class SimpleNet():
def __init__(self):
n_in = 1
n_hidden_1 = 5
n_hidden_2 = 5
self.model = FunctionSet(
en1=L.Linear(n_in, n_hidden_1),
en2_mu=L.Linear(n_hidden_1, n_hidden_2),
en2_var=L.Linear(n_hidden_1, n_hidden_2),
de1=L.Linear(n_hidden_2, n_hidden_1),
de2=L.Linear(n_hidden_1, n_in)
)
self.optimizer = optimizers.Adam()
self.optimizer.setup(self.model.collect_parameters())
def encode(self, x_var):
h1 = F.tanh(self.model.en1(x_var))
mu = self.model.en2_mu(h1)
var = self.model.en2_var(h1)
return mu, var
def decode(self, z, sigmoid=True):
h1 = F.tanh(self.model.de1(z))
h2 = self.model.de2(h1)
if sigmoid:
return F.sigmoid(h2)
return h2
def cost(self, x_var, C=1.0, k=1):
mu, ln_var = self.encode(x_var)
batchsize = len(mu.data)
rec_loss = 0
for l in six.moves.range(k):
z = F.gaussian(mu, ln_var)
rec_loss += F.bernoulli_nll(x_var, self.decode(z, sigmoid=False)) \
/ (k * batchsize)
self.rec_loss = rec_loss
self.loss = self.rec_loss + C * gaussian_kl_divergence(mu, ln_var) / batchsize
return self.loss
