使用mindspore构建CNN

在使用MindSpore构建CNN之前，需要安装MindSpore并导入必要的库。以下是构建CNN的基本步骤：

导入MindSpore库和其他必要的库：

import mindspore.nn as nn
import mindspore.ops as ops
import mindspore.common.dtype as mstype
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 PY
import mindspore.dataset.vision.py_transforms as PYCV
import mindspore.dataset.transforms.vision.c_transforms as V_C

定义CNN模型：

class CNN(nn.Cell):
    def __init__(self):
        super(CNN, self).__init__()
        self.conv1 = nn.Conv2d(3, 32, kernel_size=3, stride=1, padding=1, has_bias=True)
        self.relu = nn.ReLU()
        self.max_pool2d = nn.MaxPool2d(kernel_size=2, stride=2)
        self.conv2 = nn.Conv2d(32, 64, kernel_size=3, stride=1, padding=1, has_bias=True)
        self.flatten = nn.Flatten()
        self.dense1 = nn.Dense(64 * 8 * 8, 1024)
        self.dropout = nn.Dropout(0.2)
        self.dense2 = nn.Dense(1024, 10)

    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 = self.flatten(x)
        x = self.dense1(x)
        x = self.dropout(x)
        x = self.dense2(x)
        return x

这是一个简单的CNN模型，包含两个卷积层、两个全连接层和一个dropout层。具体来说，该模型先通过一个3x3的卷积层，得到32个特征图，然后经过ReLU激活，再通过2x2的最大池化层进行下采样。接下来，再通过一个3x3的卷积层，得到64个特征图，然后再通过ReLU激活和2x2的最大池化层进行下采样。最后，将得到的特征图展平成一维向量，并通过两个全连接层和一个dropout层，最终输出10个类别的预测结果。

定义损失函数和优化器：

net = CNN()
criterion = nn.SoftmaxCrossEntropyWithLogits(sparse=True, reduction='mean')
optimizer = nn.Momentum(net.trainable_params(), learning_rate=0.001, momentum=0.9)

这里选择交叉熵损失函数和动量优化器，其中学习率为0.001，动量为0.9。

准备数据：

train_dataset = ds.Cifar10Dataset(dataset_dir='./data', num_parallel_workers=4, shuffle=True)
train_dataset = train_dataset.map(input_columns="image", operations=V_C.RandomCrop((28, 28)))
train_dataset = train_dataset.map(input_columns="image", operations=V_C.Resize((32, 32)))
train_dataset = train_dataset.map(input_columns="image", operations=V_C.RandomHorizontalFlip())
train_dataset = train_dataset.map(input_columns="image", operations=CV.Rescale(1.0 / 255.0, 0.0))
train_dataset = train_dataset.map(input_columns="image", operations=CV.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]))
train_dataset = train_dataset.map(input_columns="label", operations=C.TypeCast(mstype.int32))
train_dataset = train_dataset.batch(batch_size=32, drop_remainder=True)

这里使用Cifar10数据集，对数据进行了一些预处理，包括随机裁剪、调整大小、随机水平翻转、归一化等。

训练模型：

for epoch in range(num_epochs):
    net.set_train()
    for i, data in enumerate(train_dataset):
        images, labels = data
        loss = criterion(net(images), labels)
        loss.backward()
        optimizer.step()
        optimizer.clear_grad()
        
        if i % 100 == 0:
            print("Epoch [{}/{}], Step [{}/{}], Loss: {:.4f}".format(epoch+1, num_epochs, i+1, len(train_dataset), loss.asnumpy()))

在训练循环中，首先将模型设置为训练模式，然后遍历数据集，计算损失并进行反向传播和梯度更新。每经过100个批次，就输出一次训练状态。

评估模型：

net.set_eval()
with ops.context_mode('auto_parallel'):
    correct = 0
    total = 0
    for data in test_dataset.create_tuple_iterator():
        images, labels = data
        outputs = net(images)
        predicted = ops.Argmax(axis=1)(outputs)
        total += labels.shape[0]
        correct += (predicted == labels).sum()
    print('Accuracy of the network on the test images: %d %%' % (100 * correct / total))

这里将模型设置为评估模式，然后遍历测试集，计算模型在测试集上的准确率。

完整代码如下：

import mindspore.nn as nn
import mindspore.ops as ops
import mindspore.common.dtype as mstype
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 PY
import mindspore.dataset.vision.py_transforms as PYCV
import mindspore.dataset.transforms.vision.c_transforms as V_C

class CNN(nn.Cell):
    def __init__(self):
        super(CNN, self).__init__()
        self.conv1 = nn.Conv2d(3, 32, kernel_size=3, stride=1, padding=1, has_bias=True)
        self.relu = nn.ReLU()
        self.max_pool2d = nn.MaxPool2d(kernel_size=2, stride=2)
        self.conv2 = nn.Conv2d(32, 64, kernel_size=3, stride=1, padding=1, has_bias=True)
        self.flatten = nn.Flatten()
        self.dense1 = nn.Dense(64 * 8 * 8, 1024)
        self.dropout = nn.Dropout(0.2)
        self.dense2 = nn.Dense(1024, 10)

    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 = self.flatten(x)
        x = self.dense1(x)
        x = self.dropout(x)
        x = self.dense2(x)
        return x

net = CNN()
criterion = nn.SoftmaxCrossEntropyWithLogits(sparse=True, reduction='mean')
optimizer = nn.Momentum(net.trainable_params(), learning_rate=0.001, momentum=0.9)

train_dataset = ds.Cifar10Dataset(dataset_dir='./data', num_parallel_workers=4, shuffle=True)
train_dataset = train_dataset.map(input_columns="image", operations=V_C.RandomCrop((28, 28)))
train_dataset = train_dataset.map(input_columns="image", operations=V_C.Resize((32, 32)))
train_dataset = train_dataset.map(input_columns="image", operations=V_C.RandomHorizontalFlip())
train_dataset = train_dataset.map(input_columns="image", operations=CV.Rescale(1.0 / 255.0, 0.0))
train_dataset = train_dataset.map(input_columns="image", operations=CV.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]))
train_dataset = train_dataset.map(input_columns="label", operations=C.TypeCast(mstype.int32))
train_dataset = train_dataset.batch(batch_size=32, drop_remainder=True)

num_epochs = 10

for epoch in range(num_epochs):
    net.set_train()
    for i, data in enumerate(train_dataset):
        images, labels = data
        loss = criterion(net(images), labels)
        loss.backward()
        optimizer.step()
        optimizer.clear_grad()
        
        if i % 100 == 0:
            print("Epoch [{}/{}], Step [{}/{}], Loss: {:.4f}".format(epoch+1, num_epochs, i+1, len(train_dataset), loss.asnumpy()))

test_dataset = ds.Cifar10Dataset(dataset_dir='./data', num_parallel_workers=4, shuffle=True)
test_dataset = test_dataset.map(input_columns="image", operations=CV.Rescale(1.0 / 255.0, 0.0))
test_dataset = test_dataset.map(input_columns="image", operations=CV.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]))
test_dataset = test_dataset.map(input_columns="label", operations=C.TypeCast(mstype.int32))
test_dataset = test_dataset.batch(batch_size=32, drop_remainder=True)

net.set_eval()
with ops.context_mode('auto_parallel'):
    correct = 0
    total = 0
    for data in test_dataset.create_tuple_iterator():
        images, labels = data
        outputs = net(images)
        predicted = ops.Argmax(axis=1)(outputs)
        total += labels.shape[0]
        correct += (predicted == labels).sum()
    print('Accuracy of the network on the test images: %d %%' % (100 * correct / total))