我想使用包含正负连续值的数据集使用 keras 测试 NN 模型。 keras模型如下:
from keras.models import Sequential
from keras.layers import Dense
import numpy
#fix random seed for reproducibility
numpy.random.seed(7)
#load and read dataset
dataset = numpy.loadtxt("Phenols-toxicity.csv", delimiter=";")
# split into input (X) and output (Y) variables
X = dataset[:,2:4]
Y = dataset[:,1]
print ("Variables: \n", X)
print ("Target_outputs: \n", Y)
# create model
model = Sequential()
model.add(Dense(4, input_dim=2, activation='relu'))
#model.add(Dense(4, activation='relu'))
model.add(Dense(1, activation='relu'))
model.summary()
# Compile model
model.compile(loss='mean_squared_error', optimizer='sgd', metrics=['MSE'])
# Fit the model
model.fit(X, Y, epochs=500, batch_size=10)
#make predictions (test)
F = model.predict(X)
print ("Predicted values: \n", F)
一切似乎都很顺利，但是，所有负值都被预测为零。该程序是否在某些地方将值限制为正值？ 我的目标值如下:
[ 0.085 2.468 0.07 0.68 -0.184 0.545 -0.063 0.871 0.113 -0.208
0.688 1.638 2.03 0.078 0.573 1.036 0.015 -0.03 -0.381 0.701
0.205 0.266 1.796 2.033 0.168 2.097 1.081 -0.384 0.377 -0.326
-0.143 1.292 0.701 0.334 1.157 1.638 -0.046 0.343 1.167 1.301
0.277 1.131 0.471 0.617 0.707 0.185 0.604 0.017 0.381 0.804
0.618 2.712 -0.092 -0.826 0.122 0.932 0.281 0.854 1.276 2.574
1.125 0.73 0.796 1.145 1.569 2.664 0.034 1.398 0.393 0.612
-0.78 0.228 -1.043 -0.141 0.013 1.119 0.643 -0.242 0.757 -0.299
0.599 0.36 1.778 0.053 1.268 1.276 0.516 1.167 1.638 0.478
1.229 0.735 2.049 -0.064 1.201 1.41 1.295 0.798 1.854 0.16
-0.954 0.424 -0.51 1.638 -0.598 2.373 2.222 -0.358 -0.295 0.33
0.183 0.122 1.745 0.081 2.097 0.914 0.979 0.084 0.473 -0.302
0.879 0.366 0.172 0.45 1.307 0.886 -0.524 1.174 -0.512 0.939
0.775 -1.053 -0.814 0.475 -1.021 1.42 -0.82 0.654 0.571 -0.076
0.74 1.729 0.75 1.712 0.95 0.33 1.125 1.077 1.721 0.506
0.539 0.266 1.745 1.229 0.632 1.585 -0.155 0.463 1.638 0.67
-0.155 2.053 0.379 0.181 0.253 1.356]
预测值如下:
[[ 0. ]
[ 2.03844833]
[ 0.27423561]
[ 0.59996957]
[ 0. ]
[ 0.44271404]
[ 0. ]
[ 0.47064281]
[ 0.29890585]
[ 0. ]
[ 0.95044041]
[ 1.84322166]
[ 1.93953323]
[ 0.18019629]
[ 0.68691438]
[ 0.96168059]
[ 0.13934678]
[ 0. ]
[ 0. ]
[ 0.87886989]
[ 0.30047321]
[ 0. ]
[ 1.90942693]
[ 1.83728123]
[ 0. ]
[ 1.84627008]
[ 1.25797462]
[ 0. ]
[ 0.01434445]
[ 0. ]
[ 0. ]
[ 1.1421392 ]
[ 0.83652729]
[ 0.37334418]
[ 1.72099805]
[ 1.73340106]
[ 0.30456764]
[ 0. ]
[ 1.37316585]
[ 1.34221601]
[ 0.6739701 ]
[ 0.79646528]
[ 0.03717542]
[ 0.35218674]
[ 0.09512168]
[ 0. ]
[ 0.20107687]
[ 0. ]
[ 0.01262379]
[ 1.00669646]
[ 0.96650052]
[ 2.10064697]
[ 0. ]
[ 0. ]
[ 0.25874525]
[ 0.61007023]
[ 0.68899512]
[ 0.81215698]
[ 0.88977867]
[ 2.43740511]
[ 1.00497019]
[ 0.94933379]
[ 0.83326894]
[ 0.63394952]
[ 1.27170706]
[ 2.56578207]
[ 0. ]
[ 1.29493976]
[ 0.599581 ]
[ 0.63211834]
[ 0. ]
[ 0.31536853]
[ 0. ]
[ 0. ]
[ 0.02201092]
[ 0.84008563]
[ 0.73076451]
[ 0. ]
[ 0.4879511 ]
[ 0. ]
[ 0.77698141]
[ 0.66419512]
[ 1.56657863]
[ 0.25022489]
[ 1.36990726]
[ 1.50250816]
[ 0. ]
[ 0.61219454]
[ 0.87011993]
[ 0.72275633]
[ 1.36519527]
[ 0.72287238]
[ 2.3798852 ]
