How can I train and test a CNN using Matlab?
Can anybody give me an example code?
you can find Matlab examples in the Caffe repository: https://github.com/BVLC/caffe/tree/master/matlab
Try this simple tutorial. it contains sample code
https://ahmedibrahimvt.wordpress.com/2016/04/18/caffe-in-matlab-matcaffe-simple-tutorial/
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How to compute the Hessian matrix of a large neural network or transformer model like BERT in PyTorch? I know torch.autograd.functional.hessian, but it seems like it only calculates the Hessian of a function, but not a neural network. I also saw the answer in How to compute hessian matrix for all parameters in a network in pytorch?. The problem is, I want to compute the Hessian with respect to the weights, but for large neural networks, it is very inefficient to write it as a function of the weights. Is there a better way to do this? Any suggestion is appreciated. Thanks.
After sometime I finally found a new feature in pytorch nightly build that solves this problem. The details are described in this comment: https://github.com/pytorch/pytorch/issues/49171#issuecomment-933814662. The solution uses the function torch.autograd.functional.hessian and the new feature torch.nn.utils._stateless. Notice that you have to install the nightly version of pytorch to use this new feature.
I want to train tesseract 4.0.0. But when i searched for it, only training for version 3 is seen. Can someone suggest me some blogs that explains tesseract 4.0.0 training.
For training Neural net based LSTM Tesseract 4.0:
https://github.com/tesseract-ocr/tesseract/wiki/TrainingTesseract-4.00
I have met a problem that I train my model in pytorch 0.4.1, but I can't find a tool to convert it into caffe model.
How to use a pytorch 0.4.1 model to init pytorch 0.2.0?
Or how to convert a pytorch 0.4.1 model to a caffe model?
Assuming you are using Caffe2, you can use ONNX to convert models trained in one AI framework to another (with some limitations). In your case you need to export your PyTorch trained model to ONNX model and then import the ONNX model to Caffe2 model.
Follow these tutorials:
PyTorch to ONNX export: link
ONNX to Caffe2 import: link
The tutorials are pretty straightforward, you don't need to write much code. But there are some limitations while exporting your PyTorch model to ONNX.
Check this link for in-depth tutorial on Pytorch to ONNX.
Comment below if you have any doubts/ questions.
EDIT: Try this repo, I have tested it on a toy model it is working.
Newbie to Caffe.
I am trying to use the trained Convolutional neural network on MNIST dataset using Caffe deep learning framework.
Following the official tutorial.
Steps taken successfully:
./data/mnist/get_mnist.sh
./examples/mnist/create_mnist.sh
./examples/mnist/train_lenet.sh
Model was trained and stopped with the following message:
I1203 solver.cpp:133] Snapshotting solver state to lenet_iter_10000.solverstate
I1203 solver.cpp:78] Optimization Done.
Now, I am not sure as how to get a testing image and use the existing trained model which I believe has been snapshot by the name lenet_iter_10000.solverstate to see the predicted scores for each class.
Use the test function of caffe:
<path to caffe root>/caffe test -model <val filename>.prototxt -weights lenet_iter_10000.caffemodel
As you want to test only one image, give that image as input to your test data layer. Use the mean_image as input as well in your <val filename>.protoxt. Test batch size is 1 in this case.
Also note that lenet_iter_10000.solverstate is not your trained model. Your trained model is actually lenet_iter_10000.caffemodel. To know about the diffrence between solverstate and caffemodel files see here.
I played with the scikit-neuralnetwork, which is backed by the pylearn2 library. The pylearn2 has functions to visualize learned weights of the convolutional kernels. Can I somehow access the learned model inside the scikit wrapper and visualize the weights aswell?
I am new to python so going trough the source of scikit-nn did not really help me.
Thanks