【AI达人特训营】基于全卷积神经网络的图像分类复现

时间：2025-08-17

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【AI达人特训营】基于全卷积神经网络的图像分类复现

本文将ResNet全连接层替换为全卷积层，构建了ResNetFCN模型。训练在CIFAR-据集上进行，参数设置与原始ResNet同（、lr=。实验结果表明，ResNetFCN的准确率显著提升，且性能表现更为平滑和稳定，验证了全卷积层在保留空间信息方面具有明显优势。

ResNet50-FCN

论文地址：https://arxiv.org/abs/1411.4038

FCN：Fully Convolutional Networks

项目采用了完全不同的方法，将标准卷积神经网络模式（CNN）中的后续全连接层替换为卷积层。这样一来，整个模型的结构变成了几乎纯粹的卷积网络。最终输出不再是概率值，而是热图形式的信息标记。与传统CNN不同，它在最后并未进行全连接操作后的特征融合和softmax分类处理。如上图所示，这个新的架构摒弃了传统的全连接部分，直接将特征提取、归一化以及后向传播等关键步骤全部整合到卷积层中。该结构的创新之处在于简化了网络结构，从而提高了计算效率，并且在识别复杂对象时表现出了更强的表现力和准确性。这种设计为深度学习领域提供了全新的思考角度，尤其是在大规模图像分类任务上展现了显著优势。

假设最后一层特征图大小为则全连接层将利用滤波器对这一特征图进行卷积处理，由此可见参数量之大。

但是全卷积网络就简单多了。FCN的做法是将最终的全连接层替换为卷积核，因此得出一个二维图像，并通过上采样（反卷积）将其放大后进行分类，每个像素点应用softmax分类。

使用全卷积层的优点:

全卷积层能够兼容不同大小的尺寸输入。与global avg pooling类似，可以大大减少网络参数量

数据集介绍：Cifar10

链接：http://www.cs.toronto.edu/~kriz/cifar.html

CIFAR-Hinton的学生Alex Krizhevsky和Ilya Sutskever整理的，用于识别普适物体的小型彩色图像数据集。它包含了类别的RGB图片：飞机(airplane)、汽车(automobile)、鸟类(bird)、猫(cat)、鹿(deer)、狗(dog)、蛙类(frog)、马(horse)、船(ship)和卡车(truck)。

每个图片的尺寸为每个类目有图；共收集了张训练图片与张测试图片。

代码复现

1.引入依赖包

In [1]

from __future__ import divisionfrom __future__ import print_functionimport paddleimport paddle.nn as nnfrom paddle.nn import functional as Ffrom paddle.utils.download import get_weights_path_from_urlimport pickleimport numpy as npfrom paddle import callbacksfrom paddle.vision.transforms import ( ToTensor, RandomHorizontalFlip, RandomResizedCrop, SaturationTransform, Compose, HueTransform, BrightnessTransform, ContrastTransform, RandomCrop, Normalize, RandomRotation, Resize )from paddle.vision.datasets import Cifar10from paddle.io import DataLoaderfrom paddle.optimizer.lr import CosineAnnealingDecay, MultiStepDecay, LinearWarmupimport random登录后复制

2.定义ResNet50-FCN网络

本代码参考Paddleclas实现，代码中将分类类别设定为100类 In [2]