[ 0. ]
[ 1.23592615]
[ 1.43725252]
[ 0.95585048]
[ 0.63723856]
[ 1.8765614 ]
[ 0.31583393]
[ 0. ]
[ 0.14386666]
[ 0. ]
[ 1.68151355]
[ 0. ]
[ 1.63394952]
[ 1.97563386]
[ 0. ]
[ 0. ]
[ 0.38875413]
[ 0.18854523]
[ 0.23547113]
[ 1.13463831]
[ 0.30076784]
[ 1.61114097]
[ 0.93304199]
[ 1.04891086]
[ 0.26546735]
[ 0.62234318]
[ 0. ]
[ 0. ]
[ 0.21855426]
[ 0. ]
[ 0. ]
[ 0. ]
[ 0. ]
[ 0. ]
[ 0.39396375]
[ 0.45845711]
[ 0. ]
[ 0. ]
[ 0. ]
[ 0. ]
[ 0. ]
[ 0. ]
[ 0.4718284 ]
[ 0. ]
[ 0. ]
[ 0.91218936]
[ 0. ]
[ 0.82205164]
[ 0.78155482]
[ 0.98432505]
[ 2.15232277]
[ 0.97631133]
[ 0.59527659]
[ 0.83814716]
[ 0.80036032]
[ 1.17462301]
[ 0.51232517]
[ 0.82968521]
[ 0.9463613 ]
[ 1.69353771]
[ 1.21046495]
[ 1.36349583]
[ 0.94378138]
[ 0. ]
[ 0.98034143]
[ 1.66670561]
[ 0.52768588]
[ 0.93855476]
[ 1.26870298]
[ 0. ]
[ 0. ]
[ 0. ]
[ 1.69362605]]
[[ 0. ]
[ 2.03844833]
[ 0.27423561]
[ 0.59996957]
[ 0. ]
[ 0.44271404]
[ 0. ]
[ 0.47064281]
[ 0.29890585]
[ 0. ]
[ 0.95044041]
[ 1.84322166]
[ 1.93953323]
[ 0.18019629]
[ 0.68691438]
[ 0.96168059]
[ 0.13934678]
[ 0. ]
[ 0. ]
[ 0.87886989]
[ 0.30047321]
[ 0. ]
[ 1.90942693]
[ 1.83728123]
[ 0. ]
[ 1.84627008]
[ 1.25797462]
[ 0. ]
[ 0.01434445]
[ 0. ]
[ 0. ]
[ 1.1421392 ]
[ 0.83652729]
[ 0.37334418]
[ 1.72099805]
[ 1.73340106]
[ 0.30456764]
[ 0. ]
[ 1.37316585]
[ 1.34221601]
[ 0.6739701 ]
[ 0.79646528]
[ 0.03717542]
[ 0.35218674]
[ 0.09512168]
[ 0. ]
[ 0.20107687]
[ 0. ]
[ 0.01262379]
[ 1.00669646]
[ 0.96650052]
[ 2.10064697]
[ 0. ]
[ 0. ]
[ 0.25874525]
[ 0.61007023]
[ 0.68899512]
[ 0.81215698]
[ 0.88977867]
[ 2.43740511]
[ 1.00497019]
[ 0.94933379]
[ 0.83326894]
[ 0.63394952]
[ 1.27170706]
[ 2.56578207]
[ 0. ]
[ 1.29493976]
[ 0.599581 ]
[ 0.63211834]
[ 0. ]
[ 0.31536853]
[ 0. ]
[ 0. ]
[ 0.02201092]
[ 0.84008563]
[ 0.73076451]
[ 0. ]
[ 0.4879511 ]
[ 0. ]
[ 0.77698141]
[ 0.66419512]
[ 1.56657863]
[ 0.25022489]
[ 1.36990726]
[ 1.50250816]
[ 0. ]
[ 0.61219454]
[ 0.87011993]
[ 0.72275633]
[ 1.36519527]
[ 0.72287238]
[ 2.3798852 ]
[ 0. ]
[ 1.23592615]
[ 1.43725252]
[ 0.95585048]
[ 0.63723856]
[ 1.8765614 ]
[ 0.31583393]
[ 0. ]
[ 0.14386666]
[ 0. ]
[ 1.68151355]
[ 0. ]
[ 1.63394952]
[ 1.97563386]
[ 0. ]
[ 0. ]
[ 0.38875413]
[ 0.18854523]
[ 0.23547113]
[ 1.13463831]
[ 0.30076784]
[ 1.61114097]
[ 0.93304199]
[ 1.04891086]
[ 0.26546735]
[ 0.62234318]
[ 0. ]
[ 0. ]
[ 0.21855426]
[ 0. ]
[ 0. ]
[ 0. ]
[ 0. ]
[ 0. ]
[ 0.39396375]
[ 0.45845711]
[ 0. ]
[ 0. ]
[ 0. ]
[ 0. ]
[ 0. ]
[ 0. ]
[ 0.4718284 ]
[ 0. ]
[ 0. ]
[ 0.91218936]
[ 0. ]
[ 0.82205164]
[ 0.78155482]
[ 0.98432505]
[ 2.15232277]
[ 0.97631133]
[ 0.59527659]
[ 0.83814716]
[ 0.80036032]
[ 1.17462301]
[ 0.51232517]
[ 0.82968521]
[ 0.9463613 ]
[ 1.69353771]
[ 1.21046495]
[ 1.36349583]
[ 0.94378138]
[ 0. ]
[ 0.98034143]
[ 1.66670561]
[ 0.52768588]
[ 0.93855476]
[ 1.26870298]
[ 0. ]
[ 0. ]
[ 0. ]
[ 1.69362605]]
最佳答案
是的，您将负数限制为零。输出激活是一个 ReLU，它正是这样做的。
解决方案只是将输出激活更改为产生负数的激活，例如 tanh。请注意，该激活的范围是 [-1, 1]，因此您必须将输出标签标准化到相同的范围。
关于python - 我的 Keras 模型不预测负值，我们在Stack Overflow上找到一个类似的问题： https://stackoverflow.com/questions/48251943/