67 lines
2.0 KiB
Python
67 lines
2.0 KiB
Python
# -*- coding: utf-8 -*-
|
|
# Created by: Vincentzyx
|
|
import os
|
|
import torch
|
|
from torch import nn
|
|
from torch.utils.data import DataLoader
|
|
from torch.utils.data.dataset import Dataset
|
|
import time
|
|
|
|
|
|
def EnvToOnehot(cards):
|
|
Env2IdxMap = {3:0,4:1,5:2,6:3,7:4,8:5,9:6,10:7,11:8,12:9,13:10,14:11,17:12,20:13,30:14}
|
|
cards = [Env2IdxMap[i] for i in cards]
|
|
Onehot = torch.zeros((4,15))
|
|
for i in range(0, 15):
|
|
Onehot[:cards.count(i),i] = 1
|
|
return Onehot
|
|
|
|
def RealToOnehot(cards):
|
|
RealCard2EnvCard = {'3': 0, '4': 1, '5': 2, '6': 3, '7': 4,
|
|
'8': 5, '9': 6, 'T': 7, 'J': 8, 'Q': 9,
|
|
'K': 10, 'A': 11, '2': 12, 'X': 13, 'D': 14}
|
|
cards = [RealCard2EnvCard[c] for c in cards]
|
|
Onehot = torch.zeros((4,15))
|
|
for i in range(0, 15):
|
|
Onehot[:cards.count(i),i] = 1
|
|
return Onehot
|
|
|
|
|
|
class Net(nn.Module):
|
|
def __init__(self):
|
|
super().__init__()
|
|
|
|
self.fc1 = nn.Linear(60, 512)
|
|
self.fc2 = nn.Linear(512, 512)
|
|
self.fc3 = nn.Linear(512, 512)
|
|
self.fc4 = nn.Linear(512, 512)
|
|
self.fc5 = nn.Linear(512, 512)
|
|
self.fc6 = nn.Linear(512, 1)
|
|
self.dropout5 = nn.Dropout(0.5)
|
|
self.dropout3 = nn.Dropout(0.3)
|
|
self.dropout1 = nn.Dropout(0.1)
|
|
|
|
def forward(self, input):
|
|
x = self.fc1(input)
|
|
x = torch.relu(self.dropout3(self.fc2(x)))
|
|
x = torch.relu(self.dropout5(self.fc3(x)))
|
|
x = torch.relu(self.dropout5(self.fc4(x)))
|
|
x = torch.relu(self.dropout5(self.fc5(x)))
|
|
x = self.fc6(x)
|
|
return x
|
|
|
|
|
|
net = Net()
|
|
net.eval()
|
|
if os.path.exists("landlord_weights.pkl"):
|
|
if torch.cuda.is_available():
|
|
net.load_state_dict(torch.load('landlord_weights.pkl'))
|
|
else:
|
|
net.load_state_dict(torch.load('landlord_weights.pkl', map_location=torch.device("cpu")))
|
|
else:
|
|
print("landlord_weights.pkl not found")
|
|
|
|
def predict(cards):
|
|
cards_onehot = torch.flatten(RealToOnehot(cards))
|
|
y_predict = net(cards_onehot)
|
|
return y_predict[0].item() * 100 |