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I seem to have some problems understandind how the model described in this paper has been designed

This is what is written about the model dimension..

...In these experiments we used one convolution ply, one poolingply and two fully connected hidden layers on the top. The fullyconnected layers had 1000 units in each. The convolution andpooling parameters were: pooling size of 6, shift size of 2,filtersize of 8, 150 feature maps for FWS..

So according to ^ does the model consist of

Input

Convolution

Pooling

Input being the 150 feature maps (each with shape (8,3)

Covolution being 1d as kernel size is 8

and pooling is with size 6 and stride 2.

What was expected of output would be a shape of (1,"number of filters), but what i get is (14,"number of filters)

Which I understand why i get, but I don't understand how the paper suggest this can give an output shape of (1,"number of filters")

when using 100 filters I get these outputs from each layer

convolution1d give me (33,100)

pooling (14,100)..

Why i expect the output to be 1 instead of 14

The model is supposed to recognise phones, it takes in a 50 frames (150 including deltas) as input, these being a context frame, meaning that these are used as support to detect one single frame... That usually why context windows are used.

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Since next layer is fully connected it does not really matter what shape your pooling output would be. You have 14x100, you can rearrange them as 1x1400 as input for next layer, 1000 elements as output. The paper says that they select the size of the fully-connected layer of 1000 to be close to the output size of the pooling layer.

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  • $\begingroup$ Thanks for the answer.. It still doesn't train that properly though.. it doesn't seem to learn, and keep looping the same accuracy values. $\endgroup$ – Bob Burt May 23 '17 at 17:55

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