diff --git a/noise_holes.py b/noise_holes.py
new file mode 100644
index 0000000..8c6439b
--- /dev/null
+++ b/noise_holes.py
@@ -0,0 +1,102 @@
+import os
+
+os.environ["TF_CPP_MIN_LOG_LEVEL"] = "3"
+
+import argh
+
+import numpy as np
+import torch
+
+rand_seed: int = 21
+torch.manual_seed(rand_seed)
+torch.cuda.manual_seed(rand_seed)
+np.random.seed(rand_seed)
+
+from get_the_data_uniform import get_the_data
+
+
+def main(
+    dataset: str = "CIFAR10",  # "CIFAR10", "FashionMNIST", "MNIST"
+    only_print_network: bool = False,
+    model_name: str = "Model_iter20_lr_1.0000e-03_1.0000e-02_1.0000e-03_.pt",
+) -> None:
+
+    torch_device: torch.device = (
+        torch.device("cuda:0") if torch.cuda.is_available() else torch.device("cpu")
+    )
+    torch.set_default_dtype(torch.float32)
+
+    # Some parameters
+    batch_size_test: int = 50  # 0
+
+    loss_mode: int = 0
+    loss_coeffs_mse: float = 0.5
+    loss_coeffs_kldiv: float = 1.0
+    print(
+        "loss_mode: ",
+        loss_mode,
+        "loss_coeffs_mse: ",
+        loss_coeffs_mse,
+        "loss_coeffs_kldiv: ",
+        loss_coeffs_kldiv,
+    )
+
+    if dataset == "MNIST" or dataset == "FashionMNIST":
+        input_dim_x: int = 24
+        input_dim_y: int = 24
+    else:
+        input_dim_x = 28
+        input_dim_y = 28
+
+    test_dataloader, test_processing_chain = get_the_data(
+        dataset,
+        batch_size_test,
+        torch_device,
+        input_dim_x,
+        input_dim_y,
+    )
+
+    network = torch.load(model_name)
+    network.to(device=torch_device)
+
+    print(network)
+
+    if only_print_network:
+        exit()
+
+    # Switch the network into evalution mode
+    network.eval()
+    number_of_noise_steps = 20
+    noise_scale = torch.arange(0, number_of_noise_steps + 1) / float(
+        number_of_noise_steps
+    )
+
+    results = torch.zeros_like(noise_scale)
+
+    with torch.no_grad():
+
+        for position in range(0, noise_scale.shape[0]):
+            test_correct: int = 0
+            test_number: int = 0
+            eta: float = noise_scale[position]
+            for image, target in test_dataloader:
+                noise = torch.rand_like(image) > eta
+
+                image = image * noise
+                image = image / (image.sum(dim=(1, 2, 3), keepdim=True) + 1e-20)
+                output = network(test_processing_chain(image))
+
+                test_correct += (output.argmax(dim=1) == target).sum().cpu().numpy()
+                test_number += target.shape[0]
+
+            perfomance_test_correct: float = 100.0 * test_correct / test_number
+            results[position] = perfomance_test_correct
+
+            print(f"{eta:.2f}: {perfomance_test_correct:.2f}%")
+
+    np.save("noise_holes_results.npy", results.cpu().numpy())
+    return
+
+
+if __name__ == "__main__":
+    argh.dispatch_command(main)
diff --git a/noise_holes_w_noise.py b/noise_holes_w_noise.py
new file mode 100644
index 0000000..5768e9b
--- /dev/null
+++ b/noise_holes_w_noise.py
@@ -0,0 +1,107 @@
+import os
+
+os.environ["TF_CPP_MIN_LOG_LEVEL"] = "3"
+
+import argh
+
+import numpy as np
+import torch
+
+rand_seed: int = 21
+torch.manual_seed(rand_seed)
+torch.cuda.manual_seed(rand_seed)
+np.random.seed(rand_seed)
+
+from get_the_data_uniform import get_the_data
+
+
+def main(
+    dataset: str = "CIFAR10",  # "CIFAR10", "FashionMNIST", "MNIST"
+    only_print_network: bool = False,
+    model_name: str = "Model_iter20_lr_1.0000e-03_1.0000e-02_1.0000e-03_.pt",
+) -> None:
+
+    torch_device: torch.device = (
+        torch.device("cuda:0") if torch.cuda.is_available() else torch.device("cpu")
+    )
+    torch.set_default_dtype(torch.float32)
+
+    # Some parameters
+    batch_size_test: int = 50  # 0
+
+    loss_mode: int = 0
+    loss_coeffs_mse: float = 0.5
+    loss_coeffs_kldiv: float = 1.0
+    print(
+        "loss_mode: ",
+        loss_mode,
+        "loss_coeffs_mse: ",
+        loss_coeffs_mse,
+        "loss_coeffs_kldiv: ",
+        loss_coeffs_kldiv,
+    )
+
+    if dataset == "MNIST" or dataset == "FashionMNIST":
+        input_dim_x: int = 24
+        input_dim_y: int = 24
+    else:
+        input_dim_x = 28
+        input_dim_y = 28
+
+    test_dataloader, test_processing_chain = get_the_data(
+        dataset,
+        batch_size_test,
+        torch_device,
+        input_dim_x,
+        input_dim_y,
+    )
+
+    network = torch.load(model_name)
+    network.to(device=torch_device)
+
+    print(network)
+
+    if only_print_network:
+        exit()
+
+    # Switch the network into evalution mode
+    network.eval()
+    number_of_noise_steps = 20
+    noise_scale = torch.arange(0, number_of_noise_steps + 1) / float(
+        number_of_noise_steps
+    )
+
+    results = torch.zeros_like(noise_scale)
+
+    with torch.no_grad():
+
+        for position in range(0, noise_scale.shape[0]):
+            test_correct: int = 0
+            test_number: int = 0
+            eta: float = noise_scale[position]
+            for image, target in test_dataloader:
+                noise = torch.rand_like(image) > eta
+                noise_2 = torch.rand_like(image)
+                noise_2 = noise_2 / (noise_2.sum(dim=(1, 2, 3), keepdim=True) + 1e-20)
+
+                image = (
+                    image * noise.type(dtype=torch.float32)
+                    + (1.0 - noise.type(dtype=torch.float32)) * noise_2
+                )
+                image = image / (image.sum(dim=(1, 2, 3), keepdim=True) + 1e-20)
+                output = network(test_processing_chain(image))
+
+                test_correct += (output.argmax(dim=1) == target).sum().cpu().numpy()
+                test_number += target.shape[0]
+
+            perfomance_test_correct: float = 100.0 * test_correct / test_number
+            results[position] = perfomance_test_correct
+
+            print(f"{eta:.2f}: {perfomance_test_correct:.2f}%")
+
+    np.save("noise_holes_w_noise_results.npy", results.cpu().numpy())
+    return
+
+
+if __name__ == "__main__":
+    argh.dispatch_command(main)
diff --git a/noise_picture.py b/noise_picture.py
new file mode 100644
index 0000000..4522f0e
--- /dev/null
+++ b/noise_picture.py
@@ -0,0 +1,110 @@
+import os
+
+os.environ["TF_CPP_MIN_LOG_LEVEL"] = "3"
+
+import argh
+
+import numpy as np
+import torch
+
+rand_seed: int = 21
+torch.manual_seed(rand_seed)
+torch.cuda.manual_seed(rand_seed)
+np.random.seed(rand_seed)
+
+from get_the_data_picture import get_the_data
+
+
+def main(
+    dataset: str = "CIFAR10",  # "CIFAR10", "FashionMNIST", "MNIST"
+    only_print_network: bool = False,
+    model_name: str = "Model_iter20_lr_1.0000e-03_1.0000e-02_1.0000e-03_.pt",
+) -> None:
+
+    torch_device: torch.device = (
+        torch.device("cuda:0") if torch.cuda.is_available() else torch.device("cpu")
+    )
+    torch.set_default_dtype(torch.float32)
+
+    # Some parameters
+    batch_size_test: int = 50  # 0
+
+    loss_mode: int = 0
+    loss_coeffs_mse: float = 0.5
+    loss_coeffs_kldiv: float = 1.0
+    print(
+        "loss_mode: ",
+        loss_mode,
+        "loss_coeffs_mse: ",
+        loss_coeffs_mse,
+        "loss_coeffs_kldiv: ",
+        loss_coeffs_kldiv,
+    )
+
+    if dataset == "MNIST" or dataset == "FashionMNIST":
+        input_dim_x: int = 24
+        input_dim_y: int = 24
+    else:
+        input_dim_x = 28
+        input_dim_y = 28
+
+    network = torch.load(model_name)
+    network.to(device=torch_device)
+
+    print(network)
+
+    if only_print_network:
+        exit()
+
+    # Switch the network into evalution mode
+    network.eval()
+    number_of_noise_steps = 20
+    noise_scale = (
+        0.5 * torch.arange(0, number_of_noise_steps + 1) / float(number_of_noise_steps)
+    )
+
+    results = torch.zeros_like(noise_scale)
+
+    with torch.no_grad():
+
+        for position in range(0, noise_scale.shape[0]):
+
+            train_dataloader, test_dataloader, test_processing_chain = get_the_data(
+                dataset,
+                batch_size_test,
+                batch_size_test,
+                torch_device,
+                input_dim_x,
+                input_dim_y,
+            )
+            train_dataloader_iter = iter(train_dataloader)
+            test_dataloader_iter = iter(test_dataloader)
+
+            test_correct: int = 0
+            test_number: int = 0
+            eta: float = noise_scale[position]
+            max_iters = len(test_dataloader)
+
+            for _ in range(0, max_iters):
