【深度学习PyTorch入门】6.Optimizing Model Parameters 优化模型参数
Optimizing Model Parameters 优化模型参数
文章目录
- Optimizing Model Parameters 优化模型参数
- Optimizing Model Parameters 优化模型参数
- 前置代码
- Hyperparameters 超参数
- Optimization Loop 优化循环
-
- Loss Function 损失函数
- Optimizer 优化器
- Full Implementation 全面实施
- Further Reading 进一步阅读
- 参考文献
- Github
Optimizing Model Parameters 优化模型参数
现在我们有了模型和数据，是时候通过优化数据上的参数来训练、验证和测试我们的模型了。训练模型是一个迭代过程；在每次迭代中，模型都会对输出进行猜测，计算其猜测中的误差（损失），收集相对于其参数的导数的误差（如我们在上一节中看到的），并使用梯度下降优化这些参数。有关此过程的更详细演练，请观看3Blue1Brown 的反向传播有关视频。
前置代码
我们加载前面有关数据集和数据加载器 以及构建模型的代码。
import torch
from torch import nn
from torch.utils.data import DataLoader
from torchvision import datasets
from torchvision.transforms import ToTensor
training_data = datasets.FashionMNIST(
root="data",
train=True,
download=True,
transform=ToTensor()
)
test_data = datasets.FashionMNIST(
root="data",
train=False,
download=True,
transform=ToTensor()
)
train_dataloader = DataLoader(training_data, batch_size=64)
test_dataloader = DataLoader(test_data, batch_size=64)
class NeuralNetwork(nn.Module):
def __init__(self):
super().__init__()
self.flatten = nn.Flatten()
self.linear_relu_stack = nn.Sequential(
nn.Linear(28*28, 512),
nn.ReLU(),
nn.Linear(512, 512),
nn.ReLU(),
nn.Linear(512, 10),
)
def forward(self, x):
x = self.flatten(x)
logits = self.linear_relu_stack(x)
return logits
model = NeuralNetwork()
Out:
Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/train-images-idx3-ubyte.gz
Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/train-images-idx3-ubyte.gz to data/FashionMNIST/raw/train-images-idx3-ubyte.gz
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100%|##########| 26421880/26421880 [00:01<00:00, 18174652.40it/s]
Extracting data/FashionMNIST/raw/train-images-idx3-ubyte.gz to data/FashionMNIST/raw
Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/train-labels-idx1-ubyte.gz
Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/train-labels-idx1-ubyte.gz to data/FashionMNIST/raw/train-labels-idx1-ubyte.gz
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Extracting data/FashionMNIST/raw/train-labels-idx1-ubyte.gz to data/FashionMNIST/raw
Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/t10k-images-idx3-ubyte.gz
Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/t10k-images-idx3-ubyte.gz to data/FashionMNIST/raw/t10k-images-idx3-ubyte.gz
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100%|##########| 4422102/4422102 [00:00<00:00, 6093019.93it/s]
Extracting data/FashionMNIST/raw/t10k-images-idx3-ubyte.gz to data/FashionMNIST/raw
Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/t10k-labels-idx1-ubyte.gz
Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/t10k-labels-idx1-ubyte.gz to data/FashionMNIST/raw/t10k-labels-idx1-ubyte.gz
0%| | 0/5148 [00:00<?, ?it/s]
100%|##########| 5148/5148 [00:00<00:00, 38905003.59it/s]
Extracting data/FashionMNIST/raw/t10k-labels-idx1-ubyte.gz to data/FashionMNIST/raw
Hyperparameters 超参数
超参数是可调整的参数，可让您控制模型优化过程。不同的超参数值会影响模型训练和收敛速度（阅读有关超参数调整的更多信息）
我们定义以下训练超参数：
- Number of Epochs - 迭代数据集的次数
