内容介绍：通过MindSpore的API来快速实现一个简单的深度学习模型。
具体内容：
1. 导包
import mindspore
from mindspore import nn
from mindspore.dataset import vision, transforms
from mindspore.dataset import MnistDataset2. 处理数据
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)3. 获取数据对象
train_dataset = MnistDataset('MNIST_Data/train')
test_dataset = MnistDataset('MNIST_Data/test')4. 数据处理
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
train_dataset = datapipe(train_dataset, 64)
test_dataset = datapipe(test_dataset, 64)5. 使用 create_dict_iterator或create_dict_iterator对数据集进行迭代访问
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}")breakfor 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}")break6. 网络构建
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 logitsmodel = Network()
print(model)7. 模型训练
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}]")8. 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")9. 训练过程
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!")通过训练可以看出loss不断降低，Accuracy不断升高，可以通过调参到达更好的效果。
10. 保存模型
mindspore.save_checkpoint(model, "model.ckpt")
print("Saved Model to model.ckpt")11. 加载模型
model = Network()
param_dict = mindspore.load_checkpoint("model.ckpt")
param_not_load, _ = mindspore.load_param_into_net(model, param_dict)
print(param_not_load)
