def __init__(self,
input_size,
hidden_size,
output_size,
embedder=None,
num_layers=1,
attn_mode='mlp',
dropout=0.0):
super(PointerDecoder, self).__init__()
self.input_size = input_size
self.hidden_size = hidden_size
self.output_size = output_size
self.embedder = embedder
self.num_layers = num_layers
self.attn_mode = None if attn_mode == 'none' else attn_mode
self.dropout = dropout
self.memory_size = hidden_size
self.rnn_input_size = self.input_size
self.out_input_size = self.hidden_size + self.input_size
if self.attn_mode is not None:
self.hist_attention = Attention(query_size=self.hidden_size,
memory_size=self.hidden_size,
hidden_size=self.hidden_size,
mode=self.attn_mode,
project=False)
self.fact_attention = Attention(query_size=self.hidden_size,
memory_size=self.hidden_size,
hidden_size=self.hidden_size,
mode=self.attn_mode,
project=False)
self.rnn_input_size += self.memory_size * 2
self.out_input_size += self.memory_size * 2
self.rnn = nn.GRU(input_size=self.rnn_input_size,
hidden_size=self.hidden_size,
num_layers=self.num_layers,
dropout=self.dropout if self.num_layers > 1 else 0,
batch_first=True)
self.ff = nn.Sequential(nn.Linear(self.out_input_size, 3),
nn.Softmax(dim=-1))
if self.out_input_size > self.hidden_size:
self.output_layer = nn.Sequential(
nn.Dropout(p=self.dropout),
nn.Linear(self.out_input_size, self.hidden_size),
nn.Linear(self.hidden_size, self.output_size),
nn.Softmax(dim=-1),
)
else:
self.output_layer = nn.Sequential(
nn.Dropout(p=self.dropout),
nn.Linear(self.out_input_size, self.output_size),
nn.Softmax(dim=-1),
)
def __init__(self, config, is_train=True):
super(RNNDecoder, self).__init__()
self.config = config
self.is_train = is_train
self.input_size = self.config.input_size
self.output_size = self.config.cn_vocab_size + 4
self.hidden_units = self.config.hidden_units
self.num_layers = self.config.num_layers
self.dropout = self.config.dropout
self.embedder = self.config.embedder
self.memory_size = self.config.memory_size or self.config.hidden_units
self.attention_mode = self.config.attention_mode
self.rnn_input_size = self.input_size
self.out_input_size = self.hidden_units
if self.attention_mode:
self.attention = Attention(config)
self.rnn_input_size += self.memory_size
self.rnn = nn.GRU(input_size=self.rnn_input_size,
hidden_size=self.hidden_units,
num_layers=self.num_layers,
batch_first=True,
dropout=self.dropout if self.num_layers > 1 else 0)
self.output_layer = nn.Sequential(
nn.Dropout(p=self.dropout),
nn.Linear(self.out_input_size, self.hidden_units),
nn.Linear(self.hidden_units, self.output_size),
nn.LogSoftmax(dim=-1))
def __init__(self,
vocab,
hidden_size,
hop=1,
attn_mode='dot',
padding_idx=None):
super(EncoderMemNN, self).__init__()
self.num_vocab = vocab
self.max_hops = hop
self.hidden_size = hidden_size
self.attn_mode = attn_mode
self.padding_idx = padding_idx
for hop in range(self.max_hops + 1):
C = Embedder(self.num_vocab,
self.hidden_size,
padding_idx=self.padding_idx)
C.weight.data.normal_(0, 0.1)
self.add_module("C_{}".format(hop), C)
for hop in range(self.max_hops):
A = Attention(query_size=self.hidden_size,
memory_size=self.hidden_size,
hidden_size=self.hidden_size,
mode=self.attn_mode,
return_attn_only=True)
self.add_module("A_{}".format(hop), A)
self.C = AttrProxy(self, "C_")
self.A = AttrProxy(self, "A_")
self.softmax = nn.Softmax(dim=1)
