导入数据深度神经与模糊算法相结合的python代码

抱歉，你提供的问题有些模糊。下面我将给出两个方向的代码实现，分别是深度神经网络和模糊算法。

1. 深度神经网络

以下是一个基于Keras的深度神经网络的示例代码，用于分类MNIST手写数字数据集。

import tensorflow as tf
from tensorflow import keras

# 加载数据集
mnist = keras.datasets.mnist
(train_images, train_labels), (test_images, test_labels) = mnist.load_data()

# 数据预处理
train_images = train_images / 255.0
test_images = test_images / 255.0

# 构建模型
model = keras.Sequential([
    keras.layers.Flatten(input_shape=(28, 28)),
    keras.layers.Dense(128, activation='relu'),
    keras.layers.Dense(10, activation='softmax')
])

# 编译模型
model.compile(optimizer='adam',
              loss='sparse_categorical_crossentropy',
              metrics=['accuracy'])

# 训练模型
model.fit(train_images, train_labels, epochs=10, validation_data=(test_images, test_labels))

# 评估模型
test_loss, test_acc = model.evaluate(test_images, test_labels)
print('Test accuracy:', test_acc)

2. 模糊算法

以下是一个基于Scikit-Fuzzy的模糊控制器的示例代码，用于控制一个简单的水箱水位。

import numpy as np
import skfuzzy as fuzz
from skfuzzy import control as ctrl

# 定义输入和输出变量
water_level = ctrl.Antecedent(np.arange(0, 101, 1), 'water_level')
valve_opening = ctrl.Consequent(np.arange(0, 101, 1), 'valve_opening')

# 定义模糊集
water_level['low'] = fuzz.trimf(water_level.universe, [0, 0, 50])
water_level['medium'] = fuzz.trimf(water_level.universe, [0, 50, 100])
water_level['high'] = fuzz.trimf(water_level.universe, [50, 100, 100])

valve_opening['low'] = fuzz.trimf(valve_opening.universe, [0, 0, 50])
valve_opening['medium'] = fuzz.trimf(valve_opening.universe, [0, 50, 100])
valve_opening['high'] = fuzz.trimf(valve_opening.universe, [50, 100, 100])

# 定义规则
rule1 = ctrl.Rule(water_level['low'], valve_opening['high'])
rule2 = ctrl.Rule(water_level['medium'], valve_opening['medium'])
rule3 = ctrl.Rule(water_level['high'], valve_opening['low'])

# 构建控制器
controller = ctrl.ControlSystem([rule1, rule2, rule3])
simulator = ctrl.ControlSystemSimulation(controller)

# 运行模拟
simulator.input['water_level'] = 60
simulator.compute()
print(simulator.output['valve_opening'])

以上代码仅供参考，具体实现可能需要根据数据集和问题进行调整。