手写数字识别是经典数据集
最后一次任务我们来搞一下
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
import matplotlib.pyplot as plt
from torchvision import datasets, transforms # datasets包含常用的数据集，transform 对图像进行预处理
# training settings
batch_size = 64
# MNIST Dataset，注意这里的关键工具，torch.utils, data.Dataloader，这个可以有效的读取数据，是一个得到batch的生成器
# 引入MNIST数据集通过datasets函数包进行导入
# root是数据的位置，train=True是下载训练有关的集合，download是决定下不下载数据，一斤固有数据集就download=False
train_dataset = datasets.MNIST(root='./data/',
train=True,
transform=transforms.ToTensor(),
download=True)
test_dataset = datasets.MNIST(root='./data_set',
train=False,
transform=transforms.ToTensor())
# Data Loader（Input Pipeline)是一个迭代器，torch.utils.data.DataLoader作用就是随机的在样本中选取数据组成一个小的batch。shuffle决定数据是否打乱
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)
# 可视化数据图像
for i in range(5):
plt.figure()
plt.imshow(train_loader.dataset.train_data[i].numpy())
x = torch.randn(2, 2, 2)
# firstly change the data into diresed dimension, then reshape the tensor according to what I want
x.view(-1, 1, 4)
# 理解迭代器的深层含义，torch.utils.data.DataLoader的作用理解
for (data, target) in train_loader:
for i in range(4):
plt.figure()
print(target[1])
plt.imshow(data[i].numpy()[0])
break
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from torch.autograd import Variable
import matplotlib.pyplot as plt
from torchvision import datasets, transforms # datasets包含常用的数据集，transform 对图像进行预处理
# training settings
batch_size = 64
# MNIST Dataset，注意这里的关键工具，torch.utils, data.Dataloader，这个可以有效的读取数据，是一个得到batch的生成器
# 引入MNIST数据集通过datasets函数包进行导入
# root是数据的位置，train=True是下载训练有关的集合，download是决定下不下载数据，一斤固有数据集就download=False
train_dataset = datasets.MNIST(root='./data_set/',
train=True,
transform=transforms.ToTensor(),
download=True)
test_dataset = datasets.MNIST(root='./data_set',
train=False,
transform=transforms.ToTensor())
# Data Loader（Input Pipeline)是一个迭代器，torch.utils.data.DataLoader作用就是随机的在样本中选取数据组成一个小的batch。shuffle决定数据是否打乱
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)
# 可视化数据图像
for i in range(5):
plt.figure()
plt.imshow(train_loader.dataset.train_data[i].numpy())
x = torch.randn(2, 2, 2)
# firstly change the data into diresed dimension, then reshape the tensor according to what I want
x.view(-1, 1, 4)
# 理解迭代器的深层含义，torch.utils.data.DataLoader的作用理解
for (data, target) in train_loader:
for i in range(4):
plt.figure()
print(target[1])
plt.imshow(data[i].numpy()[0])
break
class LeNet5(nn.Module):
def __init__(self):
super(LeNet5, self).__init__()
self.conv1 = nn.Conv2d(1, 6, 5, padding=2) #pytorch文档，torch.nn.Conv2d函数参数定义
self.conv2 = nn.Conv2d(6, 16, 5)
self.fc1 = nn.Linear(16 * 5 * 5, 120) #全连接层就是线性层
self.fc2 = nn.Linear(120, 84)
self.fc3 = nn.Linear(84, 10)
def forward(self, x):
x = F.max_pool2d(F.tanh(self.conv1(x)), (2, 2))
x = F.dropout(x, p = 0.3, training=self.training)
x = F.max_pool2d(F.tanh(self.conv2(x)), (2, 2))
x = F.dropout(x, p = 0.3, training=self.training)
x = x.view(-1, self.num_flat_features(x)) # view函数用来改变维度，-1是占位符
x = F.tanh(self.fc1(x))
x = F.dropout(x, p = 0.3, training=self.training)
x = F.tanh(self.fc2(x))
x = F.dropout(x, p = 0.3, training=self.training)
x = self.fc3(x)
# 定义num_flat_features函数进行尺度的变换
def num_flat_features(self, x):
size = x.size()[1:]
num_features = 1
for s in size:
num_features *= s
return num_features
model = LeNet5()
optimizer = torch.optim.SGD(model.parameters(), lr = 0.1, momentum=0.9)
criterion = nn.CrossEntropyLoss()
def train(epoch):
model.train() # 第一行固定，model.train是用来实现训练期间用的网络
for batch_idx, (data, target) in enumerate(train_loader):
data, target = Variable(data), Variable(target)
optimizer.zero_grad() # tidings清零
output = model(data)
loss = criterion(output, target)
loss.backward() # 反向传播
optimizer.step()
if batch_idx % 10 == 0:
Loss.append(loss.data[0])
print('Train Epoch:{} [{}/{} ({:.0f}%)]\tLoss: {:.6f}'.format(
epoch, batch_idx * len(data), len(train_loader.dataset),
100.*batch_idx / len(train_loader), loss.data[0]))
return loss.data[0]
def test():
model.eval() # 测试期间用的网络
test_loss = 0
correct = 0
# test数据集进行测试
for data, target in test_loader:
data, target = Variable(data, volatile=True), Variable(target)
output = model(data)
# sum up batch loss
test_loss += criterion(output, target).data[0]
# get the index of the max log-probability
pred = output.data.max(1, keepdim=True)[1] # 预测输出的结果
correct += pred.eq(target.data.view_as(pred)).cpu().sum()
test_loss /= len(test_loader.dataset)
print('\nTest set: Average loss: {:.4f}, Accuracy: {}/{}({:.0f}%)\n'.format(
test_loss, correct, len(test_loader.dataset),
100. * correct / len(test_loader.dataset)))
Loss = []
for epoch in range(60):
loss = train(epoch)
Loss.append(loss)
test()
