php服装网站模板,正规网站建设加盟合作,服务器网站扩容 一年1G价格,太原cms建站模板#x1f368; 本文为#x1f517;365天深度学习训练营 中的学习记录博客#x1f356; 原作者#xff1a;K同学啊 Inception v1算法实战与解析 导入数据数据预处理划分数据集搭建模型训练模型正式训练结果可视化详细网络结构图个人总结 import os, PIL, random, pathlib
imp… 本文为365天深度学习训练营 中的学习记录博客 原作者K同学啊 Inception v1算法实战与解析 导入数据数据预处理划分数据集搭建模型训练模型正式训练结果可视化详细网络结构图个人总结 import os, PIL, random, pathlib
import torch
import torch.nn as nn
import torchvision.transforms as transforms
import torchvision
from torchvision import transforms, datasets
import torch.nn.functional as Fdevice torch.device(cuda if torch.cuda.is_available() else cpu)devicedevice(typecuda)导入数据 data_dir rC:\Users\11054\Desktop\kLearning\p4_learning\data
data_dir pathlib.Path(data_dir)data_paths list(data_dir.glob(*))
classeNames [str(path).split(\\)[-1] for path in data_paths]
print(classeNames)image_count len(list(data_dir.glob(*/*)))
print(图片总数为, image_count)[Monkeypox, Others]
图片总数为 2142数据预处理 train_transforms transforms.Compose([transforms.Resize([224, 224]), # 将输入图片resize成统一尺寸# transforms.RandomHorizontalFlip(), # 随机水平翻转transforms.ToTensor(), # 将PIL Image或numpy.ndarray转换为tensor并归一化到[0,1]之间transforms.Normalize( # 标准化处理--转换为标准正太分布高斯分布使模型更容易收敛mean[0.485, 0.456, 0.406],std[0.229, 0.224, 0.225]) # 其中 mean[0.485,0.456,0.406]与std[0.229,0.224,0.225] 从数据集中随机抽样计算得到的。
])test_transform transforms.Compose([transforms.Resize([224, 224]), # 将输入图片resize成统一尺寸transforms.ToTensor(), # 将PIL Image或numpy.ndarray转换为tensor并归一化到[0,1]之间transforms.Normalize( # 标准化处理--转换为标准正太分布高斯分布使模型更容易收敛mean[0.485, 0.456, 0.406],std[0.229, 0.224, 0.225]) # 其中 mean[0.485,0.456,0.406]与std[0.229,0.224,0.225] 从数据集中随机抽样计算得到的。
])total_data datasets.ImageFolder(data_dir, transformtrain_transforms)
print(total_data.class_to_idx)
{Monkeypox: 0, Others: 1}划分数据集 train_size int(0.8 * len(total_data))
test_size len(total_data) - train_size
train_dataset, test_dataset torch.utils.data.random_split(total_data, [train_size, test_size])batch_size 8 #根据自己的显卡选择合适的batch_size大小
train_dl torch.utils.data.DataLoader(train_dataset,batch_sizebatch_size,shuffleTrue,num_workers0)
test_dl torch.utils.data.DataLoader(test_dataset,batch_sizebatch_size,shuffleTrue,num_workers0)
for X, y in test_dl:print(Shape of X [N, C, H, W]: , X.shape)print(Shape of y: , y.shape, y.dtype)break
Shape of X [N, C, H, W]: torch.Size([8, 3, 224, 224])
Shape of y: torch.Size([8]) torch.int64搭建模型 class inception_block(nn.Module):def __init__(self, in_channels, ch1x1, ch3x3red, ch3x3, ch5x5red, ch5x5, pool_proj):super(inception_block, self).__init__()# 1x1 conv branchself.branch1 nn.Sequential(nn.Conv2d(in_channels, ch1x1, kernel_size1),nn.BatchNorm2d(ch1x1),nn.ReLU(inplaceTrue))# 1x1 conv - 3x3 conv branchself.branch2 nn.Sequential(nn.Conv2d(in_channels, ch3x3red, kernel_size1),nn.BatchNorm2d(ch3x3red),nn.ReLU(inplaceTrue),nn.Conv2d(ch3x3red, ch3x3, kernel_size3, padding1),nn.BatchNorm2d(ch3x3),nn.ReLU(inplaceTrue))# 1x1 conv - 5x5 conv branchself.branch3 nn.Sequential(nn.Conv2d(in_channels, ch5x5red, kernel_size1),nn.BatchNorm2d(ch5x5red),nn.ReLU(inplaceTrue),nn.Conv2d(ch5x5red, ch5x5, kernel_size5, padding2),nn.BatchNorm2d(ch5x5),nn.ReLU(inplaceTrue))# 3x3 max pooling - 1x1 conv branchself.branch4 nn.Sequential(nn.MaxPool2d(kernel_size3, stride1, padding1),nn.Conv2d(in_channels, pool_proj, kernel_size1),nn.BatchNorm2d(pool_proj),nn.ReLU(inplaceTrue))def forward(self, x):# Compute forward pass through all branches and concatenate the output feature mapsbranch1_output self.branch1(x)branch2_output self.branch2(x)branch3_output self.branch3(x)branch4_output self.branch4(x)outputs [branch1_output, branch2_output, branch3_output, branch4_output]return torch.cat(outputs, 1)
