def get_multi_encode_softmax_weighting_model(rnn_dim, multi_rnn_dim, num_encodings, keep_rate=0.8, map_embed=True):
recurrent_layer = CudnnGru(rnn_dim, w_init=TruncatedNormal(stddev=0.05))
multi_recurrent_layer = CudnnGru(multi_rnn_dim, w_init=TruncatedNormal(stddev=0.05))
answer_encoder = BinaryAnswerEncoder()
return SingleContextMultipleEncodingWeightedSoftmaxModel(
encoder=QuestionsAndParagraphsEncoder(answer_encoder),
word_embed=FixedWordEmbedder(vec_name="glove.840B.300d", word_vec_init_scale=0, learn_unk=False, cpu=True),
char_embed=CharWordEmbedder(
LearnedCharEmbedder(word_size_th=14, char_th=50, char_dim=20, init_scale=0.05, force_cpu=True),
MaxPool(Conv1d(100, 5, 0.8)),
shared_parameters=True
),
embed_mapper=SequenceMapperSeq(
VariationalDropoutLayer(keep_rate),
recurrent_layer,
) if map_embed else None,
sequence_multi_encoder=MultiMapThenEncode(
mapper=SequenceMapperSeq(VariationalDropoutLayer(keep_rate), multi_recurrent_layer),
encoder=MaxPool(map_layer=None, min_val=0, regular_reshape=True),
num_encodings=num_encodings
),
weight_layer=MultiEncodingWeights(weight_mode='mlp'),
merger=ConcatWithProduct(),
post_merger=None,
predictor=BinaryWeightedMultipleFixedPredictor()
)
def get_context_with_bottleneck_to_question_model(rnn_dim: int, q2c: bool, res_rnn: bool, res_self_att: bool):
recurrent_layer = CudnnGru(rnn_dim, w_init=TruncatedNormal(stddev=0.05))
answer_encoder = BinaryAnswerEncoder()
res_model = get_res_fc_seq_fc(model_rnn_dim=rnn_dim, rnn=res_rnn, self_att=res_self_att)
question_to_context = \
AttentionWithPostMapper(BiAttention(TriLinear(bias=True), True), post_mapper=res_model) if q2c else None
context_to_question = AttentionWithPostMapper(BiAttention(TriLinear(bias=True), True), post_mapper=res_model)
return SingleContextWithBottleneckToQuestionModel(
encoder=QuestionsAndParagraphsEncoder(answer_encoder),
word_embed=FixedWordEmbedder(vec_name="glove.840B.300d", word_vec_init_scale=0, learn_unk=False, cpu=True),
char_embed=CharWordEmbedder(
LearnedCharEmbedder(word_size_th=14, char_th=50, char_dim=20, init_scale=0.05, force_cpu=True),
MaxPool(Conv1d(100, 5, 0.8)),
shared_parameters=True
),
embed_mapper=SequenceMapperSeq(
VariationalDropoutLayer(0.8),
recurrent_layer,
VariationalDropoutLayer(0.8),
),
question_to_context_attention=question_to_context,
context_to_question_attention=context_to_question,
sequence_encoder=CudnnGruEncoder(rnn_dim, w_init=TruncatedNormal(stddev=0.05)),
# MaxPool(map_layer=None, min_val=0, regular_reshape=True),
rep_merge=ConcatLayer(),
predictor=BinaryFixedPredictor()
)
def get_multi_hop_model(rnn_dim, c2c: bool, q2c: bool, res_rnn: bool,
res_self_att: bool, post_merge: bool, encoder: str,
merge_type: str, num_c2c_hops: int):
recurrent_layer = CudnnGru(rnn_dim, w_init=TruncatedNormal(stddev=0.05))
answer_encoder = BinaryAnswerEncoder()
res_model = get_res_fc_seq_fc(model_rnn_dim=rnn_dim,
rnn=res_rnn,
self_att=res_self_att)
context_to_context = \
AttentionWithPostMapper(BiAttention(TriLinear(bias=True), True), post_mapper=res_model) if c2c else None
question_to_context = \
AttentionWithPostMapper(BiAttention(TriLinear(bias=True), True), post_mapper=res_model) if q2c else None
if encoder == 'max':
