Aggregate TimeDistributed layers by sum or average

[Alexander Schindler]

Hi,

I'm using the TimeDistributed wrapper to process a set of images grouped together in one tensor. The goal is to have the model predict a cummulative maximum, similar to a majority vote prediction.

But, I have problems implementing the accumulation.

The TimeDistributed wrapper outputs the softmax layers for the shared CNN-stacks. The wrapper's output shape is (None, 5, 10). My intention was to merge this layer along the 1st axis to get the desired cummulated results with an expected shape of (None, 10).

inp = Input(shape=(5, 1, 40, 40))
   
# ===== frequency filter =====

cnn = TimeDistributed(Convolution2D(16,3,3, border_mode='same', activation="relu"))(inp)
cnn = TimeDistributed(MaxPooling2D(pool_size=(2,2))(cnn)
cnn = TimeDistributed(Convolution2D(32,3,3, border_mode='same', activation="relu"))(cnn)
cnn = TimeDistributed(MaxPooling2D(pool_size=(2,2))(cnn)
cnn = TimeDistributed(Convolution2D(64, 3, 3, border_mode='same', activation="relu"))(cnn)
cnn = TimeDistributed(MaxPooling2D(pool_size=(2,2)))(cnn)
cnn = TimeDistributed(Flatten())(cnn)  
cnn = TimeDistributed(Dense(512, activation="relu"))(cnn)
cnn = TimeDistributed(Dense(10,  activation='softmax'))(cnn)

# output shape of cnn == (None, 5, 10)

mean = merge(cnn, mode='average', concat_axis=0) # <= this fails

model = Model(input=inp, output=mean)

# expected output shape of mode == (None, 10)

Unfortunately this does not work. I get the following error:

TypeError: TensorType does not support iteration. Maybe you are using builtin.sum instead of theano.tensor.sum? (Maybe .max?)

Can anybody please point me in the right direction?

Best,
Alex

[Daπid]

merge takes a list of inputs, not a single tensor, you should use GlobalAveragePooling1D instead.

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[Alexander Schindler]

Hi David,

thanks for your help. The GlobalAveragePooling1D layer works perfectly.

Alex.

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