用mindspore搭建lenet5网络模型

好的，以下是使用MindSpore搭建LeNet-5网络模型的步骤：

1. 导入需要的库和模块：

import mindspore.nn as nn
import mindspore.ops.operations as P
from mindspore.common.initializer import Normal

2. 定义LeNet-5网络模型：

class LeNet5(nn.Cell):
    def __init__(self, num_class=10, num_channel=1):
        super(LeNet5, self).__init__()
        self.conv1 = nn.Conv2d(num_channel, 6, 5, pad_mode='valid')
        self.conv2 = nn.Conv2d(6, 16, 5, pad_mode='valid')
        self.fc1 = nn.Dense(16 * 5 * 5, 120, weight_init=Normal(0.02))
        self.fc2 = nn.Dense(120, 84, weight_init=Normal(0.02))
        self.fc3 = nn.Dense(84, num_class, 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.max_pool2d(self.relu(self.conv1(x)))
        x = self.max_pool2d(self.relu(self.conv2(x)))
        x = x.reshape(x.shape[0], -1)
        x = self.relu(self.fc1(x))
        x = self.relu(self.fc2(x))
        x = self.fc3(x)
        return x

3. 实例化网络模型：

net = LeNet5(num_class=10, num_channel=1)

4. 定义损失函数和优化器：

criterion = nn.SoftmaxCrossEntropyWithLogits(sparse=True, reduction='mean')
optimizer = nn.Momentum(net.trainable_params(), learning_rate=0.01, momentum=0.9)

5. 训练模型：

from mindspore import Tensor
from mindspore import context

context.set_context(mode=context.GRAPH_MODE, device_target="CPU")

# 定义数据
train_dataset = # TODO: 定义训练数据集
test_dataset = # TODO: 定义测试数据集

# 定义超参数
epoch = 10
batch_size = 32

# 训练模型
net.set_train()
for i in range(epoch):
    for data, label in train_dataset.create_dict_iterator():
        data = Tensor(data)
        label = Tensor(label)
        loss = criterion(net(data), label)
        loss.backward()
        optimizer.step()
        optimizer.clear_grad()

    # 测试模型
    net.set_eval()
    total_correct = 0
    total_num = 0
    for data, label in test_dataset.create_dict_iterator():
        data = Tensor(data)
        label = Tensor(label)
        output = net(data)
        total_num += label.shape[0]
        total_correct += (output.argmax(axis=1) == label).sum().asnumpy()
    acc = total_correct / total_num
    print(f"Epoch {i}: accuracy = {acc}")
    net.set_train()

以上就是使用MindSpore搭建LeNet-5网络模型的步骤。需要注意的是，这里只是一个简单的示例，实际应用时还需要根据具体的任务进行调整。