sequence_encoder = MaxPool(map_layer=None,
min_val=0,
regular_reshape=True)
elif encoder == 'rnn':
sequence_encoder = CudnnGruEncoder(rnn_dim,
w_init=TruncatedNormal(stddev=0.05))
else:
raise NotImplementedError()
if merge_type == 'max':
attention_merger = MaxMerge(
pre_map_layer=None,
post_map_layer=(res_model if post_merge else None))
else:
attention_merger = WeightedMerge(
pre_map_layer=None,
post_map_layer=(res_model if post_merge else None),
weight_type=merge_type)
return MultiHopContextsToQuestionModel(
encoder=QuestionsAndParagraphsEncoder(answer_encoder),
word_embed=FixedWordEmbedder(vec_name="glove.840B.300d",
word_vec_init_scale=0,
learn_unk=False,
cpu=True),
char_embed=CharWordEmbedder(LearnedCharEmbedder(word_size_th=14,
char_th=50,
char_dim=20,
init_scale=0.05,
force_cpu=True),
MaxPool(Conv1d(100, 5, 0.8)),
shared_parameters=True),
embed_mapper=SequenceMapperSeq(
VariationalDropoutLayer(0.8),
recurrent_layer,
VariationalDropoutLayer(0.8),
),
question_to_context_attention=question_to_context,
context_to_context_attention=context_to_context,
c2c_hops=num_c2c_hops,
context_to_question_attention=BiAttention(TriLinear(bias=True), True),
attention_merger=attention_merger,
sequence_encoder=sequence_encoder,
predictor=BinaryFixedPredictor())
def get_model_with_yes_no(rnn_dim: int, use_elmo, keep_rate=0.8):
recurrent_layer = CudnnGru(rnn_dim, w_init=TruncatedNormal(stddev=0.05))
embed_mapper = SequenceMapperSeq(
VariationalDropoutLayer(keep_rate),
recurrent_layer,
VariationalDropoutLayer(keep_rate),
)
elmo_model = None
if use_elmo:
print("Using Elmo!")
elmo_model = get_hotpot_elmo()
lm_reduce = MapperSeq(
ElmoLayer(0, layer_norm=False, top_layer_only=False),
DropoutLayer(0.5),
)
embed_mapper = ElmoWrapper(input_append=True, output_append=False, rnn_layer=embed_mapper, lm_reduce=lm_reduce)
answer_encoder = GroupedSpanAnswerEncoderWithYesNo(group=True)
predictor = BoundsPredictor(
ChainBiMapper(
first_layer=recurrent_layer,
second_layer=recurrent_layer
),
span_predictor=IndependentBoundsGroupedWithYesNo()
)
return AttentionQAWithYesNo(
encoder=QuestionsAndParagraphsEncoder(answer_encoder, use_sentence_segments=False),
word_embed=FixedWordEmbedder(vec_name="glove.840B.300d", word_vec_init_scale=0, learn_unk=False, cpu=True),
char_embed=CharWordEmbedder(
LearnedCharEmbedder(word_size_th=14, char_th=50, char_dim=20, init_scale=0.05, force_cpu=True),
MaxPool(Conv1d(100, 5, 0.8)),
shared_parameters=True
),
elmo_model=elmo_model,
embed_mapper=embed_mapper,
question_mapper=None,
context_mapper=None,
memory_builder=NullBiMapper(),
attention=BiAttention(TriLinear(bias=True), True),
match_encoder=SequenceMapperSeq(FullyConnected(rnn_dim * 2, activation="relu"),
ResidualLayer(SequenceMapperSeq(
VariationalDropoutLayer(keep_rate),
recurrent_layer,
VariationalDropoutLayer(keep_rate),
StaticAttentionSelf(TriLinear(bias=True), ConcatWithProduct()),
FullyConnected(rnn_dim * 2, activation="relu"),
)),
VariationalDropoutLayer(keep_rate)),
predictor=predictor,
yes_no_question_encoder=MaxPool(map_layer=None, min_val=0, regular_reshape=True),
yes_no_context_encoder=MaxPool(map_layer=None, min_val=0, regular_reshape=True)
)
def get_res_fc_seq_fc(model_rnn_dim, rnn: bool, self_att: bool):
