diff --git a/deeptrack/image.py b/deeptrack/image.py
index 6a221a3fd..8420d6a85 100644
--- a/deeptrack/image.py
+++ b/deeptrack/image.py
@@ -59,11 +59,11 @@ class is central to DeepTrack2, acting as a container for numerical data
 - `pad_image_to_fft(image, axes)`
 
     pad_image_to_fft(
-        image: Image | np.ndarray,
+        image: Image | np.ndarray | torch.tensor,
         axes: Iterable[int] = (0, 1),
-    ) -> Image | np.ndarray
+    ) -> Image | np.ndarray | torch.tensor
 
-    Pads an image to optimize Fast Fourier Transform (FFT) performance.
+    Pad an image to optimize Fast Fourier Transform (FFT) performance.
 
 Examples
 --------
@@ -96,11 +96,17 @@ class is central to DeepTrack2, acting as a container for numerical data
 import operator as ops
 from typing import Any, Callable, Iterable
 
+import array_api_compat as apc
 import numpy as np
+from numpy.typing import NDArray
 
+from deeptrack.backend import config, TORCH_AVAILABLE, xp
 from deeptrack.properties import Property
 from deeptrack.types import NumberLike
 
+if TORCH_AVAILABLE:
+    import torch
+
 
 #TODO ***??*** revise _binary_method - typing, docstring, unit test
 def _binary_method(
@@ -1694,12 +1700,11 @@ def coerce(
 _FASTEST_SIZES = np.sort(_FASTEST_SIZES)
 
 
-#TODO ***??*** revise pad_image_to_fft - typing, docstring, unit test
 def pad_image_to_fft(
-    image: Image | np.ndarray | np.ndarray,
+    image: Image | NDArray | torch.Tensor,
     axes: Iterable[int] = (0, 1),
-) -> Image | np.ndarray:
-    """Pads an image to optimize Fast Fourier Transform (FFT) performance.
+) -> Image | NDArray | torch.Tensor:
+    """Pad an image to optimize Fast Fourier Transform (FFT) performance.
 
     This function pads an image by adding zeros to the end of specified axes
     so that their lengths match the nearest larger size in `_FASTEST_SIZES`.
@@ -1707,7 +1712,7 @@ def pad_image_to_fft(
 
     Parameters
     ----------
-    image: Image | np.ndarray
+    image: Image | np.ndarray | torch.tensor
         The input image to pad. It should be an instance of the `Image` class
         or any array-like structure compatible with FFT operations.
     axes: Iterable[int], optional
@@ -1715,7 +1720,7 @@ def pad_image_to_fft(
 
     Returns
     -------
-    Image | np.ndarray
+    Image | np.ndarray | torch.tensor
         The padded image with dimensions optimized for FFT performance.
 
     Raises
@@ -1725,30 +1730,37 @@ def pad_image_to_fft(
 
     Examples
     --------
-    >>> import numpy as np
     >>> from deeptrack.image import Image, pad_image_to_fft
 
     Pad an Image object:
-
-    >>> img = Image(np.zeros((7, 13)))
+    >>> import numpy as np
+    >>>
+    >>> img = Image(np.ones((7, 13)))
     >>> padded_img = pad_image_to_fft(img)
     >>> print(padded_img.shape)
     (8, 16)
 
     Pad a NumPy array:
-
-    >>> img = np.zeros((5, 11)))
+    >>> img = np.ones((5, 11))
     >>> padded_img = pad_image_to_fft(img)
     >>> print(padded_img.shape)
     (6, 12)
 
+    Pad a PyTorch tensor:
+    >>> import torch
+    >>>
+    >>> img = torch.ones(7, 11)
+    >>> padded_img = pad_image_to_fft(img)
+    >>> print(padded_img.shape)
+    (8, 12)
+
     """
 
     def _closest(
         dim: int,
     ) -> int:
 
-        # Returns the smallest value frin _FASTEST_SIZES larger than dim.
+        # Return the smallest value from _FASTEST_SIZES that is >= dim.
         for size in _FASTEST_SIZES:
             if size >= dim:
                 return size
@@ -1763,7 +1775,18 @@ def _closest(
         new_shape[axis] = _closest(new_shape[axis])
 
     # Calculate the padding for each axis.
-    pad_width = [(0, increase) for increase in np.array(new_shape) - image.shape]
+    pad_width = [
+        (0, increase)
+        for increase in np.array(new_shape) - np.array(image.shape)
+    ]
+
+    # Apply zero-padding with torch.nn.functional.pad if the input is a
+    # PyTorch tensor
+    if apc.is_torch_array(image):
+        pad = []
+        for before, after in reversed(pad_width):
+            pad.extend([before, after])
+        return torch.nn.functional.pad(image, pad, mode="constant", value=0)
 
-    # Pad the image using constant mode (add zeros).
+    # Apply zero-padding with np.pad if the input is a NumPy array or an Image
     return np.pad(image, pad_width, mode="constant")
diff --git a/deeptrack/noises.py b/deeptrack/noises.py
index eaaa5afe1..04b8e609f 100644
--- a/deeptrack/noises.py
+++ b/deeptrack/noises.py
@@ -217,7 +217,11 @@ def get(
 
         # For a Torch backend.
         elif self.get_backend() == "torch":
-            noisy_image = mu + image + torch.randn(*image.shape) * sigma
+            noisy_image = (
+                mu
+                + image
+                + torch.randn(*image.shape, device=image.device) * sigma
+            )
 
         return noisy_image
 
diff --git a/deeptrack/optics.py b/deeptrack/optics.py
index 5149bdae2..a37956d38 100644
--- a/deeptrack/optics.py
+++ b/deeptrack/optics.py
@@ -97,12 +97,12 @@ def _create_volume(
 - `_pad_volume(volume, limits, padding, output_region, **kwargs)`
 
     def _pad_volume(
-        volume: np.ndarray,
-        limits: np.ndarray,
+        volume: np.ndarray | torch.tensor,
+        limits: np.ndarray | torch.tensor,
         padding: tuple[int, int, int, int],
         output_region: tuple[int, int, int, int],
         **kwargs: Any,
-    ) -> tuple[np.ndarray, np.ndarray]
+    ) -> tuple[np.ndarray, np.ndarray] | tuple[torch.tensor, torch.tensor]
 
     Pads a volume with zeros to avoid edge effects during imaging.
 
@@ -140,9 +140,12 @@ def _pad_volume(
 from typing import Any
 import warnings
 
+import array_api_compat as apc
 import numpy as np
+from numpy.typing import NDArray
 from scipy.ndimage import convolve
 
+from deeptrack.backend import config, TORCH_AVAILABLE, xp
 from deeptrack.backend.units import (
     ConversionTable,
     create_context,
@@ -158,6 +161,8 @@ def _pad_volume(
 from deeptrack import image
 from deeptrack import units_registry as u
 
+if TORCH_AVAILABLE:
+    import torch
 
 #TODO ***??*** revise Microscope - torch, typing, docstring, unit test
 class Microscope(StructuralFeature):
@@ -407,21 +412,21 @@ class Optics(Feature):
     magnification: float, optional
         Magnification of the optical system, by default 10.
     resolution: float or array_like[float], optional
-        Distance between pixels in the camera (meters). A third value can 
+        Distance between pixels in the camera (meters). A third value can
         define the resolution in the z-direction, by default 1e-6.
     refractive_index_medium: float, optional
         Refractive index of the medium, by default 1.33.
     padding: array_like[int, int, int, int], optional
-        Padding applied to the sample volume to avoid edge effects, 
+        Padding applied to the sample volume to avoid edge effects,
         by default (10, 10, 10, 10).
     output_region: array_like[int, int, int, int], optional
-        Region of the image to output (x, y, width, height). If None, the 
+        Region of the image to output (x, y, width, height). If None, the
         entire image is returned, by default (0, 0, 128, 128).
     pupil: Feature, optional
         Feature-set resolving the pupil function at focus. By default, no pupil
         is applied.
     illumination: Feature, optional
-        Feature-set resolving the illumination source. By default, no specific 
+        Feature-set resolving the illumination source. By default, no specific
         illumination is applied.
     upscale: int, optional
         Scaling factor for the resolution of the optical system, by default 1.
@@ -461,10 +466,24 @@ class Optics(Feature):
     -------
     `_process_properties(propertydict: dict[str, Any]) -> dict[str, Any]`
         Processes and validates the input properties.
-    `_pupil(shape:  array_like[int, int], NA: float, wavelength: float, refractive_index_medium: float, include_aberration: bool, defocus: float, **kwargs: Any) -> array_like[complex]`
-        Calculates the pupil function at different focal points.
-    `_pad_volume(volume: array_like[complex], limits: array_like[int, int], padding: array_like[int], output_region: array_like[int], **kwargs: Any) -> tuple`
-        Pads the volume with zeros to avoid edge effects.
+    `_pupil(
+        shape: array_like[int, int],
+        NA: float,
+        wavelength: float,
+        refractive_index_medium: float,
+        include_aberration: bool,
+        defocus: float,
+        **kwargs: Any,
+    ) -> array_like[complex]`
+        Calculate the complex pupil function at one or more focal points.
+    `_pad_volume(
+        volume: array_like[complex],
+        limits: array_like[int, int],
+        padding: array_like[int],
+        output_region: array_like[int],
+        **kwargs: Any,
+    ) -> tuple`
+        Pad the volume with zeros to avoid edge effects.
     `__call__(sample: Feature, **kwargs: Any) -> Microscope`
         Creates a Microscope instance with the given sample and optics.
 
@@ -694,11 +713,11 @@ def _pupil(
         NA: float,
         wavelength: float,
         refractive_index_medium: float,
-        include_aberration: bool = True,   
+        include_aberration: bool = True,
         defocus: float | ArrayLike[float] = 0,
         **kwargs: Any,
-    ):
-        """Calculates the pupil function at different focal points.
+    ) -> NDArray | torch.Tensor:
+        """Calculate the complex pupil function at one or more focal points.
 
         Parameters
         ----------
@@ -710,26 +729,30 @@ def _pupil(
             The wavelength of the scattered light in meters.
         refractive_index_medium: float
             The refractive index of the medium.
-        voxel_size: array_like[float (, float, float)]
-            The distance between pixels in the camera. A third value can be
-            included to define the resolution in the z-direction.
         include_aberration: bool
-            If True, the aberration is included in the pupil function.
+            If True, the aberration is included in the pupil function. Default
+            is `True`.
         defocus: float or list[float]
             The defocus of the system. If a list is given, the pupil is
             calculated for each focal point. Defocus is given in meters.
+            Default is `0`.
 
         Returns
         -------
         pupil: array_like[complex]
-            The pupil function. Shape is (z, y, x).
+            The complex pupil function(s) at the specified defocus positions.
+            Shape is (z, y, x).
+
+        Notes
+        -----
+        The backend (NumPy or PyTorch) is determined by `self.get_backend()`
+        and can be switched using the global backend configuration.
 
         Examples
         --------
-        Calculating the pupil function:
-
         >>> import deeptrack as dt
 
+        Calculating the pupil function:
         >>> optics = dt.Optics()
         >>> pupil = optics._pupil(
         ...     shape=(128, 128),
@@ -737,75 +760,131 @@ def _pupil(
         ...     wavelength=0.55e-6,
         ...     refractive_index_medium=1.33,
         ... )
-        >>> print(pupil.shape)
-        (1, 128, 128)
-        
+        >>> print(pupil.shape, pupil.dtype)
+        (1, 128, 128) complex128
+
+        Calculating the pupil function with a PyTorch backend:
+        >>> from deeptrack.backend import config
+        >>> config.set_backend("torch")
+        >>>
+        >>> optics = dt.Optics()
+        >>> pupil = optics._pupil(
+        ...     shape=(128, 128),
+        ...     NA=0.8,
+        ...     wavelength=0.55e-6,
+        ...     refractive_index_medium=1.33,
+        ... )
+        >>> print(pupil.shape, pupil.dtype)
+        torch.Size([1, 128, 128]) torch.complex128
+
         """
 
-        # Calculates the pupil at each z-position in defocus.
+        # Calculate the pupil at each z-position in defocus.
         voxel_size = get_active_voxel_size()
-        shape = np.array(shape)
 
-        # Pupil radius
-        R = NA / wavelength * np.array(voxel_size)[:2]
+        if self.get_backend() == "numpy":
+            shape = np.array(shape)
+
+            # Pupil radius
+            R = NA / wavelength * np.array(voxel_size)[:2]
+
+        elif self.get_backend() == "torch":
+            shape = torch.tensor(shape, dtype=torch.float64)
+
+            # Pupil radius
+            R = NA / wavelength * torch.tensor(voxel_size)[:2]
+
+            device = config.get_device()  # could be 'cuda' or 'cpu'
+
+        else:
+            raise ValueError(f"Unsupported backend: {self.get_backend()}")
 
         x_radius = R[0] * shape[0]
         y_radius = R[1] * shape[1]
 
-        x = (np.linspace(-(shape[0] / 2), shape[0] / 2 - 1, shape[0])) / x_radius + 1e-8
-        y = (np.linspace(-(shape[1] / 2), shape[1] / 2 - 1, shape[1])) / y_radius + 1e-8
-
-        W, H = np.meshgrid(y, x)
-        RHO = (W ** 2 + H ** 2).astype(complex)
-        pupil_function = Image((RHO < 1) + 0.0j, copy=False)
-        # Defocus
-        z_shift = Image(
-            2
-            * np.pi
-            * refractive_index_medium
-            / wavelength
-            * voxel_size[2]
-            * np.sqrt(1 - (NA / refractive_index_medium) ** 2 * RHO),
-            copy=False,
-        )
+        x = (
+            xp.linspace(-(shape[0] / 2), shape[0] / 2 - 1, int(shape[0]))
+        ) / x_radius + 1e-8
+        y = (
+            xp.linspace(-(shape[1] / 2), shape[1] / 2 - 1, int(shape[1]))
+        ) / y_radius + 1e-8
+
+        W, H = xp.meshgrid(y, x, indexing="xy")
+
+        if self.get_backend() == "numpy":
+            RHO = (W**2 + H**2).astype(complex)
+            pupil_function = Image((RHO < 1) + 0.0j, copy=False)
+
+            # Defocus
+            z_shift = Image(
+                2
+                * np.pi
+                * refractive_index_medium
+                / wavelength
+                * voxel_size[2]
+                * np.sqrt(1 - (NA / refractive_index_medium) ** 2 * RHO),
+                copy=False,
+            )
 
-        z_shift._value[z_shift._value.imag != 0] = 0
+            z_shift._value[z_shift._value.imag != 0] = 0
+
+        else:
+            RHO = W**2 + H**2
+            pupil_function = (RHO < 1).to(dtype=torch.complex128) + 0.0j
+            pupil_function = pupil_function.to(device)
+            RHO = RHO.to(dtype=torch.complex128)
+
+            # Defocus
+            z_shift = (
+                2
+                * xp.pi
+                * refractive_index_medium
+                / wavelength
+                * voxel_size[2]
+                * xp.sqrt(1 - (NA / refractive_index_medium) ** 2 * RHO)
+            )
+
+            z_shift[z_shift.imag != 0] = 0
 
         try:
-            z_shift = np.nan_to_num(z_shift, False, 0, 0, 0)
+            z_shift = xp.nan_to_num(z_shift, nan=0.0, posinf=None, neginf=None)
         except TypeError:
-            np.nan_to_num(z_shift, z_shift)
+            xp.nan_to_num(z_shift, z_shift)
+
+        if self.get_backend() == "numpy":
+            defocus = np.reshape(defocus, (-1, 1, 1))
+            z_shift = defocus * np.expand_dims(z_shift, axis=0)
+        else:            
+            defocus = torch.reshape(torch.as_tensor(defocus), (-1, 1, 1)).to(device)
+            z_shift = defocus * torch.unsqueeze(z_shift.to(device), dim=0)
 
-        defocus = np.reshape(defocus, (-1, 1, 1))
-        z_shift = defocus * np.expand_dims(z_shift, axis=0)
-        
         if include_aberration:
             pupil = self.pupil
             if isinstance(pupil, Feature):
 
                 pupil_function = pupil(pupil_function)
 
-            elif isinstance(pupil, np.ndarray):
+            elif isinstance(pupil, np.ndarray) or torch.is_tensor(pupil):
                 pupil_function *= pupil
 
-        pupil_functions = pupil_function * np.exp(1j * z_shift)
+        pupil_functions = pupil_function * xp.exp(1j * z_shift)
 
         return pupil_functions
 
     def _pad_volume(
         self: Optics,
-        volume: ArrayLike[complex],
+        volume: NDArray | torch.Tensor,
         limits: ArrayLike[int] = None,
         padding: ArrayLike[int] = None,
         output_region: ArrayLike[int] = None,
         **kwargs: Any,
     ) -> tuple:
-        """Pads the volume with zeros to avoid edge effects.
+        """Pad the input volume with zeros to avoid edge effects.
 
         Parameters
         ----------
-        volume: array_like[complex]
-            The volume to pad.
+        volume: NDArray | torch.Tensor
+            The complex-valued volume to pad.
         limits: array_like[int, int]
             The limits of the volume.
         padding: array_like[int]
@@ -817,76 +896,143 @@ def _pad_volume(
 
         Returns
         -------
-        new_volume: array_like[complex]
-            The padded volume.
+        new_volume: NDArray | torch.Tensor
+            The padded, complex valued volume.
         new_limits: array_like[int, int]
             The new limits of the volume.
 
