本文的主要内容对沐神提供的代码中个人不太理解的内容进行笔记记录，内容不会特别严谨仅供参考。
1.函数目录
1.1 torch
2. LeNet
- LeNet是早期成功的神经网络
- 先使用卷积层来学习图片空间信息
- 然后使用全连接层来转换到类别空间
LeNet-5网络参数详解
3. 代码实现
3.1 model
import torch
from torch import nn
class Reshape(torch.nn.Module):
def forward(self, x):
return x.view(-1, 1, 28, 28)
net = nn.Sequential(
Reshape(),
nn.Conv2d(in_channels=1, out_channels=6, kernel_size=5, padding=2),nn.Sigmoid(),
nn.AvgPool2d(kernel_size=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((1,1,28,28), dtype=torch.float32)
for layer in net:
X = layer(X)
print(layer.__class__.__name__, 'output shape:\t',X.shape)
3.2 train
import torch
from torch import nn
import model
import tools
from model import net
from d2l import torch as d2l
import pandas as pd
from tools import *
def train_ch6(net, train_iter, test_iter, num_epochs, lr, device):
"""用GPU训练模型(在第六章定义)"""
#模型参数初始化
def init_weights(m):
if type(m) == nn.Linear or type(m) == nn.Conv2d:
nn.init.xavier_uniform_(m.weight)
net.apply(init_weights)
print("training on", device)
net.to(device)
# 定义优化器
ptimizer = torch.optim.SGD(net.parameters(), lr=lr)
# 定义损失函数
loss = nn.CrossEntropyLoss()
# 训练集损失函数
# 训练集损失列表
train_loss_all = []
train_acc_all = []
# 验证集损失列表
val_loss_all = []
val_acc_all = []
timer = tools.Timer()
timer.start()
for epoch in range(num_epochs):
train_loss, train_acc = train_epoch_gpu(net, train_iter, loss, ptimizer, device)
val_loss, val_acc = evalution_loss_accuracy_gpu(net, test_iter, loss, device)
train_loss_all.append(train_loss)
train_acc_all.append(train_acc)
val_loss_all.append(val_loss)
val_acc_all.append(val_acc)
print("{} train loss:{:.4f} train acc: {:.4f}".format(epoch, train_loss_all[-1], train_acc_all[-1]))
print("{} val loss:{:.4f} val acc: {:.4f}".format(epoch, val_loss_all[-1], val_acc_all[-1]))
print("训练和验证耗费的时间{:.0f}m{:.0f}s".format(timer.stop() // 60, timer.stop() % 60))
train_process = pd.DataFrame(data={"epoch": range(num_epochs),
"train_loss_all": train_loss_all,
"val_loss_all": val_loss_all,
"train_acc_all": train_acc_all,
"val_acc_all": val_acc_all, })
return train_process
if __name__ == "__main__":
batch_size = 256
train_iter, test_iter = d2l.load_data_fashion_mnist(batch_size=batch_size)
LeNet5 = model.net
train_process = train_ch6(LeNet5,train_iter,test_iter,10,0.8,tools.try_gpu())
tools.matplot_acc_loss(train_process)
自定义的tools文件
import pandas as pd
import torch
import matplotlib.pyplot as plt
from torch import nn
import time
import numpy as np
class Timer: #@save
"""记录多次运行时间"""
def __init__(self):
self.times = []
self.start()
def start(self):
"""启动计时器"""
self.tik = time.time()
def stop(self):
"""停止计时器并将时间记录在列表中"""
self.times.append(time.time() - self.tik)
return self.times[-1]
def avg(self):
"""返回平均时间"""
return sum(self.times) / len(self.times)
def sum(self):
"""返回时间总和"""
return sum(self.times)
def cumsum(self):
"""返回累计时间"""
return np.array(self.times).cumsum().tolist()
argmax = lambda x, *args, **kwargs: x.argmax(*args, **kwargs) #返回最大值的索引下标
astype = lambda x, *args, **kwargs: x.type(*args, **kwargs) # 转换数据类型
reduce_sum = lambda x, *args, **kwargs: x.sum(*args, **kwargs) # 求和
# 对多个变量累加
class Accumulator:
"""For accumulating sums over `n` variables."""
def __init__(self, n):
"""Defined in :numref:`sec_utils`"""
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]
# 计算正确预测的数量
def accuracy(y_hat, y):
"""Compute the number of correct predictions.
