從頭學pytorch(五) 多層感知機及其實現

多層感知機

上圖所示的多層感知機中，輸入和輸出個數分別為4和3，中間的隱藏層中包含了5個隱藏單元（hidden unit）。由於輸入層不涉及計算，圖3.3中的多層感知機的層數為2。由圖3.3可見，隱藏層中的神經元和輸入層中各個輸入完全連接，輸出層中的神經元和隱藏層中的各個神經元也完全連接。因此，多層感知機中的隱藏層和輸出層都是全連接層。

具體來說，給定一個小批量樣本\(\boldsymbol{X} \in \mathbb{R}^{n \times d}\)，其批量大小為\(n\)，輸入個數為\(d\)。假設多層感知機只有一個隱藏層，其中隱藏單元個數為\(h\)。記隱藏層的輸出（也稱為隱藏層變量或隱藏變量）為\(\boldsymbol{H}\)，有\(\boldsymbol{H} \in \mathbb{R}^{n \times h}\)。因為隱藏層和輸出層均是全連接層，可以設隱藏層的權重參數和偏差參數分別為\(\boldsymbol{W}_h \in \mathbb{R}^{d \times h}\)和 \(\boldsymbol{b}_h \in \mathbb{R}^{1 \times h}\)，輸出層的權重和偏差參數分別為\(\boldsymbol{W}_o \in \mathbb{R}^{h \times q}\)和\(\boldsymbol{b}_o \in \mathbb{R}^{1 \times q}\)。

我們先來看一種含單隱藏層的多層感知機的設計。其輸出\(\boldsymbol{O} \in \mathbb{R}^{n \times q}\)的計算為

\[\begin{aligned} \boldsymbol{H} &= \boldsymbol{X} \boldsymbol{W}_h + \boldsymbol{b}_h,\\ \boldsymbol{O} &= \boldsymbol{H} \boldsymbol{W}_o + \boldsymbol{b}_o, \end{aligned} \]

也就是將隱藏層的輸出直接作為輸出層的輸入。如果將以上兩個式子聯立起來，可以得到

\[\boldsymbol{O} = (\boldsymbol{X} \boldsymbol{W}_h + \boldsymbol{b}_h)\boldsymbol{W}_o + \boldsymbol{b}_o = \boldsymbol{X} \boldsymbol{W}_h\boldsymbol{W}_o + \boldsymbol{b}_h \boldsymbol{W}_o + \boldsymbol{b}_o. \]

從聯立后的式子可以看出，雖然神經網絡引入了隱藏層，卻依然等價於一個單層神經網絡：其中輸出層權重參數為\(\boldsymbol{W}_h\boldsymbol{W}_o\)，偏差參數為\(\boldsymbol{b}_h \boldsymbol{W}_o + \boldsymbol{b}_o\)。不難發現，即便再添加更多的隱藏層，以上設計依然只能與僅含輸出層的單層神經網絡等價。

激活函數

上面問題的根源就在於每一層的變換都是線性變換．線性變換的疊加依然是線性變換,所以,我們需要引入非線性.即對隱藏層的輸出經過激活函數后,再作為輸入輸入到下一層.　　

幾種常見的激活函數：

• relu
• sigmoid
• tanh

relu

\[\text{ReLU}(x) = \max(x, 0). \]

其曲線及導數的曲線圖繪制如下:

sigmoid

其曲線及導數的曲線圖繪制如下:

\[\text{sigmoid}(x) = \frac{1}{1 + \exp(-x)}. \]

tanh

\[\text{tanh}(x) = \frac{1 - \exp(-2x)}{1 + \exp(-2x)}. \]

其曲線及導數的曲線圖繪制如下:

---

從頭實現多層感知機

必要的模塊導入

import torch
import numpy as np
import matplotlib.pylab as plt
import sys
import torchvision
import torchvision.transforms as transforms

獲取和讀取數據

依然是之前用到的FashionMNIST數據集

batch_size = 256
num_workers = 4  # 多進程同時讀取
def load_data(batch_size,num_workers):
    mnist_train = torchvision.datasets.FashionMNIST(root='/home/sc/disk/keepgoing/learn_pytorch/Datasets/FashionMNIST',
                                                    train=True, download=True,
                                                    transform=transforms.ToTensor())
    mnist_test = torchvision.datasets.FashionMNIST(root='/home/sc/disk/keepgoing/learn_pytorch/Datasets/FashionMNIST',
                                                train=False, download=True,
                                                transform=transforms.ToTensor())

    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)
    
    return train_iter,test_iter

train_iter,test_iter = load_data(batch_size,num_workers)

模型參數初始化

我們的神經網絡有２層,所以相應的參數變成[W1,b1,W2,b2]

## 
num_inputs, num_outputs, num_hiddens = 784, 10, 256  #假設隱藏層有256個神經元
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)

模型定義

模型需要用到激活函數relu以及將輸出轉換為概率的函數softmax．
所以首先定義好relu和softmax．

relu定義:

def relu(X):
    #print(X.shape)
    return torch.max(input=X,other=torch.zeros(X.shape))

torch.max用法

softmax定義:

def softmax(X):  # X.shape=[樣本數,類別數]
    Ｘ_exp = X.exp()
    partion = X_exp.sum(dim=1, keepdim=True)  # 沿着列方向求和,即對每一行求和
    #print(partion.shape)
    return X_exp/partion  # 廣播機制,partion被擴展成與Ｘ_exp同shape的,對應位置元素做除法

