From 69699d65e34cc1098e8bb3e67db41bd262a69f82 Mon Sep 17 00:00:00 2001
From: BIGAUD Nathan <nathan.bigaud@inria.fr>
Date: Thu, 30 Mar 2023 20:59:24 +0200
Subject: [PATCH] Simplyfing defualt config and model
---
examples/quickrun/config.toml | 57 +++++++++++++++++------------------
examples/quickrun/model.py | 36 ++--------------------
2 files changed, 30 insertions(+), 63 deletions(-)
diff --git a/examples/quickrun/config.toml b/examples/quickrun/config.toml
index a25f77f7..eaa59344 100644
--- a/examples/quickrun/config.toml
+++ b/examples/quickrun/config.toml
@@ -1,40 +1,39 @@
-[network]
-protocol = "websockets"
-host = "127.0.0.1"
-port = 8765
+# This is a minimal TOML file for the MNIST example
+# It contains the bare minimum to make the experiment run
+# It can be used as a template for other experiments.
+# See `examples/quickrun/readme.md` for more details.
-[optim]
-aggregator = "averaging" # The chosen aggregation strategy
-server_opt = 1.0 # The server learning rate
- [optim.client_opt]
- lrate = 0.001 # The client learning rate
- modules = ["adam"] # The optimzer modules
+[network] # Network configuration used by both client and server
+protocol = "websockets" # Protocol used, to keep things simple use websocket
+host = "127.0.0.1" # Address used, works as is on most set ups
+port = 8765 # Port used, works as is on most set ups
-[run]
-rounds = 10 # Number of training rounds
+[model] # Information on where to find the model file
- [run.register]
- min_clients = 1
- max_clients = 6
- timeout = 5
+[data] # How to split your data
+scheme = "iid" # SPlit your data iid between simulated clients
- [run.training]
- n_epoch = 1 # Number of local epochs
- batch_size = 48 # Training batch size
- drop_remainder = false # Whether to drop the last trainig examples
+[optim] # Optimizers options for both client and server
+aggregator = "averaging" # Server aggregation strategy
- [run.evaluate]
- batch_size = 128 # Evaluation batch size
+ [optim.client_opt] # Client optimization strategy
+ lrate = 0.001 # Client learning rate
+ modules = ["adam"] # List of optimizer modules used
+
+[run] # Training process option for both client and server
+rounds = 10 # Number of overall training rounds
+ [run.register] # Client registration options
+ timeout = 5 # How long to wait for clients, in seconds
-[experiment]
-# all args for parse_data_folder
+ [run.training] # Client training procedure
+ batch_size = 48 # Training batch size
+
+ [run.evaluate]
+ # batch_size = 128 # Evaluation batch size
-[model]
-# information on where to find the model file
+[experiment] # What to report during the experiment and where to report it
+metrics=[["multi-classif",{labels = [0,1,2,3,4,5,6,7,8,9]}]]
-[data]
-# all args from split_data argparser
-scheme = "labels"
\ No newline at end of file
diff --git a/examples/quickrun/model.py b/examples/quickrun/model.py
index 8e5080bc..0b5e53cc 100644
--- a/examples/quickrun/model.py
+++ b/examples/quickrun/model.py
@@ -1,41 +1,8 @@
-"""Wrapping a torch model"""
+"""Wrapping a simple CNN for the MNIST example"""
import tensorflow as tf
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
from declearn.model.tensorflow import TensorflowModel
-from declearn.model.torch import TorchModel
-
-# class Net(nn.Module):
-# def __init__(self):
-# super(Net, self).__init__()
-# self.conv1 = nn.Conv2d(1, 32, 3, 1)
-# self.conv2 = nn.Conv2d(32, 64, 3, 1)
-# self.dropout1 = nn.Dropout(0.25)
-# self.dropout2 = nn.Dropout(0.5)
-# self.fc1 = nn.Linear(9216, 128)
-# self.fc2 = nn.Linear(128, 10)
-
-# def forward(self, x):
-# x = torch.transpose(x, 3, 1)
-# x = self.conv1(x)
-# x = F.relu(x)
-# x = self.conv2(x)
-# x = F.relu(x)
-# x = F.max_pool2d(x, 2)
-# x = self.dropout1(x)
-# x = torch.flatten(x, 1)
-# x = self.fc1(x)
-# x = F.relu(x)
-# x = self.dropout2(x)
-# x = self.fc2(x)
-# output = F.log_softmax(x, dim=1)
-# return output
-
-
-# MyModel = TorchModel(Net(), loss=nn.NLLLoss())
stack = [
tf.keras.layers.InputLayer(input_shape=(28, 28, 1)),
@@ -49,4 +16,5 @@ stack = [
tf.keras.layers.Dense(10, activation="softmax"),
]
model = tf.keras.models.Sequential(stack)
+
MyModel = TensorflowModel(model, loss="sparse_categorical_crossentropy")
--
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