3 网络模块

# 3 网络模块

主要用于构建深度学习模型。

参数理解：假设你的数据集包含32个句子，每个句子的长度不超过10个单词，每个单词用100维的词嵌入向量表示。那么，你的输入数据的形状（tensor形状）就是：

(batch_size, input_length(sequence_length), input_size) = (32, 10, 100)

参数名	含义	全称	典型用途
`N`	批大小	Batch size	一次处理的数据样本数量
`Cin`	输入通道数	Channels in	输入特征的通道数，例如 RGB 图像是 3
`Cout`	输出通道数	Channels out	网络输出的通道数，通常是卷积核数量
`Hin`	输入高度	Height in	图像或特征图的垂直维度
`Win`	输入宽度	Width in	图像或特征图的水平维度
`Hout`	输出高度	Height out	经过网络后的垂直维度
`Wout`	输出宽度	Width out	经过网络后的水平维度
`Lin`	输入序列长度	Length in	序列的时间步或长度，常用于 RNN/LSTM
`Lout`	输出序列长度	Length out	LSTM 输出序列的长度（通常等于 `Lin`）
`D`	特征维度	Dim	每个时间步或样本的特征维数（用于 LSTM）

# 3.1 Torch.nn使用

# nn.Linear()

https://pytorch.org/docs/stable/generated/torch.nn.Linear.html

import torch
from torch import nn
linear = torch.nn.Linear(in_features=64, out_features=3)
input = torch.rand(10,64)
output = linear(input)

print(output)
print(output.shape) # 10*3

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tensor([[-0.0043, -0.3533,  0.7606],
        [-0.2995,  0.1500,  0.7348],
        [-0.2207, -0.4110,  0.7303],
        [ 0.1700, -0.0216,  0.4602],
        [-0.3685, -0.1015,  0.4496],
        [-0.1225, -0.0397,  0.8009],
        [ 0.0735, -0.0215,  0.7582],
        [-0.1960, -0.0924,  0.5687],
        [-0.1162, -0.2948,  0.5116],
        [-0.4587, -0.2869,  0.5024]], grad_fn=<AddmmBackward0>)
torch.Size([10, 3])

# nn.Conv1d()

https://pytorch.org/docs/stable/generated/torch.nn.Conv1d.html

其中：

conv1 = nn.Conv1d(in_channels=256, out_channels=10, kernel_size=2, stride=1, padding=0)
input = torch.randn(32,32,256)
# 发现Cin != in_channels，修正
input_new = input.permute(0,2,1)

out = conv1(input_new)
print(out.shape) # 31=(32+2*0-1*1-1)/1+1

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torch.Size([32, 10, 31])

# nn.RNN

import torch
from torch import nn

num_steps = 35
batch_size = 2
feature_size = 32
num_hiddens = 2

x = torch.rand(num_steps, batch_size, feature_size)
RNN_X = nn.RNN(input_size=feature_size, hidden_size=num_hiddens)
y, state_new = RNN_X(x)
print(x.shape, y.shape)

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torch.Size([35, 2, 32]) torch.Size([35, 2, 2])

# 3.2 自建深度学习类

使用torch.nn.Module作为父类，重点重写__init__、forward方法

# init()

常用于模型参数初始化

# forward()

常用于厘清数据流动关系

# 示例代码：
import torch
from torch import nn
class MyNet(nn.Module):
    def __init__(self):
        super(MyNet, self).__init__() # 使用父类的方法初始化子类
        self.linear1 = torch.nn.Linear(96, 1024)  # [96,1024]
        self.relu1 = torch.nn.ReLU(True)
        self.batchnorm1d_1 = torch.nn.BatchNorm1d(1024)
        self.linear2 = torch.nn.Linear(1024, 7 * 7 * 128)  # [1024,6272]
        self.relu2 = torch.nn.ReLU(True)
        self.batchnorm1d_2 = torch.nn.BatchNorm1d(7 * 7 * 128)
        self.ConvTranspose2d = nn.ConvTranspose2d(128, 64, 4, 2, padding=1)

    def forward(self, x):
        x = self.linear1(x)
        x = self.relu1(x)
        x = self.batchnorm1d_1(x)
        x = self.linear2(x)
        x = self.relu2(x)
        x = self.batchnorm1d_2(x)
        x = self.ConvTranspose2d(x)
        return x

model = MyNet()
print(model)
# 运行结果为：
# MyNet(
#   (linear1): Linear(in_features=96, out_features=1024, bias=True)
#   (relu1): ReLU(inplace=True)
#   (batchnorm1d_1): BatchNorm1d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
#   (linear2): Linear(in_features=1024, out_features=6272, bias=True)
#   (relu2): ReLU(inplace=True)
#   (batchnorm1d_2): BatchNorm1d(6272, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
#   (ConvTranspose2d): ConvTranspose2d(128, 64, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
# )

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MyNet(
  (linear1): Linear(in_features=96, out_features=1024, bias=True)
  (relu1): ReLU(inplace=True)
  (batchnorm1d_1): BatchNorm1d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
  (linear2): Linear(in_features=1024, out_features=6272, bias=True)
  (relu2): ReLU(inplace=True)
  (batchnorm1d_2): BatchNorm1d(6272, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
  (ConvTranspose2d): ConvTranspose2d(128, 64, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
)

# 3.3 模型类的使用

构造函数有无参数

2数据模块 4激活函数

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