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22.02.2020 · Validation loss not decreasing. murali_perumalla (murali perumalla) February 22, 2020, 4:54pm #1. @ptrblck hi ,i have tried vgg16,vgg16, densenet ,resnet… and i tired chaging a lot parameters but validation loss doesnt decrease . i tried with loss functions: adam,SGD, lr_schedulars: reduceonpleatue , stepLR. lr=[0.1,0.001,0 ...

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 ...

Jan 11, 2018 · Minimum validation loss i obtained so far is 0.069. Its not decreasing further and results are also not good when plotting prediction vs y_test data. Below are details about the same.

05.02.2020 · Validation Loss is not decreasing - Regression model. Ask Question Asked 1 year, 11 months ago. Active 1 year, 10 months ago. Viewed 1k times 0 $\begingroup$ I'm trying to train a regression model with 6 input features. No. of tuples - 7287. Train set - 5465 Test set ...

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 layers, I opted for 8 layers. Instead of scaling within range (-1,1), I choose (0,1), this right there reduced my validation loss by the magnitude of one order

Jan 09, 2019 · My loss is not reducing and training accuracy doesn't fluctuate much. I'm guessing I have something wrong with the model. ... Training acc decreasing, validation ...

But, I found that cross validation error is never decreasing and is continuously in. When you learn on a part of your training set that is not representative of ...

Data Preprocessing: Standardizing and Normalizing the data. · Model compelxity: Check if the model is too complex. Add dropout, reduce number of layers or number ...

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 layers, I opted for 8 layers. Instead of scaling within range (-1,1), I choose (0,1), this right there reduced my validation loss by the magnitude of one order

11.01.2018 · Validation loss not decreasing for Non Linear regression problem #9051. sumeet30 opened this issue Jan 11, 2018 · 1 comment Comments. Copy link sumeet30 commented Jan 11, 2018. Hi, I am trying to build ANN model for this data. It is ANN for non linear regression problem.

Hi, I am traning the model and I have tried few different learning rates but my validation loss is not decrasing. below is the learning rate ...

Feb 05, 2020 · I've tried changing no. of hidden layers and hidden neurons, early stopping, shuffling the data, changing learning and decay rates and my inputs are standardized (Python Standard Scaler). Validation loss doesn't decrease. Mean square error is very high and r2 score is 0.5276 for the train set and 0.3383 for the test set.

Yeah it's not changing because your model has reached its max capacity to minimize the validation error. That's why it's not changing. At some ...

Is the network size is too small / large? Check overfitting or underfitting by train history, then chose the best epoch size. Try initialise weights with ...

Feb 22, 2020 · class sample counts : class 1 contains 3451 samples ,…. {1: 3451, 2: 1014, 4: 792, 3: 626, 7: 569, 5: 560, 6: 453, 0: 444} train accuracy:71%. validation loss never decreased 0.84 with vgg16,vgg19,densenet,alexnet,resnet18. Test Loss: 0.826629. confusion matrix:

26.11.2021 · Train/validation loss not decreasing. Mukesh1729 November 26, 2021, 9:23am #1. 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 is doesn’t decrease and is stuck around the same point. I figured the problem is using the ...

Nov 26, 2021 · Train/validation loss not decreasing. Mukesh1729 November 26, 2021, 9:23am #1. 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 is doesn’t decrease and is stuck around the same point. I figured the problem is using the ...

Validation loss not decreasing · Normalize the data with mean and standard deviation. · Use a lot of data augmentation like flips, rotation, color ...

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 have some complex surface with countless peaks and valleys. A fast learning rate means you descend down quickly because you likely are far away from any minimum.

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 have some complex surface with countless peaks and valleys. A fast learning rate means you descend down quickly because you likely are far away from any minimum.

Since neural nets are generally not able to extrapolate data, this could be the cause of the problem. A solution is again a better choice of ...

[D] Validation loss not decreasing, no matter what regularization I do. So I have a face verification model training with siamese network.