train_loss_sum / len( train_iter.dataset)

This expression calculates the average training loss per data point in a given dataset.

Explanation:

- `train_loss_sum`: This is the sum of all training losses across all batches in the dataset.
- `len(train_iter.dataset)`: This is the total number of data points in the dataset.
- Dividing `train_loss_sum` by `len(train_iter.dataset)` gives us the average training loss per data point in the dataset.

For example, if `train_loss_sum` is 100 and `len(train_iter.dataset)` is 50, then the expression would evaluate to 2.0, indicating that the average training loss per data point is 2.0.

相关问题

基于pytorch的rnn_lstm模型代码

好的，下面是一个基于PyTorch框架的RNN-LSTM模型的代码示例，用于实现MNIST手写数字分类：

import torch
import torch.nn as nn
import torch.optim as optim
from torch.autograd import Variable
from torchvision.datasets import MNIST
from torchvision.transforms import ToTensor
from torch.utils.data import DataLoader

# 准备数据集
train_dataset = MNIST(root='./data', train=True, transform=ToTensor(), download=True)
test_dataset = MNIST(root='./data', train=False, transform=ToTensor())

train_loader = DataLoader(train_dataset, batch_size=128, shuffle=True)
test_loader = DataLoader(test_dataset, batch_size=128, shuffle=False)

# 构建模型
class RNN(nn.Module):
    def __init__(self, input_size, hidden_size, num_layers, num_classes):
        super(RNN, self).__init__()
        self.hidden_size = hidden_size
        self.num_layers = num_layers
        self.lstm = nn.LSTM(input_size, hidden_size, num_layers, batch_first=True)
        self.fc = nn.Linear(hidden_size, num_classes)

    def forward(self, x):
        h0 = Variable(torch.zeros(self.num_layers, x.size(0), self.hidden_size)).cuda()
        c0 = Variable(torch.zeros(self.num_layers, x.size(0), self.hidden_size)).cuda()

        out, _ = self.lstm(x, (h0, c0))
        out = self.fc(out[:, -1, :])
        return out

model = RNN(28, 128, 2, 10).cuda()

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

# 训练模型
num_epochs = 10
for epoch in range(num_epochs):
    for i, (images, labels) in enumerate(train_loader):
        images = Variable(images.view(-1, 28, 28)).cuda()
        labels = Variable(labels).cuda()

        optimizer.zero_grad()
        outputs = model(images)
        loss = criterion(outputs, labels)
        loss.backward()
        optimizer.step()

        if (i+1) % 100 == 0:
            print('Epoch [%d/%d], Iter [%d/%d] Loss: %.4f' % (epoch+1, num_epochs, i+1, len(train_dataset)//128, loss.data[0]))

# 评估模型
correct = 0
total = 0
for images, labels in test_loader:
    images = Variable(images.view(-1, 28, 28)).cuda()
    outputs = model(images)
    _, predicted = torch.max(outputs.data, 1)
    total += labels.size(0)
    correct += (predicted.cpu() == labels).sum()

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

该模型包含一个LSTM层和一个全连接层，其中LSTM层的输入形状为(28,28)，表示每个手写数字图像的像素为28x28。在模型训练时，使用交叉熵作为损失函数，Adam作为优化器。在训练过程中，使用128个样本的批量训练，共进行10个epoch的训练。最后输出测试集上的准确率。需要注意的是，该代码示例使用了GPU进行训练，如果没有GPU可以将`.cuda()`去掉。

将本地词向量应用到简单的情感分类任务中，使用本地数据集，并且计算其准确率的pytorch代码

好的，以下是一个简单的情感分类任务的PyTorch代码，使用了本地词向量和数据集：

import torch
import torch.nn as nn
import torch.optim as optim
from torchtext.datasets import text_classification
from torchtext.vocab import Vectors

# 定义模型
class TextSentiment(nn.Module):
    def __init__(self, vocab_size, embed_dim, num_class):
        super().__init__()
        self.embedding = nn.EmbeddingBag(vocab_size, embed_dim, sparse=True)
        self.fc = nn.Linear(embed_dim, num_class)
        self.init_weights()

    def init_weights(self):
        initrange = 0.5
        self.embedding.weight.data.uniform_(-initrange, initrange)
        self.fc.weight.data.uniform_(-initrange, initrange)
        self.fc.bias.data.zero_()

    def forward(self, text):
        embedded = self.embedding(text)
        return self.fc(embedded)

# 加载数据集
NGRAMS = 2
import os
if not os.path.isdir('./.data'):
    os.mkdir('./.data')
train_dataset, test_dataset = text_classification.DATASETS['AG_NEWS'](
    root='./.data', ngrams=NGRAMS, vocab=None)

# 加载词向量
url = 'path/to/your/embedding_file.txt'
vector = Vectors(name=url)

# 构建词汇表
from torchtext.vocab import build_vocab_from_iterator
train_iter = torch.utils.data.DataLoader(train_dataset, batch_size=32, shuffle=False)
vocab = build_vocab_from_iterator(train_iter, vectors=vector, specials=["<unk>"])
vocab.set_vectors(vector.stoi, vector.vectors, vector.dim)

# 定义超参数
VOCAB_SIZE = len(vocab)
EMBED_DIM = 32
NUM_CLASS = len(train_dataset.get_labels())
BATCH_SIZE = 16
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")

# 构建模型和优化器
model = TextSentiment(VOCAB_SIZE, EMBED_DIM, NUM_CLASS).to(device)
optimizer = optim.SGD(model.parameters(), lr=4.0)
criterion = nn.CrossEntropyLoss().to(device)

# 定义训练和测试函数
def train_func(sub_train_):
    train_loss = 0
    train_acc = 0
    data = torch.cat(sub_train_, dim=0)
    targets = data.get('target').to(device)
    text = data.get('text').to(device)
    optimizer.zero_grad()
    output = model(text)
    loss = criterion(output, targets)
    train_loss += loss.item()
    loss.backward()
    optimizer.step()
    train_acc += (output.argmax(1) == targets).sum().item()
    return train_loss / len(sub_train_), train_acc / len(sub_train_)

def test(data_):
    loss = 0
    acc = 0
    data = data_.to(device)
    targets = data.get('target').to(device)
    text = data.get('text').to(device)
    with torch.no_grad():
        output = model(text)
        loss = criterion(output, targets)
        loss += loss.item()
        acc = (output.argmax(1) == targets).sum().item()
    return loss / len(data_), acc / len(data_)

# 开始训练
from torch.utils.data import DataLoader
N_EPOCHS = 5
min_valid_loss = float('inf')
for epoch in range(N_EPOCHS):
    train_loss = 0
    train_acc = 0
    valid_loss = 0
    valid_acc = 0
    n_train = len(train_dataset)
    n_valid = len(test_dataset)
    train_iter = DataLoader(train_dataset, batch_size=BATCH_SIZE, shuffle=True, collate_fn=text_classification.collate_tensors)
    valid_iter = DataLoader(test_dataset, batch_size=BATCH_SIZE, shuffle=True, collate_fn=text_classification.collate_tensors)
    # 训练
    for idx, (inputs, sub_train) in enumerate(train_iter):
        loss, acc = train_func(sub_train)
        train_loss += loss
        train_acc += acc
    # 测试
    for data in valid_iter:
        loss, acc = test(data)
        valid_loss += loss
        valid_acc += acc
    train_loss /= len(train_iter)
    train_acc /= len(train_iter)
    valid_loss /= len(valid_iter)
    valid_acc /= len(valid_iter)
    print(f'Epoch: {epoch+1:02}')
    print(f'\tTrain Loss: {train_loss:.3f} | Train Acc: {train_acc*100:.2f}%')
    print(f'\t Val. Loss: {valid_loss:.3f} |  Val. Acc: {valid_acc*100:.2f}%')

关于代码的解释：

1. 定义了一个 `TextSentiment` 类作为模型，并使用了 `EmbeddingBag` 层和 `Linear` 层，其中 `EmbeddingBag` 层将文本转换为向量表示，`Linear` 层将向量表示转换为类别概率。
2. 使用 `text_classification` 包中的 `AG_NEWS` 数据集作为训练集和测试集。
3. 加载了本地词向量，并使用 `build_vocab_from_iterator` 函数构建了词汇表。
4. 定义了超参数，包括词汇表大小、嵌入维度、类别数、批大小和设备类型。
5. 定义了模型和优化器，使用了交叉熵损失函数。
6. 定义了 `train_func` 和 `test` 函数，分别用于训练和测试模型。
7. 在主循环中，使用 `DataLoader` 对训练集和测试集进行批处理，并调用 `train_func` 和 `test` 函数对模型进行训练和测试。最后输出每个 epoch 的损失和准确率。