PyTorch学习笔记 2. 运行官网训练、推理的入门示例

PyTorch学习笔记 2. 运行官网训练、推理的入门示例

• ​​一、加载数据​​
• ​​二、创建模型​​

• ​​torch.nn.Sequential介绍：​​
• ​​torch.nn.Linear​​
• ​​3. torch.nn.ReLU​​

• ​​三、调整模型参数​​
• ​​四、保存模型​​
• ​​五、加载模型​​

一、加载数据

首先引用必要的库：

import torch
from torch import nn
from torch.utils.data import DataLoader
from torchvision import datasets
from torchvision.transforms import ToTensor, Lambda, Compose
import matplotlib.pyplot as plt

与其它机器深度学习类似，Pytorch运行时需要数据集和标签。
Pytorch提供了文本、语音、视频等多个领域的数据库，本文使用的是FashionMNIST视频库数据集。
每个Torch视频库包含两个重要的参数：样本和标签。

# 从开放库下载训练集
training_data = datasets.FashionMNIST(
    root="data",
    train=True,
    download=True,
    transform=ToTensor(),
)

# 从开放库下载测试数据集
test_data = datasets.FashionMNIST(
    root="data",
    train=False,
    download=True,
    transform=ToTensor(),
)

下面在数据集上进行迭代，可以打印一些数据信息：

batch_size = 64

# Create data loaders.
train_dataloader = DataLoader(training_data, batch_size=batch_size)
test_dataloader = DataLoader(test_data, batch_size=batch_size)

for X, y in test_dataloader:
    print("Shape of X [N, C, H, W]: ", X.shape)
    print("Shape of y: ", y.shape, y.dtype)
    break

输出结果如下，我们定义的批大小64，迭代器每批返回64个特征和标签。

Shape of X [N, C, H, W]:  torch.Size([64, 1, 28, 28])
Shape of y:  torch.Size([64]) torch.int64

二、创建模型

使用​​nn.Module​​​ 用来在PyTorch里创建神经网络。这里定义的是顺序网络，
下面定义了神经网络层，可以使用GPU来加速运算。

# Get cpu or gpu device for training.
device = "cuda" if torch.cuda.is_available() else "cpu"
print("Using {} device".format(device))

# Define model
class NeuralNetwork(nn.Module):
    def __init__(self):
        super(NeuralNetwork, self).__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().to(device)
print(model)

• ​​torch.nn.Linear(input_data,hidden_layer)​​ 完成从输入层到隐藏层的线性变换；
• ​​torch.nn.ReLU()​​ 为激活函数；
• ​​torch.nn.Linear(hidden_layer, output_data)​​ 完成从隐藏层到输出层的线性变换；

torch.nn.Sequential介绍：

torch.nn.Sequential 类是 torch.nn 中的一种序列容器，可以在其中嵌套各种实现神经网络中来完成对神经网络模型的搭建，而参数会按照我们定义好的序列自动传递下去。
另一种参数传递的方式是orderdict，

torch.nn.Linear

torch.nn.Linear 类用于定义模型的线性层，即完成前面提到的不同的层之间的线性变换。

3. torch.nn.ReLU

torch.nn.ReLU 类属于非线性激活分类，在定义时默认不需要传入参数。

这一步打印网络结构如下：

三、调整模型参数

为了训练模型，这里要定义损失函数和优化器。

loss_fn = nn.CrossEntropyLoss()
optimizer = torch.optim.SGD(model.parameters(), lr=1e-3)

在每一轮的训练中，模型要在训练集上执行预测，并且根据反射传播来调整模型参数。

def train(dataloader, model, loss_fn, optimizer):
    size = len(dataloader.dataset)
    model.train()
    for batch, (X, y) in enumerate(dataloader):
        X, y = X.to(device), y.to(device)

        # Compute prediction error
        pred = model(X)
        loss = loss_fn(pred, y)

        # Backpropagation
        optimizer.zero_grad()
        loss.backward()
        optimizer.step()

        if batch % 100 == 0:
            loss, current = loss.item(), batch * len(X)
            print(f"loss: {loss:>7f}  [{current:>5d}/{size:>5d}]")

另外需要测试集来优化、检查模型。

def test(dataloader, model, loss_fn):
    size = len(dataloader.dataset)
    num_batches = len(dataloader)
    model.eval()
    test_loss, correct = 0, 0
    with torch.no_grad():
        for X, y in dataloader:
            X, y = X.to(device), y.to(device)
            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")

训练过程包含多个周期，每一轮模型都要通过参数来学习，以获得更好的推理模型。迭代过程的精度和损失函数在下面代码中打印出来：

epochs = 5
for t in range(epochs):
    print(f"Epoch {t+1}\n-------------------------------")
    train(train_dataloader, model, loss_fn, optimizer)
    test(test_dataloader, model, loss_fn)
print("Done!")

