Question

我写了一个GRU模型来预测功率输出。
我已经注意到，尽管在5-6个时期之后，我的val_loss开始增加，并且在每个时期之后都没有停止，并且它的拟合度非常差。
我尝试了很多事情，但没有任何效果：

我已经尝试过relu，sigmoid和linear激活功能。
我从1层开始，其中具有不同数量的神经元和增加的层等。
我尝试使用dropout和L2正则化器，但没有任何效果，它要么在几个时期后就过拟合，要么保持不变

我的功能是不同的物理参数，例如温度，电压，风速等，我试图预测输出功率。我使用了错误的激活或优化程序，我真的不明白为什么它过度拟合。

以下代码：

import pandas as pd
import tensorflow as tf
import matplotlib.pyplot as plt
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import MinMaxScaler
from google.colab import files
from tensorboardcolab import TensorBoardColab, TensorBoardColabCallback
tbc=TensorBoardColab() # Tensorboard
from keras.layers.core import Dense
from keras.layers.recurrent import GRU
from keras.models import Sequential
from keras.callbacks import EarlyStopping
from keras import regularizers
from keras.layers import Dropout





df10=pd.read_csv('/content/drive/My Drive/Isolation Forest/IF 10 PERCENT.csv',index_col=None)
df2_10= pd.read_csv('/content/drive/My Drive/2019 Dataframe/2019 10minutes IF 10 PERCENT.csv',index_col=None)

X10_train= df10[['WindSpeed_mps','AmbTemp_DegC','RotorSpeed_rpm','RotorSpeedAve','NacelleOrientation_Deg','MeasuredYawError','Pitch_Deg','WindSpeed1','WindSpeed2','WindSpeed3','GeneratorTemperature_DegC','GearBoxTemperature_DegC']]
X10_train=X10_train.values

y10_train= df10['Power_kW']
y10_train=y10_train.values

X10_test= df2_10[['WindSpeed_mps','AmbTemp_DegC','RotorSpeed_rpm','RotorSpeedAve','NacelleOrientation_Deg','MeasuredYawError','Pitch_Deg','WindSpeed1','WindSpeed2','WindSpeed3','GeneratorTemperature_DegC','GearBoxTemperature_DegC']]
X10_test=X10_test.values

y10_test= df2_10['Power_kW']
y10_test=y10_test.values




# scaling values for the model


x_scale = MinMaxScaler()
y_scale = MinMaxScaler()

X10_train= x_scale.fit_transform(X10_train)
y10_train= y_scale.fit_transform(y10_train.reshape(-1,1))
X10_test=  x_scale.fit_transform(X10_test)
y10_test=  y_scale.fit_transform(y10_test.reshape(-1,1))


X10_train = X10_train.reshape((-1,1,12)) 
X10_test = X10_test.reshape((-1,1,12))



Early_Stop=EarlyStopping(monitor='val_loss', patience=5,mode='min',restore_best_weights=True)



# creating model using Keras
model10 = Sequential()
model10.add(GRU(units=256, return_sequences=True, kernel_regularizer=regularizers.l2(0.001), input_shape=(1,12)))
model10.add(GRU(units=256, return_sequences=True,activation='linear'))
model10.add(GRU(units=128,activation='linear'))
#model10.add(GRU(units=256))
model10.add(Dense(units=1, activation='linear'))
model10.compile(loss=['mse'], optimizer='adam',metrics=['mse']) 
model10.summary() 


history10=model10.fit(X10_train, y10_train, batch_size=256, epochs=10,validation_split=0.10, verbose=1, callbacks=[TensorBoardColabCallback(tbc),Early_Stop])


score = model10.evaluate(X10_test, y10_test)
print('Score: {}'.format(score))



y10_predicted = model10.predict(X10_test)
y10_predicted = y_scale.inverse_transform(y10_predicted)

y10_test = y_scale.inverse_transform(y10_test)

plt.plot( y10_predicted, label='Predicted')
plt.plot( y10_test, label='Measurements')
plt.legend()
plt.savefig('/content/drive/My Drive/Figures/Power Prediction 10 Percent.png')
plt.show()

GRU模型过拟合

0 个答案: