Data Science

Questions tagged [multi-output]

Ask Question

The tag has no usage guidance.

22 questions
Newest
Active
Bountied
Unanswered
Filter by
Sorted by
Tagged with
0
votes
1answer
43 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: ...
0
votes
1answer
23 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 ...
1
vote
1answer
22 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 ...
0
votes
0answers
23 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 ...
0
votes
0answers
40 views

Multioutput classification: How do I get probabilities of continuous dependent variables?

I'm new to ML and want to try several methods on my data set to compare it and develop a better feeling for the individual approaches. My data set has several independent variables which I want to ...
0
votes
0answers
14 views

Multi input multi output mapping with keras in R

I have five continuous variables I would like to predict ($y_1,...,y_5$). I have some prior belief that these variables are related in such a way that $y_1 \rightarrow y_2 \rightarrow y_3 \rightarrow ...
0
votes
0answers
16 views

Backprop doesnt passing across custom keras layer

I write FAST RCNN, and I ran into a problem, back propagation does not go through custom layers without weights, the gradient does not go through the user layer (as I think), the loss function at the ...
1
vote
0answers
15 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 ...
0
votes
0answers
9 views

Multi target model with only one input variable?

I am about to develop a manual model relating different input and output features regarding the operation of a powerplant with its power generation. Some features have a linear tendency with energy, ...
0
votes
0answers
36 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 ...
0
votes
0answers
12 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-...
2
votes
1answer
29 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: ...
1
vote
0answers
13 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 ...
0
votes
0answers
36 views

Multi-output and heterogeneous models

What models (including software) support multiple outputs/responses which can have their own loss functions? For example, suppose I would like to fit a model with multiple outputs where one output ...
3
votes
1answer
24 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 ...
4
votes
2answers
260 views

Are there any Python libraries for predicting the closest value to a correct label out of a variable-size list of possible label values?

Imagine I have the following dataset: ...
2
votes
1answer
46 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, ...
0
votes
0answers
17 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 ...
0
votes
0answers
17 views

Is it feasable to “predict” 2d (~2x20 array) output using a CNN

I'm trying to generate a "beatmap" (from osu!mania) based on an audio file. To do this, I've applied fast fourier transform (this can change to Mel-frequency cepstrum, but that's not super relevant) ...
0
votes
1answer
1k 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, ...
0
votes
1answer
240 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} = \...
1
vote
2answers
192 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 ...