__all__ = [] model_urls = { 'resnet18': ('https://paddle-hapi.bj.bcebos.com/models/resnet18.pdparams', 'cf548f46534aa3560945be4b95cd11c4'), 'resnet34': ('https://paddle-hapi.bj.bcebos.com/models/resnet34.pdparams', '8d2275cf8706028345f78ac0e1d31969'), 'resnet50': ('https://paddle-hapi.bj.bcebos.com/models/resnet50.pdparams', 'ca6f485ee1ab0492d38f323885b0ad80'), 'resnet101': ('https://paddle-hapi.bj.bcebos.com/models/resnet101.pdparams', '02f35f034ca3858e1e54d4036443c92d'), 'resnet152': ('https://paddle-hapi.bj.bcebos.com/models/resnet152.pdparams', '7ad16a2f1e7333859ff986138630fd7a'), }class BottleneckBlock(nn.Layer): expansion = 4 def __init__(self, inplanes, planes, stride=1, downsample=None, groups=1, base_width=64, dilation=1, norm_layer=None): super(BottleneckBlock, self).__init__() if norm_layer is None: norm_layer = nn.BatchNorm2D width = int(planes * (base_width / 64.)) * groups self.width = width self.conv1 = nn.Conv2D(inplanes, width, 1, bias_attr=False) self.bn1 = norm_layer(width) self.conv2 = nn.Conv2D( width, width, 3, padding=dilation, stride=stride, groups=groups, dilation=dilation, bias_attr=False) self.bn2 = norm_layer(width) self.conv3 = nn.Conv2D( width, planes * self.expansion, 1, bias_attr=False) self.width_2 = planes * self.expansion self.bn3 = norm_layer(planes * self.expansion) self.relu = nn.ReLU() self.downsample = downsample self.stride = stride def forward(self, x): identity = x out = self.conv1(x) out = self.bn1(out) out = self.relu(out) out = self.conv2(out) out = self.bn2(out) out = self.relu(out) out = self.conv3(out) out = self.bn3(out) if self.downsample is not None: identity = self.downsample(x) out += identity out = self.relu(out) return outclass ResNet(nn.Layer): """ResNet model from `"Deep Residual Learning for Image Recognition" <https://arxiv.org/pdf/1512.03385.pdf>`_ Args: Block (BasicBlock|BottleneckBlock): block module of model. depth (int): layers of resnet, default: 50. num_classes (int): output dim of last fc layer. If num_classes <=0, last fc layer will not be defined. Default: 1000. with_pool (bool): use pool before the last fc layer or not. Default: True. Examples: .. code-block:: python from paddle.vision.models import ResNet from paddle.vision.models.resnet import BottleneckBlock, BasicBlock resnet50 = ResNet(BottleneckBlock, 50) resnet18 = ResNet(BasicBlock, 18) """ def __init__(self, block, depth, num_classes=10, with_pool=False): super(ResNet, self).__init__() layer_cfg = { 18: [2, 2, 2, 2], 34: [3, 4, 6, 3], 50: [3, 4, 6, 3], 101: [3, 4, 23, 3], 152: [3, 8, 36, 3] } layers = layer_cfg[depth] self.num_classes = num_classes self.with_pool = with_pool self._norm_layer = nn.BatchNorm2D self.inplanes = 64 self.dilation = 1 self.conv1 = nn.Conv2D( 3, self.inplanes, kernel_size=7, stride=2, padding=3, bias_attr=False) self.bn1 = self._norm_layer(self.inplanes) self.relu = nn.ReLU() # self.maxpool = nn.MaxPool2D(kernel_size=3, stride=2, padding=1) self.layer1 = self._make_layer(block, 64, layers[0]) self.layer2 = self._make_layer(block, 128, layers[1], stride=2) self.layer3 = self._make_layer(block, 256, layers[2], stride=2) self.layer4 = self._make_layer(block, 512, layers[3], stride=2) if with_pool: self.avgpool = nn.AdaptiveAvgPool2D((1, 1)) if num_classes > 0: self.fc = nn.Linear(512 * block.expansion, num_classes) self.final_conv = nn.Conv2D(512 * block.expansion, 1024, 2) self.final_conv2 = nn.Conv2D(1024, 10, 1) def _make_layer(self, block, planes, blocks, stride=1, dilate=False): norm_layer = self._norm_layer downsample = None previous_dilation = self.dilation if dilate: self.dilation *= stride stride = 1 if stride != 1 or self.inplanes != planes * block.expansion: downsample = nn.Sequential( nn.Conv2D( self.inplanes, planes * block.expansion, 1, stride=stride, bias_attr=False), norm_layer(planes * block.expansion), ) layers = [] layers.append( block(self.inplanes, planes, stride, downsample, 1, 64, previous_dilation, norm_layer)) self.inplanes = planes * block.expansion for _ in range(1, blocks): layers.append(block(self.inplanes, planes, norm_layer=norm_layer)) return nn.Sequential(*layers) def forward(self, x): x = self.conv1(x) x = self.bn1(x) x = self.relu(x) # x = self.maxpool(x) x = self.layer1(x) x = self.layer2(x) x = self.layer3(x) x = self.layer4(x) # 更改为全卷积层 # if self.with_pool: # x = self.avgpool(x) # if self.num_classes > 0: # x = paddle.flatten(x, 1) # x = self.fc(x) # 全卷积层 x = self.final_conv(x) x = self.final_conv2(x) x = x.reshape([-1, 10], -1) return xdef _resnet(arch, Block, depth, pretrained, **kwargs): model = ResNet(Block, depth, **kwargs) if pretrained: assert arch in model_urls, "{} model do not have a pretrained model now, you should set pretrained=False".format( arch) weight_path = get_weights_path_from_url(model_urls[arch][0], model_urls[arch][1]) param = paddle.load(weight_path) model.set_dict(param) return modeldef resnet50(pretrained=False, **kwargs): """ResNet 50-layer model Args: pretrained (bool): If True, returns a model pre-trained on ImageNet Examples: .. code-block:: python from paddle.vision.models import resnet50 # build model model = resnet50() # build model and load imagenet pretrained weight # model = resnet50(pretrained=True) """ return _resnet('resnet50', BottleneckBlock, 50, pretrained, **kwargs)登录后复制 In [3]

