-
Notifications
You must be signed in to change notification settings - Fork 0
/
nn_v1.py
699 lines (546 loc) · 22.7 KB
/
nn_v1.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
442
443
444
445
446
447
448
449
450
451
452
453
454
455
456
457
458
459
460
461
462
463
464
465
466
467
468
469
470
471
472
473
474
475
476
477
478
479
480
481
482
483
484
485
486
487
488
489
490
491
492
493
494
495
496
497
498
499
500
501
502
503
504
505
506
507
508
509
510
511
512
513
514
515
516
517
518
519
520
521
522
523
524
525
526
527
528
529
530
531
532
533
534
535
536
537
538
539
540
541
542
543
544
545
546
547
548
549
550
551
552
553
554
555
556
557
558
559
560
561
562
563
564
565
566
567
568
569
570
571
572
573
574
575
576
577
578
579
580
581
582
583
584
585
586
587
588
589
590
591
592
593
594
595
596
597
598
599
600
601
602
603
604
605
606
607
608
609
610
611
612
613
614
615
616
617
618
619
620
621
622
623
624
625
626
627
628
629
630
631
632
633
634
635
636
637
638
639
640
641
642
643
644
645
646
647
648
649
650
651
652
653
654
655
656
657
658
659
660
661
662
663
664
665
666
667
668
669
670
671
672
673
674
675
676
677
678
679
680
681
682
683
684
685
686
687
688
689
690
691
692
693
694
695
696
697
698
699
import dynet as dy
import numpy as np
from dy_utils import ParamManager as pm
# dy.renew_cg(immediate_compute = True, check_validity = True)
def sigmoid(x):
return dy.logistic(x)
def tanh(x):
return dy.tanh(x)
def penalized_tanh(x):
alpha = 0.25
tanh_x = dy.tanh(x)
return dy.bmax(tanh_x, alpha*tanh_x)
def tanh_list(rep_list):
return [dy.tanh(x) for x in rep_list]
def relu(x):
return dy.rectify(x)
def relu_list(rep_list):
return [dy.rectify(x) for x in rep_list]
def cube(x):
return dy.cube(x)
def selu(x):
return dy.selu(x)
def elu(x, alpha=1.0):
return dy.elu(x)
def log_sigmoid(x):
return dy.log_sigmoid(x)
def softmax(x, dim=0):
# warning: dynet only implement 2d and 1d softmax
if dim == -1:
dim = len(x.dim()[0]) - 1
return dy.softmax(x, d=dim)
def log_softmax(x):
# warning: dynet only implement 2d and 1d log_softmax
return dy.log_softmax(x)
def dot_transpose(x, y):
return dy.dot_product(x, y)
def tranpose(x):
return dy.transpose(x)
class Linear(object):
def __init__(self, n_in, n_out, bias=True, activation='linear', model=None, init_w=None):
if model is None:
model = pm.global_collection()
if init_w is not None:
self.W = model.parameters_from_numpy(init_w)
else:
self.W = model.add_parameters((n_out, n_in), init='glorot', name='linearW')
self.bias = bias
self.act = activation
if bias:
self.b = model.add_parameters((n_out), init=0, name='linearBias')
def __call__(self, input):
if isinstance(input, list):
return [self._compute(x) for x in input]
else:
return self._compute(input)
def _compute(self, input):
if not self.bias:
output = self.W * input
else:
output = dy.affine_transform([self.b, self.W, input])
if self.act == 'linear':
return output
elif self.act == 'sigmoid':
return sigmoid(output)
elif self.act == 'tanh':
return tanh(output)
elif self.act == 'ptanh':
return penalized_tanh(output)
elif self.act == 'relu':
return relu(output)
elif self.act == 'elu':
return elu(output)
elif self.act == 'softmax':
return softmax(output)
