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tensorflowkerasdeep-learningmultilabel-classificationmulticlass-classification
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Reputation: 43

Multi-Output Classification with Keras

I am using keras to build a multi-output classification model. My dataset is such as

[x1,x2,x3,x4,y1,y2,y3]

x1,x2,x3 are the features, and y1,y2,y3 are the labels, the y1,y2,y3 are multi-classes.

And I already built a model (I ingore some hidden layers):

def baseline_model(input_dim=23,output_dim=3):
    model_in = Input(shape=(input_dim,))
    model = Dense(input_dim*5,kernel_initializer='uniform',input_dim=input_dim)(model_in)
    model = Activation(activation='relu')(model)
    model = Dropout(0.5)(model)

    ...................

    model = Dense(output_dim,kernel_initializer='uniform')(model)
    model = Activation(activation='sigmoid')(model)

    model = Model(model_in,model)
    model.compile(optimizer='adam',loss='binary_crossentropy', metrics=['accuracy'])
    return model

And then I try to use the method of keras to make it support classification:

estimator = KerasClassifier(build_fn=baseline_model)
estimator.fit()
estimator.predict(df[0:10])

But I found that the result is not multi-output, only one dimension is output.

[0,0,0,0,0,0,0,0,0,0]

So for the multi-output classification problem, we can not use KerasClassifier function to learn it?

Upvotes: 2

Views: 6827

Answers (1)

Jeremy Bare
Jeremy Bare

Reputation: 550

You do not need to wrap the model in KerasClassifier. That wrapper is so that you can use the Keras model with Scikit-Learn. The type of model (classifier, regression, multiclass classifier, etc) is ultimately determined by the shape and activation of the final layer of your model.

You can simply use model.fit() function that is part of Keras. Make sure that you pass the data into the function. You can see more info on the fit function here: https://keras.io/models/model/#fit

Also your loss is setup as binary_crossentropy. For a multi-class problem you will want to use categorical_crossentropy.

model.compile(optimizer='adam',loss='categorical_crossentropy', metrics=['accuracy'])

This model isn't really what Keras refers to as multi-output as far as I can tell. With multi-output you are trying to get the output from several different layers and possibly apply different loss functions to them.

Base on the setup in your question you would be able to use the Keras Sequential model instead of the Functional model if you wanted. Keras recommends using the Sequential model if you can because its simpler. https://keras.io/getting-started/sequential-model-guide/

Upvotes: 5

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