keras GAN 自å‰ãƒ‡ãƒ¼ã‚¿ã‚»ãƒƒãƒˆã§ç”»åƒç”Ÿæˆ - Qiitaå‰å›žã€è‡ªå‰ã®ãƒ‡ãƒ¼ã‚¿ã‚»ãƒƒãƒˆã‚’使ã£ã¦ç”»åƒåˆ†é¡žï¼ˆCNN)ã‚’ã—ãŸã®ã§ä»Šå›žã¯GANã«ã‚ˆã‚Šç”»åƒã‚’ç”Ÿæˆ ã—ã¦ã¿ã‚ˆã†ã¨æ€ã„ã¾ã™ã€‚データセットã«ä½¿ã†ã®ã¯å¤šéƒ¨æœªè¯åã¡ã‚ƒã‚“ã§ã„ã“ã†ã¨æ€ã„ã¾ã™ ###データセット作æˆç”¨ç”»åƒ ###ãƒ‡ãƒ¼ã‚¿ã‚»ãƒƒãƒˆä½œæˆ GANã§ã¯ãƒ†ã‚¹ãƒˆãƒ‡ãƒ¼ã‚¿ã¨ã‹ã€ãƒ©ãƒ™ãƒ«å¿…è¦ãªã„ã®ã§ã™ãŒå‰ã®ã‚¹ã‚¯ãƒªãƒ—トをæµç”¨ã—ãŸã®ã§ ç®±ã ã‘用æ„ã™ã‚‹æ„Ÿã˜ã«ãªã£ã¦ã—ã¾ã£ã¦ã„ã¾ã™ import matplotlib.pyplot as plt import sys from keras.preprocessing.image import array_to_img,img_to_array,load_img import numpy as np import os X_train=[] Y_train=[] X_test=[] Y_test=[] i=os.listdir("/home/MyDataSets/face/_3")
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