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for cpu and for python3, keyword_predictor.py #17

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for cpu and for python3, keyword_predictor.py #17

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757925a
Update keyword_predictor.py
guotong1988 Oct 29, 2019
458bfbb
Update keyword_predictor.py
guotong1988 Oct 29, 2019
4f175e8
Update keyword_predictor.py
guotong1988 Oct 29, 2019
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19 changes: 13 additions & 6 deletions models/keyword_predictor.py
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Original file line number Diff line number Diff line change
Expand Up @@ -16,15 +16,15 @@ def __init__(self, N_word, N_h, N_depth, gpu, use_hs):
self.gpu = gpu
self.use_hs = use_hs

self.q_lstm = nn.LSTM(input_size=N_word, hidden_size=N_h/2,
self.q_lstm = nn.LSTM(input_size=N_word, hidden_size=int(N_h/2),
num_layers=N_depth, batch_first=True,
dropout=0.3, bidirectional=True)

self.hs_lstm = nn.LSTM(input_size=N_word, hidden_size=N_h/2,
self.hs_lstm = nn.LSTM(input_size=N_word, hidden_size=int(N_h/2),
num_layers=N_depth, batch_first=True,
dropout=0.3, bidirectional=True)

self.kw_lstm = nn.LSTM(input_size=N_word, hidden_size=N_h/2,
self.kw_lstm = nn.LSTM(input_size=N_word, hidden_size=int(N_h/2),
num_layers=N_depth, batch_first=True,
dropout=0.3, bidirectional=True)

Expand All @@ -41,7 +41,7 @@ def __init__(self, N_word, N_h, N_depth, gpu, use_hs):
self.kw_out_kw = nn.Linear(N_h, N_h)
self.kw_out = nn.Sequential(nn.Tanh(), nn.Linear(N_h, 1))

self.softmax = nn.Softmax() #dim=1
self.softmax = nn.Softmax(dim=1) #dim=1
self.CE = nn.CrossEntropyLoss()
self.log_softmax = nn.LogSoftmax()
self.mlsml = nn.MultiLabelSoftMarginLoss()
Expand Down Expand Up @@ -110,15 +110,22 @@ def loss(self, score, truth):
#loss for the key word number
truth_num = [len(t) for t in truth] # double check to exclude select
data = torch.from_numpy(np.array(truth_num))
truth_num_var = Variable(data.cuda())
data = torch._cast_Long(data)
if self.gpu:
truth_num_var = Variable(data.cuda())
else:
truth_num_var = Variable(data)
loss += self.CE(kw_num_score, truth_num_var)
#loss for the key words
T = len(kw_score[0])
truth_prob = np.zeros((B, T), dtype=np.float32)
for b in range(B):
truth_prob[b][truth[b]] = 1
data = torch.from_numpy(truth_prob)
truth_var = Variable(data.cuda())
if self.gpu:
truth_var = Variable(data.cuda())
else:
truth_var = Variable(data)
#loss += self.mlsml(kw_score, truth_var)
#loss += self.bce_logit(kw_score, truth_var) # double check no sigmoid for kw
pred_prob = self.sigm(kw_score)
Expand Down