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costs.py
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costs.py
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import theano
import theano.tensor as T
import numpy as np
eps = 1e-9
#output-Prediction of the network
#y - True value
def meanSquare(output,y):
return T.mean(((output - y)**2))
def meanSquareLog(output,y):
return T.mean(((T.log(output + eps) - T.log(y + eps))**2))
def meanAbsolute(output,y):
return T.mean((abs(output - y)))
def crossEntropy(output,y):
return -T.mean((y * T.log(output + eps) + (1-y) * T.log(1 - output - eps))[T.arange(y.shape[0]), y])
def negativeLogLikelihood(output,y):
return -T.mean(T.log(output + eps)[T.arange(y.shape[0]), y])
def kullbackLeiblerDivergence(output, y):
return T.mean((y * (T.log(y + eps)-T.log(output + eps)))[T.arange(y.shape[0]), y])
def poisson(output,y):
return T.mean( (output - y * T.log(output + eps))[T.arange(y.shape[0]), y])
def cosine_proximity(output,y):
return -T.mean((output * y)[T.arange(y.shape[0]), y])
def tester():
'''y1 = theano.shared(np.asarray([[1],[2],[3],[4],[5],[6],[7],[8],[7],[9]],dtype=theano.config.floatX))
out1 = theano.shared(np.asarray([[1],[2],[3],[4],[5],[6],[7],[8],[9],[7]],dtype=theano.config.floatX))
y2 = theano.shared(np.asarray([[1],[2],[3],[4],[5],[6],[7],[8],[9],[10]],dtype=theano.config.floatX))
out2 = theano.shared(np.asarray([[1],[2],[3],[4],[5],[6],[7],[8],[9],[10]],dtype=theano.config.floatX))
# np.asarray([[0.1, 0.1, 0.1,0.1,0.1,0.1,0.1,0.1,0.1,0.1],[0.1, 0.1, 0.1,0.1,0.1,0.1,0.1,0.1,0.1,0.1],[0.1, 0.1, 0.1,0.1,0.1,0.1,0.1,0.1,0.1,0.1],[0.1, 0.1, 0.1,0.1,0.1,0.1,0.1,0.1,0.1,0.1],[0.1, 0.1, 0.1,0.1,0.1,0.1,0.1,0.1,0.1,0.1],[0.1, 0.1, 0.1,0.1,0.1,0.1,0.1,0.1,0.1,0.1],[0.1, 0.1, 0.1,0.1,0.1,0.1,0.1,0.1,0.1,0.1],[0.1, 0.1, 0.1,0.1,0.1,0.1,0.1,0.1,0.1,0.1],[0.1, 0.1, 0.1,0.1,0.1,0.1,0.1,0.1,0.1,0.1],[0.1, 0.1, 0.1,0.1,0.1,0.1,0.1,0.1,0.1,0.1],[0.1, 0.1, 0.1,0.1,0.1,0.1,0.1,0.1,0.1,0.1],[0.1, 0.1, 0.1,0.1,0.1,0.1,0.1,0.1,0.1,0.1],[0.1, 0.1, 0.1,0.1,0.1,0.1,0.1,0.1,0.1,0.1],[0.1, 0.1, 0.1,0.1,0.1,0.1,0.1,0.1,0.1,0.1],[0.1, 0.1, 0.1,0.1,0.1,0.1,0.1,0.1,0.1,0.1],[0.1, 0.1, 0.1,0.1,0.1,0.1,0.1,0.1,0.1,0.1],[0.1, 0.1, 0.1,0.1,0.1,0.1,0.1,0.1,0.1,0.1],[0.1, 0.1, 0.1,0.1,0.1,0.1,0.1,0.1,0.1,0.1],[0.1, 0.1, 0.1,0.1,0.1,0.1,0.1,0.1,0.1,0.1],[0.1, 0.1, 0.1,0.1,0.1,0.1,0.1,0.1,0.1,0.1] ]
out3 = theano.shared(np.asarray([[0.2, 0.05, 0.05,0.1,0.1,0.1,0.1,0.1,0.1,0.1],[0.1, 0.1, 0.1,0.1,0.1,0.1,0.1,0.1,0.1,0.1],[0.1, 0.1, 0.1,0.1,0.1,0.1,0.1,0.1,0.1,0.1],[0.1, 0.1, 0.1,0.1,0.1,0.1,0.1,0.1,0.1,0.1],[0.1, 0.1, 0.1,0.1,0.1,0.1,0.1,0.1,0.1,0.1],[0.1, 0.1, 0.1,0.1,0.1,0.1,0.1,0.1,0.1,0.1],[0.1, 0.1, 0.1,0.1,0.1,0.1,0.1,0.1,0.1,0.1],[0.1, 0.1, 0.1,0.1,0.1,0.1,0.1,0.1,0.1,0.1],[0.1, 0.1, 0.1,0.1,0.1,0.1,0.1,0.1,0.1,0.1],[0.1, 