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I try 2 class (dog/cat) classification with cnn.
But I found its graph of training is strange.
Why accuracy values shakes greatly? And is it correct training?
optimizer: adam
learning rate: 1e-4
network: https://gist.github.com/elect000/130acbdb0a3779910082593db4296254
optimizer: adam
learning rate: 1e-6
Likely your learning rate is too high.
When the learning rate is too high, the network takes large leaps when changing the weights, and this can cause it to overshoot the local minimum it's approaching.
Have a read of this article for a better description, and a nice diagram:
https://www.quora.com/In-an-artificial-neural-network-algorithm-what-happens-if-my-learning-rate-is-wrong-too-high-or-too-low
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I am training a semantic segmentation model consists of 3 classes(counting with the background).
The background is the dominant class, and the problem is that the model predicts every pixel as background.
I am currently using cross entropy loss function.
What are the solutions for this situation?
This is a typical strong imbalance for image segmentation; down below there are a couple of solutions to tackle this problem.
Use Jaccard(IoU) loss or dice loss; rather than optimizing for accuracy, you will optimise for the intersection over union, for example, and it has been demonstrated that they work much better than cross_entropy in case of imbalanced problems.
You may try to use class weights(sample weights in Keras/TF) in order to assign a greater importance for class 2 and 3 which are not background.
The Focal Loss has shown improvements in MLPs or other deep learning tasks, in which the dataset is strongly imbalanced. Focal loss can be combined with a loss from (1) and (3); it has the potential to improve your results.
You should expect to get the best performance improvement by employing (1) alone.
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I am trying to classify some images if they contain object : Yes or No.
I firstly run 50 epochs with rmsprop optimizer and continue with second run of SGD optimizer with more 50 epochs.
My first run ends with loss ~ 0.4 and model is saved . Second run starts with the saved model.
The problem that at start of second run Keras shows that loss is ~ 0.8 at 1 epoch.
Why could it happen for same loss function ?
Your new SGD optimizer is not optimized for that model. The moments and the adaptive learning rate were forgotten.
Thus, there is indeed a high chance that this new compilation start badly.
You can try to restart with lower learning rates, and also try to add moment to the SGD.
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I am using around 250K images in total with 6 different labels and I am using VGG , with its last layer changed to accommodate 6 categories. What will be the value of learning rate and momentum if SGD optimiser is used?
It depends on a lot of factor, including your training data, batch size, network... You should try different learning rates and see how fast they converge. The Keras LearningRateScheduler callback is also helpful.
Generally, in fine-tuning, the learning rate is kept small. The convention used is 10x smaller than the learning rate used to train the model from scratch.
Momentum is used to dampen the oscillations in the optimization procedure. (when a reduction in one dimension is higher than the other dimension). Higher the momentum more forcefully the optimization procedure is forced to move in directions where the gradient is consistent (in direction) and dampens movement in directions where gradient direction changes. Default values are good to go.
Generally used values lr = 1e-4, momentum=0.9.
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With boosting and random forests, I used early stopping and plotted the train AND test error while training in order to get a feeling when the model is overfitting.
a) in tensorflow (multilayer perceptron), I can plot the training error while training (cost/epoch graph), but how can I get the test error graph while training?
b) is there a built-in cross-validation function in tensorflow (eg 5-fold cv)? If not, what is the most efficient way to do cv with tensorflow?
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Hi I'm new to deep learning and convolutional neural network. Could someone please explain the problem in the figure below? Someone told me that the fluctuation of validation accuracy is the problem here. But I don't quite understand the negative effect of this fluctuation. Why don't we just look at the last point of the figure?
enter image description here
When training a deep learning module you have to validate it.
Which means you are showing the unseen data to algorithm.
So validation accuracy can be less that the training accuracy. Because there's an scenario called over-fitting. Where your training algorithm is too much attached to training data and does not generalize well to other unseen data.
On the fluctuating issue it can be normal. Because we training and testing the algorithm is a stochastic manner.