model.add(BatchNormalization())

model.add(ConvLSTM2D(
        filters=40,
        kernel_size=(3, 3),
        padding='same',
        return_sequences=True))
model.add(BatchNormalization())

model.add(ConvLSTM2D(
        filters=40,
        kernel_size=(3, 3),
        padding='same',
        return_sequences=True))
model.add(BatchNormalization())

model.add(AveragePooling3D((1, 135, 240)))
model.add(Reshape((-1, 40)))
model.add(Dense(
        units=9,
        activation='sigmoid'))

model.compile(
        loss='categorical_crossentropy',
        optimizer='adadelta'
)

Answer 1

AveragePooling3D is used to reduce each frame in a sequence to a single value + to reduce the #parameters in the Dense Layer. So, the dimension becomes (None, 40 , 1 , 1 ,1 ) . Then, using Reshape allows it to use for fully-connected part.

Also, as in Keras ConvLSTM2D: ValueError on output layer , AveragePooling3D is used instead of GlobalMaxPooling2D since data is 5D and Global operations leaves only (batch_size, channels) which is not desirable in your case.