此次测试发现老是发现缺包,后面发现装的位置不对。先看一下环境文件
发现自己的用户名是gluon ,所以应该在d21-zh文件目录下进cmd应该输入conda activate gluon
导入包
import torch
from IPython import display
from d2l import torch as d2l
读取数据集
batch_size = 256
train_iter,test_iter = d2l.load_data_fashion_mnist(batch_size)
展开每个图像,看作784向量。数据集有10个类别,则输出维度10
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)
给定一个矩阵,对所有矩阵求和
X = torch.tensor([[1.0,2.0,3.0],[4.0,5.0,6.0]])
X.sum(0,keepdim=True),X.sum(1,keepdim=True)#分别以维度0和1求和 6=【1,2,3】相加 15=【4,5,6】自己相加
实现softmax
def softmax(X):
X_exp = torch.exp(X)#指数计算
partition = X_exp.sum(1,keepdim=True)
return X_exp/partition
将每个元素变成一个非负数。概率原理,每行总和为1
X = torch.normal(0,1,(2,5))
X_prob = softmax(X)
X_prob,X_prob.sum(1)
具体实现softmax
def net(X):
return softmax(torch.matmul(X.reshape((-1,W.shape[0])),W)+b) #-1表示批量大小
常见一个数据y_hat,包含两个样本在3个类别的预测概率,使用y作为y_haht中概率大索引
y = torch.tensor([0,2]) #两个真实的标号
y_hat = torch.tensor([[0.1,0.3,0.6],[0.3,0.2,0.5]])
y_hat[[0,1],y] #0.1是y_hat[0]在[0.1,0.3,0.6]里第y[0] 0.5是y_hat[1]在[0.3,0.,0.5]里第y[2]
实现交叉熵损失函数
#print(y_hat,y) y_hat是预测集 y是预测的预测集标号
#print(y_hat[range(len(y_hat)),y])
def cross_entropy(y_hat,y):
return -torch.log(y_hat[range(len(y_hat)),y]) #y_hat[range(len(y_hat)),y]与上段测试相同
cross_entropy(y_hat,y)
将预测类别与真实y元素比较
def accuracy(y_hat,y):
"""计算正确预测的数量"""
if len(y_hat.shape)>1 and y_hat.shape[1]>1: #判断y_hat是否为矩阵
y_hat = y_hat.argmax(axis=1)
cmp = y_hat.type(y.dtype) == y
return float(cmp.type(y.dtype).sum()) #y是int类型 cmp转int求和再转float
accuracy(y_hat,y)/len(y) #测试结果
评估在任意模型net的准确率
def evaluate_accuracy(net, data_iter): #@save
"""计算在指定数据集上模型的精度。"""
if isinstance(net, torch.nn.Module):
net.eval() # 将模型设置为评估模式
metric = Accumulator(2) # 正确预测数、预测总数
for X, y in train_iter:
metric.add(accuracy(net(X), y), y.numel())
return metric[0] / metric[1] # metric[0]是分类正确 metric[1]是分类总数
Accumulator实例创建2个变量,分别存储正确预测数量和预测总数量
class Accumulator: #@save
"""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)]
def reset(self):
self.data = [0.0] * len(self.data)
def __getitem__(self, idx):
return self.data[idx]
evaluate_accuracy(net,test_iter)
Softmax回归训练
def train_epoch_ch3(net,train_iter,loss,updater):
if isinstance(net,torch.nn.Module):
net.train()
metric = Accumulator(3)
#for X,y in data_iter(batch_size,features,labels):此线性的
for X,y in train_iter: #(batch_size,features,labels)在上文集合了
#l = loss(net(X),y)
y_hat = net(X)
l = loss(y_hat,y) #上下不可改为 l = loss(net(X),y) 因为y_hat类型是float
if isinstance(updater,torch.optim.Optimizer):
updater.zero_grad()
l.backward()
updater.step()
metric.add(
float(1)*len(y),accuracy(y_hat,y),y.size().numel()
)
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]
定义一个在动画中绘制数据的实用程序类
class Animator:
"""在动画中绘制数据。"""
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)):
# 增量地绘制多条线
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, ]
# 使用lambda函数捕获参数
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):
# 向图表中添加多个数据点
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)
训练函数
def train_ch3(net, train_iter, test_iter, loss, num_epochs, updater):
"""训练模型。"""
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):
train_metrics = train_epoch_ch3(net, train_iter, loss, updater)
test_acc = evaluate_accuracy(net, test_iter)
animator.add(epoch + 1, train_metrics + (test_acc,))
train_loss, train_acc = train_metrics
小批量随机梯度下降优化模型店损失函数
lr = 0.1
def updater(batch_size):
return d2l.sgd([W,b],lr,batch_size)
训练模型10个迭代周期
num_epochs = 10
train_ch3(net,train_iter,test_iter,cross_entropy,num_epochs,updater)
显示
对图像进行分类预测
def predict_ch3(net, test_iter, n=6):
"""预测标签(定义见第3章)。"""
for X, y in test_iter:
break
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)
显示