Is it possible to create a CNN with real output?

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Is it possible to create a CNN with real output?

The output type of the trainNetwork() must be categorical(). How can I create a CNN with float/real output(s)?

I mean the following command gives the following error:

>> convnet = trainNetwork(input_datas, [0.0, 0.1, 0.2, 0.3], networkLayers, opts);
Error using trainNetwork>iAssertCategoricalResponseVector (line 269)
Y must be a vector of categorical responses.

(The error message corresponds the [0.0, 0.1, 0.2, 0.3] vector), But I need real outputs, not categories.

The networkLayers is the following:

>> networkLayers= 5x1 Layer array with layers:     1   ''   Image Input       1x6000x1 images with 'zerocenter' normalization
2 '' Convolution 10 1x100 convolutions with stride [1 1] and padding [0 0]
3 '' Max Pooling 1x20 max pooling with stride [10 10] and padding [0 0]
4 '' Fully Connected 200 fully connected layer
5 '' Fully Connected 1 fully connected layer

ANSWER

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The simplest way is to represent the categories with integers

>> categories = [ 1 3 5 4 2]

>> target = full(ind2vec(categories))

target     =   1     0     0     0     0
0 0 0 0 1
0 1 0 0 0
0 0 0 1 0
0 0 1 0 0

>> output = target + 0.1*randn(5,5)

output =   0.8902   -0.1361   -0.0874    0.0327   -0.0846
-0.1416 0.0780 0.0415 -0.0515 0.9827
0.0060 1.0439 0.0348 -0.0896 -0.1209
-0.0411 -0.0090 0.0349 0.8797 -0.0297
-0.0368 0.1021 0.9271 0.1038 -0.3232

>> answer = vec2ind(output)

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