def __init__(self, params):
super(Encoder, self).__init__()
self.params = params
self.hw1 = Highway(self.params.sum_depth + self.params.word_embed_size,
2, F.relu)
self.rnn = nn.LSTM(input_size=self.params.word_embed_size +
self.params.sum_depth,
hidden_size=self.params.encoder_rnn_size,
num_layers=self.params.encoder_num_layers,
dropout=0.5,
batch_first=True,
bidirectional=True)
self.hw2 = Highway(self.params.encoder_rnn_size * 2, 3, F.relu)
def __init__(self, params):
super(Encoder, self).__init__()
self.params = params
self.hw1 = Highway(self.params.sum_depth + self.params.word_embed_size,
2, F.relu)
self.rnn = nn.LSTM(input_size=self.params.word_embed_size +
self.params.sum_depth,
hidden_size=self.params.encoder_rnn_size,
num_layers=self.params.encoder_num_layers,
batch_first=True,
bidirectional=True)
self.hw2 = Highway(self.rnn.hidden_size * 2, 2, F.relu)
self.fc = nn.Linear(self.rnn.hidden_size * 2,
self.params.latent_variable_size)
def __init__(self, params):
super(Encoder, self).__init__()
self.params = params
isize = (
self.params.sum_depth + self.params.word_embed_size
) if not self.params.word_is_char else self.params.word_embed_size
self.hw1 = Highway(isize, 10,
F.relu) if self.params.use_highway else None
self.rnn = nn.LSTM(input_size=isize,
hidden_size=self.params.encoder_rnn_size,
num_layers=self.params.encoder_num_layers,
batch_first=True,
bidirectional=True)
self.hw2 = Highway(self.params.encoder_rnn_size *
2, 20, F.relu) if self.params.use_highway else None
def __init__(self, params):
super(Inference, self).__init__()
self.params = params
self.rnn = nn.GRU(input_size=self.params.word_embed_size,
hidden_size=self.params.encoder_size,
num_layers=self.params.encoder_num_layers,
batch_first=True,
bidirectional=True)
self.highway = Highway(self.params.encoder_size * 2, 3, F.elu)
def __init__(self, params):
super(Generator, self).__init__()
self.params = params
self.rnn = nn.GRU(input_size=self.params.latent_variable_size + self.params.word_embed_size,
hidden_size=self.params.decoder_size,
num_layers=self.params.decoder_num_layers,
batch_first=True)
self.highway = Highway(self.params.decoder_size, 3, F.elu)
self.fc = nn.Linear(self.params.decoder_size, self.params.word_embed_size)
def __init__(self, params):
super(DecoderLSTM, self).__init__()
self.params = params
self.rnn = nn.LSTM(input_size=self.params.latent_variable_size +
self.params.word_embed_size,
hidden_size=self.params.decoder_rnn_size,
num_layers=self.params.decoder_num_layers,
batch_first=True)
self.highway = Highway(self.params.decoder_rnn_size, 10,
F.elu) if self.params.use_highway else None
self.fc = nn.Linear(self.params.decoder_rnn_size,
self.params.word_vocab_size)
def __init__(self, params):
super(OriginalEncoder, self).__init__()
self.params = params
self.bidirectional = self.params.bidirectional
self.hw1 = Highway(self.params.sum_depth + self.params.word_embed_size,
2, F.relu)
self.rnn = nn.LSTM(input_size=self.params.word_embed_size +
self.params.sum_depth,
hidden_size=self.params.encoder_rnn_size,
num_layers=self.params.encoder_num_layers,
batch_first=True,
bidirectional=self.bidirectional)
self.mul_factor = 2
if not self.bidirectional:
self.mul_factor = 1
def __init__(self, params):
super(EncoderHR, self).__init__()
self.params = params
self.hw1 = Highway(self.params.sum_depth + self.params.word_embed_size,
2, F.relu)
self.bi = True
self.rnn = nn.LSTM(
input_size=self.params.word_embed_size + self.params.sum_depth,
hidden_size=self.params.encoder_rnn_size,
num_layers=self.params.encoder_num_layers,
batch_first=True,
bidirectional=self.bi,
)
self.layer_dim = (self.params.encoder_num_layers *
2) * self.params.encoder_rnn_size
self.linear_mu = nn.Linear(self.layer_dim * 2, self.layer_dim * 2)
self.linear_var = nn.Linear(self.layer_dim * 2, self.layer_dim * 2)
