def __call__(self, x):
# Obtain parameters for q(z|x)
encoding_time = time.time()
self.encode(x)
encoding_time = float(time.time() - encoding_time)
decoding_time_average = 0.
xp = cuda.cupy
self.importance_weights = 0
self.w_holder = []
self.kl = 0
self.logp = 0
for j in xrange(self.num_zsamples):
# Sample z ~ q(z|x)
z = F.gaussian(self.qmu, self.qln_var)
# Compute log q(z|x)
encoder_log = gaussian_logp(z, self.qmu, self.qln_var)
# Obtain parameters for p(x|z)
decoding_time = time.time()
self.decode(z)
decoding_time = time.time() - decoding_time
decoding_time_average += decoding_time
# Compute log p(x|z)
decoder_log = gaussian_logp(x, self.pmu, self.pln_var)
# Compute log p(z). The odd notation being used is to supply a mean of 0 and covariance of 1
prior_log = gaussian_logp(z, self.qmu*0, self.qln_var/self.qln_var)
# Store the latest log weight'
current_temperature = min(self.temperature['value'],1.0)
self.w_holder.append(decoder_log + current_temperature*(prior_log - encoder_log))
# Store the KL and Logp equivalents. They are not used for computation but for recording and reporting.
self.kl += (encoder_log-prior_log)
self.logp += (decoder_log)
self.temperature['value'] += self.temperature['increment']
# Compute w' for this sample (batch)
logps = F.stack(self.w_holder)
self.obj_batch = F.logsumexp(logps, axis=0) - np.log(self.num_zsamples)
self.kl /= self.num_zsamples
self.logp /= self.num_zsamples
decoding_time_average /= self.num_zsamples
batch_size = self.obj_batch.shape[0]
self.obj = -F.sum(self.obj_batch)/batch_size
self.timing_info = np.array([encoding_time,decoding_time_average])
return self.obj
def __call__(self, x):
# Compute q(z|x)
# pdb.set_trace()
encoding_time = time.time()
self.encode(x)
encoding_time = float(time.time() - encoding_time)
decoding_time_average = 0.
self.kl = 0
self.logp = 0
for j in xrange(self.num_zsamples):
# z ~ q(z|x)
z = F.gaussian(self.qmu, self.qln_var)
# pdb.set_trace()
# Compute log q(z|x)
encoder_log = gaussian_logp(z, self.qmu, self.qln_var)
# Compute p(x|z)
decoding_time = time.time()
self.decode(z)
decoding_time = time.time() - decoding_time
decoding_time_average += decoding_time
# Computer p(z)
prior_log = gaussian_logp0(z)
# Compute objective
self.kl += (encoder_log-prior_log)
self.logp += bernoulli_logp(x, self.p_ber_prob_logit)
# pdb.set_trace()
current_temperature = min(self.temperature['value'],1.0)
self.temperature['value'] += self.temperature['increment']
decoding_time_average /= self.num_zsamples
self.logp /= self.num_zsamples
self.kl /= self.num_zsamples
# pdb.set_trace()
self.obj_batch = self.logp - (current_temperature*self.kl)
self.timing_info = np.array([encoding_time,decoding_time_average])
batch_size = self.obj_batch.shape[0]
self.obj = -F.sum(self.obj_batch)/batch_size
# pdb.set_trace()
return self.obj
def __call__(self, x):
# Obtain parameters for q(z|x)
encoding_time = time.time()
qmu, qln_var, qh_vec_0 = self.encode(x)
encoding_time = float(time.time() - encoding_time)
decoding_time_average = 0.