class InceptionV1(nn.Module):def __init__(self, num_classes1000):super(InceptionV1, self).__init__()self.conv1 nn.Conv2d(3, 64, kernel_size7, stride2, padding3)self.maxpool1 nn.MaxPool2d(kernel_size3, stride2, padding1)self.conv2 nn.Conv2d(64, 64, kernel_size1, stride1, padding0)self.conv3 nn.Conv2d(64, 192, kernel_size3, stride1, padding1)self.maxpool2 nn.MaxPool2d(kernel_size3, stride2, padding1)self.inception3a inception_block(192, 64, 96, 128, 16, 32, 32)self.inception3b inception_block(256, 128, 128, 192, 32, 96, 64)self.maxpool3 nn.MaxPool2d(kernel_size3, stride2, padding1)self.inception4a inception_block(480, 192, 96, 208, 16, 48, 64)self.inception4b inception_block(512, 160, 112, 224, 24, 64, 64)self.inception4c inception_block(512, 128, 128, 256, 24, 64, 64)self.inception4d inception_block(512, 112, 144, 288, 32, 64, 64)self.inception4e inception_block(528, 256, 160, 320, 32, 128, 128)self.maxpool4 nn.MaxPool2d(kernel_size3, stride2, padding1)self.inception5a inception_block(832, 256, 160, 320, 32, 128, 128)self.inception5bnn.Sequential(inception_block(832, 384, 192, 384, 48, 128, 128),nn.AvgPool2d(kernel_size7,stride1,padding0),nn.Dropout(0.4))# 全连接网络层用于分类self.classifier nn.Sequential(nn.Linear(in_features1024, out_features1024),nn.ReLU(),nn.Linear(in_features1024, out_featuresnum_classes),nn.Softmax(dim1))def forward(self, x):x self.conv1(x)x F.relu(x)x self.maxpool1(x)x self.conv2(x)x F.relu(x)x self.conv3(x)x F.relu(x)x self.maxpool2(x)x self.inception3a(x)x self.inception3b(x)x self.maxpool3(x)x self.inception4a(x)x self.inception4b(x)x self.inception4c(x)x self.inception4d(x)x self.inception4e(x)x self.maxpool4(x)x self.inception5a(x)x self.inception5b(x)x torch.flatten(x, start_dim1)x self.classifier(x)return x# 统计模型参数量以及其他指标
import torchsummary# 调用并将模型转移到GPU中
model InceptionV1(num_classes2).to(device)# 显示网络结构
torchsummary.summary(model, (3, 224, 224))
print(model)
----------------------------------------------------------------Layer (type) Output Shape Param #
Conv2d-1 [-1, 64, 112, 112] 9,472MaxPool2d-2 [-1, 64, 56, 56] 0Conv2d-3 [-1, 64, 56, 56] 4,160Conv2d-4 [-1, 192, 56, 56] 110,784MaxPool2d-5 [-1, 192, 28, 28] 0Conv2d-6 [-1, 64, 28, 28] 12,352BatchNorm2d-7 [-1, 64, 28, 28] 128ReLU-8 [-1, 64, 28, 28] 0Conv2d-9 [-1, 96, 28, 28] 18,528BatchNorm2d-10 [-1, 96, 28, 28] 192ReLU-11 [-1, 96, 28, 28] 0Conv2d-12 [-1, 128, 28, 28] 110,720BatchNorm2d-13 [-1, 128, 28, 28] 256ReLU-14 [-1, 128, 28, 28] 0Conv2d-15 [-1, 16, 28, 28] 3,088BatchNorm2d-16 [-1, 16, 28, 28] 32ReLU-17 [-1, 16, 28, 28] 0Conv2d-18 [-1, 32, 28, 28] 12,832BatchNorm2d-19 [-1, 32, 28, 28] 64ReLU-20 [-1, 32, 28, 28] 0MaxPool2d-21 [-1, 192, 28, 28] 0Conv2d-22 [-1, 32, 28, 28] 6,176BatchNorm2d-23 [-1, 32, 28, 28] 64ReLU-24 [-1, 32, 28, 28] 0inception_block-25 [-1, 256, 28, 28] 0Conv2d-26 [-1, 128, 28, 28] 32,896BatchNorm2d-27 [-1, 128, 28, 28] 256ReLU-28 [-1, 128, 28, 28] 0Conv2d-29 [-1, 128, 28, 28] 32,896BatchNorm2d-30 [-1, 128, 28, 28] 256ReLU-31 [-1, 128, 28, 28] 0Conv2d-32 [-1, 192, 28, 28] 221,376BatchNorm2d-33 [-1, 192, 28, 28] 384ReLU-34 [-1, 192, 28, 28] 0Conv2d-35 [-1, 32, 28, 28] 8,224BatchNorm2d-36 [-1, 32, 28, 28] 64ReLU-37 [-1, 32, 28, 28] 0Conv2d-38 [-1, 96, 28, 28] 76,896BatchNorm2d-39 [-1, 96, 28, 28] 192ReLU-40 [-1, 96, 28, 28] 0MaxPool2d-41 [-1, 256, 28, 28] 0Conv2d-42 [-1, 64, 28, 28] 16,448BatchNorm2d-43 [-1, 64, 28, 28] 128ReLU-44 [-1, 64, 28, 28] 0inception_block-45 [-1, 480, 28, 28] 0MaxPool2d-46 [-1, 480, 14, 14] 0Conv2d-47 [-1, 192, 14, 14] 92,352BatchNorm2d-48 [-1, 192, 14, 14] 384ReLU-49 [-1, 192, 14, 14] 0Conv2d-50 [-1, 96, 14, 14] 46,176BatchNorm2d-51 [-1, 96, 14, 14] 192ReLU-52 [-1, 96, 14, 14] 0Conv2d-53 [-1, 208, 14, 14] 179,920BatchNorm2d-54 [-1, 208, 14, 14] 416ReLU-55 [-1, 208, 14, 14] 0Conv2d-56 [-1, 16, 14, 14] 7,696BatchNorm2d-57 [-1, 16, 14, 14] 32ReLU-58 [-1, 16, 14, 14] 0Conv2d-59 [-1, 48, 14, 14] 19,248BatchNorm2d-60 [-1, 48, 14, 14] 96ReLU-61 [-1, 48, 14, 14] 0MaxPool2d-62 [-1, 480, 14, 14] 0Conv2d-63 [-1, 64, 14, 14] 30,784BatchNorm2d-64 [-1, 64, 14, 14] 128ReLU-65 [-1, 64, 14, 14] 0inception_block-66 [-1, 512, 14, 14] 0Conv2d-67 [-1, 160, 14, 14] 82,080BatchNorm2d-68 [-1, 160, 14, 14] 320ReLU-69 [-1, 160, 14, 14] 0Conv2d-70 [-1, 112, 14, 14] 57,456BatchNorm2d-71 [-1, 112, 14, 14] 224ReLU-72 [-1, 112, 14, 14] 0Conv2d-73 [-1, 224, 14, 14] 226,016BatchNorm2d-74 [-1, 224, 14, 14] 448ReLU-75 [-1, 224, 14, 14] 0Conv2d-76 [-1, 24, 14, 14] 12,312BatchNorm2d-77 [-1, 24, 14, 14] 48ReLU-78 [-1, 24, 14, 14] 0Conv2d-79 [-1, 64, 14, 14] 38,464BatchNorm2d-80 [-1, 64, 14, 14] 128ReLU-81 [-1, 64, 14, 14] 0MaxPool2d-82 [-1, 512, 14, 14] 0Conv2d-83 [-1, 64, 14, 14] 32,832BatchNorm2d-84 [-1, 64, 14, 14] 128ReLU-85 [-1, 64, 14, 14] 0inception_block-86 [-1, 512, 14, 14] 0Conv2d-87 [-1, 128, 14, 14] 65,664BatchNorm2d-88 [-1, 128, 14, 14] 256ReLU-89 [-1, 128, 14, 14] 0Conv2d-90 [-1, 128, 14, 14] 65,664BatchNorm2d-91 [-1, 128, 14, 14] 256ReLU-92 [-1, 128, 14, 14] 0Conv2d-93 [-1, 256, 14, 14] 295,168BatchNorm2d-94 [-1, 256, 14, 14] 512ReLU-95 [-1, 256, 14, 14] 0Conv2d-96 [-1, 24, 14, 14] 12,312BatchNorm2d-97 [-1, 24, 14, 14] 48ReLU-98 [-1, 24, 14, 14] 0Conv2d-99 [-1, 64, 14, 14] 38,464BatchNorm2d-100 [-1, 64, 14, 14] 128ReLU-101 [-1, 64, 14, 14] 0MaxPool2d-102 [-1, 512, 14, 14] 0Conv2d-103 [-1, 64, 14, 14] 32,832BatchNorm2d-104 [-1, 64, 14, 14] 128ReLU-105 [-1, 64, 14, 14] 0inception_block-106 [-1, 512, 14, 14] 0Conv2d-107 [-1, 112, 14, 14] 57,456BatchNorm2d-108 [-1, 112, 14, 14] 224ReLU-109 [-1, 112, 14, 14] 0Conv2d-110 [-1, 144, 14, 14] 73,872BatchNorm2d-111 [-1, 144, 14, 14] 288ReLU-112 [-1, 144, 14, 14] 0Conv2d-113 [-1, 288, 14, 14] 373,536BatchNorm2d-114 [-1, 288, 14, 14] 576ReLU-115 [-1, 288, 14, 14] 0Conv2d-116 [-1, 32, 14, 14] 16,416BatchNorm2d-117 [-1, 32, 14, 14] 64ReLU-118 [-1, 32, 14, 14] 0Conv2d-119 [-1, 64, 14, 14] 51,264BatchNorm2d-120 [-1, 64, 14, 14] 128ReLU-121 [-1, 64, 14, 14] 0MaxPool2d-122 [-1, 512, 14, 14] 0Conv2d-123 [-1, 64, 14, 14] 32,832BatchNorm2d-124 [-1, 64, 14, 14] 128ReLU-125 [-1, 64, 14, 14] 0inception_block-126 [-1, 528, 14, 14] 0Conv2d-127 [-1, 256, 14, 14] 135,424BatchNorm2d-128 [-1, 256, 14, 14] 512ReLU-129 [-1, 256, 14, 14] 0Conv2d-130 [-1, 160, 14, 14] 84,640BatchNorm2d-131 [-1, 160, 14, 14] 320ReLU-132 [-1, 160, 14, 14] 0Conv2d-133 [-1, 320, 14, 14] 461,120BatchNorm2d-134 [-1, 320, 14, 14] 640ReLU-135 [-1, 320, 14, 14] 0Conv2d-136 [-1, 32, 14, 14] 16,928BatchNorm2d-137 [-1, 32, 14, 14] 64ReLU-138 [-1, 32, 14, 14] 0Conv2d-139 [-1, 128, 14, 14] 102,528BatchNorm2d-140 [-1, 128, 14, 14] 256ReLU-141 [-1, 128, 14, 14] 0MaxPool2d-142 [-1, 528, 14, 14] 0Conv2d-143 [-1, 128, 14, 14] 67,712BatchNorm2d-144 [-1, 128, 14, 14] 256ReLU-145 [-1, 128, 14, 14] 0inception_block-146 [-1, 832, 14, 14] 0MaxPool2d-147 [-1, 832, 7, 7] 0Conv2d-148 [-1, 256, 7, 7] 213,248BatchNorm2d-149 [-1, 256, 7, 7] 512ReLU-150 [-1, 256, 7, 7] 0Conv2d-151 [-1, 160, 7, 7] 133,280BatchNorm2d-152 [-1, 160, 7, 7] 320ReLU-153 [-1, 160, 7, 7] 0Conv2d-154 [-1, 320, 7, 7] 461,120BatchNorm2d-155 [-1, 320, 7, 7] 640ReLU-156 [-1, 320, 7, 7] 0Conv2d-157 [-1, 32, 7, 7] 26,656BatchNorm2d-158 [-1, 32, 7, 7] 64ReLU-159 [-1, 32, 7, 7] 0Conv2d-160 [-1, 128, 7, 7] 102,528BatchNorm2d-161 [-1, 128, 7, 7] 256ReLU-162 [-1, 128, 7, 7] 0MaxPool2d-163 [-1, 832, 7, 7] 0Conv2d-164 [-1, 128, 7, 7] 106,624BatchNorm2d-165 [-1, 128, 7, 7] 256ReLU-166 [-1, 128, 7, 7] 0inception_block-167 [-1, 832, 7, 7] 0Conv2d-168 [-1, 384, 7, 7] 319,872BatchNorm2d-169 [-1, 384, 7, 7] 768ReLU-170 [-1, 384, 7, 7] 0Conv2d-171 [-1, 192, 7, 7] 159,936BatchNorm2d-172 [-1, 192, 7, 7] 384ReLU-173 [-1, 192, 7, 7] 0Conv2d-174 [-1, 384, 7, 7] 663,936BatchNorm2d-175 [-1, 384, 7, 7] 768ReLU-176 [-1, 384, 7, 7] 0Conv2d-177 [-1, 48, 7, 7] 39,984BatchNorm2d-178 [-1, 48, 7, 7] 96ReLU-179 [-1, 48, 7, 7] 0Conv2d-180 [-1, 128, 7, 7] 153,728BatchNorm2d-181 [-1, 128, 7, 7] 256ReLU-182 [-1, 128, 7, 7] 0MaxPool2d-183 [-1, 832, 7, 7] 0Conv2d-184 [-1, 128, 7, 7] 106,624BatchNorm2d-185 [-1, 128, 7, 7] 256ReLU-186 [-1, 128, 7, 7] 0inception_block-187 [-1, 1024, 7, 7] 0AvgPool2d-188 [-1, 1024, 1, 1] 0Dropout-189 [-1, 1024, 1, 1] 0Linear-190 [-1, 1024] 1,049,600ReLU-191 [-1, 1024] 0Linear-192 [-1, 2] 2,050Softmax-193 [-1, 2] 0Total params: 7,039,122
Trainable params: 7,039,122
Non-trainable params: 0
----------------------------------------------------------------
