请给出基于卷积神经网络的二分类识别玫瑰花和向日葵的python代码要使accuracy和val_accuracy的值变换足够平缓。使用如下如下代码进行数据划分：import numpy as npimport tensorflow as tffrom keraspreprocessingimage import ImageDataGeneratorimport osfrom PIL import

下面是基于卷积神经网络的二分类识别玫瑰花和向日葵的Python代码示例：

import numpy as np
import tensorflow as tf
from keras.preprocessing.image import ImageDataGenerator
import os
from PIL import Image
from keras.utils import to_categorical
from sklearn.model_selection import train_test_split
from keras.models import Sequential
from keras.layers import Conv2D, MaxPooling2D, Flatten, Dense, Dropout

img_x = 64
img_y = 64

# 对读取图片进行预处理
# 读取图片
def read_image(imageName):
    img = Image.open(imageName)
    # 读取为64*64
    img = img.resize((img_x, img_y), Image.ANTIALIAS)
    number_data = img.getdata()
    # 归一化处理
    number_data_array = np.array(number_data)
    number_data_array = number_data_array.astype(float)
    number_data_normalize = number_data_array / 255

    return number_data_normalize

images = []
labels = []
# 文件路径
path = os.listdir("./mgh")

# 读取路径下各文件夹对应的图片
# 遍历文件路径下包含的所有文件夹
for textPath in path:
    print(textPath)
    # 将文件路径下各文件夹名与文件路径结合变为图片的路径
    for fn in os.listdir(os.path.join('mgh', textPath)):
        # 读取所有路径下的图片
        if fn.endswith('.jpg'):
            fd = os.path.join('./mgh', textPath, fn)
            images.append(read_image(fd))
            labels.append(textPath)
X = np.array(images)
y = np.array(labels)

# 将标签转换为整数形式
label_mapping = {label: i for i, label in enumerate(np.unique(y))}
y = np.array([label_mapping[label] for label in y])

# 划分训练集与测试集
x_train, x_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=30)

# 转换标签为one-hot编码
y_train = to_categorical(y_train)
y_test = to_categorical(y_test)

# 构建卷积神经网络模型
model = Sequential()
model.add(Conv2D(32, (3, 3), activation='relu', input_shape=(img_x, img_y, 3)))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Conv2D(64, (3, 3), activation='relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Flatten())
model.add(Dense(128, activation='relu'))
model.add(Dropout(0.5))
model.add(Dense(2, activation='softmax'))

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

# 数据增强
datagen = ImageDataGenerator(
    rotation_range=20,
    width_shift_range=0.2,
    height_shift_range=0.2,
    shear_range=0.2,
    zoom_range=0.2,
    horizontal_flip=True,
    fill_mode='nearest')

# 训练模型
model.fit(datagen.flow(x_train, y_train, batch_size=32), epochs=50, validation_data=(x_test, y_test))

# 评估模型
score = model.evaluate(x_test, y_test, verbose=0)
print('Test loss:', score[0])
print('Test accuracy:', score[1])

这个代码示例中使用了卷积神经网络模型，包括两个卷积层和一个全连接层，以及数据增强技术用于增加训练样本的多样性。训练过程中使用了ImageDataGenerator类来进行数据增强，通过调整各种参数可以实现不同程度的变换，从而使得accuracy和val_accuracy的值变换足够平缓。训练完成后，使用测试集评估模型的性能，并打印出测试集的loss和accuracy值