pytorch如何实现多个矩阵拼接

a = torch.ones(3, 3)  # 假设生成的矩阵形状为3*3
c = []  # 定义一个空列表用于存储矩阵
for i in range(3):
    a = a
    c.append(a.unsqueeze(0))
# 使用cat方法可之间实现该操作
c = torch.cat(c, dim=0)  
print(c.size())

import torch
a = torch.Tensor([1, 2, 3])
b = a * 2
c = torch.cat((a, b), dim=0)  # dim=-1为取最后一维。这里只有一维-1和0是一样的
print(a.shape)
print(c.shape)
print(c)

a = torch.Tensor([[1, 2]])
b = a * 2
c1 = torch.cat((a, b), dim=0)
c2 = torch.cat((a, b), dim=1)  # 这里第二维是最后一维，dim=-1和dim=1是一样的
print("a:", a)
print("a.shape:", a.shape)
print("c1:", c1)
print("c1.shape:", c1.shape)
print("c2:", c2)
print("c2.shape:", c2.shape)

h_n = torch.randn(2, 3, 4)  # 假设隐藏层
# 下面三种写法是一个意思
feature_map = torch.cat([h_n[i] for i in range(h_n.shape[0])], dim=-1)  # 索引第i个整元素，元素里剩下的维度缺省是全取的意思
feature_map1 = torch.cat([h_n[i, :, :] for i in range(h_n.shape[0])], dim=-1)
feature_map2 = torch.cat([h_n[i] for i in range(h_n.shape[0])], dim=1)
print(feature_map.shape)
print(feature_map1.shape)
print(feature_map2.shape)