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What kind of activation is used by ScikitLearn's MLPClasssifier in output layer?

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I am currently working on a classification task with given class labels 0 and 1. For this I am using ScikitLearn's MLPClassifier providing an output of either 0 or 1 for each training example. However, I can not find any documentation, what the output layer of the MLPClassifier is exactly doing (which activation function? encoding?).

Since there is an output of only one class I assume something like One-hot_encoding is used. Is this assumption correct? Is there any documentation tackling this question for the MLPClassifier?

scikit-learn neural-network mlp

share|improve this question

edited Mar 26 at 5:32

ai_learning

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asked Mar 25 at 12:30

S.MariaS.Maria

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add a comment | 

2

I am currently working on a classification task with given class labels 0 and 1. For this I am using ScikitLearn's MLPClassifier providing an output of either 0 or 1 for each training example. However, I can not find any documentation, what the output layer of the MLPClassifier is exactly doing (which activation function? encoding?).

Since there is an output of only one class I assume something like One-hot_encoding is used. Is this assumption correct? Is there any documentation tackling this question for the MLPClassifier?

scikit-learn neural-network mlp

share|improve this question

edited Mar 26 at 5:32

ai_learning

5,61244 gold badges1515 silver badges4040 bronze badges

asked Mar 25 at 12:30

S.MariaS.Maria

4455 bronze badges

add a comment | 

I am currently working on a classification task with given class labels 0 and 1. For this I am using ScikitLearn's MLPClassifier providing an output of either 0 or 1 for each training example. However, I can not find any documentation, what the output layer of the MLPClassifier is exactly doing (which activation function? encoding?).

Since there is an output of only one class I assume something like One-hot_encoding is used. Is this assumption correct? Is there any documentation tackling this question for the MLPClassifier?

scikit-learn neural-network mlp

share|improve this question

edited Mar 26 at 5:32

ai_learning

5,61244 gold badges1515 silver badges4040 bronze badges

asked Mar 25 at 12:30

S.MariaS.Maria

4455 bronze badges

share|improve this question

edited Mar 26 at 5:32

ai_learning

5,61244 gold badges1515 silver badges4040 bronze badges

asked Mar 25 at 12:30

S.MariaS.Maria

4455 bronze badges

share|improve this question

edited Mar 26 at 5:32

ai_learning

5,61244 gold badges1515 silver badges4040 bronze badges

asked Mar 25 at 12:30

S.MariaS.Maria

4455 bronze badges

edited Mar 26 at 5:32

ai_learning

5,61244 gold badges1515 silver badges4040 bronze badges

edited Mar 26 at 5:32

ai_learning

5,61244 gold badges1515 silver badges4040 bronze badges

asked Mar 25 at 12:30

S.MariaS.Maria

4455 bronze badges

asked Mar 25 at 12:30

S.MariaS.Maria

4455 bronze badges

2 Answers
2

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1

out_activation_ attribute would give you the type of activation used in the output layer of your MLPClassifier.

From Documentation:

out_activation_ : string
Name of the output activation function.

The activation param just sets the hidden layer's activation function.

activation : ‘identity’, ‘logistic’, ‘tanh’, ‘relu’, default ‘relu’
Activation function for the hidden layer.

The output layer is decided internally in this piece of code.

# Output for regression
if not is_classifier(self):
 self.out_activation_ = 'identity'
# Output for multi class
elif self._label_binarizer.y_type_ == 'multiclass':
 self.out_activation_ = 'softmax'
# Output for binary class and multi-label
else:
 self.out_activation_ = 'logistic'

Hence, for binary classification it would be logistic and for multi-class it would be softmax.

To know more details about these activations, see here.

share|improve this answer

answered Mar 26 at 5:31

ai_learningai_learning

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add a comment | 

2

You have most of the information in the docs. The MLP is a simple neural network. It can use several activation functions, the default is relu.

It doesn't use one-hot encoding, rather you need to feed in a y (target) vector with class labels.

share|improve this answer

answered Mar 25 at 12:40

Josh FriedlanderJosh Friedlander

3,32811 gold badge1010 silver badges3535 bronze badges

• 
  
  
  

  
  

  
  
  
  
  
  

  
  
  Hello Josh, thank you for your answer. I know that I can choose relu, identity, tanh and logistic, but my question is about the output layer. Usually, for multilayer classification you would get either probabilities for each class or something like [100], [001],... . MLPClassifier is giving me ONE output. So how is it processed in the output layer? That is actually my question.
  
  – S.Maria
  Mar 25 at 17:16
  

  
  
  
  

add a comment | 

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1

out_activation_ attribute would give you the type of activation used in the output layer of your MLPClassifier.

From Documentation:

out_activation_ : string
Name of the output activation function.

The activation param just sets the hidden layer's activation function.

activation : ‘identity’, ‘logistic’, ‘tanh’, ‘relu’, default ‘relu’
Activation function for the hidden layer.

The output layer is decided internally in this piece of code.

# Output for regression
if not is_classifier(self):
 self.out_activation_ = 'identity'
# Output for multi class
elif self._label_binarizer.y_type_ == 'multiclass':
 self.out_activation_ = 'softmax'
# Output for binary class and multi-label
else:
 self.out_activation_ = 'logistic'

Hence, for binary classification it would be logistic and for multi-class it would be softmax.

