def _encode(self):
PL, QL, CL, d, dc, nh = self._params()
with tf.variable_scope("Embedding_Encoder_Layer"):
self.c_embed_encoding = residual_block(
self.c_emb,
num_blocks=1,
num_conv_layers=4,
kernel_size=5,
mask=self.c_mask,
num_filters=d,
num_heads=nh,
seq_len=self.c_len,
scope="Encoder_Residual_Block",
bias=False,
dropout=self.dropout)
self.q_embed_encoding = residual_block(
self.q_emb,
num_blocks=1,
num_conv_layers=4,
kernel_size=5,
mask=self.q_mask,
num_filters=d,
num_heads=nh,
seq_len=self.q_len,
scope="Encoder_Residual_Block",
reuse=True, # Share the weights between passage and question
bias=False,
dropout=self.dropout)
def _encode(self):
N, PL, QL, CL, d, dc, nh = self._params()
if self.config.fix_pretrained_vector:
dc = self.char_mat.get_shape()[-1]
with tf.variable_scope("Embedding_Encoder_Layer"):
self.c_embed_encoding = residual_block(
self.c_emb,
num_blocks=1,
num_conv_layers=2,
kernel_size=7,
mask=self.c_mask,
num_filters=d,
num_heads=nh,
seq_len=self.c_len,
scope="Encoder_Residual_Block",
bias=False,
dropout=self.dropout)
self.q_embed_encoding = residual_block(
self.q_emb,
num_blocks=1,
num_conv_layers=2,
kernel_size=7,
mask=self.q_mask,
num_filters=d,
num_heads=nh,
seq_len=self.q_len,
scope="Encoder_Residual_Block",
reuse=True, # Share the weights between passage and question
bias=False,
dropout=self.dropout)
def style_transformer_network(inputs, style_params):
with tf.variable_scope('style_transformer'):
with tf.variable_scope('encode') as scope:
conv1 = layers.conv2d(inputs, 32, 9, 1, "conv1", scope.name, style_params)
conv2 = layers.conv2d(conv1, 64, 3, 2, "conv2", scope.name, style_params)
conv3 = layers.conv2d(conv2, 128, 3, 2, "conv3", scope.name, style_params)
with tf.variable_scope('residual') as scope:
res1 = layers.residual_block(conv3, 3, "residual1", scope.name, style_params)
res2 = layers.residual_block(res1, 3, "residual2", scope.name, style_params)
res3 = layers.residual_block(res2, 3, "residual3", scope.name, style_params)
with tf.variable_scope('upsample') as scope:
up1 = layers.upsampling(res3, 3, 2, 64, 'upsample1', scope.name, style_params)
up2 = layers.upsampling(up1, 3, 2, 32, 'upsample2', scope.name, style_params)
return layers.upsampling(up2, 9, 2, 3, 'upsample3', scope.name, style_params, tf.nn.sigmoid)
def discriminator(sequence,training=tf.constant(True)):
num = tf.shape(sequence)[0]
x = lyr.conv('discriminator.conv1.filter','discriminator.conv1.bias','discriminator',(5,encode_length,64),sequence,max_size)
x = tf.nn.leaky_relu(x)
x = lyr.residual_block('discriminator.res1.filter1','discriminator.res1.bias1','discriminator.res1.filter2','discriminator.res1.bias1','discriminator',64,64,x,max_size)
x = lyr.layernorm(x,num)
x = lyr.residual_block('discriminator.res4.filter1','discriminator.res4.bias1','discriminator.res4.filter2','discriminator.res4.bias1','discriminator',64,64,x,max_size)
x = lyr.layernorm(x,num)
x = lyr.residual_block('discriminator.res5.filter1','discriminator.res5.bias1','discriminator.res5.filter2','discriminator.res5.bias1','discriminator',64,64,x,max_size)
x = lyr.layernorm(x,num)
x = tf.reshape(x,(num,max_size*64))
output = lyr.dense('discriminator.dense1.matrix','discriminator.dense1.bias','discriminator',max_size*64,1,x)
return output
def discriminator(sequence):
x = lyr.conv('discriminator.conv1.filter', 'discriminator.conv1.bias',
'discriminator', (5, encode_length, 64), sequence,
max_size)
x = tf.nn.leaky_relu(x)
x = lyr.residual_block('discriminator.res1.filter1',
'discriminator.res1.bias1',
'discriminator.res1.filter2',
'discriminator.res1.bias1', 'discriminator', 64,
64, x, max_size)
x = lyr.layernorm(x, batch_size)
x = lyr.residual_block('discriminator.res2.filter1',
'discriminator.res2.bias1',
'discriminator.res2.filter2',
'discriminator.res2.bias1', 'discriminator', 64,
64, x, max_size)
x = lyr.layernorm(x, batch_size)
x = lyr.residual_block('discriminator.res3.filter1',
'discriminator.res3.bias1',
'discriminator.res3.filter2',
'discriminator.res3.bias1', 'discriminator', 64,
64, x, max_size)
x = lyr.layernorm(x, batch_size)
x = lyr.residual_block('discriminator.res4.filter1',
'discriminator.res4.bias1',
'discriminator.res4.filter2',
'discriminator.res4.bias1', 'discriminator', 64,
64, x, max_size)
x = lyr.layernorm(x, batch_size)
x = lyr.residual_block('discriminator.res5.filter1',
'discriminator.res5.bias1',
'discriminator.res5.filter2',
'discriminator.res5.bias1', 'discriminator', 64,
64, x, max_size)
x = lyr.layernorm(x, batch_size)
x = tf.reshape(x, (batch_size, max_size * 64))
output = lyr.dense('discriminator.dense1.matrix',
'discriminator.dense1.bias', 'discriminator',
max_size * 64, 1, x)
return output
def _fuse(self):
with tf.variable_scope("Context_to_Query_Attention_Layer"):
C = tf.tile(tf.expand_dims(self.c_embed_encoding, 2),
[1, 1, self.max_q_len, 1])
Q = tf.tile(tf.expand_dims(self.q_embed_encoding, 1),
[1, self.max_p_len, 1, 1])
S = trilinear([C, Q, C * Q], input_keep_prob=1.0 - self.dropout)
mask_q = tf.expand_dims(self.q_mask, 1)
S_ = tf.nn.softmax(mask_logits(S, mask=mask_q))
mask_c = tf.expand_dims(self.c_mask, 2)
S_T = tf.transpose(
tf.nn.softmax(mask_logits(S, mask=mask_c), dim=1), (0, 2, 1))
self.c2q = tf.matmul(S_, self.q_embed_encoding)
self.q2c = tf.matmul(tf.matmul(S_, S_T), self.c_embed_encoding)
self.attention_outputs = [
self.c_embed_encoding, self.c2q,
self.c_embed_encoding * self.c2q,
self.c_embed_encoding * self.q2c
]
# self.config.batch_size if not self.demo else 1,
# self.max_p_len,
# self.max_q_len,
# self.config.max_ch_len,
# self.config.hidden_size,
# self.config.char_embed_size,
# self.config.head_size
N, PL, QL, CL, d, dc, nh = self._params()
if self.config.fix_pretrained_vector:
dc = self.char_mat.get_shape()[-1]
with tf.variable_scope("Model_Encoder_Layer"):
inputs = tf.concat(self.attention_outputs, axis=-1)
self.enc = [conv(inputs, d, name="input_projection")]
for i in range(3):
if i % 2 == 0:
self.enc[i] = tf.nn.dropout(self.enc[i],
1.0 - self.dropout)
self.enc.append(
residual_block(self.enc[i],
num_blocks=1,
num_conv_layers=2,
kernel_size=5,
mask=self.c_mask,
num_filters=d,
num_heads=nh,
seq_len=self.c_len,
scope="Model_Encoder",
bias=True,
reuse=True if i > 0 else None,
dropout=self.dropout))
for i, item in enumerate(self.enc):
self.enc[i] = tf.reshape(self.enc[i],
[N, -1, self.enc[i].get_shape()[-1]])
def predictor(sequence):
num = tf.shape(sequence)[0]
x = lyr.conv('predictor.conv1.filter','predictor.conv1.bias','predictor',(5,encode_length,16),sequence,max_size)
x = tf.nn.leaky_relu(x)
x = lyr.residual_block('predictor.res1.filter1','predictor.res1.bias1','predictor.res1.filter2','predictor.res1.bias1','predictor',16,16,x,max_size,channels=16)
x = tf.reshape(x,(num,max_size*16))
output = lyr.dense('predictor.dense1.matrix','predictor.dense1.bias','predictor',max_size*16,num_classes,x)
return output
def generator(seed, training=True):
seed = tf.reshape(seed, (batch_size, 100))
seed2 = lyr.dense('generator.dense1.matrix', 'generator.dense1.bias',
'generator', 100, max_size * 64, seed)
seed2 = tf.nn.leaky_relu(seed2)
seed2 = tf.reshape(seed2, [batch_size, max_size, 64])
x = lyr.residual_block('generator.res1.filter1',
'generator.res1.bias1',
'generator.res1.filter2',
'generator.res1.bias2', 'generator', 64, 64,
seed2, max_size)
x = lyr.residual_block('generator.res2.filter1',
'generator.res2.bias1',
'generator.res2.filter2',
'generator.res2.bias2', 'generator', 64, 64, x,
max_size)
x = lyr.residual_block('generator.res3.filter1',
'generator.res3.bias1',
'generator.res3.filter2',
'generator.res3.bias2', 'generator', 64, 64, x,
max_size)
x = lyr.residual_block('generator.res4.filter1',
'generator.res4.bias1',
'generator.res4.filter2',
'generator.res4.bias2', 'generator', 64, 64, x,
max_size)
x = lyr.residual_block('generator.res5.filter1',
'generator.res5.bias1',
'generator.res5.filter2',
'generator.res5.bias2', 'generator', 64, 64, x,
max_size)
x = lyr.conv('generator.conv1.filter', 'generator.conv1.bias',
'generator', (5, 64, encode_length), x, max_size)
x = tf.nn.softmax(x)
return x
def predictor_stem(sequence):
x = lyr.conv('predictor_stem.conv1.filter',
'predictor_stem.conv1.bias', 'predictor_stem',
(5, encode_length, 64), sequence, stem_size)
x = tf.nn.leaky_relu(x)
x = lyr.residual_block('predictor_stem.res1.filter1',
'predictor_stem.res1.bias1',
'predictor_stem.res1.filter2',
'predictor_stem.res1.bias1', 'predictor_stem',
64, 64, x, stem_size)
x = lyr.residual_block('predictor_stem.res2.filter1',
'predictor_stem.res2.bias1',
'predictor_stem.res2.filter2',
'predictor_stem.res2.bias1', 'predictor_stem',
64, 64, x, stem_size)
x = lyr.residual_block('predictor_stem.res3.filter1',
'predictor_stem.res3.bias1',
'predictor_stem.res3.filter2',
'predictor_stem.res3.bias1', 'predictor_stem',
64, 64, x, stem_size)
x = lyr.residual_block('predictor_stem.res4.filter1',
'predictor_stem.res4.bias1',
'predictor_stem.res4.filter2',
'predictor_stem.res4.bias1', 'predictor_stem',
64, 64, x, stem_size)
x = lyr.residual_block('predictor_stem.res5.filter1',
'predictor_stem.res5.bias1',
'predictor_stem.res5.filter2',
'predictor_stem.res5.bias1', 'predictor_stem',
64, 64, x, stem_size)
x = tf.reshape(x, (batch_size, stem_size * 64))
output = lyr.dense('predictor_stem.dense1.matrix',
'predictor_stem.dense1.bias', 'predictor_stem',
stem_size * 64, num_classes, x)
return output
def generator(seed,training=tf.constant(True)):
num = tf.shape(seed)[0]
seed = tf.reshape(seed,(num,100))
seed2 = lyr.dense('generator.dense1.matrix','generator.dense1.bias','generator',100,max_size*64,seed)
seed2 = tf.nn.leaky_relu(seed2)
seed2 = lyr.batchnorm(seed2,'generator.batchnorm1.offset','generator.batchnorm1.scale','generator.batchnorm1.average_means','generator.batchnorm1.average_variances','generator.num_means','generator',(max_size*64,),training=training)
seed2 = tf.reshape(seed2,[num,max_size,64])
x = lyr.residual_block('generator.res1.filter1','generator.res1.bias1','generator.res1.filter2','generator.res1.bias2','generator',64,64,seed2,max_size)
x = lyr.batchnorm(x,'generator.batchnorm2.offset','generator.batchnorm2.scale','generator.batchnorm2.average_means','generator.batchnorm2.average_variances','generator.num_means','generator',(max_size,64),training=training)
x = lyr.residual_block('generator.res2.filter1','generator.res2.bias1','generator.res2.filter2','generator.res2.bias2','generator',64,64,x,max_size)
x = lyr.batchnorm(x,'generator.batchnorm3.offset','generator.batchnorm3.scale','generator.batchnorm3.average_means','generator.batchnorm3.average_variances','generator.num_means','generator',(max_size,64),training=training)
x = lyr.residual_block('generator.res3.filter1','generator.res3.bias1','generator.res3.filter2','generator.res3.bias2','generator',64,64,x,max_size)
x = lyr.batchnorm(x,'generator.batchnorm4.offset','generator.batchnorm4.scale','generator.batchnorm4.average_means','generator.batchnorm4.average_variances','generator.num_means','generator',(max_size,64),training=training)
x = lyr.conv('generator.conv1.filter','generator.conv1.bias','generator',(5,64,encode_length),x,max_size)
x = tf.nn.softmax(x)
return x
def encoder(sequence,training=True):
num = tf.shape(sequence)[0]
x = lyr.conv('encoder.conv1.filter','encoder.conv1.bias','encoder',(5,encode_length,args.channels),sequence,max_size)
x = tf.nn.leaky_relu(x)
x = lyr.batchnorm(x,'encoder.batchnorm1.offset','encoder.batchnorm1.scale','encoder.batchnorm1.average_means','encoder.batchnorm1.average_variances','encoder.num_means','encoder',(max_size,args.channels),training=training)
x = lyr.residual_block('encoder.res1.filter1','encoder.res1.bias1','encoder.res1.filter2','encoder.res1.bias1','encoder',args.channels,args.channels,x,max_size,channels=args.channels)
x = lyr.batchnorm(x,'encoder.batchnorm2.offset','encoder.batchnorm2.scale','encoder.batchnorm2.average_means','encoder.batchnorm2.average_variances','encoder.num_means','encoder',(max_size,args.channels),training=training)
x = tf.reshape(x,(num,max_size*args.channels))
x = lyr.dense('encoder.dense1.matrix','encoder.dense1.bias','encoder',max_size*args.channels,2*latent_dim,x)
x = tf.nn.leaky_relu(x)
output = lyr.batchnorm(x,'encoder.batchnorm3.offset','encoder.batchnorm3.scale','encoder.batchnorm3.average_means','encoder.batchnorm3.average_variances','encoder.num_means','encoder',(2*latent_dim),training=training)
return output
def _fuse(self):
with tf.variable_scope("Context_to_Query_Attention_Layer"):
C = tf.tile(tf.expand_dims(self.c_embed_encoding, 2),
[1, 1, self.max_q_len, 1])
Q = tf.tile(tf.expand_dims(self.q_embed_encoding, 1),
[1, self.max_p_len, 1, 1])
S = trilinear([C, Q, C * Q], input_keep_prob=1.0 - self.dropout)
mask_q = tf.expand_dims(self.q_mask, 1)
S_ = tf.nn.softmax(mask_logits(S, mask=mask_q))
mask_c = tf.expand_dims(self.c_mask, 2)
S_T = tf.transpose(
tf.nn.softmax(mask_logits(S, mask=mask_c), dim=1), (0, 2, 1))
self.c2q = tf.matmul(S_, self.q_embed_encoding)
self.q2c = tf.matmul(tf.matmul(S_, S_T), self.c_embed_encoding)
self.attention_outputs = [
self.c_embed_encoding, self.c2q,
self.c_embed_encoding * self.c2q,
self.c_embed_encoding * self.q2c
]
PL, QL, CL, d, dc, nh = self._params()
with tf.variable_scope("Model_Encoder_Layer"):
inputs = tf.concat(self.attention_outputs, axis=-1)
self.enc = [conv(inputs, d, name="input_projection")]
for i in range(3):
if i % 2 == 0:
self.enc[i] = tf.nn.dropout(self.enc[i],
1.0 - self.dropout)
self.enc.append(
residual_block(self.enc[i],
num_blocks=7,
num_conv_layers=2,
kernel_size=3,
mask=self.c_mask,
num_filters=d,
num_heads=nh,
seq_len=self.c_len,
scope="Model_Encoder",
bias=False,
reuse=True if i > 0 else None,
dropout=self.dropout))
def forward(self):
config = self.config
N, PL, QL, CL, d, dc, nh, dw = config.test_batch_size if self.loop_function else config.batch_size, self.c_maxlen, self.q_maxlen, \
config.char_limit, config.hidden, config.char_dim, config.num_heads, config.glove_dim
with tf.variable_scope("Input_Embedding_Layer"):
ch_emb = tf.reshape(tf.nn.embedding_lookup(self.char_mat, self.ch),
[N * PL, CL, dc])
qh_emb = tf.reshape(tf.nn.embedding_lookup(self.char_mat, self.qh),
[N * QL, CL, dc])
ch_emb = tf.nn.dropout(ch_emb, 1.0 - 0.5 * self.dropout)
qh_emb = tf.nn.dropout(qh_emb, 1.0 - 0.5 * self.dropout)
# Bidaf style conv-highway encoder
ch_emb = conv(ch_emb,
d,
bias=True,
activation=tf.nn.relu,
kernel_size=5,
name="char_conv",
reuse=None)
qh_emb = conv(qh_emb,
d,
bias=True,
activation=tf.nn.relu,
kernel_size=5,
name="char_conv",
reuse=True)
ch_emb = tf.reduce_max(ch_emb, axis=1)
qh_emb = tf.reduce_max(qh_emb, axis=1)
ch_emb = tf.reshape(ch_emb, [N, PL, ch_emb.shape[-1]])
qh_emb = tf.reshape(qh_emb, [N, QL, ch_emb.shape[-1]])
c_emb = tf.nn.dropout(
tf.nn.embedding_lookup(self.word_mat, self.c),
1.0 - self.dropout)
q_emb = tf.nn.dropout(
tf.nn.embedding_lookup(self.word_mat, self.q),
1.0 - self.dropout)
c_emb = tf.concat([c_emb, ch_emb], axis=2)
q_emb = tf.concat([q_emb, qh_emb], axis=2)
c_emb = highway(c_emb,
size=d,
scope="highway",
dropout=self.dropout,
reuse=None)
q_emb = highway(q_emb,
size=d,
scope="highway",
dropout=self.dropout,
reuse=True)
with tf.variable_scope("Embedding_Encoder_Layer"):
c = residual_block(c_emb,
num_blocks=1,
num_conv_layers=2,
kernel_size=7,
mask=self.c_mask,
num_filters=d,
num_heads=nh,
seq_len=self.c_len,
scope="Encoder_Residual_Block",
bias=False,
dropout=self.dropout)
q = residual_block(
q_emb,
num_blocks=1,
num_conv_layers=2,
kernel_size=7,
mask=self.q_mask,
num_filters=d,
num_heads=nh,
seq_len=self.q_len,
scope="Encoder_Residual_Block",
reuse=True, # Share the weights between passage and question
bias=False,
dropout=self.dropout)
with tf.variable_scope("Context_to_Query_Attention_Layer"):
# C = tf.tile(tf.expand_dims(c,2),[1,1,self.q_maxlen,1])
# Q = tf.tile(tf.expand_dims(q,1),[1,self.c_maxlen,1,1])
# S = trilinear([C, Q, C*Q], input_keep_prob = 1.0 - self.dropout)
S = optimized_trilinear_for_attention([c, q],
