mindspore手写识别代码

以下是使用MindSpore实现手写数字识别的简单代码：

import numpy as np
import mindspore.dataset as ds
import mindspore.dataset.transforms.c_transforms as C
import mindspore.dataset.vision.c_transforms as CV
import mindspore.dataset.transforms.py_transforms as pyC
import mindspore.nn as nn
from mindspore import context, Tensor
from mindspore.train.callback import ModelCheckpoint, CheckpointConfig, LossMonitor
from mindspore.train.model import Model
from mindspore.nn.metrics import Accuracy
from mindspore.common.initializer import Normal

# 加载数据集
def create_dataset(data_path, batch_size=32, repeat_size=1,
                   num_parallel_workers=1):
    # 定义图片转换操作
    trans = []
    trans.append(CV.Resize((32, 32)))
    trans.append(CV.RandomCrop((28, 28)))
    trans.append(CV.Rescale(1 / 255.0, 0))
    trans.append(C.Reshape([-1]))
    type_cast_op = pyC.TypeCast(np.int32)
    trans = C.Compose(trans)
    # 加载数据集
    mnist_ds = ds.MnistDataset(data_path, shuffle=True)
    # 应用转换操作
    mnist_ds = mnist_ds.map(operations=trans, input_columns="image",
                            num_parallel_workers=num_parallel_workers)
    mnist_ds = mnist_ds.map(operations=type_cast_op, input_columns="label",
                            num_parallel_workers=num_parallel_workers)
    # 对数据集进行批处理、重复和预取
    mnist_ds = mnist_ds.batch(batch_size=batch_size, drop_remainder=True)
    mnist_ds = mnist_ds.repeat(repeat_size)
    mnist_ds = mnist_ds.prefetch(buffer_size=num_parallel_workers)
    return mnist_ds

# 定义模型
class LeNet5(nn.Cell):
    def __init__(self):
        super(LeNet5, self).__init__()
        self.conv1 = nn.Conv2d(1, 6, kernel_size=5, pad_mode='valid')
        self.conv2 = nn.Conv2d(6, 16, kernel_size=5, pad_mode='valid')
        self.fc1 = nn.Dense(16 * 4 * 4, 120, weight_init=Normal(0.02))
        self.fc2 = nn.Dense(120, 84, weight_init=Normal(0.02))
        self.fc3 = nn.Dense(84, 10, weight_init=Normal(0.02))
        self.relu = nn.ReLU()
        self.max_pool2d = nn.MaxPool2d(kernel_size=2, stride=2)

    def construct(self, x):
        x = self.conv1(x)
        x = self.relu(x)
        x = self.max_pool2d(x)
        x = self.conv2(x)
        x = self.relu(x)
        x = self.max_pool2d(x)
        x = nn.Flatten()(x)
        x = self.fc1(x)
        x = self.relu(x)
        x = self.fc2(x)
        x = self.relu(x)
        x = self.fc3(x)
        return x

if __name__ == '__main__':
    # 设置设备环境为CPU
    context.set_context(mode=context.GRAPH_MODE, device_target="CPU")
    # 加载数据集
    data_path = "./MNIST_unzip/train"
    mnist_ds = create_dataset(data_path)
    # 定义模型
    network = LeNet5()
    # 定义损失函数和优化器
    loss = nn.SoftmaxCrossEntropyWithLogits(sparse=True, reduction='mean')
    opt = nn.Momentum(network.trainable_params(), learning_rate=0.01, momentum=0.9)
    # 定义模型训练和评估
    model = Model(network, loss_fn=loss, optimizer=opt, metrics={"Accuracy": Accuracy()})
    # 定义回调函数
    ckpt_config = CheckpointConfig(save_checkpoint_steps=1875, keep_checkpoint_max=10)
    ckpt_cb = ModelCheckpoint(prefix="checkpoint_lenet", config=ckpt_config)
    # 开始训练
    model.train(epoch=1, train_dataset=mnist_ds, callbacks=[ckpt_cb, LossMonitor()], dataset_sink_mode=False)

以上代码使用MindSpore实现了一个简单的LeNet-5模型，用于识别手写数字。代码中使用了MindSpore提供的数据集加载、数据增强、模型定义、损失函数、优化器、回调函数等功能，同时对代码进行了解释，可供初学者参考学习。