【Pytorch学习笔记】3.深度学习基础

曾经终败给现在 2022-09-04 02:59 302阅读 0赞

### 文章目录 ###

*  13.梯度
 *  14.激活函数
 *  15.感知机
 *  16.链式法则
 *  17.反向传播
 *  18.2D函数优化实例
 *  19.Logistic Regression
 *  20.交叉熵
 *  21.多分类
 *  22.全连接层
 *  23.激活函数与GPU加速
 *  24. 测试

根据龙良曲Pytorch学习视频整理，视频链接：  
[【计算机-AI】PyTorch学这个就够了！][-AI_PyTorch]  
[(好课推荐)深度学习与PyTorch入门实战——主讲人龙良曲][PyTorch]

# 13.梯度 #

*  导数 derivative
 *  偏微分 partial derivate
 *  梯度 gradient（向量）

How to search for minima?

*   θ t + 1 = θ t − α t ▽ f ( θ t ) \\theta\_\{t+1\}=\\theta\_t-\\alpha\_t\\triangledown f(\\theta\_t) θt\+1=θt−αt▽f(θt)

Optimizer performance

*  initialization status [何恺明初始化方法][Link 1]
 *  learning rate （learning\_rate\_decay）
 *  momentum

# 14.激活函数 #

*  连续不可导
 *  Sigmoid / Logistic  σ ′ = σ ( 1 − σ ) \\sigma'=\\sigma(1-\\sigma) σ′=σ(1−σ)  
    `torch.sigmoid()`  
    `F.sigmoid() (import torch.nn.functional as F)`
 *  Tanh  
    `torch.tanh()`
 *  Relu  
    `torch.relu()`  
    `F.relu() (import torch.nn.functional as F)`

Typical Loss

*  Mean Squared Error  
    MSE  l o s s = ∑ \[ y − ( x w + b ) \] 2 loss = \\sum \[y-(xw+b)\]^2 loss=∑\[y−(xw\+b)\]2  
     L 2 − n o r m = ∣ ∣ y − ( x w + b ) ∣ ∣ 2 L2-norm=||y-(xw+b)||\_2 L2−norm=∣∣y−(xw\+b)∣∣2
 *  Cross Entropy Loss  
    binary  
    multi-class  
    \+softmax  
    Leave it to Logistic Regression Part
 *  Softmax  
    soft version of max  
     S ( y i ) = e y i ∑ j e y j S(y\_i)=\\frac\{e^\{y\_i\}\}\{\\sum\_je^\{y\_j\}\} S(yi)=∑jeyjeyi  
     ∂ p i ∂ p j = \{ p i ( 1 − p i ) i = j − p j ∗ p i i ≠ j \\frac\{\\partial p\_i\}\{\\partial p\_j\}=\\left\\\{\\begin\{matrix\} p\_i(1-p\_i)&i=j \\\\ -p\_j\*p\_i& i\\neq j \\end\{matrix\}\\right. ∂pj∂pi=\{ pi(1−pi)−pj∗pii=ji=j

Gradient API

*  `torch.autograd.grad(loss, [w1, w2,...])`
 *  `loss.backward()`

import torch
    import torch.nn.functional as F
    
    x = torch.ones(1)
    w = torch.full([1], 2.)
    mse = F.mse_loss(torch.ones(1), x*w)
    print(mse)  # tensor(1., grad_fn=<MseLossBackward>)
    
    # torch.autograd.grad(mse, [w]) # RuntimeError: element 0 of tensors does not require grad and does not have a grad_fn
    
    print(w.requires_grad_())   # tensor([2.], requires_grad=True)
    
    # print(torch.autograd.grad(mse, [w])) # 动态图未更新会报错RuntimeError: element 0 of tensors does not require grad and does not have a grad_fn
    
    mse = F.mse_loss(torch.ones(1), x*w)
    # print(torch.autograd.grad(mse, [w])) # (tensor([2.]),
    
    mse.backward()
    print(w.grad)   # tensor([2.])
    
