学习pytorch (下)
——莫烦python之CNN
我通过莫烦python教学视频入门pytorch,通过分析一篇cnn代码样例进行学习。
1. 全局代码
import torch
import torch.nn as nn
from torch.autograd import Variable
import torch.utils.data as Data
import torchvision
import matplotlib.pyplot as plt
#Hyper parameter
EPOCH = 1
BATCH_SIZE = 50
LR = 0.001
DOWNLOAD_MNIST = False
train_data = torchvision.datasets.MNIST(
root = './mnist',
train = True,
transform = torchvision.transform.ToTensor(),
download = DOWNLOAD_MNIST
)
# #plot one example
# print(train_data.train_data.size())
# print(train_data.train_labels.size())
# plt.imshow(train_data.train_data[0].numpy(),cmap='gray')
# plt.title('%i'%train_data.train_labels[0])
# plt.show()
train_loader = Data.DataLoader(
dataset = train_data,
batch_size = BATCH_SIZE,
shuffle = True,
num_workers = 2
)
test_data = torchvision.datasets.MNIST(
root = './mnist',
train = False
)
test_x = Variable(torch.unsequeeze(test_data.test_data,dim = 1),volatile=True).type(torch.FloatTensor)[:2000]/255
test_y = test_data.test_lables[:2000]
class CNN(nn.Module):
def __init__(self):
super(CNN,self).__init__()
self.conv1 = nn.Sequential(
nn.Conv2d(
in_channels = 1,
out_channels = 16,
kernerl_size = 5,
stride = 1,
padding =2,
),
nn.ReLU(),
nn.MaxPool2d(kernerl_size = 2),
)
self.conv2 = nn.Sequential(
nn.Conv2d(16,32,5,1,2),
nn.ReLU(),
nn.MaxPool2d(2),
)
self.out = nn.Linear(32*7*7,10)
def forward(self,x):
x = self.conv1(x)
x = self.conv2(x) # (batch, 32, 7, 7)
x = x.view(x.size(0),-1) # (batch, 32 *7 *7) x.size(0) keep the size of batch
output = self.out(x)
return output
cnn = CNN()
print(cnn)
optimizer = torch.optim.Adam(cnn.parameters(),lr=LR)
loss_func = nn.CrossEntropyLoss()
#training and testing
for epoch in range(EPOCH):
for step,(x,y) in enumerate(train_loader):
b_x = Variable(x)
b_y = Variable(y)
output = cnn(b_x)
loss = loss_func(output,b_y)
optimizer.zero_grad()
loss.backward()
optimizer.step()
if step % 50 == 0:
test_output = cnn(test_x)
pred_y = torch.max(test_output,1)[1].data.squeeze()
accuracy = sum(pred_y == test_y)/test_.size(0)
print('EPOCH:',epoch,'| train_loss:', loss.data[0], '| test accuracy:',accuracy)
#print 10 predictions from test data
test_output = cnn(test_x[:10])
pred_y = torch.max(test_output,1)[1].data.numpy().squeeze()
print(pred_y,'redictin number')
print(test_y[:10].numpy(),'real number')
2. 局部分析
加载相应的库
import torch
import torch.nn as nn
from torch.autograd import Variable
import torch.utils.data as Data
import torchvision
import matplotlib.pyplot as plt
超参数
EPOCH = 1
BATCH_SIZE = 50
LR = 0.001
DOWNLOAD_MNIST = False
EPOCH:遍历几次数据集
BATCH_SIZE: 批训练大小
LR:学习率
DOWNLOAD_MNIST: 标志位(是否下载数据集,第一次置True,之后置False)
通过torchvision下载数据集MNIST
train_data = torchvision.datasets.MNIST(
root = './mnist', #数据集存放目录
train = True, #取训练数据部分
transform = torchvision.transform.ToTensor(),#转至Tensor格式
download = DOWNLOAD_MNIST #标志位,是否下载
)
通过Loader准备好训练数据
train_loader = Data.DataLoader( #Data.DataLoader是必须步骤
dataset = train_data, #加载数据
batch_size = BATCH_SIZE, #批训练
shuffle = True, #标志位,是否打乱数据集顺序
num_workers = 2 #开几个线程
)
准备测试数据集
test_data = torchvision.datasets.MNIST(
root = './mnist',
train = False
)
选择前2000个数据,将训练数据变成Variable
test_x = Variable(torch.unsequeeze(test_data.test_data,dim = 1),volatile=True).type(torch.FloatTensor)[:2000]/255
test_y = test_data.test_lables[:2000]
搭建网络结构
class CNN(nn.Module):
def __init__(self):
super(CNN,self).__init__()
self.conv1 = nn.Sequential( #卷积神经网络
nn.Conv2d(
in_channels = 1, #输入通道
out_channels = 16,#输出通道
kernerl_size = 5,#核的大小
stride = 1,
padding =2,
),
nn.ReLU(),
nn.MaxPool2d(kernerl_size = 2),#池化,2*2
)
self.conv2 = nn.Sequential(
nn.Conv2d(16,32,5,1,2),
nn.ReLU(),
nn.MaxPool2d(2),
)
self.out = nn.Linear(32*7*7,10)
def forward(self,x): #定义数据传输
x = self.conv1(x)
x = self.conv2(x) # (batch, 32, 7, 7)
x = x.view(x.size(0),-1) # (batch, 32 *7 *7) x.size(0) keep the size of batch
output = self.out(x)
return output
定义优化器核损失函数
optimizer = torch.optim.Adam(cnn.parameters(),lr=LR)
loss_func = nn.CrossEntropyLoss()
训练过程
for epoch in range(EPOCH):
for step,(x,y) in enumerate(train_loader):
b_x = Variable(x) #将训练数据里面的特征变成Variable
b_y = Variable(y) #将训练数据里面的标签变成Variable
output = cnn(b_x) #预测结果
loss = loss_func(output,b_y) #损失
optimizer.zero_grad() #每次将优化器的导数先置为0
loss.backward() #后向传播
optimizer.step() #优化
if step % 50 == 0: #每隔50步测试一下网络效果
test_output = cnn(test_x) #输入测试数据,Variable形式
pred_y = torch.max(test_output,1)[1].data.squeeze()
accuracy = sum(pred_y == test_y)/test_.size(0)
print('EPOCH:',epoch,'| train_loss:', loss.data[0], '| test accuracy:',accuracy)
3.总结
综上,步骤可以总结为: