1. 损失函数

① Loss损失函数一方面计算实际输出和目标之间的差距。

② Loss损失函数另一方面为我们更新输出提供一定的依据。

2. L1loss损失函数

① L1loss数学公式如下图所示，例子如下下图所示。

import torch
from torch.nn import L1Loss
inputs = torch.tensor([1,2,3],dtype=torch.float32)
targets = torch.tensor([1,2,5],dtype=torch.float32)
inputs = torch.reshape(inputs,(1,1,1,3))
targets = torch.reshape(targets,(1,1,1,3))
loss = L1Loss()  # 默认为 maen
result = loss(inputs,targets)
print(result)

tensor(0.6667)

import torch
from torch.nn import L1Loss
inputs = torch.tensor([1,2,3],dtype=torch.float32)
targets = torch.tensor([1,2,5],dtype=torch.float32)
inputs = torch.reshape(inputs,(1,1,1,3))
targets = torch.reshape(targets,(1,1,1,3))
loss = L1Loss(reduction='sum') # 修改为sum，三个值的差值，然后取和
result = loss(inputs,targets)
print(result)

tensor(2.)

3. MSE损失函数

① MSE损失函数数学公式如下图所示。

import torch
from torch.nn import L1Loss
from torch import nn
inputs = torch.tensor([1,2,3],dtype=torch.float32)
targets = torch.tensor([1,2,5],dtype=torch.float32)
inputs = torch.reshape(inputs,(1,1,1,3))
targets = torch.reshape(targets,(1,1,1,3))
loss_mse = nn.MSELoss()
result_mse = loss_mse(inputs,targets)
print(result_mse)

tensor(1.3333)

4. 交叉熵损失函数

① 交叉熵损失函数数学公式如下图所示。

import torch
from torch.nn import L1Loss
from torch import nn

x = torch.tensor([0.1,0.2,0.3])
y = torch.tensor([1])
x = torch.reshape(x,(1,3)) # 1的 batch_size，有三类
loss_cross = nn.CrossEntropyLoss()
result_cross = loss_cross(x,y)
print(result_cross)

tensor(1.1019)

5. 搭建神经网络

import torch
import torchvision
from torch import nn 
from torch.nn import Conv2d, MaxPool2d, Flatten, Linear, Sequential
from torch.utils.data import DataLoader
from torch.utils.tensorboard import SummaryWriter

dataset = torchvision.datasets.CIFAR10("./dataset",train=False,transform=torchvision.transforms.ToTensor(),download=True)       
dataloader = DataLoader(dataset, batch_size=1,drop_last=True)

class Tudui(nn.Module):
    def __init__(self):
        super(Tudui, self).__init__()        
        self.model1 = Sequential(
            Conv2d(3,32,5,padding=2),
            MaxPool2d(2),
            Conv2d(32,32,5,padding=2),
            MaxPool2d(2),
            Conv2d(32,64,5,padding=2),
            MaxPool2d(2),
            Flatten(),
            Linear(1024,64),
            Linear(64,10)
        )
        
    def forward(self, x):
        x = self.model1(x)
        return x
    
tudui = Tudui()
for data in dataloader:
    imgs, targets = data
    outputs = tudui(imgs)
    print(outputs)
    print(targets)