net = resnet50() paddle.summary(net, (1,3,32,32))登录后复制

W0616 14:52:04.385969 2132 gpu_context.cc:278] Please NOTE: device: 0, GPU Compute Capability: 7.0, Driver API Version: 11.2, Runtime API Version: 10.1 W0616 14:52:04.390283 2132 gpu_context.cc:306] device: 0, cuDNN Version: 7.6.登录后复制

------------------------------------------------------------------------------ Layer (type) Input Shape Output Shape Param # ============================================================================== Conv2D-1 [[1, 3, 32, 32]] [1, 64, 16, 16] 9,408 BatchNorm2D-1 [[1, 64, 16, 16]] [1, 64, 16, 16] 256 ReLU-1 [[1, 64, 16, 16]] [1, 64, 16, 16] 0 Conv2D-3 [[1, 64, 16, 16]] [1, 64, 16, 16] 4,096 BatchNorm2D-3 [[1, 64, 16, 16]] [1, 64, 16, 16] 256 ReLU-2 [[1, 256, 16, 16]] [1, 256, 16, 16] 0 Conv2D-4 [[1, 64, 16, 16]] [1, 64, 16, 16] 36,864 BatchNorm2D-4 [[1, 64, 16, 16]] [1, 64, 16, 16] 256 Conv2D-5 [[1, 64, 16, 16]] [1, 256, 16, 16] 16,384 BatchNorm2D-5 [[1, 256, 16, 16]] [1, 256, 16, 16] 1,024 Conv2D-2 [[1, 64, 16, 16]] [1, 256, 16, 16] 16,384 BatchNorm2D-2 [[1, 256, 16, 16]] [1, 256, 16, 16] 1,024 BottleneckBlock-1 [[1, 64, 16, 16]] [1, 256, 16, 16] 0 Conv2D-6 [[1, 256, 16, 16]] [1, 64, 16, 16] 16,384 BatchNorm2D-6 [[1, 64, 16, 16]] [1, 64, 16, 16] 256 ReLU-3 [[1, 256, 16, 16]] [1, 256, 16, 16] 0 Conv2D-7 [[1, 64, 16, 16]] [1, 64, 16, 16] 36,864 BatchNorm2D-7 [[1, 64, 16, 16]] [1, 64, 16, 16] 256 Conv2D-8 [[1, 64, 16, 16]] [1, 256, 16, 16] 16,384 BatchNorm2D-8 [[1, 256, 16, 16]] [1, 256, 16, 16] 1,024 BottleneckBlock-2 [[1, 256, 16, 16]] [1, 256, 16, 16] 0 Conv2D-9 [[1, 256, 16, 16]] [1, 64, 16, 16] 16,384 BatchNorm2D-9 [[1, 64, 16, 16]] [1, 64, 16, 16] 256 ReLU-4 [[1, 256, 16, 16]] [1, 256, 16, 16] 0 Conv2D-10 [[1, 64, 16, 16]] [1, 64, 16, 16] 36,864 BatchNorm2D-10 [[1, 64, 16, 16]] [1, 64, 16, 16] 256 Conv2D-11 [[1, 64, 16, 16]] [1, 256, 16, 16] 16,384 BatchNorm2D-11 [[1, 256, 16, 16]] [1, 256, 16, 16] 1,024 BottleneckBlock-3 [[1, 256, 16, 16]] [1, 256, 16, 16] 0 Conv2D-13 [[1, 256, 16, 16]] [1, 128, 16, 16] 32,768 