'pooling_method': "segment_mean"
},
use_set2set=False, # not in original paper
)
# Define learning rate and epochs
learning_rate_start = 1e-4
learning_rate_stop = 1e-5
epo = 500
epomin = 400
epostep = 10
# Compile model and optimizer
optimizer = tf.keras.optimizers.Adam(lr=learning_rate_start)
cbks = tf.keras.callbacks.LearningRateScheduler(
lr_lin_reduction(learning_rate_start, learning_rate_stop, epomin, epo))
model.compile(loss='binary_crossentropy',
optimizer=optimizer,
metrics=['accuracy'])
print(model.summary())
# Start and time training
start = time.process_time()
hist = model.fit(xtrain,
ytrain,
epochs=epo,
batch_size=32,
callbacks=[cbks],
validation_freq=epostep,
validation_data=(xtest, ytest),
verbose=2)
"gcn_args": {"units": 64, "use_bias": True, "activation": "relu", "has_unconnected": True,
"is_sorted": False, "pooling_method": 'segment_mean'}
}
model = make_gcn(**model_args)
model_node_weights = make_gcn_node_weights(**model_args)
# Set learning rate and epochs
learning_rate_start = 1e-3
learning_rate_stop = 1e-4
epo = 150
epomin = 100
epostep = 10
# Compile model with optimizer and loss
optimizer = tf.keras.optimizers.Adam(lr=learning_rate_start)
cbks = tf.keras.callbacks.LearningRateScheduler(lr_lin_reduction(learning_rate_start, learning_rate_stop, epomin, epo))
model.compile(loss='binary_crossentropy',
optimizer=optimizer,
weighted_metrics=['accuracy'])
print(model.summary())
# Start and time training
start = time.process_time()
hist = model.fit(xtrain, ytrain,
epochs=epo,
batch_size=32,
callbacks=[cbks],
validation_freq=epostep,
validation_data=(xtest, ytest),
verbose=2
)
