卷积神经网络（CNN）
问题描述：
利用卷积神经网络，实现对MNIST数据集的分类问题
数据集：
MNIST数据集包括60000张训练图片和10000张测试图片。图片样本的数量已经足够训练一个很复杂的模型（例如 CNN的深层神经网络）。它经常被用来作为一个新 的模式识别模型的测试用例。而且它也是一个方便学生和研究者们执行用例的数据集。除此之外，MNIST数据集是一个相对较小的数据集，可以在你的笔记本CPUs上面直接执行
题目要求
Pytorch版本的卷积神经网络需要补齐self.conv1中的nn.Conv2d()和self.conv2()的参数，还需要填写x=x.view()中的内容。
训练精度应该在96%以上。
import os
import torch
import torch.nn as nn
from torch.autograd import Variable
import torch.utils.data as Data
import torchvision
import torch.nn.functional as F
import numpy as np
learning_rate = 1e-4
keep_prob_rate = 0.7 #
max_epoch = 3
BATCH_SIZE = 50
DOWNLOAD_MNIST = False
if not(os.path.exists('./mnist/')) or not os.listdir('./mnist/'):
# not mnist dir or mnist is empyt dir
DOWNLOAD_MNIST = True
train_data = torchvision.datasets.MNIST(root='./mnist/',train=True, transform=torchvision.transforms.ToTensor(), download=DOWNLOAD_MNIST,)
train_loader = Data.DataLoader(dataset = train_data ,batch_size= BATCH_SIZE ,shuffle= True)
test_data = torchvision.datasets.MNIST(root = './mnist/',train = False)
test_x = Variable(torch.unsqueeze(test_data.test_data,dim = 1),volatile = True).type(torch.FloatTensor)[:500]/255.
test_y = test_data.test_labels[:500].numpy()
class CNN(nn.Module):
def __init__(self):
super(CNN, self).__init__()
self.conv1 = nn.Sequential(
nn.Conv2d( # ???
# patch 7 * 7 ; 1 in channels ; 32 out channels ; ; stride is 1
# padding style is same(that means the convolution opration's input and output have the same size)
in_channels=1,
out_channels=32,
kernel_size=7,
stride=1,
padding=3,
),
nn.ReLU(), # activation function
nn.MaxPool2d(2), # pooling operation
)
self.conv2 = nn.Sequential( # ???
# line 1 : convolution function, patch 5*5 , 32 in channels ;64 out channels; padding style is same; stride is 1
# line 2 : choosing your activation funciont
# line 3 : pooling operation function.
nn.Conv2d(in_channels=32, out_channels=64, kernel_size=5, padding=2, stride=1),
nn.ReLU(),
nn.AvgPool2d(2),
)
self.out1 = nn.Linear( 7*7*64 , 1024 , bias= True) # full connection layer one
self.dropout = nn.Dropout(keep_prob_rate)
self.out2 = nn.Linear(1024,10,bias=True)
def forward(self, x):
x = self.conv1(x)
x = self.conv2(x)
x = x.view(-1, 7*7*64) # flatten the output of coonv2 to (batch_size ,32 * 7 * 7) # ???