    
    a = torch.rand(3, requires_grad=True)
    print(a)    # tensor([0.0377, 0.4542, 0.1386], requires_grad=True)
    p = F.softmax(a, dim=0)
    # p.backward() # 报错 RuntimeError: grad can be implicitly created only for scalar outputs
    # retain_graph=True 不会清除计算图
    print(torch.autograd.grad(p[0], [a], retain_graph=True))    # (tensor([ 0.1998, -0.1156, -0.0843]),)
    print(torch.autograd.grad(p[1], [a], retain_graph=True))    # (tensor([-0.1156, 0.2434, -0.1278]),)
    print(torch.autograd.grad(p[2], [a], retain_graph=True))    # (tensor([-0.0843, -0.1278, 0.2121]),)

# 15.感知机 #

**单一输出感知机求导**  
![P32][]  
 ∂ E ∂ w j 0 = ( O 0 − t ) O 0 ( 1 − O 0 ) x j 0 \\frac\{\\partial E\}\{\\partial w\_\{j0\}\}=(O\_0-t)O\_0(1-O\_0)x^0\_j ∂wj0∂E=(O0−t)O0(1−O0)xj0

**多输出Loss层 (Multi-output Perception)**  
![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L2tlaXZlbl8_size_16_color_FFFFFF_t_70]  
 ∂ E ∂ w j k = ( O k − t k ) O k ( 1 − O k ) x j 0 \\frac\{\\partial E\}\{\\partial w\_\{jk\}\}=(O\_k-t\_k)O\_k(1-O\_k)x^0\_j ∂wjk∂E=(Ok−tk)Ok(1−Ok)xj0

import torch
    import torch.nn.functional as F
    
    x = torch.randn(1, 10)
    # w = torch.randn(1, 10, requires_grad=True) # 单一层感知机
    w = torch.randn(2, 10, requires_grad=True)  # 多输出Loss层
    o = torch.sigmoid(x@w.t())
    print(o.shape)  # torch.Size([1, 2])
    
    loss = F.mse_loss(torch.ones(1, 1), o)  # broadcasting
    print(loss.shape)   # torch.Size([])
    print(loss)   # tensor(0.2094, grad_fn=<MseLossBackward>)
    
    loss.backward()
    print(w.grad)
    """ tensor([[-2.0498e-01, 2.4619e-02, -8.0208e-04, -1.3723e-01, -1.3014e-01, -1.4648e-01, -7.5119e-02, 4.9381e-02, 2.7161e-01, 4.8075e-02], [-4.8705e-03, 5.8495e-04, -1.9058e-05, -3.2607e-03, -3.0922e-03, -3.4804e-03, -1.7849e-03, 1.1733e-03, 6.4536e-03, 1.1423e-03]]) """

# 16.链式法则 #

import torch
    import torch.nn.functional as F
    
    x = torch.tensor(1.)
    w1 = torch.tensor(2., requires_grad=True)
    b1 = torch.tensor(1.)
    w2 = torch.tensor(2., requires_grad=True)
    b2 = torch.tensor(1.)
    y1 = x * w1 + b1
    y2 = y1 * w2 + b2
    
    dy2_dy1 = torch.autograd.grad(y2, [y1], retain_graph=True)[0]
    dy1_dw1 = torch.autograd.grad(y1, [w1], retain_graph=True)[0]
    dy2_dw1 = torch.autograd.grad(y2, [w1], retain_graph=True)[0]
    
    print(dy2_dy1 * dy1_dw1)    # tensor(2.)
    print(dy2_dw1)  # tensor(2.)

# 17.反向传播 #

For an output layer node k  ∈ \\in ∈ K  ∂ E ∂ W j k = O j δ k \\frac\{\\partial E\}\{\\partial W\_\{jk\}\}=O\_j\\delta\_k ∂Wjk∂E=Ojδk

where  δ k = O k ( 1 − O k ) ( O k − t k ) \\delta \_k = O\_k(1-O\_k)(O\_k-t\_k) δk=Ok(1−Ok)(Ok−tk)

For a hidden layer node j  ∈ \\in ∈ J  ∂ E ∂ W i j = O i δ j \\frac\{\\partial E\}\{\\partial W\_\{ij\}\}=O\_i\\delta\_j ∂Wij∂E=Oiδj

where  δ j = O j ( 1 − O j ) ∑ k ∈ K δ k W j k \\delta \_j = O\_j(1-O\_j)\\sum \_\{k \\in K\}\\delta \_k W\_\{jk\} δj=Oj(1−Oj)k∈K∑δkWjk

