diff --git a/14_recurrent_neural_networks.ipynb b/14_recurrent_neural_networks.ipynb
index ba49bcf..d84171e 100644
--- a/14_recurrent_neural_networks.ipynb
+++ b/14_recurrent_neural_networks.ipynb
@@ -162,6 +162,13 @@
     "## Using `static_rnn()`"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Note**: `tf.contrib.rnn` was partially moved to the core API in TensorFlow 1.2. Most of the `*Cell` and `*Wrapper` classes are now available in `tf.nn.rnn_cell`, and the `tf.contrib.rnn.static_rnn()` function is available as `tf.nn.static_rnn()`."
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 7,
@@ -183,9 +190,9 @@
     "X0 = tf.placeholder(tf.float32, [None, n_inputs])\n",
     "X1 = tf.placeholder(tf.float32, [None, n_inputs])\n",
     "\n",
-    "basic_cell = tf.contrib.rnn.BasicRNNCell(num_units=n_neurons)\n",
-    "output_seqs, states = tf.contrib.rnn.static_rnn(basic_cell, [X0, X1],\n",
-    "                                                dtype=tf.float32)\n",
+    "basic_cell = tf.nn.rnn_cell.BasicRNNCell(num_units=n_neurons)\n",
+    "output_seqs, states = tf.nn.static_rnn(basic_cell, [X0, X1],\n",
+    "                                       dtype=tf.float32)\n",
     "Y0, Y1 = output_seqs"
    ]
   },
@@ -277,9 +284,9 @@
     "X = tf.placeholder(tf.float32, [None, n_steps, n_inputs])\n",
     "X_seqs = tf.unstack(tf.transpose(X, perm=[1, 0, 2]))\n",
     "\n",
-    "basic_cell = tf.contrib.rnn.BasicRNNCell(num_units=n_neurons)\n",
-    "output_seqs, states = tf.contrib.rnn.static_rnn(basic_cell, X_seqs,\n",
-    "                                                dtype=tf.float32)\n",
+    "basic_cell = tf.nn.rnn_cell.BasicRNNCell(num_units=n_neurons)\n",
+    "output_seqs, states = tf.nn.static_rnn(basic_cell, X_seqs,\n",
+    "                                       dtype=tf.float32)\n",
     "outputs = tf.transpose(tf.stack(output_seqs), perm=[1, 0, 2])"
    ]
   },
@@ -357,7 +364,7 @@
     "\n",
     "X = tf.placeholder(tf.float32, [None, n_steps, n_inputs])\n",
     "\n",
-    "basic_cell = tf.contrib.rnn.BasicRNNCell(num_units=n_neurons)\n",
+    "basic_cell = tf.nn.rnn_cell.BasicRNNCell(num_units=n_neurons)\n",
     "outputs, states = tf.nn.dynamic_rnn(basic_cell, X, dtype=tf.float32)"
    ]
   },
@@ -426,7 +433,7 @@
     "reset_graph()\n",
     "\n",
     "X = tf.placeholder(tf.float32, [None, n_steps, n_inputs])\n",
-    "basic_cell = tf.contrib.rnn.BasicRNNCell(num_units=n_neurons)"
+    "basic_cell = tf.nn.rnn_cell.BasicRNNCell(num_units=n_neurons)"
    ]
   },
   {
@@ -529,7 +536,7 @@
     "X = tf.placeholder(tf.float32, [None, n_steps, n_inputs])\n",
     "y = tf.placeholder(tf.int32, [None])\n",
     "\n",
-    "basic_cell = tf.contrib.rnn.BasicRNNCell(num_units=n_neurons)\n",
+    "basic_cell = tf.nn.rnn_cell.BasicRNNCell(num_units=n_neurons)\n",
     "outputs, states = tf.nn.dynamic_rnn(basic_cell, X, dtype=tf.float32)\n",
     "\n",
     "logits = tf.layers.dense(states, n_outputs)\n",
@@ -544,16 +551,26 @@
     "init = tf.global_variables_initializer()"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Warning**: `tf.examples.tutorials.mnist` is deprecated. We will use `tf.keras.datasets.mnist` instead."
