class_names= image_datasets['train'].classes#获取类别名称#加载预训练的ResNet50模型model = models.resnet50(pretrained=True)#冻结预训练模型的参数,这样我们在训练时只会更新最后一层的参数forparaminresnet.parameters(): param.requires_grad=False#获取最后一层全连接层的输入特征数量num_ftrs =model.fc....
model = resnet18_model #定义优化器,这里使用Adam优化器以及l2正则化策略,相关内容在7.3.3.2和7.6.2中会进行详细介绍 optimizer = opt.Adam(learning_rate=lr, parameters=model.parameters(), weight_decay=0.005) #定义损失函数 loss_fn = F.cross_entropy #定义评价指标 metric = Accuracy(is_logist=True)...
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ResNet-50 is a pretrained model that has been trained on a subset of the ImageNet database and that won the ImageNet Large-Scale Visual Recognition Challenge (ILSVRC) competition in 2015. The model is trained on more than a million images, has 177 layers in total, corresponding to a...
An existing model is used: ResNet50. ResNet50 model ResNet is a Residual neural Network structure. It is an innovative neural network created for image classification. The ResNet model architecture allows the training error to be reduced with a deeper network through conn...
使用ImageNet 2012 classification dataset,共有1000类,其中训练图片1.28 million张,验证图片50k张,测试图片100k张。评估的参数有 top-1 和 top-5 error rate Plain Network:这里作者评估了 18-layer 和 34-layer 两个网络。34-layer plain net如Fig.3所示,而18-layer net则是一种与之相似的网路。详细结构参见...
##定义输入层 image = fluid.layers.data(name='image', shape=train_core2["input_size"], dtype='float32') label = fluid.layers.data(name='label', shape=[1], dtype='int64') logger.info("成功定义输入层") ##获取分类器 model = resnet(image,train_core2["class_dim"]) ##获取损失函数...
Model 关于模型的配置,该字段必须。 字段下又含模型名(如上述例子中的ResNet)、优化器(OPT)、Scheduler和初始化方法(Init)。模型名和优化器必须,其它可选。每个模型可配置参数可在模型文件中的make_networ中找到。例如,ResNet的可配置参数(classification/resnet.py): ...
Explore and run machine learning code with Kaggle Notebooks | Using data from Animal Image Dataset(DOG, CAT and PANDA)
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