def __init__(self,
input_size,
hidden_size,
embedder=None,
num_layers=1,
attn_mode=None,
memory_size=None,
dropout=0.0):
super(RNNDecoder, self).__init__()
self.input_size = input_size
self.hidden_size = hidden_size
self.embedder = embedder
self.num_layers = num_layers
self.attn_mode = None if attn_mode == 'none' else attn_mode
self.memory_size = memory_size or hidden_size
self.dropout = dropout
self.out_input_size = hidden_size
self.class_num=3
self.rnn_input_size=input_size+hidden_size
if self.attn_mode is not None:
self.attention = Attention(query_size=self.hidden_size,
memory_size=self.memory_size,
hidden_size=self.memory_size,
mode=self.attn_mode,
project=False)
self.rnn = nn.LSTM(input_size=self.rnn_input_size,
hidden_size=self.hidden_size,
num_layers=self.num_layers,
dropout=self.dropout if self.num_layers > 1 else 0,
batch_first=True)
self.fc1=nn.Linear(2*self.hidden_size, self.hidden_size)
self.output_layer = nn.Sequential(
nn.Linear(self.out_input_size, self.out_input_size//2),
nn.Dropout(p=self.dropout),
nn.ReLU(),
nn.Linear(self.out_input_size//2, self.class_num),
nn.Softmax(dim=-1)
)
self.fusion=nn.Sequential(
nn.Linear(2*self.hidden_size,1),
nn.Sigmoid()
)
def __init__(self, vocab, embedding_dim, hidden_size, hop,
dropout=0.0, num_layers=1, padding_idx=None, attn_mode=None):
super(DecoderMemNN, self).__init__()
self.num_vocab = vocab
self.max_hops = hop
self.embedding_dim = embedding_dim
self.hidden_size = hidden_size
self.dropout = dropout
self.num_layers = num_layers
self.padding_idx = padding_idx
self.attn_mode = attn_mode
self.rnn_input_size = self.embedding_dim
self.out_input_size = self.hidden_size
for hop in range(self.max_hops + 1):
C = Embedder(self.num_vocab, embedding_dim,
padding_idx=self.padding_idx)
C.weight.data.normal_(0, 0.1)
self.add_module("C_{}".format(hop), C)
self.C = AttrProxy(self, "C_")
if self.attn_mode is not None:
self.attention = Attention(query_size=self.hidden_size,
memory_size=self.hidden_size,
hidden_size=self.hidden_size,
mode=self.attn_mode,
project=False)
self.rnn_input_size += self.hidden_size
self.softmax = nn.Softmax(dim=1)
self.log_softmax = nn.LogSoftmax(dim=-1)
self.W = nn.Linear(self.embedding_dim, 1)
self.W1 = nn.Linear(2 * self.embedding_dim, self.num_vocab)
self.gru = nn.GRU(input_size=self.rnn_input_size,
hidden_size=self.embedding_dim,
num_layers=self.num_layers,
dropout=self.dropout if self.num_layers > 1 else 0,
batch_first=True)
def __init__(self,
src_vocab_size,
tgt_vocab_size,
embed_size,
hidden_size,
padding_idx=None,
num_layers=1,
bidirectional=True,
attn_mode="mlp",
attn_hidden_size=None,
with_bridge=False,
tie_embedding=False,
dropout=0.0,
use_gpu=False,
use_bow=False,
use_kd=False,
use_dssm=False,
use_posterior=False,
weight_control=False,
use_pg=False,
use_gs=False,
concat=False,
pretrain_epoch=0):
super(KnowledgeSeq2Seq, self).__init__()
self.src_vocab_size = src_vocab_size
self.tgt_vocab_size = tgt_vocab_size
self.embed_size = embed_size
self.hidden_size = hidden_size
self.padding_idx = padding_idx
self.num_layers = num_layers
self.bidirectional = bidirectional
self.attn_mode = attn_mode
self.attn_hidden_size = attn_hidden_size
self.with_bridge = with_bridge
self.tie_embedding = tie_embedding
self.dropout = dropout
self.use_gpu = use_gpu
self.use_bow = use_bow
self.use_dssm = use_dssm
