Questions tagged [multi-output]

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Output representation for a neural network to learn grid-based game with multiple units

I have a round based game played on a grid map with multiple units that I would like to control in some fashion using neural network (NN). All of the units are moved at once. Each unit can move in any ...
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1answer
35 views

How to calculate a single accuracy for a model with multiple outputs in Keras?

Consider the following, rather simple, model: ...
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1answer
19 views

Early stopping based on average val_loss of last ten epoches and with some n partiences

I am training a DNN with CNN in Keras. Though, I can write an EarlyStopping criteria based on val_loss but due to minor oscillations in the val_loss, I want to monitor the average validation loss over ...
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25 views

Would it make sense to have an output layer connected to other output layers in a NN?

I'm working with data that has multiple variables which could be predicted, nonetheless I need to predict just one that is directly correlated to all of the others. Would it make sense to have a NN ...
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41 views

How to top n values from .predict_proba in multioutputclassifier?

I am following MultiOutputClassifier technique to predict roles (the data are transformed to numeric so that's not a concern) I want to use .predict_proba() and ...
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5 views

Feature selection algorithm for psychometrics, when there is several predicted variables

I'm on a psychometric study. It is a survey. All variables are on a scale of 7. So these are considered as continuous variables. I have this dataset: 600 features 100 predicted variables 100 survey ...
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25 views

Multi-Label Regression of Categorical Probability Distribution that adds up to one

What would an ideal Tensorflow/Keras architecture look like, if the target is a multi-regression with values that add up to one? Toy Example: Tv Channels You work for a big TV-Station and your boss ...
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1answer
70 views

Multi-target regression tree with additional constraint

I have a regression problem where I need to predict three dependent variables ($y$) based on a set of independent variables ($x$): $$ (y_1,y_2,y_3) = \beta_0 + \beta_1 x_1 + \beta_2 x_2 + \dots + \...
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18 views

How to handle partial labels in multi output classifier using Keras API?

I am training a model with multiple categorical inputs/outputs. Some samples have partial labels. Currently, I am dropping samples with any missing label but I am wasting a lot of data. I would like ...
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1answer
26 views

Multiple targets in a classification problem

I have a vector of length $n \gt 4$ which has exactly 4 targets, so for example [0, 0, 0, 1, 0, 1, 0, 1, 1]. I would like to know how I can modify the softmax ...
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0answers
63 views

How does Keras optimization for a network with multiple outputs

I currently have a neural network that takes in 3 numbers as inputs and outputs 3 numbers. I've attached a picture of the network below and my code is accessible through the following link: [Google ...
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1answer
25 views

What input for a combined model (3 nets)

I have this architecture, made of 3 NNs: In code: ...
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1answer
209 views

Multi-output, multi-timestep sequence prediction with Keras

I've been searching for about three hours and I can't find an answer to a very simple question. I have a time series prediction problem. I am trying to use a Keras LSTM model (with a Dense at the end) ...
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25 views

aggregating multi output regression outputs vs single output (=top down) approach: is it worth it?

basically I have this problem where I need to forecast the sales of some stores. such stores have multiple product lines of which I have the split data (say Y1,Y2,Y3 where Y1+Y2+Y3=Y). I have also ...
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1answer
24 views

Given a regression based model with many feature variables; what tools would you utilize to figure out which feature variables add the most variance?

Given a hypothetical dataset {S} with 100 X feature variables and 10 predicted Y variables. X1 ... X100 Y1 .... Y10 1 .. 2 3 .. 4 4 .. 3 2 .. 1 Let's say I want to improve the accuracy of Y1. I am ...
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1answer
55 views

What method/algorithm for constrained multi-target regression

I am working with three dimensional measurement data and want to model them using a multivariate linear regression. I have already implemented a simple gradient descent algorithm to solve the classic ...
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1answer
52 views

How To Motivate A Neural Network

Suppose a training dataset contains the following inputs: company size number of employees turnover average salary country years of operation ...and outputs: ...
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1answer
151 views

Neural Network for Multiple Dependent Outputs

I have a dataset with approx 6 input features and 5 output values to be predicted. I am trying to understand what kind of neural network would be most suitable to assign probability across multiple ...
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1answer
161 views

Multiple output size in neural network

In the paper "A NOVEL FOCAL TVERSKY LOSS FUNCTION WITH IMPROVED ATTENTIONU-NETFOR LESION SEGMENTATION" the author use deep supervision by outputing multiple outputmask which have different ...
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2answers
1k views

Custom output names for keras model

I have a model like this with multiple outputs and i want to change it's output names ...
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0answers
28 views

Derivative of multi-output Gaussian Process

I am working on a project where I estimate transition and measurements models for a kalman filter using Gaussian Processes. In order to linearize the models I require the Jacobian of the estimated ...
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1answer
175 views

Keras loaded model output is different from the training model output

When I train my model it has a two-dimension output - it is (none, 1) - corresponding to the time series I'm trying to predict. But whenever I load the saved model in order to make predictions, it has ...
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0answers
73 views

Validation Accuracy greater than train accuracy, validation loss lesser than training loss MTL

I am training a multi task model using VGG16. Datase: Dataset contain 11K images. There are two tasks: The dataset is imbalanced, 1) PFR classification: 10 classes 0 --- 5776 10-12 --- 1066 6-...
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1answer
42 views

Control which features are used for every task in multioutput classification?

I would like to perform a multiclass-multioutput classification task, on vectorized textual data. I started by using a random forest classifier in a multioutput startegy: ...
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0answers
19 views

What toolbox to use to create multi-output random forest(reggression) with custom spltting function at each node?

I am trying to implement "Real Time Head Pose Estimation fromConsumer Depth Cameras" by Fanelli et al. I need to train a random forest(regression) with the following criterion The predicted output is ...
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1answer
93 views

Physical modelling with neural networks - single output + stack ensemble vs multi-output

We are trying to replace an existing physical model (8 inputs/7 outputs) with artificial neural networks. The physics behind the existing model is mainly thermodynamics of humid air for air ...
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2answers
286 views
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1answer
81 views

unique predictions for "multi-label multi-output" classification task

Let’s assume that four participants (A, B, C and D) take on five sport-challenges (e.g. swimming, running, ...). Our goal is to predict the placement of each participant for each challenge. Moreover, ...
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23 views

Combining several Multi-Output-Models into a single Multi-Output-Model

I'm trying to create a k-Nearest-Neighbor based model of 76-dimensional input data $I$ and 44-dimensional output data $O$. Through domain knowledge I know that only certain input dimensions are ...
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2answers
3k views

How to feed data to multi-output Keras model from a single TFRecords file

I know how to feed data to a multi-output Keras model using numpy arrays for the training data. However, I have all my data in a single TFRecords file comprising several feature columns: an image, ...
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1answer
565 views

Minimal example: Keras functional API & multi-input/multi-output regression

Problem: I have a regression problem, where I want to predict two or more numerical outcomes $y_i$ based on a number of numerical features $X_i$. The model would look like: $$y_{1,i}, y_{2,i} = \...
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2answers
237 views

More output neurons than labels?

When we train a neural network model for a classification problem, we usually have a dense output layer of size equal to the number of labels we have. If the layer size was greater, the model can ...