Input size (MB): 0.57
Forward/backward pass size (MB): 69.61
Params size (MB): 26.85
Estimated Total Size (MB): 97.04
----------------------------------------------------------------
InceptionV1((conv1): Conv2d(3, 64, kernel_size(7, 7), stride(2, 2), padding(3, 3))(maxpool1): MaxPool2d(kernel_size3, stride2, padding1, dilation1, ceil_modeFalse)(conv2): Conv2d(64, 64, kernel_size(1, 1), stride(1, 1))(conv3): Conv2d(64, 192, kernel_size(3, 3), stride(1, 1), padding(1, 1))(maxpool2): MaxPool2d(kernel_size3, stride2, padding1, dilation1, ceil_modeFalse)(inception3a): inception_block((branch1): Sequential((0): Conv2d(192, 64, kernel_size(1, 1), stride(1, 1))(1): BatchNorm2d(64, eps1e-05, momentum0.1, affineTrue, track_running_statsTrue)(2): ReLU(inplaceTrue))(branch2): Sequential((0): Conv2d(192, 96, kernel_size(1, 1), stride(1, 1))(1): BatchNorm2d(96, eps1e-05, momentum0.1, affineTrue, track_running_statsTrue)(2): ReLU(inplaceTrue)(3): Conv2d(96, 128, kernel_size(3, 3), stride(1, 1), padding(1, 1))(4): BatchNorm2d(128, eps1e-05, momentum0.1, affineTrue, track_running_statsTrue)(5): ReLU(inplaceTrue))(branch3): Sequential((0): Conv2d(192, 16, kernel_size(1, 1), stride(1, 1))(1): BatchNorm2d(16, eps1e-05, momentum0.1, affineTrue, track_running_statsTrue)(2): ReLU(inplaceTrue)(3): Conv2d(16, 32, kernel_size(5, 5), stride(1, 1), padding(2, 2))(4): BatchNorm2d(32, eps1e-05, momentum0.1, affineTrue, track_running_statsTrue)(5): ReLU(inplaceTrue))(branch4): Sequential((0): MaxPool2d(kernel_size3, stride1, padding1, dilation1, ceil_modeFalse)(1): Conv2d(192, 32, kernel_size(1, 1), stride(1, 1))(2): BatchNorm2d(32, eps1e-05, momentum0.1, affineTrue, track_running_statsTrue)(3): ReLU(inplaceTrue)))(inception3b): inception_block((branch1): Sequential((0): Conv2d(256, 128, kernel_size(1, 1), stride(1, 1))(1): BatchNorm2d(128, eps1e-05, momentum0.1, affineTrue, track_running_statsTrue)(2): ReLU(inplaceTrue))(branch2): Sequential((0): Conv2d(256, 128, kernel_size(1, 1), stride(1, 1))(1): BatchNorm2d(128, eps1e-05, momentum0.1, affineTrue, track_running_statsTrue)(2): ReLU(inplaceTrue)(3): Conv2d(128, 192, kernel_size(3, 3), stride(1, 1), padding(1, 1))(4): BatchNorm2d(192, eps1e-05, momentum0.1, affineTrue, track_running_statsTrue)(5): ReLU(inplaceTrue))(branch3): Sequential((0): Conv2d(256, 32, kernel_size(1, 1), stride(1, 1))(1): BatchNorm2d(32, eps1e-05, momentum0.1, affineTrue, track_running_statsTrue)(2): ReLU(inplaceTrue)(3): Conv2d(32, 96, kernel_size(5, 5), stride(1, 1), padding(2, 2))(4): BatchNorm2d(96, eps1e-05, momentum0.1, affineTrue, track_running_statsTrue)(5): ReLU(inplaceTrue))(branch4): Sequential((0): MaxPool2d(kernel_size3, stride1, padding1, dilation1, ceil_modeFalse)(1): Conv2d(256, 64, kernel_size(1, 1), stride(1, 1))(2): BatchNorm2d(64, eps1e-05, momentum0.1, affineTrue, track_running_statsTrue)(3): ReLU(inplaceTrue)))(maxpool3): MaxPool2d(kernel_size3, stride2, padding1, dilation1, ceil_modeFalse)(inception4a): inception_block((branch1): Sequential((0): Conv2d(480, 192, kernel_size(1, 1), stride(1, 1))(1): BatchNorm2d(192, eps1e-05, momentum0.1, affineTrue, track_running_statsTrue)(2): ReLU(inplaceTrue))(branch2): Sequential((0): Conv2d(480, 96, kernel_size(1, 1), stride(1, 1))(1): BatchNorm2d(96, eps1e-05, momentum0.1, affineTrue, track_running_statsTrue)(2): ReLU(inplaceTrue)(3): Conv2d(96, 208, kernel_size(3, 3), stride(1, 1), padding(1, 1))(4): BatchNorm2d(208, eps1e-05, momentum0.1, affineTrue, track_running_statsTrue)(5): ReLU(inplaceTrue))(branch3): Sequential((0): Conv2d(480, 16, kernel_size(1, 1), stride(1, 1))(1): BatchNorm2d(16, eps1e-05, momentum0.1, affineTrue, track_running_statsTrue)(2): ReLU(inplaceTrue)(3): Conv2d(16, 48, kernel_size(5, 5), stride(1, 1), padding(2, 2))(4): BatchNorm2d(48, eps1e-05, momentum0.1, affineTrue, track_running_statsTrue)(5): ReLU(inplaceTrue))(branch4): Sequential((0): MaxPool2d(kernel_size3, stride1, padding1, dilation1, ceil_modeFalse)(1): Conv2d(480, 64, kernel_size(1, 