在训练网络模型的过程中，实际上我们希望保存中间和最后的结果，用于微调（fine-tune）和后续的模型推理与部署，本章节我们将介绍如何保存与加载模型。

import numpy as np
import mindspore
from mindspore import nn
from mindspore import Tensor
import time

def network():
    model = nn.SequentialCell(
                nn.Flatten(),
                nn.Dense(28*28, 512),
                nn.ReLU(),
                nn.Dense(512, 512),
                nn.ReLU(),
                nn.Dense(512, 10))
    return model

model = network()
mindspore.save_checkpoint(model, "model.ckpt")

model = network()
param_dict = mindspore.load_checkpoint("model.ckpt")
param_not_load, _ = mindspore.load_param_into_net(model, param_dict)
print(param_not_load)

model = network()
inputs = Tensor(np.ones([1, 1, 28, 28]).astype(np.float32))
mindspore.export(model, inputs, file_name="model", file_format="MINDIR")

mindspore.set_context(mode=mindspore.GRAPH_MODE)

graph = mindspore.load("model.mindir")
model = nn.GraphCell(graph)
outputs = model(inputs)
print(outputs.shape)
import time

print(time.strftime("%Y-%m-%d %H:%M:%S", time.localtime()),'skywp')

该章节比较简单，相当于Java中查表后写入到中间表，或直接创建视图的操作。