diff --git a/x_ksh/SA_cnn_ps3.py b/x_ksh/SA_cnn_ps3.py
index 3c308b8..de5dc55 100644
--- a/x_ksh/SA_cnn_ps3.py
+++ b/x_ksh/SA_cnn_ps3.py
@@ -39,32 +39,32 @@
 p_keep_hidden = tf.placeholder(tf.float32, name="p_keep_hidden")
 
 # L1 SoundIn shape=(?, 20, 100, 1)
-W1 = tf.get_variable("W1", shape=[2, 2, 1, 32],initializer=tf.contrib.layers.xavier_initializer())
+W1 = tf.get_variable("W1", shape=[2, 10, 1, 32],initializer=tf.contrib.layers.xavier_initializer())
 L1 = tf.nn.conv2d(X_sound, W1, strides=[1, 1, 1, 1], padding='SAME')
 L1 = tf.nn.elu(L1)
-L1 = tf.nn.max_pool(L1, ksize=[1, 2, 2, 1],strides=[1, 2, 2, 1], padding='SAME') 
+L1 = tf.nn.max_pool(L1, ksize=[1, 3, 3, 1],strides=[1, 3, 3, 1], padding='SAME') 
 L1 = tf.nn.dropout(L1, p_keep_conv)
 
-# L2 Input shape=(?,10,50,32)
-W2 = tf.get_variable("W2", shape=[2, 2, 32, 64],initializer=tf.contrib.layers.xavier_initializer())
+# L2 Input shape=(?,7,34,32)
+W2 = tf.get_variable("W2", shape=[2, 10, 32, 64],initializer=tf.contrib.layers.xavier_initializer())
 L2 = tf.nn.conv2d(L1, W2, strides=[1, 1, 1, 1], padding='SAME')
 L2 = tf.nn.elu(L2)
-L2 = tf.nn.max_pool(L2, ksize=[1, 2, 2, 1],strides=[1, 2, 2, 1], padding='SAME') 
+L2 = tf.nn.max_pool(L2, ksize=[1, 3, 3, 1],strides=[1, 3, 3, 1], padding='SAME') 
 L2 = tf.nn.dropout(L2, p_keep_conv)
 
-# L3 Input shape=(?,5,25,64)
-W3 = tf.get_variable("W3", shape=[2, 2, 64, 128],initializer=tf.contrib.layers.xavier_initializer())
+# L3 Input shape=(?,3,12,64)
+W3 = tf.get_variable("W3", shape=[2, 10, 64, 128],initializer=tf.contrib.layers.xavier_initializer())
 L3 = tf.nn.conv2d(L2, W3, strides=[1, 1, 1, 1], padding='SAME')
 L3 = tf.nn.elu(L3)
-L3 = tf.nn.max_pool(L3, ksize=[1, 2, 2, 1],strides=[1, 2, 2, 1], padding='SAME') 
+L3 = tf.nn.max_pool(L3, ksize=[1, 3, 3, 1],strides=[1, 3, 3, 1], padding='SAME') 
 L3 = tf.nn.dropout(L3, p_keep_conv)
-L3_flat= tf.reshape(L3, shape=[-1, 3*13*128])
+L3_flat= tf.reshape(L3, shape=[-1, 4*128])
 
 # Final FC 2*3*128 inputs -> 41 outputs
-W4 = tf.get_variable("W4", shape=[3*13*128, 615],initializer=tf.contrib.layers.xavier_initializer())
+W4 = tf.get_variable("W4", shape=[4*128, 512],initializer=tf.contrib.layers.xavier_initializer())
 L4 = tf.nn.elu(tf.matmul(L3_flat, W4))
 L4 = tf.nn.dropout(L4, p_keep_hidden)
-W_o = tf.get_variable("W_o", shape=[615,41],initializer=tf.contrib.layers.xavier_initializer())
+W_o = tf.get_variable("W_o", shape=[512,41],initializer=tf.contrib.layers.xavier_initializer())
 b = tf.Variable(tf.random_normal([41]))
 logits = tf.matmul(L4, W_o) + b
 
@@ -111,13 +111,15 @@
 3) con2d layer * 3 + FC
 lr=0.0002, epoch = 300    
 p_keep_conv, p_keep_hidden = 0.8, 0.7
+win : (2, 10), (2,2), (2,2)
+max_pool : (2,5), (3,3), (3,3)
 accuracy: 51~60%
-4) window를 정사각형 모양으로 바꿈
-lr=0.0002, epoch = 300    
-p_keep_conv, p_keep_hidden = 0.8, 0.7
+4) 3에서 윈도우 조절
 win : (2, 10), (2,4), (2,3)
 max_pool : (2,5), (3,3), (3,3)
 accuracy: 53~65%
-    
-    
+5) 3에서 윈도우 조절2
+win : (2, 10), (2,10), (2,10)
+max_pool : (3,3), (3,3), (3,3)
+ 
 """
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