I am using Bidirectional Recurrent Neural Network(Bi-RNN) for binary classification.
I searched lots of paper related to that and finally get to know that the output of last time step or all of outputs are used for learning and prediction.
However, I'd like to use the output of certain step, which may not be last step, depending on the input. If Cth input is what i want to know, I will use Cth output. Because Cth output is resutled by all of other hidden states in Bi-RNN, I don't have to use other outputs of time step If I am only interested in Cth one.
I know this is little special case but I can't find any example for this case.
(Attention based approach is diffrent from what i want to know. I want to use only one output,which may not be last output, for learning a input sequnece)
Is there any example and research paper related what i mentioned?
Related
What architecture/methods are used to make a neural network which can get infinite big input and/or return infinite big output?
I have an idea how to make infinite big output.
I just need extra input neurons and after the first calculation send output (or part of it) to input neurons.
But I have no clue how to make extensible input.
Maybe use multiple iterations, and plug output to input, and change the rest of the input neurons accordingly to the next portion of input data?
Artificial intelligence is new for me, so it is possible that I'm asking something that I don't want or something impossible. Please provide simple answers.
The short answer is any RNN is capable of consuming, and producing, arbitrary length sequences. Depending on the structure of the data CNNs, Graph Nets etc. can also work with arbitrarily large inputs.
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'm working on school project about data prediction in NN I have my data normalized and I have three input and one output
My questions is
what is the different between the taring data and test data (is the training data supposed to be the input data and the test the output data)
what is testing rate is it any random number or is there rule to find it
what is training error
and my final question is after training my data I remember something about error I'm not quite sure but do I need to find the error of my prediction and how to find it
I know my questions might not be clear but I'm just confused and tried to explain it as much as I can
Answering in a school spirit: Let's suppose you are given 10 solved exercises to study. You do study them, and then the teacher tests you on these exact exercises. You do well on the test. However, there is an important question. Why did you do well?? Did you really understand the exercises, or did you just memorize them?? And how can the teacher know ??
There is only one way: The teacher must test you on a set of similar but different exercises. If you also do well on them, you have gotten a feel for the subject, and you are able to generalize the knowledge you acquired. If not, you probably memorized them, without understanding a thing. This kind of knowledge is useless.
The same happens with neural networks. You use some patterns to (training set) to train them. But, to check if they are able to generalize, you have to test them on a different set of patterns (test set) without the network knowing the correct answers. Ideally, you should have small differences in performance between the two sets, that is good generalization ability.
So, both train and tests sets are inputs, not outputs. The only difference is when you use them, the training set during the training, and the test set after it. The training/test set rate is the percentage you got correct of the training/test sets respectively. The training/test error is the complementary, that is, the percentage you got wrong.
I know this reply might come late but I will just complement the previous answer by saying that in supervised learning both the training set and the test set are input-output pairs. By structure alone they are exactly the same, a set of input and their corresponding output(or label) pairs. There is no difference in structure between both.
As blue_note said, they are just used in different occasions: one during training and one after that
I am trying to design neural network in Matlab,
I see in many source that the data that used with training neural network
better to be normalize, use [pn,ps] = mapstd(Input) to normalize the input and target,
then I train the network, last thing i test the network by a=sim(net,pn);
my problem is:
how to convert the result to normal result?
last thing, is there any way to train the network with new data to increase the performance?
i mean train with more data where the weighing change slightly to increase the old performance
it is clear that normalize is mean by this function [pn,ps] = mapstd(Input)
all value will be in range of -1 to 1 as i think, the sim of neural network will be normalize result while i have to convert it again to the original range how?
To answer the first question you dont need to go very far. Read the documentation of mapstd(). In there you have a section called "Definition" you have exactly what you are looking for. It is explained why/how to use mapstd() and how to reverse the results in a network (ANN in your case) results after simulation. Read that and you'll now how to do it!
For the second question I will reffer to another SO post, where it is explained better than I what I would do, here it is! Read that carefully because it is very well explained.