- Batch Size - 参数更新之前，通过网络传播的数据样本数量
- Learning Rate- 每个batch/epoch更新模型参数的量。较小的值会导致学习速度较慢，而较大的值可能会导致训练期间出现不可预测的行为。
learning_rate = 1e-3
batch_size = 64
epochs = 5
Optimization Loop 优化循环
一旦我们设置了超参数，我们就可以使用优化循环来训练和优化我们的模型。优化循环的每次迭代称为一个epoch。
每个 epoch由两个主要部分组成：
- The Train Loop- 迭代训练数据集并尝试收敛到最佳参数。
- **The Validation/Test Loop **- 迭代测试数据集以检查模型性能是否有所改善。
让我们简单熟悉一下训练循环中使用的一些概念。向前跳转查看优化循环的完整实现。
Loss Function 损失函数
当提供一些训练数据时，我们未经训练的网络可能不会给出正确的答案。损失函数衡量的是得到的结果与目标值的不相似程度，它是我们在训练时想要最小化的损失函数。为了计算损失，我们使用给定数据样本的输入进行预测，并将其与真实数据标签值进行比较。
常见的损失函数包括用于回归任务的nn.MSELoss（Mean Square Error 均方误差）和 用于分类的nn.NLLLoss（Negative Log Likelihood 负对数似然）。 nn.CrossEntropyLoss结合了nn.LogSoftmax和nn.NLLLoss。
我们将模型的输出 logits 传递给nn.CrossEntropyLoss，这将标准化 logits 并计算预测误差。
# Initialize the loss function
loss_fn = nn.CrossEntropyLoss()
Optimizer 优化器
优化是调整模型参数以减少每个训练步骤中模型误差的过程。Optimization algorithms定义了如何执行此过程（在本例中我们使用随机梯度下降）。所有优化逻辑都封装在optimizer对象中。这里，我们使用SGD优化器；此外，PyTorch 中还有许多不同的优化器 ，例如 ADAM 和 RMSProp，它们可以更好地处理不同类型的模型和数据。
注册需要训练的模型参数，并传入学习率超参数。我们通过这种方式，来初始化优化器。
optimizer = torch.optim.SGD(model.parameters(), lr=learning_rate)
在训练循环中，优化分三个步骤进行：
- 调用
optimizer.zero_grad()重置模型参数的梯度。默认情况下渐变相加；为了防止重复计算，我们在每次迭代时明确地将它们归零。 - 通过调用
loss.backward()来反向传播预测损失。PyTorch 存储每个参数的损失梯度。 - 一旦我们有了梯度，通过后向传递中收集的梯度，我们就可以调用
optimizer.step()来调整参数。
Full Implementation 全面实施
我们定义了train_loop优化代码的循环，test_loop根据我们的测试数据评估模型的性能。
def train_loop(dataloader, model, loss_fn, optimizer):
size = len(dataloader.dataset)
# Set the model to training mode - important for batch normalization and dropout layers
# Unnecessary in this situation but added for best practices
model.train()
for batch, (X, y) in enumerate(dataloader):
# Compute prediction and loss
pred = model(X)
loss = loss_fn(pred, y)
# Backpropagation
loss.backward()
optimizer.step()
optimizer.zero_grad()
if batch % 100 == 0:
loss, current = loss.item(), (batch + 1) * len(X)
print(f"loss: {loss:>7f} [{current:>5d}/{size:>5d}]")
def test_loop(dataloader, model, loss_fn):
# Set the model to evaluation mode - important for batch normalization and dropout layers
# Unnecessary in this situation but added for best practices
model.eval()
size = len(dataloader.dataset)
num_batches = len(dataloader)
test_loss, correct = 0, 0
# Evaluating the model with torch.no_grad() ensures that no gradients are computed during test mode
# also serves to reduce unnecessary gradient computations and memory usage for tensors with requires_grad=True
with torch.no_grad():
for X, y in dataloader:
pred = model(X)
test_loss += loss_fn(pred, y).item()
correct += (pred.argmax(1) == y).type(torch.float).sum().item()
test_loss /= num_batches
correct /= size
print(f"Test Error: \n Accuracy: {(100*correct):>0.1f}%, Avg loss: {test_loss:>8f} \n")
我们初始化损失函数和优化器，并将其传递给train_loop和test_loop。请随意增加epoch数来跟踪模型改进的性能。
loss_fn = nn.CrossEntropyLoss()
optimizer = torch.optim.SGD(model.parameters(), lr=learning_rate)
epochs = 10
for t in range(epochs):
print(f"Epoch {t+1}\n-------------------------------")
train_loop(train_dataloader, model, loss_fn, optimizer)
test_loop(test_dataloader, model, loss_fn)
print("Done!")
