Update bidirectional rnn for TF1.0

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

notebooks/3_NeuralNetworks/bidirectional_rnn.ipynb

@@ -1,10 +1,10 @@
 {
  "cells": [
   {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "cell_type": "markdown",
+   "metadata": {
+    "collapsed": true
+   },
    "source": [
     "'''\n",
     "A Bidirectional Reccurent Neural Network (LSTM) implementation example using TensorFlow library.\n",
@@ -18,35 +18,26 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Extracting /tmp/data/train-images-idx3-ubyte.gz\n",
-      "Extracting /tmp/data/train-labels-idx1-ubyte.gz\n",
-      "Extracting /tmp/data/t10k-images-idx3-ubyte.gz\n",
-      "Extracting /tmp/data/t10k-labels-idx1-ubyte.gz\n"
-     ]
-    }
-   ],
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
    "source": [
     "import tensorflow as tf\n",
-    "from tensorflow.models.rnn import rnn, rnn_cell\n",
+    "from tensorflow.contrib import rnn\n",
     "import numpy as np\n",
     "\n",
     "# Import MINST data\n",
     "from tensorflow.examples.tutorials.mnist import input_data\n",
-    "mnist = input_data.read_data_sets(\"/tmp/data/\", one_hot=True)"
+    "mnist = input_data.read_data_sets(\"MNIST_data/\", one_hot=True)"
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "cell_type": "markdown",
+   "metadata": {
+    "collapsed": true
+   },
    "source": [
     "'''\n",
     "To classify images using a bidirectional reccurent neural network, we consider\n",
@@ -58,7 +49,9 @@
   {
    "cell_type": "code",
    "execution_count": 2,
-   "metadata": {},
+   "metadata": {
+    "collapsed": true
+   },
    "outputs": [],
    "source": [
     "# Parameters\n",
@@ -90,7 +83,9 @@
   {
    "cell_type": "code",
    "execution_count": 3,
-   "metadata": {},
+   "metadata": {
+    "collapsed": false
+   },
    "outputs": [],
    "source": [
     "def BiRNN(x, weights, biases):\n",
@@ -104,20 +99,20 @@
     "    # Reshape to (n_steps*batch_size, n_input)\n",
     "    x = tf.reshape(x, [-1, n_input])\n",
     "    # Split to get a list of 'n_steps' tensors of shape (batch_size, n_input)\n",
-    "    x = tf.split(0, n_steps, x)\n",
+    "    x = tf.split(x, n_steps, 0)\n",
     "\n",
     "    # Define lstm cells with tensorflow\n",
     "    # Forward direction cell\n",
-    "    lstm_fw_cell = rnn_cell.BasicLSTMCell(n_hidden, forget_bias=1.0)\n",
+    "    lstm_fw_cell = rnn.BasicLSTMCell(n_hidden, forget_bias=1.0)\n",
     "    # Backward direction cell\n",
-    "    lstm_bw_cell = rnn_cell.BasicLSTMCell(n_hidden, forget_bias=1.0)\n",
+    "    lstm_bw_cell = rnn.BasicLSTMCell(n_hidden, forget_bias=1.0)\n",
     "\n",
     "    # Get lstm cell output\n",
     "    try:\n",
-    "        outputs, _, _ = rnn.bidirectional_rnn(lstm_fw_cell, lstm_bw_cell, x,\n",
+    "        outputs, _, _ = rnn.static_bidirectional_rnn(lstm_fw_cell, lstm_bw_cell, x,\n",
     "                                              dtype=tf.float32)\n",
     "    except Exception: # Old TensorFlow version only returns outputs not states\n",
-    "        outputs = rnn.bidirectional_rnn(lstm_fw_cell, lstm_bw_cell, x,\n",
+    "        outputs = rnn.static_bidirectional_rnn(lstm_fw_cell, lstm_bw_cell, x,\n",
     "                                        dtype=tf.float32)\n",
     "\n",
     "    # Linear activation, using rnn inner loop last output\n",
@@ -126,7 +121,7 @@
     "pred = BiRNN(x, weights, biases)\n",
     "\n",
     "# Define loss and optimizer\n",
-    "cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(pred, y))\n",
+    "cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=pred, labels=y))\n",
     "optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate).minimize(cost)\n",
     "\n",
     "# Evaluate model\n",
@@ -134,101 +129,16 @@
     "accuracy = tf.reduce_mean(tf.cast(correct_pred, tf.float32))\n",
     "\n",
     "# Initializing the variables\n",
-    "init = tf.initialize_all_variables()"
+    "init = tf.global_variables_initializer()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Iter 1280, Minibatch Loss= 1.689740, Training Accuracy= 0.36719\n",
-      "Iter 2560, Minibatch Loss= 1.477009, Training Accuracy= 0.44531\n",
-      "Iter 3840, Minibatch Loss= 1.245874, Training Accuracy= 0.53125\n",
-      "Iter 5120, Minibatch Loss= 0.990923, Training Accuracy= 0.64062\n",
-      "Iter 6400, Minibatch Loss= 0.752950, Training Accuracy= 0.71875\n",
-      "Iter 7680, Minibatch Loss= 1.023025, Training Accuracy= 0.61719\n",
