精华内容
下载资源
问答
  • 今天小编就为大家分享一篇pytorch打印网络结构的实例,具有很好的参考价值,希望对大家有所帮助。一起跟随小编过来看看吧
  • pytorch打印网络结构

    万次阅读 2018-06-12 10:10:06
    最简单的方法当然可以直接print(net),但是这样网络比较复杂的时候效果不太好,看着比较乱;以前使用caffe的时候有一个...pytorch没有这样的API,但是可以用代码来完成。(1)安装环境:graphvizconda install -...

    最简单的方法当然可以直接print(net),但是这样网络比较复杂的时候效果不太好,看着比较乱;以前使用caffe的时候有一个网站可以在线生成网络框图,tensorflow可以用tensor board,keras中可以用model.summary()、或者plot_model()。pytorch没有这样的API,但是可以用代码来完成。

    (1)安装环境:graphviz

    conda install -n pytorch python-graphviz

    或:

    sudo apt-get install graphviz

    或者从官网下载,按此教程


    (2)生成网络结构的代码:

    def make_dot(var, params=None):
        """ Produces Graphviz representation of PyTorch autograd graph
        Blue nodes are the Variables that require grad, orange are Tensors
        saved for backward in torch.autograd.Function
        Args:
            var: output Variable
            params: dict of (name, Variable) to add names to node that
                require grad (TODO: make optional)
        """
        if params is not None:
            assert isinstance(params.values()[0], Variable)
            param_map = {id(v): k for k, v in params.items()}
    
        node_attr = dict(style='filled',
                         shape='box',
                         align='left',
                         fontsize='12',
                         ranksep='0.1',
                         height='0.2')
        dot = Digraph(node_attr=node_attr, graph_attr=dict(size="12,12"))
        seen = set()
    
        def size_to_str(size):
            return '('+(', ').join(['%d' % v for v in size])+')'
    
        def add_nodes(var):
            if var not in seen:
                if torch.is_tensor(var):
                    dot.node(str(id(var)), size_to_str(var.size()), fillcolor='orange')
                elif hasattr(var, 'variable'):
                    u = var.variable
                    name = param_map[id(u)] if params is not None else ''
                    node_name = '%s\n %s' % (name, size_to_str(u.size()))
                    dot.node(str(id(var)), node_name, fillcolor='lightblue')
                else:
                    dot.node(str(id(var)), str(type(var).__name__))
                seen.add(var)
                if hasattr(var, 'next_functions'):
                    for u in var.next_functions:
                        if u[0] is not None:
                            dot.edge(str(id(u[0])), str(id(var)))
                            add_nodes(u[0])
                if hasattr(var, 'saved_tensors'):
                    for t in var.saved_tensors:
                        dot.edge(str(id(t)), str(id(var)))
                        add_nodes(t)
        add_nodes(var.grad_fn)
        return dot


    (3)打印网络结构:

    import torch  
    from torch.autograd import Variable  
    import torch.nn as nn  
    from graphviz import Digraph
    
    class CNN(nn.module):
        def __init__(self):
         ******
         def forward(self,x):
          ******
          return out
    
    *****************************
    def make_dot():  #复制上面的代码
    *****************************
    
    if __name__ == '__main__':  
        net = CNN()  
        x = Variable(torch.randn(1, 1, 1024,1024))  
        y = net(x)  
        g = make_dot(y)  
        g.view()  
      
        params = list(net.parameters())  
        k = 0  
        for i in params:  
            l = 1  
            print("该层的结构:" + str(list(i.size())))  
            for j in i.size():  
                l *= j  
            print("该层参数和:" + str(l))  
            k = k + l  
        print("总参数数量和:" + str(k))

    (4)结果展示(例如这是一个resnet block类型的网络):





    展开全文
  • pytorch Resnet 网络结构

    千次阅读 2018-08-28 10:38:00
    最近在学习廖老师的pytorch教程,学到Resnet 这部分着实的烧脑,这个模型都捣鼓了好长时间才弄懂,附上我学习过程中最为不解的网络的具体结构连接(网上一直没有找到对应网络结构,对与一个自学的学渣般的我,很是...