self.kl = 0
self.logp = 0
for j in xrange(self.num_zsamples):
# z_0 ~ q(z|x)
z_0 = F.gaussian(qmu, qln_var)
# Perform Householder flow transformation, Equation (8)
decoding_time = time.time()
z_T = self.house_transform(z_0)
# Obtain parameters for p(x|z_T)
pmu, pln_var = self.decode(z_T)
decoding_time = time.time() - decoding_time
decoding_time_average += decoding_time
# Compute objective
self.logp += gaussian_logp(x, self.pmu, self.pln_var)
self.kl += gaussian_kl_divergence(z_0, qmu, qln_var, z_T)
decoding_time_average /= self.num_zsamples
self.logp /= self.num_zsamples
self.kl /= self.num_zsamples
current_temperature = min(self.temperature['value'],1.0)
self.obj_batch = self.logp - (current_temperature*self.kl)
self.temperature['value'] += self.temperature['increment']
self.timing_info = np.array([encoding_time,decoding_time_average])
batch_size = self.obj_batch.shape[0]
self.obj = -F.sum(self.obj_batch)/batch_size
return self.obj
def __call__(self, x):
# Compute q(z|x)
encoding_time = time.time()
self.encode(x)
encoding_time = float(time.time() - encoding_time)
decoding_time_average = 0.
self.kl = gaussian_kl_divergence_standard(self.qmu, self.qln_var)
self.logp = 0
for j in xrange(self.num_zsamples):
# z ~ q(z|x)
z = F.gaussian(self.qmu, self.qln_var)
# Compute p(x|z)
decoding_time = time.time()
self.decode(z)
decoding_time = time.time() - decoding_time
decoding_time_average += decoding_time
# Compute objective
self.logp += gaussian_logp(x, self.pmu, self.pln_var)
current_temperature = min(self.temperature['value'], 1.0)
self.temperature['value'] += self.temperature['increment']
# pdb.set_trace()
decoding_time_average /= self.num_zsamples
self.logp /= self.num_zsamples
self.obj_batch = self.logp - (current_temperature * self.kl)
self.timing_info = np.array([encoding_time, decoding_time_average])
batch_size = self.obj_batch.shape[0]
self.obj = -F.sum(self.obj_batch) / batch_size
return self.obj
def __call__(self, x):
# Obtain parameters for q(z|x)
encoding_time = time.time()
self.encode(x)
encoding_time = float(time.time() - encoding_time)
self.logp_xz = 0
self.logq = 0
# For reporting purposes only
self.logp = 0
self.kl = 0
decoding_time_average = 0.
current_temperature = min(self.temperature['value'], 1.0)
self.temperature['value'] += self.temperature['increment']
for j in xrange(self.num_zsamples):
# z ~ q(z|x)
z = F.gaussian(self.qmu, self.qln_var)
decoding_time = time.time()
# Apply inverse autoregressive flow (IAF)
self.logq += gaussian_logp(z, self.qmu,
self.qln_var) # - log q(z|x)
for i in range(self.num_trans):
a_layer_name = 'qiaf_a' + str(i + 1)
b_layer_name = 'qiaf_b' + str(i + 1)
z, delta_logq = self.iaf(z, self.qh, self[a_layer_name],
self[b_layer_name])
self.logq += delta_logq
self.logq *= current_temperature
# Compute p(x|z)
self.decode(z)
decoding_time = time.time() - decoding_time
decoding_time_average += decoding_time
# Compute objective, p(x,z)
logx_given_z = gaussian_logp(x, self.pmu, self.pln_var) # p(x|z)
logz = (current_temperature * gaussian_logp0(z)) # p(z)
self.logp_xz += (logx_given_z + logz)
# For reporting purposes only
self.logp += logx_given_z
self.kl += (self.logq - logz)
decoding_time_average /= self.num_zsamples
# self.logp_xz /= self.num_zsamples
# self.logq /= self.num_zsamples
# For reporting purposes only
self.logp /= self.num_zsamples
self.kl /= self.num_zsamples
self.obj_batch = self.logp_xz - self.logq # variational free energy
self.obj_batch /= self.num_zsamples
self.timing_info = np.array([encoding_time, decoding_time_average])
batch_size = self.obj_batch.shape[0]
self.obj = -F.sum(self.obj_batch) / batch_size
return self.obj
def __call__(self, x):
# Compute parameters for q(z|x, a)
encoding_time_1 = time.time()
qmu_a, qln_var_a = self.encode_a(x)
encoding_time_1 = float(time.time() - encoding_time_1)
a_enc = F.gaussian(qmu_a, qln_var_a)
encoding_time_2 = time.time()
qmu_z, qln_var_z = self.encode_z(x, a_enc)
encoding_time_2 = float(time.time() - encoding_time_2)
encoding_time = encoding_time_1 + encoding_time_2
decoding_time_average = 0.