         Examples
         --------
-        Padding a volume:
-
         >>> import deeptrack as dt
-        >>> import numpy as np
 
+        Padding a volume:        
+        >>> import numpy as np
+        >>>
         >>> volume = np.ones((10, 10, 10), dtype=complex)
         >>> limits = np.array([[0, 10], [0, 10], [0, 10]])
         >>> optics = dt.Optics()
         >>> padded_volume, new_limits = optics._pad_volume(
-        ...     volume, limits=limits, padding=[5, 5, 5, 5],
+        ...     volume,
+        ...     limits=limits,
+        ...     padding=[5, 5, 5, 5],
         ...     output_region=[0, 0, 10, 10],
         ... )
         >>> print(padded_volume.shape)
         (20, 20, 10)
+
         >>> print(new_limits)
         [[-5 15]
          [-5 15]
          [ 0 10]]
+
+        Padding a volume using PyTorch: 
+        >>> import torch
+        >>>
+        >>> volume = torch.ones(10, 10, 10, dtype=complex)
+        >>> limits = torch.tensor([[0, 10], [0, 10], [0, 10]])
+        >>> optics = dt.Optics()
+        >>> padded_volume, new_limits = optics._pad_volume(
+        ...     volume,
+        ...     limits=limits,
+        ...     padding=[5, 5, 5, 5],
+        ...     output_region=[0, 0, 10, 10],
+        ... )
+        >>> print(padded_volume.shape)
+        torch.Size([20, 20, 10])
+
+        >>> print(padded_volume.dtype)
+        torch.complex128
+
+        >>> print(new_limits)
+        tensor([[-5, 15],
+        [-5, 15],
+        [ 0, 10]])
         
         """
-        
-        if limits is None:
-            limits = np.zeros((3, 2))
 
-        new_limits = np.array(limits)
-        output_region = np.array(output_region)
+        if self.get_backend() == "torch":
+            if limits is None:
+                limits = torch.zeros(3, 2)
+
+            if isinstance(limits, np.ndarray):     # quick fix to make it work with scatterers that are not
+                limits = torch.tensor(limits)      # implemented with a torch backend (to be removed later)
+
+            # quick fix to make it work with scatterers that are not implemented with a torch backend (to be removed later)
+            volume = torch.tensor(volume)
+
+            new_limits = limits.clone()
+
+        elif self.get_backend() == "numpy":
+            if limits is None:
+                limits = np.zeros((3, 2))
+
+            new_limits = np.array(limits)
+
+        else:
+            raise ValueError(f"Unsupported backend: {self.get_backend()}")
+
+        if output_region is None:
+            output_region = [None] * 4
+        output_region = list(output_region)
 
         # Replace None entries with current limit
         output_region[0] = (
-            output_region[0] if not output_region[0] is None else new_limits[0, 0]
+            output_region[0]
+            if not output_region[0] is None
+            else new_limits[0, 0]
         )
         output_region[1] = (
-            output_region[1] if not output_region[1] is None else new_limits[0, 1]
+            output_region[1]
+            if not output_region[1] is None
+            else new_limits[0, 1]
         )
         output_region[2] = (
-            output_region[2] if not output_region[2] is None else new_limits[1, 0]
+            output_region[2]
+            if not output_region[2] is None
+            else new_limits[1, 0]
         )
         output_region[3] = (
-            output_region[3] if not output_region[3] is None else new_limits[1, 1]
+            output_region[3]
+            if not output_region[3] is None
+            else new_limits[1, 1]
         )
 
+        # Update the new limits based on padding and output region
         for i in range(2):
-            new_limits[i, :] = (
-                np.min([new_limits[i, 0], output_region[i] - padding[i]]),
-                np.max(
-                    [
-                        new_limits[i, 1],
-                        output_region[i + 2] + padding[i + 2],
-                    ]
-                ),
+            new_limits[i, 0] = min(
+                new_limits[i, 0], output_region[i] - padding[i]
+            )
+            new_limits[i, 1] = max(
+                new_limits[i, 1], output_region[i + 2] + padding[i + 2]
             )
-        new_volume = np.zeros(
-            np.diff(new_limits, axis=1)[:, 0].astype(np.int32),
-            dtype=complex,
-        )
 
-        old_region = (limits - new_limits).astype(np.int32)
-        limits = limits.astype(np.int32)
+        # Determine shape for the new volume
+        if self.get_backend() == "torch":
+            new_volume = torch.zeros(
+                tuple(new_limits[:, 1] - new_limits[:, 0]), dtype=volume.dtype
+            )
+        else:
+            new_volume = np.zeros(
+                np.diff(new_limits, axis=1)[:, 0].astype(np.int32),
+                dtype=complex,
+            )
+
+        # Compute where to place the old volume in the new one
+        old_region = (limits - new_limits)
+        
+        if self.get_backend() == "torch":
+            old_region = old_region.to(torch.int32)
+            limits = limits.to(torch.int32)
+        else:
+            old_region = old_region.astype(np.int32)
+            limits = limits.astype(np.int32)
+
         new_volume[
             old_region[0, 0] : old_region[0, 0] + limits[0, 1] - limits[0, 0],
             old_region[1, 0] : old_region[1, 0] + limits[1, 1] - limits[1, 0],
             old_region[2, 0] : old_region[2, 0] + limits[2, 1] - limits[2, 0],
         ] = volume
+
         return new_volume, new_limits
 
     def __call__(
@@ -948,8 +1094,8 @@ class Fluorescence(Optics):
     """Optical device for fluorescent imaging.
 
     The `Fluorescence` class simulates the imaging process in fluorescence
-    microscopy by creating a discretized volume where each pixel represents 
-    the intensity of light emitted by fluorophores in the sample. It extends 
+    microscopy by creating a discretized volume where each pixel represents
+    the intensity of light emitted by fluorophores in the sample. It extends
     the `Optics` class to include fluorescence-specific functionalities.
 
     Parameters
@@ -967,10 +1113,10 @@ class Fluorescence(Optics):
     padding: array_like[int, int, int, int]
         Padding applied to the sample volume to reduce edge effects.
     output_region: array_like[int, int, int, int], optional
-        Region of the output image to extract (x, y, width, height). If None, 
+        Region of the output image to extract (x, y, width, height). If None,
         returns the full image.
     pupil: Feature, optional
-        A feature set defining the pupil function at focus. The input is 
+        A feature set defining the pupil function at focus. The input is
         the unaberrated pupil.
     illumination: Feature, optional
         A feature set defining the illumination source.
@@ -994,7 +1140,7 @@ class Fluorescence(Optics):
         Padding applied to the sample volume to reduce edge effects.
     output_region: array_like[int, int, int, int]
         Region of the output image to extract (x, y, width, height).
-    voxel_size: function
+    voxel_size: function           # TODO Do we want the user to be able to set the voxel size here? If so, we would have to modify the super().__init__ of Optics
         Function returning the voxel size of the optical system.
     pixel_size: function
         Function returning the pixel size of the optical system.
@@ -1007,7 +1153,11 @@ class Fluorescence(Optics):
 
     Methods
     -------
-    `get(illuminated_volume: array_like[complex], limits: array_like[int, int], **kwargs: Any) -> Image`
+    `get(
+        illuminated_volume: array_like[complex],
+        limits: array_like[int, int],
+        **kwargs: Any,
+    ) -> Image`
         Simulates the imaging process using a fluorescence microscope.
 
     Examples
@@ -1025,14 +1175,14 @@ class Fluorescence(Optics):
     """
 
     def get(
-        self:  Fluorescence,
+        self: Fluorescence,
         illuminated_volume: ArrayLike[complex],
         limits: ArrayLike[int],
         **kwargs: Any,
     ) -> Image:
         """Simulates the imaging process using a fluorescence microscope.
 
-        This method convolves the 3D illuminated volume with a pupil function 
+        This method convolves the 3D illuminated volume with a pupil function
         to generate a 2D image projection.
 
         Parameters
@@ -1058,11 +1208,11 @@ def get(
 
         Examples
         --------
-        Simulate imaging a volume:
-
         >>> import deeptrack as dt
+        
+        Simulate imaging a volume:
         >>> import numpy as np
-
+        >>>
         >>> optics = dt.Fluorescence(
         ...     NA=1.4, wavelength=0.52e-6, magnification=60,
         ... )
@@ -1070,14 +1220,34 @@ def get(
         >>> limits = np.array([[0, 128], [0, 128], [0, 10]])
         >>> properties = optics.properties()
         >>> filtered_properties = {
-        ...     k: v for k, v in properties.items() 
-        ...     if k in {"padding", "output_region", "NA", 
+        ...     k: v for k, v in properties.items()
+        ...     if k in {"padding", "output_region", "NA",
         ...              "wavelength", "refractive_index_medium"}
         ... }
         >>> image = optics.get(volume, limits, **filtered_properties)
         >>> print(image.shape)
         (128, 128, 1)
-        
+
+        Simulate imaging a volume using torch backend:
+        >>> from deeptrack.backend import config
+        >>> config.set_backend("torch")
+        >>> import torch
+        >>>
+        >>> optics = dt.Fluorescence(
+        ...     NA=1.4, wavelength=0.52e-6, magnification=60,
+        ... )
+        >>> volume = torch.ones((128, 128, 10), dtype=torch.complex128)
+        >>> limits = torch.tensor([[0, 128], [0, 128], [0, 10]])
+        >>> properties = optics.properties()
+        >>> filtered_properties = {
+        ...     k: v for k, v in properties.items()
+        ...     if k in {"padding", "output_region", "NA",
+        ...              "wavelength", "refractive_index_medium"}
+        ... }
+        >>> image = optics.get(volume, limits, **filtered_properties)
+        >>> print(image.shape)
+        torch.Size([128, 128, 1])
+
         """
 
         # Pad volume
@@ -1087,7 +1257,9 @@ def get(
 
         # Extract indexes of the output region
         pad = kwargs.get("padding", (0, 0, 0, 0))
-        output_region = np.array(kwargs.get("output_region", (None, None, None, None)))
+        output_region = list(
+            kwargs.get("output_region", (None, None, None, None))
+        )
 
         # Calculate the how much to crop from the volume
         output_region[0] = (
@@ -1118,20 +1290,44 @@ def get(
         ]
         z_limits = limits[2, :]
 
-        output_image = Image(
-            np.zeros((*padded_volume.shape[0:2], 1)), copy=False
-        )
+        if self.get_backend() == "numpy":
+            output_image = Image(
+                np.zeros((*padded_volume.shape[0:2], 1)), copy=False
+            )
+        elif self.get_backend() == "torch":
+            device = config.get_device()
+            padded_volume = padded_volume.to(device)
+            output_image = Image(
+                torch.zeros((*padded_volume.shape[0:2], 1)), copy=False
+            )
+            output_image._value = output_image._value.to(device)
+        else:
+            raise ValueError(f"Unsupported backend: {self.get_backend()}")
 
         index_iterator = range(padded_volume.shape[2])
 
         # Find planes that are not empty for optimization
-        z_iterator = np.linspace(
-            z_limits[0],
-            z_limits[1],
-            num=padded_volume.shape[2],
-            endpoint=False,
-        )
-        zero_plane = np.all(padded_volume == 0, axis=(0, 1), keepdims=False)
+        if self.get_backend() == "torch":
+            z_iterator = torch.linspace(
+                z_limits[0],
+                z_limits[1],
+                steps=padded_volume.shape[2] + 1,
+            )[:-1]  # exclude endpoint
+            zero_plane = torch.all(padded_volume == 0, dim=(0, 1))
+
+            z_iterator = z_iterator.to(device)
+            zero_plane = zero_plane.to(device)
+        else:
+            z_iterator = np.linspace(
+                z_limits[0],
+                z_limits[1],
+                num=padded_volume.shape[2],
+                endpoint=False,
+            )
+            zero_plane = np.all(
+                padded_volume == 0, axis=(0, 1), keepdims=False
+            )
+
         z_values = z_iterator[~zero_plane]
 
         # Further pad image to speed up fft (multiples of 2 and 3)
@@ -1149,19 +1345,27 @@ def get(
             pupil = pupils[z_index]
             z_index += 1
 
-            psf = np.square(np.abs(np.fft.ifft2(np.fft.fftshift(pupil))))
-            optical_transfer_function = np.fft.fft2(psf)
-            fourier_field = np.fft.fft2(volume[:, :, i])
-            convolved_fourier_field = fourier_field * optical_transfer_function
-            field = np.fft.ifft2(convolved_fourier_field)
-            # # Discard remaining imaginary part (should be 0 up to rounding error)
-            field = np.real(field)
+            psf = xp.square(xp.abs(xp.fft.ifft2(xp.fft.fftshift(pupil))))
+            optical_transfer_function = xp.fft.fft2(psf)
+            fourier_field = xp.fft.fft2(volume[:, :, i])
+            convolved_fourier_field = (
+                    fourier_field * optical_transfer_function
+                )
+            field = xp.fft.ifft2(convolved_fourier_field)
+            field = xp.real(field)
+
             output_image._value[:, :, 0] += field[
                 : padded_volume.shape[0], : padded_volume.shape[1]
             ]
 
         output_image = output_image[pad[0] : -pad[2], pad[1] : -pad[3]]
-        output_image.properties = illuminated_volume.properties + pupils.properties
+
+        if self.get_backend() == "numpy":
+            output_image.properties = (
+                illuminated_volume.properties + pupils.properties
+            )
+
+        # TODO: handle the properties also when using torch backend
 
         return output_image
 
diff --git a/deeptrack/tests/test_image.py b/deeptrack/tests/test_image.py
index d413c8da5..5d4901a82 100644
--- a/deeptrack/tests/test_image.py
+++ b/deeptrack/tests/test_image.py
@@ -12,7 +12,10 @@
 
 import numpy as np
 
-from deeptrack import features, image
+from deeptrack import features, image, TORCH_AVAILABLE
+
+if TORCH_AVAILABLE:
+    import torch
 
 
 class TestImage(unittest.TestCase):
@@ -389,6 +392,7 @@ def test_Image__view(self):
 
     def test_pad_image_to_fft(self):
 
+        # Test with dt.Image
         input_image = image.Image(np.zeros((7, 25)))
         padded_image = image.pad_image_to_fft(input_image)
         self.assertEqual(padded_image.shape, (8, 27))
@@ -401,6 +405,33 @@ def test_pad_image_to_fft(self):
         padded_image = image.pad_image_to_fft(input_image)
         self.assertEqual(padded_image.shape, (324, 432))
 
+        # Test with NumPy array
+        input_image = np.ones((7, 13))
+        padded_image = image.pad_image_to_fft(input_image)
+        self.assertEqual(padded_image.shape, (8, 16))
+
+        input_image = np.ones((5,))
+        padded_image = image.pad_image_to_fft(input_image, axes=(0,))
+        self.assertEqual(padded_image.shape, (6,))
+
+        ### Test with PyTorch tensor (if available)
+        if TORCH_AVAILABLE:
+            input_image = torch.ones(3, 5)
+            padded_image = image.pad_image_to_fft(input_image)
+            self.assertEqual(padded_image.shape, (3, 6))
+            self.assertIsInstance(padded_image, torch.Tensor)
+
+            input_image = torch.ones(5, 7, 11, 13)
+            padded_image = image.pad_image_to_fft(input_image, axes=(0, 1, 3))
+            padded_image_np = image.pad_image_to_fft(
+                input_image.numpy(), axes=(0, 1, 3)
+            )
+            self.assertEqual(padded_image.shape, (6, 8, 11, 16))
+            self.assertIsInstance(padded_image, torch.Tensor)
+            np.testing.assert_allclose(
+                padded_image.numpy(), padded_image_np, atol=1e-6
+            )
+
 
