#需要匯入的庫
import torch
import torch.nn.functional as F
from collections import OrderedDict

#Method 1------------------------------
class Net1(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 out
print("Method 4:")
model4 = Net4()
print(model4)

#Method 2-------------------------------
class Net2(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 x
print("Method 1:")
model1 = Net1()
print(model1)

#Method 3-------------------------------
class Net3(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 out
print("Method 2:")
model2 = Net2()
print(model2)

#Method 4--------------------------------
class Net4(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 out
print("Method 3:")
model3 = Net3()
print(model3)