def train(
train_nodes,
train_targets,
val_nodes,
val_targets,
generator,
dropout,
layer_sizes,
learning_rate,
activations,
num_epochs,
):
"""
Train a GCN model on the specified graph G with given parameters, evaluate it, and save the model.
Args:
train_nodes: A list of train nodes
train_targets: Labels of train nodes
val_nodes: A list of validation nodes
val_targets: Labels of validation nodes
generator: A FullBatchNodeGenerator
dropout: The dropout (0->1) Initial Learning rate
layer_sizes: A list of number of hidden nodes in each layer
learning_rate: Initial Learning rate
activations: A list of number of activation functions in each layer
"""
train_gen = generator.flow(train_nodes, train_targets)
val_gen = generator.flow(val_nodes, val_targets)
gcnModel = GCN(
layer_sizes,
generator,
bias=True,
dropout=dropout,
kernel_regularizer=regularizers.l2(5e-4),
activations=activations,
)
# Expose the input and output sockets of the model:
x_inp, x_out = gcnModel.node_model()
# Create Keras model for training
model = keras.Model(inputs=x_inp, outputs=x_out)
model.compile(
loss=losses.categorical_crossentropy,
metrics=[metrics.categorical_accuracy],
optimizer=optimizers.Adam(lr=learning_rate),
)
# Train model
history = model.fit_generator(
train_gen, epochs=num_epochs, validation_data=val_gen, verbose=2, shuffle=False
)
return model
def create_GCN_model(graph):
generator = FullBatchNodeGenerator(graph)
train_gen = generator.flow([1, 2], np.array([[1, 0], [0, 1]]))
base_model = GCN(
layer_sizes=[8, 2],
generator=generator,
bias=True,
dropout=0.5,
activations=["elu", "softmax"],
)
x_inp, x_out = base_model.node_model()
keras_model = Model(inputs=x_inp, outputs=x_out)
return base_model, keras_model, generator, train_gen
def create_GCN_model_sparse(graph):
generator = FullBatchNodeGenerator(graph, sparse=True, method="gcn")
train_gen = generator.flow([0, 1], np.array([[1, 0], [0, 1]]))
layer_sizes = [2, 2]
gcn = GCN(
layer_sizes=layer_sizes,
activations=["elu", "elu"],
generator=generator,
dropout=0.3,
kernel_regularizer=regularizers.l2(5e-4),
)
for layer in gcn._layers:
layer._initializer = "ones"
x_inp, x_out = gcn.node_model()
keras_model = Model(inputs=x_inp, outputs=x_out)
return gcn, keras_model, generator, train_gen