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 35,
    "metadata": {},
    "outputs": [],
    "source": [
-    "from tensorflow.examples.tutorials.mnist import input_data\n",
-    "mnist = input_data.read_data_sets(\"/tmp/data/\")\n",
-    "X_test = mnist.test.images.reshape((-1, n_steps, n_inputs))\n",
-    "y_test = mnist.test.labels"
+    "(X_train, y_train), (X_test, y_test) = tf.keras.datasets.mnist.load_data()\n",
+    "X_train = X_train.astype(np.float32).reshape(-1, 28*28) / 255.0\n",
+    "X_test = X_test.astype(np.float32).reshape(-1, 28*28) / 255.0\n",
+    "y_train = y_train.astype(np.int32)\n",
+    "y_test = y_test.astype(np.int32)\n",
+    "X_valid, X_train = X_train[:5000], X_train[5000:]\n",
+    "y_valid, y_train = y_train[:5000], y_train[5000:]"
    ]
   },
   {
@@ -561,6 +578,29 @@
    "execution_count": 36,
    "metadata": {},
    "outputs": [],
+   "source": [
+    "def shuffle_batch(X, y, batch_size):\n",
+    "    rnd_idx = np.random.permutation(len(X))\n",
+    "    n_batches = len(X) // batch_size\n",
+    "    for batch_idx in np.array_split(rnd_idx, n_batches):\n",
+    "        X_batch, y_batch = X[batch_idx], y[batch_idx]\n",
+    "        yield X_batch, y_batch"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "X_test = X_test.reshape((-1, n_steps, n_inputs))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "n_epochs = 100\n",
     "batch_size = 150\n",
@@ -568,13 +608,12 @@
     "with tf.Session() as sess:\n",
     "    init.run()\n",
     "    for epoch in range(n_epochs):\n",
-    "        for iteration in range(mnist.train.num_examples // batch_size):\n",
-    "            X_batch, y_batch = mnist.train.next_batch(batch_size)\n",
+    "        for X_batch, y_batch in shuffle_batch(X_train, y_train, batch_size):\n",
     "            X_batch = X_batch.reshape((-1, n_steps, n_inputs))\n",
     "            sess.run(training_op, feed_dict={X: X_batch, y: y_batch})\n",
-    "        acc_train = accuracy.eval(feed_dict={X: X_batch, y: y_batch})\n",
+    "        acc_batch = accuracy.eval(feed_dict={X: X_batch, y: y_batch})\n",
     "        acc_test = accuracy.eval(feed_dict={X: X_test, y: y_test})\n",
-    "        print(epoch, \"Train accuracy:\", acc_train, \"Test accuracy:\", acc_test)"
+    "        print(epoch, \"Last batch accuracy:\", acc_batch, \"Test accuracy:\", acc_test)"
    ]
   },
   {
@@ -586,7 +625,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 37,
+   "execution_count": 39,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -604,23 +643,23 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 38,
+   "execution_count": 40,
    "metadata": {},
    "outputs": [],
    "source": [
     "n_neurons = 100\n",
     "n_layers = 3\n",
     "\n",
-    "layers = [tf.contrib.rnn.BasicRNNCell(num_units=n_neurons,\n",
+    "layers = [tf.nn.rnn_cell.BasicRNNCell(num_units=n_neurons,\n",
     "                                      activation=tf.nn.relu)\n",
     "          for layer in range(n_layers)]\n",
-    "multi_layer_cell = tf.contrib.rnn.MultiRNNCell(layers)\n",
+    "multi_layer_cell = tf.nn.rnn_cell.MultiRNNCell(layers)\n",
     "outputs, states = tf.nn.dynamic_rnn(multi_layer_cell, X, dtype=tf.float32)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 39,
+   "execution_count": 41,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -638,7 +677,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 40,
+   "execution_count": 42,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -648,13 +687,12 @@
     "with tf.Session() as sess:\n",
     "    init.run()\n",
     "    for epoch in range(n_epochs):\n",
-    "        for iteration in range(mnist.train.num_examples // batch_size):\n",
-    "            X_batch, y_batch = mnist.train.next_batch(batch_size)\n",
+    "        for X_batch, y_batch in shuffle_batch(X_train, y_train, batch_size):\n",
     "            X_batch = X_batch.reshape((-1, n_steps, n_inputs))\n",
     "            sess.run(training_op, feed_dict={X: X_batch, y: y_batch})\n",
-    "        acc_train = accuracy.eval(feed_dict={X: X_batch, y: y_batch})\n",
+    "        acc_batch = accuracy.eval(feed_dict={X: X_batch, y: y_batch})\n",
     "        acc_test = accuracy.eval(feed_dict={X: X_test, y: y_test})\n",
-    "        print(epoch, \"Train accuracy:\", acc_train, \"Test accuracy:\", acc_test)"
+    "        print(epoch, \"Last batch accuracy:\", acc_batch, \"Test accuracy:\", acc_test)"
    ]
   },
   {
@@ -666,7 +704,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 41,
+   "execution_count": 43,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -685,7 +723,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 42,
+   "execution_count": 44,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -718,7 +756,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 43,
+   "execution_count": 45,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -727,7 +765,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 44,
