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二十六、含并行连结的网络（ GoogLeNet ）
import torch
import torchvision
import time
from torch import nn
from torch.nn import functional as F
from IPython import display
from torchvision import transforms
from torch.utils import data
import matplotlib.pyplot as plt
from matplotlib_inline import backend_inline
mydevice = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
def accuracy(y_hat, y): # 定义一个函数来为预测正确的数量计数
"""计算预测正确的数量"""
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 # bool 类型，若预测结果与实际结果一致，则为 True
return float(cmp.type(y.dtype).sum())
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, nn.Module):
net.eval() # 设置为评估模式
if not device:
device = next(iter(net.parameters())).device
# 正确预测的数量，总预测的数量
metric = Accumulator(2)
with torch.no_grad():
for X, y in data_iter:
if isinstance(X, list):
# BERT微调所需的（之后将介绍）
X = [x.to(device) for x in X]
else:
X = X.to(device)
y = y.to(device)
metric.add(accuracy(net(X), y), y.numel())
return metric[0] / metric[1]
def set_axes(axes, xlabel, ylabel, xlim, ylim, xscale, yscale, legend):
axes.set_xlabel(xlabel), axes.set_ylabel(ylabel)
axes.set_xscale(xscale), axes.set_yscale(yscale)
axes.set_xlim(xlim), axes.set_ylim(ylim)
if legend:
axes.legend(legend)
axes.grid()
class Accumulator: # 定义一个实用程序类 Accumulator，用于对多个变量进行累加
"""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]
class Animator: # 定义一个在动画中绘制数据的实用程序类 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 = []
backend_inline.set_matplotlib_formats('svg')
self.fig, self.axes = 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: 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):
# Add multiple data points into the figure
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)
# 通过以下两行代码实现了在PyCharm中显示动图
# plt.draw()
# plt.pause(interval=0.001)
display.clear_output(wait=True)
plt.rcParams['font.sans-serif'] = ['Microsoft YaHei']
class Timer:
def __init__(self):
self.times = []
self.start()
def start(self):
self.tik = time.time()
def stop(self):
self.times.append(time.time() - self.tik)
return self.times[-1]
def sum(self):
"""Return the sum of time."""
return sum(self.times)
def load_data_fashion_mnist(batch_size, resize=None):
"""下载 Fashion-MNIST 数据集，然后将其加载到内存中"""
trans = [transforms.ToTensor()]
if resize:
trans.insert(0, transforms.Resize(resize))
trans = transforms.Compose(trans)
mnist_train = torchvision.datasets.FashionMNIST(
root="../data", train=True, transform=trans, download=False)
mnist_test = torchvision.datasets.FashionMNIST(
root="../data", train=False, transform=trans, download=False)
return (data.DataLoader(mnist_train, batch_size, shuffle=True,
num_workers=4),
data.DataLoader(mnist_test, batch_size, shuffle=False,
num_workers=4))
def train(net, train_iter, test_iter, num_epochs, lr, device):
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', torch.cuda.get_device_name(device))
net.to(device)
optimizer = torch.optim.SGD(net.parameters(), lr=lr)
loss = nn.CrossEntropyLoss()
animator = Animator(xlabel='epoch', xlim=[1, num_epochs],
legend=['train loss', 'train acc', 'test acc'])
timer, num_batches = Timer(), len(train_iter)
for epoch in range(num_epochs):
# 训练损失之和，训练准确率之和，样本数
metric = 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], 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))
plt.title(f'loss {train_l:.3f}, train acc {train_acc:.3f}, test acc {test_acc:.3f}\n'
f'{metric[2] * num_epochs / timer.sum():.1f} examples/sec on {str(device)}')
plt.show()
class Inception(nn.Module):
# c1--c4是每条路径的输出通道数
def __init__(self, in_channels, c1, c2, c3, c4, **kwargs):
super(Inception, self).__init__(**kwargs)
# 线路1，单1x1卷积层
self.p1_1 = nn.Conv2d(in_channels, c1, kernel_size=1)
# 线路2，1x1卷积层后接3x3卷积层
self.p2_1 = nn.Conv2d(in_channels, c2[0], kernel_size=1)
self.p2_2 = nn.Conv2d(c2[0], c2[1], kernel_size=3, padding=1)
