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))