新乡网站搜索引擎优化,网站新域名查询,做网站哪个语言好,门户类网站建设本文为为#x1f517;365天深度学习训练营内部文章 原作者#xff1a;K同学啊 一 DenseNet算法结构 其基本思路与ResNet一致#xff0c;但是它建立的是前面所有层和后面层的密集连接#xff0c;它的另一大特色是通过特征在channel上的连接来实现特征重用。 二 设计理念 三… 本文为为365天深度学习训练营内部文章 原作者K同学啊 一 DenseNet算法结构 其基本思路与ResNet一致但是它建立的是前面所有层和后面层的密集连接它的另一大特色是通过特征在channel上的连接来实现特征重用。 二 设计理念 三 结构 四 算法代码 import torch
import torch.nn as nn
import torchvision.transforms as transforms
import torchvision
from torchvision import transforms, datasetsimport os,PIL,pathlib,randomdevice torch.device(cuda if torch.cuda.is_available() else cpu)device
data_dir ./data/
data_dir pathlib.Path(data_dir)data_paths list(data_dir.glob(*))
classeNames [str(path).split(\\)[1] for path in data_paths]
classeNamesimport matplotlib.pyplot as plt
from PIL import Image# 指定图像文件夹路径
image_folder ./data/OSCC/# 获取文件夹中的所有图像文件
image_files [f for f in os.listdir(image_folder) if f.endswith((.jpg, .png, .jpeg))]# 创建Matplotlib图像
fig, axes plt.subplots(3, 8, figsize(16, 6))# 使用列表推导式加载和显示图像
for ax, img_file in zip(axes.flat, image_files):img_path os.path.join(image_folder, img_file)img Image.open(img_path)ax.imshow(img)ax.axis(off)# 显示图像
plt.tight_layout()
plt.show() total_datadir ./data/# 关于transforms.Compose的更多介绍可以参考https://blog.csdn.net/qq_38251616/article/details/124878863
train_transforms 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(total_datadir,transformtrain_transforms)
total_data# 划分训练集
train_size int(0.7 * 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])
train_dataset, test_datasetbatch_size 32train_dl torch.utils.data.DataLoader(train_dataset,batch_sizebatch_size,shuffleTrue,num_workers1)
test_dl torch.utils.data.DataLoader(test_dataset,batch_sizebatch_size,shuffleTrue,num_workers1)
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([32, 3, 224, 224])
Shape of y: torch.Size([32]) torch.int64
import torch.nn as nn
import torch.nn.functional as F
from collections import OrderedDict
import re
import torch
from torch.utils import model_zoo
from torchvision.models.video.resnet import model_urls
_DenseLayer 类实现了 DenseNet 的关键机制
通过使用批归一化、ReLU 激活和卷积层来提取特征并通过密集连接促进特征的共享和再利用。class _DenseLayer(nn.Sequential):def __init__(self, num_input_features, growth_rate, bn_size, drop_rate)::param num_input_features: 输入特征数:param growth_rate: 每层增长的特征数:param bn_size: 批归一化层的大小:param drop_rate: 丢弃率super(_DenseLayer, self).__init__()# 添加一个批归一化层BatchNorm2d用于对输入特征进行标准化self.add_module(norm1, nn.BatchNorm2d(num_input_features))# 添加一个 ReLU 激活函数self.add_module(relu1, nn.ReLU(inplaceTrue))# 添加第一个卷积层Conv2d其输入通道数为 num_input_features输出通道数为 bn_size * growth_rate。# 这里使用 1x1 卷积主要用于减少特征图的维度并引入更多特征self.add_module(conv1, nn.Conv2d(num_input_features, bn_size * growth_rate,kernel_size1, stride1, biasFalse))# 添加第二个批归一化层应用于第一个卷积层的输出self.add_module(norm2, nn.BatchNorm2d(bn_size * growth_rate))# 添加第二个 ReLU 激活函数。