Hi! @ptrblck.
Thank you for your help me.
I do not understand why this:
x = F.dropout (x, p = 0.5, self.training)
F.dropout has a training attribute that by default is True.
Yes, the first thing I tried was with CNN but it did not work out. The explanation I give is that the CNN really give good results when we try to get information from the image but in the spatial domain (something closer to the human view). But in this case the classification is given by frequency characteristics. For example, one of the most used DCT transforms. Imagine that an image is calculated by the 2D DCT and wants to classify them but given the information of the DCT.
Yes, I read in other articles that when the image is small as it is the case (8x8) I must decrease the learning rate. I also tried several values ​​for the dropout (0.2, 0.3 and 0.5)
Yes, I tried the same model but with a similar dataset and got acceptable results. This gives me the direction that the problem may be in the dataset used.