Fix bugs and Add test data

README.md

@@ -34,3 +34,7 @@ Added a trainable version of the VGG19 `vgg19_trainable`. It support train from
 A very simple testing is added `test_vgg19_trainable`, switch has demo about how to train, switch off train mode for verification, and how to save.
 
 A seperated file is added (instead of changing existing one) because I want to keep the simplicity of the original VGG networks.
+##Update 2018.03.07:
+fix the tensorflow-version-bugs in vgg16.py, which are tf.split() and tf.concat().
+modify the sytle of print in Vgg16.print_prob() in utils.py, and the result of prediction is more neat and clear after the modifications. 
+add a few pictures to the test_data floder, which can be used to assess the ability of prediction of VGG16

utils.py

@@ -32,12 +32,16 @@ def print_prob(prob, file_path):
     # print prob
     pred = np.argsort(prob)[::-1]
 
+    print('\n')#to make the displayed information more clear and neat
+    print("-------The predictions are as follow-------")
     # Get top1 label
     top1 = synset[pred[0]]
     print("Top1: ", top1, prob[pred[0]])
     # Get top5 label
     top5 = [(synset[pred[i]], prob[pred[i]]) for i in range(5)]
-    print("Top5: ", top5)
+    print("Top5: ")#to make the displayed information more clear and neat
+    for i in range(5):
+        print(top5[i])
     return top1
 
 

vgg16.py

@@ -15,7 +15,7 @@ def __init__(self, vgg16_npy_path=None):
             path = os.path.abspath(os.path.join(path, os.pardir))
             path = os.path.join(path, "vgg16.npy")
             vgg16_npy_path = path
-            print path
+            print(path)#Python version 3.6 and above don not support the using"print path" but "print(path)"
 
         self.data_dict = np.load(vgg16_npy_path, encoding='latin1').item()
         print("npy file loaded")
@@ -25,18 +25,21 @@ def build(self, rgb):
         load variable from npy to build the VGG
 
         :param rgb: rgb image [batch, height, width, 3] values scaled [0, 1]
+         param rgb must be a tensor.
         """
 
         start_time = time.time()
         print("build model started")
         rgb_scaled = rgb * 255.0
 
         # Convert RGB to BGR
-        red, green, blue = tf.split(3, 3, rgb_scaled)
+        #In version 1.6.0, tf.split(value, num_or_size_splits, axis=0, num=None, name='split')
+        red, green, blue = tf.split(value=rgb_scaled,num_or_size_splits=[1,1,1],axis=3)
         assert red.get_shape().as_list()[1:] == [224, 224, 1]
         assert green.get_shape().as_list()[1:] == [224, 224, 1]
         assert blue.get_shape().as_list()[1:] == [224, 224, 1]
-        bgr = tf.concat(3, [
+        #when the function is called, it is better that the keyword parameters is transmitted explicitly. 
+        bgr = tf.concat(axis=3, values=[
             blue - VGG_MEAN[0],
             green - VGG_MEAN[1],
             red - VGG_MEAN[2],