Pytorch搭建DenseNet[通俗易懂]

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文章目录

- DenseNet与ResNeXt

DenseNet与ResNeXt

首先回顾一下DenseNet的结构，DenseNet的每一层都都与前面层相连，实现了特征重用。

下图表示一个DenseBlock
在这里插入图片描述

如图所示,在一个DenseBlock中,第i层的输入不仅与i-1层的输出相关,还有所有之前层的输出有关.记作:

在这里插入图片描述

DenseNet网络的搭建

Growth_rate

在一个DenseBlock里面，每个非线性变换H输出的channels数为恒定的Growth_rate，那么第i层的输入的channels数便是k+i* Growth_rate, k为Input

的channels数，比如，假设我们把Growth_rate设为4，上图中H1的输入的size为8 * 32 * 32，输出为4 * 32 * 32，则H2的输入的size为12 * 32 * 32，

输出还是4 * 32 * 32，H3、H4以此类推，在实验中，用较小的Growth_rate就能实现较好的效果。

Transition Layer

请注意，在一个DenseBlock里面，feature size并没有发生改变，因为需要对不同层的feature map进行concatenate操作，这需要保持相同的feature size。

因此在相邻的DenseBlock中间使用Down Sampling来增大感受野（卷积神经网络每一层输出的特征图（feature map）上的像素点在原始图像上映射的区域大小），即使用Transition Layer来实现，一般的Transition Layer包含BN、Conv和Avg_pool，

同时减少维度，压缩率(compress rate)通常为0.5，即减少一半的维度。
在这里插入图片描述

例如，假设block1的输出c * w * h是24 * 32 * 32，那么经过transition之后，block2的输入就是12 * 16 * 16。

Bottleneck

为了减少参数和计算量，DenseNet的非线性变换H采用了Bottleneck结构BN-ReLU-Conv(1×1)-BN-ReLU-Conv(3×3)，1×1的卷积用于降低维度，将channels数降

低至4 * Growth_rate。

Bottleneck是这样一种网络，其输入输出channel差距较大，就像一个瓶颈一样，上窄下宽亦或上宽下窄，特征图的大小会因为最后一步的cat从N×in_planes×H×W变成N×(in_planes+growth_rate)×H×W。而transition则用来把N×in_planes×H×W变成N×growth_rate×0.5H×0.5W

定义网络

C=torch.cat((A,B),1)就表示按维数1（两位情况是列，四维是channel）拼接A和B，也就是横着拼接，A左B右。此时需要注意：行数必须一致，即维数0数值要相同，方能行对齐。拼接后的C的第1维是两个维数1数值和。

kernelsize为1，padding为0和kernelsize为3，padding为1都会让卷积后的特征图的大小与原来的图片一致：一个是(N-1)/1+1,一个是(N+2*1-3)/1+1，由此我们可以得出结论，在stride为1时，只要kernelsize=2*padding+1，就不会改变特征图的大小。

注意：stride会默认为核的大小，因此在下面的实验里,stride的值为4。一般情况下，池化层的输出大小为(N-kernelsize)/stride+1，当stride等于kernelsize的时候，也就是不填stride时，输出的大小为N/kernel。

# Load necessary modules here
import math
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
import torch.backends.cudnn as cudnn
import os
from tensorboardX import SummaryWriter
writer = SummaryWriter()

class Bottleneck(nn.Module):
    ''' the above mentioned bottleneck, including two conv layer, one's kernel size is 1×1, another's is 3×3 in_planes可以理解成channel after non-linear operation, concatenate the input to the output '''
    def __init__(self, in_planes, growth_rate):
        super(Bottleneck, self).__init__()
        self.bn1 = nn.BatchNorm2d(in_planes)
        self.conv1 = nn.Conv2d(in_planes, 4*growth_rate, kernel_size=1, bias=False)
        self.bn2 = nn.BatchNorm2d(4*growth_rate)
        self.conv2 = nn.Conv2d(4*growth_rate, growth_rate, kernel_size=3, padding=1, bias=False)

    def forward(self, x):
        out = self.conv1(F.relu(self.bn1(x)))
        out = self.conv2(F.relu(self.bn2(out)))
        
