给我写一份基于cnn卷积神经网络的水果识别python文件

好的，下面是一个简单的基于CNN的水果识别Python文件示例：

# 导入必要的库
import numpy as np
import pandas as pd
import tensorflow as tf
from sklearn.model_selection import train_test_split
from tensorflow.keras.utils import to_categorical
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense, Conv2D, Flatten, MaxPooling2D, Dropout
import cv2
import os

# 定义图像大小和类别数
IMG_HEIGHT = 100
IMG_WIDTH = 100
NUM_CLASSES = 5

# 加载数据集
def load_data():
    data = []
    labels = []
    for fruit_class in os.listdir("fruits/"):
        for image_name in os.listdir("fruits/" + fruit_class):
            image = cv2.imread("fruits/" + fruit_class + "/" + image_name)
            image = cv2.resize(image, (IMG_HEIGHT, IMG_WIDTH))
            data.append(image)
            labels.append(fruit_class)
    data = np.array(data)
    labels = pd.get_dummies(labels).values
    return data, labels

# 数据预处理
def preprocess_data(data, labels):
    data = data.astype('float32') / 255.0
    x_train, x_test, y_train, y_test = train_test_split(data, labels, test_size=0.2, random_state=42)
    y_train = to_categorical(y_train, NUM_CLASSES)
    y_test = to_categorical(y_test, NUM_CLASSES)
    return x_train, x_test, y_train, y_test

# 定义CNN模型
def create_model():
    model = Sequential([
        Conv2D(32, kernel_size=(3, 3), activation='relu', input_shape=(IMG_HEIGHT, IMG_WIDTH, 3)),
        MaxPooling2D(pool_size=(2, 2)),
        Conv2D(64, kernel_size=(3, 3), activation='relu'),
        MaxPooling2D(pool_size=(2, 2)),
        Flatten(),
        Dense(128, activation='relu'),
        Dropout(0.5),
        Dense(NUM_CLASSES, activation='softmax')
    ])
    model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])
    return model

# 加载数据集
data, labels = load_data()

# 数据预处理
x_train, x_test, y_train, y_test = preprocess_data(data, labels)

# 定义模型并进行训练
model = create_model()
model.fit(x_train, y_train, batch_size=32, epochs=10, validation_data=(x_test, y_test))

# 保存模型
model.save("fruit_classification_cnn.h5")

这份代码实现了基于CNN的水果分类任务。具体来说，它首先加载数据集，然后对图像进行预处理，接着定义CNN模型并进行训练。最后，将训练好的模型保存到文件中。您可以根据自己的需求进行修改和扩展。