07 神經網絡的基本骨架 —— 非線性激活

1 ReLU

# 非線性激活
# ReLU （讀：瑞盧） 是目前最常用的

import torch
import torchvision
from torch import nn
from torch.nn import ReLU


'''
Parameters
    inplace – can optionally do the operation in-place. Default: False
        inplace = True  原位直接替換
        inplace = False  原位保留，返回變換後的結果，（推薦false,可以保留原始數據）
'''
class Test_NN(nn.Module):
    def __init__(self):
        super(Test_NN, self).__init__()
        self.relu1 = ReLU()

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



input = torch.tensor([[1, -0.5],
                      [-1, 3]])

input = torch.reshape(input, (-1, 1, 2, 2))
print(input.shape)

testNN = Test_NN()
output = testNN(input)
print(output)

得到結果爲：

[[ 1,  0],

[ 0,   3]]

 

2 Sigmoid

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

'''
Parameters
    inplace – can optionally do the operation in-place. Default: False
        inplace = True  原位直接替換
        inplace = False  原位保留，返回變換後的結果，（推薦false,可以保留原始數據）
'''
class Test_NN(nn.Module):
    def __init__(self):
        super(Test_NN, self).__init__()
        self.relu1 = ReLU()
        self.sigmoid1 = Sigmoid()

    def forward(self, input):
        # output = self.relu1(input)
        output = self.sigmoid1(input)
        return output



dataset = torchvision.datasets.CIFAR10(root="D:/Learn/dataSet/Pytorch", train=False, transform=torchvision.transforms.ToTensor(), download=False)
dataloader = DataLoader(dataset, batch_size=64)
writer = SummaryWriter("logs_NonLinearActivation")

testNN = Test_NN()

step = 0
for data in dataloader:
    imgs, targets = data
    writer.add_images("input", imgs, step)
    output = testNN(imgs)
    writer.add_images("output", output, step)
    step += 1


writer.close()