readme: ---
  license: mit
  ---
  If you cannot load a model, please create a model here.
  ```
  import torch
  import torch.nn as nn
  class Janken(nn.Module):
      def __init__(self):
          super().__init__()
          self.fc1=nn.Linear(2,20,bias=False)
          self.fc2=nn.Linear(20,60,bias=False)
          self.fc3=nn.Linear(60,70,bias=False)
          self.fc4=nn.Linear(70,70,bias=False)
          self.fc5=nn.Linear(70,30,bias=False)
          self.fc6=nn.Linear(30,10,bias=False)
          self.fc7=nn.Linear(10,2,bias=False)
      def forward(self,x):
          x=self.fc1(x)
          x=self.fc2(x)
          x=self.fc3(x)
          x=self.fc4(x)
          x=self.fc5(x)
          x=self.fc6(x)
          x=self.fc7(x)
          return x        
  model=Janken()
  optimizer=torch.optim.RMSprop(model.parameters(),lr=0.00011)
  criterion=nn.MSELoss()
  epochs=250
  patterns=[[[1,0],[0,1]],[[0,1],[0,0]],[[0,0],[1,0]]]
  for epoch in range(epochs):
      optimizer.zero_grad()
      for pattern in patterns:
          inputs=torch.Tensor(pattern[0])
          targets=torch.Tensor(pattern[1])
          outputs=model(inputs)
          loss=criterion(targets,outputs)
          loss.backward()
          optimizer.step()
      print(f"{epoch+1}/{epochs}\t\t{loss.item()}")
  torch.save("janken.bin",model)
  ```
  code<br>
    1,0=Paper<br>
    0,1=Knife<br>
    0,0=Rock<br>
  <img src="https://i.imgur.com/FyAKxKB.png">