def resize_cast(x, shape):
H, W, C = shape
x = x.reshape(-1, 28, 28, 3)
resized_x = np.empty((len(x), H, W, 3), dtype='float32')
for i, img in enumerate(x):
# imresize returns uint8
resized_x[i] = u2t(scipy.misc.imresize(img, (H, W)))
return resized_x
def resize_cast(x, shape):
H, W, C = shape
x = x.reshape(-1, 28, 28, 3)
resized_x = np.empty((len(x), H, W, 3), dtype='float32')
for i, img in enumerate(x):
# imresize returns uint8
resized_x[i] = u2t(scipy.misc.imresize(img, (H, W)))
return resized_x
def __init__(self):
print('load Simg...')
trainx = np.load(args.datadir + 'trainX.npy')
trainy = np.load(args.datadir + 'trainY.npy')
trainx = u2t(trainx).astype('float32')
trainy = np.eye(10)[trainy].astype('float32')
self.train = Data(trainx[:4000], trainy[:4000], cast=True)
self.test = Data(trainx[4000:], trainy[4000:], casts=True)
def preprocess(self, x):
if self.cast:
return u2t(x)
else:
return x
def preprocess(self, x):
if self.cast:
return u2t(x)
else:
return x