四、保存模型

通常会把内部的状态词典序列化后作为模型保存下来。

torch.save(model.state_dict(), "model.pth")
print("Saved PyTorch Model State to model.pth")

五、加载模型

model = NeuralNetwork()
model.load_state_dict(torch.load("model.pth"))

下面使用模型执行推理：

classes = [
    "T-shirt/top",
    "Trouser",
    "Pullover",
    "Dress",
    "Coat",
    "Sandal",
    "Shirt",
    "Sneaker",
    "Bag",
    "Ankle boot",
]

model.eval()
x, y = test_data[0][0], test_data[0][1]
with torch.no_grad():
    pred = model(x)
    predicted, actual = classes[pred[0].argmax(0)], classes[y]
    print(f'Predicted: "{predicted}", Actual: "{actual}"')

推理结果：

完整源代码：

import torch
from torch import nn
from torch.utils.data import DataLoader
from torchvision import datasets
from torchvision.transforms import ToTensor, Lambda, Compose
import matplotlib.pyplot as plt



# Download training data from open datasets.
training_data = datasets.FashionMNIST(
    root="data",
    train=True,
    download=True,
    transform=ToTensor(),
)

# Download test data from open datasets.
test_data = datasets.FashionMNIST(
    root="data",
    train=False,
    download=True,
    transform=ToTensor(),
)

batch_size = 64

# Create data loaders.
train_dataloader = DataLoader(training_data, batch_size=batch_size)
test_dataloader = DataLoader(test_data, batch_size=batch_size)

for X, y in test_dataloader:
    print("Shape of X [N, C, H, W]: ", X.shape)
    print("Shape of y: ", y.shape, y.dtype)
    break


# Get cpu or gpu device for training.
device = "cuda" if torch.cuda.is_available() else "cpu"
print("Using {} device".format(device))

# Define model
class NeuralNetwork(nn.Module):
    def __init__(self):
        super(NeuralNetwork, self).__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().to(device)
print(model)

loss_fn = nn.CrossEntropyLoss()
optimizer = torch.optim.SGD(model.parameters(), lr=1e-3)


def train(dataloader, model, loss_fn, optimizer):
    size = len(dataloader.dataset)
    model.train()
    for batch, (X, y) in enumerate(dataloader):
        X, y = X.to(device), y.to(device)

        # Compute prediction error
        pred = model(X)
        loss = loss_fn(pred, y)

        # Backpropagation
        optimizer.zero_grad()
        loss.backward()
        optimizer.step()

        if batch % 100 == 0:
            loss, current = loss.item(), batch * len(X)
            print(f"loss: {loss:>7f}  [{current:>5d}/{size:>5d}]")


def test(dataloader, model, loss_fn):
    size = len(dataloader.dataset)
    num_batches = len(dataloader)
    model.eval()
    test_loss, correct = 0, 0
    with torch.no_grad():
        for X, y in dataloader:
            X, y = X.to(device), y.to(device)
            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")


epochs = 5
for t in range(epochs):
    print(f"Epoch {t+1}\n-------------------------------")
    train(train_dataloader, model, loss_fn, optimizer)
    test(test_dataloader, model, loss_fn)
print("Done!")


torch.save(model.state_dict(), "model.pth")
print("Saved PyTorch Model State to model.pth")

model = NeuralNetwork()
model.load_state_dict(torch.load("model.pth"))


classes = [
    "T-shirt/top",
    "Trouser",
    "Pullover",
    "Dress",
    "Coat",
    "Sandal",
    "Shirt",
    "Sneaker",
    "Bag",
    "Ankle boot",
]

model.eval()
x, y = test_data[0][0], test_data[0][1]
with torch.no_grad():
    pred = model(x)
    predicted, actual = classes[pred[0].argmax(0)], classes[y]
    print(f'Predicted: "{predicted}", Actual: "{actual}"')