Files already downloaded and verified
tensor([[-0.1256, -0.1614, -0.0474, -0.0409,  0.0259, -0.1314, -0.1198, -0.0682,
         -0.1246, -0.1217]], grad_fn=<AddmmBackward0>)
tensor([3])
tensor([[-0.1278, -0.1590, -0.0487, -0.0508,  0.0202, -0.1075, -0.0921, -0.0896,
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tensor([8])
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tensor([8])
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tensor([0])
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tensor([6])
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tensor([6])
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tensor([1])
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tensor([6])
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tensor([3])
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tensor([1])
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tensor([0])
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tensor([9])
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tensor([5])
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tensor([7])
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tensor([9])
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tensor([8])
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tensor([5])
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tensor([7])
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tensor([8])
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tensor([6])
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tensor([7])
tensor([[-0.1221, -0.1825, -0.0374, -0.0700,  0.0211, -0.1297, -0.0816, -0.0851,
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tensor([0])
tensor([[-0.1263, -0.1471, -0.0424, -0.0346,  0.0274, -0.1253, -0.0980, -0.0809,
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tensor([4])
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tensor([9])
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tensor([5])
tensor([[-0.1315, -0.1589, -0.0469, -0.0392,  0.0237, -0.1134, -0.1137, -0.0835,
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tensor([2])
tensor([[-0.1237, -0.1630, -0.0434, -0.0283,  0.0310, -0.1174, -0.1258, -0.0745,
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tensor([4])
tensor([[-0.1225, -0.1509, -0.0488, -0.0479,  0.0244, -0.1125, -0.0882, -0.0956,
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tensor([0])
tensor([[-0.1245, -0.1512, -0.0332, -0.0294,  0.0428, -0.1038, -0.1036, -0.0971,
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tensor([9])
tensor([[-0.1252, -0.1393, -0.0469, -0.0215,  0.0278, -0.1156, -0.1093, -0.0802,
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tensor([6])
tensor([[-0.1235, -0.1323, -0.0388, -0.0139,  0.0388, -0.1146, -0.1099, -0.0776,
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tensor([6])
tensor([[-0.1166, -0.1517, -0.0444, -0.0333,  0.0222, -0.1178, -0.0961, -0.0788,
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tensor([5])
tensor([[-0.1272, -0.1624, -0.0555, -0.0450,  0.0264, -0.1286, -0.1142, -0.0632,
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tensor([4])
tensor([[-0.1225, -0.1664, -0.0399, -0.0295,  0.0262, -0.1197, -0.1303, -0.0751,
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tensor([5])
tensor([[-0.1302, -0.1304, -0.0432, -0.0302,  0.0359, -0.0957, -0.0713, -0.0990,
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tensor([9])
tensor([[-0.1275, -0.1868, -0.0453, -0.0352,  0.0182, -0.1452, -0.1307, -0.0617,
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tensor([2])
tensor([[-0.1179, -0.1421, -0.0482, -0.0414,  0.0210, -0.1183, -0.1012, -0.0772,
         -0.1309, -0.1180]], grad_fn=<AddmmBackward0>)
tensor([4])
tensor([[-0.1286, -0.1622, -0.0549, -0.0662,  0.0222, -0.0999, -0.1112, -0.0764,
         -0.1522, -0.1182]], grad_fn=<AddmmBackward0>)
tensor([1])
tensor([[-0.1175, -0.1486, -0.0418, -0.0337,  0.0255, -0.0982, -0.1069, -0.0869,
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tensor([9])
tensor([[-0.1313, -0.1584, -0.0458, -0.0510,  0.0158, -0.1352, -0.1135, -0.0694,
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tensor([5])
tensor([[-0.1281, -0.1710, -0.0381, -0.0487,  0.0164, -0.1144, -0.0941, -0.0999,
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tensor([4])
tensor([[-0.1241, -0.1383, -0.0407, -0.0075,  0.0367, -0.1165, -0.1280, -0.0769,
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tensor([6])
tensor([[-0.1202, -0.1504, -0.0396, -0.0264,  0.0305, -0.1174, -0.0945, -0.0944,
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tensor([5])
tensor([[-0.1234, -0.1696, -0.0374, -0.0331,  0.0295, -0.1284, -0.1252, -0.0708,
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tensor([6])
tensor([[-0.1399, -0.1446, -0.0465, -0.0542,  0.0311, -0.1092, -0.1004, -0.0914,
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tensor([0])
tensor([[-0.1363, -0.1602, -0.0574, -0.0523,  0.0230, -0.1163, -0.1003, -0.0711,
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tensor([9])
tensor([[-0.1198, -0.1429, -0.0465, -0.0309,  0.0375, -0.1282, -0.0960, -0.0725,
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tensor([3])
tensor([[-0.1256, -0.1470, -0.0431, -0.0336,  0.0304, -0.1213, -0.0977, -0.0707,
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tensor([9])
tensor([[-0.1269, -0.1463, -0.0541, -0.0331,  0.0299, -0.1212, -0.1126, -0.0708,
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tensor([7])
tensor([[-0.1236, -0.1405, -0.0449, -0.0229,  0.0258, -0.1186, -0.1084, -0.0756,
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tensor([6])
tensor([[-0.1349, -0.1592, -0.0465, -0.0401,  0.0161, -0.1175, -0.0877, -0.0946,
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tensor([9])
tensor([[-0.1293, -0.1516, -0.0467, -0.0350,  0.0297, -0.1058, -0.1112, -0.0819,
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tensor([8])
tensor([[-0.1171, -0.1566, -0.0423, -0.0199,  0.0289, -0.1192, -0.1231, -0.0705,
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tensor([0])
tensor([[-0.1180, -0.1438, -0.0374, -0.0287,  0.0381, -0.1024, -0.1027, -0.0864,
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tensor([3])
tensor([[-0.1283, -0.1576, -0.0557, -0.0619,  0.0131, -0.1090, -0.0922, -0.0852,
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tensor([8])
tensor([[-0.1303, -0.1445, -0.0563, -0.0490,  0.0148, -0.1129, -0.0991, -0.0761,
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tensor([8])
tensor([[-0.1243, -0.1721, -0.0463, -0.0375,  0.0361, -0.1230, -0.1268, -0.0771,
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tensor([7])
tensor([[-0.1082, -0.1452, -0.0374, -0.0319,  0.0206, -0.1279, -0.1202, -0.0817,
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tensor([7])
tensor([[-0.1292, -0.1648, -0.0546, -0.0479,  0.0225, -0.1200, -0.1174, -0.0719,
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tensor([4])
tensor([[-0.1350, -0.1697, -0.0309, -0.0450,  0.0308, -0.1253, -0.1128, -0.0918,
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tensor([6])
tensor([[-0.1307, -0.1591, -0.0501, -0.0407,  0.0214, -0.1232, -0.1125, -0.0713,
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tensor([7])
tensor([[-0.1214, -0.1507, -0.0391, -0.0169,  0.0354, -0.1199, -0.1092, -0.0684,
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tensor([3])
tensor([[-0.1223, -0.1657, -0.0366, -0.0205,  0.0285, -0.1331, -0.1248, -0.0615,
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tensor([6])
tensor([[-0.1172, -0.1406, -0.0356, -0.0449,  0.0357, -0.1077, -0.0814, -0.1030,
         -0.1340, -0.1167]], grad_fn=<AddmmBackward0>)
tensor([3])
tensor([[-0.1263, -0.1558, -0.0473, -0.0252,  0.0244, -0.1149, -0.1142, -0.0721,
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tensor([6])
tensor([[-0.1236, -0.1599, -0.0454, -0.0284,  0.0297, -0.1272, -0.1178, -0.0750,
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tensor([2])
tensor([[-0.1165, -0.1574, -0.0414, -0.0328,  0.0123, -0.1058, -0.1146, -0.0846,
         -0.1480, -0.1126]], grad_fn=<AddmmBackward0>)
tensor([1])
tensor([[-0.1236, -0.1834, -0.0557, -0.0632,  0.0312, -0.1359, -0.0924, -0.0802,
         -0.1561, -0.1215]], grad_fn=<AddmmBackward0>)
tensor([2])
tensor([[-0.1193, -0.1480, -0.0396, -0.0175,  0.0506, -0.1092, -0.1074, -0.0875,
         -0.1199, -0.1148]], grad_fn=<AddmmBackward0>)
tensor([3])
tensor([[-0.1235, -0.1555, -0.0455, -0.0351,  0.0304, -0.1053, -0.1010, -0.0957,
         -0.1335, -0.1197]], grad_fn=<AddmmBackward0>)
tensor([7])
tensor([[-0.1378, -0.1529, -0.0590, -0.0461,  0.0340, -0.1096, -0.1127, -0.0718,
         -0.1476, -0.1224]], grad_fn=<AddmmBackward0>)
tensor([2])
tensor([[-0.1240, -0.1494, -0.0453, -0.0277,  0.0239, -0.1221, -0.1242, -0.0800,
         -0.1212, -0.1054]], grad_fn=<AddmmBackward0>)
tensor([6])
tensor([[-0.1289, -0.1581, -0.0518, -0.0483,  0.0183, -0.1098, -0.1036, -0.0755,
         -0.1489, -0.1298]], grad_fn=<AddmmBackward0>)
tensor([8])
tensor([[-0.1231, -0.1440, -0.0546, -0.0551,  0.0175, -0.1225, -0.0971, -0.0781,
         -0.1359, -0.1232]], grad_fn=<AddmmBackward0>)
tensor([8])
tensor([[-0.1219, -0.1313, -0.0446, -0.0305,  0.0334, -0.0887, -0.0883, -0.1025,
         -0.1545, -0.1033]], grad_fn=<AddmmBackward0>)
tensor([0])
tensor([[-0.1251, -0.1542, -0.0416, -0.0334,  0.0427, -0.1159, -0.0978, -0.0632,
         -0.1340, -0.1166]], grad_fn=<AddmmBackward0>)
tensor([2])
tensor([[-0.1183, -0.1669, -0.0487, -0.0520,  0.0200, -0.1113, -0.1097, -0.0950,
         -0.1372, -0.1183]], grad_fn=<AddmmBackward0>)
tensor([9])
tensor([[-0.1157, -0.1488, -0.0449, -0.0585,  0.0108, -0.1375, -0.0951, -0.0765,
         -0.1313, -0.1343]], grad_fn=<AddmmBackward0>)
tensor([3])
tensor([[-0.1154, -0.1439, -0.0324, -0.0401,  0.0303, -0.1268, -0.1075, -0.0731,
         -0.1161, -0.1206]], grad_fn=<AddmmBackward0>)
tensor([3])
tensor([[-0.1380, -0.1498, -0.0655, -0.0652,  0.0165, -0.0988, -0.0843, -0.0903,
         -0.1702, -0.1296]], grad_fn=<AddmmBackward0>)
tensor([8])
tensor([[-0.1245, -0.1498, -0.0603, -0.0668,  0.0179, -0.1186, -0.0847, -0.0779,
         -0.1474, -0.1207]], grad_fn=<AddmmBackward0>)
tensor([8])
tensor([[-0.1168, -0.1449, -0.0354, -0.0221,  0.0122, -0.1211, -0.1084, -0.0785,
         -0.1220, -0.1134]], grad_fn=<AddmmBackward0>)
tensor([1])
tensor([[-0.1252, -0.1567, -0.0469, -0.0140,  0.0255, -0.1284, -0.0953, -0.0736,
         -0.1280, -0.1234]], grad_fn=<AddmmBackward0>)
tensor([1])
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tensor([8])
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tensor([8])
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tensor([8])
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tensor([9])
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tensor([7])
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tensor([8])
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tensor([9])
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tensor([9])
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tensor([9])
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tensor([9])
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tensor([7])
