I am working with a Siamese network adaptation for a ranking task as I have sequential data, the first network (for the queries) has a transformer (as it is simple it is an encoder) followed by an embedding layer, the second network (for the indices) is simply a fully connected followed by an embedding layer identical to the previous network. At the end of everything, it uses the similarity of the cosine to make the ranking between the output of both networks. It gives good results but I am interested in knowing what is happening at the output of both networks. Any advice?
PD: I was thinking of using a t-SNE to visualize the output of the embedding layers in 2D - 3D but I am mainly interested in knowing how the clusters are organized in order to make the appropriate modifications in the network.
I have designed a 3 layer neural network whose inputs are the concatenated features from a CNN and RNN. The weights learned by network take very small values. What is the reasonable explanation for this? and how to interpret the weight histograms and distributions in Tensorflow? Any good resource for it?
This is the weight distribution of the first hidden layer of a 3 layer neural network visualized using tensorboard. How to interpret this? all the weights are taking up zero value?
This is the weight distribution of the second hidden layer of a 3 layer neural:
how to interpret the weight histograms and distributions in Tensorflow?
Well, you probably didn't realize it, but you have just asked the 1 million dollar question in ML & AI...
Model interpretability is a hyper-active and hyper-hot area of current research (think of holy grail, or something), which has been brought forward lately not least due to the (often tremendous) success of deep learning models in various tasks; these models are currently only black boxes, and we naturally feel uncomfortable about it...
Any good resource for it?
Probably not exactly the kind of resources you were thinking of, and we are well off a SO-appropriate topic here, but since you asked...:
A recent (July 2017) article in Science provides a nice overview of the current status & research: How AI detectives are cracking open the black box of deep learning (no in-text links, but googling names & terms will pay off)
DARPA itself is currently running a program on Explainable Artificial Intelligence (XAI)
There was a workshop in NIPS 2016 on Interpretable Machine Learning for Complex Systems
On a more practical level:
The Layer-wise Relevance Propagation (LRP) toolbox for neural networks (paper, project page, code, TF Slim wrapper)
FairML: Auditing Black-Box Predictive Models, by Fast Forward Labs (blog post, paper, code)
A very recent (November 2017) paper by Geoff Hinton, Distilling a Neural Network Into a Soft Decision Tree, with an independent PyTorch implementation
SHAP: A Unified Approach to Interpreting Model Predictions (paper, authors' code)
These should be enough for starters, and to give you a general idea of the subject about which you asked...
UPDATE (Oct 2018): I have put up a much more detailed list of practical resources in my answer to the question Predictive Analytics - “Why” factor?
The weights learned by network take very small values. What is the reasonable explanation for this? How to interpret this? all the weights are taking up zero value?
Not all weights are zero, but many are. One reason is regularization (in combination with a large, i.e. wide layers, network) Regularization makes weights small (both L1 and L2). If your network is large, most weights are not needed, i.e., they can be set to zero and the model still performs well.
How to interpret the weight histograms and distributions in Tensorflow? Any good resource for it?
I am not so sure about weight distributions. There is some work that analysis them, but I am not aware of a general interpretation, e.g., for CNNs it is known that center weights of a filter/feature usually have larger magnitude than those in corners, see [Locality-Promoting Representation Learning, 2021, ICPR, https://arxiv.org/abs/1905.10661]
For CNNs you can also visualize weights directly, if you have large filters. For example, for (simpl)e networks you can see that weights first converge towards some kind of class average before overfitting starts. This is shown in Figure 2 of [The learning phases in NN: From Fitting the Majority to Fitting a Few, 2022, http://arxiv.org/abs/2202.08299]
Rather than going for weights, you can also look at what samples trigger the strongest activations for specific features. If you don't want to look at single features, there is also the possibility to visualize what the network actually remembers on the input, e.g., see [Explaining Neural Networks by Decoding Layer Activations, https://arxiv.org/abs/2005.13630].
These are just a few examples (Disclaimer I authored these works) - there is thousands of other works on explainability out there.
