Add runtime output to notebooks.

Signed-off-by: Norman Heckscher <norman.heckscher@gmail.com>

notebooks/3_NeuralNetworks/bidirectional_rnn.ipynb

@@ -134,11 +134,98 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 4,
    "metadata": {
     "collapsed": false
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Iter 1280, Minibatch Loss= 1.557283, Training Accuracy= 0.49219\n",
+      "Iter 2560, Minibatch Loss= 1.358445, Training Accuracy= 0.56250\n",
+      "Iter 3840, Minibatch Loss= 1.043732, Training Accuracy= 0.64062\n",
+      "Iter 5120, Minibatch Loss= 0.796770, Training Accuracy= 0.72656\n",
+      "Iter 6400, Minibatch Loss= 0.626206, Training Accuracy= 0.72656\n",
+      "Iter 7680, Minibatch Loss= 1.025919, Training Accuracy= 0.65625\n",
+      "Iter 8960, Minibatch Loss= 0.744850, Training Accuracy= 0.76562\n",
+      "Iter 10240, Minibatch Loss= 0.530111, Training Accuracy= 0.84375\n",
+      "Iter 11520, Minibatch Loss= 0.383806, Training Accuracy= 0.86719\n",
+      "Iter 12800, Minibatch Loss= 0.607816, Training Accuracy= 0.82812\n",
+      "Iter 14080, Minibatch Loss= 0.410879, Training Accuracy= 0.89062\n",
+      "Iter 15360, Minibatch Loss= 0.335351, Training Accuracy= 0.89844\n",
+      "Iter 16640, Minibatch Loss= 0.428004, Training Accuracy= 0.91406\n",
+      "Iter 17920, Minibatch Loss= 0.307468, Training Accuracy= 0.91406\n",
+      "Iter 19200, Minibatch Loss= 0.249527, Training Accuracy= 0.92188\n",
+      "Iter 20480, Minibatch Loss= 0.148163, Training Accuracy= 0.96094\n",
+      "Iter 21760, Minibatch Loss= 0.445275, Training Accuracy= 0.83594\n",
+      "Iter 23040, Minibatch Loss= 0.173083, Training Accuracy= 0.93750\n",
+      "Iter 24320, Minibatch Loss= 0.373696, Training Accuracy= 0.87500\n",
+      "Iter 25600, Minibatch Loss= 0.509869, Training Accuracy= 0.85938\n",
+      "Iter 26880, Minibatch Loss= 0.198096, Training Accuracy= 0.92969\n",
+      "Iter 28160, Minibatch Loss= 0.228221, Training Accuracy= 0.92188\n",
+      "Iter 29440, Minibatch Loss= 0.280088, Training Accuracy= 0.89844\n",
+      "Iter 30720, Minibatch Loss= 0.300495, Training Accuracy= 0.91406\n",
+      "Iter 32000, Minibatch Loss= 0.171746, Training Accuracy= 0.95312\n",
+      "Iter 33280, Minibatch Loss= 0.263745, Training Accuracy= 0.89844\n",
+      "Iter 34560, Minibatch Loss= 0.177300, Training Accuracy= 0.93750\n",
+      "Iter 35840, Minibatch Loss= 0.160621, Training Accuracy= 0.95312\n",
+      "Iter 37120, Minibatch Loss= 0.321745, Training Accuracy= 0.91406\n",
+      "Iter 38400, Minibatch Loss= 0.188322, Training Accuracy= 0.93750\n",
+      "Iter 39680, Minibatch Loss= 0.104025, Training Accuracy= 0.96875\n",
+      "Iter 40960, Minibatch Loss= 0.291053, Training Accuracy= 0.89062\n",
+      "Iter 42240, Minibatch Loss= 0.131189, Training Accuracy= 0.95312\n",
+      "Iter 43520, Minibatch Loss= 0.154949, Training Accuracy= 0.92969\n",
+      "Iter 44800, Minibatch Loss= 0.150411, Training Accuracy= 0.93750\n",