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tensor([8])
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tensor([9])
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tensor([9])
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tensor([8])
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tensor([8])
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tensor([4])
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tensor([3])
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tensor([2])
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tensor([8])
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tensor([4])
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tensor([5])
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tensor([5])
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tensor([4])
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tensor([1])
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tensor([4])
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tensor([5])
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tensor([6])
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tensor([3])
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tensor([4])
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tensor([4])
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tensor([0])
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tensor([8])
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tensor([8])
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tensor([4])
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tensor([5])
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tensor([7])
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tensor([5])
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tensor([6])
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tensor([9])
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tensor([5])
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tensor([6])
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tensor([0])
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tensor([7])
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tensor([5])
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tensor([6])
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tensor([6])
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tensor([9])
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tensor([7])
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tensor([5])
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tensor([6])
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tensor([4])
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tensor([8])
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tensor([9])
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tensor([7])
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tensor([8])
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tensor([7])
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tensor([5])
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tensor([9])
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tensor([9])
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tensor([4])
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tensor([9])
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tensor([9])
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tensor([8])
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tensor([7])
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tensor([7])
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tensor([8])
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tensor([4])
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tensor([8])
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tensor([7])
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tensor([7])
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tensor([6])
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tensor([8])
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tensor([3])
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tensor([9])
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tensor([9])
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tensor([9])
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tensor([5])
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tensor([4])
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tensor([5])
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tensor([9])
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tensor([9])
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tensor([9])
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tensor([8])
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tensor([9])
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tensor([7])
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tensor([6])
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tensor([8])
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tensor([9])
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tensor([9])
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tensor([9])
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tensor([4])
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tensor([6])
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tensor([2])
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tensor([9])
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tensor([2])
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tensor([4])
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tensor([9])
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tensor([0])
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tensor([7])
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tensor([3])
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tensor([4])
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tensor([8])
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tensor([0])
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tensor([2])
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tensor([6])
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tensor([8])
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tensor([9])
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tensor([7])
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tensor([1])
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tensor([9])
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tensor([3])
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tensor([7])
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tensor([7])
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tensor([9])
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tensor([6])
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tensor([9])
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tensor([5])
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tensor([8])
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tensor([5])
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tensor([9])
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tensor([8])
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tensor([3])
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tensor([9])
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tensor([8])
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tensor([0])
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tensor([5])
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tensor([5])
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tensor([7])
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tensor([7])
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tensor([5])
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tensor([8])
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tensor([9])
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tensor([9])
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tensor([9])
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tensor([9])
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tensor([9])
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tensor([9])
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tensor([8])
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tensor([8])
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tensor([6])
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tensor([8])
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tensor([5])
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tensor([9])
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tensor([6])
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tensor([9])
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tensor([5])
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tensor([3])
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tensor([4])
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tensor([4])
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tensor([7])
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tensor([4])
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tensor([8])
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tensor([4])
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tensor([5])
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tensor([6])
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tensor([8])
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tensor([7])
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tensor([8])
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tensor([7])
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tensor([4])
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tensor([3])
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tensor([3])
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tensor([3])
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tensor([7])
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tensor([2])
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tensor([5])
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tensor([6])
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tensor([7])
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tensor([9])
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tensor([2])
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tensor([3])
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tensor([9])
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tensor([8])
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tensor([9])
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tensor([5])
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tensor([0])
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tensor([7])
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tensor([6])
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tensor([3])
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tensor([3])
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tensor([8])
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tensor([1])
