def cnn_for_sentence_encoding( # kim
rep_tensor, rep_mask, filter_sizes=(3,4,5), num_filters=200, scope=None,
is_train=None, keep_prob=1., wd=0.):
"""
:param rep_tensor:
:param rep_mask:
:param filter_sizes:
:param num_filters:
:param scope:
:param is_train:
:param keep_prob:
:param wd:
:return:
"""
bs, sl, vec = tf.shape(rep_tensor)[0], tf.shape(rep_tensor)[1], tf.shape(rep_tensor)[2]
ivec = rep_tensor.get_shape().as_list()[2]
with tf.variable_scope(scope or 'cnn_for_sentence_encoding'):
rep_tensor = mask_for_high_rank(rep_tensor, rep_mask)
rep_tensor_expand = tf.expand_dims(rep_tensor, 3)
rep_tensor_expand_dp = dropout(rep_tensor_expand, keep_prob, is_train)
# Create a convolution + maxpool layer for each filter size
pooled_outputs = []
for i, filter_size in enumerate(filter_sizes):
with tf.variable_scope("conv-maxpool-%s" % filter_size):
# Convolution Layer
filter_shape = [filter_size, ivec, 1, num_filters]
W = tf.get_variable('W', filter_shape, tf.float32)
b = tf.get_variable('b', [num_filters], tf.float32)
conv = tf.nn.conv2d(
rep_tensor_expand_dp,
W,
strides=[1, 1, 1, 1],
padding="VALID",
name="conv")
# Apply nonlinearity
h = tf.nn.relu(tf.nn.bias_add(conv, b), name="relu") # bs, sl-fs+1, 1, fn
# Maxpooling over the outputs
# pooled = tf.nn.max_pool(
# h,
# ksize=[1, sl - filter_size + 1, 1, 1],
# strides=[1, 1, 1, 1],
# padding='VALID',
# name="pool")
pooled = tf.reduce_max(h, 1, True) # bs, 1, 1, fn
pooled_outputs.append(pooled)
# Combine all the pooled features
num_filters_total = num_filters * len(filter_sizes)
h_pool = tf.concat(pooled_outputs, 3)
h_pool_flat = tf.reshape(h_pool, [-1, num_filters_total])
if wd > 0.:
add_reg_without_bias()
return h_pool_flat
def hierarchical_cnn_res_gate(
rep_tensor, rep_mask, n_gram=5, layer_num=5, hn=None, scope=None,
is_train=None, keep_prob=1., wd=0.):
# padding
if n_gram % 2 == 1:
padding_front = padding_back = int((n_gram - 1) / 2)
else:
padding_front = (n_gram - 1) // 2
padding_back = padding_front + 1
padding = [[0, 0], [padding_front, padding_back], [0, 0], [0, 0]]
# lengths
bs, sl, vec = tf.shape(rep_tensor)[0], tf.shape(rep_tensor)[1], tf.shape(rep_tensor)[2]
org_ivec = rep_tensor.get_shape().as_list()[2]
ivec = hn or org_ivec
with tf.variable_scope(scope or 'cnn_for_sentence_encoding'):
rep_tensor = mask_for_high_rank(rep_tensor, rep_mask) # bs, sl, hn
iter_rep = rep_tensor
layer_res_list = []
for layer_idx in range(layer_num):
with tf.variable_scope("conv_maxpool_%s" % layer_idx):
iter_rep_etd = tf.expand_dims(iter_rep, 3) # bs,sl,hn,1
iter_rep_etd_dp = dropout(iter_rep_etd, keep_prob, is_train)
# Convolution Layer
feature_size = org_ivec if layer_idx == 0 else ivec
filter_shape = [n_gram, feature_size, 1, 2 * ivec]
W = tf.get_variable('W', filter_shape, tf.float32)
b = tf.get_variable('b', [2 * ivec], tf.float32)
iter_rep_etd_pad = tf.pad(iter_rep_etd_dp, padding)
conv = tf.nn.conv2d(
iter_rep_etd_pad,
W,
strides=[1, 1, 1, 1],
padding="VALID",
name="conv")
map_res = tf.nn.relu(tf.nn.bias_add(conv, b), name="relu") # bs,sl,1,2hn
map_res = tf.squeeze(map_res, [2]) # bs,sl,2*hn
# gate
map_res_a, map_res_b = tf.split(map_res, num_or_size_splits=2, axis=2)
iter_rep = map_res_a * tf.nn.sigmoid(map_res_b)
# res
if len(layer_res_list) > 0:
iter_rep = iter_rep + layer_res_list[-1]
layer_res_list.append(iter_rep)
if wd > 0.:
add_reg_without_bias()
return iter_rep
def cnn_for_context_fusion(
