import nltk
import numpy as np
import tensorflow as tf
from tensorflow.python.ops.rnn_cell import BasicLSTMCell
from my.nltk_utils import tree2matrix, find_max_f1_subtree, load_compressed_tree, set_span
from tree.read_data import DataSet
from my.tensorflow import exp_mask, get_initializer
from my.tensorflow.nn import linear
from my.tensorflow.rnn import bidirectional_dynamic_rnn, dynamic_rnn
from my.tensorflow.rnn_cell import SwitchableDropoutWrapper, NoOpCell, TreeRNNCell
class Model(object):
def __init__(self, config):
self.config = config
self.global_step = tf.get_variable('global_step', shape=[], dtype='int32',
initializer=tf.constant_initializer(0), trainable=False)
# Define forward inputs here
N, M, JX, JQ, VW, VC, W, H = \
config.batch_size, config.max_num_sents, config.max_sent_size, \
config.max_ques_size, config.word_vocab_size, config.char_vocab_size, config.max_word_size, config.max_tree_height
self.x = tf.placeholder('int32', [None, M, JX], name='x')
self.cx = tf.placeholder('int32', [None, M, JX, W], name='cx')
self.q = tf.placeholder('int32', [None, JQ], name='q')
self.cq = tf.placeholder('int32', [None, JQ, W], name='cq')
self.tx = tf.placeholder('int32', [None, M, H, JX], name='tx')
self.tx_edge_mask = tf.placeholder('bool', [None, M, H, JX, JX], name='tx_edge_mask')
self.y = tf.placeholder('bool', [None, M, H, JX], name='y')
self.is_train = tf.placeholder('bool', [], name='is_train')
# Define misc
# Forward outputs / loss inputs
self.logits = None
self.yp = None
self.var_list = None
# Loss outputs
self.loss = None
self._build_forward()
self._build_loss()
self.ema_op = self._get_ema_op()
self.summary = tf.merge_all_summaries()
def _build_forward(self):
config = self.config
N, M, JX, JQ, VW, VC, d, dc, W = \
config.batch_size, config.max_num_sents, config.max_sent_size, \
config.max_ques_size, config.word_vocab_size, config.char_vocab_size, config.hidden_size, \
config.char_emb_size, config.max_word_size
H = config.max_tree_height
x_mask = self.x > 0
q_mask = self.q > 0
tx_mask = self.tx > 0 # [N, M, H, JX]
with tf.variable_scope("char_emb"):
char_emb_mat = tf.get_variable("char_emb_mat", shape=[VC, dc], dtype='float')
Acx = tf.nn.embedding_lookup(char_emb_mat, self.cx) # [N, M, JX, W, dc]
Acq = tf.nn.embedding_lookup(char_emb_mat, self.cq) # [N, JQ, W, dc]
filter = tf.get_variable("filter", shape=[1, config.char_filter_height, dc, d], dtype='float')
bias = tf.get_variable("bias", shape=[d], dtype='float')
strides = [1, 1, 1, 1]
Acx = tf.reshape(Acx, [-1, JX, W, dc])
Acq = tf.reshape(Acq, [-1, JQ, W, dc])
xxc = tf.nn.conv2d(Acx, filter, strides, "VALID") + bias # [N*M, JX, W/filter_stride, d]
qqc = tf.nn.conv2d(Acq, filter, strides, "VALID") + bias # [N, JQ, W/filter_stride, d]
xxc = tf.reshape(tf.reduce_max(tf.nn.relu(xxc), 2), [-1, M, JX, d])
qqc = tf.reshape(tf.reduce_max(tf.nn.relu(qqc), 2), [-1, JQ, d])
with tf.variable_scope("word_emb"):
if config.mode == 'train':
word_emb_mat = tf.get_variable("word_emb_mat", dtype='float', shape=[VW, config.word_emb_size], initializer=get_initializer(config.emb_mat))