模型結構定義:

def net(X):
    X = X.view((-1,num_inputs))
    #print(X.shape)
    H = relu(torch.matmul(X,W1) + b1)
    #print(H.shape)
    output = torch.matmul(H,W2) + b2

    return softmax(output)

損失函數定義

def cross_entropy(y_hat, y):
    y_hat_prob = y_hat.gather(1, y.view(-1, 1))  # ,沿着列方向,即選取出每一行下標為y的元素
    return -torch.log(y_hat_prob)

優化器定義

def sgd(params, lr, batch_size):
    for param in params:
        param.data -= lr * param.grad / batch_size  # 注意這里更改param時用的param.data

模型訓練

定義精度評估函數

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

訓練:

• 數據加載
• 前向傳播
• 計算loss
• 反向傳播,計算梯度
• 根據梯度值,更新參數
• 清空梯度
  加載下一個batch的數據,循環往復.

num_epochs, lr = 5, 0.1
def train():
    for epoch in range(num_epochs):
        train_l_sum, train_acc_sum, n = 0.0, 0.0, 0
        for X, y in train_iter:
            #print(X.shape,y.shape)
            y_hat = net(X)
            l = cross_entropy(y_hat, y).sum()  # 求loss
            l.backward()  # 反向傳播,計算梯度
            sgd(params, lr, batch_size)  # 根據梯度,更新參數

            Ｗ1.grad.data.zero_()  # 清空梯度
            b1.grad.data.zero_()
            Ｗ2.grad.data.zero_()  # 清空梯度
            b2.grad.data.zero_()

            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()

輸出如下:

epoch 1, loss 1.0535, train_acc 0.629，test_acc 0.760
epoch 2, loss 0.6004, train_acc 0.789，test_acc 0.788
epoch 3, loss 0.5185, train_acc 0.819，test_acc 0.824
epoch 4, loss 0.4783, train_acc 0.833，test_acc 0.830
epoch 5, loss 0.4521, train_acc 0.842，test_acc 0.832

---

多層感知機的簡單實現

必要的模塊導入

import torch
import torch.nn as nn
import torch.nn.init as init
import numpy as np
#import matplotlib.pylab as plt
import sys
import torchvision
import torchvision.transforms as transforms

獲取和讀取數據

batch_size = 256
num_workers = 4  # 多進程同時讀取
def load_data(batch_size,num_workers):
    mnist_train = torchvision.datasets.FashionMNIST(root='/home/sc/disk/keepgoing/learn_pytorch/Datasets/FashionMNIST',
                                                    train=True, download=True,
                                                    transform=transforms.ToTensor())
    mnist_test = torchvision.datasets.FashionMNIST(root='/home/sc/disk/keepgoing/learn_pytorch/Datasets/FashionMNIST',
                                                train=False, download=True,
                                                transform=transforms.ToTensor())

    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)
    
    return train_iter,test_iter

train_iter,test_iter = load_data(batch_size,num_workers)

模型定義及參數初始化

這里我們使用torch.nn中自帶的實現. 由於后續要定義的損失函數nn.nn.CrossEntropyLoss中包含了softmax的操作,所以這里不再需要定義relu和softmax．

class Net(nn.Module):
    def __init__(self,num_inputs, num_outputs, num_hiddens):
        super(Net,self).__init__()
        self.l1 = nn.Linear(num_inputs,num_hiddens)
        self.relu1 = nn.ReLU()
        self.l2 = nn.Linear(num_hiddens,num_outputs)

    def forward(self,X):
        X=X.view(X.shape[0],-1)
        o1 = self.relu1(self.l1(X))
        o2 = self.l2(o1)

        return o2

    def init_params(self):
        for param in self.parameters():
            #print(param.shape)
            init.normal_(param,mean=0,std=0.01)

num_inputs, num_outputs, num_hiddens = 28*28,10,256
net = Net(num_inputs,num_outputs,num_hiddens)
net.init_params()

定義損失函數

loss = nn.CrossEntropyLoss()

定義優化器

optimizer = torch.optim.SGD(net.parameters(),lr=0.5)

訓練模型

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

num_epochs=5
def train():
    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)   #前向傳播
            l = loss(y_hat,y).sum()#計算loss
            l.backward()#反向傳播

            optimizer.step()#參數更新
            optimizer.zero_grad()#清空梯度

            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()

輸出如下:

epoch 1, loss 0.0031, train_acc 0.709，test_acc 0.785
epoch 2, loss 0.0019, train_acc 0.823，test_acc 0.831
epoch 3, loss 0.0016, train_acc 0.844，test_acc 0.830
epoch 4, loss 0.0015, train_acc 0.855，test_acc 0.854
epoch 5, loss 0.0014, train_acc 0.866，test_acc 0.836

可以看到這里的loss相比我們自己實現的loss小了很多,是因為torch里在計算loss的時候求的是這個batch的平均loss．我們自己實現的損失函數並沒有求平均.