cleanup

Author: Z80coder

neurallogic/hard_majority.py

@@ -32,11 +32,8 @@ class SoftMajorityLayer(nn.Module):
         layer_size: The number of neurons in the layer.
         weights_init: The initializer function for the weight matrix.
     """
-    dtype: jax.numpy.dtype = jax.numpy.float32
-
     @nn.compact
     def __call__(self, x):
-        x = jax.numpy.asarray(x, self.dtype)  # TODO: remove me?
         return soft_majority_layer(x)
 
 
@@ -57,7 +54,7 @@ def __call__(self, x):
 
 
 majority_layer = neural_logic_net.select(
-    lambda dtype=jax.numpy.float32: SoftMajorityLayer(dtype),
-    lambda dtype=jax.numpy.float32: HardMajorityLayer(),
-    lambda dtype=jax.numpy.float32: SymbolicMajorityLayer()
+    lambda: SoftMajorityLayer(),
+    lambda: HardMajorityLayer(),
+    lambda: SymbolicMajorityLayer()
 )

neurallogic/hard_not.py

@@ -19,7 +19,6 @@ def soft_not(w: float, x: float) -> float:
 
 
 def hard_not(w: bool, x: bool) -> bool:
-    # ~(x ^ w)
     return jax.numpy.logical_not(jax.numpy.logical_xor(x, w))
 
 

neurallogic/hard_or.py

@@ -61,7 +61,7 @@ def __call__(self, x):
         weights_shape = (self.layer_size, jax.numpy.shape(x)[-1])
         weights = self.param(
             'bit_weights', self.weights_init, weights_shape, self.dtype)
-        x = jax.numpy.asarray(x, self.dtype) # TODO: remove me?
+        x = jax.numpy.asarray(x, self.dtype) 
         return soft_or_layer(weights, x)
 
 

neurallogic/hard_xor.py

@@ -53,7 +53,7 @@ def __call__(self, x):
         weights_shape = (self.layer_size, jax.numpy.shape(x)[-1])
         weights = self.param(
             'bit_weights', self.weights_init, weights_shape, self.dtype)
-        x = jax.numpy.asarray(x, self.dtype)  # TODO is this needed?
+        x = jax.numpy.asarray(x, self.dtype) 
         return soft_xor_layer(weights, x)
 
 

neurallogic/harden.py

@@ -62,17 +62,6 @@ def harden(x: flax.core.FrozenDict):
     return harden(x.unfreeze())
 
 
-"""
-@dispatch
-def harden(*args):
-    if len(args) == 1:
-        print(f'args = {args} of type {type(args)}')
-        arg = args[0]
-        print(f'args[0] = {arg}')
-        return tuple(harden(arg))
-    return tuple([harden(arg) for arg in args])
-"""
-
 @dispatch
 def map_keys_nested(f, d: dict) -> dict:
     return {

neurallogic/neural_logic_net.py

@@ -1,6 +1,5 @@
 from enum import Enum
 from flax import linen as nn
-from neurallogic import symbolic_generation
 
 NetType = Enum('NetType', ['Soft', 'Hard', 'Symbolic'])