举一个使用tensorflow中MultiHeadAttention包的例子

可以参考以下代码：

import tensorflow as tf

# 定义输入
query = tf.random.normal([4, 10, 8])  # [batch_size, seq_len, d_model]
key = tf.random.normal([4, 12, 8])  # [batch_size, seq_len, d_model]
value = tf.random.normal([4, 12, 8])  # [batch_size, seq_len, d_model]

# 定义MultiHeadAttention层
multi_head_attn = tf.keras.layers.MultiHeadAttention(
    num_heads=2, key_dim=4, dropout=0.2)

# 使用MultiHeadAttention层
output = multi_head_attn(query, key, value)

print(output.shape)  # (4, 10, 8)

这里使用了`tf.keras.layers.MultiHeadAttention`类来定义MultiHeadAttention层，并将`query`、`key`、`value`作为输入传入该层。最后输出的`output`的形状为`(batch_size, seq_len, d_model)`。

相关问题

编写基于TensorFlow的transformer模型代码例子

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：

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！

用tensorflow写一个cnn和transformer组合的分类代码

好的，以下是一个使用TensorFlow实现CNN和Transformer组合的分类代码：

import tensorflow as tf
from tensorflow.keras.layers import Input, Conv1D, MaxPooling1D, Flatten, Dense, Dropout
from tensorflow.keras.layers import LayerNormalization, MultiHeadAttention, Dense, Input, Flatten
from tensorflow.keras.models import Model

class CNNTransformer(Model):
    def __init__(self, num_filters, filter_sizes, d_model, num_heads, ff_dim, num_classes, max_seq_len, dropout_rate=0.1):
        super(CNNTransformer, self).__init__()
        
        # Convolution Layers
        self.conv_layers = []
        for filter_size in filter_sizes:
            conv_layer = Conv1D(num_filters, filter_size, activation='relu')
            self.conv_layers.append(conv_layer)
        
        # Transformer Layers
        self.transformer_layers = []
        for i in range(2):
            transformer_layer = tf.keras.Sequential([
                LayerNormalization(epsilon=1e-6),
                MultiHeadAttention(num_heads=num_heads, key_dim=d_model),
                Dropout(dropout_rate),
                LayerNormalization(epsilon=1e-6),
                Dense(ff_dim, activation='relu'),
                Dropout(dropout_rate),
                Dense(d_model),
                Dropout(dropout_rate)
            ])
            self.transformer_layers.append(transformer_layer)
        
        # Flatten Layer
        self.flatten = Flatten()
        
        # Output Layer
        self.output_layer = Dense(num_classes, activation='softmax')
        
        # Set max sequence length
        self.max_seq_len = max_seq_len
        
    def call(self, inputs, training=True):
        # Convolution Layers
        conv_output = []
        for conv_layer in self.conv_layers:
            conv_output.append(conv_layer(inputs))
        conv_output = tf.concat(conv_output, axis=-1)
        
        # Transformer Layers
        transformer_output = tf.reshape(conv_output, [-1, self.max_seq_len, conv_output.shape[-1]])
        for transformer_layer in self.transformer_layers:
            transformer_output = transformer_layer(transformer_output, training=training)
        
        # Flatten Layer
        flatten_output = self.flatten(transformer_output)
        
        # Output Layer
        output = self.output_layer(flatten_output)
        
        return output

在上面的代码中，我们首先定义了一个`CNNTransformer`类，它继承自`tf.keras.Model`。在类的构造函数中，我们传入了许多超参数，包括卷积层的数量、卷积核的数量和大小、Transformer层的数量、多头注意力机制的头数、前馈神经网络的维度、分类的类别数量、序列的最大长度和dropout率等。然后我们在构造函数中定义了卷积层、Transformer层、Flatten层和输出层。

在`call()`方法中，我们首先将输入传递给卷积层，然后将它们的输出连接在一起。然后我们将卷积层的输出重塑为一个三维张量，并将其传递给Transformer层。最后，我们将Transformer层的输出展平，并将其传递给输出层进行分类。

接下来，我们可以使用这个类来构建我们的模型，如下所示：

num_filters = 64
filter_sizes = [3, 4, 5]
d_model = 128
num_heads = 8
ff_dim = 512
num_classes = 10
max_seq_len = 100

model = CNNTransformer(num_filters, filter_sizes, d_model, num_heads, ff_dim, num_classes, max_seq_len, dropout_rate=0.1)
model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])

在这个例子中，我们使用了三个不同大小的卷积核，分别为3、4和5，每个卷积核有64个过滤器。我们使用了两个Transformer层，每个层有8个头和128个隐藏单元。我们使用了一个512维的前馈神经网络，并且使用了dropout率为0.1。最后，我们使用分类交叉熵作为损失函数，Adam优化器进行优化，并使用准确度作为评估指标。

希望这个例子能够帮助你理解如何在TensorFlow中实现CNN和Transformer组合的分类模型。