PyTorch深度学习（7）非线性激活及线性层

一、Non-Linear Activations  非线性激活

1、RELU 

input > 0时，取原始值；input < 0时，取0

Input：(N, *) N - batch_size

Output：(N, *) N - batch_size

nn.ReLU(inplace:bool=false) 

inplace为True, input = -1, ReLU(input, inplace=True) —— input = 0

inplace为False，input = -1, output = ReLU(input, inplace=False) —— input = -1, output = 0

即是否替换原input的值（原位操作）

2、SIGMOID

3、使用Sigmoid后得到的图像

原图像

Sigmoid后的图像

 4、具体代码

import ssl

import torch
import torchvision
from torch import nn
from torch.nn import Sigmoid
from torch.nn import ReLU
from torch.utils.data import DataLoader
from torch.utils.tensorboard import SummaryWriter

ssl._create_default_https_context = ssl._create_unverified_context

dataset = torchvision.datasets.CIFAR10("./dataset", train=False, transform=torchvision.transforms.ToTensor(),
                                       download=True)
dataLoader = DataLoader(dataset, batch_size=64)

inputData = torch.tensor([[-2, 1.],
                          [2., -3]])

inputData = torch.reshape(inputData, (-1, 1, 2, 2))
print(inputData)

class TestRelu(nn.Module):
    def __init__(self):
        super(TestRelu, self).__init__()
        self.relu1 = ReLU()

    def forward(self, input):
        output = self.relu1(input)
        return output


tr = TestRelu()
outputData = tr(inputData)
print(outputData)


class TestSigmoid(nn.Module):
    def __init__(self):
        super(TestSigmoid, self).__init__()
        self.sigmoid1 = Sigmoid()

    def forward(self, input):
        output = self.sigmoid1(input)
        return output


ts = TestSigmoid()
writer = SummaryWriter("logs")
step = 0
for data in dataLoader:
    imgs, target = data
    writer.add_images("input_image", imgs, step, dataformats="NCHW")

    output = ts(imgs)
    writer.add_images("output_image", output, step, dataformats="NCHW")
    step += 1

writer.close()

二、线性层

1、基础原理

k1 × x1 + b1

k2 × x2 + b2

...

kd × xd + bd

ki 为 权重， bi 为 偏移量

VGG16

 使用torch.flatten(图片)  将图片展成一行

2、详细代码

import ssl

import torch
import torchvision
from torch import nn
from torch.nn import Linear
from torch.utils.data import DataLoader

ssl._create_default_https_context = ssl._create_unverified_context

dataset = torchvision.datasets.CIFAR10("./dataset", train=False,
                                       transform=torchvision.transforms.ToTensor(),  download=True)
dataLoader = DataLoader(dataset, batch_size=64, drop_last=True)   # drop_last True 最后一页不足舍弃

class TestLinear(nn.Module):
    def __init__(self):
        super(TestLinear, self).__init__()
        self.linear1 = Linear(196608, 10)

    def forward(self, input):
        output = self.linear1(input)
        return output


tl = TestLinear()
for data in dataLoader:
    imgs, target = data
    print(imgs.shape)

    # output = torch.reshape(imgs, (1, 1, 1, -1))  将图像展开成一行
    output = torch.flatten(imgs)  # 将[64, 3, 32, 32] -> 转换为 [1, 1, 1, 196608]
    print(output.shape)

    output = tl(output)
    print(output.shape)