Update README.md

README.md

@@ -17,26 +17,6 @@ Install all the packages from requirments.txt
 * Experiments are run on Mnist, Fashion Mnist and Cifar.
 * To use your own dataset: Move your dataset to data directory and write a wrapper on pytorch dataset class.
 
-## Options
-The default values for various paramters parsed to the experiment are given in ```options.py```. Details are given some of those parameters:
-
-* ```--dataset:```  Default: 'mnist'. Options: 'mnist', 'fmnist', 'cifar'
-* ```--model:```    Default: 'mlp'. Options: 'mlp', 'cnn'
-* ```--gpu:```      Default: None (runs on CPU). Can also be set to the specific gpu id.
-* ```--epochs:```   Number of rounds of training.
-* ```--lr:```       Learning rate set to 0.01 by default.
-* ```--verbose:```  Detailed log outputs. Activated by default, set to 0 to deactivate.
-* ```--seed:```     Random Seed. Default set to 1.
-
-#### Federated Parameters
-* ```--iid:```      Distribution of data amongst users. Default set to IID. Set to 0 for non-IID.
-* ```--num_users:```Number of users. Default is 100.
-* ```--frac:```     Fraction of users to be used for federated updates. Default is 0.1.
-* ```--local_ep:``` Number of local training epochs in each user. Default is 10.
-* ```--local_bs:``` Batch size of local updates in each user. Default is 10.
-* ```--unequal:```  Used in non-iid setting. Option to split the data amongst users equally or unequally. Default set to 0 for equal splits. Set to 1 for unequal splits.
-
-
 ## Running the experiments
 The baseline experiment trains the model in the conventional way.
 
@@ -63,13 +43,32 @@ python federated_main --model=cnn --dataset=cifar --gpu=0 --epochs=10
 
 You can change the default values of other parameters to simulate different conditions. Refer to the options section.
 
+## Options
+The default values for various paramters parsed to the experiment are given in ```options.py```. Details are given some of those parameters:
+
+* ```--dataset:```  Default: 'mnist'. Options: 'mnist', 'fmnist', 'cifar'
+* ```--model:```    Default: 'mlp'. Options: 'mlp', 'cnn'
+* ```--gpu:```      Default: None (runs on CPU). Can also be set to the specific gpu id.
+* ```--epochs:```   Number of rounds of training.
+* ```--lr:```       Learning rate set to 0.01 by default.
+* ```--verbose:```  Detailed log outputs. Activated by default, set to 0 to deactivate.
+* ```--seed:```     Random Seed. Default set to 1.
+
+#### Federated Parameters
+* ```--iid:```      Distribution of data amongst users. Default set to IID. Set to 0 for non-IID.
+* ```--num_users:```Number of users. Default is 100.
+* ```--frac:```     Fraction of users to be used for federated updates. Default is 0.1.
+* ```--local_ep:``` Number of local training epochs in each user. Default is 10.
+* ```--local_bs:``` Batch size of local updates in each user. Default is 10.
+* ```--unequal:```  Used in non-iid setting. Option to split the data amongst users equally or unequally. Default set to 0 for equal splits. Set to 1 for unequal splits.
+
 ## Results on MNIST
 #### Baseline Experiment:
 The experiment involves training a single model in the conventional way.
 
-```Parameters:``` <br />
-    Optimizer    : SGD <br />
-    Learning Rate: 0.01
+Parameters: <br />
+* ```Optimizer:```    : SGD 
+* ```Learning Rate:``` 0.01
 
 ```Table 1:``` Test accuracy after training for 10 epochs:
 
@@ -83,12 +82,12 @@ The experiment involves training a single model in the conventional way.
 #### Federated Experiment:
 The experiment involves training a global model in the federated setting.
 
-* ```Federated parameters:```` <br />
-    Fraction of users (C): 0.1 <br />
-    Local Batch size  (B): 10 <br />
-    Local Epochs      (E): 10 <br />
-    Optimizer            : SGD <br />
-    Learning Rate        : 0.01 <br />
+Federated parameters (default values):
+* ```Fraction of users (C)```: 0.1 
+* ```Local Batch size  (B)```: 10 
+* ```Local Epochs      (E)```: 10 
+* ```Optimizer            ```: SGD 
+* ```Learning Rate        ```: 0.01 <br />
 
 ```Table 2:``` Test accuracy after training for 10 global epochs with: