编写基于TensorFlow的transformer模型代码例子
时间: 2023-11-19 08:03:28 浏览: 97
当然,我很乐意回答你的问题!以下是基于TensorFlow的transformer模型代码例子:
```python
import tensorflow as tf
from tensorflow import keras
from tensorflow.keras import layers
# Define vocab size
vocab_size = 5000
# Define input sequence length and output sequence length
max_len = 100
# Define number of attention heads and transformer layers
num_heads = 8
num_layers = 4
# Define embedding size and feedforward size
embedding_size = 256
ffn_size = 512
# Define input token with shape (batch_size, max_len)
input_token = layers.Input(shape=(max_len,))
# Define embedding layer with shape (batch_size, max_len, embedding_size)
embedding_layer = layers.Embedding(vocab_size, embedding_size)(input_token)
# Define position embedding layer with shape (batch_size, max_len, embedding_size)
position_embedding_layer = layers.Embedding(
input_dim=max_len, output_dim=embedding_size
)(tf.range(start=0, limit=max_len, delta=1))
# Combine embedding layer with position embedding layer
combined_embedding_layer = embedding_layer + position_embedding_layer
# Define transformer layers
for i in range(num_layers):
# Define multi-head attention layer with input shape (batch_size, seq_length, embedding_size)
attention_layer = layers.MultiHeadAttention(
num_heads=num_heads, key_dim=embedding_size
)
# Define normalization layers for multi-head attention layer output
norm_layer_1 = layers.LayerNormalization(
epsilon=1e-6
)
add_and_norm_layer_1 = layers.Add()([combined_embedding_layer, attention_layer(combined_embedding_layer)])
attention_output_layer = norm_layer_1(add_and_norm_layer_1)
# Define feedforward layer with input shape (batch_size, seq_length, ffn_size)
ffn_layer = keras.Sequential(
[
layers.Dense(ffn_size, activation="relu"),
layers.Dense(embedding_size),
]
)
# Define normalization layers for feedforward layer output
norm_layer_2 = layers.LayerNormalization(
epsilon=1e-6
)
add_and_norm_layer_2 = layers.Add()([attention_output_layer, ffn_layer(attention_output_layer)])
ffn_output_layer = norm_layer_2(add_and_norm_layer_2)
# If this is not the last layer, set the combined_embedding_layer to be the current ffn_output_layer
if i != num_layers-1:
combined_embedding_layer = ffn_output_layer
# Define output token with shape (batch_size, max_len, vocab_size)
output_token = layers.Dense(vocab_size)(ffn_output_layer)
# Define model with input layer of shape (batch_size, max_len) and output layer of shape (batch_size, max_len, vocab_size)
model = keras.models.Model(inputs=[input_token], outputs=[output_token])
```
希望对你有所帮助!
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