        # input and output are concatenated here
        out = torch.cat([out,x], 1)
        return out


class Transition(nn.Module):
    ''' transition layer is used for down sampling the feature when compress rate is 0.5, out_planes is a half of in_planes '''
    def __init__(self, in_planes, out_planes):
        super(Transition, self).__init__()
        self.bn = nn.BatchNorm2d(in_planes)
        self.conv = nn.Conv2d(in_planes, out_planes, kernel_size=1, bias=False)

    def forward(self, x):
        
        out = self.conv(F.relu(self.bn(x)))
        # use average pooling change the size of feature map here
        out = F.avg_pool2d(out, 2)
        return out 

    
class DenseNet(nn.Module):
    def __init__(self, block, nblocks, growth_rate=12, reduction=0.5, num_classes=10):
        super(DenseNet, self).__init__()
        ''' Args: block: bottleneck nblock: a list, the elements is number of bottleneck in each denseblock growth_rate: channel size of bottleneck's output reduction: '''
        self.growth_rate = growth_rate

        num_planes = 2*growth_rate
        self.conv1 = nn.Conv2d(3, num_planes, kernel_size=3, padding=1, bias=False)
        
        # a DenseBlock and a transition layer
        self.dense1 = self._make_dense_layers(block, num_planes, nblocks[0])
        num_planes += nblocks[0]*growth_rate
        # the channel size is superposed, mutiply by reduction to cut it down here, the reduction is also known as compress rate
        out_planes = int(math.floor(num_planes*reduction))
        self.trans1 = Transition(num_planes, out_planes)
        num_planes = out_planes
        
        # a DenseBlock and a transition layer
        self.dense2 = self._make_dense_layers(block, num_planes, nblocks[1])
        num_planes += nblocks[1]*growth_rate
        # the channel size is superposed, mutiply by reduction to cut it down here, the reduction is also known as compress rate
        out_planes = int(math.floor(num_planes*reduction))
        self.trans2 = Transition(num_planes, out_planes)
        num_planes = out_planes

        # a DenseBlock and a transition layer
        self.dense3 = self._make_dense_layers(block, num_planes, nblocks[2])
        num_planes += nblocks[2]*growth_rate
        # the channel size is superposed, mutiply by reduction to cut it down here, the reduction is also known as compress rate
        out_planes = int(math.floor(num_planes*reduction))
        self.trans3 = Transition(num_planes, out_planes)
        num_planes = out_planes

        # only one DenseBlock 
        self.dense4 = self._make_dense_layers(block, num_planes, nblocks[3])
        num_planes += nblocks[3]*growth_rate

        # the last part is a linear layer as a classifier
        self.bn = nn.BatchNorm2d(num_planes)
        self.linear = nn.Linear(num_planes, num_classes)

    def _make_dense_layers(self, block, in_planes, nblock):
        layers = []
        
        # number of non-linear transformations in one DenseBlock depends on the parameter you set
        for i in range(nblock):
            layers.append(block(in_planes, self.growth_rate))
            in_planes += self.growth_rate
        return nn.Sequential(*layers)

    def forward(self, x):
        out = self.conv1(x)
        out = self.trans1(self.dense1(out))
        out = self.trans2(self.dense2(out))
        out = self.trans3(self.dense3(out))
        out = self.dense4(out)
        out = F.avg_pool2d(F.relu(self.bn(out)), 4)
        out = out.view(out.size(0), -1)
        out = self.linear(out)
        return out


def densenet():
    return DenseNet(Bottleneck, [2, 5, 4, 6])

print(densenet())