    最近在学习廖老师的pytorch教程,学到Resnet 这部分着实的烧脑,这个模型都捣鼓了好长时间才弄懂,附上我学习过程中最为不解的网络的具体结构连接(网上一直没有找到对应网络结构,对与一个自学的学渣般的我,很是无奈,所以搞懂后我就...分享给有需要的的你了)

    我们先大致了解一下残差模型

    ResNet在2015年被提出,在ImageNet比赛classification任务上获得第一名,因为它“简单与实用”并存,之后很多方法都建立在ResNet50或者ResNet101的基础上完成的,检测,分割,识别等领域都纷纷使用ResNet,Alpha zero也使用了ResNet,所以可见ResNet确实很好用。
    下面我们从实用的角度去看看ResNet。

    1.ResNet意义

    随着网络的加深,出现了训练集准确率下降的现象,我们可以确定这不是由于Overfit过拟合造成的(过拟合的情况训练集应该准确率很高);所以作者针对这个问题提出了一种全新的网络,叫深度残差网络,它允许网络尽可能的加深,其中引入了全新的结构如图1;
    这里问大家一个问题
    残差指的是什么
    其中ResNet提出了两种mapping:一种是identity mapping,指的就是图1中”弯弯的曲线”,另一种residual mapping,指的就是除了”弯弯的曲线“那部分,所以最后的输出是 y=F(x)+x


    identity mapping顾名思义,就是指本身,也就是公式中的x,而residual mapping指的是“”,也就是y−x,所以残差指的就是F(x)部分。

     

     我们可以看到一个“弯弯的弧线“这个就是所谓的”shortcut connection“,也是文中提到identity mapping,这张图也诠释了ResNet的真谛,当然残差的结构可不会像图中这样单一,

    下面是对通过Resnet 对cafir10数据的训练代码 以及网络结构图

    import torch
    import torch.nn as nn
    import torchvision.datasets as normal_datasets
    import torchvision.transforms as transforms
    from torch.autograd import Variable
    
    num_epochs = 2
    lr = 0.001
    
    
    def get_variable(x):
        x = Variable(x)
        return x.cuda() if torch.cuda.is_available() else x
    
    
    # 图像预处理
    transform = transforms.Compose([
        transforms.Scale(40),
        transforms.RandomHorizontalFlip(),
        transforms.RandomCrop(32),
        transforms.ToTensor()])
    
    # 加载CIFAR-10
    train_dataset = normal_datasets.CIFAR10(root='./data/',
                                            train=True,
                                            transform=transform,
                                            download=False)
    
    test_dataset = normal_datasets.CIFAR10(root='./data/',
                                           train=False,
                                           transform=transforms.ToTensor())
    
    train_loader = torch.utils.data.DataLoader(dataset=train_dataset,
                                               batch_size=100,
                                               shuffle=True)
    
    test_loader = torch.utils.data.DataLoader(dataset=test_dataset,
                                              batch_size=100,
                                              shuffle=False)
    
    
    # 3x3 卷积
    def conv3x3(in_channels, out_channels, stride=1):
        return nn.Conv2d(in_channels, out_channels, kernel_size=3,
                         stride=stride, padding=1, bias=False)
    
    
    # Residual Block
    class ResidualBlock(nn.Module):
        def __init__(self, in_channels, out_channels, stride=1, downsample=None):
            
            super(ResidualBlock, self).__init__()
            self.conv1 = conv3x3(in_channels, out_channels, stride)
            self.bn1 = nn.BatchNorm2d(out_channels)
            self.relu = nn.ReLU(inplace=True)
            self.conv2 = conv3x3(out_channels, out_channels)
            self.bn2 = nn.BatchNorm2d(out_channels)
            self.downsample = downsample
    
        def forward(self, x):
            residual = x
            out = self.conv1(x)
            out = self.bn1(out)
            out = self.relu(out)
            out = self.conv2(out)
            out = self.bn2(out)
            if self.downsample:
                residual = self.downsample(x)
            out += residual
            out = self.relu(out)
            return out
    
