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

Question

我试图使用StellarGraph库来训练一个基本的图形神经网络，特别是从本文提供的例子开始。

这个例子很好，但现在我想重复同样的努力，删除N折叠交叉验证，并提供特定的培训、验证和测试集。我试图用以下代码来实现这个目标：

# One hot encoding
graph_training_set_labels_encoded = pd.get_dummies(graphs_training_set_labels, drop_first=True)
graph_validation_set_labels_encoded = pd.get_dummies(graphs_validation_set_labels, drop_first=True)

graphs = graphs_training_set + graphs_validation_set

# Graph generator preparation
generator = PaddedGraphGenerator(graphs=graphs)

train_gen = generator.flow([x for x in range(0, len(graphs_training_set))],
                           targets=graph_training_set_labels_encoded,
                           batch_size=batch_size)

valid_gen = generator.flow([x for x in range(len(graphs_training_set),
                                            len(graphs_training_set) + len(graphs_validation_set))],
                          targets=graph_validation_set_labels_encoded,
                          batch_size=batch_size)

# 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=dropout_value)

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(adam_value), loss=binary_crossentropy, metrics=["acc"])

# GNN Training
history = model.fit(train_gen, epochs=num_epochs, validation_data=valid_gen, verbose=0, callbacks=[es])

# Calculate performance on the validation data
test_metrics = model.evaluate(valid_gen, verbose=0)
valid_acc = test_metrics[model.metrics_names.index("acc")]

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

其中graphs_training_set和graphs_validation_set是StellarDiGraphs的列表。

我能够运行这段代码，但结果是它提供了NaN。有什么问题吗？

因为这是我第一次使用StellarGraph，尤其是PaddedGraphGenerator。我认为我的错误依赖于这个生成器的使用，但是以不同的方式提供培训集和验证集并没有产生更好的结果。

提前谢谢你。

UPDATE修正了我在代码中的错误，正如here所指出的(感谢george123)。

0

Answer 1

我在StellarGraph documentation for PaddedGraphGenerator和GCN Neural Network Class GCNSupervisedGraphClassification中找到了一个解决方案。此外，我还在StellarGraph Issue Tracker上发现了一个类似的问题，并指出了解决方案。

# Graph generator preparation
generator = PaddedGraphGenerator(graphs=graphs)

train_gen = generator.flow([x for x in range(0, num_graphs_for_training)],
                            targets=training_graphs_labels,
                            batch_size=35)
valid_gen = generator.flow([x for x in range(num_graphs_for_training, num_graphs_for_training + num_graphs_for_validation)],
                            targets=validation_graphs_labels,
                            batch_size=35)

# Stopping criterium
es = EarlyStopping(monitor="val_loss",
                    min_delta=0.001,
                    patience=10,
                    restore_best_weights=True)

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

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)

# Let's create the Keras model and prepare it for training
model = Model(inputs=x_inp, outputs=predictions)
model.compile(optimizer=Adam(adam_value), loss=binary_crossentropy, metrics=["acc"])

# GNN Training
history = model.fit(train_gen, epochs=num_epochs, validation_data=valid_gen, verbose=1, callbacks=[es])

# Evaluate performance on the validation data
valid_metrics = model.evaluate(valid_gen, verbose=0)
valid_acc = valid_metrics[model.metrics_names.index("acc")]

# Define test set indices temporary vars
index_begin_test_set = num_graphs_for_training + num_graphs_for_validation
index_end_test_set = index_begin_test_set + num_graphs_for_testing

test_set_indices = [x for x in range(index_begin_test_set, index_end_test_set)]

# Evaluate performance on test set
generator_for_test_set = PaddedGraphGenerator(graphs=graphs)
test_gen = generator_for_test_set.flow(test_set_indices)
result = model.predict(test_gen)