与第一个激活函数相同提供非线性变换self.add_module(relu2, nn.ReLU(inplaceTrue))# 添加第二个卷积层输入通道数为 bn_size * growth_rate输出通道数为 growth_rate。# 这里使用 3x3 卷积通常用于提取更复杂的特征self.add_module(conv2, nn.Conv2d(bn_size * growth_rate, growth_rate,kernel_size3, stride1, padding1, biasFalse))# 保存丢弃率drop rate用于在前向传播中进行 dropout 操作以防止过拟合self.drop_rate drop_ratedef forward(self, x):# 调用父类 nn.Sequential 的 forward 方法将输入 x 传递给之前添加的所有层。# 输出 new_features 是经过所有层处理后的特征new_features super(_DenseLayer, self).forward(x)# 检查丢弃率是否大于 0如果是则进行 dropout 操作if self.drop_rate 0:# 对新特征应用 dropoutp 是丢弃概率training 参数指示当前是否在训练模式。这将随机将一部分特征置为零从而帮助减少过拟合new_features F.dropout(new_features, pself.drop_rate, trainingself.training)# 将输入 x 和新特征 new_features 在通道维度即维度 1上连接。这样可以实现密集连接允许模型利用前面层的所有特征return torch.cat([x, new_features], 1)
创建一个包含多个密集层的模块每个层都会根据前面层的输出特征动态调整输入特征数量形成一个密集连接的网络结构。class _DenseBlock(nn.Sequential):def __init__(self, num_layers, num_input_features, bn_size, growth_rate, drop_rate):num_layers: 该密集块中层的数量。num_input_features: 输入特征的数量。bn_size: 批量归一化的大小。growth_rate: 每层输出特征的增长率。drop_rate: dropout 率用于防止过拟合super(_DenseBlock, self).__init__()# 开始一个循环迭代 num_layers 次为每一层创建一个密集层for i in range(num_layers):# 在每次迭代中创建一个新的 _DenseLayer 实例。该层的输入特征数量为 num_input_features i * growth_rate即前面所有层的输出特征总和layer _DenseLayer(num_input_features i * growth_rate, growth_rate, bn_size, drop_rate)# 将创建的密集层添加到模块中并命名为 denselayer1、denselayer2依此类推。这样可以方便后续访问和调试self.add_module(denselayer%d % (i 1,), layer)
构建神经网络的一个过渡层,在神经网络中通常用于特征的转换和下采样class _Transition(nn.Sequential):def __init__(self,num_input_feature,num_output_features):super(_Transition,self).__init__()# 添加一个批归一化层标准化输入特征self.add_module(norm,nn.BatchNorm2d(num_input_feature))# 添加一个 ReLU 激活函数self.add_module(relu,nn.ReLU(inplaceTrue))# 添加一个卷积层使用 1x1 的卷积核连接输入特征和输出特征。self.add_module(conv,nn.Conv2d(num_input_feature,num_output_features,kernel_size1,stride1,biasFalse))# 添加一个 2x2 的平均池化层步幅为 2用于减少特征图的大小self.add_module(pool,nn.AvgPool2d(2,stride2))class DenseNet(nn.Module):def __init__(self,growth_rate32,block_config(6,12,24,16),num_init_features64,bn_size4,compression_rate0.5,drop_rate0,num_classes1000):growth_rate: 每个DenseBlock中每层输出特征图的增长率。block_config: 一个元组指定每个DenseBlock中的层数。num_init_features: 第一层卷积的输出特征数量。bn_size: Batch Normalization的大小compression_rate: 每个Transition层中输出特征数量的压缩比例。drop_rate: Dropout的概率num_classes: 最终分类的类别数。super(DenseNet,self).__init__()# 第一层卷积self.features nn.Sequential(OrderedDict([(conv0,nn.Conv2d(3,num_init_features,kernel_size7,stride2,padding3,biasFalse)),(norm0,nn.BatchNorm2d(num_init_features)),(relu0,nn.ReLU(inplaceTrue)),(pool0,nn.MaxPool2d(3,stride2,padding1))]))# DenseBlocknum_features num_init_features# 遍历block_config为每个DenseBlock构建模型for i,num_layers in enumerate(block_config):block _DenseBlock(num_layers,num_features,bn_size,growth_rate,drop_rate)self.features.add_module(denseblock%d%(i1),block)# 