数据挖掘 - 如何改进具有更少数据的深度学习模型 - 吾爱随笔录

我已经为回归训练了一个深度学习模型。模型的准确性很差。我对深度学习很陌生。我该如何改进它？目标变量Y是通过将特征X1与相乘得到的X2。

数据集（5800 行）

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

模型

#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()))

输出

Results: -83.81452 (170.38108) MSE