 if __name__ == "__main__":
     unittest.main()
\ No newline at end of file
diff --git a/tutorials/2-examples/DTEx252_phase_mask_optimization.ipynb b/tutorials/2-examples/DTEx252_phase_mask_optimization.ipynb
new file mode 100644
index 000000000..3f57b389f
--- /dev/null
+++ b/tutorials/2-examples/DTEx252_phase_mask_optimization.ipynb
@@ -0,0 +1,3493 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "5e65db32",
+   "metadata": {},
+   "source": [
+    "# Particle Localization and Phase Mask Optimization"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d4aa8b6b",
+   "metadata": {},
+   "source": [
+    "This tutorial demonstrates how to jointly optimize an optical phase mask and a neural network to improve the localization of closely spaced particles in microscopy images.\n",
+    "\n",
+    "The phase mask, inserted in the optical pupil plane, shapes the microscope’s point spread function (PSF). Simultaneously, a convolutional neural network (CNN) is trained to reconstruct the 3D particle positions from simulated images. Through backpropagation, gradients flow not only through the network but also through the optical model. As a result, the optical system learns to produce images that are easier for the CNN to interpret, leading to improved localization accuracy."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "82dfa30b",
+   "metadata": {},
+   "source": [
+    "# 1. Create a simulation pipeline\n",
+    "The first step is to define a simulation pipeline that generates synthetic data consisting of fluorescent particles imaged through a fluorescence microscope. For each simulated image, the pipeline also provides the true 3D positions of the particles, which serve as the ground truth during training."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "89d98126",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "WARNING:pint.util:Redefining '[magnetic_flux]' (<class 'pint.delegates.txt_defparser.plain.DerivedDimensionDefinition'>)\n"
+     ]
+    }
+   ],
+   "source": [
+    "import deeptrack as dt"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8a7638e5",
+   "metadata": {},
+   "source": [
+    "## 1.1 Set the backend\n",
+    "DeepTrack supports both NumPy and PyTorch backends. In this tutorial, the PyTorch backend is selected to enable backpropagation through the optical setup. Enabling CUDA (optional) allows the computations to run on the GPU, which accelerates both simulation and training."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "1c8abc46",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "torch cuda\n"
+     ]
+    }
+   ],
+   "source": [
+    "from deeptrack.backend import config\n",
+    "\n",
+    "config.set_backend(\"torch\")\n",
+    "config.set_device(\"cuda\")\n",
+    "\n",
+    "device = config.get_device()\n",
+    "\n",
+    "print(config.get_backend(), config.get_device())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "568fe6fb",
+   "metadata": {},
+   "source": [
+    "## 1.2 Define the trainable phase mask\n",
+    "A phase mask modifies the wavefront of light in the microscope pupil plane, shaping the resulting point spread function (PSF). By making the phase mask trainable, we allow the optical system itself to learn — optimizing its design through gradient descent to improve particle localization performance.\n",
+    "\n",
+    "The LearnablePhaseMask class inherits from both `nn.Module` and `dt.Aberration`. `nn.Module` makes the mask compatible with PyTorch’s autograd and optimizer system, allowing the phase parameters to be learned, while `dt.Aberration` ensures the class integrates seamlessly into DeepTrack’s feature graph, making it compatible with the rest of the optical pipeline."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "3470f1bf",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import torch\n",
+    "import torch.nn as nn\n",
+    "\n",
+    "class LearnablePhaseMask(nn.Module, dt.Aberration):\n",
+    "    \"\"\"Applies a learnable phase mask to the pupil function.\"\"\"\n",
+    "\n",
+    "    def __init__(self, shape=(144, 144), **kwargs):\n",
+    "        dt.Aberration.__init__(self, **kwargs)\n",
+    "        super().__init__(**kwargs)\n",
+    "        \n",
+    "        # Create a trainable tensor representing the phase\n",
+    "        self.phase = nn.Parameter(torch.zeros(shape, dtype=torch.float32))\n",
+    "\n",
+    "    def forward(self, pupil: torch.Tensor, **kwargs) -> torch.Tensor:\n",
+    "        \"\"\"PyTorch forward pass for use in training\"\"\"\n",
+    "        return self.apply_phase(pupil)\n",
+    "\n",
+    "    def get(self, pupil: torch.Tensor, **kwargs) -> torch.Tensor:\n",
+    "        \"\"\"DeepTrack Feature graph call\"\"\"\n",
+    "        return self.apply_phase(pupil)\n",
+    "\n",
+    "    def apply_phase(self, pupil: torch.Tensor) -> torch.Tensor:\n",
+    "        \"\"\"Shared implementation of phase modulation\"\"\"\n",
+    "        phase = self.phase.to(device)\n",
+    "        phase_mask = torch.cos(phase) + 1j * torch.sin(phase)\n",
+    "        return pupil * phase_mask"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e8a1553c",
+   "metadata": {},
+   "source": [
+    "## 1.3 Define the optical setup\n",
+    "The optical setup is simulated using `dt.Fluorescence`, incorporating the trainable phase mask as the pupil function."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "6d1f7321",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "image_size = 121\n",
+    "depth = 30"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "44058db9",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "phase_mask = LearnablePhaseMask()\n",
+    "\n",
+    "optics = dt.Fluorescence(\n",
+    "    NA=1.45,\n",
+    "    refractive_index_medium=1.33,   \n",
+    "    output_region=(0,0, image_size, image_size),\n",
+    "    pupil=phase_mask,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8fe88c92",
+   "metadata": {},
+   "source": [
+    "## 1.4 Simulate particles\n",
+    "A simulation pipeline is defined to generate images of randomly positioned 3D particles, together with their corresponding ground-truth localization targets.\n",
+    "\n",
+    "A custom DeepTrack feature, `Positions`, creates a binary 3D mask containing randomly placed particles. Each voxel corresponding to a particle is assigned a value of 1, while background voxels remain 0. The true particle coordinates are stored in self.points for later retrieval."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "9eecebff",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "class Positions(dt.Feature):\n",
+    "    def get(self, image, num_points, **kwargs):\n",
+    "        mask = torch.zeros((image.shape[0], image.shape[1], image.shape[2]), dtype=float)\n",
+    "\n",
+    "        points = torch.rand(size=(num_points, 3)) * torch.tensor(image.shape)\n",
+    "\n",
+    "        for i in range(len(points)):\n",
+    "            mask[\n",
+    "                torch.round(points[i, 0]).long().clamp(0, image.shape[0]-1),\n",
+    "                torch.round(points[i, 1]).long().clamp(0, image.shape[1]-1),\n",
+    "                torch.round(points[i, 2]).long().clamp(0, image.shape[2]-1)\n",
+    "            ] = 1\n",
+    "\n",
+    "        self.points = points\n",
+    "\n",
+    "        return mask + image"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "38c4a606",
+   "metadata": {},
+   "source": [
+    "The number of points for each sample is randomized between 25 and 50 to provide diverse training examples."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "544624c6",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "num_points = lambda: int(torch.randint(low=25, high=50, size=(1,)))\n",
+    "xyz = Positions(num_points=num_points)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "446fee06",
+   "metadata": {},
+   "source": [
+    "To make the training data physically plausible, a combination of Poisson noise and Gaussian noise is added.\n",
+    "\n",
+    "Since true Poisson noise is not differentiable, a Gaussian approximation that preserves differentiability during backpropagation is used."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "7d339aaf",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "class poisson_noise_approx(dt.Noise):\n",
+    "    def __init__(\n",
+    "        self,\n",
+    "        **kwargs,\n",
+    "    ):\n",
+    "        super().__init__(\n",
+    "            **kwargs,\n",
+    "        )\n",
+    "\n",
+    "    def get(self, image, noise_scale=1e-2, **kwargs):\n",
+    "\n",
+    "        image = torch.clamp(image, min=0.0)\n",
+    "\n",
+    "        # Gaussian approximation of Poisson noise\n",
+    "        noise = torch.sqrt(image) * torch.randn_like(image, device=self.device)\n",
+    "        noisy_image = image + noise * noise_scale\n",
+    "\n",
+    "        return noisy_image"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9ac539dc",
+   "metadata": {},
+   "source": [
+    "Next, the components, including the particle positions, the optics, and the noise, are combined to form the simulation pipeline."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "3cb3b33f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "i = -1\n",
+    "def next_position():\n",
+    "    global i\n",
+    "    gt_pip.resolve()\n",
+    "    i = (i + 1) % len(xyz.points)\n",
+    "    return xyz.points[i]\n",
+    "\n",
+    "particle = dt.PointParticle(position=next_position)\n",
+    "\n",
+    "gt_pip = dt.Value(torch.zeros((image_size, image_size, depth), dtype=float)) >> xyz\n",
+    "\n",
+    "noise_gaussian = dt.Gaussian(mu=0, sigma=lambda: torch.rand(size=(1,), device=device)*1e-4)\n",
+    "noise_poisson = poisson_noise_approx(noise_scale=lambda: torch.rand(size=(1,), device=device)*1e-2)\n",
+    "im_pip = optics(particle ^ num_points) >> noise_poisson >> noise_gaussian\n",
+    "\n",
+    "pip = (im_pip & gt_pip) >> dt.MoveAxis(2, 0)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6bff75c1",
+   "metadata": {},
+   "source": [
+    "An example simulation is visualized below."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "2f78ddd7",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.image.AxesImage at 0x14d957ba2d0>"
+      ]
+     },
+     "execution_count": 36,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 800x400 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "pip.update()\n",
+    "im, gt = pip.resolve()\n",
+    "\n",
+    "fig, axs = plt.subplots(1, 2, figsize=(8,4))\n",
+    "\n",
+    "axs[0].imshow(im[0].cpu().detach().numpy(), cmap='gray')\n",
+    "axs[1].imshow(gt.max(dim=0)[0]);"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "297f7ade",
+   "metadata": {},
+   "source": [
+    "The left image shows a synthetic microscopy image produced by the optical system with the current, untrained phase mask, while the right image displays the corresponding ground-truth particle positions projected onto the x–y plane."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "96f0641d",
+   "metadata": {},
+   "source": [
+    "## 2. Defining the CNN"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "3efed9b4",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import deeplay as dl\n",
+    "\n",
+    "class CNNConcat(dl.ConvolutionalNeuralNetwork):\n",
+    "    def forward(self, input):\n",
+    "        out = None\n",
+    "\n",
+    "        for idx, block in enumerate(self.blocks):\n",
+    "\n",
+    "            if idx == 0:\n",
+    "                out = block(input)\n",
+    "\n",
+    "            elif idx < len(self.blocks) - 1:\n",
+    "                features = torch.cat((out, input), dim=1)\n",
+    "                new_out = block(features)\n",
+    "                out = new_out + out\n",
+    "\n",
+    "            else:\n",
+    "                out = block(out)\n",
+    "\n",
+    "        return out"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "23a07b70",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<deeplay.module._MethodForwarder at 0x14d7fb9b010>"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "hidden_dim = 64\n",
+    "factor = 10.0\n",
+    "\n",
+    "CNN = CNNConcat(\n",
+    "    in_channels=1,\n",
+    "    hidden_channels=[hidden_dim + 1] * 8 + [hidden_dim],\n",
+    "    out_channels=depth,\n",
+    ")\n",
+    "\n",
+    "CNN[\"blocks\", \"0\"].prepend(\n",
+    "    dl.Layer(torch.nn.BatchNorm2d, num_features=1),\n",
+    "    name=\"initial_normalization\",\n",
+    ")\n",
+    "CNN[..., \"activation\"].all.configure(\n",
+    "    nn.LeakyReLU, negative_slope=0.2, inplace=True\n",
+    ")\n",
+    "CNN[...].isinstance(dl.Conv2dBlock).all.normalized(torch.nn.BatchNorm2d)\n",
+    "CNN[..., \"normalization\"].all.configure(num_features=hidden_dim)\n",
+    "\n",
+    "CNN[..., \"layer#:-1\"].configure(out_channels=hidden_dim)\n",
+    "\n",
+    "CNN[\"blocks\", \"9\", \"layer\"].configure(kernel_size=1, padding=0)\n",
+    "CNN[\"blocks\", \"9\"].remove(\"normalization\", allow_missing=True)\n",
+    "CNN[..., \"activation#-1\"].configure(nn.Hardtanh, min_val=0.0, max_val=factor)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "0231c5c3",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "CNNConcat(\n",
+      "  (blocks): LayerList(\n",
+      "    (0): Conv2dBlock(\n",
+      "      (initial_normalization): BatchNorm2d(1, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "      (layer): Conv2d(1, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
+      "      (activation): LeakyReLU(negative_slope=0.2, inplace=True)\n",
+      "      (normalization): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "    )\n",
+      "    (1-8): 8 x Conv2dBlock(\n",
+      "      (layer): Conv2d(65, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
+      "      (activation): LeakyReLU(negative_slope=0.2, inplace=True)\n",
+      "      (normalization): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "    )\n",
+      "    (9): Conv2dBlock(\n",
+      "      (layer): Conv2d(64, 30, kernel_size=(1, 1), stride=(1, 1))\n",
+      "      (activation): Hardtanh(min_val=0.0, max_val=10.0, inplace=True)\n",
+      "    )\n",
+      "  )\n",
+      ")\n"
+     ]
+    }
+   ],
+   "source": [
+    "cnn = CNN.create()\n",
+    "\n",
+    "cnn.to(device)\n",
+    "print(cnn)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "20cce959",
+   "metadata": {},
+   "source": [
+    "Check that the outputs of the cnn, and the targets have the same shape:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "4d3f53fc",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Preds: torch.Size([2, 30, 121, 121])\n",
+      "Target: torch.Size([2, 30, 121, 121])\n"
+     ]
+    }
+   ],
+   "source": [
+    "images, gt = [], []\n",
+    "for _ in range(2):\n",
+    "    pip.update()\n",
+    "    img, g = pip.resolve()\n",
+    "    images.append(img)\n",
+    "    gt.append(g)\n",
+    "\n",
+    "images = torch.stack(images)\n",
+    "gt = torch.stack(gt).to(device, dtype=torch.float32)\n",
+    "\n",
+    "preds = cnn(images)\n",
+    "print(\"Preds:\", preds.shape)\n",
+    "print(\"Target:\", gt.shape)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e8a87911",
+   "metadata": {},
+   "source": [
+    "### 2.2 Loss function of DeepSTORM 3D:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "5d49c805",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import torch.nn.functional as F\n",
+    "\n",
+    "def dice_loss(pred, target):\n",
+    "    \"\"\"\n",
+    "    This definition generalize to real valued pred and target vector.\n",
+    "    pred: tensor with first dimension as batch\n",
+    "    target: tensor with first dimension as batch\n",
+    "    \"\"\"\n",
+    "\n",
+    "    smooth = 1.\n",
+    "\n",
+    "    # have to use contiguous since they may from a torch.view op\n",
+    "    iflat = pred.contiguous().view(-1)\n",
+    "    tflat = target.contiguous().view(-1)\n",
+    "    intersection = (iflat * tflat).sum()\n",
+    "\n",
+    "    A_sum = torch.sum(iflat * iflat)\n",
+    "    B_sum = torch.sum(tflat * tflat)\n",
+    "\n",
+    "\n",
+    "    return 1 - ((2. * intersection + smooth) / (A_sum + B_sum + smooth))\n",
+    "\n",
+    "\n",
+    "# create a 3D gaussian kernel\n",
+    "def GaussianKernel(shape=(3, 3, 3), sigma=0.1, normfactor=1):\n",
+    "    \"\"\"\n",
+    "    3D gaussian mask - should give the same result as MATLAB's\n",
+    "    fspecial('gaussian',[shape],[sigma]) in 3D\n",
+    "    \"\"\"\n",
+    "    m, n, p = [(ss - 1.) / 2. for ss in shape]\n",
+    "    y, x, z = np.ogrid[-m:m + 1, -n:n + 1, -p:p + 1]\n",
+    "    h = np.exp(-(x * x + y * y + z * z) / (2 * sigma ** 2))\n",
+    "    # sigma_D, sigma_H, sigma_W = sigma\n",
+    "    # h = np.exp(-( (z**2)/(2*sigma_D**2) + (y**2)/(2*sigma_H**2) + (x**2)/(2*sigma_W**2) ))\n",
+    "    h[h < np.finfo(h.dtype).eps * h.max()] = 0\n",
+    "    \"\"\"\n",
+    "    sumh = h.sum()\n",
+    "    if sumh != 0:\n",
+    "        h /= sumh\n",
+    "        h = h * normfactor\n",
+    "    \"\"\"\n",
+    "    maxh = h.max()\n",
+    "    if maxh != 0:\n",
+    "        h /= maxh\n",
+    "        h = h * normfactor\n",
+    "    h = torch.from_numpy(h).type(torch.FloatTensor)\n",
+    "    h = h.unsqueeze(0)\n",
+    "    h = h.unsqueeze(1)\n",
+    "    return h\n",
+    "\n",
+    "\n",
+    "# define the 3D extended loss function from DeepSTORM\n",
+    "class KDE_loss3D(nn.Module):\n",
+    "    def __init__(self, factor, device):\n",
+    "        super(KDE_loss3D, self).__init__()\n",
+    "        self.kernel = GaussianKernel().to(device)\n",
+    "        self.factor = factor\n",
+    "\n",
+    "    def forward(self, pred_bol, target_bol):\n",
+    "\n",
+    "        # extract kernel dimensions\n",
+    "        N, C, D, H, W = self.kernel.size()\n",
+    "        \n",
+    "        # extend prediction and target to have a single channel\n",
+    "        target_bol = target_bol.unsqueeze(1)\n",
+    "        pred_bol = pred_bol.unsqueeze(1)\n",
+    "\n",
+    "        # KDE for both input and ground truth spikes\n",
+    "        Din = F.conv3d(pred_bol, self.kernel, padding=(int(np.round((D - 1) / 2)), 0, 0))\n",
+    "        Dtar = F.conv3d(target_bol, self.factor*self.kernel, padding=(int(np.round((D - 1) / 2)), 0, 0))\n",
+    "\n",
+    "        # kde loss\n",
+    "        kde_loss = nn.MSELoss()(Din, Dtar)      # add a higher weight for the z-loss?\n",
+    "        \n",
+    "        # final loss\n",
+    "        final_loss = kde_loss + dice_loss(pred_bol/self.factor, target_bol)\n",
+    "\n",
+    "        return final_loss"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "41248a71",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# loss function\n",
+    "loss_fn = KDE_loss3D(factor=factor, device=device)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1863bbeb",
+   "metadata": {},
+   "source": [
+    "### 2.3 Optimizer\n",
+    "For both the CNN and the phase mask"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "2b4ca296",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "optimizer = torch.optim.Adam(\n",
+    "    list(phase_mask.parameters()) + list(cnn.parameters()),\n",
+    "    lr=1e-3\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1c27be15",
+   "metadata": {},
+   "source": [
+    "## 3. Training the CNN and the phase mask"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "85f1f354",
+   "metadata": {},
+   "source": [
+    "### 3.1 Visualize the phase mask before training"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "63aabfd0",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.0 0.0 0.0\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "phase_mask_before = phase_mask.phase.cpu().detach().clone()\n",
+    "print(phase_mask.phase.min().item(), phase_mask.phase.max().item(), phase_mask.phase.mean().item())\n",
+    "\n",
+    "plt.imshow(phase_mask_before, cmap='coolwarm')\n",
+    "plt.colorbar()\n",
+    "plt.show();"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f70f4a2c",
+   "metadata": {},
+   "source": [
+    "### 3.2 Training the phase mask and CNN together using a custom training loop\n",
+    "\n",
+    "Explain here that we need to update the simulation pipline each epoch to be able to update the phase mask during training."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "9b1f6b57",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 0, Loss: 1.9283\n",
+      "Epoch 1, Loss: 1.7205\n",
+      "Epoch 2, Loss: 1.3499\n",
+      "Epoch 3, Loss: 1.2026\n",
+      "Epoch 4, Loss: 1.1495\n",
+      "Epoch 5, Loss: 1.1327\n",
+      "Epoch 6, Loss: 1.0827\n",
+      "Epoch 7, Loss: 1.0692\n",
+      "Epoch 8, Loss: 1.0659\n",
+      "Epoch 9, Loss: 1.0489\n",
+      "Epoch 10, Loss: 1.0335\n",
+      "Epoch 11, Loss: 1.0276\n",
+      "Epoch 12, Loss: 1.0265\n",
+      "Epoch 13, Loss: 1.0171\n",
+      "Epoch 14, Loss: 1.0123\n",
+      "Epoch 15, Loss: 1.0201\n",
+      "Epoch 16, Loss: 0.9983\n",
+      "Epoch 17, Loss: 0.9934\n",
+      "Epoch 18, Loss: 0.9929\n",
+      "Epoch 19, Loss: 0.9874\n",
+      "Epoch 20, Loss: 0.9995\n",
+      "Epoch 21, Loss: 0.9588\n",
+      "Epoch 22, Loss: 0.9733\n",
+      "Epoch 23, Loss: 0.9641\n",
+      "Epoch 24, Loss: 0.9486\n",
+      "Epoch 25, Loss: 0.9589\n",
+      "Epoch 26, Loss: 0.9637\n",
+      "Epoch 27, Loss: 0.9400\n",
+      "Epoch 28, Loss: 0.9528\n",
+      "Epoch 29, Loss: 0.9425\n",
+      "Epoch 30, Loss: 0.9227\n",
+      "Epoch 31, Loss: 0.9320\n",
+      "Epoch 32, Loss: 0.9469\n",
+      "Epoch 33, Loss: 0.9106\n",
+      "Epoch 34, Loss: 0.8920\n",
+      "Epoch 35, Loss: 0.9343\n",
+      "Epoch 36, Loss: 0.9100\n",
+      "Epoch 37, Loss: 0.9160\n",
+      "Epoch 38, Loss: 0.9023\n",
+      "Epoch 39, Loss: 0.9026\n",
+      "Epoch 40, Loss: 0.8989\n",
+      "Epoch 41, Loss: 0.8820\n",
+      "Epoch 42, Loss: 0.8888\n",
+      "Epoch 43, Loss: 0.9189\n",
+      "Epoch 44, Loss: 0.9067\n",
+      "Epoch 45, Loss: 0.8894\n",
+      "Epoch 46, Loss: 0.9028\n",
+      "Epoch 47, Loss: 0.9017\n",
+      "Epoch 48, Loss: 0.8821\n",
+      "Epoch 49, Loss: 0.8891\n",
+      "Epoch 50, Loss: 0.9007\n",
+      "Epoch 51, Loss: 0.8982\n",
+      "Epoch 52, Loss: 0.8801\n",
+      "Epoch 53, Loss: 0.8718\n",
+      "Epoch 54, Loss: 0.8688\n",
+      "Epoch 55, Loss: 0.8843\n",
+      "Epoch 56, Loss: 0.8716\n",
+      "Epoch 57, Loss: 0.8860\n",
+      "Epoch 58, Loss: 0.8751\n",
+      "Epoch 59, Loss: 0.8710\n",
+      "Epoch 60, Loss: 0.8777\n",
+      "Epoch 61, Loss: 0.8957\n",
+      "Epoch 62, Loss: 0.8601\n",
+      "Epoch 63, Loss: 0.8530\n",
+      "Epoch 64, Loss: 0.8600\n",
+      "Epoch 65, Loss: 0.8509\n",
+      "Epoch 66, Loss: 0.8728\n",
+      "Epoch 67, Loss: 0.8496\n",
+      "Epoch 68, Loss: 0.8803\n",
+      "Epoch 69, Loss: 0.8652\n",
+      "Epoch 70, Loss: 0.8378\n",
+      "Epoch 71, Loss: 0.8362\n",
+      "Epoch 72, Loss: 0.8377\n",
+      "Epoch 73, Loss: 0.8511\n",
+      "Epoch 74, Loss: 0.8655\n",
+      "Epoch 75, Loss: 0.8495\n",
+      "Epoch 76, Loss: 0.8330\n",
+      "Epoch 77, Loss: 0.8402\n",
+      "Epoch 78, Loss: 0.8403\n",
+      "Epoch 79, Loss: 0.8552\n",
+      "Epoch 80, Loss: 0.8463\n",
+      "Epoch 81, Loss: 0.8079\n",
+      "Epoch 82, Loss: 0.8193\n",
+      "Epoch 83, Loss: 0.8349\n",
+      "Epoch 84, Loss: 0.8005\n",
+      "Epoch 85, Loss: 0.8290\n",
+      "Epoch 86, Loss: 0.8408\n",
+      "Epoch 87, Loss: 0.8498\n",
+      "Epoch 88, Loss: 0.8380\n",
+      "Epoch 89, Loss: 0.8441\n",
+      "Epoch 90, Loss: 0.8166\n",
+      "Epoch 91, Loss: 0.8379\n",
+      "Epoch 92, Loss: 0.8180\n",
+      "Epoch 93, Loss: 0.7870\n",
+      "Epoch 94, Loss: 0.8085\n",
+      "Epoch 95, Loss: 0.8348\n",
+      "Epoch 96, Loss: 0.8062\n",
+      "Epoch 97, Loss: 0.8489\n",
+      "Epoch 98, Loss: 0.7888\n",
+      "Epoch 99, Loss: 0.7850\n",
+      "Epoch 100, Loss: 0.8022\n",
+      "Epoch 101, Loss: 0.8360\n",
+      "Epoch 102, Loss: 0.8116\n",
+      "Epoch 103, Loss: 0.7769\n",
+      "Epoch 104, Loss: 0.7958\n",
+      "Epoch 105, Loss: 0.8121\n",
+      "Epoch 106, Loss: 0.8064\n",
+      "Epoch 107, Loss: 0.8255\n",
+      "Epoch 108, Loss: 0.8106\n",
+      "Epoch 109, Loss: 0.7920\n",
+      "Epoch 110, Loss: 0.8130\n",
+      "Epoch 111, Loss: 0.8069\n",
+      "Epoch 112, Loss: 0.8091\n",
+      "Epoch 113, Loss: 0.7870\n",
+      "Epoch 114, Loss: 0.8037\n",
+      "Epoch 115, Loss: 0.7794\n",
+      "Epoch 116, Loss: 0.8060\n",
+      "Epoch 117, Loss: 0.7925\n",
+      "Epoch 118, Loss: 0.7938\n",
+      "Epoch 119, Loss: 0.8147\n",
+      "Epoch 120, Loss: 0.7740\n",
+      "Epoch 121, Loss: 0.7659\n",
+      "Epoch 122, Loss: 0.7724\n",
+      "Epoch 123, Loss: 0.7765\n",
+      "Epoch 124, Loss: 0.7762\n",
+      "Epoch 125, Loss: 0.7673\n",
+      "Epoch 126, Loss: 0.7734\n",
+      "Epoch 127, Loss: 0.7593\n",
+      "Epoch 128, Loss: 0.7334\n",
+      "Epoch 129, Loss: 0.7766\n",
+      "Epoch 130, Loss: 0.7837\n",
+      "Epoch 131, Loss: 0.8115\n",
+      "Epoch 132, Loss: 0.7623\n",
+      "Epoch 133, Loss: 0.7919\n",
+      "Epoch 134, Loss: 0.8100\n",
+      "Epoch 135, Loss: 0.7778\n",
+      "Epoch 136, Loss: 0.7437\n",
+      "Epoch 137, Loss: 0.7848\n",
+      "Epoch 138, Loss: 0.7620\n",
+      "Epoch 139, Loss: 0.7534\n",
+      "Epoch 140, Loss: 0.7592\n",
+      "Epoch 141, Loss: 0.7886\n",
+      "Epoch 142, Loss: 0.7485\n",
+      "Epoch 143, Loss: 0.7732\n",
+      "Epoch 144, Loss: 0.7651\n",
+      "Epoch 145, Loss: 0.7808\n",
+      "Epoch 146, Loss: 0.7783\n",
+      "Epoch 147, Loss: 0.7766\n",
+      "Epoch 148, Loss: 0.7530\n",
+      "Epoch 149, Loss: 0.7734\n",
+      "Epoch 150, Loss: 0.7772\n",
+      "Epoch 151, Loss: 0.7636\n",
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+      "Epoch 601, Loss: 0.5279\n",
+      "Epoch 602, Loss: 0.5936\n",
+      "Epoch 603, Loss: 0.5490\n",
+      "Epoch 604, Loss: 0.5520\n",
+      "Epoch 605, Loss: 0.4972\n",
+      "Epoch 606, Loss: 0.5708\n",
+      "Epoch 607, Loss: 0.5194\n",
+      "Epoch 608, Loss: 0.5357\n",
+      "Epoch 609, Loss: 0.5438\n",
+      "Epoch 610, Loss: 0.5325\n",
+      "Epoch 611, Loss: 0.5259\n",
+      "Epoch 612, Loss: 0.5158\n",
+      "Epoch 613, Loss: 0.5233\n",
+      "Epoch 614, Loss: 0.5266\n",
+      "Epoch 615, Loss: 0.5230\n",
+      "Epoch 616, Loss: 0.5733\n",
+      "Epoch 617, Loss: 0.5418\n",
+      "Epoch 618, Loss: 0.5151\n",
+      "Epoch 619, Loss: 0.5148\n",
+      "Epoch 620, Loss: 0.5096\n",
+      "Epoch 621, Loss: 0.5397\n",
+      "Epoch 622, Loss: 0.5035\n",
+      "Epoch 623, Loss: 0.4989\n",
+      "Epoch 624, Loss: 0.5165\n",
+      "Epoch 625, Loss: 0.5273\n",
+      "Epoch 626, Loss: 0.5048\n",
+      "Epoch 627, Loss: 0.5103\n",
+      "Epoch 628, Loss: 0.5575\n",
+      "Epoch 629, Loss: 0.5051\n",
+      "Epoch 630, Loss: 0.5586\n",
+      "Epoch 631, Loss: 0.5027\n",
+      "Epoch 632, Loss: 0.5070\n",
+      "Epoch 633, Loss: 0.5687\n",
+      "Epoch 634, Loss: 0.4824\n",
+      "Epoch 635, Loss: 0.4875\n",
+      "Epoch 636, Loss: 0.5441\n",
+      "Epoch 637, Loss: 0.5191\n",
+      "Epoch 638, Loss: 0.5613\n",
+      "Epoch 639, Loss: 0.5105\n",
+      "Epoch 640, Loss: 0.5284\n",
+      "Epoch 641, Loss: 0.5680\n",
+      "Epoch 642, Loss: 0.5296\n",
+      "Epoch 643, Loss: 0.4783\n",
+      "Epoch 644, Loss: 0.5202\n",
+      "Epoch 645, Loss: 0.4981\n",
+      "Epoch 646, Loss: 0.5049\n",
+      "Epoch 647, Loss: 0.5651\n",
+      "Epoch 648, Loss: 0.5349\n",
+      "Epoch 649, Loss: 0.5347\n",
+      "Epoch 650, Loss: 0.5380\n",
+      "Epoch 651, Loss: 0.4905\n",
+      "Epoch 652, Loss: 0.5636\n",
+      "Epoch 653, Loss: 0.5391\n",
+      "Epoch 654, Loss: 0.5202\n",
+      "Epoch 655, Loss: 0.5049\n",
+      "Epoch 656, Loss: 0.5595\n",
+      "Epoch 657, Loss: 0.5057\n",
+      "Epoch 658, Loss: 0.5100\n",
+      "Epoch 659, Loss: 0.5162\n",
+      "Epoch 660, Loss: 0.5225\n",
+      "Epoch 661, Loss: 0.5555\n",
+      "Epoch 662, Loss: 0.5671\n",
+      "Epoch 663, Loss: 0.5319\n",
+      "Epoch 664, Loss: 0.4989\n",
+      "Epoch 665, Loss: 0.5460\n",
+      "Epoch 666, Loss: 0.5509\n",
+      "Epoch 667, Loss: 0.5125\n",
+      "Epoch 668, Loss: 0.5565\n",
+      "Epoch 669, Loss: 0.5537\n",
+      "Epoch 670, Loss: 0.5469\n",
+      "Epoch 671, Loss: 0.5121\n",
+      "Epoch 672, Loss: 0.5427\n",
+      "Epoch 673, Loss: 0.5394\n",
+      "Epoch 674, Loss: 0.5276\n",
+      "Epoch 675, Loss: 0.5489\n",
+      "Epoch 676, Loss: 0.5217\n",
+      "Epoch 677, Loss: 0.5069\n",
+      "Epoch 678, Loss: 0.5532\n",
+      "Epoch 679, Loss: 0.5098\n",
+      "Epoch 680, Loss: 0.5094\n",
+      "Epoch 681, Loss: 0.5027\n",
+      "Epoch 682, Loss: 0.5267\n",
+      "Epoch 683, Loss: 0.5167\n",
+      "Epoch 684, Loss: 0.5279\n",
+      "Epoch 685, Loss: 0.5079\n",
+      "Epoch 686, Loss: 0.5189\n",
+      "Epoch 687, Loss: 0.5464\n",
+      "Epoch 688, Loss: 0.5145\n",
+      "Epoch 689, Loss: 0.5414\n",
+      "Epoch 690, Loss: 0.5108\n",
+      "Epoch 691, Loss: 0.5209\n",
+      "Epoch 692, Loss: 0.5189\n",
+      "Epoch 693, Loss: 0.5058\n",
+      "Epoch 694, Loss: 0.5154\n",
+      "Epoch 695, Loss: 0.4889\n",
+      "Epoch 696, Loss: 0.5295\n",
+      "Epoch 697, Loss: 0.4929\n",
+      "Epoch 698, Loss: 0.5361\n",
+      "Epoch 699, Loss: 0.5277\n",
+      "Epoch 700, Loss: 0.5346\n",
+      "Epoch 701, Loss: 0.5523\n",
+      "Epoch 702, Loss: 0.4949\n",
+      "Epoch 703, Loss: 0.5135\n",
+      "Epoch 704, Loss: 0.5193\n",
+      "Epoch 705, Loss: 0.4866\n",
+      "Epoch 706, Loss: 0.5508\n",
+      "Epoch 707, Loss: 0.5332\n",
+      "Epoch 708, Loss: 0.5190\n",
+      "Epoch 709, Loss: 0.4864\n",
+      "Epoch 710, Loss: 0.5120\n",
+      "Epoch 711, Loss: 0.5252\n",
+      "Epoch 712, Loss: 0.4953\n",
+      "Epoch 713, Loss: 0.4954\n",
+      "Epoch 714, Loss: 0.5320\n",
+      "Epoch 715, Loss: 0.5072\n",
+      "Epoch 716, Loss: 0.5045\n",
+      "Epoch 717, Loss: 0.5246\n",
+      "Epoch 718, Loss: 0.5353\n",
+      "Epoch 719, Loss: 0.5120\n",
+      "Epoch 720, Loss: 0.5559\n",
+      "Epoch 721, Loss: 0.5356\n",
+      "Epoch 722, Loss: 0.4803\n",
+      "Epoch 723, Loss: 0.4768\n",
+      "Epoch 724, Loss: 0.5181\n",
+      "Epoch 725, Loss: 0.4775\n",
+      "Epoch 726, Loss: 0.5055\n",
+      "Epoch 727, Loss: 0.5116\n",
+      "Epoch 728, Loss: 0.5273\n",
+      "Epoch 729, Loss: 0.5311\n",
+      "Epoch 730, Loss: 0.4821\n",
+      "Epoch 731, Loss: 0.5078\n",
+      "Epoch 732, Loss: 0.5331\n",
+      "Epoch 733, Loss: 0.4970\n",
+      "Epoch 734, Loss: 0.5237\n",
+      "Epoch 735, Loss: 0.5376\n",
+      "Epoch 736, Loss: 0.5324\n",
+      "Epoch 737, Loss: 0.4970\n",
+      "Epoch 738, Loss: 0.5135\n",
+      "Epoch 739, Loss: 0.5140\n",
+      "Epoch 740, Loss: 0.5082\n",
+      "Epoch 741, Loss: 0.4960\n",
+      "Epoch 742, Loss: 0.5110\n",
+      "Epoch 743, Loss: 0.5583\n",
+      "Epoch 744, Loss: 0.5317\n",
+      "Epoch 745, Loss: 0.5353\n",
+      "Epoch 746, Loss: 0.5690\n",
+      "Epoch 747, Loss: 0.5154\n",
+      "Epoch 748, Loss: 0.4970\n",
+      "Epoch 749, Loss: 0.4820\n",
+      "Epoch 750, Loss: 0.4635\n",
+      "Epoch 751, Loss: 0.4874\n",
