def _build_forward(self):
#import pdb
#pdb.set_trace()
with tf.variable_scope("emb"):
with tf.variable_scope("emb_var"), tf.device("/cpu:0"):
if self.config.mode == 'train':
assert self.emb_mat is not None
word_emb_mat = tf.get_variable("word_emb_mat",
shape=[self.vocab_size-np.shape(self.emb_mat)[0],
self.config.word_emb_size],
dtype='float', trainable=True)
pretrained_emb_mat = tf.get_variable("pretrained_emb_mat", shape=[np.shape(self.emb_mat)[0],
self.config.word_emb_size],
dtype='float', initializer=get_initializer(self.emb_mat),
trainable=self.config.fine_tune)
else:
word_emb_mat = tf.get_variable("word_emb_mat",
shape=[self.vocab_size-np.shape(self.emb_mat)[0],
self.config.word_emb_size],
dtype='float')
pretrained_emb_mat = tf.get_variable("pretrained_emb_mat",
shape=[np.shape(self.emb_mat)[0], self.config.word_emb_size],
dtype='float')
self.word_emb_mat = tf.concat(values=[word_emb_mat, pretrained_emb_mat], axis=0)
with tf.name_scope("word"):
self.doc_word_emb = tf.nn.embedding_lookup(self.word_emb_mat, self.doc) # [N, M, d]
self.que_word_emb = tf.nn.embedding_lookup(self.word_emb_mat, self.que) # [N, M, d]
self.ans_word_emb = tf.nn.embedding_lookup(self.word_emb_mat, self.c) # [N, 10, d]
with tf.variable_scope("sentence_modeling"):
self.doc_repre, _ = ops.m_cudnn_lstm(inputs=self.doc_word_emb, num_layers=1,
hidden_size=self.config.hidden_size,
weight_noise_std=None, is_training=self.is_train, scope='doc',
input_drop_prob=0, i_m="linear_input",
use_gru=self.config.use_gru) # [N,M,JX,d]
self.que_repre, _ = ops.m_cudnn_lstm(inputs=self.que_word_emb, num_layers=1, hidden_size=self.config.hidden_size,
weight_noise_std=None, is_training=self.is_train, scope='que',
input_drop_prob=0, i_m="linear_input",
use_gru=self.config.use_gru) # [N,M,JX,d]
self.attend_passage = self._bi_attention(self.config, self.is_train, self.doc_repre, self.que_repre,
h_mask=self.doc_mask, scope="bi_att", tensor_dict=self.tensor_dict)
self.passage, _ = ops.m_cudnn_lstm(inputs=self.attend_passage, num_layers=1, hidden_size=self.config.hidden_size,
weight_noise_std=None, is_training=self.is_train, scope='aggregate',
input_drop_prob=0, i_m="linear_input",
use_gru=self.config.use_gru)
# Concatenate outputs and states of the forward and backward RNNs
with tf.variable_scope("summary"):
logit = tf.layers.dense(self.passage, 1, activation=None)
rc_logits = tf.nn.softmax(logit, 1)
passage = tf.reduce_sum(self.passage * rc_logits, 1) # [N,dim]
with tf.variable_scope('get_answer'):
self.prediction = self._answer_layer(passage, self.ans_word_emb)
def _build_forward(self):
#import pdb
#pdb.set_trace()
with tf.variable_scope("emb"):
with tf.variable_scope("emb_var"), tf.device("/cpu:0"):
if self.config.mode == 'train':
assert self.emb_mat is not None
word_emb_mat = tf.get_variable("word_emb_mat",
shape=[self.vocab_size-np.shape(self.emb_mat)[0], self.config.word_emb_size],
dtype='float', trainable=True)
pretrained_emb_mat = tf.get_variable("pretrained_emb_mat",
shape=[np.shape(self.emb_mat)[0], self.config.word_emb_size],
dtype='float', initializer=get_initializer(self.emb_mat),
trainable=self.config.fine_tune)
else:
word_emb_mat = tf.get_variable("word_emb_mat", shape=[self.vocab_size-np.shape(self.emb_mat)[0], self.config.word_emb_size],dtype='float')
pretrained_emb_mat = tf.get_variable("pretrained_emb_mat",
shape=[np.shape(self.emb_mat)[0], self.config.word_emb_size],
dtype='float')
word_emb_mat = tf.concat(values=[word_emb_mat, pretrained_emb_mat],axis=0)
with tf.name_scope("word"):
Ax = tf.nn.embedding_lookup(word_emb_mat, self.doc) # [N, M, JX, d]
Aq = tf.nn.embedding_lookup(word_emb_mat, self.que) # [N, JQ, d]
passage, _ = ops.m_cudnn_lstm(inputs=Ax, num_layers=1, hidden_size=self.config.hidden_size,
weight_noise_std=None, is_training=self.is_train, scope='doc',
input_drop_prob=0, i_m="linear_input",
use_gru=self.config.use_gru) # [N,M,JX,d]
question, _ = ops.m_cudnn_lstm(inputs=Aq, num_layers=1, hidden_size=self.config.hidden_size,
weight_noise_std=None, is_training=self.is_train, scope='question',
input_drop_prob=0, i_m="linear_input",
use_gru=self.config.use_gru) # [N,M,JX,d]
with tf.variable_scope("bidaf"):
passage = self.bi_attention(self.config, self.is_train, passage, question, self.doc_mask)
passage, _ = ops.m_cudnn_lstm(inputs=passage, num_layers=1, hidden_size=self.config.hidden_size,
weight_noise_std=None, is_training=self.is_train, scope='aggregate',
input_drop_prob=0, i_m="linear_input",
use_gru=self.config.use_gru)
with tf.variable_scope('get_answer'):
prediction = tf.layers.dense(passage, 1)
prediction = tf.reshape(prediction, [tf.shape(prediction)[0], tf.shape(prediction)[1]])
self.prediction = prediction + self.answer_mask
def _build_forward(self):
config = self.config
N, M, JX, JQ, VW, VC, d, W = \
config.batch_size, config.max_num_sents, config.max_sent_size, \
config.max_ques_size, config.word_vocab_size, config.char_vocab_size, config.hidden_size, \
config.max_word_size
JX = tf.shape(self.x)[2]
JQ = tf.shape(self.q)[1]
M = tf.shape(self.x)[1]
dc, dw, dco = config.char_emb_size, config.word_emb_size, config.char_out_size
with tf.variable_scope("emb"):
if config.use_char_emb:
with tf.variable_scope("emb_var"), tf.device("/cpu:0"):
char_emb_mat = tf.get_variable("char_emb_mat",
shape=[VC, dc],
dtype='float')
with tf.variable_scope("char"):
Acx = tf.nn.embedding_lookup(char_emb_mat,
self.cx) # [N, M, JX, W, dc]
Acq = tf.nn.embedding_lookup(char_emb_mat,
self.cq) # [N, JQ, W, dc]
Acx = tf.reshape(Acx, [-1, JX, W, dc])
Acq = tf.reshape(Acq, [-1, JQ, W, dc])
filter_sizes = list(
map(int, config.out_channel_dims.split(',')))
heights = list(map(int, config.filter_heights.split(',')))
assert sum(filter_sizes) == dco, (filter_sizes, dco)
with tf.variable_scope("conv"):
xx = multi_conv1d(Acx,
filter_sizes,
heights,
"VALID",
self.is_train,
config.keep_prob,
scope="xx")
if config.share_cnn_weights:
tf.get_variable_scope().reuse_variables()
qq = multi_conv1d(Acq,
filter_sizes,
heights,
"VALID",
self.is_train,
config.keep_prob,
scope="xx")
else:
qq = multi_conv1d(Acq,
filter_sizes,
heights,
"VALID",
self.is_train,
config.keep_prob,
scope="qq")
xx = tf.reshape(xx, [-1, M, JX, dco])
qq = tf.reshape(qq, [-1, JQ, dco])
if config.use_word_emb:
with tf.variable_scope("emb_var"), tf.device("/cpu:0"):
if config.mode == 'train':
word_emb_mat = tf.get_variable(
"word_emb_mat",
dtype='float',
shape=[VW, dw],
initializer=get_initializer(config.emb_mat))
else:
word_emb_mat = tf.get_variable("word_emb_mat",
shape=[VW, dw],
dtype='float')
if config.use_glove_for_unk:
word_emb_mat = tf.concat(
axis=0, values=[word_emb_mat, self.new_emb_mat])
with tf.name_scope("word"):
Ax = tf.nn.embedding_lookup(word_emb_mat,
self.x) # [N, M, JX, d]
Aq = tf.nn.embedding_lookup(word_emb_mat,
self.q) # [N, JQ, d]
self.tensor_dict['x'] = Ax
self.tensor_dict['q'] = Aq
if config.use_char_emb:
xx = tf.concat(axis=3, values=[xx, Ax]) # [N, M, JX, di]
qq = tf.concat(axis=2, values=[qq, Aq]) # [N, JQ, di]
else:
xx = Ax
qq = Aq
# highway network
if config.highway:
with tf.variable_scope("highway"):
xx = highway_network(xx,
config.highway_num_layers,
True,
wd=config.wd,
is_train=self.is_train)
tf.get_variable_scope().reuse_variables()
qq = highway_network(qq,
config.highway_num_layers,
True,
wd=config.wd,
is_train=self.is_train)
self.tensor_dict['xx'] = xx
self.tensor_dict['qq'] = qq
cell_fw = BasicLSTMCell(d,
state_is_tuple=True,
reuse=tf.get_variable_scope().reuse)
cell_bw = BasicLSTMCell(d,
state_is_tuple=True,
reuse=tf.get_variable_scope().reuse)
d_cell_fw = SwitchableDropoutWrapper(
cell_fw, self.is_train, input_keep_prob=config.input_keep_prob)
d_cell_bw = SwitchableDropoutWrapper(
cell_bw, self.is_train, input_keep_prob=config.input_keep_prob)
cell2_fw = BasicLSTMCell(d,
state_is_tuple=True,
reuse=tf.get_variable_scope().reuse)
cell2_bw = BasicLSTMCell(d,
state_is_tuple=True,
reuse=tf.get_variable_scope().reuse)
d_cell2_fw = SwitchableDropoutWrapper(
cell2_fw, self.is_train, input_keep_prob=config.input_keep_prob)
d_cell2_bw = SwitchableDropoutWrapper(
cell2_bw, self.is_train, input_keep_prob=config.input_keep_prob)
cell3_fw = BasicLSTMCell(d,
state_is_tuple=True,
reuse=tf.get_variable_scope().reuse)
cell3_bw = BasicLSTMCell(d,
state_is_tuple=True,
reuse=tf.get_variable_scope().reuse)
d_cell3_fw = SwitchableDropoutWrapper(
cell3_fw, self.is_train, input_keep_prob=config.input_keep_prob)
d_cell3_bw = SwitchableDropoutWrapper(
cell3_bw, self.is_train, input_keep_prob=config.input_keep_prob)
cell4_fw = BasicLSTMCell(d,
state_is_tuple=True,
reuse=tf.get_variable_scope().reuse)
cell4_bw = BasicLSTMCell(d,
state_is_tuple=True,
reuse=tf.get_variable_scope().reuse)
d_cell4_fw = SwitchableDropoutWrapper(
cell4_fw, self.is_train, input_keep_prob=config.input_keep_prob)
d_cell4_bw = SwitchableDropoutWrapper(
cell4_bw, self.is_train, input_keep_prob=config.input_keep_prob)
x_len = tf.reduce_sum(tf.cast(self.x_mask, 'int32'), 2) # [N, M]
q_len = tf.reduce_sum(tf.cast(self.q_mask, 'int32'), 1) # [N]
with tf.variable_scope("prepro"):
(fw_u, bw_u), ((_, fw_u_f), (_,
bw_u_f)) = bidirectional_dynamic_rnn(
d_cell_fw,
d_cell_bw,
qq,
q_len,
dtype='float',
scope='u1') # [N, J, d], [N, d]
u = tf.concat(axis=2, values=[fw_u, bw_u])
if config.share_lstm_weights:
tf.get_variable_scope().reuse_variables()
(fw_h, bw_h), _ = bidirectional_dynamic_rnn(
cell_fw, cell_bw, xx, x_len, dtype='float',
scope='u1') # [N, M, JX, 2d]
h = tf.concat(axis=3, values=[fw_h, bw_h]) # [N, M, JX, 2d]
else:
(fw_h, bw_h), _ = bidirectional_dynamic_rnn(
cell_fw, cell_bw, xx, x_len, dtype='float',
scope='h1') # [N, M, JX, 2d]
h = tf.concat(axis=3, values=[fw_h, bw_h]) # [N, M, JX, 2d]
self.tensor_dict['u'] = u
self.tensor_dict['h'] = h
if not config.structured_sparsity:
add_sparsity_regularization(config.l1wd,
collection_name=SPARSITY_VARS,
scope=tf.get_variable_scope().name)
else:
add_mixedlasso(config.row_col_wd,
config.l1wd,
coef_scaling=config.coef_scaling,
collection_name=SPARSITY_VARS,
scope=tf.get_variable_scope().name)
with tf.variable_scope("main"):
if config.dynamic_att:
p0 = h
u = tf.reshape(tf.tile(tf.expand_dims(u, 1), [1, M, 1, 1]),
[N * M, JQ, 2 * d])
q_mask = tf.reshape(
tf.tile(tf.expand_dims(self.q_mask, 1), [1, M, 1]),
[N * M, JQ])
first_cell_fw = AttentionCell(
cell2_fw,
u,
mask=q_mask,
mapper='sim',
input_keep_prob=self.config.input_keep_prob,
is_train=self.is_train)
first_cell_bw = AttentionCell(
cell2_bw,
u,
mask=q_mask,
mapper='sim',
input_keep_prob=self.config.input_keep_prob,
is_train=self.is_train)
second_cell_fw = AttentionCell(
cell3_fw,
u,
mask=q_mask,
mapper='sim',
input_keep_prob=self.config.input_keep_prob,
is_train=self.is_train)
second_cell_bw = AttentionCell(
cell3_bw,
u,
mask=q_mask,
mapper='sim',
input_keep_prob=self.config.input_keep_prob,
is_train=self.is_train)
else:
p0 = attention_layer(config,
self.is_train,
h,
u,
h_mask=self.x_mask,
u_mask=self.q_mask,
scope="p0",
tensor_dict=self.tensor_dict)
first_cell_fw = d_cell2_fw
second_cell_fw = d_cell3_fw
first_cell_bw = d_cell2_bw
second_cell_bw = d_cell3_bw
(fw_g0, bw_g0), _ = bidirectional_dynamic_rnn(
first_cell_fw,
first_cell_bw,
p0,
x_len,
dtype='float',
scope='g0') # [N, M, JX, 2d]
g0 = tf.concat(axis=3, values=[fw_g0, bw_g0])
(fw_g1, bw_g1), _ = bidirectional_dynamic_rnn(
second_cell_fw,
second_cell_bw,
g0,
x_len,
dtype='float',
scope='g1') # [N, M, JX, 2d]
g1 = tf.concat(axis=3, values=[fw_g1, bw_g1])
logits = get_logits([g1, p0],
d,
True,
wd=config.wd,
input_keep_prob=config.input_keep_prob,
mask=self.x_mask,
is_train=self.is_train,
func=config.answer_func,
scope='logits1')
a1i = softsel(tf.reshape(g1, [N, M * JX, 2 * d]),
tf.reshape(logits, [N, M * JX]))
a1i = tf.tile(tf.expand_dims(tf.expand_dims(a1i, 1), 1),
[1, M, JX, 1])
(fw_g2, bw_g2), _ = bidirectional_dynamic_rnn(
d_cell4_fw,
d_cell4_bw,
tf.concat(axis=3, values=[p0, g1, a1i, g1 * a1i]),
x_len,
dtype='float',
scope='g2') # [N, M, JX, 2d]
g2 = tf.concat(axis=3, values=[fw_g2, bw_g2])
logits2 = get_logits([g2, p0],
d,
True,
wd=config.wd,
input_keep_prob=config.input_keep_prob,
mask=self.x_mask,
is_train=self.is_train,
func=config.answer_func,
scope='logits2')
flat_logits = tf.reshape(logits, [-1, M * JX])
flat_yp = tf.nn.softmax(flat_logits) # [-1, M*JX]
flat_logits2 = tf.reshape(logits2, [-1, M * JX])
flat_yp2 = tf.nn.softmax(flat_logits2)
if not config.structured_sparsity:
add_sparsity_regularization(config.l1wd,
collection_name=SPARSITY_VARS,
scope=tf.get_variable_scope().name)
else:
add_mixedlasso(config.row_col_wd,
config.l1wd,
coef_scaling=config.coef_scaling,
collection_name=SPARSITY_VARS,
scope=tf.get_variable_scope().name)
if config.na:
na_bias = tf.get_variable("na_bias", shape=[], dtype='float')
na_bias_tiled = tf.tile(tf.reshape(na_bias, [1, 1]),
[N, 1]) # [N, 1]
concat_flat_logits = tf.concat(
axis=1, values=[na_bias_tiled, flat_logits])
concat_flat_yp = tf.nn.softmax(concat_flat_logits)
na_prob = tf.squeeze(tf.slice(concat_flat_yp, [0, 0], [-1, 1]),
[1])
flat_yp = tf.slice(concat_flat_yp, [0, 1], [-1, -1])
concat_flat_logits2 = tf.concat(
axis=1, values=[na_bias_tiled, flat_logits2])
concat_flat_yp2 = tf.nn.softmax(concat_flat_logits2)
na_prob2 = tf.squeeze(
tf.slice(concat_flat_yp2, [0, 0], [-1, 1]), [1]) # [N]
flat_yp2 = tf.slice(concat_flat_yp2, [0, 1], [-1, -1])
self.concat_logits = concat_flat_logits
self.concat_logits2 = concat_flat_logits2
self.na_prob = na_prob * na_prob2
yp = tf.reshape(flat_yp, [-1, M, JX])
yp2 = tf.reshape(flat_yp2, [-1, M, JX])
wyp = tf.nn.sigmoid(logits2)
self.tensor_dict['g1'] = g1
self.tensor_dict['g2'] = g2
self.logits = flat_logits
self.logits2 = flat_logits2
self.yp = yp
self.yp2 = yp2
self.wyp = wyp
def _build_forward(self):
config = self.config
x_len = tf.reduce_sum(tf.cast(self.x_mask, 'int32'), 2) # [N, M]
q_len = tf.reduce_sum(tf.cast(self.q_mask, 'int32'), 1) # [N]
N, M, JX, JQ, VW, VC, d, W = \
config.batch_size, config.max_num_sents, config.max_sent_size, \
config.max_ques_size, config.word_vocab_size, config.char_vocab_size, config.hidden_size, \
config.max_word_size
JX = tf.shape(self.x)[2]
JQ = tf.shape(self.q)[1]
M = tf.shape(self.x)[1]
dc, dw, dco = config.char_emb_size, config.word_emb_size, config.char_out_size
with tf.variable_scope("emb"):
if config.use_char_emb:
with tf.variable_scope("emb_var"), tf.device("/cpu:0"):
char_emb_mat = tf.get_variable("char_emb_mat",
shape=[VC, dc],
dtype='float')
with tf.variable_scope("char"):
Acx = tf.nn.embedding_lookup(char_emb_mat,
self.cx) # [N, M, JX, W, dc]
Acq = tf.nn.embedding_lookup(char_emb_mat,
self.cq) # [N, JQ, W, dc]
Acx = tf.reshape(Acx, [-1, JX, W, dc])
Acq = tf.reshape(Acq, [-1, JQ, W, dc])
filter_sizes = list(
map(int, config.out_channel_dims.split(',')))
heights = list(map(int, config.filter_heights.split(',')))
assert sum(filter_sizes) == dco, (filter_sizes, dco)
with tf.variable_scope("conv"):
xx = multi_conv1d(Acx,
filter_sizes,
heights,
"VALID",
self.is_train,
config.keep_prob,
scope="xx")
if config.share_cnn_weights:
tf.get_variable_scope().reuse_variables()
qq = multi_conv1d(Acq,
filter_sizes,
heights,
"VALID",
self.is_train,
config.keep_prob,
scope="xx")
else:
qq = multi_conv1d(Acq,
filter_sizes,
heights,
"VALID",
self.is_train,
config.keep_prob,
scope="qq")
xx = tf.reshape(xx, [-1, M, JX, dco])
qq = tf.reshape(qq, [-1, JQ, dco])
if config.use_word_emb:
with tf.variable_scope("emb_var"), tf.device("/cpu:0"):
if config.mode == 'train':
word_emb_mat = tf.get_variable(
"word_emb_mat",
dtype='float',
shape=[VW, dw],
initializer=get_initializer(self.emb_mat))
else:
word_emb_mat = tf.get_variable("word_emb_mat",
shape=[VW, dw],
dtype='float')
if config.use_glove_for_unk:
word_emb_mat = tf.concat(
axis=0, values=[word_emb_mat, self.new_emb_mat])
with tf.name_scope("word"):
Ax = tf.nn.embedding_lookup(word_emb_mat,
self.x) # [N, M, JX, d]
Aq = tf.nn.embedding_lookup(word_emb_mat,
self.q) # [N, JQ, d]
self.tensor_dict['x'] = Ax
self.tensor_dict['q'] = Aq
if config.use_char_emb:
xx = tf.concat(axis=3, values=[xx, Ax]) # [N, M, JX, di]
qq = tf.concat(axis=2, values=[qq, Aq]) # [N, JQ, di]
else:
xx = Ax
qq = Aq
# highway network
if config.highway:
with tf.variable_scope("highway"):
xx = highway_network(xx,
config.highway_num_layers,
True,
wd=config.wd,
is_train=self.is_train,
input_keep_prob=config.highway_keep_prob)
tf.get_variable_scope().reuse_variables()
qq = highway_network(qq,
config.highway_num_layers,
True,
wd=config.wd,
is_train=self.is_train,
input_keep_prob=config.highway_keep_prob)
self.tensor_dict['xx'] = xx
self.tensor_dict['qq'] = qq
with tf.variable_scope("prepro"):
with tf.variable_scope('u1'):
u, _ = bi_cudnn_rnn_encoder('lstm', config.hidden_size, 1,
1 - config.input_keep_prob, qq,
q_len, self.is_train)
if config.reasoning_layer == 'snmn':
u_st = zhong_selfatt(u[:, ax, :, :],
config.hidden_size * 2,
seq_len=q_len,
transform='squeeze')
if config.share_lstm_weights:
with tf.variable_scope('u1', reuse=True):
h, _ = bi_cudnn_rnn_encoder('lstm', config.hidden_size, 1,
1 - config.input_keep_prob,
tf.squeeze(xx, axis=1),
tf.squeeze(x_len, axis=1),
self.is_train)
h = h[:, ax, :, :]
else:
with tf.variable_scope('h1'):
h, _ = bi_cudnn_rnn_encoder('lstm', config.hidden_size, 1,
1 - config.input_keep_prob,
tf.squeeze(xx, axis=1),
tf.squeeze(x_len, axis=1),
self.is_train)
h = h[:, ax, :, :]
self.tensor_dict['u'] = u
self.tensor_dict['h'] = h
with tf.variable_scope("main"):
