MindSpore的CNN人脸识别代码示例

以下是使用MindSpore实现CNN人脸识别的代码示例：

import mindspore.nn as nn
import mindspore.ops.operations as P
import mindspore.common.dtype as mstype

class Net(nn.Cell):
    def __init__(self):
        super(Net, self).__init__()
        self.conv1 = nn.Conv2d(3, 64, kernel_size=3, stride=1, padding=1, pad_mode='pad')
        self.relu1 = nn.ReLU()
        self.pool1 = nn.MaxPool2d(kernel_size=2, stride=2)
        self.conv2 = nn.Conv2d(64, 128, kernel_size=3, stride=1, padding=1, pad_mode='pad')
        self.relu2 = nn.ReLU()
        self.pool2 = nn.MaxPool2d(kernel_size=2, stride=2)
        self.conv3 = nn.Conv2d(128, 256, kernel_size=3, stride=1, padding=1, pad_mode='pad')
        self.relu3 = nn.ReLU()
        self.pool3 = nn.MaxPool2d(kernel_size=2, stride=2)
        self.fc1 = nn.Dense(256 * 6 * 6, 1024)
        self.relu4 = nn.ReLU()
        self.fc2 = nn.Dense(1024, 128)
        self.relu5 = nn.ReLU()
        self.fc3 = nn.Dense(128, 2)

    def construct(self, x):
        x = self.conv1(x)
        x = self.relu1(x)
        x = self.pool1(x)
        x = self.conv2(x)
        x = self.relu2(x)
        x = self.pool2(x)
        x = self.conv3(x)
        x = self.relu3(x)
        x = self.pool3(x)
        x = nn.Flatten()(x)
        x = self.fc1(x)
        x = self.relu4(x)
        x = self.fc2(x)
        x = self.relu5(x)
        x = self.fc3(x)
        return x

net = Net()
criterion = nn.SoftmaxCrossEntropyWithLogits(sparse=True, reduction='mean')
optimizer = nn.Momentum(net.trainable_params(), learning_rate=0.001, momentum=0.9)
model = Model(net, criterion, optimizer)

该模型包含3个卷积层和3个全连接层，用于实现人脸识别功能。其中，卷积层用于提取图像特征，全连接层用于分类。模型使用SoftmaxCrossEntropyWithLogits作为损失函数，Momentum作为优化器。在训练过程中，可以使用model.train()方法进行训练，使用model.eval()方法进行测试。