self.c_maxlen,
self.q_maxlen,
input_keep_prob=1.0 -
self.dropout)
mask_q = tf.expand_dims(self.q_mask, 1)
S_ = tf.nn.softmax(mask_logits(S, mask=mask_q))
mask_c = tf.expand_dims(self.c_mask, 2)
S_T = tf.transpose(
tf.nn.softmax(mask_logits(S, mask=mask_c), dim=1), (0, 2, 1))
self.c2q = tf.matmul(S_, q)
self.q2c = tf.matmul(tf.matmul(S_, S_T), c)
attention_outputs = [c, self.c2q, c * self.c2q, c * self.q2c]
with tf.variable_scope("Model_Encoder_Layer"):
inputs = tf.concat(attention_outputs, axis=-1)
self.enc = [conv(inputs, d, name="input_projection")]
for i in range(3):
if i % 2 == 0: # dropout every 2 blocks
self.enc[i] = tf.nn.dropout(self.enc[i],
1.0 - self.dropout)
self.enc.append(
residual_block(self.enc[i],
num_blocks=2,
num_conv_layers=2,
kernel_size=5,
mask=self.c_mask,
num_filters=d,
num_heads=nh,
seq_len=self.c_len,
scope="Model_Encoder",
bias=False,
reuse=True if i > 0 else None,
dropout=self.dropout))
with tf.variable_scope("Decoder_Layer"):
memory = tf.concat([self.enc[1], self.enc[2], self.enc[3]],
axis=-1)
oups = tf.split(self.a, [1] * self.a_maxlen, 1)
h = tf.tanh(
_linear(tf.reduce_mean(memory, axis=1),
output_size=d,
bias=False,
scope="h_initial"))
c = tf.tanh(
_linear(tf.reduce_mean(memory, axis=1),
output_size=d,
bias=False,
scope="c_initial"))
state = (c, h)
outputs = []
prev = None
prev_probs = [0.0]
symbols = []
for i, inp in enumerate(oups):
einp = tf.reshape(tf.nn.embedding_lookup(self.word_mat, inp),
[N, dw])
if i > 0:
tf.get_variable_scope().reuse_variables()
if self.loop_function is not None and prev is not None:
with tf.variable_scope("loop_function", reuse=True):
einp, prev_probs, index, prev_symbol = self.loop_function(
prev, prev_probs, self.beam_size, i)
h = tf.gather(h, index) # update prev state
state = tuple(tf.gather(s, index)
for s in state) # update prev state
for j, symbol in enumerate(symbols):
symbols[j] = tf.gather(
symbol, index) # update prev symbols
for j, output in enumerate(outputs):
outputs[j] = tf.gather(
output, index) # update prev outputs
symbols.append(prev_symbol)
attn = tf.reshape(
multihead_attention(tf.expand_dims(h, 1),
units=d,
num_heads=nh,
memory=memory,
mask=self.c_mask,
bias=False), [-1, nh * d])
cinp = tf.concat([einp, attn], 1)
h, state = self.cell(cinp, state)
with tf.variable_scope("AttnOutputProjection"):
output = _linear([h] + [cinp],
output_size=dw * 2,
bias=False,
scope="output")
output = tf.reshape(output, [-1, dw, 2])
output = tf.reduce_max(output, 2) # maxout
outputs.append(output)
if self.loop_function is not None:
prev = output
if self.loop_function is not None:
# process the last symbol
einp, prev_probs, index, prev_symbol = self.loop_function(
prev, prev_probs, self.beam_size, i + 1)
for j, symbol in enumerate(symbols):
symbols[j] = tf.gather(symbol,
index) # update prev symbols
for j, output in enumerate(outputs):
outputs[j] = tf.gather(output,
index) # update prev outputs
symbols.append(prev_symbol)
# output the final best result of beam search
for k, symbol in enumerate(symbols):
symbols[k] = tf.gather(symbol, 0)
for k, output in enumerate(outputs):
outputs[k] = tf.expand_dims(tf.gather(output, 0), 0)
self.gen_loss = self._compute_loss(outputs, oups, N)
self.symbols = symbols
with tf.variable_scope("Output_Layer"):
start_logits = tf.squeeze(
conv(tf.concat([self.enc[1], self.enc[2]], axis=-1),
1,
bias=False,
name="start_pointer"), -1)
end_logits = tf.squeeze(
conv(tf.concat([self.enc[1], self.enc[3]], axis=-1),
1,
bias=False,
name="end_pointer"), -1)
self.logits = [
mask_logits(start_logits, mask=self.c_mask),
mask_logits(end_logits, mask=self.c_mask)
]
logits1, logits2 = [l for l in self.logits]
outer = tf.matmul(tf.expand_dims(tf.nn.softmax(logits1), axis=2),
tf.expand_dims(tf.nn.softmax(logits2), axis=1))
outer = tf.matrix_band_part(outer, 0, config.ans_limit)
self.yp1 = tf.argmax(tf.reduce_max(outer, axis=2), axis=1)
self.yp2 = tf.argmax(tf.reduce_max(outer, axis=1), axis=1)
losses = tf.nn.softmax_cross_entropy_with_logits(logits=logits1,
labels=self.y1)
losses2 = tf.nn.softmax_cross_entropy_with_logits(logits=logits2,
labels=self.y2)
self.loss = tf.reduce_mean(losses + losses2)
self.loss = self.gen_loss
if config.l2_norm is not None:
variables = tf.get_collection(tf.GraphKeys.REGULARIZATION_LOSSES)
l2_loss = tf.contrib.layers.apply_regularization(
regularizer, variables)
self.loss += l2_loss
if config.decay is not None:
self.var_ema = tf.train.ExponentialMovingAverage(config.decay)
ema_op = self.var_ema.apply(tf.trainable_variables())
with tf.control_dependencies([ema_op]):
self.loss = tf.identity(self.loss)
self.assign_vars = []
for var in tf.global_variables():
v = self.var_ema.average(var)
if v:
self.assign_vars.append(tf.assign(var, v))
def forward(self):
config = self.config
N, PL, QL, CL, d, dc, nh = config.batch_size if not self.demo else 1, self.c_maxlen, self.q_maxlen, config.char_limit, config.hidden, config.char_dim, config.num_heads
with tf.variable_scope("Input_Embedding_Layer"):
ch_emb = tf.reshape(tf.nn.embedding_lookup(
self.char_mat, self.ch), [N * PL, CL, dc])
qh_emb = tf.reshape(tf.nn.embedding_lookup(
self.char_mat, self.qh), [N * QL, CL, dc])
ch_emb = tf.nn.dropout(ch_emb, 1.0 - 0.5 * self.dropout)
qh_emb = tf.nn.dropout(qh_emb, 1.0 - 0.5 * self.dropout)
# Bidaf style conv-highway encoder
ch_emb = conv(ch_emb, d,
bias = True, activation = tf.nn.relu, kernel_size = 5, name = "char_conv", reuse = None)
qh_emb = conv(qh_emb, d,
bias = True, activation = tf.nn.relu, kernel_size = 5, name = "char_conv", reuse = True)
ch_emb = tf.reduce_max(ch_emb, axis = 1)
qh_emb = tf.reduce_max(qh_emb, axis = 1)
ch_emb = tf.reshape(ch_emb, [N, PL, ch_emb.shape[-1]])
qh_emb = tf.reshape(qh_emb, [N, QL, ch_emb.shape[-1]])
c_emb = tf.nn.dropout(tf.nn.embedding_lookup(self.word_mat, self.c), 1.0 - self.dropout)
q_emb = tf.nn.dropout(tf.nn.embedding_lookup(self.word_mat, self.q), 1.0 - self.dropout)
c_emb = tf.concat([c_emb, ch_emb], axis=2)
q_emb = tf.concat([q_emb, qh_emb], axis=2)
c_emb = highway(c_emb, size = d, scope = "highway", dropout = self.dropout, reuse = None)
q_emb = highway(q_emb, size = d, scope = "highway", dropout = self.dropout, reuse = True)
with tf.variable_scope("Embedding_Encoder_Layer"):
c = residual_block(c_emb,
num_blocks = 1,
num_conv_layers = 4,
kernel_size = 7,
mask = self.c_mask,
num_filters = d,
num_heads = nh,
seq_len = self.c_len,
scope = "Encoder_Residual_Block",
bias = False,
dropout = self.dropout)
q = residual_block(q_emb,
num_blocks = 1,
num_conv_layers = 4,
kernel_size = 7,
mask = self.q_mask,
num_filters = d,
num_heads = nh,
seq_len = self.q_len,
scope = "Encoder_Residual_Block",
reuse = True, # Share the weights between passage and question
bias = False,
dropout = self.dropout)
with tf.variable_scope("Context_to_Query_Attention_Layer"):
# C = tf.tile(tf.expand_dims(c,2),[1,1,self.q_maxlen,1])
# Q = tf.tile(tf.expand_dims(q,1),[1,self.c_maxlen,1,1])
# S = trilinear([C, Q, C*Q], input_keep_prob = 1.0 - self.dropout)
S = optimized_trilinear_for_attention([c, q], self.c_maxlen, self.q_maxlen, input_keep_prob = 1.0 - self.dropout)
mask_q = tf.expand_dims(self.q_mask, 1)
S_ = tf.nn.softmax(mask_logits(S, mask = mask_q))
mask_c = tf.expand_dims(self.c_mask, 2)
S_T = tf.transpose(tf.nn.softmax(mask_logits(S, mask = mask_c), dim = 1),(0,2,1))
self.c2q = tf.matmul(S_, q)
self.q2c = tf.matmul(tf.matmul(S_, S_T), c)
attention_outputs = [c, self.c2q, c * self.c2q, c * self.q2c]
with tf.variable_scope("Model_Encoder_Layer"):
inputs = tf.concat(attention_outputs, axis = -1)
self.enc = [conv(inputs, d, name = "input_projection")]
for i in range(3):
if i % 2 == 0: # dropout every 2 blocks
self.enc[i] = tf.nn.dropout(self.enc[i], 1.0 - self.dropout)
self.enc.append(
residual_block(self.enc[i],
num_blocks = 7,
num_conv_layers = 2,
kernel_size = 5,
mask = self.c_mask,
num_filters = d,
num_heads = nh,
seq_len = self.c_len,
scope = "Model_Encoder",
bias = False,
reuse = True if i > 0 else None,
dropout = self.dropout)
)
with tf.variable_scope("Output_Layer"):
start_logits = tf.squeeze(conv(tf.concat([self.enc[1], self.enc[2]],axis = -1),1, bias = False, name = "start_pointer"),-1)
end_logits = tf.squeeze(conv(tf.concat([self.enc[1], self.enc[3]],axis = -1),1, bias = False, name = "end_pointer"), -1)
self.logits = [mask_logits(start_logits, mask = self.c_mask),
mask_logits(end_logits, mask = self.c_mask)]
logits1, logits2 = [l for l in self.logits]
outer = tf.matmul(tf.expand_dims(tf.nn.softmax(logits1), axis=2),
tf.expand_dims(tf.nn.softmax(logits2), axis=1))
outer = tf.matrix_band_part(outer, 0, config.ans_limit)
self.yp1 = tf.argmax(tf.reduce_max(outer, axis=2), axis=1)
self.yp2 = tf.argmax(tf.reduce_max(outer, axis=1), axis=1)
losses = tf.nn.softmax_cross_entropy_with_logits(
logits=logits1, labels=self.y1)
losses2 = tf.nn.softmax_cross_entropy_with_logits(
logits=logits2, labels=self.y2)
self.loss = tf.reduce_mean(losses + losses2)
if config.l2_norm is not None:
variables = tf.get_collection(tf.GraphKeys.REGULARIZATION_LOSSES)
l2_loss = tf.contrib.layers.apply_regularization(regularizer, variables)
self.loss += l2_loss
if config.decay is not None:
self.var_ema = tf.train.ExponentialMovingAverage(config.decay)
ema_op = self.var_ema.apply(tf.trainable_variables())
with tf.control_dependencies([ema_op]):
self.loss = tf.identity(self.loss)
self.assign_vars = []
for var in tf.global_variables():
v = self.var_ema.average(var)
if v:
self.assign_vars.append(tf.assign(var,v))
def pred(self):
with tf.variable_scope("embedding_layer"):
(self.questions, question_lengths), (
self.contexts,
context_lengths), self.answers = self.iterator.get_next()
max_context_length = tf.reduce_max(context_lengths)
max_question_length = tf.reduce_max(question_lengths)
#max_context_length = self.train_max_context_length
#max_question_length = self.train_max_question_length
context_mask = tf.sequence_mask(context_lengths,
maxlen=max_context_length)
question_mask = tf.sequence_mask(question_lengths,
maxlen=max_question_length)
question_embeddings = tf.nn.embedding_lookup(
self.embedding, self.questions)
context_embeddings = tf.nn.embedding_lookup(
self.embedding, self.contexts)
print('question_embeddings',
question_embeddings.get_shape().as_list())
print('context_embeddings',
context_embeddings.get_shape().as_list())
with tf.variable_scope("embedding_layer"):
c = residual_block(context_embeddings,
num_blocks=1,
num_conv_layers=1,
kernel_size=7,
mask=context_mask,
num_filters=self.lstm_hidden_size,
num_heads=1,
seq_len=max_context_length,
scope="Encoder_Residual_Block",
bias=False,
dropout=1.0 - self.keep_prob)
print('c', c.get_shape().as_list())
q = residual_block(
question_embeddings,
num_blocks=1,
num_conv_layers=1,
kernel_size=7,
mask=question_mask,
num_filters=self.lstm_hidden_size,
num_heads=1,
seq_len=max_question_length,
scope="Encoder_Residual_Block",
reuse=True, # Share the weights between passage and question
bias=False,
dropout=1.0 - self.keep_prob)
print('q', q.get_shape().as_list())
# context_output dimension is BS * max_context_length * d
# where d = 2*lstm_hidden_size
with tf.variable_scope("attention_layer"):
S = optimized_trilinear_for_attention(
[c, q],
max_context_length,
max_question_length,
input_keep_prob=self.keep_prob)
mask_q = tf.expand_dims(question_mask, 1)
S_ = tf.nn.softmax(mask_logits(S, mask=mask_q))
mask_c = tf.expand_dims(context_mask, 2)
S_T = tf.transpose(
tf.nn.softmax(mask_logits(S, mask=mask_c), dim=1), (0, 2, 1))
self.c2q = tf.matmul(S_, q)
self.q2c = tf.matmul(tf.matmul(S_, S_T), c)
attention_outputs = [c, self.c2q, c * self.c2q, c * self.q2c]
with tf.variable_scope("modeling_layer"):
attention = tf.concat(attention_outputs, axis=-1)
self.enc = [
conv(attention, self.lstm_hidden_size, name="input_projection")
]
for i in range(3):
if i % 2 == 0: # dropout every 2 blocks
self.enc[i] = tf.nn.dropout(self.enc[i], self.keep_prob)
self.enc.append(
residual_block(self.enc[i],
num_blocks=1,
num_conv_layers=1,
kernel_size=5,
mask=context_mask,
num_filters=self.lstm_hidden_size,
num_heads=1,
seq_len=max_context_length,
scope="Model_Encoder",
bias=False,
reuse=True if i > 0 else None,
dropout=1.0 - self.keep_prob))
print('self.enc[i]', self.enc[i].get_shape().as_list())
with tf.variable_scope("output_layer_start"):
pred_start = tf.squeeze(
conv(tf.concat([self.enc[1], self.enc[2]], axis=-1),
1,
bias=False,
name="start_pointer"), -1)
print('pred_start', pred_start.get_shape().as_list())
self.pred_start = preprocess_softmax(pred_start, context_mask)
print('self.pred_start', self.pred_start.get_shape().as_list())
with tf.variable_scope("output_layer_end"):
pred_end = tf.squeeze(
conv(tf.concat([self.enc[1], self.enc[3]], axis=-1),
1,
bias=False,
name="end_pointer"), -1)
print('pred_end', pred_end.get_shape().as_list())
self.pred_end = preprocess_softmax(pred_end, context_mask)
print('self.pred_end', self.pred_end.get_shape().as_list())
self.preds = tf.transpose([
tf.argmax(self.pred_start, axis=1),
tf.argmax(self.pred_end, axis=1)
])
def forward(self):
config = self.config
N, PL, QL, CL, d, dc, nh = config.batch_size if not self.demo else 1, self.c_maxlen, self.q_maxlen, config.char_limit, config.hidden, config.char_dim, config.num_heads
d_cell = tf.contrib.rnn.BasicLSTMCell(d,
forget_bias=1.0,
state_is_tuple=True)
with tf.variable_scope("Input_Embedding_Layer"):
ch_emb = tf.reshape(tf.nn.embedding_lookup(self.char_mat, self.ch),
[N * PL, CL, dc])
qh_emb = tf.reshape(tf.nn.embedding_lookup(self.char_mat, self.qh),
[N * QL, CL, dc])
ch_emb = tf.nn.dropout(ch_emb, 1.0 - 0.5 * self.dropout)
qh_emb = tf.nn.dropout(qh_emb, 1.0 - 0.5 * self.dropout)
# Bidaf style conv-highway encoder
ch_emb = conv(ch_emb,
d,
bias=True,
activation=tf.nn.relu,
kernel_size=5,
name="char_conv",
reuse=None)
qh_emb = conv(qh_emb,
d,
bias=True,
activation=tf.nn.relu,
kernel_size=5,
name="char_conv",
reuse=True)
ch_emb = tf.reduce_max(ch_emb, axis=1)
qh_emb = tf.reduce_max(qh_emb, axis=1)
print "ch_emb before", ch_emb.shape[-1]
print "qh_emb before", qh_emb.shape[-1]
ch_emb = tf.reshape(ch_emb, [N, PL, ch_emb.shape[-1]])
qh_emb = tf.reshape(qh_emb, [N, QL, ch_emb.shape[-1]])
print "N", N, "PL", PL, "QL", QL
print "ch_emb", ch_emb.shape
print "qh_emb", qh_emb.shape
c_emb = tf.nn.dropout(
tf.nn.embedding_lookup(self.word_mat, self.c),
1.0 - self.dropout)
q_emb = tf.nn.dropout(
tf.nn.embedding_lookup(self.word_mat, self.q),
1.0 - self.dropout)
c_emb = tf.concat([c_emb, ch_emb], axis=2)
q_emb = tf.concat([q_emb, qh_emb], axis=2)
c_emb = highway(c_emb,
size=d,
scope="highway",
dropout=self.dropout,
reuse=None)
q_emb = highway(q_emb,
size=d,
scope="highway",
dropout=self.dropout,
reuse=True)
print "c_emb high", c_emb.shape
print "q_emb high", q_emb.shape
with tf.variable_scope("Embedding_Encoder_Layer"):
c_tmp = residual_block(c_emb,
num_blocks=1,
num_conv_layers=4,
kernel_size=7,
mask=self.c_mask,
num_filters=d,
num_heads=nh,
seq_len=self.c_len,
scope="Encoder_Residual_Block",
bias=False,
dropout=self.dropout)
# c_cell = tf.contrib.rnn.BasicLSTMCell(d, forget_bias=1.0, state_is_tuple=True)
c = drnn(d_cell, c_tmp, d)
q_tmp = residual_block(
q_emb,
num_blocks=1,
num_conv_layers=4,
kernel_size=7,
mask=self.q_mask,
num_filters=d,
num_heads=nh,
seq_len=self.q_len,
scope="Encoder_Residual_Block",
reuse=True, # Share the weights between passage and question
bias=False,
dropout=self.dropout)
# q_cell = tf.contrib.rnn.BasicLSTMCell(d, forget_bias=1.0, state_is_tuple=True)
q = drnn(d_cell, q_tmp, d)
print "embd enc output c", c.shape
print "embd enc output q", q.shape
# exit()
with tf.variable_scope("Context_to_Query_Attention_Layer"):
# C = tf.tile(tf.expand_dims(c,2),[1,1,self.q_maxlen,1])
# Q = tf.tile(tf.expand_dims(q,1),[1,self.c_maxlen,1,1])
# S = trilinear([C, Q, C*Q], input_keep_prob = 1.0 - self.dropout)
S = optimized_trilinear_for_attention([c, q],
self.c_maxlen,
self.q_maxlen,
input_keep_prob=1.0 -
self.dropout)
mask_q = tf.expand_dims(self.q_mask, 1)
S_ = tf.nn.softmax(mask_logits(S, mask=mask_q))
