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在TensorFlow RNN 深度学习下 BiLSTM+CRF 实现 sequence labeling
双向LSTM+CRF 序列标注问题
源码
去年底样子一直在做NLP相关task,是个关于序列标注问题。这 sequence labeling属于NLP的经典问题了，开始尝试用HMM，哦不，用CRF做baseline，by the way, 用的CRF++。
关于CRF的理论就不再啰嗦了，街货。顺便提下，CRF比HMM在理论上以及实际效果上都要好不少。但我要说的是CRF跑我这task还是不太乐观。P值0.6样子，R低的离谱，所以F1很不乐观。mentor告诉我说是特征不足，师兄说是这个task本身就比较难做，F1低算是正常了。
CRF做完baseline后，一直在着手用BiLSTM+CRF跑 sequence labeling，奈何项目繁多，没有多余的精力去按照正常的计划做出来。后来还是一点一点的，按照大牛们的步骤以及参考现有的代码，把 BiLSTM+CRF的实现拿下了。后来发现，跑出来的效果也不太理想……可能是这个task确实变态……抑或模型还要加强吧~
这里对比下CRF与LSTM的cell，先说RNN吧，RNN其实是比CNN更适合做序列问题的模型，RNN隐层当前时刻的输入有一部分是前一时刻的隐层输出，这使得他能通过循环反馈连接看到前面的信息，将一段序列的前面的context capture 过来参与此刻的计算，并且还具备非线性的拟合能力，这都是CRF无法超越的地方。而LSTM的cell很好的将RNN的梯度弥散问题优化解决了，他对门卫gate说：老兄，有的不太重要的信息，你该忘掉就忘掉吧，免得占用现在的资源。而双向LSTM就更厉害了，不仅看得到过去，还能将未来的序列考虑进来，使得上下文信息充分被利用。而CRF，他不像LSTM能够考虑长远的上下文信息，它更多地考虑整个句子的局部特征的线性加权组合(通过特征模板扫描整个句子)，特别的一点，他计算的是联合概率，优化了整个序列，而不是拼接每个时刻的最优值。那么，将BILSTM与CRF一起就构成了还比较不错的组合，这目前也是学术界的流行做法~
另外针对目前的跑通结果提几个改进点：
1.+CNN，通过CNN的卷积操作去提取英文单词的字母细节。
2.+char representation,作用与上相似，提取更细粒度的细节。
3.more joint model to go.
fine，叨了不少。codes time:
完整代码以及相关预处理的数据请移步github:
requirements:
ubuntu14
python2.7
tensorflow 0.8
numpy
pandas0.15
BILSTM_CRF.py
import mathimport helperimport numpy as npimport tensorflow as tffrom tensorflow.models.rnn import rnn, rnn_cellclass BILSTM_CRF(object): def __init__(self, num_chars, num_classes, num_steps=200, num_epochs=100, embedding_matrix=None, is_training=True, is_crf=True, weight=False): # Parameter self.max_f1 = 0 self.learning_rate = 0.002 self.dropout_rate = 0.5 self.batch_size = 128 self.num_layers = 1 self.emb_dim = 100 self.hidden_dim = 100 self.num_epochs = num_epochs self.num_steps = num_steps self.num_chars = num_chars self.num_classes = num_classes # placeholder of x, y and weight self.inputs = tf.placeholder(tf.int32, [None, self.num_steps]) self.targets = tf.placeholder(tf.int32, [None, self.num_steps]) self.targets_weight = tf.placeholder(tf.float32, [None, self.num_steps]) self.targets_transition = tf.placeholder(tf.int32, [None]) # char embedding if embedding_matrix != None: self.embedding = tf.Variable(embedding_matrix, trainable=False, name="emb", dtype=tf.float32) else: self.embedding = tf.get_variable("emb", [self.num_chars, self.emb_dim]) self.inputs_emb = tf.nn.embedding_lookup(self.embedding, self.inputs) self.inputs_emb = tf.transpose(self.inputs_emb, [1, 0, 2]) self.inputs_emb = tf.reshape(self.inputs_emb, [-1, self.emb_dim]) self.inputs_emb = tf.split(0, self.num_steps, self.inputs_emb) # lstm cell lstm_cell_fw = tf.nn.rnn_cell.BasicLSTMCell(self.hidden_dim) lstm_cell_bw = tf.nn.rnn_cell.BasicLSTMCell(self.hidden_dim) # dropout if is_training: lstm_cell_fw = tf.nn.rnn_cell.DropoutWrapper(lstm_cell_fw, output_keep_prob=(1 - self.dropout_rate)) lstm_cell_bw = tf.nn.rnn_cell.DropoutWrapper(lstm_cell_bw, output_keep_prob=(1 - self.dropout_rate)) lstm_cell_fw = tf.nn.rnn_cell.MultiRNNCell([lstm_cell_fw] * self.num_layers) lstm_cell_bw = tf.nn.rnn_cell.MultiRNNCell([lstm_cell_bw] * self.num_layers) # get the length of each sample self.length = tf.reduce_sum(tf.sign(self.inputs), reduction_indices=1) self.length = tf.cast(self.length, tf.int32) # forward and backward self.outputs, _, _ = rnn.bidirectional_rnn( lstm_cell_fw, lstm_cell_bw, self.inputs_emb, dtype=tf.float32, sequence_length=self.length ) # softmax self.outputs = tf.reshape(tf.concat(1, self.outputs), [-1, self.hidden_dim * 2]) self.softmax_w = tf.get_variable("softmax_w", [self.hidden_dim * 2, self.num_classes]) self.softmax_b = tf.get_variable("softmax_b", [self.num_classes]) self.logits = tf.matmul(self.outputs, self.softmax_w) + self.softmax_b if not is_crf: pass else: self.tags_scores = tf.reshape(self.logits, [self.batch_size, self.num_steps, self.num_classes]) self.transitions = tf.get_variable("transitions", [self.num_classes + 1, self.num_classes + 1]) dummy_val = -1000 class_pad = tf.Variable(dummy_val * np.ones((self.batch_size, self.num_steps, 1)), dtype=tf.float32) self.observations = tf.concat(2, [self.tags_scores, class_pad]) begin_vec = tf.Variable(np.array([[dummy_val] * self.num_classes + [0] for _ in range(self.batch_size)]), trainable=False, dtype=tf.float32) end_vec = tf.Variable(np.array([[0] + [dummy_val] * self.num_classes for _ in range(self.batch_size)]), trainable=False, dtype=tf.float32) begin_vec = tf.reshape(begin_vec, [self.batch_size, 1, self.num_classes + 1]) end_vec = tf.reshape(end_vec, [self.batch_size, 1, self.num_classes + 1]) self.observations = tf.concat(1, [begin_vec, self.observations, end_vec]) self.mask = tf.cast(tf.reshape(tf.sign(self.targets),[self.batch_size * self.num_steps]), tf.float32) # point score self.point_score = tf.gather(tf.reshape(self.tags_scores, [-1]), tf.range(0, self.batch_size * self.num_steps) * self.num_classes + tf.reshape(self.targets,[self.batch_size * self.num_steps])) self.point_score *= self.mask # transition score self.trans_score = tf.gather(tf.reshape(self.transitions, [-1]), self.targets_transition) # real score self.target_path_score = tf.reduce_sum(self.point_score) + tf.reduce_sum(self.trans_score) # all path score self.total_path_score, self.max_scores, self.max_scores_pre = self.forward(self.observations, self.transitions, self.length) # loss self.loss = - (self.target_path_score - self.total_path_score) # summary self.train_summary = tf.scalar_summary("loss", self.loss) self.val_summary = tf.scalar_summary("loss", self.loss) self.optimizer = tf.train.AdamOptimizer(learning_rate=self.learning_rate).minimize(self.loss) def logsumexp(self, x, axis=None): x_max = tf.reduce_max(x, reduction_indices=axis, keep_dims=True) x_max_ = tf.reduce_max(x, reduction_indices=axis) return x_max_ + tf.log(tf.reduce_sum(tf.exp(x - x_max), reduction_indices=axis)) def forward(self, observations, transitions, length, is_viterbi=True, return_best_seq=True): length = tf.reshape(length, [self.batch_size]) transitions = tf.reshape(tf.concat(0, [transitions] * self.batch_size), [self.batch_size, 6, 6]) observations = tf.reshape(observations, [self.batch_size, self.num_steps + 2, 6, 1]) observations = tf.transpose(observations, [1, 0, 2, 3]) previous = observations[0, :, :, :] max_scores = [] max_scores_pre = [] alphas = [previous] for t in range(1, self.num_steps + 2): previous = tf.reshape(previous, [self.batch_size, 6, 1]) current = tf.reshape(observations[t, :, :, :], [self.batch_size, 1, 6]) alpha_t = previous + current + transitions if is_viterbi: max_scores.append(tf.reduce_max(alpha_t, reduction_indices=1)) max_scores_pre.append(tf.argmax(alpha_t, dimension=1)) alpha_t = tf.reshape(self.logsumexp(alpha_t, axis=1), [self.batch_size, 6, 1]) alphas.append(alpha_t) previous = alpha_t alphas = tf.reshape(tf.concat(0, alphas), [self.num_steps + 2, self.batch_size, 6, 1]) alphas = tf.transpose(alphas, [1, 