context_dim = config.hidden_size * 2
### Reconstruct before bidaf because otherwise we need to build a larger query tensor.
x_mask = self.x_mask
x_len_squeeze = tf.squeeze(x_len, axis=1)
p0 = h
### Main model
if config.reasoning_layer == 'snmn':
module_names = ['_Find', '_Compare', '_Relocate', '_NoOp']
self.snmn = NMN_Model(config, u, qq, u_st, self.q_mask, q_len, p0, x_mask, x_len, module_names, \
self.is_train)
self.u_weights = self.snmn.cv_list # question word distribution at each step
self.module_prob_list = self.snmn.module_prob_list # module probability at each step
g0 = tf.squeeze(self.snmn.att_second, axis=-1)
if config.supervise_bridge_entity:
self.hop0_logits = self.snmn.bridge_logits
if config.self_att:
with tf.variable_scope('g0'):
g0, _ = bi_cudnn_rnn_encoder(
'lstm', config.hidden_size,
1, 1 - config.input_keep_prob,
tf.squeeze(g0,
axis=1), x_len_squeeze, self.is_train)
g0 = g0[:, ax, :, :]
g0 = hotpot_biattention(config,
self.is_train,
g0,
tf.squeeze(g0, axis=1),
h_mask=x_mask,
u_mask=tf.squeeze(x_mask,
axis=1),
scope="self_att",
tensor_dict=self.tensor_dict)
g0 = tf.layers.dense(g0, config.hidden_size * 2)
with tf.variable_scope('g1'):
g1, _ = bi_cudnn_rnn_encoder('lstm', config.hidden_size, 1,
1 - config.input_keep_prob,
tf.squeeze(g0, axis=1),
tf.squeeze(x_len, axis=1),
self.is_train)
g1 = g1[:, ax, :, :]
logits = get_logits([g1, g0],
d,
True,
wd=config.wd,
input_keep_prob=config.input_keep_prob,
mask=x_mask,
is_train=self.is_train,
func=config.answer_func,
scope='logits1')
with tf.variable_scope('g2'):
a1i = softsel(tf.reshape(g1, [N, M * JX, 2 * d]),
tf.reshape(logits, [N, M * JX]))
a1i = tf.tile(a1i[:, ax, ax, :], [1, M, JX, 1])
g2, _ = bi_cudnn_rnn_encoder(
'lstm', config.hidden_size, 1,
1 - config.input_keep_prob,
tf.squeeze(tf.concat(axis=3,
values=[g0, g1, a1i, g0 * a1i]),
axis=1), x_len_squeeze, self.is_train)
g2 = g2[:, ax, :, :]
logits2 = get_logits([g2, g1],
d,
True,
wd=config.wd,
input_keep_prob=config.input_keep_prob,
mask=x_mask,
is_train=self.is_train,
func=config.answer_func,
scope='logits2')
if config.dataset == 'hotpotqa':
with tf.variable_scope('g3'):
if config.nmn_qtype_class == 'mem_last':
g3 = tf.concat(
[self.snmn.mem_last[:, ax, :], u_st[:, ax, :]],
axis=-1)
elif config.nmn_qtype_class == 'ctrl_st':
g3 = self.snmn.c_st_list[0][:, ax, :]
else:
raise NotImplementedError
self.predict_type = dense(g3, 2, scope='predict_type')
g3_1 = self.snmn.mem_last[:, ax, :]
self.predict_yesno = dense(g3_1,
2,
scope='predict_yesno')
flat_logits = tf.reshape(logits, [-1, M * JX])
flat_yp = tf.nn.softmax(flat_logits) # [-1, M * JX]
flat_logits2 = tf.reshape(logits2, [-1, M * JX])
flat_yp2 = tf.nn.softmax(flat_logits2)
yp = tf.reshape(flat_yp, [-1, M, JX])
yp2 = tf.reshape(flat_yp2, [-1, M, JX])
wyp = tf.nn.sigmoid(logits2)
self.logits = flat_logits
self.logits2 = flat_logits2
self.yp = yp
self.yp2 = yp2
self.wyp = wyp
if config.dataset == 'hotpotqa':
flat_predict_type = tf.reshape(self.predict_type, [-1, 2])
flat_yp3 = tf.nn.softmax(flat_predict_type)
self.yp3 = tf.reshape(flat_yp3, [-1, 1, 2])
flat_predict_yesno = tf.reshape(self.predict_yesno, [-1, 2])
flat_yp3_yesno = tf.nn.softmax(flat_predict_yesno)
self.yp3_yesno = tf.reshape(flat_yp3_yesno, [-1, 1, 2])
def _build_forward(self):
config = self.config
N, M, JX, JQ, VW, VC, d, W = \
config.batch_size, config.max_num_sents, config.max_sent_size, \
config.max_ques_size, config.word_vocab_size, config.char_vocab_size, config.hidden_size, \
config.max_word_size
beam_width = config.beam_width
GO_TOKEN = 0
EOS_TOKEN = 1
JX = tf.shape(self.x)[2]
JQ = tf.shape(self.q)[1]
M = tf.shape(self.x)[1]
dc, dw, dco = config.char_emb_size, config.word_emb_size, config.char_out_size
with tf.variable_scope("emb"):
if config.use_char_emb:
with tf.variable_scope("emb_var"), tf.device("/cpu:0"):
char_emb_mat = tf.get_variable("char_emb_mat",
shape=[VC, dc],
dtype='float')
with tf.variable_scope("char"):
Acx = tf.nn.embedding_lookup(char_emb_mat,
self.cx) # [N, M, JX, W, dc]
Acq = tf.nn.embedding_lookup(char_emb_mat,
self.cq) # [N, JQ, W, dc]
Acx = tf.reshape(Acx, [-1, JX, W, dc])
Acq = tf.reshape(Acq, [-1, JQ, W, dc])
filter_sizes = list(
map(int, config.out_channel_dims.split(',')))
heights = list(map(int, config.filter_heights.split(',')))
assert sum(filter_sizes) == dco, (filter_sizes, dco)
with tf.variable_scope("conv"):
xx = multi_conv1d(Acx,
filter_sizes,
heights,
"VALID",
self.is_train,
config.keep_prob,
scope="xx")
if config.share_cnn_weights:
tf.get_variable_scope().reuse_variables()
qq = multi_conv1d(Acq,
filter_sizes,
heights,
"VALID",
self.is_train,
config.keep_prob,
scope="xx")
else:
qq = multi_conv1d(Acq,
filter_sizes,
heights,
"VALID",
self.is_train,
config.keep_prob,
scope="qq")
xx = tf.reshape(xx, [-1, M, JX, dco])
qq = tf.reshape(qq, [-1, JQ, dco])
if config.use_word_emb:
with tf.variable_scope("emb_var"), tf.device("/cpu:0"):
if config.mode == 'train':
word_emb_mat = tf.get_variable(
"word_emb_mat",
dtype='float',
shape=[VW, dw],
initializer=get_initializer(config.emb_mat),
trainable=True)
else:
word_emb_mat = tf.get_variable("word_emb_mat",
shape=[VW, dw],
dtype='float')
if config.use_glove_for_unk:
word_emb_mat = tf.concat(
axis=0, values=[word_emb_mat, self.new_emb_mat])
with tf.name_scope("word"):
Ax = tf.nn.embedding_lookup(word_emb_mat,
self.x) # [N, M, JX, d]
Aq = tf.nn.embedding_lookup(word_emb_mat,
self.q) # [N, JQ, d]
self.tensor_dict['x'] = Ax
self.tensor_dict['q'] = Aq
if config.use_char_emb:
xx = tf.concat(axis=3, values=[xx, Ax]) # [N, M, JX, di]
qq = tf.concat(axis=2, values=[qq, Aq]) # [N, JQ, di]
else:
xx = Ax
qq = Aq
# highway network
if config.highway:
with tf.variable_scope("highway"):
xx = highway_network(xx,
config.highway_num_layers,
True,
wd=config.wd,
is_train=self.is_train)
tf.get_variable_scope().reuse_variables()
qq = highway_network(qq,
config.highway_num_layers,
True,
wd=config.wd,
is_train=self.is_train)
self.tensor_dict['xx'] = xx
self.tensor_dict['qq'] = qq
cell_fw = BasicLSTMCell(d, state_is_tuple=True)
cell_bw = BasicLSTMCell(d, state_is_tuple=True)
d_cell_fw = SwitchableDropoutWrapper(
cell_fw, self.is_train, input_keep_prob=config.input_keep_prob)
d_cell_bw = SwitchableDropoutWrapper(
cell_bw, self.is_train, input_keep_prob=config.input_keep_prob)
cell2_fw = BasicLSTMCell(d, state_is_tuple=True)
cell2_bw = BasicLSTMCell(d, state_is_tuple=True)
d_cell2_fw = SwitchableDropoutWrapper(
cell2_fw, self.is_train, input_keep_prob=config.input_keep_prob)
d_cell2_bw = SwitchableDropoutWrapper(
cell2_bw, self.is_train, input_keep_prob=config.input_keep_prob)
cell3_fw = BasicLSTMCell(d, state_is_tuple=True)
cell3_bw = BasicLSTMCell(d, state_is_tuple=True)
d_cell3_fw = SwitchableDropoutWrapper(
cell3_fw, self.is_train, input_keep_prob=config.input_keep_prob)
d_cell3_bw = SwitchableDropoutWrapper(
cell3_bw, self.is_train, input_keep_prob=config.input_keep_prob)
cell4_fw = BasicLSTMCell(d, state_is_tuple=True)
cell4_bw = BasicLSTMCell(d, state_is_tuple=True)
d_cell4_fw = SwitchableDropoutWrapper(
cell4_fw, self.is_train, input_keep_prob=config.input_keep_prob)
d_cell4_bw = SwitchableDropoutWrapper(
cell4_bw, self.is_train, input_keep_prob=config.input_keep_prob)
x_len = tf.reduce_sum(tf.cast(self.x_mask, 'int32'), 2) # [N, M]
q_len = tf.reduce_sum(tf.cast(self.q_mask, 'int32'), 1) # [N]
with tf.variable_scope("prepro"):
(fw_u, bw_u), ((_, fw_u_f), (_,
bw_u_f)) = bidirectional_dynamic_rnn(
d_cell_fw,
d_cell_bw,
qq,
q_len,
dtype='float',
scope='u1') # [N, J, d], [N, d]
u = tf.concat(axis=2, values=[fw_u, bw_u])
if config.share_lstm_weights:
tf.get_variable_scope().reuse_variables()
(fw_h, bw_h), ((_, fw_h_f),
(_, bw_h_f)) = bidirectional_dynamic_rnn(
cell_fw,
cell_bw,
xx,
x_len,
dtype='float',
scope='u1') # [N, M, JX, 2d]
h = tf.concat(axis=3, values=[fw_h, bw_h]) # [N, M, JX, 2d]
else:
(fw_h, bw_h), ((_, fw_h_f),
(_, bw_h_f)) = bidirectional_dynamic_rnn(
cell_fw,
cell_bw,
xx,
x_len,
dtype='float',
scope='h1') # [N, M, JX, 2d]
h = tf.concat(axis=3, values=[fw_h, bw_h]) # [N, M, JX, 2d]
self.tensor_dict['u'] = u
self.tensor_dict['h'] = h
with tf.variable_scope("main"):
if config.dynamic_att:
p0 = h
u = tf.reshape(tf.tile(tf.expand_dims(u, 1), [1, M, 1, 1]),
[N * M, JQ, 2 * d])
q_mask = tf.reshape(
tf.tile(tf.expand_dims(self.q_mask, 1), [1, M, 1]),
[N * M, JQ])
first_cell_fw = AttentionCell(
cell2_fw,
u,
mask=q_mask,
mapper='sim',
input_keep_prob=self.config.input_keep_prob,
is_train=self.is_train)
first_cell_bw = AttentionCell(
cell2_bw,
u,
mask=q_mask,
mapper='sim',
input_keep_prob=self.config.input_keep_prob,
is_train=self.is_train)
second_cell_fw = AttentionCell(
cell3_fw,
u,
mask=q_mask,
mapper='sim',
input_keep_prob=self.config.input_keep_prob,
is_train=self.is_train)
second_cell_bw = AttentionCell(
cell3_bw,
u,
mask=q_mask,
mapper='sim',
input_keep_prob=self.config.input_keep_prob,
is_train=self.is_train)
else:
p0 = attention_layer(config,
self.is_train,
h,
u,
h_mask=self.x_mask,
u_mask=self.q_mask,
scope="p0",
tensor_dict=self.tensor_dict)
first_cell_fw = d_cell2_fw
second_cell_fw = d_cell3_fw
first_cell_bw = d_cell2_bw
second_cell_bw = d_cell3_bw
(fw_g0, bw_g0), _ = bidirectional_dynamic_rnn(
first_cell_fw,
first_cell_bw,
p0,
x_len,
dtype='float',
scope='g0') # [N, M, JX, 2d]
g0 = tf.concat(axis=3, values=[fw_g0, bw_g0])
(fw_g1, bw_g1), _ = bidirectional_dynamic_rnn(
second_cell_fw,
second_cell_bw,
g0,
x_len,
dtype='float',
scope='g1') # [N, M, JX, 2d]
g1 = tf.concat(axis=3, values=[fw_g1, bw_g1])
logits = get_logits([g1, p0],
d,
True,
wd=config.wd,
input_keep_prob=config.input_keep_prob,
mask=self.x_mask,
is_train=self.is_train,
func=config.answer_func,
scope='logits1')
a1i = softsel(tf.reshape(g1, [N, M * JX, 2 * d]),
tf.reshape(logits, [N, M * JX]))
a1i = tf.tile(tf.expand_dims(tf.expand_dims(a1i, 1), 1),
[1, M, JX, 1])
(fw_g2, bw_g2), _ = bidirectional_dynamic_rnn(
d_cell4_fw,
d_cell4_bw,
tf.concat(axis=3, values=[p0, g1, a1i, g1 * a1i]),
x_len,
dtype='float',
scope='g2') # [N, M, JX, 2d]
g2 = tf.concat(axis=3, values=[fw_g2, bw_g2])
logits2 = get_logits([g2, p0],
d,
True,
wd=config.wd,
input_keep_prob=config.input_keep_prob,
mask=self.x_mask,
is_train=self.is_train,
func=config.answer_func,
scope='logits2')
flat_logits = tf.reshape(logits, [-1, M * JX])
flat_yp = tf.nn.softmax(flat_logits) # [-1, M*JX]
flat_logits2 = tf.reshape(logits2, [-1, M * JX])
flat_yp2 = tf.nn.softmax(flat_logits2)
if config.na:
na_bias = tf.get_variable("na_bias", shape=[], dtype='float')
na_bias_tiled = tf.tile(tf.reshape(na_bias, [1, 1]),
[N, 1]) # [N, 1]
concat_flat_logits = tf.concat(
axis=1, values=[na_bias_tiled, flat_logits])
concat_flat_yp = tf.nn.softmax(concat_flat_logits)
na_prob = tf.squeeze(tf.slice(concat_flat_yp, [0, 0], [-1, 1]),
[1])
flat_yp = tf.slice(concat_flat_yp, [0, 1], [-1, -1])
concat_flat_logits2 = tf.concat(
axis=1, values=[na_bias_tiled, flat_logits2])
concat_flat_yp2 = tf.nn.softmax(concat_flat_logits2)
na_prob2 = tf.squeeze(
tf.slice(concat_flat_yp2, [0, 0], [-1, 1]), [1]) # [N]
flat_yp2 = tf.slice(concat_flat_yp2, [0, 1], [-1, -1])
self.concat_logits = concat_flat_logits
self.concat_logits2 = concat_flat_logits2
self.na_prob = na_prob * na_prob2
yp = tf.reshape(flat_yp, [-1, M, JX])
yp2 = tf.reshape(flat_yp2, [-1, M, JX])
wyp = tf.nn.sigmoid(logits2)
self.tensor_dict['g1'] = g1
self.tensor_dict['g2'] = g2
self.logits = flat_logits
self.logits2 = flat_logits2
self.yp = yp
self.yp2 = yp2
self.wyp = wyp
with tf.variable_scope("q_gen"):
# Question Generation Using (Paragraph & Predicted Ans Pos)
NM = config.max_num_sents * config.batch_size
# Separated encoder
#ss = tf.reshape(xx, (-1, JX, dw+dco))
q_worthy = tf.reduce_sum(
tf.to_int32(self.y), axis=2
) # so we get probability distribution of answer-likely. (N, M)
q_worthy = tf.expand_dims(tf.to_int32(tf.argmax(q_worthy, axis=1)),
axis=1) # (N) -> (N, 1)
q_worthy = tf.concat([
tf.expand_dims(tf.range(0, N, dtype=tf.int32), axis=1),
q_worthy
],
axis=1)
# example : [0, 9], [1, 11], [2, 8], [3, 5], [4, 0], [5, 1] ...
ss = tf.gather_nd(xx, q_worthy)
syp = tf.expand_dims(tf.gather_nd(yp, q_worthy), axis=-1)
syp2 = tf.expand_dims(tf.gather_nd(yp2, q_worthy), axis=-1)
ss_with_ans = tf.concat([ss, syp, syp2], axis=2)
qg_dim = 600
cell_fw, cell_bw = rnn.DropoutWrapper(rnn.GRUCell(qg_dim), input_keep_prob=config.input_keep_prob), \
rnn.DropoutWrapper(rnn.GRUCell(qg_dim), input_keep_prob=config.input_keep_prob)
s_outputs, s_states = tf.nn.bidirectional_dynamic_rnn(
cell_fw, cell_bw, ss_with_ans, dtype=tf.float32)
s_outputs = tf.concat(s_outputs, axis=2)
s_states = tf.concat(s_states, axis=1)
start_tokens = tf.zeros([N], dtype=tf.int32)
self.inp_q_with_GO = tf.concat(
[tf.expand_dims(start_tokens, axis=1), self.q], axis=1)
# supervise if mode is train
if config.mode == "train":
emb_q = tf.nn.embedding_lookup(params=word_emb_mat,
ids=self.inp_q_with_GO)
#emb_q = tf.reshape(tf.tile(tf.expand_dims(emb_q, axis=1), [1, M, 1, 1]), (NM, JQ+1, dw))
train_helper = seq2seq.TrainingHelper(emb_q, [JQ] * N)
else:
s_outputs = seq2seq.tile_batch(s_outputs,
multiplier=beam_width)
s_states = seq2seq.tile_batch(s_states, multiplier=beam_width)
cell = rnn.DropoutWrapper(rnn.GRUCell(num_units=qg_dim * 2),
input_keep_prob=config.input_keep_prob)
attention_mechanism = seq2seq.BahdanauAttention(num_units=qg_dim *
2,
memory=s_outputs)
attn_cell = seq2seq.AttentionWrapper(cell,
attention_mechanism,
attention_layer_size=qg_dim *
2,
output_attention=True,
alignment_history=False)
total_glove_vocab_size = 78878 #72686
out_cell = rnn.OutputProjectionWrapper(attn_cell,
VW + total_glove_vocab_size)
if config.mode == "train":
decoder_initial_states = out_cell.zero_state(
batch_size=N, dtype=tf.float32).clone(cell_state=s_states)
decoder = seq2seq.BasicDecoder(
cell=out_cell,
helper=train_helper,
initial_state=decoder_initial_states)
else:
decoder_initial_states = out_cell.zero_state(
batch_size=N * beam_width,
dtype=tf.float32).clone(cell_state=s_states)
decoder = seq2seq.BeamSearchDecoder(
cell=out_cell,
embedding=word_emb_mat,
start_tokens=start_tokens,
end_token=EOS_TOKEN,
initial_state=decoder_initial_states,
beam_width=beam_width,
length_penalty_weight=0.0)
outputs = seq2seq.dynamic_decode(decoder=decoder,
maximum_iterations=JQ)
if config.mode == "train":
gen_q = outputs[0].sample_id
gen_q_prob = outputs[0].rnn_output
gen_q_states = outputs[1]
else:
gen_q = outputs[0].predicted_ids[:, :, 0]
gen_q_prob = tf.nn.embedding_lookup(
params=word_emb_mat, ids=outputs[0].predicted_ids[:, :, 0])
gen_q_states = outputs[1]
self.gen_q = gen_q
self.gen_q_prob = gen_q_prob
self.gen_q_states = gen_q_states
def _build_forward(self):
config = self.config
N, M, JX, JQ, VW, d, W = \
config.batch_size, config.max_num_sents, config.max_sent_size, \
config.max_ques_size, config.word_vocab_size, config.hidden_size, \
config.max_word_size
JX = tf.shape(self.x)[2]
JQ = tf.shape(self.q)[1]
M = tf.shape(self.x)[1]
dc, dw, dco = config.char_emb_size, config.word_emb_size, config.char_out_size
with tf.variable_scope("emb"):
print('word embedding')
# if config.use_char_emb:
# with tf.variable_scope("emb_var"), tf.device("/cpu:0"):
# char_emb_mat = tf.get_variable("char_emb_mat", shape=[VC, dc], dtype='float')
# with tf.variable_scope("char"):
# Acx = tf.nn.embedding_lookup(char_emb_mat, self.cx) # [N, M, JX, W, dc]
# Acq = tf.nn.embedding_lookup(char_emb_mat, self.cq) # [N, JQ, W, dc]
# Acx = tf.reshape(Acx, [-1, JX, W, dc])
# Acq = tf.reshape(Acq, [-1, JQ, W, dc])
# filter_sizes = list(map(int, config.out_channel_dims.split(',')))
# heights = list(map(int, config.filter_heights.split(',')))
# assert sum(filter_sizes) == dco, (filter_sizes, dco)
# with tf.variable_scope("conv"):
# xx = multi_conv1d(Acx, filter_sizes, heights, "VALID", self.is_train, config.keep_prob, scope="xx")
# if config.share_cnn_weights:
# tf.get_variable_scope().reuse_variables()
# qq = multi_conv1d(Acq, filter_sizes, heights, "VALID", self.is_train, config.keep_prob, scope="xx")
# else:
# qq = multi_conv1d(Acq, filter_sizes, heights, "VALID", self.is_train, config.keep_prob, scope="qq")
# xx = tf.reshape(xx, [-1, M, JX, dco])
# qq = tf.reshape(qq, [-1, JQ, dco])
if config.use_word_emb:
with tf.variable_scope("emb_var"), tf.device("/gpu:7"):
if config.mode == 'train':
word_emb_mat = tf.get_variable("word_emb_mat", dtype='float', shape=[VW, dw], initializer=get_initializer(config.emb_mat))
else:
word_emb_mat = tf.get_variable("word_emb_mat", shape=[VW, dw], dtype='float')
# if config.use_glove_for_unk:
# word_emb_mat = tf.concat(0, [word_emb_mat])
print(word_emb_mat)
with tf.name_scope("word"):
print('embedding lookup')
Ax = tf.nn.embedding_lookup(word_emb_mat, self.x) # [N, M, JX, d]
Aq = tf.nn.embedding_lookup(word_emb_mat, self.q) # [N, JQ, d]
self.tensor_dict['x'] = Ax
self.tensor_dict['q'] = Aq
print('embedding lookup ready')
# if config.use_char_emb:
# xx = tf.concat(3, [xx, Ax]) # [N, M, JX, di]
# qq = tf.concat(2, [qq, Aq]) # [N, JQ, di]
# else:
xx = Ax
qq = Aq
# highway network
#if config.highway:
