I am modeling a perceptual process in tensorflow. In the setup I am interested in, the modeled agent is playing a resource game: it has to choose 1 out of n resouces, by relying only on the label that a classifier gives to the resource. Each resource is an ordered pair of two reals. The classifier only sees the first real, but payoffs depend on the second. There is a function taking first to second.
Anyway, ideally I'd like to train the classifier in the following way:
In each run, the classifier give labels to n resources.
The agent then gets the payoff of the resource corresponding to the highest label in some predetermined ranking (say, A > B > C > D), and randomly in case of draw.
The loss is taken to be the normalized absolute difference between the payoff thus obtained and the maximum payoff in the set of resources. I.e., (Payoff_max - Payoff) / Payoff_max
For this to work, one needs to run inference n times, once for each resource, before calculating the loss. Is there a way to do this in tensorflow? If I am tackling the problem in the wrong way feel free to say so, too.
I don't have much knowledge in ML aspects of this, but from programming point of view, I can see doing it in two ways. One is by copying your model n times. All the copies can share the same variables. The output of all of these copies would go into some function that determines the the highest label. As long as this function is differentiable, variables are shared, and n is not too large, it should work. You would need to feed all n inputs together. Note that, backprop will run through each copy and update your weights n times. This is generally not a problem, but if it is, I heart about some fancy tricks one can do by using partial_run.
Another way is to use tf.while_loop. It is pretty clever - it stores activations from each run of the loop and can do backprop through them. The only tricky part should be to accumulate the inference results before feeding them to your loss. Take a look at TensorArray for this. This question can be helpful: Using TensorArrays in the context of a while_loop to accumulate values
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In neural networks, in general, which model should yield a better and accurate output between both for time series?
As you rightly mentioned, We can use linear regression with time series data as long as:
The inclusion of lagged terms as regressors does not create a collinearity problem.
Both the regressors and the explained variable are stationary.
Your errors are not correlated with each other.
The other linear regression assumptions apply.
No autocorrelation is the single most important assumption in linear regression. If autocorrelation is present the consequences are the following:
Bias: Your “best fit line” will likely be way off because it will be pulled away from the “true line” by the effect of the lagged errors.
Inconsistency: Given the above, your sample estimators are unlikely to converge to the population parameters.
Inefficiency: While it is theoretically possible, your residuals are unlikely to be homoskedastic if they are autocorrelated. Thus, your confidence intervals and your hypothesis tests will be unreliable.
While, The Long Short Term Memory neural network is a type of a Recurrent Neural Network (RNN). RNNs use previous time events to inform the later ones. For example, to classify what kind of event is happening in a movie, the model needs to use information about previous events. RNNs work well if the problem requires only recent information to perform the present task. If the problem requires long term dependencies, RNN would struggle to model it. The LSTM was designed to learn long term dependencies. It remembers the information for long periods.
To focus on the 1st sequence. The model takes the feature of the time bar at index 0 and it tries to predict the target of the time bar at index 1. Then it takes the feature of the time bar at index 1 and it tries to predict the target of the time bar at index 2, etc. The feature of 2nd sequence is shifted by 1 time bar from the feature of 1st sequence, the feature of 3rd sequence is shifted by 1 time bar from 2nd sequence, etc. With this procedure, we get many shorter sequences that are shifted by a single time bar.
Maybe this is a silly question but I didn't find much about it when I google it.
I have a dataset and I use it for regression but a normal regression with FFNN didn't worked so I thought why not try an LSTM since my data is time dependent I think because it was token from a vehicle while driving so the data is monotonic and maybe I can use LSTM in this Case to do a regression to predict a continuous value (if this doesn't make sense please tell me).
Now the first step is to prepare my data for using LSTM, since I ll predict the future I think my target(Ground truth or labels) should be shifted to the up, am I right?
So if I have a pandas dataframe where each row hold the features and the target(at the end of the row), I assume that the features should stay where they are and the target would be shifted it one step up so that the features in the first row will correspond to the target of the second row (am I wrong).
This way the LSTM will be able to predict the future value from those features.
I didn't find much about this in the internet so please can you provide me how can I do this with some Code?
I also know what I can use pandas.DataFrame.shift to shift a dataset but the last value will hold a NaN I think! how to deal with this? it would be great if you show me some examples or code.
We might need a bit more information regarding the data you are using. Also, I would suggest starting with a more simple recurrent neural network before you start going for LSTMs. The way these networks work is by you feeding the first bit of information, then the next bit of information, then the next bit etc. Let's say that when you feed the first bit of information in, it occurs at time t, then the second bit of information is fed at time t+1 ... etc. up until time t+n.
You can have the neural network output a value at each time step (so a value is outputted at time t, t+1... t+n after each respective input has been fed in). This is a many-to-many network. Or you can have the neural network output a value after all inputs have been provided (i.e. the value is outputted at time t+n). This is called a many-to-one network. What you need is dependednt on your use-case.
For example, say you were recording vehicle behaviour every 100ms and after 10 seconds (i.e. the 100th time step), you wanted to predict the likelihood that the driver was under the influence of alcohol. In this case, you would use a many-to-one network where you put in subsequent vehicle behaviour recordings at subsequent time steps (the first recording at time t, then the next recording at time t+1 etc.) and then the final timestep has the probability value outputted.
If you want a value outputted after every time step, you use a many-to-many design. It's also possible to output a value every k timesteps.