1), stride(1, 1))(2): BatchNorm2d(64, eps1e-05, momentum0.1, affineTrue, track_running_statsTrue)(3): ReLU(inplaceTrue)))(inception4b): inception_block((branch1): Sequential((0): Conv2d(512, 160, kernel_size(1, 1), stride(1, 1))(1): BatchNorm2d(160, eps1e-05, momentum0.1, affineTrue, track_running_statsTrue)(2): ReLU(inplaceTrue))(branch2): Sequential((0): Conv2d(512, 112, kernel_size(1, 1), stride(1, 1))(1): BatchNorm2d(112, eps1e-05, momentum0.1, affineTrue, track_running_statsTrue)(2): ReLU(inplaceTrue)(3): Conv2d(112, 224, kernel_size(3, 3), stride(1, 1), padding(1, 1))(4): BatchNorm2d(224, eps1e-05, momentum0.1, affineTrue, track_running_statsTrue)(5): ReLU(inplaceTrue))(branch3): Sequential((0): Conv2d(512, 24, kernel_size(1, 1), stride(1, 1))(1): BatchNorm2d(24, eps1e-05, momentum0.1, affineTrue, track_running_statsTrue)(2): ReLU(inplaceTrue)(3): Conv2d(24, 64, kernel_size(5, 5), stride(1, 1), padding(2, 2))(4): BatchNorm2d(64, eps1e-05, momentum0.1, affineTrue, track_running_statsTrue)(5): ReLU(inplaceTrue))(branch4): Sequential((0): MaxPool2d(kernel_size3, stride1, padding1, dilation1, ceil_modeFalse)(1): Conv2d(512, 64, kernel_size(1, 1), stride(1, 1))(2): BatchNorm2d(64, eps1e-05, momentum0.1, affineTrue, track_running_statsTrue)(3): ReLU(inplaceTrue)))(inception4c): inception_block((branch1): Sequential((0): Conv2d(512, 128, kernel_size(1, 1), stride(1, 1))(1): BatchNorm2d(128, eps1e-05, momentum0.1, affineTrue, track_running_statsTrue)(2): ReLU(inplaceTrue))(branch2): Sequential((0): Conv2d(512, 128, kernel_size(1, 1), stride(1, 1))(1): BatchNorm2d(128, eps1e-05, momentum0.1, affineTrue, track_running_statsTrue)(2): ReLU(inplaceTrue)(3): Conv2d(128, 256, kernel_size(3, 3), stride(1, 1), padding(1, 1))(4): BatchNorm2d(256, eps1e-05, momentum0.1, affineTrue, track_running_statsTrue)(5): ReLU(inplaceTrue))(branch3): Sequential((0): Conv2d(512, 24, kernel_size(1, 1), stride(1, 1))(1): BatchNorm2d(24, eps1e-05, momentum0.1, affineTrue, track_running_statsTrue)(2): ReLU(inplaceTrue)(3): Conv2d(24, 64, kernel_size(5, 5), stride(1, 1), padding(2, 2))(4): BatchNorm2d(64, eps1e-05, momentum0.1, affineTrue, track_running_statsTrue)(5): ReLU(inplaceTrue))(branch4): Sequential((0): MaxPool2d(kernel_size3, stride1, padding1, dilation1, ceil_modeFalse)(1): Conv2d(512, 64, kernel_size(1, 1), stride(1, 1))(2): BatchNorm2d(64, eps1e-05, momentum0.1, affineTrue, track_running_statsTrue)(3): ReLU(inplaceTrue)))(inception4d): inception_block((branch1): Sequential((0): Conv2d(512, 112, kernel_size(1, 1), stride(1, 1))(1): BatchNorm2d(112, eps1e-05, momentum0.1, affineTrue, track_running_statsTrue)(2): ReLU(inplaceTrue))(branch2): Sequential((0): Conv2d(512, 144, kernel_size(1, 1), stride(1, 1))(1): BatchNorm2d(144, eps1e-05, momentum0.1, affineTrue, track_running_statsTrue)(2): ReLU(inplaceTrue)(3): Conv2d(144, 288, kernel_size(3, 3), stride(1, 1), padding(1, 1))(4): BatchNorm2d(288, eps1e-05, momentum0.1, affineTrue, track_running_statsTrue)(5): ReLU(inplaceTrue))(branch3): Sequential((0): Conv2d(512, 32, kernel_size(1, 1), stride(1, 1))(1): BatchNorm2d(32, eps1e-05, momentum0.1, affineTrue, track_running_statsTrue)(2): ReLU(inplaceTrue)(3): Conv2d(32, 64, kernel_size(5, 5), stride(1, 1), padding(2, 2))(4): BatchNorm2d(64, eps1e-05, momentum0.1, affineTrue, track_running_statsTrue)(5): ReLU(inplaceTrue))(branch4): Sequential((0): MaxPool2d(kernel_size3, stride1, padding1, dilation1, ceil_modeFalse)(1): Conv2d(512, 64, kernel_size(1, 1), stride(1, 1))(2): BatchNorm2d(64, eps1e-05, momentum0.1, affineTrue, track_running_statsTrue)(3): ReLU(inplaceTrue)))(inception4e): inception_block((branch1): Sequential((0): Conv2d(528, 256, kernel_size(1, 1), stride(1, 1))(1): BatchNorm2d(256, eps1e-05, momentum0.1, affineTrue, track_running_statsTrue)(2): ReLU(inplaceTrue))(branch2): Sequential((0): Conv2d(528, 160, kernel_size(1, 1), stride(1, 1))(1): BatchNorm2d(160, eps1e-05, momentum0.1, affineTrue, track_running_statsTrue)(2): ReLU(inplaceTrue)(3): Conv2d(160, 