模型训练
模型训练一般分为四个步骤：
- 构建数据集。
- 定义神经网络模型。
- 定义超参、损失函数及优化器。
- 输入数据集进行训练与评估。
现在我们有了数据集和模型后，可以进行模型的训练与评估。
ps：这里的训练和Stable Diffusion中的训练是一样的吗？欢迎评论。
超参
超参（Hyperparameters）是可以调整的参数，可以控制模型训练优化的过程，不同的超参数值可能会影响模型训练和收敛速度。
损失函数
损失函数（loss function）用于评估模型的预测值（logits）和目标值（targets）之间的误差
优化器
模型优化（Optimization）是在每个训练步骤中调整模型参数以减少模型误差的过程。
训练与评估
设置了超参、损失函数和优化器后，我们就可以循环输入数据来训练模型。一次数据集的完整迭代循环称为一轮（epoch）。每轮执行训练时包括两个步骤：
- 训练：迭代训练数据集，并尝试收敛到最佳参数。
- 验证/测试：迭代测试数据集，以检查模型性能是否提升。
import mindspore
from mindspore import nn
from mindspore.dataset import vision, transforms
from mindspore.dataset import MnistDataset
import time
# Download data from open datasets
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)
epochs = 3
batch_size = 64
learning_rate = 1e-2
def datapipe(path, 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 = MnistDataset(path)
dataset = dataset.map(image_transforms, 'image')
dataset = dataset.map(label_transform, 'label')
dataset = dataset.batch(batch_size)
return dataset
train_dataset = datapipe('MNIST_Data/train', batch_size=64)
test_dataset = datapipe('MNIST_Data/test', batch_size=64)
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()
# Define forward function
def forward_fn(data, label):
logits = model(data)
loss = loss_fn(logits, label)
return loss, logits
loss_fn = nn.CrossEntropyLoss()
optimizer = nn.SGD(model.trainable_params(), learning_rate=learning_rate)
# Get gradient function
grad_fn = mindspore.value_and_grad(forward_fn, None, optimizer.parameters, has_aux=True)
# Define function of one-step training
def train_step(data, label):
(loss, _), grads = grad_fn(data, label)
optimizer(grads)
return loss
def train_loop(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}]")
def test_loop(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")
loss_fn = nn.CrossEntropyLoss()
optimizer = nn.SGD(model.trainable_params(), learning_rate=learning_rate)
for t in range(epochs):
print(f"Epoch {t+1}\n-------------------------------")
train_loop(model, train_dataset)
test_loop(model, test_dataset, loss_fn)
print("Done!")
print(time.strftime("%Y-%m-%d %H:%M:%S", time.localtime()),'skywp')
