def calculate_likelihood(self, X, dir, mode='test', S=5000):
# set auxiliary variables for number of training and test sets
N_test = X.size(0)
# init list
likelihood_test = []
MB = 100
if S <= MB:
R = 1
else:
R = S / MB
S = MB
for j in range(N_test):
if j % 100 == 0:
print('{:.2f}%'.format(j / (1. * N_test) * 100))
# Take x*
x_single = X[j].unsqueeze(0)
a = []
for r in range(0, R):
# Repeat it for all training points
x = x_single.expand(S, x_single.size(1))
# pass through VAE
x_mean, x_logvar, z1_q, z1_q_mean, z1_q_logvar, z2_q, z2_q_mean, z2_q_logvar, z1_p_mean, z1_p_logvar = self.forward(
x)
# RE
RE = log_Bernoulli(x, x_mean, dim=1)
# KL
log_p_z2 = self.log_p_z2(z2_q)
log_p_z1 = log_Normal_diag(z1_q, z1_p_mean, z1_p_logvar, dim=1)
log_q_z1 = log_Normal_diag(z1_q, z1_q_mean, z1_q_logvar, dim=1)
log_q_z2 = log_Normal_diag(z2_q, z2_q_mean, z2_q_logvar, dim=1)
KL = -(log_p_z1 + log_p_z2 - log_q_z1 - log_q_z2)
a_tmp = (RE - KL)
a.append(a_tmp.cpu().data.numpy())
# calculate max
a = np.asarray(a)
a = np.reshape(a, (a.shape[0] * a.shape[1], 1))
likelihood_x = logsumexp(a)
likelihood_test.append(likelihood_x - np.log(S))
likelihood_test = np.array(likelihood_test)
plot_histogram(-likelihood_test, dir, mode)
return -np.mean(likelihood_test)
def calculate_likelihood(self, X, dir, mode='test', S=5000, MB=100):
# set auxiliary variables for number of training and test sets
X = X.view(-1, np.prod(self.args.input_size))
N_test = X.size(0)
# init list
likelihood_test = []
if S <= MB:
R = 1
else:
R = S / MB
S = MB
for j in range(N_test):
if j % 100 == 0:
print('{:.2f}%'.format(j / (1. * N_test) * 100))
# Take x*
x_single = X[j].unsqueeze(0)
a = []
for r in range(0, R):
# Repeat it for all training points
x = x_single.expand(S, x_single.size(1)).contiguous()
a_tmp, _, _, _, _ = self.calculate_loss(x)
a.append(-a_tmp.cpu().data.numpy())
# calculate max
a = np.asarray(a)
a = np.reshape(a, (a.shape[0] * a.shape[1], 1))
likelihood_x = logsumexp(a)
likelihood_test.append(likelihood_x - np.log(len(a)))
likelihood_test = np.array(likelihood_test)
plot_histogram(-likelihood_test, dir, mode)
return -np.mean(likelihood_test)
def calculate_likelihood(self, X, dir, mode='test', S=5000, MB=100):
# set auxiliary variables for number of training and test sets
N_test = X.size(0)
# init list
likelihood_test = []
if S <= MB:
R = 1
else:
R = S / MB
S = MB
for j in range(N_test):
if j % 100 == 0:
print('{:.2f}%'.format(j / (1. * N_test) * 100))
# Take x*
x_single = X[j].unsqueeze(0)
a = []
for r in range(0, R):
# Repeat it for all training points
x = x_single.expand(S, x_single.size(1))
a_tmp, _, _ = self.calculate_loss(x)
a.append( -a_tmp.cpu().data.numpy() )
# calculate max
a = np.asarray(a)
a = np.reshape(a, (a.shape[0] * a.shape[1], 1))
likelihood_x = logsumexp( a )
likelihood_test.append(likelihood_x - np.log(len(a)))
likelihood_test = np.array(likelihood_test)
plot_histogram(-likelihood_test, dir, mode)
return -np.mean(likelihood_test)
def calculate_likelihood(self,
data,
mode='valid',
s=5000,
display_rate=100):
# set auxiliary variables for number of training and valid sets
n_test = data.size(0)
# init list
likelihood = []
mb = 500
if s <= mb:
r = 1
else:
r = s / mb
s = mb
for j in range(n_test):
n = 100 * (j / (1. * n_test))
if j % display_rate == 0:
print("\revaluating likelihood:",
j,
"/",
n_test,
-np.mean(likelihood),
end="",
flush=True)
# Take x* print("\rProgress: {:.2f}%".format(progress), end="", flush=True)
x_single = data[j].unsqueeze(0).view(self.input_shape[0],
self.input_size)
a = []
for _ in range(0, int(r)):
# Repeat it for all training points
x = x_single.expand(s, x_single.size(1))
# pass through VAE
if self.flavour in ["ccLinIAF", "hf", "vanilla", "normflow"]:
loss, rec, kl, _ = self.calculate_losses(x)
elif self.flavour in [
"o-sylvester", "h-sylvester", "t-sylvester"
]:
reconstruction, z_mu, z_var, ldj, z0, zk = self(x)
loss, rec, kl = calculate_losses(reconstruction, x, z_mu,
z_var, z0, zk, ldj)
else:
print(self.flavour, "is not a flavour, quiting.")
exit()
a.append(loss.cpu().data.numpy())
# calculate max
a = np.asarray(a)
a = np.reshape(a, (a.shape[0], 1))
likelihood_x = logsumexp(a)
likelihood.append(likelihood_x - np.log(len(a)))
likelihood = np.array(likelihood)
plot_histogram(-likelihood, self.model_history_path, mode)
return -np.mean(likelihood)
