[人工智能]Pytorch学习笔记--常用函数总结2

1-- torch.nn.Sequential( )函数

作用：通过顺序序列创建神经网络结构

示例：调用Block函数时，将顺序执行括号内的Function。

import torch

x = torch.randn(1, 1, 64, 64)

Block = torch.nn.Sequential(
    torch.nn.Conv2d(in_channels = 1, out_channels = 32, kernel_size = 3, stride = 1),
    torch.nn.ReLU(),
    torch.nn.Conv2d(in_channels = 32, out_channels = 32, kernel_size = 3, stride = 1),
    torch.nn.ReLU()
)

result = Block(x)
print(result)

上述代码等同于：

import torch

Conv1 = torch.nn.Conv2d(in_channels = 1, out_channels = 32, kernel_size = 3, stride = 1)
Conv2 = torch.nn.Conv2d(in_channels = 32, out_channels = 32, kernel_size = 3, stride = 1),
Relu = torch.nn.ReLU()

x = torch.randn(1, 1, 64, 64)
print(x)

x = Conv1(x)
x = Relu(x)
x = Conv2(x)
result = Relu(x)

print(result)

############################

2--torch.flatten()函数

作用：按维度进行拼接；torch.flatten(input, start_dim = 0, end_dim = -1)；input为输入tensor数据，start_dim为拼接的起始维度，end_dim为拼接的终止维度。

示例：

import torch

x = torch.randn(2, 3, 3)
print(x)

result1 = torch.flatten(x, start_dim = 0, end_dim = 2)
print(result1)

result2 = torch.flatten(x, start_dim = 0, end_dim = 1)
print(result2)

result3 = torch.flatten(x, start_dim = 1, end_dim = 2)
print(result3)

结果：

tensor([[[ 0.3546, -0.8551,  2.3490],
         [-0.0920,  0.0773, -0.4556],
         [-1.6943,  1.4517, -0.0767]],

        [[-0.6950,  0.4382, -1.2691],
         [-0.0252, -0.4980, -0.5994],
         [-0.2581, -0.2544, -0.6787]]]) #X

tensor([ 0.3546, -0.8551,  2.3490, -0.0920,  0.0773, -0.4556, -1.6943,  1.4517,
        -0.0767, -0.6950,  0.4382, -1.2691, -0.0252, -0.4980, -0.5994, -0.2581,
        -0.2544, -0.6787]) #result1

tensor([[ 0.3546, -0.8551,  2.3490],
        [-0.0920,  0.0773, -0.4556],
        [-1.6943,  1.4517, -0.0767],
        [-0.6950,  0.4382, -1.2691],
        [-0.0252, -0.4980, -0.5994],
        [-0.2581, -0.2544, -0.6787]]) #result2

tensor([[ 0.3546, -0.8551,  2.3490, -0.0920,  0.0773, -0.4556, -1.6943,  1.4517,
         -0.0767],
        [-0.6950,  0.4382, -1.2691, -0.0252, -0.4980, -0.5994, -0.2581, -0.2544,
         -0.6787]]) #result3