#
hoge = verify_class(qqq_trn,yyy_trn)
print(np.sum(hoge[:,0]==hoge[:,1]),"/",hoge.shape[0],"\n")
np.savetxt("out1/verify_128_{}.txt".format(stamp),hoge,fmt="%d")
fuga = verify_class(qqq_vld,yyy_vld)
print(np.sum(fuga[:,0]==fuga[:,1]),"/",fuga.shape[0],"\n")
np.savetxt("out1/verify_cross_128_{}.txt".format(stamp),fuga,fmt="%d")
#
# save parameters
#
if(trainable1):
save_stage1()
print('stamp1 = \'{}\''.format(stamp))
if(trainable2):
save_stage2()
print('stamp2 = \'{}\''.format(stamp))
if(trainable3):
save_stage3()
print('stamp3 = \'{}\''.format(stamp))
save_stage4()
myutil.timestamp()
print('stamp4 = \'{}\''.format(stamp))
score_out = np.mean([xxx[2] for xxx in tmp])
print('tt error sparce score', tmax, error_out, entropy_out, score_out)
img_org = tensorflow_util.get_image_from_qqq(qqq_trn[0:8])
qqq_deconv1 = tf_deconv1.eval({tf_input: qqq_trn[0:batch_size]})
img_out = tensorflow_util.get_image_from_qqq(qqq_deconv1[0:8])
img_cmp = myutil.rbind_image(img_org, img_out)
myutil.showsave(img_cmp, file_img="vld_risa.{}.jpg".format(stamp))
print('error:', np.mean((qqq_deconv1 - qqq_trn[0:batch_size])**2))
ww_out = ww.eval()
myutil.saveObject(ww_out, 'ww_risa.{}.pkl'.format(stamp))
myutil.timestamp()
print('stamp1 = \'{}\''.format(stamp))
if (False):
img_tmp1 = get_image_from_ww(ww_out[:, :, :, 0])
img_tmp2 = get_image_from_ww(ww_out[:, :, :, 1])
img_tmp3 = myutil.rbind_image(img_tmp1, img_tmp2)
img_tmp4 = img_tmp3
for bb in range(1, 12):
img_tmp1 = get_image_from_ww(ww_out[:, :, :, bb * 2])
img_tmp2 = get_image_from_ww(ww_out[:, :, :, bb * 2 + 1])
img_tmp3 = myutil.rbind_image(img_tmp1, img_tmp2)
img_tmp4 = myutil.cbind_image(img_tmp4, img_tmp3)
myutil.showsave(img_tmp4, file_img='tmp.jpg')
# endif
# set default values if they are not defined yet
#
for k,v in extern_params.items():
if(not k in globals()):
if(type(v)==str):
print('{} = \'{}\''.format(k,v))
exec( '{} = \'{}\''.format(k,v),globals(),locals())
else:
print('{} = {}'.format(k,v))
exec('{} = {}'.format(k,v),globals(),locals())
#
#
# current time stamp
#
stamp = myutil.timestamp()
print('stamp = ',stamp)
#
# random seed
#
if(random_seed=='NA'):
np.random.seed()
else:
np.random.seed(random_seed)
#
# data paths
#
# it may depends on machines...
username = os.environ['USER']