I try to create a speaker recognition system. The training time is so long so I wonder if there is a way I can store the model and load it from the memory when the GUI launchs?
You can save your trained network with
save net
this will save the network called net as net.mat, you can then load it to the workspace with
load net
To do your prediction, you can use
sim(net, inputs)
Type
doc sim
for detailed help
(more details on this can be easily found on MATLAB central)
Related
I'm having a cold start in building a restaurant recommendation system, and I can not find a pre-trained model that I can use directly or through transfer learning
Suppose you created a model save its weights in h5 file .
Then use that h5 file in your algorithm
I have trained the neural network for a particular time series in MATLAB. After that I have saved the network. So if I want to simulate the network using different parameters like changing the number of neurons,number of hidden layer, transfer functions, learning ratio,momentum coefficient, Can I do it without again training the network?
If not what is the criteria to select the best parameter for my neural network?
How should I configure my neural network in MATLAB to do all these?
No because you save whole model to file, with including weights + activation function and whole structure (layers). You can train few networks, and save to file if you want to check in future on real data (validation data) which networks is better.
Check this also ;) http://people.cs.umass.edu/~btaylor/publications/PSI000008.pdf
Using nntool(Neural Network Manager) in Matlab, we have created a neural network named network1, the network type is Feed Forward backprop. Training function is TRAINLM, learning function is LEARNGDM, performance function is MSE. No. of layers are 2 and transfer function is TRANSIG. No. of Inputs is 2.
We have trained it using known datasets.
Now, we want to use this trained Neural Network on real time values(dynamically one by one) to get the output.
We are unable to use the network on real time values.
So, please guide us through the steps to use trained neural network on real time values.
if you created a ann via
network1 = feedforwardnet;
or something of that kind and then trained it with your known data, you should be able to use said net via
outputs = network1(inputs);
You can create a function from the neural network that you have trained and use it as regular MATLAB functions.
You can either create it with genFun command or using the GUI in neural network toolbox.
genFunction(net,pathname)
If you want the function to accept only matrix elements you should use this command:
genFunction(net,pathname,'MatrixOnly','yes')
I built and trained a neural network using FANN library. This is an initial training; majority of data will be collected online.
When online data becomes available I want to improve the network using this new data (not re-train, but make previous training more accurate).
How to do this kind of incremental training with FANN?
Train from a file that change to:
set_training_algorithm(FANN_TRAIN_INCREMENTAL)
and subsequently train incrementally (online)
Otherwise consult this:
http://fann.sourceforge.net/fann.html
i'm working on a project dealing with character recognition using artificial neural network, I need to train my neural network object 'net', dynamically, based on the training set I have.
The problem is that each time i train my neural network object 'net', its value is overwritten by the recent training set, and the previous weights and other pertinent data is lost. Need help with updating the net object than overwriting, what functions need to be used. Thanks
Move the net object to a new name and continue your work
net1 = net;
The naive way to address the issue is to train your neural net with both the new and old data, if possible.
If not, you can try to make it learn slower -- try to play with the learning rate parameters.