更新当前特征数量每个DenseBlock后增加num_layers * growth_ratenum_features num_layers*growth_rateif i ! len(block_config) - 1:# 定义Transition层连接DenseBlock减小特征图尺寸通过compression_ratetransition _Transition(num_features,int(num_features*compression_rate))# 将DenseBlock和Transition层添加到模型中self.features.add_module(transition%d%(i1),transition)num_features int(num_features * compression_rate)# final bnrelu# 在所有DenseBlock和Transition层之后添加一个Batch Normalization层和ReLU激活层self.features.add_module(norm5,nn.BatchNorm2d(num_features))self.features.add_module(relu5,nn.ReLU(inplaceTrue))# classification layer# 定义全连接层将特征映射到类别数self.classifier nn.Linear(num_features,num_classes)# 参数初始化遍历所有模块初始化权重。卷积层: 使用Kaiming正态分布初始化。BatchNorm层: 将偏置初始化为0权重初始化为1。全连接层: 将偏置初始化为0。for m in self.modules():if isinstance(m,nn.Conv2d):nn.init.kaiming_normal_(m.weight)elif isinstance(m,nn.BatchNorm2d):nn.init.constant_(m.bias,0)nn.init.constant_(m.weight,1)elif isinstance(m,nn.Linear):nn.init.constant_(m.bias,0)def forward(self,x):self.features(x): 将输入x传递通过所有特征层。F.avg_pool2d: 在特征图上进行全局平均池化。view(features.size(0), -1): 将池化后的特征展平。self.classifier(out): 通过分类层得到输出。return out: 返回最终的分类结果。features self.features(x)out F.avg_pool2d(features,7,stride1).view(features.size(0),-1)out self.classifier(out)return outdef densetnet121(pretrainedFalse, **kwargs):model DenseNet(num_init_features64, growth_rate32, block_config(6, 12, 24, 16), num_classeslen(classeNames))if pretrained:pattern re.compile(r^(.*denselayer\d\.(?:norm|relu|conv))\.((?:[12])\.(?:weight|bias|running_mean|running_var))$)# 从指定的 URL 加载 DenseNet-121 的预训练权重存储在 state_dictstate_dict model_zoo.load_url(model_urls[densenet121])for key in list(state_dict.keys()):res pattern.match(key)if res:# 创建一个新键组合匹配结果的前半部分和后半部分new_key res.group(1) res.group(2)state_dict[new_key] state_dict[key]del state_dict[key]# 将处理后的权重加载到模型中model.load_state_dict(state_dict)return modelmodel densetnet121()
model
import torchsummary as summary
summary.summary(model,(3,224,224))
----------------------------------------------------------------Layer (type) Output Shape Param #
Conv2d-1 [-1, 64, 112, 112] 9,408BatchNorm2d-2 [-1, 64, 112, 112] 128ReLU-3 [-1, 64, 112, 112] 0MaxPool2d-4 [-1, 64, 56, 56] 0BatchNorm2d-5 [-1, 64, 56, 56] 128ReLU-6 [-1, 64, 56, 56] 0Conv2d-7 [-1, 128, 56, 56] 8,192BatchNorm2d-8 [-1, 128, 56, 56] 256ReLU-9 [-1, 128, 56, 56] 0Conv2d-10 [-1, 32, 56, 56] 36,864BatchNorm2d-11 [-1, 96, 56, 56] 192ReLU-12 [-1, 96, 56, 56] 0Conv2d-13 [-1, 128, 56, 56] 12,288BatchNorm2d-14 [-1, 128, 56, 56] 256ReLU-15 [-1, 128, 56, 56] 0Conv2d-16 [-1, 32, 56, 56] 36,864BatchNorm2d-17 [-1, 128, 56, 56] 256ReLU-18 [-1, 128, 56, 56] 0Conv2d-19 [-1, 128, 56, 56] 16,384BatchNorm2d-20 [-1, 128, 56, 56] 256ReLU-21 [-1, 128, 56, 56] 0Conv2d-22 [-1, 32, 56, 56] 36,864BatchNorm2d-23 [-1, 160, 56, 56] 320ReLU-24 [-1, 160, 56, 56] 0Conv2d-25 [-1, 128, 56, 56] 