Update AlphaS2S.py

AlphaS2S.py

@@ -182,15 +182,7 @@ class SwiGLU(layers.Layer):
         x_proj = self.proj(x)
         x_val, x_gate = tf.split(x_proj, 2, axis=-1)
         return self.out(x_val * tf.nn.silu(x_gate))
-        
-class CrossBlock(layers.Layer):
-    def __init__(self):
-        super().__init__()
-        self.alpha = layers.Dense(1, activation='sigmoid', dtype='float32')
-    def call(self, x, z):
-        a = self.alpha(x)
-        y = a * x + (1.0 - a) * z
-        return y
+     
 class gMLPBlock(layers.Layer):
     def __init__(self, d_model, seq_len, dropout=0.1):
         super().__init__()
@@ -248,8 +240,6 @@ class LoU(layers.Layer):
         self.norm1 = layers.LayerNormalization(epsilon=1e-5, dtype='float32')
         
         self.alpha_linear = layers.Dense(1, activation='sigmoid', dtype='float32')
-
-        self.cross = CrossBlock()
         self.glu = SwiGLU(d_model, d_model)
 
     def _ema_over_time(self, score, alpha_dynamic):
@@ -295,7 +285,7 @@ class LoU(layers.Layer):
         
         # LoU 블록에서는 x_comb + residual 후 CrossBlock을 통과
         out = self.norm(x_comb + residual)
-        out = self.cross(out, z) # z는 인코더 출력 (enc_out)
+        out = out + z
         out = self.glu(out)
         return tf.cast(out, x.dtype)