{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import json\n",
    "\n",
    "for i in ['MobileNet','ResNet']:\n",
    "    for j in [True,False]:\n",
    "        for k,l in [(20,1),(1,20),(10,10)]:\n",
    "            if (i=='MobileNet'):\n",
    "                for m in [150,164]:\n",
    "                    args = {\n",
    "                            \"epochs\" : k,                   # Number of Global Aggregation Rounds\n",
    "                            \"num_users\":10,                  # Number of Clients\n",
    "                            \"local_ep\": l,                   # Number of local epochs on each client\n",
    "                            \"local_bs\" : 128,                 # Batch size for each client\n",
    "                            \"bs\" : 128,                       # Batch size for global model\n",
    "                            \"lr\" : 0.01,                     # Learning rate (alpha)\n",
    "                            \"momentum\" : 0.9,                # Momentum for SGD\n",
    "                            \"split_ratio\" : 0.9,             # Ratio for splitting client data into training and testing \n",
    "                            \"overlapping_classes\" : 4,       # In non-iid distribution, no of classes from which a particular user gets the data from\n",
    "                            \"base_layers\" : m,             # Base layers of local models\n",
    "                            \"model\" : i,           # Model used for training\n",
    "                            \"dataset\" :'cifar',              # Dataset \n",
    "                            \"iid\":j,                     # Data Distribution\n",
    "                            \"num_classes\" : 10,              # Number of classes in the dataset\n",
    "                            \"gpu\": 0,                        # GPU id\n",
    "                            \"seed\" : 1,                      # Seeding\n",
    "                        }\n",
    "\n",
    "\n",
    "                    with open('./{}_{}_{}_{}.txt'.format(i,str(j),k,m),'w') as outfile:\n",
    "                        json.dump(args,outfile,indent=4)\n",
    "                    \n",
    "            else:\n",
    "                for m in [204,218]:\n",
    "                    args = {\n",
    "                            \"epochs\" : k,                   # Number of Global Aggregation Rounds\n",
    "                            \"num_users\":10,                  # Number of Clients\n",
    "                            \"local_ep\": l,                   # Number of local epochs on each client\n",
    "                            \"local_bs\" : 128,                 # Batch size for each client\n",
    "                            \"bs\" : 128,                       # Batch size for global model\n",
    "                            \"lr\" : 0.01,                     # Learning rate (alpha)\n",
    "                            \"momentum\" : 0.9,                # Momentum for SGD\n",
    "                            \"split_ratio\" : 0.9,             # Ratio for splitting client data into training and testing \n",
    "                            \"overlapping_classes\" : 4,       # In non-iid distribution, no of classes from which a particular user gets the data from\n",
    "                            \"base_layers\" : m,             # Base layers of local models\n",
    "                            \"model\" : i,           # Model used for training\n",
    "                            \"dataset\" :'cifar',              # Dataset \n",
    "                            \"iid\":j,                     # Data Distribution\n",
    "                            \"num_classes\" : 10,              # Number of classes in the dataset\n",
    "                            \"gpu\": 0,                        # GPU id\n",
    "                            \"seed\" : 1,                      # Seeding\n",
    "                        }\n",
    "\n",
    "\n",
    "                    with open('./{}_{}_{}_{}.txt'.format(i,str(j),k,m),'w') as outfile:\n",
    "                        json.dump(args,outfile,indent=4)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import json\n",
    "\n",
    "                    args = {\n",
    "                            \"epochs\" : 2,                   # Number of Global Aggregation Rounds\n",
    "                            \"num_users\":10,                  # Number of Clients\n",
    "                            \"local_ep\": 2,                   # Number of local epochs on each client\n",
    "                            \"local_bs\" : 128,                 # Batch size for each client\n",
    "                            \"bs\" : 128,                       # Batch size for global model\n",
    "                            \"lr\" : 0.01,                     # Learning rate (alpha)\n",
    "                            \"momentum\" : 0.9,                # Momentum for SGD\n",
    "                            \"split_ratio\" : 0.9,             # Ratio for splitting client data into training and testing \n",
    "                            \"overlapping_classes\" : 4,       # In non-iid distribution, no of classes from which a particular user gets the data from\n",
    "                            \"base_layers\" : 204,             # Base layers of local models\n",
    "                            \"model\" : 'ResNet' ,           # Model used for training\n",
    "                            \"dataset\" :'cifar',              # Dataset \n",
    "                            \"iid\":True,                     # Data Distribution\n",
    "                            \"num_classes\" : 10,              # Number of classes in the dataset\n",
    "                            \"gpu\": 0,                        # GPU id\n",
    "                            \"seed\" : 1,                      # Seeding\n",
    "                        }\n",
    "\n",
    "\n",
    "                    with open('./{}_{}_{}_{}.txt'.format(args.model,,k,m),'w') as outfile:\n",
    "                        json.dump(args,outfile,indent=4)"
   ]
  }
 ],
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