I have a few matlab trained deep learning networks to classify images, and I want to use them in the LabVIEW. And I want to preload the networks at the very beginning, instead of loading the networks between codes. But the MATLAB node in LabView cannot output a network.
I have tried to use the Deep Learning Module in LabView, but the supported tensorflow version is too low for my network to work. I read that DataPlugin for MATLAB could work, but I didn't find any example of how it works.
Related
I have a structure for my CNN that will be used in image enhancement. and I want to know how to use Caffe package in Matlab to design and train the network. I don't need to import pretrained network from Caffe as I have a specific structure for the CNN. Does any one have a link or example that guide me on how to do that? Any help will be appreciated.
The Caffe official documentation has a simple example showcasing basic interfaces for Matlab including adding conv layers to the network:
http://caffe.berkeleyvision.org/tutorial/interfaces.html#matlab
Once you get the basics for matlab, the interfaces are mostly similar to python.
Hope that helps
Is it possible to convert a trained convolutional neural network saved as .mat into a .pb format?
I was thinking to import it in opencv using cv::dnn::readNetFromTensorflow()
Are there any alteratives, o I just should retrain the network directly in tensorflow or keras?
I just found about Open Neural Network Exchange.
You can simply save the network from matlab using exportONNXNetwork(net,filename) and load the network inside opencv using cv2.dnn.readNetFromONNX()
I'm working on a virtual machine on a remote server and I want to train a neural network on it but I don't have GPUs to use in this VM. is it possible to train the net on this VM using CPU only? and if that the case, does it work with a large dataset or that will be a problem?
I have used Tensorflow for training a deep neural network. I have used it with GPU and CPU only. The rest of my response is in context of Tensorflow.
Please be aware that Convolution Neural Nets are generally more resource hungry than standard regular feed forward neural networks because CNNs deal with much higher dimensional data. If you are not working deep CNNs then you may be all right to use CPU to and restrict to smaller datasets.
In my scenario, initially I was training with CPU only and then moved on to GPU mode because of speed improvements.
Example of speed
I was able to train the entire MNIST with when using GPU in under 15 minutes. Training on CPU was much slower but you can still learn by cutting down the on the size of the training data set.
Tensorflow with GPU
https://www.tensorflow.org/install/gpu
You will need to go through all the installation steps. This involves not only installing Tensorflow but also CUDA libraries.
What is CUDA?
CUDA is the specification developed by NVIDIA for programming with GPU. They provide their native libraries which talk to the underlying hardware.
https://docs.nvidia.com/cuda/
How to use TensorFlow GPU?
I am new to matlab and my project requires 1D Convolutional Neural Network. Can anyone tell me how to design 1D CNN in matlab? Does it require any special hardware? Refrence to some blogs will be really helpful.
Thank you.
As I know CNN implementation requires GPU. Matlab 2016 version have some inbuilt function but requires GPU installed to work. Python and tensorflow works great and quick , I would like you suggest to try those than Matlab.
I've tried to follow the example provided at mathworks for training a deep sparse autoencoder (4 layers), so i pre-trained the autoencoders separately and then stacked then into a deep network. When i try to finetune this network though, via the
train(deepnet, InputDataset)
instruction, the training stops instantly and i receive a "performance goals met" message.
Is there a way to train and finetune a deep autoencoder network in an unsupervised manner in Matlab (no labels provided)?
Have you set the "MSE" goal? Secondly, for fine tuning of network use a conventional back-propagation algorithm in supervised fashion.