《动手学深度学习》Pytorch版学习笔记（一）：Task01
课程背景
在疫情的影响下，不少学校已经做出了延迟开学的决定，：伯禹教育、Datawhale、和鲸科技牵头与多家AI企业合作，让在家的同学也能免费学习优质的付费课程，同时为学习者创建好的学习环境，提供就业绿色通道。
主修课程
《动手学深度学习》 代码讲解Pytorch版：该书是2019年国内最受欢迎的人工智能学习教材之一，是一本面向中文读者的能运行、可讨论的深度学习教科书，书籍作者之一亚马逊首席科学家李沐，毕业于上海交大。伯禹教育携手上海交通大学团队，基于此书籍，将其中的代码框架由MXNET迁移至PyTorch，并对这些代码制作了讲解视频。帮助大家边动手写代码边巩固理论知识，从原理到实践，上手深度学习。
1 任务
【第一次打卡】内容（2月11日-14日）
Task01：线性回归；Softmax与分类模型、多层感知机（1天）
Task02：过拟合、欠拟合及其解决方案；梯度消失、梯度爆炸、梯度偏移；卷积神经网络基础（1天）
Task03：LeNet；卷积神经网络进阶；批量归一化和残差网络（1天）
打卡时间：【2020-02-11 08:00 -- 2020-02-14 22:00】
打卡链接：学习开始放出
2 线性回归
2.1 线性回归的基本要素
这里就不对线性回归的概念进行展开介绍了，具体概念可见：伯禹学习平台
2.2 线性回归模型从零开始的实现
# 导入包
%matplotlib inline
import torch
from IPython import display
from matplotlib import pyplot as plt
import numpy as np
import random
print(torch.__version__)
#生成数据集：使用线性模型来生成数据集，生成一个1000个样本的数据集
# set input feature number
num_inputs = 2
# set example number
num_examples = 1000
# set true weight and bias in order to generate corresponded label
true_w = [2, -3.4]
true_b = 4.2
features = torch.randn(num_examples, num_inputs,
dtype=torch.float32)
labels = true_w[0] * features[:, 0] + true_w[1] * features[:, 1] + true_b
labels += torch.tensor(np.random.normal(0, 0.01, size=labels.size()),
dtype=torch.float32)
#使用图像来展示生成的数据
plt.scatter(features[:, 1].numpy(), labels.numpy(), 1);
#读取数据集
def data_iter(batch_size, features, labels):
num_examples = len(features)
indices = list(range(num_examples))
random.shuffle(indices) # random read 10 samples
for i in range(0, num_examples, batch_size):
j = torch.LongTensor(indices[i: min(i + batch_size, num_examples)]) # the last time may be not enough for a whole batch
yield features.index_select(0, j), labels.index_select(0, j)
batch_size = 10
for X, y in data_iter(batch_size, features, labels):
print(X, '\n', y)
break
#初始化模型参数
w = torch.tensor(np.random.normal(0, 0.01, (num_inputs, 1)), dtype=torch.float32)
b = torch.zeros(1, dtype=torch.float32)
w.requires_grad_(requires_grad=True)
b.requires_grad_(requires_grad=True)
#定义模型
def linreg(X, w, b):
return torch.mm(X, w) + b
#定义损失函数：用均方误差损失函数
def squared_loss(y_hat, y):
return (y_hat - y.view(y_hat.size())) ** 2 / 2
#定义优化函数：使用小批量随机梯度下降
def sgd(params, lr, batch_size):
for param in params:
param.data -= lr * param.grad / batch_size # ues .data to operate param without gradient track
#训练
# super parameters init
lr = 0.03
num_epochs = 5
net = linreg
loss = squared_loss
# training
for epoch in range(num_epochs): # training repeats num_epochs times
# in each epoch, all the samples in dataset will be used once
# X is the feature and y is the label of a batch sample
for X, y in data_iter(batch_size, features, labels):
l = loss(net(X, w, b), y).sum()
# calculate the gradient of batch sample loss
l.backward()
