Now a days very high power shared GPUs are available for deep learning training at very cheap price. But protecting your training dataset and code is problem there because GPU owner can see your files. I know that it is impossible to completely prevent, reverse engineering of the code and data, but I want to make it very very hard. I am thinking to encrypt my datasets images and decrypt problematically before feeding to the model.But the problem is, anyone intelligent enough can see my decrypted data when i feed to the model.
What are the options to hide TensorFlow code and build c++ binary? I tried Nuitka, its not working with tensorflow. I searched that Cython will also not work. There is tf-encrypted but, it seems like that work with older version of tensorflow (1.x). Please suggest some clever way which works.
Related
I haven't tried Tensorflow yet but still curious, how does it store, and in what form, data type, file type, the acquired learning of a machine learning code for later use?
For example, Tensorflow was used to sort cucumbers in Japan. The computer used took a long time to learn from the example images given about what good cucumbers look like. In what form the learning was saved for future use?
Because I think it would be inefficient if the program should have to re-learn the images again everytime it needs to sort cucumbers.
Ultimately, a high level way to think about a machine learning model is three components - the code for the model, the data for that model, and metadata needed to make this model run.
In Tensorflow, the code for this model is written in Python, and is saved in what is known as a GraphDef. This uses a serialization format created at Google called Protobuf. Common serialization formats include Python's native Pickle for other libraries.
The main reason you write this code is to "learn" from some training data - which is ultimately a large set of matrices, full of numbers. These are the "weights" of the model - and this too is stored using ProtoBuf, although other formats like HDF5 exist.
Tensorflow also stores Metadata associated with this model - for instance, what should the input look like (eg: an image? some text?), and the output (eg: a class of image aka - cucumber1, or 2? with scores, or without?). This too is stored in Protobuf.
During prediction time, your code loads up the graph, the weights and the meta - and takes some input data to give out an output. More information here.
Are you talking about the symbolic math library, or the idea of tensor flow in general? Please be more specific here.
Here are some resources that discuss the library and tensor flow
These are some tutorials
And here is some background on the field
And this is the github page
If you want a more specific answer, please give more details as to what sort of work you are interested in.
Edit: So I'm presuming your question is more related to the general field of tensor flow than any particular application. Your question still is too vague for this website, but I'll try to point you toward a few resources you might find interesting.
The tensorflow used in image recognition often uses an ANN (Artificial Neural Network) as the object on which to act. What this means is that the tensorflow library helps in the number crunching for the neural network, which I'm sure you can read all about with a quick google search.
The point is that tensorflow isn't a form of machine learning itself, it more serves as a useful number crunching library, similar to something like numpy in python, in large scale deep learning simulations. You should read more here.
I'm actualy new in Machine Learning, but this theme is vary interesting for me, so Im using TensorFlow to classify some images from MNIST datasets...I run this code on Compute Engine(VM) at Google Cloud, because my computer is to weak for this. And the code actualy run well, but the problam is that when I each time enter to my VM and run the same code I need to wait while my model is training on CNN, and after I can make some tests or experiment with my data to plot or import some external images to impruve my accuracy etc.
Is There is some way to save my result of trainin model just once, some where, that when I will decide for example to enter to the same VM tomorrow...and dont wait anymore while my model is training. Is that possible to do this ?
Or there is maybe some another way to do something similar ?
You can save a trained model in TensorFlow and then use it later by loading it; that way you only have to train your model once, and use it as many times as you want. To do that, you can follow the TensorFlow documentation regarding that topic, where you can find information on how to save and load the model. In short, you will have to use the SavedModelBuilder class to define the type and location of your saved model, and then add the MetaGraphs and variables you want to save. Loading the saved model for posterior usage is even easier, as you will only have to run a command pointing to the location of the file in which the model was exported.
On the other hand, I would strongly recommend you to change your working environment in such a way that it can be more profitable for you. In Google Cloud you have the Cloud ML Engine service, which might be good for the type of work you are developing. It allows you to train your models and perform predictions without the need of an instance running all the required software. I happen to have worked a little bit with TensorFlow recently, and at first I was also working with a virtualized instance, but after following some tutorials I was able to save some money by migrating my work to ML Engine, as you are only charged for the usage. If you are using your VM only with that purpose, take a look at it.
You can of course consult all the available documentation, but as a first quickstart, if you are interested in ML Engine, I recommend you to have a look at how to train your models and how to get your predictions.
This is a newbie question for the tensorflow experts:
I reading lot of data from power transformer connected to an array of solar panels using arduinos, my question is can I use tensorflow to predict the power generation in future.
I am completely new to tensorflow, if can point me to something similar I can start with that or any github repo which is doing similar predictive modeling.
Edit: Kyle pointed me to the MNIST data, which I believe is a Image Dataset. Again, not sure if tensorflow is the right computation library for this problem or does it only work on Image datasets?
thanks, Rajesh
Surely you can use tensorflow to solve your problem.
TensorFlow™ is an open source software library for numerical
computation using data flow graphs.
So it works not only on Image dataset but also others. Don't worry about this.
And about prediction, first you need to train a model(such as linear regression) on you dataset, then predict. The tutorial code can be found in tensorflow homepage .
Get your hand dirty, you will find it works on your dataset.
Good luck.
You can absolutely use TensorFlow to predict time series. There are plenty of examples out there, like this one. And this is a really interesting one on using RNN to predict basketball trajectories.
In general, TF is a very flexible platform for solving problems with machine learning. You can create any kind of network you can think of in it, and train that network to act as a model for your process. Depending on what kind of costs you define and how you train it, you can build a network to classify data into categories, predict a time series forward a number of steps, and other cool stuff.
There is, sadly, no short answer for how to do this, but that's just because the possibilities are endless! Have fun!
I have just started experimenting with Deep Learning and Computer Vision technologies. I came across this awesome tutorial. I have setup the TensorFlow environment using docker and trained my own sets of objects and it provided greater accuracy when I tested it out.
Now I want to make the same more real-time. For example, instead of giving an image of an object as the input, I want to utilize a webcam and make it recognize the object with the help of TensorFlow. Can you guys guide me with the right place to start with this work?
You may want to look at TensorFlow Serving so that you can decouple compute from sensors (and distribute the computation), or our C++ api. Beyond that, tensorflow was written emphasizing throughput rather than latency, so batch samples as much as you can. You don't need to run tensorflow at every frame, so input from a webcam should definitely be in the realm of possibilities. Making the network smaller, and buying better hardware are popular options.
The tensorflow text summarization model as described here https://github.com/tensorflow/models/tree/master/textsum requires a multi GPU architecture in order to train. My repeated attempts at training the model has resulted in memory exceptions, machine crashing for various reasons. Is the trained summarisation model available so can make use of the summarization model without the need for training? The summarization model is trained using the not free Gigaword dataset, if the trained model is not available from Google is this a factor in reason why ?
So as far as I can tell, no one has put the trained model out there that is referenced. I too was originally running into memory issues on my macbook pro and eventually ended up using my gaming laptop which had a much better GPU.
The other option of course is to take advantage of AWS and use something like their g2.2xlarge instance. They also have their P2 instances as well, but I have not checked that out yet.
With regards to the Gigaword dataset, it simply comes down to licensing. It is not a free license from LDC and often many of the academics working on this have the dataset provided to them via their Universities or companies. I have not had luck finding it, however LDC did get back to me and advised that they do have other article datasets that have a pricetag of around $300 which is much more reasonable for those of use just trying to learn TF. That said, if you didn't want to buy anything, you can always write your own page scraper and format the data for the textsum model. https://github.com/tensorflow/models/pull/379/files
Hope this helps some. Good luck!