Smiling & Weeping
---- 下次你撑伞低头看水洼，
就会想起我说雨是神的烟花。
简介：主要是看刘二大人的视频讲解：https://www.bilibili.com/video/BV1Y7411d7Ys/?spm_id_from=333.337.search-card.all.click
题目及提交链接:Digit Recognizer | Kaggle
深度学习入门的学习项目，使用CNN(Convolutional Nerual Network)
对于Basic CNN的理解：
- 分成两个部分：前一个部分叫做Feature Extraction,后一部分叫做Classification(其中Feature Extraction又可以分为Convolution,Subsampling等)
- 其中要求卷积核的通道数量与输入通道数量一致。这种卷积核的总数和输出通道数目的总数一致（详见链接PDF）
- 卷积(convolution)后,C(channels)，W(width)，H(height)，其中padding和pooling（小技巧：若要卷积W,H不变，取整kernel_size/2）
- 卷积层：保存图像的空间信息
- 卷积层要求输入输出是四维张量（B,C,W,H），全连接层的输入输出都是二维张量（B,Input_feature）
- 卷积（线性变换），激活函数（非线性变换），池化；这个过程若干次后，view打平，进入全连接层
1 import torch
2 import torch.nn.functional as F
3 import torch.nn as nn
4 import torch.optim as optim
5 import torch.autograd as lr_scheduler
6 from torch.utils.data import DataLoader, Dataset
7 from torchvision import transforms
8 from torchvision.utils import make_grid
9 from torchvision import datasets
10 from torch.autograd import Variable
11 from sklearn.model_selection import train_test_split
12 import pandas as pd
13 import numpy as np
14 import matplotlib.pyplot as plt
15
16 batch_size = 64
17 transform = transforms.Compose([transforms.ToTensor()])
18 train_dataset = datasets.MNIST(root='../dataset/mnist/', train=True, download=True, transform=transform)
19 train_loader = torch.utils.data.DataLoader(dataset=train_dataset, shuffle=True, batch_size=batch_size)
20 #同样的方式加载一下测试集
21 test_dataset = datasets.MNIST(root='../dataset/mnist/', train=False, download=True, transform=transform)
22 test_loader = torch.utils.data.DataLoader(dataset=test_dataset, shuffle=False, batch_size=batch_size)
23
24 # 使用卷积神经网络进行图像特征提取
25 # (batch, 1, 28, 28) -> (batch, 10, 24, 24) -> 池化 (batch, 10, 12, 12) -> (batch, 20, 8, 8) -> (batch, 20 , 4, 4) -> (batch, 320) -> (batch, 10)
26 class Net(torch.nn.Module):
27 def __init__(self):
28 super(Net, self).__init__()
29 self.conv1 = torch.nn.Conv2d(1, 10, kernel_size = 5)
30 self.conv2 = torch.nn.Conv2d(10, 20, kernel_size = 5)
31 self.pooling = torch.nn.MaxPool2d(2)
32 self.fc = torch.nn.Linear(320, 10)
33
34 def forward(self, x):
35 # Flatten data from (n, 1, 28, 28) to (n, 784)
36 batch_size = x.size(0)
37 x = F.relu(self.pooling(self.conv1(x)))
38 x = F.relu(self.pooling(self.conv2(x)))
39 x = x.view(batch_size, -1) # Flatten
40 x = self.fc(x)
41 return x
42
43 model = Net()
44 # print(model)
45 device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
46 model.to(device)
47 criterion = torch.nn.CrossEntropyLoss(size_average=True)
48 optimizer = torch.optim.SGD(model.parameters(), lr=0.01, momentum=0.5)
49
50 def train(epoch):
51 running_loss = 0.0
52 for batch_idx, data in enumerate(train_loader, 0):
53 inputs, target = data
54 inputs, target = inputs.to(device), target.to(device)
55 optimizer.zero_grad()
56
57 # forward + backward + update
58 outputs = model(inputs)
59 # 计算真实值 和 测量值 之间的误差
60 loss = criterion(outputs, target)
61 loss.backward()
62 optimizer.step()
63
64 running_loss += loss.item()
65 if batch_idx % 300 == 299:
66 print('[%d, %5d] loss: %3f' % (epoch + 1, batch_idx+1, running_loss / 2000))
67 running_loss = 0.0
68
69 def test():
70 correct = 0
71 total = 0
72 with torch.no_grad():
73 for data in test_loader:
74 inputs, target = data
75 inputs, target = inputs.to(device), target.to(device)
76 outputs = model(inputs)
77 _, prediction = torch.max(outputs.data, dim=1)
78 total += target.size(0)
79 correct += (prediction == target).sum().item()
80 print('Accuracy on test set: %d %% [%d/%d]' % (100*correct / total, correct, total))
81 return correct/total
82
83 epoch_list = []
84 acc_list = []
85 for epoch in range(10):
86 train(epoch)
87 acc = test()
88 epoch_list.append(epoch)
89 acc_list.append(acc)
90
91 plt.plot(epoch_list, acc_list)
92 plt.ylabel("accuracy")
93 plt.xlabel("epoch")
94 plt.show()
95
96 class DatasetSubmissionMNIST(torch.utils.data.Dataset):
97 def __init__(self, file_path, transform=None):
98 self.data = pd.read_csv(file_path)
99 self.transform = transform
100
101 def __len__(self):
102 return len(self.data)
103
104 def __getitem__(self, index):
105 image = self.data.iloc[index].values.astype(np.uint8).reshape((28, 28, 1))
106
107
108 if self.transform is not None:
109 image = self.transform(image)
110
111 return image
112
113 transform = transforms.Compose([
114 transforms.ToPILImage(),
115 transforms.ToTensor(),
116 transforms.Normalize(mean=(0.5,), std=(0.5,))
117 ])
118
119 submissionset = DatasetSubmissionMNIST('/kaggle/input/digit-recognizer/test.csv', transform=transform)
120 submissionloader = torch.utils.data.DataLoader(submissionset, batch_size=batch_size, shuffle=False)
121
122 submission = [['ImageId', 'Label']]
123
124 with torch.no_grad():
125 model.eval()
126 image_id = 1
127
128 for images in submissionloader:
129 images = images.cuda()
130 log_ps = model(images)
131 ps = torch.exp(log_ps)
132 top_p, top_class = ps.topk(1, dim=1)
133
134 for prediction in top_class:
135 submission.append([image_id, prediction.item()])
136 image_id += 1
137
138 print(len(submission) - 1)
139 import csv
140
141 with open('submission.csv', 'w') as submissionFile:
142 writer = csv.writer(submissionFile)
143 writer.writerows(submission)
144
145 print('Submission Complete!')
146 # summission.to_csv('/kaggle/working/submission.csv', index=False)
就效果来说，也就一般，后面的Advance CNN 会有更高的效率和准确性，大家可以敲一下代码放在自己的编译器上跑一下
对了，这是GPU版本，若用CPU，把所有的device删除就可以，--<-<-<@
文章到此结束，我们下次再见
一束光线，可能会摔碎
但仍旧光芒四射
标签：--,self,torch,batch,CNN,import,data,MNIST,size From： https://www.cnblogs.com/smiling-weeping-zhr/p/17730663.html