动手学深度学习PyTorch版--Task1--线性回归；Softmax与分类模型、多层感知机

一.线性回归

主要内容包括：
1.线性回归的基本要素
2.线性回归模型从零开始的实现
3.线性回归模型使用pytorch的简洁实现

1.线性回归的基本要素

2.线性回归模型从零开始的实现

# import packages and modules
%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

'''
tensor([[ 0.3160,  0.3861],
        [ 0.1074, -0.0064],
        [-0.1780, -1.6962],
        [ 1.0320,  1.8323],
        [-2.3878,  0.0096],
        [-0.1305,  0.0978],
        [ 0.7010, -0.4422],
        [ 1.1862,  0.5082],
        [-1.1331,  0.1133],
        [-0.7101, -0.0068]]) 
 tensor([ 3.5243,  4.4241,  9.6077,  0.0345, -0.6091,  3.5965,  7.1098,  4.8407,
         1.5405,  2.7867])
'''

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

def squared_loss(y_hat, y): 
    return (y_hat - y.view(y_hat.size())) ** 2 / 2

定义优化函数

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

当数据集、模型、损失函数和优化函数定义完了之后就可来准备进行模型的训练了。

# 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()))
'''
epoch 1, loss 0.036041
epoch 2, loss 0.000131
epoch 3, loss 0.000047
epoch 4, loss 0.000047
epoch 5, loss 0.000047
'''

w, true_w, b, true_b
'''
(tensor([[ 2.0002],
         [-3.4004]], requires_grad=True),
 [2, -3.4],
 tensor([4.1996], requires_grad=True),
 4.2)
'''

3.线性回归模型使用pytorch的简洁实现

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

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
)

for X, y in data_iter:
    print(X, '\n', y)
    break
'''
tensor([[-0.5937, -0.3618],
        [-1.5855, -0.4107],
        [ 0.7470,  0.7923],
        [-1.0760, -0.4435],
        [-1.4019, -0.2581],
        [-0.9036,  0.6937],
        [-0.9090, -1.2165],
        [-0.1176,  1.3189],
        [-0.7570,  0.9984],
        [ 0.6586, -1.7189]]) 
 tensor([ 4.2312,  2.4144,  2.9873,  3.5418,  2.2676,  0.0164,  6.5226, -0.5134,
        -0.7016, 11.3717])
'''

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)

'''
LinearNet(
  (linear): Linear(in_features=2, out_features=1, bias=True)
)
'''

# 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])

'''
Sequential(
  (linear): Linear(in_features=2, out_features=1, bias=True)
)
Linear(in_features=2, out_features=1, bias=True)
'''

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
'''
Parameter containing:
tensor([0.], requires_grad=True)
'''
for param in net.parameters():
    print(param)
'''
Parameter containing:
tensor([[-0.0142, -0.0161]], requires_grad=True)
Parameter containing:
tensor([0.], requires_grad=True)
'''

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)`
'''
SGD (
Parameter Group 0
    dampening: 0
    lr: 0.03
    momentum: 0
    nesterov: False
    weight_decay: 0
)
'''

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()))
'''
epoch 1, loss: 0.000338
epoch 2, loss: 0.000061
epoch 3, loss: 0.000056
'''

# result comparision
dense = net[0]
print(true_w, dense.weight.data)
print(true_b, dense.bias.data)
'''
[2, -3.4] tensor([[ 1.9998, -3.4002]])
4.2 tensor([4.2008])
'''

4.小结：

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

二.softmax和分类模型

内容包含：
1.softmax回归的基本概念
2.如何获取Fashion-MNIST数据集和读取数据
3.softmax回归模型的从零开始实现，实现一个对Fashion-MNIST训练集中的图像数据进行分类的模型
4.使用pytorch重新实现softmax回归模型

1.softmax回归的基本概念

2.如何获取Fashion-MNIST数据集和读取数据

# 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__)

'''
1.3.0
0.4.1a0+d94043a
'''

mnist_train = torchvision.datasets.FashionMNIST(root='/home/kesci/input/FashionMNIST2065', train=True, download=True, transform=transforms.ToTensor())
mnist_test = torchvision.datasets.FashionMNIST(root='/home/kesci/input/FashionMNIST2065', train=False, download=True, transform=transforms.ToTensor())

class torchvision.datasets.FashionMNIST(root, train=True, transform=None, target_transform=None, download=False)
root（string）– 数据集的根目录，其中存放processed/training.pt和processed/test.pt文件。
train（bool, 可选）– 如果设置为True，从training.pt创建数据集，否则从test.pt创建。
download（bool, 可选）– 如果设置为True，从互联网下载数据并放到root文件夹下。如果root目录下已经存在数据，不会再次下载。
transform（可被调用 , 可选）– 一种函数或变换，输入PIL图片，返回变换之后的数据。如：transforms.RandomCrop。
target_transform（可被调用 , 可选）– 一种函数或变换，输入目标，进行变换。

# show result 
print(type(mnist_train))
print(len(mnist_train), len(mnist_test))
'''

60000 10000
'''

# 我们可以通过下标来访问任意一个样本
feature, label = mnist_train[0]
print(feature.shape, label)  # Channel x Height x Width
'''
torch.Size([1, 28, 28]) 9
'''