+      "Iter 46080, Minibatch Loss= 0.117008, Training Accuracy= 0.96094\n",
+      "Iter 47360, Minibatch Loss= 0.181344, Training Accuracy= 0.96094\n",
+      "Iter 48640, Minibatch Loss= 0.209197, Training Accuracy= 0.94531\n",
+      "Iter 49920, Minibatch Loss= 0.159350, Training Accuracy= 0.96094\n",
+      "Iter 51200, Minibatch Loss= 0.124001, Training Accuracy= 0.95312\n",
+      "Iter 52480, Minibatch Loss= 0.165183, Training Accuracy= 0.94531\n",
+      "Iter 53760, Minibatch Loss= 0.046438, Training Accuracy= 0.97656\n",
+      "Iter 55040, Minibatch Loss= 0.199995, Training Accuracy= 0.91406\n",
+      "Iter 56320, Minibatch Loss= 0.057071, Training Accuracy= 0.97656\n",
+      "Iter 57600, Minibatch Loss= 0.177065, Training Accuracy= 0.92188\n",
+      "Iter 58880, Minibatch Loss= 0.091666, Training Accuracy= 0.96094\n",
+      "Iter 60160, Minibatch Loss= 0.069232, Training Accuracy= 0.96875\n",
+      "Iter 61440, Minibatch Loss= 0.127353, Training Accuracy= 0.94531\n",
+      "Iter 62720, Minibatch Loss= 0.095795, Training Accuracy= 0.96094\n",
+      "Iter 64000, Minibatch Loss= 0.202651, Training Accuracy= 0.96875\n",
+      "Iter 65280, Minibatch Loss= 0.118779, Training Accuracy= 0.95312\n",
+      "Iter 66560, Minibatch Loss= 0.043173, Training Accuracy= 0.98438\n",
+      "Iter 67840, Minibatch Loss= 0.152280, Training Accuracy= 0.95312\n",
+      "Iter 69120, Minibatch Loss= 0.085301, Training Accuracy= 0.96875\n",
+      "Iter 70400, Minibatch Loss= 0.093421, Training Accuracy= 0.96094\n",
+      "Iter 71680, Minibatch Loss= 0.096358, Training Accuracy= 0.96875\n",
+      "Iter 72960, Minibatch Loss= 0.053386, Training Accuracy= 0.98438\n",
+      "Iter 74240, Minibatch Loss= 0.065237, Training Accuracy= 0.97656\n",
+      "Iter 75520, Minibatch Loss= 0.228090, Training Accuracy= 0.92188\n",
+      "Iter 76800, Minibatch Loss= 0.106751, Training Accuracy= 0.95312\n",
+      "Iter 78080, Minibatch Loss= 0.187795, Training Accuracy= 0.94531\n",
+      "Iter 79360, Minibatch Loss= 0.092611, Training Accuracy= 0.96094\n",
+      "Iter 80640, Minibatch Loss= 0.137386, Training Accuracy= 0.96875\n",
+      "Iter 81920, Minibatch Loss= 0.106634, Training Accuracy= 0.98438\n",
+      "Iter 83200, Minibatch Loss= 0.111749, Training Accuracy= 0.94531\n",
+      "Iter 84480, Minibatch Loss= 0.191184, Training Accuracy= 0.94531\n",
+      "Iter 85760, Minibatch Loss= 0.063982, Training Accuracy= 0.96094\n",
+      "Iter 87040, Minibatch Loss= 0.092380, Training Accuracy= 0.96875\n",
+      "Iter 88320, Minibatch Loss= 0.089899, Training Accuracy= 0.97656\n",
+      "Iter 89600, Minibatch Loss= 0.141107, Training Accuracy= 0.94531\n",
+      "Iter 90880, Minibatch Loss= 0.075549, Training Accuracy= 0.96094\n",
+      "Iter 92160, Minibatch Loss= 0.186539, Training Accuracy= 0.94531\n",
+      "Iter 93440, Minibatch Loss= 0.079639, Training Accuracy= 0.97656\n",
+      "Iter 94720, Minibatch Loss= 0.156895, Training Accuracy= 0.95312\n",