20,480BatchNorm2d-26 [-1, 128, 56, 56] 256ReLU-27 [-1, 128, 56, 56] 0Conv2d-28 [-1, 32, 56, 56] 36,864BatchNorm2d-29 [-1, 192, 56, 56] 384ReLU-30 [-1, 192, 56, 56] 0Conv2d-31 [-1, 128, 56, 56] 24,576BatchNorm2d-32 [-1, 128, 56, 56] 256ReLU-33 [-1, 128, 56, 56] 0Conv2d-34 [-1, 32, 56, 56] 36,864BatchNorm2d-35 [-1, 224, 56, 56] 448ReLU-36 [-1, 224, 56, 56] 0Conv2d-37 [-1, 128, 56, 56] 28,672BatchNorm2d-38 [-1, 128, 56, 56] 256ReLU-39 [-1, 128, 56, 56] 0Conv2d-40 [-1, 32, 56, 56] 36,864BatchNorm2d-41 [-1, 256, 56, 56] 512ReLU-42 [-1, 256, 56, 56] 0Conv2d-43 [-1, 128, 56, 56] 32,768AvgPool2d-44 [-1, 128, 28, 28] 0BatchNorm2d-45 [-1, 128, 28, 28] 256ReLU-46 [-1, 128, 28, 28] 0Conv2d-47 [-1, 128, 28, 28] 16,384BatchNorm2d-48 [-1, 128, 28, 28] 256ReLU-49 [-1, 128, 28, 28] 0Conv2d-50 [-1, 32, 28, 28] 36,864BatchNorm2d-51 [-1, 160, 28, 28] 320ReLU-52 [-1, 160, 28, 28] 0Conv2d-53 [-1, 128, 28, 28] 20,480BatchNorm2d-54 [-1, 128, 28, 28] 256ReLU-55 [-1, 128, 28, 28] 0Conv2d-56 [-1, 32, 28, 28] 36,864BatchNorm2d-57 [-1, 192, 28, 28] 384ReLU-58 [-1, 192, 28, 28] 0Conv2d-59 [-1, 128, 28, 28] 24,576BatchNorm2d-60 [-1, 128, 28, 28] 256ReLU-61 [-1, 128, 28, 28] 0Conv2d-62 [-1, 32, 28, 28] 36,864BatchNorm2d-63 [-1, 224, 28, 28] 448ReLU-64 [-1, 224, 28, 28] 0Conv2d-65 [-1, 128, 28, 28] 28,672BatchNorm2d-66 [-1, 128, 28, 28] 256ReLU-67 [-1, 128, 28, 28] 0Conv2d-68 [-1, 32, 28, 28] 36,864BatchNorm2d-69 [-1, 256, 28, 28] 512ReLU-70 [-1, 256, 28, 28] 0Conv2d-71 [-1, 128, 28, 28] 32,768BatchNorm2d-72 [-1, 128, 28, 28] 256ReLU-73 [-1, 128, 28, 28] 0Conv2d-74 [-1, 32, 28, 28] 36,864BatchNorm2d-75 [-1, 288, 28, 28] 576ReLU-76 [-1, 288, 28, 28] 0Conv2d-77 [-1, 128, 28, 28] 36,864BatchNorm2d-78 [-1, 128, 28, 28] 256ReLU-79 [-1, 128, 28, 28] 0Conv2d-80 [-1, 32, 28, 28] 36,864BatchNorm2d-81 [-1, 320, 28, 28] 640ReLU-82 [-1, 320, 28, 28] 0Conv2d-83 [-1, 128, 28, 28] 40,960BatchNorm2d-84 [-1, 128, 28, 28] 256ReLU-85 [-1, 128, 28, 28] 0Conv2d-86 [-1, 32, 28, 28] 36,864BatchNorm2d-87 [-1, 352, 28, 28] 704ReLU-88 [-1, 352, 28, 28] 0Conv2d-89 [-1, 128, 28, 28] 45,056BatchNorm2d-90 [-1, 128, 28, 28] 256ReLU-91 [-1, 128, 28, 28] 0Conv2d-92 [-1, 32, 28, 28] 36,864BatchNorm2d-93 [-1, 384, 28, 28] 768ReLU-94 [-1, 384, 28, 28] 0Conv2d-95 [-1, 128, 28, 28] 49,152BatchNorm2d-96 [-1, 128, 28, 28] 256ReLU-97 [-1, 128, 28, 28] 0Conv2d-98 [-1, 32, 28, 28] 36,864BatchNorm2d-99 [-1, 416, 28, 28] 832ReLU-100 [-1, 416, 28, 28] 0Conv2d-101 [-1, 128, 28, 28] 53,248BatchNorm2d-102 [-1, 128, 28, 28] 256ReLU-103 [-1, 128, 28, 28] 0Conv2d-104 [-1, 32, 28, 28] 36,864BatchNorm2d-105 [-1, 448, 28, 28] 896ReLU-106 [-1, 448, 28, 28] 0Conv2d-107 [-1, 128, 28, 28] 57,344BatchNorm2d-108 [-1, 128, 28, 28] 256ReLU-109 [-1, 128, 28, 28] 0Conv2d-110 [-1, 32, 28, 28] 36,864BatchNorm2d-111 [-1, 480, 28, 28] 960ReLU-112 [-1, 480, 28, 28] 0Conv2d-113 [-1, 