from torch_sparse import spspmm, spmm
import matplotlib.pyplot as plt
learning_rate = 0.1
weight_decay = 5e-4
epochs = 200
device = "cuda" if torch.cuda.is_available() else "cpu"
model = GHNN_Net().to(device)
criterion = nn.CrossEntropyLoss().to(device)
optimizer = optim.Adam(model.parameters(), lr=learning_rate, weight_decay=weight_decay)
dataset = CoraData().data
x = dataset.x / dataset.x.sum(1, keepdims=True)
tensor_x = torch.from_numpy(x).to(device)
tensor_y = torch.from_numpy(dataset.y).to(device)
tensor_train_mask = torch.from_numpy(dataset.train_mask).to(device)
tensor_val_mask = torch.from_numpy(dataset.val_mask).to(device)
tensor_test_mask = torch.from_numpy(dataset.test_mask).to(device)
normalized_Laplacian = CoraData.normalization(dataset.adjacency)
indices = torch.from_numpy(np.asarray([normalized_Laplacian.row,
normalized_Laplacian.col]).astype('int64')).long()
values = torch.from_numpy(normalized_Laplacian.data.astype(np.float32))
Laplacian_tensor_ = torch.sparse.FloatTensor(indices, values,
(2708, 2708)).to(device)
identity_list_ = [1 for i in range(2708)]
identity_coo_ = sp.spdiags(identity_list_, diags=[0], m=2708, n=2708, format="coo")
INPUT_DIM = 1433 # 输入维度
# Note: 采样的邻居阶数需要与GCN的层数保持一致
HIDDEN_DIM = [156, 7] # 隐藏单元节点数
NUM_NEIGHBORS_LIST = [15, 10] # 每阶采样邻居的节点数
assert len(HIDDEN_DIM) == len(NUM_NEIGHBORS_LIST)
BTACH_SIZE = 32 # 批处理大小
EPOCHS = 50
NUM_BATCH_PER_EPOCH = 40 # 每个epoch循环的批次数
LEARNING_RATE = 0.01 # 学习率
DEVICE = "cuda" if torch.cuda.is_available() else "cpu"
Data = namedtuple(
"Data",
['x', 'y', 'adjacency_dict', 'train_mask', 'val_mask', 'test_mask'])
data = CoraData().data
x = data.x / data.x.sum(1, keepdims=True) # 归一化数据，使得每一行和为1
train_index = np.where(data.train_mask)[0]
train_label = data.y[train_index]
test_index = np.where(data.test_mask)[0]
model = GraphSage(input_dim=INPUT_DIM,
hidden_dim=HIDDEN_DIM,
num_neighbors_list=NUM_NEIGHBORS_LIST).to(DEVICE)
print(model)
criterion = nn.CrossEntropyLoss().to(DEVICE)
optimizer = optim.Adam(model.parameters(), lr=LEARNING_RATE, weight_decay=5e-4)
def train():
model.train()
# 超参数定义
learning_rate = 0.1
weight_decay = 5e-4
epochs = 200
# 模型定义：Model, Loss, Optimizer
device = "cuda" if torch.cuda.is_available() else "cpu"
model = GraphHeat_Net().to(device)
criterion = nn.CrossEntropyLoss().to(device)
optimizer = optim.Adam(model.parameters(),
lr=learning_rate,
weight_decay=weight_decay)
# 加载数据，并转换为torch.Tensor
dataset = CoraData().data
x = dataset.x / dataset.x.sum(1, keepdims=True) # 归一化数据，使得每一行和为1
tensor_x = torch.from_numpy(x).to(device)
tensor_y = torch.from_numpy(dataset.y).to(device)
tensor_train_mask = torch.from_numpy(dataset.train_mask).to(device)
tensor_val_mask = torch.from_numpy(dataset.val_mask).to(device)
tensor_test_mask = torch.from_numpy(dataset.test_mask).to(device)
normalize_adjacency = CoraData.normalization(dataset.adjacency) # 规范化邻接矩阵
indices = torch.from_numpy(
np.asarray([normalize_adjacency.row,
normalize_adjacency.col]).astype('int64')).long()
values = torch.from_numpy(normalize_adjacency.data.astype(np.float32))
tensor_adjacency = torch.sparse.FloatTensor(indices, values,
(2708, 2708)).to(device)
heatkernel = chebyshev_polynomials(dataset.adjacency, 3)