谁能建议如何改进模型
sklearn LinearRegression()
中的常规模型预测的温度误差为1,而手动在tensorflow上构建的模型的误差不会低于5.5,无论激活函数、层数或年代如何
这些数据被标准化并导出为正值
def createModelG(inputShape, dropout, initW):
model = Sequential()
model.add(Dense(4096,
kernel_regularizer=keras.regularizers.l2(0.001),
activation = 'elu',
kernel_initializer = initW,
input_dim = inputShape
))
model.add(Dropout(dropout))
#for i in range(3):
# model.add(Dense(512, activation = 'relu'))
# model.add(Dropout(dropout))
model.add(Dense(1024,
kernel_regularizer=keras.regularizers.l2(0.001),
activation = 'elu'
))
model.add(Dropout(dropout))
model.add(Dense(1))
model.compile(
loss = 'mae',
optimizer = tf.keras.optimizers.Adam(learning_rate = 0.0000005),
metrics = ['mse', 'mae']
)
return model
startModelTest = crossValdation(createModelG, trainDataXS, 0.01, 'truncated_normal', 'VancouverT', PrintDot())
modelTest = startModelTest[1]
hist = startModelTest[2]
startModelTest[0]
loss mse mae val_loss val_mse val_mae
0 22.6255 737.889 21.3214 7.32549 55.3201 6.02149
1 21.6446 677.313 20.3387 7.83092 64.0345 6.5251
2 21.1013 646.857 19.7952 7.00224 49.6842 5.69622
3 22.3446 712.008 21.0386 8.07596 68.7968 6.77008
4 24.2565 874.824 22.9531 7.71605 65.3973 6.41274
0 --- --- --- --- --- ---
0 22.3945
链接到my keras模型和ready sklearn模型的所有代码和结果:
https://www.kaggle.com/alihanurumov/weather-prediction-network
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