From 00c6cf9d438518b596be285ee75749a1d280aee6 Mon Sep 17 00:00:00 2001
From: francotheengineer <yatesf@tcd.ie>
Date: Fri, 7 Jul 2017 12:04:17 +0100
Subject: [PATCH 1/2] Fixed Deep RNN Multi-gpu implementation to use tf

Fixed Deep RNN Multi-gpu implementation to use tf.python.ops.nn.rnn_cell DeviceWrapper class instead of implementing custom class to achieve the same result.  It would appear that the custom class shown with the code does not function.
---
 14_recurrent_neural_networks.ipynb | 37 +++++++++++++++++++++++++-----
 1 file changed, 31 insertions(+), 6 deletions(-)

diff --git a/14_recurrent_neural_networks.ipynb b/14_recurrent_neural_networks.ipynb
index bc16d31..21394ec 100644
--- a/14_recurrent_neural_networks.ipynb
+++ b/14_recurrent_neural_networks.ipynb
@@ -107,11 +107,13 @@
    ]
   },
   {
-   "cell_type": "markdown",
+   "cell_type": "code",
+   "execution_count": null,
    "metadata": {
     "deletable": true,
     "editable": true
    },
+   "outputs": [],
    "source": [
     "# Basic RNNs"
    ]
@@ -431,7 +433,7 @@
       ]
      },
      "metadata": {},
-     "output_type": "display_data"
+     "output_type": "execute_result"
     }
    ],
    "source": [
@@ -711,7 +713,7 @@
       ]
      },
      "metadata": {},
-     "output_type": "display_data"
+     "output_type": "execute_result"
     }
    ],
    "source": [
@@ -2210,6 +2212,29 @@
     "outputs, states = tf.nn.dynamic_rnn(multi_layer_cell, X, dtype=tf.float32)"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Alternatively, you can use the Tensorflow class DeviceWrapper - note you can define more than one layer per gpu"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "devices = [\"/gpu:0\", \"/gpu:1\", \"/gpu:2\"]  \n",
+    "cells = []\n",
+    "for dev in devices:\n",
+    "        cell = DeviceWrapper(rnn_cell.BasicRNNCell(num_units=n_neurons), dev)\n",
+    "        cells.append(cell)\n",
+    "\n",
+    "self.multiple_lstm_cells = tf.contrib.rnn.MultiRNNCell(cells, state_is_tuple=True)\n",
+    "outputs, states = tf.nn.dynamic_rnn(multi_layer_cell, X, dtype=tf.float32)"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 76,
@@ -3524,7 +3549,7 @@
   "language_info": {
    "codemirror_mode": {
     "name": "ipython",
-    "version": 3
+    "version": 3.0
    },
    "file_extension": ".py",
    "mimetype": "text/x-python",
@@ -3538,7 +3563,7 @@
    "navigate_menu": true,
    "number_sections": true,
    "sideBar": true,
-   "threshold": 6,
+   "threshold": 6.0,
    "toc_cell": false,
    "toc_section_display": "block",
    "toc_window_display": false
@@ -3546,4 +3571,4 @@
  },
  "nbformat": 4,
  "nbformat_minor": 0
-}
+}
\ No newline at end of file

From d5707a189801450b483d14d7d06bbd81aef66ee5 Mon Sep 17 00:00:00 2001
From: Aurelien Geron <aurelien.geron@gmail.com>
Date: Wed, 18 Oct 2017 09:44:57 +0200
Subject: [PATCH 2/2] Add comment about the DeviceWrapper class since TF 1.1

---
 14_recurrent_neural_networks.ipynb | 28 ++++++----------------------
 1 file changed, 6 insertions(+), 22 deletions(-)

diff --git a/14_recurrent_neural_networks.ipynb b/14_recurrent_neural_networks.ipynb
index 833eb37..1f2c6a8 100644
--- a/14_recurrent_neural_networks.ipynb
+++ b/14_recurrent_neural_networks.ipynb
@@ -369,7 +369,7 @@
       ]
      },
      "metadata": {},
-     "output_type": "execute_result"
+     "output_type": "display_data"
     }
    ],
    "source": [
@@ -615,7 +615,7 @@
       ]
      },
      "metadata": {},
-     "output_type": "execute_result"
+     "output_type": "display_data"
     }
    ],
    "source": [
@@ -1939,23 +1939,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Alternatively, you can use the Tensorflow class DeviceWrapper - note you can define more than one layer per gpu"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "devices = [\"/gpu:0\", \"/gpu:1\", \"/gpu:2\"]  \n",
-    "cells = []\n",
-    "for dev in devices:\n",
-    "        cell = DeviceWrapper(rnn_cell.BasicRNNCell(num_units=n_neurons), dev)\n",
-    "        cells.append(cell)\n",
-    "\n",
-    "self.multiple_lstm_cells = tf.contrib.rnn.MultiRNNCell(cells, state_is_tuple=True)\n",
-    "outputs, states = tf.nn.dynamic_rnn(multi_layer_cell, X, dtype=tf.float32)"
+    "Alternatively, since TensorFlow 1.1, you can use the `tf.contrib.rnn.DeviceWrapper` class (alias `tf.nn.rnn_cell.DeviceWrapper` since TF 1.2)."
    ]
   },
   {
@@ -3720,21 +3704,21 @@
   "language_info": {
    "codemirror_mode": {
     "name": "ipython",
-    "version": 3.0
+    "version": 3
    },
    "file_extension": ".py",
    "mimetype": "text/x-python",
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.5.2"
+   "version": "3.6.2"
   },
   "nav_menu": {},
   "toc": {
    "navigate_menu": true,
    "number_sections": true,
    "sideBar": true,
-   "threshold": 6.0,
+   "threshold": 6,
    "toc_cell": false,
    "toc_section_display": "block",
    "toc_window_display": false