nn.Embedding(1, output_size)
时间: 2024-08-14 08:09:39 浏览: 24
`nn.Embedding(1, output_size)`是PyTorch库中的一个神经网络层,通常用于处理词嵌入(word embeddings)。它属于`torch.nn.EmbeddingBag`模块的一部分,但这里简化版本是指单维度的嵌入层,常用于单一维度的数据如标签或类别id。其中:
- `1` 表示输入维度,这里是1维的,例如只有一列特征或者是一个离散的类别值。
- `output_size` 是嵌入向量的大小,即每个输入元素(在这种情况下是个类别ID)将被转换成一个`output_size`长度的向量。这个向量通常用于捕捉输入项的复杂表示,比如词汇表中的单词可以用高维稠密向量表示。
这个层的主要作用是在模型训练过程中,将输入的整数编码(例如,索引)映射为固定长度的实数值向量,这在诸如循环神经网络(RNNs)、卷积神经网络(CNNs)等序列模型中非常常见,用于捕获输入数据的上下文信息。
相关问题
class PoetryModel(nn.Module): def init(self, vocab_size, embedding_dim, hidden_dim): super(PoetryModel, self).init() self.hidden_dim = hidden_dim self.embedding = nn.Embedding(vocab_size, embedding_dim) self.lstm = nn.LSTM(embedding_dim, self.hidden_dim, num_layers=3) self.classifier=nn.Sequential( nn.Linear(self.hidden_dim, 512), nn.ReLU(inplace=True), nn.Linear(512, 2048), nn.ReLU(inplace=True), nn.Linear(2048, vocab_size) ) def forward(self, input, hidden = None): seq_len, batch_size = input.size() if hidden is None: h_0 = input.data.new(3, batch_size, self.hidden_dim).fill_(0).float() c_0 = input.data.new(3, batch_size, self.hidden_dim).fill_(0).float() else: h_0, c_0 = hidden embeds = self.embedding(input) output, hidden = self.lstm(embeds, (h_0, c_0)) output = self.classifier(output.view(seq_len * batch_size, -1)) return output, hidden 请优化这段代码
这段代码已经写得比较好了,但是有几个可以改进的地方:
1. 在初始化函数中使用 super() 函数时,应该将 PoetryModel 类的名称作为第一个参数,而不是使用 super() 函数的默认参数。
2. 在 forward 函数中,应该使用 new_zeros() 方法来初始化 h_0 和 c_0,而不是使用 fill_() 方法。
3. 在 forward 函数中,应该使用 contiguous() 方法来确保输入的张量在内存中是连续的,这样可以加快计算速度。
改进后的代码如下所示:
```python
class PoetryModel(nn.Module):
def __init__(self, vocab_size, embedding_dim, hidden_dim):
super(PoetryModel, self).__init__()
self.hidden_dim = hidden_dim
self.embedding = nn.Embedding(vocab_size, embedding_dim)
self.lstm = nn.LSTM(embedding_dim, self.hidden_dim, num_layers=3)
self.classifier = nn.Sequential(
nn.Linear(self.hidden_dim, 512),
nn.ReLU(inplace=True),
nn.Linear(512, 2048),
nn.ReLU(inplace=True),
nn.Linear(2048, vocab_size)
)
def forward(self, input, hidden=None):
seq_len, batch_size = input.size()
if hidden is None:
h_0 = input.data.new_zeros(3, batch_size, self.hidden_dim).float()
c_0 = input.data.new_zeros(3, batch_size, self.hidden_dim).float()
else:
h_0, c_0 = hidden
embeds = self.embedding(input)
embeds = embeds.contiguous()
output, hidden = self.lstm(embeds, (h_0, c_0))
output = self.classifier(output.view(seq_len * batch_size, -1))
return output, hidden
```
这个优化版本主要是将 super() 函数中的参数修改为 PoetryModel 类的名称,使用了 new_zeros() 方法初始化 h_0 和 c_0,使用了 contiguous() 方法确保输入的张量在内存中是连续的。这样可以提高代码的效率和可读性。
import torch import torch.nn as nn import torch.optim as optim import numpy as np 定义基本循环神经网络模型 class RNNModel(nn.Module): def init(self, rnn_type, input_size, hidden_size, output_size, num_layers=1): super(RNNModel, self).init() self.rnn_type = rnn_type self.input_size = input_size self.hidden_size = hidden_size self.output_size = output_size self.num_layers = num_layers self.encoder = nn.Embedding(input_size, hidden_size) if rnn_type == 'RNN': self.rnn = nn.RNN(hidden_size, hidden_size, num_layers) elif rnn_type == 'GRU': self.rnn = nn.GRU(hidden_size, hidden_size, num_layers) self.decoder = nn.Linear(hidden_size, output_size) def forward(self, input, hidden): input = self.encoder(input) output, hidden = self.rnn(input, hidden) output = output.view(-1, self.hidden_size) output = self.decoder(output) return output, hidden def init_hidden(self, batch_size): if self.rnn_type == 'RNN': return torch.zeros(self.num_layers, batch_size, self.hidden_size) elif self.rnn_type == 'GRU': return torch.zeros(self.num_layers, batch_size, self.hidden_size) 定义数据集 with open('汉语音节表.txt', encoding='utf-8') as f: chars = f.readline() chars = list(chars) idx_to_char = list(set(chars)) char_to_idx = dict([(char, i) for i, char in enumerate(idx_to_char)]) corpus_indices = [char_to_idx[char] for char in chars] 定义超参数 input_size = len(idx_to_char) hidden_size = 256 output_size = len(idx_to_char) num_layers = 1 batch_size = 32 num_steps = 5 learning_rate = 0.01 num_epochs = 100 定义模型、损失函数和优化器 model = RNNModel('RNN', input_size, hidden_size, output_size, num_layers) criterion = nn.CrossEntropyLoss() optimizer = optim.Adam(model.parameters(), lr=learning_rate) 训练模型 for epoch in range(num_epochs): model.train() hidden = model.init_hidden(batch_size) loss = 0 for X, Y in data_iter_consecutive(corpus_indices, batch_size, num_steps): optimizer.zero_grad() hidden = hidden.detach() output, hidden = model(X, hidden) loss = criterion(output, Y.view(-1)) loss.backward() torch.nn.utils.clip_grad_norm_(model.parameters(), max_norm=1.0) optimizer.step() if epoch % 10 == 0: print(f"Epoch {epoch}, Loss: {loss.item()}")请正确缩进代码
下面是正确缩进的代码:
```python
import torch
import torch.nn as nn
import torch.optim as optim
import numpy as np
# 定义基本循环神经网络模型
class RNNModel(nn.Module):
def __init__(self, rnn_type, input_size, hidden_size, output_size, num_layers=1):
super(RNNModel, self).__init__()
self.rnn_type = rnn_type
self.input_size = input_size
self.hidden_size = hidden_size
self.output_size = output_size
self.num_layers = num_layers
self.encoder = nn.Embedding(input_size, hidden_size)
if rnn_type == 'RNN':
self.rnn = nn.RNN(hidden_size, hidden_size, num_layers)
elif rnn_type == 'GRU':
self.rnn = nn.GRU(hidden_size, hidden_size, num_layers)
self.decoder = nn.Linear(hidden_size, output_size)
def forward(self, input, hidden):
input = self.encoder(input)
output, hidden = self.rnn(input, hidden)
output = output.view(-1, self.hidden_size)
output = self.decoder(output)
return output, hidden
def init_hidden(self, batch_size):
if self.rnn_type == 'RNN':
return torch.zeros(self.num_layers, batch_size, self.hidden_size)
elif self.rnn_type == 'GRU':
return torch.zeros(self.num_layers, batch_size, self.hidden_size)
# 定义数据集
with open('汉语音节表.txt', encoding='utf-8') as f:
chars = f.readline()
chars = list(chars)
idx_to_char = list(set(chars))
char_to_idx = dict([(char, i) for i, char in enumerate(idx_to_char)])
corpus_indices = [char_to_idx[char] for char in chars]
# 定义超参数
input_size = len(idx_to_char)
hidden_size = 256
output_size = len(idx_to_char)
num_layers = 1
batch_size = 32
num_steps = 5
learning_rate = 0.01
num_epochs = 100
# 定义模型、损失函数和优化器
model = RNNModel('RNN', input_size, hidden_size, output_size, num_layers)
criterion = nn.CrossEntropyLoss()
optimizer = optim.Adam(model.parameters(), lr=learning_rate)
# 训练模型
for epoch in range(num_epochs):
model.train()
hidden = model.init_hidden(batch_size)
loss = 0
for X, Y in data_iter_consecutive(corpus_indices, batch_size, num_steps):
optimizer.zero_grad()
hidden = hidden.detach()
output, hidden = model(X, hidden)
loss = criterion(output, Y.view(-1))
loss.backward()
torch.nn.utils.clip_grad_norm_(model.parameters(), max_norm=1.0)
optimizer.step()
if epoch % 10 == 0:
print(f"Epoch {epoch}, Loss: {loss.item()}")
```