def predict(self, inputs, is_training=False):
output = self(inputs, is_training=is_training)
return output
model = BERT_NER(param)
model.build(input_shape=(3, param.batch_size, param.maxlen))
model.summary()
# 写入数据 通过check_exist=True参数控制仅在第一次调用时写入
writer = TFWriter(param.maxlen, vocab_file,
modes=["valid"], check_exist=False)
ner_load = TFLoader(param.maxlen, param.batch_size, epoch=3)
# Metrics
f1score = Metric.SparseF1Score(average="macro")
precsionscore = Metric.SparsePrecisionScore(average="macro")
recallscore = Metric.SparseRecallScore(average="macro")
accuarcyscore = Metric.SparseAccuracy()
# 保存模型
checkpoint = tf.train.Checkpoint(model=model)
checkpoint.restore(tf.train.latest_checkpoint('./save'))
# For test model
Batch = 0
f1s = []
precisions = []
recalls = []
model = BERT_NER(param)
model.build(input_shape=(3, param.batch_size, param.maxlen))
model.summary()
# 写入数据 通过check_exist=True参数控制仅在第一次调用时写入
writer = TFWriter(param.maxlen,
vocab_file,
modes=["valid"],
task='cls',
check_exist=True)
load = TFLoader(param.maxlen, param.batch_size, task='cls')
# Metrics
f1score = Metric.SparseF1Score(average="macro")
precsionscore = Metric.SparsePrecisionScore(average="macro")
recallscore = Metric.SparseRecallScore(average="macro")
accuarcyscore = Metric.SparseAccuracy()
# 保存模型
checkpoint = tf.train.Checkpoint(model=model)
checkpoint.restore(tf.train.latest_checkpoint('./save'))
# For train model
Batch = 0
f1s = []
precisions = []
maxlen = 128
batch_size = 64
embedding_dims = 100
vocab_file = "Input/vocab.txt"
word2vec = "./corpus/word2vec.vector"
class_num = 2
vocab_size = 30522 # line in vocab.txt
# 写入数据 通过check_exist=True参数控制仅在第一次调用时写入
writer = TFWriter(maxlen,
vocab_file,
modes=["train"],
task='cls',
check_exist=False)
load = TFLoader(maxlen, batch_size, task='cls', epoch=3)
# init_weights = writer.get_init_weight(word2vec,
# vocab_size,
# embedding_dims)
model = TextCNN.TextCNN(
maxlen,
vocab_size,
embedding_dims,
class_num,
# init_weights,
weights_trainable=True)
# model = TextCNN.TextCNN(maxlen, vocab_size, embedding_dims, class_num)
mask_sparse_categotical_loss = tf.keras.losses.SparseCategoricalCrossentropy(
from_logits=False)
# # 初始化参数
bert_init_weights_from_checkpoint(model,
model_path,
param.num_hidden_layers,
pooler=True)
# 写入数据 通过check_exist=True参数控制仅在第一次调用时写入
writer = TFWriter(param.maxlen,
vocab_file,
modes=["train"],
task='cls',
check_exist=True)
load = TFLoader(param.maxlen, param.batch_size, task='cls', epoch=5)
# 训练模型
# 使用tensorboard
summary_writer = tf.summary.create_file_writer("./tensorboard")
# Metrics
f1score = Metric.SparseF1Score(average="macro")
precsionscore = Metric.SparsePrecisionScore(average="macro")
recallscore = Metric.SparseRecallScore(average="macro")
accuarcyscore = Metric.SparseAccuracy()
# 保存模型
checkpoint = tf.train.Checkpoint(model=model)
manager = tf.train.CheckpointManager(checkpoint,
directory="./save",