TRAINING A CLASSIFIER 訓練一個分類器

就這樣. 你已經知道怎樣定義神經網絡, 計算loss和更新網絡權重.
現在你可能會想:

數據呢?

一般, 當處理圖片, 文本, 音頻或者視頻數據時, 你可以使用標準的python包,將數據載入爲numpy array. 然後你可以將array轉化爲torch.*Tensor.
對於圖片, Pillow, OpenCV之類的包較爲有用.
對於音頻, 可以用scipy和librosa
對於文本, 基於原生Python或Cython的載入功能, NLTK和SpaCy之類的較爲有用.
特別對於視覺(vision), 我們創建了一個包叫torchvision. 它有常用datasets(如ImageNet, CIFAR10, MNIST等)的數據載入工具(data loader)和圖片轉換工具, 也就是torchvision.datasets和torch.utils.data.DataLoader.

用起來很方便.

這個教程裏. 我們用CIFAR10數據庫. 有 ‘airplane’, ‘automobile’, ‘bird’, ‘cat’, ‘deer’, ‘dog’, ‘frog’, ‘horse’, ‘ship’, ‘truck’十個類別的圖片. 圖片3x32x32. 也就是3通道, 32x32大小.

Training an image classifier 訓練一個圖像分類器

步驟:

用torchvision載入和歸一化訓練和測試數據,
定義卷積神經網絡(CNN)
定義loss函數
訓練
測試

1. 載入和歸一化CIFAR10

使用torchvision, 很簡單.

import torch
import torchvision
import torchvision.transforms as transforms

torchvision的輸出是PILImage, 範圍[0,1]. 我們將其轉化爲歸一化到[-1, 1]範圍的Tensor.

注意:
如果在Windows上出現BrokenPipeError, 將torch.utils.data.DataLoader() 的 num_worker 置爲0.

transform = transforms.Compose(
    [transforms.ToTensor(),
     transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))])

trainset = torchvision.datasets.CIFAR10(root='./data', train=True,
                                        download=True, transform=transform)
trainloader = torch.utils.data.DataLoader(trainset, batch_size=4,
                                          shuffle=True, num_workers=2)

testset = torchvision.datasets.CIFAR10(root='./data', train=False,
                                       download=True, transform=transform)
testloader = torch.utils.data.DataLoader(testset, batch_size=4,
                                         shuffle=False, num_workers=2)

classes = ('plane', 'car', 'bird', 'cat',
           'deer', 'dog', 'frog', 'horse', 'ship', 'truck')

Out:

Downloading https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz to ./data/cifar-10-python.tar.gz
Extracting ./data/cifar-10-python.tar.gz to ./data
Files already downloaded and verified

讓我們看看訓練數據什麼樣.

import matplotlib.pyplot as plt
import numpy as np

# functions to show an image


def imshow(img):
    img = img / 2 + 0.5     # unnormalize
    npimg = img.numpy()
    plt.imshow(np.transpose(npimg, (1, 2, 0)))
    plt.show()


# get some random training images
dataiter = iter(trainloader)
images, labels = dataiter.next()

# show images
imshow(torchvision.utils.make_grid(images))
# print labels
print(' '.join('%5s' % classes[labels[j]] for j in range(4)))
../../_images/sphx_glr_cifar10_tutorial_001.png

Out:

dog  bird  ship   dog

2. Define a Convolutional Neural Network 定義一個卷積神經網絡

將之前定義的網絡拷貝過來, 修改爲接收3通道圖片(之前的接收單通道).

import torch.nn as nn
import torch.nn.functional as F


class Net(nn.Module):
    def __init__(self):
        super(Net, self).__init__()
        self.conv1 = nn.Conv2d(3, 6, 5)
        self.pool = nn.MaxPool2d(2, 2)
        self.conv2 = nn.Conv2d(6, 16, 5)
        self.fc1 = nn.Linear(16 * 5 * 5, 120)
        self.fc2 = nn.Linear(120, 84)
        self.fc3 = nn.Linear(84, 10)

    def forward(self, x):
        x = self.pool(F.relu(self.conv1(x)))
        x = self.pool(F.relu(self.conv2(x)))
        x = x.view(-1, 16 * 5 * 5)
        x = F.relu(self.fc1(x))
        x = F.relu(self.fc2(x))
        x = self.fc3(x)
        return x


net = Net()

3. Define a Loss function and optimizer 定義一個loss函數和優化器

用分類交叉熵和帶動量(momentum)的SGD.

import torch.optim as optim

criterion = nn.CrossEntropyLoss()
optimizer = optim.SGD(net.parameters(), lr=0.001, momentum=0.9)

4. Train the network 訓練網絡

有意思的地方來了. 我們簡單地在數據迭代器(data iterator)上循環遍歷, 將輸入餵給網絡並優化.

for epoch in range(2):  # loop over the dataset multiple times

    running_loss = 0.0
    for i, data in enumerate(trainloader, 0):
        # get the inputs; data is a list of [inputs, labels]
        inputs, labels = data

        # zero the parameter gradients
        optimizer.zero_grad()

        # forward + backward + optimize
        outputs = net(inputs)
        loss = criterion(outputs, labels)
        loss.backward()
        optimizer.step()

        # print statistics
        running_loss += loss.item()
        if i % 2000 == 1999:    # print every 2000 mini-batches
            print('[%d, %5d] loss: %.3f' %
                  (epoch + 1, i + 1, running_loss / 2000))
            running_loss = 0.0

print('Finished Training')

Out:

[1,  2000] loss: 2.235
[1,  4000] loss: 1.902
[1,  6000] loss: 1.674
[1,  8000] loss: 1.571
[1, 10000] loss: 1.529
[1, 12000] loss: 1.478
[2,  2000] loss: 1.389
[2,  4000] loss: 1.391
[2,  6000] loss: 1.359
[2,  8000] loss: 1.335
[2, 10000] loss: 1.330
[2, 12000] loss: 1.289
Finished Training

保存訓練好的模型:

PATH = './cifar_net.pth'
torch.save(net.state_dict(), PATH)

這裏有更多的保存模型的細節.