mask_c = tf.expand_dims(self.c_mask, 2)
S_T = tf.transpose(
tf.nn.softmax(mask_logits(S, mask=mask_c), dim=1), (0, 2, 1))
self.c2q = tf.matmul(S_, q)
self.q2c = tf.matmul(tf.matmul(S_, S_T), c)
attention_outputs = [c, self.c2q, c * self.c2q, c * self.q2c]
with tf.variable_scope("Model_Encoder_Layer"):
inputs = tf.concat(attention_outputs, axis=-1)
self.enc = [conv(inputs, d, name="input_projection")]
print "enc len", len(self.enc)
# print self.ch_len.shape
# print self.qh_len.shape
# print self.c_len.shape
# print self.q_len.shape
# print ip_len.shape
print "qh shape", self.qh.shape
print "qh type", self.qh.dtype
print "ip shape", inputs.shape
print "ip type", inputs.dtype
ip_len = tf.reshape(
tf.reduce_sum(tf.cast(tf.cast(inputs, tf.bool), tf.float32),
axis=2), [-1])
print "ip_len", ip_len.shape
# fw0 = drnn(d_cell, self.enc[0], d)
# f_cell = tf.contrib.rnn.BasicLSTMCell(fw0[2], forget_bias=1.0, state_is_tuple=True)
# fw1 = drnn(d_cell, fw0, d)
# fw2 = drnn(d_cell, fw1, d)
# self.enc.append(fw0)
# self.enc.append(fw1)
# self.enc.append(fw2)
# print "fw1 shape", fw1
#
# (fw0, bw0), _ = bidirectional_dynamic_rnn(cell_fw, cell_bw, inputs, sequence_length=None,
# initial_state_fw=None, initial_state_bw=None,
# dtype=None, parallel_iterations=None,
# swap_memory=False, time_major=False, scope=None):
# bw_cell = tf.contrib.rnn.BasicLSTMCell(d, forget_bias=1.0, state_is_tuple=True)
# g0 = bidirlstm(fw_cell, bw_cell, inputs, d)
# g1 = bidirlstm(fw_cell, bw_cell, g0, d)
# g2 = bidirlstm(fw_cell, bw_cell, g1, d)
# fw0 = bidirlstm(d_cell, d_cell, inputs, d)
# d_cell1 = tf.contrib.rnn.BasicLSTMCell(fw0[1], forget_bias=1.0, state_is_tuple=True)
# fw1 = bidirlstm(d_cell1, d_cell1, fw0, d)
# (fw_g0, bw_g0), _ = bidirectional_dynamic_rnn(d_cell, d_cell, self.enc[0], dtype='float', scope='g0') # [N, M, JX, 2d]
# g0 = tf.concat([fw_g0, bw_g0], 4)
# (fw_g1, bw_g1) = bidirectional_dynamic_rnn(d_cell, d_cell, fw_g0, dtype='float', scope='g1') # [N, M, JX, 2d]
# print "fw_g0", fw_g0.shape
# print "bw_g0", bw_g0.shape
# print g0.shape
# (fw_g1, bw_g1), _ = bidirlstm(d_cell, d_cell, g0, dtype='float', scope='g1') # [N, M, JX, 2d]
# g1 = tf.concat([fw_g1, bw_g1], 3)
# flat_output_fw = nest.flatten(fw_g0)
# flat_output_bw = nest.flatten(bw_g0)
# flat_outputs = tuple(array_ops.concat(1, [fw, bw])
# for fw, bw in zip(flat_output_fw, flat_output_bw))
# outputs = nest.pack_sequence_as(structure=output_fw,
# flat_sequence=flat_outputs)
# print "output", outputs.shape
for i in range(3):
if i % 2 == 0: # dropout every 2 blocks
self.enc[i] = tf.nn.dropout(self.enc[i],
1.0 - self.dropout)
self.enc.append(
drnn(
d_cell,
residual_block(self.enc[i],
num_blocks=7,
num_conv_layers=2,
kernel_size=5,
mask=self.c_mask,
num_filters=d,
num_heads=nh,
seq_len=self.c_len,
scope="Model_Encoder",
bias=False,
reuse=True if i > 0 else None,
dropout=self.dropout), d))
# print "enc[0] shape", self.enc[0].shape
print "chalala"
# exit()
with tf.variable_scope("Output_Layer"):
start_logits = tf.squeeze(
conv(tf.concat([self.enc[1], self.enc[2]], axis=-1),
1,
bias=False,
name="start_pointer"), -1)
end_logits = tf.squeeze(
conv(tf.concat([self.enc[1], self.enc[3]], axis=-1),
1,
bias=False,
name="end_pointer"), -1)
self.logits = [
mask_logits(start_logits, mask=self.c_mask),
mask_logits(end_logits, mask=self.c_mask)
]
logits1, logits2 = [l for l in self.logits]
outer = tf.matmul(tf.expand_dims(tf.nn.softmax(logits1), axis=2),
tf.expand_dims(tf.nn.softmax(logits2), axis=1))
outer = tf.matrix_band_part(outer, 0, config.ans_limit)
self.yp1 = tf.argmax(tf.reduce_max(outer, axis=2), axis=1)
self.yp2 = tf.argmax(tf.reduce_max(outer, axis=1), axis=1)
losses = tf.nn.softmax_cross_entropy_with_logits(logits=logits1,
labels=self.y1)
losses2 = tf.nn.softmax_cross_entropy_with_logits(logits=logits2,
labels=self.y2)
self.loss = tf.reduce_mean(losses + losses2)
if config.l2_norm is not None:
variables = tf.get_collection(tf.GraphKeys.REGULARIZATION_LOSSES)
l2_loss = tf.contrib.layers.apply_regularization(
regularizer, variables)
self.loss += l2_loss
if config.decay is not None:
self.var_ema = tf.train.ExponentialMovingAverage(config.decay)
ema_op = self.var_ema.apply(tf.trainable_variables())
with tf.control_dependencies([ema_op]):
self.loss = tf.identity(self.loss)
self.assign_vars = []
for var in tf.global_variables():
v = self.var_ema.average(var)
if v:
self.assign_vars.append(tf.assign(var, v))
def build_model(self):
PL, QL, CL, d, dc, nh = self.c_maxlen, self.q_maxlen, self.char_limit, self.filters, self.char_dim, self.num_heads
with tf.variable_scope("Input_Embedding_Layer"):
ch_emb = tf.reshape(
tf.nn.embedding_lookup(self.char_mat, self.contc_input),
[-1, CL, dc])
qh_emb = tf.reshape(
tf.nn.embedding_lookup(self.char_mat, self.quesc_input),
[-1, CL, dc])
ch_emb = tf.nn.dropout(ch_emb, 1.0 - 0.5 * self.dropout)
qh_emb = tf.nn.dropout(qh_emb, 1.0 - 0.5 * self.dropout)
# Bidaf style conv-highway encoder
ch_emb = conv(ch_emb,
d,
bias=True,
activation=tf.nn.relu,
kernel_size=5,
name="char_conv",
reuse=None)
qh_emb = conv(qh_emb,
d,
bias=True,
activation=tf.nn.relu,
kernel_size=5,
name="char_conv",
reuse=True)
ch_emb = tf.reduce_max(ch_emb, axis=1)
qh_emb = tf.reduce_max(qh_emb, axis=1)
ch_emb = tf.reshape(ch_emb, [-1, PL, ch_emb.shape[-1]])
qh_emb = tf.reshape(qh_emb, [-1, QL, ch_emb.shape[-1]])
c_emb = tf.nn.dropout(
tf.nn.embedding_lookup(self.word_mat, self.contw_input),
1.0 - self.dropout)
q_emb = tf.nn.dropout(
tf.nn.embedding_lookup(self.word_mat, self.quesw_input),
1.0 - self.dropout)
# if self.use_cove:
# c_emb_cove = self.cove_model(c_emb)
# q_emb_cove = self.cove_model(q_emb)
# c_emb = tf.concat([c_emb, c_emb_cove], axis=-1)
# q_emb = tf.concat([q_emb, q_emb_cove], axis=-1)
c_emb = tf.concat([c_emb, ch_emb], axis=2)
q_emb = tf.concat([q_emb, qh_emb], axis=2)
if self.use_elmo:
c_emb = tf.concat([c_emb, self.cont_elmo], axis=-1)
q_emb = tf.concat([q_emb, self.ques_elmo], axis=-1)
c_emb = highway(c_emb,
size=d,
scope="highway",
dropout=self.dropout,
reuse=None)
q_emb = highway(q_emb,
size=d,
scope="highway",
dropout=self.dropout,
reuse=True)
with tf.variable_scope("Embedding_Encoder_Layer"):
c = residual_block(c_emb,
num_blocks=1,
num_conv_layers=4,
kernel_size=7,
mask=self.c_mask,
num_filters=d,
num_heads=nh,
seq_len=self.cont_len,
scope="Encoder_Residual_Block",
bias=False,
dropout=self.dropout)
q = residual_block(q_emb,
num_blocks=1,
num_conv_layers=4,
kernel_size=7,
mask=self.q_mask,
num_filters=d,
num_heads=nh,
seq_len=self.ques_len,
scope="Encoder_Residual_Block",
reuse=True,
bias=False,
dropout=self.dropout)
with tf.variable_scope("Context_to_Query_Attention_Layer"):
S = optimized_trilinear_for_attention([c, q],
self.c_maxlen,
self.q_maxlen,
input_keep_prob=1.0 -
self.dropout)
mask_q = tf.expand_dims(self.q_mask, 1)
S_ = tf.nn.softmax(mask_logits(S, mask=mask_q))
mask_c = tf.expand_dims(self.c_mask, 2)
S_T = tf.transpose(
tf.nn.softmax(mask_logits(S, mask=mask_c), dim=1), (0, 2, 1))
c2q = tf.matmul(S_, q)
q2c = tf.matmul(tf.matmul(S_, S_T), c)
attention_outputs = [c, c2q, c * c2q, c * q2c]
with tf.variable_scope("Model_Encoder_Layer"):
attention_inputs = tf.concat(attention_outputs, axis=-1)
enc = [conv(attention_inputs, d, name="input_projection")]
for i in range(3):
if i % 2 == 0: # dropout every 2 blocks
enc[i] = tf.nn.dropout(enc[i], 1.0 - self.dropout)
enc.append(
residual_block(enc[i],
num_blocks=7,
num_conv_layers=2,
kernel_size=5,
mask=self.c_mask,
num_filters=d,
num_heads=nh,
seq_len=self.cont_len,
scope="Model_Encoder",
bias=False,
reuse=True if i > 0 else None,
dropout=self.dropout))
with tf.variable_scope("Output_Layer"):
start_logits = tf.concat([enc[1], enc[2]], axis=-1)
end_logits = tf.concat([enc[1], enc[3]], axis=-1)
if self.use_elmo:
start_logits = tf.concat((start_logits, self.cont_elmo),
axis=-1)
end_logits = tf.concat((end_logits, self.cont_elmo), axis=-1)
start_logits = tf.squeeze(
conv(start_logits, 1, bias=False, name="start_pointer"), -1)
end_logits = tf.squeeze(
conv(end_logits, 1, bias=False, name="end_pointer"), -1)
unanswer_bias = tf.get_variable(
"unanswer_bias", [1],
regularizer=tf.contrib.layers.l2_regularizer(scale=3e-7),
initializer=tf.zeros_initializer())
unanswer_bias = tf.reshape(
tf.tile(unanswer_bias, [self.batch_size]), [-1, 1])
self.logits1 = tf.concat(
(unanswer_bias, mask_logits(start_logits, mask=self.c_mask)),
axis=-1)
self.logits2 = tf.concat(
(unanswer_bias, mask_logits(end_logits, mask=self.c_mask)),
axis=-1)
start_loss = tf.nn.softmax_cross_entropy_with_logits(
logits=self.logits1, labels=self.y_start)
end_loss = tf.nn.softmax_cross_entropy_with_logits(
logits=self.logits2, labels=self.y_end)
self.loss = tf.reduce_mean(start_loss + end_loss)
if self.l2_norm is not None:
variables = tf.get_collection(
tf.GraphKeys.REGULARIZATION_LOSSES)
l2_loss = tf.contrib.layers.apply_regularization(
regularizer, variables)
self.loss += l2_loss
# output
outer = tf.matmul(
tf.expand_dims(tf.nn.softmax(self.logits1), axis=2),
tf.expand_dims(tf.nn.softmax(self.logits2), axis=1))
outer = tf.matrix_band_part(outer, 0, self.ans_limit)
self.output1 = tf.argmax(tf.reduce_max(outer, axis=2), axis=1) - 1
self.output2 = tf.argmax(tf.reduce_max(outer, axis=1), axis=1) - 1
if self.decay is not None:
self.var_ema = tf.train.ExponentialMovingAverage(self.decay)
ema_op = self.var_ema.apply(tf.trainable_variables())
with tf.control_dependencies([ema_op]):
self.loss = tf.identity(self.loss)
self.assign_vars = []
for var in tf.global_variables():
v = self.var_ema.average(var)
if v is not None:
self.assign_vars.append(tf.assign(var, v))
def forward(self):
config = self.config
N = config.batch_size if not self.demo else 1
PL = self.c_maxlen
QL = self.q_maxlen
XL = self.x_maxlen
# DEBUG
self.debug_ops.extend([PL, QL, XL])
CL = config.char_limit # 16
d = config.hidden # 96
dc = config.char_dim # 64
nh = config.num_heads # 1
with tf.variable_scope("Input_Embedding_Layer"):
'''
self.ch : (N, c_maxlen, 16)
self.qh : (N, q_maxlen, 16)
self.xh : (N, x_maxlen, 16)
'''
######################################
#get elmo embeddings
######################################
datadir = "/data/elmo_experiment_20180906/20180906_model"
vocab_file = os.path.join(datadir, 'vocab-2016-09-10.txt')
options_file = os.path.join(datadir, 'options.json')
weight_file = os.path.join(datadir, 'weights.hdf5')
print(vocab_file)
print(options_file)
print(weight_file)
# Create a Batcher to map text to character ids.
batcher = Batcher(vocab_file, 50)
# Input placeholders to the biLM.
#context_character_ids = tf.placeholder('int32', shape=(None, None, 50))
#question_character_ids = tf.placeholder('int32', shape=(None, None, 50))
# Build the biLM graph.
bilm = BidirectionalLanguageModel(options_file, weight_file)
# Get ops to compute the LM embeddings.
print(self.c)
print(self.c.shape)
#print(self.ch)
#print(self.ch.shape)
print(self.c_elmo)
print(self.c_elmo.shape)
print(self.q_elmo)
print(self.q_elmo.shape)
print(self.x_elmo)
print(self.x_elmo.shape)
context_embeddings_op = bilm(self.c_elmo)
question_embeddings_op = bilm(self.q_elmo)
candidate_embeddings_op = bilm(self.x_elmo)
# Get an op to compute ELMo (weighted average of the internal biLM layers)
# Our SQuAD model includes ELMo at both the input and output layers
# of the task GRU, so we need 4x ELMo representations for the question
# and context at each of the input and output.
# We use the same ELMo weights for both the question and context
# at each of the input and output.
#context elmo
elmo_context_input = weight_layers('input', context_embeddings_op, l2_coef=0.0)
with tf.variable_scope('', reuse=True):
# the reuse=True scope reuses weights from the context for the question
elmo_question_input = weight_layers(
'input', question_embeddings_op, l2_coef=0.0
)
elmo_candidate_input = weight_layers(
'input', candidate_embeddings_op, l2_coef=0.0
)
elmo_context_output = weight_layers(
'output', context_embeddings_op, l2_coef=0.0
)
with tf.variable_scope('', reuse=True):
# the reuse=True scope reuses weights from the context for the question
elmo_question_output = weight_layers(
'output', question_embeddings_op, l2_coef=0.0
)
elmo_candidate_output = weight_layers(
'output', candidate_embeddings_op, l2_coef=0.0
)
ch_emb = tf.reshape(tf.nn.embedding_lookup(self.char_mat, self.ch), [N * PL, CL, dc]) #(N*PL,16,64)
qh_emb = tf.reshape(tf.nn.embedding_lookup(self.char_mat, self.qh), [N * QL, CL, dc]) #(N*QL,16,64)
xh_emb = tf.reshape(tf.nn.embedding_lookup(self.char_mat, self.xh), [N * XL, CL, dc]) #(N*XL,16,64)
ch_emb = tf.nn.dropout(ch_emb, 1.0 - 0.5 * self.dropout)
qh_emb = tf.nn.dropout(qh_emb, 1.0 - 0.5 * self.dropout)
xh_emb = tf.nn.dropout(xh_emb, 1.0 - 0.5 * self.dropout)
# BiDAF style conv-highway encoder: conv over chars in each word in a batch of passages
ch_emb = conv(ch_emb, d, bias = True, activation = tf.nn.relu, kernel_size = 5,
name = "char_conv", reuse = None) # (N*c_maxlen, 16-5+1, 96)
qh_emb = conv(qh_emb, d, bias = True, activation = tf.nn.relu, kernel_size = 5,
name = "char_conv", reuse = True) # (N*q_maxlen, 16-5+1, 96)
xh_emb = conv(xh_emb, d, bias = True, activation = tf.nn.relu, kernel_size = 5,
name="char_conv", reuse=True) # (N*x_maxlen, 16-5+1, 96)
# Max Pooling
ch_emb = tf.reduce_max(ch_emb, axis = 1) # (N*c_maxlen, 96)
qh_emb = tf.reduce_max(qh_emb, axis = 1) # (N*q_maxlen, 96)
xh_emb = tf.reduce_max(xh_emb, axis = 1) # (N*x_maxlen, 96)
ch_emb = tf.reshape(ch_emb, [N, PL, ch_emb.shape[-1]]) # (N, c_maxlen, 96)
qh_emb = tf.reshape(qh_emb, [N, QL, qh_emb.shape[-1]]) # (N, q_maxlen, 96)
xh_emb = tf.reshape(xh_emb, [N, XL, xh_emb.shape[-1]]) # (N, x_maxlen, 96)
'''
self.c : (N, c_maxlen)
self.q : (N, q_maxlen)
self.x : (N, x_maxlen)
'''
#print(self.c)
#print(self.q)
#print(self.x)
c_emb = tf.nn.dropout(tf.nn.embedding_lookup(self.word_mat, self.c), 1.0 - self.dropout)#(N,c_maxlen,300)
q_emb = tf.nn.dropout(tf.nn.embedding_lookup(self.word_mat, self.q), 1.0 - self.dropout)#(N,q_maxlen,300)
x_emb = tf.nn.dropout(tf.nn.embedding_lookup(self.word_mat, self.x), 1.0 - self.dropout)#(N,x_maxlen,300)
#c_emb_elmo =
#q_emb_elmo =
#x_emb_elmo =
c_emb = tf.concat([c_emb, ch_emb], axis=2) # (N, c_maxlen, 396)
q_emb = tf.concat([q_emb, qh_emb], axis=2) # (N, q_maxlen, 396)
x_emb = tf.concat([x_emb, xh_emb], axis=2) # (N, x_maxlen, 396)
print(c_emb)
print(c_emb.shape)
c_emb = tf.concat([elmo_context_output['weighted_op'], c_emb], axis=2) # (N, c_maxlen, 1024 + 396)
q_emb = tf.concat([elmo_question_output['weighted_op'], q_emb], axis=2) # (N, q_maxlen, 1024 + 396)
x_emb = tf.concat([elmo_candidate_output['weighted_op'], x_emb], axis=2) # (N, x_maxlen, 1024 + 396)
print(c_emb)
print(c_emb.shape)
c_emb = highway(c_emb, size = d, scope = "highway", dropout = self.dropout, reuse = None)#(N,c_maxlen,96)
q_emb = highway(q_emb, size = d, scope = "highway", dropout = self.dropout, reuse = True)#(N,q_maxlen,96)
x_emb = highway(x_emb, size = d, scope = "highway", dropout = self.dropout, reuse = True)#(N,x_maxlen,96)
with tf.variable_scope("Embedding_Encoder_Layer"):
'''
-> positional encoding
-> layer_normalization
-> depth-wise separable convolution
-> self attention
-> feed forward network
In the paper: The total number of encoder blocks is 1
'''
# (N, c_maxlen, 96)
c = residual_block(c_emb,
num_blocks = 1,
num_conv_layers = 4,
kernel_size = 7,
mask = self.c_mask,
num_filters = d,
num_heads = nh,
seq_len = self.c_len,
scope = "Encoder_Residual_Block",
bias = False,
dropout = self.dropout)
# (N, q_maxlen, 96)
q = residual_block(q_emb,
num_blocks = 1,
num_conv_layers = 4,
kernel_size = 7,
mask = self.q_mask,
num_filters = d,
num_heads = nh,
seq_len = self.q_len,
scope = "Encoder_Residual_Block",
reuse = True, # Share the weights between passage and question
bias = False,
dropout = self.dropout)
with tf.variable_scope("Context_to_Query_Attention_Layer"):
'''
tf.tile(input, multiples, name=None): creates a new tensor by replicating input multiples times.
The output tensor's i'th dimension has input.dims(i) * multiples[i] elements,
and the values of input are replicated multiples[i] times along the 'i'th dimension.
Paper: The layer parameters are the same as the Embedding Encoder Layer
except that convolution layer number is 2 within a block
and the total number of blocks is 7
'''
'''
c: (N, c_maxlen, d)
q: (N, q_maxlen, d)
ch_emb: (N, c_maxlen, d)
qh_emb: (N, q_maxlen, d)
C: (N, c_maxlen, q_maxlen, d)
Q: (N, c_maxlen, q_maxlen, d)
S: (N, c_maxlen, q_maxlen)
mask_q: (N, 1, q_maxlen)
mask_c: (N, c_maxlen, 1)
S_: (N, c_maxlen, q_maxlen)
S_T: (N, q_maxlen, c_maxlen)
self.c2q: (N, c_maxlen, d) = tf.matmul(S_, q)
self.q2c: (N, c_maxlen, d) = tf.matmul(tf.matmul(S_, S_T), c)
'''
# change from commit f0c79cc93dc1dfdad2bc8abb712a53d078814a56 by Min on 27 Apr 18
# C = tf.tile(tf.expand_dims(c,2),[1,1,self.q_maxlen,1])
# Q = tf.tile(tf.expand_dims(q,1),[1,self.c_maxlen,1,1])
# S = trilinear([C, Q, C*Q], input_keep_prob = 1.0 - self.dropout)
# optimization from jasonwbw
S = optimized_trilinear_for_attention([c, q], self.c_maxlen, self.q_maxlen,
input_keep_prob=1.0 - self.dropout)
mask_q = tf.expand_dims(self.q_mask, 1)
S_ = tf.nn.softmax(mask_logits(S, mask = mask_q))
mask_c = tf.expand_dims(self.c_mask, 2)
S_T = tf.transpose(tf.nn.softmax(mask_logits(S, mask = mask_c), axis = 1),(0,2,1))
self.c2q = tf.matmul(S_, q)
self.q2c = tf.matmul(tf.matmul(S_, S_T), c)
# change from commit f0c79cc93dc1dfdad2bc8abb712a53d078814a56 by Min on 27 Apr 18
attention_outputs = [c, self.c2q, c * self.c2q, c * self.q2c]
# if config.q2c:
# attention_outputs.append(c * self.q2c)
# with tf.variable_scope("Model_Encoder_Layer"):
# inputs = tf.concat(attention_outputs, axis = -1)
#
# # same as a dxd MLP layer
# self.enc = [conv(inputs, d, name = "input_projection")] # d=hidden=96
#
# for i in range(3):
# if i % 2 == 0: # dropout every 2 blocks
# self.enc[i] = tf.nn.dropout(self.enc[i], 1.0 - self.dropout)
# self.enc.append(
# residual_block(self.enc[i],
# num_blocks = 7,
# num_conv_layers = 2,
# kernel_size = 5,
# mask = self.c_mask,
# num_filters = d,
# num_heads = nh,
# seq_len = self.c_len,
# scope = "Model_Encoder",
# bias = False,
# reuse = True if i > 0 else None,
# dropout = self.dropout)
# )
# DEBUG
# self.debug_ops.append(inputs)
# self.debug_ops.extend(self.enc)
with tf.variable_scope("Output_Layer"):
'''
broadcasting:dimensions with size 1 are stretched or "copied" to match the other
'''
'''
x_emb: (N, x_maxlen, d)
inputs: (N, c_maxlen, 4*d)
mask_x: (N, x_maxlen, 1)
c_proj: (N, c_maxlen, d)
S_xc/S_xc_: (N, x_maxlen, c_maxlen)
x2c: (N, x_maxlen, d)
xp_exp: (N, x_maxlen, c_maxlen, 1)
c_proj_exp: (N, 1, c_maxlen, d)
cand_context: (N, x_maxlen, c_maxlen, d)
cand_context_pool: (N, x_maxlen, d)
cand_condense: (N, x_maxlen, d*2)
self.cand_condense: (N, x_maxlen, d)
self.cand_logits: (N, x_maxlen, 1)
'''
inputs = tf.concat(attention_outputs, axis = -1)
# masking candidate embedding
mask_x = tf.expand_dims(self.x_mask, 2)
c_proj = conv(inputs, d, name="context_projection")
S_xc = optimized_trilinear_for_attention([x_emb, c_proj], self.x_maxlen, self.c_maxlen,
input_keep_prob=1.0 - self.dropout)
S_xc_ = tf.nn.softmax(mask_logits(S_xc, mask = mask_x))
self.x2c = tf.matmul(S_xc_, c_proj)
self.cand_condense = self.x2c
if self.config.cand_condense_vector:
xp_exp = tf.expand_dims(self.xp, axis=-1)
c_proj_exp = tf.expand_dims(c_proj, axis=1)
cand_context = tf.multiply(c_proj_exp, xp_exp)
if self.config.cand_condense_conv:
cand_context = tf.reshape(cand_context, [N*XL, PL, d])
cand_context = conv(cand_context, d, bias=True, activation=tf.nn.relu,
kernel_size=3, name="candidate_from_context")
cand_context = tf.reshape(cand_context, [N, XL, -1, d])
if self.config.cand_condense_pool:
cand_context_pool = tf.reduce_max(cand_context, axis=-2)
else:
cand_context_pool = tf.reduce_mean(cand_context, axis=-2)
cand_condense = tf.concat([self.x2c, cand_context_pool], axis = -1)
self.cand_condense = conv(cand_condense, d, name="candidate_projection")
if self.config.cand_fuse_vector:
raise NotImplementedError
# DEBUG
self.debug_ops.extend([xp_exp, c_proj_exp, cand_context, cand_context_pool,
cand_condense, self.cand_condense])
if not config.max_margin:
cand_logits = tf.squeeze(conv(self.cand_condense, 1, bias=False, name="candidate_logits_1"), -1)
self.cand_logits = mask_logits(cand_logits, mask=self.x_mask)
loss = tf.nn.softmax_cross_entropy_with_logits(logits=self.cand_logits, labels=self.yx)
# DEBUG
self.debug_ops.extend([loss, x_emb, c_proj, S_xc, S_xc_, self.x2c,
self.x_mask, self.cand_logits, self.yx])
else:
cand_logits = conv(self.cand_condense, 1, bias=False, name="candidate_logits_1")
cand_logits = tf.tanh(cand_logits)
cand_logits = tf.squeeze(conv(cand_logits, 1, bias=False, name="candidate_logits_2"), -1)
self.cand_logits = tf.sigmoid(cand_logits)
pos = tf.multiply(self.cand_logits, self.yx)
pos = tf.reduce_max(pos, axis=-1)
negs = tf.multiply(self.cand_logits, self.yx_inv)
neg = tf.reduce_max(negs, axis=-1)
loss = tf.maximum(tf.add(tf.subtract(neg, pos), config.margin), 0.0)
# DEBUG
self.debug_ops.extend([loss, x_emb, c_proj, S_xc, S_xc_, self.x2c,
self.x_mask, self.cand_logits, self.yx,
pos, negs, neg, self.yx, self.yx_inv])
self.loss = tf.reduce_mean(loss)
# with tf.variable_scope("Output_Layer"):
# '''
# tf.matrix_band_part: Copy a tensor setting everything outside a central band
# in each innermost matrix to zero.
# self.enc[i]: (N, c_maxlen, d)
# start_logits: (N, c_maxlen)
# end_logits: (N, c_maxlen)
# logits1: (N, c_maxlen)
# logits2: (N, c_maxlen)
# outer: (N, c_maxlen, c_maxlen)
# self.c_mask: (N, c_maxlen)
# yp1, yp2, losses, losses2: (N,)
# '''
#
# # map vectors to scalars
# start_logits = tf.squeeze(conv(tf.concat([self.enc[1], self.enc[2]],axis = -1),1,
# bias = False, name = "start_pointer"),-1)
# end_logits = tf.squeeze(conv(tf.concat([self.enc[1], self.enc[3]],axis = -1),1,
# bias = False, name = "end_pointer"), -1)
# self.logits = [mask_logits(start_logits, mask = self.c_mask), mask_logits(end_logits, mask = self.c_mask)]
#
# logits1, logits2 = [l for l in self.logits]
#
# losses = tf.nn.softmax_cross_entropy_with_logits(logits=logits1, labels=self.y1)
# losses2 = tf.nn.softmax_cross_entropy_with_logits(logits=logits2, labels=self.y2)
# self.loss = tf.reduce_mean(losses + losses2)
#
# # find max-score span
# outer = tf.matmul(tf.expand_dims(tf.nn.softmax(logits1), axis=2),
# tf.expand_dims(tf.nn.softmax(logits2), axis=1))
# # change from commit f0c79cc93dc1dfdad2bc8abb712a53d078814a56 by Min on 27 Apr 18
# outer = tf.matrix_band_part(outer, 0, config.ans_limit)
# self.yp1 = tf.argmax(tf.reduce_max(outer, axis=2), axis=1)
# self.yp2 = tf.argmax(tf.reduce_max(outer, axis=1), axis=1)
#
# # DEBUG
# self.debug_ops.extend([start_logits, end_logits, logits1, logits2,
# outer, self.yp1, self.yp2, losses, losses2, self.loss])
if config.l2_norm is not None:
variables = tf.get_collection(tf.GraphKeys.REGULARIZATION_LOSSES)
l2_loss = tf.contrib.layers.apply_regularization(regularizer, variables)
self.loss += l2_loss
if config.decay is not None:
self.var_ema = tf.train.ExponentialMovingAverage(config.decay)
ema_op = self.var_ema.apply(tf.trainable_variables())
with tf.control_dependencies([ema_op]):
self.loss = tf.identity(self.loss)
# change from commit f0c79cc93dc1dfdad2bc8abb712a53d078814a56 by Min on 27 Apr 18
self.assign_vars = []
# self.shadow_vars = []
# self.global_vars = []
for var in tf.global_variables():
v = self.var_ema.average(var)
if v:
self.assign_vars.append(tf.assign(var, v))
def darknet(inputs):
with tf.name_scope("darknet"):
inputs = layers.conv_layer(name="darknet/conv_0",
inputs=inputs,
filters=32,
kernel_size=3)
inputs = layers.conv_layer(name="darknet/conv_1",
inputs=inputs,
filters=64,
kernel_size=3,
downsample=True)
for i in range(1):
inputs = layers.residual_block(
f"darknet/residual_group_0/residual_{i}",
inputs,
num_filters=32)
inputs = layers.conv_layer(name="darknet/residual_group_0/conv_2",
inputs=inputs,
filters=128,
kernel_size=3,
downsample=True)
for i in range(2):
inputs = layers.residual_block(
f"darknet/residual_group_1/residual_{i}",
inputs,
num_filters=64)
inputs = layers.conv_layer(name="darknet/residual_group_1/conv_3",
inputs=inputs,
filters=256,
kernel_size=3,
downsample=True)
for i in range(8):
inputs = layers.residual_block(
f"darknet/residual_group_2/residual_{i}",
inputs,
num_filters=128)
darknet_route_1 = inputs
inputs = layers.conv_layer(name="darknet/residual_group_3/conv_4",
inputs=inputs,
filters=512,
kernel_size=3,
downsample=True)
for i in range(8):
inputs = layers.residual_block(
f"darknet/residual_group_3/residual_{i}",
inputs,
num_filters=256)
darknet_route_2 = inputs
inputs = layers.conv_layer(name="darknet/residual_group_4/conv_5",
inputs=inputs,
filters=1024,
kernel_size=3,
downsample=True)
for i in range(4):
inputs = layers.residual_block(
f"darknet/residual_group_4/residual_{i}",
inputs,
num_filters=512)
return darknet_route_1, darknet_route_2, inputs
def forward(self, trainable):
config = self.config
N, PL, QL, CL, d, dc, nh= config.batch_size,self.c_maxlen, self.q_maxlen,\
config.char_limit, config.hidden, config.char_dim, \
config.num_heads,
with tf.variable_scope("Input_Embedding_Layer"):
ch_emb = tf.reshape(
tf.nn.embedding_lookup(self.char_mat, self.ch),
[N * PL, CL, dc]) #[一个句子共有多少单词,每个单词的字符个数,每一个字符的维度]
qh_emb = tf.reshape(tf.nn.embedding_lookup(self.char_mat, self.qh),
[N * QL, CL, dc])
ch_emb = tf.nn.dropout(ch_emb, 1.0 - 0.5 * self.dropout)
qh_emb = tf.nn.dropout(qh_emb, 1.0 - 0.5 * self.dropout)
# Bidaf style conv-highway encoder以下是得到卷积之后的特征输出
ch_emb = conv(ch_emb,
d,
bias=True,
activation=tf.nn.relu,
kernel_size=5,
name="char_conv",
reuse=None) #[batch,feature_len,d]
qh_emb = conv(qh_emb,
d,
bias=True,
activation=tf.nn.relu,
kernel_size=5,
name="char_conv",
reuse=True)
ch_emb = tf.reduce_max(
ch_emb, axis=1) #求出横向唯独的最大特征，这里可以用k_max尝试,而没有用max_pooling
qh_emb = tf.reduce_max(qh_emb, axis=1)
ch_emb = tf.reshape(ch_emb,
[N, PL, ch_emb.shape[-1]]) #最终转变为句子长度对应的维度，
qh_emb = tf.reshape(qh_emb, [N, QL, qh_emb.shape[-1]])
c_emb = tf.nn.dropout(
tf.nn.embedding_lookup(self.word_mat, self.c),
1.0 - self.dropout)
q_emb = tf.nn.dropout(
tf.nn.embedding_lookup(self.word_mat, self.q),
1.0 - self.dropout)
c_emb = tf.concat(
[c_emb, ch_emb],
axis=2) #把字符与对应的特征进行连接[batch,sequence_len,对应的输出维度]
q_emb = tf.concat([q_emb, qh_emb], axis=2)
c_emb = highway(
c_emb,
size=d,
scope="highway",
dropout=self.dropout,
reuse=None) #相当于对信息进行一次筛选,并且让表示的维度降低到75,[batch,sql_len,75]
q_emb = highway(q_emb,
size=d,
scope="highway",
dropout=self.dropout,
reuse=True)
with tf.variable_scope("Embedding_Encoder_Layer"):
c = residual_block(c_emb,
num_blocks=1,
num_conv_layers=4,
kernel_size=7,
mask=self.c_mask,
num_filters=d,
num_heads=nh,
seq_len=self.c_len,
scope="Encoder_Residual_Block",
bias=False,
dropout=self.dropout)
q = residual_block(
q_emb,
num_blocks=1,
num_conv_layers=4,
kernel_size=7,
mask=self.q_mask,
num_filters=d,
num_heads=nh,
seq_len=self.q_len,
scope="Encoder_Residual_Block",
reuse=True, # Share the weights between passage and question
bias=False,
dropout=self.dropout)
with tf.variable_scope("Context_to_Query_Attention_Layer"):
# C = tf.tile(tf.expand_dims(c,2),[1,1,self.q_maxlen,1])
# Q = tf.tile(tf.expand_dims(q,1),[1,self.c_maxlen,1,1])
# S = trilinear([C, Q, C*Q], input_keep_prob = 1.0 - self.dropout)
S = optimized_trilinear_for_attention([c, q],
self.c_maxlen,
self.q_maxlen,
input_keep_prob=1.0 -
self.dropout)
mask_q = tf.expand_dims(self.q_mask, 1)
S_ = tf.nn.softmax(mask_logits(S, mask=mask_q))
mask_c = tf.expand_dims(self.c_mask, 2)
S_T = tf.transpose(
tf.nn.softmax(mask_logits(S, mask=mask_c), dim=1), (0, 2, 1))
self.c2q = tf.matmul(S_, q)
self.q2c = tf.matmul(tf.matmul(S_, S_T), c)
attention_outputs = [c, self.c2q, c * self.c2q, c * self.q2c]
with tf.variable_scope("Model_Encoder_Layer"):
inputs = tf.concat(attention_outputs, axis=-1)
self.enc = [conv(inputs, d, name="input_projection")]
for i in range(3):
if i % 2 == 0: # dropout every 2 blocks
self.enc[i] = tf.nn.dropout(self.enc[i],
1.0 - self.dropout)
self.enc.append(
residual_block(self.enc[i],
num_blocks=7,
num_conv_layers=2,
kernel_size=5,
mask=self.c_mask,
num_filters=d,
num_heads=nh,
seq_len=self.c_len,
scope="Model_Encoder",
bias=False,
reuse=True if i > 0 else None,
dropout=self.dropout))
with tf.variable_scope('question_rnn'):
self.gru = tf.contrib.rnn.GRUCell(d)
initstate = self.gru.zero_state(batch_size=N, dtype=tf.float32)
output, state = tf.nn.dynamic_rnn(self.gru,
q,
initial_state=initstate)
# self.qandc=tf.concat([self.q2c,self.c2q],axis=2)
# self.qandc=dense(self.qandc,d)
# output,state=tf.nn.dynamic_rnn(self.gru,self.qandc,initial_state=initstate)#(32,?,75)
state = tf.expand_dims(state, axis=2)
weight1 = tf.matmul(self.enc[1], state)
weight2 = tf.matmul(self.enc[2], state)
weight3 = tf.matmul(self.enc[3], state)
weight_enc1 = tf.multiply(self.enc[1], weight1)
weight_enc1 = tf.reduce_sum(weight_enc1, axis=1)
weight_enc2 = tf.multiply(self.enc[2], weight2)
weight_enc2 = tf.reduce_sum(weight_enc2, axis=1)
weight_enc3 = tf.multiply(self.enc[3], weight3)
weight_enc3 = tf.reduce_sum(weight_enc3, axis=1)
with tf.variable_scope("Output_Layer"):
print(weight_enc1, "ggggggggggggggggg")
inputs_shape = weight_enc1.get_shape().as_list()
W = tf.get_variable(
"W",
shape=[inputs_shape[-1], 3],
initializer=tf.contrib.layers.xavier_initializer())
b = tf.Variable(tf.constant(0.1, shape=[3]), name="b")
self.l2_loss += tf.nn.l2_loss(W)
self.l2_loss += tf.nn.l2_loss(b)
self.scores1 = tf.nn.xw_plus_b(weight_enc1, W, b, name="scores")
self.scores2 = tf.nn.xw_plus_b(weight_enc2, W, b, name="scores")
self.scores3 = tf.nn.xw_plus_b(weight_enc3, W, b, name="scores")
self.scores = (self.scores1 + self.scores2 + self.scores3) / 3.0
print(self.scores)
self.predictions = tf.argmax(self.scores, 1, name="predictions")
if trainable:
with tf.name_scope("loss"):
print(self.scores, self.input_y, "llllllllllllllll")
losses = tf.nn.softmax_cross_entropy_with_logits(
logits=self.scores, labels=self.input_y)
self.loss = tf.reduce_mean(
losses) + self.l2_reg_lambda * self.l2_loss
# Accuracy
with tf.name_scope("accuracy"):
correct_predictions = tf.equal(self.predictions,
tf.argmax(self.input_y, 1))
self.accuracy = tf.reduce_mean(tf.cast(
correct_predictions, "float"),
name="accuracy")
# losses2 = tf.nn.softmax_cross_entropy_with_logits(
# logits=logits2, labels=self.y2)
if config.decay is not None:
self.var_ema = tf.train.ExponentialMovingAverage(
config.decay)
ema_op = self.var_ema.apply(tf.trainable_variables())
