使用MindSpore框架实现一个卷积神经网络，用于手写字体识别的任务，代码对数据形式为这样的进行处理：resize = (32, 32) rescale = 1 / (255 * 0.3081)

好的，以下是使用MindSpore框架实现手写字体识别的卷积神经网络的代码，同时对数据进行resize和rescale处理：

import mindspore.nn as nn
import mindspore.ops as ops
from mindspore import Tensor
import mindspore.dataset as ds
import mindspore.dataset.transforms.c_transforms as C

class Net(nn.Cell):
    def __init__(self):
        super(Net, self).__init__()
        self.conv1 = nn.Conv2d(in_channels=1, out_channels=32, kernel_size=5, stride=1, padding=0)
        self.relu1 = nn.ReLU()
        self.pool1 = nn.MaxPool2d(kernel_size=2, stride=2)
        self.conv2 = nn.Conv2d(in_channels=32, out_channels=64, kernel_size=5, stride=1, padding=0)
        self.relu2 = nn.ReLU()
        self.pool2 = nn.MaxPool2d(kernel_size=2, stride=2)
        self.flatten = nn.Flatten()
        self.fc1 = nn.Dense(in_channels=1024, out_channels=256)
        self.relu3 = nn.ReLU()
        self.fc2 = nn.Dense(in_channels=256, out_channels=10)
        
    def construct(self, x):
        x = self.conv1(x)
        x = self.relu1(x)
        x = self.pool1(x)
        x = self.conv2(x)
        x = self.relu2(x)
        x = self.pool2(x)
        x = self.flatten(x)
        x = self.fc1(x)
        x = self.relu3(x)
        x = self.fc2(x)
        return x

resize = (32, 32)
rescale = 1 / (255 * 0.3081)
DATA_DIR = "./data"

train_ds = ds.MnistDataset(DATA_DIR, num_samples=60000, shuffle=True)
train_ds = train_ds.map(input_columns="image", operations=C.Resize(resize))
train_ds = train_ds.map(input_columns="image", operations=C.Rescale(rescale))
train_ds = train_ds.map(input_columns="image", operations=C.HWC2CHW())
train_ds = train_ds.map(input_columns="label", operations=C.TypeCast(mstype.int32))
train_ds = train_ds.batch(BATCH_SIZE, drop_remainder=True)

test_ds = ds.MnistDataset(DATA_DIR, num_samples=10000, shuffle=False)
test_ds = test_ds.map(input_columns="image", operations=C.Resize(resize))
test_ds = test_ds.map(input_columns="image", operations=C.Rescale(rescale))
test_ds = test_ds.map(input_columns="image", operations=C.HWC2CHW())
test_ds = test_ds.map(input_columns="label", operations=C.TypeCast(mstype.int32))
test_ds = test_ds.batch(BATCH_SIZE, drop_remainder=True)

net = Net()
criterion = SoftmaxCrossEntropyWithLogits(sparse=True, reduction='mean')
optimizer = nn.Adam(params=net.trainable_params())

model = Model(net, loss_fn=criterion, optimizer=optimizer, metrics={"acc"})
model.train(EPOCH_SIZE, train_ds, callbacks=[LossMonitor()], dataset_sink_mode=False)

在这里，我们对MNIST数据集进行了resize操作，将原始的(28, 28)大小的图片调整为(32, 32)大小的图片。然后，我们使用了Rescale操作，将像素值缩放到[0, 1]之间，并且乘以一个系数1 / (255 * 0.3081)。最后，我们将数据格式从HWC转换为CHW，即将图片的通道数放在第一个维度上。这些操作可以通过MindSpore中的C.Resize、C.Rescale和C.HWC2CHW等变换函数来实现。