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) )