Multi-Label Classification with Deep Learning

By Jason Brownlee on August 31, 2020 in Deep Learning

Last Updated on August 31, 2020

Multi-label classification involves predicting zero or more class labels.

Unlike normal classification tasks where class labels are mutually exclusive, multi-label classification requires specialized machine learning algorithms that support predicting multiple mutually non-exclusive classes or “labels.”

Deep learning neural networks are an example of an algorithm that natively supports multi-label classification problems. Neural network models for multi-label classification 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-label classification.

After completing this tutorial, you will know:

Multi-label classification is a predictive modeling task that involves predicting zero or more mutually non-exclusive class labels.
Neural network models can be configured for multi-label classification tasks.
How to evaluate a neural network for multi-label classification and make a prediction for new data.

Let’s get started.

Multi-Label Classification with Deep Learning
Photo by Trevor Marron, some rights reserved.

Tutorial Overview

This tutorial is divided into three parts; they are:

Multi-Label Classification
Neural Networks for Multiple Labels
Neural Network for Multi-Label Classification

Multi-Label Classification

Classification is a predictive modeling problem that involves outputting a class label given some input

It is different from regression tasks that involve predicting a numeric value.

Typically, a classification task involves predicting a single label. Alternately, it might involve predicting the likelihood across two or more class labels. In these cases, the classes are mutually exclusive, meaning the classification task assumes that the input belongs to one class only.

Some classification tasks require predicting more than one class label. This means that class labels or class membership are not mutually exclusive. These tasks are referred to as multiple label classification, or multi-label classification for short.

In multi-label classification, zero or more labels are required as output for each input sample, and the outputs are required simultaneously. The assumption is that the output labels are a function of the inputs.

We can create a synthetic multi-label classification dataset using the make_multilabel_classification() function in the scikit-learn library.

Our dataset will have 1,000 samples with 10 input features. The dataset will have three class label outputs for each sample and each class will have one or two values (0 or 1, e.g. present or not present).

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

# example of a multi-label classification task
from sklearn.datasets import make_multilabel_classification
# define dataset
X, y = make_multilabel_classification(n_samples=1000, n_features=10, n_classes=3, n_labels=2, random_state=1)
# summarize dataset shape
print(X.shape, y.shape)
# summarize first few examples
for i in range(10):
	print(X[i], y[i])

# example of a multi-label classification task

from sklearn.datasets import make_multilabel_classification

# define dataset

X, y = make_multilabel_classification(n_samples=1000, n_features=10, n_classes=3, n_labels=2, random_state=1)

# summarize dataset shape

print(X.shape, y.shape)

# summarize first few examples

for i in range(10):

print(X[i], y[i])

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.

The first 10 rows of inputs and outputs are summarized and we can see that all inputs for this dataset are numeric and that output class labels have 0 or 1 values for each of the three class labels.

(1000, 10) (1000, 3)

[ 3.  3.  6.  7.  8.  2. 11. 11.  1.  3.] [1 1 0]
[7. 6. 4. 4. 6. 8. 3. 4. 6. 4.] [0 0 0]
[ 5.  5. 13.  7.  6.  3.  6. 11.  4.  2.] [1 1 0]
[1. 1. 5. 5. 7. 3. 4. 6. 4. 4.] [1 1 1]
[ 4.  2.  3. 13.  7.  2.  4. 12.  1.  7.] [0 1 0]
[ 4.  3.  3.  2.  5.  2.  3.  7.  2. 10.] [0 0 0]
[ 3.  3.  3. 11.  6.  3.  4. 14.  1.  3.] [0 1 0]
[ 2.  1.  7.  8.  4.  5. 10.  4.  6.  6.] [1 1 1]
[ 5.  1.  9.  5.  3.  4. 11.  8.  1.  8.] [1 1 1]
[ 2. 11.  7.  6.  2.  2.  9. 11.  9.  3.] [1 1 1]

(1000, 10) (1000, 3)

[ 3. 3. 6. 7. 8. 2. 11. 11. 1. 3.] [1 1 0]

[7. 6. 4. 4. 6. 8. 3. 4. 6. 4.] [0 0 0]

[ 5. 5. 13. 7. 6. 3. 6. 11. 4. 2.] [1 1 0]

