一起读《动手学深度学习（PyTorch版）》- 多层感知机 - Tanh、RELU训练对比
<div class='showpostmsg'><article data-content="[{"type":"block","id":"2vrE-1729868216059","name":"paragraph","data":{},"nodes":[{"type":"text","id":"Y6MA-1729868216060","leaves":[{"text":"使用RELU激活函数","marks":[]}]}],"state":{}}]"><p>使用RELU激活函数</p>
<pre>
<code>import torch
import torchvision
from torch.utils import data
from torchvision import transforms
import matplotlib.pyplot as plt
from torch import nn
def get_dataloader_workers():
return 6
def load_data_fashion_mnist(batch_size, resize=None):
trans =
if resize:
trans.insert(0, transforms.Resize(resize))
trans = transforms.Compose(trans)
mnist_train = torchvision.datasets.FashionMNIST(root="./data", train=True, transform=trans, download=True)
mnist_test = torchvision.datasets.FashionMNIST(root="./data", 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()))
def accurancy(y_hat, y):
if len(y_hat.shape) > 1 and y_hat.shape > 1:
y_hat = y_hat.argmax(axis=1)
cmp = y_hat.type(y.dtype) == y
return float(cmp.type(y.dtype).sum())
class Accumulator:
def __init__(self, n) -> None:
self.data = *n
def add(self, *args):
self.data =
def reset(self):
self.data = * len(self.data)
def __getitem__(self, idx):
return self.data
def evaluate_accurancy(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(accurancy(net(X), y), y.numel())
return metric / metric
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)
metric.add(float(l.sum()), accurancy(y_hat, y), y.numel())
return metric / metric, metric / metric
def set_axes(axes, xlable, ylable, xlim, ylim, xscale, yscale, legend):
axes.set_xlabel(xlable)
axes.set_ylabel(ylable)
axes.set_xscale(xscale)
axes.set_yscale(yscale)
axes.set_xlim(xlim)
axes.set_ylim(ylim)
if legend:
axes.legend(legend)
axes.grid()
class Animator:
def __init__(self, xlable=None, ylable=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 = []
self.fig, self.axes = plt.subplots(nrows, ncols, figsize=figsize)
if nrows * ncols == 1:
self.axes =
self.config_axes = lambda: set_axes(self.axes, xlable, ylable, 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=
n = len(y)
if not hasattr(x, "__len__"):
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.append(a)
self.Y.append(b)
self.axes.cla()
for x, y, fmt in zip(self.X, self.Y, self.fmts):
self.axes.plot(x, y, fmt)
self.config_axes()
def train_ch3(net, train_iter, test_iter, loss, num_epochs, updater):
animator = Animator(xlable='epoch', xlim=, ylim=, 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_accurancy(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
net = nn.Sequential(nn.Flatten(),
nn.Linear(784, 256),
nn.ReLU(),
nn.Linear(256, 10))
def init_weights(m):
if type(m) == nn.Linear:
nn.init.normal_(m.weight, std=0.01)
net.apply(init_weights)
batch_size, lr, num_epochs = 256, 0.1, 10
loss = nn.CrossEntropyLoss(reduction='none')
trainer = torch.optim.SGD(net.parameters(), lr=lr)
train_iter, test_iter = load_data_fashion_mnist(batch_size)
train_ch3(net, train_iter, test_iter, loss, num_epochs, trainer)
plt.show()</code></pre>
<article data-content="[{"type":"block","id":"0pVh-1729868216061","name":"paragraph","data":{},"nodes":[{"type":"text","id":"ogdd-1729868216062","leaves":[{"text":"训练结果","marks":[]}]}],"state":{}}]">
<p>训练结果</p>
<p> </p>
<article data-content="[{"type":"block","id":"D8gb-1729868241686","name":"paragraph","data":{},"nodes":[{"type":"text","id":"wN1A-1729868241685","leaves":[{"text":"更换激活函数为Tanh","marks":[]}]}],"state":{}}]">
<p>更换激活函数为Tanh</p>
<pre>
<code>net = nn.Sequential(nn.Flatten(),
nn.Linear(784, 256),
nn.Tanh(),
nn.Linear(256, 10))</code></pre>
<p> </p>
<p> </p>
</article>
</article>
</article>
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Jacktang 发表于 2024-10-26 14:22
多层感知机 - Tanh、RELU训练对比核心也是算法
<p>是的啊</p>
zgnasd1950 发表于 2024-10-26 22:30
学习了，内容非常清晰，非常感谢楼主的分享。好文，有需要的可以看看。
<p>感谢</p>
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