def __init__(self, embedding_matrix, opt, memory_weighter='no'):
super(LCRS, self).__init__()
self.opt = opt
self.memory_weighter = memory_weighter
self.embed = nn.Embedding.from_pretrained(
torch.tensor(embedding_matrix, dtype=torch.float))
self.blstm_l = DynamicLSTM(opt.embed_dim,
opt.hidden_dim,
num_layers=1,
batch_first=True,
rnn_type='LSTM')
self.blstm_c = DynamicLSTM(opt.embed_dim,
opt.hidden_dim,
num_layers=1,
batch_first=True,
rnn_type='LSTM')
self.blstm_r = DynamicLSTM(opt.embed_dim,
opt.hidden_dim,
num_layers=1,
batch_first=True,
rnn_type='LSTM')
self.dense = nn.Linear(opt.hidden_dim * 4, opt.polarities_dim)
# target to context attention
self.t2c_l_attention = Attention(opt.hidden_dim,
score_function='bi_linear')
self.t2c_r_attention = Attention(opt.hidden_dim,
score_function='bi_linear')
# context to target attention
self.c2t_l_attention = Attention(opt.hidden_dim,
score_function='bi_linear')
self.c2t_r_attention = Attention(opt.hidden_dim,
score_function='bi_linear')
def __init__(self, bert, opt):
"""
注意力编码器网络, Attentional Encoder Network for Targeted Sentiment Classification
:param bert:
:param opt:
"""
super(AEN_BERT, self).__init__()
self.opt = opt
self.bert = bert
self.squeeze_embedding = SqueezeEmbedding()
self.dropout = nn.Dropout(opt.dropout)
# attn_k和 attn_q的初始化
self.attn_k = Attention(opt.bert_dim,
out_dim=opt.hidden_dim,
n_head=8,
score_function='mlp',
dropout=opt.dropout)
self.attn_q = Attention(opt.bert_dim,
out_dim=opt.hidden_dim,
n_head=8,
score_function='mlp',
dropout=opt.dropout)
# 初始化ffn_c, PCT层
self.ffn_c = PositionwiseFeedForward(opt.hidden_dim,
dropout=opt.dropout)
self.ffn_t = PositionwiseFeedForward(opt.hidden_dim,
dropout=opt.dropout)
# 目标特定的注意力层初始化
self.attn_s1 = Attention(opt.hidden_dim,
n_head=8,
score_function='mlp',
dropout=opt.dropout)
# 最终输出层定义
self.dense = nn.Linear(opt.hidden_dim * 3, opt.polarities_dim)
def __init__(self, embedding_matrix, opt):
super(IAN, self).__init__()
self.opt = opt
self.embed = nn.Embedding.from_pretrained(torch.tensor(
embedding_matrix, dtype=torch.float),
freeze=True)
self.lstm_entity1 = DynamicLSTM(opt.embed_dim,
opt.hidden_dim,
num_layers=1,
batch_first=True,
bidirectional=False)
self.lstm_entity2 = DynamicLSTM(opt.embed_dim,
opt.hidden_dim,
num_layers=1,
batch_first=True,
bidirectional=False)
self.lstm_context = DynamicLSTM(opt.embed_dim,
opt.hidden_dim,
num_layers=1,
batch_first=True,
bidirectional=False)
self.attention_entity1 = Attention(opt.hidden_dim,
score_function='bi_linear')
self.attention_entity2 = Attention(opt.hidden_dim,
score_function='bi_linear')
self.attention_context1 = Attention(opt.hidden_dim,
score_function='bi_linear')
self.attention_context2 = Attention(opt.hidden_dim,
score_function='bi_linear')
self.dense = nn.Linear(opt.hidden_dim * 2, opt.polarities_dim)
def __init__(self, embedding_matrix, opt):
super(RAM2m, self).__init__()
self.opt = opt
self.embed = nn.Embedding.from_pretrained(torch.tensor(
embedding_matrix, dtype=torch.float),
freeze=False)