Defined in :numref:`sec_utils`"""
if len(y_hat.shape) > 1 and y_hat.shape[1] > 1:
y_hat = argmax(y_hat, axis=1)
cmp = astype(y_hat, y.dtype) == y
return float(reduce_sum(astype(cmp, y.dtype)))
# 单轮训练
def train_epoch(net, train_iter, loss, trainer):
if isinstance(net, nn.Module):
net.train()
metric_train = Accumulator(3)
for X, y in train_iter:
y_hat = net(X)
l = loss(y_hat, y)
if isinstance(trainer, torch.optim.Optimizer):
trainer.zero_grad()
l.mean().backward()
trainer.step()
else:
l.sum().backward()
trainer(X.shape[0])
metric_train.add(float(l.sum()), accuracy(y_hat, y), y.numel())
#返回训练损失和训练精度
return metric_train[0]/metric_train[2], metric_train[1]/metric_train[2]
# 单轮训练
def train_epoch_gpu(net, train_iter, loss, trainer,device):
if isinstance(net, nn.Module):
net.train()
metric_train = Accumulator(3)
for i, (X, y) in enumerate(train_iter):
X, y = X.to(device), y.to(device)
y_hat = net(X)
l = loss(y_hat, y)
if isinstance(trainer, torch.optim.Optimizer):
trainer.zero_grad()
l.backward()
trainer.step()
else:
l.sum().backward()
trainer(X.shape[0])
metric_train.add(l * X.shape[0], accuracy(y_hat, y), X.shape[0])
#返回训练损失和训练精度
return metric_train[0]/metric_train[2], metric_train[1]/metric_train[2]
# 用于计算验证集上的准确率
def evalution_loss_accuracy(net, data_iter, loss):
if isinstance(net, torch.nn.Module):
net.eval()
meteric = Accumulator(3)
with torch.no_grad():
for X, y in data_iter:
l = loss(net(X), y)
meteric.add(float(l.sum())*X.shape[0], accuracy(net(X), y), X.shape[0])
return meteric[0]/meteric[2], meteric[1]/meteric[2]
# 用于计算验证集上的准确率
def evalution_loss_accuracy_gpu(net, data_iter, loss, device='None'):
if isinstance(net, torch.nn.Module):
net.eval()
if not device:
#将net层的第一个元素拿出来看其在那个设备上
device = next(iter(net.parameters())).device
meteric = Accumulator(3)
with torch.no_grad():
for X, y in data_iter:
if isinstance(X, list):
X = [x.to(device) for x in X]
else:
X = X.to(device) # 赋值给 X，将数据移动到GPU中
y = y.to(device) # 赋值给 y，将数据移动到GPU中
l = loss(net(X), y)
meteric.add(l * X.shape[0], accuracy(net(X), y), X.shape[0])
# meteric.add(float(l.sum()), accuracy(net(X), y), y.numel()) # 转为浮点数
return meteric[0]/meteric[2], meteric[1]/meteric[2]
def matplot_acc_loss(train_process):
# 显示每一次迭代后的训练集和验证集的损失函数和准确率
plt.figure(figsize=(12, 4))
plt.subplot(1, 2, 1)
plt.plot(train_process['epoch'], train_process.train_loss_all, "ro-", label="Train loss")
plt.plot(train_process['epoch'], train_process.val_loss_all, "bs-", label="Val loss")
plt.legend()
plt.xlabel("epoch")
plt.ylabel("Loss")
plt.subplot(1, 2, 2)
plt.plot(train_process['epoch'], train_process.train_acc_all, "ro-", label="Train acc")
plt.plot(train_process['epoch'], train_process.val_acc_all, "bs-", label="Val acc")
plt.xlabel("epoch")
plt.ylabel("acc")
plt.legend()
plt.show()
def gpu(i=0):
"""Get a GPU device.
Defined in :numref:`sec_use_gpu`"""
return torch.device(f'cuda:{i}')
def cpu():
"""Get the CPU device.
Defined in :numref:`sec_use_gpu`"""
return torch.device('cpu')
def num_gpus():
"""Get the number of available GPUs.
Defined in :numref:`sec_use_gpu`"""
return torch.cuda.device_count()
def try_gpu(i=0):
"""Return gpu(i) if exists, otherwise return cpu().
Defined in :numref:`sec_use_gpu`"""
if num_gpus() >= i + 1:
return gpu(i)
return cpu()