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I would like to test NN model with keras using a dataset containing positive and negative continuous values. The keras model is as follows:
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)
Everything seem going fine, However, all the negative values are predicted to zeros. Do the program soemwhere constrains values to be positive? My target values are as follow:
[ 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]
The predicted values are as follow:
[[ 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]]
874
Negative predictive value (NPV) Although sometimes used synonymously, a negative predictive value generally refers to what is established by control groups, while a negative post-test probability rather refers to a probability for an individual.
predict passes the input vector through the model and returns the output tensor for each datapoint. Since the last layer in your model is a single Dense neuron, the output for any datapoint is a single value. And since you didn't specify an activation for the last layer, it will default to linear activation.
Yes, you are constraining the negatives to zeros. The output activation is a ReLU, which does exactly that.
The solution is just to change the output activation to one that produces negative numbers, like the tanh. Note that the range of that activation is [-1, 1], so you will have to normalize your output labels to the same range.
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