with tf.control_dependencies([ema_op]):
self.loss = tf.identity(self.loss)
self.assign_vars = []
for var in tf.global_variables():
v = self.var_ema.average(var)
if v:
self.assign_vars.append(tf.assign(var, v))
self.lr = tf.minimum(
config.init_lr, 0.001 / tf.log(999.) *
tf.log(tf.cast(self.global_step, tf.float32) + 1))
self.opt = tf.train.AdamOptimizer(learning_rate=self.lr,
beta1=0.8,
beta2=0.999,
epsilon=1e-7)
grads = self.opt.compute_gradients(self.loss)
gradients, variables = zip(*grads)
capped_grads, _ = tf.clip_by_global_norm(
gradients, config.grad_clip)
self.train_op = self.opt.apply_gradients(
zip(capped_grads, variables), global_step=self.global_step)
self.saver = tf.train.Saver(tf.global_variables(),
max_to_keep=3)
def pred(self):
with tf.variable_scope("embedding_layer"):
(self.questions, question_lengths), (
self.contexts,
context_lengths), self.answers = self.iterator.get_next()
#max_context_length = tf.reduce_max(context_lengths)
#max_question_length = tf.reduce_max(question_lengths)
max_context_length = self.train_max_context_length
max_question_length = self.train_max_question_length
context_mask = tf.sequence_mask(context_lengths,
maxlen=max_context_length)
question_mask = tf.sequence_mask(question_lengths,
maxlen=max_question_length)
question_embeddings = tf.nn.embedding_lookup(
self.embedding, self.questions)
context_embeddings = tf.nn.embedding_lookup(
self.embedding, self.contexts)
print('question_embeddings',
question_embeddings.get_shape().as_list())
print('context_embeddings',
context_embeddings.get_shape().as_list())
with tf.variable_scope("embedding_layer"):
context_output = residual_block(context_embeddings,
num_blocks=1,
num_conv_layers=4,
kernel_size=7,
mask=context_mask,
num_filters=self.lstm_hidden_size,
num_heads=1,
seq_len=max_context_length,
scope="Encoder_Residual_Block",
bias=False,
dropout=1.0 - self.keep_prob)
print('context_output', context_output.get_shape().as_list())
question_output = residual_block(
question_embeddings,
num_blocks=1,
num_conv_layers=4,
kernel_size=7,
mask=question_mask,
num_filters=self.lstm_hidden_size,
num_heads=1,
seq_len=max_question_length,
scope="Encoder_Residual_Block",
reuse=True, # Share the weights between passage and question
bias=False,
dropout=1.0 - self.keep_prob)
print('question_output', question_output.get_shape().as_list())
# context_output dimension is BS * max_context_length * d
# where d = 2*lstm_hidden_size
with tf.variable_scope("attention_layer"):
# d is equal to 2*self.lstm_hidden_size
similarity_matrix = tf.matmul(
context_output, tf.transpose(question_output, [0, 2, 1]))
print('similarity_matrix', similarity_matrix.get_shape().as_list())
mask_aug = tf.expand_dims(context_mask, 2) & tf.expand_dims(
question_mask, 1)
similarity_matrix = preprocess_softmax(similarity_matrix, mask_aug)
print('similarity_matrix', similarity_matrix.get_shape().as_list())
context_to_query_attention_weights = tf.nn.softmax(
similarity_matrix, axis=2)
print('context_to_query_attention_weights',
context_to_query_attention_weights.get_shape().as_list())
context_to_query = tf.matmul(context_to_query_attention_weights,
question_output)
print('context_to_query', context_to_query.get_shape().as_list())
max_col_similarity = tf.reduce_max(similarity_matrix, axis=2)
print('max_col_similarity',
max_col_similarity.get_shape().as_list())
b = tf.nn.softmax(max_col_similarity, axis=1)
print('b', b.get_shape().as_list())
b = tf.expand_dims(b, 1)
print('b', b.get_shape().as_list())
query_to_context = tf.matmul(b, context_output)
print('query_to_context', query_to_context.get_shape().as_list())
context_output_with_context_to_query = context_output * context_to_query
print('context_output_with_context_to_query',
context_output_with_context_to_query.get_shape().as_list())
context_output_with_query_to_context = context_output * query_to_context
print('context_output_with_query_to_context',
context_output_with_query_to_context.get_shape().as_list())
attention = tf.concat([
context_output, context_to_query,
context_output_with_context_to_query,
context_output_with_query_to_context
],
axis=2)
print('attention', attention.get_shape().as_list())
with tf.variable_scope("modeling_layer"):
self.enc = [
conv(attention, self.lstm_hidden_size, name="input_projection")
]
for i in range(3):
if i % 2 == 0: # dropout every 2 blocks
self.enc[i] = tf.nn.dropout(self.enc[i], self.keep_prob)
self.enc.append(
residual_block(self.enc[i],
num_blocks=7,
num_conv_layers=2,
kernel_size=5,
mask=context_mask,
num_filters=self.lstm_hidden_size,
num_heads=1,
seq_len=max_context_length,
scope="Model_Encoder",
bias=False,
reuse=True if i > 0 else None,
dropout=1.0 - self.keep_prob))
print('self.enc[i]', self.enc[i].get_shape().as_list())
with tf.variable_scope("output_layer_start"):
pred_start = tf.squeeze(
conv(tf.concat([self.enc[1], self.enc[2]], axis=-1),
1,
bias=False,
name="start_pointer"), -1)
print('pred_start', pred_start.get_shape().as_list())
self.pred_start = preprocess_softmax(pred_start, context_mask)
print('self.pred_start', self.pred_start.get_shape().as_list())
with tf.variable_scope("output_layer_end"):
pred_end = tf.squeeze(
conv(tf.concat([self.enc[1], self.enc[3]], axis=-1),
1,
bias=False,
name="end_pointer"), -1)
print('pred_end', pred_end.get_shape().as_list())
self.pred_end = preprocess_softmax(pred_end, context_mask)
print('self.pred_end', self.pred_end.get_shape().as_list())
self.preds = tf.transpose([
tf.argmax(self.pred_start, axis=1),
tf.argmax(self.pred_end, axis=1)
])
def forward(self):
config = self.config
N, PL, QL, CL, d, dc, nh, AL1,AL2,AL3= config.batch_size,self.c_maxlen, self.q_maxlen, config.char_limit, config.hidden, config.char_dim, config.num_heads,self.aletr1_maxlen, \
self.aletr2_maxlen,self.aletr3_maxlen
with tf.variable_scope("Input_Embedding_Layer"):
ch_emb = tf.reshape(
tf.nn.embedding_lookup(self.char_mat, self.ch),
[N * PL, CL, dc]) #[一个句子共有多少单词,每个单词的字符个数,每一个字符的维度]
qh_emb = tf.reshape(tf.nn.embedding_lookup(self.char_mat, self.qh),
[N * QL, CL, dc])
self.alternati_emb1 = tf.reshape(
tf.nn.embedding_lookup(self.char_mat, self.alter1h),
[N * AL1, CL, dc]) # (875, 25, 20)
self.alternati_emb2 = tf.reshape(
tf.nn.embedding_lookup(self.char_mat, self.alter2h),
[N * AL2, CL, dc]) # (768, 16, 300)
self.alternati_emb3 = tf.reshape(
tf.nn.embedding_lookup(self.char_mat, self.alter3h),
[N * AL3, CL, dc]) # (768, 16, 300)
ch_emb = tf.nn.dropout(ch_emb, 1.0 - 0.5 * self.dropout)
qh_emb = tf.nn.dropout(qh_emb, 1.0 - 0.5 * self.dropout)
alternati_emb1 = tf.nn.dropout(self.alternati_emb1,
1.0 - 0.5 * self.dropout)
alternati_emb2 = tf.nn.dropout(self.alternati_emb2,
1.0 - 0.5 * self.dropout)
alternati_emb3 = tf.nn.dropout(self.alternati_emb3,
1.0 - 0.5 * self.dropout)
# Bidaf style conv-highway encoder以下是得到卷积之后的特征输出
ch_emb = conv(ch_emb,
d,
bias=True,
activation=tf.nn.relu,
kernel_size=5,
name="char_conv",
reuse=None) #[batch,feature_len,d]
qh_emb = conv(qh_emb,
d,
bias=True,
activation=tf.nn.relu,
kernel_size=5,
name="char_conv",
reuse=True)
alternati_emb1 = conv(alternati_emb1,
d,
bias=True,
activation=tf.nn.relu,
kernel_size=5,
name="char_conv",
reuse=True)
alternati_emb2 = conv(alternati_emb2,
d,
bias=True,
activation=tf.nn.relu,
kernel_size=5,
name="char_conv",
reuse=True)
alternati_emb3 = conv(alternati_emb3,
d,
bias=True,
activation=tf.nn.relu,
kernel_size=5,
name="char_conv",
reuse=True)
ch_emb = tf.reduce_max(
ch_emb, axis=1) #求出横向唯独的最大特征，这里可以用k_max尝试,而没有用max_pooling
qh_emb = tf.reduce_max(qh_emb, axis=1)
alternati_emb1 = tf.reduce_max(alternati_emb1, axis=1)
alternati_emb2 = tf.reduce_max(alternati_emb2, axis=1)
alternati_emb3 = tf.reduce_max(alternati_emb3, axis=1)
ch_emb = tf.reshape(ch_emb,
[N, PL, ch_emb.shape[-1]]) #最终转变为句子长度对应的维度，
qh_emb = tf.reshape(qh_emb, [N, QL, qh_emb.shape[-1]])
alternati_emb1 = tf.reshape(alternati_emb1,
[N, AL1, qh_emb.shape[-1]])
alternati_emb2 = tf.reshape(alternati_emb2,
[N, AL2, qh_emb.shape[-1]])
alternati_emb3 = tf.reshape(alternati_emb3,
[N, AL3, qh_emb.shape[-1]])
c_emb = tf.nn.dropout(
tf.nn.embedding_lookup(self.word_mat, self.c),
1.0 - self.dropout)
q_emb = tf.nn.dropout(
tf.nn.embedding_lookup(self.word_mat, self.q),
1.0 - self.dropout)
alter_embedding1 = tf.nn.embedding_lookup(self.word_mat,
self.alter1) # 上下文
alter_embedding2 = tf.nn.embedding_lookup(self.word_mat,
self.alter2) # 上下文
alter_embedding3 = tf.nn.embedding_lookup(self.word_mat,
self.alter3) # 上下文
c_emb = tf.concat(
[c_emb, ch_emb],
axis=2) #把字符与对应的特征进行连接[batch,sequence_len,对应的输出维度]
q_emb = tf.concat([q_emb, qh_emb], axis=2)
alter_embedding1 = tf.concat([alter_embedding1, alternati_emb1],
axis=2)
alter_embedding2 = tf.concat([alter_embedding2, alternati_emb2],
axis=2)
alter_embedding3 = tf.concat([alter_embedding3, alternati_emb3],
axis=2)
c_emb = highway(
c_emb,
size=d,
scope="highway",
dropout=self.dropout,
reuse=None) #相当于对信息进行一次筛选,并且让表示的维度降低到75,[batch,sql_len,75]
self.alter_embedding1 = c_emb
q_emb = highway(q_emb,
size=d,
scope="highway",
dropout=self.dropout,
reuse=True)
alter_embedding1 = highway(alter_embedding1,
size=d,
scope="highway",
dropout=self.dropout,
reuse=True)
alter_embedding2 = highway(alter_embedding2,
size=d,
scope="highway",
dropout=self.dropout,
reuse=True)
alter_embedding3 = highway(alter_embedding3,
size=d,
scope="highway",
dropout=self.dropout,
reuse=True)
with tf.variable_scope("Embedding_Encoder_Layer"):
c = residual_block(c_emb,
num_blocks=1,
num_conv_layers=4,
kernel_size=7,
mask=self.c_mask,
num_filters=d,
num_heads=nh,
seq_len=self.c_len,
scope="Encoder_Residual_Block",
bias=False,
dropout=self.dropout)
q = residual_block(
q_emb,
num_blocks=1,
num_conv_layers=4,
kernel_size=7,
mask=self.q_mask,
num_filters=d,
num_heads=nh,
seq_len=self.q_len,
scope="Encoder_Residual_Block",
reuse=True, # Share the weights between passage and question
bias=False,
dropout=self.dropout)
alter1 = residual_block(
alter_embedding1,
num_blocks=1,
num_conv_layers=4,
kernel_size=7,
mask=self.alter1_mask,
num_filters=d,
num_heads=nh,
seq_len=self.alterh1_len,
scope="Encoder_Residual_Block",
reuse=True, # Share the weights between passage and question
bias=False,
dropout=self.dropout)
alter2 = residual_block(
alter_embedding2,
num_blocks=1,
num_conv_layers=4,
kernel_size=7,
mask=self.alter2_mask,
num_filters=d,
num_heads=nh,
seq_len=self.alter2_len,
scope="Encoder_Residual_Block",
reuse=True, # Share the weights between passage and question
bias=False,
dropout=self.dropout)
alter3 = residual_block(
alter_embedding3,
num_blocks=1,
num_conv_layers=4,
kernel_size=7,
mask=self.alter3_mask,
num_filters=d,
num_heads=nh,
seq_len=self.alter3_len,
scope="Encoder_Residual_Block",
reuse=True, # Share the weights between passage and question
bias=False,
dropout=self.dropout)
with tf.variable_scope("Context_to_Query_Attention_Layer"):
# C = tf.tile(tf.expand_dims(c,2),[1,1,self.q_maxlen,1])
# Q = tf.tile(tf.expand_dims(q,1),[1,self.c_maxlen,1,1])
# S = trilinear([C, Q, C*Q], input_keep_prob = 1.0 - self.dropout)
S = optimized_trilinear_for_attention([c, q],
self.c_maxlen,
self.q_maxlen,
input_keep_prob=1.0 -
self.dropout)
mask_q = tf.expand_dims(self.q_mask, 1)
S_ = tf.nn.softmax(mask_logits(S, mask=mask_q))
mask_c = tf.expand_dims(self.c_mask, 2)
S_T = tf.transpose(
tf.nn.softmax(mask_logits(S, mask=mask_c), dim=1), (0, 2, 1))
self.c2q = tf.matmul(S_, q)
self.q2c = tf.matmul(tf.matmul(S_, S_T), c)
attention_outputs = [c, self.c2q, c * self.c2q, c * self.q2c]
with tf.variable_scope("Model_Encoder_Layer"):
inputs = tf.concat(attention_outputs, axis=-1)
self.enc = [conv(inputs, d, name="input_projection")]
for i in range(3):
if i % 2 == 0: # dropout every 2 blocks
self.enc[i] = tf.nn.dropout(self.enc[i],
1.0 - self.dropout)
self.enc.append(
residual_block(self.enc[i],
num_blocks=7,
num_conv_layers=2,
kernel_size=5,
mask=self.c_mask,
num_filters=d,
num_heads=nh,
seq_len=self.c_len,
scope="Model_Encoder",
bias=False,
reuse=True if i > 0 else None,
dropout=self.dropout))
with tf.variable_scope('question_rnn'):
self.gru = tf.contrib.rnn.GRUCell(d)
initstate = self.gru.zero_state(batch_size=N, dtype=tf.float32)
output, state = tf.nn.dynamic_rnn(self.gru,
q,
initial_state=initstate)
# self.qandc=tf.concat([self.q2c,self.c2q],axis=2)
# self.qandc=dense(self.qandc,d)
# output,state=tf.nn.dynamic_rnn(self.gru,self.qandc,initial_state=initstate)#(32,?,75)
output1, state1 = tf.nn.dynamic_rnn(self.gru,
alter1,
initial_state=state)
output2, state2 = tf.nn.dynamic_rnn(self.gru,
alter2,
initial_state=state)
output3, state3 = tf.nn.dynamic_rnn(self.gru,
alter3,
initial_state=state)
state = tf.expand_dims(state, axis=2)
weight1 = tf.matmul(self.enc[1], state)
weight2 = tf.matmul(self.enc[2], state)
weight3 = tf.matmul(self.enc[3], state)
weight_enc1 = tf.multiply(self.enc[1], weight1)
weight_enc1 = tf.reduce_sum(weight_enc1, axis=1)
weight_enc2 = tf.multiply(self.enc[2], weight2)
weight_enc2 = tf.reduce_sum(weight_enc2, axis=1)
weight_enc3 = tf.multiply(self.enc[3], weight3)
weight_enc3 = tf.reduce_sum(weight_enc3, axis=1)
with tf.variable_scope("Output_Layer"):
# start_logits = tf.squeeze(
# conv(tf.concat([self.enc[1], self.enc[2]], axis=-1), 1, bias=False, name="start_pointer"), -1)
# end_logits = tf.squeeze(
# conv(tf.concat([self.enc[1], self.enc[3]], axis=-1), 1, bias=False, name="end_pointer"), -1)
# self.logits = [mask_logits(start_logits, mask=self.c_mask),
# mask_logits(end_logits, mask=self.c_mask)]
#
# logits1, logits2 = [l for l in self.logits]
#
# outer = tf.matmul(tf.expand_dims(tf.nn.softmax(logits1), axis=2),
# tf.expand_dims(tf.nn.softmax(logits2), axis=1))
# outer = tf.matrix_band_part(outer, 0, config.ans_limit)
# self.yp1 = tf.argmax(tf.reduce_max(outer, axis=2), axis=1)
# self.yp2 = tf.argmax(tf.reduce_max(outer, axis=1), axis=1)
similary1 = tf.expand_dims(self.cos_sine(weight_enc1, state1),
axis=1)
similary2 = tf.expand_dims(self.cos_sine(weight_enc2, state2),
axis=1)
similary3 = tf.expand_dims(self.cos_sine(weight_enc3, state3),
axis=1)
self.logits1 = tf.nn.softmax(
tf.concat([similary1, similary2, similary3], axis=1))
print(self.logits1, "lllllllllllllllllllllllllllllllllllll")
def __init__(self, config, batch, word_mat=None,char_mat=None, filter_sizes=None, embedding_size=None,num_filters=None,trainable=True, l2_reg_lambda=0.0, keep_prob=0.9, graph=None):
# Placeholders for input, output and dropout
self.config = config
self.graph = graph if graph is not None else tf.Graph()
self.trainable = trainable
gru = cudnn_gru if config.use_cudnn else native_gru
self.is_train = tf.get_variable("is_train", shape=[], dtype=tf.bool, trainable=True)