320, kernel_size(3, 3), stride(1, 1), padding(1, 1))(4): BatchNorm2d(320, eps1e-05, momentum0.1, affineTrue, track_running_statsTrue)(5): ReLU(inplaceTrue))(branch3): Sequential((0): Conv2d(528, 32, kernel_size(1, 1), stride(1, 1))(1): BatchNorm2d(32, eps1e-05, momentum0.1, affineTrue, track_running_statsTrue)(2): ReLU(inplaceTrue)(3): Conv2d(32, 128, kernel_size(5, 5), stride(1, 1), padding(2, 2))(4): BatchNorm2d(128, eps1e-05, momentum0.1, affineTrue, track_running_statsTrue)(5): ReLU(inplaceTrue))(branch4): Sequential((0): MaxPool2d(kernel_size3, stride1, padding1, dilation1, ceil_modeFalse)(1): Conv2d(528, 128, kernel_size(1, 1), stride(1, 1))(2): BatchNorm2d(128, eps1e-05, momentum0.1, affineTrue, track_running_statsTrue)(3): ReLU(inplaceTrue)))(maxpool4): MaxPool2d(kernel_size3, stride2, padding1, dilation1, ceil_modeFalse)(inception5a): inception_block((branch1): Sequential((0): Conv2d(832, 256, kernel_size(1, 1), stride(1, 1))(1): BatchNorm2d(256, eps1e-05, momentum0.1, affineTrue, track_running_statsTrue)(2): ReLU(inplaceTrue))(branch2): Sequential((0): Conv2d(832, 160, kernel_size(1, 1), stride(1, 1))(1): BatchNorm2d(160, eps1e-05, momentum0.1, affineTrue, track_running_statsTrue)(2): ReLU(inplaceTrue)(3): Conv2d(160, 320, kernel_size(3, 3), stride(1, 1), padding(1, 1))(4): BatchNorm2d(320, eps1e-05, momentum0.1, affineTrue, track_running_statsTrue)(5): ReLU(inplaceTrue))(branch3): Sequential((0): Conv2d(832, 32, kernel_size(1, 1), stride(1, 1))(1): BatchNorm2d(32, eps1e-05, momentum0.1, affineTrue, track_running_statsTrue)(2): ReLU(inplaceTrue)(3): Conv2d(32, 128, kernel_size(5, 5), stride(1, 1), padding(2, 2))(4): BatchNorm2d(128, eps1e-05, momentum0.1, affineTrue, track_running_statsTrue)(5): ReLU(inplaceTrue))(branch4): Sequential((0): MaxPool2d(kernel_size3, stride1, padding1, dilation1, ceil_modeFalse)(1): Conv2d(832, 128, kernel_size(1, 1), stride(1, 1))(2): BatchNorm2d(128, eps1e-05, momentum0.1, affineTrue, track_running_statsTrue)(3): ReLU(inplaceTrue)))(inception5b): Sequential((0): inception_block((branch1): Sequential((0): Conv2d(832, 384, kernel_size(1, 1), stride(1, 1))(1): BatchNorm2d(384, eps1e-05, momentum0.1, affineTrue, track_running_statsTrue)(2): ReLU(inplaceTrue))(branch2): Sequential((0): Conv2d(832, 192, kernel_size(1, 1), stride(1, 1))(1): BatchNorm2d(192, eps1e-05, momentum0.1, affineTrue, track_running_statsTrue)(2): ReLU(inplaceTrue)(3): Conv2d(192, 384, kernel_size(3, 3), stride(1, 1), padding(1, 1))(4): BatchNorm2d(384, eps1e-05, momentum0.1, affineTrue, track_running_statsTrue)(5): ReLU(inplaceTrue))(branch3): Sequential((0): Conv2d(832, 48, kernel_size(1, 1), stride(1, 1))(1): BatchNorm2d(48, eps1e-05, momentum0.1, affineTrue, track_running_statsTrue)(2): ReLU(inplaceTrue)(3): Conv2d(48, 128, kernel_size(5, 5), stride(1, 1), padding(2, 2))(4): BatchNorm2d(128, eps1e-05, momentum0.1, affineTrue, track_running_statsTrue)(5): ReLU(inplaceTrue))(branch4): Sequential((0): MaxPool2d(kernel_size3, stride1, padding1, dilation1, ceil_modeFalse)(1): Conv2d(832, 128, kernel_size(1, 1), stride(1, 1))(2): BatchNorm2d(128, eps1e-05, momentum0.1, affineTrue, track_running_statsTrue)(3): ReLU(inplaceTrue)))(1): AvgPool2d(kernel_size7, stride1, padding0)(2): Dropout(p0.4, inplaceFalse))(classifier): Sequential((0): Linear(in_features1024, out_features1024, biasTrue)(1): ReLU()(2): Linear(in_features1024, out_features2, biasTrue)(3): Softmax(dim1))
