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]