if trainable == True:
self.input_x, self.input_x1, self.ch, self.qh, self.input_y, self.qa_id,self.alternatives_tokens = batch.get_next() # self.y1 is (64, 3)self.alterh batch_size is[batch,3,alternative_len,chara_len]
else:
self.input_x, self.input_x1, self.ch, self.qh,self.alternatives_tokens= batch.get_next() # self.y1 is (64, 3)self.alterh batch_size is[batch,3,alternative_len,chara_len]
self.dropout_keep_prob =keep_prob
self.global_step = tf.get_variable('global_step', shape=[], dtype=tf.int32,
initializer=tf.constant_initializer(0), trainable=False)
self.dropout = tf.placeholder_with_default(0.5, (), name="dropout")
# Keeping track of l2 regularization loss (optional)
l2_loss = tf.constant(0.0)
self.c_mask = tf.cast(self.input_x, tf.bool) # 这里是判断出每一个数据集的context对应实际句子长度的位置(64,400)
self.q_mask = tf.cast(self.input_x1, tf.bool) # 同上(64,50)
self.c_len = tf.reduce_sum(tf.cast(self.c_mask, tf.int32), axis=1) # 每一个训练数据集实际长度
self.q_len = tf.reduce_sum(tf.cast(self.q_mask, tf.int32), axis=1) # 每一个问题的实际长度
self.ch_len = tf.reshape(tf.reduce_sum(tf.cast(tf.cast(self.ch, tf.bool), tf.int32), axis=2), [-1])
self.qh_len = tf.reshape(tf.reduce_sum(tf.cast(tf.cast(self.qh, tf.bool), tf.int32), axis=2), [-1])
# Embedding layer
N, PL, QL, CL, d, dc,dg,nh= config.batch_size,config.para_limit,config.ques_limit,config.char_limit,\
config.hidden, config.char_dim,config.char_hidden,config.num_heads
with tf.variable_scope("Input_Embedding_Layer"):
self.char_mat = tf.get_variable("char_mat", initializer=tf.constant(char_mat, dtype=tf.float32),trainable=True)
ch_emb = tf.reshape(tf.nn.embedding_lookup(self.char_mat, self.ch), [N * PL, CL, dc])
qh_emb = tf.reshape(tf.nn.embedding_lookup(self.char_mat, self.qh), [N * QL, CL, dc])
ch_emb = tf.nn.dropout(ch_emb, 1.0 - 0.5 * self.dropout)
qh_emb = tf.nn.dropout(qh_emb, 1.0 - 0.5 * self.dropout)
cell_fw = tf.contrib.rnn.GRUCell(dg) # 按照字符有多少个gru神经单元
cell_bw = tf.contrib.rnn.GRUCell(dg)
_, (state_fw, state_bw) = tf.nn.bidirectional_dynamic_rnn(
cell_fw, cell_bw, ch_emb, self.ch_len,
dtype=tf.float32) # self.ch_len表示训练数据集所有字符平摊之后，实际字符的长度,sequence_length=[bacth_size] is N * PL, because
# char_hidden is 100 so state_fw and state_bw is [N * PL,100]
ch_emb = tf.concat([state_fw, state_bw], axis=1) # [N * PL,200]
_, (state_fw, state_bw) = tf.nn.bidirectional_dynamic_rnn(cell_fw, cell_bw, qh_emb, self.qh_len,dtype=tf.float32) # state_* [N*QL]
qh_emb = tf.concat([state_fw, state_bw], axis=1) # question_emd is [,200]
qh_emb = tf.reshape(qh_emb, [N, QL, 2 * dg]) # [batch_size,que_len,200]
ch_emb = tf.reshape(ch_emb, [N, PL, 2 * dg]) # 以上过程对应了论文里边的 the character-level embedding are generate by ...in the token
#这样就把每一个单词的字符转化为单词的字符级别embedding信息，tf.reshape(ch_emb, [N, PL, 2 * dg])
# 从这里可以看出作者最后那字符的state状态作为字符信息与原始单词embedding进行连接，那么是否可以用拼音
# 作为汉语的字符级别信息呢，可以尝试
print(qh_emb,"llllllllllllll")
with tf.name_scope("embedding"):
self.W = tf.get_variable("word_mat", initializer=tf.constant(word_mat, dtype=tf.float32),
trainable=True)
self.c_mask = tf.cast(self.input_x, tf.bool) # self.c为填充之后的长度是一致的，用0进行填充
self.q_mask = tf.cast(self.input_x1, tf.bool)
if trainable:
self.c_maxlen, self.q_maxlen, = config.para_limit, config.ques_limit,
else:
self.c_maxlen, self.q_maxlen = config.test_para_limit, config.test_ques_limit
self.embedded_chars = tf.nn.embedding_lookup(self.W, self.input_x)
self.embedded_chars1 = tf.nn.embedding_lookup(self.W, self.input_x1)
c_emb = tf.concat([self.embedded_chars, ch_emb], axis=2)
q_emb= tf.concat([self.embedded_chars1, qh_emb], axis=2)
# self.embedded_chars_expanded = tf.expand_dims(self.embedded_chars, -1)
# self.embedded_chars_expanded1 = tf.expand_dims(self.embedded_chars1, -1)
with tf.variable_scope("cnn_predict"):
pooled_outputs = []
c_emb = highway(c_emb, size=d, scope="highway", dropout=self.dropout, reuse=None) # 相当于对信息进行一次筛选,并且让表示的维度降低到75,[batch,sql_len,75]
q_emb = highway(q_emb, size=d, scope="highway", dropout=self.dropout, reuse=True)
c = residual_block(c_emb,
num_blocks=1,
num_conv_layers=4,
kernel_size=7,
mask=self.c_mask,
num_filters=d,
num_heads=nh,
seq_len=self.c_len,
scope="Encoder_Residual_Block",
bias=False,
dropout=self.dropout)
q = residual_block(q_emb,
num_blocks=1,
num_conv_layers=4,
kernel_size=7,
mask=self.q_mask,
num_filters=d,
num_heads=nh,
seq_len=self.q_len,
scope="Encoder_Residual_Block",
reuse=True, # Share the weights between passage and question
bias=False,
dropout=self.dropout)
qc_att = dot_attention(c, q, mask=self.q_mask, hidden=d,
keep_prob=config.keep_prob, is_train=self.is_train) # 这个函数实现的是公式（4）中的所有
rnn = gru(num_layers=1, num_units=d, batch_size=N, input_size=qc_att.get_shape(
).as_list()[-1], keep_prob=config.keep_prob, is_train=self.is_train)
att = rnn(qc_att, seq_len=self.c_len) # this is 公式(3) #[batch,c_maxlen,150]
print(att,"111111111111111111111111")
c_emb_expanded_shape=att.get_shape().as_list()
c_emb_expanded=tf.expand_dims(att, -1)
for i, filter_size in enumerate(filter_sizes):
with tf.name_scope("conv-maxpool-%s" % filter_size):
# Convolution Layer
filter_shape = [filter_size,c_emb_expanded_shape[-1], 1, num_filters]
W = tf.Variable(tf.truncated_normal(filter_shape, stddev=0.1), name="W")
l2_loss += tf.nn.l2_loss(W)
b = tf.Variable(tf.constant(0.1, shape=[num_filters]), name="b")
l2_loss += tf.nn.l2_loss(b)
conv_ouput = tf.nn.conv2d(
c_emb_expanded,
W,
strides=[1, 1, 1, 1],
padding="VALID",
name="conv")
# Apply nonlinearity
h = tf.nn.relu(tf.nn.bias_add(conv_ouput, b), name="relu")
# Maxpooling over the outputs
pooled = tf.nn.max_pool(
h,
ksize=[1, c_emb_expanded_shape[1]- filter_size + 1, 1, 1],
strides=[1, 1, 1, 1],
padding='VALID',
name="pool")
print(pooled,"222222222222222222222")
pooled_outputs.append(pooled)
# Combine all the pooled features
num_filters_total = num_filters * len(filter_sizes)
self.h_pool = tf.concat(pooled_outputs, 3)
self.h_pool_flat_cnn = tf.reshape(self.h_pool, [-1, num_filters_total])
with tf.variable_scope("encoding"):
rnn = gru(num_layers=3, num_units=d, batch_size=N, input_size=c_emb.get_shape(
).as_list()[-1], keep_prob=config.keep_prob, is_train=self.is_train) #input_size对应embedding的长度,此过程是初始化一个gru，双向lstm，包括他们的初始状态
c = rnn(c_emb, seq_len=self.c_len) #上下文编码输出为batch ，c_maxlen,以及lstm输出长度 [batch_size,sequncen_length,150*3] num_layers is 3 so concat each layers
#each layer is 150 because each layers has back_forword and feed_forword(75+75)
q = rnn(q_emb, seq_len=self.q_len) #问题编码
with tf.variable_scope("attention"):
qc_att = dot_attention(c, q, mask=self.q_mask, hidden=d,
keep_prob=config.keep_prob, is_train=self.is_train) # 这个函数实现的是公式（4）中的所有公式
rnn = gru(num_layers=1, num_units=d, batch_size=N, input_size=qc_att.get_shape(
).as_list()[-1], keep_prob=config.keep_prob, is_train=self.is_train)
att = rnn(qc_att, seq_len=self.c_len) # this is 公式(3) #[batch,c_maxlen,150]
# Create a convolution + maxpool layer for each filter size
input_shape=att.get_shape().as_list()
print(att,"rrrr")
att=tf.expand_dims(att,-1)
print(att,"hhhhhhhhhhhh")
pooled_outputs = []
for i, filter_size in enumerate(filter_sizes):
with tf.name_scope("conv-maxpool-%s" % filter_size):
# Convolution Layer
filter_shape = [filter_size, input_shape[-1], 1, num_filters]
W = tf.Variable(tf.truncated_normal(filter_shape, stddev=0.1), name="W")
l2_loss += tf.nn.l2_loss(W)
b = tf.Variable(tf.constant(0.1, shape=[num_filters]), name="b")
l2_loss += tf.nn.l2_loss(b)
conv_ouput = tf.nn.conv2d(
att,
W,
strides=[1, 1, 1, 1],
padding="VALID",
name="conv")
# Apply nonlinearity
h = tf.nn.relu(tf.nn.bias_add(conv_ouput, b), name="relu")
# Maxpooling over the outputs
pooled = tf.nn.max_pool(
h,
ksize=[1, config.para_limit - filter_size + 1, 1, 1],
strides=[1, 1, 1, 1],
padding='VALID',
name="pool")
print(pooled,"3333333333333333333333333")
pooled_outputs.append(pooled)
# Combine all the pooled features
num_filters_total = num_filters * len(filter_sizes)
self.h_pool = tf.concat(pooled_outputs, 3)
self.h_pool_flat = tf.reshape(self.h_pool, [-1, num_filters_total])
# Add dropout
with tf.name_scope("dropout"):
self.h_drop_lstm = tf.nn.dropout(self.h_pool_flat, self.dropout_keep_prob)
self.h_drop_cnn=tf.nn.dropout(self.h_pool_flat_cnn, self.dropout_keep_prob)
self.h_drop=tf.concat([self.h_drop_lstm,self.h_drop_cnn],axis=-1)
# Final (unnormalized) scores and predictions
with tf.name_scope("output"):
W = tf.get_variable(
"W",
shape=[num_filters_total*2, 3],
initializer=tf.contrib.layers.xavier_initializer())
b = tf.Variable(tf.constant(0.1, shape=[3]), name="b")
l2_loss += tf.nn.l2_loss(W)
l2_loss += tf.nn.l2_loss(b)
self.scores = tf.nn.xw_plus_b(self.h_drop, W, b, name="scores")
self.predictions = tf.argmax(self.scores, 1, name="predictions")
# Calculate mean cross-entropy loss
if trainable:
with tf.name_scope("loss"):
print(self.scores,self.input_y, "llllllllllllllll")
losses = tf.nn.softmax_cross_entropy_with_logits(logits=self.scores, labels=self.input_y)
self.loss = tf.reduce_mean(losses) + l2_reg_lambda * l2_loss
# Accuracy
with tf.name_scope("accuracy"):
correct_predictions = tf.equal(self.predictions, tf.argmax(self.input_y, 1))
self.accuracy = tf.reduce_mean(tf.cast(correct_predictions, "float"), name="accuracy")
# if config.decay is not None:
# self.var_ema = tf.train.ExponentialMovingAverage(config.decay)
# ema_op = self.var_ema.apply(tf.trainable_variables())
# with tf.control_dependencies([ema_op]):
# self.loss = tf.identity(self.loss)
#
# self.assign_vars = []
# for var in tf.global_variables():
# v = self.var_ema.average(var)
# if v:
# self.assign_vars.append(tf.assign(var, v))
self.lr = tf.minimum(config.init_lr,
0.001 / tf.log(999.) * tf.log(tf.cast(self.global_step, tf.float32) + 1))
self.opt = tf.train.AdamOptimizer(learning_rate=self.lr, beta1=0.8, beta2=0.999, epsilon=1e-7)
grads = self.opt.compute_gradients(self.loss)
gradients, variables = zip(*grads)
capped_grads, _ = tf.clip_by_global_norm(
gradients, config.grad_clip)
self.train_op = self.opt.apply_gradients(
zip(capped_grads, variables), global_step=self.global_step)
self.saver = tf.train.Saver(tf.global_variables(), max_to_keep=3)
def forward(self):
config = self.config
N, PL, QL, CL, d, dc, nh = config.batch_size if not self.demo else 1, self.c_maxlen, self.q_maxlen, config.char_limit, config.hidden, config.char_dim, config.num_heads
with tf.variable_scope("Input_Embedding_Layer"):
ch_emb = tf.reshape(tf.nn.embedding_lookup(self.char_mat, self.ch),
[N * PL, CL, dc])
qh_emb = tf.reshape(tf.nn.embedding_lookup(self.char_mat, self.qh),
[N * QL, CL, dc])
# shape = (?, 16, 64)
ch_emb = tf.nn.dropout(ch_emb, 1.0 - 0.5 * self.dropout)
qh_emb = tf.nn.dropout(qh_emb, 1.0 - 0.5 * self.dropout)
# Bidaf style conv-highway encoder
# d(hidden_size) = 96
ch_emb = conv(ch_emb,
d,
bias=True,
activation=tf.nn.relu,
kernel_size=5,
name="char_conv",
reuse=None)
qh_emb = conv(qh_emb,
d,
bias=True,
activation=tf.nn.relu,
kernel_size=5,
name="char_conv",
reuse=True)
# shape = (?, 12, 96)
ch_emb = tf.reduce_max(ch_emb, axis=1)
qh_emb = tf.reduce_max(qh_emb, axis=1)
# shape = (?, 96)
ch_emb = tf.reshape(ch_emb, [N, PL, ch_emb.shape[-1]])
qh_emb = tf.reshape(qh_emb, [N, QL, ch_emb.shape[-1]])
# shape = (32, ?, 96)
c_emb = tf.nn.dropout(
tf.nn.embedding_lookup(self.word_mat, self.c),
1.0 - self.dropout)
q_emb = tf.nn.dropout(
tf.nn.embedding_lookup(self.word_mat, self.q),
1.0 - self.dropout)
c_emb = tf.concat([c_emb, ch_emb], axis=2)
q_emb = tf.concat([q_emb, qh_emb], axis=2)
c_emb = highway(c_emb,
size=d,
scope="highway",
dropout=self.dropout,
reuse=None)
q_emb = highway(q_emb,
size=d,
scope="highway",
dropout=self.dropout,
reuse=True)
with tf.variable_scope("Embedding_Encoder_Layer"):
c = residual_block(c_emb,
num_blocks=1,
num_conv_layers=4,
kernel_size=7,
mask=self.c_mask,
num_filters=d,
num_heads=nh,
seq_len=self.c_len,
scope="Encoder_Residual_Block",
bias=False,
dropout=self.dropout)
q = residual_block(
q_emb,
num_blocks=1,
num_conv_layers=4,
kernel_size=7,
mask=self.q_mask,
num_filters=d,
num_heads=nh,
seq_len=self.q_len,
scope="Encoder_Residual_Block",
reuse=True, # Share the weights between passage and question
bias=False,
dropout=self.dropout)
with tf.variable_scope("Context_to_Query_Attention_Layer"):
# C = tf.tile(tf.expand_dims(c,2),[1,1,self.q_maxlen,1])
# Q = tf.tile(tf.expand_dims(q,1),[1,self.c_maxlen,1,1])
# S = trilinear([C, Q, C*Q], input_keep_prob = 1.0 - self.dropout)
S = optimized_trilinear_for_attention([c, q],
self.c_maxlen,
self.q_maxlen,
input_keep_prob=1.0 -
self.dropout)
mask_q = tf.expand_dims(self.q_mask, 1)
S_ = tf.nn.softmax(mask_logits(S, mask=mask_q))
mask_c = tf.expand_dims(self.c_mask, 2)
S_T = tf.transpose(
tf.nn.softmax(mask_logits(S, mask=mask_c), axis=1), (0, 2, 1))
self.c2q = tf.matmul(S_, q)
self.q2c = tf.matmul(tf.matmul(S_, S_T), c)
attention_outputs = [c, self.c2q, c * self.c2q, c * self.q2c]
with tf.variable_scope("Model_Encoder_Layer"):
inputs = tf.concat(attention_outputs, axis=-1)
self.enc = [conv(inputs, d, name="input_projection")]
for i in range(3):
if i % 2 == 0: # dropout every 2 blocks
self.enc[i] = tf.nn.dropout(self.enc[i],
1.0 - self.dropout)
self.enc.append(
residual_block(self.enc[i],
num_blocks=7,
num_conv_layers=2,
kernel_size=5,
mask=self.c_mask,
num_filters=d,
num_heads=nh,
seq_len=self.c_len,
scope="Model_Encoder",
bias=False,
reuse=True if i > 0 else None,
dropout=self.dropout))
with tf.variable_scope("Output_Layer"):
# self.enc[1] = (32, ?, 96)
conv1 = conv(tf.concat([self.enc[1], self.enc[2]], axis=-1),
1,
bias=False,
name="start_pointer")
# tf.shape(conv1) = (32, ?, 1)
start_logits = tf.squeeze(conv1, -1)
# tf.shape(start_logits) = (32, ?)
conv2 = conv(tf.concat([self.enc[1], self.enc[3]], axis=-1),
1,
bias=False,
name="end_pointer")
end_logits = tf.squeeze(conv2, -1)
# mask ??
self.logits = [
mask_logits(start_logits, mask=self.c_mask),
mask_logits(end_logits, mask=self.c_mask)
]
logits1, logits2 = [l for l in self.logits]
# shape = (32, ?) -> cause the context length is variable
# matmul([32, ?, 1] x [32, 1, ?])
outer = tf.matmul(tf.expand_dims(tf.nn.softmax(logits1), axis=2),
tf.expand_dims(tf.nn.softmax(logits2), axis=1))
# outer = (32, ?, ?)
outer = tf.matrix_band_part(outer, 0, config.ans_limit)
reduced1 = tf.reduce_max(outer, axis=2)
reduced2 = tf.reduce_max(outer, axis=1)
# tf.shape(reduced) = (32, ?)
# ###############################################
paddings = [[0, 0], [0, self.MAX_PL - tf.shape(reduced1)[0]]]
reduced1 = tf.pad(reduced1, paddings, "CONSTANT")
reduced2 = tf.pad(reduced2, paddings, "CONSTANT")
reduced1 = tf.slice(reduced1, [0, 0], [N, self.MAX_PL])
reduced2 = tf.slice(reduced2, [0, 0], [N, self.MAX_PL])