# 18.2D函数优化实例 #

import torch
    import numpy as np
    import matplotlib.pyplot as plt
    
    def himmelblau(x):
        return (x[0] ** 2 + x[1] - 11) ** 2 + (x[0] + x[1] ** 2 - 7) ** 2
    
    x = np.arange(-6, 6, 0.1)
    y = np.arange(-6, 6, 0.1)
    print('x, y range:', x.shape, y.shape)
    X, Y = np.meshgrid(x, y)
    print('X, Y maps', X.shape, Y.shape)
    Z = himmelblau([X, Y])
    
    fig = plt.figure('himmelblau')
    ax = fig.gca(projection='3d')
    ax.plot_surface(X, Y, Z)
    ax.view_init(60, -30)
    ax.set_xlabel('x')
    ax.set_ylabel('y')
    plt.show()
    
    x = torch.tensor([0., 0.], requires_grad=True)
    optimizer = torch.optim.Adam([x], lr=1e-3)
    for step in range(20000):
        pred = himmelblau(x)
        optimizer.zero_grad()
        pred.backward()
        optimizer.step()
        if step % 2000 == 0:
            print('step {}: x = {}, f(x) = {}'.format(step, x.tolist(), pred.item()))
    """ x, y range: (120,) (120,) X, Y maps (120, 120) (120, 120) G:/Project/PYTHON/Demo/Pytorch21_7_29/12himmelblau.py:17: MatplotlibDeprecationWarning: Calling gca() with keyword arguments was deprecated in Matplotlib 3.4. Starting two minor releases later, gca() will take no keyword arguments. The gca() function should only be used to get the current axes, or if no axes exist, create new axes with default keyword arguments. To create a new axes with non-default arguments, use plt.axes() or plt.subplot(). ax = fig.gca(projection='3d') step 0: x = [0.0009999999310821295, 0.0009999999310821295], f(x) = 170.0 step 2000: x = [2.3331806659698486, 1.9540694952011108], f(x) = 13.730916023254395 step 4000: x = [2.9820079803466797, 2.0270984172821045], f(x) = 0.014858869835734367 step 6000: x = [2.999983549118042, 2.0000221729278564], f(x) = 1.1074007488787174e-08 step 8000: x = [2.9999938011169434, 2.0000083446502686], f(x) = 1.5572823031106964e-09 step 10000: x = [2.999997854232788, 2.000002861022949], f(x) = 1.8189894035458565e-10 step 12000: x = [2.9999992847442627, 2.0000009536743164], f(x) = 1.6370904631912708e-11 step 14000: x = [2.999999761581421, 2.000000238418579], f(x) = 1.8189894035458565e-12 step 16000: x = [3.0, 2.0], f(x) = 0.0 step 18000: x = [3.0, 2.0], f(x) = 0.0 """

![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L2tlaXZlbl8_size_16_color_FFFFFF_t_70 1]  
不同的初始化会产生不同的解

# 19.Logistic Regression #

Goal v.s. Approach

*  For regression  
    Goal:  p r e d = y pred = y pred=y  
    Approach: minimize  d i s t ( p r e d , y ) dist(pred, y) dist(pred,y)
 *  For classification  
    Goal: maximize benchmark, e.g. accuracy  
    Approach1: minimize  d i s t ( p θ ( y ∣ x ) , p r ( y ∣ x ) ) dist(p\_\\theta(y|x), p\_r(y|x)) dist(pθ(y∣x),pr(y∣x))  
    Approach2: minimize  d i v e r g e n c e ( p θ ( y ∣ x ) , p r ( y ∣ x ) ) divergence(p\_\\theta(y|x), p\_r(y|x)) divergence(pθ(y∣x),pr(y∣x))

Q1. why not maximize accuracy?

*   a c c . = ∑ I ( p r e d i = = y i ) l e n ( Y ) acc.=\\frac\{\\sum I(pred\_i==y\_i)\}\{len(Y)\} acc.=len(Y)∑I(predi==yi)
 *  issues 1. gradient = 0 if accuracy unchanged but weights changed
 *  issues 2. gradient not continuous since the number of correct is not continuous

Q2. why call logistic regression

*  use sigmoid
 *  Controversial  
    Mse => regression  
    Cross Entropy => classification

# 20.交叉熵 #

Entropy

*  Uncertainty
 *  measure of surprise
 *  higher entropy = less info.  
     E n t r o p y = − ∑ i P ( i ) l o g P ( i ) Entropy=-\\sum\_iP(i)logP(i) Entropy=−i∑P(i)logP(i)

