英文:
How do I extract tensors in a tensor, into a 2D-numpy array?
问题
我正在尝试从一个较大的张量中提取张量，并将其转换为一个二维的NumPy数组。（这个张量包含了通过图神经网络后的节点嵌入）。我正在使用PyTorch（Geometric）进行我的项目。我需要将这些单独的嵌入进一步处理。
这是我的张量：
tensor([[-0.7863, 0.8097],
[-1.0679, 1.1331],
...
[-0.7821, 0.7521]], grad_fn=<AddmmBackward0>)
这是我编写的代码，用来将嵌入提取为NumPy数组：
final = []
for element in final_embeddings:
element.detach().numpy()
final.append(element)
print(final)
这仍然给我一个张量列表，而不是一个二维的NumPy数组。
只使用element.numpy() 会给我一个错误：
RuntimeError: Can't call numpy() on Tensor that requires grad. Use tensor.detach().numpy() instead.
有人能告诉我可能出了什么问题吗？
英文:
I'm trying to extract tensors in a larger tensor, into a 2D-numpy array. (The tensor of tensors holds node embeddings after passing through a graph neural network). I'm using PyTorch (Geometric) for my project. I need the individual embeddings to go further.
This is my tensor:
tensor([[-0.7863, 0.8097],
[-1.0679, 1.1331],
[-1.8162, 1.9160],
[ 2.0584, -2.2741],
[-1.8818, 1.9333],
[ 0.7870, -0.8974],
[ 6.1731, -6.8074],
[ 7.3219, -8.0852],
[-0.9933, 0.9439],
[ 4.6217, -5.1856],
[-1.3747, 1.4614],
[ 4.6429, -5.0861],
[ 3.1141, -3.4420],
[ 2.6417, -2.9173],
[-2.9696, 3.0740],
[ 4.0654, -4.5340],
[ 1.7143, -1.9558],
[-1.7497, 1.8496],
[-1.9055, 1.9934],
[ 3.9273, -4.3356],
[ 4.0350, -4.4137],
[ 1.2770, -1.4225],
[-1.7447, 1.8458],
[ 1.3937, -1.5936],
[ 3.2471, -3.5991],
[ 2.2516, -2.6034],
[ 1.3096, -1.4573],
[-1.7823, 1.8775],
[ 0.9923, -1.2175],
[-1.1818, 1.2430],
[ 1.0997, -1.2466],
[ 0.4841, -0.5800],
[ 4.1609, -4.5518],
[ 3.6211, -3.9535],
[-1.6287, 1.7216],
[ 2.1960, -2.5067],
[ 1.9977, -2.2448],
[-0.9295, 0.9438],
[ 2.2512, -2.5578],
[-2.5360, 2.6436],
[-1.8890, 1.9787],
[ 2.4500, -2.7050],
[ 3.5502, -3.9974],
[ 7.8740, -8.7413],
[ 1.9768, -2.2287],
[-0.9723, 1.0192],
[ 5.3840, -5.9153],
[-1.2483, 1.2866],
[-1.4501, 1.5467],
[-1.0471, 1.0899],
[ 2.3409, -2.5763],
[ 3.1816, -3.5639],
[-1.8847, 1.9865],
[-2.2041, 2.2781],
[-2.7572, 2.8656],
[-2.3390, 2.4441],
[ 3.0862, -3.3945],
[ 1.0977, -1.2327],
[-1.7125, 1.7395],
[ 2.8744, -3.2442],
[ 1.8027, -2.0044],
[-0.7821, 0.7521]], grad_fn=<AddmmBackward0>)
This is the code I wrote to get the embeddings as numpy arrays:
final = []
for element in final_embeddings:
element.detach().numpy()
final.append(element)
print(final)
This still gives me a list of tensors, not a 2D-numpy array.
Using just element.numpy() gives me an error:
RuntimeError Traceback (most recent call last)
Cell In[139], line 3
1 final = []
2 for element in final_embeddings:
----> 3 element.numpy()
4 final.append(element)
6 print(final)
RuntimeError: Can't call numpy() on Tensor that requires grad. Use tensor.detach().numpy() instead.
Can someone tell me what might be going wrong?
答案1
得分: 1
不需要遍历条目。你应该可以直接执行：
final = final_embeddings.detach().numpy()
英文:
There is no need to iterate through the entries. You should be able to just do:
final = final_embeddings.detach().numpy()
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