# using small batch random gradient descent to iter model parameters
sgd([w, b], lr, batch_size)
# reset parameter gradient
w.grad.data.zero_()
b.grad.data.zero_()
train_l = loss(net(features, w, b), labels)
print('epoch %d, loss %f' % (epoch + 1, train_l.mean().item()))
2.3 线性回归模型使用pytorch的简洁实现
#导入包
import torch
from torch import nn
import numpy as np
torch.manual_seed(1)
print(torch.__version__)
torch.set_default_tensor_type('torch.FloatTensor')
#生成数据集：在这里生成数据集跟从零开始的实现中是完全一样的。
num_inputs = 2
num_examples = 1000
true_w = [2, -3.4]
true_b = 4.2
features = torch.tensor(np.random.normal(0, 1, (num_examples, num_inputs)), dtype=torch.float)
labels = true_w[0] * features[:, 0] + true_w[1] * features[:, 1] + true_b
labels += torch.tensor(np.random.normal(0, 0.01, size=labels.size()), dtype=torch.float)
#读取数据集
import torch.utils.data as Data
batch_size = 10
# combine featues and labels of dataset
dataset = Data.TensorDataset(features, labels)
# put dataset into DataLoader
data_iter = Data.DataLoader(
dataset=dataset, # torch TensorDataset format
batch_size=batch_size, # mini batch size
shuffle=True, # whether shuffle the data or not
num_workers=2, # read data in multithreading
)
#定义模型
class LinearNet(nn.Module):
def __init__(self, n_feature):
super(LinearNet, self).__init__() # call father function to init
self.linear = nn.Linear(n_feature, 1) # function prototype: `torch.nn.Linear(in_features, out_features, bias=True)`
def forward(self, x):
y = self.linear(x)
return y
net = LinearNet(num_inputs)
print(net)
# ways to init a multilayer network
# method one
net = nn.Sequential(
nn.Linear(num_inputs, 1)
# other layers can be added here
)
# method two
net = nn.Sequential()
net.add_module('linear', nn.Linear(num_inputs, 1))
# net.add_module ......
# method three
from collections import OrderedDict
net = nn.Sequential(OrderedDict([
('linear', nn.Linear(num_inputs, 1))
# ......
]))
print(net)
print(net[0])
#初始化模型参数
from torch.nn import init
init.normal_(net[0].weight, mean=0.0, std=0.01)
init.constant_(net[0].bias, val=0.0) # or you can use `net[0].bias.data.fill_(0)` to modify it directly
for param in net.parameters():
print(param)
#定义损失函数
loss = nn.MSELoss() # nn built-in squared loss function
# function prototype: `torch.nn.MSELoss(size_average=None, reduce=None, reduction='mean')`
#定义优化函数
import torch.optim as optim
optimizer = optim.SGD(net.parameters(), lr=0.03) # built-in random gradient descent function
print(optimizer) # function prototype: `torch.optim.SGD(params, lr=, momentum=0, dampening=0, weight_decay=0, nesterov=False)`
#训练
num_epochs = 3
for epoch in range(1, num_epochs + 1):
for X, y in data_iter:
output = net(X)
l = loss(output, y.view(-1, 1))
optimizer.zero_grad() # reset gradient, equal to net.zero_grad()
l.backward()
optimizer.step()
print('epoch %d, loss: %f' % (epoch, l.item()))
# result comparision
dense = net[0]
print(true_w, dense.weight.data)
print(true_b, dense.bias.data)
2.4 两种实现方式的比较
- 从零开始的实现（推荐用来学习）能够更好的理解模型和神经网络底层的原理；
- 使用pytorch的简洁实现能够更加快速地完成模型的设计与实现。
3 softmax和分类模型
3.1 softmax回归的基本概念
这里就不对概念进行展开介绍了，具体概念可见：伯禹学习平台
3.2 如何获取Fashion-MNIST数据集和读取数据
在介绍softmax回归的实现前我们先引入一个多类图像分类数据集。它将在后面的章节中被多次使用，以方便我们观察比较算法之间在模型精度和计算效率上的区别。图像分类数据集中最常用的是手写数字识别数据集MNIST[1]。但大部分模型在MNIST上的分类精度都超过了95%。为了更直观地观察算法之间的差异，我们将使用一个图像内容更加复杂的数据集Fashion-MNIST[2]。
我这里我们会使用torchvision包，它是服务于PyTorch深度学习框架的，主要用来构建计算机视觉模型。torchvision主要由以下几部分构成：
- torchvision.datasets: 一些加载数据的函数及常用的数据集接口；
- torchvision.models: 包含常用的模型结构（含预训练模型），例如AlexNet、VGG、ResNet等；
- torchvision.transforms: 常用的图片变换，例如裁剪、旋转等；
- torchvision.utils: 其他的一些有用的方法。
#导入包
# import needed package
%matplotlib inline
from IPython import display
import matplotlib.pyplot as plt
import torch
import torchvision
import torchvision.transforms as transforms
import time
import sys
sys.path.append("/home/kesci/input")
import d2lzh1981 as d2l
print(torch.__version__)
print(torchvision.__version__)
# 本函数已保存在d2lzh包中方便以后使用
def get_fashion_mnist_labels(labels):
text_labels = ['t-shirt', 'trouser', 'pullover', 'dress', 'coat',
'sandal', 'shirt', 'sneaker', 'bag', 'ankle boot']
return [text_labels[int(i)] for i in labels]
def show_fashion_mnist(images, labels):
d2l.use_svg_display()
# 这里的_表示我们忽略（不使用）的变量
_, figs = plt.subplots(1, len(images), figsize=(12, 12))
for f, img, lbl in zip(figs, images, labels):
f.imshow(img.view((28, 28)).numpy())
f.set_title(lbl)
f.axes.get_xaxis().set_visible(False)
f.axes.get_yaxis().set_visible(False)
plt.show()
# 读取数据
batch_size = 256
num_workers = 4
train_iter = torch.utils.data.DataLoader(mnist_train, batch_size=batch_size, shuffle=True, num_workers=num_workers)
test_iter = torch.utils.data.DataLoader(mnist_test, batch_size=batch_size, shuffle=False, num_workers=num_workers)
3.3 softmax回归模型的从零开始实现
import torch
import torchvision
import numpy as np
import sys
sys.path.append("/home/kesci/input")
import d2lzh1981 as d2l
print(torch.__version__)
print(torchvision.__version__)
#获取训练集数据和测试集数据
batch_size = 256
train_iter, test_iter = d2l.load_data_fashion_mnist(batch_size)
#模型参数初始化
num_inputs = 784
print(28*28)
num_outputs = 10
W = torch.tensor(np.random.normal(0, 0.01, (num_inputs, num_outputs)), dtype=torch.float)
b = torch.zeros(num_outputs, dtype=torch.float)
#对多维Tensor按维度操作
X = torch.tensor([[1, 2, 3], [4, 5, 6]])
print(X.sum(dim=0, keepdim=True)) # dim为0，按照相同的列求和，并在结果中保留列特征
print(X.sum(dim=1, keepdim=True)) # dim为1，按照相同的行求和，并在结果中保留行特征
print(X.sum(dim=0, keepdim=False)) # dim为0，按照相同的列求和，不在结果中保留列特征
print(X.sum(dim=1, keepdim=False)) # dim为1，按照相同的行求和，不在结果中保留行特征
#定义softmax操作
def softmax(X):
X_exp = X.exp()
