卷积神经网络

关于卷积神经网络的原理这篇文章先不做介绍,推荐机器视角:长文揭秘图像处理和卷积神经网络架构卷积:如何成为一个很厉害的神经网络这两篇文章,这里记录一下PyTorch对卷积神经网络的实现。
代码清单 model.py

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import torch.nn as nn

# 卷积神经网络(两个卷积层)
class ConvNet(nn.Module):
def __init__(self, num_classes=10):
super(ConvNet, self).__init__()
self.layer1 = nn.Sequential(
nn.Conv2d(1, 16, kernel_size=5, stride=1, padding=2),
nn.BatchNorm2d(16),
nn.ReLU(),
nn.MaxPool2d(kernel_size=2, stride=2))
self.layer2 = nn.Sequential(
nn.Conv2d(16, 32, kernel_size=5, stride=1, padding=2),
nn.BatchNorm2d(32),
nn.ReLU(),
nn.MaxPool2d(kernel_size=2, stride=2))
self.fc = nn.Linear(7*7*32, num_classes) # full connection,即输出层

def forward(self, x):
out = self.layer1(x)
out = self.layer2(out)
out = out.reshape(out.size(0), -1)
out = self.fc(out)
return out

这里首先构建了一个两层的卷积神经网络,torch.nn.Squential是一个顺序的容器,模块按照构造函数顺序加入。
torch.nn.Conv2d是将2D卷积(卷积核是2D的)应用到输入,其参数依次为:
- in_channels 输入图像的通道数 - out_channels 卷积产生的通道数 - kernel_size 卷积核大小 - stride 卷积的步长 - padding 在输入的各边0填充

输入为一个图像的Tensor形式,其中包含\(N\)词袋大小,\(C\)是通道数,\(H\)是输入的高度,\(W\)是输入的宽度,单位是pixel。下面的demo.py中可以看到一个图像的Tensor如何构造。
torch.nn.BatchNorm2dBatch Normalization对每个隐藏层的输入进行标准化,有加速收敛等好处。
torch.nn.ReLU是一个非线性激活函数,\(ReLU(x) = max(0, x)\)正数不变,负数都转化为0。 torch.nn.MaxPool2d是对输入进行一个2D最大池化。
torch.nn.Linear对输入的数据进行一个线性转变\(y = Ax + b\),参数依次为: - in_features 输入样本的大小 - out_features 输出样本的大小,这里是10个数字 - bias

代码清单 train.py

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import torch
import torch.nn as nn
import torchvision
import torchvision.transforms as transforms
from model import ConvNet

# Device configuration
device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')

# Hyper parameters
num_epochs = 5
num_classes = 10
batch_size = 100
learning_rate = 0.001

# MNIST dataset
train_dataset = torchvision.datasets.MNIST(root='./data/',
train=True,
transform=transforms.ToTensor(),
download=True)

test_dataset = torchvision.datasets.MNIST(root='./data/',
train=False,
transform=transforms.ToTensor())

# Data loader
train_loader = torch.utils.data.DataLoader(dataset=train_dataset,
batch_size=batch_size,
shuffle=True)

test_loader = torch.utils.data.DataLoader(dataset=test_dataset,
batch_size=batch_size,
shuffle=False)

model = ConvNet(num_classes).to(device)

# Loss and optimizer
criterion = nn.CrossEntropyLoss()
optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate)

# Train the model
total_step = len(train_loader)
for epoch in range(num_epochs):
for i, (images, labels) in enumerate(train_loader):
images = images.to(device)
labels = labels.to(device)

# Forward pass
outputs = model(images)
loss = criterion(outputs, labels)

# Backward and optimize
optimizer.zero_grad()
loss.backward()
optimizer.step()

if (i+1) % 100 == 0:
print ('Epoch [{}/{}], Step [{}/{}], Loss: {:.4f}'
.format(epoch+1, num_epochs, i+1, total_step, loss.item()))

# Test the model
model.eval() # eval mode (batchnorm uses moving mean/variance instead of mini-batch mean/variance)
with torch.no_grad():
correct = 0
total = 0
for images, labels in test_loader:
images = images.to(device)
labels = labels.to(device)
outputs = model(images)
_, predicted = torch.max(outputs.data, 1)
total += labels.size(0)
correct += (predicted == labels).sum().item()

print('Test Accuracy of the model on the 10000 test images: {} %'.format(100 * correct / total))

# Save the model checkpoint
torch.save(model.state_dict(), 'model.ckpt')

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$ python train.py
Epoch [1/5], Step [100/600], Loss: 0.1743
Epoch [1/5], Step [200/600], Loss: 0.1452
Epoch [1/5], Step [300/600], Loss: 0.1029
Epoch [1/5], Step [400/600], Loss: 0.0549
Epoch [1/5], Step [500/600], Loss: 0.0608
...
Test Accuracy of the model on the 10000 test images: 99 %

这里首先介绍一下训练数据,PyTorch支持多种数据集,上述代码用到的是MNIST数据集,一般在训练和测试时都是直接使用官网提供的二进制数据集,但是作为初学者,不太理解这个二进制到底是什么东西,所以先用下面的代码将二进制文件解析为原始的图片和对应的标签:
代码清单 resolve.py

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from PIL import Image
import struct

def read_image(filename):
f = open(filename, 'rb')
index = 0
buf = f.read()
f.close()
magic, images, rows, columns = struct.unpack_from('>IIII' , buf , index)
index += struct.calcsize('>IIII')
for i in xrange(images):
image = Image.new('L', (columns, rows))
for x in xrange(rows):
for y in xrange(columns):
image.putpixel((y, x), int(struct.unpack_from('>B', buf, index)[0]))
index += struct.calcsize('>B')

print 'save ' + str(i) + 'image'
image.save('test/' + str(i) + '.png')

def read_label(filename, saveFilename):
f = open(filename, 'rb')
index = 0
buf = f.read()

f.close()

magic, labels = struct.unpack_from('>II' , buf , index)
index += struct.calcsize('>II')

labelArr = [0] * labels

for x in xrange(labels):
labelArr[x] = int(struct.unpack_from('>B', buf, index)[0])
index += struct.calcsize('>B')

save = open(saveFilename, 'w')

save.write(','.join(map(lambda x: str(x), labelArr)))
save.write('\n')

save.close()
print 'save labels success'

if __name__ == '__main__':
read_image('data/raw/t10k-images-idx3-ubyte')
read_label('data/raw/t10k-labels-idx1-ubyte', 'test/label.txt')

解析出的图片
解析出数据集中的图片后,我们可以随意选取图片来测试训练的模型model.ckpt
代码清单 demo.py

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import numpy as np
import torch
import torchvision.transforms as transforms
from model import ConvNet
from PIL import Image


model_path = "./model.ckpt"
img_path = "./12.png"

model = ConvNet()
print "load pretrained model from %s" % model_path
model.load_state_dict(torch.load(model_path))

transformer = transforms.ToTensor()
# 将图像转换为灰度模式,即单通道
image = Image.open(img_path).convert('L')
#image.resize((28, 28), Image.BILINEAR)
# 将图像转换为Tensor
image = transformer(image)
# 为Tensor添加一维,表示batch
image = image.view(1, *image.size())

model.eval()
output = model(image)

preds = torch.max(output, 1)[1]

print preds.item()

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$ python demo.py
load pretrained model from ./model.ckpt
9

可以看到用训练的模型可以正确识别出图片中的数字9。
reference
https://github.com/yunjey/pytorch-tutorial/blob/master/tutorials/02-intermediate/convolutional_neural_network/main.py