- 1 Why does validation loss fluctuate?
- 2 Why does loss decrease but not accuracy?
- 3 How can validation loss be reduced?
- 4 How do you increase validation accuracy?
- 5 How do I improve my CNN validation loss?
- 6 How can we reduce loss in keras?
- 7 Are there any possible explanations for loss increasing?
- 8 Why does my loss function stay the same?
Why does validation loss fluctuate?
Your validation accuracy on a binary classification problem (I assume) is “fluctuating” around 50%, that means your model is giving completely random predictions (sometimes it guesses correctly few samples more, sometimes a few samples less). Generally, your model is not better than flipping a coin.
Why does loss decrease but not accuracy?
2 Answers. A decrease in binary cross-entropy loss does not imply an increase in accuracy. Consider label 1, predictions 0.2, 0.4 and 0.6 at timesteps 1, 2, 3 and classification threshold 0.5. timesteps 1 and 2 will produce a decrease in loss but no increase in accuracy.
What does it mean if validation loss is lower than training loss?
Training Loss. If your training loss is much lower than validation loss then this means the network might be overfitting. Solutions to this are to decrease your network size, or to increase dropout. For example you could try dropout of 0.5 and so on.
Why does VAL loss increase?
If the loss increases and the accuracy increase too is because your regularization techniques are working well and you’re fighting the overfitting problem. This is true only if the loss, then, starts to decrease whilst the accuracy continues to increase.
How can validation loss be reduced?
- Data Preprocessing: Standardizing and Normalizing the data.
- Model compelxity: Check if the model is too complex. Add dropout, reduce number of layers or number of neurons in each layer.
- Learning Rate and Decay Rate: Reduce the learning rate, a good starting value is usually between 0.0005 to 0.001.
How do you increase validation accuracy?
- Use weight regularization. It tries to keep weights low which very often leads to better generalization.
- Corrupt your input (e.g., randomly substitute some pixels with black or white).
- Expand your training set.
- Pre-train your layers with denoising critera.
- Experiment with network architecture.
What should you do if your accuracy is low?
Now we’ll check out the proven way to improve the accuracy of a model:
- Add more data. Having more data is always a good idea.
- Treat missing and Outlier values.
- Feature Engineering.
- Feature Selection.
- Multiple algorithms.
- Algorithm Tuning.
- Ensemble methods.
What is more important loss or accuracy?
There is no relationship between these two metrics. Loss can be seen as a distance between the true values of the problem and the values predicted by the model. Greater the loss is, more huge is the errors you made on the data. Accuracy can be seen as the number of error you made on the data.
How do I improve my CNN validation loss?
We have the following options.
- Use a single model, the one with the highest accuracy or loss.
- Use all the models. Create a prediction with all the models and average the result.
- Retrain an alternative model using the same settings as the one used for the cross-validation. But now use the entire dataset.
How can we reduce loss in keras?
Here are things you could adjust:
- You have a very low batch size.
- Try different activation functions (but always have softmax or sigmoid in the last layer because you want numbers between 0 and 1 ).
- Increase the number of units in the first and/or second layer (if you have enough data).
Why would the loss decrease while the accuracy stays the same?
I expect that either both losses should decrease while both accuracies increase, or the network will overfit and the validation loss and accuracy won’t change much. Either way, shouldn’t the loss and its corresponding accuracy value be directly linked and move inversely to each other?
Why does my loss increase as I deepen my network?
I started with a small network of 3 conv->relu->pool layers and then added 3 more to deepen the network since the learning task is not straightforward. My loss is doing this (with both the 3 and 6 layer networks):: The loss actually starts kind of smooth and declines for a few hundred steps, but then starts creeping up.
Are there any possible explanations for loss increasing?
Or better yet use the tf.nn.sparse_softmax_cross_entropy_with_logits (…) function which takes care of numerical stability for you. Since the cost is so high for your crossentropy it sounds like the network is outputting almost all zeros (or values close to zero). Since you did not post any code I can not say why.
Why does my loss function stay the same?
Similarly My loss seems to stay the same, here is an interesting read on the loss function. I really am still unsure as to what I may be doing wrong. SGD learning rate from 0.000000001 to 0.1