BatchNorm2D-13 [[1, 128, 16, 16]] [1, 128, 16, 16] 512 ReLU-5 [[1, 512, 8, 8]] [1, 512, 8, 8] 0 Conv2D-14 [[1, 128, 16, 16]] [1, 128, 8, 8] 147,456 BatchNorm2D-14 [[1, 128, 8, 8]] [1, 128, 8, 8] 512 Conv2D-15 [[1, 128, 8, 8]] [1, 512, 8, 8] 65,536 BatchNorm2D-15 [[1, 512, 8, 8]] [1, 512, 8, 8] 2,048 Conv2D-12 [[1, 256, 16, 16]] [1, 512, 8, 8] 131,072 BatchNorm2D-12 [[1, 512, 8, 8]] [1, 512, 8, 8] 2,048 BottleneckBlock-4 [[1, 256, 16, 16]] [1, 512, 8, 8] 0 Conv2D-16 [[1, 512, 8, 8]] [1, 128, 8, 8] 65,536 BatchNorm2D-16 [[1, 128, 8, 8]] [1, 128, 8, 8] 512 ReLU-6 [[1, 512, 8, 8]] [1, 512, 8, 8] 0 Conv2D-17 [[1, 128, 8, 8]] [1, 128, 8, 8] 147,456 BatchNorm2D-17 [[1, 128, 8, 8]] [1, 128, 8, 8] 512 Conv2D-18 [[1, 128, 8, 8]] [1, 512, 8, 8] 65,536 BatchNorm2D-18 [[1, 512, 8, 8]] [1, 512, 8, 8] 2,048 BottleneckBlock-5 [[1, 512, 8, 8]] [1, 512, 8, 8] 0 Conv2D-19 [[1, 512, 8, 8]] [1, 128, 8, 8] 65,536 BatchNorm2D-19 [[1, 128, 8, 8]] [1, 128, 8, 8] 512 ReLU-7 [[1, 512, 8, 8]] [1, 512, 8, 8] 0 Conv2D-20 [[1, 128, 8, 8]] [1, 128, 8, 8] 147,456 BatchNorm2D-20 [[1, 128, 8, 8]] [1, 128, 8, 8] 512 Conv2D-21 [[1, 128, 8, 8]] [1, 512, 8, 8] 65,536 BatchNorm2D-21 [[1, 512, 8, 8]] [1, 512, 8, 8] 2,048 BottleneckBlock-6 [[1, 512, 8, 8]] [1, 512, 8, 8] 0 Conv2D-22 [[1, 512, 8, 8]] [1, 128, 8, 8] 65,536 BatchNorm2D-22 [[1, 128, 8, 8]] [1, 128, 8, 8] 512 ReLU-8 [[1, 512, 8, 8]] [1, 512, 8, 8] 0 Conv2D-23 [[1, 128, 8, 8]] [1, 128, 8, 8] 147,456 BatchNorm2D-23 [[1, 128, 8, 8]] [1, 128, 8, 8] 512 Conv2D-24 [[1, 128, 8, 8]] [1, 512, 8, 8] 65,536 BatchNorm2D-24 [[1, 512, 8, 8]] [1, 512, 8, 8] 2,048 BottleneckBlock-7 [[1, 512, 8, 8]] [1, 512, 8, 8] 0 Conv2D-26 [[1, 512, 8, 8]] [1, 256, 8, 8] 131,072 BatchNorm2D-26 [[1, 256, 8, 8]] [1, 256, 8, 8] 1,024 ReLU-9 [[1, 1024, 4, 4]] [1, 1024, 4, 4] 0 Conv2D-27 [[1, 256, 8, 8]] [1, 256, 4, 4] 589,824 BatchNorm2D-27 [[1, 256, 4, 4]] [1, 256, 4, 4] 1,024 Conv2D-28 [[1, 256, 4, 4]] [1, 1024, 4, 4] 262,144 BatchNorm2D-28 [[1, 1024, 4, 4]] [1, 1024, 4, 4] 4,096 Conv2D-25 [[1, 512, 8, 8]] [1, 1024, 4, 4] 