# with tf.variable_scope("highway"):
# xx = highway_network(xx, config.highway_num_layers, True, wd=config.wd, is_train=self.is_train)
# tf.get_variable_scope().reuse_variables()
# qq = highway_network(qq, config.highway_num_layers, True, wd=config.wd, is_train=self.is_train)
self.tensor_dict['xx'] = xx
self.tensor_dict['qq'] = qq
print('context emmbedding')
cell = BasicLSTMCell(d, state_is_tuple=True)
d_cell = SwitchableDropoutWrapper(cell, self.is_train, input_keep_prob=config.input_keep_prob)
x_len = tf.reduce_sum(tf.cast(self.x_mask, 'int32'), 2) # [N, M]
q_len = tf.reduce_sum(tf.cast(self.q_mask, 'int32'), 1) # [N]
print('prepro')
with tf.variable_scope("prepro"):
(fw_u, bw_u), ((_, fw_u_f), (_, bw_u_f)) = bidirectional_dynamic_rnn(d_cell, d_cell, qq, q_len, dtype='float32', scope='u1') # [N, J, d], [N, d]
u = tf.concat(2, [fw_u, bw_u])
if config.share_lstm_weights:
tf.get_variable_scope().reuse_variables()
(fw_h, bw_h), _ = bidirectional_dynamic_rnn(cell, cell, xx, x_len, dtype='float', scope='u1') # [N, M, JX, 2d]
h = tf.concat(3, [fw_h, bw_h]) # [N, M, JX, 2d]
else:
(fw_h, bw_h), _ = bidirectional_dynamic_rnn(cell, cell, xx, x_len, dtype='float', scope='h1') # [N, M, JX, 2d]
h = tf.concat(3, [fw_h, bw_h]) # [N, M, JX, 2d]
self.tensor_dict['u'] = u
self.tensor_dict['h'] = h
print('main pro')
with tf.variable_scope("main"):
if config.dynamic_att:
p0 = h
u = tf.reshape(tf.tile(tf.expand_dims(u, 1), [1, M, 1, 1]), [N * M, JQ, 2 * d])
q_mask = tf.reshape(tf.tile(tf.expand_dims(self.q_mask, 1), [1, M, 1]), [N * M, JQ])
first_cell = AttentionCell(cell, u, mask=q_mask, mapper='sim',
input_keep_prob=self.config.input_keep_prob, is_train=self.is_train)
else:
p0 = attention_layer(config, self.is_train, h, u, h_mask=self.x_mask, u_mask=self.q_mask, scope="p0", tensor_dict=self.tensor_dict)
first_cell = d_cell
(fw_g0, bw_g0), _ = bidirectional_dynamic_rnn(first_cell, first_cell, p0, x_len, dtype='float', scope='g0') # [N, M, JX, 2d]
g0 = tf.concat(3, [fw_g0, bw_g0])
(fw_g1, bw_g1), _ = bidirectional_dynamic_rnn(first_cell, first_cell, g0, x_len, dtype='float', scope='g1') # [N, M, JX, 2d]
g1 = tf.concat(3, [fw_g1, bw_g1])
logits = get_logits([g1, p0], [config.batch_size,config.max_num_sents] , True, wd=config.wd, input_keep_prob=config.input_keep_prob,
mask=self.x_mask, is_train=self.is_train, func='sigmoid', scope='logits1')
# a1i = softsel(tf.reshape(g1, [N, M * JX, 2 * d]), tf.reshape(logits, [N, M * JX]))
# a1i = tf.tile(tf.expand_dims(tf.expand_dims(a1i, 1), 1), [1, M, JX, 1])
# (fw_g2, bw_g2), _ = bidirectional_dynamic_rnn(d_cell, d_cell, tf.concat(3, [p0, g1, a1i, g1 * a1i]),
# x_len, dtype='float', scope='g2') # [N, M, JX, 2d]
# g2 = tf.concat(3, [fw_g2, bw_g2])
# logits2 = get_logits([g2, p0], d, True, wd=config.wd, input_keep_prob=config.input_keep_prob,
# mask=self.x_mask,
# is_train=self.is_train, func=config.answer_func, scope='logits2')
# flat_logits = tf.reshape(logits, [-1, M * JX])
# flat_yp = tf.nn.softmax(flat_logits) # [-1, M*JX]
# yp = tf.reshape(flat_yp, [-1, M, JX])
yp = tf.greater(0.5, logits)
# flat_logits2 = tf.reshape(logits2, [-1, M * JX])
# flat_yp2 = tf.nn.softmax(flat_logits2)
# yp2 = tf.reshape(flat_yp2, [-1, M, JX])
self.tensor_dict['g1'] = g1
# self.tensor_dict['g2'] = g2
self.logits = logits
# self.logits2 = flat_logits2
self.yp = yp
def _build_forward(self):
config = self.config
N, M, JX, JQ, VW, VC, d, W = \
config.batch_size, config.max_num_sents, config.max_sent_size, \
config.max_ques_size, config.word_vocab_size, config.char_vocab_size, config.hidden_size, \
config.max_word_size
JX = tf.shape(self.x)[2]
JQ = tf.shape(self.q)[1]
M = tf.shape(self.x)[1]
dc, dw, dco = config.char_emb_size, config.word_emb_size, config.char_out_size
with tf.variable_scope("emb"):
if config.use_char_emb:
with tf.variable_scope("emb_var"), tf.device("/cpu:0"):
char_emb_mat = tf.get_variable("char_emb_mat",
shape=[VC, dc],
dtype='float')
with tf.variable_scope("char"):
Acx = tf.nn.embedding_lookup(char_emb_mat,
self.cx) # [N, M, JX, W, dc]
Acq = tf.nn.embedding_lookup(char_emb_mat,
self.cq) # [N, JQ, W, dc]
Acx = tf.reshape(Acx, [-1, JX, W, dc])
Acq = tf.reshape(Acq, [-1, JQ, W, dc])
filter_sizes = list(
map(int, config.out_channel_dims.split(',')))
heights = list(map(int, config.filter_heights.split(',')))
assert sum(filter_sizes) == dco, (filter_sizes, dco)
with tf.variable_scope("conv"):
xx = multi_conv1d(Acx,
filter_sizes,
heights,
"VALID",
self.is_train,
config.keep_prob,
scope="xx")
if config.share_cnn_weights:
tf.get_variable_scope().reuse_variables()
qq = multi_conv1d(Acq,
filter_sizes,
heights,
"VALID",
self.is_train,
config.keep_prob,
scope="xx")
else:
qq = multi_conv1d(Acq,
filter_sizes,
heights,
"VALID",
self.is_train,
config.keep_prob,
scope="qq")
xx = tf.reshape(xx, [-1, M, JX, dco])
qq = tf.reshape(qq, [-1, JQ, dco])
if config.use_word_emb:
with tf.variable_scope("emb_var"), tf.device("/cpu:0"):
if config.mode == 'train':
word_emb_mat = tf.get_variable(
"word_emb_mat",
dtype='float',
shape=[VW, dw],
initializer=get_initializer(config.emb_mat))
else:
word_emb_mat = tf.get_variable("word_emb_mat",
shape=[VW, dw],
dtype='float')
if config.use_glove_for_unk:
word_emb_mat = tf.concat(
axis=0, values=[word_emb_mat, self.new_emb_mat])
with tf.name_scope("word"):
Ax = tf.nn.embedding_lookup(word_emb_mat,
self.x) # [N, M, JX, d]
Aq = tf.nn.embedding_lookup(word_emb_mat,
self.q) # [N, JQ, d]
self.tensor_dict['x'] = Ax
self.tensor_dict['q'] = Aq
if config.use_char_emb:
xx = tf.concat(axis=3, values=[xx, Ax]) # [N, M, JX, di]
qq = tf.concat(axis=2, values=[qq, Aq]) # [N, JQ, di]
else:
xx = Ax
qq = Aq
# highway network
if config.highway:
with tf.variable_scope("highway"):
xx = highway_network(xx,
config.highway_num_layers,
True,
wd=config.wd,
is_train=self.is_train)
tf.get_variable_scope().reuse_variables()
qq = highway_network(qq,
config.highway_num_layers,
True,
wd=config.wd,
is_train=self.is_train)
self.tensor_dict['xx'] = xx
self.tensor_dict['qq'] = qq
cell_fw = BasicLSTMCell(d, state_is_tuple=True)
cell_bw = BasicLSTMCell(d, state_is_tuple=True)
d_cell_fw = SwitchableDropoutWrapper(
cell_fw, self.is_train, input_keep_prob=config.input_keep_prob)
d_cell_bw = SwitchableDropoutWrapper(
cell_bw, self.is_train, input_keep_prob=config.input_keep_prob)
cell2_fw = BasicLSTMCell(d, state_is_tuple=True)
cell2_bw = BasicLSTMCell(d, state_is_tuple=True)
d_cell2_fw = SwitchableDropoutWrapper(
cell2_fw, self.is_train, input_keep_prob=config.input_keep_prob)
d_cell2_bw = SwitchableDropoutWrapper(
cell2_bw, self.is_train, input_keep_prob=config.input_keep_prob)
cell3_fw = BasicLSTMCell(d, state_is_tuple=True)
cell3_bw = BasicLSTMCell(d, state_is_tuple=True)
d_cell3_fw = SwitchableDropoutWrapper(
cell3_fw, self.is_train, input_keep_prob=config.input_keep_prob)
d_cell3_bw = SwitchableDropoutWrapper(
cell3_bw, self.is_train, input_keep_prob=config.input_keep_prob)
cell4_fw = BasicLSTMCell(d, state_is_tuple=True)
cell4_bw = BasicLSTMCell(d, state_is_tuple=True)
d_cell4_fw = SwitchableDropoutWrapper(
cell4_fw, self.is_train, input_keep_prob=config.input_keep_prob)
d_cell4_bw = SwitchableDropoutWrapper(
cell4_bw, self.is_train, input_keep_prob=config.input_keep_prob)
x_len = tf.reduce_sum(tf.cast(self.x_mask, 'int32'), 2) # [N, M]
q_len = tf.reduce_sum(tf.cast(self.q_mask, 'int32'), 1) # [N]
with tf.variable_scope("prepro"):
(fw_u, bw_u), ((_, fw_u_f), (_,
bw_u_f)) = bidirectional_dynamic_rnn(
d_cell_fw,
d_cell_bw,
qq,
q_len,
dtype='float',
scope='u1') # [N, J, d], [N, d]
u = tf.concat(axis=2, values=[fw_u, bw_u])
if config.share_lstm_weights:
tf.get_variable_scope().reuse_variables()
(fw_h, bw_h), _ = bidirectional_dynamic_rnn(
cell_fw, cell_bw, xx, x_len, dtype='float',
scope='u1') # [N, M, JX, 2d]
h = tf.concat(axis=3, values=[fw_h, bw_h]) # [N, M, JX, 2d]
else:
(fw_h, bw_h), _ = bidirectional_dynamic_rnn(
cell_fw, cell_bw, xx, x_len, dtype='float',
scope='h1') # [N, M, JX, 2d]
h = tf.concat(axis=3, values=[fw_h, bw_h]) # [N, M, JX, 2d]
self.tensor_dict['u'] = u
self.tensor_dict['h'] = h
with tf.variable_scope("main"):
if config.dynamic_att:
p0 = h
u = tf.reshape(tf.tile(tf.expand_dims(u, 1), [1, M, 1, 1]),
[N * M, JQ, 2 * d])
q_mask = tf.reshape(
tf.tile(tf.expand_dims(self.q_mask, 1), [1, M, 1]),
[N * M, JQ])
first_cell_fw = AttentionCell(
cell2_fw,
u,
mask=q_mask,
mapper='sim',
input_keep_prob=self.config.input_keep_prob,
is_train=self.is_train)
first_cell_bw = AttentionCell(
cell2_bw,
u,
mask=q_mask,
mapper='sim',
input_keep_prob=self.config.input_keep_prob,
is_train=self.is_train)
second_cell_fw = AttentionCell(
cell3_fw,
u,
mask=q_mask,
mapper='sim',
input_keep_prob=self.config.input_keep_prob,
is_train=self.is_train)
second_cell_bw = AttentionCell(
cell3_bw,
u,
mask=q_mask,
mapper='sim',
input_keep_prob=self.config.input_keep_prob,
is_train=self.is_train)
else:
p0 = attention_layer(config,
self.is_train,
h,
u,
h_mask=self.x_mask,
u_mask=self.q_mask,
scope="p0",
tensor_dict=self.tensor_dict)
first_cell_fw = d_cell2_fw
second_cell_fw = d_cell3_fw
first_cell_bw = d_cell2_bw
second_cell_bw = d_cell3_bw
config.ruminating_layer = True
if config.ruminating_layer:
'''
RUMINATING LAYER
'''
with tf.variable_scope('rum_layer'):
print('-' * 5 + "RUMINATING LAYER" + '-' * 5)
print("Context", xx) #[N,M,JX,2d]
print("Question", qq) #[N,JQ,2d]
print("p0", p0) #[N,M,JX,8D]
sum_cell = BasicLSTMCell(d, state_is_tuple=True)
(s_f,
s_b), _ = bidirectional_dynamic_rnn(sum_cell,
sum_cell,
p0,
x_len,
dtype=tf.float32,
scope="sum_layer")
batch_lens = (tf.reshape(x_len, [N * M]))
s_f = tf.reshape(s_f, [N * M, JX, d])
s_b = tf.reshape(s_b, [N * M, JX, d])
s_fout = tf.reshape(extract_axis_1(s_f, batch_lens),
[N, M, d])
s_bout = tf.reshape(extract_axis_1(s_b, batch_lens),
[N, M, d])
s = tf.concat(axis=2, values=[s_fout, s_bout]) # [N,M,2d]
print("summarization layer", s)
print('-' * 5 + "QUESTION RUMINATE LAYER" + '-' * 5)
S_Q = tf.tile(tf.expand_dims(s, 2),
[1, 1, JQ, 1]) # [N,M,JQ,2d]
S_cell_fw = BasicLSTMCell(d, state_is_tuple=True)
S_cell_bw = BasicLSTMCell(d, state_is_tuple=True)
(fw_hq,
bw_hq), _ = bidirectional_dynamic_rnn(S_cell_fw,
S_cell_bw,
S_Q,
q_len,
dtype=tf.float32,
scope="S_Q")
S_Q = tf.concat(axis=3, values=[fw_hq, bw_hq])
q_m = tf.reshape(tf.expand_dims(qq, 1), [N, M, JQ, 2 * d])
with tf.variable_scope("question_rum_layer"):
Q_hat = ruminating_layer(S_Q, q_m, N, M, JQ, d)
print("Q_hat", Q_hat) #[N,M,JQ,2d]
print('-' * 5 + "CONTEXT RUMINATE LAYER" + '-' * 5)
S_C = tf.tile(tf.expand_dims(s, 2),
[1, 1, JX, 1]) # [N,M,JX,2d]
C_cell_fw = BasicLSTMCell(d, state_is_tuple=True)
C_cell_bw = BasicLSTMCell(d, state_is_tuple=True)
(fw_h,
bw_h), _ = bidirectional_dynamic_rnn(C_cell_fw,
C_cell_bw,
S_C,
x_len,
dtype=tf.float32,
scope="S_C")
S_C = tf.concat(axis=3, values=[fw_h, bw_h]) #[N,M,JX,2d]
with tf.variable_scope("context_rum_layer"):
C_hat = ruminating_layer(S_C, xx, N, M, JX, d)
print("C_hat", C_hat) #[N,M,JX,2d]
#Second Hop bi-Attention
print('-' * 5 + "SECOND HOP ATTENTION" + '-' * 5)
sh_aug = tf.tile(tf.expand_dims(C_hat, 3),
[1, 1, 1, JQ, 1]) #[N,M,JX,2d]
su_aug = tf.tile(tf.expand_dims(Q_hat, 2),
[1, 1, JX, 1, 1]) #[N,M,JQ,2d]
sh_mask_aug = tf.tile(tf.expand_dims(self.x_mask, -1),
[1, 1, 1, JQ])
su_mask_aug = tf.tile(
tf.expand_dims(tf.expand_dims(self.q_mask, 1), 1),
[1, M, JX, 1])
shu_mask = sh_mask_aug & su_mask_aug
su_logits = get_logits([sh_aug, su_aug],
None,
True,
wd=config.wd,
mask=shu_mask,
is_train=True,
func=config.logit_func,
scope='su_logits')
su_a = softsel(su_aug, su_logits)
sh_a = softsel(C_hat, tf.reduce_max(su_logits, 3))
sh_a = tf.tile(tf.expand_dims(sh_a, 2), [1, 1, JX, 1])
p00 = tf.concat(
axis=3,
values=[C_hat, su_a, C_hat * su_a, C_hat * sh_a])
print("p00", p00) #[N,M,JX,8d]
p0 = p00
print('-' * 5 + "END RUMINATING LAYER" + '-' * 5)
(fw_g0, bw_g0), _ = bidirectional_dynamic_rnn(
first_cell_fw,
first_cell_bw,
p0,
x_len,
dtype='float',
scope='g0') # [N, M, JX, 2d]
g0 = tf.concat(axis=3, values=[fw_g0, bw_g0])
(fw_g1, bw_g1), _ = bidirectional_dynamic_rnn(
second_cell_fw,
second_cell_bw,
g0,
x_len,
dtype='float',
scope='g1') # [N, M, JX, 2d]
g1 = tf.concat(axis=3, values=[fw_g1, bw_g1])
logits = get_logits([g1, p0],
d,
True,
wd=config.wd,
input_keep_prob=config.input_keep_prob,
mask=self.x_mask,
is_train=self.is_train,
func=config.answer_func,
scope='logits1')
a1i = softsel(tf.reshape(g1, [N, M * JX, 2 * d]),
tf.reshape(logits, [N, M * JX]))
a1i = tf.tile(tf.expand_dims(tf.expand_dims(a1i, 1), 1),
[1, M, JX, 1])
(fw_g2, bw_g2), _ = bidirectional_dynamic_rnn(
d_cell4_fw,
d_cell4_bw,
tf.concat(axis=3, values=[p0, g1, a1i, g1 * a1i]),
x_len,
dtype='float',
scope='g2') # [N, M, JX, 2d]
g2 = tf.concat(axis=3, values=[fw_g2, bw_g2])
logits2 = get_logits([g2, p0],
d,
True,
wd=config.wd,
input_keep_prob=config.input_keep_prob,
mask=self.x_mask,
is_train=self.is_train,
func=config.answer_func,
scope='logits2')
flat_logits = tf.reshape(logits, [-1, M * JX])
flat_yp = tf.nn.softmax(flat_logits) # [-1, M*JX]
flat_logits2 = tf.reshape(logits2, [-1, M * JX])
flat_yp2 = tf.nn.softmax(flat_logits2)
self.tensor_dict['g1'] = g1
self.tensor_dict['g2'] = g2
if config.na:
na_bias = tf.get_variable("na_bias", shape=[], dtype='float')
na_bias_tiled = tf.tile(tf.reshape(na_bias, [1, 1]),
[N, 1]) # [N, 1]
concat_flat_logits = tf.concat(
axis=1, values=[na_bias_tiled, flat_logits])
concat_flat_yp = tf.nn.softmax(concat_flat_logits)
na_prob = tf.squeeze(tf.slice(concat_flat_yp, [0, 0], [-1, 1]),
[1])
flat_yp = tf.slice(concat_flat_yp, [0, 1], [-1, -1])
concat_flat_logits2 = tf.concat(
axis=1, values=[na_bias_tiled, flat_logits2])
concat_flat_yp2 = tf.nn.softmax(concat_flat_logits2)
na_prob2 = tf.squeeze(
tf.slice(concat_flat_yp2, [0, 0], [-1, 1]), [1]) # [N]
flat_yp2 = tf.slice(concat_flat_yp2, [0, 1], [-1, -1])
self.concat_logits = concat_flat_logits
self.concat_logits2 = concat_flat_logits2
self.na_prob = na_prob * na_prob2
yp = tf.reshape(flat_yp, [-1, M, JX])
yp2 = tf.reshape(flat_yp2, [-1, M, JX])
wyp = tf.nn.sigmoid(logits2)
self.logits = flat_logits
self.logits2 = flat_logits2
self.yp = yp
self.yp2 = yp2
self.wyp = wyp
def _build_forward(self):
config = self.config
N, M, JX, JQ, VW, VC, d, W = \
config.batch_size, config.max_num_sents, config.max_sent_size, \
config.max_ques_size, config.word_vocab_size, config.char_vocab_size, config.hidden_size, \
config.max_word_size
JX = tf.shape(self.x)[2]
JQ = tf.shape(self.q)[1]
M = tf.shape(self.x)[1]
dc, dw, dco = config.char_emb_size, config.word_emb_size, config.char_out_size
with tf.variable_scope("emb"):
if config.use_char_emb:
with tf.variable_scope("emb_var"), tf.device("/cpu:0"):
char_emb_mat = tf.get_variable("char_emb_mat",
shape=[VC, dc],
dtype='float')
with tf.variable_scope("char"):
Acx = tf.nn.embedding_lookup(char_emb_mat,
self.cx) # [N, M, JX, W, dc]
Acq = tf.nn.embedding_lookup(char_emb_mat,
self.cq) # [N, JQ, W, dc]
Acx = tf.reshape(Acx, [-1, JX, W, dc])
Acq = tf.reshape(Acq, [-1, JQ, W, dc])
filter_sizes = list(
map(int, config.out_channel_dims.split(',')))
heights = list(map(int, config.filter_heights.split(',')))
assert sum(filter_sizes) == dco
with tf.variable_scope("conv"):
xx = multi_conv1d(Acx,
filter_sizes,
heights,
"VALID",
self.is_train,
config.keep_prob,
scope="xx")
if config.share_cnn_weights:
tf.get_variable_scope().reuse_variables()
qq = multi_conv1d(Acq,
filter_sizes,
heights,
"VALID",
self.is_train,
config.keep_prob,
scope="xx")
else:
qq = multi_conv1d(Acq,
filter_sizes,
heights,
"VALID",
self.is_train,
config.keep_prob,
scope="qq")
xx = tf.reshape(xx, [-1, M, JX, dco])
qq = tf.reshape(qq, [-1, JQ, dco])
if config.use_word_emb:
with tf.variable_scope("emb_var"), tf.device("/cpu:0"):
if config.mode == 'train':
word_emb_mat = tf.get_variable(
"word_emb_mat",
dtype='float',
shape=[VW, dw],
initializer=get_initializer(config.emb_mat))
else:
word_emb_mat = tf.get_variable("word_emb_mat",
shape=[VW, dw],
dtype='float')
if config.use_glove_for_unk:
word_emb_mat = tf.concat(
0, [word_emb_mat, self.new_emb_mat])
with tf.name_scope("word"):
Ax = tf.nn.embedding_lookup(word_emb_mat,
self.x) # [N, M, JX, d]
Aq = tf.nn.embedding_lookup(word_emb_mat,
self.q) # [N, JQ, d]
self.tensor_dict['x'] = Ax
self.tensor_dict['q'] = Aq
if config.use_char_emb:
xx = tf.concat(3, [xx, Ax]) # [N, M, JX, di]
qq = tf.concat(2, [qq, Aq]) # [N, JQ, di]
else:
xx = Ax
qq = Aq
# highway network
if config.highway:
with tf.variable_scope("highway"):
xx = highway_network(xx,
config.highway_num_layers,
True,
wd=config.wd,
is_train=self.is_train)
tf.get_variable_scope().reuse_variables()
qq = highway_network(qq,
config.highway_num_layers,
True,
wd=config.wd,
is_train=self.is_train)
self.tensor_dict['xx'] = xx
self.tensor_dict['qq'] = qq
cell = BasicLSTMCell(d, state_is_tuple=True)
d_cell = SwitchableDropoutWrapper(
cell, self.is_train, input_keep_prob=config.input_keep_prob)
x_len = tf.reduce_sum(tf.cast(self.x_mask, 'int32'), 2) # [N, M]
q_len = tf.reduce_sum(tf.cast(self.q_mask, 'int32'), 1) # [N]
with tf.variable_scope("prepro"):
(fw_u, bw_u), ((_, fw_u_f), (_,
bw_u_f)) = bidirectional_dynamic_rnn(
d_cell,
d_cell,
qq,
q_len,
dtype='float',
scope='u1') # [N, J, d], [N, d]
u = tf.concat(2, [fw_u, bw_u])
if config.two_prepro_layers:
(fw_u, bw_u), ((_, fw_u_f),
(_, bw_u_f)) = bidirectional_dynamic_rnn(
d_cell,
d_cell,
u,
q_len,
dtype='float',
scope='u2') # [N, J, d], [N, d]
u = tf.concat(2, [fw_u, bw_u])
if config.share_lstm_weights:
tf.get_variable_scope().reuse_variables()
(fw_h, bw_h), _ = bidirectional_dynamic_rnn(
cell, cell, xx, x_len, dtype='float',
scope='u1') # [N, M, JX, 2d]
h = tf.concat(3, [fw_h, bw_h]) # [N, M, JX, 2d]
if config.two_prepro_layers:
(fw_h, bw_h), _ = bidirectional_dynamic_rnn(
cell, cell, h, x_len, dtype='float',
scope='u2') # [N, M, JX, 2d]
h = tf.concat(3, [fw_h, bw_h]) # [N, M, JX, 2d]
else:
(fw_h, bw_h), _ = bidirectional_dynamic_rnn(
cell, cell, xx, x_len, dtype='float',
scope='h1') # [N, M, JX, 2d]
h = tf.concat(3, [fw_h, bw_h]) # [N, M, JX, 2d]
if config.two_prepro_layers:
(fw_h, bw_h), _ = bidirectional_dynamic_rnn(
cell, cell, h, x_len, dtype='float',
scope='h2') # [N, M, JX, 2d]
h = tf.concat(3, [fw_h, bw_h]) # [N, M, JX, 2d]
self.tensor_dict['u'] = u
self.tensor_dict['h'] = h
with tf.variable_scope("main"):
if config.dynamic_att:
p0 = h
u = tf.reshape(tf.tile(tf.expand_dims(u, 1), [1, M, 1, 1]),
[N * M, JQ, 2 * d])
q_mask = tf.reshape(
tf.tile(tf.expand_dims(self.q_mask, 1), [1, M, 1]),
[N * M, JQ])
first_cell = AttentionCell(
cell,
u,
mask=q_mask,
mapper='sim',
input_keep_prob=self.config.input_keep_prob,
is_train=self.is_train)
else:
p0 = attention_layer(config,
self.is_train,
h,
u,
h_mask=self.x_mask,
u_mask=self.q_mask,
scope="p0",
tensor_dict=self.tensor_dict)
first_cell = d_cell
(fw_g0, bw_g0), _ = bidirectional_dynamic_rnn(
first_cell, first_cell, p0, x_len, dtype='float',
scope='g0') # [N, M, JX, 2d]
g0 = tf.concat(3, [fw_g0, bw_g0])
(fw_g1, bw_g1), _ = bidirectional_dynamic_rnn(
first_cell, first_cell, g0, x_len, dtype='float',
scope='g1') # [N, M, JX, 2d]
g1 = tf.concat(3, [fw_g1, bw_g1])
if config.late:
(fw_g2, bw_g2), _ = bidirectional_dynamic_rnn(
d_cell,
d_cell,
tf.concat(3, [g1, p0]),
x_len,
dtype='float',
scope='g2') # [N, M, JX, 2d]
g2 = tf.concat(3, [fw_g2, bw_g2])
# logits2 = u_logits(config, self.is_train, tf.concat(3, [g1, a1i]), u, h_mask=self.x_mask, u_mask=self.q_mask, scope="logits2")
logits = get_logits([g1, g2, p0],
d,
True,
wd=config.wd,
input_keep_prob=config.input_keep_prob,
mask=self.x_mask,
is_train=self.is_train,
func=config.answer_func,
scope='logits1')
if config.feed_gt:
logy = tf.log(tf.cast(self.y, 'float') + VERY_SMALL_NUMBER)
logits = tf.cond(self.is_train, lambda: logy,
lambda: logits)
if config.feed_hard:
hard_yp = tf.argmax(tf.reshape(logits, [N, M * JX]), 1)
hard_logits = tf.reshape(tf.one_hot(hard_yp, M * JX),
[N, M, JX]) # [N, M, JX]
logits = tf.cond(self.is_train, lambda: logits,
lambda: hard_logits)
flat_logits = tf.reshape(logits, [-1, M * JX])
flat_yp = tf.nn.softmax(flat_logits) # [-1, M*JX]
yp = tf.reshape(flat_yp, [-1, M, JX])
logits2 = get_logits([g1, g2, p0],
d,
True,
wd=config.wd,
input_keep_prob=config.input_keep_prob,
mask=self.x_mask,
is_train=self.is_train,
func=config.answer_func,
scope='logits2')
flat_logits2 = tf.reshape(logits2, [-1, M * JX])
flat_yp2 = tf.nn.softmax(flat_logits2)
yp2 = tf.reshape(flat_yp2, [-1, M, JX])
else:
logits = get_logits([g1, p0],
d,
True,
wd=config.wd,
input_keep_prob=config.input_keep_prob,
mask=self.x_mask,
is_train=self.is_train,
func=config.answer_func,
scope='logits1')
a1i = softsel(tf.reshape(g1, [N, M * JX, 2 * d]),
tf.reshape(logits, [N, M * JX]))
if config.feed_gt:
logy = tf.log(tf.cast(self.y, 'float') + VERY_SMALL_NUMBER)
logits = tf.cond(self.is_train, lambda: logy,
lambda: logits)
if config.feed_hard:
hard_yp = tf.argmax(tf.reshape(logits, [N, M * JX]), 1)
hard_logits = tf.reshape(tf.one_hot(hard_yp, M * JX),
[N, M, JX]) # [N, M, JX]
logits = tf.cond(self.is_train, lambda: logits,
lambda: hard_logits)
flat_logits = tf.reshape(logits, [-1, M * JX])
flat_yp = tf.nn.softmax(flat_logits) # [-1, M*JX]
yp = tf.reshape(flat_yp, [-1, M, JX])
a1i = tf.tile(tf.expand_dims(tf.expand_dims(a1i, 1), 1),
[1, M, JX, 1])
yp_aug = tf.expand_dims(yp, -1)
g1yp = g1 * yp_aug
if config.prev_mode == 'a':
prev = a1i
elif config.prev_mode == 'y':
prev = yp_aug
elif config.prev_mode == 'gy':
prev = g1yp
else:
raise Exception()
(fw_g2, bw_g2), _ = bidirectional_dynamic_rnn(
d_cell,
d_cell,
tf.concat(3, [p0, g1, prev, g1 * prev]),
x_len,
dtype='float',
scope='g2') # [N, M, JX, 2d]
g2 = tf.concat(3, [fw_g2, bw_g2])
# logits2 = u_logits(config, self.is_train, tf.concat(3, [g1, a1i]), u, h_mask=self.x_mask, u_mask=self.q_mask, scope="logits2")
logits2 = get_logits([g2, p0],
d,
True,
wd=config.wd,
input_keep_prob=config.input_keep_prob,
mask=self.x_mask,
is_train=self.is_train,
func=config.answer_func,
scope='logits2')
flat_logits2 = tf.reshape(logits2, [-1, M * JX])
flat_yp2 = tf.nn.softmax(flat_logits2)
yp2 = tf.reshape(flat_yp2, [-1, M, JX])
self.tensor_dict['g1'] = g1
self.tensor_dict['g2'] = g2
self.logits = flat_logits
self.logits2 = flat_logits2
self.yp = yp
self.yp2 = yp2
def _build_forward(self):
config = self.config
N, M, JX, JQ, VW, VC, d, W = \
config.batch_size, config.max_num_sents, config.max_sent_size, \
config.max_ques_size, config.word_vocab_size, config.char_vocab_size, config.hidden_size, \
config.max_word_size
JX = tf.shape(self.x)[2]
JQ = tf.shape(self.q)[1]
M = tf.shape(self.x)[1]
dc, dw, dco = config.char_emb_size, config.word_emb_size, config.char_out_size
with tf.variable_scope("emb"):
if config.use_word_emb:
with tf.variable_scope("emb_var"), tf.device("/cpu:0"):
if config.mode == 'train':
word_emb_mat = tf.get_variable(
"word_emb_mat",
dtype='float',
shape=[VW, dw],
initializer=get_initializer(config.emb_mat))
else:
word_emb_mat = tf.get_variable("word_emb_mat",
shape=[VW, dw],
dtype='float')
if config.use_glove_for_unk:
word_emb_mat = tf.concat(
0, [word_emb_mat, self.new_emb_mat])
print(word_emb_mat.get_shape().as_list())
with tf.name_scope("word"):
Ax = tf.nn.embedding_lookup(word_emb_mat,
self.x) # [N, M, JX, d]
Aq = tf.nn.embedding_lookup(word_emb_mat,
self.q) # [N, JQ, d]
self.tensor_dict['x'] = Ax
self.tensor_dict['q'] = Aq
xx = Ax
qq = Aq
# highway network
if config.highway:
with tf.variable_scope("highway"):
xx = highway_network(xx,
config.highway_num_layers,
True,
wd=config.wd,
is_train=self.is_train)
tf.get_variable_scope().reuse_variables()
qq = highway_network(qq,
config.highway_num_layers,
True,
wd=config.wd,
is_train=self.is_train)
cell = BasicLSTMCell(d, state_is_tuple=True)
d_cell = SwitchableDropoutWrapper(
cell, self.is_train, input_keep_prob=config.input_keep_prob)
x_len = tf.reduce_sum(tf.cast(self.x_mask, 'int32'), 2) # [N, M]
q_len = tf.reduce_sum(tf.cast(self.q_mask, 'int32'), 1) # [N]
with tf.variable_scope("prepro"):
(fw_u, bw_u), ((_, fw_u_f), (_,
bw_u_f)) = bidirectional_dynamic_rnn(
d_cell,
d_cell,
qq,
q_len,
dtype='float',
scope='u1') # [N, J, d], [N, d]
u = tf.concat(2, [fw_u, bw_u])
if config.share_lstm_weights:
tf.get_variable_scope().reuse_variables()
(fw_h, bw_h), _ = bidirectional_dynamic_rnn(
cell, cell, xx, x_len, dtype='float',
scope='u1') # [N, M, JX, 2d]
h = tf.concat(3, [fw_h, bw_h]) # [N, M, JX, 2d]
else:
(fw_h, bw_h), _ = bidirectional_dynamic_rnn(
cell, cell, xx, x_len, dtype='float',
scope='h1') # [N, M, JX, 2d]
h = tf.concat(3, [fw_h, bw_h]) # [N, M, JX, 2d]
self.tensor_dict['u'] = u
self.tensor_dict['h'] = h
with tf.variable_scope("main"):
if config.dynamic_att:
p0 = h
u = tf.reshape(tf.tile(tf.expand_dims(u, 1), [1, M, 1, 1]),
[N * M, JQ, 2 * d])
q_mask = tf.reshape(
tf.tile(tf.expand_dims(self.q_mask, 1), [1, M, 1]),
[N * M, JQ])
first_cell = AttentionCell(
cell,
u,
mask=q_mask,
mapper='sim',
input_keep_prob=self.config.input_keep_prob,
is_train=self.is_train)
else:
p0, p1 = attention_layer(config,
self.is_train,
h,
u,
h_mask=self.x_mask,
u_mask=self.q_mask,
scope="p0",
tensor_dict=self.tensor_dict)
first_cell = d_cell
# with tf.variable_scope("activate"):
# p0 = tf.nn.relu(_linear(tf.reshape(p0,[-1,1200]),300,bias=0.01,bias_start=0.0,scope='relu'))
# if config.share_lstm_weights:
# tf.get_variable_scope().reuse_variables()
# p1 = tf.nn.relu(_linear(tf.reshape(p1,[-1,1200]),300,bias=0.01,bias_start=0.0,scope='relu'))
with tf.variable_scope('two_lstm'):
p0 = tf.reshape(p0, [N, 1, -1, 300])
p1 = tf.reshape(p1, [N, 1, -1, 300])
(fw_g0, bw_g0), _ = bidirectional_dynamic_rnn(
first_cell, first_cell, p0, x_len, dtype='float',
scope='g0') # [N, M, JX, 2d]
g0 = tf.concat(3, [fw_g0, bw_g0])
q_len_new = tf.tile(tf.expand_dims(q_len, 1), [1, M])
if config.share_lstm_weights:
tf.get_variable_scope().reuse_variables()
(fw_g1, bw_g1), _ = bidirectional_dynamic_rnn(
first_cell,
first_cell,
p1,
q_len_new,
dtype='float',
scope='g0') # [N, M, JX, 2d]
g1 = tf.concat(3, [fw_g1, bw_g1])
# with tf.variable_scope('two_lstm_1'):
# (fw_g2, bw_g2), _ = bidirectional_dynamic_rnn(first_cell, first_cell, g0, x_len, dtype='float', scope='g0') # [N, M, JX, 2d]
# g2 = tf.concat(3, [fw_g2, bw_g2])
# q_len_new = tf.tile(tf.expand_dims(q_len,1),[1,M])
# if config.share_lstm_weights:
# tf.get_variable_scope().reuse_variables()
# (fw_g3, bw_g3), _ = bidirectional_dynamic_rnn(first_cell, first_cell, g1, q_len_new, dtype='float', scope='g0') # [N, M, JX, 2d]
# g3 = tf.concat(3, [fw_g3, bw_g3])
g0 = tf.reduce_sum(tf.reduce_max(g0, 2), 1)
g1 = tf.reduce_sum(tf.reduce_max(g1, 2), 1)
logits = _linear([g0, g1, tf.abs(tf.subtract(g0, g1)), g0 * g1],
2,
bias=0.01,
bias_start=0.0,
scope='logits1')
flat_logits2 = tf.reshape(logits, [N, 2])
yp = tf.nn.softmax(flat_logits2) # [-1, M*JX]
self.tensor_dict['g0'] = g0
self.tensor_dict['g1'] = g1
self.logits = flat_logits2
self.yp = yp
def _build_forward(self):
config = self.config
N, M, JX, JQ, VW, d, dc, W = \
config.batch_size, config.max_num_sents, config.max_sent_size, \
config.max_ques_size, config.word_vocab_size, config.hidden_size, \
config.char_emb_size, config.max_word_size
H = config.max_tree_height
x_mask = self.x > 0
q_mask = self.q > 0
tx_mask = self.tx > 0 # [N, M, H, JX]
with tf.variable_scope("char_emb"):
char_emb_mat = tf.get_variable("char_emb_mat", shape=[VC, dc], dtype='float')
Acx = tf.nn.embedding_lookup(char_emb_mat, self.cx) # [N, M, JX, W, dc]
Acq = tf.nn.embedding_lookup(char_emb_mat, self.cq) # [N, JQ, W, dc]
filter = tf.get_variable("filter", shape=[1, config.char_filter_height, dc, d], dtype='float')
bias = tf.get_variable("bias", shape=[d], dtype='float')
strides = [1, 1, 1, 1]
Acx = tf.reshape(Acx, [-1, JX, W, dc])
Acq = tf.reshape(Acq, [-1, JQ, W, dc])
xxc = tf.nn.conv2d(Acx, filter, strides, "VALID") + bias # [N*M, JX, W/filter_stride, d]
qqc = tf.nn.conv2d(Acq, filter, strides, "VALID") + bias # [N, JQ, W/filter_stride, d]
xxc = tf.reshape(tf.reduce_max(tf.nn.relu(xxc), 2), [-1, M, JX, d])
qqc = tf.reshape(tf.reduce_max(tf.nn.relu(qqc), 2), [-1, JQ, d])
with tf.variable_scope("word_emb"):
if config.mode == 'train':
word_emb_mat = tf.get_variable("word_emb_mat", dtype='float', shape=[VW, config.word_emb_size], initializer=get_initializer(config.emb_mat))
else:
word_emb_mat = tf.get_variable("word_emb_mat", shape=[VW, config.word_emb_size], dtype='float')
Ax = tf.nn.embedding_lookup(word_emb_mat, self.x) # [N, M, JX, d]
Aq = tf.nn.embedding_lookup(word_emb_mat, self.q) # [N, JQ, d]
# Ax = linear([Ax], d, False, scope='Ax_reshape')
# Aq = linear([Aq], d, False, scope='Aq_reshape')
xx = tf.concat(3, [xxc, Ax]) # [N, M, JX, 2d]
qq = tf.concat(2, [qqc, Aq]) # [N, JQ, 2d]
D = d + config.word_emb_size
with tf.variable_scope("pos_emb"):
pos_emb_mat = tf.get_variable("pos_emb_mat", shape=[config.pos_vocab_size, d], dtype='float')
Atx = tf.nn.embedding_lookup(pos_emb_mat, self.tx) # [N, M, H, JX, d]
cell = BasicLSTMCell(D, state_is_tuple=True)
cell = SwitchableDropoutWrapper(cell, self.is_train, input_keep_prob=config.input_keep_prob)
x_len = tf.reduce_sum(tf.cast(x_mask, 'int32'), 2) # [N, M]
q_len = tf.reduce_sum(tf.cast(q_mask, 'int32'), 1) # [N]
with tf.variable_scope("rnn"):
(fw_h, bw_h), _ = bidirectional_dynamic_rnn(cell, cell, xx, x_len, dtype='float', scope='start') # [N, M, JX, 2d]
tf.get_variable_scope().reuse_variables()
(fw_us, bw_us), (_, (fw_u, bw_u)) = bidirectional_dynamic_rnn(cell, cell, qq, q_len, dtype='float', scope='start') # [N, J, d], [N, d]
u = (fw_u + bw_u) / 2.0
h = (fw_h + bw_h) / 2.0
with tf.variable_scope("h"):
no_op_cell = NoOpCell(D)
tree_rnn_cell = TreeRNNCell(no_op_cell, d, tf.reduce_max)
initial_state = tf.reshape(h, [N*M*JX, D]) # [N*M*JX, D]
inputs = tf.concat(4, [Atx, tf.cast(self.tx_edge_mask, 'float')]) # [N, M, H, JX, d+JX]
inputs = tf.reshape(tf.transpose(inputs, [0, 1, 3, 2, 4]), [N*M*JX, H, d + JX]) # [N*M*JX, H, d+JX]
length = tf.reshape(tf.reduce_sum(tf.cast(tx_mask, 'int32'), 2), [N*M*JX])
# length = tf.reshape(tf.reduce_sum(tf.cast(tf.transpose(tx_mask, [0, 1, 3, 2]), 'float'), 3), [-1])
h, _ = dynamic_rnn(tree_rnn_cell, inputs, length, initial_state=initial_state) # [N*M*JX, H, D]
h = tf.transpose(tf.reshape(h, [N, M, JX, H, D]), [0, 1, 3, 2, 4]) # [N, M, H, JX, D]
u = tf.expand_dims(tf.expand_dims(tf.expand_dims(u, 1), 1), 1) # [N, 1, 1, 1, 4d]
dot = linear(h * u, 1, True, squeeze=True, scope='dot') # [N, M, H, JX]
# self.logits = tf.reshape(dot, [N, M * H * JX])
self.logits = tf.reshape(exp_mask(dot, tx_mask), [N, M * H * JX]) # [N, M, H, JX]
self.yp = tf.reshape(tf.nn.softmax(self.logits), [N, M, H, JX])
def _build_forward(self):
config = self.config
N, M, JX, JQ, VW, VC, d, W = \
config.batch_size, config.max_num_sents, config.max_sent_size, \
config.max_ques_size, config.word_vocab_size, config.char_vocab_size, config.hidden_size, \
config.max_word_size
JX = tf.shape(self.x)[2]
JQ = tf.shape(self.q)[1]
M = tf.shape(self.x)[1]
dc, dw, dco = config.char_emb_size, config.word_emb_size, config.char_out_size
with tf.variable_scope("emb"):
if config.use_char_emb:
with tf.variable_scope("emb_var"), tf.device("/cpu:0"):
char_emb_mat = tf.get_variable("char_emb_mat", shape=[VC, dc], dtype='float')
with tf.variable_scope("char"):
Acx = tf.nn.embedding_lookup(char_emb_mat, self.cx) # [N, M, JX, W, dc]
Acq = tf.nn.embedding_lookup(char_emb_mat, self.cq) # [N, JQ, W, dc]
Acx = tf.reshape(Acx, [-1, JX, W, dc])
Acq = tf.reshape(Acq, [-1, JQ, W, dc])
filter_sizes = list(map(int, config.out_channel_dims.split(',')))
heights = list(map(int, config.filter_heights.split(',')))
assert sum(filter_sizes) == dco, (filter_sizes, dco)
with tf.variable_scope("conv"):
xx = multi_conv1d(Acx, filter_sizes, heights, "VALID", self.is_train, config.keep_prob,
scope="xx")
if config.share_cnn_weights:
tf.get_variable_scope().reuse_variables()
qq = multi_conv1d(Acq, filter_sizes, heights, "VALID", self.is_train, config.keep_prob,
scope="xx")
else:
qq = multi_conv1d(Acq, filter_sizes, heights, "VALID", self.is_train, config.keep_prob,
scope="qq")
xx = tf.reshape(xx, [-1, M, JX, dco])
qq = tf.reshape(qq, [-1, JQ, dco])
if config.use_word_emb:
with tf.variable_scope("emb_var"), tf.device("/cpu:0"):
if config.mode == 'train':
word_emb_mat = tf.get_variable("word_emb_mat", dtype='float', shape=[VW, dw],
initializer=get_initializer(config.emb_mat))
else:
word_emb_mat = tf.get_variable("word_emb_mat", shape=[VW, dw], dtype='float')
if config.use_glove_for_unk:
word_emb_mat = tf.concat([word_emb_mat, self.new_emb_mat], 0)
with tf.name_scope("word"):
Ax = tf.nn.embedding_lookup(word_emb_mat, self.x) # [N, M, JX, d]
Aq = tf.nn.embedding_lookup(word_emb_mat, self.q) # [N, JQ, d]
self.tensor_dict['x'] = Ax
self.tensor_dict['q'] = Aq
if config.use_char_emb:
xx = tf.concat([xx, Ax], 3) # [N, M, JX, di]
qq = tf.concat([qq, Aq], 2) # [N, JQ, di]
else:
xx = Ax
qq = Aq
# highway network
if config.highway:
with tf.variable_scope("highway"):
xx = highway_network(xx, config.highway_num_layers, True, wd=config.wd, is_train=self.is_train)
tf.get_variable_scope().reuse_variables()
qq = highway_network(qq, config.highway_num_layers, True, wd=config.wd, is_train=self.is_train)
self.tensor_dict['xx'] = xx
self.tensor_dict['qq'] = qq
cell_fw = BasicLSTMCell(d, state_is_tuple=True)
cell_bw = BasicLSTMCell(d, state_is_tuple=True)
d_cell_fw = SwitchableDropoutWrapper(cell_fw, self.is_train, input_keep_prob=config.input_keep_prob)
d_cell_bw = SwitchableDropoutWrapper(cell_bw, self.is_train, input_keep_prob=config.input_keep_prob)
cell2_fw = BasicLSTMCell(d, state_is_tuple=True)
cell2_bw = BasicLSTMCell(d, state_is_tuple=True)
d_cell2_fw = SwitchableDropoutWrapper(cell2_fw, self.is_train, input_keep_prob=config.input_keep_prob)
d_cell2_bw = SwitchableDropoutWrapper(cell2_bw, self.is_train, input_keep_prob=config.input_keep_prob)
cell3_fw = BasicLSTMCell(d, state_is_tuple=True)
cell3_bw = BasicLSTMCell(d, state_is_tuple=True)
d_cell3_fw = SwitchableDropoutWrapper(cell3_fw, self.is_train, input_keep_prob=config.input_keep_prob)
d_cell3_bw = SwitchableDropoutWrapper(cell3_bw, self.is_train, input_keep_prob=config.input_keep_prob)
cell4_fw = BasicLSTMCell(d, state_is_tuple=True)
cell4_bw = BasicLSTMCell(d, state_is_tuple=True)
d_cell4_fw = SwitchableDropoutWrapper(cell4_fw, self.is_train, input_keep_prob=config.input_keep_prob)
d_cell4_bw = SwitchableDropoutWrapper(cell4_bw, self.is_train, input_keep_prob=config.input_keep_prob)