Lottery

import torch
    
    a = torch.full([4], 1/4.)
    print(a)    # tensor([0.2500, 0.2500, 0.2500, 0.2500])
    print(a * torch.log2(a))    # tensor([-0.5000, -0.5000, -0.5000, -0.5000])
    print(-(a * torch.log2(a)).sum())   # tensor(2.)
    
    a = torch.tensor([0.1, 0.1, 0.1, 0.7])
    print(a)    # tensor([0.1000, 0.1000, 0.1000, 0.7000])
    print(a * torch.log2(a))    # tensor([-0.3322, -0.3322, -0.3322, -0.3602])
    print(-(a * torch.log2(a)).sum())   #tensor(1.3568)

Cross Entropy

*   H ( p , q ) = ∑ p ( x ) l o g   q ( x ) H(p,q)=\\sum p(x)log\\space q(x) H(p,q)=∑p(x)log q(x)
 *   H ( p , q ) = H ( p ) + D K L ( p ∣ q ) H(p,q)=H(p)+D\_\{KL\}(p|q) H(p,q)=H(p)\+DKL(p∣q)  D K L D\_\{KL\} DKL指KL Divergence相对熵  
    P=Q: cross Entropy = Entropy  
    for one-hot encoding: entropy = 1log1=0

Binary Classfication  
 H ( P , Q ) = − ( y l o g ( p ) + ( 1 − y ) l o g ( 1 − p ) ) H(P, Q)=-(ylog(p)+(1-y)log(1-p)) H(P,Q)=−(ylog(p)\+(1−y)log(1−p))

Why not use MSE?

*  sigmoid+MSE  
    gradient vanish
 *  converge slower
 *  But, sometimes  
    e.g. meta-learning

Numerical Stability

import torch
    import torch.nn.functional as F
    
    x = torch.randn(1, 784)
    w = torch.randn(10, 784)
    logits = x @ w.t()
    print(logits.size())    # torch.Size([1, 10])
    
    print(F.cross_entropy(logits, torch.tensor([3])))   # tensor(0.1694)
    
    pred = F.softmax(logits, dim=1)
    print(pred.size())    # torch.Size([1, 10])
    pred_log = torch.log(pred)
    print(F.nll_loss(pred_log, torch.tensor([3])))   # tensor(0.1694)

# 21.多分类 #

import torch
    import torch.nn as nn
    import torch.nn.functional as F
    from torchvision import datasets, transforms
    
    # initialize
    batch_size = 200
    learning_rate = 0.01
    epochs = 10
    
    # load data
    train_loader = torch.utils.data.DataLoader(
        datasets.MNIST('../data', train=True, download=True,
                       transform=transforms.Compose([
                           transforms.ToTensor(),
                           transforms.Normalize((0.01307, ), (0.3081, ))
                       ])),
        batch_size=batch_size, shuffle=True
    )
    test_loader = torch.utils.data.DataLoader(
        datasets.MNIST('../data', train=False,
                       transform=transforms.Compose([
                           transforms.ToTensor(),
                           transforms.Normalize((0.01307, ), (0.3081, ))
                       ])),
        batch_size=batch_size, shuffle=True
    )
    
    # Network Architecture
    w1, b1 = torch.randn(200, 784, requires_grad=True), torch.zeros(200, requires_grad=True)
    w2, b2 = torch.randn(200, 200, requires_grad=True), torch.zeros(200, requires_grad=True)
    w3, b3 = torch.randn(10, 200, requires_grad=True), torch.zeros(10, requires_grad=True)
    
    torch.nn.init.kaiming_normal_(w1)
    torch.nn.init.kaiming_normal_(w2)
    torch.nn.init.kaiming_normal_(w3)
    
    def forward(x):
        x = x @ w1.t() + b1
        x = F.relu(x)
        x = x @ w2.t() + b2
        x = F.relu(x)
        x = x @ w3.t() + b3
        x = F.relu(x)
        return x
    
