################################################################################
A, X, y, _, _, _ = citation.load_data('cora')
A_norm = normalized_adjacency(A)
X = X.todense()
F = X.shape[-1]
y = np.argmax(y, axis=-1)
n_clusters = y.max() + 1
################################################################################
# MODEL
################################################################################
X_in = Input(shape=(F, ), name='X_in')
A_in = Input(shape=(None, ), name='A_in', sparse=True)
X_1 = GraphConvSkip(16, activation='elu')([X_in, A_in])
X_1, A_1, S = MinCutPool(n_clusters, return_mask=True)([X_1, A_in])
model = Model([X_in, A_in], [X_1, S])
################################################################################
# TRAINING
################################################################################
# Setup
inputs = [X, sp_matrix_to_sp_tensor(A_norm)]
opt = tf.keras.optimizers.Adam(learning_rate=lr)
# Fit model
loss_history = []
nmi_history = []
for _ in tqdm(range(epochs)):
outs = train_step(inputs)
################################################################################
X_in = Input(tensor=tf.placeholder(tf.float32, shape=(None, F), name='X_in'))
A_in = Input(tensor=tf.sparse_placeholder(tf.float32, shape=(None, None)),
sparse=True)
I_in = Input(
tensor=tf.placeholder(tf.int32, shape=(None, ), name='segment_ids_in'))
target = Input(
tensor=tf.placeholder(tf.float32, shape=(None, n_out), name='target'))
# Block 1
gc1 = GraphConvSkip(n_channels,
activation=activ,
kernel_regularizer=l2(GNN_l2))([X_in, A_in])
X_1, A_1, I_1, M_1 = MinCutPool(
k=int(average_N // 2),
h=mincut_H,
activation=activ,
kernel_regularizer=l2(pool_l2))([gc1, A_in, I_in])
# Block 2
gc2 = GraphConvSkip(n_channels,
activation=activ,
kernel_regularizer=l2(GNN_l2))([X_1, A_1])
X_2, A_2, I_2, M_2 = MinCutPool(
k=int(average_N // 4),
h=mincut_H,
activation=activ,
kernel_regularizer=l2(pool_l2))([gc2, A_1, I_1])
# Block 3
X_3 = GraphConvSkip(n_channels,
dataset = Cora()
adj, x, y = dataset[0].a, dataset[0].x, dataset[0].y
a_norm = normalized_adjacency(adj)
a_norm = sp_matrix_to_sp_tensor(a_norm)
F = dataset.n_node_features
y = np.argmax(y, axis=-1)
n_clusters = y.max() + 1
################################################################################
# MODEL
################################################################################
x_in = Input(shape=(F, ), name="X_in")
a_in = Input(shape=(None, ), name="A_in", sparse=True)
x_1 = GCSConv(16, activation="elu")([x_in, a_in])
x_1, a_1, s_1 = MinCutPool(n_clusters, return_selection=True)([x_1, a_in])
model = Model([x_in, a_in], [x_1, s_1])
################################################################################
# TRAINING
################################################################################
# Setup
inputs = [x, a_norm]
opt = tf.keras.optimizers.Adam(learning_rate=lr)
# Fit model
loss_history = []
nmi_history = []
for _ in tqdm(range(epochs)):
outs = train_step(inputs)
dataset = Cora()
adj, x, y = dataset[0].a, dataset[0].x, dataset[0].y
a_norm = normalized_adjacency(adj)
a_norm = sp_matrix_to_sp_tensor(a_norm)
F = dataset.n_node_features
y = np.argmax(y, axis=-1)
n_clusters = y.max() + 1
################################################################################
# MODEL
################################################################################
x_in = Input(shape=(F, ), name='X_in')
a_in = Input(shape=(None, ), name='A_in', sparse=True)
x_1 = GCSConv(16, activation='elu')([x_in, a_in])
x_1, a_1, s_1 = MinCutPool(n_clusters, return_mask=True)([x_1, a_in])
model = Model([x_in, a_in], [x_1, s_1])
################################################################################
# TRAINING
################################################################################
# Setup
inputs = [x, a_norm]
opt = tf.keras.optimizers.Adam(learning_rate=lr)
# Fit model
loss_history = []
nmi_history = []
for _ in tqdm(range(epochs)):
outs = train_step(inputs)
y = np.argmax(y, axis=-1)
n_clust = y.max() + 1
################################################################################
# MODEL
################################################################################
X_in = Input(
tensor=tf.placeholder(tf.float32, shape=(None, n_feat), name='X_in'))
A_in = Input(tensor=tf.sparse_placeholder(tf.float32, shape=(None, None)),
name='A_in',
sparse=True)
X_1 = GraphConvSkip(gnn_channels,
kernel_initializer='he_normal',
activation=gnn_activ)([X_in, A_in])
pool1, adj1, S = MinCutPool(k=n_clust, h=mlp_channels,
activation=mlp_activ)([X_1, A_in])
model = Model([X_in, A_in], [pool1, S])
model.compile('adam', None)
################################################################################
# TRAINING
################################################################################
# Setup
sess = K.get_session()
loss = model.total_loss # The full unsupervised loss of MinCutPool
mincut_loss = model.losses[0] # The minCUT loss of MinCutPool
ortho_loss = model.losses[1] # The orthogonality loss of MinCutPool
opt = tf.train.AdamOptimizer(learning_rate=lr)
X = X.todense()
n_feat = X.shape[-1]
y = np.argmax(y, axis=-1)
n_clust = y.max() + 1
################################################################################
# MODEL
################################################################################
X_in = Input(shape=(n_feat, ), name='X_in')
A_in = Input(shape=(None, ), name='A_in', sparse=True)
X_1 = GraphConvSkip(gnn_channels,
kernel_initializer='he_normal',
activation=gnn_activ)([X_in, A_in])
pool1, adj1, S = MinCutPool(n_clust,
return_mask=True,
activation=mlp_activ)([X_1, A_in])
model = Model([X_in, A_in], [pool1, S])
model.compile('adam', None)
################################################################################
# TRAINING
################################################################################
# Setup
loss = model.total_loss # The full unsupervised loss of MinCutPool
mincut_loss = model.losses[0] # The minCUT loss of MinCutPool
ortho_loss = model.losses[1] # The orthogonality loss of MinCutPool
inputs = [X, sp_matrix_to_sp_tensor(A_norm)]
opt = tf.keras.optimizers.Adam(learning_rate=lr)