+      "Iter 96000, Minibatch Loss= 0.088042, Training Accuracy= 0.97656\n",
+      "Iter 97280, Minibatch Loss= 0.076670, Training Accuracy= 0.96875\n",
+      "Iter 98560, Minibatch Loss= 0.051336, Training Accuracy= 0.97656\n",
+      "Iter 99840, Minibatch Loss= 0.086923, Training Accuracy= 0.98438\n",
+      "Optimization Finished!\n",
+      "Testing Accuracy: 0.960938\n"
+     ]
+    }
+   ],
    "source": [
     "# Launch the graph\n",
     "with tf.Session() as sess:\n",

notebooks/3_NeuralNetworks/convolutional_network.ipynb

@@ -152,11 +152,176 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 5,
    "metadata": {
     "collapsed": false
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Iter 1280, Minibatch Loss= 26574.855469, Training Accuracy= 0.25781\n",
+      "Iter 2560, Minibatch Loss= 11454.494141, Training Accuracy= 0.49219\n",
+      "Iter 3840, Minibatch Loss= 10070.515625, Training Accuracy= 0.55469\n",
+      "Iter 5120, Minibatch Loss= 4008.586426, Training Accuracy= 0.78125\n",
+      "Iter 6400, Minibatch Loss= 3148.004639, Training Accuracy= 0.80469\n",
+      "Iter 7680, Minibatch Loss= 6740.440430, Training Accuracy= 0.71875\n",
+      "Iter 8960, Minibatch Loss= 4103.991699, Training Accuracy= 0.80469\n",
+      "Iter 10240, Minibatch Loss= 2631.275391, Training Accuracy= 0.85938\n",
+      "Iter 11520, Minibatch Loss= 1428.798828, Training Accuracy= 0.91406\n",
+      "Iter 12800, Minibatch Loss= 3909.772705, Training Accuracy= 0.78906\n",
+      "Iter 14080, Minibatch Loss= 1423.095947, Training Accuracy= 0.88281\n",
+      "Iter 15360, Minibatch Loss= 1524.569824, Training Accuracy= 0.89062\n",
+      "Iter 16640, Minibatch Loss= 2234.539795, Training Accuracy= 0.86719\n",
+      "Iter 17920, Minibatch Loss= 933.932800, Training Accuracy= 0.90625\n",
+      "Iter 19200, Minibatch Loss= 2039.046021, Training Accuracy= 0.89062\n",
+      "Iter 20480, Minibatch Loss= 674.179932, Training Accuracy= 0.95312\n",
+      "Iter 21760, Minibatch Loss= 3778.958984, Training Accuracy= 0.82812\n",
+      "Iter 23040, Minibatch Loss= 1038.217773, Training Accuracy= 0.91406\n",
+      "Iter 24320, Minibatch Loss= 1689.513672, Training Accuracy= 0.89062\n",
+      "Iter 25600, Minibatch Loss= 1800.954956, Training Accuracy= 0.85938\n",
+      "Iter 26880, Minibatch Loss= 1086.292847, Training Accuracy= 0.90625\n",
+      "Iter 28160, Minibatch Loss= 656.042847, Training Accuracy= 0.94531\n",
+      "Iter 29440, Minibatch Loss= 1210.589844, Training Accuracy= 0.91406\n",
+      "Iter 30720, Minibatch Loss= 1099.606323, Training Accuracy= 0.90625\n",
+      "Iter 32000, Minibatch Loss= 1073.128174, Training Accuracy= 0.92969\n",
+      "Iter 33280, Minibatch Loss= 518.844543, Training Accuracy= 0.95312\n",
+      "Iter 34560, Minibatch Loss= 540.856689, Training Accuracy= 0.92188\n",
+      "Iter 35840, Minibatch Loss= 353.990906, Training Accuracy= 0.97656\n",
+      "Iter 37120, Minibatch Loss= 1488.962891, Training Accuracy= 0.91406\n",
+      "Iter 38400, Minibatch Loss= 231.191864, Training Accuracy= 0.98438\n",
+      "Iter 39680, Minibatch Loss= 171.154480, Training Accuracy= 0.98438\n",
+      "Iter 40960, Minibatch Loss= 2092.023682, Training Accuracy= 0.90625\n",
+      "Iter 42240, Minibatch Loss= 480.594299, Training Accuracy= 0.95312\n",
+      "Iter 43520, Minibatch Loss= 504.128143, Training Accuracy= 0.96875\n",
+      "Iter 44800, Minibatch Loss= 143.534485, Training Accuracy= 0.97656\n",
+      "Iter 46080, Minibatch Loss= 325.875580, Training Accuracy= 0.96094\n",
+      "Iter 47360, Minibatch Loss= 602.813049, Training Accuracy= 0.91406\n",
+      "Iter 48640, Minibatch Loss= 794.595093, Training Accuracy= 0.94531\n",
+      "Iter 49920, Minibatch Loss= 415.539032, Training Accuracy= 0.95312\n",
+      "Iter 51200, Minibatch Loss= 146.016022, Training Accuracy= 0.96094\n",
+      "Iter 52480, Minibatch Loss= 294.180786, Training Accuracy= 0.94531\n",
+      "Iter 53760, Minibatch Loss= 50.955730, Training Accuracy= 0.99219\n",
+      "Iter 55040, Minibatch Loss= 1026.607056, Training Accuracy= 0.92188\n",
+      "Iter 56320, Minibatch Loss= 283.756134, Training Accuracy= 0.96875\n",
+      "Iter 57600, Minibatch Loss= 691.538208, Training Accuracy= 0.95312\n",
+      "Iter 58880, Minibatch Loss= 491.075073, Training Accuracy= 0.96094\n",
+      "Iter 60160, Minibatch Loss= 571.951660, Training Accuracy= 0.95312\n",
+      "Iter 61440, Minibatch Loss= 284.041168, Training Accuracy= 0.97656\n",
+      "Iter 62720, Minibatch Loss= 1041.941528, Training Accuracy= 0.92969\n",
+      "Iter 64000, Minibatch Loss= 664.833923, Training Accuracy= 0.93750\n",
+      "Iter 65280, Minibatch Loss= 1582.112793, Training Accuracy= 0.88281\n",
+      "Iter 66560, Minibatch Loss= 783.135376, Training Accuracy= 0.94531\n",
+      "Iter 67840, Minibatch Loss= 245.942398, Training Accuracy= 0.96094\n",
+      "Iter 69120, Minibatch Loss= 752.858948, Training Accuracy= 0.96875\n",
+      "Iter 70400, Minibatch Loss= 623.243286, Training Accuracy= 0.94531\n",
+      "Iter 71680, Minibatch Loss= 846.498230, Training Accuracy= 0.93750\n",
+      "Iter 72960, Minibatch Loss= 586.516479, Training Accuracy= 0.95312\n",
+      "Iter 74240, Minibatch Loss= 92.774963, Training Accuracy= 0.98438\n",
+      "Iter 75520, Minibatch Loss= 644.039612, Training Accuracy= 0.95312\n",
+      "Iter 76800, Minibatch Loss= 693.247681, Training Accuracy= 0.96094\n",
+      "Iter 78080, Minibatch Loss= 466.491882, Training Accuracy= 0.96094\n",
+      "Iter 79360, Minibatch Loss= 964.212341, Training Accuracy= 0.93750\n",
+      "Iter 80640, Minibatch Loss= 230.451904, Training Accuracy= 0.97656\n",
+      "Iter 81920, Minibatch Loss= 280.434570, Training Accuracy= 0.95312\n",
+      "Iter 83200, Minibatch Loss= 213.208252, Training Accuracy= 0.97656\n",
+      "Iter 84480, Minibatch Loss= 774.836060, Training Accuracy= 0.94531\n",
+      "Iter 85760, Minibatch Loss= 164.687729, Training Accuracy= 0.96094\n",
