diff --git a/11_deep_learning.ipynb b/11_deep_learning.ipynb
index 27d8e7e..5c440ec 100644
--- a/11_deep_learning.ipynb
+++ b/11_deep_learning.ipynb
@@ -504,7 +504,7 @@
     "    xentropy = tf.nn.sparse_softmax_cross_entropy_with_logits(labels=y, logits=logits)\n",
     "    reg_losses = tf.get_collection(tf.GraphKeys.REGULARIZATION_LOSSES)\n",
     "    base_loss = tf.reduce_mean(xentropy, name=\"base_loss\")\n",
-    "    loss = tf.add(base_loss, reg_losses, name=\"loss\")\n",
+    "    loss = tf.add_n([base_loss] + reg_losses, name=\"loss\")\n",
     "\n",
     "with tf.name_scope(\"train\"):\n",
     "    optimizer = tf.train.MomentumOptimizer(learning_rate, momentum)\n",
@@ -749,6 +749,7 @@
     "        for iteration in range(len(mnist.test.labels)//batch_size):\n",
     "            X_batch, y_batch = mnist.train.next_batch(batch_size)\n",
     "            sess.run(training_op, feed_dict={is_training: True, X: X_batch, y: y_batch})\n",
+    "            sess.run(clip_all_weights)\n",
     "        acc_train = accuracy.eval(feed_dict={is_training: False, X: X_batch, y: y_batch})\n",
     "        acc_test = accuracy.eval(feed_dict={is_training: False, X: mnist.test.images, y: mnist.test.labels})\n",
     "        print(epoch, \"Train accuracy:\", acc_train, \"Test accuracy:\", acc_test)\n",
diff --git a/15_autoencoders.ipynb b/15_autoencoders.ipynb
index 8868cc5..e1e20f5 100644
--- a/15_autoencoders.ipynb
+++ b/15_autoencoders.ipynb
@@ -393,7 +393,7 @@
     "mse = tf.reduce_mean(tf.square(outputs - X))\n",
     "\n",
     "reg_losses = tf.get_collection(tf.GraphKeys.REGULARIZATION_LOSSES)\n",
-    "loss = mse + reg_losses\n",
+    "loss = tf.add_n([mse] + reg_losses)\n",
     "\n",
     "optimizer = tf.train.AdamOptimizer(learning_rate)\n",
     "training_op = optimizer.minimize(loss)\n",
@@ -545,7 +545,7 @@
     "        mse = tf.reduce_mean(tf.square(outputs - X))\n",
     "\n",
     "        reg_losses = tf.get_collection(tf.GraphKeys.REGULARIZATION_LOSSES)\n",
-    "        loss = mse + reg_losses\n",
+    "        loss = tf.add_n([mse] + reg_losses)\n",
     "\n",
     "        optimizer = tf.train.AdamOptimizer(learning_rate)\n",
     "        training_op = optimizer.minimize(loss)\n",