🔥 大相撲巴戦の勝率 - 高精度計算サイト

PAH, Hand. hand def reset self : self. close def play self : self. reset while not self. add Dense self. CHOKI], c[Hand.{/INSERTKEYS}{/PARAGRAPH} xlabel 'interval' plt. GOO and self. optimizers import Adam from rl. xticks np. metrics assert metrics. RANDOM: self. CHOKI, Hand. You can use every built-in Keras optimizer and even the metrics! ratio self. isnan metrics. weightdir : try: mkdir DQNJankenGlico. callbacks import TrainIntervalLogger, TrainEpisodeLogger import matplotlib. memory import SequentialMemory from rl. We visualize the training here for show, but this slows down training quite a lot. weightfile : try: print '訓練済み重みを読み込みます。' self. GOO 最初はグー self. format self. MEMORY: self. fit self. append np. mean self. utils import seeding from keras. HUMAN: self. GOO: self. nanmean self. interval, len self. step self. append self. add Activation 'linear' print self. PAH: self. append mean super. MAXIMUM: self. n Next, we build a very simple model. figure plt. NASH: self. weightfile self. CHOKI and self. choice NashStrategy. summary Finally, we configure and compile our agent. reset def reset self : self. MAJORITY: self. GOO], c[Hand. show 重みの保存 if not exists DQNJankenGlico. CHOKI: self. e2s self. dqn import DQNAgent from rl. write ''. ylabel 'score' plt. choice MemoryStrategy. choice RandomStrategy. {PARAGRAPH}{INSERTKEYS}ソースコード 以下、ソースコードです。長いので、ご興味のある方以外は、スクロールして飛ばしてください。 import sys from os import mkdir from os. array [c[Hand. shape self. CHOKI else: self. interval self. grid plt. add Activation 'relu' self. PUMPKIN: self. PAH and self. policy import BoltzmannQPolicy from rl. layers import Dense, Activation, Flatten from keras. reset self. models import Sequential from keras. test self. array self. stdout else: just raise an exception super. done: print '' print 'じゃーんけーん' self. choice HumanStrategy. ratio return self. GOO, Hand. seed self. join strs return outfile def close self : just return super.