self.kl = 0
self.logp = 0
logp_a_xz = 0
logp_x_z = 0
logp_z = 0
logq_a_x = 0
logq_z_ax = 0
current_temperature = min(self.temperature['value'], 1.0)
self.temperature['value'] += self.temperature['increment']
for j in xrange(self.num_zsamples):
# z ~ q(z|x, a)
z = F.gaussian(self.qmu_z, self.qln_var_z)
# Compute p(x|z)
decoding_time = time.time()
pmu_a, pln_var_a = self.decode_a(z, x)
pmu_x, pln_var_x = self.decode(z)
decoding_time = time.time() - decoding_time
decoding_time_average += decoding_time
logp_a_xz += gaussian_logp(a_enc, pmu_a, pln_var_a)
logp_x_z += gaussian_logp(x, pmu_x, pln_var_x)
logp_z += current_temperature * gaussian_logp0(z)
logq_a_x += gaussian_logp(a_enc, qmu_a, qln_var_a)
logq_z_ax += current_temperature * gaussian_logp(
z, qmu_z, qln_var_z)
logp_a_xz /= self.num_zsamples
logp_x_z /= self.num_zsamples
logp_z /= self.num_zsamples
logq_a_x /= self.num_zsamples
logq_z_ax /= self.num_zsamples
decoding_time_average /= self.num_zsamples
self.logp /= self.num_zsamples
self.obj_batch = logp_a_xz + logp_x_z + logp_z - logq_a_x - logq_z_ax
self.kl = logq_z_ax - logp_z
self.logp = logp_x_z
self.timing_info = np.array([encoding_time, decoding_time_average])
batch_size = self.obj_batch.shape[0]
self.obj = -F.sum(self.obj_batch) / batch_size
return self.obj
def __call__(self, x):
# Compute q(z|x)
encoding_time = time.time()
qmu, qln_var = self.encode(x)
encoding_time = float(time.time() - encoding_time)
decoding_time_average = 0.
self.logp = 0
self.kl = 0
current_temperature = min(self.temperature['value'], 1.0)
self.temperature['value'] += self.temperature['increment']
for j in xrange(self.num_zsamples):
# Sample z ~ q(z_0|x)
z_0 = F.gaussian(self.qmu, self.qln_var)
# Perform planar flow mappings, Equation (10)
decoding_time = time.time()
z_K = self.planar_flows(z_0)
# Obtain parameters for p(x|z_K)
pmu, pln_var = self.decode(z_K)
decoding_time = time.time() - decoding_time
decoding_time_average += decoding_time
# Compute log q(z_0)
q_prior_log = current_temperature * gaussian_logp0(z_0)
# Compute log p(x|z_K)
decoder_log = gaussian_logp(x, pmu, pln_var)
# Compute log p(z_K)
p_prior_log = current_temperature * gaussian_logp0(z_K)
# Compute log p(x,z_K) which is log p(x|z_K) + log p(z_K)
joint_log = decoder_log + p_prior_log
# Compute second term of log q(z_K)
q_K_log = 0
for i in range(self.num_trans):
flow_u_name = 'flow_u_' + str(i)
lodget_jacobian = F.sum(self[flow_u_name](self.phi[i]), axis=1)
q_K_log += F.log(1 + lodget_jacobian)
q_K_log *= current_temperature
# For recording purposes only
self.logp += decoder_log
self.kl += -(q_prior_log - p_prior_log - q_K_log)
decoding_time_average /= self.num_zsamples
# pdb.set_trace()
self.obj_batch = -((q_prior_log - joint_log) - q_K_log)
self.obj_batch /= self.num_zsamples
batch_size = self.obj_batch.shape[0]
self.obj = F.sum(self.obj_batch) / batch_size
self.kl /= self.num_zsamples
self.logp /= self.num_zsamples
self.timing_info = np.array([encoding_time, decoding_time])
return -self.obj
