Answers.Body: <p>The perceptron convergence theorem states that any architecture will lead to a correlation between the data.</p>
  
  <p>Yes, you can!</p>
  
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Answers.CreationDate: 2020-01-24T05:09:13.810Z
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Answers.UserDisplayName: Ramedlaw Vocinnas
Answers.Body: <p>There is nothing stopping you, you can setup Dense Neural Networks to have any size inputs or outputs (simple proof is to imagine a single layer NN with no activation is just a linear transform and given input dim <span class="math-container">$n$</span> and output dim <span class="math-container">$m$</span>, it's just a matrix of <span class="math-container">$n$</span> x <span class="math-container">$m$</span>, trivially this works though with any number of hidden layers)</p>
  
  <p>The better question is <em>should you?</em>. In all honesty, it depends on the data that you have, but, usually, with only 120 examples you'll either overfit completely or do relatively well if the true solution is a very simple function, but, in general, in the common situations where that isn't the case I find myself more likely or not using Bayesian approaches, so I can actually consider confidence (with little data, this is really nice)</p>
  
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Answers.CreationDate: 2020-01-17T16:31:29.307Z
Answers.Text: Are there any recommendations for choosing the number of layers and neurons in them, as well as activation functions?
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Answers.Text: @AndrewSol For the most part no, usually grid searches or previous empirical results are used.
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Answers.Text: @ mshlis - Re: Please see my adjusted starting message.
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Body: <p>The selection of experimental data includes a set of vectors of different dimensions. The input is a 3-dimensional vector, and the output is a 12-dimensional vector. The sample size is 120 pairs of input 3-dimensional and output 12-dimensional vectors. </p>
  
  <p>Is it possible to train such a neural network (in MATLAB)? Which structure of the neural network is best suited for this?</p>
  
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2020-01-17T12:51:26.247Z
Comments.Text: Hello, could you provide an overview of the data and/or problem you're trying to solve? Without such information I can not give you a full answer.
Comments.UserDisplayName: razvanc92
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Comments.Text: Hello, I do direct kinematics of the robot. The input 3-dimensional vector consists of the rotation angles of the corresponding drive. The working body of the robot is a triangular platform. As the output, I record the coordinates of the vertices of the triangle in the base coordinate system (we get three 3-dimensional vectors = only 9 coordinates) and three angular velocities along the x, y and z axes. In total, a 12-dimensional output vector is obtained.
Comments.UserDisplayName: ayr
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Comments.CreationDate: 2020-01-17T13:10:10.707Z
Comments.Text: I need to approximate data using a neural network, i.e. train it when applying the appropriate 3-dimensional vector to produce the corresponding 12-dimensional vector. I have a training database.
Comments.UserDisplayName: ayr
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2020-01-17T18:07:03.397Z
Comments.Text: Please see my adjusted starting communication.
Comments.UserDisplayName: ayr
Tags: <neural-networks><matlab><neural-architecture-search><thought-vectors>
Title: Is it possible to train a neural network with 3 inputs and 12 outputs?
UserDisplayName: ayr
ThreadText: # Is it possible to train a neural network with 3 inputs and 12 outputs?
  
  (Asked by: username_0 on 2020-01-17)
  
  The selection of experimental data includes a set of vectors of different dimensions. The input is a 3-dimensional vector, and the output is a 12-dimensional vector. The sample size is 120 pairs of input 3-dimensional and output 12-dimensional vectors.
  
  Is it possible to train such a neural network (in MATLAB)? Which structure of the neural network is best suited for this?
  
  **Tags:** neural-networks, matlab, neural-architecture-search, thought-vectors
  
  > **Comments:**
  > **username_1**: Hello, could you provide an overview of the data and/or problem you're trying to solve? Without such information I can not give you a full answer.  
  > **username_0**: Hello, I do direct kinematics of the robot. The input 3-dimensional vector consists of the rotation angles of the corresponding drive. The working body of the robot is a triangular platform. As the output, I record the coordinates of the vertices of the triangle in the base coordinate system (we get three 3-dimensional vectors = only 9 coordinates) and three angular velocities along the x, y and z axes. In total, a 12-dimensional output vector is obtained.  
  > **username_0**: I need to approximate data using a neural network, i.e. train it when applying the appropriate 3-dimensional vector to produce the corresponding 12-dimensional vector. I have a training database.  
  > **username_0**: Please see my adjusted starting communication.  
  
  ---
  
  ## 2 answers
  
  ### Answer by username_3 on 2020-01-17. Score: 4 | Accepted answer
  
  There is nothing stopping you, you can setup Dense Neural Networks to have any size inputs or outputs (simple proof is to imagine a single layer NN with no activation is just a linear transform and given input dim $n$ and output dim $m$, it's just a matrix of $n$ x $m$, trivially this works though with any number of hidden layers)
  
  The better question is *should you?*. In all honesty, it depends on the data that you have, but, usually, with only 120 examples you'll either overfit completely or do relatively well if the true solution is a very simple function, but, in general, in the common situations where that isn't the case I find myself more likely or not using Bayesian approaches, so I can actually consider confidence (with little data, this is really nice)
  
  > **Comments:**
  > **username_0**: Are there any recommendations for choosing the number of layers and neurons in them, as well as activation functions?  
  > **username_3**: @AndrewSol For the most part no, usually grid searches or previous empirical results are used.  
  > **username_0**: @ username_3 - Re: Please see my adjusted starting message.  
  
  ---
  
  ### Answer by username_2 on 2020-01-24. Score: 1
  
  The perceptron convergence theorem states that any architecture will lead to a correlation between the data.
  
  Yes, you can!
  
  
  ---
  
  