)训练模型 def train(dataloader, model, loss_fn, optimizer):size len(dataloader.dataset) # 训练集的大小num_batches len(dataloader) # 批次数目, (size/batch_size向上取整)train_loss, train_acc 0, 0 # 初始化训练损失和正确率for X, y in dataloader: # 获取图片及其标签X, y X.to(device), y.to(device)# 计算预测误差pred model(X) # 网络输出loss loss_fn(pred, y) # 计算网络输出和真实值之间的差距targets为真实值计算二者差值即为损失# 反向传播optimizer.zero_grad() # grad属性归零loss.backward() # 反向传播optimizer.step() # 每一步自动更新# 记录acc与losstrain_acc (pred.argmax(1) y).type(torch.float).sum().item()train_loss loss.item()train_acc / sizetrain_loss / num_batchesreturn train_acc, train_lossdef test(dataloader, model, loss_fn):size len(dataloader.dataset) # 测试集的大小num_batches len(dataloader) # 批次数目test_loss, test_acc 0, 0# 当不进行训练时停止梯度更新节省计算内存消耗with torch.no_grad():for imgs, target in dataloader:imgs, target imgs.to(device), target.to(device)# 计算losstarget_pred model(imgs)loss loss_fn(target_pred, target)test_loss loss.item()test_acc (target_pred.argmax(1) target).type(torch.float).sum().item()test_acc / sizetest_loss / num_batchesreturn test_acc, test_loss正式训练 import copyoptimizer torch.optim.Adam(model.parameters(), lr1e-4)
loss_fn nn.CrossEntropyLoss() # 创建损失函数epochs 50train_loss []
train_acc []
test_loss []
test_acc []best_acc 0 # 设置一个最佳准确率作为最佳模型的判别指标for epoch in range(epochs):# 更新学习率使用自定义学习率时使用# adjust_learning_rate(optimizer, epoch, learn_rate)model.train()epoch_train_acc, epoch_train_loss train(train_dl, model, loss_fn, optimizer)# scheduler.step() # 更新学习率调用官方动态学习率接口时使用model.eval()epoch_test_acc, epoch_test_loss test(test_dl, model, loss_fn)# 保存最佳模型到 best_modelif epoch_test_acc best_acc:best_acc epoch_test_accbest_model copy.deepcopy(model)train_acc.append(epoch_train_acc)train_loss.append(epoch_train_loss)test_acc.append(epoch_test_acc)test_loss.append(epoch_test_loss)# 获取当前的学习率lr optimizer.state_dict()[param_groups][0][lr]template (Epoch:{:2d}, Train_acc:{:.1f}%, Train_loss:{:.3f}, Test_acc:{:.1f}%, Test_loss:{:.3f}, Lr:{:.2E})print(template.format(epoch 1, epoch_train_acc * 100, epoch_train_loss,epoch_test_acc * 100, epoch_test_loss, lr))# 保存最佳模型到文件中
PATH rC:/Users/11054/Desktop/kLearning/J8_learning/best_model.pth # 保存的参数文件名
torch.save(model.state_dict(), PATH)print(Done)Epoch: 1, Train_acc:62.8%, Train_loss:0.650, Test_acc:66.9%, Test_loss:0.622, Lr:1.00E-04
Epoch: 2, Train_acc:65.6%, Train_loss:0.635, Test_acc:66.2%, Test_loss:0.612, Lr:1.00E-04
Epoch: 3, Train_acc:67.7%, Train_loss:0.612, Test_acc:68.5%, Test_loss:0.621, Lr:1.00E-04
Epoch: 4, Train_acc:71.7%, Train_loss:0.582, Test_acc:73.0%, Test_loss:0.576, Lr:1.00E-04
Epoch: 5, Train_acc:72.1%, Train_loss:0.575, Test_acc:74.4%, Test_loss:0.562, Lr:1.00E-04
Epoch: 6, Train_acc:74.2%, Train_loss:0.556, Test_acc:75.1%, Test_loss:0.548, Lr:1.00E-04
Epoch: 7, Train_acc:75.8%, Train_loss:0.549, Test_acc:78.1%, Test_loss:0.517, Lr:1.00E-04
Epoch: 8, Train_acc:76.9%, Train_loss:0.531, Test_acc:79.5%, Test_loss:0.510, Lr:1.00E-04
Epoch: 9, Train_acc:81.2%, Train_loss:0.498, Test_acc:83.7%, Test_loss:0.478, Lr:1.00E-04
Epoch:10, Train_acc:81.1%, Train_loss:0.497, Test_acc:82.3%, Test_loss:0.486, Lr:1.00E-04
Epoch:11, Train_acc:81.7%, Train_loss:0.490, Test_acc:83.0%, Test_loss:0.476, Lr:1.00E-04
Epoch:12, Train_acc:83.9%, Train_loss:0.472, Test_acc:85.5%, Test_loss:0.454, Lr:1.00E-04
Epoch:13, Train_acc:83.7%, Train_loss:0.474, Test_acc:83.9%, Test_loss:0.467, Lr:1.00E-04
Epoch:14, Train_acc:84.4%, Train_loss:0.462, Test_acc:86.0%, Test_loss:0.444, Lr:1.00E-04
Epoch:15, Train_acc:86.2%, Train_loss:0.446, Test_acc:80.7%, Test_loss:0.490, Lr:1.00E-04
Epoch:16, Train_acc:85.9%, Train_loss:0.449, Test_acc:86.0%, Test_loss:0.445, Lr:1.00E-04
Epoch:17, Train_acc:86.6%, Train_loss:0.444, Test_acc:80.9%, Test_loss:0.501, Lr:1.00E-04
Epoch:18, Train_acc:86.6%, Train_loss:0.446, Test_acc:83.4%, Test_loss:0.468, Lr:1.00E-04
Epoch:19, Train_acc:89.1%, Train_loss:0.417, Test_acc:85.8%, Test_loss:0.453, Lr:1.00E-04
Epoch:20, Train_acc:88.2%, Train_loss:0.425, Test_acc:90.4%, Test_loss:0.404, Lr:1.00E-04
Epoch:21, Train_acc:90.4%, Train_loss:0.407, Test_acc:87.9%, Test_loss:0.428, Lr:1.00E-04
Epoch:22, Train_acc:90.3%, Train_loss:0.411, Test_acc:89.0%, Test_loss:0.422, Lr:1.00E-04
Epoch:23, Train_acc:89.5%, Train_loss:0.415, Test_acc:85.3%, Test_loss:0.449, Lr:1.00E-04
Epoch:24, Train_acc:89.8%, Train_loss:0.412, Test_acc:89.0%, Test_loss:0.416, Lr:1.00E-04
Epoch:25, Train_acc:88.5%, Train_loss:0.428, Test_acc:90.2%, Test_loss:0.411, Lr:1.00E-04
Epoch:26, Train_acc:90.4%, Train_loss:0.406, Test_acc:89.5%, Test_loss:0.413, Lr:1.00E-04