self.weight_control = weight_control
self.use_kd = use_kd
self.use_pg = use_pg
self.use_gs = use_gs
self.use_posterior = use_posterior
self.pretrain_epoch = pretrain_epoch
self.baseline = 0
enc_embedder = Embedder(num_embeddings=self.src_vocab_size,
embedding_dim=self.embed_size,
padding_idx=self.padding_idx)
self.encoder = RNNEncoder(input_size=self.embed_size,
hidden_size=self.hidden_size,
embedder=enc_embedder,
num_layers=self.num_layers,
bidirectional=self.bidirectional,
dropout=self.dropout)
if self.with_bridge:
self.bridge = nn.Sequential(
nn.Linear(self.hidden_size, self.hidden_size), nn.Tanh())
if self.tie_embedding:
assert self.src_vocab_size == self.tgt_vocab_size
dec_embedder = enc_embedder
knowledge_embedder = enc_embedder
else:
dec_embedder = Embedder(num_embeddings=self.tgt_vocab_size,
embedding_dim=self.embed_size,
padding_idx=self.padding_idx)
knowledge_embedder = Embedder(num_embeddings=self.tgt_vocab_size,
embedding_dim=self.embed_size,
padding_idx=self.padding_idx)
self.knowledge_encoder = RNNEncoder(input_size=self.embed_size,
hidden_size=self.hidden_size,
embedder=knowledge_embedder,
num_layers=self.num_layers,
bidirectional=self.bidirectional,
dropout=self.dropout)
self.prior_attention = Attention(query_size=self.hidden_size,
memory_size=self.hidden_size,
hidden_size=self.hidden_size,
mode="dot")
self.posterior_attention = Attention(query_size=self.hidden_size,
memory_size=self.hidden_size,
hidden_size=self.hidden_size,
mode="dot")
self.decoder = RNNDecoder(input_size=self.embed_size,
hidden_size=self.hidden_size,
output_size=self.tgt_vocab_size,
embedder=dec_embedder,
num_layers=self.num_layers,
attn_mode=self.attn_mode,
memory_size=self.hidden_size,
feature_size=None,
dropout=self.dropout,
concat=concat)
self.log_softmax = nn.LogSoftmax(dim=-1)
self.softmax = nn.Softmax(dim=-1)
self.sigmoid = nn.Sigmoid()
self.softplus = nn.Softplus()
if self.use_bow:
self.bow_output_layer = nn.Sequential(
nn.Linear(in_features=self.hidden_size,
out_features=self.hidden_size), nn.Tanh(),
nn.Linear(in_features=self.hidden_size,
out_features=self.tgt_vocab_size),
nn.LogSoftmax(dim=-1))
if self.use_dssm:
self.dssm_project = nn.Linear(in_features=self.hidden_size,
out_features=self.hidden_size)
self.mse_loss = torch.nn.MSELoss(reduction='mean')
if self.use_kd:
self.knowledge_dropout = nn.Dropout()
if self.padding_idx is not None:
self.weight = torch.ones(self.tgt_vocab_size)
self.weight[self.padding_idx] = 0
else:
self.weight = None
self.nll_loss = NLLLoss(weight=self.weight,
ignore_index=self.padding_idx,
reduction='mean')
self.kl_loss = torch.nn.KLDivLoss(size_average=True)
if self.use_gpu:
self.cuda()
self.weight = self.weight.cuda()
def __init__(self,
input_size,
hidden_size,
output_size,
embedder=None,
num_layers=1,
attn_mode=None,
attn_hidden_size=None,
memory_size=None,
feature_size=None,
dropout=0.0,
concat=False):
super(RNNDecoder, self).__init__()
self.input_size = input_size
self.hidden_size = hidden_size
self.output_size = output_size
self.embedder = embedder
self.num_layers = num_layers
self.attn_mode = None if attn_mode == 'none' else attn_mode
self.attn_hidden_size = attn_hidden_size or hidden_size // 2
self.memory_size = memory_size or hidden_size
self.feature_size = feature_size
self.dropout = dropout
self.concat = concat
self.rnn_input_size = self.input_size
