Updata readme

README.md

 # candock
 这是一个用于记录毕业设计的日志仓库，其目的是尝试多种不同的深度神经网络结构(如LSTM,RESNET,DFCNN等)对单通道EEG进行自动化睡眠阶段分期.我们相信这些代码同时可以用于其他生理信号(如ECG,EMG等)的分类.希望这将有助于您的研究.<br>
+![image](https://github.com/HypoX64/candock/blob/master/image/compare.png)
 ## 如何运行
 如果你需要运行这些代码（训练自己的模型或者使用预训练模型进行测试）请进入以下页面<br>
 [How to run codes](https://github.com/HypoX64/candock/blob/master/how_to_run.md)<br>
@@ -30,7 +31,7 @@
 
 * multi_scale_resnet_1d 网络结构<br>
   该网络参考[geekfeiw / Multi-Scale-1D-ResNet](https://github.com/geekfeiw/Multi-Scale-1D-ResNet)<br>
-  修改后的[网络结构如图](https://github.com/HypoX64/candock/blob/master/image/multi_scale_resnet_1d_network.png)<br>
+  修改后的[网络结构](https://github.com/HypoX64/candock/blob/master/image/multi_scale_resnet_1d_network.png)<br>
 
 * 关于交叉验证<br>
   为了便于与其他文献中的方法便于比较，使用了两种交叉验证方法<br>
@@ -42,14 +43,13 @@
   accuracy = (TP+TN)/(TP+FN+TN+FP)<br>
   recall = sensitivity = (TP)/(TP+FN)<br>
   对于总体:<br>
-
-  Top1.err.
+  Top1.err.<br>
 
 * 关于代码<br>
   目前的代码仍然在不断修改与更新中,不能确保其能工作.详细内容将会在毕业设计完成后抽空更新.<br>
 ## 部分实验结果
 该部分将持续更新... ...<br>
-[[Confusion matrix]](https://github.com/HypoX64/candock/blob/master/image/confusion_mat)<br>
+[[Confusion matrix]](https://github.com/HypoX64/candock/blob/master/confusion_mat)<br>
 
 ####  5-Fold Cross-Validation Results
 * sleep-edf<br>

how_to_run.md

@@ -2,7 +2,7 @@
 - Linux, Windows,mac
 - CPU or NVIDIA GPU + CUDA CuDNN
 - Python 3.5+
-- Pytroch 1.0+
+- Pytroch 1.0
 
 ## Dependencies
 This code depends on torchvision, numpy, scipy, h5py, matplotlib, mne , requests, hashlib, available via pip install.<br>
@@ -23,20 +23,20 @@ git clone https://github.com/HypoX64/candock
 cd candock
 ```
 ### Train
-* download datasets
+* Download datasets
 ```bash
 python3 download_dataset.py
 ```
-* choose your options and run
+* 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
 ```
 * Notes<br>
-If want to use cpu to train or test, please use --no_cuda
+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)
-* choose your options and run
+* Download pretrained model & simple test data  [[Google Drive]](https://drive.google.com/open?id=1pup2_tZFGQQwB-hoXRjpMxiD4Vmpn0Lf)
+* Input your options and run
 ```bash
 python3 simple_test.py
 ```
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