rep_tensor, rep_mask, filter_sizes=(3,4,5), num_filters=200, scope=None,
is_train=None, keep_prob=1., wd=0.):
bs, sl, vec = tf.shape(rep_tensor)[0], tf.shape(rep_tensor)[1], tf.shape(rep_tensor)[2]
ivec = rep_tensor.get_shape().as_list()[2]
with tf.variable_scope(scope or 'cnn_for_sentence_encoding'):
rep_tensor = mask_for_high_rank(rep_tensor, rep_mask)
rep_tensor_expand = tf.expand_dims(rep_tensor, 3) # bs, sl,
rep_tensor_expand_dp = dropout(rep_tensor_expand, keep_prob, is_train)
# Create a convolution + maxpool layer for each filter size
pooled_outputs = []
for i, filter_size in enumerate(filter_sizes):
with tf.variable_scope("conv-maxpool-%s" % filter_size):
# Convolution Layer
filter_shape = [filter_size, ivec, 1, num_filters]
W = tf.get_variable('W', filter_shape, tf.float32)
b = tf.get_variable('b', [num_filters], tf.float32)
# # pading in the sequence
if filter_size % 2 == 1:
padding_front = padding_back = int((filter_size - 1) / 2)
else:
padding_front = (filter_size - 1) // 2
padding_back = padding_front + 1
padding = [[0, 0], [padding_front, padding_back], [0, 0], [0, 0]]
rep_tensor_expand_dp_pad = tf.pad(rep_tensor_expand_dp, padding)
conv = tf.nn.conv2d(
rep_tensor_expand_dp_pad,
W,
strides=[1, 1, 1, 1],
padding="VALID",
name="conv")
# Apply nonlinearity
h = tf.nn.relu(tf.nn.bias_add(conv, b), name="relu") # bs, sl, 1, fn
h_squeeze = tf.squeeze(h, [2]) # bs, sl, fn
pooled_outputs.append(h_squeeze)
# Combine all the pooled features
result = tf.concat(pooled_outputs, 2) # bs, sl, 3 * fn
if wd > 0.:
add_reg_without_bias()
return result
def visit_sa_with_dense(rep_tensor,
keep_prob=1.,
is_train=None,
wd=0.,
activation='relu',
hn=None,
is_scale=True,
is_plus_sa=True):
batch_size, sw_len, vec_size = tf.shape(rep_tensor)[0], tf.shape(
rep_tensor)[1], tf.shape(rep_tensor)[2]
ivec = rep_tensor.get_shape().as_list()[2]
ivec = hn or ivec
with tf.variable_scope('temporal_attention'):
# mask generation
attn_mask = tf.cast(
tf.diag(-tf.ones([sw_len], tf.int32)) + 1,
tf.bool) # batch_size, code_len, code_len
# non-linear for context
rep_map = bn_dense_layer(rep_tensor, ivec, True, 0., 'bn_dense_map',
activation, False, wd, keep_prob, is_train)
rep_map_tile = tf.tile(tf.expand_dims(rep_map, 1),
[1, sw_len, 1, 1]) # bs,sl,sl,vec
rep_map_dp = dropout(rep_map, keep_prob, is_train)
# attention
with tf.variable_scope('attention'): # bs,sl,sl,vec
f_bias = tf.get_variable('f_bias', [ivec], tf.float32,
tf.constant_initializer(0.))
dependent = linear(
rep_map_dp, ivec, False,
scope='linear_dependent') # batch_size, code_len, vec_size
dependent_etd = tf.expand_dims(
dependent, 1) # batch_size, code_len,code_len, vec_size
head = linear(
rep_map_dp, ivec, False,
scope='linear_head') # batch_size, code_len, vec_size
head_etd = tf.expand_dims(
head, 2) # batch_size, code_len,code_len, vec_size
if is_plus_sa:
attention_fact = dependent_etd + head_etd + f_bias
else:
return rep_map
if is_scale:
logits = scaled_tanh(attention_fact, 5.0) # bs,sl,sl,vec
else:
logits = linear(tf.nn.tanh(attention_fact),
ivec,
True,
scope='linear_attn_fact')
logits_masked = exp_mask_for_high_rank(logits, attn_mask)
attn_score = tf.nn.softmax(logits_masked, 2) # bs,sl,sl,vec
attn_score = mask_for_high_rank(attn_score, attn_mask)
attn_result = tf.reduce_sum(attn_score * rep_map_tile,
2) # bs,sl,vec
with tf.variable_scope('output'):
o_bias = tf.get_variable('o_bias', [ivec], tf.float32,
tf.constant_initializer(0.))