x_len = tf.reduce_sum(tf.cast(self.x_mask, 'int32'), 2) # [N, M]
q_len = tf.reduce_sum(tf.cast(self.q_mask, 'int32'), 1) # [N]
with tf.variable_scope("prepro"):
(fw_u, bw_u), ((_, fw_u_f), (_, bw_u_f)) = bidirectional_dynamic_rnn(d_cell_fw, d_cell_bw, qq, q_len,
dtype='float',
scope='u1') # [N, J, d], [N, d]
u = tf.concat(axis=2, values=[fw_u, bw_u])
if config.share_lstm_weights:
tf.get_variable_scope().reuse_variables()
(fw_h, bw_h), _ = bidirectional_dynamic_rnn(cell_fw, cell_bw, xx, x_len, dtype='float',
scope='u1') # [N, M, JX, 2d]
h = tf.concat(axis=3, values=[fw_h, bw_h]) # [N, M, JX, 2d]
else:
(fw_h, bw_h), _ = bidirectional_dynamic_rnn(cell_fw, cell_bw, xx, x_len, dtype='float',
scope='h1') # [N, M, JX, 2d]
h = tf.concat(axis=3, values=[fw_h, bw_h]) # [N, M, JX, 2d]
self.tensor_dict['u'] = u
self.tensor_dict['h'] = h
with tf.variable_scope("main"):
if config.dynamic_att:
p0 = h
u = tf.reshape(tf.tile(tf.expand_dims(u, 1), [1, M, 1, 1]), [N * M, JQ, 2 * d])
q_mask = tf.reshape(tf.tile(tf.expand_dims(self.q_mask, 1), [1, M, 1]), [N * M, JQ])
first_cell_fw = AttentionCell(cell2_fw, u, mask=q_mask, mapper='sim',
input_keep_prob=self.config.input_keep_prob, is_train=self.is_train)
first_cell_bw = AttentionCell(cell2_bw, u, mask=q_mask, mapper='sim',
input_keep_prob=self.config.input_keep_prob, is_train=self.is_train)
second_cell_fw = AttentionCell(cell3_fw, u, mask=q_mask, mapper='sim',
input_keep_prob=self.config.input_keep_prob, is_train=self.is_train)
second_cell_bw = AttentionCell(cell3_bw, u, mask=q_mask, mapper='sim',
input_keep_prob=self.config.input_keep_prob, is_train=self.is_train)
else:
p0 = attention_layer(config, self.is_train, h, u, h_mask=self.x_mask, u_mask=self.q_mask, scope="p0",
tensor_dict=self.tensor_dict)
first_cell_fw = d_cell2_fw
second_cell_fw = d_cell3_fw
first_cell_bw = d_cell2_bw
second_cell_bw = d_cell3_bw
(fw_g0, bw_g0), _ = bidirectional_dynamic_rnn(first_cell_fw, first_cell_bw, p0, x_len, dtype='float',
scope='g0') # [N, M, JX, 2d]
g0 = tf.concat(axis=3, values=[fw_g0, bw_g0])
(fw_g1, bw_g1), _ = bidirectional_dynamic_rnn(second_cell_fw, second_cell_bw, g0, x_len, dtype='float',
scope='g1') # [N, M, JX, 2d]
g1 = tf.concat(axis=3, values=[fw_g1, bw_g1])
with tf.variable_scope("output"):
if config.model_name == "basic":
logits = get_logits([g1, p0], d, True, wd=config.wd,
input_keep_prob=config.input_keep_prob,
mask=self.x_mask, is_train=self.is_train,
func=config.answer_func, scope='logits1')
a1i = softsel(tf.reshape(g1, [N, M * JX, 2 * d]),
tf.reshape(logits, [N, M * JX]))
a1i = tf.tile(tf.expand_dims(tf.expand_dims(a1i, 1), 1),
[1, M, JX, 1])
(fw_g2, bw_g2), _ = bidirectional_dynamic_rnn(d_cell4_fw, d_cell4_bw,
tf.concat([p0, g1, a1i, g1 * a1i], 3),
x_len, dtype='float', scope='g2') # [N, M, JX, 2d]
g2 = tf.concat([fw_g2, bw_g2], 3)
logits2 = get_logits([g2, p0], d, True, wd=config.wd,
input_keep_prob=config.input_keep_prob, mask=self.x_mask,
is_train=self.is_train, func=config.answer_func,
scope='logits2')
flat_logits = tf.reshape(logits, [-1, M * JX])
flat_yp = tf.nn.softmax(flat_logits) # [-1, M*JX]
yp = tf.reshape(flat_yp, [-1, M, JX])
flat_logits2 = tf.reshape(logits2, [-1, M * JX])
flat_yp2 = tf.nn.softmax(flat_logits2)
yp2 = tf.reshape(flat_yp2, [-1, M, JX])
self.tensor_dict['g1'] = g1
self.tensor_dict['g2'] = g2
self.logits = flat_logits
self.logits2 = flat_logits2
self.yp = yp
self.yp2 = yp2
elif config.model_name == "basic-class":
C = 3 if config.data_dir.startswith('data/snli') else 2
(fw_g2, bw_g2) = (fw_g1, bw_g1)
if config.classifier == 'maxpool':
g2 = tf.concat([fw_g2, bw_g2], 3) # [N, M, JX, 2d]
g2 = tf.reduce_max(g2, 2) # [N, M, 2d]
g2_dim = 2 * d
elif config.classifier == 'sumpool':
g2 = tf.concat([fw_g2, bw_g2], 3)
g2 = tf.reduce_sum(g2, 2)
g2_dim = 2 * d
else:
fw_g2_ = tf.gather(tf.transpose(fw_g2, [2, 0, 1, 3]), JX - 1)
bw_g2_ = tf.gather(tf.transpose(bw_g2, [2, 0, 1, 3]), 0)
g2 = tf.concat([fw_g2_, bw_g2_], 2)
g2_dim = 2 * d
g2_ = tf.reshape(g2, [N, g2_dim])
logits0 = linear(g2_, C, True, wd=config.wd, input_keep_prob=config.input_keep_prob,
is_train=self.is_train, scope='classifier')
flat_yp0 = tf.nn.softmax(logits0)
yp0 = tf.reshape(flat_yp0, [N, M, C])
self.tensor_dict['g1'] = g1
self.logits0 = logits0
self.yp0 = yp0
self.logits = logits0
self.yp = yp0
def _build_forward(self):
with tf.variable_scope("emb"):
with tf.variable_scope("bert_emb"):
bs = self.doc.get_shape().as_list()[0]
self.doc_bert_model = BertModel(config=self.bert_config,
is_training=self.is_train,
input_ids=self.doc,
input_mask=self.doc_mask)
tvars = tf.trainable_variables()
initialized_variable_names = {}
(assignment_map, initialized_variable_names
) = get_assignment_map_from_checkpoint(
tvars, self.config.init_checkpoint)
tf.train.init_from_checkpoint(self.config.init_checkpoint,
assignment_map)
tf.logging.info("**** Trainable Variables ****")
for var in tvars:
init_string = ""
if var.name in initialized_variable_names:
init_string = ", *INIT_FROM_CKPT*"
tf.logging.info(" name = %s, shape = %s%s", var.name,
var.shape, init_string)
self.doc_bert_repre = self.doc_bert_model.get_sequence_output()
with tf.variable_scope("emb_var"), tf.device("/cpu:0"):
if self.config.mode == 'train':
assert self.emb_mat is not None
word_emb_mat = tf.get_variable(
"word_emb_mat",
shape=[
self.vocab_size - np.shape(self.emb_mat)[0],
self.config.word_emb_size
],
dtype='float',
trainable=True)
pretrained_emb_mat = tf.get_variable(
"pretrained_emb_mat",
shape=[
np.shape(self.emb_mat)[0],
self.config.word_emb_size
],
dtype='float',
initializer=get_initializer(self.emb_mat),
trainable=self.config.fine_tune)
else:
word_emb_mat = tf.get_variable(
"word_emb_mat",
shape=[
self.vocab_size - np.shape(self.emb_mat)[0],
self.config.word_emb_size
],
dtype='float')
pretrained_emb_mat = tf.get_variable(
"pretrained_emb_mat",
shape=[
np.shape(self.emb_mat)[0],
self.config.word_emb_size
],
dtype='float')
self.word_emb_mat = tf.concat(
values=[word_emb_mat, pretrained_emb_mat], axis=0)
self.que_word_emb = tf.nn.embedding_lookup(
self.word_emb_mat, self.que) # [N, M, d]
self.ans_word_emb = tf.nn.embedding_lookup(
self.word_emb_mat, self.c) # [N, 10, d]
with tf.variable_scope("sentence_modeling"):
self.que_repre, _ = ops.m_cudnn_lstm(
inputs=self.que_word_emb,
num_layers=1,
hidden_size=self.config.hidden_size,
weight_noise_std=None,
is_training=self.is_train,
scope='que',
input_drop_prob=0,
i_m="linear_input",
use_gru=self.config.use_gru) # [N,M,d]
self.attended_passage = self._bi_attention(
self.config,
self.is_train,
self.doc_bert_repre,
self.que_repre,
h_mask=self.doc_mask,
scope="bi_att",
tensor_dict=self.tensor_dict)
# Concatenate outputs and states of the forward and backward RNNs
with tf.variable_scope("summary"):
logit = tf.layers.dense(self.attended_passage, 1, activation=None)
rc_logits = tf.nn.softmax(logit, 1)
self.doc_final_repre = tf.reduce_sum(self.attended_passage *
rc_logits, 1) # [N,dim]
with tf.variable_scope('get_answer'):
self.prediction = self._answer_layer(self.doc_final_repre,
self.ans_word_emb)
def _build_forward(self):
config = self.config
N, M, JX, JQ, VW, VC, d, W = \
config.batch_size, config.max_num_sents, config.max_sent_size, \
config.max_ques_size, config.word_vocab_size, config.char_vocab_size, config.hidden_size, \
config.max_word_size
print("parameters", N, M, JX, JQ, VW, VC, d, W)
JX = tf.shape(self.x)[2]
JQ = tf.shape(self.q)[1]
M = tf.shape(self.x)[1]
dc, dw, dco = config.char_emb_size, config.word_emb_size, config.char_out_size
with tf.variable_scope("emb"):
if config.use_char_emb:
with tf.variable_scope("emb_var"), tf.device("/cpu:0"):
char_emb_mat = tf.get_variable("char_emb_mat", shape=[VC, dc], dtype='float')
with tf.variable_scope("char"):
Acx = tf.nn.embedding_lookup(char_emb_mat, self.cx) # [N, M, JX, W, dc]
Acq = tf.nn.embedding_lookup(char_emb_mat, self.cq) # [N, JQ, W, dc]
Acx = tf.reshape(Acx, [-1, JX, W, dc])
Acq = tf.reshape(Acq, [-1, JQ, W, dc])
filter_sizes = list(map(int, config.out_channel_dims.split(',')))
heights = list(map(int, config.filter_heights.split(',')))
assert sum(filter_sizes) == dco, (filter_sizes, dco)
with tf.variable_scope("conv"):
xx = multi_conv1d(Acx, filter_sizes, heights, "VALID", self.is_train, config.keep_prob, scope="xx")
if config.share_cnn_weights:
tf.get_variable_scope().reuse_variables()
qq = multi_conv1d(Acq, filter_sizes, heights, "VALID", self.is_train, config.keep_prob, scope="xx")
else:
qq = multi_conv1d(Acq, filter_sizes, heights, "VALID", self.is_train, config.keep_prob, scope="qq")
xx = tf.reshape(xx, [-1, M, JX, dco])
qq = tf.reshape(qq, [-1, JQ, dco])
if config.use_word_emb:
with tf.variable_scope("emb_var"), tf.device("/cpu:0"):
if config.mode == 'train':
word_emb_mat = tf.get_variable("word_emb_mat", dtype='float', shape=[VW, dw], initializer=get_initializer(config.emb_mat))
else:
word_emb_mat = tf.get_variable("word_emb_mat", shape=[VW, dw], dtype='float')
if config.use_glove_for_unk:
word_emb_mat = tf.concat(axis=0, values=[word_emb_mat, self.new_emb_mat])
with tf.name_scope("word"):
Ax = tf.nn.embedding_lookup(word_emb_mat, self.x) # [N, M, JX, d]
Aq = tf.nn.embedding_lookup(word_emb_mat, self.q) # [N, JQ, d]
self.tensor_dict['x'] = Ax
self.tensor_dict['q'] = Aq
if config.use_char_emb:
xx = tf.concat(axis=3, values=[xx, Ax]) # [N, M, JX, di]
qq = tf.concat(axis=2, values=[qq, Aq]) # [N, JQ, di]
else:
xx = Ax
qq = Aq
# highway network
if config.highway:
with tf.variable_scope("highway"):
xx = highway_network(xx, config.highway_num_layers, True, wd=config.wd, is_train=self.is_train)
tf.get_variable_scope().reuse_variables()
qq = highway_network(qq, config.highway_num_layers, True, wd=config.wd, is_train=self.is_train)
self.tensor_dict['xx'] = xx
self.tensor_dict['qq'] = qq
# prepro layer cell
cell_fw = BasicLSTMCell(d, state_is_tuple=True)
cell_bw = BasicLSTMCell(d, state_is_tuple=True)
d_cell_fw = SwitchableDropoutWrapper(cell_fw, self.is_train, input_keep_prob=config.input_keep_prob)
d_cell_bw = SwitchableDropoutWrapper(cell_bw, self.is_train, input_keep_prob=config.input_keep_prob)
cell_fwh = BasicLSTMCell(d, state_is_tuple=True)
cell_bwh = BasicLSTMCell(d, state_is_tuple=True)
d_cell_fwh = SwitchableDropoutWrapper(cell_fwh, self.is_train, input_keep_prob=config.input_keep_prob)
d_cell_bwh = SwitchableDropoutWrapper(cell_bwh, self.is_train, input_keep_prob=config.input_keep_prob)
# attention layer cell
cell2_fw = BasicLSTMCell(d, state_is_tuple=True)
cell2_bw = BasicLSTMCell(d, state_is_tuple=True)
d_cell2_fw = SwitchableDropoutWrapper(cell2_fw, self.is_train, input_keep_prob=config.input_keep_prob)
d_cell2_bw = SwitchableDropoutWrapper(cell2_bw, self.is_train, input_keep_prob=config.input_keep_prob)
cell3_fw = BasicLSTMCell(d, state_is_tuple=True)
cell3_bw = BasicLSTMCell(d, state_is_tuple=True)
d_cell3_fw = SwitchableDropoutWrapper(cell3_fw, self.is_train, input_keep_prob=config.input_keep_prob)
d_cell3_bw = SwitchableDropoutWrapper(cell3_bw, self.is_train, input_keep_prob=config.input_keep_prob)
# out layer cell_bw
cell4_fw = BasicLSTMCell(d, state_is_tuple=True)
cell4_bw = BasicLSTMCell(d, state_is_tuple=True)
d_cell4_fw = SwitchableDropoutWrapper(cell4_fw, self.is_train, input_keep_prob=config.input_keep_prob)
d_cell4_bw = SwitchableDropoutWrapper(cell4_bw, self.is_train, input_keep_prob=config.input_keep_prob)
x_len = tf.reduce_sum(tf.cast(self.x_mask, 'int32'), 2) # [N, M]
q_len = tf.reduce_sum(tf.cast(self.q_mask, 'int32'), 1) # [N]
with tf.variable_scope("prepro"):
(fw_h, bw_h), ((_, fw_h_f), (_, bw_h_f)) = bidirectional_dynamic_rnn(cell_fw, cell_bw, xx, x_len, dtype='float', scope='h1')
h = tf.concat(axis=3, values=[fw_h, bw_h])
(fw_u, bw_u), ((_, fw_h_f), (_, bw_h_f)) = bidirectional_dynamic_rnn(d_cell_fw, d_cell_bw, qq, q_len, dtype='float', scope='u1') # [N, J, d], [N, d]
u = tf.concat(axis=2, values=[fw_u, bw_u])
# if config.share_lstm_weights:
# tf.get_variable_scope().reuse_variables()
# (fw_h, bw_h), _ = bidirectional_dynamic_rnn(cell_fw, cell_bw, xx, x_len, dtype='float', scope='u1') # [N, M, JX, 2d]
# h = tf.concat(axis=3, values=[fw_h, bw_h]) # [N, M, JX, 2d]
# else:
# (fw_h, bw_h), _ = bidirectional_dynamic_rnn(cell_fw, cell_bw, xx, x_len, dtype='float', scope='h1') # [N, M, JX, 2d]
# h = tf.concat(axis=3, values=[fw_h, bw_h]) # [N, M, JX, 2d]
self.tensor_dict['u'] = u
self.tensor_dict['h'] = h
# h = tf.Print(h, [tf.reduce_max(h), tf.reduce_min(h), "h"], summarize=1000)
with tf.variable_scope("main"):
if config.dynamic_att:
p0 = h
u = tf.reshape(tf.tile(tf.expand_dims(u, 1), [1, M, 1, 1]), [N * M, JQ, 2 * d])
q_mask = tf.reshape(tf.tile(tf.expand_dims(self.q_mask, 1), [1, M, 1]), [N * M, JQ])
first_cell_fw = AttentionCell(cell2_fw, u, mask=q_mask, mapper='sim',
input_keep_prob=self.config.input_keep_prob, is_train=self.is_train)
first_cell_bw = AttentionCell(cell2_bw, u, mask=q_mask, mapper='sim',
input_keep_prob=self.config.input_keep_prob, is_train=self.is_train)
second_cell_fw = AttentionCell(cell3_fw, u, mask=q_mask, mapper='sim',
input_keep_prob=self.config.input_keep_prob, is_train=self.is_train)
second_cell_bw = AttentionCell(cell3_bw, u, mask=q_mask, mapper='sim',
input_keep_prob=self.config.input_keep_prob, is_train=self.is_train)
else:
p0 = attention_layer(config, self.is_train, h, u, h_mask=self.x_mask, u_mask=self.q_mask, scope="p0", tensor_dict=self.tensor_dict)
first_cell_fw = d_cell2_fw
second_cell_fw = d_cell3_fw
first_cell_bw = d_cell2_bw
second_cell_bw = d_cell3_bw
# p0 = tf.Print(p0, [p0, "lstm output-1:"], summarize=200)
# two layer LSTM
(fw_g0, bw_g0), _ = bidirectional_dynamic_rnn(first_cell_fw, first_cell_bw, p0, x_len, dtype='float', scope='g0') # [N, M, JX, 2d]
g0 = tf.concat(axis=3, values=[fw_g0, bw_g0])
# g0 = tf.Print(g0, [g0, "lstm output0:"], summarize=200)
(fw_g1, bw_g1), _ = bidirectional_dynamic_rnn(second_cell_fw, second_cell_bw, g0, x_len, dtype='float', scope='g1') # [N, M, JX, 2d]
g1 = tf.concat(axis=3, values=[fw_g1, bw_g1])
# g1 = tf.Print(g1, [g1, "lstm output:"], summarize=200)
# output through a denselayer
with tf.variable_scope("output"):
#lstm_out = g1[:,0,-1,:]
lstm_out = tf.reduce_sum(g1, axis = 2)
lstm_out = tf.reshape(lstm_out, [-1, 2*d])
lstm_out = tf.nn.dropout(lstm_out, 0.5)
#logits = get_logits([g1, p0], d, True, wd=config.wd, input_keep_prob=config.input_keep_prob,
# mask=self.x_mask, is_train=self.is_train, func='linear', scope='logits1')
#print (logits)
#lstm_out.set_shape([N, M*JX*2*d])
dense1 = tf.layers.dense(inputs=lstm_out, units=64, activation = tf.nn.relu, kernel_regularizer=tf.contrib.layers.l2_regularizer(0.003))
score = tf.layers.dense(inputs=dense1, units = 2, activation = None, kernel_regularizer=tf.contrib.layers.l2_regularizer(0.003))
self.probs = tf.nn.softmax(score)
# self.tensor_dict['g1'] = g1
# self.tensor_dict['g2'] = g2
self.score = score
def _build_forward(self):
config = self.config
N, M, JX, JQ, VW, VC, d, W ,EW, WOW= \
config.batch_size, config.max_num_sents, config.max_sent_size, \
config.max_ques_size, config.word_vocab_size, config.char_vocab_size, config.hidden_size, \
config.max_word_size,config.word_vocab_size-config.vw_wo_entity_size,config.vw_wo_entity_size
JX = tf.shape(self.x)[2] # words
JQ = tf.shape(self.q)[1] # words
M = tf.shape(self.x)[1]
dc, dw, dco = config.char_emb_size, config.word_emb_size, config.char_out_size
#print ("dhruv is here",N, self.x.get_shape(), JX, self.q.get_shape(), VW, VC, d, W,dc, dw, dco)
with tf.variable_scope("emb"):
if config.use_char_emb:
with tf.variable_scope("emb_var"), tf.device("/cpu:0"):
char_emb_mat = tf.get_variable("char_emb_mat", shape=[VC, dc], dtype='float')
with tf.variable_scope("char"):
Acx = tf.nn.embedding_lookup(char_emb_mat, self.cx) # [N, M, JX, W, dc]
Acq = tf.nn.embedding_lookup(char_emb_mat, self.cq) # [N, JQ, W, dc]
Acx = tf.reshape(Acx, [-1, JX, W, dc])
Acq = tf.reshape(Acq, [-1, JQ, W, dc])
filter_sizes = list(map(int, config.out_channel_dims.split(',')))
heights = list(map(int, config.filter_heights.split(',')))
assert sum(filter_sizes) == dco, (filter_sizes, dco)
with tf.variable_scope("conv"):
xx = multi_conv1d(Acx, filter_sizes, heights, "VALID", self.is_train, config.keep_prob, scope="xx")
if config.share_cnn_weights:
tf.get_variable_scope().reuse_variables()
qq = multi_conv1d(Acq, filter_sizes, heights, "VALID", self.is_train, config.keep_prob, scope="xx")
else:
qq = multi_conv1d(Acq, filter_sizes, heights, "VALID", self.is_train, config.keep_prob, scope="qq")
xx = tf.reshape(xx, [-1, M, JX, dco])
qq = tf.reshape(qq, [-1, JQ, dco])
if config.use_word_emb:
with tf.variable_scope("emb_var"), tf.device("/cpu:0"):
if config.mode == 'train':
entity_emb_mat = tf.get_variable("entity_emb_mat", dtype='float', shape=[EW, EW], initializer=get_initializer(config.onehot_encoded))
entity_emb_out = _linear(entity_emb_mat, dw, True, bias_initializer=tf.constant_initializer(0.0))
word_emb_mat = tf.get_variable("word_emb_mat", dtype='float', shape=[WOW, dw], initializer=get_initializer(config.emb_mat))
word_emb_mat = tf.concat(axis=0,values=[word_emb_mat, entity_emb_out])
else:
word_emb_mat = tf.get_variable("word_emb_mat", shape=[VW, dw], dtype='float')
if config.use_glove_for_unk:
word_emb_mat = tf.concat(axis=0, values=[word_emb_mat, self.new_emb_mat])
with tf.name_scope("word"):
Ax = tf.nn.embedding_lookup(word_emb_mat, self.x) # [N, M, JX, d] i.e. [batch size, max sentences, max words, embedding size]
Aq = tf.nn.embedding_lookup(word_emb_mat, self.q) # [N, JQ, d] i.e. [batch size, max words, embedding size]
self.tensor_dict['x'] = Ax
self.tensor_dict['q'] = Aq
if config.use_char_emb:
xx = tf.concat(axis=3, values=[xx, Ax]) # [N, M, JX, di]
qq = tf.concat(axis=2, values=[qq, Aq]) # [N, JQ, di]
else:
xx = Ax
qq = Aq
# highway network
if config.highway:
with tf.variable_scope("highway"):
xx = highway_network(xx, config.highway_num_layers, True, wd=config.wd, is_train=self.is_train)
tf.get_variable_scope().reuse_variables()
qq = highway_network(qq, config.highway_num_layers, True, wd=config.wd, is_train=self.is_train)
self.tensor_dict['xx'] = xx
self.tensor_dict['qq'] = qq
#xx = tf.Print(xx,[tf.shape(xx),xx],message="DHRUV xx=",summarize=20)
cell_fw = BasicLSTMCell(d, state_is_tuple=True)
cell_bw = BasicLSTMCell(d, state_is_tuple=True)
d_cell_fw = SwitchableDropoutWrapper(cell_fw, self.is_train, input_keep_prob=config.input_keep_prob)
d_cell_bw = SwitchableDropoutWrapper(cell_bw, self.is_train, input_keep_prob=config.input_keep_prob)
cell2_fw = BasicLSTMCell(d, state_is_tuple=True)
cell2_bw = BasicLSTMCell(d, state_is_tuple=True)
d_cell2_fw = SwitchableDropoutWrapper(cell2_fw, self.is_train, input_keep_prob=config.input_keep_prob)
d_cell2_bw = SwitchableDropoutWrapper(cell2_bw, self.is_train, input_keep_prob=config.input_keep_prob)
cell3_fw = BasicLSTMCell(d, state_is_tuple=True)
cell3_bw = BasicLSTMCell(d, state_is_tuple=True)
d_cell3_fw = SwitchableDropoutWrapper(cell3_fw, self.is_train, input_keep_prob=config.input_keep_prob)
d_cell3_bw = SwitchableDropoutWrapper(cell3_bw, self.is_train, input_keep_prob=config.input_keep_prob)
cell4_fw = BasicLSTMCell(d, state_is_tuple=True)
cell4_bw = BasicLSTMCell(d, state_is_tuple=True)
d_cell4_fw = SwitchableDropoutWrapper(cell4_fw, self.is_train, input_keep_prob=config.input_keep_prob)
d_cell4_bw = SwitchableDropoutWrapper(cell4_bw, self.is_train, input_keep_prob=config.input_keep_prob)
x_len = tf.reduce_sum(tf.cast(self.x_mask, 'int32'), 2) # [N, M]
q_len = tf.reduce_sum(tf.cast(self.q_mask, 'int32'), 1) # [N]
with tf.variable_scope("prepro"):
(fw_u, bw_u), ((_, fw_u_f), (_, bw_u_f)) = bidirectional_dynamic_rnn(d_cell_fw, d_cell_bw, qq, q_len, dtype='float', scope='u1') # [N, J, d], [N, d]
u = tf.concat(axis=2, values=[fw_u, bw_u])
if config.share_lstm_weights:
tf.get_variable_scope().reuse_variables()
(fw_h, bw_h), _ = bidirectional_dynamic_rnn(cell_fw, cell_bw, xx, x_len, dtype='float', scope='u1') # [N, M, JX, 2d]
h = tf.concat(axis=3, values=[fw_h, bw_h]) # [N, M, JX, 2d]
else:
(fw_h, bw_h), _ = bidirectional_dynamic_rnn(cell_fw, cell_bw, xx, x_len, dtype='float', scope='h1') # [N, M, JX, 2d]
h = tf.concat(axis=3, values=[fw_h, bw_h]) # [N, M, JX, 2d]
self.tensor_dict['u'] = u
self.tensor_dict['h'] = h
with tf.variable_scope("main"):
if config.dynamic_att: # not true
p0 = h
u = tf.reshape(tf.tile(tf.expand_dims(u, 1), [1, M, 1, 1]), [N * M, JQ, 2 * d])
q_mask = tf.reshape(tf.tile(tf.expand_dims(self.q_mask, 1), [1, M, 1]), [N * M, JQ])
first_cell_fw = AttentionCell(cell2_fw, u, mask=q_mask, mapper='sim',
input_keep_prob=self.config.input_keep_prob, is_train=self.is_train)
first_cell_bw = AttentionCell(cell2_bw, u, mask=q_mask, mapper='sim',
input_keep_prob=self.config.input_keep_prob, is_train=self.is_train)
second_cell_fw = AttentionCell(cell3_fw, u, mask=q_mask, mapper='sim',
input_keep_prob=self.config.input_keep_prob, is_train=self.is_train)
second_cell_bw = AttentionCell(cell3_bw, u, mask=q_mask, mapper='sim',
input_keep_prob=self.config.input_keep_prob, is_train=self.is_train)
else:
p0 = attention_layer(config, self.is_train, h, u, h_mask=self.x_mask, u_mask=self.q_mask, scope="p0", tensor_dict=self.tensor_dict) # p0 seems to be G in paper
first_cell_fw = d_cell2_fw
second_cell_fw = d_cell3_fw
first_cell_bw = d_cell2_bw
second_cell_bw = d_cell3_bw
(fw_g0, bw_g0), _ = bidirectional_dynamic_rnn(first_cell_fw, first_cell_bw, p0, x_len, dtype='float', scope='g0') # [N, M, JX, 2d]
g0 = tf.concat(axis=3, values=[fw_g0, bw_g0])
(fw_g1, bw_g1), _ = bidirectional_dynamic_rnn(second_cell_fw, second_cell_bw, g0, x_len, dtype='float', scope='g1') # [N, M, JX, 2d]
g1 = tf.concat(axis=3, values=[fw_g1, bw_g1]) # g1 seems to be M in paper
g1= tf.Print(g1,[tf.shape(g1)],message="g1 shape",first_n=5,summarize=200)
p0 = tf.Print(p0, [tf.shape(p0)], message="p0 shape", first_n=5, summarize=200)
my_cell_fw = BasicLSTMCell(d, state_is_tuple=True)
my_cell_fw_d = SwitchableDropoutWrapper(my_cell_fw, self.is_train, input_keep_prob=config.input_keep_prob)
my_cell_bw = BasicLSTMCell(d, state_is_tuple=True)
my_cell_bw_d = SwitchableDropoutWrapper(my_cell_bw, self.is_train, input_keep_prob=config.input_keep_prob)
(fw_g11,bw_g11),(my_fw_final_state, my_bw_final_state),g11_len = my_bidirectional_dynamic_rnn(my_cell_fw_d, my_cell_bw_d, g1, x_len, dtype='float', scope='my_g2') # [N, M, JX, 2d]
g11 = tf.concat(axis=2, values=[fw_g11, bw_g11])
my_encoder_final_state_c = tf.concat(values = (my_fw_final_state.c, my_bw_final_state.c), axis = 1, name = "my_encoder_final_state_c")
my_encoder_final_state_h = tf.concat(values = (my_fw_final_state.h, my_bw_final_state.h), axis = 1, name = "my_encoder_final_state_h")
my_encoder_final_state = tf.contrib.rnn.LSTMStateTuple(c = my_encoder_final_state_c, h = my_encoder_final_state_h)
#compute indices for finding span as the second task in multi task learning
logits = get_logits([g1, p0], d, True, wd=config.wd, input_keep_prob=config.input_keep_prob,
mask=self.x_mask, is_train=self.is_train, func=config.answer_func, scope='logits1')
logits = tf.Print(logits, [tf.shape(logits)], message="logits shape", first_n=5, summarize=200)
a1i = softsel(tf.reshape(g1, [N, M * JX, 2 * d]), tf.reshape(logits, [N, M * JX]))
a1i = tf.tile(tf.expand_dims(tf.expand_dims(a1i, 1), 1), [1, M, JX, 1])
(fw_g2, bw_g2), _ = bidirectional_dynamic_rnn(d_cell4_fw, d_cell4_bw,
tf.concat(axis=3, values=[p0, g1, a1i, g1 * a1i]),
x_len, dtype='float', scope='g2') # [N, M, JX, 2d]
g2 = tf.concat(axis=3, values=[fw_g2, bw_g2])
logits2 = get_logits([g2, p0], d, True, wd=config.wd, input_keep_prob=config.input_keep_prob,
mask=self.x_mask,
is_train=self.is_train, func=config.answer_func, scope='logits2')
flat_logits = tf.reshape(logits, [-1, M * JX])
flat_logits = tf.Print(flat_logits, [tf.shape(flat_logits),flat_logits], message="flat_logits shape and contents", first_n=5, summarize=200)
flat_yp = tf.nn.softmax(flat_logits) # [-1, M*JX]
flat_logits2 = tf.reshape(logits2, [-1, M * JX])
flat_yp2 = tf.nn.softmax(flat_logits2)
tgt_vocab_size = config.len_new_emb_mat # hparam # FIXME: Obtain embeddings differently?
print("length is",config.len_new_emb_mat)
tgt_embedding_size = dw # hparam
# Look up embedding
decoder_emb_inp = tf.nn.embedding_lookup(word_emb_mat, self.decoder_inputs) # [batch_size, max words, embedding_size]
def decode_with_attention(helper, scope, reuse=None,maximum_iterations=None):
with tf.variable_scope(scope, reuse=reuse):
attention_mechanism = tf.contrib.seq2seq.BahdanauAttention(num_units=d, memory=g11)
cell = tf.contrib.rnn.GRUCell(num_units=d)
attn_cell = tf.contrib.seq2seq.AttentionWrapper(cell, attention_mechanism,attention_layer_size=d /2)
out_cell = tf.contrib.rnn.OutputProjectionWrapper(attn_cell, tgt_vocab_size, reuse=reuse)
decoder = tf.contrib.seq2seq.BasicDecoder(cell=out_cell, helper=helper,initial_state=out_cell.zero_state(
dtype=tf.float32, batch_size=N))
# initial_state=encoder_final_state)
outputs = tf.contrib.seq2seq.dynamic_decode(decoder=decoder, output_time_major=False,
impute_finished=True, maximum_iterations=maximum_iterations)
return outputs[0]
def decode(helper, scope, reuse=None, maximum_iterations=None):
with tf.variable_scope(scope, reuse=reuse):
decoder_cell = BasicLSTMCell(2 * d, state_is_tuple=True) # hparam
projection_layer = layers_core.Dense(tgt_vocab_size, use_bias=False) # hparam
decoder = tf.contrib.seq2seq.BasicDecoder(decoder_cell, helper, my_encoder_final_state,output_layer=projection_layer) # decoder
final_outputs, _ ,_= tf.contrib.seq2seq.dynamic_decode(decoder, output_time_major=False, impute_finished=True,
maximum_iterations=maximum_iterations) # dynamic decoding
return final_outputs
# Decoder
if config.mode == 'train': #TODO:doesnt seem to be correct to use this variable for dev
training_helper = tf.contrib.seq2seq.TrainingHelper(decoder_emb_inp, self.target_sequence_length,time_major=False)
#final_outputs = decode(helper=training_helper, scope="HAHA", reuse=None)
final_outputs = decode_with_attention(helper=training_helper, scope="HAHA", reuse=None)
else:
training_helper = tf.contrib.seq2seq.GreedyEmbeddingHelper(word_emb_mat, tf.fill([N], self.tgt_sos_id),self.tgt_eos_id)
#final_outputs= decode(helper=training_helper, scope="HAHA", reuse=True,maximum_iterations=100)
final_outputs= decode_with_attention(helper=training_helper, scope="HAHA", reuse=True,maximum_iterations=100)
self.decoder_logits_train = final_outputs.rnn_output
self.index_start = flat_logits
self.index_end = flat_logits2
def _build_forward(self):
config = self.config
N, M, JX, JQ, VW, VC, d, dc, W = \
config.batch_size, config.max_num_sents, config.max_sent_size, \
config.max_ques_size, config.word_vocab_size, config.char_vocab_size, config.hidden_size, \
config.char_emb_size, config.max_word_size
H = config.max_tree_height
x_mask = self.x > 0
q_mask = self.q > 0
tx_mask = self.tx > 0 # [N, M, H, JX]
with tf.variable_scope("char_emb"):
char_emb_mat = tf.get_variable("char_emb_mat", shape=[VC, dc], dtype='float')
Acx = tf.nn.embedding_lookup(char_emb_mat, self.cx) # [N, M, JX, W, dc]
Acq = tf.nn.embedding_lookup(char_emb_mat, self.cq) # [N, JQ, W, dc]
filter = tf.get_variable("filter", shape=[1, config.char_filter_height, dc, d], dtype='float')
bias = tf.get_variable("bias", shape=[d], dtype='float')
strides = [1, 1, 1, 1]
Acx = tf.reshape(Acx, [-1, JX, W, dc])
Acq = tf.reshape(Acq, [-1, JQ, W, dc])
xxc = tf.nn.conv2d(Acx, filter, strides, "VALID") + bias # [N*M, JX, W/filter_stride, d]
qqc = tf.nn.conv2d(Acq, filter, strides, "VALID") + bias # [N, JQ, W/filter_stride, d]
xxc = tf.reshape(tf.reduce_max(tf.nn.relu(xxc), 2), [-1, M, JX, d])
qqc = tf.reshape(tf.reduce_max(tf.nn.relu(qqc), 2), [-1, JQ, d])
with tf.variable_scope("word_emb"):
if config.mode == 'train':
word_emb_mat = tf.get_variable("word_emb_mat", dtype='float', shape=[VW, config.word_emb_size], initializer=get_initializer(config.emb_mat))
else:
word_emb_mat = tf.get_variable("word_emb_mat", shape=[VW, config.word_emb_size], dtype='float')
Ax = tf.nn.embedding_lookup(word_emb_mat, self.x) # [N, M, JX, d]
Aq = tf.nn.embedding_lookup(word_emb_mat, self.q) # [N, JQ, d]
# Ax = linear([Ax], d, False, scope='Ax_reshape')
# Aq = linear([Aq], d, False, scope='Aq_reshape')
xx = tf.concat(3, [xxc, Ax]) # [N, M, JX, 2d]
qq = tf.concat(2, [qqc, Aq]) # [N, JQ, 2d]
D = d + config.word_emb_size
with tf.variable_scope("pos_emb"):
pos_emb_mat = tf.get_variable("pos_emb_mat", shape=[config.pos_vocab_size, d], dtype='float')
Atx = tf.nn.embedding_lookup(pos_emb_mat, self.tx) # [N, M, H, JX, d]
cell = BasicLSTMCell(D, state_is_tuple=True)
cell = SwitchableDropoutWrapper(cell, self.is_train, input_keep_prob=config.input_keep_prob)
x_len = tf.reduce_sum(tf.cast(x_mask, 'int32'), 2) # [N, M]
q_len = tf.reduce_sum(tf.cast(q_mask, 'int32'), 1) # [N]
with tf.variable_scope("rnn"):
(fw_h, bw_h), _ = bidirectional_dynamic_rnn(cell, cell, xx, x_len, dtype='float', scope='start') # [N, M, JX, 2d]
tf.get_variable_scope().reuse_variables()
(fw_us, bw_us), (_, (fw_u, bw_u)) = bidirectional_dynamic_rnn(cell, cell, qq, q_len, dtype='float', scope='start') # [N, J, d], [N, d]
u = (fw_u + bw_u) / 2.0
h = (fw_h + bw_h) / 2.0
with tf.variable_scope("h"):
no_op_cell = NoOpCell(D)
tree_rnn_cell = TreeRNNCell(no_op_cell, d, tf.reduce_max)
initial_state = tf.reshape(h, [N*M*JX, D]) # [N*M*JX, D]
inputs = tf.concat(4, [Atx, tf.cast(self.tx_edge_mask, 'float')]) # [N, M, H, JX, d+JX]
inputs = tf.reshape(tf.transpose(inputs, [0, 1, 3, 2, 4]), [N*M*JX, H, d + JX]) # [N*M*JX, H, d+JX]
length = tf.reshape(tf.reduce_sum(tf.cast(tx_mask, 'int32'), 2), [N*M*JX])
# length = tf.reshape(tf.reduce_sum(tf.cast(tf.transpose(tx_mask, [0, 1, 3, 2]), 'float'), 3), [-1])
h, _ = dynamic_rnn(tree_rnn_cell, inputs, length, initial_state=initial_state) # [N*M*JX, H, D]
h = tf.transpose(tf.reshape(h, [N, M, JX, H, D]), [0, 1, 3, 2, 4]) # [N, M, H, JX, D]
u = tf.expand_dims(tf.expand_dims(tf.expand_dims(u, 1), 1), 1) # [N, 1, 1, 1, 4d]
dot = linear(h * u, 1, True, squeeze=True, scope='dot') # [N, M, H, JX]
# self.logits = tf.reshape(dot, [N, M * H * JX])
self.logits = tf.reshape(exp_mask(dot, tx_mask), [N, M * H * JX]) # [N, M, H, JX]
self.yp = tf.reshape(tf.nn.softmax(self.logits), [N, M, H, JX])
def _build_forward(self):
config = self.config
N, M, JX, JQ, VW, VC, d, W = \
config.batch_size, config.max_num_sents, config.max_sent_size, \
config.max_ques_size, config.word_vocab_size, config.char_vocab_size, config.hidden_size, \
config.max_word_size
print("VW:", VW, "N:", N, "M:", M, "JX:", JX, "JQ:", JQ)
JA = config.max_answer_length
JX = tf.shape(self.x)[2]
JQ = tf.shape(self.q)[1]
M = tf.shape(self.x)[1]
print("VW:", VW, "N:", N, "M:", M, "JX:", JX, "JQ:", JQ)
dc, dw, dco = config.char_emb_size, config.word_emb_size, config.char_out_size
with tf.variable_scope("emb"):
# Char-CNN Embedding
if config.use_char_emb:
with tf.variable_scope("emb_var"), tf.device("/cpu:0"):
char_emb_mat = tf.get_variable("char_emb_mat",
shape=[VC, dc],
dtype='float')
with tf.variable_scope("char"):
Acx = tf.nn.embedding_lookup(char_emb_mat,
self.cx) # [N, M, JX, W, dc]
Acq = tf.nn.embedding_lookup(char_emb_mat,
self.cq) # [N, JQ, W, dc]
Acx = tf.reshape(Acx, [-1, JX, W, dc])
Acq = tf.reshape(Acq, [-1, JQ, W, dc])
filter_sizes = list(
map(int, config.out_channel_dims.split(',')))
heights = list(map(int, config.filter_heights.split(',')))
assert sum(filter_sizes) == dco, (filter_sizes, dco)
with tf.variable_scope("conv"):
xx = multi_conv1d(Acx,
filter_sizes,
heights,
"VALID",
self.is_train,
config.keep_prob,
scope="xx")
if config.share_cnn_weights:
tf.get_variable_scope().reuse_variables()
qq = multi_conv1d(Acq,
filter_sizes,
heights,
"VALID",
self.is_train,
config.keep_prob,
scope="xx")
else:
qq = multi_conv1d(Acq,
filter_sizes,
heights,
"VALID",
self.is_train,
config.keep_prob,
scope="qq")
xx = tf.reshape(xx, [-1, M, JX, dco])
qq = tf.reshape(qq, [-1, JQ, dco])
# Word Embedding
if config.use_word_emb:
with tf.variable_scope("emb_var") as scope, tf.device(
"/cpu:0"):
if config.mode == 'train':
word_emb_mat = tf.get_variable(
"word_emb_mat",
dtype='float',
shape=[VW, dw],
initializer=get_initializer(config.emb_mat))
else:
word_emb_mat = tf.get_variable("word_emb_mat",
shape=[VW, dw],
dtype='float')
tf.get_variable_scope().reuse_variables()
self.word_emb_scope = scope
if config.use_glove_for_unk:
word_emb_mat = tf.concat(
[word_emb_mat, self.new_emb_mat], 0)
with tf.name_scope("word"):
Ax = tf.nn.embedding_lookup(word_emb_mat,
self.x) # [N, M, JX, d]
Aq = tf.nn.embedding_lookup(word_emb_mat,
self.q) # [N, JQ, d]
self.tensor_dict['x'] = Ax
self.tensor_dict['q'] = Aq
# Concat Char-CNN Embedding and Word Embedding
if config.use_char_emb:
xx = tf.concat([xx, Ax], 3) # [N, M, JX, di]
qq = tf.concat([qq, Aq], 2) # [N, JQ, di]
else:
xx = Ax
qq = Aq
# exact match
if config.use_exact_match:
emx = tf.expand_dims(tf.cast(self.emx, tf.float32), -1)
xx = tf.concat([xx, emx], 3) # [N, M, JX, di+1]
emq = tf.expand_dims(tf.cast(self.emq, tf.float32), -1)
qq = tf.concat([qq, emq], 2) # [N, JQ, di+1]
# 2 layer highway network on Concat Embedding
if config.highway:
with tf.variable_scope("highway"):
xx = highway_network(xx,
config.highway_num_layers,
True,
wd=config.wd,
is_train=self.is_train)
tf.get_variable_scope().reuse_variables()
qq = highway_network(qq,
config.highway_num_layers,
True,
wd=config.wd,
is_train=self.is_train)
self.tensor_dict['xx'] = xx
self.tensor_dict['qq'] = qq
# Bidirection-LSTM (3rd layer on paper)
cell = GRUCell(d) if config.GRU else BasicLSTMCell(d,
state_is_tuple=True)
d_cell = SwitchableDropoutWrapper(
cell, self.is_train, input_keep_prob=config.input_keep_prob)
x_len = tf.reduce_sum(tf.cast(self.x_mask, 'int32'), 2) # [N, M]
q_len = tf.reduce_sum(tf.cast(self.q_mask, 'int32'), 1) # [N]
with tf.variable_scope("prepro"):
(fw_u, bw_u), _ = bidirectional_dynamic_rnn(
d_cell, d_cell, qq, q_len, dtype='float',
scope='u1') # [N, J, d], [N, d]
u = tf.concat([fw_u, bw_u], 2)
if config.share_lstm_weights:
tf.get_variable_scope().reuse_variables()
(fw_h, bw_h), _ = bidirectional_dynamic_rnn(
cell, cell, xx, x_len, dtype='float',
scope='u1') # [N, M, JX, 2d]
h = tf.concat([fw_h, bw_h], 3) # [N, M, JX, 2d]
else:
(fw_h, bw_h), _ = bidirectional_dynamic_rnn(
cell, cell, xx, x_len, dtype='float',
scope='h1') # [N, M, JX, 2d]
h = tf.concat([fw_h, bw_h], 3) # [N, M, JX, 2d]
self.tensor_dict['u'] = u
self.tensor_dict['h'] = h
# Attention Flow Layer (4th layer on paper)
with tf.variable_scope("main"):
if config.dynamic_att:
p0 = h
u = tf.reshape(tf.tile(tf.expand_dims(u, 1), [1, M, 1, 1]),
[N * M, JQ, 2 * d])
q_mask = tf.reshape(
tf.tile(tf.expand_dims(self.q_mask, 1), [1, M, 1]),
[N * M, JQ])
first_cell = AttentionCell(
cell,
u,
size=d,
mask=q_mask,
mapper='sim',
input_keep_prob=self.config.input_keep_prob,
is_train=self.is_train)
else:
p0 = attention_layer(config,
self.is_train,
h,
u,
h_mask=self.x_mask,
u_mask=self.q_mask,
scope="p0",
tensor_dict=self.tensor_dict)
first_cell = d_cell
# Modeling layer (5th layer on paper)
tp0 = p0
for layer_idx in range(config.LSTM_num_layers - 1):
(fw_g0, bw_g0), _ = bidirectional_dynamic_rnn(
first_cell,
first_cell,
p0,
x_len,
dtype='float',
scope="g_{}".format(layer_idx)) # [N, M, JX, 2d]
p0 = tf.concat([fw_g0, bw_g0], 3)
(fw_g1, bw_g1), _ = bidirectional_dynamic_rnn(
first_cell, first_cell, p0, x_len, dtype='float',
scope='g1') # [N, M, JX, 2d]
g1 = tf.concat([fw_g1, bw_g1], 3) # [N, M, JX, 2d]
# Self match layer
with tf.variable_scope("SelfMatch"):
s0 = tf.reshape(g1, [N * M, JX, 2 * d]) # [N * M, JX, 2d]
x_mask = tf.reshape(self.x_mask, [N * M, JX])
first_cell = AttentionCell(cell,
s0,
size=d,
mask=x_mask,
is_train=self.is_train)
(fw_s, bw_s), (fw_s_f, bw_s_f) = bidirectional_dynamic_rnn(
first_cell, first_cell, s0, x_len, dtype='float',
scope='s') # [N, M, JX, 2d]
s1 = tf.concat([fw_s, bw_s], 2) # [N * M, JX, 2d], M == 1
# prepare for PtrNet
encoder_output = tf.expand_dims(s1, 1) # [N, M, JX, 2d]
encoder_output = tf.expand_dims(
tf.cast(self.x_mask,
tf.float32), -1) * encoder_output # [N, M, JX, 2d]
if config.GRU:
encoder_state_final = tf.concat((fw_s_f, bw_s_f),
1,
name='encoder_concat')
else:
if isinstance(fw_s_f, LSTMStateTuple):
encoder_state_c = tf.concat((fw_s_f.c, bw_s_f.c),
1,
name='encoder_concat_c')
encoder_state_h = tf.concat((fw_s_f.h, bw_s_f.h),
1,
name='encoder_concat_h')
encoder_state_final = LSTMStateTuple(c=encoder_state_c,
h=encoder_state_h)
elif isinstance(fw_s_f, tf.Tensor):
encoder_state_final = tf.concat((fw_s_f, bw_s_f),
1,
name='encoder_concat')
else:
encoder_state_final = None
tf.logging.error("encoder_state_final not set")
print("encoder_state_final:", encoder_state_final)
with tf.variable_scope("output"):
# eos_symbol = config.eos_symbol
# next_symbol = config.next_symbol
tf.assert_equal(
M,
1) # currently dynamic M is not supported, thus we assume M==1
answer_string = tf.placeholder(
shape=(N, 1, JA + 1), dtype=tf.int32,
name='answer_string') # [N, M, JA + 1]
answer_string_mask = tf.placeholder(
shape=(N, 1, JA + 1), dtype=tf.bool,
name='answer_string_mask') # [N, M, JA + 1]
answer_string_length = tf.placeholder(
shape=(N, 1),
dtype=tf.int32,
name='answer_string_length',
) # [N, M]
self.tensor_dict['answer_string'] = answer_string
self.tensor_dict['answer_string_mask'] = answer_string_mask
self.tensor_dict['answer_string_length'] = answer_string_length
self.answer_string = answer_string
self.answer_string_mask = answer_string_mask
self.answer_string_length = answer_string_length
answer_string_flattened = tf.reshape(answer_string,
[N * M, JA + 1])
self.answer_string_flattened = answer_string_flattened # [N * M, JA+1]
print("answer_string_flattened:", answer_string_flattened)
answer_string_length_flattened = tf.reshape(
answer_string_length, [N * M])
self.answer_string_length_flattened = answer_string_length_flattened # [N * M]
print("answer_string_length_flattened:",
answer_string_length_flattened)
decoder_cell = GRUCell(2 * d) if config.GRU else BasicLSTMCell(
2 * d, state_is_tuple=True)
with tf.variable_scope("Decoder"):
decoder_train_logits = ptr_decoder(
decoder_cell,
tf.reshape(tp0, [N * M, JX, 2 * d]), # [N * M, JX, 2d]
tf.reshape(encoder_output,
[N * M, JX, 2 * d]), # [N * M, JX, 2d]
encoder_final_state=encoder_state_final,
max_encoder_length=config.sent_size_th,
decoder_output_length=
answer_string_length_flattened, # [N * M]
batch_size=N, # N * M (M=1)
attention_proj_dim=self.config.decoder_proj_dim,
scope='ptr_decoder'
) # [batch_size, dec_len*, enc_seq_len + 1]
self.decoder_train_logits = decoder_train_logits
print("decoder_train_logits:", decoder_train_logits)
self.decoder_train_softmax = tf.nn.softmax(
self.decoder_train_logits)
self.decoder_inference = tf.argmax(
decoder_train_logits, axis=2,
name='decoder_inference') # [N, JA + 1]
self.yp = tf.ones([N, M, JX], dtype=tf.int32) * -1
self.yp2 = tf.ones([N, M, JX], dtype=tf.int32) * -1
def _build_forward(self):
#import pdb
#pdb.set_trace()
with tf.variable_scope("emb"):
with tf.variable_scope("emb_var"), tf.device("/cpu:0"):
if self.config.mode == 'train':
assert self.emb_mat is not None
word_emb_mat = tf.get_variable(
"word_emb_mat",
shape=[
self.vocab_size - np.shape(self.emb_mat)[0],
self.config.word_emb_size
],
dtype='float',
trainable=True)
pretrained_emb_mat = tf.get_variable(
"pretrained_emb_mat",
shape=[
np.shape(self.emb_mat)[0],
self.config.word_emb_size
],
dtype='float',
initializer=get_initializer(self.emb_mat),
trainable=self.config.fine_tune)
else:
word_emb_mat = tf.get_variable(
"word_emb_mat",
shape=[
self.vocab_size - np.shape(self.emb_mat)[0],
self.config.word_emb_size
],
dtype='float')
pretrained_emb_mat = tf.get_variable(
"pretrained_emb_mat",
shape=[
np.shape(self.emb_mat)[0],
self.config.word_emb_size
],
dtype='float')
word_emb_mat = tf.concat(
values=[word_emb_mat, pretrained_emb_mat], axis=0)
with tf.name_scope("word"):
Aq = tf.nn.embedding_lookup(word_emb_mat,
self.que) # [N, JQ, d]
candidates = tf.nn.embedding_lookup(word_emb_mat, self.c)
Aq = nn.dropout(Aq, self.config.keep_prob, self.is_train)
question, _ = ops.m_cudnn_lstm(
inputs=Aq,
num_layers=1,
hidden_size=self.config.hidden_size,
weight_noise_std=None,
is_training=self.is_train,
scope='question',
input_drop_prob=0,
i_m="linear_input",
use_gru=self.config.use_gru) # [N,M,JX,d]
question = nn.dropout(question, self.config.keep_prob, self.is_train)
with tf.variable_scope("summary"):
rc_logits = tf.layers.dense(question, 1, activation=None)
rc_logits = nn.dropout(rc_logits, self.config.keep_prob,
self.is_train)
rc_logits = tf.nn.softmax(rc_logits, 1)
question = tf.reduce_sum(question * rc_logits, 1) # [N,dim]
with tf.variable_scope('get_answer'):
self.prediction = self._answer_layer(question, candidates)
def _build_forward(self):
config = self.config
N, M, JX, JQ, VW, VC, d, W = \
config.batch_size, config.max_num_sents, config.max_sent_size, \
config.max_ques_size, config.word_vocab_size, config.char_vocab_size, config.hidden_size, \
config.max_word_size
JX = tf.shape(self.x)[2]
JQ = tf.shape(self.q)[1]
M = tf.shape(self.x)[1]
dc, dw, dco = config.char_emb_size, config.word_emb_size, config.char_out_size
with tf.variable_scope("emb"):
if config.use_char_emb: # 计算字符emb
with tf.variable_scope("emb_var"), tf.device("/cpu:0"):
char_emb_mat = tf.get_variable("char_emb_mat",
shape=[VC, dc],
dtype='float')
with tf.variable_scope("char"):
Acx = tf.nn.embedding_lookup(char_emb_mat,
self.cx) # [N, M, JX, W, dc]
Acq = tf.nn.embedding_lookup(char_emb_mat,
self.cq) # [N, JQ, W, dc]
Acx = tf.reshape(Acx, [-1, JX, W, dc])
Acq = tf.reshape(Acq, [-1, JQ, W, dc])
filter_sizes = list(
map(int,
config.out_channel_dims.split(','))) # TODO What?
heights = list(map(int, config.filter_heights.split(',')))
assert sum(filter_sizes) == dco, (filter_sizes, dco)
with tf.variable_scope("conv"):
xx = multi_conv1d(Acx,
filter_sizes,
heights,
"VALID",
self.is_train,
config.keep_prob,
scope="xx")
if config.share_cnn_weights:
with tf.variable_scope(tf.get_variable_scope(),
reuse=True):
qq = multi_conv1d(Acq,
filter_sizes,
heights,
"VALID",
self.is_train,
config.keep_prob,
scope="xx")
else:
qq = multi_conv1d(Acq,
filter_sizes,
heights,
"VALID",
self.is_train,
config.keep_prob,
scope="qq")
xx = tf.reshape(xx, [-1, M, JX, dco])
qq = tf.reshape(qq, [-1, JQ, dco])
if config.use_word_emb:
with tf.variable_scope("emb_var"), tf.device("/cpu:0"):
if config.mode == 'train':
word_emb_mat = tf.get_variable(
"word_emb_mat",
dtype='float',
shape=[VW, dw],
initializer=get_initializer(
config.emb_mat)) # emb_mat is glove
else:
word_emb_mat = tf.get_variable("word_emb_mat",
shape=[VW, dw],
dtype='float')
if config.use_glove_for_unk:
word_emb_mat = tf.concat(
[word_emb_mat, self.new_emb_mat], 0)
with tf.name_scope("word"):
Ax = tf.nn.embedding_lookup(word_emb_mat,
self.x) # [N, M, JX, d]
Aq = tf.nn.embedding_lookup(word_emb_mat,
self.q) # [N, JQ, d]
self.tensor_dict['x'] = Ax
self.tensor_dict['q'] = Aq
if config.use_char_emb:
xx = tf.concat([xx, Ax], 3) # [N, M, JX, di]
qq = tf.concat([qq, Aq], 2) # [N, JQ, di]
else:
xx = Ax
qq = Aq
# highway network
if config.highway:
with tf.variable_scope("highway"):
xx = highway_network(xx,
config.highway_num_layers,
True,
wd=config.wd,
is_train=self.is_train)
with tf.variable_scope(tf.get_variable_scope(), reuse=True):
qq = highway_network(qq,
config.highway_num_layers,
True,
wd=config.wd,
is_train=self.is_train)
self.tensor_dict['xx'] = xx
self.tensor_dict['qq'] = qq
cell = BasicLSTMCell(d, state_is_tuple=True)
d_cell = SwitchableDropoutWrapper(
cell, self.is_train, input_keep_prob=config.input_keep_prob)
x_len = tf.reduce_sum(tf.cast(self.x_mask, 'int32'), 2) # [N, M]
q_len = tf.reduce_sum(tf.cast(self.q_mask, 'int32'), 1) # [N]
with tf.variable_scope("prepro"):
(fw_u, bw_u), ((_, fw_u_f), (_,
bw_u_f)) = bidirectional_dynamic_rnn(
d_cell,
d_cell,
qq,
q_len,
dtype='float',
scope='u1') # [N, J, d], [N, d]
u = tf.concat([fw_u, bw_u], 2)
if config.share_lstm_weights:
with tf.variable_scope(tf.get_variable_scope(), reuse=True):
(fw_h, bw_h), _ = bidirectional_dynamic_rnn(
cell, cell, xx, x_len, dtype='float',
scope='u1') # [N, M, JX, 2d] TODO JX == x_len?
h = tf.concat([fw_h, bw_h], 3) # [N, M, JX, 2d]
else:
(fw_h, bw_h), _ = bidirectional_dynamic_rnn(
cell, cell, xx, x_len, dtype='float',
scope='h1') # [N, M, JX, 2d]
h = tf.concat([fw_h, bw_h], 3) # [N, M, JX, 2d]
self.tensor_dict['u'] = u
self.tensor_dict['h'] = h
with tf.variable_scope("main"):
if config.dynamic_att:
p0 = h
u = tf.reshape(tf.tile(tf.expand_dims(u, 1), [1, M, 1, 1]),
[N * M, JQ, 2 * d])
q_mask = tf.reshape(
tf.tile(tf.expand_dims(self.q_mask, 1), [1, M, 1]),
[N * M, JQ])
first_cell = AttentionCell(
cell,
u,
mask=q_mask,
mapper='sim',
input_keep_prob=self.config.input_keep_prob,
is_train=self.is_train)
else:
p0 = attention_layer(config,
self.is_train,
h,
u,
h_mask=self.x_mask,
u_mask=self.q_mask,
scope="p0",
tensor_dict=self.tensor_dict)
first_cell = d_cell
(fw_g0, bw_g0), _ = bidirectional_dynamic_rnn(
first_cell, first_cell, p0, x_len, dtype='float',
scope='g0') # [N, M, JX, 2d]
g0 = tf.concat([fw_g0, bw_g0], 3)
(fw_g1, bw_g1), _ = bidirectional_dynamic_rnn(
first_cell, first_cell, g0, x_len, dtype='float',
scope='g1') # [N, M, JX, 2d]
g1 = tf.concat([fw_g1, bw_g1], 3)
# Output Layer
logits = get_logits([g1, p0],
d,
True,
wd=config.wd,
input_keep_prob=config.input_keep_prob,
mask=self.x_mask,
is_train=self.is_train,
func=config.answer_func,
scope='logits1')
a1i = softsel(tf.reshape(g1, [N, M * JX, 2 * d]),
tf.reshape(logits, [N, M * JX]))
a1i = tf.tile(tf.expand_dims(tf.expand_dims(a1i, 1), 1),
[1, M, JX, 1])
(fw_g2, bw_g2), _ = bidirectional_dynamic_rnn(
d_cell,
d_cell,
tf.concat([p0, g1, a1i, g1 * a1i], 3),
x_len,
dtype='float',
scope='g2') # [N, M, JX, 2d]
g2 = tf.concat([fw_g2, bw_g2], 3)
logits2 = get_logits([g2, p0],
d,
True,
wd=config.wd,
input_keep_prob=config.input_keep_prob,
mask=self.x_mask,
is_train=self.is_train,
func=config.answer_func,
scope='logits2')
flat_logits = tf.reshape(logits, [-1, M * JX])
flat_yp = tf.nn.softmax(flat_logits) # [-1, M*JX]
yp = tf.reshape(flat_yp, [-1, M, JX])
flat_logits2 = tf.reshape(logits2, [-1, M * JX])
flat_yp2 = tf.nn.softmax(flat_logits2)
yp2 = tf.reshape(flat_yp2, [-1, M, JX])
self.tensor_dict['g1'] = g1
self.tensor_dict['g2'] = g2
self.logits = flat_logits
self.logits2 = flat_logits2
self.yp = yp
self.yp2 = yp2
def _build_forward(self):
config = self.config
N = config.batch_size
M = config.max_num_sents
JX = config.max_sent_size
JQ = config.max_ques_size
VW = config.word_vocab_size
VC = config.char_vocab_size
W = config.max_word_size
d = config.hidden_size
JX = tf.shape(self.x)[2] # JX max sentence size, length,
JQ = tf.shape(self.q)[1] # JQ max questions size, length, is the
M = tf.shape(self.x)[1] # m is the max number of sentences
dc, dw, dco = config.char_emb_size, config.word_emb_size, config.char_out_size
# dc = 8, each char will be map to 8-number vector, "char-level word embedding size [100]"
with tf.variable_scope("emb"):
if config.use_char_emb:
with tf.variable_scope("emb_var"), tf.device("/cpu:0"):
char_emb_mat = tf.get_variable("char_emb_mat",
shape=[VC, dc],
dtype='float')
# 330,8 a matrix for each char to its 8-number vector
with tf.variable_scope("char"):
Acx = tf.nn.embedding_lookup(char_emb_mat, self.cx)
# [N, M, JX, W, dc] 60,None,None,16,8, batch-size,
# N is the number of batch_size
# M the max number of sentences
# JX is the max sentence length
# W is the max length of a word
# dc is the vector for each char
# map each char to a vector
Acq = tf.nn.embedding_lookup(char_emb_mat,
self.cq) # [N, JQ, W, dc]
# JQ the max length of question
# W the max length of words
# mao each char in questiosn to vectors
Acx = tf.reshape(Acx, [-1, JX, W, dc])
Acq = tf.reshape(Acq, [-1, JQ, W, dc])
# max questions size, length, max_word_size(16), char_emb_size(8)
filter_sizes = list(
map(int, config.out_channel_dims.split(',')))
heights = list(map(int, config.filter_heights.split(',')))
# so here, there are 100 filters and the size of each filter is 5
# different heights and there are different number of these filter, but here just 100 5-long filters
assert sum(filter_sizes) == dco, (filter_sizes, dco)
with tf.variable_scope("conv"):
xx = multi_conv1d(Acx,
filter_sizes,
heights,
"VALID",
self.is_train,
config.keep_prob,
scope="xx")
if config.share_cnn_weights:
tf.get_variable_scope().reuse_variables()
qq = multi_conv1d(Acq,
filter_sizes,
heights,
"VALID",
self.is_train,
config.keep_prob,
scope="xx")
else:
qq = multi_conv1d(Acq,
filter_sizes,
heights,
"VALID",
self.is_train,
config.keep_prob,
scope="qq")
xx = tf.reshape(xx, [-1, M, JX, dco])
qq = tf.reshape(
qq, [-1, JQ, dco
]) # here, xx and qq are the output of cnn,
if config.use_word_emb:
with tf.variable_scope("emb_var"), tf.device("/cpu:0"):
if config.mode == 'train':
word_emb_mat = tf.get_variable(
"word_emb_mat",
dtype='float',
shape=[VW, dw],
initializer=get_initializer(config.emb_mat))
else:
word_emb_mat = tf.get_variable("word_emb_mat",
shape=[VW, dw],
dtype='float')
if config.use_glove_for_unk: # create a new word embedding or use the glove?
word_emb_mat = tf.concat(
[word_emb_mat, self.new_emb_mat], 0)
with tf.name_scope("word"):
Ax = tf.nn.embedding_lookup(word_emb_mat,
self.x) # [N, M, JX, d]
Aq = tf.nn.embedding_lookup(word_emb_mat,
self.q) # [N, JQ, d]
self.tensor_dict['x'] = Ax
self.tensor_dict['q'] = Aq
if config.use_char_emb:
xx = tf.concat([xx, Ax], 3) # [N, M, JX, di]
qq = tf.concat([qq, Aq], 2) # [N, JQ, di]
else:
xx = Ax
qq = Aq # here we used cnn and word embedding represented each word with a 200-unit vector
# so for, xx, (batch_size, sentence#, word#, embedding), qq (batch_size, word#, embedding)
# highway network
if config.highway:
with tf.variable_scope("highway"):
xx = highway_network(xx,
config.highway_num_layers,
True,
wd=config.wd,
is_train=self.is_train)
tf.get_variable_scope().reuse_variables()
qq = highway_network(qq,
config.highway_num_layers,
True,
wd=config.wd,
is_train=self.is_train)
self.tensor_dict['xx'] = xx
self.tensor_dict['qq'] = qq
# same shape with line 173
cell = BasicLSTMCell(
d, state_is_tuple=True) # d = 100, hidden state number
d_cell = SwitchableDropoutWrapper(
cell, self.is_train, input_keep_prob=config.input_keep_prob)
x_len = tf.reduce_sum(tf.cast(self.x_mask, 'int32'),
2) # [N, M], [60,?]
q_len = tf.reduce_sum(tf.cast(self.q_mask, 'int32'), 1) # [N] [60]
# masks are true and false, here, he sums up those truths,
with tf.variable_scope("prepro"):
(fw_u, bw_u), ((_, fw_u_f), (_,
bw_u_f)) = bidirectional_dynamic_rnn(
d_cell,
d_cell,
qq,
q_len,
dtype='float',
scope='u1') # [N, J, d], [N, d]
u = tf.concat(
[fw_u, bw_u],
2) # (60, ?, 200) | 200 becahse combined 2 100 hidden states
if config.share_lstm_weights:
tf.get_variable_scope().reuse_variables()
(fw_h, bw_h), _ = bidirectional_dynamic_rnn(
cell, cell, xx, x_len, dtype='float',
scope='u1') # [N, M, JX, 2d]
h = tf.concat([fw_h, bw_h], 3) # [N, M, JX, 2d]
else:
(fw_h, bw_h), _ = bidirectional_dynamic_rnn(
cell, cell, xx, x_len, dtype='float',
scope='h1') # [N, M, JX, 2d]
h = tf.concat([fw_h, bw_h], 3) # [N, M, JX, 2d]
self.tensor_dict['u'] = u # [60, ?, 200] for question
self.tensor_dict['h'] = h # [60, ?, ?, 200] for article
with tf.variable_scope("main"):
if config.dynamic_att: # todo what is this dynamic attention.