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tensor([4])
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tensor([5])
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tensor([4])
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tensor([3])
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tensor([6])
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tensor([8])
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tensor([8])
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tensor([6])
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tensor([3])
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tensor([3])
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tensor([9])
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tensor([9])
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tensor([9])
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tensor([4])
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tensor([5])
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tensor([9])
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tensor([8])
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tensor([5])
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tensor([9])
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tensor([5])
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tensor([8])
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tensor([8])
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tensor([9])
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tensor([7])
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tensor([6])
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tensor([0])
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tensor([6])
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tensor([9])
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tensor([3])
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tensor([9])
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tensor([6])
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tensor([6])
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tensor([7])
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tensor([7])
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tensor([8])
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tensor([7])
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tensor([9])
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tensor([9])
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tensor([8])
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tensor([9])
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tensor([8])
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tensor([8])
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tensor([8])
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tensor([9])
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tensor([9])
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tensor([9])
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tensor([9])
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tensor([8])
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tensor([8])
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tensor([4])
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tensor([8])
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tensor([9])
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tensor([8])
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tensor([9])
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tensor([8])
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tensor([9])
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tensor([8])
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tensor([9])
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tensor([9])
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tensor([9])
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tensor([8])
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tensor([9])
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tensor([9])
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tensor([5])
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tensor([6])
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tensor([9])
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tensor([7])
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tensor([7])
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tensor([6])
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tensor([3])
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tensor([7])
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tensor([9])
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tensor([4])
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tensor([2])
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tensor([6])
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tensor([7])
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tensor([6])
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tensor([3])
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tensor([9])
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tensor([2])
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tensor([7])
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tensor([8])
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tensor([5])
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tensor([9])
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tensor([5])
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tensor([8])
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tensor([5])
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tensor([4])
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tensor([3])
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tensor([7])
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tensor([9])
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tensor([8])
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tensor([1])
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tensor([8])
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tensor([9])
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tensor([3])
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tensor([0])
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tensor([8])
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tensor([4])
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tensor([8])
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tensor([7])
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tensor([9])
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tensor([8])
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tensor([9])
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tensor([9])
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tensor([8])
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tensor([7])
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tensor([9])
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tensor([8])
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tensor([5])
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tensor([9])
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tensor([9])
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tensor([8])
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tensor([9])
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tensor([8])
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tensor([7])
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tensor([6])
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tensor([4])
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tensor([9])
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tensor([9])
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tensor([6])
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tensor([9])
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tensor([7])
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tensor([8])
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tensor([9])
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tensor([9])
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tensor([6])
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tensor([8])
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tensor([7])
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tensor([4])
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tensor([8])
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tensor([9])
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tensor([6])
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tensor([8])
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tensor([8])
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tensor([7])
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tensor([9])
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tensor([9])
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tensor([7])
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tensor([8])
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tensor([9])
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tensor([5])
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tensor([6])
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tensor([9])
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tensor([4])
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tensor([3])
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tensor([9])
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tensor([9])
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tensor([7])
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tensor([9])
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tensor([9])
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tensor([6])
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tensor([9])
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tensor([8])
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tensor([9])
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tensor([9])
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tensor([7])
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tensor([9])
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tensor([9])
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tensor([8])
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tensor([9])
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tensor([8])
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tensor([9])
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tensor([9])
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tensor([8])
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tensor([8])
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tensor([7])
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tensor([9])
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tensor([4])
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tensor([8])
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tensor([9])
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tensor([8])
