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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 …
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 …
I'm able to run your inference model and the results looks good. But I'm not able to train the PWCNet caffe model following the instructions. I compiled Caffe from the official …
The training iteration loss is over the minibatches, not the whole training set. Oscillation is expected, not only because the batches differ but because the optimization is …
Moreover, in the last experiments it seems to be almost constant around the value 0.69 (attached a plot of the loss during training) The other issue is related to the accuracy on …
I'm using the training scripts from the examples. I used batch size 15 instead of 30 due to memory limits. I tried learning 1e-4 or 5e-5. The training loss does not decrease once reaching …
I am wondering why validation loss of this regression problem is not decreasing while I have implemented several methods such as making the model simpler, adding early …
I am trying to solve a problem that I found in deep learning with pytorch course on Udacity: “Predict whether a student will get selected or rejected by the university”. But i am …
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 …
Hi, I am taking the output from my final convolutional transpose layer into a softmax layer and then trying to measure the mse loss with my target. Problem is that my loss …
The default learning rate for Adam is 1e-3, which is much too high for training Transformer models. Try learning rates in the range 1e-5 to 1e-4. If training loss is still not …
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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 …
3rd May, 2021. Jacob Blevins. North Carolina State University. The loss function being cyclical seems to be a more dire issue, but I have not seen something like this before. Cite. 4th May, …
The only way the NN can learn now is by memorising the training set, which means that the training loss will decrease very slowly, while the test loss will increase very quickly. In …
Answer (1 of 3): When the validation loss is not decreasing, that means the model might be overfitting to the training data. It helps to think about it from a geometric perspective. Say you …
I am trying to train caffe cifar 10 model for 3 custom classes. I have created the LMDB for training and validation. Data is shuffled before creating LMDB. I tried to plot the …
The model is overfitting right from epoch 10, the validation loss is increasing while the training loss is decreasing.. Dealing with such a Model: Data Preprocessing: Standardizing …
I tried playing around with learning rates, .01, .001, .0001 however my model loss and val loss are not decreasing. I am using Densenet from Pytorch models, and have copied …
First one is a simplest one. Set up a very small step and train it. The second one is to decrease your learning rate monotonically. Here is a simple formula: α ( t + 1) = α ( 0) 1 + t m. Where a is …
to Caffe Users That looks like a case of overfitting. The only possible explanation I can have right now for the low validation loss is there being an inherent similarity in the training …
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 …
I followed a few blog posts and PyTorch portal to implement variable length input sequencing with pack_padded and pad_packed sequence which appears to work well. …
The loss value is not decreasing training the Roberta model. 🤗Transformers. zahraabbasian October 30, 2020, 5:05pm #1. Hi, I load the Roberta pre-trained model from the …
Training loss not decrease after certain epochs.
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I1202 21:40:37.956634 52642 solver.cpp:229] Train net output #0: loss/loss = 3.13329e-05 (* 1 = 3.13329e-05 loss) I am using a K20 GPU with 5GB graphic memory. In order …
1 Answer. This is not unusual for reinforcement learning and does not indicate anything is wrong. As the agent gets better at playing, estimating the reward does get more …
In fact, with decaying the learning rate by 0.1, the network actually ends up giving worse loss. The network does overfit on a very small dataset of 4 samples (giving training loss …
However a couple of epochs later I notice that the training loss increases and that my accuracy drops. This seems weird to me as I would expect that on the training set the …
1. I cannot make my neural network - MLP with 1 hidden layer fit the training data perfectly. Here is the data: xs1 = c (-1, 0, 1) ys1 = c (-0.2445248, 0.1232554, 0.1713998) This …
I was training my deepspeech model and I noticed that the training loss decrease between epoch otherwise the test loss increase . Is it normal ? alchemi5t (alchemi5t) July 30, …
But only your validation set loss will tell whether it is overfitting or not. So your model is getting slightly overfit, becuase train loss is lower than the val loss. You can look into …
My training data has input as a sequence of 80 numbers in which each represent a word and target value is just a number between 1 and 3. I pass it through this model:
Figure 8. The loss plots when using early stopping. The training stopped when the validation loss did not improve for 5 epochs in total. The accuracy and loss plots show the …
During training, the training loss keeps decreasing and training accuracy keeps increasing until convergence. But the validation loss started increasing while the validation …
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