Steps to think of while trying to increase accuracy of network - neural-network

How do you proceed to increasing accuracy of your neural network?
I have tried lots of architectures yet in my image detection ( classification + localization ) I can only get 75% accuracy.
I am using VOC2007 dataset, and I extracted only data where 1 person is present.
What are the steps I can think of to increase the accuracy of my object detector?
thanks for help.

You might want to have a look at my masters thesis Analysis and Optimization of Convolutional Neural Network Architectures, chapter 2.5 page 15:
A machine learning developer has the following choices to improve the model’s quality:
(I1) Change the problem definition (e.g., the classes which are to be distinguished)
(I2) Get more training data
(I3) Clean the training data
(I4) Change the preprocessing (see Appendix B.1)
(I5) Augment the training data set (see Appendix B.2)
(I6) Change the training setup (see Appendices B.3 to B.5)
(I7) Change the model (see Appendices B.6 and B.7)
It's always good to check thoroughly where exactly the problem is and compare it with a human baseline. Reliably getting better than a human is super hard.

Related

How do I determine the architecture for deep NN training according to the number of examples?

As the title says, how can I determine the architecture or build a reasonable model for training a neural network with regards to the number of examples?
For example, assuming that I have roughly 50 thousand images and I have successfully converted all data to fit the model which means they are ready for training, how can I choose a model that is suitable for training a neural network? I am a little bit confused sometimes when I have data but I did not know how to initiate a model for training NN.
Fine tuning is the way
Sometimes you have a pre-trained CNN that you can use as a starting point for your domain. For more about fine tuning You can check here.
According to this, my advice is to fine tune a pre-trained Neural Network that you can find in Keras (This page, under "Available models") or TensorFlow. You can go deeper as far as you are confident with your training set!
In any case, you need to see the number of samples per class rather than the absolute number of images in your training set. If you are confident you can choose a Deep Learning SOA architecture and try to train it from zero.

Use a trained neural network to imitate its training data

I'm in the overtures of designing a prose imitation system. It will read a bunch of prose, then mimic it. It's mostly for fun so the mimicking prose doesn't need to make too much sense, but I'd like to make it as good as I can, with a minimal amount of effort.
My first idea is to use my example prose to train a classifying feed-forward neural network, which classifies its input as either part of the training data or not part. Then I'd like to somehow invert the neural network, finding new random inputs that also get classified by the trained network as being part of the training data. The obvious and stupid way of doing this is to randomly generate word lists and only output the ones that get classified above a certain threshold, but I think there is a better way, using the network itself to limit the search to certain regions of the input space. For example, maybe you could start with a random vector and do gradient descent optimisation to find a local maximum around the random starting point. Is there a word for this kind of imitation process? What are some of the known methods?
How about Generative Adversarial Networks (GAN, Goodfellow 2014) and their more advanced siblings like Deep Convolutional Generative Adversarial Networks? There are plenty of proper research articles out there, and also more gentle introductions like this one on DCGAN and this on GAN. To quote the latter:
GANs are an interesting idea that were first introduced in 2014 by a
group of researchers at the University of Montreal lead by Ian
Goodfellow (now at OpenAI). The main idea behind a GAN is to have two
competing neural network models. One takes noise as input and
generates samples (and so is called the generator). The other model
(called the discriminator) receives samples from both the generator
and the training data, and has to be able to distinguish between the
two sources. These two networks play a continuous game, where the
generator is learning to produce more and more realistic samples, and
the discriminator is learning to get better and better at
distinguishing generated data from real data. These two networks are
trained simultaneously, and the hope is that the competition will
drive the generated samples to be indistinguishable from real data.
(DC)GAN should fit your task quite well.