5. Test the network on the test data 用測試數據測試網絡

我們已經訓練了兩輪了, 我們看看網絡是否學到東西了.
我們將模型輸出的結果和真實結果(ground-truth)作比較. 如果預測是正確的, 我們將這個sample加到正確預測的list中.

dataiter = iter(testloader)
images, labels = dataiter.next()
# print images
imshow(torchvision.utils.make_grid(images))
print('GroundTruth: ', ' '.join('%5s' % classes[labels[j]] for j in range(4)))

Out:

GroundTruth:    cat  ship  ship plane

然後, 重新載入保存的模型(注意: 保存和重新載入模型不是必要的, 我們這裏只是爲了說明怎樣做):

net = Net()
net.load_state_dict(torch.load(PATH))

好, 我們來看看模型分類結果如何:

outputs = net(images)

輸出是10類的置信度(energy). 某一類的置信度越高, 網絡認爲圖片是此類的可能性越大. 所以, 我們來獲取最高的置信度:

_, predicted = torch.max(outputs, 1)

print('Predicted: ', ' '.join('%5s' % classes[predicted[j]]
                              for j in range(4)))

Out:

Predicted:    cat  ship  ship  ship

結果還不錯, 我們來看看整個數據集上的表現:

correct = 0
total = 0
with torch.no_grad():
    for data in testloader:
        images, labels = data
        outputs = net(images)
        _, predicted = torch.max(outputs.data, 1)
        total += labels.size(0)
        correct += (predicted == labels).sum().item()

print('Accuracy of the network on the 10000 test images: %d %%' % (
    100 * correct / total))

Out:

Accuracy of the network on the 10000 test images: 53 %

看起來比隨機猜測要好. 隨機猜測正確率是10%. 看來網絡確實學到了東西.
看看哪些類表現好, 哪些類表現不好:

class_correct = list(0. for i in range(10))
class_total = list(0. for i in range(10))
with torch.no_grad():
    for data in testloader:
        images, labels = data
        outputs = net(images)
        _, predicted = torch.max(outputs, 1)
        c = (predicted == labels).squeeze()
        for i in range(4):
            label = labels[i]
            class_correct[label] += c[i].item()
            class_total[label] += 1


for i in range(10):
    print('Accuracy of %5s : %2d %%' % (
        classes[i], 100 * class_correct[i] / class_total[i]))

Out:

Accuracy of plane : 56 %
Accuracy of   car : 74 %
Accuracy of  bird : 30 %
Accuracy of   cat : 29 %
Accuracy of  deer : 57 %
Accuracy of   dog : 37 %
Accuracy of  frog : 73 %
Accuracy of horse : 62 %
Accuracy of  ship : 56 %
Accuracy of truck : 53 %

好了, 接下來做什麼呢?
怎樣在GPU上跑神經網絡呢?

Training on GPU GPU上訓練

正如將Tensor轉到GPU上一樣, 你可以將網絡轉到GPU上.
如果CUDA有效的話, 將device定義爲第一個可用的cuda設備.

device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")

# Assuming that we are on a CUDA machine, this should print a CUDA device:

print(device)

Out:

cuda:0

剩下的部分, 我們認爲device就是CUDA device.
然後這些方法將會遞歸地遍歷我們的模型部件(module)並將他們的參數和buffer轉爲CUDA tensor:

net.to(device)

記住, 你必須要在每一步中將輸入圖像和目標輸出都轉到GPU上:

inputs, labels = data[0].to(device), data[1].to(device)

爲什麼我們有看到很大的速度提升? 因爲你的模型太小了.

練習: 嘗試增大網絡寬度(第一個nn.Conv2d的第二個參數, 第二個nn.Conv2d的第一個參數, 兩者要相等), 看看提速效果如何.

目標達成:

在高層(high level)上理解Pytorch Tensor庫和神經網絡.
訓練一個分類圖片的小神經網絡.

Training on multiple GPUs 在多GPU上訓練.

如果你想用多塊GPU訓練, 請看: Data Parallelism.

Where do I go next? 下面做什麼?

Total running time of the script: ( 3 minutes 29.428 seconds)

Pytorch 60分鐘入門之(四) TRAINING A CLASSIFIER 訓練一個分類器

目錄

TRAINING A CLASSIFIER 訓練一個分類器

數據呢?

Training an image classifier 訓練一個圖像分類器

1. 載入和歸一化CIFAR10

2. Define a Convolutional Neural Network 定義一個卷積神經網絡

3. Define a Loss function and optimizer 定義一個loss函數和優化器

4. Train the network 訓練網絡

5. Test the network on the test data 用測試數據測試網絡

Training on GPU GPU上訓練

Training on multiple GPUs 在多GPU上訓練.

Where do I go next? 下面做什麼?

CUDA編程怎樣給kernel函數傳入函數指針

Pytorch 60分鐘入門之(二) Autograd:自動求導

Pytorch 60分鐘入門之(三) NEURAL NETWORKS 神經網絡

CUDA內存(一) 寄存器

Pytorch 60分鐘入門之(一) What is Pytorch?

Mac下配置sublime實現LaTeX

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