一、环境准备
安装minspore模块
!pip uninstall mindspore -y
!pip install -i https://pypi.mirrors.ustc.edu.cn/simple mindspore==2.3.0rc1导入minsporei
mport mindspore
from mindspore import nn
from mindspore.dataset import vision, transforms
from mindspore.dataset import MnistDataset二、学习内容
用MindSpore API实现一个简单的深度学习模型。
1.使用download下载数据
from download import download
url = ("https://mindspore-website.obs.cn-north-4.myhuaweicloud.com/""notebook/datasets/MNIST_Data.zip"
)
path = download(url, "./", kind="zip", replace=True)输出：
Downloading data from https://mindspore-website.obs.cn-north-4.myhuaweicloud.com/notebook/datasets/MNIST_Data.zip (10.3 MB)file_sizes: 100%|███████████████████████████| 10.8M/10.8M [00:00<00:00, 150MB/s]
Extracting zip file...
Successfully downloaded / unzipped to ./下载的MNIST数据集目录结构：
MNIST_Data
└── train
├── train-images-idx3-ubyte (60000个训练图片)
├── train-labels-idx1-ubyte (60000个训练标签)
└── test
├── t10k-images-idx3-ubyte (10000个测试图片)
├── t10k-labels-idx1-ubyte (10000个测试标签)
2.加载数据
train_dataset = MnistDataset("MNIST_Data/train")
test_dataset = MnistDataset("MNIST_Data/test")打印训练数据的列名
print(train_dataset.get_col_names())输出：
['image', 'label']3.数据预处理
定义数据处理流水线
用到的模块：数据集Dataset、数据变换Transforms
用到的操作：map、batch
def datapipe(dataset, batch_size):image_transforms = [vision.Rescale(1.0 / 255.0, 0),vision.Normalize(mean=(0.1307,), std=(0.3081,)),vision.HWC2CHW(),]label_transform = transforms.TypeCast(mindspore.int32)dataset = dataset.map(image_transforms, "image")dataset = dataset.map(label_transform, "label")dataset = dataset.batch(batch_size)return dataset
# Map vision transforms and batch dataset预处理训练数据和测试数据
train_dataset = datapipe(train_dataset, 64)
test_dataset = datapipe(test_dataset, 64)迭代查看数据和标签的shape和datatype
for image, label in test_dataset.create_tuple_iterator():print(f"Shape of image [N, C, H, W]: {image.shape} {image.dtype}")print(f"Shape of label: {label.shape} {label.dtype}")
Break输出：
Shape of image [N, C, H, W]: (64, 1, 28, 28) Float32
Shape of label: (64,) Int32for data in test_dataset.create_dict_iterator():print(f"Shape of image [N, C, H, W]: {data['image'].shape} {data['image'].dtype}")print(f"Shape of label: {data['label'].shape} {data['label'].dtype}")
Break输出：
Shape of image [N, C, H, W]: (64, 1, 28, 28) Float32
Shape of label: (64,) Int324.构建神经网络
mindspore.nn类 所有神经网络的基类。
nn.Cell类 自定义网络时可以继承
__init__ 包含所有网络层的定义
Construct 数据（Tensor）的变换过程
# Define model
class Network(nn.Cell):def __init__(self):super().__init__()self.flatten = nn.Flatten()self.dense_relu_sequential = nn.SequentialCell(nn.Dense(28 * 28, 512),nn.ReLU(),nn.Dense(512, 512),nn.ReLU(),nn.Dense(512, 10),)def construct(self, x):x = self.flatten(x)logits = self.dense_relu_sequential(x)return logitsmodel = Network()
print(model)输出：
Network<(flatten): Flatten<>(dense_relu_sequential): SequentialCell<(0): Dense<input_channels=784, output_channels=512, has_bias=True>(1): ReLU<>(2): Dense<input_channels=512, output_channels=512, has_bias=True>(3): ReLU<>(4): Dense<input_channels=512, output_channels=10, has_bias=True>>>5.模型训练
训练三步：
（1）正向计算
模型预测结果（logits），与正确标签（label）求预测损失（loss）。
（2）反向传播
自动微分求模型参数（parameters）对于loss的梯度（gradients）。