    # Train
    optimizer = torch.optim.SGD([w1, b1, w2, b2, w3, b3], lr=learning_rate)
    criteon = nn.CrossEntropyLoss()
    
    for epoch in range(epochs):
        for batch_idx, (data, target) in enumerate(train_loader):
            data = data.view(-1, 28 * 28)
            logits = forward(data)
            loss = criteon(logits, target)
            optimizer.zero_grad()
            loss.backward()
            # print(w1.grad.norm(), w2.grad.norm())
            optimizer.step()
    
            if batch_idx % 100 == 0:
                print('Train Epoch:{} [{} / {} ({:.0f}%)]\tLoss: {:.6f}'.format(
                    epoch, batch_idx * len(data), len(train_loader.dataset),
                    100. * batch_idx / len(train_loader), loss.item()
                ))
    
        test_loss = 0
        correct = 0
        for data, target in test_loader:
            data = data.view(-1, 28 * 28)
            logits = forward(data)
            test_loss += criteon(logits, target).item()
            pred = logits.data.max(1)[1]
            correct += pred.eq(target.data).sum()
    
        test_loss /= len(test_loader.dataset)
        print('\nTest set Average loss:{:.4f}, Accuracy: {} / {} ({:.0f}%)\n'.format(
            test_loss, correct, len(test_loader.dataset),
                  100. * correct / len(test_loader.dataset)
        ))
    
    """ Train Epoch:0 [0 / 60000 (0%)] Loss: 2.486867 Train Epoch:0 [20000 / 60000 (33%)] Loss: 0.724861 Train Epoch:0 [40000 / 60000 (67%)] Loss: 0.376032 Test set Average loss:0.0018, Accuracy: 8983 / 10000 (90%) Train Epoch:1 [0 / 60000 (0%)] Loss: 0.366988 Train Epoch:1 [20000 / 60000 (33%)] Loss: 0.377176 Train Epoch:1 [40000 / 60000 (67%)] Loss: 0.399104 Test set Average loss:0.0014, Accuracy: 9186 / 10000 (92%) Train Epoch:2 [0 / 60000 (0%)] Loss: 0.252696 Train Epoch:2 [20000 / 60000 (33%)] Loss: 0.302346 Train Epoch:2 [40000 / 60000 (67%)] Loss: 0.266919 Test set Average loss:0.0012, Accuracy: 9284 / 10000 (93%) Train Epoch:3 [0 / 60000 (0%)] Loss: 0.320602 Train Epoch:3 [20000 / 60000 (33%)] Loss: 0.223881 Train Epoch:3 [40000 / 60000 (67%)] Loss: 0.198832 Test set Average loss:0.0011, Accuracy: 9364 / 10000 (94%) Train Epoch:4 [0 / 60000 (0%)] Loss: 0.253680 Train Epoch:4 [20000 / 60000 (33%)] Loss: 0.147065 Train Epoch:4 [40000 / 60000 (67%)] Loss: 0.194152 Test set Average loss:0.0010, Accuracy: 9406 / 10000 (94%) Train Epoch:5 [0 / 60000 (0%)] Loss: 0.163504 Train Epoch:5 [20000 / 60000 (33%)] Loss: 0.216691 Train Epoch:5 [40000 / 60000 (67%)] Loss: 0.166883 Test set Average loss:0.0010, Accuracy: 9460 / 10000 (95%) Train Epoch:6 [0 / 60000 (0%)] Loss: 0.120956 Train Epoch:6 [20000 / 60000 (33%)] Loss: 0.122348 Train Epoch:6 [40000 / 60000 (67%)] Loss: 0.167381 Test set Average loss:0.0009, Accuracy: 9484 / 10000 (95%) Train Epoch:7 [0 / 60000 (0%)] Loss: 0.218382 Train Epoch:7 [20000 / 60000 (33%)] Loss: 0.141006 Train Epoch:7 [40000 / 60000 (67%)] Loss: 0.156644 Test set Average loss:0.0009, Accuracy: 9501 / 10000 (95%) Train Epoch:8 [0 / 60000 (0%)] Loss: 0.152702 Train Epoch:8 [20000 / 60000 (33%)] Loss: 0.167587 Train Epoch:8 [40000 / 60000 (67%)] Loss: 0.182679 Test set Average loss:0.0008, Accuracy: 9528 / 10000 (95%) Train Epoch:9 [0 / 60000 (0%)] Loss: 0.210252 Train Epoch:9 [20000 / 60000 (33%)] Loss: 0.150022 Train Epoch:9 [40000 / 60000 (67%)] Loss: 0.097077 Test set Average loss:0.0008, Accuracy: 9559 / 10000 (96%) """