self.bi_lstm_context = DynamicLSTM(opt.embed_dim,
opt.hidden_dim,
num_layers=1,
batch_first=True,
bidirectional=True)
self.bi_lstm_aspect = DynamicLSTM(opt.embed_dim,
opt.hidden_dim,
num_layers=1,
batch_first=True,
bidirectional=True)
self.bi_lstm_img = DynamicLSTM(opt.embed_dim_img,
opt.hidden_dim,
num_layers=1,
batch_first=True,
bidirectional=True)
self.attention_text = Attention(opt.hidden_dim * 2,
score_function='mlp')
self.attention_img = Attention(opt.hidden_dim * 2,
score_function='mlp')
self.gru_cell_text = nn.GRUCell(opt.hidden_dim * 2, opt.hidden_dim * 2)
self.gru_cell_img = nn.GRUCell(opt.hidden_dim * 2, opt.hidden_dim * 2)
self.bn = nn.BatchNorm1d(opt.hidden_dim * 2, affine=False)
self.fc = nn.Linear(opt.hidden_dim * 4, opt.polarities_dim)
def __init__(self, embedding_matrix, opt):
super(GC_IAN1, self).__init__()
self.opt = opt
kernel_sizes = [1, 3, 5, 7]
self.embed = nn.Embedding.from_pretrained(
torch.tensor(embedding_matrix, dtype=torch.float))
self.cnn_context = nn.ModuleList([
self.cnn_layer(opt.embed_dim, opt.in_channels, k)
for k in kernel_sizes
])
self.cnn_context_ = nn.ModuleList([
self.cnn_layer(opt.embed_dim, opt.in_channels, k)
for k in kernel_sizes
])
self.cnn_aspect = nn.ModuleList([
self.cnn_layer(opt.embed_dim, opt.in_channels, k)
for k in kernel_sizes
])
self.attention_1 = Attention(len(kernel_sizes) * opt.in_channels,
score_function='bi_linear')
self.attention_2 = Attention(len(kernel_sizes) * opt.in_channels,
score_function='bi_linear')
self.dropout = nn.Dropout(opt.dropout)
self.dense = nn.Linear(2 * len(kernel_sizes) * opt.in_channels,
opt.polarities_dim)
def __init__(self, embedding_matrix, opt):
super(AEN, self).__init__()
self.opt = opt
self.embed = nn.Embedding.from_pretrained(
torch.tensor(embedding_matrix, dtype=torch.float))
self.squeeze_embedding = SqueezeEmbedding()
self.attn_k = Attention(opt.embed_dim,
out_dim=opt.hidden_dim,
n_head=8,
score_function='mlp',
dropout=opt.dropout)
self.attn_q = Attention(opt.embed_dim,
out_dim=opt.hidden_dim,
n_head=8,
score_function='mlp',
dropout=opt.dropout)
self.ffn_c = PositionwiseFeedForward(opt.hidden_dim,
dropout=opt.dropout)
self.ffn_t = PositionwiseFeedForward(opt.hidden_dim,
dropout=opt.dropout)
self.attn_s1 = Attention(opt.hidden_dim,
n_head=8,
score_function='mlp',
dropout=opt.dropout)
self.dense = nn.Linear(opt.hidden_dim * 3, opt.polarities_dim)
def __init__(self, bert, opt):
super(AEN_BERT, self).__init__()
#print(" 1 In AEN_BERT ")
self.opt = opt
self.bert = bert
self.squeeze_embedding = SqueezeEmbedding()
self.dropout = nn.Dropout(opt.dropout)
#print(" 2 In AEN_BERT ")
self.attn_k = Attention(opt.bert_dim,
out_dim=opt.hidden_dim,
n_head=8,
score_function='mlp',
dropout=opt.dropout)
self.attn_q = Attention(opt.bert_dim,
out_dim=opt.hidden_dim,
n_head=8,
score_function='mlp',
dropout=opt.dropout)
self.ffn_c = PositionwiseFeedForward(opt.hidden_dim,
dropout=opt.dropout)
self.ffn_t = PositionwiseFeedForward(opt.hidden_dim,
dropout=opt.dropout)
#print(" 3 In AEN_BERT ")
