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Is it true that there is no support for time-delayed neural nets in Caffe? I am interested in training a net with variable size input (I cannot scale them all to the same size) where I can set the targets dynamically based on all the output activations.
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That's great! I saw #594, but wasn't sure if it will allow TDNN. Just to make sure we are on the same page, this is what I want to do: the input would be a 2D array M X N. I will have several hidden layers (some convolutional). The output would be m X n. Then I will be setting the m X n targets based on the entire output array and then backward feed.
For the loss over the whole sequence you will have to develop that yourself. Otherwise Caffe does recurrent models through weight sharing, defining a model for each length, or making use of variable size inputs in #594.
Is it true that there is no support for time-delayed neural nets in Caffe? I am interested in training a net with variable size input (I cannot scale them all to the same size) where I can set the targets dynamically based on all the output activations.
The text was updated successfully, but these errors were encountered: