diff --git a/test_it_noise.py b/test_it_noise.py
new file mode 100644
index 0000000..9a66cf0
--- /dev/null
+++ b/test_it_noise.py
@@ -0,0 +1,188 @@
+# %%
+import os
+
+os.environ["TF_CPP_MIN_LOG_LEVEL"] = "3"
+
+import torch
+import dataconf
+import logging
+from datetime import datetime
+
+from network.Parameter import Config
+
+from network.build_network import build_network
+from network.build_optimizer import build_optimizer
+from network.build_lr_scheduler import build_lr_scheduler
+from network.build_datasets import build_datasets
+from network.load_previous_weights import load_previous_weights
+
+from network.loop_train_test import (
+    loop_test,
+)
+
+import numpy as np
+
+
+# ######################################################################
+# We want to log what is going on into a file and screen
+# ######################################################################
+
+now = datetime.now()
+dt_string_filename = now.strftime("%Y_%m_%d_%H_%M_%S")
+logging.basicConfig(
+    filename="log_" + dt_string_filename + ".txt",
+    filemode="w",
+    level=logging.INFO,
+    format="%(asctime)s %(message)s",
+)
+logging.getLogger().addHandler(logging.StreamHandler())
+
+# ######################################################################
+# Load the config data from the json file
+# ######################################################################
+
+if os.path.exists("def.json") is False:
+    raise Exception("Config file not found! def.json")
+
+if os.path.exists("network.json") is False:
+    raise Exception("Config file not found! network.json")
+
+if os.path.exists("dataset.json") is False:
+    raise Exception("Config file not found! dataset.json")
+
+
+cfg = (
+    dataconf.multi.file("network.json").file("dataset.json").file("def.json").on(Config)
+)
+logging.info(cfg)
+
+logging.info(f"Number of spikes: {cfg.number_of_spikes}")
+logging.info(f"Cooldown after spikes: {cfg.cooldown_after_number_of_spikes}")
+logging.info(f"Reduction cooldown: {cfg.reduction_cooldown}")
+logging.info("")
+logging.info(f"Epsilon 0: {cfg.epsilon_0}")
+logging.info(f"Batch size: {cfg.batch_size}")
+logging.info(f"Data mode: {cfg.data_mode}")
+logging.info("")
+logging.info("*** Config loaded.")
+logging.info("")
+
+
+# ###########################################
+# GPU Yes / NO ?
+# ###########################################
+default_dtype = torch.float32
+torch.set_default_dtype(default_dtype)
+torch_device: str = "cuda:0" if torch.cuda.is_available() else "cpu"
+use_gpu: bool = True if torch.cuda.is_available() else False
+logging.info(f"Using {torch_device} device")
+device = torch.device(torch_device)
+
+# ######################################################################
+# Prepare the test and training data
+# ######################################################################
+
+the_dataset_train, the_dataset_test, my_loader_test, my_loader_train = build_datasets(
+    cfg
+)
+
+logging.info("*** Data loaded.")
+
+# ######################################################################
+# Build the network, Optimizer, and LR Scheduler                                                                   #
+# ######################################################################
+
+network = build_network(
+    cfg=cfg, device=device, default_dtype=default_dtype, logging=logging
+)
+logging.info("")
+
+optimizer = build_optimizer(network=network, cfg=cfg, logging=logging)
+
+lr_scheduler = build_lr_scheduler(optimizer=optimizer, cfg=cfg, logging=logging)
+
+logging.info("*** Network generated.")
+
+load_previous_weights(
+    network=network,
+    overload_path=cfg.learning_parameters.overload_path,
+    logging=logging,
+    device=device,
+    default_dtype=default_dtype,
+)
+
+logging.info("")
+
+last_test_performance: float = -1.0
+
+spike_list: list[int] = [
+    1,
+    2,
+    3,
+    4,
+    5,
+    6,
+    7,
+    8,
+    9,
+    10,
+    20,
+    30,
+    40,
+    50,
+    60,
+    70,
+    80,
+    90,
+    100,
+    200,
+    300,
+    400,
+    500,
+    600,
+    700,
+    800,
+    900,
+    1000,
+    2000,
+    3000,
+    4000,
+    5000,
+    6000,
+    7000,
+    8000,
+    9000,
+    10000,
+]
+
+
+# ##############################################
+# Run test data
+# ##############################################
+network.eval()
+
+results = torch.zeros((2, len(spike_list)), dtype=torch.float32)
+
+for sp_id, spikes_number in enumerate(spike_list):
+
+    print(f"Number of spikes: {spikes_number}")
+
+    last_test_performance = loop_test(
+        epoch_id=cfg.epoch_id,
+        cfg=cfg,
+        network=network,
+        my_loader_test=my_loader_test,
+        the_dataset_test=the_dataset_test,
+        device=device,
+        default_dtype=default_dtype,
+        logging=logging,
+        tb=None,
+        overwrite_number_of_spikes=spikes_number,
+    )
+
+    results[0, sp_id] = spikes_number
+    results[1, sp_id] = last_test_performance
+
+np.save("results.npy", results.cpu().numpy())
+
+# %%