+   "execution_count": 46,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -750,7 +788,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 45,
+   "execution_count": 47,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -764,7 +802,7 @@
     "X = tf.placeholder(tf.float32, [None, n_steps, n_inputs])\n",
     "y = tf.placeholder(tf.float32, [None, n_steps, n_outputs])\n",
     "\n",
-    "cell = tf.contrib.rnn.BasicRNNCell(num_units=n_neurons, activation=tf.nn.relu)\n",
+    "cell = tf.nn.rnn_cell.BasicRNNCell(num_units=n_neurons, activation=tf.nn.relu)\n",
     "outputs, states = tf.nn.dynamic_rnn(cell, X, dtype=tf.float32)"
    ]
   },
@@ -777,7 +815,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 46,
+   "execution_count": 48,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -794,18 +832,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 47,
+   "execution_count": 50,
    "metadata": {},
    "outputs": [],
    "source": [
     "cell = tf.contrib.rnn.OutputProjectionWrapper(\n",
-    "    tf.contrib.rnn.BasicRNNCell(num_units=n_neurons, activation=tf.nn.relu),\n",
+    "    tf.nn.rnn_cell.BasicRNNCell(num_units=n_neurons, activation=tf.nn.relu),\n",
     "    output_size=n_outputs)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 48,
+   "execution_count": 51,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -814,7 +852,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 49,
+   "execution_count": 52,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -829,7 +867,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 50,
+   "execution_count": 53,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -838,7 +876,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 51,
+   "execution_count": 54,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -859,7 +897,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 52,
+   "execution_count": 55,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -872,7 +910,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 53,
+   "execution_count": 56,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -881,7 +919,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 54,
+   "execution_count": 57,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -905,7 +943,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 55,
+   "execution_count": 58,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -921,17 +959,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 56,
+   "execution_count": 59,
    "metadata": {},
    "outputs": [],
    "source": [
-    "cell = tf.contrib.rnn.BasicRNNCell(num_units=n_neurons, activation=tf.nn.relu)\n",
+    "cell = tf.nn.rnn_cell.BasicRNNCell(num_units=n_neurons, activation=tf.nn.relu)\n",
     "rnn_outputs, states = tf.nn.dynamic_rnn(cell, X, dtype=tf.float32)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 57,
+   "execution_count": 60,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -941,7 +979,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 58,
+   "execution_count": 61,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -952,7 +990,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 59,
+   "execution_count": 62,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -966,7 +1004,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 60,
+   "execution_count": 63,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -990,7 +1028,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 61,
+   "execution_count": 64,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -999,7 +1037,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 62,
+   "execution_count": 65,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1022,7 +1060,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 63,
+   "execution_count": 66,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1038,7 +1076,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 64,
+   "execution_count": 67,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1052,7 +1090,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 65,
+   "execution_count": 68,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1102,7 +1140,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 66,
+   "execution_count": 69,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1116,22 +1154,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 67,
+   "execution_count": 70,
    "metadata": {},
    "outputs": [],
    "source": [
     "n_neurons = 100\n",
     "n_layers = 3\n",
     "\n",
-    "layers = [tf.contrib.rnn.BasicRNNCell(num_units=n_neurons)\n",
+    "layers = [tf.nn.rnn_cell.BasicRNNCell(num_units=n_neurons)\n",
     "          for layer in range(n_layers)]\n",
-    "multi_layer_cell = tf.contrib.rnn.MultiRNNCell(layers)\n",
+    "multi_layer_cell = tf.nn.rnn_cell.MultiRNNCell(layers)\n",
