What should the dimension of the mean and variance of the latent distribution in variational auto-encoders be?

I’m having a problem to understand the needed dimensions of an VAE, especially for mu, logvar and z layer. Let’s say I have an input of 512×512, 1 color channel (CT images), batch size 32. Then my encoder/decoder looks like the following: self.encoder = nn.Sequential( nn.Conv2d(1, 32, kernel_size=3, stride=1, padding=1), # 32x512x512 nn.ReLU(True), nn.Conv2d(32, 32,…

faster rcnn loss problems

The RPN loss in faster rcnn is L({pi},{ti}) = 1/Ncls sigma(i)Lcls(pi,pi) + lambda 1/Nreg sigma(i)piLreg(ti,t*i) and Lreg=SmoothL1(t−t∗) which tx=(x−xa)/wa, ty=(y−ya)/ha, tw=log(w/wa), th=log(h/ha) (1) t∗x=(x∗−xa)/wa, t∗y=(y∗−ya)/ha, t∗w=log(w∗/wa), t∗h=log(h∗/ha) (2) x*,y*,w*,h* = GT(label) xa,ya,wa,ha = anchor(Rpn generated) x,y,w,h = predict box so how i can get predict box? And Lreg = SmoothL1(t−t∗), is that add up tx,ty,tw,th and minus the sum of tx,ty,tw,th?

Does CNN Model accuracy increase if I fit it twice

As I train my CNN model I realised that fitting model twice, ie. running again this code history = model_2.fit([X_train_1,X_train_2], y_train, epochs = 120, batch_size = 256, validation_split = 0.2, callbacks = [keras.callbacks.EarlyStopping(monitor=’val_loss’, patience=20)]) after first trial will lead to better accuracy level most of the time. I don’t understand why this is happening. Is…