写一个用花分类数据集做分类的添加了注意力机制的卷积神经网络代码

好的，以下是一个用花分类数据集做分类的添加了注意力机制的卷积神经网络代码：

import tensorflow as tf
from tensorflow.keras.layers import Input, Conv2D, MaxPooling2D, Flatten, Dense, Dropout, GlobalAveragePooling2D, GlobalMaxPooling2D, multiply, Reshape, add
from tensorflow.keras.models import Model
from tensorflow.keras.preprocessing.image import ImageDataGenerator

# 设置数据集路径
train_data_dir = 'path/to/train'
validation_data_dir = 'path/to/validation'

# 设置超参数
img_width, img_height = 224, 224
batch_size = 32
epochs = 10
class_num = 5

# 创建数据生成器
train_datagen = ImageDataGenerator(
    rescale=1. / 255,
    shear_range=0.2,
    zoom_range=0.2,
    horizontal_flip=True)

validation_datagen = ImageDataGenerator(rescale=1. / 255)

train_generator = train_datagen.flow_from_directory(
    train_data_dir,
    target_size=(img_width, img_height),
    batch_size=batch_size,
    class_mode='categorical')

validation_generator = validation_datagen.flow_from_directory(
    validation_data_dir,
    target_size=(img_width, img_height),
    batch_size=batch_size,
    class_mode='categorical')

# 定义注意力机制的卷积神经网络
def attention_module(inputs, attention_factor=8):
    x = inputs
    filters = x.shape[-1]
    x = GlobalAveragePooling2D()(x)
    x = Dense(filters // attention_factor, activation='relu')(x)
    x = Dense(filters, activation='sigmoid')(x)
    x = Reshape((1, 1, filters))(x)
    x = multiply([inputs, x])
    x = add([inputs, x])
    return x

def conv_block(inputs, filters, kernel_size=(3, 3), strides=(1, 1), padding='same', activation='relu'):
    x = Conv2D(filters, kernel_size, strides=strides, padding=padding)(inputs)
    x = BatchNormalization()(x)
    x = Activation(activation)(x)
    return x

def dense_block(inputs, units, activation='relu', dropout_rate=0.2):
    x = Dense(units)(inputs)
    x = BatchNormalization()(x)
    x = Activation(activation)(x)
    x = Dropout(dropout_rate)(x)
    return x

inputs = Input(shape=(img_width, img_height, 3))
x = conv_block(inputs, 64)
x = conv_block(x, 64)
x = attention_module(x)
x = MaxPooling2D()(x)

x = conv_block(x, 128)
x = conv_block(x, 128)
x = attention_module(x)
x = MaxPooling2D()(x)

x = conv_block(x, 256)
x = conv_block(x, 256)
x = conv_block(x, 256)
x = attention_module(x)
x = MaxPooling2D()(x)

x = conv_block(x, 512)
x = conv_block(x, 512)
x = conv_block(x, 512)
x = attention_module(x)
x = GlobalAveragePooling2D()(x)

x = dense_block(x, 1024)
x = dense_block(x, 512)

predictions = Dense(class_num, activation='softmax')(x)

model = Model(inputs=inputs, outputs=predictions)

model.compile(loss='categorical_crossentropy',
              optimizer='adam',
              metrics=['accuracy'])

# 训练模型
model.fit(train_generator,
          steps_per_epoch=train_generator.samples // batch_size,
          epochs=epochs,
          validation_data=validation_generator,
          validation_steps=validation_generator.samples // batch_size)

# 评估模型
model.evaluate(validation_generator)

这个模型包含了注意力机制，能够在卷积神经网络中增加权重的重要性，提高模型的准确率。同时，也使用了数据增强来防止过拟合。