out1 = self.out1(x)
out1 = F.relu(out1)
out1 = self.dropout(out1)
out2 = self.out2(out1)
output = F.softmax(out2)
return output
def test(cnn):
global prediction
y_pre = cnn(test_x)
_,pre_index= torch.max(y_pre,1)
pre_index= pre_index.view(-1)
prediction = pre_index.data.numpy()
correct = np.sum(prediction == test_y)
return correct / 500.0
def train(cnn):
optimizer = torch.optim.Adam(cnn.parameters(), lr=learning_rate )
loss_func = nn.CrossEntropyLoss()
for epoch in range(max_epoch):
for step, (x_, y_) in enumerate(train_loader):
x ,y= Variable(x_),Variable(y_)
output = cnn(x)
loss = loss_func(output,y)
optimizer.zero_grad()
loss.backward()
optimizer.step()
if step != 0 and step % 20 ==0:
print("=" * 10,step,"="*5,"="*5, "test accuracy is ",test(cnn) ,"=" * 10 )
if __name__ == '__main__':
cnn = CNN()
train(cnn)
训练结果为：
========== 20 ===== ===== test accuracy is 0.224 ==========
========== 40 ===== ===== test accuracy is 0.362 ==========
========== 60 ===== ===== test accuracy is 0.402 ==========
========== 80 ===== ===== test accuracy is 0.51 ==========
========== 100 ===== ===== test accuracy is 0.608 ==========
========== 120 ===== ===== test accuracy is 0.624 ==========
========== 140 ===== ===== test accuracy is 0.708 ==========
========== 160 ===== ===== test accuracy is 0.684 ==========
========== 180 ===== ===== test accuracy is 0.738 ==========
========== 200 ===== ===== test accuracy is 0.766 ==========
========== 220 ===== ===== test accuracy is 0.778 ==========
========== 240 ===== ===== test accuracy is 0.796 ==========
========== 260 ===== ===== test accuracy is 0.802 ==========
========== 280 ===== ===== test accuracy is 0.81 ==========
========== 300 ===== ===== test accuracy is 0.812 ==========
========== 320 ===== ===== test accuracy is 0.82 ==========
========== 340 ===== ===== test accuracy is 0.848 ==========
========== 360 ===== ===== test accuracy is 0.83 ==========
========== 380 ===== ===== test accuracy is 0.852 ==========
========== 400 ===== ===== test accuracy is 0.852 ==========
========== 420 ===== ===== test accuracy is 0.856 ==========
========== 440 ===== ===== test accuracy is 0.874 ==========
========== 460 ===== ===== test accuracy is 0.85 ==========
========== 480 ===== ===== test accuracy is 0.874 ==========
========== 500 ===== ===== test accuracy is 0.864 ==========
========== 520 ===== ===== test accuracy is 0.858 ==========
========== 540 ===== ===== test accuracy is 0.884 ==========
========== 560 ===== ===== test accuracy is 0.872 ==========
========== 580 ===== ===== test accuracy is 0.9 ==========
========== 600 ===== ===== test accuracy is 0.88 ==========
========== 620 ===== ===== test accuracy is 0.886 ==========
========== 640 ===== ===== test accuracy is 0.882 ==========
========== 660 ===== ===== test accuracy is 0.886 ==========
========== 680 ===== ===== test accuracy is 0.876 ==========
========== 700 ===== ===== test accuracy is 0.882 ==========
========== 720 ===== ===== test accuracy is 0.886 ==========
========== 740 ===== ===== test accuracy is 0.894 ==========
========== 760 ===== ===== test accuracy is 0.894 ==========
========== 780 ===== ===== test accuracy is 0.9 ==========
========== 800 ===== ===== test accuracy is 0.898 ==========
========== 820 ===== ===== test accuracy is 0.912 ==========
========== 840 ===== ===== test accuracy is 0.894 ==========
========== 860 ===== ===== test accuracy is 0.898 ==========
========== 880 ===== ===== test accuracy is 0.888 ==========
========== 900 ===== ===== test accuracy is 0.896 ==========
========== 920 ===== ===== test accuracy is 0.888 ==========
========== 940 ===== ===== test accuracy is 0.91 ==========
========== 960 ===== ===== test accuracy is 0.908 ==========
========== 980 ===== ===== test accuracy is 0.918 ==========
========== 1000 ===== ===== test accuracy is 0.906 ==========
