如何在StellarGraph PaddedGraphGenerator中提供特定的培训、验证和测试集-

Question

我试图使用StellarGraph库来训练一个图形卷积神经网络。我想运行这个例子https://stellargraph.readthedocs.io/en/stable/demos/graph-classification/gcn-supervised-graph-classification.html，但没有N折叠交叉验证，通过提供我自己的培训，验证和测试集。这是我正在使用的代码(取自这个post)

generator = PaddedGraphGenerator(graphs=graphs)

train_gen = generator.flow([x for x in range(0, len(graphs_train))],
                           targets=graphs_train_labels,
                           batch_size=35)

test_gen = generator.flow([x for x in range(len(graphs_train),len(graphs_train) + len(graphs_test))],
                          targets=graphs_test_labels,
                          batch_size=35)

# Stopping criterium
es = EarlyStopping(monitor="val_loss",
                   min_delta=0,
                   patience=20,
                   restore_best_weights=True)

# Model definition
gc_model = GCNSupervisedGraphClassification(layer_sizes=[64, 64],
                                            activations=["relu", "relu"],
                                            generator=generator,
                                            dropout=0.5)

x_inp, x_out = gc_model.in_out_tensors()
predictions = Dense(units=32, activation="relu")(x_out)
predictions = Dense(units=16, activation="relu")(predictions)
predictions = Dense(units=1, activation="sigmoid")(predictions)

# Creating Keras model and preparing it for training
model = Model(inputs=x_inp, outputs=predictions)
model.compile(optimizer=Adam(0.001), loss=binary_crossentropy, metrics=["acc"])

# GNN Training
history = model.fit(train_gen, epochs=10, validation_data=test_gen, verbose=1)
model.fit(x=graphs_train,
          y=graphs_train_labels,
          epochs=10,
          verbose=1,
          callbacks=[es])


# Calculate performance on the validation data
test_metrics = model.evaluate(valid_gen, verbose=1)
valid_acc = test_metrics[model.metrics_names.index("acc")]

print(f"Test Accuracy model = {valid_acc}")

但到了最后，我得到了这个错误

ValueError:未能找到能够处理输入的数据适配器：(包含类型{"}“的值)，

我在这里错过了什么？是因为我创建图表的方式吗？在我的例子中，图是一个包含恒星图的列表。

Answer 1

问题解决了。我在打电话

model.fit(x=graphs_train,
          y=graphs_train_labels,
          epochs=10,
          verbose=1,
          callbacks=[es])

在线后

history = model.fit(train_gen, epochs=10, validation_data=test_gen, verbose=1)