mindspore识别手写体数字

以下是使用MindSpore框架进行手写数字识别的步骤：

1.准备数据集

首先，需要准备手写数字的数据集。可以使用MNIST数据集，该数据集包含60,000个训练图像和10,000个测试图像，每个图像都是28x28像素的灰度图像。

2.安装MindSpore及其套件

安装MindSpore框架及其套件，可以使用pip命令进行安装：

pip install mindspore

3.程序撰写

接下来，需要编写MindSpore程序来训练和测试模型。以下是一个简单的程序示例：

import mindspore.dataset as ds
import mindspore.dataset.transforms.c_transforms as C
import mindspore.dataset.vision.c_transforms as CV
import mindspore.nn as nn
import mindspore.ops.operations as P
import mindspore.dataset.transforms.py_transforms as py_transforms
import mindspore.common.dtype as mstype
from mindspore import Model, Tensor
from mindspore.train.callback import LossMonitor, ModelCheckpoint, CheckpointConfig, TimeMonitor
from mindspore.train.serialization import load_checkpoint, load_param_into_net
from mindspore.train.callback import Callback
from mindspore.nn.metrics import Accuracy
import os
import numpy as np

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

    def construct(self, x):
        x = self.max_pool2d(self.relu(self.conv1(x)))
        x = self.max_pool2d(self.relu(self.conv2(x)))
        x = x.view(x.shape[0], -1)
        x = self.relu(self.fc1(x))
        x = self.relu(self.fc2(x))
        x = self.fc3(x)
        return x

# 定义数据集
def create_dataset(data_path, batch_size=32, repeat_size=1, num_parallel_workers=1):
    # 定义数据集
    mnist_ds = ds.MnistDataset(data_path)

    # 定义数据增强
    trans = []
    trans.append(CV.Resize((32, 32)))
    trans.append(CV.Rescale(1.0 / 255.0, 0.0))
    trans.append(CV.Normalize([0.1307], [0.3081]))
    trans.append(C.HWC2CHW())

    # 应用数据增强
    mnist_ds = mnist_ds.map(input_columns="image", num_parallel_workers=num_parallel_workers, operations=py_transforms.Compose(trans))

    # 对数据集进行shuffle和batch处理
    mnist_ds = mnist_ds.shuffle(buffer_size=10000)
    mnist_ds = mnist_ds.batch(batch_size, drop_remainder=True)
    mnist_ds = mnist_ds.repeat(repeat_size)

    return mnist_ds

# 定义训练函数
def train_net(network, train_dataset, eval_dataset, epochs=1, lr=0.01, momentum=0.9, ckpt_path=None):
    # 定义损失函数和优化器
    loss = nn.SoftmaxCrossEntropyWithLogits(sparse=True, reduction='mean')
    opt = nn.Momentum(network.trainable_params(), lr, momentum)

    # 定义模型
    model = Model(network, loss_fn=loss, optimizer=opt, metrics={"Accuracy": Accuracy()})

    # 定义回调函数
    callbacks = [LossMonitor(), TimeMonitor(data_size=train_dataset.get_dataset_size())]
    if ckpt_path:
        config_ck = CheckpointConfig(save_checkpoint_steps=train_dataset.get_dataset_size(), keep_checkpoint_max=10)
        ckpt_cb = ModelCheckpoint(prefix="checkpoint_lenet", directory=ckpt_path, config=config_ck)
        callbacks.append(ckpt_cb)

    # 训练模型
    model.train(epochs, train_dataset, callbacks=callbacks, dataset_sink_mode=False)

    # 评估模型
    metrics = model.eval(eval_dataset)
    print("metrics: ", metrics)

    # 保存模型
    if ckpt_path:
        model.save_checkpoint(os.path.join(ckpt_path, "lenet.ckpt"))

# 加载数据集
data_path = "/path/to/mnist_dataset"
train_dataset = create_dataset(os.path.join(data_path, "train"), batch_size=32, repeat_size=1)
eval_dataset = create_dataset(os.path.join(data_path, "test"), batch_size=32, repeat_size=1)

# 定义网络
network = LeNet5()

# 训练模型
train_net(network, train_dataset, eval_dataset, epochs=10, lr=0.01, momentum=0.9, ckpt_path="/path/to/checkpoints")

4.总结

以上就是使用MindSpore框架进行手写数字识别的步骤。通过准备数据集、安装MindSpore及其套件、编写MindSpore程序来训练和测试模型，可以实现手写数字识别的任务。