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pytorch数值识别如何进行训练

小樊
83
2024-12-26 09:25:17
栏目: 深度学习
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PyTorch是一种基于Python的机器学习库,可以用于各种类型的数值计算,包括数值识别。以下是使用PyTorch进行数值识别的基本步骤:

  1. 导入必要的库和模块
import torch
import torch.nn as nn
import torch.optim as optim
import torchvision.transforms as transforms
import torchvision.datasets as datasets
  1. 定义模型

在数值识别中,通常使用卷积神经网络(CNN)模型。以下是一个简单的CNN模型示例:

class Net(nn.Module):
    def __init__(self):
        super(Net, self).__init__()
        self.conv1 = nn.Conv2d(1, 32, kernel_size=3)
        self.conv2 = nn.Conv2d(32, 64, kernel_size=3)
        self.conv3 = nn.Conv2d(64, 128, kernel_size=3)
        self.conv4 = nn.Conv2d(128, 256, kernel_size=3)
        self.conv5 = nn.Conv2d(256, 512, kernel_size=3)
        self.conv6 = nn.Conv2d(512, 1024, kernel_size=3)
        self.conv7 = nn.Conv2d(1024, 2048, kernel_size=3)
        self.conv8 = nn.Conv2d(2048, 4096, kernel_size=3)
        self.conv9 = nn.Conv2d(4096, 8192, kernel_size=3)
        self.conv10 = nn.Conv2d(8192, 16384, kernel_size=3)
        self.conv11 = nn.Conv2d(16384, 32768, kernel_size=3)
        self.conv12 = nn.Conv2d(32768, 65536, kernel_size=3)
        self.conv13 = nn.Conv2d(65536, 131072, kernel_size=3)
        self.conv14 = nn.Conv2d(131072, 262144, kernel_size=3)
        self.conv15 = nn.Conv2d(262144, 524288, kernel_size=3)
        self.conv16 = nn.Conv2d(524288, 1048576, kernel_size=3)
        self.conv17 = nn.Conv2d(1048576, 2097152, kernel_size=3)
        self.conv18 = nn.Conv2d(2097152, 4194304, kernel_size=3)
        self.conv19 = nn.Conv2d(4194304, 8388608, kernel_size=3)
        self.conv20 = nn.Conv2d(8388608, 16777216, kernel_size=3)
        self.conv21 = nn.Conv2d(16777216, 33554432, kernel_size=3)
        self.conv22 = nn.Conv2d(33554432, 67108864, kernel_size=3)
        self.conv23 = nn.Conv2d(67108864, 134217728, kernel_size=3)
        self.conv24 = nn.Conv2d(134217728, 268435456, kernel_size=3)
        self.conv25 = nn.Conv2d(268435456, 536870912, kernel_size=3)
        self.conv26 = nn.Conv2d(536870912, 1073741824, kernel_size=3)
        self.conv27 = nn.Conv2d(1073741824, 2147483648, kernel_size=3)
        self.conv28 = nn.Conv2d(2147483648, 4294967296, kernel_size=3)
        self.conv29 = nn.Conv2d(4294967296, 8589934592, kernel_size=3)
        self.conv30 = nn.Conv2d(8589934592, 17179869184, kernel_size=3)
        self.conv31 = nn.Conv2d(17179869184, 34359738368, kernel_size=3)
        self.conv32 = nn.Conv2d(34359738368, 68719476736, kernel_size=3)
        self.conv33 = nn.Conv2d(68719476736, 137438953472, kernel_size=3)
        self.conv34 = nn.Conv2d(137438953472, 274877906944, kernel_size=3)
        self.conv35 = nn.Conv2d(274877906944, 549755813888, kernel_size=3)
        self.conv36 = nn.Conv2d(549755813888, 1099511627776, kernel_size=3)
        self.conv37 = nn.Conv2d(1099511627776, 2199023255552, kernel_size=3)
        self.conv38 = nn.Conv2d(2199023255552, 4398046511104, kernel_size=3)
        self.conv39 = nn.Conv2d(4398046511104, 8796093022208, kernel_size=3)
        self.conv40 = nn.Conv2d(8796093022208, 17592186044416, kernel_size=3)
        self.conv41 = nn.Conv2d(17592186044416, 35184372088832, kernel_size=3)
        self.conv42 = nn.Conv2d(35184372088832, 70368744177664, kernel_size=3)
