帮我写一段ResNet的测试代码

以下是一段关于ResNet的测试代码：import torch
import torch.nn as nn# 创建ResNet模型
model = nn.Sequential(
nn.Conv2d(3, 16, 3, padding=1),
nn.ReLU(),
nn.MaxPool2d(2, 2),
nn.Conv2d(16, 32, 3, padding=1),
nn.ReLU(),
nn.MaxPool2d(2, 2),
nn.Conv2d(32, 64, 3, padding=1),
nn.ReLU(),
nn.MaxPool2d(2, 2)
)# 创建测试输入
x = torch.rand(1, 3, 32, 32)# 执行前向传播
y = model(x)# 打印结果
print(y.size())

相关问题

生成一段利用resnet读取cifar10的python代码

以下是使用ResNet模型读取CIFAR10数据集的Python代码：

import tensorflow as tf
from tensorflow.keras.datasets import cifar10
from tensorflow.keras.layers import Input, Conv2D, BatchNormalization, Activation, MaxPooling2D, GlobalAveragePooling2D, Dense, Dropout
from tensorflow.keras.models import Model


# Load CIFAR10 dataset
(x_train, y_train), (x_test, y_test) = cifar10.load_data()

# Normalize pixel values
x_train = x_train / 255.0
x_test = x_test / 255.0

# Define ResNet block
def resnet_block(inputs, filters, strides):
    x = Conv2D(filters, kernel_size=3, strides=strides, padding='same')(inputs)
    x = BatchNormalization()(x)
    x = Activation('relu')(x)
    x = Conv2D(filters, kernel_size=3, strides=1, padding='same')(x)
    x = BatchNormalization()(x)
    shortcut = Conv2D(filters, kernel_size=1, strides=strides, padding='same')(inputs)
    shortcut = BatchNormalization()(shortcut)
    x = tf.keras.layers.add([x, shortcut])
    x = Activation('relu')(x)
    return x

# Define ResNet model
inputs = Input(shape=(32, 32, 3))
x = Conv2D(64, kernel_size=3, strides=1, padding='same')(inputs)
x = BatchNormalization()(x)
x = Activation('relu')(x)
x = resnet_block(x, filters=64, strides=1)
x = resnet_block(x, filters=64, strides=1)
x = resnet_block(x, filters=64, strides=1)
x = resnet_block(x, filters=128, strides=2)
x = resnet_block(x, filters=128, strides=1)
x = resnet_block(x, filters=128, strides=1)
x = resnet_block(x, filters=256, strides=2)
x = resnet_block(x, filters=256, strides=1)
x = resnet_block(x, filters=256, strides=1)
x = resnet_block(x, filters=512, strides=2)
x = resnet_block(x, filters=512, strides=1)
x = resnet_block(x, filters=512, strides=1)
x = GlobalAveragePooling2D()(x)
outputs = Dense(10, activation='softmax')(x)
model = Model(inputs=inputs, outputs=outputs)

# Compile model
model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy'])

# Train model
model.fit(x_train, y_train, batch_size=64, epochs=10, validation_data=(x_test, y_test))

# Evaluate model
model.evaluate(x_test, y_test)

该代码使用了ResNet模型，其中定义了一个ResNet块，用于构建ResNet模型。在构建ResNet模型时，使用了多个ResNet块来实现深度网络。最后，编译和训练模型，并使用测试集评估模型性能。

我希望使用pytorch训练resnet18模型，帮我写出完成代码

好的，以下是使用PyTorch训练ResNet18模型的代码：

import torch
import torch.nn as nn
import torch.optim as optim
import torchvision
import torchvision.transforms as transforms

# 定义transforms
transform_train = transforms.Compose(
    [transforms.RandomCrop(32, padding=4),
     transforms.RandomHorizontalFlip(),
     transforms.ToTensor(),
     transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))])

transform_test = transforms.Compose(
    [transforms.ToTensor(),
     transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))])

# 加载数据集
trainset = torchvision.datasets.CIFAR10(root='./data', train=True,
                                        download=True, transform=transform_train)
trainloader = torch.utils.data.DataLoader(trainset, batch_size=128,
                                          shuffle=True, num_workers=2)

testset = torchvision.datasets.CIFAR10(root='./data', train=False,
                                       download=True, transform=transform_test)
testloader = torch.utils.data.DataLoader(testset, batch_size=100,
                                         shuffle=False, num_workers=2)

# 定义ResNet18模型
net = torchvision.models.resnet18()

# 定义损失函数和优化器
criterion = nn.CrossEntropyLoss()
optimizer = optim.SGD(net.parameters(), lr=0.1, momentum=0.9, weight_decay=5e-4)

# 训练网络
for epoch in range(100):  # 训练100个epoch
    running_loss = 0.0
    for i, data in enumerate(trainloader, 0):
        # 输入数据
        inputs, labels = data

        # 梯度清零
        optimizer.zero_grad()

        # forward + backward
        outputs = net(inputs)
        loss = criterion(outputs, labels)
        loss.backward()
        optimizer.step()

        # 输出统计信息
        running_loss += loss.item()
        if i % 100 == 99:    # 每100个batch输出一次统计信息
            print('[%d, %5d] loss: %.3f' %
                  (epoch + 1, i + 1, running_loss / 100))
            running_loss = 0.0

print('Finished Training')

# 测试网络
correct = 0
total = 0
with torch.no_grad():
    for data in testloader:
        images, labels = data
        outputs = net(images)
        _, predicted = torch.max(outputs.data, 1)
        total += labels.size(0)
        correct += (predicted == labels).sum().item()

print('Accuracy of the network on the 10000 test images: %d %%' % (
    100 * correct / total))

这段代码会下载CIFAR-10数据集，使用ResNet18模型训练数据集，并在测试集上测试模型的准确率。你可以根据自己的需求修改代码中的参数和超参数。