I am training a DNN in MATLAB , while optimizing my network, I am observing a decrement in accuracy while increasing the epochs. Is it possible?
The loss values in on the other hand decreases during training while increasing epochs. Please guide.
tldr; absolutely.
When entire training dataset is seen once by the model (feed forwarded once), it's termed as 1 epoch.
The below graph shows the general behaviour of accuracy with the number of epochs. Training on more number of epochs can result in low accuracy on validation, even though loss will continue to reduce (training accuracy will be high). This is termed as overfitting.
No. of epochs to train also a hyperparameter that needs fine tuning.
It is absolutely possible:
Especially when you are training in batches
When your learning rate is too high
Related
I am training a neural network with dropout. It happens that as I decrease dropout from 0.9 to 0.7, the loss (cross-validation error) also decreases for the training data data. I noticed also that accuracy increases as I reduce dropout parameter.
It seems odd to me. Does it make sense?
Dropout is a regularization technique. You should use it only to reduce variance (validation performance vs training performance).It is not intended to reduce the bias, and you should not use it in this way. it is very misleading.
Probably the reason for which you see this behavior is that you use a very high value for dropout. 0.9 means you neutralize too many neurons. It makes sense that once you put there 0.7 instead, the network has higher neurons to use while learning on training set. So the performance will increase for lower values.
You usually should see the training performance dropping a bit, while increasing the performance on the validation set (if you do not have one, at least on the test set). This is the desired behavior you are looking for, when using dropout. The current behavior you get is because if the very high values for dropout.
Start with 0.2 or 0.3 and compare the bias vs. variance in order to get a good value for dropout.
My clear recommendation: don't use it to improve bias, but to reduce variance (error on validation set).
In order to fit better on the training set I recommend :
find a better architecture (or change the number of neurons per
layer)
try different optimizers
hyperparameter tunning
maybe train the network a bit longer
Hopefully this helps !
Dropout works by probabilistically removing, or “dropping out,” inputs to a layer, which may be input variables in the data sample or activations from a previous layer. It has the effect of simulating a large number of networks with a very different network structure and, in turn, making nodes in the network generally more robust to the inputs.
With dropout (dropout rate less than some small value), the accuracy will gradually increase and loss will gradually decrease first(That is what is happening in your case).
When you increase dropout beyond a certain threshold, it results in the model not being able to fit properly. Intuitively, a higher dropout rate would result in a higher variance to some of the layers, which also degrades training.
What you should always remember is that Dropout is like all other forms of regularization in that it reduces model capacity. If you reduce the capacity too much, it is sure that you will get bad results.
Hope this may help you.
When training a neural neural network using batches, should accuracy (training and validation) increase after every epoch (after seeing the whole data an additional time)?
I want to be able to quickly judge if the network settings (learning rate, number of nodes.. etc) is reasonable. It also seemed necessary that the more the whole dataset is seen, the better the performance should be.
So, if the performance decreases at an epoch, should I be worried that something is wrong (high learning rate, high bias)? (Or do I always have to wait several epochs to judge?)
I would say it depends on dataset and architecture. Hence, fluctuations are normal, but in general loss should improve. You can have a look at these practical guides to better interpret loss curves:
http://cs231n.github.io/neural-networks-3/#loss
https://blog.slavv.com/37-reasons-why-your-neural-network-is-not-working-4020854bd607
Yes, in a perfect world one would expect the test accuracy to increase. If the test accuracy starts to decrease it might be that your network is overfitting. You might want to stop the learning just before you reach that point or take other steps to counter the overfitting problem.
Also, it could be a result of noise in the test dataset, i.e. wrongly labeled examples.
I have a neural network which does image segmentation. I trained it ~100 epochs. The current effect is that the validation loss is constant (0.2 +/- 0.03) and the training accuracy is still decreasing (currently 0.07), but very slow.
The result of the neural network is quite well.
What does this mean? Is it overfitting? Should i stop the training?
I currently use dropout in the first layer (50%). Would it make sense to add dropout to every layer (there are about ~15 layers)? Or should i also add L2 regularization? Does it make sense to use L2 and also droput?
Thank you very much
It is recommended to use L2 when you use dropout. I think that your dropout at 50% is a little too high. People usually use it around 20% depending on the operations.
Moreover, 100 epochs may not be enough, it depends on the size of your training set and the size of your neural network.
What do you mean by "quite well"? Please quantify it and share an example. The validation and accuracy are just "indicators", their value also depend on the NN and the training set, so 0.2 can be either bad or good depending on your problem.
I've made digit recognition (56x56 digits) using Neural Networks, but I'm getting 89.5% accuracy on test set and 100% on training set. I know that it's possible to get >95% on test set using this training set. Is there any way to improve my training so I can get better predictions? Changing iterations from 300 to 1000 gave me +0.12% accuracy. I'm also file size limited so increasing number of nodes can be impossible, but if that's the case maybe I could cut some pixels/nodes from the input layer.
To train I'm using:
input layer: 3136 nodes
hidden layer: 220 nodes
labels: 36
regularized cost function with lambda=0.1
fmincg to calculate weights (1000 iterations)
As mentioned in the comments, the easiest and most promising way is to switch to a Convolutional Neural Network. But with you current model you can:
Add more layers with less neurons each, which increases learning capacity and should increase accuracy by a bit. Problem is that you might start overfitting. Use regularization to counter this.
Use batch Normalization (BN). While you are already using regularization, BN accelerates training and also does regularization, and is a NN specific algorithm that might work better.
Make an ensemble. Train several NNs on the same dataset, but with a different initialization. This will produce slightly different classifiers and you can combine their output to get a small increase in accuracy.
Cross-entropy loss. You don't mention what loss function you are using, if its not Cross-entropy, then you should start using it. All the high accuracy classifiers use cross-entropy loss.
Switch to backpropagation and Stochastic Gradient Descent. I do not know the effect of using a different optimization algorithm, but backpropagation might outperform the optimization algorithm you are currently using, and you could combine this with other optimizers such as Adagrad or ADAM.
Other small changes that might increase accuracy are changing the activation functions (like ReLU), shuffle training samples after every epoch, and do data augmentation.
I am training the neural network with input vector of 85*650 and target vector of 26*650. Here is the list of parameters that I have used
net.trainParam.max_fail = 6;
net.trainParam.min_grad=1e-5;
net.trainParam.show=10;
net.trainParam.lr=0.9;
net.trainParam.epochs=13500;
net.trainParam.goal=0.001;
Number of hidden nodes=76
As you can see ,I have set the number of epochs to 13500. Is it OK to set the number of epochs to such a large number?. Performance goal is not reaching if the number of epochs is decreased and I am getting a bad classification while testing.
Try not to focus on the number of epochs. Instead, you should have, at least, two sets of data: one for training and another for testing. Use the testing set to get a feel for how well your ANN is performing and how many epochs is needed to get a decent ANN.
For example, you want to stop training when performance on your testing set as levelled-off or has begun to decrease (get worse). This would be evidence of over-learning which is the reason why more epochs is not always better.