# 22.全连接层 #

concisely

*  inherit from nn.Module
 *  init layer in \_\_init\_\_
 *  implement forward()

nn.Relu v.s. F.relu()

*  class-style API
 *  function-style API

import torch
    import torch.nn as nn
    from torchvision import datasets, transforms
    import torch.optim as optim
    from visdom import Visdom
    
    # initialize
    batch_size = 200
    learning_rate = 0.01
    epochs = 10
    
    # load data
    train_loader = torch.utils.data.DataLoader(
        datasets.MNIST('../data', train=True, download=True,
                       transform=transforms.Compose([
                           transforms.ToTensor(),
                           transforms.Normalize((0.01307, ), (0.3081, ))
                       ])),
        batch_size=batch_size, shuffle=True
    )
    test_loader = torch.utils.data.DataLoader(
        datasets.MNIST('../data', train=False,
                       transform=transforms.Compose([
                           transforms.ToTensor(),
                           transforms.Normalize((0.01307, ), (0.3081, ))
                       ])),
        batch_size=batch_size, shuffle=True
    )
    
    
    class MLP(nn.Module):
        def __init__(self):
            super(MLP, self).__init__()
    
            self.model = nn.Sequential(
                nn.Linear(784, 200),
                nn.LeakyReLU(inplace=True), # inplace计算可以节省内（显）存，还可省去反复申请和释放内存时间,但会对原变量覆盖
                nn.Linear(200,200),
                nn.LeakyReLU(inplace=True),
                nn.Linear(200,10),
                nn.LeakyReLU(inplace=True),
            )
    
        def forward(self, x):
            x = self.model(x)
            return x
    
    device = torch.device('cuda:0')
    net = MLP().to(device)
    optimizer = optim.SGD(net.parameters(), lr=learning_rate)
    criteon = nn.CrossEntropyLoss().to(device)
    
    viz = Visdom()
    viz.line([0.], [0.], win='train_loss', opts=dict(title='train_loss'))
    viz.line([[0.0, 0.0]], [0.], win='test', opts=dict(title='test loss&acc.', legend=['loss', 'acc.']))
    global_step = -1
    
    for epoch in range(epochs):
        for batch_idx, (data, target) in enumerate(train_loader):
            data = data.view(-1, 28 * 28)
            data, target = data.to(device), target.cuda()
    
            logits = net(data)
            loss = criteon(logits, target)
            optimizer.zero_grad()
            loss.backward()
            # print(w1.grad.norm(), w2.grad.norm())
            optimizer.step()
    
            if batch_idx % 100 == 0:
                print('Train Epoch:{} [{} / {} ({:.0f}%)]\tLoss: {:.6f}'.format(
                    epoch, batch_idx * len(data), len(train_loader.dataset),
                           100. * batch_idx / len(train_loader), loss.item()
                ))
    
            # lines: single trace
            global_step += 1
            viz.line([loss.item()], [global_step], win='train_loss', update='append')
    
        test_loss = 0
        correct = 0
        for data, target in test_loader:
            data = data.view(-1, 28 * 28)
            data, target = data.to(device), target.cuda()
    
            logits = net(data)
            test_loss += criteon(logits, target).item()
            # pred = logits.data.max(1)[1]
            pred = logits.argmax(dim=1)
            correct += pred.eq(target).float().sum().item()
    
        # lines: multi-traces
        viz.line([[test_loss, correct / len(test_loader.dataset)]], [global_step], win='test', update='append')
        # visual X
        viz.images(data.view(-1, 1, 28, 28), win='x')
        viz.text(str(pred.detach().cpu().numpy()), win='pred', opts=dict(title='pred'))
    
        test_loss /= len(test_loader.dataset)
        print('\nTest set Average loss:{:.4f}, Accuracy: {} / {} ({:.0f}%)\n'.format(
            test_loss, correct, len(test_loader.dataset),
                  100. * correct / len(test_loader.dataset)
        ))
    
