From dab33070234188f33862fa00cba1238279b10044 Mon Sep 17 00:00:00 2001
From: Leahcimali <michaelbenalipro@gmail.com>
Date: Fri, 11 Oct 2024 10:25:03 +0200
Subject: [PATCH] Correct problem with Quantity Skew
---
src/utils_data.py | 3 +--
src/utils_training.py | 2 +-
2 files changed, 2 insertions(+), 3 deletions(-)
diff --git a/src/utils_data.py b/src/utils_data.py
index 8385860..1a0058d 100644
--- a/src/utils_data.py
+++ b/src/utils_data.py
@@ -518,9 +518,8 @@ def apply_quantity_skew(list_clients : list, row_exp : dict, list_skews : list)
dict_clients = [get_clients_data(n_clients_by_skew,
int(n_max_samples * skew),
row_exp['dataset'],
- seed=row_exp['seed'])
+ row_exp['nn_model'])
for skew in list_skews]
-
list_clients = []
for c in range(n_clients_by_skew):
diff --git a/src/utils_training.py b/src/utils_training.py
index ca2e523..d1cc6d0 100644
--- a/src/utils_training.py
+++ b/src/utils_training.py
@@ -212,7 +212,7 @@ def train_central(model: ImageClassificationBase, train_loader: DataLoader, val_
# Move the model to the appropriate device
model.to(device)
- opt_func = torch.optim.SGD # if row_exp['nn_model'] == "linear" else torch.optim.Adam
+ opt_func = torch.optim.Adam # if row_exp['nn_model'] == "linear" else torch.optim.Adam
lr = 0.001
history = []
optimizer = opt_func(model.parameters(), lr)
--
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