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.
Related
I'm a beginner in computer vision. Could anyone tell me whether what I'm considering to do is correct or not? I wanted to detect a certain cyst in teeth. So my dataset consists of a part of the dental x-ray that contains that cyst. I train my model with these pictures. The one with the colored area contains cyst (infected teeth), and the one below it is the uninfected teet.
Image with cyst
Uninfected teeth
After training my model, I want to use it on a full dental x-ray, and determine if this picture has the cyst or not. A full dental x-ray is shown below.
Full dental X-Ray
Does this work? Or I'm completely wrong?
Instead of treating this as an object detection problem, you would get far better results if you were to treat this as a classification problem.
There are already various architectures for such classification tasks.
There are various architectures in TensorFlow to get you started.
Take a look at this. If you have enough data you can train them from scratch instead of using pre-trained weights
Note - The architecture provided in TensorFlow will almost always give you better results than the architectures that you create.
Object detection is suitable for cases where you have well-defined objects. If you take a look at recently published research papers you can see that these types of problems are considered as classification problems instead of object detection problems.
I successfully trained multi-classificator model, that was really easy with simple class related folder structure and keras.preprocessing.image.ImageDataGenerator with flow_from_directory (no one-hot encoding by hand btw!) after i just compile fit and evaluate - extremely well done pipeline by Keras!
BUT! when i decided to make my own (not cats, not dogs, not you_named) object detector - this is became a nightmare...
TFRecord and tf.Example are just madness! but ok, i almost get it (my dataset is small, i have plenty of ram, but who cares, write f. boilerplate, so much meh...)
The main thing - i just can't find any docs/tutorial how to make it with plain simple tf/keras, everyone just want to build up it on top of someone model, YOLO SSD FRCNN, even if they trying to detect completely new objects!!!
There two links about OD in official docs, and they both using some models underneath.
So my main question WHY ??? or i just blind..? -__-
It becomes a nightmare because Object Detection is way way harder than classification. The most simple object detector is this: first train a classifier on all your objects. Then when you want to detect objects in your image, slide a window over your image, and classify each window. Then, if your classifier is certain that a certain window is one of the objects, mark it as a successful detection.
But this approach has a lot of problems, mainly it's way (like waaaay) too slow. So, researcher improved it and invented RCNNs. That had it problems, so they invented Faster-RCNN, YOLO and SSD, all to make it faster and more accurate.
You won't find any tutorials online on how to implement the sliding window technique because it's not useful anyway, and you won't find any tutorials on how to implement the more advanced stuff because, well, the networks get complicated pretty quick.
Also note that using YOLO doesn't mean you should use the same weights as in YOLO. You can always train YOLO from scratch on your own data if you want by randomly initiliazing all the weights in the network layers. So the even if they trying to detect completely new objects!!! you mentioned isn't really valid. Also also note that I still would advise you to do use the weights they used in Yolo network. Transfer Learning is generally looked at as being a good idea, especially when starting out and especially in the image processing world, as many images share common features (like edges, for example).
I am having pretty much the same problem as my images are B/W diagrams, quite different from regular pictures, I want to train a custom model on just only diagrams.
I have found this documentation section in Tensorflow models repo:
https://github.com/tensorflow/models/blob/master/research/object_detection/README.md
It has a couple of sections explaining how to bring your own model and dataset in "extras" that could be a starting point.
I am working on a project that aims to detect objects in certain difficult circumstances. I ran a test with Mask_RCNN on a dataset that contains that specific type of difficult examples and it did a pretty good job in some of them.
But some other examples didn't get detected surprisingly, when there is no obvious reason. To understand the reason behind this performance difference, I've been adviced to use Tensorboard. But then I realized that its mostly used for training phase, as I understood from this video.
At the end of the video, however, they mention about an integration project of Tensorboard, namely the Tensorflow Debugger Integration. But unfortunately I could not find further information regarding the continuation about that feature.
Is there any way to visualize weights and activation maps inside a CNN during inference/evaluation phase?
The main difference between training and inference time for tensorboard will be the global_step value. Most graphs display global step as the x-axis. You can supply your own global step counter if you like, but you'll have to decide what the x-axis should represent to you in this case since "time" isn't really a logical construct during inference. Other tabs such as the images tab don't have a time component, so using them should be the same as during training.
The tensorflow debugger is a nice terminal debugger, but wouldn't really be related to what you're trying to do here. It's certainly not a visualization tool.
Another approach might be to simply generate your own plots and output a set of PDFs with the various visualizations you need using standard tools like matplotlib for each test image. I've found tools like XnView make it really easy to look through a lot of PDF visualizations to understand what's going on. I've used this approach quite effectively. If you want to view many hundreds or thousands of results quickly you might have an easier time if all the visuals are just dumped out to a directory.
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.
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!