PyTorch深度学习（7）非线性激活及线性层

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）

原图像

Sigmoid后的图像

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()

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

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)