self.out_input_size = self.hidden_size
self.cue_input_size = self.hidden_size
if self.feature_size is not None:
self.rnn_input_size += self.feature_size
self.cue_input_size += self.feature_size
if self.attn_mode is not None:
self.attention = Attention(query_size=self.hidden_size,
memory_size=self.memory_size,
hidden_size=self.attn_hidden_size,
mode=self.attn_mode,
project=False)
self.rnn_input_size += self.memory_size
self.cue_input_size += self.memory_size
self.out_input_size += self.memory_size
self.rnn = nn.GRU(input_size=self.rnn_input_size,
hidden_size=self.hidden_size,
num_layers=self.num_layers,
dropout=self.dropout if self.num_layers > 1 else 0,
batch_first=True)
self.cue_rnn = nn.GRU(
input_size=self.cue_input_size,
hidden_size=self.hidden_size,
num_layers=self.num_layers,
dropout=self.dropout if self.num_layers > 1 else 0,
batch_first=True)
self.fc1 = nn.Linear(self.hidden_size, self.hidden_size)
self.fc2 = nn.Linear(self.hidden_size, self.hidden_size)
if self.concat:
self.fc3 = nn.Linear(self.hidden_size * 2, self.hidden_size)
else:
self.fc3 = nn.Linear(self.hidden_size * 2, 1)
self.tanh = nn.Tanh()
self.sigmoid = nn.Sigmoid()
if self.out_input_size > self.hidden_size:
self.output_layer = nn.Sequential(
nn.Dropout(p=self.dropout),
nn.Linear(self.out_input_size, self.hidden_size),
nn.Linear(self.hidden_size, self.output_size),
nn.LogSoftmax(dim=-1),
)
else:
self.output_layer = nn.Sequential(
nn.Dropout(p=self.dropout),
nn.Linear(self.out_input_size, self.output_size),
nn.LogSoftmax(dim=-1),
)
def __init__(
self,
input_size,
hidden_size,
topic_size,
output_size,
trans_mat,
embedder=None,
num_layers=1,
attn_mode=None,
attn_hidden_size=None,
memory_size=None,
feature_size=None,
dropout=0.0,
tgt_unk_idx=3,
attention_channels='ST',
):
super(RNNDecoder, self).__init__()
self.input_size = input_size
self.hidden_size = hidden_size
self.topic_size = topic_size
self.output_size = output_size
self.trans_mat = trans_mat.transpose(0, 1)
self.embedder = embedder
self.num_layers = num_layers
self.attn_mode = None if attn_mode == 'none' else attn_mode
self.attn_hidden_size = attn_hidden_size or hidden_size // 2
self.memory_size = memory_size or hidden_size
self.feature_size = feature_size
self.dropout = dropout
self.tgt_unk_idx = tgt_unk_idx
self.attention_channels = attention_channels
self.rnn_input_size = self.input_size + self.memory_size * 2
self.out_input_size = self.memory_size + self.hidden_size
self.topic_input_size = self.hidden_size + self.memory_size
self.soft_prob_layer = nn.Linear(self.hidden_size, 2)
self.tgv_layer = nn.Sequential(
nn.Linear(self.memory_size * 2, self.memory_size), nn.Tanh())
self.tgv_fc = nn.Linear(self.memory_size * 2, self.memory_size)
self.attention = Attention(query_size=self.hidden_size,
memory_size=self.memory_size,
hidden_size=self.attn_hidden_size,
mode=self.attn_mode,
project=False)
self.rnn = nn.GRU(input_size=self.rnn_input_size,
hidden_size=self.hidden_size,
num_layers=self.num_layers,
dropout=self.dropout if self.num_layers > 1 else 0,
batch_first=True)
attention_channels_length = len(self.attention_channels)
self.tgv_layer = nn.Sequential(
nn.Linear(self.memory_size * attention_channels_length,
self.memory_size), nn.Tanh())
self.output_out_layer = nn.Sequential(
nn.Dropout(p=self.dropout),
nn.Linear(self.out_input_size, self.hidden_size),