DenseNet(
  (conv1): Conv2d(3, 24, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
  (dense1): Sequential(
    (0): Bottleneck(
      (bn1): BatchNorm2d(24, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (conv1): Conv2d(24, 48, kernel_size=(1, 1), stride=(1, 1), bias=False)
      (bn2): BatchNorm2d(48, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (conv2): Conv2d(48, 12, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
    )
    (1): Bottleneck(
      (bn1): BatchNorm2d(36, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (conv1): Conv2d(36, 48, kernel_size=(1, 1), stride=(1, 1), bias=False)
      (bn2): BatchNorm2d(48, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (conv2): Conv2d(48, 12, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
    )
  )
  (trans1): Transition(
    (bn): BatchNorm2d(48, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
    (conv): Conv2d(48, 24, kernel_size=(1, 1), stride=(1, 1), bias=False)
  )
  (dense2): Sequential(
    (0): Bottleneck(
      (bn1): BatchNorm2d(24, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (conv1): Conv2d(24, 48, kernel_size=(1, 1), stride=(1, 1), bias=False)
      (bn2): BatchNorm2d(48, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (conv2): Conv2d(48, 12, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
    )
    (1): Bottleneck(
      (bn1): BatchNorm2d(36, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (conv1): Conv2d(36, 48, kernel_size=(1, 1), stride=(1, 1), bias=False)
      (bn2): BatchNorm2d(48, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (conv2): Conv2d(48, 12, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
    )
    (2): Bottleneck(
      (bn1): BatchNorm2d(48, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (conv1): Conv2d(48, 48, kernel_size=(1, 1), stride=(1, 1), bias=False)
      (bn2): BatchNorm2d(48, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (conv2): Conv2d(48, 12, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
    )
    (3): Bottleneck(
      (bn1): BatchNorm2d(60, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (conv1): Conv2d(60, 48, kernel_size=(1, 1), stride=(1, 1), bias=False)
      (bn2): BatchNorm2d(48, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (conv2): Conv2d(48, 12, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
    )
    (4): Bottleneck(
      (bn1): BatchNorm2d(72, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (conv1): Conv2d(72, 48, kernel_size=(1, 1), stride=(1, 1), bias=False)
      (bn2): BatchNorm2d(48, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (conv2): Conv2d(48, 12, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
    )
  )
  (trans2): Transition(
    (bn): BatchNorm2d(84, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
    (conv): Conv2d(84, 42, kernel_size=(1, 1), stride=(1, 1), bias=False)
  )
  (dense3): Sequential(
    (0): Bottleneck(
      (bn1): BatchNorm2d(42, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (conv1): Conv2d(42, 48, kernel_size=(1, 1), stride=(1, 1), bias=False)
      (bn2): BatchNorm2d(48, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (conv2): Conv2d(48, 12, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
    )
    (1): Bottleneck(
      (bn1): BatchNorm2d(54, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (conv1): Conv2d(54, 48, kernel_size=(1, 1), stride=(1, 1), bias=False)
      (bn2): BatchNorm2d(48, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (conv2): Conv2d(48, 12, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
    )
    (2): Bottleneck(
      (bn1): BatchNorm2d(66, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (conv1): Conv2d(66, 48, kernel_size=(1, 1), stride=(1, 1), bias=False)
      (bn2): BatchNorm2d(48, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (conv2): Conv2d(48, 12, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
    )
    (3): Bottleneck(
      (bn1): BatchNorm2d(78, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (conv1): Conv2d(78, 48, kernel_size=(1, 1), stride=(1, 1), bias=False)
      (bn2): BatchNorm2d(48, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (conv2): Conv2d(48, 12, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
    )
  )
  (trans3): Transition(
    (bn): BatchNorm2d(90, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
    (conv): Conv2d(90, 45, kernel_size=(1, 1), stride=(1, 1), bias=False)
  )
  (dense4): Sequential(
    (0): Bottleneck(
      (bn1): BatchNorm2d(45, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (conv1): Conv2d(45, 48, kernel_size=(1, 1), stride=(1, 1), bias=False)
      (bn2): BatchNorm2d(48, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (conv2): Conv2d(48, 12, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
    )
    (1): Bottleneck(
      (bn1): BatchNorm2d(57, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (conv1): Conv2d(57, 48, kernel_size=(1, 1), stride=(1, 1), bias=False)
      (bn2): BatchNorm2d(48, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (conv2): Conv2d(48, 12, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
    )
    (2): Bottleneck(
      (bn1): BatchNorm2d(69, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (conv1): Conv2d(69, 48, kernel_size=(1, 1), stride=(1, 1), bias=False)
      (bn2): BatchNorm2d(48, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (conv2): Conv2d(48, 12, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
    )
    (3): Bottleneck(
      (bn1): BatchNorm2d(81, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (conv1): Conv2d(81, 48, kernel_size=(1, 1), stride=(1, 1), bias=False)
      (bn2): BatchNorm2d(48, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (conv2): Conv2d(48, 12, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
    )
    (4): Bottleneck(
      (bn1): BatchNorm2d(93, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (conv1): Conv2d(93, 48, kernel_size=(1, 1), stride=(1, 1), bias=False)
      (bn2): BatchNorm2d(48, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (conv2): Conv2d(48, 12, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
    )
    (5): Bottleneck(
      (bn1): BatchNorm2d(105, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (conv1): Conv2d(105, 48, kernel_size=(1, 1), stride=(1, 1), bias=False)
      (bn2): BatchNorm2d(48, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (conv2): Conv2d(48, 12, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
    )
  )
  (bn): BatchNorm2d(117, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
  (linear): Linear(in_features=117, out_features=10, bias=True)
)