raise ValueError('Unknown activation function :'+self.act)
import ops
class ActionGenerator(object):
def __init__(self, n_in, n_out, bias=True, activation='linear', model=None, init_w=None):
if model is None:
model = pm.global_collection()
if init_w is not None:
self.W = model.parameters_from_numpy(init_w)
else:
self.W = model.add_parameters((n_out, n_in), init='glorot', name='linearW')
# self.W_1 = model.add_parameters((n_out, n_in), init='glorot')
self.bias = bias
self.act = activation
if bias:
self.b = model.add_parameters((n_out), init=0, name='linearBias')
self.attn_hidden = Linear(n_in+n_out, 50, activation='tanh')
self.attn_out = Linear(50, 1)
self.empty_embedding = model.add_parameters((n_out,), name='stackGuardEmb')
def __call__(self, input, arg_prd_distributions_prd=None):
if isinstance(input, list):
return [self._compute(x) for x in input]
else:
return self._compute(input)
# else:
# if len(arg_prd_distributions_prd) > 1:
# rep = ops.cat([self.arg_prd_distributions_prd_attn(input, arg_prd_distributions_prd), input], 0)
# else:
# rep = ops.cat([self.empty_embedding, input], 0)
# return self._compute(rep)
def arg_prd_distributions_prd_attn(self, inputs, arg_prd_distributions_prd):
inputs_ = [inputs for _ in range(len(arg_prd_distributions_prd))]
# (action_len + inputs_dim, his_pair_len)
# arg_prd_distributions_prd = [self._compute(x) for x in arg_prd_distributions_prd]
arg_prd_distributions_prd = ops.cat(arg_prd_distributions_prd, 1)
inputs_ = ops.cat(inputs_, 1)
att_input = dy.concatenate([arg_prd_distributions_prd, inputs_], 0)
# print("att_input", att_input.npvalue().shape) # (300, 9)
# (50, his_pair_len)
hidden = self.attn_hidden(att_input)
# print(hidden.npvalue().shape)
# (1, his_pair_len)
attn_out = self.attn_out(hidden)
# print(attn_out.npvalue().shape)
attn_prob = softmax(attn_out, dim=1)
# (action_len, his_pair_len) *(his_pair_len, 1) -> (action_len, 1)
rep = arg_prd_distributions_prd * dy.transpose(attn_prob)
return rep
def _compute(self, input):
if not self.bias:
output = self.W * input
else:
output = dy.affine_transform([self.b, self.W, input])
if self.act == 'linear':
return output
elif self.act == 'sigmoid':
return sigmoid(output)
elif self.act == 'tanh':
return tanh(output)
elif self.act == 'ptanh':
return penalized_tanh(output)
elif self.act == 'relu':
return relu(output)
elif self.act == 'elu':
return elu(output)
raise ValueError('Unknown activation function :'+self.act)
class Embedding(object):
def __init__(self, n_vocab, n_dim, init_weight=None, trainable=True, model=None, name='embed'):
if model is None:
model = pm.global_collection()
self.trainable = trainable
if init_weight is not None:
self.embed = model.lookup_parameters_from_numpy(init_weight, name=name)
else:
self.embed = model.add_lookup_parameters((n_vocab, n_dim), name=name)
def __call__(self, input):
output = [dy.lookup(self.embed, x, update=self.trainable) for x in input]
# output = dy.lookup_batch(self.embed, input, update=self.trainable)
return output
def __getitem__(self, item):
return dy.lookup(self.embed, item, update=self.trainable)
from TreeUtils import *
class TreeLSTMEncoder(object):
""" The standard RNN encoder.