else:
word_emb_mat = tf.get_variable("word_emb_mat", shape=[VW, config.word_emb_size], dtype='float')
Ax = tf.nn.embedding_lookup(word_emb_mat, self.x) # [N, M, JX, d]
Aq = tf.nn.embedding_lookup(word_emb_mat, self.q) # [N, JQ, d]
# Ax = linear([Ax], d, False, scope='Ax_reshape')
# Aq = linear([Aq], d, False, scope='Aq_reshape')
xx = tf.concat(3, [xxc, Ax]) # [N, M, JX, 2d]
qq = tf.concat(2, [qqc, Aq]) # [N, JQ, 2d]
D = d + config.word_emb_size
with tf.variable_scope("pos_emb"):
pos_emb_mat = tf.get_variable("pos_emb_mat", shape=[config.pos_vocab_size, d], dtype='float')
Atx = tf.nn.embedding_lookup(pos_emb_mat, self.tx) # [N, M, H, JX, d]
cell = BasicLSTMCell(D, state_is_tuple=True)
cell = SwitchableDropoutWrapper(cell, self.is_train, input_keep_prob=config.input_keep_prob)
x_len = tf.reduce_sum(tf.cast(x_mask, 'int32'), 2) # [N, M]
q_len = tf.reduce_sum(tf.cast(q_mask, 'int32'), 1) # [N]
with tf.variable_scope("rnn"):
(fw_h, bw_h), _ = bidirectional_dynamic_rnn(cell, cell, xx, x_len, dtype='float', scope='start') # [N, M, JX, 2d]
tf.get_variable_scope().reuse_variables()
(fw_us, bw_us), (_, (fw_u, bw_u)) = bidirectional_dynamic_rnn(cell, cell, qq, q_len, dtype='float', scope='start') # [N, J, d], [N, d]
u = (fw_u + bw_u) / 2.0
h = (fw_h + bw_h) / 2.0
with tf.variable_scope("h"):
no_op_cell = NoOpCell(D)
tree_rnn_cell = TreeRNNCell(no_op_cell, d, tf.reduce_max)
initial_state = tf.reshape(h, [N*M*JX, D]) # [N*M*JX, D]
inputs = tf.concat(4, [Atx, tf.cast(self.tx_edge_mask, 'float')]) # [N, M, H, JX, d+JX]
inputs = tf.reshape(tf.transpose(inputs, [0, 1, 3, 2, 4]), [N*M*JX, H, d + JX]) # [N*M*JX, H, d+JX]
length = tf.reshape(tf.reduce_sum(tf.cast(tx_mask, 'int32'), 2), [N*M*JX])
# length = tf.reshape(tf.reduce_sum(tf.cast(tf.transpose(tx_mask, [0, 1, 3, 2]), 'float'), 3), [-1])
h, _ = dynamic_rnn(tree_rnn_cell, inputs, length, initial_state=initial_state) # [N*M*JX, H, D]
h = tf.transpose(tf.reshape(h, [N, M, JX, H, D]), [0, 1, 3, 2, 4]) # [N, M, H, JX, D]
u = tf.expand_dims(tf.expand_dims(tf.expand_dims(u, 1), 1), 1) # [N, 1, 1, 1, 4d]
dot = linear(h * u, 1, True, squeeze=True, scope='dot') # [N, M, H, JX]
# self.logits = tf.reshape(dot, [N, M * H * JX])
self.logits = tf.reshape(exp_mask(dot, tx_mask), [N, M * H * JX]) # [N, M, H, JX]
self.yp = tf.reshape(tf.nn.softmax(self.logits), [N, M, H, JX])
def _build_loss(self):
config = self.config
N, M, JX, JQ, VW, VC = \
config.batch_size, config.max_num_sents, config.max_sent_size, \
config.max_ques_size, config.word_vocab_size, config.char_vocab_size
H = config.max_tree_height
ce_loss = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(
self.logits, tf.cast(tf.reshape(self.y, [N, M * H * JX]), 'float')))
tf.add_to_collection('losses', ce_loss)
self.loss = tf.add_n(tf.get_collection('losses'), name='loss')
tf.scalar_summary(self.loss.op.name, self.loss)
tf.add_to_collection('ema/scalar', self.loss)
def _get_ema_op(self):
ema = tf.train.ExponentialMovingAverage(self.config.decay)
