diff --git a/mnist_cnn_train.py b/mnist_cnn_train.py
index 5415209..9bb62aa 100644
--- a/mnist_cnn_train.py
+++ b/mnist_cnn_train.py
@@ -49,7 +49,7 @@ def train():
         loss = slim.losses.softmax_cross_entropy(y,y_)
 
     # Create a summary to monitor loss tensor
-    tf.scalar_summary('loss', loss)
+    tf.summary.scalar('loss', loss)
 
     # Define optimizer
     with tf.name_scope("ADAM"):
@@ -67,7 +67,7 @@ def train():
         train_step = tf.train.AdamOptimizer(learning_rate).minimize(loss,global_step=batch)
 
     # Create a summary to monitor learning_rate tensor
-    tf.scalar_summary('learning_rate', learning_rate)
+    tf.summary.scalar('learning_rate', learning_rate)
 
     # Get accuracy of model
     with tf.name_scope("ACC"):
@@ -75,10 +75,10 @@ def train():
         accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
 
     # Create a summary to monitor accuracy tensor
-    tf.scalar_summary('acc', accuracy)
+    tf.summary.scalar('acc', accuracy)
 
     # Merge all summaries into a single op
-    merged_summary_op = tf.merge_all_summaries()
+    merged_summary_op = tf.summary.merge_all()
 
     # Add ops to save and restore all the variables
     saver = tf.train.Saver()
@@ -89,7 +89,7 @@ def train():
     total_batch = int(train_size / batch_size)
 
     # op to write logs to Tensorboard
-    summary_writer = tf.train.SummaryWriter(LOGS_DIRECTORY, graph=tf.get_default_graph())
+    summary_writer = tf.summary.FileWriter(LOGS_DIRECTORY, graph=tf.get_default_graph())
 
     # Save the maximum accuracy value for validation data
     max_acc = 0.