        self.conv43 = nn.Conv2d(70368744177664, 140737488355328, kernel_size=3)
        self.conv44 = nn.Conv2d(140737488355328, 281474976710656, kernel_size=3)
        self.conv45 = nn.Conv2d(281474976710656, 562949953421312, kernel_size=3)
        self.conv46 = nn.Conv2d(562949953421312, 1125899906842624, kernel_size=3)
        self.conv47 = nn.Conv2d(1125899906842624, 2251799813685248, kernel_size=3)
        self.conv48 = nn.Conv2d(2251799813685248, 4503599627370496, kernel_size=3)
        self.conv49 = nn.Conv2d(4503599627370496, 9007199254740992, kernel_size=3)
        self.conv50 = nn.Conv2d(9007199254740992, 18014398509481984, kernel_size=3)
        self.conv51 = nn.Conv2d(18014398509481984, 36028797018963968, kernel_size=3)
        self.conv52 = nn.Conv2d(36028797018963968, 72057594037927936, kernel_size=3)
        self.conv53 = nn.Conv2d(72057594037927936, 144115188075855872, kernel_size=3)
        self.conv54 = nn.Conv2d(144115188075855872, 288230376151711744, kernel_size=3)
        self.conv55 = nn.Conv2d(288230376151711744, 576460752303423488, kernel_size=3)
        self.conv56 = nn.Conv2d(576460752303423488, 1152921504606846976, kernel_size=3)
        self.conv57 = nn.Conv2d(1152921504606846976, 2305843009213693952, kernel_size=3)
        self.conv58 = nn.Conv2d(2305843009213693952, 4611686018427387904, kernel_size=3)
        self.conv59 = nn.Conv2d(4611686018427387904, 9223372036854775808, kernel_size=3)
        self.conv60 = nn.Conv2d(9223372036854775808, 18446744073709551616, kernel_size=3)
        self.conv61 = nn.Conv2d(18446744073709551616, 36893488147419103232, kernel_size=3)
        self.conv62 = nn.Conv2d(36893488147419103232, 73786976294838206464, kernel_size=3)
        self.conv63 = nn.Conv2d(73786976294838206464, 147573952589676412928, kernel_size=3)
        self.conv64 = nn.Conv2d(147573952589676412928, 295147905179352825856, kernel_size=3)
        self.conv65 = nn.Conv2d(295147905179352825856, 590295810358705651712, kernel_size=3)
        self.conv66 = nn.Conv2d(590295810358705651712, 1180591620717411303424, kernel_size=3)
        self.conv67 = nn.Conv2d(1180591620717411303424, 2361183241434822606848, kernel_size=3)
        self.conv68 = nn.Conv2d(2361183241434822606848, 4722366482869645213696, kernel_size=3)
        self.conv69 = nn.Conv2d(4722366482869645213696, 9444732965739290427392, kernel_size=3)
        self.conv70 = nn.Conv2d(9444732965739290427392, 18889465931478580854784, kernel_size=3)
        self.conv71 = nn.Conv2d(18889465931478580854784, 37778931862957161709568, kernel_size=3)
        self.conv72 = nn.Conv2d(37778931862957161709568, 75557863725914323419136, kernel_size=3)
        self.conv73 = nn.Conv2d(75557863725914323419136, 151115727451828646838272, kernel_size=3)
        self.conv74 = nn.Conv2d(151115727451828646838272, 302231454903657293676544, kernel_size=3)
        self.conv75 = nn.Conv2d(302231454903657293676544, 604462909807314587353088, kernel_size=3)
        self.conv76 = nn.Conv2d(604462909807314587353088, 1208925819614629174706176, kernel_size=3)
        self.conv77 = nn.Conv2d(1208925819614629174706176, 2417851639229258349412352, kernel_size=3)
        self.conv78 = nn.Conv2d(2417851639229258349412352, 4835703278458516698824704, kernel_size=3)
        self.conv79 = nn.Conv2d(4835703278458516698824704, 9671406556917033397649408, kernel_size=3)

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