parser.add_argument("--ep", default=100, type=int, help="训练的总轮数")
parser.add_argument('--re', default=False, help='是否从checkpoint处开始训练')
parser.add_argument("--ckpt", default="./checkpoints/res/CNN-LSTM-60.t7", help="checkpoint的地址")
parser.add_argument("--gpu", default=False, help="是否使用GPU进行训T练")
parser.add_argument("--cuda", default="0，1，2，3", type=str, help="用于训练GPU的代号")
cfg, unknown = parser.parse_known_args()
os.environ["CUDA_VISIBLE_DEVICES"] = cfg.cuda # 配置环境中的GPU
start_epoch = 0 # 开始训练的轮数
best_test_acc = 0 # 最好的测试准确率
best_test_acc_epoch = 0 # 最好的测试准确率时的轮数
print("--------------加载训练数据中------------")
trainRoot = cfg.dr + "/train"
X_train, y_train, mask_train = dataLoader.window_process(trainRoot, "./data/r.txt", cfg.dl, 20, cfg.ts, cfg.lb)
X_train = np.array(X_train)
y_train = np.array(y_train)
mask_train = np.array(mask_train)
y_train = to_categorical(y_train, 22)
# 对数据进行随机打乱
permutation = np.random.permutation(X_train.shape[0])
X_train = X_train[permutation, :, :, :]
y_train = y_train[permutation, :]
mask_train = mask_train[permutation, :, :]
trainData_sum = len(X_train) # 训练样本总数
print("--------------加载训练数据完成------------")
print("--------------加载测试数据中------------")
parser.add_argument('--re', default=True, help='是否从checkpoint处开始训练')
parser.add_argument("--ckpt", default="./checkpoints/res1_52/CNN-LSTM-82.t7", help="checkpoint的地址")
parser.add_argument("--gpu", default=False, help="是否使用GPU进行训T练")
parser.add_argument("--cuda", default="0，1，2，3", type=str, help="用于训练GPU的代号")
cfg, unknown = parser.parse_known_args()
os.environ["CUDA_VISIBLE_DEVICES"] = cfg.cuda # 配置环境中的GPUF
start_epoch = 0 # 开始训练的轮数
best_test_acc = 0 # 最好的测试准确率
best_test_acc_epoch = 0 # 最好的测试准确率时的轮数
print("--------------加载测试数据中------------")
testRoot = cfg.dr + "/test"
X_test, y_test, mask_test = dataLoader.window_process(testRoot, cfg.dl, 160, cfg.ts, cfg.lb)
X_test = np.array(X_test)
y_test = np.array(y_test)
mask_test = np.array(mask_test)
y_test = to_categorical(y_test, 22)
# 对数据进行随机打乱
permutation = np.random.permutation(X_test.shape[0])
X_test = X_test[permutation, :, :, :]
y_test = y_test[permutation, :]
mask_test = mask_test[permutation, :, :]
testData_sum = len(X_test) # 训练样本总数
print("--------------加载测试数据完成------------")
use_cuda = torch.cuda.is_available() # 环境中是否有GPU
parser.add_argument("--ckpt",
default="./checkpoints/res1_52/CNN-LSTM-82.t7",
help="checkpoint的地址")
parser.add_argument("--gpu", default=False, help="是否使用GPU进行训T练")
parser.add_argument("--cuda", default="0，1，2，3", type=str, help="用于训练GPU的代号")
cfg, unknown = parser.parse_known_args()
os.environ["CUDA_VISIBLE_DEVICES"] = cfg.cuda # 配置环境中的GPUF
start_epoch = 0 # 开始训练的轮数
best_test_acc = 0 # 最好的测试准确率
best_test_acc_epoch = 0 # 最好的测试准确率时的轮数
print("--------------加载训练数据中------------")
trainRoot = cfg.dr + "/train"
X_train, y_train, mask_train = dataLoader.window_process(
trainRoot, cfg.rr, cfg.dl, cfg.ts, cfg.lb)
X_train = np.array(X_train)
y_train = np.array(y_train)
mask_train = np.array(mask_train)
y_train = to_categorical(y_train, 2)
# 对数据进行随机打乱
permutation = np.random.permutation(X_train.shape[0])
X_train = X_train[permutation, :, :, :]
y_train = y_train[permutation, :]
mask_train = mask_train[permutation, :, :]
trainData_sum = len(X_train) # 训练样本总数
print("--------------加载训练数据完成------------")
print("--------------加载测试数据中------------")
if iter == 100:
P = P / 4.
# Return solution
return Y
if __name__ == "__main__":
testRoot = "./data/test"
dl = 0
ts = 52
lb = 1
print("--------------加载测试数据中------------")
X_test, y_test, mask_test = dataLoader.window_process(
testRoot, dl, 160, ts, lb)
X_test = np.array(X_test)
y_test = np.array(y_test)
mask_test = np.array(mask_test)
y_test = to_categorical(y_test, 22)
# 对数据进行随机打乱
permutation = np.random.permutation(X_test.shape[0])
X_test = X_test[permutation, :, :, :]
y_test = y_test[permutation, :]
mask_test = mask_test[permutation, :, :]
testData_sum = len(X_test) # 训练样本总数
print("--------------加载测试数据完成------------")
inputs = torch.Tensor(X_test)
