Deep Learning Models for Multi-Output Regression

By Jason Brownlee on August 28, 2020 in Deep Learning

Last Updated on August 28, 2020

Multi-output regression involves predicting two or more numerical variables.

Unlike normal regression where a single value is predicted for each sample, multi-output regression requires specialized machine learning algorithms that support outputting multiple variables for each prediction.

Deep learning neural networks are an example of an algorithm that natively supports multi-output regression problems. Neural network models for multi-output regression tasks can be easily defined and evaluated using the Keras deep learning library.

In this tutorial, you will discover how to develop deep learning models for multi-output regression.

After completing this tutorial, you will know:

Multi-output regression is a predictive modeling task that involves two or more numerical output variables.
Neural network models can be configured for multi-output regression tasks.
How to evaluate a neural network for multi-output regression and make a prediction for new data.

Let’s get started.

Deep Learning Models for Multi-Output Regression
Photo by Christian Collins, some rights reserved.

Tutorial Overview

This tutorial is divided into three parts; they are:

Multi-Output Regression
Neural Networks for Multi-Outputs
Neural Network for Multi-Output Regression

Multi-Output Regression

Regression is a predictive modeling task that involves predicting a numerical output given some input.

It is different from classification tasks that involve predicting a class label.

Typically, a regression task involves predicting a single numeric value. Although, some tasks require predicting more than one numeric value. These tasks are referred to as multiple-output regression, or multi-output regression for short.

In multi-output regression, two or more outputs are required for each input sample, and the outputs are required simultaneously. The assumption is that the outputs are a function of the inputs.

We can create a synthetic multi-output regression dataset using the make_regression() function in the scikit-learn library.

Our dataset will have 1,000 samples with 10 input features, five of which will be relevant to the output and five of which will be redundant. The dataset will have three numeric outputs for each sample.

The complete example of creating and summarizing the synthetic multi-output regression dataset is listed below.

# example of a multi-output regression problem
from sklearn.datasets import make_regression
# create dataset
X, y = make_regression(n_samples=1000, n_features=10, n_informative=5, n_targets=3, random_state=2)
# summarize shape
print(X.shape, y.shape)

# example of a multi-output regression problem

from sklearn.datasets import make_regression

# create dataset

X, y = make_regression(n_samples=1000, n_features=10, n_informative=5, n_targets=3, random_state=2)

# summarize shape

print(X.shape, y.shape)

Running the example creates the dataset and summarizes the shape of the input and output elements.

We can see that, as expected, there are 1,000 samples, each with 10 input features and three output features.

(1000, 10) (1000, 3)

1	(1000, 10) (1000, 3)

Next, let’s look at how we can develop neural network models for multiple-output regression tasks.

Neural Networks for Multi-Outputs

Many machine learning algorithms support multi-output regression natively.

Popular examples are decision trees and ensembles of decision trees. A limitation of decision trees for multi-output regression is that the relationships between inputs and outputs can be blocky or highly structured based on the training data.

Neural network models also support multi-output regression and have the benefit of learning a continuous function that can model a more graceful relationship between changes in input and output.

Multi-output regression can be supported directly by neural networks simply by specifying the number of target variables there are in the problem as the number of nodes in the output layer. For example, a task that has three output variables will require a neural network output layer with three nodes in the output layer, each with the linear (default) activation function.

We can demonstrate this using the Keras deep learning library.

We will define a multilayer perceptron (MLP) model for the multi-output regression task defined in the previous section.

Each sample has 10 inputs and three outputs, therefore, the network requires an input layer that expects 10 inputs specified via the “input_dim” argument in the first hidden layer and three nodes in the output layer.

We will use the popular ReLU activation function in the hidden layer. The hidden layer has 20 nodes, which were chosen after some trial and error. We will fit the model using mean absolute error (MAE) loss and the Adam version of stochastic gradient descent.

The definition of the network for the multi-output regression task is listed below.

...
# define the model
model = Sequential()
model.add(Dense(20, input_dim=10, kernel_initializer='he_uniform', activation='relu'))
model.add(Dense(3))
model.compile(loss='mae', optimizer='adam')

...

# define the model

model = Sequential()

model.add(Dense(20, input_dim=10, kernel_initializer='he_uniform', activation='relu'))

model.add(Dense(3))

model.compile(loss='mae', optimizer='adam')

You may want to adapt this model for your own multi-output regression task, therefore, we can create a function to define and return the model where the number of input and number of output variables are provided as arguments.

# get the model
def get_model(n_inputs, n_outputs):
	model = Sequential()
	model.add(Dense(20, input_dim=n_inputs, kernel_initializer='he_uniform', activation='relu'))
	model.add(Dense(n_outputs))
	model.compile(loss='mae', optimizer='adam')
	return model

# get the model

def get_model(n_inputs, n_outputs):

model = Sequential()

model.add(Dense(20, input_dim=n_inputs, kernel_initializer='he_uniform', activation='relu'))

model.add(Dense(n_outputs))

model.compile(loss='mae', optimizer='adam')

return model

Now that we are familiar with how to define an MLP for multi-output regression, let’s explore how this model can be evaluated.

Neural Network for Multi-Output Regression

If the dataset is small, it is good practice to evaluate neural network models repeatedly on the same dataset and report the mean performance across the repeats.

This is because of the stochastic nature of the learning algorithm.

Additionally, it is good practice to use k-fold cross-validation instead of train/test splits of a dataset to get an unbiased estimate of model performance when making predictions on new data. Again, only if there is not too much data and the process can be completed in a reasonable time.

Taking this into account, we will evaluate the MLP model on the multi-output regression task using repeated k-fold cross-validation with 10 folds and three repeats.

Each fold the model is defined, fit, and evaluated. The scores are collected and can be summarized by reporting the mean and standard deviation.

The evaluate_model() function below takes the dataset, evaluates the model, and returns a list of evaluation scores, in this case, MAE scores.

