refactored

src/rbm/layer.py

@@ -20,7 +20,7 @@ class RbmLayer:
 	def train_batch(self, v_states: np.ndarray, cd_func: Callable):
 		for epochs in range(self.params.num_epochs):
 			# Contrastive divergence learning: calculate gradients
-			cd_func(v_states, dwhv, dbh, dbv);
+			dwhv, dbh, dbv = cd_func(v_states)
 
 
 
@@ -36,9 +36,9 @@ if __name__ == "__main__":
 	v0 = uniform((1,2))
 	h0 = uniform((1,3))
 
-	h1 = l1.v_to_h(v0)
+	h1 = l1.state.v_to_h(v0)
 	print(h1.shape)
-	v1 = l1.h_to_v(h1)
+	v1 = l1.state.h_to_v(h1)
 	s_v1 = sample(v1)
 	p_h = prob(s_v1)