self.bn8_2= torch.nn.BatchNorm2d(512) self.relu8_2=torch.nn.ReLU()#第18层self.fc = torch.nn.Linear(512, num_classes)defforward(self, x):#batch_size, 3, 224, 224x = self.conv0_1(x)#bs, 64, 112, 112x =self.bn0_1(x) x=self.relu0_1(x) x1= self.dmp(x)#bs, 64,...
def resnet(input_shape,depth,num_classes=10): """ResNet Arguments: input_shape (tensor): 输入尺寸 depth (int): 网络层数 num_classes (int): 预测类别数 Return: model (Model): 模型 """ if (depth - 2) % 6 != 0: raise ValueError('depth should be 6n+2') #超参数 num_filters = ...
# 定义模型 model=resnet18(num_classes=2) # 分类数=2 W0421 15:39:54.739560 3881 gpu_resources.cc:61] Please NOTE: device: 0, GPU Compute Capability: 7.0, Driver API Version: 11.2, Runtime API Version: 11.2 W0421 15:39:54.744657 3881 gpu_resources.cc:91] device: 0, cuDNN Version:...
fc(out) return out # Make model,使用cpu model=ResNet(ResidualBlock, [2,2,2,2], num_classes).to(device=device) # 打印model结构 print(f"Model structure: {model}\n\n") # Loss and optimizer criterion = nn.CrossEntropyLoss() #交叉熵损失函数 optimizer = torch.optim.Adam(model.parameters...
output = layers.Dense(num_classes, activation='softmax')(x) model = Model(input, output) return model # 加载CIFAR-10数据集并预处理 (x_train, y_train), (x_test, y_test) = cifar10.load_data() x_train = x_train.astype('float32') / 255.0 ...
importtorchimporttorch.nnasnnimporttorchvision.modelsasmodels# 加载预训练的ResNet18模型resnet=models.resnet18(pretrained=True)# 替换最后一层全连接层num_classes=10resnet.fc=nn.Linear(resnet.fc.in_features,num_classes)# 定义损失函数和优化器criterion=nn.CrossEntropyLoss()optimizer=torch.optim.SGD(re...
修改yolov3.py源码,将num_classes设置为非空 按照文档要求配置default_config.yaml脚本 执行python train.py启动训练进程 Describe the current behavior 执行之后出现如下报错: Traceback (most recent call last): File "train.py", line 200, in <module> run_train() File "/dataset/mindspore/model_zoo/offi...
def __init__(self, blocks, num_classes=1000): super(ResNet, self).__init__() self.model_name = 'resnet34' # 前几层: 图像转换 self.pre = nn.Sequential( nn.Conv2d(3, 64, 7, 2, 3, bias=False), nn.BatchNorm2d(64), ...
from paddle.static import InputSpec # 模型结构 network = paddle.vision.models.resnet18(pretrained=True,num_classes=get('num_classes')) # 模型封装 model_2 = paddle.Model(network, inputs=[InputSpec(shape=[-1] + get('image_shape'), dtype='float32', name='image')]) # 训练好的模型加载...
# 假设新任务的类别数为num_classes num_classes = 10 # 示例值,请根据实际情况修改 # 获取原模型全连接层的输入特征数 n_features = model.fc.in_features # 替换原模型的全连接层 model.fc = torch.nn.Linear(n_features, num_classes) 3. 冻结ResNet18模型中除最后一层外的所有层 在迁移学习中,我...