seq_mapper = []
if not rnn and not self_att:
raise NotImplementedError()
if rnn:
seq_mapper.extend([VariationalDropoutLayer(0.8), CudnnGru(model_rnn_dim, w_init=TruncatedNormal(stddev=0.05))])
if self_att:
seq_mapper.extend([VariationalDropoutLayer(0.8),
StaticAttentionSelf(TriLinear(bias=True), ConcatWithProduct())])
seq_mapper.append(FullyConnected(model_rnn_dim * 2, activation="relu"))
return SequenceMapperSeq(FullyConnected(model_rnn_dim * 2, activation="relu"),
ResidualLayer(SequenceMapperSeq(
*seq_mapper
)))
def get_reread_simple_score(rnn_dim, use_elmo, keep_rate=0.8, two_phase_att=False, res_rnn=True, res_self_att=False,
reformulate_by_context=False, rank_first=False, rank_second=False, reread_rnn_dim=None):
recurrent_layer = CudnnGru(rnn_dim, w_init=TruncatedNormal(stddev=0.05))
answer_encoder = IterativeAnswerEncoder(group=rank_first or rank_second)
embed_mapper = SequenceMapperSeq(VariationalDropoutLayer(keep_rate), recurrent_layer)
if res_rnn or res_self_att:
res_model = get_res_fc_seq_fc(model_rnn_dim=rnn_dim, rnn=res_rnn, self_att=res_self_att, keep_rate=keep_rate)
else:
res_model = FullyConnected(rnn_dim * 2, activation="relu")
attention = AttentionWithPostMapper(BiAttention(TriLinear(bias=True), True),
post_mapper=res_model)
use_c2q = two_phase_att or not reformulate_by_context
use_q2c = two_phase_att or reformulate_by_context
elmo_model = None
if use_elmo:
print("Using Elmo!")
elmo_model = get_hotpot_elmo()
lm_reduce = MapperSeq(
ElmoLayer(0, layer_norm=False, top_layer_only=False),
DropoutLayer(0.5),
)
embed_mapper = ElmoWrapper(input_append=True, output_append=False, rnn_layer=embed_mapper, lm_reduce=lm_reduce)
return IterativeContextReReadSimpleScoreModel(
encoder=QuestionsAndParagraphsEncoder(answer_encoder, use_sentence_segments=True),
word_embed=FixedWordEmbedder(vec_name="glove.840B.300d", word_vec_init_scale=0, learn_unk=False, cpu=True),
char_embed=CharWordEmbedder(
LearnedCharEmbedder(word_size_th=14, char_th=50, char_dim=20, init_scale=0.05, force_cpu=True),
MaxPool(Conv1d(100, 5, 0.8)),
shared_parameters=True
),
elmo_model=elmo_model,
embed_mapper=embed_mapper,
sequence_encoder=EncodeMap(encoder=MaxPool(map_layer=None, min_val=VERY_NEGATIVE_NUMBER, regular_reshape=True),
mapper=FullyConnected(rnn_dim * 2, activation=None)),
sentences_encoder=SentenceMaxEncoder(),
sentence_mapper=FullyConnected(rnn_dim * 2, activation=None),
reread_mapper=None if reread_rnn_dim is None else CudnnGru(reread_rnn_dim, w_init=TruncatedNormal(stddev=0.05)),
pre_attention_mapper=VariationalDropoutLayer(keep_rate),
context_to_question_attention=attention if use_c2q else None,
question_to_context_attention=attention if use_q2c else None,
reformulate_by_context=reformulate_by_context,
first_predictor=BinaryNullPredictor(rank_first),
second_predictor=BinaryNullPredictor(rank_second),
max_batch_size=512
)
def get_sentences_model(rnn_dim, use_elmo, keep_rate=0.8):
recurrent_layer = CudnnGru(rnn_dim, w_init=TruncatedNormal(stddev=0.05))
answer_encoder = BinaryAnswerEncoder()
embed_mapper = SequenceMapperSeq(VariationalDropoutLayer(keep_rate), recurrent_layer)
elmo_model = None
if use_elmo:
print("Using Elmo!")