To know more details about these activations, see here.

share|improve this answer

answered Mar 26 at 5:31

ai_learningai_learning

5,61244 gold badges1515 silver badges4040 bronze badges

add a comment | 

1

out_activation_ attribute would give you the type of activation used in the output layer of your MLPClassifier.

From Documentation:

out_activation_ : string
Name of the output activation function.

The activation param just sets the hidden layer's activation function.

activation : ‘identity’, ‘logistic’, ‘tanh’, ‘relu’, default ‘relu’
Activation function for the hidden layer.

The output layer is decided internally in this piece of code.

# Output for regression
if not is_classifier(self):
 self.out_activation_ = 'identity'
# Output for multi class
elif self._label_binarizer.y_type_ == 'multiclass':
 self.out_activation_ = 'softmax'
# Output for binary class and multi-label
else:
 self.out_activation_ = 'logistic'

Hence, for binary classification it would be logistic and for multi-class it would be softmax.

To know more details about these activations, see here.

share|improve this answer

answered Mar 26 at 5:31

ai_learningai_learning

5,61244 gold badges1515 silver badges4040 bronze badges

add a comment | 

out_activation_ attribute would give you the type of activation used in the output layer of your MLPClassifier.

From Documentation:

out_activation_ : string
Name of the output activation function.

The activation param just sets the hidden layer's activation function.

activation : ‘identity’, ‘logistic’, ‘tanh’, ‘relu’, default ‘relu’
Activation function for the hidden layer.

The output layer is decided internally in this piece of code.

# Output for regression
if not is_classifier(self):
 self.out_activation_ = 'identity'
# Output for multi class
elif self._label_binarizer.y_type_ == 'multiclass':
 self.out_activation_ = 'softmax'
# Output for binary class and multi-label
else:
 self.out_activation_ = 'logistic'

Hence, for binary classification it would be logistic and for multi-class it would be softmax.

To know more details about these activations, see here.

share|improve this answer

answered Mar 26 at 5:31

ai_learningai_learning

5,61244 gold badges1515 silver badges4040 bronze badges

# Output for regression
if not is_classifier(self):
 self.out_activation_ = 'identity'
# Output for multi class
elif self._label_binarizer.y_type_ == 'multiclass':
 self.out_activation_ = 'softmax'
# Output for binary class and multi-label
else:
 self.out_activation_ = 'logistic'

share|improve this answer

answered Mar 26 at 5:31

ai_learningai_learning

5,61244 gold badges1515 silver badges4040 bronze badges

answered Mar 26 at 5:31

ai_learningai_learning

5,61244 gold badges1515 silver badges4040 bronze badges

answered Mar 26 at 5:31

ai_learningai_learning

5,61244 gold badges1515 silver badges4040 bronze badges

2

You have most of the information in the docs. The MLP is a simple neural network. It can use several activation functions, the default is relu.

It doesn't use one-hot encoding, rather you need to feed in a y (target) vector with class labels.

share|improve this answer

answered Mar 25 at 12:40

Josh FriedlanderJosh Friedlander

3,32811 gold badge1010 silver badges3535 bronze badges

• 
  
  
  

  
  

  
  
  
  
  
  

  
  
  Hello Josh, thank you for your answer. I know that I can choose relu, identity, tanh and logistic, but my question is about the output layer. Usually, for multilayer classification you would get either probabilities for each class or something like [100], [001],... . MLPClassifier is giving me ONE output. So how is it processed in the output layer? That is actually my question.
  
  – S.Maria
  Mar 25 at 17:16
  

  
  
  
  

add a comment | 

2

You have most of the information in the docs. The MLP is a simple neural network. It can use several activation functions, the default is relu.

It doesn't use one-hot encoding, rather you need to feed in a y (target) vector with class labels.

share|improve this answer

answered Mar 25 at 12:40

Josh FriedlanderJosh Friedlander

3,32811 gold badge1010 silver badges3535 bronze badges

• 
  
  
  

  
  

  
  
  
  
  
  

  
  
  Hello Josh, thank you for your answer. I know that I can choose relu, identity, tanh and logistic, but my question is about the output layer. Usually, for multilayer classification you would get either probabilities for each class or something like [100], [001],... . MLPClassifier is giving me ONE output. So how is it processed in the output layer? That is actually my question.
  
  – S.Maria
  Mar 25 at 17:16
  

  
  
  
  

add a comment | 

You have most of the information in the docs. The MLP is a simple neural network. It can use several activation functions, the default is relu.

It doesn't use one-hot encoding, rather you need to feed in a y (target) vector with class labels.

share|improve this answer

answered Mar 25 at 12:40

Josh FriedlanderJosh Friedlander

3,32811 gold badge1010 silver badges3535 bronze badges

share|improve this answer

answered Mar 25 at 12:40

Josh FriedlanderJosh Friedlander

3,32811 gold badge1010 silver badges3535 bronze badges

answered Mar 25 at 12:40

Josh FriedlanderJosh Friedlander

3,32811 gold badge1010 silver badges3535 bronze badges

answered Mar 25 at 12:40

Josh FriedlanderJosh Friedlander

3,32811 gold badge1010 silver badges3535 bronze badges