+      "Iter 87040, Minibatch Loss= 419.967407, Training Accuracy= 0.96875\n",
+      "Iter 88320, Minibatch Loss= 160.920151, Training Accuracy= 0.96875\n",
+      "Iter 89600, Minibatch Loss= 586.063599, Training Accuracy= 0.96094\n",
+      "Iter 90880, Minibatch Loss= 345.598145, Training Accuracy= 0.96875\n",
+      "Iter 92160, Minibatch Loss= 931.361145, Training Accuracy= 0.92188\n",
+      "Iter 93440, Minibatch Loss= 170.107117, Training Accuracy= 0.97656\n",
+      "Iter 94720, Minibatch Loss= 497.162750, Training Accuracy= 0.93750\n",
+      "Iter 96000, Minibatch Loss= 906.600464, Training Accuracy= 0.94531\n",
+      "Iter 97280, Minibatch Loss= 303.382202, Training Accuracy= 0.92969\n",
+      "Iter 98560, Minibatch Loss= 509.161652, Training Accuracy= 0.97656\n",
+      "Iter 99840, Minibatch Loss= 359.561981, Training Accuracy= 0.97656\n",
+      "Iter 101120, Minibatch Loss= 136.516541, Training Accuracy= 0.97656\n",
+      "Iter 102400, Minibatch Loss= 517.199341, Training Accuracy= 0.96875\n",
+      "Iter 103680, Minibatch Loss= 487.793335, Training Accuracy= 0.95312\n",
+      "Iter 104960, Minibatch Loss= 407.351929, Training Accuracy= 0.96094\n",
+      "Iter 106240, Minibatch Loss= 70.495193, Training Accuracy= 0.98438\n",
+      "Iter 107520, Minibatch Loss= 344.783508, Training Accuracy= 0.96094\n",
+      "Iter 108800, Minibatch Loss= 242.682465, Training Accuracy= 0.95312\n",
+      "Iter 110080, Minibatch Loss= 169.181458, Training Accuracy= 0.96094\n",
+      "Iter 111360, Minibatch Loss= 152.638245, Training Accuracy= 0.98438\n",
+      "Iter 112640, Minibatch Loss= 170.795868, Training Accuracy= 0.96875\n",
+      "Iter 113920, Minibatch Loss= 133.262726, Training Accuracy= 0.98438\n",
+      "Iter 115200, Minibatch Loss= 296.063293, Training Accuracy= 0.95312\n",
+      "Iter 116480, Minibatch Loss= 254.247543, Training Accuracy= 0.96094\n",
+      "Iter 117760, Minibatch Loss= 506.795715, Training Accuracy= 0.94531\n",
+      "Iter 119040, Minibatch Loss= 446.006897, Training Accuracy= 0.96094\n",
+      "Iter 120320, Minibatch Loss= 149.467377, Training Accuracy= 0.97656\n",
+      "Iter 121600, Minibatch Loss= 52.783600, Training Accuracy= 0.98438\n",
+      "Iter 122880, Minibatch Loss= 49.041794, Training Accuracy= 0.98438\n",
+      "Iter 124160, Minibatch Loss= 184.371246, Training Accuracy= 0.97656\n",
+      "Iter 125440, Minibatch Loss= 129.838501, Training Accuracy= 0.97656\n",
+      "Iter 126720, Minibatch Loss= 288.006531, Training Accuracy= 0.96875\n",
+      "Iter 128000, Minibatch Loss= 187.284653, Training Accuracy= 0.97656\n",
+      "Iter 129280, Minibatch Loss= 197.969955, Training Accuracy= 0.96875\n",
+      "Iter 130560, Minibatch Loss= 299.969818, Training Accuracy= 0.96875\n",
+      "Iter 131840, Minibatch Loss= 537.602173, Training Accuracy= 0.96094\n",
+      "Iter 133120, Minibatch Loss= 4.519302, Training Accuracy= 0.99219\n",