self.attn_s1 = Attention(opt.hidden_dim,
n_head=8,
score_function='mlp',
dropout=opt.dropout)
self.hat = False
self.last = torch.nn.ModuleList()
for t in range(self.opt.taskcla):
self.last.append(nn.Linear(opt.hidden_dim * 3, opt.polarities_dim))
def __init__(self, bert, opt):
super(AEN_BERT, self).__init__()
self.opt = opt
self.bert = bert
self.squeeze_embedding = SqueezeEmbedding()
self.dropout = nn.Dropout(opt.dropout)
self.attn_k = Attention(opt.bert_dim,
out_dim=opt.hidden_dim,
n_head=8,
score_function='mlp',
dropout=opt.dropout)
self.attn_q = Attention(opt.bert_dim,
out_dim=opt.hidden_dim,
n_head=8,
score_function='mlp',
dropout=opt.dropout)
self.ffn_c = PositionwiseFeedForward(opt.hidden_dim,
dropout=opt.dropout)
self.ffn_t = PositionwiseFeedForward(opt.hidden_dim,
dropout=opt.dropout)
self.attn_s1 = Attention(opt.hidden_dim,
n_head=8,
score_function='mlp',
dropout=opt.dropout)
self.dense = nn.Linear(opt.hidden_dim * 3, opt.polarities_dim)
def __init__(self, config, opt):
super(BERT_IAN, self).__init__()
self.opt = opt
self.bert = BertModel(config)
self.attention_aspect = Attention(opt.hidden_dim,
score_function='bi_linear')
self.attention_context = Attention(opt.hidden_dim,
score_function='bi_linear')
self.dense = nn.Linear(opt.hidden_dim * 2, opt.output_dim)
def __init__(self, config, opt):
super(IAN, self).__init__()
self.opt = opt
self.bert = BertModel(config)
self.lstm_context = DynamicLSTM(opt.embed_dim, opt.hidden_dim, num_layers=1, batch_first=True)
self.lstm_aspect = DynamicLSTM(opt.embed_dim, opt.hidden_dim, num_layers=1, batch_first=True)
self.attention_aspect = Attention(opt.hidden_dim, score_function='bi_linear')
self.attention_context = Attention(opt.hidden_dim, score_function='bi_linear')
self.dense = nn.Linear(opt.hidden_dim*2, opt.output_dim)
def __init__(self, embedding_matrix, opt):
super(IAN, self).__init__()
self.opt = opt
self.embed = nn.Embedding.from_pretrained(torch.tensor(embedding_matrix, dtype=torch.float))
self.lstm_context = DynamicLSTM(opt.embed_dim, opt.hidden_dim, num_layers=1, batch_first=True)
self.lstm_aspect = DynamicLSTM(opt.embed_dim, opt.hidden_dim, num_layers=1, batch_first=True)
self.attention_aspect = Attention(opt.hidden_dim, score_function='bi_linear',n_head=2)
self.attention_context = Attention(opt.hidden_dim, score_function='bi_linear',n_head=2)
self.dense = nn.Linear(opt.hidden_dim*2, opt.polarities_dim)
def _encoder_decoder_attention(self, q, k, v):
with tf.variable_scope("encoder-decoder-attention"):
attention = Attention(num_heads=self.num_heads,
masked=False,
linear_key_dim=self.linear_key_dim,
linear_value_dim=self.linear_value_dim,
model_dim=self.model_dim,
dropout=self.dropout)
return attention.multi_head(q, k, v)
def _masked_self_attention(self, q, k, v):
with tf.variable_scope("masked-self-attention"):
attention = Attention(
num_heads=self.num_heads,
masked=True, # Not implemented yet
linear_key_dim=self.linear_key_dim,
linear_value_dim=self.linear_value_dim,
model_dim=self.model_dim,
dropout=self.dropout)
return attention.multi_head(q, k, v)
def __init__(self, embedding_matrix, opt): super(InterAttNet, self).__init__() self.model_name = "IAN" self.opt = opt self.embed = nn.Embedding.from_pretrained(torch.FloatTensor(embedding_matrix)) self.lstm_context = DynamicLSTM(opt.embed_dim, opt.hidden_dim, num_layer=1, batch_first=True,return_use_tuple=True) self.lstm_aspect = DynamicLSTM(opt.embed_dim, opt.hidden_dim, num_layer=1, batch_first=True, return_use_tuple=True) self.attention_aspect = Attention(opt.hidden_dim, score_function="bi_linear", dropout=0.5) self.attention_context = Attention(opt.hidden_dim, score_function="bi_linear", dropout=0.5) self.dense = nn.Linear(opt.hidden_dim * 2, opt.polarity_dim)
def __init__(self, embedding_matrix, opt):
super(AEGCN, self).__init__()
self.opt = opt
self.embed = nn.Embedding.from_pretrained(
torch.tensor(embedding_matrix, dtype=torch.float))
self.squeeze_embedding = SqueezeEmbedding() #
self.text_lstm = DynamicLSTM(opt.embed_dim,
opt.hidden_dim,
num_layers=1,
batch_first=True,
bidirectional=True)
self.aspect_lstm = DynamicLSTM(opt.embed_dim,
opt.hidden_dim,
num_layers=1,
batch_first=True,
bidirectional=True)
self.attn_k = Attention(opt.embed_dim * 2,
out_dim=opt.hidden_dim,
n_head=opt.head,
score_function='mlp',
dropout=opt.dropout) #
self.attn_a = Attention(opt.embed_dim * 2,
out_dim=opt.hidden_dim,
n_head=opt.head,
score_function='mlp',
dropout=opt.dropout) #
# self.attn_s1 = Attention(opt.embed_dim*2, out_dim=opt.hidden_dim, n_head=3, score_function='mlp', dropout=0.5)
self.attn_q = Attention(opt.embed_dim * 2,
out_dim=opt.hidden_dim,
n_head=opt.head,
score_function='mlp',
dropout=opt.dropout) #
self.gc1 = GraphConvolution(2 * opt.hidden_dim, 2 * opt.hidden_dim,
opt)
self.gc2 = GraphConvolution(2 * opt.hidden_dim, 2 * opt.hidden_dim,
opt)
self.attn_k_q = Attention(opt.hidden_dim,
n_head=opt.head,
score_function='mlp',
dropout=opt.dropout) #
self.attn_k_a = Attention(opt.hidden_dim,
n_head=opt.head,
score_function='mlp',
dropout=opt.dropout)
#self.fc = nn.Linear(2*opt.hidden_dim, opt.polarities_dim)
self.text_embed_dropout = nn.Dropout(opt.dropout)
self.aspect_embed_dropout = nn.Dropout(opt.dropout)
self.dense = nn.Linear(opt.hidden_dim * 3, opt.polarities_dim)
def _self_attention(self, q, k, v, key_masks):
with tf.variable_scope("self-attention"):
attention = Attention(
num_heads=self.num_heads,
masked=False,
linear_key_dim=self.linear_key_dim,
linear_value_dim=self.linear_value_dim,
model_dim=self.model_dim,
max_seq_len=self.max_seq_len,
dropout=self.dropout,
)
#self.att = attention
return attention.multi_head(q, k, v, key_masks)
def __init__(self, bert, opt):
super(SDGCN_NEW, self).__init__()
self.opt = opt
self.bert = bert
self.squeeze_embedding = SqueezeEmbedding()
self.dropout = nn.Dropout(opt.dropout)
self.lstm_context = sLSTM(opt.bert_dim, 768, window_size=2)
self.lstm_aspect = sLSTM(opt.bert_dim, 768, window_size=2)
