Updata README.md and loss weight

README.md

@@ -58,7 +58,7 @@
 
 | Network                     | Parameters | Top1.err. | Avg. Acc. | Avg. Re. | Need to extract feature |
 | --------------------------- | ---------- | --------- | --------- | -------- | ----------------------- |
-| lstm                        | 1.25M      | 26.32%    | 93.56%    | 68.57%   | Yes                     |
-| micro_multi_scale_resnet_1d | 2.11M      | 25.33%    | 93.87%    | 72.61%   | No                      |
-| resnet18_1d                 | 3.85M      | 24.21%    | 94.07%    | 72.87%   | No                      |
-| multi_scale_resnet_1d       | 8.42M      | 24.01%    | 94.06%    | 72.37%   | No                      |
\ No newline at end of file
+| lstm                        | 1.25M      | 26.32%    | 89.47%    | 68.57%   | Yes                     |
+| micro_multi_scale_resnet_1d | 2.11M      | 25.33%    | 89.87%    | 72.61%   | No                      |
+| resnet18_1d                 | 3.85M      | 24.21%    | 90.31%    | 72.87%   | No                      |
+| multi_scale_resnet_1d       | 8.42M      | 24.01%    | 90.40%    | 72.37%   | No                      |
\ No newline at end of file

how_to_run.md

 ## Prerequisites
 - Linux, Windows,mac
 - CPU or NVIDIA GPU + CUDA CuDNN
-- Python 3.5+
-- Pytroch 1.0
+- Python 3
+- Pytroch 1.0+
 
 ## Dependencies
 This code depends on torchvision, numpy, scipy, h5py, matplotlib, mne , requests, hashlib, available via pip install.<br>
@@ -29,14 +29,14 @@ python3 download_dataset.py
 ```
 * Input your options and run
 ```bash
-python3 train.py --dataset_dir './datasets/sleep-edfx/' --dataset_name sleep-edfx --signal_name 'EEG Fpz-Cz' --sample_num 10 --model_name lstm --batchsize 64 --network_save_freq 5 --epochs 20 --lr 0.0005 --BID 5_95_th --select_sleep_time --cross_validation subject
+python3 train.py --dataset_dir './datasets/sleep-edfx/' --dataset_name sleep-edfx --signal_name 'EEG Fpz-Cz' --sample_num 20 --model_name lstm --batchsize 64 --network_save_freq 5 --epochs 20 --lr 0.001 --BID 5_95_th --select_sleep_time
 ```
 * Notes<br>
 If want to use cpu to train or test, please input --no_cuda
 
 ### Simple Test
-* Download pretrained model & simple test data  [[Google Drive]](https://drive.google.com/open?id=1pup2_tZFGQQwB-hoXRjpMxiD4Vmpn0Lf)   [[百度云,dh88]](https://pan.baidu.com/s/1dGobTMVa_4u2HLky6bmbsA)
+* Download pretrained model & simple test data  [[Google Drive]](https://drive.google.com/open?id=1NTtLmT02jqlc81lhtzQ7GlPK8epuHfU5)   [[百度云,y4ks]](https://pan.baidu.com/s/1WKWZL91SekrSlhOoEC1bQA)
 * Input your options and run
 ```bash
-python3 simple_test.py
+python3 simple_test.py --model_name lstm
 ```
\ No newline at end of file

options.py

@@ -15,7 +15,7 @@ class Options():
         self.parser.add_argument('--no_cuda', action='store_true', help='if input, do not use gpu')
         self.parser.add_argument('--no_cudnn', action='store_true', help='if input, do not use cudnn')
         self.parser.add_argument('--pretrained', action='store_true', help='if input, use pretrained models')
-        self.parser.add_argument('--lr', type=float, default=0.0005,help='learning rate')
+        self.parser.add_argument('--lr', type=float, default=0.001,help='learning rate')
         self.parser.add_argument('--BID', type=str, default='5_95_th',help='Balance individualized differences  5_95_th | median |None')
         self.parser.add_argument('--batchsize', type=int, default=64,help='batchsize')
         self.parser.add_argument('--dataset_dir', type=str, default='./datasets/sleep-edfx/',

simple_test.py

@@ -17,7 +17,7 @@ https://drive.google.com/open?id=1pup2_tZFGQQwB-hoXRjpMxiD4Vmpn0Lf
 '''
 opt = Options().getparse()
 #choose and creat model
-opt.model_name = 'micro_multi_scale_resnet_1d'
+opt.model_name = 'lstm'
 net=CreatNet(opt.model_name)
 
 if not opt.no_cuda:

train.py

@@ -47,14 +47,14 @@ t2 = time.time()
 print('load data cost time: %.2f'% (t2-t1),'s')
 
 net=CreatNet(opt.model_name)
-torch.save(net.cpu().state_dict(),'./checkpoints/'+opt.model_name+'.pth')
 util.show_paramsnumber(net)
 
 weight = np.array([1,1,1,1,1])
 if opt.weight_mod == 'avg_best':
     weight = np.log(1/stage_cnt_per)
     weight[2] = weight[2]+1
-    weight = np.clip(weight,1,3)
+    weight = weight/np.median(weight)
+    weight = np.clip(weight, 0.8, 2)
 print('Loss_weight:',weight)
 weight = torch.from_numpy(weight).float()
 # print(net)