this might seem like a silly question, but I am trying to understand to what extent the testing phase in caffe is important for good results. Of course the training phase is important, but is the testing phase simply to test out how much loss is obtained periodically on a set that is not trained? If this is the case, does the size of my test set really matter? Does testing even matter at all? I ask because I currently have some serious overfit problems. If I have a large dataset (>50 000 images), how should I go about splitting them between test and train?
Caffe never use the result of the test sets while doing training and modify some parameter to fix some issues like overfitting.
The usage of a validation set (test set during training) is for us to visualize whether the model overfits the data by looking at the accuracy or loss values, by plotting them or looking at the outputs.
For example, if the loss of the training set keeps reducing at every iterations and the loss of the test set keeps increasing, this is a solid case of the model overfitting the training set. For getting such conclusions, the images selected for the test set shouldn't be the same as that of the training set. Its ideal to keep a 1:10 ratio for test-train image count. If the test set was using a subset of the trainset, the loss of the testset would have decreased and we may not detect the overfitting behaviour of the model.
Related
I'm training a CNN (one using a series of ConvTranspose2D in pytorch) that uses input data from JSON to constitute an image. Unlike natural language, the input data can be in any order, as it contains info about various sprites in a scene.
In my first attempts to train the model, I didn't change the order of the input data (meaning, on each epoch, each sprite was represented in the same place in the input data). The model learned for about 10 epochs, but then there started to be divergence between the training loss (which continued to go down) and the test loss. So classic overfitting.
I tried to solve this by doing a form of data augmentation where the output data (in this case an image) stayed the same but I shuffled the order of the input data. As I have around 400 sprites, the maximum shuffling is 400!, so theoretically this can vastly expand the amount of training data. For example, instead of 100k JSON documents corresponding to 100K images, by shuffling the order of sprites in the input data, you have 400!*100000 training data points. In practice of course this amount of data is impractical, so I went with around 2m data points for an initial test. The issue I ran into here was that the model was not learning at all - after getting to a certain loss very quickly (after the first few mini-batches), it didn't learn at all for around 4 epochs. So classic underfitting.
Like Goldilocks, I'd like to find "just right" between the initial overfitting and subsequent underfitting. I'm wondering other strategies I could try out. One idea I had was letting the model train on a predetermined order of sprites (the overfitting case) and then, once overfitting starts (ie two straight epochs with divergence between the test and training loss) shuffling the data. I can also play with changing the model, although it can only be so big because of constraints with the hardware and the fact that inference needs to happen in under 20ms.
Are there any papers or techniques that are recommended in this scenario where data augmentation can lead to vastly more data points but results in a model ceasing to learn? Thanks in advance for any tips!
I am trying to train and test a pytorch GCN model that is supposed to identify person. But the test accuracy is quite jumpy like it gives 49% at 23 epoch then goes below near 45% at 41 epoch. So it's not increasing all the time though loss seems to decrease at every epoch.
My question is not about implementation errors rather I want to know why this happens. I don't think there is something wrong in my coding as I saw SOTA architecture has this type of behavior as well. The author just picked the best result and published saying that their models gives that result.
Is it normal for the accuracy to be jumpy (up-down) and am I just to take the best ever weights that produce that?
Accuracy is naturally more "jumpy", as you put it. In terms of accuracy, you have a discrete outcome for each sample - you either get it right or wrong. This makes it so that the result fluctuate, especially if you have a relatively low number of samples (as you have a higher sampling variance).
On the other hand, the loss function should vary more smoothly. It is based on the probabilities for each class calculated at your softmax layer, which means that they vary continuously. With a small enough learning rate, the loss function should vary monotonically. Any bumps you see are due to the optimization algorithm taking discrete steps, with the assumption that the loss function is roughly linear in the vicinity of the current point.
Currently I am using the convolutional neural networks to solve the binary classification problem. The data I use is 2D-images and the number of training data is only about 20,000-30,000. In deep learning, it is generally known that overfitting problems can arise if the model is too complex relative to the amount of the training data. So, to prevent overfitting, the simplified model or transfer learning is used.
Previous developers in the same field did not use high-capacity models (high-capacity means a large number of model parameters) due to the small amount of training data. Most of them used small-capacity models and transfer learning.
But, when I was trying to train the data on high-capacity models (based on ResNet50, InceptionV3, DenseNet101) from scratch, which have about 10 million to 20 million parameters in, I got a high accuracy in the test set.