0.1, 0.1,0.1,0.1,0.1,0.1,0.1,0.1,0.1],[0.1, 0.1, 0.1,0.1,0.1,0.1,0.1,0.1,0.1,0.1],[0.1, 0.1, 0.1,0.1,0.1,0.1,0.1,0.1,0.1,0.1],[0.1, 0.1, 0.1,0.1,0.1,0.1,0.1,0.1,0.1,0.1],[0.1, 0.1, 0.1,0.1,0.1,0.1,0.1,0.1,0.1,0.1],[0.1, 0.1, 0.1,0.1,0.1,0.1,0.1,0.1,0.1,0.1],[0.1, 0.1, 0.1,0.1,0.1,0.1,0.1,0.1,0.1,0.1],[0.1, 0.1, 0.1,0.1,0.1,0.1,0.1,0.1,0.1,0.1],[0.1, 0.1, 0.1,0.1,0.1,0.1,0.1,0.1,0.1,0.1],[0.1, 0.1, 0.1,0.1,0.1,0.1,0.1,0.1,0.1,0.1],[0.1, 0.1, 0.1,0.1,0.1,0.1,0.1,0.1,0.1,0.1] ]
,dtype=theano.config.floatX))
y3 = theano.shared(np.asarray([0,1,2,3,4,5,6,7,8,9,0,1,2,3,4,5,6,7,8,9]))
out4 = theano.shared(np.asarray([[0.9, 0.0, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.1],
[0.9, 0.0, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.1],
[0.9, 0.0, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.1],
[0.9, 0.0, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.1],
[0.9, 0.0, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.1],
[0.9, 0.0, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.1],
[0.9, 0.0, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.1],
[0.9, 0.0, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.1],
[0.9, 0.0, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.1],
[0.9, 0.0, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.1],
[0.9, 0.0, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.1],
[0.9, 0.0, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.1],
[0.9, 0.0, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.1],
[0.9, 0.0, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.1],
[0.9, 0.0, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.1],
[0.9, 0.0, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.1],
[0.9, 0.0, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.1],
[0.9, 0.0, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.1],
[0.9, 0.0, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.1],
[0.9, 0.0, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.1]]
,dtype=theano.config.floatX))
y4 = theano.shared(np.asarray([0,1,2,3,4,5,6,7,8,9,0,1,2,3,4,5,6,7,8,9]))
out5 = theano.shared(np.asarray([[0.9, 0.0, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.1],
[0.0, 0.9, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.1],
[0.0, 0.0, 0.9,0.0,0.0,0.0,0.0,0.0,0.0,0.1],
[0.0, 0.0, 0.0,0.9,0.0,0.0,0.0,0.0,0.0,0.1],
[0.0, 0.0, 0.0,0.0,0.9,0.0,0.0,0.0,0.0,0.1],
[0.0, 0.0, 0.0,0.0,0.0,0.9,0.0,0.0,0.0,0.1],
[0.0, 0.0, 0.0,0.0,0.0,0.0,0.9,0.0,0.0,0.1],
[0.0, 0.0, 0.0,0.0,0.0,0.0,0.0,0.9,0.0,0.1],
[0.0, 0.0, 0.0,0.0,0.0,0.0,0.0,0.0,0.9,0.1],
[0.1, 0.0, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.9],
[0.9, 0.0, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.1],
[0.0, 0.9, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.1],
