keras注意力机制代码
时间: 2023-11-20 15:53:47 浏览: 34
Attention机制是一种分配权重的思路,可以用于深度学习中的各种任务。Keras提供了多种实现Attention机制的代码,包括基于Dense层和LSTM层的实现。其中,基于Dense层的实现可以参考来源1中的代码,需要注意Keras版本的兼容性问题。另外,基于LSTM层的实现可以参考【深度学习】 基于Keras的Attention机制代码实现及剖析——LSTM+Attention。这些代码可以帮助我们更好地理解Attention机制的实现方式和原理。
相关问题
注意力机制代码Keras
下面是一个简单的注意力机制的实现示例,使用Keras:
```python
from keras.layers import Input, Dense, Embedding, LSTM, concatenate, Dot, Activation, Flatten
from keras.models import Model
# 构建模型
input1 = Input(shape=(max_len, ))
input2 = Input(shape=(max_len, ))
embedding = Embedding(input_dim=vocab_size, output_dim=embedding_dim)
lstm1 = LSTM(units=hidden_size, return_sequences=True)
lstm2 = LSTM(units=hidden_size, return_sequences=True)
embed1 = embedding(input1)
embed2 = embedding(input2)
h1 = lstm1(embed1)
h2 = lstm2(embed2)
# 计算注意力权重
attention = Dot(axes=-1)([h1, h2])
attention = Activation('softmax')(attention)
# 对注意力权重进行加权平均
context1 = Dot(axes=1)([attention, h2])
context2 = Dot(axes=1)([attention, h1])
# 拼接输出
concat = concatenate([context1, context2])
flatten = Flatten()(concat)
output = Dense(1, activation='sigmoid')(flatten)
model = Model(inputs=[input1, input2], outputs=output)
model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])
```
在上述代码中,我们首先定义了两个输入张量`input1`和`input2`,分别表示两个句子。接着使用`Embedding`层将输入序列转化为词向量。然后使用`LSTM`层对每个句子进行编码,得到两个隐藏状态张量`h1`和`h2`。注意力机制的计算通过`Dot`层和`Activation`层实现,其中`Dot(axes=-1)`表示两个张量的最后一维进行点积,得到一个形状为`(batch_size, max_len1, max_len2)`的张量,然后使用`Activation('softmax')`将每个位置的值归一化为注意力权重。接下来,我们使用`Dot(axes=1)`将注意力权重与另一个隐藏状态张量相乘,得到每个句子的上下文向量`context1`和`context2`。最后,我们将两个上下文向量拼接起来,并通过全连接层输出二分类结果。
在训练时,我们需要将两个句子的序列作为输入,同时提供它们的标签作为输出。具体的训练代码可以参考Keras的文档。
自注意力机制代码keras
下面是一个简单的自注意力机制的代码示例,使用了Keras库:
```python
import tensorflow as tf
from tensorflow.keras.layers import Layer, Dense, Dropout, LayerNormalization
class SelfAttention(Layer):
def __init__(self, d_model, num_heads):
super(SelfAttention, self).__init__()
self.num_heads = num_heads
self.d_model = d_model
assert d_model % self.num_heads == 0
self.depth = d_model // self.num_heads
self.query_dense = Dense(d_model)
self.key_dense = Dense(d_model)
self.value_dense = Dense(d_model)
self.dense = Dense(d_model)
def split_heads(self, x, batch_size):
x = tf.reshape(x, (batch_size, -1, self.num_heads, self.depth))
return tf.transpose(x, perm=[0, 2, 1, 3])
def call(self, inputs):
query = inputs
key = inputs
value = inputs
batch_size = tf.shape(query)[0]
query = self.query_dense(query)
key = self.key_dense(key)
value = self.value_dense(value)
query = self.split_heads(query, batch_size)
key = self.split_heads(key, batch_size)
value = self.split_heads(value, batch_size)
scaled_attention_logits = tf.matmul(query, key, transpose_b=True)
scaled_attention_logits /= tf.math.sqrt(tf.cast(self.depth, tf.float32))
attention_weights = tf.nn.softmax(scaled_attention_logits, axis=-1)
attention_output = tf.matmul(attention_weights, value)
attention_output = tf.transpose(attention_output, perm=[0, 2, 1, 3])
attention_output = tf.reshape(attention_output, (batch_size, -1, self.d_model))
output = self.dense(attention_output)
return output
class TransformerBlock(Layer):
def __init__(self, d_model, num_heads, dff, rate=0.1):
super(TransformerBlock, self).__init__()
self.attention = SelfAttention(d_model, num_heads)
self.ffn = tf.keras.Sequential([
Dense(dff, activation='relu'),
Dense(d_model)
])
self.layernorm1 = LayerNormalization(epsilon=1e-6)
self.layernorm2 = LayerNormalization(epsilon=1e-6)
self.dropout1 = Dropout(rate)
self.dropout2 = Dropout(rate)
def call(self, inputs):
attention_output = self.attention(inputs)
attention_output = self.dropout1(attention_output)
out1 = self.layernorm1(inputs + attention_output)
ffn_output = self.ffn(out1)
ffn_output = self.dropout2(ffn_output)
out2 = self.layernorm2(out1 + ffn_output)
return out2
class Transformer(Layer):
def __init__(self, num_layers, d_model, num_heads, dff, input_vocab_size, maximum_position_encoding, rate=0.1):
super(Transformer, self).__init__()
self.d_model = d_model
self.num_layers = num_layers
self.embedding = Embedding(input_vocab_size, d_model)
self.pos_encoding = positional_encoding(maximum_position_encoding, self.d_model)
self.transformer_blocks = [TransformerBlock(d_model, num_heads, dff, rate) for _ in range(num_layers)]
self.dropout = Dropout(rate)
def call(self, inputs):
seq_len = tf.shape(inputs)[1]
word_emb = self.embedding(inputs)
word_emb *= tf.math.sqrt(tf.cast(self.d_model, tf.float32))
word_emb += self.pos_encoding[:, :seq_len, :]
x = self.dropout(word_emb)
for i in range(self.num_layers):
x = self.transformer_blocks[i](x)
return x
```
这是一个基本的Transformer模型,包括自注意力机制和前馈神经网络。你可以根据自己的需求进行修改和扩展。注意,这个代码示例可能需要一些其他的辅助函数和类,例如`positional_encoding`和`Embedding`,你可以根据需要自行添加。