+      "Epoch 752, Loss: 0.5303\n",
+      "Epoch 753, Loss: 0.5491\n",
+      "Epoch 754, Loss: 0.5054\n",
+      "Epoch 755, Loss: 0.5597\n",
+      "Epoch 756, Loss: 0.5236\n",
+      "Epoch 757, Loss: 0.5229\n",
+      "Epoch 758, Loss: 0.5286\n",
+      "Epoch 759, Loss: 0.4837\n",
+      "Epoch 760, Loss: 0.4697\n",
+      "Epoch 761, Loss: 0.5153\n",
+      "Epoch 762, Loss: 0.4904\n",
+      "Epoch 763, Loss: 0.5146\n",
+      "Epoch 764, Loss: 0.5074\n",
+      "Epoch 765, Loss: 0.5424\n",
+      "Epoch 766, Loss: 0.5609\n",
+      "Epoch 767, Loss: 0.5122\n",
+      "Epoch 768, Loss: 0.5238\n",
+      "Epoch 769, Loss: 0.5219\n",
+      "Epoch 770, Loss: 0.4919\n",
+      "Epoch 771, Loss: 0.5432\n",
+      "Epoch 772, Loss: 0.4881\n",
+      "Epoch 773, Loss: 0.5047\n",
+      "Epoch 774, Loss: 0.5112\n",
+      "Epoch 775, Loss: 0.5379\n",
+      "Epoch 776, Loss: 0.5065\n",
+      "Epoch 777, Loss: 0.5177\n",
+      "Epoch 778, Loss: 0.4848\n",
+      "Epoch 779, Loss: 0.5123\n",
+      "Epoch 780, Loss: 0.4648\n",
+      "Epoch 781, Loss: 0.4958\n",
+      "Epoch 782, Loss: 0.5393\n",
+      "Epoch 783, Loss: 0.5433\n",
+      "Epoch 784, Loss: 0.5222\n",
+      "Epoch 785, Loss: 0.4890\n",
+      "Epoch 786, Loss: 0.5392\n",
+      "Epoch 787, Loss: 0.4808\n",
+      "Epoch 788, Loss: 0.5293\n",
+      "Epoch 789, Loss: 0.4818\n",
+      "Epoch 790, Loss: 0.5057\n",
+      "Epoch 791, Loss: 0.5204\n",
+      "Epoch 792, Loss: 0.5090\n",
+      "Epoch 793, Loss: 0.5379\n",
+      "Epoch 794, Loss: 0.5129\n",
+      "Epoch 795, Loss: 0.4998\n",
+      "Epoch 796, Loss: 0.4808\n",
+      "Epoch 797, Loss: 0.5078\n",
+      "Epoch 798, Loss: 0.5000\n",
+      "Epoch 799, Loss: 0.5238\n",
+      "Epoch 800, Loss: 0.5415\n",
+      "Epoch 801, Loss: 0.5358\n",
+      "Epoch 802, Loss: 0.5027\n",
+      "Epoch 803, Loss: 0.4896\n",
+      "Epoch 804, Loss: 0.5151\n",
+      "Epoch 805, Loss: 0.4835\n",
+      "Epoch 806, Loss: 0.5181\n",
+      "Epoch 807, Loss: 0.5464\n",
+      "Epoch 808, Loss: 0.5051\n",
+      "Epoch 809, Loss: 0.4726\n",
+      "Epoch 810, Loss: 0.5130\n",
+      "Epoch 811, Loss: 0.4856\n",
+      "Epoch 812, Loss: 0.5181\n",
+      "Epoch 813, Loss: 0.5169\n",
+      "Epoch 814, Loss: 0.4924\n",
+      "Epoch 815, Loss: 0.5174\n",
+      "Epoch 816, Loss: 0.4822\n",
+      "Epoch 817, Loss: 0.4842\n",
+      "Epoch 818, Loss: 0.5177\n",
+      "Epoch 819, Loss: 0.5051\n",
+      "Epoch 820, Loss: 0.4946\n",
+      "Epoch 821, Loss: 0.5109\n",
+      "Epoch 822, Loss: 0.5494\n",
+      "Epoch 823, Loss: 0.4975\n",
+      "Epoch 824, Loss: 0.5279\n",
+      "Epoch 825, Loss: 0.5244\n",
+      "Epoch 826, Loss: 0.4914\n",
+      "Epoch 827, Loss: 0.5223\n",
+      "Epoch 828, Loss: 0.4992\n",
+      "Epoch 829, Loss: 0.5017\n",
+      "Epoch 830, Loss: 0.5424\n",
+      "Epoch 831, Loss: 0.4961\n",
+      "Epoch 832, Loss: 0.4788\n",
+      "Epoch 833, Loss: 0.5026\n",
+      "Epoch 834, Loss: 0.5438\n",
+      "Epoch 835, Loss: 0.5202\n",
+      "Epoch 836, Loss: 0.5085\n",
+      "Epoch 837, Loss: 0.5242\n",
+      "Epoch 838, Loss: 0.5260\n",
+      "Epoch 839, Loss: 0.4871\n",
+      "Epoch 840, Loss: 0.4726\n",
+      "Epoch 841, Loss: 0.4898\n",
+      "Epoch 842, Loss: 0.4946\n",
+      "Epoch 843, Loss: 0.5212\n",
+      "Epoch 844, Loss: 0.4928\n",
+      "Epoch 845, Loss: 0.5125\n",
+      "Epoch 846, Loss: 0.5102\n",
+      "Epoch 847, Loss: 0.4899\n",
+      "Epoch 848, Loss: 0.5224\n",
+      "Epoch 849, Loss: 0.5312\n",
+      "Epoch 850, Loss: 0.4765\n",
+      "Epoch 851, Loss: 0.4782\n",
+      "Epoch 852, Loss: 0.5528\n",
+      "Epoch 853, Loss: 0.4821\n",
+      "Epoch 854, Loss: 0.5406\n",
+      "Epoch 855, Loss: 0.5050\n",
+      "Epoch 856, Loss: 0.4995\n",
+      "Epoch 857, Loss: 0.5158\n",
+      "Epoch 858, Loss: 0.5133\n",
+      "Epoch 859, Loss: 0.4855\n",
+      "Epoch 860, Loss: 0.5315\n",
+      "Epoch 861, Loss: 0.4812\n",
+      "Epoch 862, Loss: 0.5116\n",
+      "Epoch 863, Loss: 0.5145\n",
+      "Epoch 864, Loss: 0.5180\n",
+      "Epoch 865, Loss: 0.5110\n",
+      "Epoch 866, Loss: 0.4838\n",
+      "Epoch 867, Loss: 0.4988\n",
+      "Epoch 868, Loss: 0.5254\n",
+      "Epoch 869, Loss: 0.4973\n",
+      "Epoch 870, Loss: 0.5015\n",
+      "Epoch 871, Loss: 0.4901\n",
+      "Epoch 872, Loss: 0.4790\n",
+      "Epoch 873, Loss: 0.4788\n",
+      "Epoch 874, Loss: 0.5275\n",
+      "Epoch 875, Loss: 0.4815\n",
+      "Epoch 876, Loss: 0.4785\n",
+      "Epoch 877, Loss: 0.5081\n",
+      "Epoch 878, Loss: 0.4949\n",
+      "Epoch 879, Loss: 0.5140\n",
+      "Epoch 880, Loss: 0.4793\n",
+      "Epoch 881, Loss: 0.5153\n",
+      "Epoch 882, Loss: 0.4929\n",
+      "Epoch 883, Loss: 0.5017\n",
+      "Epoch 884, Loss: 0.4800\n",
+      "Epoch 885, Loss: 0.5018\n",
+      "Epoch 886, Loss: 0.4817\n",
+      "Epoch 887, Loss: 0.5007\n",
+      "Epoch 888, Loss: 0.4425\n",
+      "Epoch 889, Loss: 0.4949\n",
+      "Epoch 890, Loss: 0.5041\n",
+      "Epoch 891, Loss: 0.4997\n",
+      "Epoch 892, Loss: 0.4534\n",
+      "Epoch 893, Loss: 0.4924\n",
+      "Epoch 894, Loss: 0.4921\n",
+      "Epoch 895, Loss: 0.5070\n",
+      "Epoch 896, Loss: 0.5152\n",
+      "Epoch 897, Loss: 0.5251\n",
+      "Epoch 898, Loss: 0.5209\n",
+      "Epoch 899, Loss: 0.4941\n",
+      "Epoch 900, Loss: 0.4856\n",
+      "Epoch 901, Loss: 0.4965\n",
+      "Epoch 902, Loss: 0.5515\n",
+      "Epoch 903, Loss: 0.4787\n",
+      "Epoch 904, Loss: 0.4554\n",
+      "Epoch 905, Loss: 0.5123\n",
+      "Epoch 906, Loss: 0.4843\n",
+      "Epoch 907, Loss: 0.5164\n",
+      "Epoch 908, Loss: 0.5029\n",
+      "Epoch 909, Loss: 0.4693\n",
+      "Epoch 910, Loss: 0.4779\n",
+      "Epoch 911, Loss: 0.4765\n",
+      "Epoch 912, Loss: 0.4975\n",
+      "Epoch 913, Loss: 0.4881\n",
+      "Epoch 914, Loss: 0.5065\n",
+      "Epoch 915, Loss: 0.5175\n",
+      "Epoch 916, Loss: 0.5016\n",
+      "Epoch 917, Loss: 0.5202\n",
+      "Epoch 918, Loss: 0.5361\n",
+      "Epoch 919, Loss: 0.5246\n",
+      "Epoch 920, Loss: 0.5153\n",
+      "Epoch 921, Loss: 0.5226\n",
+      "Epoch 922, Loss: 0.5060\n",
+      "Epoch 923, Loss: 0.5166\n",
+      "Epoch 924, Loss: 0.4462\n",
+      "Epoch 925, Loss: 0.5218\n",
+      "Epoch 926, Loss: 0.4523\n",
+      "Epoch 927, Loss: 0.5080\n",
+      "Epoch 928, Loss: 0.4797\n",
+      "Epoch 929, Loss: 0.5042\n",
+      "Epoch 930, Loss: 0.4846\n",
+      "Epoch 931, Loss: 0.5051\n",
+      "Epoch 932, Loss: 0.4631\n",
+      "Epoch 933, Loss: 0.4816\n",
+      "Epoch 934, Loss: 0.4850\n",
+      "Epoch 935, Loss: 0.5260\n",
+      "Epoch 936, Loss: 0.4795\n",
+      "Epoch 937, Loss: 0.4939\n",
+      "Epoch 938, Loss: 0.5185\n",
+      "Epoch 939, Loss: 0.4924\n",
+      "Epoch 940, Loss: 0.4790\n",
+      "Epoch 941, Loss: 0.5052\n",
+      "Epoch 942, Loss: 0.5417\n",
+      "Epoch 943, Loss: 0.5205\n",
+      "Epoch 944, Loss: 0.4921\n",
+      "Epoch 945, Loss: 0.4876\n",
+      "Epoch 946, Loss: 0.4980\n",
+      "Epoch 947, Loss: 0.5026\n",
+      "Epoch 948, Loss: 0.5151\n",
+      "Epoch 949, Loss: 0.5361\n",
+      "Epoch 950, Loss: 0.4674\n",
+      "Epoch 951, Loss: 0.4961\n",
+      "Epoch 952, Loss: 0.5211\n",
+      "Epoch 953, Loss: 0.4850\n",
+      "Epoch 954, Loss: 0.4899\n",
+      "Epoch 955, Loss: 0.4905\n",
+      "Epoch 956, Loss: 0.4798\n",
+      "Epoch 957, Loss: 0.4927\n",
+      "Epoch 958, Loss: 0.5028\n",
+      "Epoch 959, Loss: 0.5152\n",
+      "Epoch 960, Loss: 0.4982\n",
+      "Epoch 961, Loss: 0.4738\n",
+      "Epoch 962, Loss: 0.4909\n",
+      "Epoch 963, Loss: 0.4891\n",
+      "Epoch 964, Loss: 0.5214\n",
+      "Epoch 965, Loss: 0.4868\n",
+      "Epoch 966, Loss: 0.5030\n",
+      "Epoch 967, Loss: 0.4762\n",
+      "Epoch 968, Loss: 0.4669\n",
+      "Epoch 969, Loss: 0.5154\n",
+      "Epoch 970, Loss: 0.4756\n",
+      "Epoch 971, Loss: 0.5457\n",
+      "Epoch 972, Loss: 0.4996\n",
+      "Epoch 973, Loss: 0.5119\n",
+      "Epoch 974, Loss: 0.5633\n",
+      "Epoch 975, Loss: 0.5013\n",
+      "Epoch 976, Loss: 0.4668\n",
+      "Epoch 977, Loss: 0.5063\n",
+      "Epoch 978, Loss: 0.5091\n",
+      "Epoch 979, Loss: 0.5004\n",
+      "Epoch 980, Loss: 0.5031\n",
+      "Epoch 981, Loss: 0.4968\n",
+      "Epoch 982, Loss: 0.4896\n",
+      "Epoch 983, Loss: 0.4759\n",
+      "Epoch 984, Loss: 0.5004\n",
+      "Epoch 985, Loss: 0.5018\n",
+      "Epoch 986, Loss: 0.4738\n",
+      "Epoch 987, Loss: 0.5177\n",
+      "Epoch 988, Loss: 0.5038\n",
+      "Epoch 989, Loss: 0.5106\n",
+      "Epoch 990, Loss: 0.4671\n",
+      "Epoch 991, Loss: 0.4986\n",
+      "Epoch 992, Loss: 0.4965\n",
+      "Epoch 993, Loss: 0.5082\n",
+      "Epoch 994, Loss: 0.5075\n",
+      "Epoch 995, Loss: 0.5034\n",
+      "Epoch 996, Loss: 0.4936\n",
+      "Epoch 997, Loss: 0.4910\n",
+      "Epoch 998, Loss: 0.5078\n",
+      "Epoch 999, Loss: 0.4805\n"
+     ]
+    }
+   ],
+   "source": [
+    "num_epochs = 1000\n",
+    "batch_size = 10\n",
+    "loss_history = []\n",
+    "\n",
+    "for epoch in range(num_epochs):\n",
+    "    cnn.train()\n",
+    "\n",
+    "    images, gt = [], []\n",
+    "    for _ in range(batch_size):\n",
+    "        pip.update()\n",
+    "        img, g = pip.resolve()\n",
+    "        images.append(img)\n",
+    "        gt.append(g)\n",
+    "\n",
+    "    images = torch.stack(images)\n",
+    "    gt = torch.stack(gt).to(device, dtype=torch.float32)\n",
+    "\n",
+    "    optimizer.zero_grad(set_to_none=True)\n",
+    "    preds = cnn(images)\n",
+    "    loss = loss_fn(preds, gt)\n",
+    "    loss.backward()\n",
+    "    optimizer.step()\n",
+    "\n",
+    "    loss_history.append(loss.detach().cpu().item())\n",
+    "\n",
+    "    print(f\"Epoch {epoch}, Loss: {loss.detach().cpu().item():.4f}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "a5d574c7",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 1500x500 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize=(15, 5))\n",
+    "plt.plot(range(len(loss_history)), loss_history, label='Training Loss')\n",
+    "plt.yscale('log')\n",
+    "plt.xlabel('Epoch')\n",
+    "plt.ylabel('Loss')\n",
+    "plt.title('Training Loss over Time')\n",
+    "plt.grid(True)\n",
+    "plt.legend()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8ba5c7eb",
+   "metadata": {},
+   "source": [
+    "### 3.3 Visualize the phase mask after training"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "id": "8df31c19",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "-0.795109748840332 0.9175896644592285 0.0021575456485152245\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "phase_mask_after = phase_mask.phase.cpu().detach().clone()\n",
+    "print(phase_mask.phase.min().item(), phase_mask.phase.max().item(), phase_mask.phase.mean().item())\n",
+    "\n",
+    "plt.imshow(phase_mask_after, cmap='coolwarm')\n",
+    "plt.colorbar()\n",
+    "plt.show();"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5be6c7c1",
+   "metadata": {},
+   "source": [
+    "## 4. Post-processing\n",
+    "Copied from DeepSTORM3D"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "id": "7566e960",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from torch.nn import Module, MaxPool3d, ConstantPad3d, MaxPool2d\n",
+    "from torch.nn.functional import conv3d\n",
+    "\n",
+    "# convert gpu tensors to numpy\n",
+    "def tensor_to_np(x):\n",
+    "    return np.squeeze(x.detach().cpu().numpy())\n",
+    "\n",
+    "# post-processing module: thresholding and local maxima finding\n",
+    "class Postprocess(Module):\n",
+    "    def __init__(self, device, thresh=40, radius=4, keep_singlez=False):\n",
+    "        \"\"\"\n",
+    "        Parameters:\n",
+    "        -----------\n",
+    "        device : torch device\n",
+    "            GPU/CPU device to run on\n",
+    "        thresh : float\n",
+    "            Minimum confidence threshold for candidate detections\n",
+    "        radius : int\n",
+    "            Neighborhood radius (defines local region size = 2r+1)\n",
+    "        keep_singlez : bool\n",
+    "            If True, keep only one z-maximum per (x, y) location\n",
+    "        \"\"\"\n",
+    "        super().__init__()\n",
+    "        self.device = device\n",
+    "        self.thresh = thresh\n",
+    "        self.r = radius\n",
+    "        self.keep_singlez = keep_singlez\n",
+    "\n",
+    "        # Max pooling layers for local maxima detection\n",
+    "        self.maxpool = MaxPool3d(kernel_size=2*self.r + 1, stride=1, padding=self.r)\n",
+    "        self.maxpool2 = MaxPool2d(kernel_size=2*self.r + 1, stride=1, padding=self.r)\n",
+    "        \n",
+    "        self.pad = ConstantPad3d(self.r, 0.0)\n",
+    "        self.zero = torch.FloatTensor([0.0]).to(self.device)\n",
+    "\n",
+    "        # Construct local filters for sub-voxel refinement\n",
+    "        filt_vec = np.arange(-self.r, self.r + 1)\n",
+    "        yfilter, zfilter, xfilter = np.meshgrid(filt_vec, filt_vec, filt_vec)\n",
+    "        xfilter = torch.FloatTensor(xfilter).unsqueeze(0).unsqueeze(0)\n",
+    "        yfilter = torch.FloatTensor(yfilter).unsqueeze(0).unsqueeze(0)\n",
+    "        zfilter = torch.FloatTensor(zfilter).unsqueeze(0).unsqueeze(0)\n",
+    "        sfilter = torch.ones_like(xfilter)\n",
+    "        self.local_filter = torch.cat((sfilter, xfilter, yfilter, zfilter), 0).to(self.device)\n",
+    "\n",
+    "    def keep_maxz(self, conf_vol):\n",
+    "        \"\"\"Keep only the strongest maximum along z for each (x, y). \"\"\"\n",
+    "\n",
+    "        D, H, W = conf_vol.shape\n",
+    "\n",
+    "        max_proj, _ = torch.max(conf_vol, dim=0, keepdim=True)\n",
+    "\n",
+    "        # keep only local maxima in 2d\n",
+    "        max_proj = self.maxpool2(max_proj.unsqueeze(0))\n",
+    "        max_proj = max_proj.squeeze(0)\n",
+    "\n",
+    "        # keep only maximum\n",
+    "        conf_vol_out = torch.where(\n",
+    "            conf_vol == max_proj.expand(D, H, W),\n",
+    "            conf_vol,\n",
+    "            self.zero\n",
+    "        )\n",
+    "\n",
+    "        return conf_vol_out\n",
+    "    \n",
+    "    def local_avg(self, xbool, ybool, zbool, pred_vol_pad, num_pts, device):\n",
+    "        \"\"\"Compute sub-voxel position refinement using local weighted averages.\n",
+    "        \"\"\"\n",
+    "\n",
+    "        # Collect local (2r+1)^3 cubes around each candidate\n",
+    "        pred_vol_all = torch.zeros(num_pts, 1, self.r*2 + 1, self.r*2 + 1, self.r*2 + 1).to(device)\n",
+    "        for pt in range(num_pts):\n",
+    "\n",
+    "            # local 3D volume\n",
+    "            xpt = [xbool[pt], xbool[pt] + 2 * self.r + 1]\n",
+    "            ypt = [ybool[pt], ybool[pt] + 2 * self.r + 1]\n",
+    "            zpt = [zbool[pt], zbool[pt] + 2 * self.r + 1]\n",
+    "            pred_vol_all[pt, :] = pred_vol_pad[:, :, zpt[0]:zpt[1], ypt[0]:ypt[1], xpt[0]:xpt[1]]\n",
+    "\n",
+    "        # convolve it using conv3d\n",
+    "        sums = conv3d(pred_vol_all, self.local_filter)\n",
+    "\n",
+    "        # squeeze the sums and convert them to local perturbations\n",
+    "        xloc = sums[:, 1] / sums[:, 0]\n",
+    "        yloc = sums[:, 2] / sums[:, 0]\n",
+    "        zloc = sums[:, 3] / sums[:, 0]\n",
+    "\n",
+    "        return xloc, yloc, zloc\n",
+    "\n",
+    "    def forward(self, pred_vol):\n",
+    "        \"\"\"Main postprocessing pipeline:\n",
+    "        Input:  3D confidence volume (output of network)\n",
+    "        Output: xyz_rec (N×3 array of particle coordinates),\n",
