I have a pre-trained network with which I would like to test my data. I defined the network architecture using a .prototxt and my data layer is a custom Python Layer that receives a .txt file with the path of my data and its label, preprocess it and then feed to the network.
At the end of the network, I have a custom Python layer that get the class prediction made by the net and the label (from the first layer) and print, for example, the accuracy regarding all batches.
I would like to run the network until all examples have passed through the net.
However, while searching for the command to test a network, I've found:
caffe test -model architecture.prototxt -weights model.caffemodel -gpu 0 -iterations 100
If I don't set the -iterations, it uses the default value (50).
Does any of you know a way to run caffe test without setting the number of iterations?
Thank you very much for your help!
No, Caffe does not have a facility to detect that it has run exactly one epoch (use each input vector exactly once). You could write a validation input routine to do that, but Caffe expects you to supply the quantity. This way, you can generate easily comparable results for a variety of validation data sets. However, I agree that it would be a convenient feature.
The lack of this feature might be related to its lack for training and the interstitial testing.
In training, we tune the hyper-parameters to get the most accurate model for a given application. As it turns out, this is more closely dependent on TOTAL_NUM than on the number of epochs (given a sufficiently large training set).
With a fixed training set, we often graph accuracy (y-axis) against epochs (x-axis), because that gives tractable results as we adjust batch size. However, if we cut the size of the training set in half, the most comparable graph would scale on TOTAL_NUM rather than the epoch number.
Also, by restricting the size of the test set, we avoid long waits for that feedback during training. For instance, in training against the ImageNet data set (1.2M images), I generally test with around 1000 images, typically no more than 5 times per epoch.
Related
I have a convolutional neural network and my input data are 10.000 images of the same object from different views (angles in 3D around the image). My network converges, but I am not sure if the network has memorized all the different angles / views or not. Since I only have one object I cannot really check test it with different data.
My training / test plot looks like this (red trainig, green test):
Since the test is lower than training I expect the network to learn all the images by heart? Even though I have 10.000 kind of different images.
First, "memorize" is not a term we apply to the learning process, since it's not exact regurgitation of prior examples.
This is a matter of your experimental process. You get to define the success criteria. Is 95% accuracy good enough for your intended application? What, to you, is good enough performance to declare success?
One way to build a more convincing argument is to make the typical third partition: besides training and test sets, save part of your data for validation. You do the training and testing as you've already done. When the model has converged, you apply it to the validation set to predict results. If that test passes your success criterion, then you have a finished model.
My apologies since my question may sound stupid question. But I am quite new in deep learning and caffe.
How can we detect how many iterations are required to fine-tune a pre-trained on our own dataset? For example, I am running fcn32 for my own data with 5 classes. When can I stop the fine-tuning process by looking at the loss and accuracy of training phase?
Many thanks
You shouldn't do it by looking at the loss or accuracy of training phase. Theoretically, the training accuracy should always be increasing (also means the training loss should always be decreasing) because you train the network to decrease the training loss. But a high training accuracy doesn't necessary mean a high test accuracy, that's what we referred as over-fitting problem. So what you need to find is a point where the accuracy of test set (or validation set if you have it) stops increasing. And you can simply do it by specifying a relatively larger number of iteration at first, then monitor the test accuracy or test loss, if the test accuracy stops increasing (or the loss stops decreasing) in consistently N iterations (or epochs), where N could be 10 or other number specified by you, then stop the training process.
The best thing to do is to track training and validation accuracy and store snapshots of the weights every k iterations. To compute validation accuracy you need to have a sparate set of held out data which you do not use for training.
Then, you can stop once the validation accuracy stops increasing or starts decreasing. This is called early stopping in the literature. Keras, for example, provides functionality for this: https://keras.io/callbacks/#earlystopping
Also, it's good practice to plot the above quantities, because it gives you important insights into the training process. See http://cs231n.github.io/neural-networks-3/#accuracy for a great illustration (not specific to early stopping).
Hope this helps
Normally you converge to a specific validation accuracy for your model. In practice you normally stop training, if the validation loss did not increase in x epochs. Depending on your epoch duration x may vary most commonly between 5 and 20.
Edit:
An epoch is one iteration over your dataset for trainig in ML terms. You do not seem to have a validation set. Normally the data is split into training and validation data so you can see how well your model performs on unseen data and made decisions about which model to take by looking at this data. You might want to take a look at http://caffe.berkeleyvision.org/gathered/examples/mnist.html to see the usage of a validation set, even though they call it test set.
I am using caffe, or more likely pycaffe to train and create my network. I am having a dataset with 5 labels at the end. I had the idea to create one network for each label that can just simply say the score for one class. After having then trained 5 networks I want to compare the outputs of the networks and which one has the highest score.
Sadly I do only know how to create one network , but not how to let them interact and moreover how to do something like a max function at the end. I add a picture to describe what I want to do.
Moreover, I do not know if this would have a better outcome than just a normal deep neuronal network.
I don't see what you expect to have as the input to this "max" function. Even if you use some sort of is / is not boundary training, your approach appears to be an inferior version of the softmax layer available in all popular frameworks.
Yes, you can build a multi-channel model, train each channel with a different data set, and then accept the most confident prediction -- but the result will take longer and be less accurate than a cooperative training pass. Your five channels wind up negotiating their boundaries after they've made other parametric assumptions.
Feed a single model all the information available from the outset; you'll get faster convergence and more accurate classification.
I am using caffe to train a deep network, and have set the display at 200 iterations in my solver prototext file. However, instead of getting the loss and accuracy, I am getting a large number of lines of the form
solver.cpp:245] Train net output #{no.}: prob = {no.}.
The prob values are the probabilities of output of softmax layer(the last layer in my network).
This display is of no real use to me. I am interested in seeing only the rate at which the accuracy is evolving with the number of iterations. Can someone suggest a way in which I may print only the relevant parameters to stdout? Is there a way to control in general what is printed by caffe to stdout?
Thanks.
(Note: I am using caffe executable for training on ubuntu)
When training a network, the snapshots taken every N iterations come in two forms together. One is the .solverstate file, which I presume is exactly what it sounds like, storing the state of the loss functions and gradients, etc. The other is the .caffemodel file which I know stores the trained parameters.
The .caffemodel is the file you need if you want a pre-trained model, so I imagine it's also the file you want if you are going to test your network.
WWhat is the .solverstate good for? In this tutorial it looks like you can restart training from it, but how does that differ than using the .caffemodel? Does .solverstate also include the same info as .caffemodel? Put another way, is .caffemodel just a subset of .solverstate?
The solverstate file, as its name conveys, stores the state of the solver and not any information related to classification results. The model is saved as caffemodel file, which you can use to obtain classification results for your data. If you want to fine-tune your network you may use a pre-trained caffemodel file. This will save time as your network does not need to learn from scratch. But, in case your present training needs to be halted, due to a power cut or an unexpected reboot, you may resume your training form the previous snapshot of the solverstate. The difference between using the solverstate and the caffemodel files is that the former allows you to complete your training in the pre-determined manner while the latter may require changes in certain training parameters such as the maximum number of iterations.