Out:
Epoch 1
-------------------------------
loss: 2.298730 [ 64/60000]
loss: 2.289123 [ 6464/60000]
loss: 2.273286 [12864/60000]
loss: 2.269406 [19264/60000]
loss: 2.249603 [25664/60000]
loss: 2.229407 [32064/60000]
loss: 2.227368 [38464/60000]
loss: 2.204261 [44864/60000]
loss: 2.206193 [51264/60000]
loss: 2.166651 [57664/60000]
Test Error:
Accuracy: 50.9%, Avg loss: 2.166725
Epoch 2
-------------------------------
loss: 2.176750 [ 64/60000]
loss: 2.169595 [ 6464/60000]
loss: 2.117500 [12864/60000]
loss: 2.129272 [19264/60000]
loss: 2.079674 [25664/60000]
loss: 2.032928 [32064/60000]
loss: 2.050115 [38464/60000]
loss: 1.985236 [44864/60000]
loss: 1.987887 [51264/60000]
loss: 1.907162 [57664/60000]
Test Error:
Accuracy: 55.9%, Avg loss: 1.915486
Epoch 3
-------------------------------
loss: 1.951612 [ 64/60000]
loss: 1.928685 [ 6464/60000]
loss: 1.815709 [12864/60000]
loss: 1.841552 [19264/60000]
loss: 1.732467 [25664/60000]
loss: 1.692914 [32064/60000]
loss: 1.701714 [38464/60000]
loss: 1.610632 [44864/60000]
loss: 1.632870 [51264/60000]
loss: 1.514263 [57664/60000]
Test Error:
Accuracy: 58.8%, Avg loss: 1.541525
Epoch 4
-------------------------------
loss: 1.616448 [ 64/60000]
loss: 1.582892 [ 6464/60000]
loss: 1.427595 [12864/60000]
loss: 1.487950 [19264/60000]
loss: 1.359332 [25664/60000]
loss: 1.364817 [32064/60000]
loss: 1.371491 [38464/60000]
loss: 1.298706 [44864/60000]
loss: 1.336201 [51264/60000]
loss: 1.232145 [57664/60000]
Test Error:
Accuracy: 62.2%, Avg loss: 1.260237
Epoch 5
-------------------------------
loss: 1.345538 [ 64/60000]
loss: 1.327798 [ 6464/60000]
loss: 1.153802 [12864/60000]
loss: 1.254829 [19264/60000]
loss: 1.117322 [25664/60000]
loss: 1.153248 [32064/60000]
loss: 1.171765 [38464/60000]
loss: 1.110263 [44864/60000]
loss: 1.154467 [51264/60000]
loss: 1.070921 [57664/60000]
Test Error:
Accuracy: 64.1%, Avg loss: 1.089831
Epoch 6
-------------------------------
loss: 1.166889 [ 64/60000]
loss: 1.170514 [ 6464/60000]
loss: 0.979435 [12864/60000]
loss: 1.113774 [19264/60000]
loss: 0.973411 [25664/60000]
loss: 1.015192 [32064/60000]
loss: 1.051113 [38464/60000]
loss: 0.993591 [44864/60000]
loss: 1.039709 [51264/60000]
loss: 0.971077 [57664/60000]
Test Error:
Accuracy: 65.8%, Avg loss: 0.982440
Epoch 7
-------------------------------
loss: 1.045165 [ 64/60000]
loss: 1.070583 [ 6464/60000]
loss: 0.862304 [12864/60000]
loss: 1.022265 [19264/60000]
loss: 0.885213 [25664/60000]
loss: 0.919528 [32064/60000]
loss: 0.972762 [38464/60000]
loss: 0.918728 [44864/60000]
loss: 0.961629 [51264/60000]
loss: 0.904379 [57664/60000]
Test Error:
Accuracy: 66.9%, Avg loss: 0.910167
Epoch 8
-------------------------------
loss: 0.956964 [ 64/60000]
loss: 1.002171 [ 6464/60000]
loss: 0.779057 [12864/60000]
loss: 0.958409 [19264/60000]
loss: 0.827240 [25664/60000]
loss: 0.850262 [32064/60000]
loss: 0.917320 [38464/60000]
loss: 0.868384 [44864/60000]
loss: 0.905506 [51264/60000]
loss: 0.856353 [57664/60000]
Test Error:
Accuracy: 68.3%, Avg loss: 0.858248
Epoch 9
-------------------------------
loss: 0.889765 [ 64/60000]
loss: 0.951220 [ 6464/60000]
loss: 0.717035 [12864/60000]
loss: 0.911042 [19264/60000]
loss: 0.786085 [25664/60000]
loss: 0.798370 [32064/60000]
loss: 0.874939 [38464/60000]
loss: 0.832796 [44864/60000]
loss: 0.863254 [51264/60000]
loss: 0.819742 [57664/60000]
Test Error:
Accuracy: 69.5%, Avg loss: 0.818780
Epoch 10
-------------------------------
loss: 0.836395 [ 64/60000]
loss: 0.910220 [ 6464/60000]
loss: 0.668506 [12864/60000]
loss: 0.874338 [19264/60000]
loss: 0.754805 [25664/60000]
loss: 0.758453 [32064/60000]
loss: 0.840451 [38464/60000]
loss: 0.806153 [44864/60000]
loss: 0.830360 [51264/60000]
loss: 0.790281 [57664/60000]
Test Error:
Accuracy: 71.0%, Avg loss: 0.787271
Done!
Further Reading 进一步阅读
- Loss Functions
- torch.optim
- Warmstart Training a Model
参考文献
Optimizing Model Parameters — PyTorch Tutorials 2.2.0+cu121 documentation
Optimizing Model Parameters — PyTorch Tutorials 2.2.0+cu121 documentation
Github
storm-ice/Get_started_with_PyTorch
storm-ice/Get_started_with_PyTorch