elmo_model = get_wiki_elmo()
lm_reduce = MapperSeq(
ElmoLayer(0, layer_norm=False, top_layer_only=False),
DropoutLayer(0.5),
)
embed_mapper = ElmoWrapper(input_append=True, output_append=False, rnn_layer=embed_mapper, lm_reduce=lm_reduce)
return SingleContextMaxSentenceModel(
encoder=QuestionsAndParagraphsEncoder(answer_encoder, use_sentence_segments=True, paragraph_as_sentence=True),
word_embed=FixedWordEmbedder(vec_name="glove.840B.300d", word_vec_init_scale=0, learn_unk=False, cpu=True),
char_embed=CharWordEmbedder(
LearnedCharEmbedder(word_size_th=14, char_th=50, char_dim=20, init_scale=0.05, force_cpu=True),
MaxPool(Conv1d(100, 5, 0.8)),
shared_parameters=True
),
elmo_model=elmo_model,
embed_mapper=embed_mapper,
sequence_encoder=MaxPool(map_layer=None, min_val=VERY_NEGATIVE_NUMBER, regular_reshape=True),
sentences_encoder=SentenceMaxEncoder(),
post_merger=None,
merger=WithConcatOptions(sub=False, hadamard=True, dot=True, raw=True),
max_batch_size=256
)
def get_model(rnn_dim):
recurrent_layer = CudnnGru(rnn_dim, w_init=TruncatedNormal(stddev=0.05))
answer_encoder = BinaryAnswerEncoder()
return ContextPairRelevanceModel(
encoder=QuestionsAndParagraphsEncoder(answer_encoder),
word_embed=FixedWordEmbedder(vec_name="glove.840B.300d",
word_vec_init_scale=0,
learn_unk=False,
cpu=True),
char_embed=CharWordEmbedder(LearnedCharEmbedder(word_size_th=14,
char_th=50,
char_dim=20,
init_scale=0.05,
force_cpu=True),
MaxPool(Conv1d(100, 5, 0.8)),
shared_parameters=True),
embed_mapper=SequenceMapperSeq(
VariationalDropoutLayer(0.8),
recurrent_layer,
# VariationalDropoutLayer(0.8), # fixme probably doesn't belong here
),
question_to_context_attention=None,
context_to_context_attention=None,
context_to_question_attention=None,
sequence_encoder=MaxPool(map_layer=None,
min_val=0,
regular_reshape=True),
merger=MergeTwoContextsConcatQuestion(),
predictor=BinaryFixedPredictor())
def get_fixed_context_to_question(rnn_dim):
recurrent_layer = CudnnGru(rnn_dim, w_init=TruncatedNormal(stddev=0.05))
answer_encoder = BinaryAnswerEncoder()
return SingleFixedContextToQuestionModel(
encoder=QuestionsAndParagraphsEncoder(answer_encoder),
word_embed=FixedWordEmbedder(vec_name="glove.840B.300d", word_vec_init_scale=0, learn_unk=False, cpu=True),
char_embed=CharWordEmbedder(
LearnedCharEmbedder(word_size_th=14, char_th=50, char_dim=20, init_scale=0.05, force_cpu=True),
MaxPool(Conv1d(100, 5, 0.8)),
shared_parameters=True
),
embed_mapper=SequenceMapperSeq(
SequenceMapperSeq(VariationalDropoutLayer(0.8), recurrent_layer),
ResidualLayer(SequenceMapperSeq(VariationalDropoutLayer(0.8), recurrent_layer)),
ResidualLayer(SequenceMapperSeq(VariationalDropoutLayer(0.8), recurrent_layer))
),
context_mapper=None,
# ResidualLayer(
# SequenceMapperSeq(
# VariationalDropoutLayer(0.8),
# StaticAttentionSelf(TriLinear(bias=True), ConcatWithProduct()),
# FullyConnected(rnn_dim*2, activation=None))),
context_encoder=MaxPool(map_layer=None, min_val=0, regular_reshape=True),