如果不做变换输入的数据是图像，我们可以看一下图片的类型参数：

mnist_PIL = torchvision.datasets.FashionMNIST(root='/home/kesci/input/FashionMNIST2065', train=True, download=True)
PIL_feature, label = mnist_PIL[0]
print(PIL_feature)
'''

'''

# 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()

X, y = [], []
for i in range(10):
    X.append(mnist_train[i][0]) # 将第i个feature加到X中
    y.append(mnist_train[i][1]) # 将第i个label加到y中
show_fashion_mnist(X, get_fashion_mnist_labels(y))

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)

start = time.time()
for X, y in train_iter:
    continue
print('%.2f sec' % (time.time() - start))
'''
4.95 sec
'''

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__)
'''
1.3.0
0.4.1a0+d94043a
'''

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, root='/home/kesci/input/FashionMNIST2065')

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)
'''
784
'''

W.requires_grad_(requires_grad=True)
b.requires_grad_(requires_grad=True)
'''
tensor([0., 0., 0., 0., 0., 0., 0., 0., 0., 0.], requires_grad=True)
'''

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
tensor([[5, 7, 9]])
tensor([[ 6],
        [15]])
tensor([5, 7, 9])
tensor([ 6, 15])

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
    
X = torch.rand((2, 5))
X_prob = softmax(X)
print(X_prob, '\n', X_prob.sum(dim=1))
'''
tensor([[0.2253, 0.1823, 0.1943, 0.2275, 0.1706],
        [0.1588, 0.2409, 0.2310, 0.1670, 0.2024]]) 
 tensor([1.0000, 1.0000])
'''

def net(X):
    return softmax(torch.mm(X.view((-1, num_inputs)), W) + b)

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))
'''
tensor([[0.1000],
        [0.5000]])
'''     
  
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()
print(accuracy(y_hat, y))
'''
0.5
'''

# 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
print(evaluate_accuracy(test_iter, net))
'''
0.1445
'''

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)
'''
epoch 1, loss 0.7851, train acc 0.750, test acc 0.791
epoch 2, loss 0.5704, train acc 0.814, test acc 0.810
epoch 3, loss 0.5258, train acc 0.825, test acc 0.819
epoch 4, loss 0.5014, train acc 0.832, test acc 0.824
epoch 5, loss 0.4865, train acc 0.836, test acc 0.827
'''

现在我们的模型训练完了，可以进行一下预测，我们的这个模型训练的到底准确不准确。 现在就可以演示如何对图像进行分类了。给定一系列图像（第三行图像输出），我们比较一下它们的真实标签（第一行文本输出）和模型预测结果（第二行文本输出）。

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])

4.使用pytorch重新实现softmax回归模型

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__)
'''
1.3.0
0.4.1a0+d94043a
'''

初始化参数和获取数据

batch_size = 256
train_iter, test_iter = d2l.load_data_fashion_mnist(batch_size, root='/home/kesci/input/FashionMNIST2065')

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)
Parameter containing:
tensor([0., 0., 0., 0., 0., 0., 0., 0., 0., 0.], requires_grad=True)

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)
'''
epoch 1, loss 0.0031, train acc 0.751, test acc 0.795
epoch 2, loss 0.0022, train acc 0.813, test acc 0.809
epoch 3, loss 0.0021, train acc 0.825, test acc 0.806
epoch 4, loss 0.0020, train acc 0.833, test acc 0.813
epoch 5, loss 0.0019, train acc 0.837, test acc 0.822
'''

三.多层感知机的基本知识

1.多层感知机的基本知识
2.使用多层感知机图像分类的从零开始的实现
3.使用pytorch的简洁实现

1.多层感知机的基本知识

关于激活函数的选择
ReLu函数是一个通用的激活函数，目前在大多数情况下使用。但是，ReLU函数只能在隐藏层中使用。
用于分类器时，sigmoid函数及其组合通常效果更好。由于梯度消失问题，有时要避免使用sigmoid和tanh函数。
在神经网络层数较多的时候，最好使用ReLu函数，ReLu函数比较简单计算量少，而sigmoid和tanh函数计算量大很多。
在选择激活函数的时候可以先选用ReLu函数如果效果不理想可以尝试其他激活函数。

2.使用多层感知机图像分类的从零开始的实现

import torch
import numpy as np
import sys
sys.path.append("/home/kesci/input")
import d2lzh1981 as d2l
print(torch.__version__)
# ==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)
'''
epoch 1, loss 0.0030, train acc 0.712, test acc 0.806
epoch 2, loss 0.0019, train acc 0.821, test acc 0.806
epoch 3, loss 0.0017, train acc 0.847, test acc 0.825
epoch 4, loss 0.0015, train acc 0.856, test acc 0.834
epoch 5, loss 0.0015, train acc 0.863, test acc 0.847
'''

3.使用pytorch的简洁实现

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__)
1.3.0
# ==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)
'''
epoch 1, loss 0.0031, train acc 0.701, test acc 0.774
epoch 2, loss 0.0019, train acc 0.821, test acc 0.806
epoch 3, loss 0.0017, train acc 0.841, test acc 0.805
epoch 4, loss 0.0015, train acc 0.855, test acc 0.834
epoch 5, loss 0.0014, train acc 0.866, test acc 0.840
'''

课后习题

线性回归课后习题

Softmax课后习题

多层感知机课后习题