"""
def __init__(self, input_dim, h_dim, dropout=0.1):
self.treeLSTM = ChiSumTreeLSTM(input_dim, h_dim)
def __call__(self, input, heads, lengths=None, hidden=None):
# trees=None
root, tree = creatTree(heads)
self.treeLSTM.expr_for_tree(input, root, decorate=True, training=True)
state = []
for i in range(len(tree)):
assert tree[i].index == i, 'tree[i].index != i'
state.append(tree[i]._e)
# node._e for node in tree]
return state
class ChiSumTreeLSTM(object):
def __init__(self, input_dim, h_dim, model=None):
if model is None:
model = pm.global_collection()
self.WS = [model.add_parameters((h_dim, input_dim)) for _ in "iou"]
self.US = [model.add_parameters((h_dim, h_dim)) for _ in "iou"]
self.UFS = [model.add_parameters((h_dim, h_dim)) for _ in "ff"]
self.BS = [model.add_parameters(h_dim) for _ in "iouf"]
def expr_for_tree(self, input_rep, tree_node, decorate=False, training=True):
if tree_node.isleaf():
emb = input_rep[tree_node.index]
Wi, Wo, Wu = [dy.parameter(w) for w in self.WS]
bi, bo, bu, _ = [dy.parameter(b) for b in self.BS]
i = dy.logistic(dy.affine_transform([bi, Wi, emb]))
o = dy.logistic(dy.affine_transform([bo, Wo, emb]))
u = dy.tanh(dy.affine_transform([bu, Wu, emb]))
c = dy.cmult(i, u)
h = dy.cmult(o, dy.tanh(c))
if decorate: tree_node._e = h
return h, c
es, cs = [], []
for node in tree_node.left_children + tree_node.right_children:
e_, c_ = self.expr_for_tree(input_rep, node, decorate)
es.append(e_)
cs.append(c_)
es_ = dy.average(es) * len(es)
Ui, Uo, Uu = [dy.parameter(u) for u in self.US]
Uf1, Uf2 = [dy.parameter(u) for u in self.UFS]
bi, bo, bu, bf = [dy.parameter(b) for b in self.BS]
i = dy.logistic(dy.affine_transform([bi, Ui, es_]))
o = dy.logistic(dy.affine_transform([bo, Uo, es_]))
u = dy.tanh(dy.affine_transform([bu, Uu, es_]))
c = dy.cmult(i, u)
for idx in range(len(es)):
f_ = dy.logistic(dy.affine_transform([bf, Uf1, es[idx]]))
f_ = dy.cmult(f_, cs[idx])
c += f_
h = dy.cmult(o, dy.tanh(c))
if decorate:
tree_node._e = h
return h, c
class NarayTreeLSTM(object):
'''
for binary tree
'''
def __init__(self, input_dim, h_dim, model=None):
if model is None:
model = pm.global_collection()
self.WS = [model.add_parameters((h_dim, input_dim)) for _ in "iou"]
self.US = [model.add_parameters((h_dim, 2 * h_dim)) for _ in "iou"]
self.UFS = [model.add_parameters((h_dim, 2 * h_dim)) for _ in "ff"]
self.BS = [model.add_parameters(h_dim) for _ in "iouf"]
def expr_for_tree(self, input_rep, tree_node, decorate=False, training=True):
# if tree_node.isleaf(): raise RuntimeError('Tree structure error: meet with leaves')
# if len(tree_node.children) == 1:
if tree_node.isleaf():
# if not tree_node.children[0].isleaf():
# raise RuntimeError(
# 'Tree structure error: tree nodes with one child should be a leaf')
emb = input_rep[tree_node.index] #self.E[self.w2i.get(tree.children[0].label, 0)]
Wi, Wo, Wu = [dy.parameter(w) for w in self.WS]
bi, bo, bu, _ = [dy.parameter(b) for b in self.BS]
i = dy.logistic(dy.affine_transform([bi, Wi, emb]))
o = dy.logistic(dy.affine_transform([bo, Wo, emb]))
u = dy.tanh(dy.affine_transform([bu, Wu, emb]))
c = dy.cmult(i, u)
h = dy.cmult(o, dy.tanh(c))
