AdjToSpTensor()])
# We convert the binary masks to sample weights so that we can compute the
# average loss over the nodes (following original implementation by
# Kipf & Welling)
def mask_to_weights(mask):
return mask.astype(np.float32) / np.count_nonzero(mask)
weights_tr, weights_va, weights_te = (mask_to_weights(mask)
for mask in (dataset.mask_tr,
dataset.mask_va,
dataset.mask_te))
model = GCN(n_labels=dataset.n_labels,
n_input_channels=dataset.n_node_features)
model.compile(
optimizer=Adam(learning_rate),
loss=CategoricalCrossentropy(reduction="sum"),
weighted_metrics=["acc"],
)
# Train model
loader_tr = SingleLoader(dataset, sample_weights=weights_tr)
loader_va = SingleLoader(dataset, sample_weights=weights_va)
model.fit(
loader_tr.load(),
steps_per_epoch=loader_tr.steps_per_epoch,
validation_data=loader_va.load(),
validation_steps=loader_va.steps_per_epoch,
epochs=epochs,
from spektral.layers import GCNConv
from spektral.models.gcn import GCN
from spektral.transforms import AdjToSpTensor, LayerPreprocess
from spektral.utils import tic, toc
tf.random.set_seed(seed=0) # make weight initialization reproducible
# Load data
dataset = Cora(normalize_x=True,
transforms=[LayerPreprocess(GCNConv),
AdjToSpTensor()])
graph = dataset[0]
x, a, y = graph.x, graph.a, graph.y
mask_tr, mask_va, mask_te = dataset.mask_tr, dataset.mask_va, dataset.mask_te
model = GCN(n_labels=dataset.n_labels)
optimizer = Adam(lr=1e-2)
loss_fn = CategoricalCrossentropy()
# Training step
@tf.function
def train():
with tf.GradientTape() as tape:
predictions = model([x, a], training=True)
loss = loss_fn(y[mask_tr], predictions[mask_tr])
loss += sum(model.losses)
gradients = tape.gradient(loss, model.trainable_variables)
optimizer.apply_gradients(zip(gradients, model.trainable_variables))
return loss
from spektral.layers import GCNConv
from spektral.models.gcn import GCN
from spektral.transforms import AdjToSpTensor, LayerPreprocess
from spektral.utils import tic, toc
tf.random.set_seed(seed=0) # make weight initialization reproducible
# Load data
dataset = Cora(normalize_x=True,
transforms=[LayerPreprocess(GCNConv),
AdjToSpTensor()])
graph = dataset[0]
x, a, y = graph.x, graph.a, graph.y
mask_tr, mask_va, mask_te = dataset.mask_tr, dataset.mask_va, dataset.mask_te
model = GCN(n_labels=dataset.n_labels,
n_input_channels=dataset.n_node_features)
optimizer = Adam(lr=1e-2)
loss_fn = CategoricalCrossentropy()
# Training step
@tf.function
def train():
with tf.GradientTape() as tape:
predictions = model([x, a], training=True)
loss = loss_fn(y[mask_tr], predictions[mask_tr])
loss += sum(model.losses)
gradients = tape.gradient(loss, model.trainable_variables)
optimizer.apply_gradients(zip(gradients, model.trainable_variables))
return loss