Merge branch 'master' of https://github.com/Vastlab/ObjectoSphere

Author: akshay-raj-dhamija

README.md

@@ -16,15 +16,15 @@ You may want to update the dataset locations in [MNIST/data_prep.py](https://git
 
 ## Training
 [MNIST/Mnist_Training.py](https://github.com/Vastlab/ObjectoSphere/blob/master/MNIST/Mnist_Training.py) provides the script to train models with all the loss functions used in different experiments. 
-The random models used to initalize the networks trained in the paper are also provided at [MNIST/LeNet++/Random_Models](https://github.com/Vastlab/ObjectoSphere/tree/master/MNIST/LeNet%2B%2B/Random_Models).
+The random models used to initialize the networks trained in the paper are also provided at [MNIST/LeNet++/Random_Models](https://github.com/Vastlab/ObjectoSphere/tree/master/MNIST/LeNet%2B%2B/Random_Models).
 The types of networks supported by this script contain:
 
 1. Original LeNet/LeNet++ with Softmax loss.
 2. LeNet/LeNet++ with Background Class.
 3. LeNet/LeNet++ with Entropic OpenSet Loss.
 4. LeNet/LeNet++ with ObjectoSphere Loss.
 
-In the current training process we run the training for the given network for 70 epochs and save only the model with the least validation loss.
+In the current training process, we run the training for the given network for 70 epochs and save only the model with the least validation loss.
 The parameters used for training are provided as the default parameters for the script.
 For details please refer to the help provided in [MNIST/Mnist_Training.py](https://github.com/Vastlab/ObjectoSphere/blob/master/MNIST/Mnist_Training.py)
 
@@ -34,6 +34,6 @@ The training and testing process for the OpenMax technique is provided in [MNIST
 All the visualizations for the MNIST experiments provided in the paper can be reproduced following the Jupyter Notebook at [MNIST/Fig_creator.ipynb](https://github.com/Vastlab/ObjectoSphere/blob/master/MNIST/Fig_creator.ipynb).
 It utilizes the tools available in [Tools](https://github.com/Vastlab/ObjectoSphere/tree/master/Tools).
 The [Tools/model_tools.py](https://github.com/Vastlab/ObjectoSphere/tree/master/Tools/model_tools.py) contains functions to define the network architecture, extract feature vectors from a specific layer of the network and preprocess data for implementing in the loss function.
-The [Tools/visualizing_tools.py](https://github.com/Vastlab/ObjectoSphere/tree/master/Tools/visualizing_tools.py) contains the plotting functions for the two dimensional plots and the histograms.
+The [Tools/visualizing_tools.py](https://github.com/Vastlab/ObjectoSphere/tree/master/Tools/visualizing_tools.py) contains the plotting functions for the two-dimensional plots and the histograms.
 And, the [Tools/evaluation_tools.py](https://github.com/Vastlab/ObjectoSphere/tree/master/Tools/evaluation_tools.py) contains the plotting functions for the DIR curves in the paper as well as a function to write the results into a preliminary file.
 Please refer to these files for a detailed understanding.