# self.lstm_context = DynamicLSTM(opt.bert_dim, opt.hidden_dim, num_layers=1, batch_first=True,bidirectional=True)
# self.lstm_aspect = DynamicLSTM(opt.bert_dim, opt.hidden_dim, num_layers=1, batch_first=True,bidirectional=True)
self.attention_aspect = Attention(768, score_function='bi_linear')
self.attention_context = Attention(768, score_function='bi_linear')
self.dense = nn.Linear(768 * 2, opt.polarities_dim)
def __init__(self, word_embedding, char_embedding, opt):
super(LSTM_CNN, self).__init__()
self.opt = opt
self.word_char_embed = WordCharEmbedding(word_embedding,
char_embedding, opt)
self.text_lstm = DynamicLSTM(opt.embed_dim,
opt.hidden_dim,
num_layers=1,
batch_first=True,
bidirectional=True)
self.bi_lstm_aspect = DynamicLSTM(opt.embed_dim,
opt.hidden_dim,
num_layers=1,
batch_first=True,
bidirectional=True)
D = opt.embed_dim
C = opt.polarities_dim
# A = opt.aspect_num
Co = 100
# Ks = [i for i in range(3, 10)]
Ks = [3, 4]
self.convs1 = nn.ModuleList([nn.Conv1d(D, Co, K) for K in Ks])
self.convs2 = nn.ModuleList([nn.Conv1d(D, Co, K) for K in Ks])
self.convs3 = nn.ModuleList(
[nn.Conv1d(D, Co, K, padding=K - 2) for K in [3]])
self.fc_aspect = nn.Linear(100, Co)
self.attention = Attention(opt.hidden_dim * 2, score_function='mlp')
self.l1 = nn.Linear(opt.hidden_dim * 2, Co * len(Ks))
self.dropout = nn.Dropout(opt.dropout)
# self.dense = nn.Linear(self.kernel_num, opt.polarities_dim)
self.gru_cell = nn.GRUCell(opt.hidden_dim * 2, opt.hidden_dim * 2)
self.gru = nn.GRUCell(Co * len(Ks), Co * len(Ks))
self.dense = nn.Linear(Co * len(Ks) * 2, opt.polarities_dim)
self.w1 = nn.Linear(Co * len(Ks), Co * len(Ks))
self.w2 = nn.Linear(Co * len(Ks), Co * len(Ks))
def __init__(self):
super(ATAE_LSTM, self).__init__()
self.uniform_rate = config.uniform_rate
self.lstm = nn.LSTM(2 * config.embed_size,
config.hidden_size,
batch_first=True)
self.text_embed = nn.Embedding.from_pretrained(
config.text_vocab.vectors,
freeze=False if config.if_embed_trainable else True)
self.aspect_embed = nn.Embedding.from_pretrained(
config.aspect_vocab.vectors,
freeze=False if config.if_embed_trainable else True)
self.aspect_mean = AspectMean(config.max_sen_len)
self.attention = Attention()
self.proj1 = nn.Linear(config.hidden_size,
config.hidden_size,
bias=False)
self.proj2 = nn.Linear(config.hidden_size,
config.hidden_size,
bias=False)
self.fc = nn.Linear(config.hidden_size, config.target_size)
self.dropout = nn.Dropout(config.dropout_rate)
self.tanh = nn.Tanh()
self.so1ftmax = nn.Softmax(dim=1)
# reset parameters
self.reset_param()
def __init__(self, embedding_matrix, opt, type='cabasc'):
super(Cabasc, self).__init__()
self.opt = opt
self.embed = nn.Embedding.from_pretrained(
torch.tensor(embedding_matrix, dtype=torch.float))
self.squeeze_embedding = SqueezeEmbedding(batch_first=True)
self.attention = Attention(opt.embed_dim,
score_function='mlp',
dropout=opt.dropout) # content attention
self.m_linear = nn.Linear(opt.embed_dim, opt.embed_dim, bias=False)
self.mlp = nn.Linear(opt.embed_dim, opt.embed_dim) # W4
self.dense = nn.Linear(opt.embed_dim, opt.polarities_dim) # W5
