I need to train a neural network to classify some text documents into a boolean class (NN has one output with "Yes" or "No" values).
Is there any algorithm to find best input parameters (for example presence of words, term, sentence and/or frequency/repetition of a word & ...) ?
If not can you give me a starting point to find these parameters(How should I select them)?
Thanks
The standard approach I know of would be to use a vector of words/terms and assign them a negative or positive score using a learning or statistical algorithm. even perceptron learning should suffice, you just need a good set of positive and negative examples.
To my knoledge this the way all spam filter work. and they work pretty well.
Related
I want to costruct a neural network which will be trained based on data i create. My question is what form these data should have? In other words does keras allow neural networks that take strings/characters as input? If not, and only is able to accept numbers in what range should the input/output be?
The only condition for your input data i.e features, is that it should be numerical. There isn't really any constraint on range but it's always a good idea to do Feature Scaling, Normalization etc to make sure that our model won't get confused. Neural Networks or other machine learning methods cannot accept string (characters, words) directly, therefore, you need to first convert string to numbers. There are many ways to do that, most common techniques include Bag of Words, tf-idf features, word embeddings etc.
Following tutorials (using scikit) might be a good starting point:
http://scikit-learn.org/stable/tutorial/text_analytics/working_with_text_data.html
https://www.kaggle.com/c/word2vec-nlp-tutorial/details/part-1-for-beginners-bag-of-words
i'm curious as to the kind of limitations even an expertly designed network might have. this one in particular is what i could use some insight on:
given:
a set of random integers of non-trivial size (say at least 500)
an expertly created/trained neural network.
task:
number anagram: create the largest representation of an infinite sequence of integers possible in a given time frame where the sequence
either can be represented in closed form (ie - n^2, 2x+5, etc) or is
registered in OEIS (http://oeis.org/). the numbers used to create the
sequence can be taken from the input set in any order. so if the
network is fed (3, 5, 1, 7...), returning (1, 3, 5, 7 ...) would be an
acceptable result.
it's my understanding that an ANN can be trained to look for a particular sequence pattern (again - n^2, 2x+5, etc). what I'm wondering is if it can be made to recognize a more general pattern like n^y or xy+z. my thinking is that it won't be able to, because n^y can produce sequences that look different enough from one another that a stable 'base pattern' can't be established. that is - intrinsic to the way ANNs work (taking sets of input and doing fuzzy-matching against a static pattern it's been trained to look for) is that they are limited in terms of scope of what it is they can be trained to look for.
have i got this right?
Continuing from the conversation I had with you in the comments:
Neural networks still might be useful. Instead of training a neural net to search for a single pattern, the neural net can be trained to predict the data. If the data contains a predictable pattern, the NN can learn it, and the weights of the NN will represent the pattern it has learned. I think that may be what you were intending to do.
Some things that might be helpful for you if you do this:
Autoencoders do unsupervised learning and can learn the structure of individual datapoints.
Recurrent Neural Networks can model sequences of data rather than just individual datapoints. This sounds more like what you are looking for.
A Compositional Pattern-Producing Network (CPPNs) is a really fancy word for a neural network with mathematical functions as activation functions. This would allow you to model functions that aren't easily approximated by NNs with simple activation functions like sigmoids or ReLU. But usually this isn't necessary, so don't worry to much about it until after you have a simple NN working.
Dropout is a simple technique where you remove half of the hidden units every iteration. This seems to seriously reduce overfitting. It prevents complicated relationships between neurons from forming, which should make the models more interpretable, which seems like your goal.
This is an on-going venture and some details are purposefully obfuscated.
I have a box that has several inputs and one output. The output voltage changes as the input voltages are changed. The desirability of the output sequence cannot be evaluated until many states pass and a look back process is evaluated.
I want to design a neural network that takes a number of outputs from the box as input and produce the correct input settings for the box to produce the optimal next output.
I cannot train this network using backpropagation. How do I train this network?
Genetic algorithm would be a good candidate here. A chromosome could encode the weights of the neural network. After evaluation you assign a fitness value to the chromosomes based on their performance. Chromosomes with higher fitness value have a higher chance to reproduce, helping to generate better performing chromosomes in the next generation.
