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
Related
Is it possible to use Tensorflow or some similar library to make a model that you can efficiently train and use at the same time.
An example/use case for this would be a chat bot that you give feedback to. Somewhat like how pets learn (i.e. replicating what they just did for a reward). Or being able to add new entries or new responses they can use.
I think what you are asking is whether a model can be trained continuously without having to retrain it from scratch each time new labelled data comes in.
Answer to that is - Online models
There are models that can be trained continuously on data without worrying about training them from scratch. As per Wikipedia definition
Online machine learning is a method of machine learning in which data becomes available in sequential order and is used to update the best predictor for future data at each step, as opposed to batch learning techniques which generate the best predictor by learning on the entire training data set at once.
Some examples of such algorithms are
BernoulliNB
GaussianNB
MiniBatchKMeans
MultinomialNB
PassiveAggressiveClassifier
PassiveAggressiveRegressor
Perceptron
SGDClassifier
SGDRegressor
DNNs
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)
So after I have spent a few days cleaning my data, preproceasing, and experimenting with a few different models (e.g. in R Studio) how do I realistically deploy the solution.
Its straightforward if the model is a simple Model e.g Decision Tree, Logistic regression, as the model is obvious and the R Predictor model deployed into an commercial R Server with http endpoints etc.
My question is, what about complex pre processing ( e.g. PCA transforms, RBF kernels, or Random forests of 100 trees.) just as in the Validation phase, I would presume I would have to deploy R Scripts to preprocess, and PCA or apply RBF pre processing scripts etc to my deployment server ?
Does this mean for RBF I have to host all the original Training data set alongside my SVM predictor ? RBF transform being a function of Training set or at least the support vectors.
And for Random Forest, I assume I have to upload all 500 or so Trees, as part of a very big model.
First, export your R solution (data pre-processing steps and the model) into PMML data format using either the combo of pmml and pmmlTransformations packages, or the r2pmml package. Second, deploy the PMML solution using Openscoring REST web service, JPMML-Spark, or whatever else PMML integration that fits your deployment needs.
PMML has no problem representing PCA, SVM with RBF kernel, tree ensembles, etc.
A pure R vanilla solution for this problem. Most of the ensemble method provide utility to dump/save the learnt model. Learning is very time consuming and iterative process that should be done once. Post learning save/dump your R object. In the deployment have only scoring code. Scoring code will do all the data transformations and later on scoring.
For normal preprocessing you can reuse R code which was used in training. For complex processing like PCA again save the final model and just score/run data over saved PCA R object. Lastly post preprocessing score/run your data on learnt model and get final results.
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
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