+    "                conf_rec (N array of confidences)\n",
+    "        \"\"\"\n",
+    "\n",
+    "        # Ensure 5D tensor shape: (B, C, D, H, W)\n",
+    "        num_dims = len(pred_vol.size())\n",
+    "        if np.not_equal(num_dims, 5):\n",
+    "            if num_dims == 4:\n",
+    "                pred_vol = pred_vol.unsqueeze(0)\n",
+    "            else:\n",
+    "                pred_vol = pred_vol.unsqueeze(0)\n",
+    "                pred_vol = pred_vol.unsqueeze(0)\n",
+    "\n",
+    "        # Thresholding\n",
+    "        pred_thresh = torch.where(pred_vol > self.thresh, pred_vol, self.zero)\n",
+    "\n",
+    "        # 3D local maxima detection\n",
+    "        conf_vol = self.maxpool(pred_thresh)\n",
+    "        conf_vol = torch.where((conf_vol > self.zero) & (conf_vol == pred_thresh), conf_vol, self.zero)\n",
+    "        \n",
+    "        # Optionally keep only a single z in each xy sub-pixel\n",
+    "        if self.keep_singlez:\n",
+    "            conf_vol = torch.squeeze(conf_vol)\n",
+    "            conf_vol = self.keep_maxz(conf_vol)\n",
+    "\n",
+    "        # Find locations of confs (bigger than 0)\n",
+    "        conf_vol = torch.squeeze(conf_vol)\n",
+    "        batch_indices = torch.nonzero(conf_vol)\n",
+    "        zbool, ybool, xbool = batch_indices[:, 0], batch_indices[:, 1], batch_indices[:, 2]\n",
+    "\n",
+    "       # If no detections, return None\n",
+    "        if len(zbool) == 0:\n",
+    "            xyz_rec = None\n",
+    "            conf_rec = None\n",
+    "\n",
+    "        else:\n",
+    "            # Sub-voxel refinement\n",
+    "            # pad the result with radius_px 0's for average calc.\n",
+    "            pred_vol_pad = self.pad(pred_vol)\n",
+    "\n",
+    "            # for each point calculate local weighted average\n",
+    "            num_pts = len(zbool)\n",
+    "            xloc, yloc, zloc = self.local_avg(xbool, ybool, zbool, pred_vol_pad, num_pts, self.device)\n",
+    "\n",
+    "            # Convert lists and tensors to NumPy\n",
+    "            xloc, yloc, zloc = tensor_to_np(xloc), tensor_to_np(yloc), tensor_to_np(zloc)\n",
+    "            xbool, ybool, zbool = tensor_to_np(xbool), tensor_to_np(ybool), tensor_to_np(zbool)\n",
+    "\n",
+    "            # Calculate the recovered positions assuming mid-voxel\n",
+    "            xrec = xbool + xloc\n",
+    "            yrec = ybool + yloc            \n",
+    "            zrec = zbool + zloc\n",
+    "\n",
+    "            # rearrange the result into a Nx3 array\n",
+    "            xyz_rec = np.column_stack((xrec, yrec, zrec))\n",
+    "\n",
+    "            # Extract confidence values\n",
+    "            conf_rec = conf_vol[zbool, ybool, xbool]\n",
+    "            conf_rec = tensor_to_np(conf_rec)\n",
+    "\n",
+    "            xyz_rec, conf_rec = self.remove_duplicates(xyz_rec, conf_rec)       # added to remove duplicates\n",
+    "\n",
+    "        return xyz_rec, conf_rec\n",
+    "    \n",
+    "    # added, to remove duplicates   \n",
+    "    def remove_duplicates(self, xyz_rec, conf_rec, tol=1e-4):\n",
+    "        \"\"\"\n",
+    "        Remove duplicate positions (within tolerance) while keeping\n",
+    "        confidence values aligned.\n",
+    "        \"\"\"\n",
+    "        if xyz_rec is None or len(xyz_rec) == 0:\n",
+    "            return xyz_rec, conf_rec\n",
+    "\n",
+    "        rounded = np.round(xyz_rec / tol).astype(np.int64)\n",
+    "        _, unique_idx = np.unique(rounded, axis=0, return_index=True)\n",
+    "        unique_idx = np.sort(unique_idx)\n",
+    "\n",
+    "        xyz_rec = xyz_rec[unique_idx]\n",
+    "        if conf_rec is not None:\n",
+    "            conf_rec = conf_rec[unique_idx]\n",
+    "\n",
+    "        return xyz_rec, conf_rec\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "33995ae0",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "radius = 2\n",
+    "threshold = 2\n",
+    "\n",
+    "postprocessing_module = Postprocess(device, thresh=threshold, radius=radius, keep_singlez=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0caad544",
+   "metadata": {},
+   "source": [
+    "## 5. Check the performance of the combination of the optimized optical setup and the CNN"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "23a4fb4e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def jaccard_coeff(pred, target, postprocessing, max_dist=2):\n",
+    "    \"\"\"\n",
+    "    jaccard index = TP / (TP + FP + FN)\n",
+    "    pred: tensor with first dimension as batch\n",
+    "    target: tensor with first dimension as batch\n",
+    "    \"\"\"\n",
+    "    xyz_rec, conf_rec = postprocessing(pred)     # this already takes care of removing duplicates/predicted particles that are too close to each other\n",
+    "    xyz_target = torch.nonzero(target)[:, [2,1,0]]\n",
+    "\n",
+    "    D = torch.cdist(torch.tensor(xyz_rec), xyz_target.cpu().to(torch.float64))\n",
+    "\n",
+    "    # Each prediction and ground truth can only be part of one TP\n",
+    "    matched_pred = set()\n",
+    "    matched_gt = set()\n",
+    "    TP = 0\n",
+    "    while True:\n",
+    "        min_val, idx = torch.min(D.view(-1), dim=0)\n",
+    "        if min_val > max_dist or not torch.isfinite(min_val):\n",
+    "            break\n",
+    "        i = idx // D.size(1)\n",
+    "        j = idx % D.size(1)\n",
+    "        TP += 1\n",
+    "        matched_pred.add(i.item())\n",
+    "        matched_gt.add(j.item())\n",
+    "        D[i, :] = float('inf')\n",
+    "        D[:, j] = float('inf')\n",
+    "\n",
+    "    FP = len(xyz_rec) - TP\n",
+    "    FN = len(xyz_target) - TP\n",
+    "\n",
+    "    JI = TP / (TP + FP + FN + 1e-6)\n",
+    "\n",
+    "    return JI"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "02c70efc",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "tensor(0.7731)\n"
+     ]
+    }
+   ],
+   "source": [
+    "num_tests = 100\n",
+    "jaccard_index = []\n",
+    "jaccard_index_coords = []\n",
+    "\n",
+    "for j in range(num_tests):\n",
+    "    pip.update()\n",
+    "    img, g = pip.resolve()\n",
+    "    g = g.to(device)\n",
+    "\n",
+    "    pred = cnn(img.unsqueeze(0))\n",
+    "        \n",
+    "    jaccard_index.append(jaccard_coeff(pred, g, postprocessing_module))\n",
+    "\n",
+    "average_jaccard_index = torch.mean(torch.tensor(jaccard_index))\n",
+    "print(average_jaccard_index)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f98f2249",
+   "metadata": {},
+   "source": [
+    "### 5.1 Plot the average over z"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 73,
+   "id": "4d20b8de",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 2000x2500 with 20 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "num_samples = 5\n",
+    "fig, ax = plt.subplots(num_samples, 4, figsize=(20, 5*num_samples))\n",
+    "\n",
+    "images, gt, exact_pos = [], [], []\n",
+    "for _ in range(num_samples):\n",
+    "    pip.update()\n",
+    "    img, g = pip.resolve()\n",
+    "    pos = xyz.points\n",
+    "    images.append(img)\n",
+    "    gt.append(g)\n",
+    "    exact_pos.append(pos)\n",
+    "\n",
+    "images = torch.stack(images)\n",
+    "gt = torch.stack(gt)\n",
+    "\n",
+    "preds = cnn(images)\n",
+    "\n",
+    "for i in range(num_samples):\n",
+    "\n",
+    "    xyz_rec, conf_rec = postprocessing_module(preds[i])\n",
+    "    xyz_target = torch.nonzero(gt[i])[:, [2,1,0]]\n",
+    "\n",
+    "    ax[i, 0].imshow(images[i, 0].cpu().detach().numpy(), cmap='gray')\n",
+    "    ax[i, 1].imshow(preds[i].max(dim=0)[0].cpu().detach().numpy())\n",
+    "    ax[i, 2].imshow(gt[i].max(dim=0)[0].cpu().detach().numpy())\n",
+    "    ax[i, 2].plot(xyz_rec[:, 0], xyz_rec[:,1], c='red', marker='.', ls=' ')\n",
+    "    ax[i, 3].imshow(gt[i].max(dim=0)[0].cpu().detach().numpy())\n",
+    "\n",
+    "ax[0, 0].set_title(\"Input image\")\n",
+    "ax[0, 1].set_title(\"Prediction\")\n",
+    "ax[0, 2].set_title(\"Ground truth + prediction in red\")\n",
+    "ax[0, 3].set_title(\"Ground truth\");"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a0152702",
+   "metadata": {},
+   "source": [
+    "### 5.2 Visualize the predicted positions in 3D"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "id": "5d4137ed",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 1400x700 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sample = 1\n",
+    "\n",
+    "fig = plt.figure(figsize=(14, 7))\n",
+    "\n",
+    "ax1 = fig.add_subplot(121)\n",
+    "ax1.imshow(images[sample, 0].cpu().detach().numpy(), cmap='gray')\n",
+    "ax1.set_xlabel(\"y\")\n",
+    "ax1.set_ylabel(\"x\")\n",
+    "ax1.set_title(\"Input image\")\n",
+    "\n",
+    "\n",
+    "xyz_rec, conf_rec = postprocessing_module(preds[sample])\n",
+    "xyz_target = torch.nonzero(gt[sample])[:, [2,1,0]]\n",
+    "\n",
+    "ax2 = fig.add_subplot(122, projection='3d')\n",
+    "# ax2.scatter(xyz_target[:, 0], xyz_target[:, 1], xyz_target[:, 2], c='red', marker='o', s=100, label='Ground truth', alpha=0.3)\n",
+    "ax2.scatter(exact_pos[sample][:, 1], exact_pos[sample][:, 0], exact_pos[sample][:, 2], c='red', marker='o', s=100, label='Ground truth', alpha=0.3)\n",
+    "ax2.scatter(xyz_rec[:, 0], xyz_rec[:, 1], xyz_rec[:, 2], c='blue', marker='o', s=30, label='Prediction', alpha=1)\n",
+    "\n",
+    "ax2.set_xlabel('y')\n",
+    "ax2.set_ylabel('x')\n",
+    "ax2.set_zlabel('z')\n",
+    "ax2.set_xlim(0, image_size)\n",
+    "ax2.set_ylim(0, image_size)\n",
+    "ax2.set_zlim(0, depth)\n",
+    "ax2.set_title('Predicted & Ground truth positions')\n",
+    "ax2.legend();"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ef0268f1",
+   "metadata": {},
+   "source": [
+    "## 6. Compare with not having a trainable phase mask:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "edd1bcd0",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "optics_no_phase_mask = dt.Fluorescence(\n",
+    "    NA=1.45,\n",
+    "    refractive_index_medium=1.33,   \n",
+    "    output_region=(0,0, image_size, image_size),\n",
+    ")\n",
+    "\n",
+    "im_pip_no_phase_mask = optics_no_phase_mask(particle ^ num_points) ## Add noise!\n",
+    "\n",
+    "pip_no_phase_mask = (im_pip_no_phase_mask & gt_pip) >> dt.MoveAxis(2, 0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "id": "87b30e58",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "cnn_no_phase_mask = CNN.create()\n",
+    "cnn_no_phase_mask.to(device)\n",
+    "\n",
+    "optimizer_no_phase_mask = torch.optim.Adam(list(cnn_no_phase_mask.parameters()), lr=1e-3)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "id": "640f4d39",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 0, Loss: 3.6640\n",
+      "Epoch 1, Loss: 3.4355\n",
+      "Epoch 2, Loss: 2.5561\n",
+      "Epoch 3, Loss: 2.4490\n",
+      "Epoch 4, Loss: 2.2709\n",
+      "Epoch 5, Loss: 2.2155\n",
+      "Epoch 6, Loss: 2.1898\n",
+      "Epoch 7, Loss: 2.1486\n",
+      "Epoch 8, Loss: 2.1223\n",
+      "Epoch 9, Loss: 2.1003\n",
+      "Epoch 10, Loss: 2.0874\n",
+      "Epoch 11, Loss: 2.0605\n",
+      "Epoch 12, Loss: 2.0553\n",
+      "Epoch 13, Loss: 2.0427\n",
+      "Epoch 14, Loss: 2.0426\n",
+      "Epoch 15, Loss: 2.0279\n",
+      "Epoch 16, Loss: 2.0182\n",
+      "Epoch 17, Loss: 2.0158\n",
+      "Epoch 18, Loss: 2.0069\n",
+      "Epoch 19, Loss: 1.9978\n",
+      "Epoch 20, Loss: 1.9850\n",
+      "Epoch 21, Loss: 1.9794\n",
+      "Epoch 22, Loss: 1.9711\n",
+      "Epoch 23, Loss: 1.9532\n",
+      "Epoch 24, Loss: 1.9606\n",
+      "Epoch 25, Loss: 1.9188\n",
+      "Epoch 26, Loss: 1.9222\n",
+      "Epoch 27, Loss: 1.8994\n",
+      "Epoch 28, Loss: 1.9259\n",
+      "Epoch 29, Loss: 1.9094\n",
+      "Epoch 30, Loss: 1.9118\n",
+      "Epoch 31, Loss: 1.8966\n",
+      "Epoch 32, Loss: 1.8568\n",
+      "Epoch 33, Loss: 1.8212\n",
+      "Epoch 34, Loss: 1.8413\n",
+      "Epoch 35, Loss: 1.8214\n",
+      "Epoch 36, Loss: 1.7948\n",
+      "Epoch 37, Loss: 1.8015\n",
+      "Epoch 38, Loss: 1.7824\n",
+      "Epoch 39, Loss: 1.7767\n",
+      "Epoch 40, Loss: 1.7810\n",
+      "Epoch 41, Loss: 1.7439\n",
+      "Epoch 42, Loss: 1.7442\n",
+      "Epoch 43, Loss: 1.7384\n",
+      "Epoch 44, Loss: 1.7808\n",
+      "Epoch 45, Loss: 1.6939\n",
+      "Epoch 46, Loss: 1.7518\n",
+      "Epoch 47, Loss: 1.7423\n",
+      "Epoch 48, Loss: 1.7124\n",
+      "Epoch 49, Loss: 1.7123\n",
+      "Epoch 50, Loss: 1.7734\n",
+      "Epoch 51, Loss: 1.6863\n",
+      "Epoch 52, Loss: 1.6599\n",
+      "Epoch 53, Loss: 1.6682\n",
+      "Epoch 54, Loss: 1.6792\n",
+      "Epoch 55, Loss: 1.6384\n",
+      "Epoch 56, Loss: 1.6538\n",
+      "Epoch 57, Loss: 1.6668\n",
+      "Epoch 58, Loss: 1.6692\n",
+      "Epoch 59, Loss: 1.6617\n",
+      "Epoch 60, Loss: 1.6316\n",
+      "Epoch 61, Loss: 1.6507\n",
+      "Epoch 62, Loss: 1.7154\n",
+      "Epoch 63, Loss: 1.6557\n",
+      "Epoch 64, Loss: 1.6587\n",
+      "Epoch 65, Loss: 1.6510\n",
+      "Epoch 66, Loss: 1.6009\n",
+      "Epoch 67, Loss: 1.6638\n",
+      "Epoch 68, Loss: 1.6560\n",
+      "Epoch 69, Loss: 1.5970\n",
+      "Epoch 70, Loss: 1.5877\n",
+      "Epoch 71, Loss: 1.6391\n",
+      "Epoch 72, Loss: 1.5865\n",
+      "Epoch 73, Loss: 1.6247\n",
+      "Epoch 74, Loss: 1.6330\n",
+      "Epoch 75, Loss: 1.6120\n",
+      "Epoch 76, Loss: 1.6082\n",
+      "Epoch 77, Loss: 1.6159\n",
+      "Epoch 78, Loss: 1.6034\n",
+      "Epoch 79, Loss: 1.6106\n",
+      "Epoch 80, Loss: 1.5930\n",
+      "Epoch 81, Loss: 1.5845\n",
+      "Epoch 82, Loss: 1.5938\n",
+      "Epoch 83, Loss: 1.5705\n",
+      "Epoch 84, Loss: 1.5578\n",
+      "Epoch 85, Loss: 1.5451\n",
+      "Epoch 86, Loss: 1.5260\n",
+      "Epoch 87, Loss: 1.5447\n",
+      "Epoch 88, Loss: 1.5063\n",
+      "Epoch 89, Loss: 1.5654\n",
+      "Epoch 90, Loss: 1.5393\n",
+      "Epoch 91, Loss: 1.5200\n",
+      "Epoch 92, Loss: 1.5003\n",
+      "Epoch 93, Loss: 1.5267\n",
+      "Epoch 94, Loss: 1.4899\n",
+      "Epoch 95, Loss: 1.5092\n",
+      "Epoch 96, Loss: 1.5423\n",
+      "Epoch 97, Loss: 1.5143\n",
+      "Epoch 98, Loss: 1.4779\n",
+      "Epoch 99, Loss: 1.4522\n",
+      "Epoch 100, Loss: 1.4637\n",
+      "Epoch 101, Loss: 1.5140\n",
+      "Epoch 102, Loss: 1.4810\n",
+      "Epoch 103, Loss: 1.4758\n",
+      "Epoch 104, Loss: 1.4829\n",
+      "Epoch 105, Loss: 1.4967\n",
+      "Epoch 106, Loss: 1.4788\n",
+      "Epoch 107, Loss: 1.4588\n",
+      "Epoch 108, Loss: 1.4771\n",
+      "Epoch 109, Loss: 1.4286\n",
+      "Epoch 110, Loss: 1.5002\n",
+      "Epoch 111, Loss: 1.4500\n",
+      "Epoch 112, Loss: 1.4247\n",
+      "Epoch 113, Loss: 1.4266\n",
+      "Epoch 114, Loss: 1.4246\n",
+      "Epoch 115, Loss: 1.4228\n",
+      "Epoch 116, Loss: 1.4807\n",
+      "Epoch 117, Loss: 1.4577\n",
+      "Epoch 118, Loss: 1.4621\n",
+      "Epoch 119, Loss: 1.4096\n",
+      "Epoch 120, Loss: 1.4512\n",
+      "Epoch 121, Loss: 1.4150\n",
+      "Epoch 122, Loss: 1.4063\n",
+      "Epoch 123, Loss: 1.4518\n",
+      "Epoch 124, Loss: 1.4261\n",
+      "Epoch 125, Loss: 1.4491\n",
+      "Epoch 126, Loss: 1.4457\n",
+      "Epoch 127, Loss: 1.4335\n",
+      "Epoch 128, Loss: 1.4275\n",
+      "Epoch 129, Loss: 1.4538\n",
+      "Epoch 130, Loss: 1.4646\n",
+      "Epoch 131, Loss: 1.4642\n",
+      "Epoch 132, Loss: 1.4033\n",
+      "Epoch 133, Loss: 1.4290\n",
+      "Epoch 134, Loss: 1.4646\n",
+      "Epoch 135, Loss: 1.3712\n",
+      "Epoch 136, Loss: 1.4111\n",
+      "Epoch 137, Loss: 1.4529\n",
+      "Epoch 138, Loss: 1.4236\n",
+      "Epoch 139, Loss: 1.4804\n",
