crf_lianxi
crf:
# @TIME : 2019/3/17 下午10:52
# @File : chainer_implementation.py
# 参考：https://github.com/createmomo/CRF-Layer-on-the-Top-of-BiLSTM
import numpy as np
import chainer
import chainer.links as L
import chainer.functions as F
from chainer import variable
from chainer.functions.math import sum as _sum
from chainer.functions.math import exponential as _exponential
# More details: https://github.com/createmomo/CRF-Layer-on-the-Top-of-BiLSTM
# This code is modified based on https://github.com/glample/tagger
# 1） 总体结构: 初始化 + 计算Loss + 预测
class My_CRF(L.CRF1d):
def __init__(self, n_label):
"""
randomly initialize transition score
"""
super(My_CRF, self).__init__(n_label)
with self.init_scope():
'''
[Initialization]
'''
# Generate random values for transition matrix.
# The shape of transition matrix is (n_label+2, n_label+2).
# "2" means the extra added labels, START and END. (see 3.2)
drange = np.sqrt(6. / (np.sum((n_label + 2, n_label + 2))))
value = drange * np.random.uniform(low=-1.0, high=1.0, size=(n_label + 2, n_label + 2))
transitions = np.array(value, dtype=np.float32)
self.cost = variable.Parameter(transitions)
# The number of unique labels in training data set (e.g B-Person, I-Person, O)
self.n_label = n_label
# The small value will fill the expanded emission score matrix as described in 3.2
self.small = -1000
def __call__(self, xs, ys):
# [ Loss Function]
# Compute CRF Loss
"""
for sentence in training_data_set:
1) The real path score of current sentence according the true labels
2) The log total score of all the possible paths of current sentence
3) Compute the cos on this sentence using result 1) and 2)
4) Total_Cost += Cost of this sentence
"""
'''
:param xs: the outputs of BiLSTM layer (the emission score matrix)
:param ys: the ground truth labels
:return: CRF loss
'''
'''
Loss Function
'''
# Assign new id for extra added labels (START and END)
b_id = np.array([self.n_label], dtype='i')
e_id = np.array([self.n_label + 1], dtype='i')
total_loss = 0.0
small = self.small
# Compute crf loss for each sentence
for xs_i, ys_i in zip(xs, ys):
s_len = len(xs_i) # how many words does the sentence have
# Expand the emission score matrix by adding two extra labels (START and END).
# For more details, please see the example in 3.2
b_s = np.array([[small] * self.n_label + [0, small]]).astype(np.float32)
e_s = np.array([[small] * self.n_label + [small, 0]]).astype(np.float32)
observations = F.concat((xs_i, small * np.ones((s_len, 2), dtype='f')), axis=1)
observations = F.concat((b_s, observations, e_s), axis=0)
# Compute the real path score according the ground truth labels (see 2.4)
# Emission score of the real path
real_path_score = _sum.sum(xs_i[list(range(s_len)), ys_i])
# Transition score of the real path
padded_tags_ids = F.concat((b_id, ys_i, e_id), axis=0)
real_path_score += _sum.sum(self.cost[
padded_tags_ids[list(range(s_len + 1))].data,
padded_tags_ids[
[current_item + 1 for current_item in range(s_len + 1)]].data
])
# Compute the score of all the possible paths of current sentence (see 2.5)
all_paths_scores = self.forward(observations, self.cost)
# The crf cost of current sentence (see 2.5)
current_cost = - (real_path_score - all_paths_scores)
total_loss += current_cost
return total_loss
def log_sum_exp(self, x, axis=None):
'''
Compute the sum of scores in log space (see 2.5).
This function is used in forward.
'''
xmax = F.max(x, axis=axis, keepdims=True)
xmax_ = F.max(x, axis=axis)
second_item = _exponential.log(_sum.sum(_exponential.exp(x - F.broadcast_to(xmax,x.shape)),axis=axis))
return xmax_ + second_item
def forward(self, observations, transitions,
viterbi=False,
return_best_sequence=False
):
'''
:param observations: (see 2.5) In 2.5, 'obs' are the observations here.
:param transitions: Transition score matrix
:param viterbi: When the viterbi and return_best_sequence are true, this method will return the predicted best paths.
If false, this function will return the sum of scores in log space
:param return_best_sequence: Please see above.
:return: The sum of scores in log space or the predicted best sequence
'''
'''
This function is described in detail in 2.5 and 2.6.