nn.Linear(self.hidden_size, self.output_size),
)
if self.topic_size is not None:
self.topic_out_layer = nn.Sequential(
nn.Dropout(p=self.dropout),
nn.Linear(self.topic_input_size, self.hidden_size),
nn.Linear(self.hidden_size, self.topic_size),
)
self.lsf = nn.LogSoftmax(dim=-1)
def __init__(self,
src_vocab_size,
tgt_vocab_size,
embed_size,
hidden_size,
padding_idx=None,
num_layers=1,
bidirectional=True,
attn_mode="mlp",
attn_hidden_size=None,
with_bridge=False,
tie_embedding=False,
dropout=0.0,
use_gpu=False,
use_bow=False,
use_kd=False,
use_dssm=False,
use_posterior=False,
weight_control=False,
use_pg=False,
use_gs=False,
concat=False,
pretrain_epoch=0):
super(KnowledgeSeq2Seq, self).__init__()
self.src_vocab_size = src_vocab_size
self.tgt_vocab_size = tgt_vocab_size
self.embed_size = embed_size
self.hidden_size = hidden_size
self.padding_idx = padding_idx
self.num_layers = num_layers
self.bidirectional = bidirectional
self.attn_mode = attn_mode
self.attn_hidden_size = attn_hidden_size
self.with_bridge = with_bridge
self.tie_embedding = tie_embedding
self.dropout = dropout
self.use_gpu = use_gpu
self.use_bow = use_bow
self.use_dssm = use_dssm
self.weight_control = weight_control
self.use_kd = use_kd
self.use_pg = use_pg
self.use_gs = use_gs
self.use_posterior = use_posterior
self.pretrain_epoch = pretrain_epoch
self.baseline = 0
bc = BertClient()
enc_embedder = bc.encode(['你好', '吃饭了么'])
# enc_embedder = Embedder(num_embeddings=self.src_vocab_size,
# embedding_dim=self.embed_size, padding_idx=self.padding_idx) # Embedder(30004, 300, padding_idx=0)
self.encoder = RNNEncoder(input_size=self.embed_size,
hidden_size=self.hidden_size,
embedder=enc_embedder,
num_layers=self.num_layers,
bidirectional=self.bidirectional,
dropout=self.dropout)
if self.with_bridge:
self.bridge = nn.Sequential(
nn.Linear(self.hidden_size, self.hidden_size),
nn.Tanh()) #定义一个linear层 ,通过Squential将网络层和激活函数结合起来，输出激活后的网络节点
if self.tie_embedding:
assert self.src_vocab_size == self.tgt_vocab_size
dec_embedder = enc_embedder
knowledge_embedder = enc_embedder
else:
dec_embedder = Embedder(num_embeddings=self.tgt_vocab_size,
embedding_dim=self.embed_size,
padding_idx=self.padding_idx)
knowledge_embedder = Embedder(num_embeddings=self.tgt_vocab_size,
embedding_dim=self.embed_size,
padding_idx=self.padding_idx)
self.knowledge_encoder = RNNEncoder(input_size=self.embed_size,
hidden_size=self.hidden_size,
embedder=knowledge_embedder,
num_layers=self.num_layers,
bidirectional=self.bidirectional,
dropout=self.dropout)
self.prior_attention = Attention(query_size=self.hidden_size,
memory_size=self.hidden_size,
hidden_size=self.hidden_size,
mode="dot")
self.posterior_attention = Attention(query_size=self.hidden_size,
memory_size=self.hidden_size,
hidden_size=self.hidden_size,
mode="dot")
self.decoder = RNNDecoder(input_size=self.embed_size,
hidden_size=self.hidden_size,
output_size=self.tgt_vocab_size,
embedder=dec_embedder,
num_layers=self.num_layers,
attn_mode=self.attn_mode,
memory_size=self.hidden_size,
feature_size=None,
dropout=self.dropout,
concat=concat)
self.log_softmax = nn.LogSoftmax(dim=-1)
self.softmax = nn.Softmax(dim=-1)
self.sigmoid = nn.Sigmoid()
self.softplus = nn.Softplus()
"""
Softplus():a smooth approximation to the ReLU function and can be used to constrain the output of a machine to always be positive.
