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Regarding Multi-label transfer learning with googlenet

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I have a dataset with pictures with presence of objects of different classes. I want to perform a multilabel classification, which means I need to classify the pictures into different classes with the picture belonging to more than one class at the same time. That is, for pictures with objects of type A and type B, the net should output both the labels A and B.
If I am designing a CNN for this from scratch, I will have a sigmoid activation at the last layer. The number of output neurons will be equal to the number of classes with the output of each neuron giving 1 if the picture belongs to the particular class or 0 if not. However, there seems to be no provision for adding a sigmoid function and the Image datastore cannot hold binary vectors as the label. How do I overcome this?
  1 Comment
SC P
SC P on 12 Oct 2019
@Balakrishnan Rajan ,how you have resolved this problem? ( how you did this?:defining classes which are unique combination of the previous class occurences). Is there any code of it

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Accepted Answer

Shounak Mitra
Shounak Mitra on 24 Aug 2018
We do not support sigmoid activation. You can use the softmax activation function. You don't need to define the neurons in the softmaxLayer. Define the no of neurons (= no of classes) you want in the fullyConnectedLayer. So, your network structure would be like:
inputLayer -- -- fullyConnectedLayer softmaxLayer ClassificationLayer
HTH Shounak
  3 Comments
SC P
SC P on 7 Jul 2020
@Kira, Have you found any solution of it? if yes please let me know. Thanks

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More Answers (3)

Antonio Quvera
Antonio Quvera on 21 May 2019
Edited: Antonio Quvera on 21 May 2019
I'm also interested in this application (i.e. multi-label classification using CNN/LSTM). Any news? Does the latest deep learning toolbox resolve this issue?

cui
cui on 14 May 2019
Can I define multiple softmaxLayer at the end of the network? Each softmaxLayer is independent of each other, and each layer is used to classify a label so that there can be multiple loss functions, shared by the previous convolutional layer? But how do you enter the network goals?

Greg Heath
Greg Heath on 22 Dec 2018
Decades old solution:
Divide each output by the sum to obtain the relative probability of each class
Hope this helps.
Thank you for formally accepting my answer
Greg
  4 Comments
o.cefet cefet
o.cefet cefet on 29 May 2020
Hello all?
As the images do not have a single class, how can I build the ImageDataStore, because the images cannot be separated by folders, that is, I cannot endow "Labels" with "Folders".
The images are in the same folder and a CSV file destines them. Like this:
Image, Class A, Class B, Class C, Class D
00000001_000.png, 1,1,0,0
00000001_001.png, 1,1,0,1
00000001_002.png, 0,0,0,1
00000002_000.png, 0,0,0,0
00000003_000.png, 0,0,1, -1
00000003_001.png, 0, -1,0,1
00000003_002.png, 1,0,0,0
00000003_003.png, 0,0,0,1
00000003_004.png, 0,1,0,0
00000003_005.png, 0,0,1,0
00000003_006.png, 0,1,1,0
00000003_007.png, 1,0,0,1
00000004_000.png, 0,0,1,0
00000005_000.png, 0,1,0,0
00000005_001.png, -1, -1,1,0
00000005_002.png, 0.1, -1.0
00000005_003.png, 0,0,0,1
00000005_004.png, 0,0,1,0
00000005_005.png, 0,1,0,0
00000005_006.png, 0,0, -1,1
00000005_007.png, 0,1,0, -1
00000006_000.png, 0,0,0,1
00000007_000.png, 0,1,0,0
00000008_000.png, 0,0,1,0
00000008_001.png, 0,0,0,1
......
......
......
......

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