========== 1020 ===== ===== test accuracy is 0.908 ==========
========== 1040 ===== ===== test accuracy is 0.906 ==========
========== 1060 ===== ===== test accuracy is 0.914 ==========
========== 1080 ===== ===== test accuracy is 0.908 ==========
========== 1100 ===== ===== test accuracy is 0.906 ==========
========== 1120 ===== ===== test accuracy is 0.906 ==========
========== 1140 ===== ===== test accuracy is 0.924 ==========
========== 1160 ===== ===== test accuracy is 0.918 ==========
========== 1180 ===== ===== test accuracy is 0.904 ==========
========== 20 ===== ===== test accuracy is 0.924 ==========
========== 40 ===== ===== test accuracy is 0.908 ==========
========== 60 ===== ===== test accuracy is 0.92 ==========
========== 80 ===== ===== test accuracy is 0.91 ==========
========== 100 ===== ===== test accuracy is 0.926 ==========
========== 120 ===== ===== test accuracy is 0.91 ==========
========== 140 ===== ===== test accuracy is 0.922 ==========
========== 160 ===== ===== test accuracy is 0.932 ==========
========== 180 ===== ===== test accuracy is 0.932 ==========
========== 200 ===== ===== test accuracy is 0.93 ==========
========== 220 ===== ===== test accuracy is 0.94 ==========
========== 240 ===== ===== test accuracy is 0.918 ==========
========== 260 ===== ===== test accuracy is 0.934 ==========
========== 280 ===== ===== test accuracy is 0.93 ==========
========== 300 ===== ===== test accuracy is 0.934 ==========
========== 320 ===== ===== test accuracy is 0.934 ==========
========== 340 ===== ===== test accuracy is 0.93 ==========
========== 360 ===== ===== test accuracy is 0.944 ==========
========== 380 ===== ===== test accuracy is 0.938 ==========
========== 400 ===== ===== test accuracy is 0.92 ==========
========== 420 ===== ===== test accuracy is 0.936 ==========
========== 440 ===== ===== test accuracy is 0.948 ==========
========== 460 ===== ===== test accuracy is 0.934 ==========
========== 480 ===== ===== test accuracy is 0.938 ==========
========== 500 ===== ===== test accuracy is 0.916 ==========
========== 520 ===== ===== test accuracy is 0.916 ==========
========== 540 ===== ===== test accuracy is 0.928 ==========
========== 560 ===== ===== test accuracy is 0.936 ==========
========== 580 ===== ===== test accuracy is 0.942 ==========
========== 600 ===== ===== test accuracy is 0.922 ==========
========== 620 ===== ===== test accuracy is 0.94 ==========
========== 640 ===== ===== test accuracy is 0.94 ==========
========== 660 ===== ===== test accuracy is 0.96 ==========
========== 680 ===== ===== test accuracy is 0.938 ==========
========== 700 ===== ===== test accuracy is 0.936 ==========
========== 720 ===== ===== test accuracy is 0.94 ==========
========== 740 ===== ===== test accuracy is 0.946 ==========
========== 760 ===== ===== test accuracy is 0.946 ==========
========== 780 ===== ===== test accuracy is 0.948 ==========
========== 800 ===== ===== test accuracy is 0.95 ==========
========== 820 ===== ===== test accuracy is 0.948 ==========
========== 840 ===== ===== test accuracy is 0.95 ==========
========== 860 ===== ===== test accuracy is 0.94 ==========
========== 880 ===== ===== test accuracy is 0.956 ==========
========== 900 ===== ===== test accuracy is 0.944 ==========
========== 920 ===== ===== test accuracy is 0.948 ==========
========== 940 ===== ===== test accuracy is 0.95 ==========
========== 960 ===== ===== test accuracy is 0.944 ==========
========== 980 ===== ===== test accuracy is 0.94 ==========
========== 1000 ===== ===== test accuracy is 0.946 ==========
========== 1020 ===== ===== test accuracy is 0.952 ==========
========== 1040 ===== ===== test accuracy is 0.952 ==========
========== 1060 ===== ===== test accuracy is 0.944 ==========
========== 1080 ===== ===== test accuracy is 0.956 ==========
========== 1100 ===== ===== test accuracy is 0.96 ==========
