transformer代码
时间: 2023-09-03 12:15:54 浏览: 54
下面是一个简单的 Transformer 模型的代码,用于进行序列到序列的翻译任务:
```
import tensorflow as tf
from tensorflow.keras.layers import Input, Dense, Dropout, Embedding, Concatenate, Add
from tensorflow.keras.layers import LayerNormalization, MultiHeadAttention, TimeDistributed
from tensorflow.keras.models import Model
def positional_encoding(max_length, d_model):
position = tf.range(max_length, dtype=tf.float32)[:, tf.newaxis]
div_term = tf.math.exp(tf.range(0, d_model, 2, dtype=tf.float32) * -(tf.math.log(10000.0) / d_model))
sin = tf.sin(position * div_term)
cos = tf.cos(position * div_term)
positional_encoding = tf.concat([sin, cos], axis=-1)
return positional_encoding
def transformer(d_model, num_heads, max_length, input_vocab_size, target_vocab_size):
inputs = Input(shape=(None,))
targets = Input(shape=(None,))
input_embedding = Embedding(input_vocab_size, d_model)(inputs)
target_embedding = Embedding(target_vocab_size, d_model)(targets)
input_pos_encoding = positional_encoding(max_length, d_model)(inputs)
target_pos_encoding = positional_encoding(max_length, d_model)(targets)
encoder_input = Dropout(0.1)(input_embedding + input_pos_encoding)
decoder_input = Dropout(0.1)(target_embedding + target_pos_encoding)
for i in range(num_heads):
attention = MultiHeadAttention(num_heads=num_heads, key_dim=d_model // num_heads)(encoder_input, decoder_input)
attention = Dropout(0.1)(attention)
attention = LayerNormalization(epsilon=1e-6)(Add()([decoder_input, attention]))
feed_forward = TimeDistributed(Dense(d_model, activation='relu'))(attention)
feed_forward = TimeDistributed(Dropout(0.1))(feed_forward)
feed_forward = TimeDistributed(Dense(d_model))(feed_forward)
feed_forward = TimeDistributed(Dropout(0.1))(feed_forward)
decoder_input = LayerNormalization(epsilon=1e-6)(Add()([attention, feed_forward]))
outputs = TimeDistributed(Dense(target_vocab_size, activation='softmax'))(decoder_input)
model = Model(inputs=[inputs, targets], outputs=outputs)
return model
```
这个模型使用了 Keras 的 API 进行构建,包含了以下几个组件:
- Embedding 层:将输入的序列转换为 d_model 维度的向量表示。
- MultiHeadAttention 层:对输入序列和目标序列进行多头注意力计算。
- LayerNormalization 层:对输入序列进行层归一化。
- TimeDistributed 层:对序列中的每个时间步进行相同的计算。
- Dense 层:计算输出序列的概率分布。
这个模型并不包含训练过程,只是一个构建模型的例子。在使用时,需要针对不同的任务进行修改。