小测试

x = torch.randn((3,3,4,8))
y = F.max_pool2d(x, 4)
print(x.shape)
print(y.shape)
b = Bottleneck(3, 5)
print(b(x).shape)
t = Transition(3, 5)
print(t(x).shape)

torch.Size([3, 3, 4, 8])
torch.Size([3, 3, 1, 2])
torch.Size([3, 8, 4, 8])
torch.Size([3, 5, 2, 4])

问题1

上面的定义的DenseNet为多少层DenseNet（只计算卷积层与全连接层）？请定义一个卷积层总数为52层的DenseNet。

答：每个Bottleneck包含了两个卷积层，每个Transition包含一个卷积层，总共有2+5+4+6=17个Bottleneck和3个transition，也就是37个卷积层，再加上最前面的卷积层和最后的全连接层，最后总共有39层。

def densenet52():
    return DenseNet(Bottleneck, [6, 4, 8, 6])

训练与测试

import torchvision
import torchvision.transforms as transforms
from torch.autograd import Variable


def train(epoch, model, lossFunction, optimizer, device, trainloader):
    """train model using loss_fn and optimizer. When this function is called, model trains for one epoch. Args: train_loader: train data model: prediction model loss_fn: loss function to judge the distance between target and outputs optimizer: optimize the loss function get_grad: True, False output: total_loss: loss average_grad2: average grad for hidden 2 in this epoch average_grad3: average grad for hidden 3 in this epoch """
    print('\nEpoch: %d' % epoch)
    model.train()     # enter train mode
    train_loss = 0    # accumulate every batch loss in a epoch
    correct = 0       # count when model' prediction is correct i train set
    total = 0         # total number of prediction in train set
    for batch_idx, (inputs, targets) in enumerate(trainloader):
        inputs, targets = inputs.to(device), targets.to(device) # load data to gpu device
        inputs, targets = Variable(inputs), Variable(targets)
        optimizer.zero_grad()            # clear gradients of all optimized torch.Tensors'
        outputs = model(inputs)          # forward propagation return the value of softmax function
        loss = lossFunction(outputs, targets) #compute loss
        loss.backward()                  # compute gradient of loss over parameters 
        optimizer.step()                 # update parameters with gradient descent 