partition = X_exp.sum(dim=1, keepdim=True)
# print("X size is ", X_exp.size())
# print("partition size is ", partition, partition.size())
return X_exp / partition # 这里应用了广播机制
#softmax回归模型
def net(X):
return softmax(torch.mm(X.view((-1, num_inputs)), W) + b)
#定义损失函数
y_hat = torch.tensor([[0.1, 0.3, 0.6], [0.3, 0.2, 0.5]])
y = torch.LongTensor([0, 2])
y_hat.gather(1, y.view(-1, 1))
def cross_entropy(y_hat, y):
return - torch.log(y_hat.gather(1, y.view(-1, 1)))
#定义准确率
def accuracy(y_hat, y):
return (y_hat.argmax(dim=1) == y).float().mean().item()
# 本函数已保存在d2lzh_pytorch包中方便以后使用。该函数将被逐步改进：它的完整实现将在“图像增广”一节中描述
def evaluate_accuracy(data_iter, net):
acc_sum, n = 0.0, 0
for X, y in data_iter:
acc_sum += (net(X).argmax(dim=1) == y).float().sum().item()
n += y.shape[0]
return acc_sum / n
#训练模型
num_epochs, lr = 5, 0.1
# 本函数已保存在d2lzh_pytorch包中方便以后使用
def train_ch3(net, train_iter, test_iter, loss, num_epochs, batch_size,
params=None, lr=None, optimizer=None):
for epoch in range(num_epochs):
train_l_sum, train_acc_sum, n = 0.0, 0.0, 0
for X, y in train_iter:
y_hat = net(X)
l = loss(y_hat, y).sum()
# 梯度清零
if optimizer is not None:
optimizer.zero_grad()
elif params is not None and params[0].grad is not None:
for param in params:
param.grad.data.zero_()
l.backward()
if optimizer is None:
d2l.sgd(params, lr, batch_size)
else:
optimizer.step()
train_l_sum += l.item()
train_acc_sum += (y_hat.argmax(dim=1) == y).sum().item()
n += y.shape[0]
test_acc = evaluate_accuracy(test_iter, net)
print('epoch %d, loss %.4f, train acc %.3f, test acc %.3f'
% (epoch + 1, train_l_sum / n, train_acc_sum / n, test_acc))
train_ch3(net, train_iter, test_iter, cross_entropy, num_epochs, batch_size, [W, b], lr)
#模型预测
X, y = iter(test_iter).next()
true_labels = d2l.get_fashion_mnist_labels(y.numpy())
pred_labels = d2l.get_fashion_mnist_labels(net(X).argmax(dim=1).numpy())
titles = [true + '\n' + pred for true, pred in zip(true_labels, pred_labels)]
d2l.show_fashion_mnist(X[0:9], titles[0:9])
3.4 使用pytorch重新实现softmax回归模型
# 加载各种包或者模块
import torch
from torch import nn
from torch.nn import init
import numpy as np
import sys
sys.path.append("/home/kesci/input")
import d2lzh1981 as d2l
print(torch.__version__)
#初始化参数和获取数据
batch_size = 256
train_iter, test_iter = d2l.load_data_fashion_mnist(batch_size)
#定义网络模型
num_inputs = 784
num_outputs = 10
class LinearNet(nn.Module):
def __init__(self, num_inputs, num_outputs):
super(LinearNet, self).__init__()
self.linear = nn.Linear(num_inputs, num_outputs)
def forward(self, x): # x 的形状: (batch, 1, 28, 28)
y = self.linear(x.view(x.shape[0], -1))
return y
# net = LinearNet(num_inputs, num_outputs)
class FlattenLayer(nn.Module):
def __init__(self):
super(FlattenLayer, self).__init__()
def forward(self, x): # x 的形状: (batch, *, *, ...)
