转 Pytorch之搭建神经网络的四种方法

目录

第一种方法

第二种方法

第三种方法

第四种方法

完整代码

---

 转载自这里

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

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 torch
import torch.nn.functional as F
from 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)

</span><span style="color: #0000ff;">def</span><span style="color: #000000;"> forward(self, x):
    x </span>= F.max_pool2d(F.relu(self.conv(x)), 2<span style="color: #000000;">)
    x </span>= x.view(x.size(0), -1<span style="color: #000000;">)
    x </span>=<span style="color: #000000;"> F.relu(self.dense1(x))
    x </span>=<span style="color: #000000;"> self.dense2(x)
    </span><span style="color: #0000ff;">return</span><span style="color: #000000;"> x

print(“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)
        )
</span><span style="color: #0000ff;">def</span><span style="color: #000000;"> forward(self, x):
    conv_out </span>=<span style="color: #000000;"> self.conv1(x)
    res </span>= conv_out.view(conv_out.size(0), -1<span style="color: #000000;">)
    out </span>=<span style="color: #000000;"> self.dense(res)
    </span><span style="color: #0000ff;">return</span><span style="color: #000000;"> out

print(“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))
</span><span style="color: #0000ff;">def</span><span style="color: #000000;"> forward(self, x):
    conv_out </span>=<span style="color: #000000;"> self.conv1(x)
    res </span>= conv_out.view(conv_out.size(0), -1<span style="color: #000000;">)
    out </span>=<span style="color: #000000;"> self.dense(res)
    </span><span style="color: #0000ff;">return</span><span style="color: #000000;"> out

print(“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 </span>=<span style="color: #000000;"> torch.nn.Sequential(
        OrderedDict([
            (</span><span style="color: #800000;">"</span><span style="color: #800000;">dense1</span><span style="color: #800000;">"</span>, torch.nn.Linear(32 * 3 * 3, 128<span style="color: #000000;">)),
            (</span><span style="color: #800000;">"</span><span style="color: #800000;">relu2</span><span style="color: #800000;">"</span><span style="color: #000000;">, torch.nn.ReLU()),
            (</span><span style="color: #800000;">"</span><span style="color: #800000;">dense2</span><span style="color: #800000;">"</span>, torch.nn.Linear(128, 10<span style="color: #000000;">))
        ])
    )

</span><span style="color: #0000ff;">def</span><span style="color: #000000;"> forward(self, x):
    conv_out </span>=<span style="color: #000000;"> self.conv1(x)
    res </span>= conv_out.view(conv_out.size(0), -1<span style="color: #000000;">)
    out </span>=<span style="color: #000000;"> self.dense(res)
    </span><span style="color: #0000ff;">return</span><span style="color: #000000;"> out

print(“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 torch
import torch.nn.functional as F
from 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)

</span><span style="color: #0000ff;">def</span><span style="color: #000000;"> forward(self, x):
    x </span>= F.max_pool2d(F.relu(self.conv(x)), 2<span style="color: #000000;">)
    x </span>= x.view(x.size(0), -1<span style="color: #000000;">)
    x </span>=<span style="color: #000000;"> F.relu(self.dense1(x))
    x </span>=<span style="color: #000000;"> self.dense2()
    </span><span style="color: #0000ff;">return</span><span style="color: #000000;"> x

print(“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)
)

</span><span style="color: #0000ff;">def</span><span style="color: #000000;"> forward(self, x):
    conv_out </span>=<span style="color: #000000;"> self.conv1(x)
    res </span>= conv_out.view(conv_out.size(0), -1<span style="color: #000000;">)
    out </span>=<span style="color: #000000;"> self.dense(res)
    </span><span style="color: #0000ff;">return</span><span style="color: #000000;"> out

print(“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))

</span><span style="color: #0000ff;">def</span><span style="color: #000000;"> forward(self, x):
    conv_out </span>=<span style="color: #000000;"> self.conv1(x)
    res </span>= conv_out.view(conv_out.size(0), -1<span style="color: #000000;">)
    out </span>=<span style="color: #000000;"> self.dense(res)
    </span><span style="color: #0000ff;">return</span><span style="color: #000000;"> out

print(“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 </span>=<span style="color: #000000;"> torch.nn.Sequential(
        OrderedDict([
            (</span><span style="color: #800000;">"</span><span style="color: #800000;">dense1</span><span style="color: #800000;">"</span>, torch.nn.Linear(32 * 3 * 3, 128<span style="color: #000000;">)),
            (</span><span style="color: #800000;">"</span><span style="color: #800000;">relu2</span><span style="color: #800000;">"</span><span style="color: #000000;">, torch.nn.ReLU()),
            (</span><span style="color: #800000;">"</span><span style="color: #800000;">dense2</span><span style="color: #800000;">"</span>, torch.nn.Linear(128, 10<span style="color: #000000;">))
        ])
    )

</span><span style="color: #0000ff;">def</span><span style="color: #000000;"> forward(self, x):
    conv_out </span>=<span style="color: #000000;"> self.conv1(x)
    res </span>= conv_out.view(conv_out.size(0), -1<span style="color: #000000;">)
    out </span>=<span style="color: #000000;"> self.dense(res)
    </span><span style="color: #0000ff;">return</span><span style="color: #000000;"> out

print(“Method 4:”)
model4 = Net4()
print(model4)

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目录