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I have a regression problem which I have to predict 3 numerical values from a provided data. For example let's say I have a data set containing X1,X2,X3,X4,X5,X6...X100,Y1,Y2,Y3 columns. In this case I have to predict Y1,Y2,Y3 values. Is there are any way to construct the model to get all the outputs at the same time using Keras.

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it's quite easy to make! One thing to note I'm using the Keras functional API.

from keras.layers import Input, Dense

Input_1= Input(shape=(shape, ))

x = Dense(100, activation='relu')(Input_1)
x = Dense(100, activation='relu')(x)
x = Dense(100, activation='relu')(x)

out1 = Dense(1,  activation='linear')(x)
out2 = Dense(1,  activation='linear')(x)
out3 = Dense(1,  activation='linear')(x)


model = Model(inputs=Input_1, outputs=[out1,out2,out3])
model.compile(optimizer = "rmsprop", loss = 'mse')
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You can set your output layer to have 3 nodes. When you train, set your output to be a vector containing $[Y_1, Y_2, Y_3]$.

model.add(Dense(3, activation='linear'))

In above it is not mentioned that we have a linear regression, also adding dense layer will let model take different $Y_i$ simultaenously in beginnging, I wil still prefer \textbf{Model} Api from keras, as multioutput model

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  • 2
    $\begingroup$ For regression tasks the output layer should have linear activation. $\endgroup$ – Media Feb 20 '18 at 5:55

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