if decorate: tree_node._e = h
return h, c
# if len(tree_node.children) != 2:
# raise RuntimeError('Tree structure error: only binary trees are supported.')
e1, c1 = self.expr_for_tree(input_rep, tree_node.left_children[0], decorate)
for node in tree_node.left_children[1:]:
e1_, c1_ = self.expr_for_tree(input_rep, node, decorate)
e1 += e1_
c1 += c1_
e2, c2 = self.expr_for_tree(input_rep, tree_node.right_children[0], decorate)
for node in tree_node.right_children[1:]:
e2_, c2_ = self.expr_for_tree(input_rep, node, decorate)
e2 += e2_
c2 += c2_
# e2, c2 = self.expr_for_tree(input_rep, tree_node.children[1], decorate)
Ui, Uo, Uu = [dy.parameter(u) for u in self.US]
Uf1, Uf2 = [dy.parameter(u) for u in self.UFS]
bi, bo, bu, bf = [dy.parameter(b) for b in self.BS]
e = dy.concatenate([e1, e2])
i = dy.logistic(dy.affine_transform([bi, Ui, e]))
o = dy.logistic(dy.affine_transform([bo, Uo, e]))
f1 = dy.logistic(dy.affine_transform([bf, Uf1, e]))
f2 = dy.logistic(dy.affine_transform([bf, Uf2, e]))
u = dy.tanh(dy.affine_transform([bu, Uu, e]))
c = dy.cmult(i, u) + dy.cmult(f1, c1) + dy.cmult(f2, c2)
h = dy.cmult(o, dy.tanh(c))
if decorate: tree_node._e = h
return h, c
class MultiLayerLSTM(object):
def __init__(self, n_in, n_hidden, n_layer=1, bidirectional=False, lstm_params=None, model=None, dropout_x=0., dropout_h=0.):
if model is None:
model = pm.global_collection()
self.bidirectional = bidirectional
self.n_layer = n_layer
rnn_builder_factory = LSTMCell
self.fwd_builders = [rnn_builder_factory(model, n_in, n_hidden, dropout_x, dropout_h)]
if bidirectional:
self.bwd_builders = [rnn_builder_factory(model, n_in, n_hidden, dropout_x, dropout_h)]
hidden_input_dim = n_hidden * 2 if bidirectional else n_hidden
for _ in range(n_layer - 1):
self.fwd_builders.append(rnn_builder_factory(model,hidden_input_dim, n_hidden))
if bidirectional:
self.bwd_builders.append(rnn_builder_factory(model, hidden_input_dim, n_hidden))
if lstm_params is not None:
self._init_param(lstm_params)
def get_cells(self):
return self.fwd_builders + self.bwd_builders
def init_sequence(self, test=False):
for fwd in self.fwd_builders:
fwd.init_sequence(test)
if self.bidirectional:
for bwd in self.bwd_builders:
bwd.init_sequence(test)
def _init_param(self, lstm_params):
'''
:param lstm_params: (forward_param_list, backward_param_list),
forward_param_list: [[layer1], [layer2], ...]
layer1: (Wx, Wh, bais)
:return:
'''
def _set_param_value(builder, Wx, Wh, bais):
model_Wx, model_Wh, model_bias = builder.get_parameters()[0]
model_Wx.set_value(Wx)
model_Wh.set_value(Wh)
bais = np.zeros_like(bais)
model_bias.set_value(bais)
if self.bidirectional:
lstm_params_fw, lstm_params_bw = lstm_params
for i in range(self.n_layer):
fwd_params, bwd_params = lstm_params_fw[i], lstm_params_bw[i]
builder_fw = self.fwd_builders[i]
builder_bw = self.bwd_builders[i]
builder_fw.init_params(fwd_params[0], fwd_params[1], fwd_params[2])
builder_bw.init_params(bwd_params[0], bwd_params[1], bwd_params[2])
else:
lstm_params_fw = lstm_params
for i in range(self.n_layer):
fwd_params = lstm_params_fw[i]
builder_fw = self.fwd_builders[i]
builder_fw.init_params(fwd_params[0], fwd_params[1], fwd_params[2])
def transduce(self, input, hx=None, cx=None):
layer_rep = input
if self.bidirectional:
fs, bs = None, None
for fw, bw in zip(self.fwd_builders, self.bwd_builders):
fs, _ = fw.transduce(layer_rep, hx, cx)
bs, _ = bw.transduce(reversed(layer_rep))