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tensor([5])
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tensor([8])
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tensor([6])
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tensor([6])
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tensor([3])
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tensor([4])
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tensor([3])
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tensor([6])
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tensor([8])
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tensor([3])
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tensor([6])
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tensor([3])
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tensor([5])
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tensor([8])
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tensor([0])
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tensor([8])
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tensor([7])
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tensor([3])
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tensor([5])
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tensor([9])
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tensor([8])
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tensor([3])
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tensor([6])
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tensor([8])
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tensor([6])
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tensor([3])
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tensor([5])
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tensor([8])
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tensor([9])
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tensor([4])
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tensor([8])
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tensor([9])
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tensor([8])
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tensor([8])
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tensor([9])
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tensor([9])
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tensor([8])
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tensor([9])
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tensor([8])
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tensor([8])
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tensor([8])
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tensor([9])
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tensor([9])
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tensor([8])
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tensor([7])
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tensor([4])
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tensor([8])
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tensor([4])
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tensor([7])
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tensor([8])
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tensor([8])
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tensor([8])
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tensor([7])
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tensor([6])
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tensor([9])
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tensor([9])
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tensor([8])
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tensor([6])
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tensor([9])
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tensor([8])
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tensor([9])
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tensor([7])
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tensor([9])
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tensor([9])
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tensor([7])
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tensor([5])
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tensor([8])
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tensor([9])
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tensor([8])
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tensor([9])
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tensor([7])
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tensor([3])
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tensor([8])
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tensor([8])
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tensor([9])
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tensor([8])
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tensor([9])
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tensor([9])
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tensor([9])
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tensor([8])
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tensor([5])
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tensor([9])
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tensor([6])
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tensor([2])
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tensor([8])
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tensor([9])
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tensor([2])
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tensor([3])
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tensor([9])
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tensor([9])
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tensor([4])
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tensor([4])
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tensor([5])
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tensor([5])
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tensor([0])
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tensor([3])
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tensor([3])
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tensor([7])
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tensor([7])
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tensor([7])
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tensor([9])
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tensor([8])
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tensor([6])
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tensor([3])
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tensor([5])
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tensor([5])
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tensor([6])
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tensor([2])
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tensor([7])
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tensor([8])
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tensor([4])
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tensor([3])
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tensor([3])
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tensor([5])
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tensor([9])
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tensor([6])
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tensor([8])
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tensor([5])
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tensor([6])
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tensor([7])
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tensor([9])
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tensor([6])
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tensor([2])
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tensor([8])
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tensor([7])
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tensor([9])
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tensor([7])
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tensor([9])
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tensor([8])
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tensor([8])
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tensor([4])
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tensor([8])
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tensor([6])
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tensor([9])
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tensor([4])
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tensor([8])
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tensor([7])
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tensor([5])
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tensor([7])
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tensor([8])
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tensor([7])
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tensor([3])
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tensor([5])
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tensor([4])
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tensor([8])
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tensor([6])
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tensor([2])
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tensor([8])
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tensor([9])
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tensor([8])
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tensor([3])
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tensor([8])
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tensor([5])
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tensor([0])
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tensor([5])
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tensor([0])
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tensor([5])
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tensor([8])
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tensor([6])
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tensor([3])
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tensor([9])
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tensor([6])
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tensor([6])
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tensor([3])
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tensor([9])
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tensor([4])
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tensor([8])
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tensor([3])
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tensor([4])
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tensor([9])
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tensor([7])
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tensor([8])