    
    
    class ResNet(nn.Module):
        
        def __init__(self, block, layers, num_classes=10):
            super(ResNet, self).__init__()
            self.in_channels = 16
            self.conv = conv3x3(3, 16)
            self.bn = nn.BatchNorm2d(16)
            self.relu = nn.ReLU(inplace=True)
            self.layer1 = self.make_layer(block, 16, layers[0])
            self.layer2 = self.make_layer(block, 32, layers[0], 2)
            self.layer3 = self.make_layer(block, 64, layers[1], 2)
           
            self.avg_pool = nn.AvgPool2d(8)
            self.fc = nn.Linear(64, num_classes)
            
        def make_layer(self, block, out_channels, blocks, stride=1,mm=0):
            #print(out_channels,blocks,'****')
            downsample = None
            if (stride != 1) or (self.in_channels != out_channels):
                downsample = nn.Sequential(
                    conv3x3(self.in_channels, out_channels, stride=stride),
                    nn.BatchNorm2d(out_channels))
                
                
            layers = []
            layers.append(block(self.in_channels, out_channels, stride, downsample))
            mm+=1
            
            self.in_channels = out_channels
            for i in range(1, blocks):
                
                layers.append(block(out_channels, out_channels))
            return nn.Sequential(*layers)
    
        def forward(self, x):
            out = self.conv(x)
            out = self.bn(out)
            out = self.relu(out)
            out = self.layer1(out)
            out = self.layer2(out)
            out = self.layer3(out)
            out = self.avg_pool(out)
            out = out.view(out.size(0), -1)
            out = self.fc(out)
            return out
    
    
    resnet = ResNet(ResidualBlock, [2,2 ,2,2])  #blocks 
    print(resnet)
    if torch.cuda.is_available():
        resnet = resnet.cuda()
    
    loss_func = nn.CrossEntropyLoss()
    optimizer = torch.optim.Adam(resnet.parameters(), lr=lr)
    
    # 训练
    for epoch in range(num_epochs):
        for i, (images, labels) in enumerate(train_loader):
            images = get_variable(images)
            labels = get_variable(labels)
    
            outputs = resnet(images)
            loss = loss_func(outputs, labels)
            optimizer.zero_grad()
            loss.backward()
            optimizer.step()
    
            if (i + 1) % 100 == 0:
                print("Epoch [%d/%d], Iter [%d/%d] Loss: %.4f" % (epoch + 1, num_epochs, i + 1, 500, loss.data[0]))
    
        # 衰减学习率
        if (epoch + 1) % 20 == 0:
            lr /= 3
            optimizer = torch.optim.Adam(resnet.parameters(), lr=lr)
    
    # 测试
    correct = 0
    total = 0
    for images, labels in test_loader:
        images = get_variable(images)
        labels = get_variable(labels)
        outputs = resnet(images)
        _, predicted = torch.max(outputs.data, 1)
        total += labels.size(0)
        correct += (predicted == labels.data).sum()
    
    print(' 测试 准确率: %d %%' % (100 * correct / total))
    
