diff --git a/.gitignore b/.gitignore
index 28665b5..8caffae 100644
--- a/.gitignore
+++ b/.gitignore
@@ -1,4 +1,4 @@
 .idea
 leela-zero
-.pyc
+*.pyc
 parameters
diff --git a/AlphaGo/Network.py b/AlphaGo/Network.py
index ef77e21..72852d6 100644
--- a/AlphaGo/Network.py
+++ b/AlphaGo/Network.py
@@ -3,6 +3,7 @@ import numpy as np
 import time
 import multi_gpu
 import tensorflow.contrib.layers as layers
+import sys
 
 def residual_block(input, is_training):
 	normalizer_params = {'is_training': is_training,
@@ -96,15 +97,30 @@ def train():
 
 def forward(board):
 	result_path = "./results/"
-	with multi_gpu.create_session() as sess:
-		sess.run(tf.global_variables_initializer())
-		ckpt_file = tf.train.latest_checkpoint(result_path)
-		if ckpt_file is not None:
-			print('Restoring model from {}...'.format(ckpt_file))
-			saver.restore(sess, ckpt_file)
-		else:
-			raise ValueError("No model loaded")
-		return sess.run([p,v], feed_dict={x:board})
+        itflag = False
+        res = None
+        if board is None:
+                board = np.load("/home/yama/tongzheng/AG/self_play_204/d7d7d552b7be4b51883de99d74a8e51b.npz")
+                board = board["boards"][100].reshape(-1, 19, 19, 17)
+                result_path = "../parameters/checkpoints"
+                itflag = True
+        with multi_gpu.create_session() as sess:
+                sess.run(tf.global_variables_initializer())
+                ckpt_file = tf.train.latest_checkpoint(result_path)
+                if ckpt_file is not None:
+                	print('Restoring model from {}...'.format(ckpt_file))
+                	saver.restore(sess, ckpt_file)
+                else:
+                	raise ValueError("No model loaded")
+                res = sess.run([tf.nn.softmax(p),v], feed_dict={x:board, is_training:itflag})
+                #res = sess.run([tf.nn.softmax(p),v], feed_dict={x:fix_board["boards"][300].reshape(-1, 19, 19, 17), is_training:False})
+                #res = sess.run([tf.nn.softmax(p),v], feed_dict={x:fix_board["boards"][50].reshape(-1, 19, 19, 17), is_training:True})
+                print(res)
+                #print(res[0].tolist()[0])
+                #print(np.argmax(res[0]))
+        return res
 
-if __name__='main':
-	train()
+if __name__=='__main__':
+	#train()
+        if sys.argv[1] == "test":
+            forward(None)