依然是pycharm环境，图像显示部分和jupyter不一样
import torch
import matplotlib.pyplot as plt
from IPython import display
from d2l import torch as d2l
d2l.use_svg_display()#!!!!
# help(d2l.use_svg_display())
batch_size=256
train_iter,test_iter=d2l.load_data_fashion_mnist(batch_size)
next(iter(test_iter))
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)
X=torch.tensor([[1.0,2.0,3.0],[4.0,5.0,6.0]])
print(X.sum(0,keepdim=True),X.sum(1,keepdim=True))
def softmax(X):
X_exp=torch.exp(X)
partition=X_exp.sum(1,keepdim=True)
return X_exp/partition
X=torch.normal(0,1,(2,5))
X_prob=softmax(X)
print(X_prob,X_prob.sum(1))
def net(X):
return softmax(torch.matmul(X.reshape(-1,W.shape[0]),W)+b)
y=torch.tensor([0,2])
y_hat=torch.tensor([[0.1,0.3,0.6],[0.3,0.2,0.5]])
print(y_hat[[0,1],y])
def cross_entropy(y_hat,y):
return -torch.log(y_hat[range(y_hat.shape[0]),y])
print(cross_entropy(y_hat,y))
print(y_hat.shape,len(y_hat.shape),y_hat.shape[1])
def accuracy(y_hat,y):
if len(y_hat.shape)>1 and y_hat.shape[1]>1:
y_hat=y_hat.argmax(axis=1)
cmp=y_hat.type(y.dtype)==y
return float(cmp.type(y.dtype).sum())
print(accuracy(y_hat,y)/len(y))
def evaluate_accuracy(net,data_iter):
if isinstance(net,torch.nn.Module):
net.eval()
metric=Accumulator(2)
with torch.no_grad():
for X,y in data_iter:
metric.add(accuracy(net(X),y),y.numel())
return metric[0]/metric[1]
class Accumulator:
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]*self.data
def __getitem__(self, item):
return self.data[item]
print(evaluate_accuracy(net,test_iter))
def train_epoch_ch3(net,train_iter,loss,updater):
if isinstance(net,torch.nn.Module):
net.train()
metric=Accumulator(3)
for X,y in train_iter:
y_hat=net(X)
l=loss(y_hat,y)
if isinstance(updater,torch.optim.Optimizer):
updater.zero_grad()
l.mean.backward()
updater.step()
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]
class Animator: #@save
"""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)):
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)
plt.pause(0.01)
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
assert train_loss<0.5,train_loss
assert train_acc<=1 and train_acc>0.7,train_acc
assert test_acc<=1 and test_acc>0.7,test_acc
lr=0.1
def updater(batch_size):
return d2l.sgd([W,b],lr,batch_size)
num_epochs=10
train_ch3(net,train_iter,test_iter,cross_entropy,num_epochs,updater)
plt.show()
def predict_ch3(net,test_iter,n=6):
"""预测标签"""
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)
plt.show()
