人工智能训练人脸识别代码
时间: 2024-10-01 09:09:42 浏览: 22
在人工智能领域,特别是深度学习中,人脸识别通常涉及到卷积神经网络(CNN)的应用。以下是一个简化的Python示例,使用了开源库如TensorFlow或PyTorch来训练人脸识别模型:
```python
# 引入必要的库
import tensorflow as tf
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Conv2D, MaxPooling2D, Flatten, Dense
# 数据预处理:假设你已经有了人脸数据集,分为训练集和测试集
from tensorflow.keras.preprocessing.image import ImageDataGenerator
train_datagen = ImageDataGenerator(rescale=1./255) # 调整像素值范围
test_datagen = ImageDataGenerator(rescale=1./255)
train_generator = train_datagen.flow_from_directory(
'train_data',
target_size=(64, 64),
batch_size=32,
class_mode='categorical')
validation_generator = test_datagen.flow_from_directory(
'validation_data',
target_size=(64, 64),
batch_size=32,
class_mode='categorical')
# 创建一个简单的卷积神经网络模型
model = Sequential([
Conv2D(32, (3, 3), activation='relu', input_shape=(64, 64, 3)),
MaxPooling2D((2, 2)),
Conv2D(64, (3, 3), activation='relu'),
MaxPooling2D((2, 2)),
Flatten(),
Dense(64, activation='relu'),
Dense(train_generator.num_classes, activation='softmax') # 类别数取决于你的数据
])
# 编译模型
model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])
# 训练模型
model.fit(train_generator, epochs=20, validation_data=validation_generator)
# 保存模型
model.save('face_recognition_model.h5')
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