def fprop(self, H_prev, H, dH):
dH = None
self.mu = H_prev[:, :self.size]
self.log_sigma = H_prev[:, self.size:]
nn.fill_randn(self.eps)
# self.eps= nn.ones(self.eps.shape)
nn.exp(self.log_sigma, self.sigma)
H[:] = self.mu + self.sigma * self.eps
return H, dH, self.sigma, self.mu
def fprop(self, H_prev, H, dH):
dH = None
self.mu = H_prev[:,:self.size]
self.log_sigma = H_prev[:,self.size:]
nn.fill_randn(self.eps)
# self.eps= nn.ones(self.eps.shape)
nn.exp(self.log_sigma,self.sigma)
H[:] = self.mu + self.sigma*self.eps
return H,dH,self.sigma,self.mu
def load_weights_from(self,net_from,lst_from = None, lst_to = None, init = None):
if lst_from == None:
assert lst_to == None
self.weights.mem[:] = net_from.weights.mem
else:
assert lst_to != None and init != None
nn.fill_randn(self.weights.mem)
nn.multiply(self.weights.mem,init)
for k in lst_from:
print k
w_from, b_from = net_from.weights[k]
w_to, b_to = self.weights[k]
assert w_from.shape == w_to.shape
w_to[:], b_to[:] = w_from, b_from
def load_weights_from(self,
net_from,
lst_from=None,
lst_to=None,
init=None):
if lst_from == None:
assert lst_to == None
self.weights.mem[:] = net_from.weights.mem
else:
assert lst_to != None and init != None
nn.fill_randn(self.weights.mem)
nn.multiply(self.weights.mem, init)
for k in lst_from:
print k
w_from, b_from = net_from.weights[k]
w_to, b_to = self.weights[k]
assert w_from.shape == w_to.shape
w_to[:], b_to[:] = w_from, b_from
def randn(self, k=1.0):
nn.fill_randn(self.mem)
self.mem *= k
def randn(self,k=1.0):
nn.fill_randn(self.mem)
self.mem *= k