# evaluate a model using repeated k-fold cross-validation
def evaluate_model(X, y):
	results = list()
	n_inputs, n_outputs = X.shape[1], y.shape[1]
	# define evaluation procedure
	cv = RepeatedKFold(n_splits=10, n_repeats=3, random_state=1)
	# enumerate folds
	for train_ix, test_ix in cv.split(X):
		# prepare data
		X_train, X_test = X[train_ix], X[test_ix]
		y_train, y_test = y[train_ix], y[test_ix]
		# define model
		model = get_model(n_inputs, n_outputs)
		# fit model
		model.fit(X_train, y_train, verbose=0, epochs=100)
		# evaluate model on test set
		mae = model.evaluate(X_test, y_test, verbose=0)
		# store result
		print('>%.3f' % mae)
		results.append(mae)
	return results

# evaluate a model using repeated k-fold cross-validation

def evaluate_model(X, y):

results = list()

n_inputs, n_outputs = X.shape[1], y.shape[1]

# define evaluation procedure

cv = RepeatedKFold(n_splits=10, n_repeats=3, random_state=1)

# enumerate folds

for train_ix, test_ix in cv.split(X):

# prepare data

X_train, X_test = X[train_ix], X[test_ix]

y_train, y_test = y[train_ix], y[test_ix]

# define model

model = get_model(n_inputs, n_outputs)

# fit model

model.fit(X_train, y_train, verbose=0, epochs=100)

# evaluate model on test set

mae = model.evaluate(X_test, y_test, verbose=0)

# store result

print('>%.3f' % mae)

results.append(mae)

return results

We can then load our dataset and evaluate the model and report the mean performance.

Tying this together, the complete example is listed below.

# mlp for multi-output regression
from numpy import mean
from numpy import std
from sklearn.datasets import make_regression
from sklearn.model_selection import RepeatedKFold
from keras.models import Sequential
from keras.layers import Dense

# get the dataset
def get_dataset():
	X, y = make_regression(n_samples=1000, n_features=10, n_informative=5, n_targets=3, random_state=2)
	return X, y

# get the model
def get_model(n_inputs, n_outputs):
	model = Sequential()
	model.add(Dense(20, input_dim=n_inputs, kernel_initializer='he_uniform', activation='relu'))
	model.add(Dense(n_outputs))
	model.compile(loss='mae', optimizer='adam')
	return model

# evaluate a model using repeated k-fold cross-validation
def evaluate_model(X, y):
	results = list()
	n_inputs, n_outputs = X.shape[1], y.shape[1]
	# define evaluation procedure
	cv = RepeatedKFold(n_splits=10, n_repeats=3, random_state=1)
	# enumerate folds
	for train_ix, test_ix in cv.split(X):
		# prepare data
		X_train, X_test = X[train_ix], X[test_ix]
		y_train, y_test = y[train_ix], y[test_ix]
		# define model
		model = get_model(n_inputs, n_outputs)
		# fit model
		model.fit(X_train, y_train, verbose=0, epochs=100)
		# evaluate model on test set
		mae = model.evaluate(X_test, y_test, verbose=0)
		# store result
		print('>%.3f' % mae)
		results.append(mae)
	return results

# load dataset
X, y = get_dataset()
# evaluate model
results = evaluate_model(X, y)
# summarize performance
print('MAE: %.3f (%.3f)' % (mean(results), std(results)))

# mlp for multi-output regression

from numpy import mean

from numpy import std

from sklearn.datasets import make_regression

from sklearn.model_selection import RepeatedKFold

from keras.models import Sequential

from keras.layers import Dense

# get the dataset

def get_dataset():

X, y = make_regression(n_samples=1000, n_features=10, n_informative=5, n_targets=3, random_state=2)

return X, y

# get the model

def get_model(n_inputs, n_outputs):

model = Sequential()

model.add(Dense(20, input_dim=n_inputs, kernel_initializer='he_uniform', activation='relu'))

model.add(Dense(n_outputs))

model.compile(loss='mae', optimizer='adam')

return model

# evaluate a model using repeated k-fold cross-validation

def evaluate_model(X, y):

results = list()

n_inputs, n_outputs = X.shape[1], y.shape[1]

# define evaluation procedure

cv = RepeatedKFold(n_splits=10, n_repeats=3, random_state=1)

# enumerate folds

for train_ix, test_ix in cv.split(X):

# prepare data

X_train, X_test = X[train_ix], X[test_ix]

y_train, y_test = y[train_ix], y[test_ix]

# define model

model = get_model(n_inputs, n_outputs)

# fit model

model.fit(X_train, y_train, verbose=0, epochs=100)

# evaluate model on test set

mae = model.evaluate(X_test, y_test, verbose=0)

# store result

print('>%.3f' % mae)

results.append(mae)

return results

# load dataset

X, y = get_dataset()

# evaluate model

results = evaluate_model(X, y)

# summarize performance

print('MAE: %.3f (%.3f)' % (mean(results), std(results)))

Running the example reports the MAE for each fold and each repeat, to give an idea of the evaluation progress.

Note: Your results may vary given the stochastic nature of the algorithm or evaluation procedure, or differences in numerical precision. Consider running the example a few times and compare the average outcome.

At the end, the mean and standard deviation MAE is reported. In this case, the model is shown to achieve a MAE of about 8.184.

You can use this code as a template for evaluating MLP models on your own multi-output regression tasks. The number of nodes and layers in the model can easily be adapted and tailored to the complexity of your dataset.

...
>8.054
>7.562
>9.026
>8.541
>6.744
MAE: 8.184 (1.032)

...

>8.054

>7.562

>9.026

>8.541

>6.744

MAE: 8.184 (1.032)

Once a model configuration is chosen, we can use it to fit a final model on all available data and make a prediction for new data.

The example below demonstrates this by first fitting the MLP model on the entire multi-output regression dataset, then calling the predict() function on the saved model in order to make a prediction for a new row of data.