# 线路3，1x1卷积层后接5x5卷积层
self.p3_1 = nn.Conv2d(in_channels, c3[0], kernel_size=1)
self.p3_2 = nn.Conv2d(c3[0], c3[1], kernel_size=5, padding=2)
# 线路4，3x3最大汇聚层后接1x1卷积层
self.p4_1 = nn.MaxPool2d(kernel_size=3, stride=1, padding=1)
self.p4_2 = nn.Conv2d(in_channels, c4, kernel_size=1)
def forward(self, x):
p1 = F.relu(self.p1_1(x))
p2 = F.relu(self.p2_2(F.relu(self.p2_1(x))))
p3 = F.relu(self.p3_2(F.relu(self.p3_1(x))))
p4 = F.relu(self.p4_2(self.p4_1(x)))
# 在通道维度上连结输出
return torch.cat((p1, p2, p3, p4), dim=1)
b1 = nn.Sequential(nn.Conv2d(1, 64, kernel_size=7, stride=2, padding=3),
nn.ReLU(),
nn.MaxPool2d(kernel_size=3, stride=2, padding=1))
b2 = nn.Sequential(nn.Conv2d(64, 64, kernel_size=1),
nn.ReLU(),
nn.Conv2d(64, 192, kernel_size=3, padding=1),
nn.ReLU(),
nn.MaxPool2d(kernel_size=3, stride=2, padding=1))
b3 = nn.Sequential(Inception(192, 64, (96, 128), (16, 32), 32),
Inception(256, 128, (128, 192), (32, 96), 64),
nn.MaxPool2d(kernel_size=3, stride=2, padding=1))
b4 = nn.Sequential(Inception(480, 192, (96, 208), (16, 48), 64),
Inception(512, 160, (112, 224), (24, 64), 64),
Inception(512, 128, (128, 256), (24, 64), 64),
Inception(512, 112, (144, 288), (32, 64), 64),
Inception(528, 256, (160, 320), (32, 128), 128),
nn.MaxPool2d(kernel_size=3, stride=2, padding=1))
b5 = nn.Sequential(Inception(832, 256, (160, 320), (32, 128), 128),
Inception(832, 384, (192, 384), (48, 128), 128),
nn.AdaptiveAvgPool2d((1,1)),
nn.Flatten())
net = nn.Sequential(b1, b2, b3, b4, b5, nn.Linear(1024, 10))
X = torch.rand(size=(1, 1, 96, 96))
for layer in net:
X = layer(X)
print(layer.__class__.__name__, 'output shape:\t\t', X.shape)
lr, num_epochs, batch_size = 0.1, 5, 128
train_iter, test_iter = load_data_fashion_mnist(batch_size, resize=96)
train(net, train_iter, test_iter, num_epochs, lr, mydevice)
二十七、批量规范化
import torch
import torchvision
import time
from torch import nn
from IPython import display
from torchvision import transforms
from torch.utils import data
import matplotlib.pyplot as plt
from matplotlib_inline import backend_inline
mydevice = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
def accuracy(y_hat, y): # 定义一个函数来为预测正确的数量计数
"""计算预测正确的数量"""
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 # bool 类型，若预测结果与实际结果一致，则为 True
return float(cmp.type(y.dtype).sum())
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, nn.Module):
net.eval() # 设置为评估模式
if not device:
device = next(iter(net.parameters())).device
# 正确预测的数量，总预测的数量
metric = Accumulator(2)
with torch.no_grad():
for X, y in data_iter:
if isinstance(X, list):
# BERT微调所需的（之后将介绍）
X = [x.to(device) for x in X]
else:
X = X.to(device)
y = y.to(device)
metric.add(accuracy(net(X), y), y.numel())
return metric[0] / metric[1]
def set_axes(axes, xlabel, ylabel, xlim, ylim, xscale, yscale, legend):
axes.set_xlabel(xlabel), axes.set_ylabel(ylabel)
axes.set_xscale(xscale), axes.set_yscale(yscale)
axes.set_xlim(xlim), axes.set_ylim(ylim)
if legend:
axes.legend(legend)
axes.grid()
class Accumulator: # 定义一个实用程序类 Accumulator，用于对多个变量进行累加
"""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]
class Animator: # 定义一个在动画中绘制数据的实用程序类 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 = []
backend_inline.set_matplotlib_formats('svg')
self.fig, self.axes = 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: 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):
# Add multiple data points into the figure
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)
# 通过以下两行代码实现了在PyCharm中显示动图
# plt.draw()
# plt.pause(interval=0.001)
display.clear_output(wait=True)
plt.rcParams['font.sans-serif'] = ['Microsoft YaHei']
class Timer:
def __init__(self):
self.times = []
self.start()
def start(self):
self.tik = time.time()
def stop(self):
self.times.append(time.time() - self.tik)
return self.times[-1]
def sum(self):
"""Return the sum of time."""
