1. LeNet网络¶
2. 总结¶
1. LeNet网络（使用自定义）¶
In [1]:
# LeNet(LeNet-5) 由两个部分组成：卷积编码器和全连接层密集块
import torch
from torch import nn
from d2l import torch as d2l
class Reshape(torch.nn.Module):
def forward(self,x):
return x.view(-1,1,28,28) # 批量数自适应得到，通道数为1，图片为28X28
net = torch.nn.Sequential(
Reshape(), nn.Conv2d(1,6,kernel_size=5,padding=2),nn.Sigmoid(),
nn.AvgPool2d(2,stride=2),
nn.Conv2d(6,16,kernel_size=5),nn.Sigmoid(),
nn.AvgPool2d(kernel_size=2,stride=2),nn.Flatten(),
nn.Linear(16 * 5 * 5, 120), nn.Sigmoid(),
nn.Linear(120, 84), nn.Sigmoid(),
nn.Linear(84,10))
X = torch.rand(size=(1,1,28,28),dtype=torch.float32)
for layer in net:
X = layer(X)
print(layer.__class__.__name__,'output shape：\t',X.shape) # 上一层的输出为这一层的输入
Reshape output shape： torch.Size([1, 1, 28, 28]) Conv2d output shape： torch.Size([1, 6, 28, 28]) Sigmoid output shape： torch.Size([1, 6, 28, 28]) AvgPool2d output shape： torch.Size([1, 6, 14, 14]) Conv2d output shape： torch.Size([1, 16, 10, 10]) Sigmoid output shape： torch.Size([1, 16, 10, 10]) AvgPool2d output shape： torch.Size([1, 16, 5, 5]) Flatten output shape： torch.Size([1, 400]) Linear output shape： torch.Size([1, 120]) Sigmoid output shape： torch.Size([1, 120]) Linear output shape： torch.Size([1, 84]) Sigmoid output shape： torch.Size([1, 84]) Linear output shape： torch.Size([1, 10])
In [2]:
help(d2l.load_data_fashion_mnist)
Help on function load_data_fashion_mnist in module d2l.torch:
load_data_fashion_mnist(batch_size, resize=None)
Download the Fashion-MNIST dataset and then load it into memory.
In [3]:
# LeNet在Fashion-MNIST数据集上的表现
batch_size = 256
train_iter, test_iter = d2l.load_data_fashion_mnist(batch_size=batch_size)
In [4]:
# 对evaluate_accuracy函数进行轻微的修改
def evaluate_accuracy_gpu(net, data_iter, device=None):
"""使用GPU计算模型在数据集上的精度"""
if isinstance(net, torch.nn.Module):
net.eval() # net.eval()开启验证模式，不用计算梯度和更新梯度
if not device:
device = next(iter(net.parameters())).device # 看net.parameters()中第一个元素的device为哪里
metric = d2l.Accumulator(2)
for X, y in data_iter:
if isinstance(X,list):
X = [x.to(device) for x in X] # 如果X是个List，则把每个元素都移到device上
else:
X = X.to(device) # 如果X是一个Tensor，则只用移动一次，直接把X移动到device上
y = y.to(device)
metric.add(d2l.accuracy(net(X),y),y.numel()) # y.numel() 为y元素个数
return metric[0]/metric[1]
In [5]:
# 为了使用GPU，还需要一点小改动
def train_ch6(net, train_iter, test_iter, num_epochs, lr, device):
"""Train a model with a GPU"""
def init_weights(m):
if type(m) == nn.Linear or type(m) == nn.Conv2d:
nn.init.xavier_uniform_(m.weight) # 根据输入、输出大小，使得随即初始化后，输入和输出的的方差是差不多的
net.apply(init_weights)
print('training on',device)
net.to(device)
optimizer = torch.optim.SGD(net.parameters(),lr=lr)
loss = nn.CrossEntropyLoss()
animator = d2l.Animator(xlabel='epoch',xlim=[1,num_epochs],
legend=['train loss', 'train acc', 'test acc'])
timer, num_batches = d2l.Timer(), len(train_iter)
for epoch in range(num_epochs):
metric = d2l.Accumulator(3)
net.train()
for i, (X,y) in enumerate(train_iter):
timer.start()
optimizer.zero_grad()
X, y = X.to(device), y.to(device)
y_hat = net(X)
l = loss(y_hat, y)
l.backward()
optimizer.step()
with torch.no_grad():
metric.add(l * X.shape[0], d2l.accuracy(y_hat,y),X.shape[0])
timer.stop()
train_l = metric[0] / metric[2]
train_acc = metric[1] / metric[2]
if(i+1) % (num_batches//5) == 0 or i == num_batches - 1:
animator.add(epoch + (i+1) / num_batches,
(train_l, train_acc, None))
test_acc = evaluate_accuracy_gpu(net, test_iter)
animator.add(epoch + 1, (None, None, test_acc))
print(f'loss {train_l:.3f},train acc {train_acc:.3f},'
f'test acc {test_acc:.3f}')
print(f'{metric[2] * num_epochs / timer.sum():.1f} examples/sec'
f'on{str(device)}')
In [6]:
help(nn.init.xavier_uniform_)
Help on function xavier_uniform_ in module torch.nn.init:
xavier_uniform_(tensor:torch.Tensor, gain:float=1.0) -> torch.Tensor
Fills the input `Tensor` with values according to the method
described in `Understanding the difficulty of training deep feedforward
neural networks` - Glorot, X. & Bengio, Y. (2010), using a uniform
distribution. The resulting tensor will have values sampled from
:math:`\mathcal{U}(-a, a)` where
.. math::
a = \text{gain} \times \sqrt{\frac{6}{\text{fan\_in} + \text{fan\_out}}}
Also known as Glorot initialization.
Args:
tensor: an n-dimensional `torch.Tensor`
gain: an optional scaling factor
Examples:
>>> w = torch.empty(3, 5)
>>> nn.init.xavier_uniform_(w, gain=nn.init.calculate_gain('relu'))
In [7]:
# 训练和评估LeNet-5模型
lr, num_epochs = 0.9, 10
train_ch6(net, train_iter, test_iter, num_epochs, lr, d2l.try_gpu())
loss 0.458,train acc 0.829,test acc 0.801 45859.5 examples/seconcuda:0
