pytorch构建网络模型的4种方法

Python  /  管理员 发布于 5年前   181

利用pytorch来构建网络模型有很多种方法，以下简单列出其中的四种。

假设构建一个网络模型如下：

卷积层--》Relu层--》池化层--》全连接层--》Relu层--》全连接层

首先导入几种方法用到的包：

import torchimport torch.nn.functional as Ffrom collections import OrderedDict

第一种方法

# Method 1 -----------------------------------------class Net1(torch.nn.Module):  def __init__(self):    super(Net1, self).__init__()    self.conv1 = torch.nn.Conv2d(3, 32, 3, 1, 1)    self.dense1 = torch.nn.Linear(32 * 3 * 3, 128)    self.dense2 = torch.nn.Linear(128, 10)  def forward(self, x):    x = F.max_pool2d(F.relu(self.conv(x)), 2)    x = x.view(x.size(0), -1)    x = F.relu(self.dense1(x))    x = self.dense2(x)    return xprint("Method 1:")model1 = Net1()print(model1)

这种方法比较常用，早期的教程通常就是使用这种方法。

第二种方法

# Method 2 ------------------------------------------class Net2(torch.nn.Module):  def __init__(self):    super(Net2, self).__init__()    self.conv = torch.nn.Sequential(      torch.nn.Conv2d(3, 32, 3, 1, 1),      torch.nn.ReLU(),      torch.nn.MaxPool2d(2))    self.dense = torch.nn.Sequential(      torch.nn.Linear(32 * 3 * 3, 128),      torch.nn.ReLU(),      torch.nn.Linear(128, 10)    )  def forward(self, x):    conv_out = self.conv1(x)    res = conv_out.view(conv_out.size(0), -1)    out = self.dense(res)    return outprint("Method 2:")model2 = Net2()print(model2)

这种方法利用torch.nn.Sequential（）容器进行快速搭建，模型的各层被顺序添加到容器中。缺点是每层的编号是默认的阿拉伯数字，不易区分。

第三种方法：

# Method 3 -------------------------------class Net3(torch.nn.Module):  def __init__(self):    super(Net3, self).__init__()    self.conv=torch.nn.Sequential()    self.conv.add_module("conv1",torch.nn.Conv2d(3, 32, 3, 1, 1))    self.conv.add_module("relu1",torch.nn.ReLU())    self.conv.add_module("pool1",torch.nn.MaxPool2d(2))    self.dense = torch.nn.Sequential()    self.dense.add_module("dense1",torch.nn.Linear(32 * 3 * 3, 128))    self.dense.add_module("relu2",torch.nn.ReLU())    self.dense.add_module("dense2",torch.nn.Linear(128, 10))  def forward(self, x):    conv_out = self.conv1(x)    res = conv_out.view(conv_out.size(0), -1)    out = self.dense(res)    return outprint("Method 3:")model3 = Net3()print(model3)

这种方法是对第二种方法的改进：通过add_module()添加每一层，并且为每一层增加了一个单独的名字。 

第四种方法：

# Method 4 ------------------------------------------class Net4(torch.nn.Module):  def __init__(self):    super(Net4, self).__init__()    self.conv = torch.nn.Sequential(      OrderedDict(        [          ("conv1", torch.nn.Conv2d(3, 32, 3, 1, 1)),          ("relu1", torch.nn.ReLU()),          ("pool", torch.nn.MaxPool2d(2))        ]      ))    self.dense = torch.nn.Sequential(      OrderedDict([        ("dense1", torch.nn.Linear(32 * 3 * 3, 128)),        ("relu2", torch.nn.ReLU()),        ("dense2", torch.nn.Linear(128, 10))      ])    )  def forward(self, x):    conv_out = self.conv1(x)    res = conv_out.view(conv_out.size(0), -1)    out = self.dense(res)    return outprint("Method 4:")model4 = Net4()print(model4)

是第三种方法的另外一种写法，通过字典的形式添加每一层，并且设置单独的层名称。

完整代码：

import torchimport torch.nn.functional as Ffrom collections import OrderedDict# Method 1 -----------------------------------------class Net1(torch.nn.Module):  def __init__(self):    super(Net1, self).__init__()    self.conv1 = torch.nn.Conv2d(3, 32, 3, 1, 1)    self.dense1 = torch.nn.Linear(32 * 3 * 3, 128)    self.dense2 = torch.nn.Linear(128, 10)  def forward(self, x):    x = F.max_pool2d(F.relu(self.conv(x)), 2)    x = x.view(x.size(0), -1)    x = F.relu(self.dense1(x))    x = self.dense2()    return xprint("Method 1:")model1 = Net1()print(model1)# Method 2 ------------------------------------------class Net2(torch.nn.Module):  def __init__(self):    super(Net2, self).__init__()    self.conv = torch.nn.Sequential(      torch.nn.Conv2d(3, 32, 3, 1, 1),      torch.nn.ReLU(),      torch.nn.MaxPool2d(2))    self.dense = torch.nn.Sequential(      torch.nn.Linear(32 * 3 * 3, 128),      torch.nn.ReLU(),      torch.nn.Linear(128, 10)    )  def forward(self, x):    conv_out = self.conv1(x)    res = conv_out.view(conv_out.size(0), -1)    out = self.dense(res)    return outprint("Method 2:")model2 = Net2()print(model2)# Method 3 -------------------------------class Net3(torch.nn.Module):  def __init__(self):    super(Net3, self).__init__()    self.conv=torch.nn.Sequential()    self.conv.add_module("conv1",torch.nn.Conv2d(3, 32, 3, 1, 1))    self.conv.add_module("relu1",torch.nn.ReLU())    self.conv.add_module("pool1",torch.nn.MaxPool2d(2))    self.dense = torch.nn.Sequential()    self.dense.add_module("dense1",torch.nn.Linear(32 * 3 * 3, 128))    self.dense.add_module("relu2",torch.nn.ReLU())    self.dense.add_module("dense2",torch.nn.Linear(128, 10))  def forward(self, x):    conv_out = self.conv1(x)    res = conv_out.view(conv_out.size(0), -1)    out = self.dense(res)    return outprint("Method 3:")model3 = Net3()print(model3)# Method 4 ------------------------------------------class Net4(torch.nn.Module):  def __init__(self):    super(Net4, self).__init__()    self.conv = torch.nn.Sequential(      OrderedDict(        [          ("conv1", torch.nn.Conv2d(3, 32, 3, 1, 1)),          ("relu1", torch.nn.ReLU()),          ("pool", torch.nn.MaxPool2d(2))        ]      ))    self.dense = torch.nn.Sequential(      OrderedDict([        ("dense1", torch.nn.Linear(32 * 3 * 3, 128)),        ("relu2", torch.nn.ReLU()),        ("dense2", torch.nn.Linear(128, 10))      ])    )  def forward(self, x):    conv_out = self.conv1(x)    res = conv_out.view(conv_out.size(0), -1)    out = self.dense(res)    return outprint("Method 4:")model4 = Net4()print(model4)

以上就是本文的全部内容，希望对大家的学习有所帮助，也希望大家多多支持。