# tf.shape(reduced) = (32, ?)
# no answer flag: (no_answer, answer_exist)
# TODO add additinal layer
# TODO dimenstion between reduced and weight
na_flag1 = tf.cast(
tf.argmax(tf.matmul(reduced1, self.weights1), axis=1),
tf.float32)
na_flag2 = tf.cast(
tf.argmax(tf.matmul(reduced2, self.weights2), axis=1),
tf.float32)
# Tensor("Output_Layer/ArgMax:0", shape=(32, ?), dtype=int64)
self.yp1 = tf.argmax(reduced1, axis=1)
self.yp2 = tf.argmax(reduced2, axis=1)
print(tf.reduce_sum(reduced1, axis=1))
print(tf.multiply(na_flag1, tf.reduce_sum(reduced1, axis=1)))
print(
tf.nn.softmax_cross_entropy_with_logits_v2(logits=logits1,
labels=self.y1))
# no_answer
losses = tf.where(
self.no_answer > 0,
tf.multiply(na_flag1, tf.reduce_sum(reduced1, axis=1)),
tf.nn.softmax_cross_entropy_with_logits_v2(logits=logits1,
labels=self.y1))
losses2 = tf.where(
self.no_answer > 0,
tf.multiply(na_flag2, tf.reduce_sum(reduced2, axis=1)),
tf.nn.softmax_cross_entropy_with_logits_v2(logits=logits2,
labels=self.y2))
#################################################
self.loss = tf.reduce_mean(losses + losses2)
if config.l2_norm is not None:
variables = tf.get_collection(tf.GraphKeys.REGULARIZATION_LOSSES)
l2_loss = tf.contrib.layers.apply_regularization(
regularizer, variables)
self.loss += l2_loss
if config.decay is not None:
self.var_ema = tf.train.ExponentialMovingAverage(config.decay)
ema_op = self.var_ema.apply(tf.trainable_variables())
with tf.control_dependencies([ema_op]):
self.loss = tf.identity(self.loss)
self.assign_vars = []
for var in tf.global_variables():
v = self.var_ema.average(var)
if v:
self.assign_vars.append(tf.assign(var, v))
def build_model(self):
PL, QL, CL, d, dc, nh = self.c_maxlen, self.q_maxlen, self.char_limit, self.filters, self.char_dim, self.num_heads
with tf.variable_scope("Input_Embedding_Layer"):
ch_emb = tf.reshape(tf.nn.embedding_lookup(self.char_mat, self.contc_input), [-1, CL, dc])
qh_emb = tf.reshape(tf.nn.embedding_lookup(self.char_mat, self.quesc_input), [-1, CL, dc])
ch_emb = tf.nn.dropout(ch_emb, 1.0 - 0.5 * self.dropout)
qh_emb = tf.nn.dropout(qh_emb, 1.0 - 0.5 * self.dropout)
# Bidaf style conv-highway encoder
ch_emb = conv(ch_emb, d, bias=True, activation=tf.nn.relu, kernel_size=5, name="char_conv", reuse=None)
qh_emb = conv(qh_emb, d, bias=True, activation=tf.nn.relu, kernel_size=5, name="char_conv", reuse=True)
ch_emb = tf.reduce_max(ch_emb, axis=1)
qh_emb = tf.reduce_max(qh_emb, axis=1)
ch_emb = tf.reshape(ch_emb, [-1, PL, ch_emb.shape[-1]])
qh_emb = tf.reshape(qh_emb, [-1, QL, ch_emb.shape[-1]])
c_emb = tf.nn.dropout(tf.nn.embedding_lookup(self.word_mat, self.contw_input), 1.0 - self.dropout)
q_emb = tf.nn.dropout(tf.nn.embedding_lookup(self.word_mat, self.quesw_input), 1.0 - self.dropout)
c_emb = tf.concat([c_emb, ch_emb], axis=2)
q_emb = tf.concat([q_emb, qh_emb], axis=2)
if self.use_elmo:
c_emb = tf.concat([c_emb, self.cont_elmo], axis=-1)
q_emb = tf.concat([q_emb, self.ques_elmo], axis=-1)
c_emb = highway(c_emb, size=d, scope="highway", dropout=self.dropout, reuse=None)
q_emb = highway(q_emb, size=d, scope="highway", dropout=self.dropout, reuse=True)
with tf.variable_scope("Embedding_Encoder_Layer"):
c = residual_block(c_emb,
num_blocks=1,
num_conv_layers=4,
kernel_size=7,
mask=self.c_mask,
num_filters=d,
num_heads=nh,
seq_len=self.cont_len,
scope="Encoder_Residual_Block",
bias=False,
dropout=self.dropout)
q = residual_block(q_emb,
num_blocks=1,
num_conv_layers=4,
kernel_size=7,
mask=self.q_mask,
num_filters=d,
num_heads=nh,
seq_len=self.ques_len,
scope="Encoder_Residual_Block",
reuse=True,
bias=False,
dropout=self.dropout)
with tf.variable_scope("Context_to_Query_Attention_Layer"):
S = optimized_trilinear_for_attention([c, q], self.c_maxlen, self.q_maxlen,
input_keep_prob=1.0 - self.dropout)
mask_q = tf.expand_dims(self.q_mask, 1)
S_ = tf.nn.softmax(mask_logits(S, mask=mask_q))
mask_c = tf.expand_dims(self.c_mask, 2)
S_T = tf.transpose(tf.nn.softmax(mask_logits(S, mask=mask_c), dim=1), (0, 2, 1))
c2q = tf.matmul(S_, q)
q2c = tf.matmul(tf.matmul(S_, S_T), c)
attention_outputs = [c, c2q, c * c2q, c * q2c]
with tf.variable_scope("Model_Encoder_Layer"):
attention_inputs = tf.concat(attention_outputs, axis=-1)
enc = [conv(attention_inputs, d, name="input_projection")]
for i in range(3):
if i % 2 == 0: # dropout every 2 blocks
enc[i] = tf.nn.dropout(enc[i], 1.0 - self.dropout)
enc.append(residual_block(enc[i],
num_blocks=7,
num_conv_layers=2,
kernel_size=5,
mask=self.c_mask,
num_filters=d,
num_heads=nh,
seq_len=self.cont_len,
scope="Model_Encoder",
bias=False,
reuse=True if i > 0 else None,
dropout=self.dropout))
with tf.variable_scope("Output_Layer"):
start_logits = tf.concat([enc[1], enc[2]], axis=-1)
end_logits = tf.concat([enc[1], enc[3]], axis=-1)
if self.use_elmo:
start_logits = tf.concat((start_logits, self.cont_elmo), axis=-1)
end_logits = tf.concat((end_logits, self.cont_elmo), axis=-1)
start_logits = tf.squeeze(conv(start_logits, 1, bias=False, name="start_pointer"), -1)
end_logits = tf.squeeze(conv(end_logits, 1, bias=False, name="end_pointer"), -1)
# 2.0 Dataset
# unanswer_bias = tf.get_variable("unanswer_bias", [1],
# regularizer=tf.contrib.layers.l2_regularizer(scale=3e-7),
# initializer=tf.zeros_initializer())
# unanswer_bias = tf.reshape(tf.tile(unanswer_bias, [self.batch_size]), [-1, 1])
# self.logits1 = tf.concat((unanswer_bias, mask_logits(start_logits, mask=self.c_mask)), axis=-1)
# self.logits2 = tf.concat((unanswer_bias, mask_logits(end_logits, mask=self.c_mask)), axis=-1)
self.logits1 = mask_logits(start_logits, mask=self.c_mask)
self.logits2 = mask_logits(end_logits, mask=self.c_mask)
start_loss = tf.nn.softmax_cross_entropy_with_logits(logits=self.logits1, labels=self.y_start)
end_loss = tf.nn.softmax_cross_entropy_with_logits(logits=self.logits2, labels=self.y_end)
self.loss = tf.reduce_mean(start_loss + end_loss)
# output
outer = tf.matmul(tf.expand_dims(tf.nn.softmax(self.logits1), axis=2),
tf.expand_dims(tf.nn.softmax(self.logits2), axis=1))
outer = tf.matrix_band_part(outer, 0, self.ans_limit)
self.output1 = tf.argmax(tf.reduce_max(outer, axis=2), axis=1)
self.output2 = tf.argmax(tf.reduce_max(outer, axis=1), axis=1)
if self.use_topk:
with tf.variable_scope("Topk_Layer"):
top_size = 3
outer = tf.reshape(outer, [self.batch_size, -1])
outer_inds = tf.nn.top_k(outer, top_size).indices # [N,top_size]
self.yp1 = outer_inds // tf.shape(self.logits1)[-1]
self.yp2 = outer_inds % tf.shape(self.logits2)[-1]
def sen_mask(tensor):
def sen_mask_(a, b, filters):
try:
mata = tf.zeros([a, filters], tf.int32)
except:
mata = []
matb = tf.ones([b - a, filters], tf.int32)
matc = tf.zeros([tf.shape(self.logits1)[-1] - b, filters], tf.int32)
mat = tf.concat((mata, matb, matc), axis=0)
return mat
return tf.map_fn(lambda x: sen_mask_(x[0], x[1], self.filters), tensor)
self.yp3 = self.yp2 + 1
self.yp1 = tf.expand_dims(self.yp1, -1)
self.yp2 = tf.expand_dims(self.yp2, -1)
self.yp3 = tf.expand_dims(self.yp3, -1)
self.y_mask = tf.concat([self.yp1, self.yp3], axis=-1)
self.y_mask = tf.map_fn(lambda x: sen_mask(x), self.y_mask)
# answer
c = tf.tile(tf.expand_dims(c2q, 1), [1, top_size, 1, 1])
c_topk = tf.multiply(tf.cast(self.y_mask, tf.float32), c)
W1 = tf.get_variable("W1", initializer=tf.ones([1, 1, 1, self.filters]))
W1 = tf.tile(W1, [self.batch_size, top_size, 1, 1])
alpha1 = tf.nn.softmax(tf.matmul(W1, c_topk, transpose_b=True), axis=2)
answer = tf.matmul(alpha1, c_topk) # [32,top_size,1,128]
# question
W2 = tf.get_variable("W2", initializer=tf.ones([1, 1, self.filters]))
W2 = tf.tile(W2, [self.batch_size, 1, 1])
alpha2 = tf.nn.softmax(tf.matmul(W2, q, transpose_b=True), axis=1)
ques = tf.matmul(alpha2, q)
ques = tf.tile(tf.expand_dims(ques, 1), [1, top_size, 1, 1]) # [32,top_size,1,128]
# question & answer
W3 = tf.get_variable("W3", initializer=tf.ones([1, 1, self.filters, self.filters]))
W3 = tf.tile(W3, [self.batch_size, top_size, 1, 1])
y_topk_logits = tf.nn.sigmoid(tf.matmul(ques, tf.matmul(W3, answer, transpose_b=True))) # [32,top_size,1,1]
y_topk_logits = tf.squeeze(y_topk_logits) # [32,top_size]
self.yp1 = tf.squeeze(self.yp1)
self.yp2 = tf.squeeze(self.yp2)
coeff1_topk = tf.one_hot(self.yp1, self.c_maxlen, axis=-1) # [32,top_size,400] one-hot
coeff2_topk = tf.one_hot(self.yp2, self.c_maxlen, axis=-1)
# [0,1,0,0,0][0,0,0,1,0]->[0,1,1,1,1]-[0,0,0,1,1]->[0,1,1,0,0]+[0,0,0,1,0]->[0,1,1,1,0]
coeff1_topk_cumsum = tf.cumsum(coeff1_topk, axis=-1)
coeff2_topk_cumsum = tf.cumsum(coeff2_topk, axis=-1)
self.y_d = coeff1_topk_cumsum - coeff2_topk_cumsum + coeff2_topk # [32, top_size, 400]
def clip_for_sigmoid(output):
_epsilon = tf.convert_to_tensor(1e-7, dtype=output.dtype.base_dtype)
output = tf.clip_by_value(output, _epsilon, 1 - _epsilon)
output = tf.log(output / (1 - output))
return output
if self.topk_loss=='f1':
# f1 loss
y_start_ind = tf.cumsum(self.y_start, axis=-1)
y_end_ind = tf.cumsum(self.y_end, axis=-1)
y_gtd = y_start_ind - y_end_ind + self.y_end # [32, 400]
def cal_num_same(y_pred, y_truth): # [top_size, 400] [400,]
def cal_num_same_(y_pred_, y_truth): # [400,] [400,]
return tf.reduce_sum(tf.cast(tf.logical_and(tf.cast(y_pred_, tf.bool), tf.cast(y_truth, tf.bool)), tf.float32),axis=-1)
return [tf.map_fn(lambda x:cal_num_same_(x,y_truth),y_pred),tf.map_fn(lambda x:cal_num_same_(x,y_truth),y_pred)]
num_same = tf.map_fn(lambda x:cal_num_same(x[0], x[1]), [self.y_d, y_gtd])[0] # [32, top_size]
y_precision = num_same / (tf.cast(tf.reduce_sum(self.y_d, axis=-1),tf.float32) + 1e-8) # [32, top_size]
y_recall = num_same / tf.expand_dims(tf.cast(tf.reduce_sum(y_gtd, axis=-1),tf.float32) + 1e-8, axis=-1) # [32, top_size]
y_f1 = (2.0 * y_precision * y_recall) / (tf.cast(y_precision + y_recall,tf.float32) + 1e-8) # [32, top_size]
topk_loss = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits=clip_for_sigmoid(y_topk_logits), labels=y_f1))
elif self.topk_loss=='em':
# em loss
start_em = tf.equal(tf.cast(tf.expand_dims(tf.argmax(self.y_start, axis=-1), axis=1), tf.int32),
tf.cast(self.yp1, tf.int32)) # [32, top_size]
end_em = tf.equal(tf.cast(tf.expand_dims(tf.argmax(self.y_end, axis=-1), axis=1), tf.int32),
tf.cast(self.yp2, tf.int32)) # [32, top_size]
y_em = tf.cast(tf.logical_and(start_em, end_em), tf.float32) # [32, top_size]
topk_loss = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits=clip_for_sigmoid(y_topk_logits), labels=y_em))
# final loss
self.Lambda1 = tf.get_variable("Lambda1", initializer=tf.constant([0.9]), trainable=False)
self.loss = tf.reduce_mean(self.Lambda1 * (start_loss + end_loss) + (1 - self.Lambda1) * topk_loss)
# output
outer_topk = tf.matmul(tf.expand_dims(tf.nn.softmax(self.logits1), axis=2),
tf.expand_dims(tf.nn.softmax(self.logits2), axis=1))
outer_topk = tf.matrix_band_part(outer_topk, 0, self.ans_limit)
self.output1 = tf.argmax(tf.reduce_max(outer_topk, axis=2), axis=1)
self.output2 = tf.argmax(tf.reduce_max(outer_topk, axis=1), axis=1)
# diversity loss
if self.diversity_loss:
self.Lambda2 = tf.get_variable("Lambda2", initializer=tf.constant([0.1]),trainable=False)
diversity_loss = tf.reduce_mean(tf.reduce_prod(self.y_d, axis=1),axis=-1) # [32,top_size,400]->[32,400]->[32,]
self.loss = self.loss + tf.reduce_mean(self.Lambda2 * diversity_loss)
if self.l2_norm is not None:
variables = tf.get_collection(tf.GraphKeys.REGULARIZATION_LOSSES)
l2_loss = tf.contrib.layers.apply_regularization(regularizer, variables)
self.loss += l2_loss
if self.decay is not None:
self.var_ema = tf.train.ExponentialMovingAverage(self.decay)
ema_op = self.var_ema.apply(tf.trainable_variables())
with tf.control_dependencies([ema_op]):
self.loss = tf.identity(self.loss)
self.assign_vars = []
for var in tf.global_variables():
v = self.var_ema.average(var)
if v is not None:
self.assign_vars.append(tf.assign(var, v))
def forward(self):
config = self.config
N = config.batch_size if not self.demo else 1
PL = self.c_maxlen
QL = self.q_maxlen
CL = config.char_limit # 16
d = config.hidden # 96
dc = config.char_dim # 64
nh = config.num_heads # 1
with tf.variable_scope("Input_Embedding_Layer"):
'''
self.ch : (N, c_maxlen, 16)
self.qh : (N, q_maxlen, 16)
'''
ch_emb = tf.reshape(tf.nn.embedding_lookup(self.char_mat, self.ch),
[N * PL, CL, dc]) # (N*c_maxlen, 16, 64)
qh_emb = tf.reshape(tf.nn.embedding_lookup(self.char_mat, self.qh),
[N * QL, CL, dc]) # (N*q_maxlen, 16, 64)
ch_emb = tf.nn.dropout(ch_emb, 1.0 - 0.5 * self.dropout)
qh_emb = tf.nn.dropout(qh_emb, 1.0 - 0.5 * self.dropout)
# BiDAF style conv-highway encoder: conv over chars in each word in a batch of passages
ch_emb = conv(ch_emb,
d,
bias=True,
activation=tf.nn.relu,
kernel_size=5,
name="char_conv",
reuse=None) # (N*c_maxlen, 16-5+1, 96)
qh_emb = conv(qh_emb,
d,
bias=True,
activation=tf.nn.relu,
kernel_size=5,
name="char_conv",
reuse=True) # (N*q_maxlen, 16-5+1, 96)
ch_emb = tf.reduce_max(ch_emb, axis=1) # (N*c_maxlen, 96)
qh_emb = tf.reduce_max(qh_emb, axis=1) # (N*q_maxlen, 96)
ch_emb = tf.reshape(ch_emb,
[N, PL, ch_emb.shape[-1]]) # (N, c_maxlen, 96)
qh_emb = tf.reshape(qh_emb,
[N, QL, ch_emb.shape[-1]]) # (N, q_maxlen, 96)
'''
self.c : (N, c_maxlen)
self.q : (N, q_maxlen)
'''
c_emb = tf.nn.dropout(tf.nn.embedding_lookup(
self.word_mat, self.c),
1.0 - self.dropout) # (N, c_maxlen, 300)
q_emb = tf.nn.dropout(tf.nn.embedding_lookup(
self.word_mat, self.q),
1.0 - self.dropout) # (N, q_maxlen, 300)
c_emb = tf.concat([c_emb, ch_emb], axis=2) # (N, c_maxlen, 396)
q_emb = tf.concat([q_emb, qh_emb], axis=2) # (N, q_maxlen, 396)
c_emb = highway(c_emb,
size=d,
scope="highway",
dropout=self.dropout,
reuse=None) # (N, c_maxlen, 96)
q_emb = highway(q_emb,
size=d,
scope="highway",
dropout=self.dropout,
reuse=True) # (N, q_maxlen, 96)
with tf.variable_scope("Embedding_Encoder_Layer"):
'''
-> positional encoding
-> layer_normalization
-> depth-wise separable convolution
-> self attention
-> feed forward network
In the paper: The total number of encoder blocks is 1
'''
# (N, c_maxlen, 96)
c = residual_block(c_emb,
num_blocks=1,
num_conv_layers=4,
kernel_size=7,
mask=self.c_mask,
num_filters=d,
num_heads=nh,
seq_len=self.c_len,
scope="Encoder_Residual_Block",
bias=False,
dropout=self.dropout)
# (N, q_maxlen, 96)
q = residual_block(
q_emb,
num_blocks=1,
num_conv_layers=4,
kernel_size=7,
mask=self.q_mask,
num_filters=d,
num_heads=nh,
seq_len=self.q_len,
scope="Encoder_Residual_Block",
reuse=True, # Share the weights between passage and question
bias=False,
dropout=self.dropout)
with tf.variable_scope("Context_to_Query_Attention_Layer"):
'''
tf.tile(input, multiples, name=None): creates a new tensor by replicating input multiples times.
The output tensor's i'th dimension has input.dims(i) * multiples[i] elements,
and the values of input are replicated multiples[i] times along the 'i'th dimension.