layer_rep = [dy.concatenate([f, b]) for f, b in zip(fs, reversed(bs))]
return layer_rep, (fs, bs)
else:
for fw in self.fwd_builders:
layer_rep, _ = fw.transduce(layer_rep, hx, cx)
return layer_rep
def last_step(self, input, hx=None, cx=None, separated_fw_bw=False):
layer_rep = input
if self.bidirectional:
for fw, bw in zip(self.fwd_builders[:-1], self.bwd_builders[:-1]):
fs, _ = fw.transduce(layer_rep, hx, cx)
bs, _ = bw.transduce(reversed(layer_rep))
layer_rep = [dy.concatenate([f, b]) for f, b in zip(fs, reversed(bs))]
fw, bw = self.fwd_builders[-1], self.bwd_builders[-1]
fs, fc = fw.transduce(layer_rep, hx, cx)
bs, bc = bw.transduce(reversed(layer_rep))
layer_rep = [dy.concatenate([f, b]) for f, b in zip(fs, reversed(bs))]
last_rep = dy.concatenate([fs[-1], bs[-1]])
last_c = dy.concatenate([fc[-1],bc[-1]])
if not separated_fw_bw:
return layer_rep, (last_rep, last_c)
else:
return (fw, bw), (last_rep, last_c)
else:
for fw in self.fwd_builders:
layer_rep, _ = fw.transduce(layer_rep, hx, cx)
return layer_rep, (hx, cx)
class LSTMCell:
def __init__(self, model, n_in, n_hidden, dropout_x=0., dropout_h=0.):
self.n_in = n_in
self.n_hidden = n_hidden
self.dropout_x = dropout_x
self.dropout_h = dropout_h
self.weight_ih = model.add_parameters((n_hidden * 4, n_in), name='lstmIH')
self.weight_hh = model.add_parameters((n_hidden * 4, n_hidden), name='lstmHH')
self.bias = model.add_parameters((n_hidden * 4), init=0, name='lstmBias')
def init_params(self, weight_ih=None, weight_hh=None, bias=None):
if weight_ih is not None:
self.weight_ih.set_value(weight_ih)
if weight_hh is not None:
self.weight_hh.set_value(weight_hh)
if bias is not None:
self.bias.set_value(bias)
def transduce(self, input, hx=None, cx=None):
hx = hx if hx is not None else dy.zeros((self.n_hidden))
cx = cx if cx is not None else dy.zeros((self.n_hidden))
output = []
cells = []
for x in input:
hx, cx = self.step(x, hx, cx)
output.append(hx)
cells.append(cx)
return output, cells
def init_sequence(self, test=False):
self.test = test
if not test:
self.dropout_mask_x = dy.dropout(
dy.ones((self.n_in,)), self.dropout_x
)
self.dropout_mask_h = dy.dropout(
dy.ones((self.n_hidden,)), self.dropout_h
)
def step(self, x, hx, cx):
if not self.test:
if self.dropout_x > 0:
x = dy.cmult(self.dropout_mask_x, x)
if self.dropout_h > 0:
hx = dy.cmult(self.dropout_mask_h, hx)
gates = dy.affine_transform([self.bias, self.weight_ih, x, self.weight_hh, hx])
i = dy.pickrange(gates, 0, self.n_hidden)
f = dy.pickrange(gates, self.n_hidden, self.n_hidden * 2)
g = dy.pickrange(gates, self.n_hidden * 2, self.n_hidden * 3)
o = dy.pickrange(gates, self.n_hidden * 3, self.n_hidden * 4)
i, f, g, o = dy.logistic(i), dy.logistic(f), dy.tanh(g), dy.logistic(o)
cy = dy.cmult(f, cx) + dy.cmult(i, g)
hy = dy.cmult(o, dy.tanh(cy))
return hy, cy
class LayerNorm:
def __init__(self, n_hid, model=None):
if model is None:
model = pm.global_collection()
self.p_g = model.add_parameters(dim=n_hid, init=dy.ConstInitializer(1.0))
self.p_b = model.add_parameters(dim=n_hid, init=dy.ConstInitializer(0.0))
def transform(self, x):
g = self.p_g
b = self.p_b
return dy.layer_norm(x, g, b)
def __call__(self, input):
if isinstance(input, list):
return [self.transform(x) for x in input]
else:
return self.transform(input)
class StackLSTM(object):
def __init__(self, input_size, hidden_size, dropout_x=0., dropout_h=0.):
super(StackLSTM, self).__init__()
self.hidden_size = hidden_size
model = pm.global_collection()