I'm new to neural networks/machine learning/genetic algorithms, and for my first implementation I am writing a network that learns to play snake (An example in case you haven't played it before) I have a few questions that I don't fully understand:
Before my questions I just want to make sure I understand the general idea correctly. There is a population of snakes, each with randomly generated DNA. The DNA is the weights used in the neural network. Each time the snake moves, it uses the neural net to decide where to go (using a bias). When the population dies, select some parents (maybe highest fitness), and crossover their DNA with a slight mutation chance.
1) If given the whole board as an input (about 400 spots) enough hidden layers (no idea how many, maybe 256-64-32-2?), and enough time, would it learn to not box itself in?
2) What would be good inputs? Here are some of my ideas:
400 inputs, one for each space on the board. Positive if snake should go there (the apple) and negative if it is a wall/your body. The closer to -1/1 it is the closer it is.
6 inputs: game width, game height, snake x, snake y, apple x, and apple y (may learn to play on different size boards if trained that way, but not sure how to input it's body, since it changes size)
Give it a field of view (maybe 3x3 square in front of head) that can alert the snake of a wall, apple, or it's body. (the snake would only be able to see whats right in front unfortunately, which could hinder it's learning ability)
3) Given the input method, what would be a good starting place for hidden layer sizes (of course plan on tweaking this, just don't know what a good starting place)
4) Finally, the fitness of the snake. Besides time to get the apple, it's length, and it's lifetime, should anything else be factored in? In order to get the snake to learn to not block itself in, is there anything else I could add to the fitness to help that?
Thank you!
In this post, I will advise you of:
How to map navigational instructions to action sequences with an LSTM
neural network
Resources that will help you learn how to use neural
networks to accomplish your task
How to install and configure neural
network libraries based on what I needed to learn the hard way
General opinion of your idea:
I can see what you're trying to do, and I believe that your game idea (of using randomly generated identities of adversaries that control their behavior in a way that randomly alters the way they're using artificial intelligence to behave intelligently) has a lot of potential.
Mapping navigational instructions to action sequences with a neural network
For processing your game board, because it involves dense (as opposed to sparse) data, you could find a Convolutional Neural Network (CNN) to be useful. However, because you need to translate the map to an action sequence, sequence-optimized neural networks (such as Recurrent Neural Networks) will likely be the most useful for you. I did find some studies that use neural networks to map navigational instructions to action sequences, construct the game map, and move a character through a game with many types of inputs:
Mei, H., Bansal, M., & Walter, M. R. (2015). Listen, attend, and walk: Neural mapping of navigational instructions to action sequences. arXiv preprint arXiv:1506.04089. Available at: Listen, Attend, and Walk: Neural Mapping of Navigational Instructions to Action Sequences
Lample, G., & Chaplot, D. S. (2016). Playing FPS games with deep reinforcement learning. arXiv preprint arXiv:1609.05521. Available at: Super Mario as a String: Platformer Level Generation Via LSTMs
Lample, G., & Chaplot, D. S. (2016). Playing FPS games with deep reinforcement learning. arXiv preprint arXiv:1609.05521. Available at: Playing FPS Games with Deep Reinforcement Learning
Schulz, R., Talbot, B., Lam, O., Dayoub, F., Corke, P., Upcroft, B., & Wyeth, G. (2015, May). Robot navigation using human cues: A robot navigation system for symbolic goal-directed exploration. In Robotics and Automation (ICRA), 2015 IEEE International Conference on (pp. 1100-1105). IEEE. Available at: Robot Navigation Using Human Cues: A robot navigation system for symbolic goal-directed exploration
General opinion of what will help you
It sounds like you're missing some basic understanding of how neural networks work, so my primary recommendation to you is to study more of the underlying mechanics behind neural networks in general. It's important to keep in mind that a neural network is a type of machine learning model. So, it doesn't really make sense to just construct a neural network with random parameters. A neural network is a machine learning model that is trained from sample data, and once it is trained, it can be evaluated on test data (e.g. to perform predictions).
The root of machine learning is largely influenced by Bayesian statistics, so you might benefit from getting a textbook on Bayesian statistics to gain a deeper understanding of how machine-based classification works in general.
It will also be valuable for you to learn the differences between different types of neural networks, such as Long Short Term Memory (LSTM) and Convolutional Neural Networks (CNNs).