return sum(self.times)
def load_data_fashion_mnist(batch_size, resize=None):
"""下载 Fashion-MNIST 数据集，然后将其加载到内存中"""
trans = [transforms.ToTensor()]
if resize:
trans.insert(0, transforms.Resize(resize))
trans = transforms.Compose(trans)
mnist_train = torchvision.datasets.FashionMNIST(
root="../data", train=True, transform=trans, download=False)
mnist_test = torchvision.datasets.FashionMNIST(
root="../data", train=False, transform=trans, download=False)
return (data.DataLoader(mnist_train, batch_size, shuffle=True,
num_workers=4),
data.DataLoader(mnist_test, batch_size, shuffle=False,
num_workers=4))
def train(net, train_iter, test_iter, num_epochs, lr, device):
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', torch.cuda.get_device_name(device))
net.to(device)
optimizer = torch.optim.SGD(net.parameters(), lr=lr)
loss = nn.CrossEntropyLoss()
animator = Animator(xlabel='epoch', xlim=[1, num_epochs],
legend=['train loss', 'train acc', 'test acc'])
timer, num_batches = Timer(), len(train_iter)
for epoch in range(num_epochs):
# 训练损失之和，训练准确率之和，样本数
metric = 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], 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))
plt.title(f'loss {train_l:.3f}, train acc {train_acc:.3f}, test acc {test_acc:.3f}\n'
f'{metric[2] * num_epochs / timer.sum():.1f} examples/sec on {str(device)}')
plt.show()
def batch_norm(X, gamma, beta, moving_mean, moving_var, eps, momentum):
# 通过is_grad_enabled来判断当前模式是训练模式还是预测模式
if not torch.is_grad_enabled():
# 如果是在预测模式下，直接使用传入的移动平均所得的均值和方差
X_hat = (X - moving_mean) / torch.sqrt(moving_var + eps)
else:
assert len(X.shape) in (2, 4)
if len(X.shape) == 2:
# 使用全连接层的情况，计算特征维上的均值和方差
mean = X.mean(dim=0)
var = ((X - mean) ** 2).mean(dim=0)
else:
# 使用二维卷积层的情况，计算通道维上（axis=1）的均值和方差。
# 这里我们需要保持X的形状以便后面可以做广播运算
mean = X.mean(dim=(0, 2, 3), keepdim=True)
var = ((X - mean) ** 2).mean(dim=(0, 2, 3), keepdim=True)
# 训练模式下，用当前的均值和方差做标准化
X_hat = (X - mean) / torch.sqrt(var + eps)
# 更新移动平均的均值和方差
moving_mean = momentum * moving_mean + (1.0 - momentum) * mean
moving_var = momentum * moving_var + (1.0 - momentum) * var
Y = gamma * X_hat + beta # 缩放和移位
return Y, moving_mean.data, moving_var.data
class BatchNorm(nn.Module):
# num_features：完全连接层的输出数量或卷积层的输出通道数。
# num_dims：2表示完全连接层，4表示卷积层
def __init__(self, num_features, num_dims):
super().__init__()
if num_dims == 2:
shape = (1, num_features)
else:
shape = (1, num_features, 1, 1)
# 参与求梯度和迭代的拉伸和偏移参数，分别初始化成1和0
self.gamma = nn.Parameter(torch.ones(shape))
self.beta = nn.Parameter(torch.zeros(shape))
# 非模型参数的变量初始化为0和1
self.moving_mean = torch.zeros(shape)
self.moving_var = torch.ones(shape)
def forward(self, X):
# 如果X不在内存上，将moving_mean和moving_var
# 复制到X所在显存上
if self.moving_mean.device != X.device:
self.moving_mean = self.moving_mean.to(X.device)
self.moving_var = self.moving_var.to(X.device)
# 保存更新过的moving_mean和moving_var
Y, self.moving_mean, self.moving_var = batch_norm(
X, self.gamma, self.beta, self.moving_mean,
self.moving_var, eps=1e-5, momentum=0.9)
return Y
net = nn.Sequential(
nn.Conv2d(1, 6, kernel_size=5), BatchNorm(6, num_dims=4), nn.Sigmoid(),
nn.AvgPool2d(kernel_size=2, stride=2),
nn.Conv2d(6, 16, kernel_size=5), BatchNorm(16, num_dims=4), nn.Sigmoid(),
nn.AvgPool2d(kernel_size=2, stride=2), nn.Flatten(),
nn.Linear(16*4*4, 120), BatchNorm(120, num_dims=2), nn.Sigmoid(),
nn.Linear(120, 84), BatchNorm(84, num_dims=2), nn.Sigmoid(),
nn.Linear(84, 10))
lr, num_epochs, batch_size = 1.0, 5, 256
train_iter, test_iter = load_data_fashion_mnist(batch_size)
train(net, train_iter, test_iter, num_epochs, lr, mydevice)
print("拉伸参数 gamma : ", net[1].gamma.resh1ape((-1,)))
print("偏移参数 beta : ", net[1].beta.reshape((-1,)))
# ########## 批量规范化的简明实现 ##########
# net = nn.Sequential(
# nn.Conv2d(1, 6, kernel_size=5), nn.BatchNorm2d(6), nn.Sigmoid(),
# nn.AvgPool2d(kernel_size=2, stride=2),
# nn.Conv2d(6, 16, kernel_size=5), nn.BatchNorm2d(16), nn.Sigmoid(),
# nn.AvgPool2d(kernel_size=2, stride=2), nn.Flatten(),
# nn.Linear(256, 120), nn.BatchNorm1d(120), nn.Sigmoid(),
# nn.Linear(120, 84), nn.BatchNorm1d(84), nn.Sigmoid(),
# nn.Linear(84, 10))
#
# lr, num_epochs, batch_size = 1.0, 5, 256
# train_iter, test_iter = load_data_fashion_mnist(batch_size)
# train(net, train_iter, test_iter, num_epochs, lr, mydevice)
文中部分知识参考：B 站 —— 跟李沐学AI；百度百科
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