用tensorflow2.6.0写一段transformer模型代码
时间: 2023-06-06 14:05:36 浏览: 124
好的,以下是一个简单的Transformer模型的代码(使用TensorFlow 2.6.0):
```
import tensorflow as tf
from tensorflow.keras.layers import Input, Dense, Dropout
from tensorflow.keras.models import Model
from tensorflow.keras.optimizers import Adam
from tensorflow.keras.losses import SparseCategoricalCrossentropy
from tensorflow.keras.activations import softmax
# 输入序列长度为10
input_length = 10
# 词汇表大小为1000个词
vocab_size = 1000
# 模型中的隐藏层大小
hidden_dim = 256
# 多头自注意力头数
num_heads = 8
# Feed Forward网络中间层大小
ff_dim = 1024
# Dropout概率
dropout_rate = 0.5
# 定义输入
inputs = Input(shape=(input_length,), name='inputs')
# 词嵌入层
embeddings = Embedding(input_dim=vocab_size, output_dim=hidden_dim)(inputs)
# 位置编码
pos_encoding = PositionalEncoding()(embeddings)
# 多头自注意力层
self_attention = MultiHeadAttention(num_heads=num_heads,
key_dim=hidden_dim,
value_dim=hidden_dim)(pos_encoding)
# Dropout层
dropout1 = Dropout(rate=dropout_rate)(self_attention)
# 残差连接和正则化
layer_norm1 = LayerNormalization()(inputs + dropout1)
# Feed Forward网络
ff = Dense(units=ff_dim, activation='relu')(layer_norm1)
ff = Dense(units=hidden_dim)(ff)
# Dropout层
dropout2 = Dropout(rate=dropout_rate)(ff)
# 残差连接和正则化
layer_norm2 = LayerNormalization()(layer_norm1 + dropout2)
# 输出层,使用Softmax激活函数
outputs = Dense(units=vocab_size, activation=softmax)(layer_norm2)
# 定义模型
model = Model(inputs=inputs, outputs=outputs)
# 定义优化器
optimizer = Adam(learning_rate=0.001)
# 定义损失函数
loss_fn = SparseCategoricalCrossentropy()
# 编译模型
model.compile(optimizer=optimizer, loss=loss_fn)
```
这段代码实现了一个简单的Transformer模型,包括词嵌入层、位置编码、多头自注意力层、Feed Forward网络、残差连接、Layer Normalization、Dropout和输出层。这个模型可以用来完成各种NLP任务,如文本分类、命名实体识别、情感分析等。
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