Paper: The layer parameters are the same as the Embedding Encoder Layer
except that convolution layer number is 2 within a block
and the total number of blocks is 7
'''
'''
c: (N, c_maxlen, d)
q: (N, q_maxlen, d)
ch_emb: (N, c_maxlen, d)
qh_emb: (N, q_maxlen, d)
C: (N, c_maxlen, q_maxlen, d)
Q: (N, c_maxlen, q_maxlen, d)
S: (N, c_maxlen, q_maxlen)
mask_q: (N, 1, q_maxlen)
mask_c: (N, c_maxlen, 1)
S_: (N, c_maxlen, q_maxlen)
S_T: (N, q_maxlen, c_maxlen)
self.c2q: (N, c_maxlen, d) = tf.matmul(S_, q)
self.q2c: (N, c_maxlen, d) = tf.matmul(tf.matmul(S_, S_T), c)
'''
C = tf.tile(tf.expand_dims(c, 2), [1, 1, self.q_maxlen, 1])
Q = tf.tile(tf.expand_dims(q, 1), [1, self.c_maxlen, 1, 1])
S = trilinear([C, Q, C * Q], input_keep_prob=1.0 - self.dropout)
mask_q = tf.expand_dims(self.q_mask, 1)
S_ = tf.nn.softmax(mask_logits(S, mask=mask_q))
mask_c = tf.expand_dims(self.c_mask, 2)
S_T = tf.transpose(
tf.nn.softmax(mask_logits(S, mask=mask_c), axis=1), (0, 2, 1))
self.c2q = tf.matmul(S_, q)
self.q2c = tf.matmul(tf.matmul(S_, S_T), c)
attention_outputs = [c, self.c2q, c * self.c2q]
if config.q2c:
attention_outputs.append(c * self.q2c)
with tf.variable_scope("Model_Encoder_Layer"):
inputs = tf.concat(attention_outputs, axis=-1)
self.enc = [conv(inputs, d,
name="input_projection")] # d=hidden=96
for i in range(3):
if i % 2 == 0: # dropout every 2 blocks
self.enc[i] = tf.nn.dropout(self.enc[i],
1.0 - self.dropout)
self.enc.append(
residual_block(self.enc[i],
num_blocks=7,
num_conv_layers=2,
kernel_size=5,
mask=self.c_mask,
num_filters=d,
num_heads=nh,
seq_len=self.c_len,
scope="Model_Encoder",
bias=False,
reuse=True if i > 0 else None,
dropout=self.dropout))
with tf.variable_scope("Output_Layer"):
'''
tf.matrix_band_part: Copy a tensor setting everything outside a central band
in each innermost matrix to zero.
self.enc[i]: (N, c_maxlen, d)
start_logits: (N, c_maxlen)
end_logits: (N, c_maxlen)
logits1: (N, c_maxlen)
logits2: (N, c_maxlen)
outer: (N, c_maxlen, c_maxlen)
yp1, yp2, losses, losses2: (N,)
'''
start_logits = tf.squeeze(
conv(tf.concat([self.enc[1], self.enc[2]], axis=-1),
1,
bias=False,
name="start_pointer"), -1)
end_logits = tf.squeeze(
conv(tf.concat([self.enc[1], self.enc[3]], axis=-1),
1,
bias=False,
name="end_pointer"), -1)
self.logits = [
mask_logits(start_logits, mask=self.c_mask),
mask_logits(end_logits, mask=self.c_mask)
]
logits1, logits2 = [l for l in self.logits]
losses = tf.nn.softmax_cross_entropy_with_logits(logits=logits1,
labels=self.y1)
losses2 = tf.nn.softmax_cross_entropy_with_logits(logits=logits2,
labels=self.y2)
self.loss = tf.reduce_mean(losses + losses2)
# find max-score span
outer = tf.matmul(tf.expand_dims(tf.nn.softmax(logits1), axis=2),
tf.expand_dims(tf.nn.softmax(logits2), axis=1))
outer = tf.matrix_band_part(outer, 0, 15)
self.yp1 = tf.argmax(tf.reduce_max(outer, axis=2), axis=1)
self.yp2 = tf.argmax(tf.reduce_max(outer, axis=1), axis=1)
#DEBUG
self.debug_ops.extend([
self.enc[1], start_logits, end_logits, logits1, logits2, outer,
self.yp1, self.yp2, losses, losses2, self.loss
])
if config.l2_norm is not None:
variables = tf.get_collection(tf.GraphKeys.REGULARIZATION_LOSSES)
l2_loss = tf.contrib.layers.apply_regularization(
regularizer, variables)
self.loss += l2_loss
if config.decay is not None:
self.var_ema = tf.train.ExponentialMovingAverage(config.decay)
ema_op = self.var_ema.apply(tf.trainable_variables())
with tf.control_dependencies([ema_op]):
self.loss = tf.identity(self.loss)
self.shadow_vars = []
self.global_vars = []
for var in tf.global_variables():
v = self.var_ema.average(var)
if v:
self.shadow_vars.append(v)
self.global_vars.append(var)
self.assign_vars = []
for g, v in zip(self.global_vars, self.shadow_vars):
self.assign_vars.append(tf.assign(g, v))
def forward(self):
config = self.config
N, PL, QL, CL, d, dc, nh = config.batch_size if not self.demo else 1, self.c_maxlen, \
self.q_maxlen, config.char_limit, config.hidden, config.tw_char_dim, config.num_heads
with tf.variable_scope("Input_Embedding_Layer"):
if config.type == "all":
ch_emb = tf.reshape(
tf.nn.embedding_lookup(self.char_mat, self.ch),
[N * PL, CL, dc])
qh_emb = tf.reshape(
tf.nn.embedding_lookup(self.char_mat, self.qh),
[N * QL, CL, dc])
ch_emb = tf.nn.dropout(ch_emb, 1.0 - 0.5 * self.dropout)
qh_emb = tf.nn.dropout(qh_emb, 1.0 - 0.5 * self.dropout)
# Bidaf style conv-highway encoder
ch_emb = conv(ch_emb,
d,
bias=True,
activation=tf.nn.relu,
kernel_size=5,
name="char_conv",
reuse=None)
qh_emb = conv(qh_emb,
d,
bias=True,
activation=tf.nn.relu,
kernel_size=5,
name="char_conv",
reuse=True)
ch_emb = tf.reduce_max(ch_emb, axis=1)
qh_emb = tf.reduce_max(qh_emb, axis=1)
ch_emb = tf.reshape(ch_emb, [N, PL, ch_emb.shape[-1]])
qh_emb = tf.reshape(qh_emb, [N, QL, ch_emb.shape[-1]])
c_emb = tf.nn.dropout(
tf.nn.embedding_lookup(self.word_mat, self.c),
1.0 - self.dropout)
q_emb = tf.nn.dropout(
tf.nn.embedding_lookup(self.word_mat, self.q),
1.0 - self.dropout)
c_emb = tf.concat([c_emb, ch_emb], axis=2)
q_emb = tf.concat([q_emb, qh_emb], axis=2)
c_emb = highway(c_emb,
size=d,
scope="highway",
dropout=self.dropout,
reuse=None)
q_emb = highway(q_emb,
size=d,
scope="highway",
dropout=self.dropout,
reuse=True)
elif config.type == 'char':
c_emb = tf.nn.dropout(
tf.nn.embedding_lookup(self.char_mat, self.c),
1.0 - self.dropout)
q_emb = tf.nn.dropout(
tf.nn.embedding_lookup(self.char_mat, self.q),
1.0 - self.dropout)
c_emb = highway(c_emb,
size=d,
scope="highway",
dropout=self.dropout,
reuse=None)
q_emb = highway(q_emb,
size=d,
scope="highway",
dropout=self.dropout,
reuse=True)
with tf.variable_scope("Embedding_Encoder_Layer"):
c = residual_block(c_emb,
num_blocks=1,
num_conv_layers=4,
kernel_size=7,
mask=self.c_mask,
num_filters=d,
num_heads=nh,
seq_len=self.c_len,
scope="Encoder_Residual_Block",
bias=False,
dropout=self.dropout)
q = residual_block(
q_emb,
num_blocks=1,
num_conv_layers=4,
kernel_size=7,
mask=self.q_mask,
num_filters=d,
num_heads=nh,
seq_len=self.q_len,
scope="Encoder_Residual_Block",
reuse=True, # Share the weights between passage and question
bias=False,
dropout=self.dropout)
with tf.variable_scope("Context_to_Query_Attention_Layer"):
# C = tf.tile(tf.expand_dims(c,2),[1,1,self.q_maxlen,1])
# Q = tf.tile(tf.expand_dims(q,1),[1,self.c_maxlen,1,1])
# S = trilinear([C, Q, C*Q], input_keep_prob = 1.0 - self.dropout)
S = optimized_trilinear_for_attention([c, q],
self.c_maxlen,
self.q_maxlen,
input_keep_prob=1.0 -
self.dropout)
mask_q = tf.expand_dims(self.q_mask, 1)
S_ = tf.nn.softmax(mask_logits(S, mask=mask_q))
mask_c = tf.expand_dims(self.c_mask, 2)
S_T = tf.transpose(
tf.nn.softmax(mask_logits(S, mask=mask_c), axis=1), (0, 2, 1))
self.c2q = tf.matmul(S_, q)
self.q2c = tf.matmul(tf.matmul(S_, S_T), c)
attention_outputs = [c, self.c2q, c * self.c2q, c * self.q2c]
with tf.variable_scope("Model_Encoder_Layer"):
inputs = tf.concat(attention_outputs, axis=-1)
self.enc = [conv(inputs, d, name="input_projection")]
for i in range(3):
if i % 2 == 0: # dropout every 2 blocks
self.enc[i] = tf.nn.dropout(self.enc[i],
1.0 - self.dropout)
self.enc.append(
residual_block(self.enc[i],
num_blocks=7,
num_conv_layers=2,
kernel_size=5,
mask=self.c_mask,
num_filters=d,
num_heads=nh,
seq_len=self.c_len,
scope="Model_Encoder",
bias=False,
reuse=True if i > 0 else None,
dropout=self.dropout))
with tf.variable_scope("Output_Layer"):
start_logits = tf.squeeze(
conv(tf.concat([self.enc[1], self.enc[2]], axis=-1),
1,
bias=False,
name="start_pointer"), -1)
end_logits = tf.squeeze(
conv(tf.concat([self.enc[1], self.enc[3]], axis=-1),
1,
bias=False,
name="end_pointer"), -1)
# guess : mask the padding part pad in the end of the passage
self.logits = [
mask_logits(start_logits, mask=self.c_mask),
mask_logits(end_logits, mask=self.c_mask)
]
logits1, logits2 = [l for l in self.logits]
outer = tf.matmul(tf.expand_dims(tf.nn.softmax(logits1), axis=2),
tf.expand_dims(tf.nn.softmax(logits2), axis=1))
outer = tf.matrix_band_part(outer, 0, config.ans_limit)
self.yp1 = tf.argmax(tf.reduce_max(outer, axis=2), axis=1)
self.yp2 = tf.argmax(tf.reduce_max(outer, axis=1), axis=1)
losses = tf.nn.softmax_cross_entropy_with_logits_v2(logits=logits1,
labels=self.y1)
losses2 = tf.nn.softmax_cross_entropy_with_logits_v2(
logits=logits2, labels=self.y2)
self.loss = tf.reduce_mean(losses + losses2)
if config.l2_norm is not None:
variables = tf.get_collection(tf.GraphKeys.REGULARIZATION_LOSSES)
l2_loss = tf.contrib.layers.apply_regularization(
regularizer, variables)
self.loss += l2_loss
if config.decay is not None:
self.var_ema = tf.train.ExponentialMovingAverage(config.decay)
ema_op = self.var_ema.apply(tf.trainable_variables())
with tf.control_dependencies([ema_op]):
self.loss = tf.identity(self.loss)
self.assign_vars = []
for var in tf.global_variables():
v = self.var_ema.average(var)
if v:
self.assign_vars.append(tf.assign(var, v))
def forward(self):
config = self.config
'''
N: batch_size
PL: passage最大长度
QL: question最大长度
CL: 单词最大字母长度
d: 输出通道数
dc: 字母的嵌入维度
nh: 自注意力的头数
'''
N, PL, QL, CL, d, dc, nh = config.batch_size if not self.demo else 1, self.c_maxlen, self.q_maxlen, config.char_limit, config.hidden, config.char_dim, config.num_heads
# Embedding层：获取词向量和字符向量的拼接
with tf.variable_scope("Input_Embedding_Layer"):
# # character嵌入：
# 1、先对单词的每个字母进行char2vec
ch_emb = tf.reshape(tf.nn.embedding_lookup(self.char_mat, self.ch),
[N * PL, CL, dc])
qh_emb = tf.reshape(tf.nn.embedding_lookup(self.char_mat, self.qh),
[N * QL, CL, dc])
ch_emb = tf.nn.dropout(ch_emb, 1.0 - 0.5 * self.dropout)
qh_emb = tf.nn.dropout(qh_emb, 1.0 - 0.5 * self.dropout)
# 2、将单词对应的word2vec矩阵通过conv编码成向量
# 卷积 ch_emb_shape = [N * PL, CL-5+1, d], qh_emb_shape = [N * QL, CL-5+1, d]
ch_emb = conv(ch_emb,
d,
bias=True,
activation=tf.nn.relu,
kernel_size=5,
name="char_conv",
reuse=None)
qh_emb = conv(qh_emb,
d,
bias=True,
activation=tf.nn.relu,
kernel_size=5,
name="char_conv",
reuse=True)
# max_time_pooling
# ch_emb_shape = [N * PL, d], qh_emb_shape = [N * QL, d]
ch_emb = tf.reduce_max(ch_emb, axis=1)
qh_emb = tf.reduce_max(qh_emb, axis=1)
# ch_emb_shape = [N, PL, d], qh_emb_shape = [N, QL, d]
ch_emb = tf.reshape(ch_emb, [N, PL, ch_emb.shape[-1]])
qh_emb = tf.reshape(qh_emb, [N, QL, ch_emb.shape[-1]])
# # 词嵌入：从glove获取
c_emb = tf.nn.dropout(
tf.nn.embedding_lookup(self.word_mat, self.c),
1.0 - self.dropout)
q_emb = tf.nn.dropout(
tf.nn.embedding_lookup(self.word_mat, self.q),
1.0 - self.dropout)
# 拼接词向量和字符向量
# c_emb_size = [batch, n_c, c_emb+ch_emb]
c_emb = tf.concat([c_emb, ch_emb], axis=2)
# q_emb_size = [batch, n_q, c_emb + ch_emb]
q_emb = tf.concat([q_emb, qh_emb], axis=2)
# 分别通过highway网络
# c_emb_size = [batch, n_c, d]
c_emb = highway(c_emb,
size=d,
scope="highway",
dropout=self.dropout,
reuse=None)
# c_emb_size = [batch, n_q, d]
q_emb = highway(q_emb,
size=d,
scope="highway",
dropout=self.dropout,
reuse=True)
# Stacking Embedding Encoder Block的实现：共1个encoder block，每个7个卷积层，卷积核数d=96
with tf.variable_scope("Embedding_Encoder_Layer"):
# c_size = [batch, n_c, d]
c = residual_block(c_emb,
num_blocks=1,
num_conv_layers=4,
kernel_size=7,
mask=self.c_mask,
num_filters=d,
num_heads=nh,
seq_len=self.c_len,
scope="Encoder_Residual_Block",
bias=False,
dropout=self.dropout)
# q_size = [batch, n_q, d]
q = residual_block(
q_emb,
num_blocks=1,
num_conv_layers=4,
kernel_size=7,
mask=self.q_mask,
num_filters=d,
num_heads=nh,
seq_len=self.q_len,
scope="Encoder_Residual_Block",
reuse=
True, # 共享passage和question的Stacking Embedding Encoder Block的权重
bias=False,
dropout=self.dropout)
# Context-Query-Attention实现：
with tf.variable_scope("Context_to_Query_Attention_Layer"):
# C = tf.tile(tf.expand_dims(c,2),[1,1,self.q_maxlen,1])
# Q = tf.tile(tf.expand_dims(q,1),[1,self.c_maxlen,1,1])
# S = trilinear([C, Q, C*Q], input_keep_prob = 1.0 - self.dropout)
# S_size = [batch, n_c, n_q], q_size = [batch, n_q, d], c_size = [batch, n_c, d]
S = optimized_trilinear_for_attention([c, q],
self.c_maxlen,
self.q_maxlen,
input_keep_prob=1.0 -
self.dropout)
mask_q = tf.expand_dims(self.q_mask, 1)
# n_q方向进行softmax
S_ = tf.nn.softmax(mask_logits(S, mask=mask_q), dim=-1)
mask_c = tf.expand_dims(self.c_mask, 2)
# n_c方向进行softmax
S_T = tf.transpose(
tf.nn.softmax(mask_logits(S, mask=mask_c), dim=1), (0, 2, 1))
# c2q_size = [batch, n_c, d]
self.c2q = tf.matmul(S_, q)
# q2c_size = [batch, n_c, d]
self.q2c = tf.matmul(tf.matmul(S_, S_T), c)
# attention_size = [4, batch, n_c, d]
attention_outputs = [c, self.c2q, c * self.c2q, c * self.q2c]
# Stacked Model Encoder Blocks实现：共7个encoder block，每个2个卷积层，卷积核数d=96
with tf.variable_scope("Model_Encoder_Layer"):
# c, self.c2q, c * self.c2q, c * self.q2c 按照通道维度进行合并
# input_shape = [batch, n_c, 4d]
inputs = tf.concat(attention_outputs, axis=-1)
# self.enc[i]_shape = [batch, n_c, d]
self.enc = [conv(inputs, d, name="input_projection")]
# 3个Stacked Model Encoder Blocks
for i in range(3):
if i % 2 == 0: # 每两层进行一次dropout
self.enc[i] = tf.nn.dropout(self.enc[i],
1.0 - self.dropout)
self.enc.append(
residual_block(
self.enc[i],
num_blocks=7,
num_conv_layers=2,
kernel_size=5,
mask=self.c_mask,
num_filters=d,
num_heads=nh,
seq_len=self.c_len,
scope="Model_Encoder",
bias=False,
reuse=True if i > 0 else
None, # 共享同一个Stacked Model Encoder Blocks的权重
dropout=self.dropout))
# 输出层实现：
with tf.variable_scope("Output_Layer"):
# 合并Stacked Model Encoder Blocks的第一个和第二个输出，并和并通道
# start_logits_shape = [batch, n_c, 1]
start_logits = tf.squeeze(
conv(tf.concat([self.enc[1], self.enc[2]], axis=-1),
1,
bias=False,
name="start_pointer"), -1)
# 合并Stacked Model Encoder Blocks的第一个和第三个输出，并和并通道
# end_logits_shape = [batch, n_c, 1]
end_logits = tf.squeeze(
conv(tf.concat([self.enc[1], self.enc[3]], axis=-1),
1,
bias=False,
name="end_pointer"), -1)
self.logits = [
mask_logits(start_logits, mask=self.c_mask),
mask_logits(end_logits, mask=self.c_mask)
]
logits1, logits2 = [l for l in self.logits]
# outer_shape = [bacth, n_c, n_c]
outer = tf.matmul(tf.expand_dims(tf.nn.softmax(logits1), axis=2),
tf.expand_dims(tf.nn.softmax(logits2), axis=1))
# 保留行坐标<纵坐标，且行坐标+纵坐标<=ans_limit的数据，其余置0
outer = tf.matrix_band_part(outer, 0, config.ans_limit)
# 最大值的行坐标，代表起始位置
self.yp1 = tf.argmax(tf.reduce_max(outer, axis=2), axis=1)
# 最大值的列坐标，代表结束位置
self.yp2 = tf.argmax(tf.reduce_max(outer, axis=1), axis=1)
losses = tf.nn.softmax_cross_entropy_with_logits(logits=logits1,
labels=self.y1)
losses2 = tf.nn.softmax_cross_entropy_with_logits(logits=logits2,
labels=self.y2)
self.loss = tf.reduce_mean(losses + losses2)
# L2正则化
if config.l2_norm is not None:
variables = tf.get_collection(tf.GraphKeys.REGULARIZATION_LOSSES)
l2_loss = tf.contrib.layers.apply_regularization(
regularizer, variables)
self.loss += l2_loss
if config.decay is not None:
self.var_ema = tf.train.ExponentialMovingAverage(config.decay)
ema_op = self.var_ema.apply(tf.trainable_variables())
# control_dependencies传入的操作是先于with后的操作
with tf.control_dependencies([ema_op]):
self.loss = tf.identity(self.loss)
self.assign_vars = []
for var in tf.global_variables():
v = self.var_ema.average(var)
if v:
self.assign_vars.append(tf.assign(var, v))