0, 2, 3]) alphas = tf.reshape(alphas, [self.batch_size * (self.num_steps + 2), 6, 1]) last_alphas = tf.gather(alphas, tf.range(0, self.batch_size) * (self.num_steps + 2) + length) last_alphas = tf.reshape(last_alphas, [self.batch_size, 6, 1]) max_scores = tf.reshape(tf.concat(0, max_scores), (self.num_steps + 1, self.batch_size, 6)) max_scores_pre = tf.reshape(tf.concat(0, max_scores_pre), (self.num_steps + 1, self.batch_size, 6)) max_scores = tf.transpose(max_scores, [1, 0, 2]) max_scores_pre = tf.transpose(max_scores_pre, [1, 0, 2]) return tf.reduce_sum(self.logsumexp(last_alphas, axis=1)), max_scores, max_scores_pre def train(self, sess, save_file, X_train, y_train, X_val, y_val): saver = tf.train.Saver() char2id, id2char = helper.loadMap("char2id") label2id, id2label = helper.loadMap("label2id") merged = tf.merge_all_summaries() summary_writer_train = tf.train.SummaryWriter('loss_log/train_loss', sess.graph) summary_writer_val = tf.train.SummaryWriter('loss_log/val_loss', sess.graph) num_iterations = int(math.ceil(1.0 * len(X_train) / self.batch_size)) cnt = 0 for epoch in range(self.num_epochs): # shuffle train in each epoch sh_index = np.arange(len(X_train)) np.random.shuffle(sh_index) X_train = X_train[sh_index] y_train = y_train[sh_index] print "current epoch: %d" % (epoch) for iteration in range(num_iterations): # train X_train_batch, y_train_batch = helper.nextBatch(X_train, y_train, start_index=iteration * self.batch_size, batch_size=self.batch_size) y_train_weight_batch = 1 + np.array((y_train_batch == label2id['B']) | (y_train_batch == label2id['E']), float) transition_batch = helper.getTransition(y_train_batch) _, loss_train, max_scores, max_scores_pre, length, train_summary =\ sess.run([ self.optimizer, self.loss, self.max_scores, self.max_scores_pre, self.length, self.train_summary ], feed_dict={ self.targets_transition:transition_batch, self.inputs:X_train_batch, self.targets:y_train_batch, self.targets_weight:y_train_weight_batch }) predicts_train = self.viterbi(max_scores, max_scores_pre, length, predict_size=self.batch_size) if iteration % 10 == 0: cnt += 1 precision_train, recall_train, f1_train = self.evaluate(X_train_batch, y_train_batch, predicts_train, id2char, id2label) summary_writer_train.add_summary(train_summary, cnt) print "iteration: %5d, train loss: %5d, train precision: %.5f, train recall: %.5f, train f1: %.5f" % (iteration, loss_train, precision_train, recall_train, f1_train) # validation if iteration % 100 == 0: X_val_batch, y_val_batch = helper.nextRandomBatch(X_val, y_val, batch_size=self.batch_size) y_val_weight_batch = 1 + np.array((y_val_batch == label2id['B']) | (y_val_batch == label2id['E']), float) transition_batch = helper.getTransition(y_val_batch) loss_val, max_scores, max_scores_pre, length, val_summary =\ sess.run([ self.loss, self.max_scores, self.max_scores_pre, self.length, self.val_summary ], feed_dict={ self.targets_transition:transition_batch, self.inputs:X_val_batch, self.targets:y_val_batch, self.targets_weight:y_val_weight_batch }) predicts_val = self.viterbi(max_scores, max_scores_pre, length, predict_size=self.batch_size) precision_val, recall_val, f1_val = self.evaluate(X_val_batch, y_val_batch, predicts_val, id2char, id2label) summary_writer_val.add_summary(val_summary, cnt) print "iteration: %5d, valid loss: %5d, valid precision: %.5f, valid recall: %.5f, valid f1: %.5f" % (iteration, loss_val, precision_val, recall_val, f1_val) if f1_val > self.max_f1: self.max_f1 = f1_val save_path = saver.save(sess, save_file) print "saved the best model with f1: %.5f" % (self.max_f1) def test(self, sess, X_test, X_test_str, output_path): char2id, id2char = helper.loadMap("char2id") label2id, id2label = helper.loadMap("label2id") num_iterations = int(math.ceil(1.0 * len(X_test) / self.batch_size)) print "number of iteration: " + str(num_iterations) with open(output_path, "wb") as outfile: for i in range(num_iterations): print "iteration: " + str(i + 1) results = [] X_test_batch = X_test[i * self.batch_size : (i + 1) * self.batch_size] X_test_str_batch = X_test_str[i * self.batch_size : (i + 1) * self.batch_size] if i == num_iterations - 1 and len(X_test_batch) < self.batch_size: X_test_batch = list(X_test_batch) X_test_str_batch = list(X_test_str_batch) last_size = len(X_test_batch) X_test_batch += [[0 for j in range(self.num_steps)] for i in range(self.batch_size - last_size)] X_test_str_batch += [['x' for j in range(self.num_steps)] for i in range(self.batch_size - last_size)] X_test_batch = np.array(X_test_batch) X_test_str_batch = np.array(X_test_str_batch) results = self.predictBatch(sess, X_test_batch, X_test_str_batch, id2label) results = results[:last_size] else: X_test_batch = np.array(X_test_batch) results = self.predictBatch(sess, X_test_batch, X_test_str_batch, id2label) for i in range(len(results)): doc = ''.join(X_test_str_batch[i]) outfile.write(doc + "<@>" +" ".join(results[i]).encode("utf-8") + "\n") def viterbi(self, max_scores, max_scores_pre, length, predict_size=128): best_paths = [] for m in range(predict_size): path = [] last_max_node = np.argmax(max_scores[m][length[m]]) # last_max_node = 0 for t in range(1, length[m] + 1)[::-1]: last_max_node = max_scores_pre[m][t][last_max_node] path.append(last_max_node) path = path[::-1] best_paths.append(path) return best_paths def predictBatch(self, sess, X, X_str, id2label): results = [] length, max_scores, max_scores_pre = sess.run([self.length, self.max_scores, self.max_scores_pre], feed_dict={self.inputs:X}) predicts = self.viterbi(max_scores, max_scores_pre, length, self.batch_size) for i in range(len(predicts)): x = ''.join(X_str[i]).decode("utf-8") y_pred = ''.join([id2label[val] for val in predicts[i] if val != 5 and val != 0]) entitys = helper.extractEntity(x, y_pred) results.append(entitys) return results def evaluate(self, X, y_true, y_pred, id2char, id2label): precision = -1.0 recall = -1.0 f1 = -1.0 hit_num = 0 pred_num = 0 true_num = 0 for i in range(len(y_true)): x = ''.join([str(id2char[val].encode("utf-8")) for val in X[i]]) y = ''.join([str(id2label[val].encode("utf-8")) for val in y_true[i]]) y_hat = ''.join([id2label[val] for val in y_pred[i] if val != 5]) true_labels = helper.extractEntity(x, y) pred_labels = helper.extractEntity(x, y_hat) hit_num += len(set(true_labels) & set(pred_labels)) pred_num += len(set(pred_labels)) true_num += len(set(true_labels)) if pred_num != 0: precision = 1.0 * hit_num / pred_num if true_num != 0: recall = 1.0 * hit_num / true_num if precision > 0 and recall > 0: f1 = 2.0 * (precision * recall) / (precision + recall) return precision, recall, f1 util.py