(Note that the training set and the test set were exclusively separated, and I used early stopping to prevent overfitting)
In the ImageNet image classification task, the training data is about 10 million. So, I also think that the amount of my training data is very small compared to the model capacity.
Here I have two questions.
1) Even though I got high accuracy, is there any reason why I should not use a small amount of data on the high-capacity model?
2) Why does it perform well? Even if there is a (very) large gap between the amount of data and the number of model parameters, the techniques like early stopping overcome the problems?
1) You're completely right that small amounts of training data can be problematic when working with a large model. Given that your ultimate goal is to achieve a "high accuracy" this theoretical limitation shouldn't bother you too much if the practical performance is satisfactory for you. Of course, you might always do better but I don't see a problem with your workflow if the score on the test data is legit and you're happy with it.
2) First of all, I believe ImageNet consists of 1.X million images so that puts you a little closer in terms of data. Here are a few ideas I can think of:
Your problem is easier to solve than ImageNet
You use image augmentation to synthetically increase your image data
Your test data is very similar to the training data
Also, don't forget that 30,000 samples means (30,000 * 224 * 224 * 3 =) 4.5 billion values. That should make it quite hard for a 10 million parameter network to simply memorize your data.
3) Welcome to StackOverflow
On Caffe, I am trying to implement a Fully Convolution Network for semantic segmentation. I was wondering is there a specific strategy to set up your 'solver.prototxt' values for the following hyper-parameters:
test_iter
test_interval
iter_size
max_iter
Does it depend on the number of images you have for your training set? If so, how?
In order to set these values in a meaningful manner, you need to have a few more bits of information regarding your data:
1. Training set size the total number of training examples you have, let's call this quantity T.
2. Training batch size the number of training examples processed together in a single batch, this is usually set by the input data layer in the 'train_val.prototxt'. For example, in this file the train batch size is set to 256. Let's denote this quantity by tb.
3. Validation set size the total number of examples you set aside for validating your model, let's denote this by V.
4. Validation batch size value set in batch_size for the TEST phase. In this example it is set to 50. Let's call this vb.
Now, during training, you would like to get an un-biased estimate of the performance of your net every once in a while. To do so you run your net on the validation set for test_iter iterations. To cover the entire validation set you need to have test_iter = V/vb.
How often would you like to get this estimation? It's really up to you. If you have a very large validation set and a slow net, validating too often will make the training process too long. On the other hand, not validating often enough may prevent you from noting if and when your training process failed to converge. test_interval determines how often you validate: usually for large nets you set test_interval in the order of 5K, for smaller and faster nets you may choose lower values. Again, all up to you.
In order to cover the entire training set (completing an "epoch") you need to run T/tb iterations. Usually one trains for several epochs, thus max_iter=#epochs*T/tb.
Regarding iter_size: this allows to average gradients over several training mini batches, see this thread fro more information.
On Caffe, I am trying to implement a Fully Convolution Network for semantic segmentation. I was wondering is there a specific strategy to set up your 'solver.prototxt' values for the following hyper-parameters:
test_iter
test_interval
iter_size
max_iter
Does it depend on the number of images you have for your training set? If so, how?
In order to set these values in a meaningful manner, you need to have a few more bits of information regarding your data:
1. Training set size the total number of training examples you have, let's call this quantity T.
2. Training batch size the number of training examples processed together in a single batch, this is usually set by the input data layer in the 'train_val.prototxt'. For example, in this file the train batch size is set to 256. Let's denote this quantity by tb.
3. Validation set size the total number of examples you set aside for validating your model, let's denote this by V.
4. Validation batch size value set in batch_size for the TEST phase. In this example it is set to 50. Let's call this vb.
Now, during training, you would like to get an un-biased estimate of the performance of your net every once in a while. To do so you run your net on the validation set for test_iter iterations. To cover the entire validation set you need to have test_iter = V/vb.
How often would you like to get this estimation? It's really up to you. If you have a very large validation set and a slow net, validating too often will make the training process too long. On the other hand, not validating often enough may prevent you from noting if and when your training process failed to converge. test_interval determines how often you validate: usually for large nets you set test_interval in the order of 5K, for smaller and faster nets you may choose lower values. Again, all up to you.
In order to cover the entire training set (completing an "epoch") you need to run T/tb iterations. Usually one trains for several epochs, thus max_iter=#epochs*T/tb.
Regarding iter_size: this allows to average gradients over several training mini batches, see this thread fro more information.