ema_op = ema.apply(tf.get_collection("ema/scalar") + tf.get_collection("ema/histogram"))
for var in tf.get_collection("ema/scalar"):
ema_var = ema.average(var)
tf.scalar_summary(ema_var.op.name, ema_var)
for var in tf.get_collection("ema/histogram"):
ema_var = ema.average(var)
tf.histogram_summary(ema_var.op.name, ema_var)
return ema_op
def get_loss(self):
return self.loss
def get_global_step(self):
return self.global_step
def get_var_list(self):
return self.var_list
def get_feed_dict(self, batch, is_train, supervised=True):
assert isinstance(batch, DataSet)
config = self.config
N, M, JX, JQ, VW, VC, d, W, H = \
config.batch_size, config.max_num_sents, config.max_sent_size, \
config.max_ques_size, config.word_vocab_size, config.char_vocab_size, config.hidden_size, config.max_word_size, \
config.max_tree_height
feed_dict = {}
x = np.zeros([N, M, JX], dtype='int32')
cx = np.zeros([N, M, JX, W], dtype='int32')
q = np.zeros([N, JQ], dtype='int32')
cq = np.zeros([N, JQ, W], dtype='int32')
tx = np.zeros([N, M, H, JX], dtype='int32')
tx_edge_mask = np.zeros([N, M, H, JX, JX], dtype='bool')
feed_dict[self.x] = x
feed_dict[self.cx] = cx
feed_dict[self.q] = q
feed_dict[self.cq] = cq
feed_dict[self.tx] = tx
feed_dict[self.tx_edge_mask] = tx_edge_mask
feed_dict[self.is_train] = is_train
def _get_word(word):
d = batch.shared['word2idx']
for each in (word, word.lower(), word.capitalize(), word.upper()):
if each in d:
return d[each]
return 1
def _get_char(char):
d = batch.shared['char2idx']
if char in d:
return d[char]
return 1
def _get_pos(tree):
d = batch.shared['pos2idx']
if tree.label() in d:
return d[tree.label()]
return 1
for i, xi in enumerate(batch.data['x']):
for j, xij in enumerate(xi):
for k, xijk in enumerate(xij):
x[i, j, k] = _get_word(xijk)
for i, cxi in enumerate(batch.data['cx']):
for j, cxij in enumerate(cxi):
for k, cxijk in enumerate(cxij):
for l, cxijkl in enumerate(cxijk):
cx[i, j, k, l] = _get_char(cxijkl)
if l + 1 == config.max_word_size:
break
for i, qi in enumerate(batch.data['q']):
for j, qij in enumerate(qi):
q[i, j] = _get_word(qij)
for i, cqi in enumerate(batch.data['cq']):
for j, cqij in enumerate(cqi):
for k, cqijk in enumerate(cqij):
cq[i, j, k] = _get_char(cqijk)
if k + 1 == config.max_word_size:
break
for i, txi in enumerate(batch.data['stx']):
for j, txij in enumerate(txi):
txij_mat, txij_mask = tree2matrix(nltk.tree.Tree.fromstring(txij), _get_pos, row_size=H, col_size=JX)
tx[i, j, :, :], tx_edge_mask[i, j, :, :, :] = txij_mat, txij_mask
if supervised:
y = np.zeros([N, M, H, JX], dtype='bool')
feed_dict[self.y] = y
for i, yi in enumerate(batch.data['y']):
start_idx, stop_idx = yi
sent_idx = start_idx[0]
if start_idx[0] == stop_idx[0]:
span = [start_idx[1], stop_idx[1]]
else:
span = [start_idx[1], len(batch.data['x'][sent_idx])]
tree = nltk.tree.Tree.fromstring(batch.data['stx'][i][sent_idx])
set_span(tree)
best_subtree = find_max_f1_subtree(tree, span)
def _get_y(t):
return t == best_subtree
yij, _ = tree2matrix(tree, _get_y, H, JX, dtype='bool')
y[i, sent_idx, :, :] = yij
return feed_dict