#encoding:utf-8import reimport osimport csvimport timeimport pickleimport numpy as npimport pandas as pddef getEmbedding(infile_path="embedding"): char2id, id_char = loadMap("char2id") row_index = 0 with open(infile_path, "rb") as infile: for row in infile: row = row.strip() row_index += 1 if row_index == 1: num_chars = int(row.split()[0]) emb_dim = int(row.split()[1]) emb_matrix = np.zeros((len(char2id.keys()), emb_dim)) continue items = row.split() char = items[0] emb_vec = [float(val) for val in items[1:]] if char in char2id: emb_matrix[char2id[char]] = emb_vec return emb_matrixdef nextBatch(X, y, start_index, batch_size=128): last_index = start_index + batch_size X_batch = list(X[start_index:min(last_index, len(X))]) y_batch = list(y[start_index:min(last_index, len(X))]) if last_index > len(X): left_size = last_index - (len(X)) for i in range(left_size): index = np.random.randint(len(X)) X_batch.append(X[index]) y_batch.append(y[index]) X_batch = np.array(X_batch) y_batch = np.array(y_batch) return X_batch, y_batchdef nextRandomBatch(X, y, batch_size=128): X_batch = [] y_batch = [] for i in range(batch_size): index = np.random.randint(len(X)) X_batch.append(X[index]) y_batch.append(y[index]) X_batch = np.array(X_batch) y_batch = np.array(y_batch) return X_batch, y_batch# use "0" to padding the sentencedef padding(sample, seq_max_len): for i in range(len(sample)): if len(sample[i]) < seq_max_len: sample[i] += [0 for _ in range(seq_max_len - len(sample[i]))] return sampledef prepare(chars, labels, seq_max_len, is_padding=True): X = [] y = [] tmp_x = [] tmp_y = [] for record in zip(chars, labels): c = record[0] l = record[1] # empty line if c == -1: if len(tmp_x) <= seq_max_len: X.append(tmp_x) y.append(tmp_y) tmp_x = [] tmp_y = [] else: tmp_x.append(c) tmp_y.append(l) if is_padding: X = np.array(padding(X, seq_max_len)) else: X = np.array(X) y = np.array(padding(y, seq_max_len)) return X, ydef extractEntity(sentence, labels): entitys = [] re_entity = re.compile(r'BM*E') m = re_entity.search(labels) while m: entity_labels = m.group() start_index = labels.find(entity_labels) entity = sentence[start_index:start_index + len(entity_labels)] labels = list(labels) # replace the "BM*E" with "OO*O" labels[start_index: start_index + len(entity_labels)] = ['O' for i in range(len(entity_labels))] entitys.append(entity) labels = ''.join(labels) m = re_entity.search(labels) return entitysdef loadMap(token2id_filepath): if not os.path.isfile(token2id_filepath): print "file not exist, building map" buildMap() token2id = {} id2token = {} with open(token2id_filepath) as infile: for row in infile: row = row.rstrip().decode("utf-8") token = row.split('\t')[0] token_id = int(row.split('\t')[1]) token2id[token] = token_id id2token[token_id] = token return token2id, id2tokendef saveMap(id2char, id2label): with open("char2id", "wb") as outfile: for idx in id2char: outfile.write(id2char[idx] + "\t" + str(idx) + "\r\n") with open("label2id", "wb") as outfile: for idx in id2label: outfile.write(id2label[idx] + "\t" + str(idx) + "\r\n") print "saved map between token and id"def buildMap(train_path="train.in"): df_train = pd.read_csv(train_path, delimiter='\t', quoting=csv.QUOTE_NONE, skip_blank_lines=False, header=None, names=["char", "label"]) chars = list(set(df_train["char"][df_train["char"].notnull()])) labels = list(set(df_train["label"][df_train["label"].notnull()])) char2id = dict(zip(chars, range(1, len(chars) + 1))) label2id = dict(zip(labels, range(1, len(labels) + 1))) id2char = dict(zip(range(1, len(chars) + 1), chars)) id2label = dict(zip(range(1, len(labels) + 