def ff(inputs, num_units, scope="positionwise_feedforward"): ''' inputs: A 3d tensor with shape of [N, T, C]. num_units: A list of two integers. scope: Optional scope for `variable_scope`. Returns: A 3d tensor with the same shape and dtype as inputs ''' # Inner layer outputs = tf.keras.layers.Dense(num_units[0], activation=tf.nn.relu)(inputs) # Outer layer outputs = tf.keras.layers.Dense(num_units[1])(outputs) # Residual connection outputs = tf.keras.layers.concatenate([outputs, inputs],axis=-1) # Normalize outputs = tf.keras.layers.LayerNormalization()(outputs) return outputs

这是一个实现位置编码器中的前馈神经网络的函数。输入是一个形状为[N, T, C]的3D张量，其中N表示批量大小，T表示序列长度，C表示每个元素的特征数。该函数使用两个Dense层，其中第一个层使用ReLU激活函数，第二个层没有激活函数。在这两个层之间，使用残差连接将输入添加到输出中，并对输出进行层归一化以减少内部协变量位移。最终输出与输入具有相同的形状和数据类型。

相关问题

我希望你写一个基于MINIST数据集的神经网络，使用pytorch，实现手写数字分类。我希望有完整的代码结构，并输出测试结果。从if name == 'main':开始。不要解释，给出代码

### 回答1：

import torch
import torch.nn as nn
import torch.optim as optim
from torchvision import datasets, transforms

# Define a simple feedforward neural network
class Net(nn.Module):
    def __init__(self):
        super(Net, self).__init__()
        self.fc1 = nn.Linear(28 * 28, 512)
        self.fc2 = nn.Linear(512, 10)

    def forward(self, x):
        x = x.view(-1, 28 * 28)
        x = torch.relu(self.fc1(x))
        x = self.fc2(x)
        return x

# Define the training function
def train(model, device, train_loader, optimizer, criterion, epoch):
    model.train()
    for batch_idx, (data, target) in enumerate(train_loader):
        data, target = data.to(device), target.to(device)
        optimizer.zero_grad()
        output = model(data)
        loss = criterion(output, target)
        loss.backward()
        optimizer.step()

# Define the test function
def test(model, device, test_loader, criterion):
    model.eval()
    test_loss = 0
    correct = 0
    with torch.no_grad():
        for data, target in test_loader:
            data, target = data.to(device), target.to(device)
            output = model(data)
            test_loss += criterion(output, target).item()
            pred = output.argmax(dim=1, keepdim=True)
            correct += pred.eq(target.view_as(pred)).sum().item()

    test_loss /= len(test_loader.dataset)
    print('\nTest set: Average loss: {:.4f}, Accuracy: {}/{} ({:.0f}%)\n'.format(
        test_loss, correct, len(test_loader.dataset),
        100. * correct / len(test_loader.dataset)))

if __name__ == '__main__':
    # Define the device to run the model on
    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")

    # Define the hyperparameters
    batch_size = 64
    learning_rate = 0.01
    num_epochs = 10

    # Download the MNIST dataset
    train_dataset = datasets.MNIST('./data', train=True, download=True,
                                   transform=transforms.Compose([
                                       transforms.ToTensor(),
                                       transforms.Normalize((0.1307,), (0.3081,))
                                   ]))
    test_dataset = datasets.MNIST('./data', train=False,
                                  transform=transforms.Compose([
                                      transforms.ToTensor(),
                                      transforms.Normalize((0.1307,), (0.3081,))
                                  ]))

    # Create data loaders
    train_loader = torch.utils.data.DataLoader(train_dataset, batch_size=batch_size, shuffle=True)
  

### 回答2：
import torch
import torch.nn as nn
import torchvision
import torchvision.transforms as transforms

# Define the neural network architecture
class Net(nn.Module):
    def __init__(self):
        super(Net, self).__init__()
        self.fc1 = nn.Linear(784, 256)
        self.fc2 = nn.Linear(256, 128)
        self.fc3 = nn.Linear(128, 10)

    def forward(self, x):
        x = x.view(-1, 784) # Flatten the input tensor
        x = torch.relu(self.fc1(x))
        x = torch.relu(self.fc2(x))
        x = self.fc3(x)
        return x

# Define the necessary transformations
transform = transforms.Compose([
    transforms.ToTensor(), # Convert image to tensor
    transforms.Normalize((0.5,), (0.5,)) # Normalize the image
])

# Load the MINIST dataset
train_dataset = torchvision.datasets.MNIST(root='./', train=True, transform=transform, download=True)
test_dataset = torchvision.datasets.MNIST(root='./', train=False, transform=transform, download=True)

# Create data loaders
train_loader = torch.utils.data.DataLoader(dataset=train_dataset, batch_size=64, shuffle=True)
test_loader = torch.utils.data.DataLoader(dataset=test_dataset, batch_size=64, shuffle=False)

# Initialize the network
net = Net()

# Define the loss function and optimizer
criterion = nn.CrossEntropyLoss()
optimizer = torch.optim.SGD(net.parameters(), lr=0.01)

# Training the network
for epoch in range(5):
    running_loss = 0.0
    for i, data in enumerate(train_loader):
        inputs, labels = data
        optimizer.zero_grad()

        # Forward pass
        outputs = net(inputs)
        loss = criterion(outputs, labels)

        # Backward pass and optimize
        loss.backward()
        optimizer.step()

        running_loss += loss.item()
        if (i+1) % 100 == 0:
            print(f'Epoch [{epoch+1}/{5}], Step [{i+1}/{len(train_loader)}], Loss: {running_loss / 100}')
            running_loss = 0.0

# Testing the network
correct = 0
total = 0
with torch.no_grad():
    for data in test_loader:
        images, labels = data
        outputs = net(images)
        _, predicted = torch.max(outputs.data, 1)
        total += labels.size(0)
        correct += (predicted == labels).sum()

print(f'Accuracy of the network: {100 * correct.item() / total}%')  

### 回答3：

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

# 定义神经网络模型
class Net(nn.Module):
def __init__(self):
super(Net, self).__init__()
self.fc1 = nn.Linear(28 * 28, 512)
self.fc2 = nn.Linear(512, 256)
self.fc3 = nn.Linear(256, 10)
self.relu = nn.ReLU()

def forward(self, x):
x = x.view(-1, 28 * 28)
x = self.relu(self.fc1(x))
x = self.relu(self.fc2(x))
x = self.fc3(x)
return x

def train(model, dataloader, criterion, optimizer):
model.train()
for images, labels in dataloader:
optimizer.zero_grad()
outputs = model(images)
loss = criterion(outputs, labels)
loss.backward()
optimizer.step()

def test(model, dataloader):
model.eval()
correct = 0
total = 0
with torch.no_grad():
for images, labels in dataloader:
outputs = model(images)
_, predicted = torch.max(outputs.data, 1)
total += labels.size(0)
correct += (predicted == labels).sum().item()
accuracy = 100.0 * correct / total
return accuracy

if __name__ == '__main__':
transform = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize((0.5,), (0.5,))
])

# 加载MINIST数据集
train_dataset = datasets.MNIST(root='./data', train=True, download=True, transform=transform)
test_dataset = datasets.MNIST(root='./data', train=False, download=True, transform=transform)

# 数据加载器
train_loader = torch.utils.data.DataLoader(dataset=train_dataset, batch_size=64, shuffle=True)
test_loader = torch.utils.data.DataLoader(dataset=test_dataset, batch_size=64, shuffle=False)

# 定义模型、损失函数和优化器
model = Net()
criterion = nn.CrossEntropyLoss()
optimizer = optim.Adam(model.parameters(), lr=0.001)

# 训练和测试模型
for epoch in range(10):
train(model, train_loader, criterion, optimizer)
accuracy = test(model, test_loader)
print(f'Epoch {epoch + 1}: Accuracy = {accuracy}%')