CNN卷积神经网络人脸识别 python代码

好的，以下是一个基于Python和Keras框架实现的CNN卷积神经网络人脸识别代码示例：

# 导入必要的库
import numpy as np
from keras.models import Sequential
from keras.layers import Dense, Dropout, Flatten
from keras.layers import Conv2D, MaxPooling2D
from keras.utils import np_utils
from keras.datasets import mnist
from keras import backend as K
from keras.preprocessing.image import ImageDataGenerator
from keras.preprocessing import image

# 定义数据集相关参数
batch_size = 128
num_classes = 2
epochs = 10
img_rows, img_cols = 28, 28
input_shape = (img_rows, img_cols, 3)

# 加载数据集
train_datagen = ImageDataGenerator(
        rescale=1./255,
        shear_range=0.2,
        zoom_range=0.2,
        horizontal_flip=True)

test_datagen = ImageDataGenerator(rescale=1./255)

train_generator = train_datagen.flow_from_directory(
        'train',
        target_size=(img_rows, img_cols),
        batch_size=batch_size,
        class_mode='categorical')

validation_generator = test_datagen.flow_from_directory(
        'test',
        target_size=(img_rows, img_cols),
        batch_size=batch_size,
        class_mode='categorical')

# 构建CNN模型
model = Sequential()
model.add(Conv2D(32, kernel_size=(3, 3),
                 activation='relu',
                 input_shape=input_shape))
model.add(Conv2D(64, (3, 3), activation='relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.25))
model.add(Flatten())
model.add(Dense(128, activation='relu'))
model.add(Dropout(0.5))
model.add(Dense(num_classes, activation='softmax'))

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

# 训练模型
model.fit_generator(
        train_generator,
        steps_per_epoch=2000 // batch_size,
        epochs=epochs,
        validation_data=validation_generator,
        validation_steps=800 // batch_size)

# 预测新图像
img_path = 'test/cat/cat.1.jpg'
img = image.load_img(img_path, target_size=(img_rows, img_cols))
x = image.img_to_array(img)
x = np.expand_dims(x, axis=0)

preds = model.predict_classes(x)
print('预测结果:', preds[0])

这是一个简单的人脸识别例子，其中使用了一个2层的CNN卷积神经网络模型，并且使用Keras提供的ImageDataGenerator来加载和处理数据集。你可以将此代码作为一个起点，并进行修改以满足你的具体需求。