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Bidirectional Attention #24

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Bidirectional Attention #24

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cdf8400
Update sqlnet_condition_predict.py
guotong1988 May 2, 2018
fbe2222
Update sqlnet_condition_predict.py
guotong1988 May 2, 2018
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25 changes: 20 additions & 5 deletions sqlnet/model/modules/sqlnet_condition_predict.py
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Original file line number Diff line number Diff line change
Expand Up @@ -40,7 +40,7 @@ def __init__(self, N_word, N_h, N_depth, max_col_num, max_tok_num, use_ca, gpu):
self.cond_col_name_enc = nn.LSTM(input_size=N_word, hidden_size=N_h/2,
num_layers=N_depth, batch_first=True,
dropout=0.3, bidirectional=True)
self.cond_col_out_K = nn.Linear(N_h, N_h)
self.cond_col_out_K = nn.Linear(N_h*2, N_h)
self.cond_col_out_col = nn.Linear(N_h, N_h)
self.cond_col_out = nn.Sequential(nn.ReLU(), nn.Linear(N_h, 1))

Expand All @@ -51,7 +51,7 @@ def __init__(self, N_word, N_h, N_depth, max_col_num, max_tok_num, use_ca, gpu):
self.cond_op_att = nn.Linear(N_h, N_h)
else:
self.cond_op_att = nn.Linear(N_h, 1)
self.cond_op_out_K = nn.Linear(N_h, N_h)
self.cond_op_out_K = nn.Linear(N_h*2, N_h)
self.cond_op_name_enc = nn.LSTM(input_size=N_word, hidden_size=N_h/2,
num_layers=N_depth, batch_first=True,
dropout=0.3, bidirectional=True)
Expand Down Expand Up @@ -155,6 +155,12 @@ def forward(self, x_emb_var, x_len, col_inp_var, col_name_len,
col_att = self.softmax(col_att_val.view(
(-1, max_x_len))).view(B, -1, max_x_len)
K_cond_col = (h_col_enc.unsqueeze(1) * col_att.unsqueeze(3)).sum(2)

# bi-attention
temp, _ = torch.max(col_att_val, dim=1)
temp_probs = self.softmax(temp).unsqueeze(2)
temp2 = (temp_probs*h_col_enc).sum(1)
temp2 = temp2.unsqueeze(1).expand([e_cond_col.size()[0],e_cond_col.size()[1],e_cond_col.size()[2]])
else:
col_att_val = self.cond_col_att(h_col_enc).squeeze()
for idx, num in enumerate(x_len):
Expand All @@ -164,7 +170,8 @@ def forward(self, x_emb_var, x_len, col_inp_var, col_name_len,
K_cond_col = (h_col_enc *
col_att_val.unsqueeze(2)).sum(1).unsqueeze(1)

cond_col_score = self.cond_col_out(self.cond_col_out_K(K_cond_col) +
cond_col_score = self.cond_col_out(self.cond_col_out_K(
torch.cat([K_cond_col,temp2*e_cond_col],dim=-1)) +
self.cond_col_out_col(e_cond_col)).squeeze()
max_col_num = max(col_num)
for b, num in enumerate(col_num):
Expand Down Expand Up @@ -201,6 +208,13 @@ def forward(self, x_emb_var, x_len, col_inp_var, col_name_len,
op_att_val[idx, :, num:] = -100
op_att = self.softmax(op_att_val.view(B*4, -1)).view(B, 4, -1)
K_cond_op = (h_op_enc.unsqueeze(1) * op_att.unsqueeze(3)).sum(2)

# bi-attention
temp, _ = torch.max(op_att_val, dim=1)
temp_probs = self.softmax(temp).unsqueeze(2)
temp2 = (temp_probs * h_op_enc).sum(1)
temp2 = temp2.unsqueeze(1).expand([col_emb.size()[0], col_emb.size()[1], col_emb.size()[2]])

else:
op_att_val = self.cond_op_att(h_op_enc).squeeze()
for idx, num in enumerate(x_len):
Expand All @@ -209,8 +223,9 @@ def forward(self, x_emb_var, x_len, col_inp_var, col_name_len,
op_att = self.softmax(op_att_val)
K_cond_op = (h_op_enc * op_att.unsqueeze(2)).sum(1).unsqueeze(1)

cond_op_score = self.cond_op_out(self.cond_op_out_K(K_cond_op) +
self.cond_op_out_col(col_emb)).squeeze()
cond_op_score = self.cond_op_out(self.cond_op_out_K(
torch.cat([K_cond_op,temp2*col_emb],dim=-1)
) + self.cond_op_out_col(col_emb)).squeeze()

#Predict the string of conditions
h_str_enc, _ = run_lstm(self.cond_str_lstm, x_emb_var, x_len)
Expand Down