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tensor([8])
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tensor([9])
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tensor([8])
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tensor([8])
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tensor([6])
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tensor([8])
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tensor([9])
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tensor([6])
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tensor([5])
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tensor([7])
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tensor([7])
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tensor([4])
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tensor([4])
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tensor([9])
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tensor([8])
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tensor([4])
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tensor([9])
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tensor([5])
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tensor([7])
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tensor([7])
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tensor([7])
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tensor([5])
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tensor([2])
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tensor([3])
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tensor([3])
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tensor([6])
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tensor([4])
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tensor([7])
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tensor([3])
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tensor([0])
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tensor([8])
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tensor([4])
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tensor([4])
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tensor([9])
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tensor([8])
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tensor([8])
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tensor([7])
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tensor([6])
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tensor([3])
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tensor([4])
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tensor([7])
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tensor([5])
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tensor([3])
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tensor([4])
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tensor([3])
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tensor([4])
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tensor([9])
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tensor([4])
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tensor([7])
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tensor([9])
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tensor([7])
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tensor([8])
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tensor([8])
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tensor([7])
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tensor([9])
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tensor([8])
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tensor([5])
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tensor([9])
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tensor([4])
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tensor([8])
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tensor([8])
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tensor([4])
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tensor([8])
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tensor([6])
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tensor([7])
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tensor([7])
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tensor([7])
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tensor([4])
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tensor([8])
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tensor([7])
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tensor([5])
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tensor([8])
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tensor([9])
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tensor([9])
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tensor([8])
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tensor([4])
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tensor([9])
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tensor([6])
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tensor([9])
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tensor([8])
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tensor([9])
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tensor([8])
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tensor([9])
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tensor([4])
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tensor([8])
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tensor([2])
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tensor([8])
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tensor([9])
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tensor([9])
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tensor([8])
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tensor([8])
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tensor([3])
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tensor([2])
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tensor([9])
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tensor([5])
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tensor([7])
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tensor([6])
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tensor([7])
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tensor([3])
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tensor([9])
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tensor([5])
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tensor([9])
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tensor([3])
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tensor([7])
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tensor([6])
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tensor([5])
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tensor([8])
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tensor([9])
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tensor([9])
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tensor([9])
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tensor([7])
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tensor([9])
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tensor([9])
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tensor([8])
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tensor([9])
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tensor([9])
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         -0.1542, -0.1090]], grad_fn=<AddmmBackward0>)
tensor([7])
tensor([[-0.1224, -0.1664, -0.0368, -0.0442,  0.0343, -0.1214, -0.1264, -0.0812,
         -0.1449, -0.1167]], grad_fn=<AddmmBackward0>)
tensor([5])
tensor([[-0.1412, -0.1879, -0.0389, -0.0417,  0.0154, -0.1379, -0.1436, -0.0697,
         -0.1118, -0.1223]], grad_fn=<AddmmBackward0>)
tensor([5])
tensor([[-0.1183, -0.1542, -0.0355, -0.0230,  0.0307, -0.1319, -0.1211, -0.0685,
         -0.1179, -0.1092]], grad_fn=<AddmmBackward0>)
tensor([7])
tensor([[-0.1322, -0.1432, -0.0572, -0.0512,  0.0270, -0.1077, -0.0985, -0.0713,
         -0.1341, -0.1049]], grad_fn=<AddmmBackward0>)
tensor([3])
tensor([[-0.1329, -0.1680, -0.0401, -0.0598,  0.0420, -0.1066, -0.0862, -0.1067,
         -0.1748, -0.1135]], grad_fn=<AddmmBackward0>)
tensor([0])
tensor([[-0.1350, -0.1552, -0.0355, -0.0331,  0.0329, -0.1235, -0.1303, -0.0705,
         -0.1312, -0.1131]], grad_fn=<AddmmBackward0>)
tensor([4])
tensor([[-0.1183, -0.1833, -0.0414, -0.0229,  0.0399, -0.1265, -0.1304, -0.0797,
         -0.1243, -0.1172]], grad_fn=<AddmmBackward0>)
tensor([2])
tensor([[-0.1237, -0.1485, -0.0256, -0.0218,  0.0402, -0.1205, -0.1177, -0.0847,
         -0.1269, -0.1188]], grad_fn=<AddmmBackward0>)
tensor([0])
tensor([[-0.1285, -0.1614, -0.0604, -0.0357,  0.0306, -0.1271, -0.1172, -0.0677,
         -0.1337, -0.1153]], grad_fn=<AddmmBackward0>)
tensor([7])
tensor([[-0.1113, -0.1687, -0.0431, -0.0148,  0.0296, -0.1134, -0.0944, -0.0694,
         -0.1371, -0.1075]], grad_fn=<AddmmBackward0>)
tensor([5])
tensor([[-0.1267, -0.1725, -0.0438, -0.0630,  0.0213, -0.1156, -0.1077, -0.0902,
         -0.1479, -0.1251]], grad_fn=<AddmmBackward0>)
tensor([8])
tensor([[-0.1403, -0.1494, -0.0543, -0.0501,  0.0102, -0.1189, -0.1048, -0.0720,
         -0.1386, -0.1256]], grad_fn=<AddmmBackward0>)
tensor([0])
tensor([[-0.1377, -0.1591, -0.0371, -0.0469,  0.0101, -0.0974, -0.0851, -0.0961,
         -0.1475, -0.1104]], grad_fn=<AddmmBackward0>)
tensor([8])
tensor([[-0.1367, -0.1641, -0.0496, -0.0394,  0.0246, -0.1325, -0.1300, -0.0619,
         -0.1380, -0.1242]], grad_fn=<AddmmBackward0>)
tensor([2])
tensor([[-0.1091, -0.1669, -0.0490, -0.0228,  0.0387, -0.1207, -0.0891, -0.0486,
         -0.1512, -0.1218]], grad_fn=<AddmmBackward0>)
tensor([7])
tensor([[-0.1276, -0.1433, -0.0525, -0.0503,  0.0107, -0.1040, -0.1007, -0.0876,
         -0.1459, -0.1203]], grad_fn=<AddmmBackward0>)
tensor([0])
tensor([[-0.1252, -0.1749, -0.0496, -0.0417,  0.0247, -0.1432, -0.1227, -0.0660,
         -0.1309, -0.1300]], grad_fn=<AddmmBackward0>)
tensor([3])
tensor([[-0.1232, -0.1794, -0.0534, -0.0416,  0.0223, -0.1197, -0.1212, -0.0648,
         -0.1387, -0.1232]], grad_fn=<AddmmBackward0>)
tensor([5])
tensor([[-0.1263, -0.1502, -0.0356, -0.0254,  0.0298, -0.1187, -0.1072, -0.0689,
         -0.1241, -0.1210]], grad_fn=<AddmmBackward0>)
tensor([3])
tensor([[-0.1336, -0.1777, -0.0486, -0.0679,  0.0104, -0.1461, -0.1021, -0.0791,
         -0.1319, -0.1568]], grad_fn=<AddmmBackward0>)
tensor([8])
tensor([[-0.1287, -0.1627, -0.0491, -0.0421,  0.0299, -0.1209, -0.1274, -0.0760,
         -0.1296, -0.1147]], grad_fn=<AddmmBackward0>)
tensor([3])
tensor([[-0.1154, -0.1393, -0.0361, -0.0205,  0.0449, -0.1282, -0.1012, -0.0759,
         -0.1193, -0.1259]], grad_fn=<AddmmBackward0>)
tensor([5])
tensor([[-0.1181, -0.1839, -0.0368, -0.0412,  0.0291, -0.1358, -0.1153, -0.0760,
         -0.1292, -0.1292]], grad_fn=<AddmmBackward0>)
tensor([1])
tensor([[-0.1241, -0.1493, -0.0365, -0.0312,  0.0431, -0.1140, -0.1047, -0.0975,
         -0.1279, -0.1058]], grad_fn=<AddmmBackward0>)
tensor([7])

