diff --git a/n2v/internals/N2V_DataGenerator.py b/n2v/internals/N2V_DataGenerator.py
index 33ca496..973a5ef 100644
--- a/n2v/internals/N2V_DataGenerator.py
+++ b/n2v/internals/N2V_DataGenerator.py
@@ -10,7 +10,7 @@ class N2V_DataGenerator():
     The 'N2V_DataGenerator' enables training and validation data generation for Noise2Void.
     """
 
-    def load_imgs(self, files, dims='YX'):
+    def load_imgs(self, files, to32bit, dims='YX'):
         """
         Helper to read a list of files. The images are not required to have same size,
         but have to be of same dimensionality.
@@ -21,7 +21,8 @@ def load_imgs(self, files, dims='YX'):
                  List of paths to tiff-files.
         dims   : String, optional(default='YX')
                  Dimensions of the images to read. Known dimensions are: 'TZYXC'
-
+        to32bit: makes conversion to 32 bit optional, if set to False the original datatype is used
+        
         Returns
         -------
         images : list(array(float))
@@ -65,7 +66,10 @@ def load_imgs(self, files, dims='YX'):
             else:
                 _raise("Filetype '{}' is not supported.".format(f))
 
-            img = imread(f).astype(np.float32)
+            if to32bit:
+                img = imread(f).astype(np.float32)
+            else:
+                img = imread(f)
             assert len(img.shape) == len(dims), "Number of image dimensions doesn't match 'dims'."
 
             img = np.moveaxis(img, move_axis_from, move_axis_to)
@@ -80,7 +84,7 @@ def load_imgs(self, files, dims='YX'):
 
         return imgs
 
-    def load_imgs_from_directory(self, directory, filter='*.tif', dims='YX'):
+    def load_imgs_from_directory(self, directory, filter='*.tif', dims='YX', names_back = False, to32bit = True):
         """
         Helper to read all files which match 'filter' from a directory. The images are not required to have same size,
         but have to be of same dimensionality.
@@ -94,15 +98,23 @@ def load_imgs_from_directory(self, directory, filter='*.tif', dims='YX'):
         dims      : String, optional(default='YX')
                     Dimensions of the images to read. Known dimensions are: 'TZYXC'
 
+        names_back: if set to True, the function returns the names of the input files as list
+
+        to32bit: makes conversion to 32 bit optional, if set to False the original datatype is used
+
         Returns
         -------
         images : list(array(float))
                  A list of the read tif-files. The images have dimensionality 'SZYXC' or 'SYXC'
+        (optional): files, names of the input files as list
         """
 
         files = glob(join(directory, filter))
         files.sort()
-        return self.load_imgs(files, dims=dims)
+        if names_back:
+            return files, self.load_imgs(files, to32bit, dims=dims)
+        else:
+            return self.load_imgs(files, to32bit, dims=dims)
 
 
     def generate_patches_from_list(self, data, num_patches_per_img=None, shape=(256, 256), augment=True, shuffle=False):
@@ -142,7 +154,7 @@ def generate_patches_from_list(self, data, num_patches_per_img=None, shape=(256,
 
         return patches
 
-    def generate_patches(self, data, num_patches=None, shape=(256, 256), augment=True):
+    def generate_patches(self, data, num_patches=None, shape=(256, 256), augment=True, shuffle_patches = True):
         """
         Extracts patches from 'data'. The patches can be augmented, which means they get rotated three times
         in XY-Plane and flipped along the X-Axis. Augmentation leads to an eight-fold increase in training data.
@@ -174,17 +186,18 @@ def generate_patches(self, data, num_patches=None, shape=(256, 256), augment=Tru
             if augment:
                 print("XY-Plane is not square. Omit augmentation!")
 
-        np.random.shuffle(patches)
-        print('Generated patches:', patches.shape)
+        if shuffle_patches:
+            np.random.shuffle(patches)
+        #print('Generated patches:', patches.shape)
         return patches
 
     def __extract_patches__(self, data, num_patches=None, shape=(256, 256), n_dims=2):
         if num_patches == None:
             patches = []
             if n_dims == 2:
-                if data.shape[1] > shape[0] and data.shape[2] > shape[1]:
-                    for y in range(0, data.shape[1] - shape[0], shape[0]):
-                        for x in range(0, data.shape[2] - shape[1], shape[1]):
+                if data.shape[1] >= shape[0] and data.shape[2] >= shape[1]:
+                    for y in range(0, data.shape[1] - shape[0]+1, shape[0]):
+                        for x in range(0, data.shape[2] - shape[1]+1, shape[1]):
                             patches.append(data[:, y:y + shape[0], x:x + shape[1]])
 
                     return np.concatenate(patches)
@@ -193,10 +206,10 @@ def __extract_patches__(self, data, num_patches=None, shape=(256, 256), n_dims=2
                 else:
                     print("'shape' is too big.")
             elif n_dims == 3:
-                if data.shape[1] > shape[0] and data.shape[2] > shape[1] and data.shape[3] > shape[2]:
-                    for z in range(0, data.shape[1] - shape[0],  shape[0]):
-                        for y in range(0, data.shape[2] - shape[1], shape[1]):
-                            for x in range(0, data.shape[3] - shape[2], shape[2]):
+                if data.shape[1] >= shape[0] and data.shape[2] >= shape[1] and data.shape[3] >= shape[2]:
+                    for z in range(0, data.shape[1] - shape[0]+1,  shape[0]):
+                        for y in range(0, data.shape[2] - shape[1]+1, shape[1]):
+                            for x in range(0, data.shape[3] - shape[2]+1, shape[2]):
                                 patches.append(data[:, z:z + shape[0], y:y + shape[1], x:x + shape[2]])
 
                     return np.concatenate(patches)