1), labels)) id2char[0] = " " id2label[0] = " " char2id[" "] = 0 label2id[" "] = 0 id2char[len(chars) + 1] = " " char2id[" "] = len(chars) + 1 saveMap(id2char, id2label) return char2id, id2char, label2id, id2labeldef getTrain(train_path, val_path, train_val_ratio=0.99, use_custom_val=False, seq_max_len=200): char2id, id2char, label2id, id2label = buildMap(train_path) df_train = pd.read_csv(train_path, delimiter='\t', quoting=csv.QUOTE_NONE, skip_blank_lines=False, header=None, names=["char", "label"]) # map the char and label into id df_train["char_id"] = df_train.char.map(lambda x : -1 if str(x) == str(np.nan) else char2id[x]) df_train["label_id"] = df_train.label.map(lambda x : -1 if str(x) == str(np.nan) else label2id[x]) # convert the data in maxtrix X, y = prepare(df_train["char_id"], df_train["label_id"], seq_max_len) # shuffle the samples num_samples = len(X) indexs = np.arange(num_samples) np.random.shuffle(indexs) X = X[indexs] y = y[indexs] if val_path != None: X_train = X y_train = y X_val, y_val = getTest(val_path, is_validation=True, seq_max_len=seq_max_len) else: # split the data into train and validation set X_train = X[:int(num_samples * train_val_ratio)] y_train = y[:int(num_samples * train_val_ratio)] X_val = X[int(num_samples * train_val_ratio):] y_val = y[int(num_samples * train_val_ratio):] print "train size: %d, validation size: %d" %(len(X_train), len(y_val)) return X_train, y_train, X_val, y_valdef getTest(test_path="test.in", is_validation=False, seq_max_len=200): char2id, id2char = loadMap("char2id") label2id, id2label = loadMap("label2id") df_test = pd.read_csv(test_path, delimiter='\t', quoting=csv.QUOTE_NONE, skip_blank_lines=False, header=None, names=["char", "label"]) def mapFunc(x, char2id): if str(x) == str(np.nan): return -1 elif x.decode("utf-8") not in char2id: return char2id[" "] else: return char2id[x.decode("utf-8")] df_test["char_id"] = df_test.char.map(lambda x:mapFunc(x, char2id)) df_test["label_id"] = df_test.label.map(lambda x : -1 if str(x) == str(np.nan) else label2id[x]) if is_validation: X_test, y_test = prepare(df_test["char_id"], df_test["label_id"], seq_max_len) return X_test, y_test else: df_test["char"] = df_test.char.map(lambda x : -1 if str(x) == str(np.nan) else x) X_test, _ = prepare(df_test["char_id"], df_test["char_id"], seq_max_len) X_test_str, _ = prepare(df_test["char"], df_test["char_id"], seq_max_len, is_padding=False) print "test size: %d" %(len(X_test)) return X_test, X_test_strdef getTransition(y_train_batch): transition_batch = [] for m in range(len(y_train_batch)): y = [5] + list(y_train_batch[m]) + [0] for t in range(len(y)): if t + 1 == len(y): continue i = y[t] j = y[t + 1] if i == 0: break transition_batch.append(i * 6 + j) transition_batch = np.array(transition_batch) return transition_batch train.py import timeimport helperimport argparseimport numpy as npimport pandas as pdimport tensorflow as tffrom BILSTM_CRF import BILSTM_CRF# python train.py train.in model -v validation.in -c char_emb -e 10 -g 2parser = argparse.ArgumentParser()parser.add_argument("train_path", help="the path of the train file")parser.add_argument("save_path", help="the path of the saved model")parser.add_argument("-v","--val_path", help="the path of the validation file", default=None)parser.add_argument("-e","--epoch", help="the number of epoch", default=100, type=int)parser.add_argument("-c","--char_emb", help="the