def create_GAT_model(graph):
generator = FullBatchNodeGenerator(graph, sparse=False, method=None)
train_gen = generator.flow([0, 1], np.array([[1, 0], [0, 1]]))
gat = GAT(
layer_sizes=[2, 2],
generator=generator,
bias=False,
in_dropout=0,
attn_dropout=0,
activations=["elu", "softmax"],
normalize=None,
saliency_map_support=True,
)
for layer in gat._layers:
layer._initializer = "ones"
x_inp, x_out = gat.in_out_tensors()
keras_model = Model(inputs=x_inp, outputs=x_out)
return gat, keras_model, generator, train_gen
def create_GAT_model(graph):
generator = FullBatchNodeGenerator(graph, sparse=False)
train_gen = generator.flow([1, 2], np.array([[1, 0], [0, 1]]))
base_model = GAT(
layer_sizes=[8, 8, 2],
generator=generator,
bias=True,
in_dropout=0.5,
attn_dropout=0.5,
activations=["elu", "elu", "softmax"],
normalize=None,
)
x_inp, x_out = base_model.in_out_tensors()
keras_model = Model(inputs=x_inp, outputs=x_out)
return base_model, keras_model, generator, train_gen
generator = FullBatchNodeGenerator(G, method="gat")
train_gen = generator.flow(train_subjects.index, train_targets)
gat = GAT(
layer_sizes=[16, train_targets.shape[1]],
activations=["elu", "softmax"],
attn_heads=16,
generator=generator,
in_dropout=0.5,
attn_dropout=0.5,
normalize=None,
)
x_inp, predictions = gat.in_out_tensors()
model = Model(inputs=x_inp, outputs=predictions)
model.compile(
optimizer=optimizers.Adam(lr=0.005),
loss=losses.categorical_crossentropy,
metrics=["acc"],
)
val_gen = generator.flow(val_subjects.index, val_targets)
from tensorflow.keras.callbacks import EarlyStopping, ModelCheckpoint
if not os.path.isdir("logs"):
os.makedirs("logs")
es_callback = EarlyStopping(