+                (image, target) = next(test_dataloader_iter)
+                (noise, _) = next(train_dataloader_iter)
+                noise = noise / (noise.sum(dim=(1, 2, 3), keepdim=True) + 1e-20)
+                image = image / (image.sum(dim=(1, 2, 3), keepdim=True) + 1e-20)
+                output = network(
+                    test_processing_chain((1.0 - eta) * image + eta * noise)
+                )
+
+                test_correct += (output.argmax(dim=1) == target).sum().cpu().numpy()
+                test_number += target.shape[0]
+
+            perfomance_test_correct: float = 100.0 * test_correct / test_number
+            results[position] = perfomance_test_correct
+
+            print(f"{eta:.2f}: {perfomance_test_correct:.2f}%")
+
+    np.save("noise_picture_results.npy", results.cpu().numpy())
+    return
+
+
+if __name__ == "__main__":
+    argh.dispatch_command(main)
diff --git a/noise_run.sh b/noise_run.sh
new file mode 100644
index 0000000..b0edc9b
--- /dev/null
+++ b/noise_run.sh
@@ -0,0 +1,5 @@
+/data_1/davrot/P3.12/bin/python3 noise_uniform.py
+/data_1/davrot/P3.12/bin/python3 noise_picture.py
+/data_1/davrot/P3.12/bin/python3 noise_holes.py
+/data_1/davrot/P3.12/bin/python3 noise_holes_w_noise.py
+
diff --git a/noise_uniform.py b/noise_uniform.py
new file mode 100644
index 0000000..63af795
--- /dev/null
+++ b/noise_uniform.py
@@ -0,0 +1,103 @@
+import os
+
+os.environ["TF_CPP_MIN_LOG_LEVEL"] = "3"
+
+import argh
+
+import numpy as np
+import torch
+
+rand_seed: int = 21
+torch.manual_seed(rand_seed)
+torch.cuda.manual_seed(rand_seed)
+np.random.seed(rand_seed)
+
+from get_the_data_uniform import get_the_data
+
+
+def main(
+    dataset: str = "CIFAR10",  # "CIFAR10", "FashionMNIST", "MNIST"
+    only_print_network: bool = False,
+    model_name: str = "Model_iter20_lr_1.0000e-03_1.0000e-02_1.0000e-03_.pt",
+) -> None:
+
+    torch_device: torch.device = (
+        torch.device("cuda:0") if torch.cuda.is_available() else torch.device("cpu")
+    )
+    torch.set_default_dtype(torch.float32)
+
+    # Some parameters
+    batch_size_test: int = 50  # 0
+
+    loss_mode: int = 0
+    loss_coeffs_mse: float = 0.5
+    loss_coeffs_kldiv: float = 1.0
+    print(
+        "loss_mode: ",
+        loss_mode,
+        "loss_coeffs_mse: ",
+        loss_coeffs_mse,
+        "loss_coeffs_kldiv: ",
+        loss_coeffs_kldiv,
+    )
+
+    if dataset == "MNIST" or dataset == "FashionMNIST":
+        input_dim_x: int = 24
+        input_dim_y: int = 24
+    else:
+        input_dim_x = 28
+        input_dim_y = 28
+
+    test_dataloader, test_processing_chain = get_the_data(
+        dataset,
+        batch_size_test,
+        torch_device,
+        input_dim_x,
+        input_dim_y,
+    )
+
+    network = torch.load(model_name)
+    network.to(device=torch_device)
+
+    print(network)
+
+    if only_print_network:
+        exit()
+
+    # Switch the network into evalution mode
+    network.eval()
+    number_of_noise_steps = 20
+    noise_scale = torch.arange(0, number_of_noise_steps + 1) / float(
+        number_of_noise_steps
+    )
+
+    results = torch.zeros_like(noise_scale)
+
+    with torch.no_grad():
+
+        for position in range(0, noise_scale.shape[0]):
+            test_correct: int = 0
+            test_number: int = 0
+            eta: float = noise_scale[position]
+            for image, target in test_dataloader:
+                noise = torch.rand_like(image)
+                noise = noise / (noise.sum(dim=(1, 2, 3), keepdim=True) + 1e-20)
+                image = image / (image.sum(dim=(1, 2, 3), keepdim=True) + 1e-20)
+                output = network(
+                    test_processing_chain((1.0 - eta) * image + eta * noise)
+                )
+
+                test_correct += (output.argmax(dim=1) == target).sum().cpu().numpy()
+                test_number += target.shape[0]
+
+            perfomance_test_correct: float = 100.0 * test_correct / test_number
+            results[position] = perfomance_test_correct
+
+            print(f"{eta:.2f}: {perfomance_test_correct:.2f}%")
+
+    np.save("noise_uniform_results.npy", results.cpu().numpy())
+    return
+
+
+if __name__ == "__main__":
+    argh.dispatch_command(main)