def SpCrossEntropyCostFunction(o, y):
'''
used for matrix output
:param o:
:param y:
:return:
'''
L = - ST.sp_sum(y * ST.structured_log(o) + (1 - y) * ST.structured_log(1 - o), axis=1)
cost = T.mean(L)
return cost
def SpCrossEntropyCostFunction(o, y):
'''
used for matrix output
:param o:
:param y:
:return:
'''
L = -ST.sp_sum(
y * ST.structured_log(o) + (1 - y) * ST.structured_log(1 - o), axis=1)
cost = T.mean(L)
return cost
def theanoExpr(self,env):
env.binding[self.dst] = env.binding[self.vec] * B.sp_sum(env.binding[self.weighter],sparse_grad=True)
def theanoExpr(self,env):
#convert to a sparse vector constant
c = env.db.onehot(self.onehotConst)
theanoConstVec = S.CSR(c.data, c.indices, c.indptr, c.shape)
env.binding[self.dst] = theanoConstVec * B.sp_sum(env.binding[self.weighter],sparse_grad=True)
import theano.sparse.basic as B
import matrixdb
import numpy
if __name__ == "__main__":
db = matrixdb.MatrixDB.loadFile("test/fam.cfacts")
va = db.onehot('william')
vb = db.onehot('sarah')
print 'a', va
print 'b', vb
print 'shape', va.shape
print 'f1'
tx = S.csr_matrix('x')
r1 = B.sp_sum(tx + tx + tx, sparse_grad=True)
s = tx * r1
f1 = theano.function(inputs=[tx], outputs=[s])
w = f1(va)
print w[0]
#print db.rowAsSymbolDict(w[0])
print 'f2(w=a,c=b)'
tw = S.csr_matrix('w') #weighter
tc = S.csr_matrix('c') #constant
r2 = B.sp_sum(tw * 1.7, sparse_grad=True)
s2 = tc * r2
f2 = theano.function(inputs=[tw, tc], outputs=[s2])
w = f2(va, vb)
print w[0]
p.listing()
#
# load the data
#
xs, ys = loadExamples("test/textcattoy-train.examples", p.db)
#returns inputs and outputs that are used to build the prediction
#function
mode = tensorlog.ModeDeclaration('predict(i,o)')
ins, outs = p.theanoPredictExpr(mode, ['x'])
scorex = outs[0] #the actual score vector for x
# something simple to try differentiating
toyLoss = B.sp_sum(scorex, sparse_grad=True)
print 'gradToyLoss...'
gradToyLoss = T.grad(toyLoss, p.getParamList())
#
# now define a theano function that computes loss for ONE example
#
y = S.csr_matrix('y')
prob = scorex * (1.0 / B.sp_sum(scorex, sparse_grad=True)) #scale x to 0-1
loss = B.sp_sum(-y * B.structured_log(prob),
sparse_grad=True) #cross-entropy loss
print 'loss...'
theano.printing.debugprint(loss)
lossFun = theano.function(inputs=[ins[0], y], outputs=[loss])
#
for v in a.inputs:
debugVar(v,depth=depth+1,maxdepth=maxdepth)
if __name__=="__main__":
db = matrixdb.MatrixDB.loadFile("test/fam.cfacts")
va = db.onehot('william')
vb = db.onehot('sarah')
print 'a',va
print 'b',vb
print 'shape',va.shape
print 'f1: s = x*((x+x)+x)'
tx = S.csr_matrix('x')
r1 = B.sp_sum(tx+tx+tx,sparse_grad=True)
s = tx*r1
s.name = 's'
f1 = theano.function(inputs=[tx],outputs=[s])
w = f1(va)
print w[0]
debugVar(s)
#print db.rowAsSymbolDict(w[0])
#
# print 'f2(w=a,c=b)'
# tw = S.csr_matrix('w') #weighter
# tc = S.csr_matrix('c') #constant
# r2 = B.sp_sum(tw*1.7,sparse_grad=True)
# s2 = tc*r2
import theano.sparse.basic as B
import matrixdb
import numpy
if __name__=="__main__":
db = matrixdb.MatrixDB.loadFile("test/fam.cfacts")
va = db.onehot('william')
vb = db.onehot('sarah')
print 'a',va
print 'b',vb
print 'shape',va.shape
print 'f1'
tx = S.csr_matrix('x')
r1 = B.sp_sum(tx+tx+tx,sparse_grad=True)
s = tx*r1
f1 = theano.function(inputs=[tx],outputs=[s])
w = f1(va)
print w[0]
#print db.rowAsSymbolDict(w[0])
print 'f2(w=a,c=b)'
tw = S.csr_matrix('w') #weighter
tc = S.csr_matrix('c') #constant
r2 = B.sp_sum(tw*1.7,sparse_grad=True)
s2 = tc*r2
f2 = theano.function(inputs=[tw,tc],outputs=[s2])
w = f2(va,vb)
print w[0]
def theanoExpr(self, env):
env.binding[self.dst] = env.binding[self.vec] * B.sp_sum(
env.binding[self.weighter], sparse_grad=True)
def theanoExpr(self, env):
#convert to a sparse vector constant
c = env.db.onehot(self.onehotConst)
theanoConstVec = S.CSR(c.data, c.indices, c.indptr, c.shape)
env.binding[self.dst] = theanoConstVec * B.sp_sum(
env.binding[self.weighter], sparse_grad=True)