'''
def recurrence(obs, previous, transitions):
previous = previous.reshape((previous.shape[0], 1))
obs = obs.reshape((1, obs.shape[0]))
if viterbi: # Please see 2.6
scores = F.broadcast_to(previous, (self.n_label + 2, self.n_label + 2)) + F.broadcast_to(obs, (
self.n_label + 2, self.n_label + 2)) + transitions
scores = scores.data
out = scores.max(axis=0)
if return_best_sequence:
out2 = scores.argmax(axis=0)
out2 = np.array(out2, dtype='i')
return out, out2
else: # Please see 2.5 (Return the sum of scores in log space)
previous = F.broadcast_to(previous, (self.n_label + 2, self.n_label + 2))
obs = F.broadcast_to(obs, (self.n_label + 2, self.n_label + 2))
return self.log_sum_exp(previous + obs + transitions, axis=0)
def mini_function_for_best_sequence(beta_i, previous):
return beta_i[previous]
if return_best_sequence: # Return the best predicted path for one sentence (see 2.6)
initial_0 = observations[0]
alpha_0 = np.array(initial_0.data, dtype='f')
alpha_0 = F.expand_dims(alpha_0, axis=0)
alpha_1 = None
flag = True
for obs in observations[1:]:
initial_0, initial_1 = recurrence(obs, initial_0, transitions)
alpha_0 = F.vstack((alpha_0, F.expand_dims(initial_0, axis=0)))
if flag == True:
alpha_1 = np.array(initial_1, dtype='i')
alpha_1 = F.expand_dims(alpha_1, axis=0)
flag = False
alpha_1 = F.vstack((alpha_1, F.expand_dims(initial_1, axis=0)))
alpha_0 = alpha_0.data[1:]
initial_beta = np.argmax(alpha_0[-1])
initial_beta = initial_beta.astype('i')
sequence = np.array(initial_beta, dtype='i')
sequence = F.expand_dims(sequence, axis=0)
for item in alpha_1.data[::-1].astype('i'):
initial_beta = mini_function_for_best_sequence(item, initial_beta)
sequence = F.concat((sequence, F.expand_dims(np.array(initial_beta), axis=0)), axis=0)
sequence = sequence[::-1][2:-1]
sequence = sequence.reshape(1, sequence.shape[0])
return sequence[0] # Return best path
else: # Please see 2.5 (Return the sum of scores in log space)
initial = observations[0]
alpha = []
alpha.append(initial)
for obs in observations[1:]:
initial = recurrence(obs, initial, transitions)
alpha.append(initial)
alpha = alpha[1:]
return self.log_sum_exp(alpha[-1], axis=0)
def argmax(self, xs):
'''
:param xs: The list of new sentences
:return: Predicted labels for the new sentences
'''
best_sequence = []
small = self.small
# Predict the labels for new sentences (Please see 2.6)
for xs_i in xs:
s_len = len(xs_i)
b_s = np.array([[small] * self.n_label + [0, small]]).astype(np.float32)
e_s = np.array([[small] * self.n_label + [small, 0]]).astype(np.float32)
observations = F.concat((xs_i, small * np.ones((s_len, 2), dtype='f')), axis=1)
observations = F.concat((b_s, observations, e_s), axis=0)
current_best_sequence = self.forward(observations, self.cost, viterbi=True, return_best_sequence=True)
best_sequence.append(current_best_sequence.data)
return best_sequence
# main
n_label = 2
a = np.random.uniform(-1, 1, n_label).astype('f')
b = np.random.uniform(-1, 1, n_label).astype('f')
x1 = np.stack([b, a])
x2 = np.stack([a])
xs = [x1, x2]
ys = [np.random.randint(n_label,size = x.shape[0],dtype='i') for x in xs]
my_crf = My_CRF(n_label)
loss = my_crf(xs,ys)
print('Ground Truth:')
for i,y in enumerate(ys):
print('\tsentence {0}: [{1}]'.format(str(i),' '.join([str(label) for label in y])))
from chainer import optimizers
optimizer = optimizers.SGD(lr=0.01)
optimizer.setup(my_crf)
optimizer.add_hook(chainer.optimizer.GradientClipping(5.0))
print('Predictions:')
for epoch_i in range(201):
with chainer.using_config('train', True):
loss = my_crf(xs,ys)
# update parameters
optimizer.target.zerograds()
loss.backward()
optimizer.update()
with chainer.using_config('train', False):
if epoch_i % 50 == 0:
print('\tEpoch {0}: (loss={1})'.format(str(epoch_i),str(loss.data)))
for i, prediction in enumerate(my_crf.argmax(xs)):
print('\t\tsentence {0}: [{1}]'.format(str(i), ' '.join([str(label) for label in prediction])))
# 结果：
# Ground Truth:
# sentence 0: [1 1]
# sentence 1: [0]
# Predictions:
# Epoch 0: (loss=1.901068)
# sentence 0: [1 0]
# sentence 1: [0]
# Epoch 50: (loss=1.419822)
# sentence 0: [1 0]
# sentence 1: [0]
# Epoch 100: (loss=1.1503992)
# sentence 0: [1 1]
# sentence 1: [0]
# Epoch 150: (loss=0.97296774)
# sentence 0: [1 1]
# sentence 1: [0]
# Epoch 200: (loss=0.8429147)
# sentence 0: [1 1]
# sentence 1: [0]
#
# Process finished with exit code 0