# use mlp for prediction on multi-output regression
from numpy import asarray
from sklearn.datasets import make_regression
from keras.models import Sequential
from keras.layers import Dense

# get the dataset
def get_dataset():
	X, y = make_regression(n_samples=1000, n_features=10, n_informative=5, n_targets=3, random_state=2)
	return X, y

# get the model
def get_model(n_inputs, n_outputs):
	model = Sequential()
	model.add(Dense(20, input_dim=n_inputs, kernel_initializer='he_uniform', activation='relu'))
	model.add(Dense(n_outputs, kernel_initializer='he_uniform'))
	model.compile(loss='mae', optimizer='adam')
	return model

# load dataset
X, y = get_dataset()
n_inputs, n_outputs = X.shape[1], y.shape[1]
# get model
model = get_model(n_inputs, n_outputs)
# fit the model on all data
model.fit(X, y, verbose=0, epochs=100)
# make a prediction for new data
row = [-0.99859353,2.19284309,-0.42632569,-0.21043258,-1.13655612,-0.55671602,-0.63169045,-0.87625098,-0.99445578,-0.3677487]
newX = asarray([row])
yhat = model.predict(newX)
print('Predicted: %s' % yhat[0])

# use mlp for prediction on multi-output regression

from numpy import asarray

from sklearn.datasets import make_regression

from keras.models import Sequential

from keras.layers import Dense

# get the dataset

def get_dataset():

X, y = make_regression(n_samples=1000, n_features=10, n_informative=5, n_targets=3, random_state=2)

return X, y

# get the model

def get_model(n_inputs, n_outputs):

model = Sequential()

model.add(Dense(20, input_dim=n_inputs, kernel_initializer='he_uniform', activation='relu'))

model.add(Dense(n_outputs, kernel_initializer='he_uniform'))

model.compile(loss='mae', optimizer='adam')

return model

# load dataset

X, y = get_dataset()

n_inputs, n_outputs = X.shape[1], y.shape[1]

# get model

model = get_model(n_inputs, n_outputs)

# fit the model on all data

model.fit(X, y, verbose=0, epochs=100)

# make a prediction for new data

row = [-0.99859353,2.19284309,-0.42632569,-0.21043258,-1.13655612,-0.55671602,-0.63169045,-0.87625098,-0.99445578,-0.3677487]

newX = asarray([row])

yhat = model.predict(newX)

print('Predicted: %s' % yhat[0])

Running the example fits the model and makes a prediction for a new row.

As expected, the prediction contains three output variables required for the multi-output regression task.

Predicted: [-152.22713 -78.04891 -91.97194]

1	Predicted: [-152.22713 -78.04891 -91.97194]

Summary

In this tutorial, you discovered how to develop deep learning models for multi-output regression.

Specifically, you learned:

Multi-output regression is a predictive modeling task that involves two or more numerical output variables.
Neural network models can be configured for multi-output regression tasks.
How to evaluate a neural network for multi-output regression and make a prediction for new data.

Do you have any questions?
Ask your questions in the comments below and I will do my best to answer.

102 Responses to Deep Learning Models for Multi-Output Regression

Doru August 28, 2020 at 7:30 pm #

Lors de l’exécution est générée l’erreur suivante:

File “C:\ProgramData\Anaconda3\lib\site-packages\keras\backend\tensorflow_backend.py”, line 703, in is_tensor
return isinstance(x, tf_ops._TensorLike) or tf_ops.is_dense_tensor_like(x)

AttributeError: module ‘tensorflow.python.framework.ops’ has no attribute ‘_TensorLike’

Comment résoudre?

Reply
- Jason Brownlee August 29, 2020 at 7:59 am #
  
  Sorry to hear that, perhaps confirm that your version of Keras and TensorFlow are up to date:
  https://machinelearningmastery.com/setup-python-environment-machine-learning-deep-learning-anaconda/
  
  The tips here may also help:
  https://machinelearningmastery.com/faq/single-faq/why-does-the-code-in-the-tutorial-not-work-for-me
  
  Reply
Doru August 28, 2020 at 8:50 pm #

Hy
Running this example I get the following error:

AttributeError: module ‘tensorflow.python.framework.ops’ has no attribute ‘_TensorLike’

A solution?

Reply
- Jason Brownlee August 29, 2020 at 8:00 am #
  
  What version of Keras and TensorFlow are you using?
  
  You must use the follow versions or higher:
  
  tensorflow: 2.3.0 keras: 2.4.3
  
  1
  2
  
  tensorflow: 2.3.0
  keras: 2.4.3
  
  Reply
fabou August 29, 2020 at 6:44 am #

Hi Jason,

could you tell me why a model is compiled for each cross validation fold?

Reply
- Jason Brownlee August 29, 2020 at 8:08 am #
  
  Each model requires training a new model from scratch in order to establish an unbiased estimate of model performance when making predictions on out of sample instances.
  
  If you are new to k-fold cross-validation, you can get started here:
  https://machinelearningmastery.com/k-fold-cross-validation/
  
  Reply
  - fabou August 30, 2020 at 1:14 am #
    
    I thought a model had to be instantiated once and then was passed to the cross validation loop. I missed something I guess…
    
    In the example below taken from one of your excellent article ( Add Binary Flags for Missing Values ) I understand that the model is instantiated and that this instance of the model is evaluated on each cross validation training folds :
    
    [ step 1 ] model = RandomForest()
    
    [ step 2 ] cv = RepeatedStratifiedKFold( n_splits = 10 , n_repeats = 3 )
    
    [ step3 ] scores = cross_val_score( model , X , y , scoring = ‘accuracy’ , cv = cv , n_jobs = -1 )
    
    Here it seems to me that for each training fold a model is instanciated and then evaluated on the corresponding testing folds.
    
    Both logic seems different… or I missed something.
    
