def test_predict_proba(self):
pickle_fname = 'abp_CV_fold_1_tlXlY_trn.pkl'
gcn_graph = GCNDataset.load_transkribus_pickle(pickle_fname)
gcn_graph_train = [gcn_graph[8], gcn_graph[18], gcn_graph[29]]
node_dim = gcn_graph[0].X.shape[1]
edge_dim = gcn_graph[0].E.shape[1] - 2.0
nb_class = gcn_graph[0].Y.shape[1]
gcn_model = GraphAttNet(node_dim,
nb_class,
num_layers=1,
learning_rate=0.01,
node_indim=-1,
nb_attention=3)
gcn_model.dropout_rate_node = 0.2
gcn_model.dropout_rate_attention = 0.2
gcn_model.create_model()
with tf.Session() as session:
session.run([gcn_model.init])
# Get the Test Prediction
gcn_model.train_lG(session, gcn_graph)
g_proba = gcn_model.prediction_prob(session, gcn_graph_train[1])
print(g_proba.shape)
print(type(g_proba))
print(gcn_graph_train[1].X.shape)
self.assertTrue(g_proba.shape == (gcn_graph_train[1].X.shape[0],
5))
def test_graphattnet_train_dropout(self):
pickle_fname = 'abp_CV_fold_1_tlXlY_trn.pkl'
gcn_graph = GCNDataset.load_transkribus_pickle(pickle_fname)
gcn_graph_train = [gcn_graph[8], gcn_graph[18], gcn_graph[29]]
node_dim = gcn_graph[0].X.shape[1]
edge_dim = gcn_graph[0].E.shape[1] - 2.0
nb_class = gcn_graph[0].Y.shape[1]
gcn_model = GraphAttNet(node_dim,
nb_class,
num_layers=1,
learning_rate=0.01,
node_indim=-1,
nb_attention=3)
gcn_model.dropout_rate_node = 0.2
gcn_model.dropout_rate_attention = 0.2
gcn_model.create_model()
with tf.Session() as session:
session.run([gcn_model.init])
# Get the Test Prediction
g_acc, node_acc = gcn_model.test_lG(session, gcn_graph_train)
print('Mean Accuracy', g_acc, node_acc)
gcn_model.train_lG(session, gcn_graph)
g_acc, node_acc = gcn_model.test_lG(session, gcn_graph_train)
print('Mean Accuracy', g_acc, node_acc)
def test_train_ensemble_NN_model(self):
#TODO Make a proper synthetic dataset for test Purpose
pickle_fname = 'abp_CV_fold_1_tlXlY_trn.pkl'
gcn_graph = GCNDataset.load_transkribus_pickle(pickle_fname)
gcn_graph_train = [gcn_graph[8], gcn_graph[18], gcn_graph[29]]
node_dim = gcn_graph[0].X.shape[1]
edge_dim = gcn_graph[0].E.shape[1] - 2.0
nb_class = gcn_graph[0].Y.shape[1]
gat_model = GraphAttNet(node_dim,
nb_class,
num_layers=1,
learning_rate=0.01,
node_indim=-1,
nb_attention=3)
gat_model.dropout_rate_node = 0.2
gat_model.dropout_rate_attention = 0.2
gat_model.create_model()
nb_layers = 3
lr = 0.001
nb_conv = 2
ecn_model = EdgeConvNet(
node_dim,
edge_dim,
nb_class,
num_layers=nb_layers,
learning_rate=lr,
mu=0.0,
node_indim=-1,
nconv_edge=nb_conv,
)
ecn_model.create_model()
#Check Graphs
#Are we recopying the models and graph definition implicitly ?
ensemble = EnsembleGraphNN([ecn_model, gat_model])
with tf.Session() as session:
session.run([ensemble.models[0].init])
for iter in range(500):
ensemble.train_lG(session, gcn_graph_train)
prediction = ensemble.predict_lG(session, gcn_graph_train)
print(prediction)
self.assertTrue(len(prediction) == len(gcn_graph_train))
print('Ensemble Prediction')
accs = ensemble.test_lG(session, gcn_graph_train)
print('Base Predictions')
for m in ensemble.models:
accs = m.test_lG(session, gcn_graph_train)
print(accs)
print(accs)