<class 'src_metric_learning.Data.dataset_2D.ImgDataset'>

这看起来像是Python中的一个类对象，名为`ImgDataset`，位于`src_metric_learning.Data.dataset_2D`模块中。它可能是一个自定义的数据集类，用于处理2D图像数据。可能包含了一些方法，用于加载、处理和转换图像数据，以及在训练模型时用于对数据进行批处理和取样。

相关问题

net = LeNet5() paddle.summary(net,(-1,1,img_size,img_size)) from paddle.metric import Accuracy save_dir = "model/lenet_2" epoch = 5 lr = 0.01 weight_decay = 5e-4 batch_size = 64 model = paddle.Model(net) optim = paddle.optimizer.Adam(learning_rate=lr,parameter=model.parameters(),weight_decay=weight_decay) model.prepare(optim,paddle.nn.CrossEntropyloss(),paddle.nn.Accuracy()) model.fit(train_dataset,epochs=epoch,batch_size=batch_size,save_dir=save_dir,verbose=1) best_model_path = "model/lenet_2/final.pdparams" net = LeNet5() model = paddle.Model(net) model.load(best_model_path) model.prepare(optim,paddle.nn.CrossEntropyloss(),Accuracy()) results = model.evaluate(test_dataset,batch_size=batch_size,verbose=1) print(results)在pytorch中如何表示

在 PyTorch 中，上述代码可以表示为：

import torch
import torch.nn as nn
import torch.optim as optim
from torch.utils.data import DataLoader

class LeNet5(nn.Module):
    def __init__(self):
        super(LeNet5, self).__init__()
        self.conv1 = nn.Conv2d(1, 6, 5)
        self.pool1 = nn.MaxPool2d(2)
        self.conv2 = nn.Conv2d(6, 16, 5)
        self.pool2 = nn.MaxPool2d(2)
        self.fc1 = nn.Linear(16 * 5 * 5, 120)
        self.fc2 = nn.Linear(120, 84)
        self.fc3 = nn.Linear(84, 10)
    
    def forward(self, x):
        x = self.pool1(torch.relu(self.conv1(x)))
        x = self.pool2(torch.relu(self.conv2(x)))
        x = x.view(-1, 16 * 5 * 5)
        x = torch.relu(self.fc1(x))
        x = torch.relu(self.fc2(x))
        x = self.fc3(x)
        return x

train_dataset = # 加载训练数据集
test_dataset = # 加载测试数据集
batch_size = 64
epoch = 5
lr = 0.01
weight_decay = 5e-4
save_dir = "model/lenet_2"
best_model_path = "model/lenet_2/final.pt"

train_loader = DataLoader(train_dataset, batch_size=batch_size, shuffle=True)
test_loader = DataLoader(test_dataset, batch_size=batch_size, shuffle=False)

device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
net = LeNet5().to(device)
criterion = nn.CrossEntropyLoss()
optimizer = optim.Adam(net.parameters(), lr=lr, weight_decay=weight_decay)

# 训练模型
for epoch in range(epoch):
    net.train()
    running_loss = 0.0
    for i, data in enumerate(train_loader, 0):
        inputs, labels = data
        inputs, labels = inputs.to(device), labels.to(device)
        optimizer.zero_grad()
        outputs = net(inputs)
        loss = criterion(outputs, labels)
        loss.backward()
        optimizer.step()
        running_loss += loss.item()
    print(f"Epoch {epoch+1}, loss: {running_loss/len(train_loader):.4f}")
torch.save(net.state_dict(), best_model_path)

# 测试模型
net.load_state_dict(torch.load(best_model_path))
net.eval()
correct = 0
total = 0
with torch.no_grad():
    for data in test_loader:
        images, labels = data
        images, labels = images.to(device), labels.to(device)
        outputs = net(images)
        _, predicted = torch.max(outputs.data, 1)
        total += labels.size(0)
        correct += (predicted == labels).sum().item()
accuracy = correct / total
print(f"Accuracy on test set: {accuracy:.4f}")

请同学们尝试修改以下代码，如修改网络结构、优化器、损失函数、学习率等，提升模型评估准确率，要求精度达到0.985。 In [2] # 定义模型结构 import paddle.nn.functional as F from paddle.nn import Conv2D, MaxPool2D, Linear # 多层卷积神经网络实现（可修改，例如加深网络层级） class MNIST(paddle.nn.Layer): def init(self): super(MNIST, self).init() #

# 修改后的代码
import paddle.nn.functional as F
import paddle.nn as nn
from paddle.nn import Conv2D, MaxPool2D, Linear

class MNIST(paddle.nn.Layer):
def __init__(self):
super(MNIST, self).__init__()
self.conv1 = Conv2D(in_channels=1, out_channels=32, kernel_size=3, stride=1)
self.max_pool1 = MaxPool2D(kernel_size=2, stride=2)
self.conv2 = Conv2D(in_channels=32, out_channels=64, kernel_size=3, stride=1)
self.max_pool2 = MaxPool2D(kernel_size=2, stride=2)
self.fc1 = Linear(in_features=64*5*5, out_features=512)
self.fc2 = Linear(in_features=512, out_features=10)

def forward(self, x):
x = self.conv1(x)
x = F.relu(x)
x = self.max_pool1(x)
x = self.conv2(x)
x = F.relu(x)
x = self.max_pool2(x)
x = paddle.flatten(x, start_axis=1, stop_axis=-1)
x = self.fc1(x)
x = F.relu(x)
x = F.dropout(x, p=0.5)
x = self.fc2(x)
return x

# 定义训练函数
def train(model):
model.train()
epoch_loss = 0
for batch_id, data in enumerate(train_loader()):
x_data = data[0]
y_data = data[1]
predicts = model(x_data)
loss = F.cross_entropy(predicts, y_data)
loss.backward()
optimizer.step()
optimizer.clear_grad()
epoch_loss += loss.numpy()[0]
return epoch_loss / len(train_loader())

# 定义测试函数
def test(model):
model.eval()
accuracies = []
for batch_id, data in enumerate(test_loader()):
x_data = data[0]
y_data = data[1]
predicts = model(x_data)
acc = paddle.metric.accuracy(predicts, y_data)
accuracies.append(acc.numpy()[0])
return np.mean(accuracies)

# 定义优化器
optimizer = paddle.optimizer.Adam(learning_rate=0.001, parameters=model.parameters())

# 训练模型
EPOCH_NUM = 20
model = MNIST()
train_loader = paddle.batch(paddle.dataset.mnist.train(), batch_size=64, drop_last=True)
test_loader = paddle.batch(paddle.dataset.mnist.test(), batch_size=64, drop_last=True)
for epoch_id in range(EPOCH_NUM):
train_loss = train(model)
test_acc = test(model)
print("epoch_id: {}, train_loss: {}, test_acc: {}".format(epoch_id+1, train_loss, test_acc))

# 测试模型
acc = test(model)
print("Final test acc: {}".format(acc))

# 预测样例
params_file_path = 'mnist.pdparams'
model_dict = paddle.load(params_file_path)
model.load_dict(model_dict)
model.eval()

data = paddle.dataset.mnist.test()[0][0]
label = paddle.dataset.mnist.test()[0][1]
result = model(paddle.to_tensor(data))
print("Predict digit {}, label is {}".format(np.argmax(result.numpy()), label))