    Reply
    - fabou August 30, 2020 at 1:36 am #
      
      I tested passing the model to the evaluation function :
      
      def evaluate_model( model , X , y ) :
      
      results = list()
      
      n_inputs = X.shape[ 1 ]
      
      n_outputs = y.shape[ 1 ]
      
      cv = RepeatedKFold( n_splits = 10 , n_repeats = 1 , random_state = 999 )
      
      for train_ix, test_ix in cv.split( X ) :
      
      X_train, X_test = X[train_ix], X[test_ix]
      
      y_train, y_test = y[train_ix], y[test_ix]
      
      model.fit( X_train , y_train , verbose = 0 , epochs = 100 )
      
      mae = model.evaluate( X_test , y_test , verbose = 0 )
      
      results.append( mae )
      
      print( f’mae : {mae:.3f}’ )
      
      return results
      
      mae keeps decreasing… I do not understand why…
      
      Reply
      - Jason Brownlee August 30, 2020 at 6:42 am #
        
        It is invalid as you continue to train the same model each loop.
        
        The model must be re-defined and re-fit each cross-validation loop otherwise the evaluation is optimistic.
    - Jason Brownlee August 30, 2020 at 6:41 am #
      
      It does the same thing.
      
      In that case, the model is created and fit a new for each cross-validation fold. You just don’t see it as it happens internally to the function.
      
      Reply
      - fabou August 30, 2020 at 4:58 pm #
        
        OK. I thought the model was created once and then fitted on each cross validation fold.
        
        I have been doing a little experiment.
        
        cv = RepeatedStratifiedKFold( n_splits = 10 , n_repeats = 1 , random_state = 777 )
        
        and then [ A ] :
        
        > scores_A = []
        
        > for train_ix , test_ix in cv.split( X , y ) :
        
        > X_train , X_test = X[ train_ix , : ] , X[ test_ix , : ]
        
        > y_train , y_test = y[ train_ix ] , y[ test_ix ]
        
        > model = AdaBoostClassifier()
        
        > model.fit( X_train , y_train )
        
        > y_test_pred = model.predict( X_test )
        
        > score = roc_auc_score( y_test , y_test_pred )
        
        > print( f’score : {score}’ )
        
        > scores_A.append( score )
        
        After that [ B ] :
        
        > scores_B = []
        
        > model = AdaBoostClassifier()
        
        > for train_ix , test_ix in cv.split( X , y ) :
        
        > X_train , X_test = X[ train_ix , : ] , X[ test_ix , : ]
        
        > y_train , y_test = y[ train_ix ] , y[ test_ix ]
        
        > #model = AdaBoostClassifier()
        
        > model.fit( X_train , y_train )
        
        > y_test_pred = model.predict( X_test )
        
        > score = roc_auc_score( y_test , y_test_pred )
        
        > print( f’score : {score}’ )
        
        > scores_B.append( score )
        
        The difference between [ A ] and [ B ] is that with [ B ] the model is instantiated outside the loop once whereas with [ A ] it is created anew for each fold.
        
        scores_A and _B are the same ( even when I change the random state parameter for RepeatedStratifiedKFold and/or the model used : RandomForest…).
        
        This experiment makes me think the model has to be created once and be fitted on each cross validation fold.
        
        For me, the sole difference between model_A = AdaBoostClassifier() an model_B = AdaBoostClassifier() is there memory location this is why I do not understand why the get_model function is called in the evaluate_model function…
        
        PS :
        
        I was not able to replicate the scores given by
        
        > cross_val_score( model , X , y , scoring = ‘roc_auc’ , cv = cv , n_jobs = -1 )
      - Jason Brownlee August 31, 2020 at 6:09 am #
        
        The cross-validation procedure requires the model be re-fit each evaluation.
        
        Internally cross_val_score will clone the model and refit from scratch each iteration. This is functionality equilivient to re-defining and re-fitting each iteration.
Sam August 29, 2020 at 7:42 am #

HI Jason,

Just out of curiosity, why would you build a multi-output model instead of multiple models of single outputs? When would it be better/worse?

Reply
- Jason Brownlee August 29, 2020 at 8:11 am #
  
  Try both and see what works best for your specific dataset.
  
  Reply
  - Vania June 26, 2021 at 2:08 pm #
    
    Hi Jason. How can I apply those codes into specific datasets? Since you were using random values.
    
    Reply
    - Jason Brownlee June 27, 2021 at 4:36 am #
      
      This will help you to load your dataset:
      https://machinelearningmastery.com/load-machine-learning-data-python/
      
      Reply
Konstantinos September 4, 2020 at 8:54 am #

Hi Jason,

First of all, congratulations on your website and for your detailed explanations and examples. I really appreciate it!

I have a question for you, as I’ve already spent a considerable amount of time searching online, without significant success. How can we handle a situation where we have partial ground truth for our targets? (e.g. output vectors with some NaN values)

So far, I understand that if you provide a y_train with NaNs, then the loss function won’t behave properly. If we provided a loss function for each output variable (I think keras allows that with loss=[‘mse’, ‘mse’, …]), then in theory, we could dismiss a group of NaNs within a batch of an output variable, by filtering them out (practically making both y_true and y_pred = 0).

The problem is that if we create a custom loss function and try to replace NaNs with 0s in both vectors, then keras throws an error (I think it relates to the use of non-tensorflow functions to filter nans, thus making keras unable to compute the derivative of the loss function)

Could you think of an elegant solution on this problem? Or maybe a completely different approach? (i assume that this way is sensible from the optimization perspective)

Thanks in advance for your time!

Reply
- Jason Brownlee September 4, 2020 at 1:36 pm #
  
  Thanks!
  
  It is common to replace missing inputs with an imputed value using statistics or a model.
  
  For some models, you can mark missing values with a special value and allow the model to treat missing as just another value.
  
  Also, you can mark missing values for some models and configure them to ignore them, e.g. a masking layer in neural nets.
  
  I hope that gives you some ideas.
  
  Reply
  - Konstantinos September 4, 2020 at 7:21 pm #
    
    Hi Jason,
    
    Thanks for your reply.
    
    In this case, it’s not missing inputs, and I can’t really use any statistics to learn the missing targets.
    
    Regarding the special values, I haven’t seen anything related in Keras. Are you aware of such symbol?
    
    Also, I checked the masking layers, but that’s again only for the inputs. This wouldn’t affect the y_true in the loss function.
    
