def __init__(self,setup):
N.checkDictKeys(setup,['dim','lr'],rerr=True)
super(IafLayer, self).__init__(setup)
self.dimin = self.dimout = self.dim
self.b = N.sharedScalar(0.)
self.x = T.fmatrix()
# self.w = N.sharedf(npr.rand(self.dim))
self.u = N.sharedf(npr.rand(self.dim))
self.w = N.sharedf([0.]*self.dim)
def setParams(self, w, b, u):
'''
DON'T USE
should replace the values of w,b,u in-place
'''
w,b,u = np.asarray(w), np.asarray(b), np.asarray(u)
self.w = N.sharedf(w, name='w')
self.b = N.sharedf(b, name='b')
self.u = N.sharedf(u, name='u')
self.params = [self.w, self.b, self.u]
def initWeight(self):
self.w = N.sharedf(mathZ.permutMat(self.dim))
self.detJ = N.sharedsfGpu(0.)
def setWeight(self,weights,bias=None):
self.w = N.sharedf(np.asarray(weights))
if bias is not None:
self.bias = True
self.b = N.sharedf(np.asarray(bias))
def initLogPrior(self):
noisevar = N.sharedf(np.eye(self.dim))*T.sqr(self.priorstd)
noisemu = N.sharedf(np.zeros(self.dim))
self.logPrior = mathT.gaussInit(noisemu, noisevar)
def setWeight(self,weight):
self.w = N.sharedf( np.asarray(weight) )
def __init__(self,name,dim):
super(IafPermute,self).__init__(name)
self.dimin = self.dimout = dim
self.w = N.sharedf( mathZ.permutMat(self.dimin,enforcing=True) )