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The solver. scaffolds the optimization bookkeeping and creates the training network for learning and test network (s) for evaluation. iteratively optimizes by calling forward / backward and updating parameters. (periodically) evaluates the test networks. snapshots the model and solver state throughout the optimiz… See more
caffe doesn't optimize anything but the weights, and since the test is only there for evaluation, it does exactly as expected. Assuming you're tuning hyper parameters between …
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 …
Solution 1. 1) You can use the NVIDIA-DIGITS app to monitor your networks. They provide a GUI including dataset preparation, model selection, and learning curve visualization. …
I'm a bit confused about the division between training set, test set and validation set. In creating the solver.prototxt file that defines the train parameters i have to define test_iter parame...
Start training. So we have our model and solver ready, we can start training by calling the caffe binary: caffe train \ -gpu 0 \ -solver my_model/solver.prototxt. note that we …
Caffe creates logs each time you try to train something, and its located in the tmp folder (both linux and windows). I also wrote a plotting script in python which you can easily use to …
So basically what I'm doing here is that, we limit the number of tests in the validation solver file (like instead of 100, we are using 25, which when multiplied by the batch …
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Hi @alexgkendall How can I specify the validation file (val.txt) for validation for the segnet training ? I have found no input as specifying the train.txt file path in Segnet-tutorial-master/Model...
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caffe doesn't optimize anything but the weights, and since the test is only there for evaluation, it does exactly as expected. Assuming you're tuning hyper parameters between solver …
All groups and messages ... ...
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