# context attention layer
self.rnn_l = DynamicLSTM(opt.embed_dim,
opt.hidden_dim,
num_layers=1,
batch_first=True,
rnn_type='GRU')
self.rnn_r = DynamicLSTM(opt.embed_dim,
opt.hidden_dim,
num_layers=1,
batch_first=True,
rnn_type='GRU')
self.mlp_l = nn.Linear(opt.hidden_dim, 1)
self.mlp_r = nn.Linear(opt.hidden_dim, 1)
def __init__(self, embedding_matrix, opt):
super(C_LSTM, self).__init__()
self.opt = opt
self.kernel_num = 100
self.kernel_sizes = [3, 4, 5]
self.embed = nn.Embedding.from_pretrained(
torch.FloatTensor(embedding_matrix))
self.context_lstm = DynamicLSTM(opt.embed_dim,
opt.hidden_dim,
num_layers=1,
batch_first=True,
bidirectional=True)
self.bi_lstm_aspect = DynamicLSTM(opt.embed_dim,
opt.hidden_dim,
num_layers=1,
batch_first=True,
bidirectional=True)
self.convs1 = nn.ModuleList([
nn.Conv1d(opt.embed_dim, self.kernel_num, K)
for K in self.kernel_sizes
])
self.attention = Attention(opt.hidden_dim * 2, score_function='mlp')
self.dropout = nn.Dropout(opt.dropout)
self.gru_cell = nn.GRUCell(opt.hidden_dim * 2, opt.hidden_dim * 2)
# self.dense = nn.Linear(self.kernel_num, opt.polarities_dim)
self.dense = nn.Linear(opt.hidden_dim * 2, opt.polarities_dim)
def __init__(self, input_shape):
self.model = Sequential()
[self.model.add(x) for x in Encoder(input_shape).model.layers]
[self.model.add(x) for x in Attention(self.model.layers[-1].output_shape).model.layers]
[self.model.add(x) for x in State(self.model.layers[-1].output_shape).model.layers]
[self.model.add(x) for x in Decoder(self.model.layers[-1].output_shape).model.layers]
self.model.add(Activation('softmax'))
def __init__(self, embedding_matrix, opt):
super(MMRAM, self).__init__()
self.opt = opt
self.embed = nn.Embedding.from_pretrained(
torch.tensor(embedding_matrix, dtype=torch.float))
self.bi_lstm_context = DynamicLSTM(opt.embed_dim,
opt.hidden_dim,
num_layers=1,
batch_first=True,
bidirectional=True,
dropout=opt.dropout_rate)
self.bi_lstm_aspect = DynamicLSTM(opt.embed_dim,
opt.hidden_dim,
num_layers=1,
batch_first=True,
bidirectional=True,
dropout=opt.dropout_rate)
self.attention = Attention(opt.hidden_dim * 2,
score_function='mlp',
dropout=opt.dropout_rate)
self.gru_cell = nn.GRUCell(opt.hidden_dim * 2, opt.hidden_dim * 2)
if self.opt.tfn:
self.vis2text = nn.Linear(2048, opt.hidden_dim * 2)
self.dense = nn.Linear(opt.hidden_dim * opt.hidden_dim * 4,
opt.polarities_dim)
else:
self.vis2text = nn.Linear(2048, opt.hidden_dim)
self.dense = nn.Linear(opt.hidden_dim * 3, opt.polarities_dim)
def build(self):
seq1 = Input(name='seq1', shape=[self.config['seq1_maxlen']])
seq2 = Input(name='seq2', shape=[self.config['seq2_maxlen']])
embedding = Embedding(self.config['vocab_size'],
self.config['embed_size'],
weights=[self.config['embed']],
trainable=self.config['embed_trainable'])
seq1_embed = embedding(seq1)
seq1_embed = Dropout(0.5)(seq1_embed)
seq2_embed = embedding(seq2)
seq2_embed = Dropout(0.5)(seq2_embed)
lstm = Bidirectional(
LSTM(self.config['hidden_size'],
return_sequences=True,
dropout=self.config['dropout_rate']))