     "outputs, states = tf.nn.dynamic_rnn(multi_layer_cell, X, dtype=tf.float32)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 68,
+   "execution_count": 71,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1140,7 +1178,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 69,
+   "execution_count": 72,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1149,7 +1187,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 70,
+   "execution_count": 73,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1160,7 +1198,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 71,
+   "execution_count": 74,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1183,15 +1221,15 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 72,
+   "execution_count": 75,
    "metadata": {},
    "outputs": [],
    "source": [
     "with tf.device(\"/gpu:0\"):  # BAD! This is ignored.\n",
-    "    layer1 = tf.contrib.rnn.BasicRNNCell(num_units=n_neurons)\n",
+    "    layer1 = tf.nn.rnn_cell.BasicRNNCell(num_units=n_neurons)\n",
     "\n",
     "with tf.device(\"/gpu:1\"):  # BAD! Ignored again.\n",
-    "    layer2 = tf.contrib.rnn.BasicRNNCell(num_units=n_neurons)"
+    "    layer2 = tf.nn.rnn_cell.BasicRNNCell(num_units=n_neurons)"
    ]
   },
   {
@@ -1203,13 +1241,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 73,
+   "execution_count": 76,
    "metadata": {},
    "outputs": [],
    "source": [
     "import tensorflow as tf\n",
     "\n",
-    "class DeviceCellWrapper(tf.contrib.rnn.RNNCell):\n",
+    "class DeviceCellWrapper(tf.nn.rnn_cell.RNNCell):\n",
     "  def __init__(self, device, cell):\n",
     "    self._cell = cell\n",
     "    self._device = device\n",
@@ -1229,7 +1267,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 74,
+   "execution_count": 77,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1244,14 +1282,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 75,
+   "execution_count": 78,
    "metadata": {},
    "outputs": [],
    "source": [
     "devices = [\"/cpu:0\", \"/cpu:0\", \"/cpu:0\"] # replace with [\"/gpu:0\", \"/gpu:1\", \"/gpu:2\"] if you have 3 GPUs\n",
-    "cells = [DeviceCellWrapper(dev,tf.contrib.rnn.BasicRNNCell(num_units=n_neurons))\n",
+    "cells = [DeviceCellWrapper(dev,tf.nn.rnn_cell.BasicRNNCell(num_units=n_neurons))\n",
     "         for dev in devices]\n",
-    "multi_layer_cell = tf.contrib.rnn.MultiRNNCell(cells)\n",
+    "multi_layer_cell = tf.nn.rnn_cell.MultiRNNCell(cells)\n",
     "outputs, states = tf.nn.dynamic_rnn(multi_layer_cell, X, dtype=tf.float32)"
    ]
   },
@@ -1264,7 +1302,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 76,
+   "execution_count": 79,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1273,7 +1311,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 77,
+   "execution_count": 80,
    "metadata": {
     "scrolled": true
    },
@@ -1293,7 +1331,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 78,
+   "execution_count": 81,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1308,7 +1346,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 79,
+   "execution_count": 82,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1325,22 +1363,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 80,
+   "execution_count": 83,
    "metadata": {},
    "outputs": [],
    "source": [
     "keep_prob = tf.placeholder_with_default(1.0, shape=())\n",
-    "cells = [tf.contrib.rnn.BasicRNNCell(num_units=n_neurons)\n",
+    "cells = [tf.nn.rnn_cell.BasicRNNCell(num_units=n_neurons)\n",
     "         for layer in range(n_layers)]\n",
-    "cells_drop = [tf.contrib.rnn.DropoutWrapper(cell, input_keep_prob=keep_prob)\n",
+    "cells_drop = [tf.nn.rnn_cell.DropoutWrapper(cell, input_keep_prob=keep_prob)\n",
     "              for cell in cells]\n",
-    "multi_layer_cell = tf.contrib.rnn.MultiRNNCell(cells_drop)\n",
+    "multi_layer_cell = tf.nn.rnn_cell.MultiRNNCell(cells_drop)\n",
     "rnn_outputs, states = tf.nn.dynamic_rnn(multi_layer_cell, X, dtype=tf.float32)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 81,
+   "execution_count": 84,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1360,7 +1398,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 82,
+   "execution_count": 85,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1383,7 +1421,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 83,
+   "execution_count": 86,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1396,7 +1434,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 84,
+   "execution_count": 87,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1426,18 +1464,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 85,
+   "execution_count": 88,
    "metadata": {},
    "outputs": [],
    "source": [
     "reset_graph()\n",
     "\n",
-    "lstm_cell = tf.contrib.rnn.BasicLSTMCell(num_units=n_neurons)"
+    "lstm_cell = tf.nn.rnn_cell.BasicLSTMCell(num_units=n_neurons)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 86,
