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Some of my parameters base_lr: 0.04 max_iter: 170000 lr_policy: "poly" batch_size = 8 iter_size =16 this is how the training process looks until …
Hi everyone I have been working with caffe for a while and up to nearly a week ago I only used the pre-trained available networks (namely bvlc_reference_caffenet and …
If the loss doesn't decrease, assuming that it was decreasing at some point earlier, that usually means that the learning rate is too large and needs to be decreased. A common …
to3i commented on May 8, 2014. random initialization (any modifications of random number generation from boost-eigen branch to dev branch?!) nvidia drivers ( I am still …
But have different story. Half year ago I sucsefully learned Imagenet caffe net with cuda 6.5 and cudnn_v2 (45000 iter with same resault as base model). And now i try to learn it …
The other issue is related to the accuracy on the test set. So far in all the experiments that I’ve carried out the accuracy on the test set is always 0.5. I think that this fact …
Thank you! you are really kind to reply me! Although i try use .cuda replace Variable and .to(device), it doesn`t work. But when I change my optimizer form SGD to Adam, …
Hi, I am new to deeplearning and pytorch, I write a very simple demo, but the loss can’t decreasing when training. Any comments are highly appreciated! I want to use one hot to …
In the above piece of code, my when I print my loss it does not decrease at all. It always stays the. same equal to 2.30. epoch 0 loss = 2.308579206466675. epoch 1 loss = …
It is kind of clear that a decrease in the learning rate would probably fix the oscillation. However, I am interested in an explanation for the specific results in the image, and …
VAE Loss not decreasing. I have implemented a Variational Autoencoder in Pytorch that works on SMILES strings (String representations of molecular structures). When trained to …
to Caffe Users I think it's a problem of weights initialization, to verify try to print the weights using the net->params() instructions to retrieve the vector of weights. Try to: specify …
The only possible explanation I can have right now for the low validation loss is there being an inherent similarity in the training and validation set. Try to relax the learning …
Training loss is supposed to decrease over time using a good learning rate. If not, I suggest to decrease the learning rate. Experiment on different learning rate values. Also, try …
U-net segmentation loss does not decrease. Diauro February 7, 2020, 10:37am #1. Hi all, I’m quite new to pytorch so I know I could be doing some very basic mistakes. Currently …
The loss in Caffe is computed by the Forward pass of the network. Each layer takes a set of input ( bottom) blobs and produces a set of output ( top) blobs. Some of these layers’ outputs may …
the opposite test: you keep the full training set, but you shuffle the labels. The only way the NN can learn now is by memorising the training set, which means that the training loss will …
The ValueLoss does not decrease as it IS a 'chasing the tail situation'. If I run the code with ValueLoss = a constant - Value instead of NextValue - Value, I see that the loss …
Do Not Reduce - DNR: A trade type used on an buy or sell order. It tells the broker not to decrease the limit price on buy-limit and sell-stop orders on the record date of a cash …
I had this issue - while training loss was decreasing, the validation loss was not decreasing. I checked and found while I was using LSTM: I simplified the model - instead of 20 …
Losses of keras CNN model is not decreasing. I am working on Street view house numbers dataset using CNN in Keras on tensorflow backend. I have queries regarding why loss …
If the validation loss does not decrease for the given number of `patience` epochs, then the learning rate will decrease by by given `factor`. """ def __init__( self, optimizer, …
To initialize the weights randomly. Since it is initialization, so the name suggests, it is only called in the initial stage, and is no longer called during the training process. Therefore, this value is …
Dear all, I'm fine-tuning previously trained network. Now I see that validaton loss start increase while training loss constatnly decreases. I know that it's probably overfitting, but …
Loss does not decrease during training a CNN. Learn more about deep learning, accurate image super-resolution using cnn Deep Learning Toolbox
caffe-loss has a low active ecosystem. It has 1 star(s) with 0 fork(s). It had no major release in the last 12 months. It has a neutral sentiment in the developer community.
Caffe_Loss. The loss function is an important component in deep learning. All of the optimization algorithms are LOSS-based, and the designs of loss functions can have a large extent to affect …
In the solver.prototxt, I reduced the stepsize and set the solver_mode to CPU. This is the output of training: I1205 10:42:36.951148 2041447168 solver.cpp:298] Test net output …
Caffeine helps to promote hair growth as the chemical targets DHT, which is the hormone responsible for hair loss. When the hormone DHT attaches itself to the hair follicles it …
In the short term, caffeine can boost the metabolic rate and increase fat burning, but after a while people become tolerant to the effects and it stops working. But even if coffee …
However, Dr. Sonpal says there are some caveats. Most importantly, coffee doesn't directly cause a magnesium deficiency. "The coffee isn’t leeching the magnesium out," he …
1 Answer. Discriminator consist of two loss parts (1st: detect real image as real; 2nd detect fake image as fake). 'Full discriminator loss' is sum of these two parts. The loss …
I think there are some problems with your approach. Firstly, looking at the Keras documentation, LSTM expects an input of shape (batch_size, timesteps, input_dim).You're passing an input of …
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