MindSpore LeNet-5 模型训练示例

以下示例代码使用 MindSpore 框架，基于 LeNet-5 模型实现数字识别任务的训练过程，包括数据加载、模型构建、训练和评估等步骤：

def train_lenet():
    context.set_context(mode=context.GRAPH_MODE, device_target='CPU')
    train_dataset_generator = TrainDatasetGenerator('D:/code/machine vision course/digit-mindspore/dataset')
    ds_train = ds.GeneratorDataset(train_dataset_generator, ['data', 'label'], shuffle=True)
    ds_train = ds_train.shuffle(buffer_size=10)
    ds_train = ds_train.batch(batch_size=4, drop_remainder=True)
    valid_dataset_generator = TrainDatasetGenerator('D:/code/machine vision course/digit-mindspore/test')
    ds_valid = ds.GeneratorDataset(valid_dataset_generator, ['data', 'label'], shuffle=True)
    ds_valid = ds_valid.batch(batch_size=4, drop_remainder=True)
    network = LeNet5(num_class=7)
    net_loss = nn.SoftmaxCrossEntropyWithLogits(sparse=True, reduction='mean')
    net_opt = nn.Momentum(network.trainable_params(), learning_rate=0.01, momentum=0.9)
    time_cb = TimeMonitor(data_size=ds_train.get_dataset_size())
    config_ck = CheckpointConfig(save_checkpoint_steps=10,
                                 keep_checkpoint_max=10)
    config_ckpt_path = 'D:/code/machine vision course/digit-mindspore/ckpt/'
    ckpoint_cb = ModelCheckpoint(prefix='checkpoint_lenet', directory=config_ckpt_path, config=config_ck)


    model = Model(network, net_loss, net_opt, metrics={'Accuracy': Accuracy()})
    epoch_size = 10
    print('============== Starting Training =============')
    model.train(epoch_size, ds_train, callbacks=[time_cb, ckpoint_cb, LossMonitor()])

    acc = model.eval(ds_valid)
    print('============== {} ============='.format(acc))
    epoch_size = 10
    print('============== Starting Training =============')
    model.train(epoch_size, ds_train, callbacks=[time_cb, ckpoint_cb, LossMonitor()])

    acc = model.eval(ds_valid)
    print('============== {} ============='.format(acc))
    epoch_size = 10
    print('============== Starting Training =============')
    model.train(epoch_size, ds_train, callbacks=[time_cb, ckpoint_cb, LossMonitor()])

    acc = model.eval(ds_valid)
    print('============== {} ============='.format(acc))

注意： LeNet-5 模型并不适合用于人脸识别任务。人脸识别需要使用更加复杂的卷积神经网络模型，例如 ResNet、VGG 等。同时，数据集也需要使用人脸数据集，例如 LFW、CASIA 等。建议您了解相关的人脸识别模型和数据集，并进行相关的实现。