self.cell = LSTMCell(model, input_size, hidden_size, dropout_x, dropout_h)
self.empty_embedding = model.add_parameters((self.hidden_size,), name='stackGuardEmb')
self.states = []
self.indices = []
def init_sequence(self, test=False):
self.cell.init_sequence(test)
def get_reverse_hx(self):
rev_hx = []
for i in range(len(self.states) - 1, -1, -1):
rev_hx.append(self.states[i][0])
return rev_hx
def iter(self):
for (hx, cx), idx in zip(self.states, self.indices):
yield hx, idx
def push(self, input, idx):
'''
:param input:
:param idx: word idx in buffer or action_id in vocab
:return:
'''
if len(self.states) == 0:
init_h, init_c = dy.zeros((self.hidden_size)), dy.zeros((self.hidden_size))
hx, cx = self.cell.step(input, init_h, init_c)
else:
pre_hx, pre_cx = self.states[-1]
hx, cx = self.cell.step(input, pre_hx, pre_cx)
self.states.append((hx, cx))
self.indices.append(idx)
def pop(self):
if len(self.states) == 0:
raise RuntimeError('Empty states')
hx, cx = self.states.pop()
idx = self.indices.pop()
return hx, idx
def last_state(self):
return self.states[-1][0], self.indices[-1]
def all_h(self):
return [s[0] for s in self.states]
def clear(self):
self.states.clear()
self.indices.clear()
def embedding(self):
if len(self.states) == 0:
hx = self.empty_embedding
else:
hx, cx = self.states[-1]
return hx
def is_empty(self):
return len(self.states) == 0
def idx_range(self):
return self.indices[0], self.indices[-1]
def last_idx(self):
return self.indices[-1]
def __getitem__(self, item):
hx, cx = self.states[item]
idx = self.indices[item]
return hx, idx
def __len__(self):
return len(self.states)
def __str__(self):
return str(len(self.states)) + ':' + str(len(self.indices))
class Buffer(object):
def __init__(self, bi_rnn_dim, hidden_state_list):
'''
:param state_tensor: list of (n_dim)
'''
self.hidden_states = hidden_state_list
self.seq_len = len(hidden_state_list)
self.idx = 0
def pop(self):
if self.idx == self.seq_len:
raise RuntimeError('Empty buffer')
hx = self.hidden_states[self.idx]
cur_idx = self.idx
self.idx += 1
return hx, cur_idx
def last_state(self):
return self.hidden_states[self.idx], self.idx
def buffer_idx(self):
return self.idx
def hidden_embedding(self):
return self.hidden_states[self.idx]
def hidden_idx_embedding(self, idx):
return self.hidden_states[idx]
def is_empty(self):
return (self.seq_len - self.idx) == 0
def move_pointer(self, idx):
self.idx = idx
def move_back(self):
self.idx -= 1
def __len__(self):
return self.seq_len - self.idx
class LambdaVar(object):
# PRD = 'prd'
# # ENTITY = 'e'
OTHERS = 'other'
OPINION = 'opinion'
ASPECT = 'aspect'
def __init__(self, bi_rnn_dim):
self.var = None
self.idx = -1
self.model = pm.global_collection()
self.bi_rnn_dim = bi_rnn_dim
self.lmda_empty_embedding = self.model.add_parameters((bi_rnn_dim,))
self.lambda_type = LambdaVar.OTHERS
def push(self, embedding, idx, lambda_type):
self.var = embedding
self.idx = idx
self.lambda_type = lambda_type
def pop(self):
var, idx = self.var, self.idx
self.var, self.idx = None, -1
self.lambda_type = LambdaVar.OTHERS
return var, idx
def clear(self):
self.var, self.idx = None, -1
def is_empty(self):
return self.var is None
def is_aspect(self):
return self.lambda_type == LambdaVar.ASPECT
def is_opinion(self):
return self.lambda_type == LambdaVar.OPINION
def embedding(self):
# return dy.zeros(self.bi_rnn_dim) if self.var is None else self.var
return self.lmda_empty_embedding if self.var is None else self.var