运行结果：

(pytorch) appledeMacBook-Pro:pytorch apple$ python3 learn1.py 
/opt/miniconda3/envs/pytorch/lib/python3.7/site-packages/torchvision/datasets/mnist.py:498: UserWarning: The given NumPy array is not writeable, and PyTorch does not support non-writeable tensors. This means you can write to the underlying (supposedly non-writeable) NumPy array using the tensor. You may want to copy the array to protect its data or make it writeable before converting it to a tensor. This type of warning will be suppressed for the rest of this program. (Triggered internally at  ../torch/csrc/utils/tensor_numpy.cpp:180.)
  return torch.from_numpy(parsed.astype(m[2], copy=False)).view(*s)
Shape of X [N, C, H, W]:  torch.Size([64, 1, 28, 28])
Shape of y:  torch.Size([64]) torch.int64
Using cpu device
NeuralNetwork(
  (flatten): Flatten(start_dim=1, end_dim=-1)
  (linear_relu_stack): Sequential(
    (0): Linear(in_features=784, out_features=512, bias=True)
    (1): ReLU()
    (2): Linear(in_features=512, out_features=512, bias=True)
    (3): ReLU()
    (4): Linear(in_features=512, out_features=10, bias=True)
  )
)
Epoch 1
-------------------------------
loss: 2.314974  [    0/60000]
loss: 2.301318  [ 6400/60000]
loss: 2.279395  [12800/60000]
loss: 2.260673  [19200/60000]
loss: 2.256194  [25600/60000]
loss: 2.230177  [32000/60000]
loss: 2.232735  [38400/60000]
loss: 2.197281  [44800/60000]
loss: 2.199913  [51200/60000]
loss: 2.165345  [57600/60000]
Test Error: 
 Accuracy: 46.0%, Avg loss: 2.161378 

Epoch 2
-------------------------------
loss: 2.181149  [    0/60000]
loss: 2.164842  [ 6400/60000]
loss: 2.110326  [12800/60000]
loss: 2.114110  [19200/60000]
loss: 2.074363  [25600/60000]
loss: 2.027263  [32000/60000]
loss: 2.043373  [38400/60000]
loss: 1.965748  [44800/60000]
loss: 1.974563  [51200/60000]
loss: 1.901750  [57600/60000]
Test Error: 
 Accuracy: 58.3%, Avg loss: 1.897587 

Epoch 3
-------------------------------
loss: 1.938917  [    0/60000]
loss: 1.899398  [ 6400/60000]
loss: 1.785908  [12800/60000]
loss: 1.814409  [19200/60000]
loss: 1.720331  [25600/60000]
loss: 1.681701  [32000/60000]
loss: 1.687258  [38400/60000]
loss: 1.586764  [44800/60000]
loss: 1.616351  [51200/60000]
loss: 1.508570  [57600/60000]
Test Error: 
 Accuracy: 60.8%, Avg loss: 1.523732 

Epoch 4
-------------------------------
loss: 1.598114  [    0/60000]
loss: 1.553433  [ 6400/60000]
loss: 1.402492  [12800/60000]
loss: 1.467502  [19200/60000]
loss: 1.366623  [25600/60000]
loss: 1.368079  [32000/60000]
loss: 1.371022  [38400/60000]
loss: 1.290068  [44800/60000]
loss: 1.333592  [51200/60000]
loss: 1.231188  [57600/60000]
Test Error: 
 Accuracy: 62.9%, Avg loss: 1.253851 

Epoch 5
-------------------------------
loss: 1.339722  [    0/60000]
loss: 1.310711  [ 6400/60000]
loss: 1.143826  [12800/60000]
loss: 1.242881  [19200/60000]
loss: 1.134900  [25600/60000]
loss: 1.168011  [32000/60000]
loss: 1.180709  [38400/60000]
loss: 1.111995  [44800/60000]
loss: 1.158805  [51200/60000]
loss: 1.070515  [57600/60000]
Test Error: 
 Accuracy: 64.0%, Avg loss: 1.088987 

Done!
Saved PyTorch Model State to model.pth
Predicted: "Ankle boot", Actual: "Ankle boot"