If you want to tinker with how neural networks can be used for classification tasks, try this:
Tensorflow Playground
To learn the math:
My professional opinion is that learning the underlying math of neural networks is very important. If it's intimidating, I give you my testimony that I was able to learn all of it on my own. But if you prefer learning in a classroom environment, then I recommend that you try that. A great resource and textbook for learning the mechanics and mathematics of neural networks is:
Neural Networks and Deep Learning
Tutorials for neural network libraries
I recommend that you try working through the tutorials for a neural network library, such as:
TensorFlow tutorials
Deep Learning tutorials with Theano
CNTK tutorials (CNTK 205: Artistic Style Transfer is particularly cool.)
Keras tutorial (Keras is a powerful high-level neural network library that can use either TensorFlow or Theano.)
I saw similar application. Inputs usually were snake coordinates, apple coordinates and some sensory data(is wall next to snake head or no in your case).
Using genetic algorithm is a good idea in this case. You doing only parametric learning(finding set of weights), but structure will be based on your estimation. GA can be also used for structure learning(finding topology of ANN). But using GA for both will be very computational hard.
Professor Floreano did something similar. He use GA for finding weights for neural network controller of robot. Robot was in labyrinth and perform some task. Neural network hidden layer was one neuron with recurrent joints on inputs and one lateral connection on himself. There was two outputs. Outputs were connected on input layer and hidden layer(mentioned one neuron).
But Floreano did something more interesting. He say, We don't born with determined synapses, our synapses change in our lifetime. So he use GA for finding rules for change of synapses. These rules was based on Hebbian learning. He perform node encoding(for all weights connected to neuron will apply same rule). On beginning, he initialized weights on small random values. Finding rules instead of numerical value of synapse leads to better results.
One from Floreno's articles.
And on the and my own experience. In last semester I and my schoolmate get a task finding the rules for synapse with GA but for Spiking neural network. Our SNN was controller for kinematic model of mobile robot and task was lead robot in to the chosen point. We obtained some results but not expected. You can see results here. So I recommend you use "ordinary" ANN instead off SNN because SNN brings new phenomens.
I am a newbie in machine learning and also in neural networks. Currently I'm taking a course at coursera.org about neural networks, but I don't understand everything. I have a little problem with my thesis. I should use a neural network, but I don't know how to choose the right neural network architecture for my problem.
I have a lot of data from web portals (typically online editions of newspapers, magazines). There is information about articles for example, name, text of article and release of article. There are also large amounts of sequence data that capture behavior of users.
My goal is to predict the popularity of an article (number of readers or clicks on article by unique user). I want to make vectors from this data and feed my neural network with these vectors.
I have two questions:
1. How do I create the right vector?
2. Which neural network architecture is best suited for this problem?
Those are very broad questions. You'll need to identify smaller issues if you want more exact answers.
How to create a right vector?
For text data, you usually use the vector space model. Best results are often obtained using tf-idf weighting.
Which neural network architecture is suitable for this problem?
This is very hard to say. I would start with a network with k input neurons (where k is the size of your vectors after applying tf-idf: you might also want to do some sort of feature selection to reduce the number of features. A good feature selection method is by using the chi squared test.)
Then, a standard network layout is given by using a single hidden layer with number of neurons equal to the average between the number of input neurons and output neurons. Then it looks like you only need a single output neuron that will output how popular the article is going to be (this can be a linear neuron or a sigmoid neuron).
For the neurons in your hidden layer, you can also experiment with linear and sigmoid neurons.
There are many other things you can try as well: weight decay, the momentum technique, networks with multiple layers, recurrent networks and so on. It's impossible to say what would work best for your given problem without a lot of experimentation.
I came across a way to calculate the influence score of a person on a twitter network. Here is a sample reference: http://thenoisychannel.com/2009/01/13/a-twitter-analog-to-pagerank/
On similar lines, are there any other algorithms that calculate the influence score of a subscriber on a telecom network using his/her CDR data?
Please checkout Magnusson's thesis:
http://uu.diva-portal.org/smash/record.jsf?pid=diva2:509757
This thesis aims at investigating the usefulness of social network analysis in telecommunication networks. As these networks can be very large the methods used to study them must scale linearly when the network size increases. Thus, an integral part of the study is to determine which social network analysis algorithms that have this scalability. Moreover, comparisons of software solutions are performed to find product suitable for these specific tasks.