p0 = h
u = tf.reshape(tf.tile(tf.expand_dims(u, 1), [1, M, 1, 1]),
[N * M, JQ, 2 * d])
q_mask = tf.reshape(
tf.tile(tf.expand_dims(self.q_mask, 1), [1, M, 1]),
[N * M, JQ])
first_cell = AttentionCell(
cell,
u,
mask=q_mask,
mapper='sim',
input_keep_prob=self.config.input_keep_prob,
is_train=self.is_train)
else:
p0 = attention_layer(config,
self.is_train,
h,
u,
h_mask=self.x_mask,
u_mask=self.q_mask,
scope="p0",
tensor_dict=self.tensor_dict)
cell2 = BasicLSTMCell(
d, state_is_tuple=True) # d = 100, hidden state number
first_cell = SwitchableDropoutWrapper(
cell2,
self.is_train,
input_keep_prob=config.input_keep_prob)
(fw_g0, bw_g0), _ = bidirectional_dynamic_rnn(
first_cell,
first_cell,
inputs=p0,
sequence_length=x_len,
dtype='float',
scope='g0') # [N, M, JX, 2d]
g0 = tf.concat([fw_g0, bw_g0], 3)
cell3 = BasicLSTMCell(
d, state_is_tuple=True) # d = 100, hidden state number
first_cell3 = SwitchableDropoutWrapper(
cell3, self.is_train, input_keep_prob=config.input_keep_prob)
(fw_g1, bw_g1), _ = bidirectional_dynamic_rnn(
first_cell3, first_cell3, g0, x_len, dtype='float',
scope='g1') # [N, M, JX, 2d]
g1 = tf.concat([fw_g1, bw_g1], 3)
logits = get_logits([g1, p0],
d,
True,
wd=config.wd,
input_keep_prob=config.input_keep_prob,
mask=self.x_mask,
is_train=self.is_train,
func=config.answer_func,
scope='logits1')
a1i = softsel(tf.reshape(g1, [N, M * JX, 2 * d]),
tf.reshape(logits, [N, M * JX]))
a1i = tf.tile(tf.expand_dims(tf.expand_dims(a1i, 1), 1),
[1, M, JX, 1])
cell4 = BasicLSTMCell(
d, state_is_tuple=True) # d = 100, hidden state number
first_cell4 = SwitchableDropoutWrapper(
cell4, self.is_train, input_keep_prob=config.input_keep_prob)
(fw_g2, bw_g2), _ = bidirectional_dynamic_rnn(
first_cell4,
first_cell4,
tf.concat([p0, g1, a1i, g1 * a1i], 3),
x_len,
dtype='float',
scope='g2') # [N, M, JX, 2d]
g2 = tf.concat([fw_g2, bw_g2], 3)
logits2 = get_logits([g2, p0],
d,
True,
wd=config.wd,
input_keep_prob=config.input_keep_prob,
mask=self.x_mask,
is_train=self.is_train,
func=config.answer_func,
scope='logits2')
flat_logits = tf.reshape(logits, [-1, M * JX])
flat_yp = tf.nn.softmax(flat_logits) # [-1, M*JX]
yp = tf.reshape(flat_yp, [-1, M, JX])
flat_logits2 = tf.reshape(logits2, [-1, M * JX])
flat_yp2 = tf.nn.softmax(flat_logits2)
yp2 = tf.reshape(flat_yp2, [-1, M, JX])
self.tensor_dict['g1'] = g1
self.tensor_dict['g2'] = g2
self.logits = flat_logits
self.logits2 = flat_logits2
self.yp = yp
self.yp2 = yp2
def _build_forward(self):
config = self.config
N, M, JX, JQ, VW, VC, d, W = \
config.batch_size, config.max_num_sents, config.max_sent_size, \
config.max_ques_size, config.word_vocab_size, config.char_vocab_size, config.hidden_size, \
config.max_word_size
JQ = JX
print('VC:{} NEW_EMB:{}'.format(VW, self.new_emb_mat.get_shape()))
dc, dw, dco = config.char_emb_size, config.word_emb_size, config.char_out_size
with tf.variable_scope("emb"):
if config.use_char_emb:
with tf.variable_scope("emb_var"), tf.device("/cpu:0"):
char_emb_mat = tf.get_variable("char_emb_mat",
shape=[VC, dc],
dtype='float')
with tf.variable_scope("char"):
Acx = tf.nn.embedding_lookup(char_emb_mat,
self.cx) # [N, M, JX, W, dc]
Acq = tf.nn.embedding_lookup(char_emb_mat,
self.cq) # [N, JQ, W, dc]
Acx = tf.reshape(Acx, [-1, JX, W, dc])
Acq = tf.reshape(Acq, [-1, JQ, W, dc])
filter_sizes = list(
map(int, config.out_channel_dims.split(',')))
heights = list(map(int, config.filter_heights.split(',')))
assert sum(filter_sizes) == dco, (filter_sizes, dco)
with tf.variable_scope("conv"):
xx = multi_conv1d(Acx,
filter_sizes,
heights,
"VALID",
self.is_train,
config.keep_prob,
scope="xx")
if config.share_cnn_weights:
tf.get_variable_scope().reuse_variables()
qq = multi_conv1d(Acq,
filter_sizes,
heights,
"VALID",
self.is_train,
config.keep_prob,
scope="xx")
else:
qq = multi_conv1d(Acq,
filter_sizes,
heights,
"VALID",
self.is_train,
config.keep_prob,
scope="qq")
xx = tf.reshape(xx, [-1, M, JX, dco])
qq = tf.reshape(qq, [-1, JQ, dco])
if config.use_word_emb:
with tf.variable_scope("emb_var"), tf.device("/cpu:0"):
if config.mode == 'train':
word_emb_mat = tf.get_variable(
"word_emb_mat",
dtype='float',
shape=[VW, dw],
initializer=get_initializer(config.emb_mat))
else:
word_emb_mat = tf.get_variable("word_emb_mat",
shape=[VW, dw],
dtype='float')
if config.use_glove_for_unk:
word_emb_mat = tf.concat(
axis=0, values=[word_emb_mat, self.new_emb_mat])
with tf.name_scope("word"):
Ax = tf.nn.embedding_lookup(word_emb_mat,
self.x) # [N, M, JX, d]
Aq = tf.nn.embedding_lookup(word_emb_mat,
self.q) # [N, JQ, d]
self.tensor_dict['x'] = Ax
self.tensor_dict['q'] = Aq
if config.use_char_emb:
xx = tf.concat(axis=3, values=[xx, Ax]) # [N, M, JX, di]
qq = tf.concat(axis=2, values=[qq, Aq]) # [N, JQ, di]
else:
xx = Ax
qq = Aq
xx = tf.reshape(xx, [-1, M, JX, d])
qq = tf.reshape(qq, [-1, JQ, d])
if config.use_pos_emb:
with tf.variable_scope("pos_onehot"), tf.device("/cpu:0"):
pos_x = tf.one_hot(
self.x_pos, depth=config.pos_tag_num) # [N,M,JX,depth]
pos_q = tf.one_hot(
self.q_pos, depth=config.pos_tag_num) # [N,JQ,depth]
xx = tf.concat(axis=3, values=[xx,
pos_x]) # [N, M, JX, di]
qq = tf.concat(axis=2, values=[qq, pos_q])
if config.use_sem_emb:
with tf.variable_scope("sem_onehot"), tf.device("/cpu:0"):
sem_x = tf.one_hot(self.x_sem, depth=3) # [N,M,JX,3]
sem_q = tf.one_hot(self.q_sem, depth=3) # [N,JQ,3]
xx = tf.concat(axis=3, values=[xx, sem_x])
qq = tf.concat(axis=2, values=[qq, sem_q])
if config.use_neg_emb:
with tf.variable_scope("neg_onehot"), tf.device("/cpu:0"):
neg_x = tf.one_hot(self.x_neg, depth=2) # [N,M,JX,2]
neg_q = tf.one_hot(self.q_neg, depth=2) # [N,JQ,2]
xx = tf.concat(axis=3, values=[xx, neg_x])
qq = tf.concat(axis=2, values=[qq, neg_q])
if config.highway:
with tf.variable_scope("highway"):
xx = highway_network(xx,
config.highway_num_layers,
True,
wd=config.wd,
is_train=self.is_train)
tf.get_variable_scope().reuse_variables()
qq = highway_network(qq,
config.highway_num_layers,
True,
wd=config.wd,
is_train=self.is_train)
self.tensor_dict['xx'] = xx
self.tensor_dict['qq'] = qq
cell_fw = BasicLSTMCell(d, state_is_tuple=True)
cell_bw = BasicLSTMCell(d, state_is_tuple=True)
d_cell_fw = SwitchableDropoutWrapper(
cell_fw, self.is_train, input_keep_prob=config.input_keep_prob)
d_cell_bw = SwitchableDropoutWrapper(
cell_bw, self.is_train, input_keep_prob=config.input_keep_prob)
cell_fw2 = BasicLSTMCell(d, state_is_tuple=True)
cell_bw2 = BasicLSTMCell(d, state_is_tuple=True)
d_cell_fw2 = SwitchableDropoutWrapper(
cell_fw2, self.is_train, input_keep_prob=config.input_keep_prob)
d_cell_bw2 = SwitchableDropoutWrapper(
cell_bw2, self.is_train, input_keep_prob=config.input_keep_prob)
x_len = tf.reduce_sum(tf.cast(self.x_mask, 'int32'), 2) # [N, M]
q_len = tf.reduce_sum(tf.cast(self.q_mask, 'int32'), 1) # [N]
if config.lstm:
with tf.variable_scope("prepro"):
(fw_u, bw_u), ((_, fw_u_f),
(_, bw_u_f)) = bidirectional_dynamic_rnn(
d_cell_fw,
d_cell_bw,
qq,
q_len,
dtype='float',
scope='u1') # [N, J, d], [N, d]
print('fw_u_f hsape:{}'.format(fw_u_f.get_shape()))
u = tf.concat(axis=2, values=[fw_u, bw_u]) #[N,JQ,2d]
if config.share_lstm_weights:
tf.get_variable_scope().reuse_variables()
(fw_h, bw_h), _ = bidirectional_dynamic_rnn(
cell_fw, cell_bw, xx, x_len, dtype='float',
scope='u1') # [N, M, JX, 2d]
h = tf.concat(axis=3, values=[fw_h,
bw_h]) # [N, M, JX, 2d]
print('fw_u_f nn hsape:{}'.format(fw_u_f.get_shape()))
else:
(fw_h, bw_h), _ = bidirectional_dynamic_rnn(
cell_fw, cell_bw, xx, x_len, dtype='float',
scope='h1') # [N, M, JX, 2d]
h = tf.concat(axis=3, values=[fw_h,
bw_h]) # [N, M, JX, 2d]
self.tensor_dict['u'] = u
self.tensor_dict['h'] = h
else:
h = xx
u = qq
h1 = h[:, 0, :, :]
h2 = h[:, 1, :, :]
h3 = h[:, 2, :, :]
h4 = h[:, 3, :, :]
n_1 = tf.reshape(self.x_mask[:, 0, :], [N, JX])
n_2 = tf.reshape(self.x_mask[:, 1, :], [N, JX])
n_3 = tf.reshape(self.x_mask[:, 2, :], [N, JX])
n_4 = tf.reshape(self.x_mask[:, 3, :], [N, JX])
if config.self_attention:
with tf.variable_scope("h_self_weight"):
print(h.get_shape())
for i in range(2):
with tf.variable_scope("self-attention"):
h1 = self_attention_layer(
config,
self.is_train,
h1,
p_mask=tf.expand_dims(n_1, -1),
scope="{}_layer_self_att_enc_e".format(
i)) # [N, len, dim]
tf.get_variable_scope().reuse_variables()
h2 = self_attention_layer(
config,
self.is_train,
h2,
p_mask=tf.expand_dims(n_2, -1),
scope="{}_layer_self_att_enc_e".format(i))
tf.get_variable_scope().reuse_variables()
h3 = self_attention_layer(
config,
self.is_train,
h3,
p_mask=tf.expand_dims(n_3, -1),
scope="{}_layer_self_att_enc_e".format(i))
tf.get_variable_scope().reuse_variables()
h4 = self_attention_layer(
config,
self.is_train,
h4,
p_mask=tf.expand_dims(n_4, -1),
scope="{}_layer_self_att_enc_e".format(i))
with tf.variable_scope("self-attention"):
u = self_attention_layer(
config,
self.is_train,
u,
p_mask=tf.expand_dims(self.q_mask, -1),
scope="{}_layer_self_att_enc_p".format(i))
if config.plot_encoder == "concate":
h = tf.concat([h1, h2, h3, h4], axis=1)
print("h concate shape".format(h.get_shape()))
n_n = tf.concat([n_1, n_2, n_3, n_4], axis=1)
elif config.plot_encoder == "sum":
h1 = tf.expand_dims(h1, axis=1)
h2 = tf.expand_dims(h2, axis=1)
h3 = tf.expand_dims(h3, axis=1)
h4 = tf.expand_dims(h4, axis=1)
h = tf.concat([h1, h2, h3, h4], axis=1)
h = tf.reduce_sum(h, axis=1)
print("h sum shape".format(h.get_shape()))
elif config.plot_encoder == "lstm":
# h1 = tf.reduce_sum(h1, axis=1)
h1 = tf.expand_dims(tf.reduce_sum(h1, axis=-1), axis=1)
h2 = tf.expand_dims(tf.reduce_sum(h2, axis=-1), axis=1)
h3 = tf.expand_dims(tf.reduce_sum(h3, axis=-1), axis=1)
h4 = tf.expand_dims(tf.reduce_sum(h4, axis=-1), axis=1)
(fw_u, bw_u), ((_, fw_u_f), (_,
bw_u_f)) = bidirectional_dynamic_rnn(
d_cell_fw2,
d_cell_bw2,
tf.concat([h1, h2, h3, h4],
axis=1),
dtype='float',
scope='1') # [N, J, d], [N, d]
print('fw_u_f hsape:{}'.format(fw_u_f.get_shape()))
h = tf.concat(axis=2, values=[fw_u, bw_u]) # [N,JQ,2d]
u = tf.expand_dims(tf.reduce_sum(u, axis=-1), axis=1)
tf.get_variable_scope().reuse_variables()
(fw_u, bw_u), ((_, fw_u_f), (_,
bw_u_f)) = bidirectional_dynamic_rnn(
d_cell_fw2,
d_cell_bw2,
tf.concat([u], axis=1),
dtype='float',
scope='1') # [N, J, d], [N, d]
print('fw_u_f hsape:{}'.format(fw_u_f.get_shape()))
u = tf.concat(axis=2, values=[fw_u, bw_u]) # [N,JQ,2d]
if config.interact:
with tf.variable_scope("interact"):
def get_attention(h, u, m):
JX = tf.shape(h)[1]
JQ = tf.shape(u)[1]
h = tf.expand_dims(h, 2)
u = tf.expand_dims(u, 1)
h = tf.tile(h, [1, 1, JQ, 1])
u = tf.tile(u, [1, JX, 1, 1])
attention = h * u # N,JX,JQ,2d
return attention
if config.plot_encoder == "concate":
attention = get_attention(h, u, M)
else:
attention = get_attention(h, u, 1)
with tf.variable_scope('conv_dense'):
if config.plot_encoder == "concate":
out_final = dense_net(config, attention, self.is_train)
else:
out_final = tf.reshape(attention, shape=[N, -1])
else:
h = tf.reshape(h, [-1, M * 2 * d * JX])
print("h shape {}".format(h.get_shape()))
u = tf.reshape(u, [-1, 2 * d * JQ])
print("U shape {}".format(u.get_shape()))
attention = tf.concat([h, u], axis=-1)
out_final = attention
out_final = linear(tf.concat([attention], axis=-1),
1000,
True,
bias_start=0.0,
scope="logit8",
squeeze=False,
wd=config.wd,
input_keep_prob=config.output_keep_pro,
is_train=self.is_train)
out_final = tf.nn.relu(out_final)
out_final = linear(tf.concat([out_final], axis=-1),
400,
True,
bias_start=0.0,
scope="logit9",
squeeze=False,
wd=config.wd,
input_keep_prob=config.output_keep_pro,
is_train=self.is_train)
out_final = tf.nn.relu(out_final)
out_final = linear(out_final,
300,
True,
bias_start=0.0,
scope="logit3",
squeeze=False,
wd=config.wd,
input_keep_prob=config.output_keep_pro,
is_train=self.is_train)
out_final = tf.nn.relu(out_final)
with tf.variable_scope('conv_dense'):
if config.hao:
out_final = linear(tf.concat(
[out_final, self.haoruopeng_feature], axis=-1),
200,
True,
bias_start=0.0,
scope="logit",
squeeze=False,
wd=config.wd,
input_keep_prob=config.output_keep_pro,
is_train=self.is_train)
out_final = tf.nn.relu(out_final)
out_final = linear(out_final,
100,
True,
bias_start=0.0,
scope="logit3",
squeeze=False,
wd=config.wd,
input_keep_prob=config.output_keep_pro,
is_train=self.is_train)
out_final = tf.nn.relu(out_final)
else:
out_final = linear(tf.concat([out_final], axis=-1),
200,
True,
bias_start=0.0,
scope="logit",
squeeze=False,
wd=config.wd,
input_keep_prob=config.output_keep_pro,
is_train=self.is_train)
out_final = linear(out_final,
100,
True,
bias_start=0.0,
scope="logit3",
squeeze=False,
wd=config.wd,
input_keep_prob=config.output_keep_pro,
is_train=self.is_train)
out_final = tf.nn.relu(out_final)
self.tensor_dict['outfinal'] = out_final
self.prediction = linear(tf.concat([out_final], axis=-1),
1,
True,
bias_start=0.0,
scope="logit2",
squeeze=False,
wd=config.wd,
input_keep_prob=config.output_keep_pro,
is_train=self.is_train)
def _build_forward(self):
#config为预先配置好的参数等
config = self.config
N, M, JX, JQ, VW, VC, d, W = \
config.batch_size, config.max_num_sents, config.max_sent_size, \
config.max_ques_size, config.word_vocab_size, config.char_vocab_size, config.hidden_size, \
config.max_word_size
JX = tf.shape(self.x)[2]
JQ = tf.shape(self.q)[1]
M = tf.shape(self.x)[1]
dc, dw, dco = config.char_emb_size, config.word_emb_size, config.char_out_size
#嵌入层
with tf.variable_scope("emb"):
#字符嵌入层
if config.use_char_emb: #若需要字符嵌入层
with tf.variable_scope("emb_var"), tf.device("/cpu:0"):
char_emb_mat = tf.get_variable("char_emb_mat",
shape=[VC, dc],
dtype='float')
with tf.variable_scope("char"):
Acx = tf.nn.embedding_lookup(char_emb_mat,
self.cx) # [N, M, JX, W, dc]
Acq = tf.nn.embedding_lookup(char_emb_mat,
self.cq) # [N, JQ, W, dc]
Acx = tf.reshape(Acx, [-1, JX, W, dc])
Acq = tf.reshape(Acq, [-1, JQ, W, dc])
#CNN的滤波器参数
filter_sizes = list(
map(int, config.out_channel_dims.split(',')))
heights = list(map(int, config.filter_heights.split(',')))
assert sum(filter_sizes) == dco, (filter_sizes, dco)
with tf.variable_scope("conv"):
xx = multi_conv1d(Acx,
filter_sizes,
heights,
"VALID",
self.is_train,
config.keep_prob,
scope="xx")
if config.share_cnn_weights:
tf.get_variable_scope().reuse_variables()
qq = multi_conv1d(Acq,
filter_sizes,
heights,
"VALID",
self.is_train,
config.keep_prob,
scope="xx")
else:
qq = multi_conv1d(Acq,
filter_sizes,
heights,
"VALID",
self.is_train,
config.keep_prob,
scope="qq")
xx = tf.reshape(xx, [-1, M, JX, dco])
qq = tf.reshape(qq, [-1, JQ, dco])
#词嵌入层
if config.use_word_emb:
with tf.variable_scope("emb_var"), tf.device("/cpu:0"):
if config.mode == 'train':
word_emb_mat = tf.get_variable(
"word_emb_mat",
dtype='float',
shape=[VW, dw],
initializer=get_initializer(config.emb_mat))
else:
word_emb_mat = tf.get_variable("word_emb_mat",
shape=[VW, dw],
dtype='float')
if config.use_glove_for_unk: #若调用已训练好的词嵌入文件
word_emb_mat = tf.concat(
0, [word_emb_mat, self.new_emb_mat])
with tf.name_scope("word"):
#将文章主体context：x和问题query：q转换为词向量
#embedding_lookup(params, ids),根据ids寻找params中的第id行
Ax = tf.nn.embedding_lookup(word_emb_mat,
self.x) # [N, M, JX, d]
Aq = tf.nn.embedding_lookup(word_emb_mat,
self.q) # [N, JQ, d]
self.tensor_dict['x'] = Ax
self.tensor_dict['q'] = Aq
if config.use_char_emb: #若进行了字符嵌入，在指定维度上将字符嵌入和词嵌入进行拼接
xx = tf.concat(3, [xx, Ax]) # [N, M, JX, di]
qq = tf.concat(2, [qq, Aq]) # [N, JQ, di]
else:
xx = Ax
qq = Aq
# 经过两层highway network得到context vector∈ R^(d*T)和query vectorQ∈R^(d∗J)
if config.highway:
with tf.variable_scope("highway"):
xx = highway_network(xx,
config.highway_num_layers,
True,
wd=config.wd,
is_train=self.is_train)
tf.get_variable_scope().reuse_variables()
qq = highway_network(qq,
config.highway_num_layers,
True,
wd=config.wd,
is_train=self.is_train)
self.tensor_dict['xx'] = xx
self.tensor_dict['qq'] = qq
cell = BasicLSTMCell(d, state_is_tuple=True)
#SwitchableDropoutWrapper为自定义的DropoutWrapper类
d_cell = SwitchableDropoutWrapper(
cell, self.is_train, input_keep_prob=config.input_keep_prob)
#reduce_sum在指定的维度上求和（得到x和q的非空值总数），cast将输入的tensor映射到指定类型（此处为x_mask到int32）
x_len = tf.reduce_sum(tf.cast(self.x_mask, 'int32'), 2) # [N, M]
q_len = tf.reduce_sum(tf.cast(self.q_mask, 'int32'), 1) # [N]
#Contextual Embedding Layer：对上一层得到的X和Q分别使用BiLSTM进行处理，分别捕捉X和Q中各自单词间的局部关系
with tf.variable_scope("prepro"):
(fw_u, bw_u), ((_, fw_u_f), (_,
bw_u_f)) = bidirectional_dynamic_rnn(
d_cell,
d_cell,
qq,
q_len,
dtype='float',
scope='u1') # [N, J, d], [N, d]
#fw_u和bw_u分别为双向lstm的output
u = tf.concat(2, [fw_u, bw_u]) #[N, J, 2d]
if config.share_lstm_weights:
tf.get_variable_scope().reuse_variables()
(fw_h, bw_h), _ = bidirectional_dynamic_rnn(
cell, cell, xx, x_len, dtype='float',
scope='u1') # [N, M, JX, 2d]
h = tf.concat(3, [fw_h, bw_h]) # [N, M, JX, 2d]
else:
(fw_h, bw_h), _ = bidirectional_dynamic_rnn(
cell, cell, xx, x_len, dtype='float',
scope='h1') # [N, M, JX, 2d]
h = tf.concat(3, [fw_h, bw_h]) # [N, M, JX, 2d]
self.tensor_dict['u'] = u
self.tensor_dict['h'] = h
#核心层Attention Flow Layer
with tf.variable_scope("main"):
if config.dynamic_att:
p0 = h
#expand_dims()在矩阵指定位置增加维度
#tile()对矩阵的指定维度进行复制
u = tf.reshape(tf.tile(tf.expand_dims(u, 1), [1, M, 1, 1]), [
N * M, JQ, 2 * d
]) #先在索引1的位置添加一个维度，然后复制M（context中最多的sentence数量）次，使u和h能具有相同的维度
q_mask = tf.reshape(
tf.tile(tf.expand_dims(self.q_mask, 1), [1, M, 1]),
[N * M, JQ])
first_cell = AttentionCell(
cell,
u,
mask=q_mask,
mapper='sim',
input_keep_prob=self.config.input_keep_prob,
is_train=self.is_train)
else:
p0 = attention_layer(config,
self.is_train,
h,
u,
h_mask=self.x_mask,
u_mask=self.q_mask,
scope="p0",
tensor_dict=self.tensor_dict)
first_cell = d_cell
(fw_g0, bw_g0), _ = bidirectional_dynamic_rnn(
first_cell, first_cell, p0, x_len, dtype='float',
scope='g0') # [N, M, JX, 2d]
g0 = tf.concat(3, [fw_g0, bw_g0])
(fw_g1, bw_g1), _ = bidirectional_dynamic_rnn(
first_cell, first_cell, g0, x_len, dtype='float',
scope='g1') # [N, M, JX, 2d]
g1 = tf.concat(3, [fw_g1, bw_g1])
logits = get_logits([g1, p0],
d,
True,
wd=config.wd,
input_keep_prob=config.input_keep_prob,
mask=self.x_mask,
is_train=self.is_train,
func=config.answer_func,
scope='logits1')
a1i = softsel(tf.reshape(g1, [N, M * JX, 2 * d]),
tf.reshape(logits, [N, M * JX]))
a1i = tf.tile(tf.expand_dims(tf.expand_dims(a1i, 1), 1),
[1, M, JX, 1])
(fw_g2, bw_g2), _ = bidirectional_dynamic_rnn(
d_cell,
d_cell,
tf.concat(3, [p0, g1, a1i, g1 * a1i]),
x_len,
dtype='float',
scope='g2') # [N, M, JX, 2d]
g2 = tf.concat(3, [fw_g2, bw_g2])
logits2 = get_logits([g2, p0],
d,
True,
wd=config.wd,
input_keep_prob=config.input_keep_prob,
mask=self.x_mask,
is_train=self.is_train,
func=config.answer_func,
scope='logits2')
flat_logits = tf.reshape(logits, [-1, M * JX])
flat_yp = tf.nn.softmax(flat_logits) # [-1, M*JX]
yp = tf.reshape(flat_yp, [-1, M, JX])
flat_logits2 = tf.reshape(logits2, [-1, M * JX])
flat_yp2 = tf.nn.softmax(flat_logits2)
yp2 = tf.reshape(flat_yp2, [-1, M, JX])
self.tensor_dict['g1'] = g1
self.tensor_dict['g2'] = g2
self.logits = flat_logits
self.logits2 = flat_logits2
self.yp = yp
self.yp2 = yp2