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(
embedding_dims, class_num, # init_weights, weights_trainable=True) # model = TextCNN.TextCNN(maxlen, vocab_size, embedding_dims, class_num) # 构建优化器 # lr = tf.keras.optimizers.schedules.PolynomialDecay(0.01, decay_steps=18000, # end_learning_rate=0.0001, # cycle=False) optimizer = optim.AdamWarmup(learning_rate=0.01, decay_steps=15000) # 构建损失函数 mask_sparse_categotical_loss = Losess.MaskSparseCategoricalCrossentropy( from_logits=False) 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", checkpoint_name="model.ckpt", max_to_keep=3) Batch = 0 for X, token_type_id, input_mask, Y in load.load_train():
num_class = 6 drop_rate = 0.5 epoch = 200 early_stopping = 10 penalty = 5e-4 # cora, pubmed, citeseer data = Planetoid(name="citeseer", loop=True, norm=True) features, adj, y_train, y_val, y_test, train_mask, val_mask, test_mask = data.load( ) model = GCNLayer(hidden_dim, num_class, drop_rate) optimizer = tf.keras.optimizers.Adam(0.01) crossentropy = Losess.MaskCategoricalCrossentropy() accscore = Metric.MaskAccuracy() stop_monitor = EarlyStopping(monitor="loss", patience=early_stopping) # --------------------------------------------------------- # For train for p in range(epoch): t = time.time() with tf.GradientTape() as tape: predict = model(features, adj, training=True) loss = crossentropy(y_train, predict, train_mask) loss += penalty * tf.nn.l2_loss(model.variables[0]) grads = tape.gradient(loss, model.variables) optimizer.apply_gradients(grads_and_vars=zip(grads, model.variables))