return x.view(x.shape[0], -1)
from collections import OrderedDict
net = nn.Sequential(
# FlattenLayer(),
# LinearNet(num_inputs, num_outputs)
OrderedDict([
('flatten', FlattenLayer()),
('linear', nn.Linear(num_inputs, num_outputs))]) # 或者写成我们自己定义的 LinearNet(num_inputs, num_outputs) 也可以
)
#初始化模型参数
init.normal_(net.linear.weight, mean=0, std=0.01)
init.constant_(net.linear.bias, val=0)
#定义损失函数
loss = nn.CrossEntropyLoss() # 下面是他的函数原型
# class torch.nn.CrossEntropyLoss(weight=None, size_average=None, ignore_index=-100, reduce=None, reduction='mean')
#定义优化函数
optimizer = torch.optim.SGD(net.parameters(), lr=0.1) # 下面是函数原型
# class torch.optim.SGD(params, lr=, momentum=0, dampening=0, weight_decay=0, nesterov=False)
#训练
num_epochs = 5
d2l.train_ch3(net, train_iter, test_iter, loss, num_epochs, batch_size, None, None, optimizer)
4 多层感知机
4.1 多层感知机的基本知识
这里就不对概念进行展开介绍了，具体概念可见：伯禹学习平台
4.2 使用多层感知机图像分类的从零开始的实现
#导入包
import torch
import numpy as np
import sys
sys.path.append("/home/kesci/input")
import d2lzh1981 as d2l
#获取训练集
batch_size = 256
train_iter, test_iter = d2l.load_data_fashion_mnist(batch_size,root='/home/kesci/input/FashionMNIST2065')
#定义模型参数
num_inputs, num_outputs, num_hiddens = 784, 10, 256
W1 = torch.tensor(np.random.normal(0, 0.01, (num_inputs, num_hiddens)), dtype=torch.float)
b1 = torch.zeros(num_hiddens, dtype=torch.float)
W2 = torch.tensor(np.random.normal(0, 0.01, (num_hiddens, num_outputs)), dtype=torch.float)
b2 = torch.zeros(num_outputs, dtype=torch.float)
params = [W1, b1, W2, b2]
for param in params:
param.requires_grad_(requires_grad=True)
#定义激活函数
def relu(X):
return torch.max(input=X, other=torch.tensor(0.0))
#定义网络
def net(X):
X = X.view((-1, num_inputs))
H = relu(torch.matmul(X, W1) + b1)
return torch.matmul(H, W2) + b2
#定义损失函数
loss = torch.nn.CrossEntropyLoss()
#训练
num_epochs, lr = 5, 100.0
# def train_ch3(net, train_iter, test_iter, loss, num_epochs, batch_size,
# params=None, lr=None, optimizer=None):
# for epoch in range(num_epochs):
# train_l_sum, train_acc_sum, n = 0.0, 0.0, 0
# for X, y in train_iter:
# y_hat = net(X)
# l = loss(y_hat, y).sum()
#
# # 梯度清零
# if optimizer is not None:
# optimizer.zero_grad()
# elif params is not None and params[0].grad is not None:
# for param in params:
# param.grad.data.zero_()
#
# l.backward()
# if optimizer is None:
# d2l.sgd(params, lr, batch_size)
# else:
# optimizer.step() # “softmax回归的简洁实现”一节将用到
#
#
# train_l_sum += l.item()
# train_acc_sum += (y_hat.argmax(dim=1) == y).sum().item()
# n += y.shape[0]
# test_acc = evaluate_accuracy(test_iter, net)
# print('epoch %d, loss %.4f, train acc %.3f, test acc %.3f'
# % (epoch + 1, train_l_sum / n, train_acc_sum / n, test_acc))
d2l.train_ch3(net, train_iter, test_iter, loss, num_epochs, batch_size, params, lr)
4.3 使用pytorch的简洁实现
#导入包
import torch
from torch import nn
from torch.nn import init
import numpy as np
import sys
sys.path.append("/home/kesci/input")
import d2lzh1981 as d2l
#初始化模型和各个参数
num_inputs, num_outputs, num_hiddens = 784, 10, 256
net = nn.Sequential(
d2l.FlattenLayer(),
nn.Linear(num_inputs, num_hiddens),
nn.ReLU(),
nn.Linear(num_hiddens, num_outputs),
)
for params in net.parameters():
init.normal_(params, mean=0, std=0.01)
#训练
batch_size = 256
train_iter, test_iter = d2l.load_data_fashion_mnist(batch_size,root='/home/kesci/input/FashionMNIST2065')
loss = torch.nn.CrossEntropyLoss()
optimizer = torch.optim.SGD(net.parameters(), lr=0.5)
num_epochs = 5
d2l.train_ch3(net, train_iter, test_iter, loss, num_epochs, batch_size, None, None, optimizer)
