def test_ReshapeLike(self):
blob = Blob()
other1 = Blob()
other2 = Blob()
blob.set_data(numpy.array(range(30), float))
blob.set_diff(numpy.array(range(30), float))
blob.ReshapeLike(other1)
blob.ReshapeLike(other2)
blob.ReshapeLike(other1)
def AddTrainNet(self, net):
Solver.AddTrainNet(self, net)
params = self.net_.learnable_params()
for i in range(len(params)):
s = Blob()
r = Blob()
s.ReshapeLike(params[i])
r.ReshapeLike(params[i])
self.s_.append(s)
self.r_.append(r)
def AddTrainNet(self, net):
Solver.AddTrainNet(self, net)
params = self.net_.learnable_params()
for i in range(len(params)):
h = Blob()
u = Blob()
t = Blob()
h.ReshapeLike(params[i])
u.ReshapeLike(params[i])
t.ReshapeLike(params[i])
self.history_.append(h)
self.update_.append(u)
self.temp_.append(t)
self.history_.extend(self.history_)
def AddTrainNet(self, net):
Solver.AddTrainNet(self, net)
params = self.net_.learnable_params()
for i in range(len(params)):
blob = Blob()
blob.ReshapeLike(params[i])
self.history_.append(blob)
class EuclideanLossLayer(LossLayer):
def __init__(self):
LossLayer.__init__(self)
self.diff_ = Blob(numpy.float, [6])
def Reshape(self, bottom, top):
LossLayer.Reshape(self, bottom, top)
self.diff_.ReshapeLike(bottom[0])
def type(self):
return 'EuclideanLoss'
def AllowForceBackward(self, bottom_index):
return True
def Forward_cpu(self, bottom, top):
self.diff_.set_data(bottom[0].data() - bottom[1].data())
dot = numpy.dot(self.diff_.data(), self.diff_.data())
loss = dot / bottom[0].shape()[0] / 2
top[0].set_data(loss)
def Backward_cpu(self, top, propagate_down, bottom):
print top[0].diff()
print bottom[0].shape()[0]
print self.diff_.data()
bottom[0].set_diff(top[0].diff() / bottom[0].shape()[0] *
self.diff_.data())
class DropoutLayer(NeuronLayer):
def __init__(self, threshold):
NeuronLayer.__init__(self)
self.threshold_ = threshold
self.rand_blob_ = Blob()
if 1.0 == threshold:
self.scale_ = 1.0
else:
self.scale_ = 1.0 / (1.0 - threshold)
def type(self):
return 'Dropout'
def LayerSetup(self, bottom, top):
self.rand_blob_.ReshapeLike(bottom[0])
self.rand_blob_.set_data(
numpy.random.binomial(n=1,
p=self.threshold_,
size=bottom[0].data().shape))
self.rand_blob_.ReshapeLike(bottom[0])
def Reshape(self, bottom, top):
NeuronLayer.Reshape(self, bottom, top)
self.rand_blob_.ReshapeLike(bottom[0])
def Forward_cpu(self, bottom, top):
self.rand_blob_.ReshapeLike(bottom[0])
self.rand_blob_.set_data(
numpy.random.binomial(n=1,
p=self.threshold_,
size=bottom[0].data().shape))
self.rand_blob_.ReshapeLike(bottom[0])
top[0].set_data(
numpy.multiply(bottom[0].data(),
self.rand_blob_.data() * self.scale_))
def Backward_cpu(self, top, propagate_down, bottom):
bottom[0].set_diff(
numpy.multiply(top[0].diff(),
self.rand_blob_.data() * self.scale_))
