《昇思25天学习打卡营第02天|快速入门》

快速入门

准备MindSpore环境

安装环境

python 复制代码

   !pip uninstall mindspore -y
   !pip install -i https://pypi.mirrors.ustc.edu.cn/simple mindspore==2.2.14

环境导入

python 复制代码

import mindspore
from mindspore import nn #神经网络模块
from mindspore.dataset import vision, transforms #图像模块(vision)&数据增强+预处理(transforms)
from mindspore.dataset import MnistDataset #数据集模块

数据集处理

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#数据集为开放数据集，下载完成后解压
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)
MNIST数据集目录结构如下：
	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)
	数据下载完成后，获得数据集对象。

#获取训练&测试数据集
train_dataset = MnistDataset('MNIST_Data/train')
test_dataset = MnistDataset('MNIST_Data/test')

#数据集预处理
def datapipe(dataset, batch_size):
	#定义图像转换操作
    image_transforms = [
    	#图片缩放至范围(0，1)
        vision.Rescale(1.0 / 255.0, 0), 
        #将图像数据的分布调整到均值为0，标准差为1的分布
        vision.Normalize(mean=(0.1307,), std=(0.3081,)), 
      	#HWC(Height, Width, Channel) -> (Channel, Height, Width)
        vision.HWC2CHW()
    ]
    #将标签的数据类型转化成int32，便于模型进行处理，目的是确保计算过程中的精度&性能
    label_transform = transforms.TypeCast(mindspore.int32)
	
	#对图像&标签进行数据转化
    dataset = dataset.map(image_transforms, 'image')
    dataset = dataset.map(label_transform, 'label')
    
    #分batch，便于后续训练
    dataset = dataset.batch(batch_size)
    return dataset

网络模型构建

python 复制代码

#定义模型
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 logits

model = Network()

模型训练

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#定义损失函数&优化器
loss_fn = nn.CrossEntropyLoss()
optimizer = nn.SGD(model.trainable_params(), 1e-2)

#正向计算
def forward_fn(data, label):
   logits = model(data)
   loss = loss_fn(logits, label)
   return loss, logits

#反向传播
grad_fn = mindspore.value_and_grad(forward_fn, None, optimizer.parameters, has_aux=True)

#单步训练
def train_step(data, label):
   (loss, _), grads = grad_fn(data, label)
   optimizer(grads)
   return loss
   
#训练过程
def 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(), batch
           print(f"loss: {loss:>7f}  [{current:>3d}/{size:>3d}]")

测试函数(模型评估)

python 复制代码

def test(model, dataset, loss_fn):
    num_batches = dataset.get_dataset_size()
    model.set_train(False)
    total, test_loss, correct = 0, 0, 0
    for 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_batches
    correct /= total
    print(f"Test: \n Accuracy: {(100*correct):>0.1f}%, Avg loss: {test_loss:>8f} \n")

模型保存&加载&推理

python 复制代码

#保存训练完后模型
mindspore.save_checkpoint(model, "model.ckpt")

#实例模型
model = Network()
#加载模型
param_dict = mindspore.load_checkpoint("model.ckpt")
param_not_load, _ = mindspore.load_param_into_net(model, param_dict)
#推理
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