from spektral.data import DisjointLoader
from spektral.datasets import TUDataset
from tensorflow.keras.optimizers import Adam
physical_devices = tf.config.list_physical_devices('GPU')
if len(physical_devices) > 0:
tf.config.experimental.set_memory_growth(physical_devices[0], True)
# Best config
batch_size = 32
learning_rate = 0.01
epochs = 400
# Read data
data = TUDataset('PROTEINS')
# Train/test split
np.random.shuffle(data)
split = int(0.8 * len(data))
data_tr, data_te = data[:split], data[split:]
# Data loader
loader_tr = DisjointLoader(data_tr, batch_size=batch_size, epochs=epochs)
loader_te = DisjointLoader(data_te, batch_size=batch_size)
# Create model
model = GeneralGNN(data.n_labels, activation='softmax')
optimizer = Adam(learning_rate)
model.compile('adam', 'categorical_crossentropy', metrics=['categorical_accuracy'])
import tensorflow as tf
from tensorflow.keras.losses import CategoricalCrossentropy
from tensorflow.keras.metrics import categorical_accuracy
from tensorflow.keras.optimizers import Adam
from spektral.data import DisjointLoader
from spektral.datasets import TUDataset
from spektral.models import GeneralGNN, GNNExplainer
# Config
learning_rate = 1e-2
batch_size = 32
epochs = 100
# Load data
data = TUDataset("PROTEINS")
# Train/test split
np.random.shuffle(data)
split = int(0.8 * len(data))
data_tr, data_te = data[:split], data[split:]
# Data loaders
loader_tr = DisjointLoader(data_tr, batch_size=batch_size, epochs=epochs)
loader_te = DisjointLoader(data_te, batch_size=batch_size)
# Create model
model = GeneralGNN(data.n_labels, activation="softmax")
optimizer = Adam(learning_rate)
loss_fn = CategoricalCrossentropy()
from spektral.data import BatchLoader
from spektral.datasets import TUDataset
from spektral.layers import GCSConv, GlobalSumPool, GraphMasking, MinCutPool
################################################################################
# Config
################################################################################
learning_rate = 1e-3 # Learning rate
epochs = 10 # Number of training epochs
batch_size = 32 # Batch size
################################################################################
# Load data
################################################################################
dataset = TUDataset("PROTEINS", clean=True)
# Parameters
N = max(g.n_nodes for g in dataset)
F = dataset.n_node_features # Dimension of node features
S = dataset.n_edge_features # Dimension of edge features
n_out = dataset.n_labels # Dimension of the target
# Train/test split
idxs = np.random.permutation(len(dataset))
split_va, split_te = int(0.8 * len(dataset)), int(0.9 * len(dataset))
idx_tr, idx_va, idx_te = np.split(idxs, [split_va, split_te])
dataset_tr = dataset[idx_tr]
dataset_va = dataset[idx_va]
dataset_te = dataset[idx_te]
dataset = sys.argv[1]
if (dataset[:2] == 'vs') or (dataset == 'all'):
data_type = 'MNIST'
else:
data_type = 'GRAPHS'
use_spektral = True
if data_type == 'GRAPHS':
path = path + 'data/graphs/' + dataset + '/mat/'
if use_spektral:
data = TUDataset(dataset)
for i in range(len(data)):
stfile = open(path + 'SPK' + str(i) + '_st.txt', 'w')
A = np.array(data[i].a.todense())
num_vertices = A.shape[0]
for v in range(num_vertices):
stfile.write(str(v) + ' \n')
idxs = np.argwhere(A > 0)
for v in range(len(idxs)):
stfile.write(str(idxs[v, 0]) + ' ' + str(idxs[v, 1]) + ' \n')
stfile.close()
else:
pad_size = 50
Cinit = np.ones([1, pad_size])
from spektral.data import BatchLoader
from spektral.datasets import TUDataset
from spektral.layers import GCNConv, MinCutPool, GlobalSumPool
################################################################################
# PARAMETERS
################################################################################
learning_rate = 1e-3 # Learning rate
epochs = 10 # Number of training epochs
batch_size = 32 # Batch size
################################################################################
# LOAD DATA
################################################################################
dataset = TUDataset('PROTEINS', clean=True)
# Parameters
N = max(g.n_nodes for g in dataset)
F = dataset.n_node_features # Dimension of node features
S = dataset.n_edge_features # Dimension of edge features
n_out = dataset.n_labels # Dimension of the target
# Train/test split
idxs = np.random.permutation(len(dataset))
split_va, split_te = int(0.8 * len(dataset)), int(0.9 * len(dataset))
idx_tr, idx_va, idx_te = np.split(idxs, [split_va, split_te])
dataset_tr = dataset[idx_tr]
dataset_va = dataset[idx_va]
dataset_te = dataset[idx_te]