        train_loss += loss.item()        # accumulate every batch loss in a epoch
        _, predicted = outputs.max(1)    # make prediction according to the outputs
        total += targets.size(0)
        correct += predicted.eq(targets).sum().item() # count how many predictions is correct
        
        if (batch_idx+1) % 100 == 0:
            # print loss and acc
            print( 'Train loss: %.3f | Train Acc: %.3f%% (%d/%d)'
                % (train_loss/(batch_idx+1), 100.*correct/total, correct, total))
    print( 'Train loss: %.3f | Train Acc: %.3f%% (%d/%d)'
                % (train_loss/(batch_idx+1), 100.*correct/total, correct, total))
    
    
def test(model, lossFunction, optimizer, device, testloader):
    """ test model's prediction performance on loader. When thid function is called, model is evaluated. Args: loader: data for evaluation model: prediction model loss_fn: loss function to judge the distance between target and outputs output: total_loss accuracy """
    global best_acc
    model.eval() #enter test mode
    test_loss = 0 # accumulate every batch loss in a epoch
    correct = 0
    total = 0
    with torch.no_grad():
        for batch_idx, (inputs, targets) in enumerate(testloader):
            inputs, targets = inputs.to(device), targets.to(device)
            outputs = model(inputs)
            loss = lossFunction(outputs, targets) #compute loss

            test_loss += loss.item() # accumulate every batch loss in a epoch
            _, predicted = outputs.max(1) # make prediction according to the outputs
            total += targets.size(0)
            correct += predicted.eq(targets).sum().item() # count how many predictions is correct
        # print loss and acc
        print('Test Loss: %.3f | Test Acc: %.3f%% (%d/%d)'
            % (test_loss/(batch_idx+1), 100.*correct/total, correct, total))

        
def data_loader():
    # define method of preprocessing data for evaluating
    transform_train = transforms.Compose([
        transforms.Resize(32),
        transforms.RandomCrop(32, padding=4),
        transforms.RandomHorizontalFlip(),
        transforms.ToTensor(),
        # Normalize a tensor image with mean and standard variance
        transforms.Normalize((0.4914, 0.4822, 0.4465), (0.2023, 0.1994, 0.2010)),
    ])

    transform_test = transforms.Compose([
        transforms.Resize(32),
        transforms.ToTensor(),
        # Normalize a tensor image with mean and standard variance
        transforms.Normalize((0.4914, 0.4822, 0.4465), (0.2023, 0.1994, 0.2010)),
    ])
    
    # prepare dataset by ImageFolder, data should be classified by directory
    trainset = torchvision.datasets.ImageFolder(root='./mnist/train', transform=transform_train)

    testset = torchvision.datasets.ImageFolder(root='./mnist/test', transform=transform_test)

    # Data loader. 

    # Combines a dataset and a sampler, 

    trainloader = torch.utils.data.DataLoader(trainset, batch_size=64, shuffle=True)

    testloader = torch.utils.data.DataLoader(testset, batch_size=100, shuffle=False)
    return trainloader, testloader

def run(model, num_epochs):
    
    # load model into GPU device
# device = 'cuda:0' if torch.cuda.is_available() else 'cpu'
    device = 'cpu'
    model.to(device)
# if device == 'cuda:0':
# model = torch.nn.DataParallel(model)
# cudnn.benchmark = True

    # define the loss function and optimizer

    lossFunction = nn.CrossEntropyLoss()
    lr = 0.01
    optimizer = optim.SGD(model.parameters(), lr=lr, momentum=0.9, weight_decay=5e-4)

    trainloader, testloader = data_loader()
    for epoch in range(num_epochs):
        train(epoch, model, lossFunction, optimizer, device, trainloader)
        test(model, lossFunction, optimizer, device, testloader)
        if (epoch + 1) % 50 == 0 :
            lr = lr / 10
            for param_group in optimizer.param_groups:
                param_group['lr'] = lr