    In general, it seems like a trivial problem which doesn’t seem to have a trivial solution…
    Maybe I need to check how people handle missing labels on multi-label classification.
    
    Hopefully I’ll figure it out soon!
    
    Reply
    - Jason Brownlee September 5, 2020 at 6:45 am #
      
      You can use zero padding in the putput (yhat) then manually ignore zero padded values when using the output or evaluating predoctions.
      
      This is very common in seq2seq problems in NLP.
      
      Reply
robgonz September 5, 2020 at 9:03 am #

This is a great nn-model for regression. I tried using mean sq log error for the loss, so I can interpret the reslt a bit better.

Now, when you evaluate or use the evaluation, is there a difference if you setup the repeat times as desired? Or just keep calling the evaluation function with just 2 or 3 repeats?

Just wandering with this algorithm as I never use tried RepeatedKFolds before.

Thanks for sharing J.

Reply
- Jason Brownlee September 6, 2020 at 6:01 am #
  
  Perhaps you can keep the loss as mse, and use sq log error as a metric?
  
  Repeated k-fold cross-validation is a good practice for stochastic models, if they are not too expensive to fit. 3 repeats is conservative, 10 or 30 is better.
  
  More here:
  https://machinelearningmastery.com/repeated-k-fold-cross-validation-with-python/
  
  Reply
Pratyay Mukherjee September 6, 2020 at 3:10 pm #

can you please explain how the mean absolute error in Keras loss function is calculated on multi-output vectors? Is it the average of corresponding values in vectors averaged over dimensions of the output and samples?

Reply
- Jason Brownlee September 7, 2020 at 8:25 am #
  
  I believe it is error averaged across variables and samples.
  
  Reply
zeinab September 14, 2020 at 10:35 pm #

Hello
I have a question
My data is not time dependent
Not image and video
Which Deep Learning model is more suitable for predicting my data?
Thanks for guiding me

Reply
- Jason Brownlee September 15, 2020 at 5:24 am #
  
  An MLP.
  
  Also, this will help:
  https://machinelearningmastery.com/when-to-use-mlp-cnn-and-rnn-neural-networks/
  
  Reply
j.sanjay September 29, 2020 at 11:30 am #

Hi Jason,
Can you be my teacher

Reply
- Jason Brownlee September 29, 2020 at 12:52 pm #
  
  I’m happy to answer questions about machine learning.
  
  Reply
Noodle October 7, 2020 at 11:16 pm #

Hi, thanks for the tutorial.

If the goal is simply prediction, what are the benefits of fitting a multi-output NN instead of multiple single-output NN? What are the gains in this case? Is there a paper or reference you can recommend?

Reply
- Jason Brownlee October 8, 2020 at 8:31 am #
  
  The problem will require a single or multiple outputs. You must use the model that achieves the goals of the project.
  
  Reply
Davide October 9, 2020 at 4:44 am #

Hi, thank you for this article!

I didn’t get once you go through the Cross validation step how you choose one configuration with respect to the others in order to make prediction on new data. I mean, once all the loop is finished what has to be done?

Reply
- Jason Brownlee October 9, 2020 at 6:49 am #
  
  See this:
  https://machinelearningmastery.com/make-predictions-scikit-learn/
  
  Reply
  - Davide Mori October 9, 2020 at 8:05 pm #
    
    Ok I think I’ve understood.
    Based on what you have written in this article, I evaluate the model using k-fold-cross validation and based on what the score is I change the Layers and their parameters in my model accordingly. Then I make my network learning on all the data already used during validation and finally I make the predictions.
    To conclude, Validation is used just to measure the performance of the model I have built, is that right?
    
    Reply
    - Jason Brownlee October 10, 2020 at 7:05 am #
      
      The train set is used to fit the model.
      The test set is used to evaluate the model.
      The validation set can be used to tune the model or stop training the model at the right time.
      
      More here:
      https://machinelearningmastery.com/difference-test-validation-datasets/
      
      Reply
Urs October 10, 2020 at 12:42 am #

Wondering about this: With “model.compile(loss=’mae’, optimizer=’adam’)”, I basically instruct keras to minimize the combined loss of both output values together. This averages out the two errors stemming from the two separate target values, which could lead to a large positive error on target 1 which is compensated by a large negative error on target 2.

I imagine to avoid this, I would have to create 2 separate submodels that optimize each loss individually – probably using the functional API?

Reply
- Jason Brownlee October 10, 2020 at 7:07 am #
  
  Yes.
  
  Another approach, a model with two output and two losses (so-called multi-output model):
  https://machinelearningmastery.com/keras-functional-api-deep-learning/
  
  Reply
Julm October 14, 2020 at 2:00 am #

Hi, I really appreciate your content!

I am starting with deep learning models and I have a project on mind.

What I want to do, giving certain input values (for example: 10 features), I want to predict a curve. I mean, I want to obtain a 2D curve (where Y axis will be force and X axis will be time). Can “Multi-output regression” be a solution? I will predict time steps (t0=5, t1=16, t2=26…).

What other solution can I use for this case?

thanks in advance

Reply
- Jason Brownlee October 14, 2020 at 6:22 am #
  
  If you want a curve, then perhaps use curve fitting directly:
  https://docs.scipy.org/doc/scipy/reference/generated/scipy.optimize.curve_fit.html
  
  Reply
pratyush October 22, 2020 at 4:41 am #

I tested this on my data set and found that it worked if iloc was used. ex:

X_train, X_test = X.iloc[train_ix], X.iloc[test_ix]
y_train, y_test = y.iloc[train_ix], y.iloc[test_ix]

Reply
- Jason Brownlee October 22, 2020 at 6:51 am #
  
  Nice work!
  
  Reply
Igor November 6, 2020 at 12:09 pm #

Can I do this to forecast t+1, t+2 and so on?

Reply
- Jason Brownlee November 6, 2020 at 1:15 pm #
  
  Yes.
  