    
    """ Train Epoch:0 [0 / 60000 (0%)] Loss: 2.302269 Train Epoch:0 [20000 / 60000 (33%)] Loss: 1.884911 Train Epoch:0 [40000 / 60000 (67%)] Loss: 1.336271 Test set Average loss:0.0038, Accuracy: 8169 / 10000 (82%) Train Epoch:1 [0 / 60000 (0%)] Loss: 0.721071 Train Epoch:1 [20000 / 60000 (33%)] Loss: 0.565047 Train Epoch:1 [40000 / 60000 (67%)] Loss: 0.506850 Test set Average loss:0.0021, Accuracy: 8889 / 10000 (89%) Train Epoch:2 [0 / 60000 (0%)] Loss: 0.368552 Train Epoch:2 [20000 / 60000 (33%)] Loss: 0.301212 Train Epoch:2 [40000 / 60000 (67%)] Loss: 0.406262 Test set Average loss:0.0017, Accuracy: 9061 / 10000 (91%) Train Epoch:3 [0 / 60000 (0%)] Loss: 0.372895 Train Epoch:3 [20000 / 60000 (33%)] Loss: 0.390528 Train Epoch:3 [40000 / 60000 (67%)] Loss: 0.389583 Test set Average loss:0.0015, Accuracy: 9141 / 10000 (91%) Train Epoch:4 [0 / 60000 (0%)] Loss: 0.220136 Train Epoch:4 [20000 / 60000 (33%)] Loss: 0.281799 Train Epoch:4 [40000 / 60000 (67%)] Loss: 0.291274 Test set Average loss:0.0014, Accuracy: 9211 / 10000 (92%) Train Epoch:5 [0 / 60000 (0%)] Loss: 0.280618 Train Epoch:5 [20000 / 60000 (33%)] Loss: 0.305418 Train Epoch:5 [40000 / 60000 (67%)] Loss: 0.334693 Test set Average loss:0.0014, Accuracy: 9226 / 10000 (92%) Train Epoch:6 [0 / 60000 (0%)] Loss: 0.342200 Train Epoch:6 [20000 / 60000 (33%)] Loss: 0.294665 Train Epoch:6 [40000 / 60000 (67%)] Loss: 0.220197 Test set Average loss:0.0013, Accuracy: 9280 / 10000 (93%) Train Epoch:7 [0 / 60000 (0%)] Loss: 0.211271 Train Epoch:7 [20000 / 60000 (33%)] Loss: 0.358451 Train Epoch:7 [40000 / 60000 (67%)] Loss: 0.236865 Test set Average loss:0.0012, Accuracy: 9338 / 10000 (93%) Train Epoch:8 [0 / 60000 (0%)] Loss: 0.226759 Train Epoch:8 [20000 / 60000 (33%)] Loss: 0.288015 Train Epoch:8 [40000 / 60000 (67%)] Loss: 0.263826 Test set Average loss:0.0012, Accuracy: 9349 / 10000 (93%) Train Epoch:9 [0 / 60000 (0%)] Loss: 0.144617 Train Epoch:9 [20000 / 60000 (33%)] Loss: 0.166465 Train Epoch:9 [40000 / 60000 (67%)] Loss: 0.296551 Test set Average loss:0.0011, Accuracy: 9367 / 10000 (94%) """

# 23.激活函数与GPU加速 #

大部分时候使用ReLU激活函数

*  ReLU:  R ( z ) = m a x ( 0 , z ) R(z)=max(0, z) R(z)=max(0,z)
 *  Leaky ReLU
 *  SELU
 *  softplus

GPU accelerated

*  一键切换  
    `device=torch.device('cuda: 0')` `data.to(device)`  
    `data.cuda()` 不推荐

任务管理器查看GPU使用情况  
![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L2tlaXZlbl8_size_16_color_FFFFFF_t_70 2]

# 24. 测试 #

Loss != Accuracy

When to test

*  test once per serveral batch
 *  test once per epoch
 *  epoch v.s. step?

[-AI_PyTorch]: https://www.bilibili.com/video/BV1Rv411y7oE?p=1
[PyTorch]: https://www.bilibili.com/video/BV1mh41167e3?p=1
[Link 1]: https://arxiv.org/pdf/1502.01852.pdf
[P32]: /images/20220829/0ea71ada57c24830a56366c337845cef.png
[watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L2tlaXZlbl8_size_16_color_FFFFFF_t_70]: /images/20220829/caf7aaab22fb495dab937428d59c6cca.png
[watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L2tlaXZlbl8_size_16_color_FFFFFF_t_70 1]: /images/20220829/7dc8cbf6e8eb4779a2286524e207fe82.png
[watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L2tlaXZlbl8_size_16_color_FFFFFF_t_70 2]: /images/20220829/932cce77f11541bba08d53b860c483eb.png