paddlepaddle怎么写VGG16模型
VGG13到VGG19代码。
怎么用paddlepaddle写
- 这篇博客也许可以解决你的问题👉 ：【paddlepaddle】paddlepaddle入门资料
- 除此之外, 这篇博客: PaddlePaddle2.0搭建VGG-16模型实现蝴蝶分类中的 PaddlePaddle2.0利用VGG-16预训练模型实现蝴蝶分类 部分也许能够解决你的问题, 你可以仔细阅读以下内容或跳转源博客中阅读:
本项目是百度AI Studio上图像分类课程的一个内容，之前使用ResNet101网络完成，现在用VGG-16网络实现同样的任务。数据集可以在AI Studio上搜索Butterfly20下载，如果下载不到，可用下面网盘链接。
Butterfly20数据集下载链接：
链接：https://pan.baidu.com/s/19Fqsg_rUAQi9nvf3vLhI9w
提取码：qdkx
这次使用其他网络完成同一任务，所以程序大体变化不大，数据预处理、训练测试、模型保存等都变化不大，所以更详细的分步解释见上一篇文章：
https://blog.csdn.net/weixin_43848436/article/details/114669737- 以下回答由chatgpt基于相关博客总结生成:
我可以提供使用PaddlePaddle编写VGG16模型的代码，与此相关的VGG13到VGG19的代码与VGG16类似，仅需要更改部分网络块和参数配置即可。以下是代码和相关注释：
import paddle # 定义网络块：卷积+池化 class ConvPool(paddle.nn.Layer): def __init__(self, num_channels, num_filters): super(ConvPool, self).__init__() # 定义卷积层 self.conv2d = paddle.nn.Conv2D(in_channels=num_channels, out_channels=num_filters, kernel_size=3, stride=1, padding=1) # 定义批归一化层 self.batch_norm = paddle.nn.BatchNorm2D(num_filters) # 定义ReLU激活函数 self.relu = paddle.nn.ReLU() # 定义最大池化层 self.pool2d = paddle.nn.MaxPool2D(kernel_size=2, stride=2) def forward(self, inputs): x = self.conv2d(inputs) x = self.batch_norm(x) x = self.relu(x) x = self.pool2d(x) return x # 定义VGG网络结构：由多个网络块组成 class VGG16(paddle.nn.Layer): def __init__(self, num_classes): super(VGG16, self).__init__() # 定义网络块 self.block1 = ConvPool(3, 64) self.block2 = ConvPool(64, 128) self.block3 = paddle.nn.Sequential( ConvPool(128, 256), ConvPool(256, 256) ) self.block4 = paddle.nn.Sequential( ConvPool(256, 512), ConvPool(512, 512) ) self.block5 = paddle.nn.Sequential( ConvPool(512, 512), ConvPool(512, 512) ) # 定义全连接层 self.fc1 = paddle.nn.Linear(in_features=512 * 7 * 7, out_features=4096) self.fc2 = paddle.nn.Linear(in_features=4096, out_features=4096) self.fc3 = paddle.nn.Linear(in_features=4096, out_features=num_classes) def forward(self, inputs): x = self.block1(inputs) x = self.block2(x) x = self.block3(x) x = self.block4(x) x = self.block5(x) x = paddle.flatten(x, start_axis=1, stop_axis=-1) x = self.fc1(x) x = paddle.nn.functional.relu(x) x = paddle.nn.functional.dropout(x, p=0.5, training=self.training) x = self.fc2(x) x = paddle.nn.functional.relu(x) x = paddle.nn.functional.dropout(x, p=0.5, training=self.training) x = self.fc3(x) return x # 参数配置 train_parameters = { "input_size": [3, 224, 224], "class_dim": 1000, "lr": 0.005, "epoch_num": 100, "batch_size": 64, "step_size": 30, "gamma": 0.1, "optimizer_type": "momentum", "momentum": 0.9, "l2_decay": 5e-4, "save_interval_epochs": 10, "early_stop_epochs": 4, "resume_epoch": 90, "resume_dir": "./checkpoints/vgg16_epoch_90.pdparams" } # 定义训练函数 def train(model, train_loader, valid_loader, optimizer, loss_fn, train_parameters): # 开始训练 for epoch in range(train_parameters['epoch_num']): model.train() for batch_id, data in enumerate(train_loader()): x_data = data[0] y_data = data[1] logits = model(x_data) loss = loss_fn(logits, y_data) loss.backward() optimizer.step() optimizer.clear_grad() if batch_id % 10 == 0: print('epoch: {}, batch_id: {}, loss is: {}'.format(epoch, batch_id, loss.numpy())) # 验证模型 model.eval() accuracies = [] losses = [] for batch_id, data in enumerate(valid_loader()): x_data = data[0] y_data = data[1] # 这里需要显式地告诉 PaddlePaddle 不需要计算梯度，以免浪费内存 with paddle.no_grad(): logits = model(x_data) loss = loss_fn(logits, y_data) acc = paddle.metric.accuracy(input=logits, label=y_data) accuracies.append(acc.numpy()) losses.append(loss.numpy()) avg_acc, avg_loss = np.mean(accuracies), np.mean(losses) print("[Validation] accuracy/loss: {}/{}".format(avg_acc, avg_loss)) # 保存模型 if (epoch % train_parameters['save_interval_epochs'] == 0) or (epoch == train_parameters['epoch_num'] - 1): model_save_dir = "./checkpoints" if not os.path.exists(model_save_dir): os.makedirs(model_save_dir) model_save_path = os.path.join(model_save_dir, "vgg16_epoch_{}.pdparams".format(epoch)) paddle.save(model.state_dict(), model_save_path) # 主函数 if __name__ == '__main__': # 数据准备 train_dataset = paddle.vision.datasets.Cifar10(mode='train', transform=paddle.vision.transforms.Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5])) valid_dataset = paddle.vision.datasets.Cifar10(mode='test', transform=paddle.vision.transforms.Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5])) train_loader = paddle.io.DataLoader(train_dataset, batch_size=train_parameters['batch_size'], shuffle=True, num_workers=0, drop_last=True) valid_loader = paddle.io.DataLoader(valid_dataset, batch_size=train_parameters['batch_size'], shuffle=False, num_workers=0, drop_last=True) # 模型训练 paddle.set_device('gpu' if paddle.fluid.is_compiled_with_cuda() else 'cpu') model = VGG16(num_classes=train_parameters['class_dim']) # 加载已训练好的模型 if 'resume_dir' in train_parameters.keys(): assert os.path.exists(train_parameters['resume_dir']), "The resume_dir does not exist." model_state_dict = paddle.load(train_parameters['resume_dir']) model.set_dict(model_state_dict) loss_fn = paddle.nn.CrossEntropyLoss() optimizer = paddle.optimizer.Momentum(learning_rate=train_parameters['lr'], momentum=train_parameters['momentum'], weight_decay=paddle.regularizer.L2Decay(train_parameters['l2_decay'])) train(model=model, train_loader=train_loader, valid_loader=valid_loader, optimizer=optimizer, loss_fn=loss_fn, train_parameters=train_parameters)