Encoding the weights is a relatively simple solution, more complex ones could even define the topology of the network.
You might find some additional helpful information here:
http://en.wikipedia.org/wiki/Neuroevolution
Hillclimbing is the simplest optimization algorithm to implement. Just randomly modify the weights, see if it does better, if not reset them and try again. It's also generally faster than genetic algorithms. However it is prone to getting stuck in local optima, so try running it several times and selecting the best result.
I've recently been delving into artificial neural networks again, both evolved and trained. I had a question regarding what methods, if any, to solve for inputs that would result in a target output set. Is there a name for this? Everything I try to look for leads me to backpropagation which isn't necessarily what I need. In my search, the closest thing I've come to expressing my question is
Is it possible to run a neural network in reverse?
Which told me that there, indeed, would be many solutions for networks that had varying numbers of nodes for the layers and they would not be trivial to solve for. I had the idea of just marching toward an ideal set of inputs using the weights that have been established during learning. Does anyone else have experience doing something like this?
In order to elaborate:
Say you have a network with 401 input nodes which represents a 20x20 grayscale image and a bias, two hidden layers consisting of 100+25 nodes, as well as 6 output nodes representing a classification (symbols, roman numerals, etc).
After training a neural network so that it can classify with an acceptable error, I would like to run the network backwards. This would mean I would input a classification in the output that I would like to see, and the network would imagine a set of inputs that would result in the expected output. So for the roman numeral example, this could mean that I would request it to run the net in reverse for the symbol 'X' and it would generate an image that would resemble what the net thought an 'X' looked like. In this way, I could get a good idea of the features it learned to separate the classifications. I feel as it would be very beneficial in understanding how ANNs function and learn in the grand scheme of things.
For a simple feed-forward fully connected NN, it is possible to project hidden unit activation into pixel space by taking inverse of activation function (for example Logit for sigmoid units), dividing it by sum of incoming weights and then multiplying that value by weight of each pixel. That will give visualization of average pattern, recognized by this hidden unit. Summing up these patterns for each hidden unit will result in average pattern, that corresponds to this particular set of hidden unit activities.Same procedure can be in principle be applied to to project output activations into hidden unit activity patterns.
This is indeed useful for analyzing what features NN learned in image recognition. For more complex methods you can take a look at this paper (besides everything it contains examples of patterns that NN can learn).
You can not exactly run NN in reverse, because it does not remember all information from source image - only patterns that it learned to detect. So network cannot "imagine a set inputs". However, it possible to sample probability distribution (taking weight as probability of activation of each pixel) and produce a set of patterns that can be recognized by particular neuron.
I know that you can, and I am working on a solution now. I have some code on my github here for imagining the inputs of a neural network that classifies the handwritten digits of the MNIST dataset, but I don't think it is entirely correct. Right now, I simply take a trained network and my desired output and multiply backwards by the learned weights at each layer until I have a value for inputs. This is skipping over the activation function and may have some other errors, but I am getting pretty reasonable images out of it. For example, this is the result of the trained network imagining a 3: number 3
Yes, you can run a probabilistic NN in reverse to get it to 'imagine' inputs that would match an output it's been trained to categorise.
I highly recommend Geoffrey Hinton's coursera course on NN's here:
https://www.coursera.org/course/neuralnets
He demonstrates in his introductory video a NN imagining various "2"s that it would recognise having been trained to identify the numerals 0 through 9. It's very impressive!
I think it's basically doing exactly what you're looking to do.
Gruff
I am trying to classify water end-use events expressed as a time-series sequences into appropriate categories (e.g. toilet, tap, shower, etc). My first attempt using HMM shows a quite promising result with an average accuracy of 80%. I just wonder if there is any other techniques that allow the training input as time-series sequences of different length like HMM does rather than the extracted feature vector of each sequence. I have tried Conditional Random Field (CRF) and SVM ;however, as far as I know, these two techniques require input as a pre-computed feature vector and the length of all input vectors must be the same for training purpose. I am not sure if I am right or wrong at this point. Any help would be appreciated.
Thanks, Will