========== 1120 ===== ===== test accuracy is 0.948 ==========
========== 1140 ===== ===== test accuracy is 0.942 ==========
========== 1160 ===== ===== test accuracy is 0.948 ==========
========== 1180 ===== ===== test accuracy is 0.944 ==========
========== 20 ===== ===== test accuracy is 0.952 ==========
========== 40 ===== ===== test accuracy is 0.96 ==========
========== 60 ===== ===== test accuracy is 0.948 ==========
========== 80 ===== ===== test accuracy is 0.954 ==========
========== 100 ===== ===== test accuracy is 0.948 ==========
========== 120 ===== ===== test accuracy is 0.948 ==========
========== 140 ===== ===== test accuracy is 0.958 ==========
========== 160 ===== ===== test accuracy is 0.942 ==========
========== 180 ===== ===== test accuracy is 0.948 ==========
========== 200 ===== ===== test accuracy is 0.952 ==========
========== 220 ===== ===== test accuracy is 0.952 ==========
========== 240 ===== ===== test accuracy is 0.95 ==========
========== 260 ===== ===== test accuracy is 0.966 ==========
========== 280 ===== ===== test accuracy is 0.96 ==========
========== 300 ===== ===== test accuracy is 0.956 ==========
========== 320 ===== ===== test accuracy is 0.96 ==========
========== 340 ===== ===== test accuracy is 0.956 ==========
========== 360 ===== ===== test accuracy is 0.956 ==========
========== 380 ===== ===== test accuracy is 0.954 ==========
========== 400 ===== ===== test accuracy is 0.96 ==========
========== 420 ===== ===== test accuracy is 0.966 ==========
========== 440 ===== ===== test accuracy is 0.96 ==========
========== 460 ===== ===== test accuracy is 0.954 ==========
========== 480 ===== ===== test accuracy is 0.968 ==========
========== 500 ===== ===== test accuracy is 0.958 ==========
========== 520 ===== ===== test accuracy is 0.958 ==========
========== 540 ===== ===== test accuracy is 0.962 ==========
========== 560 ===== ===== test accuracy is 0.968 ==========
========== 580 ===== ===== test accuracy is 0.958 ==========
========== 600 ===== ===== test accuracy is 0.952 ==========
========== 620 ===== ===== test accuracy is 0.95 ==========
========== 640 ===== ===== test accuracy is 0.964 ==========
========== 660 ===== ===== test accuracy is 0.962 ==========
========== 680 ===== ===== test accuracy is 0.96 ==========
========== 700 ===== ===== test accuracy is 0.962 ==========
========== 720 ===== ===== test accuracy is 0.964 ==========
========== 740 ===== ===== test accuracy is 0.958 ==========
========== 760 ===== ===== test accuracy is 0.96 ==========
========== 780 ===== ===== test accuracy is 0.972 ==========
========== 800 ===== ===== test accuracy is 0.962 ==========
========== 820 ===== ===== test accuracy is 0.968 ==========
========== 840 ===== ===== test accuracy is 0.964 ==========
========== 860 ===== ===== test accuracy is 0.96 ==========
========== 880 ===== ===== test accuracy is 0.964 ==========
========== 900 ===== ===== test accuracy is 0.96 ==========
========== 920 ===== ===== test accuracy is 0.96 ==========
========== 940 ===== ===== test accuracy is 0.97 ==========
========== 960 ===== ===== test accuracy is 0.956 ==========
========== 980 ===== ===== test accuracy is 0.966 ==========
========== 1000 ===== ===== test accuracy is 0.964 ==========
========== 1020 ===== ===== test accuracy is 0.964 ==========
========== 1040 ===== ===== test accuracy is 0.97 ==========
========== 1060 ===== ===== test accuracy is 0.974 ==========
========== 1080 ===== ===== test accuracy is 0.962 ==========
========== 1100 ===== ===== test accuracy is 0.97 ==========
========== 1120 ===== ===== test accuracy is 0.974 ==========
========== 1140 ===== ===== test accuracy is 0.978 ==========
========== 1160 ===== ===== test accuracy is 0.976 ==========
========== 1180 ===== ===== test accuracy is 0.974 ==========