question_mapper=None,
# ResidualLayer(
# SequenceMapperSeq(
# VariationalDropoutLayer(0.8),
# StaticAttentionSelf(TriLinear(bias=True), ConcatWithProduct()),
# FullyConnected(rnn_dim*2, activation=None))),
merger=WithConcatOptions(dot=True, sub=True, hadamard=True, raw=True, project=False),
post_merger=SequenceMapperSeq(
FullyConnected(rnn_dim * 2, activation='relu'),
ResidualLayer(SequenceMapperSeq(VariationalDropoutLayer(0.8), recurrent_layer,
FullyConnected(rnn_dim * 2, activation='relu')))
),
final_encoder=MaxPool(map_layer=None, min_val=0, regular_reshape=True),
predictor=BinaryFixedPredictor()
)
def get_bottleneck_to_seq_model(rnn_dim, q2c: bool, res_rnn: bool, res_self_att: bool, seq_len=50):
recurrent_layer = CudnnGru(rnn_dim, w_init=TruncatedNormal(stddev=0.05))
answer_encoder = BinaryAnswerEncoder()
res_model = get_res_fc_seq_fc(model_rnn_dim=rnn_dim, rnn=res_rnn, self_att=res_self_att)
question_to_context = \
AttentionWithPostMapper(BiAttention(TriLinear(bias=True), True), post_mapper=res_model) if q2c else None
context_to_question = AttentionWithPostMapper(BiAttention(TriLinear(bias=True), True), post_mapper=res_model)
sequence_generator = GenerativeRNN(tf.contrib.rnn.LSTMCell(num_units=rnn_dim,
initializer=tf.initializers.truncated_normal(
stddev=0.05)),
output_layer=FullyConnected(rnn_dim * 2, activation='relu'),
vec_to_in=FullyConnected(rnn_dim * 2, activation='relu'),
seq_len=seq_len, include_original_vec=False)
return SingleContextBottleneckToSeqQuestionModel(
encoder=QuestionsAndParagraphsEncoder(answer_encoder),
word_embed=FixedWordEmbedder(vec_name="glove.840B.300d", word_vec_init_scale=0, learn_unk=False, cpu=True),
char_embed=CharWordEmbedder(
LearnedCharEmbedder(word_size_th=14, char_th=50, char_dim=20, init_scale=0.05, force_cpu=True),
MaxPool(Conv1d(100, 5, 0.8)),
shared_parameters=True
),
embed_mapper=SequenceMapperSeq(
VariationalDropoutLayer(0.8),
recurrent_layer,
),
sequence_generator=sequence_generator,
pre_attention=VariationalDropoutLayer(0.8),
question_to_context_attention=question_to_context,
context_to_question_attention=context_to_question,
sequence_encoder=MaxPool(map_layer=None, min_val=0, regular_reshape=True),
predictor=BinaryFixedPredictor()
)
def get_reread_merge_model(rnn_dim, use_elmo, keep_rate=0.8, res_rnn=True, res_self_att=False,
multiply_iteration_probs=False):
recurrent_layer = CudnnGru(rnn_dim, w_init=TruncatedNormal(stddev=0.05))
answer_encoder = IterativeAnswerEncoder()
embed_mapper = SequenceMapperSeq(VariationalDropoutLayer(keep_rate), recurrent_layer)
if res_rnn or res_self_att:
res_model = get_res_fc_seq_fc(model_rnn_dim=rnn_dim, rnn=res_rnn, self_att=res_self_att)
else:
res_model = FullyConnected(rnn_dim * 2, activation="relu")
attention = AttentionWithPostMapper(BiAttention(TriLinear(bias=True), True), post_mapper=res_model)
elmo_model = None
if use_elmo:
print("Using Elmo!")