char embedding file", default=None)parser.add_argument("-g","--gpu", help="the id of gpu, the default is 0", default=0, type=int)args = parser.parse_args()train_path = args.train_pathsave_path = args.save_pathval_path = args.val_pathnum_epochs = args.epochemb_path = args.char_embgpu_config = "/cpu:0"#gpu_config = "/gpu:"+str(args.gpu)num_steps = 200 # it must consist with the teststart_time = time.time()print "preparing train and validation data"X_train, y_train, X_val, y_val = helper.getTrain(train_path=train_path, val_path=val_path, seq_max_len=num_steps)char2id, id2char = helper.loadMap("char2id")label2id, id2label = helper.loadMap("label2id")num_chars = len(id2char.keys())num_classes = len(id2label.keys())if emb_path != None: embedding_matrix = helper.getEmbedding(emb_path)else: embedding_matrix = Noneprint "building model"config = tf.ConfigProto(allow_soft_placement=True)with tf.Session(config=config) as sess: with tf.device(gpu_config): initializer = tf.random_uniform_initializer(-0.1, 0.1) with tf.variable_scope("model", reuse=None, initializer=initializer): model = BILSTM_CRF(num_chars=num_chars, num_classes=num_classes, num_steps=num_steps, num_epochs=num_epochs, embedding_matrix=embedding_matrix, is_training=True) print "training model" tf.initialize_all_variables().run() model.train(sess, save_path, X_train, y_train, X_val, y_val) print "final best f1 is: %f" % (model.max_f1) end_time = time.time() print "time used %f(hour)" % ((end_time - start_time) / 3600) test.py import timeimport helperimport argparseimport numpy as npimport pandas as pdimport tensorflow as tffrom BILSTM_CRF import BILSTM_CRF# python test.py model test.in test.out -c char_emb -g 2parser = argparse.ArgumentParser()parser.add_argument("model_path", help="the path of model file")parser.add_argument("test_path", help="the path of test file")parser.add_argument("output_path", help="the path of output file")parser.add_argument("-c","--char_emb", help="the char embedding file", default=None)parser.add_argument("-g","--gpu", help="the id of gpu, the default is 0", default=0, type=int)args = parser.parse_args()model_path = args.model_pathtest_path = args.test_pathoutput_path = args.output_pathgpu_config = "/cpu:0"emb_path = args.char_embnum_steps = 200 # it must consist with the trainstart_time = time.time()print "preparing test data"X_test, X_test_str = helper.getTest(test_path=test_path, seq_max_len=num_steps)char2id, id2char = helper.loadMap("char2id")label2id, id2label = helper.loadMap("label2id")num_chars = len(id2char.keys())num_classes = len(id2label.keys())if emb_path != None: embedding_matrix = helper.getEmbedding(emb_path)else: embedding_matrix = Noneprint "building model"config = tf.ConfigProto(allow_soft_placement=True)with tf.Session(config=config) as sess: with tf.device(gpu_config): initializer = tf.random_uniform_initializer(-0.1, 0.1) with tf.variable_scope("model", reuse=None, initializer=initializer): model = BILSTM_CRF(num_chars=num_chars, num_classes=num_classes, num_steps=num_steps, embedding_matrix=embedding_matrix, is_training=False) print "loading model parameter" saver = tf.train.Saver() saver.restore(sess, model_path) print "testing" model.test(sess, X_test, X_test_str, output_path) end_time = time.time() print "time used %f(hour)" % ((end_time - start_time) / 3600) 相关预处理的数据请参考github:
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