added metrics for object classification performance

metrics.py

+def fraction_in_top_k(y, y_pred):
+    y_list = tf.unpack(y)
+    y_pred_list = tf.unpack(y_pred)
+    accuracy = 0.0
+    for y_, y_pred_ in zip(y_list, y_pred_list):
+        k = tf.reduce_sum(y_)
+        pos_label_ids = tf.nn.top_k(y_,k)
+        pos_label_ids_list = tf.unpack(pos_label_ids)
+        num_pos_labels_in_top_k = 0.0
+        for pos_label_id in pos_label_ids_list:
+            is_present = tf.nn.in_top_k(y_pred_,pos_label_id,k)
+            num_pos_labels_in_top_k += tf.cast(is_present, tf.float32)
+
+        frac_pos_labels_in_top_k = num_pos_labels_in_top_k/tf.maximum(k,1e-5)
+        accuracy += frac_pos_labels_in_top_k
+    
+    accuracy /= len(y_list)
+    return accuracy
+
+def presence_in_top_k(y, y_pred, k):
+    max_ids = tf.argmax(y,1)
+    is_present = tf.nn.in_top_k(y_pred, max_ids, k)
+    is_present = tf.cast(is_present, tf.float32)
+    return tf.reduce_mean(is_present)
+