Stats

.gitignore

@@ -13,3 +13,4 @@
 ./protos/pipeline_pb2.py
 ./protos/train_pb2.py
 ./protos/eval_pb2.py
+mnist_data/*

helpers.py

@@ -13,22 +13,29 @@ def _xentropy_loss_op(logit,label,name):
 		return:
 	 		mean cross entropy
 	"""
-	return tf.reduce_mean(
-				tf.nn.sparse_softmax_cross_entropy_with_logits(
-					logits=logit,
-					labels=label),name=name)
+	with tf.name_scope("LOSS"):
+		loss = tf.reduce_mean(
+					tf.nn.sparse_softmax_cross_entropy_with_logits(
+						logits=logit,
+						labels=label),name=name)
+	tf.summary.scalar("LOSS", loss)
+	return loss
 
 def _eval_op(logits,label,name):
 	"""
 		Construct accuracy operation
 		Args:
 			logits -> output units of network rank 0
 			label  -> matching labels rank 0
+      		name   -> name scope of evaluation op
 		return:
 	 		mean accuracy op
 	"""
 	with tf.name_scope(name):
-		return tf.reduce_mean(tf.cast(tf.nn.in_top_k(logits,label,1),tf.float32))
+		accuracy = tf.reduce_mean(tf.cast(tf.nn.in_top_k(logits,label,1),tf.float32))
+
+	tf.summary.scalar("ACCURACY", accuracy)
+	return accuracy
 
 
 class Helper:
@@ -94,8 +101,10 @@ def get_acc(predictions,
                  batched_tensors: output of get_inputs
            Returns:
                  accs_dict : dict mapping labels to accuracy operations
+				 scalar_updates : any updates for keeping stats
         """
         accs_dict = {}
+		scalar_updates = []
         labels = {label:
                     tensor for label, tensor in batched_tensors.items()
                                 if label != "input"}
@@ -105,7 +114,26 @@ def get_acc(predictions,
                                      name=label)
             accs_dict[label] = acc
 
-        return accs_dict
+			acc_avg = tf.train.ExponentialMovingAverage(0.9, name='moving_avg')
+			acc_avg_op = acc_avg.apply([acc])
+
+			tf.summary.scalar('acc_for_' + str(label), acc_avg.average(acc))
+			scalar_updates.append(acc_avg_op)
+
+
+		'''does the total acc summary stuff'''
+		sum_acc = 0.0
+		for k, v in accs_dict.items():
+			sum_acc += v
+
+        acc_avg = tf.train.ExponentialMovingAverage(0.9,name='moving_avg')
+        acc_avg_op = acc_avg.apply([sum_acc])
+
+        tf.summary.scalar('SUM_ACC',acc_avg.average(sum_acc))
+        scalar_updates.append(acc_avg_op)
+
+
+        return accs_dict, scalar_updates
 
     @staticmethod
     def get_loss(predictions,
@@ -117,9 +145,10 @@ def get_loss(predictions,
               batched_tensors: output of get_inputs
            Returns:
               loss: the combined loss for all labels
-              scalar_updates : any updates for keeping stats
+              scalar_updates : any updates for keeping stats   **last item in list is the total loss
         """
         losses = []
+		scalar_updates = []
         labels = {label:
                     tensor for label, tensor in batched_tensors.items()
                                 if label != "input"}
@@ -130,6 +159,14 @@ def get_loss(predictions,
             tf.losses.add_loss(loss,
                                 tf.GraphKeys.LOSSES)
             losses.append(loss)
+
+			loss_avg = tf.train.ExponentialMovingAverage(0.9, name='moving_avg')
+			loss_avg_op = loss_avg.apply([loss])
+			tf.summary.scalar('loss_for_' + str(label), loss_avg.average(loss))
+			scalar_updates.append(loss_avg_op)
+
+
+		'''does the total loss summary stuff'''
         loss = tf.reduce_sum(losses,
                              name = "total_loss")
 
@@ -141,6 +178,6 @@ def get_loss(predictions,
         #log loss and shadow variables for avg loss
         #tf.summary.scalar(loss.op.name+' (raw)',loss)
         tf.summary.scalar(loss.op.name,loss_avg.average(loss))
-        scalar_updates = [loss_avg_op]
+        scalar_updates.append(loss_avg_op)
 
         return loss, scalar_updates