+      "Iter 134400, Minibatch Loss= 133.264191, Training Accuracy= 0.97656\n",
+      "Iter 135680, Minibatch Loss= 89.662292, Training Accuracy= 0.97656\n",
+      "Iter 136960, Minibatch Loss= 107.774078, Training Accuracy= 0.96875\n",
+      "Iter 138240, Minibatch Loss= 335.904572, Training Accuracy= 0.96094\n",
+      "Iter 139520, Minibatch Loss= 457.494568, Training Accuracy= 0.96094\n",
+      "Iter 140800, Minibatch Loss= 259.131531, Training Accuracy= 0.95312\n",
+      "Iter 142080, Minibatch Loss= 152.205383, Training Accuracy= 0.96094\n",
+      "Iter 143360, Minibatch Loss= 252.535828, Training Accuracy= 0.95312\n",
+      "Iter 144640, Minibatch Loss= 109.477585, Training Accuracy= 0.96875\n",
+      "Iter 145920, Minibatch Loss= 24.468613, Training Accuracy= 0.99219\n",
+      "Iter 147200, Minibatch Loss= 51.722107, Training Accuracy= 0.97656\n",
+      "Iter 148480, Minibatch Loss= 69.715233, Training Accuracy= 0.97656\n",
+      "Iter 149760, Minibatch Loss= 405.289246, Training Accuracy= 0.92969\n",
+      "Iter 151040, Minibatch Loss= 282.976379, Training Accuracy= 0.95312\n",
+      "Iter 152320, Minibatch Loss= 134.991119, Training Accuracy= 0.97656\n",
+      "Iter 153600, Minibatch Loss= 491.618103, Training Accuracy= 0.92188\n",
+      "Iter 154880, Minibatch Loss= 154.299988, Training Accuracy= 0.99219\n",
+      "Iter 156160, Minibatch Loss= 79.480019, Training Accuracy= 0.96875\n",
+      "Iter 157440, Minibatch Loss= 68.093750, Training Accuracy= 0.99219\n",
+      "Iter 158720, Minibatch Loss= 459.739685, Training Accuracy= 0.92188\n",
+      "Iter 160000, Minibatch Loss= 168.076843, Training Accuracy= 0.94531\n",
+      "Iter 161280, Minibatch Loss= 256.141846, Training Accuracy= 0.97656\n",
+      "Iter 162560, Minibatch Loss= 236.400391, Training Accuracy= 0.94531\n",
+      "Iter 163840, Minibatch Loss= 177.011261, Training Accuracy= 0.96875\n",
+      "Iter 165120, Minibatch Loss= 48.583298, Training Accuracy= 0.97656\n",
+      "Iter 166400, Minibatch Loss= 413.800293, Training Accuracy= 0.96094\n",
+      "Iter 167680, Minibatch Loss= 209.587387, Training Accuracy= 0.96875\n",
+      "Iter 168960, Minibatch Loss= 239.407318, Training Accuracy= 0.98438\n",
+      "Iter 170240, Minibatch Loss= 183.567017, Training Accuracy= 0.96875\n",
+      "Iter 171520, Minibatch Loss= 87.937515, Training Accuracy= 0.96875\n",
+      "Iter 172800, Minibatch Loss= 203.777039, Training Accuracy= 0.98438\n",
+      "Iter 174080, Minibatch Loss= 566.378052, Training Accuracy= 0.94531\n",
+      "Iter 175360, Minibatch Loss= 325.170898, Training Accuracy= 0.95312\n",
+      "Iter 176640, Minibatch Loss= 300.142212, Training Accuracy= 0.97656\n",
+      "Iter 177920, Minibatch Loss= 205.370193, Training Accuracy= 0.95312\n",
+      "Iter 179200, Minibatch Loss= 5.594437, Training Accuracy= 0.99219\n",
+      "Iter 180480, Minibatch Loss= 110.732109, Training Accuracy= 0.98438\n",
+      "Iter 181760, Minibatch Loss= 33.320297, Training Accuracy= 0.99219\n",
