edge_weight
]), ragged_tensor_from_nested_numpy([edge_index]) # One graph in batch
# Set training data. But requires mask and batch-dimension of 1
xtrain = nodes, edges, edge_indices
ytrain = np.expand_dims(labels, axis=0) # One graph in batch
model = make_gcn(
input_node_shape=[None, 1433],
input_edge_shape=[None, 1],
# Output
output_embedd={"output_mode": 'node'},
output_mlp={
"use_bias": [True, True, False],
"units": [64, 16, 7],
"activation": ['relu', 'relu', 'softmax']
},
# model specs
depth=3,
gcn_args={
"units": 124,
"use_bias": True,
"activation": "relu",
"has_unconnected": True
})
# Set learning rate and epochs
learning_rate_start = 1e-3
learning_rate_stop = 1e-4
epo = 300
epomin = 260
epostep = 10
ytrain = np.expand_dims(labels_train, axis=-1)
ytest = np.expand_dims(labels_test, axis=-1)
model_args = {"input_node_shape": [None, 14],
"input_edge_shape": [None, 1],
"input_embedd": {'input_node_vocab': 55, "input_node_embedd": 64},
# Output
"output_embedd": {"output_mode": 'graph', "output_type": 'padded'},
"output_mlp": {"use_bias": [True, True, False], "units": [140, 70, 1],
"activation": ['relu', 'relu', 'sigmoid']},
# model specs
"depth": 3,
"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'])
ytest = np.expand_dims(labels_test, axis=-1)
model = make_gcn(
input_node_shape=[None, 14],
input_edge_shape=[None, 1],
input_embedd={
'input_node_vocab': 55,
"input_node_embedd": 64
},
# Output
output_embedd={
"output_mode": 'graph',
"output_type": 'padded'
},
output_mlp={
"use_bias": [True, True, False],
"units": [140, 70, 1],
"activation": ['relu', 'relu', 'sigmoid']
},
# model specs
depth=3,
gcn_args={
"units": 64,
"use_bias": True,
"activation": "relu",
"has_unconnected": True,
"is_sorted": False,
"pooling_method": 'segment_mean'
})
# Set learning rate and epochs
