from keras.models import Sequential
from keras.layers import Dense,Dropout,Activation
from keras.wrappers.scikit_learn import KerasClassifier
+import numpy as np
class Comp():
def __init__(self):
metrics=['accuracy'])
def sente_stone(self,X_train,Y_train):
- self.model1.fit(X_train,Y_train)
+ X,Y = np.array(X_train),np.array(Y_train)
+ self.model1.fit(X,Y)
hdf5_file = './sente-model.hdf5'
self.model1.save_weights(hdf5_file)
- res = self.model1.predict(X_train,Y_train)
+ res = self.model1.predict(X,Y)
return [res % 8, res // 8]
def gote_stone(self,X_train,Y_train):
- self.model2.fit(X_train,Y_train)
+ X,Y=np.array(X_train),np.array(Y_train)
+ self.model2.fit(X,Y)
hdf5_file ='./gote-model.hdf5'
self.model2.save_weights(hdf5_file)
- res = self.model2.predict(X_train,Y_train)
+ res = self.model2.predict(X,Y)
return [res % 8, res // 8]
stone_grid.active = False
if stone_grid.NextStone(index.stone, pos) == True:
if index.stone == black:
- pre = comp.sente_stone(stone_grid.map[1:],stone_grid.arr)
+ pre = comp.sente_stone(stone_grid.map[1:],stone_grid.arr[1:])
elif index.stone == white:
- pre = comp.gote_stone(stone_grid.map[1:],stone_grid.arr)
+ pre = comp.gote_stone(stone_grid.map[1:],stone_grid.arr[1:])
if stone_grid.CanSetStone(index.stone, pre[0], pre[1], True) == False:
stone_grid.CanSetStone(index.stone, pos[0], pos[1], True)
ChangePlayer()