Epoch:27, Train_acc:91.9%, Train_loss:0.395, Test_acc:89.3%, Test_loss:0.418, Lr:1.00E-04
Epoch:28, Train_acc:92.9%, Train_loss:0.381, Test_acc:91.6%, Test_loss:0.388, Lr:1.00E-04
Epoch:29, Train_acc:92.9%, Train_loss:0.383, Test_acc:90.0%, Test_loss:0.409, Lr:1.00E-04
Epoch:30, Train_acc:91.5%, Train_loss:0.397, Test_acc:89.0%, Test_loss:0.420, Lr:1.00E-04
Epoch:31, Train_acc:91.9%, Train_loss:0.392, Test_acc:91.6%, Test_loss:0.396, Lr:1.00E-04
Epoch:32, Train_acc:89.2%, Train_loss:0.421, Test_acc:89.7%, Test_loss:0.411, Lr:1.00E-04
Epoch:33, Train_acc:92.3%, Train_loss:0.392, Test_acc:90.0%, Test_loss:0.409, Lr:1.00E-04
Epoch:34, Train_acc:92.2%, Train_loss:0.386, Test_acc:92.3%, Test_loss:0.387, Lr:1.00E-04
Epoch:35, Train_acc:92.2%, Train_loss:0.393, Test_acc:92.5%, Test_loss:0.387, Lr:1.00E-04
Epoch:36, Train_acc:95.0%, Train_loss:0.362, Test_acc:91.8%, Test_loss:0.395, Lr:1.00E-04
Epoch:37, Train_acc:93.3%, Train_loss:0.383, Test_acc:90.7%, Test_loss:0.409, Lr:1.00E-04
Epoch:38, Train_acc:93.8%, Train_loss:0.378, Test_acc:91.6%, Test_loss:0.399, Lr:1.00E-04
Epoch:39, Train_acc:93.3%, Train_loss:0.384, Test_acc:91.4%, Test_loss:0.392, Lr:1.00E-04
Epoch:40, Train_acc:94.5%, Train_loss:0.371, Test_acc:90.4%, Test_loss:0.405, Lr:1.00E-04
Epoch:41, Train_acc:95.6%, Train_loss:0.360, Test_acc:91.8%, Test_loss:0.397, Lr:1.00E-04
Epoch:42, Train_acc:91.2%, Train_loss:0.401, Test_acc:85.1%, Test_loss:0.450, Lr:1.00E-04
Epoch:43, Train_acc:92.2%, Train_loss:0.391, Test_acc:88.3%, Test_loss:0.425, Lr:1.00E-04
Epoch:44, Train_acc:93.9%, Train_loss:0.375, Test_acc:89.5%, Test_loss:0.413, Lr:1.00E-04
Epoch:45, Train_acc:95.4%, Train_loss:0.359, Test_acc:93.2%, Test_loss:0.381, Lr:1.00E-04
Epoch:46, Train_acc:93.5%, Train_loss:0.381, Test_acc:91.6%, Test_loss:0.395, Lr:1.00E-04
Epoch:47, Train_acc:95.7%, Train_loss:0.354, Test_acc:92.8%, Test_loss:0.382, Lr:1.00E-04
Epoch:48, Train_acc:95.9%, Train_loss:0.356, Test_acc:93.7%, Test_loss:0.373, Lr:1.00E-04
Epoch:49, Train_acc:95.9%, Train_loss:0.354, Test_acc:94.4%, Test_loss:0.367, Lr:1.00E-04
Epoch:50, Train_acc:95.1%, Train_loss:0.362, Test_acc:92.3%, Test_loss:0.391, Lr:1.00E-04
Done结果可视化 import matplotlib.pyplot as plt
# 隐藏警告
import warningswarnings.filterwarnings(ignore) # 忽略警告信息
plt.rcParams[font.sans-serif] [SimHei] # 用来正常显示中文标签
plt.rcParams[axes.unicode_minus] False # 用来正常显示负号
plt.rcParams[figure.dpi] 100 # 分辨率epochs_range range(epochs)plt.figure(figsize(12, 3))
plt.subplot(1, 2, 1)plt.plot(epochs_range, train_acc, labelTraining Accuracy)
plt.plot(epochs_range, test_acc, labelTest Accuracy)
plt.legend(loclower right)
plt.title(Training and Validation Accuracy)plt.subplot(1, 2, 2)
plt.plot(epochs_range, train_loss, labelTraining Loss)
plt.plot(epochs_range, test_loss, labelTest Loss)
plt.legend(locupper right)
plt.title(Training and Validation Loss)
plt.show()from PIL import Imageclasses list(total_data.class_to_idx)
print(classes)
print(total_data.class_to_idx)def predict_one_image(image_path, model, transform, classes):test_img Image.open(image_path).convert(RGB)plt.imshow(test_img) # 展示预测的图片test_img transform(test_img)img test_img.to(device).unsqueeze(0)model.eval()output model(img)_, pred torch.max(output, 1)pred_class classes[pred]print(f预测结果是{pred_class})# 预测训练集中的某张照片
predict_one_image(image_pathrC:\Users\11054\Desktop\kLearning\p4_learning\data\Monkeypox\M01_01_02.jpg,modelmodel,transformtrain_transforms,classesclasses) [Monkeypox, Others]
{Monkeypox: 0, Others: 1}
预测结果是Monkeypox详细网络结构图 个人总结
主要特点和创新点(Inception模块)
其设计理念是在同一层网络中使用多种不同尺寸的卷积核如1x1, 3x3, 5x5等和池化层然后将它们的输出拼接在一起。这种设计允许网络在同一空间维度上捕获多尺度特征从而提高了网络的表达能力。1x1卷积核的使用不仅减少了计算量还起到了降维的作用帮助减少模型的参数数量和计算复杂度。
辅助分类器 Inception v1在网络的中间层添加了两个辅助分类器。这些分类器通过添加额外的损失函数来帮助训练时的梯度传播防止梯度消失问题特别是在深层网络中。在测试时这些辅助分类器的输出会被忽略。参数效率
通过使用1x1卷积核和特殊的模块设计Inception v1在保持高性能的同时有效地减少了模型的参数数量这使得网络更加高效能够更好地推广到更大的数据集上。 一个典型的Inception模块包括以下几个部分 1x1卷积层用于降维和减少计算量。 3x3卷积层用于捕获局部细节特征。 5x5卷积层用于捕获更大范围的特征。 3x3最大池化层用于捕获空间信息。 拼接层将所有上述卷积层和池化层的输出在通道维度上拼接在一起。
