需要知道代码实现细节
这些模块在 NN 中是通用的，后面的学习是在此基础上的拓展
轮次总训练函数#
##### %matplotlib inline
import torch
import torchvision
from torch.utils import data
from torchvision import transforms
from d2l import torch as d2l
from IPython import display
def get_dataloader_workers():
"""使用4个进程来读取的数据"""
return 4
def load_data_fashion_mnist(batch_size, resize=None):
"""下载Fashion-MNIST数据集，然后将其加载到内存中"""
trans = [transforms.ToTensor()]
if resize:
trans.insert(0,transforms.Resize(resize)) # 如果有Resize参数传进来，就进行resize操作
trans = transforms.Compose(trans)
mnist_train = torchvision.datasets.FashionMNIST(root="01_data/01_DataSet_FashionMNIST",train=True,transform=trans,download=True)
mnist_test = torchvision.datasets.FashionMNIST(root="01_data/01_DataSet_FashionMNIST",train=False,transform=trans,download=True)
return (data.DataLoader(mnist_train, batch_size, shuffle=True, num_workers=get_dataloader_workers()),
data.DataLoader(mnist_test, batch_size, shuffle=False, num_workers=get_dataloader_workers()))
batch_size = 256
train_iter, test_iter = load_data_fashion_mnist(batch_size) # 返回训练集、测试集的迭代器
num_inputs = 784
num_outputs = 10
w = torch.normal(0,0.01,size=(num_inputs,num_outputs),requires_grad=True)
b = torch.zeros(num_outputs,requires_grad=True)
def softmax(X):
X_exp = torch.exp(X) # 每个都进行指数运算
partition = X_exp.sum(1,keepdim=True)
return X_exp / partition # 这里应用了广播机制
# 实现softmax回归模型
def net(X):
return softmax(torch.matmul(X.reshape((-1,w.shape[0])),w)+b) # -1为默认的批量大小，表示有多少个图片，每个图片用一维的784列个元素表示
def cross_entropy(y_hat, y):
return -torch.log(y_hat[range(len(y_hat)),y]) # y_hat[range(len(y_hat)),y]为把y的标号列表对应的值拿出来。传入的y要是最大概率的标号
def accuracy(y_hat,y):
"""计算预测正确的数量"""
if len(y_hat.shape) > 1 and y_hat.shape[1] > 1: # y_hat.shape[1]>1表示不止一个类别，每个类别有各自的概率
y_hat = y_hat.argmax(axis=1) # y_hat.argmax(axis=1)为求行最大值的索引
cmp = y_hat.type(y.dtype) == y # 先判断逻辑运算符==，再赋值给cmp，cmp为布尔类型的数据
return float(cmp.type(y.dtype).sum()) # 获得y.dtype的类型作为传入参数，将cmp的类型转为y的类型（int型），然后再求和
# 可以评估在任意模型net的准确率
def evaluate_accuracy(net,data_iter):
"""计算在指定数据集上模型的精度"""
if isinstance(net,torch.nn.Module): # 如果net模型是torch.nn.Module实现的神经网络的话，将它变成评估模式
net.eval() # 将模型设置为评估模式
metric = Accumulator(2) # 正确预测数、预测总数，metric为累加器的实例化对象，里面存了两个数
for X, y in data_iter:
metric.add(accuracy(net(X),y),y.numel()) # net(X)将X输入模型，获得预测值。y.numel()为样本总数
return metric[0] / metric[1] # 分类正确的样本数 / 总样本数
# Accumulator实例中创建了2个变量，用于分别存储正确预测的数量和预测的总数量
class Accumulator:
"""在n个变量上累加"""
def __init__(self,n):
self.data = [0.0] * n
def add(self, *args):
self.data = [a+float(b) for a,b in zip(self.data,args)] # zip函数把两个列表第一个位置元素打包、第二个位置元素打包....
def reset(self):
self.data = [0.0] * len(self.data)
def __getitem__(self,idx):
return self.data[idx]
# 训练函数
def train_epoch_ch3(net, train_iter, loss, updater):
if isinstance(net, torch.nn.Module):
net.train() # 开启训练模式
metric = Accumulator(3)
for X, y in train_iter:
y_hat = net(X)
l = loss(y_hat,y) # 计算损失
if isinstance(updater, torch.optim.Optimizer): # 如果updater是pytorch的优化器的话
updater.zero_grad()
l.mean().backward() # 这里对loss取了平均值出来
updater.step()
metric.add(float(l)*len(y),accuracy(y_hat,y),y.size().numel()) # 总的训练损失、样本正确数、样本总数
else:
l.sum().backward()
updater(X.shape[0])
metric.add(float(l.sum()),accuracy(y_hat,y),y.numel())
return metric[0] / metric[2], metric[1] / metric[2] # 所有loss累加除以样本总数，总的正确个数除以样本总数
class Animator:
def __init__(self, xlabel=None, ylabel=None, legend=None, xlim=None,
ylim=None, xscale='linear',yscale='linear',
fmts=('-','m--','g-.','r:'),nrows=1,ncols=1,
figsize=(3.5,2.5)):
if legend is None:
legend = []
d2l.use_svg_display()
self.fig, self.axes = d2l.plt.subplots(nrows,ncols,figsize=figsize)
if nrows * ncols == 1:
self.axes = [self.axes,]
self.config_axes = lambda: d2l.set_axes(self.axes[0],xlabel,ylabel,xlim,ylim,xscale,yscale,legend)
self.X, self.Y, self.fmts = None, None, fmts
def add(self, x, y):
if not hasattr(y, "__len__"):
y = [y]
n = len(y)
if not hasattr(x, "__len__"):
x = [x] * n
if not self.X:
self.X = [[] for _ in range(n)]
if not self.Y:
self.Y = [[] for _ in range(n)]
for i, (a,b) in enumerate(zip(x,y)):
if a is not None and b is not None:
self.X[i].append(a)
self.Y[i].append(b)
self.axes[0].cla()
for x, y, fmt in zip(self.X, self.Y, self.fmts):
self.axes[0].plot(x, y, fmt)
self.config_axes()
display.display(self.fig)
display.clear_output(wait=True)
# 总训练函数
def train_ch3(net,train_iter,test_iter,loss,num_epochs,updater):
animator = Animator(xlabel='epoch',xlim=[1,num_epochs],ylim=[0.3,0.9],
legend=['train loss','train acc','test acc'])
for epoch in range(num_epochs): # 变量num_epochs遍数据
train_metrics = train_epoch_ch3(net,train_iter,loss,updater) # 返回两个值，一个总损失、一个总正确率
test_acc = evaluate_accuracy(net, test_iter) # 测试数据集上评估精度，仅返回一个值，总正确率
animator.add(epoch+1,train_metrics+(test_acc,)) # train_metrics+(test_acc,) 仅将两个值的正确率相加，
train_loss, train_acc = train_metrics
# 小批量随即梯度下降来优化模型的损失函数
lr = 0.1
def updater(batch_size):
return d2l.sgd([w,b],lr,batch_size)
num_epochs = 100
train_ch3(net,train_iter,test_iter,cross_entropy,num_epochs,updater)
预测数据#
def predict_ch3(net,test_iter,n=12):
for X, y in test_iter:
break # 仅拿出一批六个数据
trues = d2l.get_fashion_mnist_labels(y)
preds = d2l.get_fashion_mnist_labels(net(X).argmax(axis=1))
titles = [true + '\n' + pred for true, pred in zip(trues,preds)]
d2l.show_images(X[0:n].reshape((n,28,28)),1,n,titles=titles[0:n])
predict_ch3(net,test_iter)