  Reply
Luz N November 7, 2020 at 1:11 pm #

Hi Jason, how would it be for multi-output binary classification?
For example, forecast if t+1 is 0 or 1 and t+2 is 0 or 1 and t+3 is 0 or 1.
1. Output layer: model.add(Dense(n_outputs, activation=’sigmoid’))?
2. Loss function: ‘binary_crossentropy’?

Thanks in advance.
Great tutorial!!!

Reply
- Jason Brownlee November 8, 2020 at 6:37 am #
  
  Yes, I believe so. Try it and see.
  
  Reply
Yuchen November 20, 2020 at 8:45 am #

Hi Jason, first of all, thanks for your great content! I am also using MLP for doing some multi-output regression, but I found that when I tested the model, the output would always be the same regardless of the input (this also happened at training stage). I have normalized my input data and the dimension of my input and output are 450 and 120, respectively, also I used tanh activation function to bound my output within range [-1,1]. Do you have any suggestions on this? Thanks in advance 🙂

Reply
- Jason Brownlee November 20, 2020 at 1:04 pm #
  
  It may suggestion your model require further tuning for your dataset.
  
  The suggestions here will help:
  https://machinelearningmastery.com/start-here/#better
  
  Reply
Chiedozie November 24, 2020 at 3:23 am #

Hey Jason, I really love your works here. I’m working on a dataset_1 where the output classes of this dataset_1 has some other features dataset, say dataset_2, that the model could also learn from. The challenge I’m facing is that dataset_1 and dataset_2 are totally different, so there’s no way I could merge them on some common features.
I’d like to know if there’s a way to train a model that would be able to learn on dataset_1 then subsequently learn on dataset_2?

I am considering using dimensionality reduction to reduce the features of the dataset_2 to a single value and then use this single value as an output to dataset_1 in a multi-output model. Do you think this is a good approach?

Reply
- Jason Brownlee November 24, 2020 at 6:21 am #
  
  Thanks!
  
  Try it and compare to other approaches.
  
  Perhaps try to ensemble their predictions?
  
  Reply
Goutam December 1, 2020 at 5:55 pm #

Thanks a lot for this amazing article. I love your works. I actually need some advice on a multioutput regression problem. In the problem, the outputs are percentage. and all the outputs need to sum up to 1.
For example, i have to predict 3 outputs A, B, C. each of them can be any value between 0 and 1. let’s say,
A = 0.6,B = 0.3,C = 0.1 or A = 1.0, B = 0.0, C = 0.0
so, A+B+C is 1. how do i approach this problem.
Thanks in advance.

Reply
- Jason Brownlee December 2, 2020 at 7:38 am #
  
  Thanks!
  
  Use a softmax activation:
  https://machinelearningmastery.com/softmax-activation-function-with-python/
  
  Reply
Dinani December 12, 2020 at 8:10 pm #

Hi
I appreciate your post. I have a question, how to determine the number of hidden layers and their dense? Is there any relation between them and the shape of input/output?

Reply
- Jason Brownlee December 13, 2020 at 6:00 am #
  
  Thanks.
  
  Good question, I answer it here:
  https://machinelearningmastery.com/faq/single-faq/how-many-layers-and-nodes-do-i-need-in-my-neural-network
  
  Reply
Joshua December 19, 2020 at 9:55 am #

Hey Jason,

Absolutely enjoy reading these concise tutorials. What are the available metrics for multi-output regression models when using grid search? When compiling the model, any other metric besides [“Accuracy”] raises an error. Is accuracy the MAE or R^2 value in this case?

Reply
- Jason Brownlee December 19, 2020 at 1:27 pm #
  
  Thanks.
  
  You can use the same old regression metrics either for overall error or per output variable error.
  
  Accuracy is inappropriate for regression, you can use MSE, RMSE or MAE as common error metrics.
  
  Reply
Maha December 30, 2020 at 6:20 am #

Hi
If I want to build deep model , as output will be vector of 20 elements and each element will take a value from on two possible values …..what kind of system i can use??,, any hep ..to solve this problem

Reply
- Jason Brownlee December 30, 2020 at 6:46 am #
  
  I recommend testing a suite of different models in order to discover what works well or best for your dataset.
  
  Perhaps start with a simple MLP.
  
  Reply
  - Maha December 30, 2020 at 7:15 am #
    
    thanks for reply
    if any link to MLP example to fellow??
    
    Reply
    - Jason Brownlee December 30, 2020 at 7:49 am #
      
      Yes, the above tutorial is exactly this, an MLP (multilayer perceptron) for multi-output regression.
      
      Reply
Katherine January 5, 2021 at 6:10 am #

Hi there, first of all thank you for the tutorial! This website has been immensely helpful for me in recent months as I’ve started to incorporate machine learning into my research. My main question for this post is why you chose he_uniform for your kernel initialization on the output layer. I know that it’s the best choice for relu activations, but I can’t find anything anywhere saying that it should also be used for linear activation. Maybe you have a good source/reason for this choice here? To be honest, I can’t find much about what is the best canned initializer for linear activation so I’m really curious to hear your thoughts.

Reply
- Jason Brownlee January 5, 2021 at 6:30 am #
  
  You’re welcome!
  
  It probably should not be used for linear activation. Probably won’t make a difference though.
  
  Reply
Maha January 14, 2021 at 8:33 am #

Hi
If there is example based on dividing data set into training and testing Multi-Output Regression

Reply
- Jason Brownlee January 14, 2021 at 8:46 am #
  
  It would be the same for single output regression:
  https://machinelearningmastery.com/train-test-split-for-evaluating-machine-learning-algorithms/
  
  Unless, you have a time series, then this will help:
  https://machinelearningmastery.com/time-series-forecasting-supervised-learning/
  
  Reply
Maha January 15, 2021 at 6:18 am #

Hi,
you mentioned that if the data set is small , can use k folds for evaluation …what about if the data set is large?? , for example ..instead of 1000 , maybe 5000 or 10000

Reply
- Jason Brownlee January 15, 2021 at 8:44 am #
  
  Those are still small datasets, and k-fold cross validation.
  
  Nevertheless, for very large data (millions/billions of samples) or for data where small samples are stable/stationary, you can use a train/test split.
  
  Reply
  - Maha January 16, 2021 at 10:45 am #
    
    okay..Thanks alots Jason
    
    Reply
Ladu Tandel January 20, 2021 at 8:29 pm #

Hi ,

May I know how to import x y data which i have as excel instead of auto generated x y data as per above code.

I want to predict multiple outputs and above concept is nice to start with, but since i am new to coding would like to get help on how to import my excel data of multiple xs and multiple ys

Thank you
Your help will be much appreciated

Reply
- Jason Brownlee January 21, 2021 at 6:48 am #
  
  Save your excel to CVS, then load your CSV as a numpy array:
  http://machinelearningmastery.com/load-machine-learning-data-python/
  
  Reply
Sada Hussain January 27, 2021 at 5:21 am #

which deep learning will be used for prediction of crop production ,humidity ,temperature and pH and also rain fall ?

Reply
Sean Murphy February 26, 2021 at 9:53 am #

Hi, just wondering, how can this neural network use multiple inputs and outputs if it is using Keras’s sequential API? Everything I’ve read online says that a Functional API is required for multiple inputs and outputs? thanks!

Reply
- Jason Brownlee February 26, 2021 at 1:27 pm #
  
  Yes, the functional API is required if you want to use separate input models or output models.
  
  To have a vector input or vector output the sequential model can be used directly.
  
  Reply
  - Sean Murphy February 27, 2021 at 3:34 am #
    
    Thank you for your reply, I really appreciate it. Would it be possible to explain the difference between using “seperate input models” and simply using “vector input or vector output” if it wouldn’t be too much trouble? For example, I have data with 5 numerical inputs and 9 numerical outputs. The inputs represent the performance characteristics of a op-amp, e.g gain and slew rate, the outputs are the widths of transistors which give those performance characteristics. Would these be vector inputs and outputs or separate models? Sorry if this is a foolish question, this is all quite new to me. Thanks again for your help and this fantastic article.
    
    Reply
    - Jason Brownlee February 27, 2021 at 6:09 am #
      
      Separate input models would be multiple disjoint vectors as input to the model for example.
      
      You can see models with separate input models here:
      https://machinelearningmastery.com/keras-functional-api-deep-learning/
      
      Yes, you’re describing a single model that takes a vector in and predicts a vector out. You could also model it with a separate “model” for output of each element in the output vector if you want – this might make sense if the elements were unrelated/uncorrelated.
      
      Reply
Peter March 10, 2021 at 4:47 am #

Hi Jason,

I have a question about activation functions in multi-output regression neural networks. I notice you choose the default linear function for the output and ReLU for the hidden layer.

In my case, my outputs can be very small numbers, and often the predictions return negative values which are physically meaningless. How can I restrict the outputs to be positive only? Is a linear output required for regression models, or can I change the output transfer function to tansig, logsig, etc.?

Thanks!

Reply
- Jason Brownlee March 10, 2021 at 6:26 am #
  
  Perhaps you can use a sigmoid activation then scale the output to the desired range.
  
  Reply
Syed Khurram Mahmud March 17, 2021 at 6:42 pm #

Hi! Nice work
Please help me here:

yhat = model.predict(newX)

model is not defined. Rest works fine I think

Why this error

Reply
- Jason Brownlee March 18, 2021 at 5:17 am #
  
  Perhaps you have skipped some lines of code in the example, this will help:
  https://machinelearningmastery.com/faq/single-faq/why-does-the-code-in-the-tutorial-not-work-for-me
  
  Reply
ali March 23, 2021 at 1:43 pm #

Hi Jason,

Great!! thank you so much for your nice tutorial. However, can I know how to develop an explainer for developing an interpretable model such as Permutation Feature Importance in a multi-output regression?

Thank you

Reply
- Jason Brownlee March 24, 2021 at 5:48 am #
  
  Sorry, I cannot help you with model interpretation – I don’t have tutorials on this topic.
  
  Reply
Sepideh April 21, 2021 at 8:20 am #

Hi Jason,

Would you please help me that how I can develop multi output classification neural network?

Thanks

Reply
- Jason Brownlee April 22, 2021 at 5:35 am #
  
  See this example:
  https://machinelearningmastery.com/multi-class-classification-tutorial-keras-deep-learning-library/
  
  Reply
Ayan Mitra April 29, 2021 at 6:37 pm #

Hi Jason,
Thanks for this tutorial, helped me on a real project.
I have a related question please.
I have a set of image matrix data set (lets say red, green, blue, white etc.) and the output colour is decided by a set of three input (x, y, z) (numerical values). Therefore the data looks like this (everything numerical, nothing categorical):

Input
X1,Y1,Z1 –> Blue Matrix (flattened 1-D arrays)
X2,Y2,Z2 –> Red Matrix
‘
‘
Xn,Yn,Zn –> White Matrix

Now I want build a NN regression model, like you showed here, where if [X,Y,Z] are input, it predicts the entire colour matrix (/ flattened 1-D array)

now, I understand this output layer will have a shape = shape(1-D colour matrix) (similar to 2 in your example). I also understand that the output layer’s shape is >> input layer’s size.

Can you please give some comments ? is this possible (technically I dont see why not)? Which algorithm you suggest is ideal.

thanks

Reply
- Jason Brownlee April 30, 2021 at 6:03 am #
  
  Perhaps the model can predict a class label and you can write code to then create the matrix – if it is the same for each class label.
  
  Reply
Ayoub AIT IDIR May 17, 2021 at 12:08 am #

Hi,

First of all, thank you for this amazing explanation, it was really helpful.
My question is, can we use RNN or LSTM to predict many output variables because I’ve been searching and found LSTM may be the best option but didn’t found any implementation to start with.

Thank you.

Reply
- Jason Brownlee May 17, 2021 at 5:39 am #
  
  You’re welcome.
  