+      "Iter 183040, Minibatch Loss= 6.885544, Training Accuracy= 0.99219\n",
+      "Iter 184320, Minibatch Loss= 221.144806, Training Accuracy= 0.96875\n",
+      "Iter 185600, Minibatch Loss= 365.337372, Training Accuracy= 0.94531\n",
+      "Iter 186880, Minibatch Loss= 186.558258, Training Accuracy= 0.96094\n",
+      "Iter 188160, Minibatch Loss= 149.720322, Training Accuracy= 0.98438\n",
+      "Iter 189440, Minibatch Loss= 105.281998, Training Accuracy= 0.97656\n",
+      "Iter 190720, Minibatch Loss= 289.980011, Training Accuracy= 0.96094\n",
+      "Iter 192000, Minibatch Loss= 214.382278, Training Accuracy= 0.96094\n",
+      "Iter 193280, Minibatch Loss= 461.044312, Training Accuracy= 0.93750\n",
+      "Iter 194560, Minibatch Loss= 138.653076, Training Accuracy= 0.98438\n",
+      "Iter 195840, Minibatch Loss= 112.004883, Training Accuracy= 0.98438\n",
+      "Iter 197120, Minibatch Loss= 212.691467, Training Accuracy= 0.97656\n",
+      "Iter 198400, Minibatch Loss= 57.642502, Training Accuracy= 0.97656\n",
+      "Iter 199680, Minibatch Loss= 80.503563, Training Accuracy= 0.96875\n",
+      "Optimization Finished!\n",
+      "Testing Accuracy: 0.984375\n"
+     ]
+    }
+   ],
    "source": [
     "# Launch the graph\n",
     "with tf.Session() as sess:\n",

notebooks/3_NeuralNetworks/recurrent_network.ipynb

@@ -125,11 +125,98 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 4,
    "metadata": {
     "collapsed": false
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Iter 1280, Minibatch Loss= 1.576423, Training Accuracy= 0.51562\n",
+      "Iter 2560, Minibatch Loss= 1.450179, Training Accuracy= 0.53906\n",
+      "Iter 3840, Minibatch Loss= 1.160066, Training Accuracy= 0.64844\n",
+      "Iter 5120, Minibatch Loss= 0.898589, Training Accuracy= 0.73438\n",
+      "Iter 6400, Minibatch Loss= 0.685712, Training Accuracy= 0.75781\n",
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+      "Iter 11520, Minibatch Loss= 0.340857, Training Accuracy= 0.92188\n",
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+      "Iter 14080, Minibatch Loss= 0.486564, Training Accuracy= 0.84375\n",
+      "Iter 15360, Minibatch Loss= 0.302493, Training Accuracy= 0.90625\n",
+      "Iter 16640, Minibatch Loss= 0.334277, Training Accuracy= 0.92188\n",
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+      "Iter 20480, Minibatch Loss= 0.150356, Training Accuracy= 0.96094\n",
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+      "Iter 23040, Minibatch Loss= 0.159742, Training Accuracy= 0.94531\n",
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+      "Iter 26880, Minibatch Loss= 0.241612, Training Accuracy= 0.91406\n",
+      "Iter 28160, Minibatch Loss= 0.200397, Training Accuracy= 0.93750\n",
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+      "Iter 35840, Minibatch Loss= 0.207867, Training Accuracy= 0.93750\n",
+      "Iter 37120, Minibatch Loss= 0.313306, Training Accuracy= 0.90625\n",