+   "execution_count": 89,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1452,9 +1490,9 @@
     "X = tf.placeholder(tf.float32, [None, n_steps, n_inputs])\n",
     "y = tf.placeholder(tf.int32, [None])\n",
     "\n",
-    "lstm_cells = [tf.contrib.rnn.BasicLSTMCell(num_units=n_neurons)\n",
+    "lstm_cells = [tf.nn.rnn_cell.BasicLSTMCell(num_units=n_neurons)\n",
     "              for layer in range(n_layers)]\n",
-    "multi_cell = tf.contrib.rnn.MultiRNNCell(lstm_cells)\n",
+    "multi_cell = tf.nn.rnn_cell.MultiRNNCell(lstm_cells)\n",
     "outputs, states = tf.nn.dynamic_rnn(multi_cell, X, dtype=tf.float32)\n",
     "top_layer_h_state = states[-1][1]\n",
     "logits = tf.layers.dense(top_layer_h_state, n_outputs, name=\"softmax\")\n",
@@ -1470,7 +1508,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 87,
+   "execution_count": 90,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1479,7 +1517,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 88,
+   "execution_count": 91,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1488,7 +1526,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 89,
+   "execution_count": 92,
    "metadata": {
     "scrolled": true
    },
@@ -1500,31 +1538,30 @@
     "with tf.Session() as sess:\n",
     "    init.run()\n",
     "    for epoch in range(n_epochs):\n",
-    "        for iteration in range(mnist.train.num_examples // batch_size):\n",
-    "            X_batch, y_batch = mnist.train.next_batch(batch_size)\n",
-    "            X_batch = X_batch.reshape((batch_size, n_steps, n_inputs))\n",
+    "        for X_batch, y_batch in shuffle_batch(X_train, y_train, batch_size):\n",
+    "            X_batch = X_batch.reshape((-1, n_steps, n_inputs))\n",
     "            sess.run(training_op, feed_dict={X: X_batch, y: y_batch})\n",
-    "        acc_train = accuracy.eval(feed_dict={X: X_batch, y: y_batch})\n",
+    "        acc_batch = accuracy.eval(feed_dict={X: X_batch, y: y_batch})\n",
     "        acc_test = accuracy.eval(feed_dict={X: X_test, y: y_test})\n",
-    "        print(\"Epoch\", epoch, \"Train accuracy =\", acc_train, \"Test accuracy =\", acc_test)"
+    "        print(epoch, \"Last batch accuracy:\", acc_batch, \"Test accuracy:\", acc_test)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 90,
+   "execution_count": 93,
    "metadata": {},
    "outputs": [],
    "source": [
-    "lstm_cell = tf.contrib.rnn.LSTMCell(num_units=n_neurons, use_peepholes=True)"
+    "lstm_cell = tf.nn.rnn_cell.LSTMCell(num_units=n_neurons, use_peepholes=True)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 91,
+   "execution_count": 94,
    "metadata": {},
    "outputs": [],
    "source": [
-    "gru_cell = tf.contrib.rnn.GRUCell(num_units=n_neurons)"
+    "gru_cell = tf.nn.rnn_cell.GRUCell(num_units=n_neurons)"
    ]
   },
   {
@@ -1550,7 +1587,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 92,
+   "execution_count": 95,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1589,7 +1626,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 93,
+   "execution_count": 96,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1598,7 +1635,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 94,
+   "execution_count": 97,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1614,7 +1651,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 95,
+   "execution_count": 98,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1630,7 +1667,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 96,
+   "execution_count": 99,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1639,7 +1676,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 97,
+   "execution_count": 100,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1648,7 +1685,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 98,
+   "execution_count": 101,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1664,7 +1701,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 99,
+   "execution_count": 102,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1697,7 +1734,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 100,
+   "execution_count": 103,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1706,7 +1743,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 101,
+   "execution_count": 104,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1716,7 +1753,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 102,
+   "execution_count": 105,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1725,7 +1762,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 103,
+   "execution_count": 106,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1741,7 +1778,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 104,
+   "execution_count": 107,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1763,7 +1800,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 105,
