# observation:[batch_size,num_step,output_dims] 神經網絡輸出
# transition:[output_dims,output_dims] 轉移矩陣
# pi:[batch_size,output_dims] 初始機率矩陣
def lstm_crf_viterbi(observation,transition,pi):
batch_size = observation.shape[].value
num_step = observation.shape[].value
output_len = transition.shape[].value
previous = [] # [B,O]
#記錄最終路徑
all_path_tag_sequence = []
batch_scores = []
#記錄最佳路徑
batch_argmax = [[] for b in xrange(batch_size)]
for b in xrange(batch_size):
previous.append(tf.transpose([observation[b][]+pi[b]]))
for b in xrange(batch_size):
for x in range(,num_step):
r_pre =tf.transpose(tf.convert_to_tensor([previous[b] for i in range(output_len)]))
r_obs = tf.convert_to_tensor([observation[b][x] for i in range(output_len)])
scores = r_pre + transition + r_obs
scores = tf.convert_to_tensor(scores)
batch_argmax[b].append(tf.squeeze(tf.argmax(scores,)))
previous[b] = tf.reduce_max(scores,)
previous[b] = tf.squeeze(previous[b])
print(batch_argmax)
#回溯 (僅最高分)
for b in xrange(batch_size):
best_path = [tf.argmax(previous[b])]
for x in xrange(num_step-,-,-):
best_path.insert(,batch_argmax[b][x][best_path[]])
all_path_tag_sequence.append(best_path)
return previous,all_path_tag_sequence
#previous:最高分
#all_path_tag_sequence:最高分路徑