[0.0, 0.0, 0.9,0.0,0.0,0.0,0.0,0.0,0.0,0.1],
[0.0, 0.0, 0.0,0.9,0.0,0.0,0.0,0.0,0.0,0.1],
[0.0, 0.0, 0.0,0.0,0.9,0.0,0.0,0.0,0.0,0.1],
[0.1, 0.0, 0.0,0.0,0.0,0.9,0.0,0.0,0.0,0.0],
[0.0, 0.0, 0.0,0.0,0.0,0.0,0.9,0.0,0.0,0.1],
[0.0, 0.0, 0.0,0.0,0.0,0.0,0.0,0.9,0.0,0.1],
[0.0, 0.0, 0.0,0.0,0.0,0.0,0.0,0.0,0.9,0.1],
[0.1, 0.0, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.9]]
,dtype=theano.config.floatX))
y5 = theano.shared(np.asarray([0,1,2,3,4,5,6,7,8,9,0,1,2,3,4,5,6,7,8,9]))
#a = meanSquareLog(out1,y1)
a1 = meanSquare(out1,y1)
a2 = meanSquare(out2,y2)
print "Mean Square error: " + str(a1.eval())
print "Mean Square error: " + str(a2.eval())
print "\n"
b1 = meanSquareLog(out1,y1)
b2 = meanSquareLog(out2,y2)
print "Mean Square Log error: " + str(b1.eval())
print "Mean Square Log error: " + str(b2.eval())
print "\n"
c1 = meanAbsolute(out1,y1)
c2 = meanAbsolute(out2,y2)
print "Mean Absolute error: " + str(c1.eval())
print "Mean Absolute error: " + str(c2.eval())
print "\n"
y3 = y3.reshape((20,1))
# print y3.eval()
d1 = negativeLogLikelihood(out3,y3)
#d2 = meanAbsolute(out2,y2)
#print y3.Print()
#print out3.Print()
print "Neg log likelihood error: " + str(d1.eval())
#print "Mean Absolute error: " + str(c2.eval())
print "\n"
y4 = y4.reshape((20,1))
e1 = crossEntropy(out4,y4)
f1 = negativeLogLikelihood(out4,y4)
g1 = kullbackLeiblerDivergence(out4, y4)
h1 = poisson(out4, y4)
i1 = cosine_proximity(out4,y4)
print "negative log likelihood error: " + str(f1.eval())
print "cross Entropy likelihood error: " + str(e1.eval())
print "kL divergence error: " + str(g1.eval())
print "Poisson error: " + str(h1.eval())
print "Cosine error: " + str(i1.eval())
#print "Mean Absolute error: " + str(c2.eval())
print "\n"
y5 = y5.reshape((20,1))
out5 = out5.reshape((20,10))
e2 = crossEntropy(out5,y5)
f2 = negativeLogLikelihood(out5,y5)
g2 = kullbackLeiblerDivergence(out5, y5)
h2 = poisson(out5, y5)
i2 = cosine_proximity(out5,y5)
print "negative log likelihood error: " + str(f2.eval())
print "cross Entropy likelihood error: " + str(e2.eval())
print "kL divergence error: " + str(g2.eval())
print "Poisson error: " + str(h2.eval())
print "Cosine error: " + str(i2.eval())
#print "Mean Absolute error: " + str(c2.eval())
print "\n"'''
y1 = np.asarray([7,1,5,2])
out1 = np.asarray([[0.1,0.2,0.05,0.05,0.1,0.1,0.1,0.1,0.1,0.1],
[0.3,0.4,0.05,0.05,0.05,0.05,0.05,0.05,0.0,0.0],
[0.03,0.04,0.5,0.05,0.05,0.5,0.05,0.05,0.0,0.0],
[0.03,0.04,0.7,0.7,0.05,0.05,0.05,0.05,0.0,0.0]])
out2 = np.asarray([[0.05,0.2,0.05,0.05,0.1,0.1,0.1,0.05,0.1,0.1],
[0.3,0.05,0.05,0.05,0.05,0.05,0.05,0.05,0.0,0.0],
[0.3,0.4,0.05,0.05,0.05,0.05,0.05,0.05,0.0,0.0],
[0.3,0.4,0.05,0.05,0.05,0.05,0.05,0.05,0.0,0.0]])
print negativeLogLikelihood(out1,y1).eval()
print negativeLogLikelihood(out2,y1).eval()
# tester()