524,288 BatchNorm2D-25 [[1, 1024, 4, 4]] [1, 1024, 4, 4] 4,096 BottleneckBlock-8 [[1, 512, 8, 8]] [1, 1024, 4, 4] 0 Conv2D-29 [[1, 1024, 4, 4]] [1, 256, 4, 4] 262,144 BatchNorm2D-29 [[1, 256, 4, 4]] [1, 256, 4, 4] 1,024 ReLU-10 [[1, 1024, 4, 4]] [1, 1024, 4, 4] 0 Conv2D-30 [[1, 256, 4, 4]] [1, 256, 4, 4] 589,824 BatchNorm2D-30 [[1, 256, 4, 4]] [1, 256, 4, 4] 1,024 Conv2D-31 [[1, 256, 4, 4]] [1, 1024, 4, 4] 262,144 BatchNorm2D-31 [[1, 1024, 4, 4]] [1, 1024, 4, 4] 4,096 BottleneckBlock-9 [[1, 1024, 4, 4]] [1, 1024, 4, 4] 0 Conv2D-32 [[1, 1024, 4, 4]] [1, 256, 4, 4] 262,144 BatchNorm2D-32 [[1, 256, 4, 4]] [1, 256, 4, 4] 1,024 ReLU-11 [[1, 1024, 4, 4]] [1, 1024, 4, 4] 0 Conv2D-33 [[1, 256, 4, 4]] [1, 256, 4, 4] 589,824 BatchNorm2D-33 [[1, 256, 4, 4]] [1, 256, 4, 4] 1,024 Conv2D-34 [[1, 256, 4, 4]] [1, 1024, 4, 4] 262,144 BatchNorm2D-34 [[1, 1024, 4, 4]] [1, 1024, 4, 4] 4,096 BottleneckBlock-10 [[1, 1024, 4, 4]] [1, 1024, 4, 4] 0 Conv2D-35 [[1, 1024, 4, 4]] [1, 256, 4, 4] 262,144 BatchNorm2D-35 [[1, 256, 4, 4]] [1, 256, 4, 4] 1,024 ReLU-12 [[1, 1024, 4, 4]] [1, 1024, 4, 4] 0 Conv2D-36 [[1, 256, 4, 4]] [1, 256, 4, 4] 589,824 BatchNorm2D-36 [[1, 256, 4, 4]] [1, 256, 4, 4] 1,024 Conv2D-37 [[1, 256, 4, 4]] [1, 1024, 4, 4] 262,144 BatchNorm2D-37 [[1, 1024, 4, 4]] [1, 1024, 4, 4] 4,096 BottleneckBlock-11 [[1, 1024, 4, 4]] [1, 1024, 4, 4] 0 Conv2D-38 [[1, 1024, 4, 4]] [1, 256, 4, 4] 262,144 BatchNorm2D-38 [[1, 256, 4, 4]] [1, 256, 4, 4] 1,024 ReLU-13 [[1, 1024, 4, 4]] [1, 1024, 4, 4] 0 Conv2D-39 [[1, 256, 4, 4]] [1, 256, 4, 4] 589,824 BatchNorm2D-39 [[1, 256, 4, 4]] [1, 256, 4, 4] 1,024 Conv2D-40 [[1, 256, 4, 4]] [1, 1024, 4, 4] 262,144 BatchNorm2D-40 [[1, 1024, 4, 4]] [1, 1024, 4, 4] 4,096 BottleneckBlock-12 [[1, 1024, 4, 4]] [1, 1024, 4, 4] 0 Conv2D-41 [[1, 1024, 4, 4]] [1, 256, 4, 4] 262,144 BatchNorm2D-41 [[1, 256, 4, 4]] [1, 256, 4, 4] 1,024 ReLU-14 [[1, 1024, 4, 4]] [1, 1024, 4, 4] 0 Conv2D-42 [[1, 256, 4, 4]] [1, 256, 4, 4] 589,824 BatchNorm2D-42 [[1, 256, 4, 4]] [1, 256, 4, 4] 1,024 Conv2D-43 [[1, 256, 4, 4]] [1, 