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I have trained a deep learning model for regression. The accuracy of the model is poor. I am quite new to deep learning. How can I improve it? The target variable Y is obtained by multiplying the features X1 and X2.

DataSet(5800 rows)

X1       |      X2      | Y
1.000000    70.000000   70.000000
0.714286    29.045455   20.746753
0.000000    35.000000   0.000000
0.538462    22.071429   11.884615
0.000000    54.000000   0.000000

Model

#Define a larger model
def larger_model():
  #Create Model
  model = Sequential()
  model.add(Dense(2, input_dim=2, kernel_initializer='normal', activation='relu'))
  model.add(Dense(6, kernel_initializer='normal', activation='relu'))
  model.add(Dense(1, kernel_initializer='normal'))
  
  #Compile Model
  model.compile(loss='mean_squared_error', optimizer='adam')
  return model

#Evaluate Model

estimator = KerasRegressor(build_fn=larger_model, epochs=10, batch_size=5)
kfold = KFold(n_splits=10)
results = cross_val_score(estimator, X, y, cv=kfold)
print("Results: %.5f (%.5f) MSE" % (results.mean(), results.std()))

Output

Results: -83.81452 (170.38108) MSE
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First have a look at your data scale. Deep learning models are sensitive to data scaling so it would be better to preprocess your data to keep in within accetable ranges:

  • scale [-2,2] - apply scaling so that all features and label are within this range
  • mean 0: try to center your data around 0

Second, deep learning models are regularised overfitting models. One thing you can try here is to progressively increase the size of your model (i.e. more hidden layers or hidden units) and also add regularisation.

Your options are:

  • weight decay - keras Dense layers can be augmented with weight decay. Have a look at the documentation

  • layer regularisation (batch norm, dropout, layer normalisation) these are a bit more advanced and work on a case by case basis - have a look at this paper as a start.

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