i need a help , i want to create a model with netlogo with Neural Network (perceptron) that alows me to take a number as an input and tell if it is Pair or Impair Number from 0 -- 9 thanks for aides guys
question : how can i do it in netlogo
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I am a total beginner to ML and Neural networks. I am currently working on a project where I have a lot of pictures stored in a MongoDB database. Each one of those pictures has a number from 0 to 1. For example "picture 1" 0.71.
I want to train my model given the database. The main goal for the project is that after the model is finished and trained, given an image the model will be able to return(predict) a number from 0 to 1. After doing some research and asking a few people I figured out some libraries that would be useful for the project are: Tenserflow and Keras. Some people told me that it is impossible, but I'm not sure therefore I came to ask here.
So my questions are: Is it possible? If so, how can I implement it? Are there any specific tools you recommend? If you specify a way that I should use for my project do I need to export my MongoDB database in a certain form? Since I am a beginner maybe there are some tutorials that you think that can help?
I'm sorry if this question is a bit too general, if there are any misunderstandings please comment and I will try to answer.
Thanks in advance!
What you want to do is totally feasible, this kind of project is called regression, since you are using images data the best type of models are called convolutional neural network (CNN), you'll need some understanding if you want to build your own model. I've done a project where I had to predict a number of bacterial colonies using an image, much like your problem except that I had no boundaries on the predicted values.
What is a CNN ? Here is a link
Basically a CNN will understand the features in the images and will use those features to predict a value.
You won't need to create your own model, most people just use well-designed one in the scientific litterature.
Go for keras, it's the easiest framework out there and work like a charm. Here is how to implement VGG16 (an architecture that is probably the best for your problem) : link
You should follow this tutorial to get going on developing with keras.
Last hint: don't use the same last layer as the one on the VGG16 implementation, use a Dense Layer with one neuron and with a sigmoid/linear/leaky relu activation.
ie:
#model.add(Dense(1000, activation='softmax'))
model.add(Dense(1, activation='sigmoid'))
This means : predict 1 number (sigmoid will bound it between 0 and 1, but maybe lrelu or linear is better)
Also, I guess you could use MongoDB to read the images as arrays, but I would just put the images on a folder.
Edit : When compiling the model, use a mean squared error as in
adam = keras.optimizers.Adam(lr=1e-4)
model.compile(optimizer=adam, loss='mse')
Here you have the "hello world program" in terms of neural networks and digits classification. You can start studying it because I think you will end up with a similar architecture for your NN. What you should focus on is the output of your model, because in this example they are performing classification on 10 classes (digits from 0 to 9) but you are trying to read a real number. You could try to use a single neurone with sigmoid or linear activation at the end of your model.
I'm currently doing research in Combinatorial Game Theory and I'm trying to develop an Artificial Intelligence using a Neural Network. My initial approach to this would be to take statistics of the game and use those as inputs, and train my Neural Network to develop the optimal weight configuration for those inputs in order to get a MAXIMAL output value for those inputs. Each set of inputs represents a move and by passing each move (input x weights) through the neural net I can find out which move has the maximal value. Thus, that move would be the best move to make.
This is all in theory and I'm just curious if constructing a neural net while not knowing the expected value is at all possible. If this doesn't seem reasonable are there any other algorithms I should look into for this sort of problem?
I appreciate any feedback, thank you in advance.
I'm developing a project for the university. I have to create a classifier for a disease. The data-set i have contains several inputs (symptoms) and each of them is associated to a multiplicative probability factor (e.g. if patient has the symptom A, he has a double probability to have that disease).
So, how can i do this type of classifier? Is there any type of neural network or other instrument to do this??
Thanks in advance
You should specify how much labeled data and unlabeled data you have.
Let's assume you have only labeled data. Then you could use neural networks, but IMHO, SVM or random forests are the best techniques for a first try.
Note that if you use machine learning techniques, your prior information about symptoms (multiplicative coefficients) are not used because the labels are used instead. If you want to use these coefficients, it's no more machine learning.
You can use neural network for this purpose also. If to speak about your situation, with binding symptom A to more chances for decease B, that is what neural network should be able to accomplish. To bind connection weights from input A ( symptom A ) to desease B. From your side, you can engrain such classification rule in case if you'll have enough training data in your training data set. Also I propose you to try two different approaches: 1. neural network with N outputs (N = number of deseases to clasif). 2. Create for each desease neural network.
I have a dataset of 60 points. I have supplied 58 points as input data to a NAR network in Matlab(using NNToolbox) and tried developing a model which would help me forecast the next two values. I wish to know how to obtain the next two forecasted values generated by the model in Matlab using NNToolbox. I didn't find any option in NNToolbox for the same.
Any help is highly appreciated.
I do not know much about NNToolbox in MATLAB, but for your dataset, I believe that you would need to train a Recurrent Neural Net (RNN) with the training examples. A RNN should forecast (by regression) the values generated by the model. And there are many tutorials available for building a RNN for regression.
I have created a neural network with 2 inputs nodes, 4 hidden nodes and 3 output nodes. The initial weights are random between -1 to 1. I used backpropagation method to update the network with TD error. However, the performance is not good.
I want to know, where the problem might be?
1. Is a bias node necessary?
2. Are eligibility traces necessary?
If anyone can provide me any sample code, I'm very grateful.
Yes, you should include the bias nodes, and yes you should use eligibility traces. The bias nodes just give one additional tunable parameter. Think of the neural network as a "function approximator" as described in Sutton and Barto's book (free online). If the neural network has parameters theta (a vector containing all of the weights in the network), then the Sarsa update is just (using LaTeX notation):
\delta_t = r_t + \gamma*Q(s_{t+1},a_{t+1},\theta_t) - Q(s_t,a_t, \theta_t)
\theta_{t+1} = \theta_t + \alpha*\delta_t*\frac{\partial Q(s,a,\theta)}{\partial \theta}
This is for any function approximator Q(s,a,\theta), which estimates Q(s,a) by tuning its parameters, \theta.
However, I must ask why you're doing this. If you're just trying to get Q learning working really well, then you should use the Fourier Basis instead of a neural network:
http://all.cs.umass.edu/pubs/2011/konidaris_o_t_11.pdf
If you really want to use a neural network for RL, then you should use a natural actor-critic (NAC). NACs follow something called the "natural gradient," which was developed by Amari specifically to speed up learning using neural networks, and it makes a huge difference.
We need more information. What is the problem domain. What are the inputs? What are the outputs?
RL can take a very long time to train and, depending on how you're training, can go from good to great to good to not-so-good during training. Therefore, you should plot the performance of your agent during learning, not just the end result.
You always should use bias nodes. Eligibility traces? Probably not.