diff --git a/15_autoencoders.ipynb b/15_autoencoders.ipynb
index cbcab18..c62c07b 100644
--- a/15_autoencoders.ipynb
+++ b/15_autoencoders.ipynb
@@ -366,7 +366,7 @@
     "\n",
     "X = tf.placeholder(tf.float32, shape=[None, n_inputs])\n",
     "\n",
-    "he_init = tf.contrib.layers.variance_scaling_initializer() # He 초기화\n",
+    "he_init = tf.variance_scaling_initializer() # He 초기화\n",
     "#아래와 동일합니다:\n",
     "#he_init = lambda shape, dtype=tf.float32: tf.truncated_normal(shape, 0., stddev=np.sqrt(2/shape[0]))\n",
     "l2_regularizer = tf.contrib.layers.l2_regularizer(l2_reg)\n",
@@ -532,7 +532,7 @@
    "source": [
     "activation = tf.nn.elu\n",
     "regularizer = tf.contrib.layers.l2_regularizer(l2_reg)\n",
-    "initializer = tf.contrib.layers.variance_scaling_initializer()\n",
+    "initializer = tf.variance_scaling_initializer()\n",
     "\n",
     "X = tf.placeholder(tf.float32, shape=[None, n_inputs])\n",
     "\n",
@@ -680,7 +680,7 @@
     "        \n",
     "        my_dense_layer = partial(\n",
     "            tf.layers.dense,\n",
-    "            kernel_initializer=tf.contrib.layers.variance_scaling_initializer(),\n",
+    "            kernel_initializer=tf.variance_scaling_initializer(),\n",
     "            kernel_regularizer=tf.contrib.layers.l2_regularizer(l2_reg))\n",
     "\n",
     "        hidden = my_dense_layer(X, n_neurons, activation=hidden_activation, name=\"hidden\")\n",
@@ -823,7 +823,7 @@
     "\n",
     "activation = tf.nn.elu\n",
     "regularizer = tf.contrib.layers.l2_regularizer(l2_reg)\n",
-    "initializer = tf.contrib.layers.variance_scaling_initializer()\n",
+    "initializer = tf.variance_scaling_initializer()\n",
     "\n",
     "X = tf.placeholder(tf.float32, shape=[None, n_inputs])\n",
     "\n",
@@ -1119,7 +1119,7 @@
     "\n",
     "activation = tf.nn.elu\n",
     "regularizer = tf.contrib.layers.l2_regularizer(l2_reg)\n",
-    "initializer = tf.contrib.layers.variance_scaling_initializer()\n",
+    "initializer = tf.variance_scaling_initializer()\n",
     "\n",
     "X = tf.placeholder(tf.float32, shape=[None, n_inputs])\n",
     "y = tf.placeholder(tf.int32, shape=[None])\n",
@@ -1791,7 +1791,7 @@
     "n_outputs = n_inputs\n",
     "learning_rate = 0.001\n",
     "\n",
-    "initializer = tf.contrib.layers.variance_scaling_initializer()\n",
+    "initializer = tf.variance_scaling_initializer()\n",
     "\n",
     "my_dense_layer = partial(\n",
     "    tf.layers.dense,\n",
@@ -1940,7 +1940,7 @@
     "n_outputs = n_inputs\n",
     "learning_rate = 0.001\n",
     "\n",
-    "initializer = tf.contrib.layers.variance_scaling_initializer()\n",
+    "initializer = tf.variance_scaling_initializer()\n",
     "my_dense_layer = partial(\n",
     "    tf.layers.dense,\n",
     "    activation=tf.nn.elu,\n",