Convolution Neural Network for image detection/classification

So here is there setup, I have a set of images (labeled train and test) and I want to train a conv net that tells me whether or not a specific object is within this image.
To do this, I followed the tensorflow tutorial on MNIST, and I train a simple conv net reduced to the area of interest (the object) which are training on image of size 128x128. The architecture is as follows : successively 3 layers consisting of 2 conv layers and 1 max pool down-sampling layers, and one fully connected softmax layers (with two class 0 and 1 whether the object is present or not)
I impleted it using tensorflow, and this works quite well, but since I have enough computing power I was wondering how I could improve the complexity of the classification:
- adding more layers ?
- adding more channel at each layer ? (currently 32,64,128 and 1024 for the fully connected)
- anything else ?
But the most important part is that now I want to detect this same object on larger images (roughle 600x600 whereas the size of the object should be around 100x100).
I was wondering how I could use the previously training "small" network used for small images, in order to pretrained a larger network on the large images ? One option could be to classify the image using a slicing window of size 128x128 and scan the whole image but I would like to try if possible to train a whole network on it.
Any suggestion on how to proceed ? Or an article / ressource tackling this kind of problem ? (I am really new to deep learning so sorry if this is stupid question...)
Thanks !
I suggest that you continue reading on the field overall. Your search keys include CNN, image classification, neural net, AlexNet, GoogleNet, and ResNet. This will return many articles, on-line classes and lectures, and other materials to help you learn about classification with neural nets.
Don't just add layers or filters: the complexity of the topology (net design) must be fitted to the task; a net that's too complex will over-fit the training data. The one you've been using is probably LeNet; the three I cite above are for the ImageNet image classification contest.
Since you are working on images, I would suggest you to use a pretrained image classification network (like VGG, Alexnet etc.)and fine tune this network with your 128x128 image data. In my experience until we have very large data set fine tuned network will give more accuracy and also save training time. After building a good image classifier on your data set you can use any popular algorithm to generate region of proposal from the image. Now take all regions of proposal and pass them to classification network one by one and check weather this network is classifying given region of proposal as positive or negative. If it classifying as positively then most probably your object is present in that region. Otherwise it's not. If there are a lot of region of proposal in which object is present according to classifier then you can use non maximal suppression algorithms to reduce number of positive proposals.

Continuously train MATLAB ANN, i.e. online training?

I would like to ask for ideas what options there is for training a MATLAB ANN (artificial neural network) continuously, i.e. not having a pre-prepared training set? The idea is to have an "online" data stream thus, when first creating the network it's completely untrained but as samples flow in the ANN is trained and converges.
The ANN will be used to classify a set of values and the implementation would visualize how the training of the ANN gets improved as samples flows through the system. I.e. each sample is used for training and then also evaluated by the ANN and the response is visualized.
The effect that I expect is that for the very first samples the response of the ANN will be more or less random but as the training progress the accuracy improves.
Any ideas are most welcome.
Regards, Ola
In MATLAB you can use the adapt function instead of train. You can do this incrementally (change weights every time you get a new piece of information) or you can do it every N-samples, batch-style.
This document gives an in-depth run-down on the different styles of training from the perspective of a time-series problem.
I'd really think about what you're trying to do here, because adaptive learning strategies can be difficult. I found that they like to flail all over compared to their batch counterparts. This was especially true in my case where I work with very noisy signals.
Are you sure that you need adaptive learning? You can't periodically re-train your NN? Or build one that generalizes well enough?

Optimization of Neural Network input data

I'm trying to build an app to detect images which are advertisements from the webpages. Once I detect those I`ll not be allowing those to be displayed on the client side.
Basically I'm using Back-propagation algorithm to train the neural network using the dataset given here: http://archive.ics.uci.edu/ml/datasets/Internet+Advertisements.
But in that dataset no. of attributes are very high. In fact one of the mentors of the project told me that If you train the Neural Network with that many attributes, it'll take lots of time to get trained. So is there a way to optimize the input dataset? Or I just have to use that many attributes?
1558 is actually a modest number of features/attributes. The # of instances(3279) is also small. The problem is not on the dataset side, but on the training algorithm side.
ANN is slow in training, I'd suggest you to use a logistic regression or svm. Both of them are very fast to train. Especially, svm has a lot of fast algorithms.
In this dataset, you are actually analyzing text, but not image. I think a linear family classifier, i.e. logistic regression or svm, is better for your job.
If you are using for production and you cannot use open source code. Logistic regression is very easy to implement compared to a good ANN and SVM.
If you decide to use logistic regression or SVM, I can future recommend some articles or source code for you to refer.
If you're actually using a backpropagation network with 1558 input nodes and only 3279 samples, then the training time is the least of your problems: Even if you have a very small network with only one hidden layer containing 10 neurons, you have 1558*10 weights between the input layer and the hidden layer. How can you expect to get a good estimate for 15580 degrees of freedom from only 3279 samples? (And that simple calculation doesn't even take the "curse of dimensionality" into account)
You have to analyze your data to find out how to optimize it. Try to understand your input data: Which (tuples of) features are (jointly) statistically significant? (use standard statistical methods for this) Are some features redundant? (Principal component analysis is a good stating point for this.) Don't expect the artificial neural network to do that work for you.
Also: remeber Duda&Hart's famous "no-free-lunch-theorem": No classification algorithm works for every problem. And for any classification algorithm X, there is a problem where flipping a coin leads to better results than X. If you take this into account, deciding what algorithm to use before analyzing your data might not be a smart idea. You might well have picked the algorithm that actually performs worse than blind guessing on your specific problem! (By the way: Duda&Hart&Storks's book about pattern classification is a great starting point to learn about this, if you haven't read it yet.)
aplly a seperate ANN for each category of features
for example
457 inputs 1 output for url terms ( ANN1 )
495 inputs 1 output for origurl ( ANN2 )
...
then train all of them
use another main ANN to join results