6. 数据集计算损失函数

import torch
import torchvision
from torch import nn 
from torch.nn import Conv2d, MaxPool2d, Flatten, Linear, Sequential
from torch.utils.data import DataLoader
from torch.utils.tensorboard import SummaryWriter

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

class Tudui(nn.Module):
    def __init__(self):
        super(Tudui, self).__init__()        
        self.model1 = Sequential(
            Conv2d(3,32,5,padding=2),
            MaxPool2d(2),
            Conv2d(32,32,5,padding=2),
            MaxPool2d(2),
            Conv2d(32,64,5,padding=2),
            MaxPool2d(2),
            Flatten(),
            Linear(1024,64),
            Linear(64,10)
        )
        
    def forward(self, x):
        x = self.model1(x)
        return x
    
loss = nn.CrossEntropyLoss() # 交叉熵    
tudui = Tudui()
for data in dataloader:
    imgs, targets = data
    outputs = tudui(imgs)
    result_loss = loss(outputs, targets) # 计算实际输出与目标输出的差距
    print(result_loss)

Files already downloaded and verified
tensor(2.2994, grad_fn=<NllLossBackward0>)
tensor(2.2952, grad_fn=<NllLossBackward0>)
tensor(2.3162, grad_fn=<NllLossBackward0>)
tensor(2.3234, grad_fn=<NllLossBackward0>)
tensor(2.2983, grad_fn=<NllLossBackward0>)
tensor(2.3051, grad_fn=<NllLossBackward0>)
tensor(2.2991, grad_fn=<NllLossBackward0>)
tensor(2.3084, grad_fn=<NllLossBackward0>)
tensor(2.3059, grad_fn=<NllLossBackward0>)
tensor(2.3066, grad_fn=<NllLossBackward0>)
tensor(2.2939, grad_fn=<NllLossBackward0>)
tensor(2.3037, grad_fn=<NllLossBackward0>)
tensor(2.2835, grad_fn=<NllLossBackward0>)
tensor(2.2898, grad_fn=<NllLossBackward0>)
tensor(2.3043, grad_fn=<NllLossBackward0>)
tensor(2.3290, grad_fn=<NllLossBackward0>)
tensor(2.2848, grad_fn=<NllLossBackward0>)
tensor(2.3043, grad_fn=<NllLossBackward0>)
tensor(2.3056, grad_fn=<NllLossBackward0>)
tensor(2.3195, grad_fn=<NllLossBackward0>)
tensor(2.3193, grad_fn=<NllLossBackward0>)
tensor(2.3130, grad_fn=<NllLossBackward0>)
tensor(2.3068, grad_fn=<NllLossBackward0>)
tensor(2.2979, grad_fn=<NllLossBackward0>)
tensor(2.3035, grad_fn=<NllLossBackward0>)
tensor(2.3011, grad_fn=<NllLossBackward0>)
tensor(2.3147, grad_fn=<NllLossBackward0>)
tensor(2.3126, grad_fn=<NllLossBackward0>)
tensor(2.3177, grad_fn=<NllLossBackward0>)
tensor(2.3124, grad_fn=<NllLossBackward0>)
tensor(2.2949, grad_fn=<NllLossBackward0>)
tensor(2.3179, grad_fn=<NllLossBackward0>)
tensor(2.3062, grad_fn=<NllLossBackward0>)
tensor(2.3132, grad_fn=<NllLossBackward0>)
tensor(2.3004, grad_fn=<NllLossBackward0>)
tensor(2.3095, grad_fn=<NllLossBackward0>)
tensor(2.3018, grad_fn=<NllLossBackward0>)
tensor(2.3118, grad_fn=<NllLossBackward0>)
tensor(2.3170, grad_fn=<NllLossBackward0>)
tensor(2.2988, grad_fn=<NllLossBackward0>)
tensor(2.2960, grad_fn=<NllLossBackward0>)
tensor(2.2833, grad_fn=<NllLossBackward0>)
tensor(2.3189, grad_fn=<NllLossBackward0>)
tensor(2.2927, grad_fn=<NllLossBackward0>)
tensor(2.2946, grad_fn=<NllLossBackward0>)
tensor(2.3104, grad_fn=<NllLossBackward0>)
tensor(2.3118, grad_fn=<NllLossBackward0>)
tensor(2.3017, grad_fn=<NllLossBackward0>)
tensor(2.3059, grad_fn=<NllLossBackward0>)
tensor(2.3191, grad_fn=<NllLossBackward0>)
tensor(2.2971, grad_fn=<NllLossBackward0>)
tensor(2.3065, grad_fn=<NllLossBackward0>)
tensor(2.3048, grad_fn=<NllLossBackward0>)
tensor(2.3074, grad_fn=<NllLossBackward0>)
tensor(2.2991, grad_fn=<NllLossBackward0>)
tensor(2.3185, grad_fn=<NllLossBackward0>)
tensor(2.2937, grad_fn=<NllLossBackward0>)
tensor(2.3057, grad_fn=<NllLossBackward0>)
tensor(2.3080, grad_fn=<NllLossBackward0>)
tensor(2.3141, grad_fn=<NllLossBackward0>)
tensor(2.3018, grad_fn=<NllLossBackward0>)
tensor(2.3121, grad_fn=<NllLossBackward0>)
tensor(2.3113, grad_fn=<NllLossBackward0>)
tensor(2.2942, grad_fn=<NllLossBackward0>)
tensor(2.3001, grad_fn=<NllLossBackward0>)
tensor(2.3064, grad_fn=<NllLossBackward0>)
tensor(2.3268, grad_fn=<NllLossBackward0>)
tensor(2.3142, grad_fn=<NllLossBackward0>)
tensor(2.3031, grad_fn=<NllLossBackward0>)
tensor(2.3125, grad_fn=<NllLossBackward0>)
tensor(2.3007, grad_fn=<NllLossBackward0>)
tensor(2.2948, grad_fn=<NllLossBackward0>)
tensor(2.2963, grad_fn=<NllLossBackward0>)
tensor(2.2952, grad_fn=<NllLossBackward0>)
tensor(2.3121, grad_fn=<NllLossBackward0>)
tensor(2.3139, grad_fn=<NllLossBackward0>)
tensor(2.3107, grad_fn=<NllLossBackward0>)
tensor(2.3138, grad_fn=<NllLossBackward0>)
tensor(2.2930, grad_fn=<NllLossBackward0>)
tensor(2.3238, grad_fn=<NllLossBackward0>)
tensor(2.3019, grad_fn=<NllLossBackward0>)
tensor(2.2962, grad_fn=<NllLossBackward0>)
tensor(2.2952, grad_fn=<NllLossBackward0>)
tensor(2.2989, grad_fn=<NllLossBackward0>)
tensor(2.2985, grad_fn=<NllLossBackward0>)
tensor(2.3154, grad_fn=<NllLossBackward0>)
tensor(2.2773, grad_fn=<NllLossBackward0>)
tensor(2.3149, grad_fn=<NllLossBackward0>)
tensor(2.2925, grad_fn=<NllLossBackward0>)
tensor(2.2947, grad_fn=<NllLossBackward0>)
tensor(2.3068, grad_fn=<NllLossBackward0>)
tensor(2.2909, grad_fn=<NllLossBackward0>)
tensor(2.2964, grad_fn=<NllLossBackward0>)
tensor(2.3057, grad_fn=<NllLossBackward0>)
tensor(2.3012, grad_fn=<NllLossBackward0>)
tensor(2.3204, grad_fn=<NllLossBackward0>)
tensor(2.3095, grad_fn=<NllLossBackward0>)
tensor(2.3012, grad_fn=<NllLossBackward0>)
tensor(2.3043, grad_fn=<NllLossBackward0>)
tensor(2.2964, grad_fn=<NllLossBackward0>)
tensor(2.3131, grad_fn=<NllLossBackward0>)
tensor(2.2971, grad_fn=<NllLossBackward0>)
tensor(2.3228, grad_fn=<NllLossBackward0>)
tensor(2.3057, grad_fn=<NllLossBackward0>)
tensor(2.3015, grad_fn=<NllLossBackward0>)
tensor(2.3196, grad_fn=<NllLossBackward0>)
tensor(2.3175, grad_fn=<NllLossBackward0>)
tensor(2.2937, grad_fn=<NllLossBackward0>)
tensor(2.3081, grad_fn=<NllLossBackward0>)
tensor(2.3053, grad_fn=<NllLossBackward0>)
tensor(2.2995, grad_fn=<NllLossBackward0>)
tensor(2.3188, grad_fn=<NllLossBackward0>)
tensor(2.2883, grad_fn=<NllLossBackward0>)
tensor(2.3213, grad_fn=<NllLossBackward0>)
tensor(2.2918, grad_fn=<NllLossBackward0>)
tensor(2.2998, grad_fn=<NllLossBackward0>)
tensor(2.3263, grad_fn=<NllLossBackward0>)
tensor(2.3028, grad_fn=<NllLossBackward0>)
tensor(2.3192, grad_fn=<NllLossBackward0>)
tensor(2.3196, grad_fn=<NllLossBackward0>)
tensor(2.3073, grad_fn=<NllLossBackward0>)
tensor(2.3012, grad_fn=<NllLossBackward0>)
tensor(2.2949, grad_fn=<NllLossBackward0>)
tensor(2.3062, grad_fn=<NllLossBackward0>)
tensor(2.3161, grad_fn=<NllLossBackward0>)
tensor(2.3182, grad_fn=<NllLossBackward0>)
tensor(2.3036, grad_fn=<NllLossBackward0>)
tensor(2.3117, grad_fn=<NllLossBackward0>)
tensor(2.2998, grad_fn=<NllLossBackward0>)
tensor(2.3089, grad_fn=<NllLossBackward0>)
tensor(2.2902, grad_fn=<NllLossBackward0>)
tensor(2.3173, grad_fn=<NllLossBackward0>)
tensor(2.3201, grad_fn=<NllLossBackward0>)
tensor(2.2994, grad_fn=<NllLossBackward0>)
tensor(2.2973, grad_fn=<NllLossBackward0>)
tensor(2.2989, grad_fn=<NllLossBackward0>)
tensor(2.3062, grad_fn=<NllLossBackward0>)
tensor(2.2890, grad_fn=<NllLossBackward0>)
tensor(2.3169, grad_fn=<NllLossBackward0>)
tensor(2.3027, grad_fn=<NllLossBackward0>)
tensor(2.3080, grad_fn=<NllLossBackward0>)
tensor(2.3102, grad_fn=<NllLossBackward0>)
tensor(2.2997, grad_fn=<NllLossBackward0>)
tensor(2.3128, grad_fn=<NllLossBackward0>)
tensor(2.3074, grad_fn=<NllLossBackward0>)
tensor(2.2941, grad_fn=<NllLossBackward0>)
tensor(2.3240, grad_fn=<NllLossBackward0>)
tensor(2.2948, grad_fn=<NllLossBackward0>)
tensor(2.3057, grad_fn=<NllLossBackward0>)
tensor(2.3135, grad_fn=<NllLossBackward0>)
tensor(2.3261, grad_fn=<NllLossBackward0>)
tensor(2.2970, grad_fn=<NllLossBackward0>)
tensor(2.3017, grad_fn=<NllLossBackward0>)
tensor(2.3292, grad_fn=<NllLossBackward0>)
tensor(2.3188, grad_fn=<NllLossBackward0>)
tensor(2.2930, grad_fn=<NllLossBackward0>)

