Spektral GCN Model

spektral-gcn.py

import tensorflow as tf
from tensorflow.keras.losses import BinaryCrossentropy
from tensorflow.keras.optimizers import Adam
from spektral.models.gcn import GCN

seed = 42
tf.random.set_seed(seed=seed) 

# 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.type(torch.float32) / np.count_nonzero(mask)

weights_tr, weights_te = (mask_to_weights(mask) for mask in (train_mask, test_mask))

# instantiate the model
model = GCN(n_labels=dataset.n_labels, 
            n_input_channels=dataset.n_node_features)

model.compile(optimizer=Adam(learning_rate),
              loss=BinaryCrossentropy(reduction="sum"),
              weighted_metrics=["acc"])