（3）参数优化
将梯度更新到参数上。
# Instantiate loss function and optimizer
loss_fn = nn.CrossEntropyLoss()
optimizer = nn.SGD(model.trainable_params(), 1e-2)# 1. Define forward function
def forward_fn(data, label):logits = model(data)loss = loss_fn(logits, label)return loss, logits# 2. Get gradient function
grad_fn = mindspore.value_and_grad(forward_fn, None, optimizer.parameters, has_aux=True)# 3. Define function of one-step training
def train_step(data, label):(loss, _), grads = grad_fn(data, label)optimizer(grads)return lossdef train(model, dataset):size = dataset.get_dataset_size()model.set_train()for batch, (data, label) in enumerate(dataset.create_tuple_iterator()):loss = train_step(data, label)if batch % 100 == 0:loss, current = loss.asnumpy(), batchprint(f"loss: {loss:>7f} [{current:>3d}/{size:>3d}]")6.测试评估
定义测试函数
def test(model, dataset, loss_fn):num_batches = dataset.get_dataset_size()model.set_train(False)total, test_loss, correct = 0, 0, 0for data, label in dataset.create_tuple_iterator():pred = model(data)total += len(data)test_loss += loss_fn(pred, label).asnumpy()correct += (pred.argmax(1) == label).asnumpy().sum()test_loss /= num_batchescorrect /= totalprint(f"Test: \n Accuracy: {(100*correct):>0.1f}%, Avg loss: {test_loss:>8f} \n")迭代测试，这里迭代3次
epochs = 3
for t in range(epochs):print(f"Epoch {t+1}\n-------------------------------")train(model, train_dataset)test(model, test_dataset, loss_fn)
print("Done!")输出：
Epoch 1
-------------------------------
loss: 2.307619 [ 0/938]
loss: 1.641935 [100/938]
loss: 0.906887 [200/938]
loss: 0.559807 [300/938]
loss: 0.402098 [400/938]
loss: 0.356145 [500/938]
loss: 0.349220 [600/938]
loss: 0.398980 [700/938]
loss: 0.428066 [800/938]
loss: 0.239198 [900/938]
Test: Accuracy: 91.0%, Avg loss: 0.313863 Epoch 2
-------------------------------
loss: 0.383306 [ 0/938]
loss: 0.262217 [100/938]
loss: 0.261673 [200/938]
loss: 0.659807 [300/938]
loss: 0.188391 [400/938]
loss: 0.288512 [500/938]
loss: 0.188541 [600/938]
loss: 0.215372 [700/938]
loss: 0.321593 [800/938]
loss: 0.179319 [900/938]
Test: Accuracy: 92.9%, Avg loss: 0.246736 Epoch 3
-------------------------------
loss: 0.374724 [ 0/938]
loss: 0.198583 [100/938]
loss: 0.129807 [200/938]
loss: 0.144729 [300/938]
loss: 0.140285 [400/938]
loss: 0.319090 [500/938]
loss: 0.408056 [600/938]
loss: 0.278954 [700/938]
loss: 0.206172 [800/938]
loss: 0.107220 [900/938]
Test: Accuracy: 94.1%, Avg loss: 0.204920 Done!7.保存导出模型
# Save checkpoint
mindspore.save_checkpoint(model, "model.ckpt")
print("Saved Model to model.ckpt")输出：
Saved Model to model.ckpt8.加载模型
重构模型
加载模型参数
# Instantiate a random initialized model
model = Network()
# Load checkpoint and load parameter to model
param_dict = mindspore.load_checkpoint("model.ckpt")
param_not_load, _ = mindspore.load_param_into_net(model, param_dict)
print(param_not_load)输出：
[]param_not_load是未加载的参数，为空表示全部参数都已加载
9.应用模型
model.set_train(False)
for data, label in test_dataset:pred = model(data)predicted = pred.argmax(1)print(f'Predicted: "{predicted[:10]}", Actual: "{label[:10]}"')break输出：
Predicted: "[9 2 5 1 1 9 1 3 0 8]", Actual: "[9 2 5 1 1 9 1 3 0 8]"
