I have trained and tested the pytorch based GAN model and now I want to deploy it to Android.
I have read about converting cnn model to onnx format and then to cafe2
but I don't know how to use it if there is more than one model i.e. generator, discriminator, encoder-decoder models.
Related
I do not know if the fine tuning of the pretrained model changes the weights value in the downloaded pretrained model or it changes the model weights of my network only.
I design a DNN with the help of ResNet50 pretrained model ... and I tried to fine tune it, so I turned the trainable into true ... did it change the weights of the downloaded ResNet model in my computer or these changes is local to my model?
I have a simple tensorflow model with lstm layers. I want to convert the model to .mlmodel format. However, I think, as of now, CoreML does not support LSTM layers and hence I need to write a custom lstm layer in swift.
How can I write that custom layer?
Why not try with Keras and use different tensorflow or other back-ends? the Keras has one of the easiest interfaces.
I suggest reading the tensorflow model using keras. Then use the link below to convert the keras to core-ml.
Try to make it simple. not complicated
https://heartbeat.fritz.ai/using-coremltools-to-convert-a-keras-model-to-core-ml-for-ios-d4a0894d4aba?gi=76f8b08071e9
I used the Core ML Converter to convert a Caffe AlexNet model to a Core ML model. The model works just fine and outputs correct classification results. However, I do not know how to access the output of a layer inside the CNN model. Say for example I want to know what the output of one of the convolution layer (e.g. conv5) is. Caffe let you do so easily, but I could not find documentation on how to do this using Core ML.
Does Core ML allow access to outputs of layers inside the CNN model like Caffe does?
I am working on speaker identification project in matlab which my goal is to check whether a test speaker is my target speaker or not.
I used mfcc and lpcc and pitch as my features in this project and I used libsvm for single class classifier to train my model but my model accuracy even when I test it on my train data is quite low.
I use pre-implement mfcc and lpcc function which I am sure of correctness of this two features so I thought this might be a problem with classifier so I decide to use Gaussian Mixture Model as my classifier in this project however how can use Gaussian Mixture Model for single class classification?
I have trained a Bayesian Regularized Neural Network model with MATLAB. This model is not available with Weka. So now I want to import my MATLAB model as Weka's .model file, so that I can directly use my model with Weka.
How do I go about it?
Weka can import models in the PMML format, so the easiest (and possibly the only avaliable) way to load the neural network trained with some "special" form of regularization.
You will have to save your network in the PMML format, some guidelines can be obtained here:
http://www.dmg.org/v3-2/NeuralNetwork.html