seq1_rep_rnn = lstm(seq1_embed)
seq2_rep_rnn = lstm(seq2_embed)
att = Attention(8, 64)
final_rep = att([seq1_rep_rnn, seq2_rep_rnn, seq2_rep_rnn])
final_rep = GlobalAveragePooling1D()(final_rep)
final_rep = Dropout(0.5)(final_rep)
output = Dense(2, activation="softmax")(final_rep)
model = Model(inputs=[seq1, seq2], outputs=output)
return model
def __init__(self, embedding_matrix, opt):
super(Test, self).__init__()
self.opt = opt
self.embed = nn.Embedding.from_pretrained(
torch.tensor(embedding_matrix, dtype=torch.float))
self.squeeze_embedding = SqueezeEmbedding()
self.layer_stack = nn.ModuleList([
EncoderLayer(d_model,
d_inner,
n_head,
d_k,
d_v,
dropout=opt.dropout) for _ in range(n_layers)
])
# self.ffn_c = PositionwiseFeedForward(opt.hidden_dim, dropout=opt.dropout)
# self.ffn_t = PositionwiseFeedForward(opt.hidden_dim, dropout=opt.dropout)
self.attn_s1 = Attention(opt.hidden_dim,
n_head=8,
score_function='mlp',
dropout=opt.dropout)
self.dense = nn.Linear(opt.hidden_dim * 3, opt.polarities_dim)
def __init__(self, embedding_matrix, opt):
super(MemNet, self).__init__()
self.opt = opt
self.embed = nn.Embedding.from_pretrained(torch.tensor(embedding_matrix, dtype=torch.float))
self.squeeze_embedding = SqueezeEmbedding(batch_first=True)
self.attention = Attention(opt.embed_dim, score_function='mlp')
self.x_linear = nn.Linear(opt.embed_dim, opt.embed_dim)
self.dense = nn.Linear(opt.embed_dim, opt.polarities_dim)
def __init__(self, config, opt):
super(MemNet, self).__init__()
self.opt = opt
self.bert = BertModel(config)
self.squeeze_embedding = SqueezeEmbedding(batch_first=True)
self.attention = Attention(opt.embed_dim, score_function='mlp')
self.x_linear = nn.Linear(opt.embed_dim, opt.embed_dim)
self.dense = nn.Linear(opt.embed_dim, opt.output_dim)
def build(self):
H = self.config['hidden_size']
v = SharedWeight(size=(H, 1), name='v')
WQ_u = SharedWeight(size=(2 * H, H), name='WQ_u')
WP_u = SharedWeight(size=(2 * H, H), name='WP_u')
WP_v = SharedWeight(size=(H, H), name='WP_v')
W_g1 = SharedWeight(size=(4 * H, 4 * H), name='W_g1')
W_g2 = SharedWeight(size=(2 * H, 2 * H), name='W_g2')
WP_h = SharedWeight(size=(2 * H, H), name='WP_h')
Wa_h = SharedWeight(size=(2 * H, H), name='Wa_h')
WQ_v = SharedWeight(size=(2 * H, H), name='WQ_v')
WPP_v = SharedWeight(size=(H, H), name='WPP_v')
VQ_r = SharedWeight(size=(H, H), name='VQ_r')
shared_weights = [
v, WQ_u, WP_u, WP_v, W_g1, W_g2, WP_h, Wa_h, WQ_v, WPP_v, VQ_r
]
seq1 = Input(name='seq1', shape=[self.config['seq1_maxlen']])
seq2 = Input(name='seq2', shape=[self.config['seq2_maxlen']])
embedding = Embedding(self.config['vocab_size'],
self.config['embed_size'],
weights=[self.config['embed']],
trainable=self.config['embed_trainable'])
seq1_embed = embedding(seq1)
seq1_embed = Dropout(0.5)(seq1_embed)
seq2_embed = embedding(seq2)
seq2_embed = Dropout(0.5)(seq2_embed)
lstm = Bidirectional(
GRU(self.config['hidden_size'],
return_sequences=True,
dropout=self.config['dropout_rate']))
seq1_rep_rnn = lstm(seq1_embed)