  Yes, see this:
  https://machinelearningmastery.com/faq/single-faq/how-do-you-use-lstms-for-multi-step-time-series-forecasting
  
  Reply
Maha May 25, 2021 at 11:13 am #

Hello
I want to know how MAE for multi regression output is calculated numerically , if you can mention the mathematical equation for better understanding?
Thanks in advance

Reply
- Jason Brownlee May 26, 2021 at 5:45 am #
  
  Mean of the absolute differences, more here:
  https://scikit-learn.org/stable/modules/model_evaluation.html#mean-absolute-error
  
  Reply
Samaneh Manavi May 26, 2021 at 9:46 pm #

Dear Jason,
Thank you for this nice tutorial.
I have a complex multi-output regression task, in which, the input elements and output elements are not independent. I have designed a network with multi-layers of 1D convolutions by trial and error. I was wondering if you could recommend a well-known network architecture for multi-output regression problems.
I guess it makes more sense to optimize the hyperparameters of a well-known model to my problem rather than designing it from the scratch.

Thank you in advance,
Samaneh

Reply
- Jason Brownlee May 27, 2021 at 5:38 am #
  
  We cannot know what architecture will work well or best for a dataset, I recommend testing a suite of models and model configurations in order to discover what works best for your specific dataset.
  
  Reply
sepideh June 17, 2021 at 3:48 am #

Hi,
I run this code: I have two features(inputs) and 13 output, but I have this error:
—————————————————————————
TypeError Traceback (most recent call last)
in
16 mae = model.evaluate(x_test, y_test, verbose=0)
17 # store result
—> 18 print(‘>%.3f’ % mae)
19 results.append(mae)
20 return results

TypeError: must be real number, not list

######################################
def evaluate_model(x, y):
results = list()
n_inputs, n_outputs = x.shape[1], y.shape[1]
# define evaluation procedure
cv = RepeatedKFold(n_splits=10, n_repeats=3, random_state=1)
# enumerate folds
for train_ix, test_ix in cv.split(x):
# prepare data
x_train, x_test = x[train_ix], x[test_ix]
y_train, y_test = y[train_ix], y[test_ix]
# define model
model = build_regression(n_inputs, n_outputs)
# fit model
model.fit(x_train, y_train, verbose=0, epochs=100)
# evaluate model on test set
mae = model.evaluate(x_test, y_test, verbose=0)
# store result
print(‘>%.3f’ % mae)
results.append(mae)
return results

Reply
- Jason Brownlee June 17, 2021 at 6:19 am #
  
  Sorry to hear that, these tips may help:
  https://machinelearningmastery.com/faq/single-faq/why-does-the-code-in-the-tutorial-not-work-for-me
  
  Reply
Jean-Rassaire June 23, 2021 at 1:09 am #

Hi Jason Brownlee,
Thank you for this pedagogically explained model.
I would like to know how I can save this code in a file. Do you have a code to save it?

Reply
- Jason Brownlee June 23, 2021 at 5:39 am #
  
  You’re welcome.
  
  This will help you to save the code:
  https://machinelearningmastery.com/faq/single-faq/how-do-i-copy-code-from-a-tutorial
  
  Reply
Jean-Rassaire June 23, 2021 at 10:30 pm #

Thanks Jason Brownlee, but sorry that was a stupid question.

Reply
- Jason Brownlee June 24, 2021 at 6:02 am #
  
  No problem.
  
  Reply
Gireesh B July 15, 2021 at 4:38 pm #

Hi Jason, Just I want to Train a model with a set of inputs in numbers and set of outputs in numbers without knowing relationship between them(input and output).

Reply
- Jason Brownlee July 16, 2021 at 5:21 am #
  
  Yes, that is the definition of regression problems.
  
  Reply
Amn September 15, 2021 at 3:06 am #

Hi, first of all, thank you for the tutorial! my question is: I have students’ id as a sample and I want to predict 3 grades in the future to evaluate their performance but the problem is that the student id is repeated because they have many homework and many quizzes and so on. how can I predict the grades for each student separately? Then I want to save these predicted grades in a new file with a unique student-id to decide the student performance. Note: the repeated for student-id are different from student to other. any suggestion? Thanks in advance

Reply
- Adrian Tam September 15, 2021 at 11:53 pm #
  
  Student id is a nominal data. This should not be input to the model. I would suggest you to create a vector for each student id, holding the grades of homework and quiz, and predict the final grade. No student id should be in this vector.
  
  Reply
wb September 15, 2021 at 9:34 pm #

Hi, I’ve bought some of your books and they are great. BTW, for multi outputs regression, are there different weights and bias for each output? Or all outputs share the same w and b? In that case, how are the performance of multi outputs regression compared to separate single output regression?

Reply
- Adrian Tam September 16, 2021 at 12:53 am #
  
  Likely different because there is nothing in the model to enforce that they should be the same (but you can construct a model that way if you insist). For performance comparison, I doubt if that is possible because it sounds like you are comparing apples to oranges.
  
  Reply
Amn September 21, 2021 at 2:08 am #

Hi, there is a shared metrics between the classification and regression? I want to compare the performance of regression NN with classification NN?

Reply
- Adrian Tam September 21, 2021 at 9:32 am #
  
  You’re comparing apples to oranges. Note, regression is about predicting continuous values that the numbers have its literal meaning. Classification, however, are predicting nominal values that the numbers at the output are just names. This vast difference causes one metric can make no sense in the wrong problem.
  
  Reply

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Deep Learning Models for Multi-Output Regression

Tutorial Overview

Multi-Output Regression

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Tutorial Overview

Multi-Output Regression

Neural Networks for Multi-Outputs

Neural Network for Multi-Output Regression

Further Reading

Summary

Develop Deep Learning Projects with Python!

What If You Could Develop A Network in Minutes

Finally Bring Deep Learning To Your Own Projects

More On This Topic

102 Responses to Deep Learning Models for Multi-Output Regression

Leave a Reply Click here to cancel reply.

Finally Bring Deep Learning To
Your Own Projects