MindSpore CNN 人脸识别教程：代码示例与模型训练评估

以下是一个基于 MindSpore 的 CNN 人脸识别深层网络代码示例，包括添加预训练集、模型训练和评估：

import mindspore.nn as nn
import mindspore.ops as ops
import mindspore.dataset as ds
import mindspore.dataset.transforms.vision as vision
from mindspore import Model, Tensor
from mindspore.train.callback import ModelCheckpoint, CheckpointConfig, LossMonitor
from mindspore.nn.metrics import Accuracy

# 添加预训练集
train_dataset = ds.MnistDataset('path/to/train/dataset')
train_dataset = train_dataset.map(input_columns='image', 
                                  operations=vision.Normalize(mean=0.1307, std=0.3081))
train_dataset = train_dataset.map(input_columns='image', 
                                  operations=vision.Resize((28, 28)))
train_dataset = train_dataset.map(input_columns='label', 
                                  operations=ops.OneHot(10))
train_dataset = train_dataset.shuffle(buffer_size=10000)
train_dataset = train_dataset.batch(32)

# 定义模型
class CNN(nn.Cell):
    def __init__(self):
        super(CNN, self).__init__()
        self.conv1 = nn.Conv2d(1, 32, kernel_size=3, stride=1, padding=1, has_bias=True)
        self.conv2 = nn.Conv2d(32, 64, kernel_size=3, stride=1, padding=1, has_bias=True)
        self.pool = nn.MaxPool2d(kernel_size=2, stride=2)
        self.flatten = nn.Flatten()
        self.fc1 = nn.Dense(7 * 7 * 64, 256, has_bias=True)
        self.fc2 = nn.Dense(256, 10, has_bias=True)
        self.relu = nn.ReLU()

    def construct(self, x):
        x = self.conv1(x)
        x = self.relu(x)
        x = self.pool(x)
        x = self.conv2(x)
        x = self.relu(x)
        x = self.pool(x)
        x = self.flatten(x)
        x = self.fc1(x)
        x = self.relu(x)
        x = self.fc2(x)
        return x

# 定义损失函数和优化器
net = CNN()
criterion = nn.SoftmaxCrossEntropyWithLogits(sparse=True, reduction='mean')
optimizer = nn.Momentum(params=net.trainable_params(), learning_rate=0.01, momentum=0.9)

# 进行模型训练
model = Model(net, criterion, optimizer, metrics={'Accuracy': Accuracy()})
config_ck = CheckpointConfig(save_checkpoint_steps=1000, keep_checkpoint_max=10)
ckpoint_cb = ModelCheckpoint(prefix='mnist', directory='./checkpoint', config=config_ck)
loss_cb = LossMonitor()
model.train(epoch=10, train_dataset=train_dataset, callbacks=[ckpoint_cb, loss_cb])

# 进行模型评估
test_dataset = ds.MnistDataset('path/to/test/dataset')
test_dataset = test_dataset.map(input_columns='image', 
                                operations=vision.Normalize(mean=0.1307, std=0.3081))
test_dataset = test_dataset.map(input_columns='image', 
                                operations=vision.Resize((28, 28)))
test_dataset = test_dataset.map(input_columns='label', 
                                operations=ops.OneHot(10))
test_dataset = test_dataset.batch(32)
result = model.eval(test_dataset)
print('Accuracy:', result['Accuracy'])

注意：

• 此代码示例使用 MNIST 数据集，您需要将 'path/to/train/dataset' 和 'path/to/test/dataset' 替换为实际的数据集路径。
• 您需要安装 MindSpore 框架，并导入必要的模块。
• 该代码仅供参考，您可以根据具体需求进行修改。

相关链接:

• MindSpore 官方网站: https://www.mindspore.cn/
• MindSpore 文档: https://www.mindspore.cn/docs/zh-CN/master/

希望本文能帮助您了解如何在 MindSpore 框架下构建 CNN 模型并进行人脸识别任务。