[1. 1. 5. 5. 7. 3. 4. 6. 4. 4.] [1 1 1]

[ 4. 2. 3. 13. 7. 2. 4. 12. 1. 7.] [0 1 0]

[ 4. 3. 3. 2. 5. 2. 3. 7. 2. 10.] [0 0 0]

[ 3. 3. 3. 11. 6. 3. 4. 14. 1. 3.] [0 1 0]

[ 2. 1. 7. 8. 4. 5. 10. 4. 6. 6.] [1 1 1]

[ 5. 1. 9. 5. 3. 4. 11. 8. 1. 8.] [1 1 1]

[ 2. 11. 7. 6. 2. 2. 9. 11. 9. 3.] [1 1 1]

Next, let’s look at how we can develop neural network models for multi-label classification tasks.

Neural Networks for Multiple Labels

Some machine learning algorithms support multi-label classification natively.

Neural network models can be configured to support multi-label classification and can perform well, depending on the specifics of the classification task.

Multi-label classification can be supported directly by neural networks simply by specifying the number of target labels there is in the problem as the number of nodes in the output layer. For example, a task that has three output labels (classes) will require a neural network output layer with three nodes in the output layer.

Each node in the output layer must use the sigmoid activation. This will predict a probability of class membership for the label, a value between 0 and 1. Finally, the model must be fit with the binary cross-entropy loss function.

In summary, to configure a neural network model for multi-label classification, the specifics are:

Number of nodes in the output layer matches the number of labels.
Sigmoid activation for each node in the output layer.
Binary cross-entropy loss function.

We can demonstrate this using the Keras deep learning library.

We will define a Multilayer Perceptron (MLP) model for the multi-label classification 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 that were chosen after some trial and error. We will fit the model using binary cross-entropy loss and the Adam version of stochastic gradient descent.

The definition of the network for the multi-label classification task is listed below.

# define the model
model = Sequential()
model.add(Dense(20, input_dim=n_inputs, kernel_initializer='he_uniform', activation='relu'))
model.add(Dense(n_outputs, activation='sigmoid'))
model.compile(loss='binary_crossentropy', optimizer='adam')

# define the model

model = Sequential()

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

model.add(Dense(n_outputs, activation='sigmoid'))

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

You may want to adapt this model for your own multi-label classification task; therefore, we can create a function to define and return the model where the number of input and output variables is 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, activation='sigmoid'))
	model.compile(loss='binary_crossentropy', 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, activation='sigmoid'))

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

return model

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

Neural Network for Multi-Label Classification

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 that 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.

The MLP model will predict the probability for each class label by default. This means it will predict three probabilities for each sample. These can be converted to crisp class labels by rounding the values to either 0 or 1. We can then calculate the classification accuracy for the crisp class labels.

...
# make a prediction on the test set
yhat = model.predict(X_test)
# round probabilities to class labels
yhat = yhat.round()
# calculate accuracy
acc = accuracy_score(y_test, yhat)

...

# make a prediction on the test set

yhat = model.predict(X_test)

# round probabilities to class labels

yhat = yhat.round()

# calculate accuracy

acc = accuracy_score(y_test, yhat)

The scores are collected and can be summarized by reporting the mean and standard deviation across all repeats and cross-validation folds.

The evaluate_model() function below takes the dataset, evaluates the model, and returns a list of evaluation scores, in this case, accuracy 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)
		# make a prediction on the test set
		yhat = model.predict(X_test)
		# round probabilities to class labels
		yhat = yhat.round()
		# calculate accuracy
		acc = accuracy_score(y_test, yhat)
		# store result
		print('>%.3f' % acc)
		results.append(acc)
	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)

# make a prediction on the test set

yhat = model.predict(X_test)

# round probabilities to class labels

yhat = yhat.round()

# calculate accuracy

acc = accuracy_score(y_test, yhat)

# store result

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

results.append(acc)

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-label classification
from numpy import mean
from numpy import std
from sklearn.datasets import make_multilabel_classification
from sklearn.model_selection import RepeatedKFold
from keras.models import Sequential
from keras.layers import Dense
from sklearn.metrics import accuracy_score

# get the dataset
def get_dataset():
	X, y = make_multilabel_classification(n_samples=1000, n_features=10, n_classes=3, n_labels=2, random_state=1)
	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, activation='sigmoid'))
	model.compile(loss='binary_crossentropy', 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)
		# make a prediction on the test set
		yhat = model.predict(X_test)
		# round probabilities to class labels
		yhat = yhat.round()
		# calculate accuracy
		acc = accuracy_score(y_test, yhat)
		# store result
		print('>%.3f' % acc)
		results.append(acc)
	return results

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

# mlp for multi-label classification

from numpy import mean

from numpy import std

from sklearn.datasets import make_multilabel_classification

from sklearn.model_selection import RepeatedKFold

from keras.models import Sequential

from keras.layers import Dense

from sklearn.metrics import accuracy_score

# get the dataset

def get_dataset():

X, y = make_multilabel_classification(n_samples=1000, n_features=10, n_classes=3, n_labels=2, random_state=1)

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, activation='sigmoid'))

model.compile(loss='binary_crossentropy', 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)

# make a prediction on the test set

yhat = model.predict(X_test)

# round probabilities to class labels

yhat = yhat.round()

# calculate accuracy

acc = accuracy_score(y_test, yhat)

# store result

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

results.append(acc)

return results

# load dataset

X, y = get_dataset()

# evaluate model

results = evaluate_model(X, y)

# summarize performance

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

Running the example reports the classification accuracy 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 accuracy is reported. In this case, the model is shown to achieve an accuracy of about 81.2 percent.

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

...
>0.780
>0.820
>0.790
>0.810
>0.840
Accuracy: 0.812 (0.032)

...

>0.780

>0.820

>0.790

>0.810

>0.840

Accuracy: 0.812 (0.032)

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-label classification 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-label classification
from numpy import asarray
from sklearn.datasets import make_multilabel_classification
from keras.models import Sequential
from keras.layers import Dense

# get the dataset
def get_dataset():
	X, y = make_multilabel_classification(n_samples=1000, n_features=10, n_classes=3, n_labels=2, random_state=1)
	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, activation='sigmoid'))
	model.compile(loss='binary_crossentropy', 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 = [3, 3, 6, 7, 8, 2, 11, 11, 1, 3]
newX = asarray([row])
yhat = model.predict(newX)
print('Predicted: %s' % yhat[0])

# use mlp for prediction on multi-label classification

from numpy import asarray

from sklearn.datasets import make_multilabel_classification

from keras.models import Sequential

from keras.layers import Dense

# get the dataset

def get_dataset():

X, y = make_multilabel_classification(n_samples=1000, n_features=10, n_classes=3, n_labels=2, random_state=1)

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, activation='sigmoid'))

model.compile(loss='binary_crossentropy', 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 = [3, 3, 6, 7, 8, 2, 11, 11, 1, 3]

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-label classification task: the probabilities of each class label.

Predicted: [0.9998627 0.9849341 0.00208042]

1	Predicted: [0.9998627 0.9849341 0.00208042]

Summary

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

Specifically, you learned:

Multi-label classification is a predictive modeling task that involves predicting zero or more mutually non-exclusive class labels.
Neural network models can be configured for multi-label classification tasks.
How to evaluate a neural network for multi-label classification 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.

Deep Learning Models for Multi-Output Regression

How to Use AutoKeras for Classification and Regression

43 Responses to Multi-Label Classification with Deep Learning

pratyush August 31, 2020 at 11:32 am #

Hi Jason,

How to deal if one or more labels are heavily imbalanced?
We might have high accuracy but F1 score would be low for those.

Reply
- Arjun Pakrashi August 31, 2020 at 11:55 am #
  
  Use label based macro or micro averaged F-Score and not accuracy or hamming loss to evaluate. It’s a good idea to perform a per-label performance analysis which is often not done in the multi-label literature.
  
  Reply
  - Jason Brownlee August 31, 2020 at 1:23 pm #
    
    Great suggestion.
    
    Reply
  - Prachi Gupta September 4, 2020 at 6:18 am #
    
    This article is nice
    
    Reply
    - Jason Brownlee September 4, 2020 at 6:33 am #
      
      Thanks!
      
      Reply
- Jason Brownlee August 31, 2020 at 1:23 pm #
  
  Perhaps you can try a class weighting, e.g. cost-sensitive learning:
  https://machinelearningmastery.com/cost-sensitive-neural-network-for-imbalanced-classification/
  
  Reply
Pathak Machine August 31, 2020 at 10:24 pm #

Hello,

This is really very informative article and thanks for sharing this usefull post.

Reply
- Jason Brownlee September 1, 2020 at 6:30 am #
  
  You’re welcome. I’m happy to hear that!
  
  Reply
SWM September 1, 2020 at 1:01 am #

Sir you are a gem! Great article!

Reply
- Jason Brownlee September 1, 2020 at 6:34 am #
  
  Thanks.
  
  Reply
David Rodriguez September 2, 2020 at 10:57 am #

For larger datasets where we would use train/test/validate would we still use average accuracy to evaluate?

Reply
- Jason Brownlee September 2, 2020 at 1:29 pm #
  
  No, you would estimate directly from your test set.
  
  Reply
MEN September 3, 2020 at 12:37 am #

Hi Jason,
Very well explained.
Thank you

Reply
- Jason Brownlee September 3, 2020 at 6:07 am #
  
  You’re welcome.
  
  Reply
Ryan Wyngard September 4, 2020 at 3:25 pm #

Jason I appreciate your tutorials

Reply
- Jason Brownlee September 5, 2020 at 6:39 am #
  
  Thank you!
  
  Reply
Berns Buenaobra September 4, 2020 at 3:44 pm #

Had some trouble installing them on Anaconda my solution is the instruction in Autokeras website.To install the package, please use the pip installation as follows:

pip3 install git+https://github.com/keras-team/keras-tuner.git@1.0.2rc1
pip3 install autokeras

Thanks for this Doc Jason will buy the book over the weekend on a Student Discount

Reply
- Jason Brownlee September 5, 2020 at 6:39 am #
  
  Great tip, thanks for sharing!
  
  Reply
Saad September 4, 2020 at 9:17 pm #

Hi Jason,
Can Random Forest or XGBoost be used for similar problem of multi-label classification? How viable would that approach be? And are you planning to do any such article in the near future?

Reply
- Jason Brownlee September 5, 2020 at 6:46 am #
  
  I’m not sure off the cuff, sorry. Perhaps try it and see.
  
  Reply
pradeep September 9, 2020 at 5:28 am #

This is really an awesome tutorial.
The hidden layer has 20 nodes . Is there any particular logic to choose the number of nodes based on number of input dimensions which is 10 in this case ?

Reply
- Jason Brownlee September 9, 2020 at 6:53 am #
  
  Thanks!
  
  No, it was configured via trial and error.
  
  Reply
George September 16, 2020 at 10:56 am #

Hi Jason,
In MultiLabel if the prediction gives as
array([[0.4774732 , 0.04919493, 0.47333184]], dtype=float32)
and data has 3 classes say ‘0’,’1′,’2′

How we know which class probability belong to which class?
Thank You

Reply
- George September 16, 2020 at 11:19 am #
  
  Also, How to get model.classes_ for keras functional model, for the multi label we do to_categorical of y values like
  
  y_train = tensorflow.keras.utils.to_categorical(y_train, num_classes= 3) y_valid = tensorflow.keras.utils.to_categorical(y_valid, num_classes= 3)
  whether y_train column names return classes in order of prediction?
  
  Reply
  - Jason Brownlee September 16, 2020 at 12:17 pm #
    
    You can call model.predict_classes(), more here:
    https://machinelearningmastery.com/how-to-make-classification-and-regression-predictions-for-deep-learning-models-in-keras/
    
    Reply
- Jason Brownlee September 16, 2020 at 12:17 pm #
  
  Index 0 is always class 0, index 1 is always class 1, etc.
  
  You can use argmax as a short cut:
  https://machinelearningmastery.com/argmax-in-machine-learning/
  
  Reply
  - George September 16, 2020 at 4:52 pm #
    
    Thanks Jason
    Does that mean in this also
    array([[0.4774732 , 0.04919493, 0.47333184]], dtype=float32)
    Index 0 belong to class 0, Index 1 belong to Class 1, etc
    
    Reply
    - Jason Brownlee September 17, 2020 at 6:41 am #
      
      Yes.
      
      Reply
George September 16, 2020 at 5:14 pm #

Because model.predict_classes(), argmax returns the max probability class

Reply
- Jason Brownlee September 17, 2020 at 6:41 am #
  
  Yes.
  
  predict_classes() does an argmax for you.
  