I'm facing my first machine learning algorithm which is knn, and the thing that has confused me the most is spliting the dataset into training and testing data. With more complicated ms algorithms I can imagine that the computer needs to have a 'training' process, but knn is more straightforward and having a training set is unnecessary. Either that or i haven't comprehended knn completely.
For the background: I'm having a dataset and have to ask for some input from the user. From there i can find k nearest neighbors of the user.
I'd be very greatful for your explanation. Thank you in advance:).
KNN usually has validation dataset to get the optimal number of neighbors to take into consideration.
Given that, on test set you check how your algorithm performs "in the wild".
If you can somehow find from the user's input the optimal number of neighbors to take into consideration you don't need either test or validation. If not (e.g. user inputs some value, but given that you can't be certain about preferable number of neighbors to consider), you should do both validation and test (or some other variant, e.g. K-Fold, to find hyperparameters).
EDIT: There are other hyperparameters like distance metric, but the idea holds.
I want to predict stock price.
Normally, people would feed the input as a sequence of stock prices.
Then they would feed the output as the same sequence but shifted to the left.
When testing, they would feed the output of the prediction into the next input timestep like this:
I have another idea, which is to fix the sequence length, for example 50 timesteps.
The input and output are exactly the same sequence.
When training, I replace last 3 elements of the input by zero to let the model know that I have no input for those timesteps.
When testing, I would feed the model a sequence of 50 elements. The last 3 are zeros. The predictions I care are the last 3 elements of the output.
Would this work or is there a flaw in this idea?
The main flaw of this idea is that it does not add anything to the model's learning, and it reduces its capacity, as you force your model to learn identity mapping for first 47 steps (50-3). Note, that providing 0 as inputs is equivalent of not providing input for an RNN, as zero input, after multiplying by a weight matrix is still zero, so the only source of information is bias and output from previous timestep - both are already there in the original formulation. Now second addon, where we have output for first 47 steps - there is nothing to be gained by learning the identity mapping, yet network will have to "pay the price" for it - it will need to use weights to encode this mapping in order not to be penalised.
So in short - yes, your idea will work, but it is nearly impossible to get better results this way as compared to the original approach (as you do not provide any new information, do not really modify learning dynamics, yet you limit capacity by requesting identity mapping to be learned per-step; especially that it is an extremely easy thing to learn, so gradient descent will discover this relation first, before even trying to "model the future").
I am being asked to take a look at a scenario where a company has many projects that they wish to complete, but with any company budget comes into play. There is a Y value of a predefined score, with multiple X inputs. There are also 3 main constraints of Capital Costs, Expense Cost and Time for Completion in Months.
The ask is could an algorithmic approach be used to optimize which projects should be done for the year given the 3 constraints. The approach also should give different results if the constraint values change. The suggested method is multiple regression. Though I have looked into different approaches in detail. I would like to ask the wider community, if anyone has dealt with a similar problem, and what approaches have you used.
Fisrt thing we should understood, a conclution of something is not base on one argument.
this is from communication theory, that every human make a frame of knowledge (understanding conclution), where the frame construct from many piece of knowledge / information).
the concequence is we cannot use single linear regression in math to create a ML / DL system.
at least we should use two different variabel to make a sub conclution. if we push to use single variable with use linear regression (y=mx+c). it's similar to push computer predict something with low accuration. what ever optimization method that you pick...it's still low accuracy..., why...because linear regresion if you use in real life, it similar with predict 'habbit' base on data, not calculating the real condition.
that's means...., we should use multiple linear regression (y=m1x1+m2x2+ ... + c) to calculate anything in order to make computer understood / have conclution / create model of regression. but, not so simple like it. because of computer try to make a conclution from data that have multiple character / varians ... you must classified the data and the conclution.
for an example, try to make computer understood phitagoras.
we know that phitagoras formula is c=((a^2)+(b^2))^(1/2), and we want our computer can make prediction the phitagoras side (c) from two input values (a and b). so to do that, we should make a model or a mutiple linear regresion formula of phitagoras.
step 1 of course we should make a multi character data of phitagoras.
this is an example
a b c
3 4 5
8 6 10
3 14 etc..., try put 10 until 20 data
try to make a conclution of regression formula with multiple regression to predic the c base on a and b values.
you will found that some data have high accuration (higher than 98%) for some value and some value is not to accurate (under 90%). example a=3 and b=14 or b=15, will give low accuration result (under 90%).
so you must make and optimization....but how to do it...
I know many method to optimize, but i found in manual way, if I exclude the data that giving low accuracy result and put them in different group then, recalculate again to the data group that excluded, i will get more significant result. do again...until you reach the accuracy target that you want.
each group data, that have a new regression, is a new class.
means i will have several multiple regression base on data that i input (the regression come from each group of data / class) and the accuracy is really high, 99% - 99.99%.
and with the several class, the regresion have a fuction as a 'label' of the class, this is what happens in the backgroud of the automation computation. but with many module, the user of the module, feel put 'string' object as label, but the truth is, the string object binding to a regresion that constructed as label.
with some conditional parameter you can get the good ML with minimum number of data train.
try it on excel / libreoffice before step more further...
try to follow the tutorial from this video
and implement it in simple data that easy to construct in excel, like pythagoras.
so the answer is yes...the multiple regression is the best approach for optimization.