def buildOutGrad(self, grad):
if len(self.fwds) == 0:
return grad
grad = [[] for _ in range(
len(self.data) if isinstance(self.data, list) else 1)]
for node, slots in self.fwds:
if slots is not None:
for slot in slots:
grad[slot].append(node.grad[self.name][slot])
else:
for i, gr in enumerate(node.grad[self.name]):
grad[i].append(gr)
for i, grads in enumerate(grad):
if len(grads) > 1:
gr = copy(None, grads[0])
for j in range(1, len(grads)):
Blas.toVectorAddVector(gr.ravel(), grads[j].ravel())
else:
gr = grads[0]
grad[i] = gr
if len(grad) == 1:
grad = grad[0]
return grad
def updateGrad(self, grad): sections = [grad.shape[self.axis] // self.times] * self.times ingrad = Utils.split(grad, sections, axis=self.axis) for i in range(1, len(ingrad)): Blas.toVectorAddVector(ingrad[0].ravel(), ingrad[i].ravel()) self.grad = ingrad[0]
def updateGrad(self, grad):
firstgrad = grad[0]
self.grad = gpuarray.empty(firstgrad.shape,
dtype=firstgrad.dtype,
allocator=memPool)
self.grad.fill(0)
for gr in grad:
Blas.toVectorAddVector(self.grad.ravel(), gr.ravel())
def updateData(self, data):
firstdata = data[0]
self.data = gpuarray.empty(firstdata.shape,
dtype=firstdata.dtype,
allocator=memPool)
self.data.fill(0)
for dat in data:
Blas.toVectorAddVector(self.data.ravel(), dat.ravel())
def updateParams(self, learnRate):
for var in self.vars.values():
Blas.toVectorAddVector(var.data.ravel(),
var.grad.ravel(),
alpha=learnRate)