elmo_model = get_hotpot_elmo()
lm_reduce = MapperSeq(
ElmoLayer(0, layer_norm=False, top_layer_only=False),
DropoutLayer(0.5),
)
embed_mapper = ElmoWrapper(input_append=True, output_append=False, rnn_layer=embed_mapper, lm_reduce=lm_reduce)
return IterativeContextReReadMergeModel(
encoder=QuestionsAndParagraphsEncoder(answer_encoder, use_sentence_segments=True),
word_embed=FixedWordEmbedder(vec_name="glove.840B.300d", word_vec_init_scale=0, learn_unk=False, cpu=True),
char_embed=CharWordEmbedder(
LearnedCharEmbedder(word_size_th=14, char_th=50, char_dim=20, init_scale=0.05, force_cpu=True),
MaxPool(Conv1d(100, 5, 0.8)),
shared_parameters=True
),
elmo_model=elmo_model,
embed_mapper=embed_mapper,
sequence_encoder=MaxPool(map_layer=None, min_val=0, regular_reshape=True),
sentences_encoder=SentenceMaxEncoder(),
sentence_mapper=None,
post_merger=None,
merger=WithConcatOptions(sub=False, hadamard=True, dot=False, raw=True),
context_to_question_attention=attention,
question_to_context_attention=attention,
reread_merger=ConcatWithProduct(),
multiply_iteration_probs=multiply_iteration_probs,
max_batch_size=128
)
def get_model(rnn_dim, use_elmo, keep_rate=0.8):
recurrent_layer = CudnnGru(rnn_dim, w_init=TruncatedNormal(stddev=0.05))
answer_encoder = IterativeAnswerEncoder()
embed_mapper = SequenceMapperSeq(VariationalDropoutLayer(keep_rate), recurrent_layer)
elmo_model = None
if use_elmo:
print("Using Elmo!")
elmo_model = get_hotpot_elmo()
lm_reduce = MapperSeq(
ElmoLayer(0, layer_norm=False, top_layer_only=False),
DropoutLayer(0.5),
)
embed_mapper = ElmoWrapper(input_append=True, output_append=False, rnn_layer=embed_mapper, lm_reduce=lm_reduce)
reformulation = ProjectMapEncodeReformulation(project_layer=None,
sequence_mapper=None,
encoder=CudnnGruEncoder(rnn_dim, w_init=TruncatedNormal(stddev=0.05)))
# reformulation = WeightedSumThenProjectReformulation(rnn_dim*2, activation='relu')
# reformulation = ProjectThenWeightedSumReformulation(rnn_dim*2, activation='relu')
return IterativeContextMaxSentenceModel(
encoder=QuestionsAndParagraphsEncoder(answer_encoder, use_sentence_segments=True),
word_embed=FixedWordEmbedder(vec_name="glove.840B.300d", word_vec_init_scale=0, learn_unk=False, cpu=True),
char_embed=CharWordEmbedder(
LearnedCharEmbedder(word_size_th=14, char_th=50, char_dim=20, init_scale=0.05, force_cpu=True),
MaxPool(Conv1d(100, 5, 0.8)),
shared_parameters=True
),
elmo_model=elmo_model,
embed_mapper=embed_mapper,
sequence_encoder=MaxPool(map_layer=None, min_val=0, regular_reshape=True),
sentences_encoder=SentenceMaxEncoder(),
sentence_mapper=recurrent_layer,
post_merger=None,
merger=WithConcatOptions(sub=False, hadamard=True, dot=True, raw=True),
reformulation_layer=reformulation,
max_batch_size=128
)
def get_basic_model(rnn_dim, post_merger_params: Optional[dict] = None, use_elmo=False, keep_rate=0.8):
recurrent_layer = CudnnGru(rnn_dim, w_init=TruncatedNormal(stddev=0.05))
answer_encoder = BinaryAnswerEncoder()
embed_mapper = SequenceMapperSeq(VariationalDropoutLayer(keep_rate), recurrent_layer)
# embed_mapper = SequenceMapperSeq(
# SequenceMapperSeq(VariationalDropoutLayer(keep_rate), recurrent_layer),
# ResidualLayer(SequenceMapperSeq(VariationalDropoutLayer(keep_rate), recurrent_layer)),
# ResidualLayer(SequenceMapperSeq(VariationalDropoutLayer(keep_rate), recurrent_layer))
# )
elmo_model = None
if use_elmo:
print("Using Elmo!")
