diff --git a/TensorFlow_eaxmple/Model_train_test/model/ChannelAttention/DCT_channelAttention.py b/TensorFlow_eaxmple/Model_train_test/model/ChannelAttention/DCT_channelAttention.py
index 8752bc6..fd54eff 100644
--- a/TensorFlow_eaxmple/Model_train_test/model/ChannelAttention/DCT_channelAttention.py
+++ b/TensorFlow_eaxmple/Model_train_test/model/ChannelAttention/DCT_channelAttention.py
@@ -59,6 +59,12 @@ from scipy.fftpack import dct
 import tensorflow as tf
 
 
+'''
+参考：
+[1] https://github.com/Zero-coder/FECAM/blob/main/layers/dctnet.py
+[2] https://arxiv.org/pdf/2212.01209v1.pdf
+'''
+
 def sdct_tf(signals, frame_length, frame_step, window_fn=tf.signal.hamming_window):
     """Compute Short-Time Discrete Cosine Transform of `signals`.
 
@@ -130,6 +136,7 @@ class DCTChannelAttention(layers.Layer):
         self.drop1 = Dropout(0.1)
         self.relu = ReLU(0.1)
         self.l2 = Dense(channel, use_bias=False)
+        self.bn = BatchNormalization()
 
     def call(self, inputs, **kwargs):
         batch_size, hidden, channel = inputs.shape
@@ -141,11 +148,13 @@ class DCTChannelAttention(layers.Layer):
         #     list.append(freq)
         # stack_dct = tf.stack(list, dim=1)
 
+        lr_weight = self.bn(stack_dct)
         lr_weight = self.l1(stack_dct)
         lr_weight = self.drop1(lr_weight)
         lr_weight = self.relu(lr_weight)
         lr_weight = self.l2(lr_weight)
+        lr_weight = tf.sigmoid(lr_weight)
 
-        lr_weight = BatchNormalization()(lr_weight)
+        lr_weight = self.bn(lr_weight)
 
         return inputs * lr_weight