128, 28, 28] 61,440BatchNorm2d-114 [-1, 128, 28, 28] 256ReLU-115 [-1, 128, 28, 28] 0Conv2d-116 [-1, 32, 28, 28] 36,864BatchNorm2d-117 [-1, 512, 28, 28] 1,024ReLU-118 [-1, 512, 28, 28] 0Conv2d-119 [-1, 256, 28, 28] 131,072AvgPool2d-120 [-1, 256, 14, 14] 0BatchNorm2d-121 [-1, 256, 14, 14] 512ReLU-122 [-1, 256, 14, 14] 0Conv2d-123 [-1, 128, 14, 14] 32,768BatchNorm2d-124 [-1, 128, 14, 14] 256ReLU-125 [-1, 128, 14, 14] 0Conv2d-126 [-1, 32, 14, 14] 36,864BatchNorm2d-127 [-1, 288, 14, 14] 576ReLU-128 [-1, 288, 14, 14] 0Conv2d-129 [-1, 128, 14, 14] 36,864BatchNorm2d-130 [-1, 128, 14, 14] 256ReLU-131 [-1, 128, 14, 14] 0Conv2d-132 [-1, 32, 14, 14] 36,864BatchNorm2d-133 [-1, 320, 14, 14] 640ReLU-134 [-1, 320, 14, 14] 0Conv2d-135 [-1, 128, 14, 14] 40,960BatchNorm2d-136 [-1, 128, 14, 14] 256ReLU-137 [-1, 128, 14, 14] 0Conv2d-138 [-1, 32, 14, 14] 36,864BatchNorm2d-139 [-1, 352, 14, 14] 704ReLU-140 [-1, 352, 14, 14] 0Conv2d-141 [-1, 128, 14, 14] 45,056BatchNorm2d-142 [-1, 128, 14, 14] 256ReLU-143 [-1, 128, 14, 14] 0Conv2d-144 [-1, 32, 14, 14] 36,864BatchNorm2d-145 [-1, 384, 14, 14] 768ReLU-146 [-1, 384, 14, 14] 0Conv2d-147 [-1, 128, 14, 14] 49,152BatchNorm2d-148 [-1, 128, 14, 14] 256ReLU-149 [-1, 128, 14, 14] 0Conv2d-150 [-1, 32, 14, 14] 36,864BatchNorm2d-151 [-1, 416, 14, 14] 832ReLU-152 [-1, 416, 14, 14] 0Conv2d-153 [-1, 128, 14, 14] 53,248BatchNorm2d-154 [-1, 128, 14, 14] 256ReLU-155 [-1, 128, 14, 14] 0Conv2d-156 [-1, 32, 14, 14] 36,864BatchNorm2d-157 [-1, 448, 14, 14] 896ReLU-158 [-1, 448, 14, 14] 0Conv2d-159 [-1, 128, 14, 14] 57,344BatchNorm2d-160 [-1, 128, 14, 14] 256ReLU-161 [-1, 128, 14, 14] 0Conv2d-162 [-1, 32, 14, 14] 36,864BatchNorm2d-163 [-1, 480, 14, 14] 960ReLU-164 [-1, 480, 14, 14] 0Conv2d-165 [-1, 128, 14, 14] 61,440BatchNorm2d-166 [-1, 128, 14, 14] 256ReLU-167 [-1, 128, 14, 14] 0Conv2d-168 [-1, 32, 14, 14] 36,864BatchNorm2d-169 [-1, 512, 14, 14] 1,024ReLU-170 [-1, 512, 14, 14] 0Conv2d-171 [-1, 128, 14, 14] 65,536BatchNorm2d-172 [-1, 128, 14, 14] 256ReLU-173 [-1, 128, 14, 14] 0Conv2d-174 [-1, 32, 14, 14] 36,864BatchNorm2d-175 [-1, 544, 14, 14] 1,088ReLU-176 [-1, 544, 14, 14] 0Conv2d-177 [-1, 128, 14, 14] 69,632BatchNorm2d-178 [-1, 128, 14, 14] 256ReLU-179 [-1, 128, 14, 14] 0Conv2d-180 [-1, 32, 14, 14] 36,864BatchNorm2d-181 [-1, 576, 14, 14] 1,152ReLU-182 [-1, 576, 14, 14] 0Conv2d-183 [-1, 128, 14, 14] 73,728BatchNorm2d-184 [-1, 128, 14, 14] 256ReLU-185 [-1, 128, 14, 14] 0Conv2d-186 [-1, 32, 14, 14] 36,864BatchNorm2d-187 [-1, 608, 14, 14] 1,216ReLU-188 [-1, 608, 14, 14] 0Conv2d-189 [-1, 128, 14, 14] 77,824BatchNorm2d-190 [-1, 128, 14, 14] 256ReLU-191 [-1, 128, 14, 14] 0Conv2d-192 [-1, 32, 14, 14] 36,864BatchNorm2d-193 [-1, 640, 14, 14] 1,280ReLU-194 [-1, 640, 14, 14] 0Conv2d-195 [-1, 128, 14, 14] 81,920BatchNorm2d-196 [-1, 128, 14, 14] 256ReLU-197 [-1, 128, 14, 14] 0Conv2d-198 [-1, 32, 14, 14] 36,864BatchNorm2d-199 [-1, 672, 14, 14] 1,344ReLU-200 [-1, 672, 14, 14] 0Conv2d-201 [-1, 128, 14, 14] 86,016BatchNorm2d-202 [-1, 128, 14, 14] 256ReLU-203 [-1, 128, 14, 14] 0Conv2d-204 [-1, 32, 14, 14] 36,864BatchNorm2d-205 [-1, 704, 14, 14] 1,408ReLU-206 [-1, 704, 14, 14] 0Conv2d-207 [-1, 128, 14, 14] 90,112BatchNorm2d-208 [-1, 128, 14, 14] 256ReLU-209 [-1, 128, 14, 14] 0Conv2d-210 [-1, 32, 14, 14] 36,864BatchNorm2d-211 [-1, 736, 14, 14] 1,472ReLU-212 [-1, 736, 14, 14] 0Conv2d-213 [-1, 128, 14, 14] 94,208BatchNorm2d-214 [-1, 128, 14, 14] 256ReLU-215 [-1, 128, 14, 14] 0Conv2d-216 [-1, 32, 14, 14] 36,864BatchNorm2d-217 [-1, 768, 14, 14] 1,536ReLU-218 [-1, 768, 14, 14] 0Conv2d-219 [-1, 128, 14, 14] 98,304BatchNorm2d-220 [-1, 128, 14, 14] 256ReLU-221 [-1, 128, 14, 14] 0Conv2d-222 [-1, 32, 14, 14] 36,864BatchNorm2d-223 [-1, 800, 14, 14] 1,600ReLU-224 [-1, 800, 14, 14] 0Conv2d-225 [-1, 128, 14, 14] 102,400BatchNorm2d-226 [-1, 128, 14, 14] 256ReLU-227 [-1, 128, 14, 14] 0Conv2d-228 [-1, 32, 14, 14] 36,864BatchNorm2d-229 [-1, 832, 14, 14] 1,664ReLU-230 [-1, 832, 14, 14] 0Conv2d-231 [-1, 128, 14, 14] 106,496BatchNorm2d-232 [-1, 128, 14, 14] 256ReLU-233 [-1, 128, 14, 14] 0Conv2d-234 [-1, 32, 14, 14] 36,864BatchNorm2d-235 [-1, 864, 14, 14] 1,728ReLU-236 [-1, 864, 14, 14] 0Conv2d-237 [-1, 128, 14, 14] 110,592BatchNorm2d-238 [-1, 128, 14, 14] 256ReLU-239 [-1, 128, 14, 14] 0Conv2d-240 [-1, 32, 14, 14] 36,864BatchNorm2d-241 [-1, 896, 14, 14] 1,792ReLU-242 [-1, 896, 14, 14] 0Conv2d-243 [-1, 128, 14, 14] 114,688BatchNorm2d-244 [-1, 128, 14, 14] 256ReLU-245 [-1, 128, 14, 14] 0Conv2d-246 [-1, 32, 14, 14] 36,864BatchNorm2d-247 [-1, 928, 14, 14] 1,856ReLU-248 [-1, 928, 14, 14] 0Conv2d-249 [-1, 128, 14, 14] 118,784BatchNorm2d-250 [-1, 128, 14, 14] 256ReLU-251 [-1, 128, 14, 14] 0Conv2d-252 [-1, 32, 14, 14] 36,864BatchNorm2d-253 [-1, 960, 14, 14] 1,920ReLU-254 [-1, 960, 14, 14] 0Conv2d-255 [-1, 128, 14, 14] 122,880BatchNorm2d-256 [-1, 128, 14, 14] 256ReLU-257 [-1, 128, 14, 14] 0Conv2d-258 [-1, 32, 14, 14] 36,864BatchNorm2d-259 [-1, 992, 14, 14] 1,984ReLU-260 [-1, 992, 14, 14] 0Conv2d-261 [-1, 128, 14, 14] 126,976BatchNorm2d-262 [-1, 128, 14, 14] 256ReLU-263 [-1, 128, 14, 14] 0Conv2d-264 [-1, 32, 14, 14] 36,864BatchNorm2d-265 [-1, 1024, 14, 14] 2,048ReLU-266 [-1, 1024, 14, 14] 0Conv2d-267 [-1, 512, 14, 14] 524,288AvgPool2d-268 [-1, 512, 7, 7] 0BatchNorm2d-269 [-1, 512, 7, 7] 1,024ReLU-270 [-1, 512, 7, 7] 0Conv2d-271 [-1, 128, 7, 7] 65,536BatchNorm2d-272 [-1, 128, 7, 7] 256ReLU-273 [-1, 128, 7, 7] 0Conv2d-274 [-1, 32, 7, 7] 36,864BatchNorm2d-275 [-1, 544, 7, 7] 1,088ReLU-276 [-1, 544, 7, 7] 0Conv2d-277 [-1, 128, 7, 7] 69,632BatchNorm2d-278 [-1, 128, 7, 7] 256ReLU-279 [-1, 128, 7, 7] 0Conv2d-280 [-1, 32, 7, 7] 36,864BatchNorm2d-281 [-1, 576, 7, 7] 1,152ReLU-282 [-1, 576, 7, 7] 0Conv2d-283 [-1, 128, 7, 7] 73,728BatchNorm2d-284 [-1, 128, 7, 7] 256ReLU-285 [-1, 128, 7, 7] 0Conv2d-286 [-1, 32, 7, 7] 36,864BatchNorm2d-287 [-1, 608, 7, 7] 1,216ReLU-288 [-1, 608, 7, 7] 0Conv2d-289 [-1, 128, 7, 7] 77,824BatchNorm2d-290 [-1, 128, 7, 7] 256ReLU-291 [-1, 128, 7, 7] 0Conv2d-292 [-1, 32, 7, 7] 36,864BatchNorm2d-293 [-1, 640, 7, 7] 1,280ReLU-294 [-1, 640, 7, 7] 0Conv2d-295 [-1, 128, 7, 7] 81,920BatchNorm2d-296 [-1, 128, 7, 7] 256ReLU-297 [-1, 128, 7, 7] 0Conv2d-298 [-1, 32, 7, 7] 36,864BatchNorm2d-299 [-1, 672, 7, 7] 1,344ReLU-300 [-1, 672, 7, 7] 0Conv2d-301 [-1, 128, 7, 7] 86,016BatchNorm2d-302 [-1, 128, 7, 7] 256ReLU-303 [-1, 128, 7, 7] 0Conv2d-304 [-1, 32, 7, 7] 36,864BatchNorm2d-305 [-1, 704, 7, 7] 1,408ReLU-306 [-1, 704, 7, 7] 0Conv2d-307 [-1, 128, 7, 7] 90,112BatchNorm2d-308 [-1, 128, 7, 7] 256ReLU-309 [-1, 128, 7, 7] 0Conv2d-310 [-1, 32, 7, 7] 36,864BatchNorm2d-311 [-1, 736, 7, 7] 1,472ReLU-312 [-1, 736, 7, 7] 0Conv2d-313 [-1, 128, 7, 7] 94,208BatchNorm2d-314 [-1, 128, 7, 7] 256ReLU-315 [-1, 128, 7, 7] 0Conv2d-316 [-1, 32, 7, 7] 36,864BatchNorm2d-317 [-1, 768, 7, 7] 1,536ReLU-318 [-1, 768, 7, 7] 0Conv2d-319 [-1, 128, 7, 7] 98,304BatchNorm2d-320 [-1, 128, 7, 7] 256ReLU-321 [-1, 128, 7, 7] 0Conv2d-322 [-1, 32, 7, 7] 36,864BatchNorm2d-323 [-1, 800, 7, 7] 1,600ReLU-324 [-1, 800, 7, 7] 0Conv2d-325 [-1, 128, 7, 7] 102,400BatchNorm2d-326 [-1, 128, 7, 7] 256ReLU-327 [-1, 128, 7, 7] 0Conv2d-328 [-1, 32, 7, 7] 36,864BatchNorm2d-329 [-1, 832, 7, 7] 1,664ReLU-330 [-1, 832, 7, 7] 0Conv2d-331 [-1, 128, 7, 7] 106,496BatchNorm2d-332 [-1, 128, 7, 7] 256ReLU-333 [-1, 128, 7, 7] 0Conv2d-334 [-1, 32, 7, 7] 36,864BatchNorm2d-335 [-1, 864, 7, 7] 1,728ReLU-336 [-1, 864, 7, 7] 0Conv2d-337 [-1, 128, 7, 7] 110,592BatchNorm2d-338 [-1, 128, 7, 7] 256ReLU-339 [-1, 128, 7, 7] 0Conv2d-340 [-1, 32, 7, 7] 36,864BatchNorm2d-341 [-1, 896, 7, 7] 1,792ReLU-342 [-1, 896, 7, 7] 0Conv2d-343 [-1, 128, 7, 7] 114,688BatchNorm2d-344 [-1, 128, 7, 7] 256ReLU-345 [-1, 128, 7, 7] 0Conv2d-346 [-1, 32, 7, 7] 36,864BatchNorm2d-347 [-1, 928, 7, 7] 1,856ReLU-348 [-1, 928, 7, 7] 0Conv2d-349 [-1, 128, 7, 7] 118,784BatchNorm2d-350 [-1, 128, 7, 7] 256ReLU-351 [-1, 128, 7, 7] 0Conv2d-352 [-1, 32, 7, 7] 36,864BatchNorm2d-353 [-1, 960, 7, 7] 1,920ReLU-354 [-1, 960, 7, 7] 0Conv2d-355 [-1, 128, 7, 7] 122,880BatchNorm2d-356 [-1, 128, 7, 7] 256ReLU-357 [-1, 128, 7, 7] 0Conv2d-358 [-1, 32, 7, 7] 36,864BatchNorm2d-359 [-1, 992, 7, 7] 1,984ReLU-360 [-1, 992, 7, 7] 0Conv2d-361 [-1, 128, 7, 7] 126,976BatchNorm2d-362 [-1, 128, 7, 7] 256ReLU-363 [-1, 128, 7, 7] 0Conv2d-364 [-1, 32, 7, 7] 36,864BatchNorm2d-365 [-1, 1024, 7, 7] 2,048ReLU-366 [-1, 1024, 7, 7] 0Linear-367 [-1, 2] 2,050Total params: 6,955,906
Trainable params: 6,955,906
Non-trainable params: 0
----------------------------------------------------------------
Input size (MB): 0.57
