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Updating variable names to be accurate #146

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26 changes: 13 additions & 13 deletions contextualized_topic_models/networks/decoding_network.py
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Original file line number Diff line number Diff line change
Expand Up @@ -121,11 +121,11 @@ def reparameterize(mu, logvar):
def forward(self, x, x_bert, labels=None):
"""Forward pass."""
# batch_size x n_components
posterior_mu, posterior_log_sigma = self.inf_net(x, x_bert, labels)
posterior_sigma = torch.exp(posterior_log_sigma)
posterior_mu, posterior_log_variance = self.inf_net(x, x_bert, labels)
posterior_variance = torch.exp(posterior_log_variance)

# generate samples from theta
theta = F.softmax(self.reparameterize(posterior_mu, posterior_log_sigma), dim=1)
theta = F.softmax(self.reparameterize(posterior_mu, posterior_log_variance), dim=1)
theta = self.drop_theta(theta)

# prodLDA vs LDA
Expand Down Expand Up @@ -156,39 +156,39 @@ def forward(self, x, x_bert, labels=None):
self.prior_mean,
self.prior_variance,
posterior_mu,
posterior_sigma,
posterior_log_sigma,
posterior_variance,
posterior_log_variance,
word_dist,
estimated_labels,
)

def get_posterior(self, x, x_bert, labels=None):
"""Get posterior distribution."""
# batch_size x n_components
posterior_mu, posterior_log_sigma = self.inf_net(x, x_bert, labels)
posterior_mu, posterior_log_variance = self.inf_net(x, x_bert, labels)

return posterior_mu, posterior_log_sigma
return posterior_mu, posterior_log_variance

def get_theta(self, x, x_bert, labels=None):
with torch.no_grad():
# batch_size x n_components
posterior_mu, posterior_log_sigma = self.get_posterior(x, x_bert, labels)
# posterior_sigma = torch.exp(posterior_log_sigma)
posterior_mu, posterior_log_variance = self.get_posterior(x, x_bert, labels)
# posterior_variance = torch.exp(posterior_log_variance)

# generate samples from theta
theta = F.softmax(
self.reparameterize(posterior_mu, posterior_log_sigma), dim=1
self.reparameterize(posterior_mu, posterior_log_variance), dim=1
)

return theta

def sample(self, posterior_mu, posterior_log_sigma, n_samples: int = 20):
def sample(self, posterior_mu, posterior_log_variance, n_samples: int = 20):
with torch.no_grad():
posterior_mu = posterior_mu.unsqueeze(0).repeat(n_samples, 1, 1)
posterior_log_sigma = posterior_log_sigma.unsqueeze(0).repeat(n_samples, 1, 1)
posterior_log_variance = posterior_log_variance.unsqueeze(0).repeat(n_samples, 1, 1)
# generate samples from theta
theta = F.softmax(
self.reparameterize(posterior_mu, posterior_log_sigma), dim=-1
self.reparameterize(posterior_mu, posterior_log_variance), dim=-1
)

return theta.mean(dim=0)
16 changes: 8 additions & 8 deletions contextualized_topic_models/networks/inference_network.py
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Original file line number Diff line number Diff line change
Expand Up @@ -49,8 +49,8 @@ def __init__(self, input_size, bert_size, output_size, hidden_sizes,
self.f_mu = nn.Linear(hidden_sizes[-1], output_size)
self.f_mu_batchnorm = nn.BatchNorm1d(output_size, affine=False)

self.f_sigma = nn.Linear(hidden_sizes[-1], output_size)
self.f_sigma_batchnorm = nn.BatchNorm1d(output_size, affine=False)
self.f_variance = nn.Linear(hidden_sizes[-1], output_size)
self.f_variance_batchnorm = nn.BatchNorm1d(output_size, affine=False)

self.dropout_enc = nn.Dropout(p=self.dropout)

Expand All @@ -67,9 +67,9 @@ def forward(self, x, x_bert, labels=None):
x = self.hiddens(x)
x = self.dropout_enc(x)
mu = self.f_mu_batchnorm(self.f_mu(x))
log_sigma = self.f_sigma_batchnorm(self.f_sigma(x))
log_variance = self.f_variance_batchnorm(self.f_variance(x))

return mu, log_sigma
return mu, log_variance


class CombinedInferenceNetwork(nn.Module):
Expand Down Expand Up @@ -119,8 +119,8 @@ def __init__(self, input_size, bert_size, output_size, hidden_sizes,
self.f_mu = nn.Linear(hidden_sizes[-1], output_size)
self.f_mu_batchnorm = nn.BatchNorm1d(output_size, affine=False)

self.f_sigma = nn.Linear(hidden_sizes[-1], output_size)
self.f_sigma_batchnorm = nn.BatchNorm1d(output_size, affine=False)
self.f_variance = nn.Linear(hidden_sizes[-1], output_size)
self.f_variance_batchnorm = nn.BatchNorm1d(output_size, affine=False)

self.dropout_enc = nn.Dropout(p=self.dropout)

Expand All @@ -139,6 +139,6 @@ def forward(self, x, x_bert, labels=None):
x = self.hiddens(x)
x = self.dropout_enc(x)
mu = self.f_mu_batchnorm(self.f_mu(x))
log_sigma = self.f_sigma_batchnorm(self.f_sigma(x))
log_variance = self.f_variance_batchnorm(self.f_variance(x))

return mu, log_sigma
return mu, log_variance