卷积神经网络 人脸图像识别 代码

以下是一个简单的卷积神经网络人脸图像识别的代码示例：

import keras
from keras.models import Sequential
from keras.layers import Dense, Dropout, Flatten
from keras.layers import Conv2D, MaxPooling2D
from keras.utils import to_categorical
from keras.datasets import cifar10

# 加载CIFAR-10数据集
(x_train, y_train), (x_test, y_test) = cifar10.load_data()

# 将标签转换为one-hot编码
num_classes = 10
y_train = to_categorical(y_train, num_classes)
y_test = to_categorical(y_test, num_classes)

# 数据预处理
x_train = x_train.astype('float32') / 255
x_test = x_test.astype('float32') / 255

# 定义模型
model = Sequential()
model.add(Conv2D(32, (3, 3), padding='same', activation='relu', input_shape=x_train.shape[1:]))
model.add(Conv2D(32, (3, 3), activation='relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.25))

model.add(Conv2D(64, (3, 3), padding='same', activation='relu'))
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(512, 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
batch_size = 128
epochs = 20
model.fit(x_train, y_train, batch_size=batch_size, epochs=epochs, 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])

这是一个基于CIFAR-10数据集的简单卷积神经网络代码示例，你可以根据你的数据集进行相应的修改。