用你自己定义的DenseNet进行测试

# start training and testing
model = densenet52()
# num_epochs is adjustable
run(model, num_epochs=20)

Epoch: 0
Train loss: 2.247 | Train Acc: 19.900% (398/2000)
Test Loss: 2.314  | Test Acc: 10.000% (100/1000)

Epoch: 1
Train loss: 1.916 | Train Acc: 38.700% (774/2000)
Test Loss: 2.055  | Test Acc: 25.300% (253/1000)

Epoch: 2
Train loss: 1.398 | Train Acc: 56.450% (1129/2000)
Test Loss: 1.486  | Test Acc: 39.300% (393/1000)

Epoch: 3
Train loss: 0.988 | Train Acc: 72.300% (1446/2000)
Test Loss: 1.224  | Test Acc: 56.600% (566/1000)

Epoch: 4
Train loss: 0.716 | Train Acc: 78.250% (1565/2000)
Test Loss: 0.755  | Test Acc: 75.400% (754/1000)

Epoch: 5
Train loss: 0.561 | Train Acc: 81.000% (1620/2000)
Test Loss: 0.795  | Test Acc: 70.300% (703/1000)

Epoch: 6
Train loss: 0.487 | Train Acc: 82.150% (1643/2000)
Test Loss: 0.634  | Test Acc: 75.500% (755/1000)

Epoch: 7
Train loss: 0.475 | Train Acc: 82.300% (1646/2000)
Test Loss: 0.644  | Test Acc: 77.800% (778/1000)

Epoch: 8
Train loss: 0.458 | Train Acc: 83.550% (1671/2000)
Test Loss: 0.395  | Test Acc: 84.300% (843/1000)

Epoch: 9
Train loss: 0.403 | Train Acc: 86.000% (1720/2000)
Test Loss: 0.551  | Test Acc: 82.500% (825/1000)

Epoch: 10
Train loss: 0.351 | Train Acc: 87.050% (1741/2000)
Test Loss: 0.674  | Test Acc: 77.100% (771/1000)

Epoch: 11
Train loss: 0.321 | Train Acc: 89.100% (1782/2000)
Test Loss: 0.338  | Test Acc: 88.900% (889/1000)

Epoch: 12
Train loss: 0.256 | Train Acc: 92.400% (1848/2000)
Test Loss: 0.394  | Test Acc: 87.800% (878/1000)

Epoch: 13
Train loss: 0.251 | Train Acc: 92.550% (1851/2000)
Test Loss: 0.448  | Test Acc: 84.300% (843/1000)

Epoch: 14
Train loss: 0.259 | Train Acc: 92.150% (1843/2000)
Test Loss: 0.414  | Test Acc: 86.000% (860/1000)

Epoch: 15
Train loss: 0.229 | Train Acc: 93.850% (1877/2000)
Test Loss: 0.321  | Test Acc: 88.300% (883/1000)

Epoch: 16
Train loss: 0.196 | Train Acc: 94.400% (1888/2000)
Test Loss: 0.393  | Test Acc: 87.100% (871/1000)

Epoch: 17
Train loss: 0.178 | Train Acc: 95.350% (1907/2000)
Test Loss: 0.295  | Test Acc: 90.600% (906/1000)

Epoch: 18
Train loss: 0.184 | Train Acc: 94.650% (1893/2000)
Test Loss: 0.351  | Test Acc: 87.700% (877/1000)

Epoch: 19
Train loss: 0.168 | Train Acc: 95.200% (1904/2000)
Test Loss: 0.164  | Test Acc: 95.500% (955/1000)

可视化网络结构

device = 'cuda:0' if torch.cuda.is_available() else 'cpu'
model.to(device)
train_loader, _ = data_loader()
dataiter = iter(train_loader)
images, _ = dataiter.next() # get a batch of images
images = images.to(device)
with SummaryWriter(comment="densenet52") as s:
    s.add_graph(model, (Variable(images),))
    s.close()