+      "Epoch 140, Loss: 1.4568\n",
+      "Epoch 141, Loss: 1.4213\n",
+      "Epoch 142, Loss: 1.4837\n",
+      "Epoch 143, Loss: 1.4576\n",
+      "Epoch 144, Loss: 1.5062\n",
+      "Epoch 145, Loss: 1.4327\n",
+      "Epoch 146, Loss: 1.4257\n",
+      "Epoch 147, Loss: 1.4610\n",
+      "Epoch 148, Loss: 1.4289\n",
+      "Epoch 149, Loss: 1.4656\n",
+      "Epoch 150, Loss: 1.4155\n",
+      "Epoch 151, Loss: 1.4051\n",
+      "Epoch 152, Loss: 1.4452\n",
+      "Epoch 153, Loss: 1.3809\n",
+      "Epoch 154, Loss: 1.4015\n",
+      "Epoch 155, Loss: 1.3772\n",
+      "Epoch 156, Loss: 1.4159\n",
+      "Epoch 157, Loss: 1.3992\n",
+      "Epoch 158, Loss: 1.4600\n",
+      "Epoch 159, Loss: 1.4002\n",
+      "Epoch 160, Loss: 1.4290\n",
+      "Epoch 161, Loss: 1.3935\n",
+      "Epoch 162, Loss: 1.3639\n",
+      "Epoch 163, Loss: 1.4162\n",
+      "Epoch 164, Loss: 1.4000\n",
+      "Epoch 165, Loss: 1.3525\n",
+      "Epoch 166, Loss: 1.4811\n",
+      "Epoch 167, Loss: 1.4199\n",
+      "Epoch 168, Loss: 1.3371\n",
+      "Epoch 169, Loss: 1.4401\n",
+      "Epoch 170, Loss: 1.3465\n",
+      "Epoch 171, Loss: 1.3900\n",
+      "Epoch 172, Loss: 1.3424\n",
+      "Epoch 173, Loss: 1.4424\n",
+      "Epoch 174, Loss: 1.3991\n",
+      "Epoch 175, Loss: 1.3588\n",
+      "Epoch 176, Loss: 1.3875\n",
+      "Epoch 177, Loss: 1.4072\n",
+      "Epoch 178, Loss: 1.3344\n",
+      "Epoch 179, Loss: 1.3863\n",
+      "Epoch 180, Loss: 1.3522\n",
+      "Epoch 181, Loss: 1.4102\n",
+      "Epoch 182, Loss: 1.3536\n",
+      "Epoch 183, Loss: 1.4285\n",
+      "Epoch 184, Loss: 1.3748\n",
+      "Epoch 185, Loss: 1.4014\n",
+      "Epoch 186, Loss: 1.4356\n",
+      "Epoch 187, Loss: 1.3139\n",
+      "Epoch 188, Loss: 1.4148\n",
+      "Epoch 189, Loss: 1.4379\n",
+      "Epoch 190, Loss: 1.3569\n",
+      "Epoch 191, Loss: 1.3948\n",
+      "Epoch 192, Loss: 1.3997\n",
+      "Epoch 193, Loss: 1.3689\n",
+      "Epoch 194, Loss: 1.3742\n",
+      "Epoch 195, Loss: 1.3001\n",
+      "Epoch 196, Loss: 1.3623\n",
+      "Epoch 197, Loss: 1.3531\n",
+      "Epoch 198, Loss: 1.2926\n",
+      "Epoch 199, Loss: 1.2988\n",
+      "Epoch 200, Loss: 1.4028\n",
+      "Epoch 201, Loss: 1.3651\n",
+      "Epoch 202, Loss: 1.3042\n",
+      "Epoch 203, Loss: 1.2902\n",
+      "Epoch 204, Loss: 1.3254\n",
+      "Epoch 205, Loss: 1.3678\n",
+      "Epoch 206, Loss: 1.2940\n",
+      "Epoch 207, Loss: 1.3235\n",
+      "Epoch 208, Loss: 1.3209\n",
+      "Epoch 209, Loss: 1.3415\n",
+      "Epoch 210, Loss: 1.3092\n",
+      "Epoch 211, Loss: 1.2711\n",
+      "Epoch 212, Loss: 1.3072\n",
+      "Epoch 213, Loss: 1.2783\n",
+      "Epoch 214, Loss: 1.3520\n",
+      "Epoch 215, Loss: 1.3174\n",
+      "Epoch 216, Loss: 1.3276\n",
+      "Epoch 217, Loss: 1.3208\n",
+      "Epoch 218, Loss: 1.2229\n",
+      "Epoch 219, Loss: 1.2922\n",
+      "Epoch 220, Loss: 1.2998\n",
+      "Epoch 221, Loss: 1.2600\n",
+      "Epoch 222, Loss: 1.3074\n",
+      "Epoch 223, Loss: 1.2677\n",
+      "Epoch 224, Loss: 1.2428\n",
+      "Epoch 225, Loss: 1.2775\n",
+      "Epoch 226, Loss: 1.2937\n",
+      "Epoch 227, Loss: 1.2623\n",
+      "Epoch 228, Loss: 1.1989\n",
+      "Epoch 229, Loss: 1.1837\n",
+      "Epoch 230, Loss: 1.2297\n",
+      "Epoch 231, Loss: 1.2379\n",
+      "Epoch 232, Loss: 1.2158\n",
+      "Epoch 233, Loss: 1.2526\n",
+      "Epoch 234, Loss: 1.2233\n",
+      "Epoch 235, Loss: 1.2344\n",
+      "Epoch 236, Loss: 1.2563\n",
+      "Epoch 237, Loss: 1.2216\n",
+      "Epoch 238, Loss: 1.2532\n",
+      "Epoch 239, Loss: 1.2171\n",
+      "Epoch 240, Loss: 1.2264\n",
+      "Epoch 241, Loss: 1.1970\n",
+      "Epoch 242, Loss: 1.2941\n",
+      "Epoch 243, Loss: 1.2312\n",
+      "Epoch 244, Loss: 1.2358\n",
+      "Epoch 245, Loss: 1.2142\n",
+      "Epoch 246, Loss: 1.2790\n",
+      "Epoch 247, Loss: 1.3548\n",
+      "Epoch 248, Loss: 1.1814\n",
+      "Epoch 249, Loss: 1.2210\n",
+      "Epoch 250, Loss: 1.2247\n",
+      "Epoch 251, Loss: 1.1598\n",
+      "Epoch 252, Loss: 1.1878\n",
+      "Epoch 253, Loss: 1.2584\n",
+      "Epoch 254, Loss: 1.2490\n",
+      "Epoch 255, Loss: 1.1856\n",
+      "Epoch 256, Loss: 1.1995\n",
+      "Epoch 257, Loss: 1.1823\n",
+      "Epoch 258, Loss: 1.2388\n",
+      "Epoch 259, Loss: 1.2057\n",
+      "Epoch 260, Loss: 1.2286\n",
+      "Epoch 261, Loss: 1.2324\n",
+      "Epoch 262, Loss: 1.2211\n",
+      "Epoch 263, Loss: 1.1983\n",
+      "Epoch 264, Loss: 1.2539\n",
+      "Epoch 265, Loss: 1.2418\n",
+      "Epoch 266, Loss: 1.1889\n",
+      "Epoch 267, Loss: 1.2436\n",
+      "Epoch 268, Loss: 1.1466\n",
+      "Epoch 269, Loss: 1.2173\n",
+      "Epoch 270, Loss: 1.2273\n",
+      "Epoch 271, Loss: 1.2897\n",
+      "Epoch 272, Loss: 1.2062\n",
+      "Epoch 273, Loss: 1.2011\n",
+      "Epoch 274, Loss: 1.2131\n",
+      "Epoch 275, Loss: 1.2359\n",
+      "Epoch 276, Loss: 1.1484\n",
+      "Epoch 277, Loss: 1.1922\n",
+      "Epoch 278, Loss: 1.1549\n",
+      "Epoch 279, Loss: 1.2327\n",
+      "Epoch 280, Loss: 1.2305\n",
+      "Epoch 281, Loss: 1.2214\n",
+      "Epoch 282, Loss: 1.2124\n",
+      "Epoch 283, Loss: 1.1765\n",
+      "Epoch 284, Loss: 1.1906\n",
+      "Epoch 285, Loss: 1.2501\n",
+      "Epoch 286, Loss: 1.1267\n",
+      "Epoch 287, Loss: 1.1852\n",
+      "Epoch 288, Loss: 1.2171\n",
+      "Epoch 289, Loss: 1.1805\n",
+      "Epoch 290, Loss: 1.1482\n",
+      "Epoch 291, Loss: 1.2256\n",
+      "Epoch 292, Loss: 1.2502\n",
+      "Epoch 293, Loss: 1.1730\n",
+      "Epoch 294, Loss: 1.2307\n",
+      "Epoch 295, Loss: 1.1314\n",
+      "Epoch 296, Loss: 1.1193\n",
+      "Epoch 297, Loss: 1.2354\n",
+      "Epoch 298, Loss: 1.1679\n",
+      "Epoch 299, Loss: 1.2145\n",
+      "Epoch 300, Loss: 1.1556\n",
+      "Epoch 301, Loss: 1.1978\n",
+      "Epoch 302, Loss: 1.1860\n",
+      "Epoch 303, Loss: 1.1528\n",
+      "Epoch 304, Loss: 1.1659\n",
+      "Epoch 305, Loss: 1.2307\n",
+      "Epoch 306, Loss: 1.1602\n",
+      "Epoch 307, Loss: 1.1642\n",
+      "Epoch 308, Loss: 1.1760\n",
+      "Epoch 309, Loss: 1.1851\n",
+      "Epoch 310, Loss: 1.1873\n",
+      "Epoch 311, Loss: 1.1635\n",
+      "Epoch 312, Loss: 1.1126\n",
+      "Epoch 313, Loss: 1.1611\n",
+      "Epoch 314, Loss: 1.1130\n",
+      "Epoch 315, Loss: 1.1752\n",
+      "Epoch 316, Loss: 1.1579\n",
+      "Epoch 317, Loss: 1.1632\n",
+      "Epoch 318, Loss: 1.1716\n",
+      "Epoch 319, Loss: 1.0868\n",
+      "Epoch 320, Loss: 1.1433\n",
+      "Epoch 321, Loss: 1.0974\n",
+      "Epoch 322, Loss: 1.1826\n",
+      "Epoch 323, Loss: 1.1800\n",
+      "Epoch 324, Loss: 1.1618\n",
+      "Epoch 325, Loss: 1.1824\n",
+      "Epoch 326, Loss: 1.1051\n",
+      "Epoch 327, Loss: 1.1046\n",
+      "Epoch 328, Loss: 1.1381\n",
+      "Epoch 329, Loss: 1.1210\n",
+      "Epoch 330, Loss: 1.1000\n",
+      "Epoch 331, Loss: 1.1179\n",
+      "Epoch 332, Loss: 1.1498\n",
+      "Epoch 333, Loss: 1.1154\n",
+      "Epoch 334, Loss: 1.1459\n",
+      "Epoch 335, Loss: 1.1939\n",
+      "Epoch 336, Loss: 1.1825\n",
+      "Epoch 337, Loss: 1.1250\n",
+      "Epoch 338, Loss: 1.2186\n",
+      "Epoch 339, Loss: 1.1974\n",
+      "Epoch 340, Loss: 1.2015\n",
+      "Epoch 341, Loss: 1.1401\n",
+      "Epoch 342, Loss: 1.1574\n",
+      "Epoch 343, Loss: 1.1467\n",
+      "Epoch 344, Loss: 1.1412\n",
+      "Epoch 345, Loss: 1.1343\n",
+      "Epoch 346, Loss: 1.1851\n",
+      "Epoch 347, Loss: 1.1358\n",
+      "Epoch 348, Loss: 1.1197\n",
+      "Epoch 349, Loss: 1.1668\n",
+      "Epoch 350, Loss: 1.1478\n",
+      "Epoch 351, Loss: 1.1546\n",
+      "Epoch 352, Loss: 1.1268\n",
+      "Epoch 353, Loss: 1.1787\n",
+      "Epoch 354, Loss: 1.1126\n",
+      "Epoch 355, Loss: 1.0711\n",
+      "Epoch 356, Loss: 1.1590\n",
+      "Epoch 357, Loss: 1.1372\n",
+      "Epoch 358, Loss: 1.1915\n",
+      "Epoch 359, Loss: 1.1581\n",
+      "Epoch 360, Loss: 1.0806\n",
+      "Epoch 361, Loss: 1.1537\n",
+      "Epoch 362, Loss: 1.1829\n",
+      "Epoch 363, Loss: 1.1321\n",
+      "Epoch 364, Loss: 1.1357\n",
+      "Epoch 365, Loss: 1.1607\n",
+      "Epoch 366, Loss: 1.1333\n",
+      "Epoch 367, Loss: 1.1117\n",
+      "Epoch 368, Loss: 1.1543\n",
+      "Epoch 369, Loss: 1.1050\n",
+      "Epoch 370, Loss: 1.1185\n",
+      "Epoch 371, Loss: 1.0682\n",
+      "Epoch 372, Loss: 1.0717\n",
+      "Epoch 373, Loss: 1.2294\n",
+      "Epoch 374, Loss: 1.1694\n",
+      "Epoch 375, Loss: 1.0999\n",
+      "Epoch 376, Loss: 1.0792\n",
+      "Epoch 377, Loss: 1.1458\n",
+      "Epoch 378, Loss: 1.1315\n",
+      "Epoch 379, Loss: 1.1043\n",
+      "Epoch 380, Loss: 1.1680\n",
+      "Epoch 381, Loss: 1.1410\n",
+      "Epoch 382, Loss: 1.0761\n",
+      "Epoch 383, Loss: 1.0999\n",
+      "Epoch 384, Loss: 1.0246\n",
+      "Epoch 385, Loss: 1.0596\n",
+      "Epoch 386, Loss: 1.1114\n",
+      "Epoch 387, Loss: 1.0532\n",
+      "Epoch 388, Loss: 1.1347\n",
+      "Epoch 389, Loss: 1.1602\n",
+      "Epoch 390, Loss: 1.1121\n",
+      "Epoch 391, Loss: 1.0932\n",
+      "Epoch 392, Loss: 1.1339\n",
+      "Epoch 393, Loss: 1.0714\n",
+      "Epoch 394, Loss: 1.1036\n",
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+      "Epoch 449, Loss: 1.1104\n",
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+      "Epoch 491, Loss: 1.0681\n",
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+      "Epoch 493, Loss: 1.0320\n",
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+      "Epoch 507, Loss: 0.9921\n",
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+      "Epoch 537, Loss: 1.1016\n",
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+      "Epoch 545, Loss: 1.0534\n",
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+      "Epoch 547, Loss: 1.0601\n",
+      "Epoch 548, Loss: 1.0483\n",
+      "Epoch 549, Loss: 1.0701\n",
+      "Epoch 550, Loss: 1.1049\n",
+      "Epoch 551, Loss: 1.0970\n",
+      "Epoch 552, Loss: 1.0297\n",
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+      "Epoch 555, Loss: 1.1245\n",
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+      "Epoch 557, Loss: 0.9387\n",
+      "Epoch 558, Loss: 1.0011\n",
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+      "Epoch 561, Loss: 1.1421\n",
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+      "Epoch 564, Loss: 1.1405\n",
+      "Epoch 565, Loss: 1.1000\n",
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+      "Epoch 568, Loss: 1.0724\n",
+      "Epoch 569, Loss: 1.0989\n",
+      "Epoch 570, Loss: 1.0511\n",
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+      "Epoch 572, Loss: 1.1169\n",
+      "Epoch 573, Loss: 1.0001\n",
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+      "Epoch 580, Loss: 1.0826\n",
+      "Epoch 581, Loss: 1.0907\n",
+      "Epoch 582, Loss: 1.0786\n",
+      "Epoch 583, Loss: 1.0412\n",
+      "Epoch 584, Loss: 1.0979\n",
+      "Epoch 585, Loss: 1.1001\n",
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+      "Epoch 589, Loss: 1.0060\n",
+      "Epoch 590, Loss: 1.0656\n",
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+      "Epoch 593, Loss: 1.0421\n",
+      "Epoch 594, Loss: 1.0089\n",
+      "Epoch 595, Loss: 1.0064\n",
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+      "Epoch 598, Loss: 1.0489\n",
+      "Epoch 599, Loss: 1.0856\n",
+      "Epoch 600, Loss: 1.0554\n",
+      "Epoch 601, Loss: 1.1248\n",
+      "Epoch 602, Loss: 1.0225\n",
+      "Epoch 603, Loss: 1.1505\n",
+      "Epoch 604, Loss: 1.0699\n",
+      "Epoch 605, Loss: 1.0552\n",
+      "Epoch 606, Loss: 1.0312\n",
+      "Epoch 607, Loss: 1.0457\n",
+      "Epoch 608, Loss: 1.1135\n",
+      "Epoch 609, Loss: 1.1348\n",
+      "Epoch 610, Loss: 1.0385\n",
+      "Epoch 611, Loss: 1.0719\n",
+      "Epoch 612, Loss: 0.9806\n",
+      "Epoch 613, Loss: 1.1098\n",
+      "Epoch 614, Loss: 1.0799\n",
+      "Epoch 615, Loss: 1.0407\n",
+      "Epoch 616, Loss: 1.0524\n",
+      "Epoch 617, Loss: 1.0583\n",
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+      "Epoch 619, Loss: 1.0398\n",
+      "Epoch 620, Loss: 1.0514\n",
+      "Epoch 621, Loss: 1.0714\n",
+      "Epoch 622, Loss: 0.9720\n",
+      "Epoch 623, Loss: 1.0871\n",
+      "Epoch 624, Loss: 1.0413\n",
+      "Epoch 625, Loss: 0.9868\n",
+      "Epoch 626, Loss: 1.0940\n",
+      "Epoch 627, Loss: 1.0104\n",
+      "Epoch 628, Loss: 1.0595\n",
+      "Epoch 629, Loss: 1.0596\n",
+      "Epoch 630, Loss: 1.0792\n",
+      "Epoch 631, Loss: 1.0304\n",
+      "Epoch 632, Loss: 0.9644\n",
+      "Epoch 633, Loss: 1.0249\n",
+      "Epoch 634, Loss: 1.1356\n",
+      "Epoch 635, Loss: 1.1028\n",
+      "Epoch 636, Loss: 1.0171\n",
+      "Epoch 637, Loss: 1.0677\n",
+      "Epoch 638, Loss: 1.0675\n",
+      "Epoch 639, Loss: 1.0435\n",
+      "Epoch 640, Loss: 1.0508\n",
+      "Epoch 641, Loss: 1.0193\n",
+      "Epoch 642, Loss: 0.9904\n",
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+      "Epoch 645, Loss: 1.0955\n",
+      "Epoch 646, Loss: 1.0605\n",
+      "Epoch 647, Loss: 1.0271\n",
+      "Epoch 648, Loss: 1.0795\n",
+      "Epoch 649, Loss: 1.0707\n",
+      "Epoch 650, Loss: 0.9856\n",
+      "Epoch 651, Loss: 1.0519\n",
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+      "Epoch 654, Loss: 1.0672\n",
+      "Epoch 655, Loss: 1.0945\n",
+      "Epoch 656, Loss: 1.0953\n",
+      "Epoch 657, Loss: 1.0444\n",
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+      "Epoch 659, Loss: 1.0308\n",
+      "Epoch 660, Loss: 1.0186\n",
+      "Epoch 661, Loss: 0.9802\n",
+      "Epoch 662, Loss: 1.1303\n",
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+      "Epoch 665, Loss: 1.1057\n",
+      "Epoch 666, Loss: 1.0067\n",
+      "Epoch 667, Loss: 1.0340\n",
+      "Epoch 668, Loss: 0.9981\n",
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+      "Epoch 671, Loss: 0.9822\n",
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+      "Epoch 673, Loss: 1.0331\n",
+      "Epoch 674, Loss: 1.0438\n",
+      "Epoch 675, Loss: 1.0582\n",
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+      "Epoch 677, Loss: 0.9774\n",
+      "Epoch 678, Loss: 1.0421\n",
+      "Epoch 679, Loss: 0.9760\n",
+      "Epoch 680, Loss: 1.0009\n",
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+      "Epoch 689, Loss: 1.0045\n",
+      "Epoch 690, Loss: 1.0261\n",
+      "Epoch 691, Loss: 1.0058\n",
+      "Epoch 692, Loss: 0.9943\n",
+      "Epoch 693, Loss: 1.0203\n",
+      "Epoch 694, Loss: 1.0193\n",
+      "Epoch 695, Loss: 1.0018\n",
+      "Epoch 696, Loss: 0.9940\n",
+      "Epoch 697, Loss: 1.0352\n",
+      "Epoch 698, Loss: 1.0242\n",
+      "Epoch 699, Loss: 1.0614\n",
+      "Epoch 700, Loss: 1.0496\n",
+      "Epoch 701, Loss: 1.0361\n",
+      "Epoch 702, Loss: 1.0027\n",
+      "Epoch 703, Loss: 0.9952\n",
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+      "Epoch 706, Loss: 0.9916\n",
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+      "Epoch 708, Loss: 0.9822\n",
+      "Epoch 709, Loss: 0.9855\n",
+      "Epoch 710, Loss: 1.0517\n",
+      "Epoch 711, Loss: 0.9874\n",
+      "Epoch 712, Loss: 1.0927\n",
+      "Epoch 713, Loss: 1.0009\n",
+      "Epoch 714, Loss: 1.1245\n",
+      "Epoch 715, Loss: 1.0600\n",
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+      "Epoch 717, Loss: 1.0701\n",
+      "Epoch 718, Loss: 0.9629\n",
+      "Epoch 719, Loss: 1.1114\n",
+      "Epoch 720, Loss: 0.9759\n",
+      "Epoch 721, Loss: 1.0449\n",
+      "Epoch 722, Loss: 1.0905\n",
+      "Epoch 723, Loss: 1.0104\n",
+      "Epoch 724, Loss: 1.0495\n",
+      "Epoch 725, Loss: 0.9844\n",
+      "Epoch 726, Loss: 1.0411\n",
