edit output logits

Author: shaohua0116

evaler.py

@@ -13,7 +13,7 @@
 import time
 import numpy as np
 import tensorflow as tf
-import h5py
+
 
 class EvalManager(object):
     def __init__(self):
@@ -183,13 +183,15 @@ def log_step_message(self, step, accuracy, step_time, is_train=False):
                          )
                )
 
+
 def check_data_path(path):
     if os.path.isfile(os.path.join(path, 'data.hy')) \
            and os.path.isfile(os.path.join(path, 'id.txt')):
         return True
     else:
         return False
 
+
 def main():
     import argparse
     parser = argparse.ArgumentParser()
@@ -204,7 +206,7 @@ def main():
     path = os.path.join('./datasets', config.dataset_path)
 
     if check_data_path(path):
-        import  sort_of_clevr as dataset
+        import sort_of_clevr as dataset
     else:
         raise ValueError(path)
 

model_baseline.py

@@ -99,8 +99,9 @@ def C(img, q, scope='Classifier'):
                 print('{} {}'.format(scope.name, fc_3))
                 return fc_3
 
-        self.all_preds = C(self.img, self.q, scope='Classifier')
-        self.loss, self.accuracy = build_loss(self.all_preds, self.a)
+        logits = C(self.img, self.q, scope='Classifier')
+        self.all_preds = tf.nn.softmax(logits)
+        self.loss, self.accuracy = build_loss(logits, self.a)
 
         # Add summaries
         def draw_iqa(img, q, target_a, pred_a):

model_rn.py

@@ -146,8 +146,9 @@ def f_phi(g, scope='f_phi'):
                 return fc_3
 
         g = CONV(self.img, self.q, scope='CONV')
-        self.all_preds = f_phi(g, scope='f_phi')
-        self.loss, self.accuracy = build_loss(self.all_preds, self.a)
+        logits = f_phi(g, scope='f_phi')
+        self.all_preds = tf.nn.softmax(logits)
+        self.loss, self.accuracy = build_loss(logits, self.a)
 
         # Add summaries
         def draw_iqa(img, q, target_a, pred_a):