请给我keras下的CNN与transformer结合的网络代码

对于这个问题，我可以提供一些参考。Keras中可以使用CNN和Transformer结合的网络，可以参考这份GitHub代码：https://github.com/tensorflow/tensorflow/blob/master/tensorflow/contrib/eager/python/examples/cnn_transformer/cnn_transformer.py。另外，还可以参考一些博客，如：https://medium.com/@zawlin_win/cnn-transformer-for-image-captioning-f0a05b34d2b1。

相关问题

transformer与cnn结合模型

将Transformer与CNN结合可以有效地编码局部处理和全局交互。一种简单的方法是将卷积和Vision Transformer结合起来。最近的研究表明，将卷积和Vision Transformer串联在一起，无论是在开始时使用卷积，还是将卷积插入到每个Transformer块中都是有益的。另外，使用卷积作为Transformer＊＊＊ Transformers See Better。

以下是一个将CNN和Transformer结合的模型的示例代码：

import tensorflow as tf
from tensorflow.keras.layers import Input, Conv2D, Dense, Dropout
from tensorflow.keras.layers import MultiHeadAttention, LayerNormalization
from tensorflow.keras.models import Model

# 定义CNN部分
input_cnn = Input(shape=(224, 224, 3))
x = Conv2D(64, (3, 3), activation='relu')(input_cnn)
x = Conv2D(64, (3, 3), activation='relu')(x)
x = Conv2D(128, (3, 3), activation='relu')(x)
x = Conv2D(128, (3, 3), activation='relu')(x)
x = Conv2D(256, (3, 3), activation='relu')(x)
x = Conv2D(256, (3, 3), activation='relu')(x)
x = Conv2D(512, (3, 3), activation='relu')(x)
x = Conv2D(512, (3, 3), activation='relu')(x)
x = Conv2D(1024, (3, 3), activation='relu')(x)
x = Conv2D(1024, (3, 3), activation='relu')(x)
x = Dropout(0.5)(x)
x = Dense(1024, activation='relu')(x)
output_cnn = Dense(512, activation='relu')(x)

# 定义Transformer部分
input_transformer = Input(shape=(512,))
x = LayerNormalization()(input_transformer)
x = MultiHeadAttention(num_heads=8, key_dim=64)(x, x)
x = Dropout(0.5)(x)
x = LayerNormalization()(x)
x = Dense(512, activation='relu')(x)
output_transformer = Dense(512, activation='relu')(x)

# 将CNN和Transformer连接起来
merged = tf.keras.layers.concatenate([output_cnn, output_transformer])
output = Dense(10, activation='softmax')(merged)

model = Model(inputs=[input_cnn, input_transformer], outputs=output)

可以找到CNN与transformer混合框架图像分割代码

以下是一个使用CNN和Transformer混合框架进行图像分割的代码示例：

import tensorflow as tf
from tensorflow import keras
from tensorflow.keras import layers


class VisionTransformer(keras.Model):
    def __init__(self, num_classes, patch_size=16, num_layers=12, d_model=768, num_heads=12, mlp_dim=3072, channels=3):
        super(VisionTransformer, self).__init__()
        self.patch_size = patch_size
        self.num_layers = num_layers
        self.d_model = d_model
        self.num_heads = num_heads
        self.mlp_dim = mlp_dim

        # CNN backbone
        self.backbone = keras.applications.ResNet50V2(include_top=False, weights='imagenet', input_shape=(None, None, channels))

        # Patch and flatten
        self.patch_and_flatten = layers.Sequential([
            layers.Reshape((-1, patch_size * patch_size * channels)),
            layers.Dense(d_model),
        ])

        # Positional encoding
        self.positional_encoding = layers.Embedding(input_dim=10000, output_dim=d_model)

        # Transformer layers
        self.transformer_layers = [TransformerBlock(d_model, num_heads, mlp_dim) for _ in range(num_layers)]

        # Classification head
        self.classification_head = layers.Dense(num_classes)

    def call(self, inputs):
        # CNN backbone
        cnn_features = self.backbone(inputs)

        # Patch and flatten
        patches = tf.image.extract_patches(cnn_features, sizes=[1, self.patch_size, self.patch_size, 1], strides=[1, self.patch_size, self.patch_size, 1], rates=[1, 1, 1, 1], padding='SAME')
        patches = tf.reshape(patches, (-1, patches.shape[1], self.patch_size * self.patch_size * 3))
        patches = self.patch_and_flatten(patches)

        # Positional encoding
        positions = tf.range(start=0, limit=patches.shape[1], delta=1)
        position_embeddings = self.positional_encoding(positions)

        # Add positional embeddings to patches
        patches += position_embeddings

        # Transformer layers
        for transformer_layer in self.transformer_layers:
            patches = transformer_layer(patches)

        # Classification head
        outputs = self.classification_head(patches[:, 0, :])
        return outputs


class TransformerBlock(keras.layers.Layer):
    def __init__(self, d_model, num_heads, mlp_dim, dropout_rate=0.1):
        super(TransformerBlock, self).__init__()

        self.multi_head_attention = keras.layers.MultiHeadAttention(num_heads=num_heads, key_dim=d_model)
        self.dropout1 = keras.layers.Dropout(dropout_rate)
        self.layer_norm1 = keras.layers.LayerNormalization(epsilon=1e-6)

        self.mlp = keras.Sequential([
            keras.layers.Dense(mlp_dim, activation=keras.activations.gelu),
            keras.layers.Dropout(dropout_rate),
            keras.layers.Dense(d_model),
            keras.layers.Dropout(dropout_rate),
        ])
        self.layer_norm2 = keras.layers.LayerNormalization(epsilon=1e-6)

    def call(self, inputs):
        attention_output = self.multi_head_attention(inputs, inputs)
        attention_output = self.dropout1(attention_output)
        attention_output = self.layer_norm1(inputs + attention_output)

        mlp_output = self.mlp(attention_output)
        mlp_output = self.dropout2(mlp_output)
        mlp_output = self.layer_norm2(attention_output + mlp_output)

        return mlp_output

这个代码使用ResNet50V2作为CNN骨干网络，然后使用Transformer块对提取的图像补丁进行处理。最后，通过全连接层进行分类。你可以通过修改这个代码来完成你的图像分割任务。