seq2_rep_rnn = lstm(seq2_embed)
vP = QuestionAttnGRU(units=H, return_sequences=True, unroll=False)(
[seq2_rep_rnn, seq1_rep_rnn, WQ_u, WP_v, WP_u, v, W_g1])
hP = Bidirectional(
SelfAttnGRU(units=H, return_sequences=True,
unroll=False))([vP, vP, WP_v, WPP_v, v, W_g2])
gP = Bidirectional(GRU(units=H, return_sequences=True,
unroll=False))(hP)
rQ = QuestionPooling()([seq1_rep_rnn, WQ_u, WQ_v, v, VQ_r])
rQ = Dropout(rate=self.config['dropout_rate'], name='rQ')(rQ)
att = Attention(8, 64)
final_rep = att([seq1_rep_rnn, seq2_rep_rnn, seq2_rep_rnn])
final_rep = GlobalAveragePooling1D()(final_rep)
final_rep = Dropout(0.5)(final_rep)
output = Dense(2, activation="softmax")(final_rep)
model = Model(inputs=[seq1, seq2], outputs=output)
return model
def __init__(self, embedding_matrix, opt):
super(MMFUSION, self).__init__()
self.opt = opt
self.embed = nn.Embedding.from_pretrained(
torch.tensor(embedding_matrix, dtype=torch.float))
self.lstm_aspect = DynamicLSTM(
opt.embed_dim, opt.hidden_dim, num_layers=1,
batch_first=True) #, dropout = opt.dropout_rate
self.lstm_l = DynamicLSTM(
opt.embed_dim, opt.hidden_dim, num_layers=1,
batch_first=True) #, dropout = opt.dropout_rate
self.lstm_r = DynamicLSTM(
opt.embed_dim, opt.hidden_dim, num_layers=1,
batch_first=True) #, dropout = opt.dropout_rate
self.attention_l = Attention(opt.hidden_dim,
score_function='bi_linear',
dropout=opt.dropout_rate)
self.attention_r = Attention(opt.hidden_dim,
score_function='bi_linear',
dropout=opt.dropout_rate)
self.visaspect_att_l = MMAttention(opt.hidden_dim,
score_function='bi_linear',
dropout=opt.dropout_rate)
self.visaspect_att_r = MMAttention(opt.hidden_dim,
score_function='bi_linear',
dropout=opt.dropout_rate)
#self.viscontext_att_aspect = MMAttention(opt.hidden_dim, score_function='mlp', dropout=opt.dropout_rate)
#self.visaspect_att_context = MMAttention(opt.hidden_dim, score_function='mlp', dropout=opt.dropout_rate)
self.aspect2text = nn.Linear(opt.hidden_dim, opt.hidden_dim)
self.vismap2text = nn.Linear(2048, opt.hidden_dim)
self.vis2text = nn.Linear(2048, opt.hidden_dim)
self.gate = nn.Linear(2048 + 3 * opt.hidden_dim, opt.hidden_dim)
self.madality_attetion = nn.Linear(opt.hidden_dim, 1)
#blinear interaction between text vectors and image vectors
self.text2hidden = nn.Linear(opt.hidden_dim * 3, opt.hidden_dim)
self.vis2hidden = nn.Linear(opt.hidden_dim, opt.hidden_dim)
self.hidden2final = nn.Linear(opt.hidden_dim, opt.hidden_dim)
self.dense_2 = nn.Linear(opt.hidden_dim * 2, opt.polarities_dim)
self.dense_3 = nn.Linear(opt.hidden_dim * 3, opt.polarities_dim)
self.dense_4 = nn.Linear(opt.hidden_dim * 4, opt.polarities_dim)
self.dense = nn.Linear(opt.hidden_dim, opt.polarities_dim)
def __init__(self, bert, opt):
super(BERT_SPC, self).__init__()
# self.squeeze_embedding = SqueezeEmbedding()
self.bert = bert
self.dropout = nn.Dropout(opt.dropout)
self.dense = nn.Linear(opt.bert_dim, opt.polarities_dim)
self.att = Attention(opt.bert_dim, score_function='mlp')
self.pool = BertPooler()
self.conv = nn.MaxPool1d(12)