    # 保存模型参数
    torch.save(resnet.state_dict(), 'resnet.pkl')

    网络结构

    ResNet(
      (conv): Conv2d(3, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      (bn): BatchNorm2d(16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (relu): ReLU(inplace)
      (layer1): Sequential(
        (0): ResidualBlock(
          (conv1): Conv2d(16, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
          (bn1): BatchNorm2d(16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (relu): ReLU(inplace)
          (conv2): Conv2d(16, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
          (bn2): BatchNorm2d(16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        )
        (1): ResidualBlock(
          (conv1): Conv2d(16, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
          (bn1): BatchNorm2d(16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (relu): ReLU(inplace)
          (conv2): Conv2d(16, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
          (bn2): BatchNorm2d(16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        )
      )
      (layer2): Sequential(
        (0): ResidualBlock(
          (conv1): Conv2d(16, 32, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
          (bn1): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (relu): ReLU(inplace)
          (conv2): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
          (bn2): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (downsample): Sequential(
            (0): Conv2d(16, 32, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
            (1): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          )
        )
        (1): ResidualBlock(
          (conv1): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
          (bn1): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (relu): ReLU(inplace)
          (conv2): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
          (bn2): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        )
      )
      (layer3): Sequential(
        (0): ResidualBlock(
          (conv1): Conv2d(32, 64, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
          (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (relu): ReLU(inplace)
          (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
          (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (downsample): Sequential(
            (0): Conv2d(32, 64, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
            (1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          )
        )
        (1): ResidualBlock(
          (conv1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
          (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (relu): ReLU(inplace)
          (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
          (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        )
      )
      (avg_pool): AvgPool2d(kernel_size=8, stride=8, padding=0)
      (fc): Linear(in_features=64, out_features=10, bias=True)
    )

    网络结构的图

     

    虽然我是个学渣但是不妨碍我学习啊,希望这个图能帮助到有希望看具体网络连接图的你,

    展开全文
  • pytorch 打印网络参数

    千次阅读 2019-06-05 14:55:40
    打印出来的是网络卷积层、池化层、激活层等内的参数信息,同时会打印网络总参数,如下,VGG19 分为两个块:features和classifier,使用时可直接使用其名字,self.features/self.classifier: 2. for name,...
    import utils
    VGG = networks.VGG19('vgg19.pth', feature_mode=True)
    VGG.to(device)
    VGG.eval()
    print('---------- Networks initialized -------------')
    utils.print_network(VGG)
    print('-----------------------------------------------')
    

    打印出来的是网络卷积层、池化层、激活层等内的参数信息,同时会打印网络总参数,如下,VGG19 分为两个块:features和classifier,使用时可直接使用其名字,self.features/self.classifier:

    2.

    for name, param in VGG.named_parameters():
    	print(name, '      ', param.size())
    

    打印的是模块名字.序号.权重名(注意此处不回打印relu,pool不需要back的层,打印结果:

    å¨è¿éæå¥å¾çæè¿°

    如果直接打印param, 即 print(name,param), 打印结果:打印出来的详细参数

    å¨è¿éæå¥å¾çæè¿°

    参考:https://blog.csdn.net/Jee_King/article/details/87368398

     

    展开全文
  • pytorch打印网络参数量

    2021-04-30 15:16:42
    def model_info(model): # Plots a line-by-line description of a PyTorch model n_p = sum(x.numel() for x in model.parameters()) # number parameters n_g = sum(x.numel() for x in model.parameters() if x...

    函数:

    def model_info(model):  # Plots a line-by-line description of a PyTorch model
        n_p = sum(x.numel() for x in model.parameters())  # number parameters
        n_g = sum(x.numel() for x in model.parameters() if x.requires_grad)  # number gradients
        print('\n%5s %50s %9s %12s %20s %12s %12s' % ('layer', 'name', 'gradient', 'parameters', 'shape', 'mu', 'sigma'))
        for i, (name, p) in enumerate(model.named_parameters()):
            name = name.replace('module_list.', '')
            print('%5g %50s %9s %12g %20s %12.3g %12.3g' % (
                i, name, p.requires_grad, p.numel(), list(p.shape), p.mean(), p.std()))
        print('Model Summary: %g layers, %g parameters, %g gradients\n' % (i + 1, n_p, n_g))
    
    

    使用方法:

    net = vgg16()
    model_info(net)
    

    输出:

    layer                                               name  gradient   parameters                shape           mu        sigma
        0                                  features.0.weight      True         1728        [64, 3, 3, 3]     -0.00188        0.251
        1                                    features.0.bias      True           64                 [64]       -0.065        0.519
        2                                  features.2.weight      True        36864       [64, 64, 3, 3]     -0.00166       0.0558
        3                                    features.2.bias      True           64                 [64]      0.00947        0.267
        4                                  features.5.weight      True        73728      [128, 64, 3, 3]     -0.00185       0.0497
        5                                    features.5.bias      True          128                [128]       0.0552        0.107
        6                                  features.7.weight      True       147456     [128, 128, 3, 3]     -0.00221       0.0385
        7                                    features.7.bias      True          128                [128]       0.0294        0.156
        8                                 features.10.weight      True       294912     [256, 128, 3, 3]     -0.00133       0.0318
        9                                   features.10.bias      True          256                [256]       0.0224       0.0941
       10                                 features.12.weight      True       589824     [256, 256, 3, 3]     -0.00144       0.0245
       11                                   features.12.bias      True          256                [256]       0.0428        0.081
       12                                 features.14.weight      True       589824     [256, 256, 3, 3]     -0.00244       0.0253
       13                                   features.14.bias      True          256                [256]       0.0219        0.107
       14                                 features.17.weight      True  1.17965e+06     [512, 256, 3, 3]     -0.00171       0.0215
       15                                   features.17.bias      True          512                [512]      0.00453       0.0991
       16                                 features.19.weight      True   2.3593e+06     [512, 512, 3, 3]     -0.00161       0.0163
       17                                   features.19.bias      True          512                [512]       0.0364         0.13
       18                                 features.21.weight      True   2.3593e+06     [512, 512, 3, 3]     -0.00224       0.0162
       19                                   features.21.bias      True          512                [512]       0.0498        0.107
       20                                 features.24.weight      True   2.3593e+06     [512, 512, 3, 3]     -0.00157       0.0173
       21                                   features.24.bias      True          512                [512]       0.0181        0.112
       22                                 features.26.weight      True   2.3593e+06     [512, 512, 3, 3]     -0.00185       0.0169
       23                                   features.26.bias      True          512                [512]       0.0555         0.16
       24                                 features.28.weight      True   2.3593e+06     [512, 512, 3, 3]     -0.00221       0.0159
       25                                   features.28.bias      True          512                [512]       0.0726        0.138
       26                                classifier.0.weight      True   1.0276e+08        [4096, 25088]    -0.000299      0.00504
       27                                  classifier.0.bias      True         4096               [4096]      0.00922       0.0197
       28                                classifier.3.weight      True  1.67772e+07         [4096, 4096]     -0.00102      0.00998
       29                                  classifier.3.bias      True         4096               [4096]       0.0547       0.0193
       30                                classifier.6.weight      True        40960           [10, 4096]        0.499        0.289
       31                                  classifier.6.bias      True           10                 [10]        0.408        0.261
    Model Summary: 32 layers, 1.34302e+08 parameters, 1.34302e+08 gradients
    
    展开全文
  • 尝试用tensorboard打印pytorch网络结构pytorch版本是1.4.0,配置过程中间走了一些弯路记录下来,最终解决方案请参考文末。 1、问题一 File "D:\Softwares\Anaconda3\lib\site-packages\tensorboardX\writer.py...
  • pytorch网络模型结构的总结打印

    千次阅读 2021-03-03 21:07:19
    在keras中可以通过model.summary()打印出模型的结构,类似这样: 在pytorch中想要实现类似的功能,直接打印模型就可以了。例如 from torchvision import models model = models.vgg16() print(model) 输出结果 VGG...
  • 打印环境:pytorch1.4.0,CUDA10.0,tensorflow1.15.0,tensorboardX 2.1 1、安装tensorboardX pip installtensorboardX 2、测试代码: 代码来源:https://blog.csdn.net/xiaoxifei/article/details/82735355 ...
  • 功能:打印显示网络结构和参数 pip install torchsummary github 看一下就明白使用 import torch from torchvision import models from torchsummary import summary device = torch.device('cuda' if torch.cuda....
  • sudo pip3 install torchsummary import torchvision.models as models from torchsummary import summary device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') vgg = models.vgg19().to...
  • Keras style model.summary() in PyTorch - 打印 PyTorch 模型结构 https://github.com/sksq96/pytorch-summary https://pypi.org/project/torchsummary/ Model summary in PyTorch similar to model.summary() in ...
  • pytorch打印自定义网络的每层的名称