1024, 4, 4] 262,144 BatchNorm2D-43 [[1, 1024, 4, 4]] [1, 1024, 4, 4] 4,096 BottleneckBlock-13 [[1, 1024, 4, 4]] [1, 1024, 4, 4] 0 Conv2D-45 [[1, 1024, 4, 4]] [1, 512, 4, 4] 524,288 BatchNorm2D-45 [[1, 512, 4, 4]] [1, 512, 4, 4] 2,048 ReLU-15 [[1, 2048, 2, 2]] [1, 2048, 2, 2] 0 Conv2D-46 [[1, 512, 4, 4]] [1, 512, 2, 2] 2,359,296 BatchNorm2D-46 [[1, 512, 2, 2]] [1, 512, 2, 2] 2,048 Conv2D-47 [[1, 512, 2, 2]] [1, 2048, 2, 2] 1,048,576 BatchNorm2D-47 [[1, 2048, 2, 2]] [1, 2048, 2, 2] 8,192 Conv2D-44 [[1, 1024, 4, 4]] [1, 2048, 2, 2] 2,097,152 BatchNorm2D-44 [[1, 2048, 2, 2]] [1, 2048, 2, 2] 8,192 BottleneckBlock-14 [[1, 1024, 4, 4]] [1, 2048, 2, 2] 0 Conv2D-48 [[1, 2048, 2, 2]] [1, 512, 2, 2] 1,048,576 BatchNorm2D-48 [[1, 512, 2, 2]] [1, 512, 2, 2] 2,048 ReLU-16 [[1, 2048, 2, 2]] [1, 2048, 2, 2] 0 Conv2D-49 [[1, 512, 2, 2]] [1, 512, 2, 2] 2,359,296 BatchNorm2D-49 [[1, 512, 2, 2]] [1, 512, 2, 2] 2,048 Conv2D-50 [[1, 512, 2, 2]] [1, 2048, 2, 2] 1,048,576 BatchNorm2D-50 [[1, 2048, 2, 2]] [1, 2048, 2, 2] 8,192 BottleneckBlock-15 [[1, 2048, 2, 2]] [1, 2048, 2, 2] 0 Conv2D-51 [[1, 2048, 2, 2]] [1, 512, 2, 2] 1,048,576 BatchNorm2D-51 [[1, 512, 2, 2]] [1, 512, 2, 2] 2,048 ReLU-17 [[1, 2048, 2, 2]] [1, 2048, 2, 2] 0 Conv2D-52 [[1, 512, 2, 2]] [1, 512, 2, 2] 2,359,296 BatchNorm2D-52 [[1, 512, 2, 2]] [1, 512, 2, 2] 2,048 Conv2D-53 [[1, 512, 2, 2]] [1, 2048, 2, 2] 1,048,576 BatchNorm2D-53 [[1, 2048, 2, 2]] [1, 2048, 2, 2] 8,192 BottleneckBlock-16 [[1, 2048, 2, 2]] [1, 2048, 2, 2] 0 Conv2D-54 [[1, 2048, 2, 2]] [1, 1024, 1, 1] 8,389,632 Conv2D-55 [[1, 1024, 1, 1]] [1, 10, 1, 1] 10,250 ============================================================================== Total params: 31,961,034 Trainable params: 31,854,794 Non-trainable params: 106,240 ------------------------------------------------------------------------------ Input size (MB): 0.01 Forward/backward pass size (MB): 20.10 Params size (MB): 121.92 Estimated Total Size (MB): 142.04 ------------------------------------------------------------------------------登录后复制

