Update README.md

README.md

@@ -2,38 +2,38 @@
 A deep learning approach to estimate stress distribution: a fast and accurate surrogate of finite-element analysis
 
 
-### Note
+## Note
 The files contain code and data associated with the paper titled "A Deep Learning Approach to Estimate Stress Distribution: A Fast and Accurate Surrogate of Finite Element Analysis". The paper is authored by Liang Liang, Minliang Liu, Caitlin Martin, and Wei Sun, and published at Journal of The Royal Society Interface, 2018.
 
 The following repository contains further investigation of the method used in the mentioned above paper.
 
 Copyright (c) 2018 by Biologically Inspired Computer Vision Group at Imperial College London. All rights reserved.
 
 
-### Files
-1. Data: 
+## Files
+1. **Data**: 
 - ShapeData.mat
 - StressData.mat
-2. Code of DL-model: 
+2. **Code of DL-model**: 
 - DLStress.py
 - im2patch.m 
 - UnsupervisedLearning.m
-3. Code for visualization: 
+3. **Code for visualization**: 
 - ReadMeshFromVTKFile.m
 - ReadPolygonMeshFromVTKFile.m
 - WritePolygonMeshAsVTKFile.m
 - Visualization.m
-4. Template meshes for visualization:
+4. **Template meshes for visualization**:
 - TemplateMesh3D.vtk
 - TemplateMesh2D.vtk
 
 
-### System Requirement
+## System Requirement
 - OS: Windows (64bit) 7 or 10
 - Hardware: Intel quad-core CPU, 32G RAM
 
 
-### Software Requirement
+## Software Requirement
 - **Anaconda 3.6**: https://www.anaconda.com/download/;
 - **Keras 2.0.4**: https://github.com/fchollet/keras. Keras can be install from Anaconda Cloud: https://anaconda.org/anaconda/keras
 - **Tensorflow 1.1.0**: https://www.tensorflow.org/. Tensorflow CPU version can be installed from Anaconda Cloud: https://anaconda.org/conda-forge/tensorflow
@@ -44,7 +44,7 @@ Copyright (c) 2018 by Biologically Inspired Computer Vision Group at Imperial Co
 - **Python 3.5**
 
 
-### Installation guide
+## Installation guide
 1. Install Matlab
 2. Install MatConvNet
 3. Install Anaconda 
@@ -69,7 +69,7 @@ c:\matlab\R2017a\extern\engines\python
 python setup.py install
 ```
 
-### Running procedure
+## Running procedure
 1. Activate the anaconda environment in a cmd window, and type spyder. Then you should see something like
 this. Spyder is a Python IDE. The current directory of Spyder is shown on top right. Open DLStress.py in Spyder, and run the code. You need to change the current directory of Spyder so that it contains DLStress.py. Change the path of MatConvnet in UnsupervisedLearning.m
 2. Create folder 'result'