    千次阅读 2019-09-14 20:19:19
    pytorch打印自定义网络的每层的名称 import torch from torchvision import models from torchsummary import summary from resnext_MulTask_clothes import resnext50_elastic data_class=[8, 7] device ...
  • 一、pytorch打印网络结构 #前提:构建了一个TransformerModel网络结构类 #实例化网络对象 model = TransformerModel(ntokens, emsize, nhead, nhid, nlayers, dropout) 法一:主要查看网络层次结构,也包括输入...
  • 文章目录构建神经元网络模型的基本范型构建网络模型选择优化和损失函数构建迭代过程结果验证 在没有任何基础的前提下,直接学习如何搭建神经网络,意义其实不大。我建议你如果因为读研或者好奇而开始学神经元网络,...
  • 最近注意到在一些caffe模型中,偏置项的学习率通常设置为普通层的两倍。具体原因可以参考... Pytorch...
  • 20210610 if config.test is True: model = load_test_model(model, config) print(model) 打印网络结构 版权声明:本文为博主原创文章,遵循 CC 4.0 BY-SA 版权协议,转载请附上原文出处链接和本声明。 本文链接:...
  • # Plot the graph 画出原y与x的曲线与网络结构拟合后的曲线 predicted = model(torch.from_numpy(x_train)).detach().numpy() plt.plot(x_train, y_train, 'ro', label='Original data') plt.plot(x_train, ...
  • Pytorch 模型的网络结构可视化

    万次阅读 2018-10-17 11:27:48
    Keras 中 keras.summary() ...总结两种pytorch网络结构的可视化方法 Pytorch使用Tensorboard可视化网络结构 GitHub地址:点击打开 1.下载可视化代码 git clone https://github.com/lanpa/tensorboard-pytorch.git ...
  • Pytorch YOLOv3 网络结构

    2018-12-07 17:59:06
    github:... Pytorch YOLOv3 结构 ModuleList( (0): Sequential( (conv_with_bn_0): Conv2d(3, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=F...
  • 建立了模型对象后直接打印模型就会输出它的比较完整的层 net = Net() print(net) net模型还没有使用sequential结构 from torch import nn from torch.nn import functional as F from torchvision import ...
  • pytorch命令行打印模型结构的两种方法及对比 当我们使用pytorch进行模型训练或测试时,有时候希望能知道模型每一层分别是什么,具有怎样的参数。此时我们可以将模型打印出来,输出每一层的名字、类型、参数等。 常用...
  • Pytorch 查看模型网络结构

    万次阅读 多人点赞 2019-04-03 18:49:07
    安装torchsummary包 sudo pip3 install torchsummary 下面以查看vgg19为例: 代码如下: import torchvision.models as models from torchsummary import summary ...device = torch.device('cuda' if torch.cuda...
  • 本文为 PyTorch 学习笔记,讲解高级神经网络结构
  • 这里写自定义目录标题新的改变功能快捷键合理的创建标题,有助于目录的生成如何改变文本的样式插入链接与图片如何插入一段漂亮...本文讲下如何使用pytorch实现yolov3网络结构中的DarkNet53网络结构。 首先上图,图片参
  • 在使用Pytorch构建神经网络模型后,我们需要看一下自己写的模型的网络结构,此时可以使用torchkeras模块中的summary函数实现该功能。以多层感知机为例,首先我们构建网络结构打印该模型的初步信息,代码如下: ...
  • pytorch如何查看网络结构所有的权值和偏置 由于需要将神经网络的参数导出,在硬件上实现,网络结构的参数总是显示一部分,省略了大部分,查阅了很多文章,总是没有很好的解决办法,无意中发现了一条命令,试了一下,...
  • 一、安装命令: sudo pip install ...- Pytorch 模型的网络结构可视化 pytorchviz - [2]:使用graphviz 生成树报错(graphviz.backend.ExecutableNotFound: failed to execute ['dot', '-Tpdf', '-O', 'Digraph.gv']) 

空空如也

空空如也

1 2 3 4 5 ... 20
收藏数 3,660
精华内容 1,464
关键字:

pytorch打印网络结构