elmo_model = get_squad_elmo()
lm_reduce = MapperSeq(
ElmoLayer(0, layer_norm=False, top_layer_only=False),
DropoutLayer(0.5),
)
embed_mapper = ElmoWrapper(input_append=True, output_append=True, rnn_layer=embed_mapper, lm_reduce=lm_reduce)
post_merger = None if post_merger_params is None else get_mlp(**post_merger_params)
return BasicSingleContextAndQuestionIndependentModel(
encoder=QuestionsAndParagraphsEncoder(answer_encoder),
word_embed=FixedWordEmbedder(vec_name="glove.840B.300d", word_vec_init_scale=0, learn_unk=False, cpu=True),
char_embed=CharWordEmbedder(
LearnedCharEmbedder(word_size_th=14, char_th=50, char_dim=20, init_scale=0.05, force_cpu=True),
MaxPool(Conv1d(100, 5, 0.8)),
shared_parameters=True
),
elmo_model=elmo_model,
embed_mapper=embed_mapper,
sequence_encoder=MaxPool(map_layer=None, min_val=0, regular_reshape=True),
merger=ConcatWithProductSub(),
post_merger=post_merger,
predictor=BinaryFixedPredictor(sigmoid=True),
max_batch_size=128
)
def get_contexts_to_question_model(rnn_dim, post_merge):
recurrent_layer = CudnnGru(rnn_dim, w_init=TruncatedNormal(stddev=0.05))
answer_encoder = BinaryAnswerEncoder()
if post_merge == 'res_rnn_self_att':
post_map_layer = SequenceMapperSeq(
FullyConnected(rnn_dim * 2, activation="relu"),
ResidualLayer(
SequenceMapperSeq(
VariationalDropoutLayer(0.8),
recurrent_layer,
VariationalDropoutLayer(0.8),
StaticAttentionSelf(TriLinear(bias=True),
ConcatWithProduct()),
FullyConnected(rnn_dim * 2, activation="relu"),
)))
elif post_merge == 'res_rnn':
post_map_layer = SequenceMapperSeq(
FullyConnected(rnn_dim * 2, activation="relu"),
ResidualLayer(
SequenceMapperSeq(
VariationalDropoutLayer(0.8),
recurrent_layer,
FullyConnected(rnn_dim * 2, activation="relu"),
)))
elif post_merge == 'res_self_att':
post_map_layer = SequenceMapperSeq(
FullyConnected(rnn_dim * 2, activation="relu"),
ResidualLayer(
SequenceMapperSeq(
VariationalDropoutLayer(0.8),
StaticAttentionSelf(TriLinear(bias=True),
ConcatWithProduct()),
FullyConnected(rnn_dim * 2, activation="relu"),
)))
else:
raise NotImplementedError()
return ContextsToQuestionModel(
encoder=QuestionsAndParagraphsEncoder(answer_encoder),
word_embed=FixedWordEmbedder(vec_name="glove.840B.300d",
word_vec_init_scale=0,
learn_unk=False,
cpu=True),
char_embed=CharWordEmbedder(LearnedCharEmbedder(word_size_th=14,
char_th=50,
char_dim=20,
init_scale=0.05,
force_cpu=True),
MaxPool(Conv1d(100, 5, 0.8)),
shared_parameters=True),
embed_mapper=SequenceMapperSeq(
VariationalDropoutLayer(0.8),
recurrent_layer,
VariationalDropoutLayer(0.8),
),
attention_merger=MaxMerge(pre_map_layer=None,
post_map_layer=post_map_layer),
context_to_question_attention=BiAttention(TriLinear(bias=True), True),
sequence_encoder=MaxPool(map_layer=None,
min_val=0,
regular_reshape=True),
predictor=BinaryFixedPredictor())