transformerblock怎么加
时间: 2023-07-29 19:14:25 浏览: 103
要在Transformer模型中添加一个TransformerBlock,你可以按照以下步骤进行操作:
1. 导入必要的库:
```python
import tensorflow as tf
from tensorflow.keras.layers import LayerNormalization, MultiHeadAttention, Dense, Dropout
```
2. 创建`TransformerBlock`类,继承自`tf.keras.layers.Layer`:
```python
class TransformerBlock(tf.keras.layers.Layer):
def __init__(self, num_heads, d_model, dff, rate=0.1):
super(TransformerBlock, self).__init__()
self.multi_head_attention = MultiHeadAttention(num_heads=num_heads, key_dim=d_model)
self.dropout1 = Dropout(rate)
self.layer_norm1 = LayerNormalization(epsilon=1e-6)
self.dense1 = Dense(units=dff, activation='relu')
self.dense2 = Dense(units=d_model)
self.dropout2 = Dropout(rate)
self.layer_norm2 = LayerNormalization(epsilon=1e-6)
def call(self, inputs, training):
attention_output = self.multi_head_attention(inputs, inputs)
attention_output = self.dropout1(attention_output, training=training)
attention_output = self.layer_norm1(inputs + attention_output)
ffn_output = self.dense1(attention_output)
ffn_output = self.dense2(ffn_output)
ffn_output = self.dropout2(ffn_output, training=training)
outputs = self.layer_norm2(attention_output + ffn_output)
return outputs
```
3. 在你的Transformer模型中使用`TransformerBlock`:
```python
class Transformer(tf.keras.Model):
def __init__(self, num_layers, num_heads, d_model, dff, input_vocab_size, target_vocab_size, rate=0.1):
super(Transformer, self).__init__()
self.embedding = tf.keras.layers.Embedding(input_vocab_size, d_model)
self.encoder_blocks = [TransformerBlock(num_heads, d_model, dff, rate) for _ in range(num_layers)]
self.dense = Dense(units=target_vocab_size)
def call(self, inputs, training):
x = self.embedding(inputs)
for encoder_block in self.encoder_blocks:
x = encoder_block(x, training)
x = self.dense(x)
return x
```
这样,你就可以使用`Transformer`模型并添加多个`TransformerBlock`了。请根据你的需求调整模型的参数和网络结构。
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