+   "execution_count": 108,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1776,7 +1813,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 106,
+   "execution_count": 109,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1790,7 +1827,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 107,
+   "execution_count": 110,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1800,7 +1837,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 108,
+   "execution_count": 111,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1840,7 +1877,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 109,
+   "execution_count": 112,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1892,7 +1929,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 110,
+   "execution_count": 113,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1908,7 +1945,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 111,
+   "execution_count": 114,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1928,7 +1965,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 112,
+   "execution_count": 115,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1957,7 +1994,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 113,
+   "execution_count": 116,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1981,9 +2018,9 @@
     "encoder_inputs = tf.unstack(tf.transpose(X)) # list of 1D tensors\n",
     "decoder_inputs = tf.unstack(tf.transpose(Y_input)) # list of 1D tensors\n",
     "\n",
-    "lstm_cells = [tf.contrib.rnn.BasicLSTMCell(num_units=n_neurons)\n",
+    "lstm_cells = [tf.nn.rnn_cell.BasicLSTMCell(num_units=n_neurons)\n",
     "              for layer in range(n_layers)]\n",
-    "cell = tf.contrib.rnn.MultiRNNCell(lstm_cells)\n",
+    "cell = tf.nn.rnn_cell.MultiRNNCell(lstm_cells)\n",
     "\n",
     "output_seqs, states = tf.contrib.legacy_seq2seq.embedding_rnn_seq2seq(\n",
     "    encoder_inputs,\n",
@@ -1998,7 +2035,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 114,
+   "execution_count": 117,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2052,7 +2089,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 115,
+   "execution_count": 118,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2097,7 +2134,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 116,
+   "execution_count": 119,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2114,7 +2151,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 117,
+   "execution_count": 120,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2131,7 +2168,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 118,
+   "execution_count": 121,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2152,7 +2189,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 119,
+   "execution_count": 122,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2169,7 +2206,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 120,
+   "execution_count": 123,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2183,7 +2220,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 121,
+   "execution_count": 124,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2199,7 +2236,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 122,
+   "execution_count": 125,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2221,7 +2258,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 123,
+   "execution_count": 126,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2237,7 +2274,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 124,
+   "execution_count": 127,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2253,7 +2290,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 125,
+   "execution_count": 128,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2269,7 +2306,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 126,
+   "execution_count": 129,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2287,7 +2324,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 127,
+   "execution_count": 130,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2305,7 +2342,7 @@
     "seq_length = tf.placeholder(tf.int32, [None], name=\"seq_length\")\n",
     "y = tf.placeholder(tf.float32, [None, 1], name=\"y\")\n",
     "\n",
-    "gru_cell = tf.contrib.rnn.GRUCell(num_units=n_neurons)\n",
+    "gru_cell = tf.nn.rnn_cell.GRUCell(num_units=n_neurons)\n",
     "outputs, states = tf.nn.dynamic_rnn(gru_cell, X, dtype=tf.float32,\n",
     "                                    sequence_length=seq_length)\n",
     "\n",
@@ -2336,7 +2373,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 128,
+   "execution_count": 131,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2345,7 +2382,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 129,
+   "execution_count": 132,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2378,7 +2415,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 130,
+   "execution_count": 133,
    "metadata": {},
    "outputs": [],
    "source": [