Created
June 16, 2020 13:34
-
-
Save khanhnamle1994/d86d79ee5e32f4098829d12dbe383d53 to your computer and use it in GitHub Desktop.
SVAE model architecture
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
class SVAE(nn.Module): | |
""" | |
Function to build the SVAE model | |
""" | |
def __init__(self, hyper_params): | |
super(Model, self).__init__() | |
self.hyper_params = hyper_params | |
self.encoder = Encoder(hyper_params) | |
self.decoder = Decoder(hyper_params) | |
self.item_embed = nn.Embedding(hyper_params['total_items'], hyper_params['item_embed_size']) | |
self.gru = nn.GRU( | |
hyper_params['item_embed_size'], hyper_params['rnn_size'], | |
batch_first=True, num_layers=1 | |
) | |
self.linear1 = nn.Linear(hyper_params['hidden_size'], 2 * hyper_params['latent_size']) | |
nn.init.xavier_normal(self.linear1.weight) | |
self.tanh = nn.Tanh() | |
def sample_latent(self, h_enc): | |
""" | |
Return the latent normal sample z ~ N(mu, sigma^2) | |
""" | |
temp_out = self.linear1(h_enc) | |
mu = temp_out[:, :self.hyper_params['latent_size']] | |
log_sigma = temp_out[:, self.hyper_params['latent_size']:] | |
sigma = torch.exp(log_sigma) | |
std_z = torch.from_numpy(np.random.normal(0, 1, size=sigma.size())).float() | |
self.z_mean = mu | |
self.z_log_sigma = log_sigma | |
return mu + sigma * Variable(std_z, requires_grad=False) # Reparameterization trick | |
def forward(self, x): | |
""" | |
Function to do a forward pass | |
:param x: the input | |
""" | |
in_shape = x.shape # [bsz x seq_len] = [1 x seq_len] | |
x = x.view(-1) # [seq_len] | |
x = self.item_embed(x) # [seq_len x embed_size] | |
x = x.view(in_shape[0], in_shape[1], -1) # [1 x seq_len x embed_size] | |
rnn_out, _ = self.gru(x) # [1 x seq_len x rnn_size] | |
rnn_out = rnn_out.view(in_shape[0] * in_shape[1], -1) # [seq_len x rnn_size] | |
enc_out = self.encoder(rnn_out) # [seq_len x hidden_size] | |
sampled_z = self.sample_latent(enc_out) # [seq_len x latent_size] | |
dec_out = self.decoder(sampled_z) # [seq_len x total_items] | |
dec_out = dec_out.view(in_shape[0], in_shape[1], -1) # [1 x seq_len x total_items] | |
return dec_out, self.z_mean, self.z_log_sigma |
Sign up for free
to join this conversation on GitHub.
Already have an account?
Sign in to comment