diff --git a/priorConditionedAnnealing/noise.py b/priorConditionedAnnealing/noise.py
index 2d5d8ea..9247e05 100644
--- a/priorConditionedAnnealing/noise.py
+++ b/priorConditionedAnnealing/noise.py
@@ -35,7 +35,9 @@ class White_Noise():
     def __init__(self, known_shape=None):
         self.known_shape = known_shape
 
-    def __call__(self, shape, latent: th.Tensor = None) -> th.Tensor:
+    def __call__(self, shape=None, latent: th.Tensor = None) -> th.Tensor:
+        if shape == None:
+            shape = self.known_shape
         return th.Tensor(np.random.normal(0, 1, shape))
 
 
@@ -99,7 +101,9 @@ class Perlin_Noise():
         self.normal_factor = 14/99
         self.reset()
 
-    def __call__(self, shape):
+    def __call__(self, shape=None):
+        if shape == None:
+            shape = self.known_shape
         self.index += 1
         noise = [self.noise([self.index*self.scale, self.magic*a]) / self.normal_factor
                  for a in range(shape[-1])]
@@ -114,16 +118,19 @@ class Harmonic_Perlin_Noise():
     def __init__(self, known_shape=None, scale=0.1, octaves=8):
         self.known_shape = known_shape
         self.scale = scale
+        assert octaves >= 1
         if type(octaves) in [int, float]:
             int_octaves = int(octaves)
             octaves_arr = [1/(i+1) for i in range(int_octaves)]
-            if type(octaves) == float:
+            if int_octaves != octaves:
                 octaves_arr += [1/(int_octaves+2)*(octaves-int_octaves)]
         octaves_arr = np.array(octaves_arr)
         self.octaves = octaves_arr / np.linalg.norm(octaves_arr)
         self.reset()
 
-    def __call__(self, shape):
+    def __call__(self, shape=None):
+        if shape == None:
+            shape = self.known_shape
         harmonics = [noise(shape)*self.octaves[i] for i, noise in enumerate(self.noises)]
         return sum(harmonics)