所以我建立了一个GRU模型,在同一个模型上比较了3个不同的数据集。我刚刚运行了第一个数据集,并将历元数设置为25,但我注意到,在第六个历元之后,我的验证丢失正在增加,这是否表明拟合过度,我是否做错了什么
import pandas as pd
import tensorflow as tf
from keras.layers.core import Dense
from keras.layers.recurrent import GRU
from keras.models import Sequential
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
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 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))
# creating model using Keras
model10 = Sequential()
model10.add(GRU(units=512, return_sequences=True, input_shape=(1,12)))
model10.add(GRU(units=256, return_sequences=True))
model10.add(GRU(units=256))
model10.add(Dense(units=1, activation='sigmoid'))
model10.compile(loss=['mse'], optimizer='adam',metrics=['mse'])
model10.summary()
history10=model10.fit(X10_train, y10_train, batch_size=256, epochs=25,validation_split=0.20, verbose=1, callbacks=[TensorBoardColabCallback(tbc)])
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()
LSTM(以及GRU,尽管其结构较轻)因容易过度装配而臭名昭著
减少每个层(32(layer1)-64(layer2)中的单位数(输出大小);也可以完全消除最后一层
第二,您正在使用激活“
sigmoid
”,但是您的损失函数+度量是mse
确保您的问题是
regression
或classification
问题。如果确实是回归问题,则激活函数在最后一步应为“linear
”。如果是分类问题,则应将损失函数更改为binary_crossentropy
,度量值更改为“accuracy
”因此,目前显示的图只是误导性的。如果您按照我的建议进行修改,并且仍然得到这样一个train val loss图,那么我们可以确定您有一个过度拟合的案例
相关问题 更多 >
编程相关推荐