transformer的output embedding
时间: 2023-08-31 17:10:08 浏览: 85
Transformer模型的output embedding是指通过将输入序列经过多层的自注意力机制和前馈神经网络处理后得到的隐藏表示。这个隐藏表示可以用来进行下游任务,比如机器翻译、文本分类等。
在Transformer模型中,输入序列经过编码器编码得到一个上下文感知的表示,然后通过解码器生成输出序列。编码器和解码器都由多个编码层(encoder layer)和解码层(decoder layer)组成。
在每个编码层和解码层中,输入序列首先通过自注意力机制(self-attention)进行编码,然后再通过前馈神经网络(feed-forward neural network)进行处理。自注意力机制能够捕捉输入序列中的长距离依赖关系,而前馈神经网络则能够引入非线性变换。
最后,经过多个编码层和解码层的处理后,输出序列会被映射到一个固定维度的向量空间中,这个向量空间即为输出序列的embedding表示,也就是output embedding。这个output embedding可以被用于词语或句子级别的表示学习和下游任务的应用。
相关问题
Transformer的
Transformer是一种用于序列到序列(seq2seq)学习的神经网络模型,由“Attention is All You Need”一文提出。Transformer使用自注意力机制来处理变长输入,可以用于各种自然语言处理(NLP)任务,如机器翻译、文本摘要等。
下面是Transformer的基本结构[^1]:
- Encoder: 由多个编码器块组成,每个编码器块包含多头自注意力层和前馈网络。
- Decoder: 由多个解码器块组成,每个解码器块包含多头自注意力层、多头编码器-解码器注意力层和前馈网络。
- 损失函数: 一般使用交叉熵损失函数来计算模型预测结果与真实结果之间的误差。
训练步骤如下[^2]:
1. 首先将原始文本转换为词向量,然后将其传入Transformer的编码器中。
2. 编码器将输入的词向量序列转换为一组上下文感知的编码向量,并将其传递给解码器。
3. 解码器接受编码器的输出,并利用它们来生成输出序列的下一个词。
4. 模型根据下一个期望的输出词,预测下一个正确的输出。
5. 计算模型预测结果与真实结果之间的误差,并使用梯度下降法更新模型的参数。
下面是一个用于英语到德语的机器翻译的Transformer的例子[^3]:
```python
import tensorflow as tf
from tensorflow import keras
from tensorflow.keras.layers import Input, Dense, LayerNormalization, Dropout, Embedding, Multiply, Add, Concatenate
from tensorflow.keras.models import Model
from tensorflow.keras.losses import SparseCategoricalCrossentropy
from tensorflow.keras.optimizers import Adam
# 构建Transformer模型
def transformer_model(src_input, tar_input, src_vocab_size, tar_vocab_size, d_model, n_heads, n_encoders, n_decoders, dropout=0.1):
# Encoder部分
# Input
src_word_embedding = Embedding(input_dim=src_vocab_size, output_dim=d_model)(src_input)
src_position_embedding = Embedding(input_dim=src_vocab_size, output_dim=d_model)(src_input)
src_encoder = Add()([src_word_embedding, src_position_embedding])
src_encoder = Dropout(dropout)(src_encoder)
# Encoder Blocks
for i in range(n_encoders):
src_encoder = encoder_block(src_encoder, d_model, n_heads, dropout)
# Decoder部分
# Input
tar_word_embedding = Embedding(input_dim=tar_vocab_size, output_dim=d_model)(tar_input)
tar_position_embedding = Embedding(input_dim=tar_vocab_size, output_dim=d_model)(tar_input)
tar_encoder = Add()([tar_word_embedding, tar_position_embedding])
tar_encoder = Dropout(dropout)(tar_encoder)
# Decoder Blocks
for i in range(n_decoders):
tar_encoder = decoder_block(tar_encoder, src_encoder, d_model, n_heads, dropout)
output = Dense(tar_vocab_size, activation='softmax')(tar_encoder)
model = Model(inputs=[src_input, tar_input], outputs=output)
return model
# Encoder Block
def encoder_block(inputs, d_model, n_heads, dropout=0.1):
# Multi-head Attention
attention_output, _ = MultiHeadAttention(n_heads, d_model)(inputs, inputs, inputs)
attention_output = Dropout(dropout)(attention_output)
attention_output = LayerNormalization(epsilon=1e-6)(Add()([inputs, attention_output]))
# Feed Forward
feed_forward_output = Dense(2048, activation='relu')(attention_output)
feed_forward_output = Dropout(dropout)(feed_forward_output)
feed_forward_output = Dense(d_model, activation=None)(feed_forward_output)
feed_forward_output = Dropout(dropout)(feed_forward_output)
feed_forward_output = LayerNormalization(epsilon=1e-6)(Add()([attention_output, feed_forward_output]))
return feed_forward_output
# Decoder Block
def decoder_block(inputs, enc_outputs, d_model, n_heads, dropout=0.1):
# Masked Multi-head Attention
attention_output_1, _ = MultiHeadAttention(n_heads, d_model)(inputs, inputs, inputs, mask='subsequent')
attention_output_1 = Dropout(dropout)(attention_output_1)
attention_output_1 = LayerNormalization(epsilon=1e-6)(Add()([inputs, attention_output_1]))
# Multi-head Attention
attention_output_2, _ = MultiHeadAttention(n_heads, d_model)(attention_output_1, enc_outputs, enc_outputs)
attention_output_2 = Dropout(dropout)(attention_output_2)
attention_output_2 = LayerNormalization(epsilon=1e-6)(Add()([attention_output_1, attention_output_2]))
# Feed Forward
feed_forward_output = Dense(2048, activation='relu')(attention_output_2)
feed_forward_output = Dropout(dropout)(feed_forward_output)
feed_forward_output = Dense(d_model, activation=None)(feed_forward_output)
feed_forward_output = Dropout(dropout)(feed_forward_output)
feed_forward_output = LayerNormalization(epsilon=1e-6)(Add()([attention_output_2, feed_forward_output]))
return feed_forward_output
# Multi-Head Attention
class MultiHeadAttention(tf.keras.layers.Layer):
def __init__(self, n_heads, d_model):
super(MultiHeadAttention, self).__init__()
self.n_heads = n_heads
self.d_model = d_model
assert d_model % self.n_heads == 0
self.depth = d_model // self.n_heads
self.query = Dense(d_model, activation=None)
self.key = Dense(d_model, activation=None)
self.value = Dense(d_model, activation=None)
self.fc = Dense(d_model, activation=None)
def split_heads(self, inputs):
batch_size = tf.shape(inputs)
inputs = tf.reshape(inputs, shape=(batch_size, -1, self.n_heads, self.depth))
return tf.transpose(inputs, perm=[0, 2, 1, 3])
def call(self, q, k, v, mask=None):
q = self.query(q)
k = self.key(k)
v = self.value(v)
q = self.split_heads(q)
k = self.split_heads(k)
v = self.split_heads(v)
scaled_attention, attention_weights = self.scaled_dot_product_attention(q, k, v, mask)
scaled_attention = tf.transpose(scaled_attention, perm=[0, 2, 1, 3])
concat_attention = tf.reshape(scaled_attention, (tf.shape(scaled_attention), -1, self.d_model))
output = self.fc(concat_attention)
return output, attention_weights
def scaled_dot_product_attention(self, q, k, v, mask=None):
matmul_qk = tf.matmul(q, k, transpose_b=True)
dk = tf.cast(tf.shape(k)[-1], tf.float32)
scaled_attention_logits = matmul_qk / tf.math.sqrt(dk)
if mask is not None:
scaled_attention_logits += (mask * -1e9)
attention_weights = tf.nn.softmax(scaled_attention_logits, axis=-1)
output = tf.matmul(attention_weights, v)
return output, attention_weights
# 构建模型
src_input = Input(shape=(None,))
tar_input = Input(shape=(None,))
model = transformer_model(src_input, tar_input, 10000, 10000, 512, 8, 6, 6)
# 训练模型
loss_fn = SparseCategoricalCrossentropy(from_logits=True, reduction='none')
model.compile(optimizer=Adam(0.0001), loss=loss_fn)
model.fit(train_dataset, epochs=20, validation_data=val_dataset)
```
Transformer modules
Transformer模块是一种用于序列到序列任务的神经网络模型,它在自然语言处理领域中得到了广泛应用。在PyTorch中,可以使用torch.nn.modules.Transformer模块来实现Transformer模型。
Transformer模块包含了多个子模块,其中最重要的是Encoder和Decoder。Encoder负责将输入序列编码为一系列隐藏表示,而Decoder则将这些隐藏表示解码为输出序列。每个Encoder和Decoder都由多个Transformer层组成,每个层都包含了多头自注意力机制和前馈神经网络。
以下是一个使用Transformer模块进行序列到序列任务的示例代码:
```python
import torch
import torch.nn as nn
from torch.nn import Transformer
class TransformerModel(nn.Module):
def __init__(self, input_dim, output_dim, hidden_dim, num_layers, num_heads):
super(TransformerModel, self).__init__()
self.embedding = nn.Embedding(input_dim, hidden_dim)
self.transformer = Transformer(
d_model=hidden_dim,
nhead=num_heads,
num_encoder_layers=num_layers,
num_decoder_layers=num_layers,
dim_feedforward=hidden_dim,
dropout=0.1
)
self.fc = nn.Linear(hidden_dim, output_dim)
def forward(self, src, tgt):
src_embedded = self.embedding(src)
tgt_embedded = self.embedding(tgt)
src_encoded = self.transformer.encoder(src_embedded)
tgt_encoded = self.transformer.decoder(tgt_embedded, src_encoded)
output = self.fc(tgt_encoded)
return output
# 创建一个Transformer模型实例
model = TransformerModel(input_dim=100, output_dim=10, hidden_dim=256, num_layers=4, num_heads=8)
# 定义输入数据
src = torch.tensor([[1, 2, 3, 4, 5]])
tgt = torch.tensor([[6, 7, 8, 9, 10]])
# 前向传播
output = model(src, tgt)
# 打印输出
print(output)
```
这个示例代码展示了如何使用Transformer模块进行序列到序列任务。首先,我们定义了一个TransformerModel类,它包含了一个嵌入层、一个Transformer模块和一个全连接层。在forward方法中,我们首先将输入序列通过嵌入层进行编码,然后将编码后的序列输入到Transformer模块中进行编码和解码,最后通过全连接层得到输出序列。