def call(self, node, adj, batch, edge_attr, training=True):
feature = tf.nn.embedding_lookup(self.embedding, node)
predict = self.model(feature, adj, batch, edge_attr, training=training)
return predict
def predict(self, nodes, adj, batch, edge_attr, training=False):
return self(nodes, adj, batch, edge_attr, training)
accs_all = []
for i in range(10):
model = TextSAGEynamicWeight(dim, num_class, drop_rate)
optimize = tf.optimizers.Adam(lr)
cross_entropy = Losess.MaskSparseCategoricalCrossentropy()
acc_score = Metric.SparseAccuracy()
stop_monitor = EarlyStopping(monitor="loss",
patience=10,
restore_best_weights=False)
for i in range(epoch):
t = time.time()
for node, label, adj, edge_attr, batch in data.load(nodes[:-500],
adjs[:-500],
labels[:-500],
edge_attrs[:-500],
batchs[:-500],
batch_size=32):
node, label, adj, edge_attr, batch = merge_batch_graph(
node, label, adj, edge_attr, batch)
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 = []
accuracys = []
for X, token_type_id, input_mask, Y in ner_load.load_valid():
predict = model.predict([X, token_type_id, input_mask]) # [batch_size, max_length,label_size]
# predict = tf.argmax(output, -1)
f1s.append(f1score(Y, predict))
precisions.append(precsionscore(Y, predict))
model = BERT_NER(param)
model.build(input_shape=(4, 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=True)
ner_load = TFLoader(param.maxlen, param.batch_size)
# Metrics
f1score = Metric.SparseF1Score("macro", predict_sparse=True)
precsionscore = Metric.SparsePrecisionScore("macro", predict_sparse=True)
recallscore = Metric.SparseRecallScore("macro", predict_sparse=True)
accuarcyscore = Metric.SparseAccuracy(predict_sparse=True)
# 保存模型
checkpoint = tf.train.Checkpoint(model=model)
checkpoint.restore(tf.train.latest_checkpoint('./save'))
# For test model
# print(dir(checkpoint))
Batch = 0
f1s = []
precisions = []
recalls = []
accuracys = []
for X, token_type_id, input_mask, Y in ner_load.load_valid():
predict = model.predict([X, token_type_id, input_mask,
Y]) # [batch_size, max_length,label_size]