{'total_params': 31961034, 'trainable_params': 31854794}登录后复制

3.自定义数据集处理方式

In [4]

class ToArray(object): def __call__(self, img): img = np.array(img) img = np.transpose(img, [2, 0, 1]) img = img / 255. return img.astype('float32')class RandomApply(object): def __init__(self, transform, p=0.5): super().__init__() self.p = p self.transform = transform def __call__(self, img): if self.p < random.random(): return img img = self.transform(img) return img class LRSchedulerM(callbacks.LRScheduler): def __init__(self, by_step=False, by_epoch=True, warm_up=True): super().__init__(by_step, by_epoch) assert by_step ^ warm_up self.warm_up = warm_up def on_epoch_end(self, epoch, logs=None): if self.by_epoch and not self.warm_up: if self.model._optimizer and hasattr( self.model._optimizer, '_learning_rate') and isinstance( self.model._optimizer._learning_rate, paddle.optimizer.lr.LRScheduler): self.model._optimizer._learning_rate.step() def on_train_batch_end(self, step, logs=None): if self.by_step or self.warm_up: if self.model._optimizer and hasattr( self.model._optimizer, '_learning_rate') and isinstance( self.model._optimizer._learning_rate, paddle.optimizer.lr.LRScheduler): self.model._optimizer._learning_rate.step() if self.model._optimizer._learning_rate.last_epoch >= self.model._optimizer._learning_rate.warmup_steps: self.warm_up = Falsedef _on_train_batch_end(self, step, logs=None): logs = logs or {} logs['lr'] = self.model._optimizer.get_lr() self.train_step += 1 if self._is_write(): self._updates(logs, 'train')def _on_train_begin(self, logs=None): self.epochs = self.params['epochs'] assert self.epochs self.train_metrics = self.params['metrics'] + ['lr'] assert self.train_metrics self._is_fit = True self.train_step = 0callbacks.VisualDL.on_train_batch_end = _on_train_batch_end callbacks.VisualDL.on_train_begin = _on_train_begin登录后复制

4.在Cifar10数据集上训练模型

使用Paddle自带的Cifar10数据集API加载 In []

在PaddlePaddle框架中，使用ResNet型进行训练并保存超参数和结果。加载预训练的ResNet型，设置学习率、权重衰减、批量大小等参数，并定义数据增强方式以增加训练的多样性。通过Compose函数创建多个图像变换来处理输入图像数据集。接着将训练数据集和测试数据集加载到内存中，然后调用fit方法进行模型训练。在训练过程中，保存超参数信息及模型状态以便后期调试或复现实验结果。