__init__(**kwargs)
self.input_spec = InputSpec(min_ndim=3)
def compute_output_shape(self, input_shape...],
[1,2,0,0],
[1,2,3,0],
[1,2,3,4]]
A = Input(shape=[4]) # None * 4
emb = Embedding(5, 3, mask_zero...3、transpose()
torch.transpose(input, dim0, dim1) – Tensor
将输入数据input的第dim0维和dim1维进行交换
#官方例子
x...([[1, 2, 3, 4],
[5, 6, 7, 8]])
torch.nn.Flatten()可以理解为一种网络结构,类似Conv2d、Linear。...(),
torch.nn.Linear(160,10))
m
Sequential(
(0): Conv2d(1, 32, kernel_size=(5, 5), stride=(1, 1)