  Reply
Anthony The Koala September 18, 2020 at 6:23 pm #

Dear Dr Jason,
The predicted yhat[0].round() returns

print(yhat[0].round()) [1. 1. 0.] #This is instead of print(yhat[0]) [0.99956924 0.9856825 0.00900584]

1
2
3
4
5

print(yhat[0].round())
[1. 1. 0.]
#This is instead of
print(yhat[0])
[0.99956924 0.9856825 0.00900584]

* Is the above example predicting multi-variate output

Thank you,
Anthony of Sydney

Reply
- Jason Brownlee September 19, 2020 at 6:50 am #
  
  A multi-label output. I guess you could call it multivariate, but I would not use that phrase.
  
  Reply
  - Anthony The Koala September 19, 2020 at 2:36 pm #
    
    Dear Dr Jason,
    Thank you,
    Anthony of Sydney
    
    Reply
  - Anthony The Koala September 19, 2020 at 8:05 pm #
    
    Dear Dr Jason,
    Given that there are multi-label outputs consisting of only 0 or 1, are there multilabel categories regression models whose outputs belong to the set of integers?
    
    By set of integers I mean numbers that are like 0,1,2,3,4? NOT 0, 1.39, 2.141, 3.142, 4.23? I mean multi-label integers output categories
    
    Thank you,
    Anthony of Sydney
    
    Reply
    - Jason Brownlee September 20, 2020 at 6:44 am #
      
      If you require a model to predict integers, you can round/scale the output of a model to meet your needs.
      
      Reply
      - Anthony The Koala September 20, 2020 at 11:53 am #
        
        Dear Dr Jason,
        Thank you for your reply.
        Could you elaborate what kind of multivariate Y models I could generate please.
        Thank you,
        Anthony of Sydney
      - Jason Brownlee September 20, 2020 at 1:35 pm #
        
        An MLP for regression can output real values in 0-1 that can be scaled by the desired range and rounded.
        
        Target values in the training data must be prepared in the same way.
Anthony The Koala September 18, 2020 at 10:53 pm #

Dear Dr Jason,
In the #define model

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

1
2

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

* We know that ’20’ means 20 neurons in the first hidden layer.
* Is there a rule of thumb to determine how many neurons are in the hidden layer?

Thank you,
Anthony of Sydney

Reply
- Jason Brownlee September 19, 2020 at 6:54 am #
  
  Good question, no, see this:
  https://machinelearningmastery.com/faq/single-faq/how-many-layers-and-nodes-do-i-need-in-my-neural-network
  
  Reply
  - Anthony The Koala September 19, 2020 at 2:41 pm #
    
    Dear Dr Jason,
    Thank you for the reply by averting me to the FAQ on adding lawyers and nodes.
    
    I have a further question not addressed by the FAQ.
    
    Is it possible that even if you add layers and nodes, would an evaluiation score or accuracy score get worse.
    
    Put it another way, could an evaluation or accuracy score peak as you add more layers and/or nodes then as you add more layers and/or nodes, the evaluation or accuracy score drops?
    
    Thank you,
    Anthony of Sydney
    
    Reply
    - Jason Brownlee September 20, 2020 at 6:39 am #
      
      Yes, it is common that adding layers and nodes results in worse performance at some point, particularly if you do not also tune the hyperparametres for the learning algorithm.
      
      Reply
Ahmed September 21, 2020 at 1:46 pm #

Hi Dr Jason,
Very well explained, Thank you.
Please , I have multi label classification dataset with large number of labels , labels equal to 1385
When use this model on my dataset
The accuracy of training data equal to 15 and
The accuracy of testing data equal to zero
How can I do to deal with my multi label dataset with this number of labels ?
Thanks alot.

Reply
- Jason Brownlee September 21, 2020 at 2:37 pm #
  
  Thanks.
  
  Good question, this may give you ideas:
  https://machinelearningmastery.com/faq/single-faq/how-do-i-handle-a-large-number-of-categories
  
  Reply

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Tutorial Overview

Multi-Label Classification

Neural Networks for Multiple Labels

Neural Network for Multi-Label Classification

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

43 Responses to Multi-Label Classification with Deep Learning

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Finally Bring Deep Learning To
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