# input gate
fusion_gate = tf.nn.sigmoid(
linear(rep_map, ivec, True, 0., 'linear_fusion_i', False, wd,
keep_prob, is_train) +
linear(attn_result, ivec, True, 0., 'linear_fusion_a', False,
wd, keep_prob, is_train) + o_bias)
output = fusion_gate * rep_map + (1 - fusion_gate) * attn_result
return output
def directional_attention_with_dense(rep_tensor,
rep_mask,
direction=None,
scope=None,
keep_prob=1.,
is_train=None,
wd=0.,
activation='elu',
tensor_dict=None,
name=None,
hn=None):
bs, sl, vec = tf.shape(rep_tensor)[0], tf.shape(rep_tensor)[1], tf.shape(
rep_tensor)[2]
ivec = rep_tensor.get_shape().as_list()[2]
ivec = hn or ivec
with tf.variable_scope(scope or 'directional_attention_%s' % direction
or 'diag'):
# mask generation
sl_indices = tf.range(sl, dtype=tf.int32)
sl_col, sl_row = tf.meshgrid(sl_indices, sl_indices)
if direction is None:
direct_mask = tf.cast(
tf.diag(-tf.ones([sl], tf.int32)) + 1, tf.bool)
else:
if direction == 'forward':
direct_mask = tf.greater(sl_row, sl_col)
else:
direct_mask = tf.greater(sl_col, sl_row)
direct_mask_tile = tf.tile(tf.expand_dims(direct_mask, 0),
[bs, 1, 1]) # bs,sl,sl
rep_mask_tile = tf.tile(tf.expand_dims(rep_mask, 1),
[1, sl, 1]) # bs,sl,sl
attn_mask = tf.logical_and(direct_mask_tile, rep_mask_tile) # bs,sl,sl
# non-linear
rep_map = bn_dense_layer(rep_tensor, ivec, True, 0., 'bn_dense_map',
activation, False, wd, keep_prob, is_train)
rep_map_tile = tf.tile(tf.expand_dims(rep_map, 1),
[1, sl, 1, 1]) # bs,sl,sl,vec
rep_map_dp = dropout(rep_map, keep_prob, is_train)
# attention
with tf.variable_scope('attention'): # bs,sl,sl,vec
f_bias = tf.get_variable('f_bias', [ivec], tf.float32,
tf.constant_initializer(0.))
dependent = linear(rep_map_dp,
ivec,
False,
scope='linear_dependent') # bs,sl,vec
dependent_etd = tf.expand_dims(dependent, 1) # bs,1,sl,vec
head = linear(rep_map_dp, ivec, False,
scope='linear_head') # bs,sl,vec
head_etd = tf.expand_dims(head, 2) # bs,sl,1,vec
logits = scaled_tanh(dependent_etd + head_etd + f_bias,
5.0) # bs,sl,sl,vec
logits_masked = exp_mask_for_high_rank(logits, attn_mask)
attn_score = tf.nn.softmax(logits_masked, 2) # bs,sl,sl,vec
attn_score = mask_for_high_rank(attn_score, attn_mask)
attn_result = tf.reduce_sum(attn_score * rep_map_tile,
2) # bs,sl,vec
with tf.variable_scope('output'):
o_bias = tf.get_variable('o_bias', [ivec], tf.float32,
tf.constant_initializer(0.))
# input gate
fusion_gate = tf.nn.sigmoid(
linear(rep_map, ivec, True, 0., 'linear_fusion_i', False, wd,
keep_prob, is_train) +
linear(attn_result, ivec, True, 0., 'linear_fusion_a', False,
wd, keep_prob, is_train) + o_bias)
output = fusion_gate * rep_map + (1 - fusion_gate) * attn_result
output = mask_for_high_rank(output, rep_mask)
# save attn
if tensor_dict is not None and name is not None:
tensor_dict[name + '_dependent'] = dependent
tensor_dict[name + '_head'] = head
tensor_dict[name] = attn_score
tensor_dict[name + '_gate'] = fusion_gate
return output