+      "Iter 38400, Minibatch Loss= 0.089850, Training Accuracy= 0.96875\n",
+      "Iter 39680, Minibatch Loss= 0.184803, Training Accuracy= 0.92188\n",
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+      "Iter 43520, Minibatch Loss= 0.127905, Training Accuracy= 0.93750\n",
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+      "Iter 46080, Minibatch Loss= 0.068337, Training Accuracy= 0.98438\n",
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+      "Iter 52480, Minibatch Loss= 0.158776, Training Accuracy= 0.96094\n",
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+      "Iter 60160, Minibatch Loss= 0.062801, Training Accuracy= 0.97656\n",
+      "Iter 61440, Minibatch Loss= 0.036346, Training Accuracy= 0.98438\n",
+      "Iter 62720, Minibatch Loss= 0.153030, Training Accuracy= 0.92969\n",
+      "Iter 64000, Minibatch Loss= 0.117716, Training Accuracy= 0.95312\n",
+      "Iter 65280, Minibatch Loss= 0.048387, Training Accuracy= 0.99219\n",
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+      "Iter 67840, Minibatch Loss= 0.221085, Training Accuracy= 0.96875\n",
+      "Iter 69120, Minibatch Loss= 0.184049, Training Accuracy= 0.93750\n",
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+      "Iter 74240, Minibatch Loss= 0.161794, Training Accuracy= 0.94531\n",
+      "Iter 75520, Minibatch Loss= 0.103779, Training Accuracy= 0.96875\n",
+      "Iter 76800, Minibatch Loss= 0.165586, Training Accuracy= 0.96094\n",
+      "Iter 78080, Minibatch Loss= 0.137721, Training Accuracy= 0.95312\n",
+      "Iter 79360, Minibatch Loss= 0.124014, Training Accuracy= 0.96094\n",
+      "Iter 80640, Minibatch Loss= 0.051460, Training Accuracy= 0.99219\n",
+      "Iter 81920, Minibatch Loss= 0.185836, Training Accuracy= 0.96094\n",
+      "Iter 83200, Minibatch Loss= 0.147694, Training Accuracy= 0.94531\n",
+      "Iter 84480, Minibatch Loss= 0.061550, Training Accuracy= 0.98438\n",
+      "Iter 85760, Minibatch Loss= 0.093457, Training Accuracy= 0.96875\n",
+      "Iter 87040, Minibatch Loss= 0.094497, Training Accuracy= 0.98438\n",
+      "Iter 88320, Minibatch Loss= 0.093934, Training Accuracy= 0.96094\n",
+      "Iter 89600, Minibatch Loss= 0.061550, Training Accuracy= 0.96875\n",
+      "Iter 90880, Minibatch Loss= 0.082452, Training Accuracy= 0.97656\n",
+      "Iter 92160, Minibatch Loss= 0.087423, Training Accuracy= 0.97656\n",
+      "Iter 93440, Minibatch Loss= 0.032694, Training Accuracy= 0.99219\n",
+      "Iter 94720, Minibatch Loss= 0.069597, Training Accuracy= 0.97656\n",
+      "Iter 96000, Minibatch Loss= 0.193636, Training Accuracy= 0.96094\n",
+      "Iter 97280, Minibatch Loss= 0.134405, Training Accuracy= 0.96094\n",
+      "Iter 98560, Minibatch Loss= 0.072992, Training Accuracy= 0.96875\n",
+      "Iter 99840, Minibatch Loss= 0.041049, Training Accuracy= 0.99219\n",
+      "Optimization Finished!\n",
+      "Testing Accuracy: 0.960938\n"
+     ]
+    }
+   ],
    "source": [
     "# Launch the graph\n",
     "with tf.Session() as sess:\n",