如何在 python 中创建包含 1000 个图形的数据集
How to create a DataSet of 1000 graphs in python
我需要创建一个包含 1000 个图表的数据集。我使用了以下代码：
data_list = []
ngraphs = 1000
for i in range(ngraphs):
num_nodes = randint(10,500)
num_edges = randint(10,num_nodes*(num_nodes - 1))
f1 = np.random.randint(10, size=(num_nodes))
f2 = np.random.randint(10,20, size=(num_nodes))
f3 = np.random.randint(20,30, size=(num_nodes))
f_final = np.stack((f1,f2,f3), axis=1)
capital = 2*f1 + f2 - f3
f1_t = torch.from_numpy(f1)
f2_t = torch.from_numpy(f2)
f3_t = torch.from_numpy(f3)
capital_t = torch.from_numpy(capital)
capital_t = capital_t.type(torch.LongTensor)
x = torch.from_numpy(f_final)
x = x.type(torch.LongTensor)
edge_index = torch.randint(low=0, high=num_nodes, size=(num_edges,2), dtype=torch.long)
edge_attr = torch.randint(low=0, high=50, size=(num_edges,1), dtype=torch.long)
data = Data(x = x, edge_index = edge_index.t().contiguous(), y = capital_t, edge_attr=edge_attr )
data_list.append(data)
这行得通。但是当我运行我的训练函数如下：
for epoch in range(1, 500):
loss = train()
print(f'Loss: {loss:.4f}')
我不断收到以下错误：
RuntimeError Traceback (most recent call
last) in ()
1 for epoch in range(1, 500):
----> 2 loss = train()
3 print(f'Loss: {loss:.4f}')
5 frames /usr/local/lib/python3.7/dist-packages/torch/nn/functional.py
in linear(input, weight, bias) 1845 if
has_torch_function_variadic(input, weight): 1846 return
handle_torch_function(linear, (input, weight), input, weight,
bias=bias)
-> 1847 return torch._C._nn.linear(input, weight, bias) 1848 1849
RuntimeError: expected scalar type Float but found Long
谁能帮我解决这个问题。或者制作一个不会抛出此错误的 1000 图数据集。
将您的 x 和 y 张量更改为 FloatTensor，因为 python 中的线性层仅接受 FloatTensor 输入
我需要创建一个包含 1000 个图表的数据集。我使用了以下代码：
data_list = []
ngraphs = 1000
for i in range(ngraphs):
num_nodes = randint(10,500)
num_edges = randint(10,num_nodes*(num_nodes - 1))
f1 = np.random.randint(10, size=(num_nodes))
f2 = np.random.randint(10,20, size=(num_nodes))
f3 = np.random.randint(20,30, size=(num_nodes))
f_final = np.stack((f1,f2,f3), axis=1)
capital = 2*f1 + f2 - f3
f1_t = torch.from_numpy(f1)
f2_t = torch.from_numpy(f2)
f3_t = torch.from_numpy(f3)
capital_t = torch.from_numpy(capital)
capital_t = capital_t.type(torch.LongTensor)
x = torch.from_numpy(f_final)
x = x.type(torch.LongTensor)
edge_index = torch.randint(low=0, high=num_nodes, size=(num_edges,2), dtype=torch.long)
edge_attr = torch.randint(low=0, high=50, size=(num_edges,1), dtype=torch.long)
data = Data(x = x, edge_index = edge_index.t().contiguous(), y = capital_t, edge_attr=edge_attr )
data_list.append(data)
这行得通。但是当我运行我的训练函数如下：
for epoch in range(1, 500):
loss = train()
print(f'Loss: {loss:.4f}')
我不断收到以下错误：
RuntimeError Traceback (most recent call last) in () 1 for epoch in range(1, 500): ----> 2 loss = train() 3 print(f'Loss: {loss:.4f}')
5 frames /usr/local/lib/python3.7/dist-packages/torch/nn/functional.py in linear(input, weight, bias) 1845 if has_torch_function_variadic(input, weight): 1846 return handle_torch_function(linear, (input, weight), input, weight, bias=bias) -> 1847 return torch._C._nn.linear(input, weight, bias) 1848 1849
RuntimeError: expected scalar type Float but found Long
谁能帮我解决这个问题。或者制作一个不会抛出此错误的 1000 图数据集。
将您的 x 和 y 张量更改为 FloatTensor，因为 python 中的线性层仅接受 FloatTensor 输入