Merge pull request #17 from hainingbaby/master

add evaluate_all_image()

Author: kevin369ml

01 cats vs dogs/training.py

@@ -161,8 +161,59 @@ def run_training():
 #                print('This is a cat with possibility %.6f' %prediction[:, 0])
 #            else:
 #                print('This is a dog with possibility %.6f' %prediction[:, 1])
-
-
+#
+def evaluate_all_image():
+	'''
+	Test all image against the saved models and parameters.
+	Return global accuracy of test_image_set
+	##############################################
+	##Notice that test image must has label to compare the prediction and real
+	##############################################
+	'''
+    # you need to change the directories to yours.
+	test_dir = '/home/kevin/tensorflow/cats_vs_dogs/data/test/'
+    N_CLASSES = 2
+    print('-------------------------')
+    test, test_label = input_data.get_files(test_dir)
+    BATCH_SIZE = len(test)
+    print('There are %d test images totally..' % BATCH_SIZE)
+    print('-------------------------')
+    test_batch, test_label_batch = input_data.get_batch(test,
+                                                          test_label,
+                                                          IMG_W,
+                                                          IMG_H,
+                                                          BATCH_SIZE, 
+                                                          CAPACITY)
+	
+	logits = model.inference(test_batch, BATCH_SIZE, N_CLASSES)
+    testloss = model.losses(logits, test_label_batch) 
+    testacc = model.evaluation(logits, test_label_batch)
+	
+	logs_train_dir = '/home/kevin/tensorflow/cats_vs_dogs/logs/train/'              
+    saver = tf.train.Saver()
+        
+    with tf.Session() as sess:
+        print("Reading checkpoints...")
+        ckpt = tf.train.get_checkpoint_state(logs_train_dir)
+        if ckpt and ckpt.model_checkpoint_path:
+            global_step = ckpt.model_checkpoint_path.split('/')[-1].split('-')[-1]
+            saver.restore(sess, ckpt.model_checkpoint_path)
+            print('Loading success, global_step is %s' % global_step)
+        else:
+            print('No checkpoint file found')
+        print('-------------------------')        
+        coord = tf.train.Coordinator()
+        threads = tf.train.start_queue_runners(sess=sess, coord=coord)
+        test_loss,test_acc = sess.run([testloss,testacc])
+        print('The model\'s loss is %.2f' %test_loss)
+        correct = int(BATCH_SIZE*test_acc)
+        print('Correct : %d' % correct)
+        print('Wrong : %d' % (BATCH_SIZE - correct))
+        print('The accuracy in test images are %.2f%%' %(test_acc*100.0))
+    coord.request_stop()
+    coord.join(threads)
+    sess.close()
+ 
 #%%