Forward/backward pass size (MB): 294.57
Params size (MB): 26.53
Estimated Total Size (MB): 321.68
----------------------------------------------------------------
loss_fn nn.CrossEntropyLoss() # 创建损失函数
learn_rate 1e-4 # 学习率
opt torch.optim.SGD(model.parameters(),lrlearn_rate)# 训练循环
def train(dataloader, model, loss_fn, optimizer):size len(dataloader.dataset) # 训练集的大小一共60000张图片num_batches len(dataloader) # 批次数目187560000/32train_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) # 测试集的大小一共10000张图片num_batches len(dataloader) # 批次数目31310000/32312.5向上取整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_lossepochs 20
train_loss []
train_acc []
test_loss []
test_acc []for epoch in range(epochs):model.train()epoch_train_acc, epoch_train_loss train(train_dl, model, loss_fn, opt)model.eval()epoch_test_acc, epoch_test_loss test(test_dl, model, loss_fn)train_acc.append(epoch_train_acc)train_loss.append(epoch_train_loss)test_acc.append(epoch_test_acc)test_loss.append(epoch_test_loss)template (Epoch:{:2d}, Train_acc:{:.1f}%, Train_loss:{:.3f}, Test_acc:{:.1f}%Test_loss:{:.3f})print(template.format(epoch1, epoch_train_acc*100, epoch_train_loss, epoch_test_acc*100, epoch_test_loss))
print(Done)
Epoch: 1, Train_acc:47.7%, Train_loss:0.725, Test_acc:47.4%Test_loss:0.708
Epoch: 2, Train_acc:50.2%, Train_loss:0.697, Test_acc:52.7%Test_loss:0.690
Epoch: 3, Train_acc:56.1%, Train_loss:0.686, Test_acc:59.9%Test_loss:0.681
Epoch: 4, Train_acc:58.5%, Train_loss:0.679, Test_acc:60.7%Test_loss:0.675
Epoch: 5, Train_acc:60.9%, Train_loss:0.673, Test_acc:60.1%Test_loss:0.671
Epoch: 6, Train_acc:61.7%, Train_loss:0.670, Test_acc:62.6%Test_loss:0.664
Epoch: 7, Train_acc:62.4%, Train_loss:0.665, Test_acc:63.5%Test_loss:0.659
Epoch: 8, Train_acc:63.0%, Train_loss:0.660, Test_acc:64.8%Test_loss:0.653
Epoch: 9, Train_acc:64.2%, Train_loss:0.656, Test_acc:65.5%Test_loss:0.649
Epoch:10, Train_acc:64.9%, Train_loss:0.652, Test_acc:65.6%Test_loss:0.644
Epoch:11, Train_acc:65.4%, Train_loss:0.649, Test_acc:66.6%Test_loss:0.641
Epoch:12, Train_acc:65.0%, Train_loss:0.646, Test_acc:66.6%Test_loss:0.638
Epoch:13, Train_acc:64.8%, Train_loss:0.643, Test_acc:67.5%Test_loss:0.634
Epoch:14, Train_acc:65.7%, Train_loss:0.641, Test_acc:67.3%Test_loss:0.633
Epoch:15, Train_acc:65.9%, Train_loss:0.638, Test_acc:67.8%Test_loss:0.629
Epoch:16, Train_acc:66.3%, Train_loss:0.635, Test_acc:67.6%Test_loss:0.626
Epoch:17, Train_acc:67.3%, Train_loss:0.632, Test_acc:67.8%Test_loss:0.624
Epoch:18, Train_acc:67.1%, Train_loss:0.628, Test_acc:68.2%Test_loss:0.618
Epoch:19, Train_acc:67.3%, Train_loss:0.628, Test_acc:68.9%Test_loss:0.618
Epoch:20, Train_acc:67.9%, Train_loss:0.624, Test_acc:68.4%Test_loss:0.614
Done
import matplotlib.pyplot as plt
#隐藏警告
import warnings
warnings.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()