+      "Epoch 727, Loss: 1.0766\n",
+      "Epoch 728, Loss: 0.9971\n",
+      "Epoch 729, Loss: 1.0387\n",
+      "Epoch 730, Loss: 0.9401\n",
+      "Epoch 731, Loss: 1.0208\n",
+      "Epoch 732, Loss: 0.9844\n",
+      "Epoch 733, Loss: 0.9645\n",
+      "Epoch 734, Loss: 1.0244\n",
+      "Epoch 735, Loss: 1.0131\n",
+      "Epoch 736, Loss: 1.1054\n",
+      "Epoch 737, Loss: 1.0494\n",
+      "Epoch 738, Loss: 1.0273\n",
+      "Epoch 739, Loss: 1.0315\n",
+      "Epoch 740, Loss: 1.0082\n",
+      "Epoch 741, Loss: 0.9805\n",
+      "Epoch 742, Loss: 0.9965\n",
+      "Epoch 743, Loss: 1.0591\n",
+      "Epoch 744, Loss: 1.0505\n",
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+      "Epoch 746, Loss: 0.9966\n",
+      "Epoch 747, Loss: 1.0208\n",
+      "Epoch 748, Loss: 0.9672\n",
+      "Epoch 749, Loss: 1.0253\n",
+      "Epoch 750, Loss: 0.9980\n",
+      "Epoch 751, Loss: 0.9580\n",
+      "Epoch 752, Loss: 1.0006\n",
+      "Epoch 753, Loss: 1.0295\n",
+      "Epoch 754, Loss: 0.9943\n",
+      "Epoch 755, Loss: 1.0126\n",
+      "Epoch 756, Loss: 1.1166\n",
+      "Epoch 757, Loss: 0.9941\n",
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+      "Epoch 760, Loss: 0.9982\n",
+      "Epoch 761, Loss: 1.0877\n",
+      "Epoch 762, Loss: 1.0032\n",
+      "Epoch 763, Loss: 1.0149\n",
+      "Epoch 764, Loss: 1.0212\n",
+      "Epoch 765, Loss: 1.0467\n",
+      "Epoch 766, Loss: 1.0839\n",
+      "Epoch 767, Loss: 0.9717\n",
+      "Epoch 768, Loss: 1.0227\n",
+      "Epoch 769, Loss: 1.0054\n",
+      "Epoch 770, Loss: 1.0243\n",
+      "Epoch 771, Loss: 0.9798\n",
+      "Epoch 772, Loss: 1.0160\n",
+      "Epoch 773, Loss: 1.0515\n",
+      "Epoch 774, Loss: 0.9757\n",
+      "Epoch 775, Loss: 1.0180\n",
+      "Epoch 776, Loss: 1.0118\n",
+      "Epoch 777, Loss: 1.1062\n",
+      "Epoch 778, Loss: 1.1067\n",
+      "Epoch 779, Loss: 1.0394\n",
+      "Epoch 780, Loss: 0.9847\n",
+      "Epoch 781, Loss: 1.0510\n",
+      "Epoch 782, Loss: 1.0717\n",
+      "Epoch 783, Loss: 0.9508\n",
+      "Epoch 784, Loss: 0.9805\n",
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+      "Epoch 786, Loss: 1.0377\n",
+      "Epoch 787, Loss: 0.9938\n",
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+      "Epoch 789, Loss: 0.9292\n",
+      "Epoch 790, Loss: 0.9976\n",
+      "Epoch 791, Loss: 1.0394\n",
+      "Epoch 792, Loss: 0.9567\n",
+      "Epoch 793, Loss: 1.0286\n",
+      "Epoch 794, Loss: 1.0730\n",
+      "Epoch 795, Loss: 1.0990\n",
+      "Epoch 796, Loss: 0.9822\n",
+      "Epoch 797, Loss: 0.9571\n",
+      "Epoch 798, Loss: 1.0560\n",
+      "Epoch 799, Loss: 1.0014\n",
+      "Epoch 800, Loss: 1.0186\n",
+      "Epoch 801, Loss: 1.0636\n",
+      "Epoch 802, Loss: 1.0197\n",
+      "Epoch 803, Loss: 1.0491\n",
+      "Epoch 804, Loss: 1.0215\n",
+      "Epoch 805, Loss: 1.0807\n",
+      "Epoch 806, Loss: 1.0357\n",
+      "Epoch 807, Loss: 1.0567\n",
+      "Epoch 808, Loss: 1.0193\n",
+      "Epoch 809, Loss: 1.0091\n",
+      "Epoch 810, Loss: 0.9913\n",
+      "Epoch 811, Loss: 1.0125\n",
+      "Epoch 812, Loss: 1.0346\n",
+      "Epoch 813, Loss: 1.0199\n",
+      "Epoch 814, Loss: 1.0436\n",
+      "Epoch 815, Loss: 0.9729\n",
+      "Epoch 816, Loss: 1.0371\n",
+      "Epoch 817, Loss: 0.9767\n",
+      "Epoch 818, Loss: 1.1002\n",
+      "Epoch 819, Loss: 1.0366\n",
+      "Epoch 820, Loss: 0.9979\n",
+      "Epoch 821, Loss: 0.9776\n",
+      "Epoch 822, Loss: 0.9950\n",
+      "Epoch 823, Loss: 1.0269\n",
+      "Epoch 824, Loss: 1.0000\n",
+      "Epoch 825, Loss: 1.0292\n",
+      "Epoch 826, Loss: 0.9678\n",
+      "Epoch 827, Loss: 0.9836\n",
+      "Epoch 828, Loss: 1.0299\n",
+      "Epoch 829, Loss: 1.0174\n",
+      "Epoch 830, Loss: 0.9700\n",
+      "Epoch 831, Loss: 0.9714\n",
+      "Epoch 832, Loss: 1.0408\n",
+      "Epoch 833, Loss: 0.9971\n",
+      "Epoch 834, Loss: 1.0677\n",
+      "Epoch 835, Loss: 1.0487\n",
+      "Epoch 836, Loss: 1.0679\n",
+      "Epoch 837, Loss: 0.9679\n",
+      "Epoch 838, Loss: 0.9696\n",
+      "Epoch 839, Loss: 0.9968\n",
+      "Epoch 840, Loss: 1.0097\n",
+      "Epoch 841, Loss: 0.9474\n",
+      "Epoch 842, Loss: 1.0146\n",
+      "Epoch 843, Loss: 1.0739\n",
+      "Epoch 844, Loss: 0.9792\n",
+      "Epoch 845, Loss: 0.9668\n",
+      "Epoch 846, Loss: 0.9960\n",
+      "Epoch 847, Loss: 1.0426\n",
+      "Epoch 848, Loss: 1.0482\n",
+      "Epoch 849, Loss: 1.0077\n",
+      "Epoch 850, Loss: 0.9295\n",
+      "Epoch 851, Loss: 0.9861\n",
+      "Epoch 852, Loss: 0.9656\n",
+      "Epoch 853, Loss: 1.0424\n",
+      "Epoch 854, Loss: 1.0462\n",
+      "Epoch 855, Loss: 0.9708\n",
+      "Epoch 856, Loss: 1.0018\n",
+      "Epoch 857, Loss: 0.9965\n",
+      "Epoch 858, Loss: 1.0618\n",
+      "Epoch 859, Loss: 1.0917\n",
+      "Epoch 860, Loss: 1.0024\n",
+      "Epoch 861, Loss: 0.9716\n",
+      "Epoch 862, Loss: 0.9820\n",
+      "Epoch 863, Loss: 0.9604\n",
+      "Epoch 864, Loss: 0.9494\n",
+      "Epoch 865, Loss: 1.0040\n",
+      "Epoch 866, Loss: 1.0725\n",
+      "Epoch 867, Loss: 0.9402\n",
+      "Epoch 868, Loss: 0.9569\n",
+      "Epoch 869, Loss: 0.9578\n",
+      "Epoch 870, Loss: 0.9792\n",
+      "Epoch 871, Loss: 1.0313\n",
+      "Epoch 872, Loss: 1.1099\n",
+      "Epoch 873, Loss: 1.0076\n",
+      "Epoch 874, Loss: 0.9819\n",
+      "Epoch 875, Loss: 0.9581\n",
+      "Epoch 876, Loss: 1.0162\n",
+      "Epoch 877, Loss: 1.0309\n",
+      "Epoch 878, Loss: 1.0234\n",
+      "Epoch 879, Loss: 0.9820\n",
+      "Epoch 880, Loss: 1.0070\n",
+      "Epoch 881, Loss: 1.0033\n",
+      "Epoch 882, Loss: 0.9928\n",
+      "Epoch 883, Loss: 0.9885\n",
+      "Epoch 884, Loss: 1.0499\n",
+      "Epoch 885, Loss: 1.0258\n",
+      "Epoch 886, Loss: 1.0012\n",
+      "Epoch 887, Loss: 1.0318\n",
+      "Epoch 888, Loss: 0.9556\n",
+      "Epoch 889, Loss: 0.9865\n",
+      "Epoch 890, Loss: 1.0286\n",
+      "Epoch 891, Loss: 0.9312\n",
+      "Epoch 892, Loss: 1.0593\n",
+      "Epoch 893, Loss: 0.9846\n",
+      "Epoch 894, Loss: 1.0161\n",
+      "Epoch 895, Loss: 0.9943\n",
+      "Epoch 896, Loss: 1.0071\n",
+      "Epoch 897, Loss: 1.0253\n",
+      "Epoch 898, Loss: 0.9788\n",
+      "Epoch 899, Loss: 1.0321\n",
+      "Epoch 900, Loss: 1.0089\n",
+      "Epoch 901, Loss: 0.9479\n",
+      "Epoch 902, Loss: 1.0200\n",
+      "Epoch 903, Loss: 0.9732\n",
+      "Epoch 904, Loss: 1.0417\n",
+      "Epoch 905, Loss: 0.9928\n",
+      "Epoch 906, Loss: 1.0513\n",
+      "Epoch 907, Loss: 0.9971\n",
+      "Epoch 908, Loss: 0.9457\n",
+      "Epoch 909, Loss: 1.0042\n",
+      "Epoch 910, Loss: 0.9338\n",
+      "Epoch 911, Loss: 0.9996\n",
+      "Epoch 912, Loss: 1.0238\n",
+      "Epoch 913, Loss: 1.0053\n",
+      "Epoch 914, Loss: 1.0221\n",
+      "Epoch 915, Loss: 0.9499\n",
+      "Epoch 916, Loss: 0.9543\n",
+      "Epoch 917, Loss: 1.0481\n",
+      "Epoch 918, Loss: 1.0399\n",
+      "Epoch 919, Loss: 0.9779\n",
+      "Epoch 920, Loss: 1.1013\n",
+      "Epoch 921, Loss: 0.9648\n",
+      "Epoch 922, Loss: 0.9859\n",
+      "Epoch 923, Loss: 1.0230\n",
+      "Epoch 924, Loss: 1.0002\n",
+      "Epoch 925, Loss: 0.9890\n",
+      "Epoch 926, Loss: 1.0753\n",
+      "Epoch 927, Loss: 0.9709\n",
+      "Epoch 928, Loss: 1.0678\n",
+      "Epoch 929, Loss: 0.9847\n",
+      "Epoch 930, Loss: 0.9916\n",
+      "Epoch 931, Loss: 1.0073\n",
+      "Epoch 932, Loss: 0.9570\n",
+      "Epoch 933, Loss: 1.0065\n",
+      "Epoch 934, Loss: 1.0107\n",
+      "Epoch 935, Loss: 0.9541\n",
+      "Epoch 936, Loss: 1.0366\n",
+      "Epoch 937, Loss: 0.9737\n",
+      "Epoch 938, Loss: 0.9837\n",
+      "Epoch 939, Loss: 0.9953\n",
+      "Epoch 940, Loss: 0.9185\n",
+      "Epoch 941, Loss: 1.0178\n",
+      "Epoch 942, Loss: 1.0525\n",
+      "Epoch 943, Loss: 1.0036\n",
+      "Epoch 944, Loss: 1.0012\n",
+      "Epoch 945, Loss: 0.9150\n",
+      "Epoch 946, Loss: 1.0049\n",
+      "Epoch 947, Loss: 1.0104\n",
+      "Epoch 948, Loss: 0.9501\n",
+      "Epoch 949, Loss: 1.0457\n",
+      "Epoch 950, Loss: 0.9321\n",
+      "Epoch 951, Loss: 1.0141\n",
+      "Epoch 952, Loss: 1.0073\n",
+      "Epoch 953, Loss: 1.0667\n",
+      "Epoch 954, Loss: 0.9527\n",
+      "Epoch 955, Loss: 1.0648\n",
+      "Epoch 956, Loss: 1.0016\n",
+      "Epoch 957, Loss: 0.9698\n",
+      "Epoch 958, Loss: 1.0739\n",
+      "Epoch 959, Loss: 1.0756\n",
+      "Epoch 960, Loss: 0.9634\n",
+      "Epoch 961, Loss: 1.0738\n",
+      "Epoch 962, Loss: 1.0723\n",
+      "Epoch 963, Loss: 0.9582\n",
+      "Epoch 964, Loss: 1.0509\n",
+      "Epoch 965, Loss: 1.0211\n",
+      "Epoch 966, Loss: 1.0141\n",
+      "Epoch 967, Loss: 1.0421\n",
+      "Epoch 968, Loss: 1.0022\n",
+      "Epoch 969, Loss: 1.0643\n",
+      "Epoch 970, Loss: 0.9596\n",
+      "Epoch 971, Loss: 1.0253\n",
+      "Epoch 972, Loss: 1.0250\n",
+      "Epoch 973, Loss: 0.9997\n",
+      "Epoch 974, Loss: 1.0384\n",
+      "Epoch 975, Loss: 1.0460\n",
+      "Epoch 976, Loss: 0.9859\n",
+      "Epoch 977, Loss: 1.0549\n",
+      "Epoch 978, Loss: 1.0294\n",
+      "Epoch 979, Loss: 1.0052\n",
+      "Epoch 980, Loss: 1.0403\n",
+      "Epoch 981, Loss: 1.0308\n",
+      "Epoch 982, Loss: 1.0407\n",
+      "Epoch 983, Loss: 0.9932\n",
+      "Epoch 984, Loss: 0.9542\n",
+      "Epoch 985, Loss: 1.0246\n",
+      "Epoch 986, Loss: 0.9303\n",
+      "Epoch 987, Loss: 0.9716\n",
+      "Epoch 988, Loss: 0.9975\n",
+      "Epoch 989, Loss: 1.0669\n",
+      "Epoch 990, Loss: 0.9814\n",
+      "Epoch 991, Loss: 0.9428\n",
+      "Epoch 992, Loss: 1.0494\n",
+      "Epoch 993, Loss: 0.9703\n",
+      "Epoch 994, Loss: 0.9299\n",
+      "Epoch 995, Loss: 1.0264\n",
+      "Epoch 996, Loss: 0.9883\n",
+      "Epoch 997, Loss: 0.9021\n",
+      "Epoch 998, Loss: 0.9277\n",
+      "Epoch 999, Loss: 1.0874\n"
+     ]
+    }
+   ],
+   "source": [
+    "loss_history = []\n",
+    "\n",
+    "for epoch in range(num_epochs):\n",
+    "    cnn_no_phase_mask.train()\n",
+    "\n",
+    "    images, gt = [], []\n",
+    "    for _ in range(batch_size):\n",
+    "        pip_no_phase_mask.update()\n",
+    "        img, g = pip_no_phase_mask.resolve()\n",
+    "        images.append(img)\n",
+    "        gt.append(g)\n",
+    "\n",
+    "    images = torch.stack(images)\n",
+    "    gt = torch.stack(gt).to(device, dtype=torch.float32)\n",
+    "\n",
+    "    optimizer_no_phase_mask.zero_grad(set_to_none=True)\n",
+    "    preds = cnn_no_phase_mask(images)\n",
+    "    # loss = loss_fn2D(preds, gt) + loss_fn(preds, gt)\n",
+    "    loss = loss_fn(preds, gt) + loss_fn(preds, gt)\n",
+    "    loss.backward()\n",
+    "    optimizer_no_phase_mask.step()\n",
+    "\n",
+    "    loss_history.append(loss.detach().cpu().item())\n",
+    "\n",
+    "    print(f\"Epoch {epoch}, Loss: {loss.detach().cpu().item():.4f}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "id": "e6621526",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x500 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize=(10, 5))\n",
+    "plt.plot(range(len(loss_history)), loss_history, label='Training Loss')\n",
+    "plt.yscale('log')\n",
+    "plt.xlabel('Epoch')\n",
+    "plt.ylabel('Loss')\n",
+    "plt.title('Training Loss over Time')\n",
+    "plt.grid(True)\n",
+    "plt.legend()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 81,
+   "id": "da9038bb",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "threshold_no_phase_mask = 1\n",
+    "\n",
+    "postprocessing_module_no_phase_mask = Postprocess(device, thresh=threshold_no_phase_mask, radius=radius, keep_singlez=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 84,
+   "id": "6bc12a53",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "tensor(0.6028)\n"
+     ]
+    }
+   ],
+   "source": [
+    "jaccard_index = []\n",
+    "jaccard_index_coords = []\n",
+    "\n",
+    "for _ in range(num_tests):\n",
+    "    pip_no_phase_mask.update()\n",
+    "    img, g = pip_no_phase_mask.resolve()\n",
+    "    g = g.to(device)\n",
+    "\n",
+    "    pred = cnn_no_phase_mask(img.unsqueeze(0))\n",
+    "    \n",
+    "    jaccard_index.append(jaccard_coeff(pred, g, postprocessing_module_no_phase_mask))\n",
+    "\n",
+    "average_jaccard_index = torch.mean(torch.tensor(jaccard_index))\n",
+    "print(average_jaccard_index)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a688d5ba",
+   "metadata": {},
+   "source": [
+    "Adjust the threshold for the data without the phase mask optimization?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 82,
+   "id": "6c077de8",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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RMAEAAAAAAAAAAJVHwAQAAAAAAAAAAFQeARMAAAAAAAAAAFB5BEwAAAAAAAAAAEDlETABAAAAAAAAAACVR8AEAAAAAAAAAABI1f1/GRsB7IS5qTAAAAAASUVORK5CYII=",
+      "text/plain": [
+       "<Figure size 2000x2500 with 20 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "num_samples = 5\n",
+    "fig, ax = plt.subplots(num_samples, 4, figsize=(20, 5*num_samples))\n",
+    "\n",
+    "images, gt = [], []\n",
+    "for _ in range(num_samples):\n",
+    "    pip_no_phase_mask.update()\n",
+    "    img, g = pip_no_phase_mask.resolve()\n",
+    "    images.append(img)\n",
+    "    gt.append(g)\n",
+    "\n",
+    "images = torch.stack(images)\n",
+    "gt = torch.stack(gt)\n",
+    "\n",
+    "preds = cnn_no_phase_mask(images)\n",
+    "\n",
+    "for i in range(num_samples):\n",
+    "\n",
+    "    xyz_rec, conf_rec = postprocessing_module_no_phase_mask(preds[i])\n",
+    "\n",
+    "    xyz_target = torch.nonzero(gt[i])[:, [2,1,0]]\n",
+    "\n",
+    "    ax[i, 0].imshow(images[i, 0].cpu().detach().numpy(), cmap='gray')\n",
+    "    ax[i, 1].imshow(preds[i].max(dim=0)[0].cpu().detach().numpy())\n",
+    "    ax[i, 2].imshow(gt[i].max(dim=0)[0].cpu().detach().numpy())\n",
+    "    ax[i, 2].plot(xyz_rec[:, 0], xyz_rec[:,1], c='red', marker='.', ls=' ')\n",
+    "    ax[i, 3].imshow(gt[i].max(dim=0)[0].cpu().detach().numpy())\n",
+    "\n",
+    "ax[0, 0].set_title(\"Input image\")\n",
+    "ax[0, 1].set_title(\"Prediction\")\n",
+    "ax[0, 2].set_title(\"Ground truth + prediction in red\")\n",
+    "ax[0, 3].set_title(\"Ground truth\");"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 83,
+   "id": "7640bb1d",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1400x700 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sample = 0\n",
+    "\n",
+    "fig = plt.figure(figsize=(14, 7))\n",
+    "\n",
+    "ax1 = fig.add_subplot(121)\n",
+    "ax1.imshow(images[sample, 0].cpu().detach().numpy(), cmap='gray')\n",
+    "ax1.set_xlabel(\"y\")\n",
+    "ax1.set_ylabel(\"x\")\n",
+    "ax1.set_title(\"Input image\")\n",
+    "\n",
+    "\n",
+    "xyz_rec, conf_rec = postprocessing_module_no_phase_mask(preds[sample])\n",
+    "# xyz_rec, conf_rec = postprocessing_module(preds[sample])\n",
+    "xyz_target = torch.nonzero(gt[sample])[:, [2,1,0]]\n",
+    "\n",
+    "ax2 = fig.add_subplot(122, projection='3d')\n",
+    "ax2.scatter(xyz_target[:, 0], xyz_target[:, 1], xyz_target[:, 2], c='red', marker='o', s=100, label='Ground truth', alpha=0.3)\n",
+    "ax2.scatter(xyz_rec[:, 0], xyz_rec[:, 1], xyz_rec[:, 2], c='blue', marker='o', s=30, label='Prediction', alpha=1)\n",
+    "\n",
+    "ax2.set_xlabel('x')\n",
+    "ax2.set_ylabel('y')\n",
+    "ax2.set_zlabel('z')\n",
+    "ax2.set_xlim(0, image_size)\n",
+    "ax2.set_ylim(0, image_size)\n",
+    "ax2.set_zlim(0, depth)\n",
+    "ax2.set_title('Predicted & Ground truth positions')\n",
+    "ax2.legend();"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "2df81a3a",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}