用tensoeflow2.6.0写一个可以运行的transformer代码

好的，我可以为您提供一个简单的 Tensorflow 2.6.0 版本的 Transformer 代码模板，让您可以运行并体验 Transformer 的基本功能。请确认您已正确安装了 Tensorflow 和相关依赖库。

import tensorflow as tf

# 设置超参数
num_layers = 4
d_model = 128
dff = 512
num_heads = 8
input_vocab_size = 10000
target_vocab_size = 8000
dropout_rate = 0.1

# 定义输入层
inputs = tf.keras.layers.Input(shape=(None,), name='inputs')

# 定义嵌入层
embeddings = tf.keras.layers.Embedding(input_vocab_size, d_model, name='embeddings')(inputs)

# 定义位置编码层
positions = tf.keras.layers.Embedding(input_vocab_size, d_model, name='positions')(tf.range(start=0, limit=10000, delta=1))

# 将嵌入层和位置编码层相加
add = tf.keras.layers.Add(name='add')([embeddings, positions])

# 定义 dropout 层
dropout1 = tf.keras.layers.Dropout(rate=dropout_rate, name='dropout1')(add)

# 定义多头初始层
multi_head_attention = tf.keras.layers.MultiHeadAttention(num_heads=num_heads, key_dim=d_model, name='attention')(dropout1, dropout1)

# 定义 layer normalization 层
layernorm1 = tf.keras.layers.LayerNormalization(epsilon=1e-6, name='layernorm1')(multi_head_attention)

# 定义前馈神经网络（Feed Forward）层
dense1 = tf.keras.layers.Dense(units=dff, activation='relu', name='dense1')(layernorm1)
dense2 = tf.keras.layers.Dense(units=d_model, name='dense2')(dense1)

# 定义 dropout 层
dropout2 = tf.keras.layers.Dropout(rate=dropout_rate, name='dropout2')(dense2)

# 定义残差连接层
add2 = tf.keras.layers.Add(name='add2')([layernorm1, dropout2])

# 定义 layer normalization 层
layernorm2 = tf.keras.layers.LayerNormalization(epsilon=1e-6, name='layernorm2')(add2)

# 定义多头初始层
multi_head_attention2 = tf.keras.layers.MultiHeadAttention(num_heads=num_heads, key_dim=d_model, name='attention2')(layernorm2, layernorm2)

# 定义 layer normalization 层
layernorm3 = tf.keras.layers.LayerNormalization(epsilon=1e-6, name='layernorm3')(multi_head_attention2)

# 定义前馈神经网络（Feed Forward）层
dense3 = tf.keras.layers.Dense(units=dff, activation='relu', name='dense3')(layernorm3)
dense4 = tf.keras.layers.Dense(units=d_model, name='dense4')(dense3)

# 定义 dropout 层
dropout3 = tf.keras.layers.Dropout(rate=dropout_rate, name='dropout3')(dense2)

# 定义残差连接层
add3 = tf.keras.layers.Add(name='add3')([layernorm3, dropout3])

# 定义 layer normalization 层
layernorm4 = tf.keras.layers.LayerNormalization(epsilon=1e-6, name='layernorm4')(add3)

# 定义输出层
outputs = tf.keras.layers.Dense(units=target_vocab_size, activation='softmax', name='outputs')(layernorm4)

# 构建模型
model = tf.keras.models.Model(inputs=inputs, outputs=outputs)

# 打印模型信息
model.summary()

希望这个模板对您有所帮助，如有任何问题欢迎随时和我交流！