"""
if self.use_bow:
self.bow_output_layer = nn.Sequential(
nn.Linear(in_features=self.hidden_size,
out_features=self.hidden_size), nn.Tanh(),
nn.Linear(in_features=self.hidden_size,
out_features=self.tgt_vocab_size),
nn.LogSoftmax(dim=-1))
if self.use_dssm:
self.dssm_project = nn.Linear(in_features=self.hidden_size,
out_features=self.hidden_size)
self.mse_loss = torch.nn.MSELoss(reduction='mean')
if self.use_kd:
self.knowledge_dropout = nn.Dropout()
if self.padding_idx is not None:
self.weight = torch.ones(self.tgt_vocab_size)
self.weight[self.padding_idx] = 0
else:
self.weight = None
self.nll_loss = NLLLoss(
weight=self.weight,
ignore_index=self.padding_idx,
reduction='mean') #量化真实回复与基线生成的回复的不同 :NLLLoss() 负对数似然
self.kl_loss = torch.nn.KLDivLoss(
size_average=True
) #KLDivLoss() #select related background knowledge -> lead the conversation
if self.use_gpu:
self.cuda()
self.weight = self.weight.cuda()
def __init__(self,
emb_size=1024,
n_layer=12,
n_head=1,
voc_size=10005,
max_position_seq_len=1024,
sent_types=2,
num_labels=2,
dropout=0.3,
use_knowledge=False,
share_embedding=False,
padding_idx=0,
use_gpu=True):
super(RetrievalModel, self).__init__()
self.emb_size = emb_size
self.n_layer = n_layer
self.n_head = n_head
self.voc_size = voc_size
self.max_position_seq_len = max_position_seq_len
self.sent_types = sent_types
self.num_labels = num_labels
self.dropout = dropout
self.use_knowledge = use_knowledge
self.share_embedding = share_embedding
self.padding_idx = padding_idx
self.use_gpu = use_gpu
self.embeddings = [nn.Embedding(self.voc_size, self.emb_size, self.padding_idx),
nn.Embedding(self.max_position_seq_len, self.emb_size),
nn.Embedding(self.sent_types, self.emb_size)]
self.transformer_encoder = TransformerEncoder(self.n_layer,
self.emb_size,
self.n_head,
self.emb_size * 4,
self.dropout,
self.embeddings)
'''
仅仅是初始化
定义
embedding
transformer:voc+position+sent_types
GRU
knowledge embedding
'''
if self.use_knowledge:
if self.share_embedding:
self.knowledge_embeddings = self.embeddings[0]
else:
self.knowledge_embeddings = \
nn.Embedding(self.voc_size, self.emb_size, self.padding_idx)
self.rnn_encoder = RNNEncoder(rnn_type="GRU",
bidirectional=True,
num_layers=1,
input_size=self.emb_size,
hidden_size=self.emb_size,
dropout=self.dropout,
embeddings=self.knowledge_embeddings,
use_bridge=True)
self.attention = Attention(query_size=self.emb_size,
memory_size=self.emb_size,
hidden_size=self.emb_size,
mode="dot",
project=True)
self.middle_linear = nn.Sequential(
nn.Linear(self.emb_size, self.emb_size),
nn.Tanh()
)
self.final_linear = nn.Sequential(
nn.Dropout(p=self.dropout),
nn.Linear(self.emb_size, self.num_labels)
)
self.softmax = nn.LogSoftmax(dim=-1)
self.criterion = nn.NLLLoss()
if self.use_gpu:
self.cuda()
def __init__(self,
src_vocab_size,
tgt_vocab_size,
embed_size,
hidden_size,
padding_idx=None,
num_layers=1,
bidirectional=True,
attn_mode="mlp",
attn_hidden_size=None,
