return chainer.get_device(numpy)
else:
import cupy
return chainer.get_device((cupy, gpu))
return chainer.get_device(args.device)
"""
3. Import datasets
データセット読み込み
FBGセンサから取得した脈波データ
参照血糖値
"""
# 1,000datas
data, teach = easy_chainer.load_Data("C:/Users/Owner/Desktop/Normalized/val/val_ebina_day1.xlsx")
data = data.astype(numpy.float32)
teach = teach
print(teach)
print(teach.shape)
# 回帰させるときに必要（分類はint型）
teach = teach.astype(numpy.float32)
id_all = numpy.arange(1, len(teach) + 1, 1).astype(numpy.int32) - 1
# print(id_all)
numpy.random.seed(11)
id_train = numpy.random.choice(id_all, 400, replace=False) #重複なし
print(id_train)
id_test = numpy.delete(id_all, id_train)
print(id_test)
if gpu is not None:
if gpu < 0:
return chainer.get_device(numpy)
else:
import cupy
return chainer.get_device((cupy, gpu))
return chainer.get_device(args.device)
"""
3. データセットのインポート
"""
# 1,000datas
data, teach = easy_chainer.load_Data(
"C:/Users/Owner/Desktop/arteries/excel/black.xlsx")
data = data.astype(numpy.float32)
teach = teach.astype(numpy.float32)
all_data_number = len(teach)
print(teach)
print(teach.shape)
# 教師値にidを割り振る
id_all = numpy.arange(1, len(teach) + 1, 1).astype(numpy.int32) - 1
print(id_all)
# seedは適当
# 訓練に使うデータ数はその時の最適なもので
numpy.random.seed(11)
id_train = numpy.random.choice(id_all, 300, replace=False)
# print(id_train)
gpu = int(args.device)
if gpu is not None:
if gpu < 0:
return chainer.get_device(numpy)
else:
import cupy
return chainer.get_device((cupy, gpu))
return chainer.get_device(args.device)
"""
3. データセットのインポート
"""
# 1,000datas
data, teach = easy_chainer.load_Data("C:/Users/Owner/Desktop/Normalized/val/new2_1.xlsx")
data = data.astype(numpy.float32)
teach = teach
all_data_number = len(teach)
print(teach)
print(teach.shape)
teach = teach.astype(numpy.float32)
# print(data.shape)
# 教師値にidを割り振る
id_all = numpy.arange(1, len(teach) + 1, 1).astype(numpy.int32) - 1
print(id_all)
# seedは適当
# 訓練に使うデータ数はその時の最適なもので
if args.gpu is not None:
gpu = args.gpu
elif re.match(r'(-|\+|)[0-9]+$', args.device):
gpu = int(args.device)
if gpu is not None:
if gpu < 0:
return chainer.get_device(numpy)
else:
import cupy
return chainer.get_device((cupy, gpu))
return chainer.get_device(args.device)
data, teach = easy_chainer.load_Data("/home/fiber_classifier/Desktop/blood_glucose_fulldata.xlsx")
data = data.astype(numpy.float32)
teach = teach
#print(teach)
print(teach.shape)
# 回帰させるときに必要（分類はint型）
teach = teach.astype(numpy.float32)
id_all = numpy.arange(1, len(teach) + 1, 1).astype(numpy.int32) - 1
print(id_all.shape)
numpy.random.seed(13)
id_train_T = numpy.random.choice(id_all, 300, replace=False) #重複なし
#print(id_train_T.dtype)
id_test = numpy.delete(id_all, id_train_T)