- 准备MindSpore环境
!pip uninstall mindspore -y
!pip install -i https://pypi.mirrors.ustc.edu.cn/simple mindspore==2.2.14
import mindspore
from mindspore import nn
from mindspore.dataset import vision, transforms
from mindspore.dataset import MnistDataset
- 数据集处理
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。
train_dataset = MnistDataset('MNIST_Data/train')
test_dataset = MnistDataset('MNIST_Data/test')
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
- 网络模型构建
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
loss_fn = nn.CrossEntropyLoss()
optimizer = nn.SGD(model.trainable_params(), 1e-2)
def forward_fn(data, label):
logits = model(data)
loss = loss_fn(logits, label)
return loss, logits
grad_fn = mindspore.value_and_grad(forward_fn, None, optimizer.parameters, has_aux=True)
def train_step(data, label):
(loss, _), grads = grad_fn(data, label)
optimizer(grads)
return loss
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)
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
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)
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