7. 损失函数反向传播

① 反向传播通过梯度来更新参数，使得loss损失最小，如下图所示。

import torch
import torchvision
from torch import nn 
from torch.nn import Conv2d, MaxPool2d, Flatten, Linear, Sequential
from torch.utils.data import DataLoader
from torch.utils.tensorboard import SummaryWriter

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

class Tudui(nn.Module):
    def __init__(self):
        super(Tudui, self).__init__()        
        self.model1 = Sequential(
            Conv2d(3,32,5,padding=2),
            MaxPool2d(2),
            Conv2d(32,32,5,padding=2),
            MaxPool2d(2),
            Conv2d(32,64,5,padding=2),
            MaxPool2d(2),
            Flatten(),
            Linear(1024,64),
            Linear(64,10)
        )
        
    def forward(self, x):
        x = self.model1(x)
        return x
    
loss = nn.CrossEntropyLoss() # 交叉熵    
tudui = Tudui()
for data in dataloader:
    imgs, targets = data
    outputs = tudui(imgs)
    result_loss = loss(outputs, targets) # 计算实际输出与目标输出的差距
    result_loss.backward()  # 计算出来的 loss 值有 backward 方法属性，反向传播来计算每个节点的更新的参数。这里查看网络的属性 grad 梯度属性刚开始没有，反向传播计算出来后才有，后面优化器会利用梯度优化网络参数。      
    print("ok")

Files already downloaded and verified
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