with_bridge=False,
tie_embedding=False,
dropout=0.0,
use_gpu=False,
use_dssm=False,
weight_control=False,
use_pg=False,
concat=False,
pretrain_epoch=0,
with_label=False):
super(TwoStagePersonaSeq2Seq, self).__init__()
self.src_vocab_size = src_vocab_size
self.tgt_vocab_size = tgt_vocab_size
self.embed_size = embed_size
self.hidden_size = hidden_size
self.padding_idx = padding_idx
self.num_layers = num_layers
self.bidirectional = bidirectional
self.attn_mode = attn_mode
self.attn_hidden_size = attn_hidden_size
self.with_bridge = with_bridge
self.tie_embedding = tie_embedding
self.dropout = dropout
self.use_gpu = use_gpu
self.use_dssm = use_dssm
self.weight_control = weight_control
self.use_pg = use_pg
self.pretrain_epoch = pretrain_epoch
self.baseline = 0
self.with_label = with_label
self.task_id = 1
enc_embedder = Embedder(num_embeddings=self.src_vocab_size,
embedding_dim=self.embed_size,
padding_idx=self.padding_idx)
self.encoder = RNNEncoder(input_size=self.embed_size,
hidden_size=self.hidden_size,
embedder=enc_embedder,
num_layers=self.num_layers,
bidirectional=self.bidirectional,
dropout=self.dropout)
if self.with_bridge:
self.bridge = nn.Sequential(
nn.Linear(self.hidden_size, self.hidden_size), nn.Tanh())
if self.tie_embedding:
assert self.src_vocab_size == self.tgt_vocab_size
dec_embedder = enc_embedder
persona_embedder = enc_embedder
else:
dec_embedder = Embedder(num_embeddings=self.tgt_vocab_size,
embedding_dim=self.embed_size,
padding_idx=self.padding_idx)
persona_embedder = Embedder(num_embeddings=self.tgt_vocab_size,
embedding_dim=self.embed_size,
padding_idx=self.padding_idx)
self.persona_encoder = RNNEncoder(input_size=self.embed_size,
hidden_size=self.hidden_size,
embedder=persona_embedder,
num_layers=self.num_layers,
bidirectional=self.bidirectional,
dropout=self.dropout)
self.persona_attention = Attention(query_size=self.hidden_size,
memory_size=self.hidden_size,
hidden_size=self.hidden_size,
mode="general")
self.decoder = RNNDecoder(input_size=self.embed_size,
hidden_size=self.hidden_size,
output_size=self.tgt_vocab_size,
embedder=dec_embedder,
num_layers=self.num_layers,
attn_mode=self.attn_mode,
memory_size=self.hidden_size,
feature_size=None,
dropout=self.dropout,
concat=concat,
with_label=self.with_label)
self.key_linear = nn.Linear(in_features=self.embed_size,
out_features=self.hidden_size)
if self.use_dssm:
self.dssm_project = nn.Linear(in_features=self.hidden_size,
out_features=self.hidden_size)
self.mse_loss = torch.nn.MSELoss(reduction='mean')
self.mse_loss = torch.nn.MSELoss(reduction='mean')
if self.padding_idx is not None:
self.weight = torch.ones(self.tgt_vocab_size)
self.weight[self.padding_idx] = 0
else:
self.weight = None
self.nll_loss = NLLLoss(weight=self.weight,
ignore_index=self.padding_idx,
reduction='mean')
self.persona_loss = NLLLoss(weight=None, reduction='mean')
self.eps = 1e-7
if self.use_gpu:
self.cuda()
self.weight = self.weight.cuda()