-      "Iter 8960, Minibatch Loss= 0.921414, Training Accuracy= 0.68750\n",
-      "Iter 10240, Minibatch Loss= 0.719829, Training Accuracy= 0.75000\n",
-      "Iter 11520, Minibatch Loss= 0.468657, Training Accuracy= 0.86719\n",
-      "Iter 12800, Minibatch Loss= 0.654315, Training Accuracy= 0.78125\n",
-      "Iter 14080, Minibatch Loss= 0.595391, Training Accuracy= 0.83594\n",
-      "Iter 15360, Minibatch Loss= 0.392862, Training Accuracy= 0.83594\n",
-      "Iter 16640, Minibatch Loss= 0.421122, Training Accuracy= 0.92188\n",
-      "Iter 17920, Minibatch Loss= 0.311471, Training Accuracy= 0.88281\n",
-      "Iter 19200, Minibatch Loss= 0.276949, Training Accuracy= 0.92188\n",
-      "Iter 20480, Minibatch Loss= 0.170499, Training Accuracy= 0.94531\n",
-      "Iter 21760, Minibatch Loss= 0.419481, Training Accuracy= 0.86719\n",
-      "Iter 23040, Minibatch Loss= 0.183765, Training Accuracy= 0.92188\n",
-      "Iter 24320, Minibatch Loss= 0.386232, Training Accuracy= 0.86719\n",
-      "Iter 25600, Minibatch Loss= 0.335571, Training Accuracy= 0.88281\n",
-      "Iter 26880, Minibatch Loss= 0.169092, Training Accuracy= 0.92969\n",
-      "Iter 28160, Minibatch Loss= 0.247623, Training Accuracy= 0.92969\n",
-      "Iter 29440, Minibatch Loss= 0.242989, Training Accuracy= 0.94531\n",
-      "Iter 30720, Minibatch Loss= 0.253811, Training Accuracy= 0.92188\n",
-      "Iter 32000, Minibatch Loss= 0.169660, Training Accuracy= 0.93750\n",
-      "Iter 33280, Minibatch Loss= 0.291349, Training Accuracy= 0.90625\n",
-      "Iter 34560, Minibatch Loss= 0.172026, Training Accuracy= 0.95312\n",
-      "Iter 35840, Minibatch Loss= 0.186019, Training Accuracy= 0.93750\n",
-      "Iter 37120, Minibatch Loss= 0.298480, Training Accuracy= 0.89062\n",
-      "Iter 38400, Minibatch Loss= 0.158750, Training Accuracy= 0.92188\n",
-      "Iter 39680, Minibatch Loss= 0.162706, Training Accuracy= 0.94531\n",
-      "Iter 40960, Minibatch Loss= 0.339814, Training Accuracy= 0.86719\n",
-      "Iter 42240, Minibatch Loss= 0.068817, Training Accuracy= 0.99219\n",
-      "Iter 43520, Minibatch Loss= 0.188742, Training Accuracy= 0.93750\n",
-      "Iter 44800, Minibatch Loss= 0.176708, Training Accuracy= 0.92969\n",
-      "Iter 46080, Minibatch Loss= 0.096726, Training Accuracy= 0.96875\n",
-      "Iter 47360, Minibatch Loss= 0.220973, Training Accuracy= 0.92969\n",
-      "Iter 48640, Minibatch Loss= 0.226749, Training Accuracy= 0.94531\n",
-      "Iter 49920, Minibatch Loss= 0.188906, Training Accuracy= 0.94531\n",
-      "Iter 51200, Minibatch Loss= 0.145194, Training Accuracy= 0.95312\n",
-      "Iter 52480, Minibatch Loss= 0.168948, Training Accuracy= 0.95312\n",
-      "Iter 53760, Minibatch Loss= 0.069116, Training Accuracy= 0.97656\n",
-      "Iter 55040, Minibatch Loss= 0.228721, Training Accuracy= 0.93750\n",
-      "Iter 56320, Minibatch Loss= 0.152915, Training Accuracy= 0.95312\n",
-      "Iter 57600, Minibatch Loss= 0.126974, Training Accuracy= 0.96875\n",
-      "Iter 58880, Minibatch Loss= 0.078870, Training Accuracy= 0.97656\n",
-      "Iter 60160, Minibatch Loss= 0.225498, Training Accuracy= 0.95312\n",
-      "Iter 61440, Minibatch Loss= 0.111760, Training Accuracy= 0.97656\n",
-      "Iter 62720, Minibatch Loss= 0.161434, Training Accuracy= 0.97656\n",
-      "Iter 64000, Minibatch Loss= 0.207190, Training Accuracy= 0.94531\n",
-      "Iter 65280, Minibatch Loss= 0.103831, Training Accuracy= 0.96094\n",
-      "Iter 66560, Minibatch Loss= 0.153846, Training Accuracy= 0.93750\n",
-      "Iter 67840, Minibatch Loss= 0.082717, Training Accuracy= 0.96875\n",
-      "Iter 69120, Minibatch Loss= 0.199301, Training Accuracy= 0.95312\n",
-      "Iter 70400, Minibatch Loss= 0.139725, Training Accuracy= 0.96875\n",
-      "Iter 71680, Minibatch Loss= 0.169596, Training Accuracy= 0.95312\n",
-      "Iter 72960, Minibatch Loss= 0.142444, Training Accuracy= 0.96094\n",
-      "Iter 74240, Minibatch Loss= 0.145822, Training Accuracy= 0.95312\n",
-      "Iter 75520, Minibatch Loss= 0.129086, Training Accuracy= 0.94531\n",
-      "Iter 76800, Minibatch Loss= 0.078082, Training Accuracy= 0.97656\n",
-      "Iter 78080, Minibatch Loss= 0.151803, Training Accuracy= 0.94531\n",
-      "Iter 79360, Minibatch Loss= 0.050142, Training Accuracy= 0.98438\n",
-      "Iter 80640, Minibatch Loss= 0.136788, Training Accuracy= 0.95312\n",
-      "Iter 81920, Minibatch Loss= 0.130100, Training Accuracy= 0.94531\n",
-      "Iter 83200, Minibatch Loss= 0.058298, Training Accuracy= 0.98438\n",
-      "Iter 84480, Minibatch Loss= 0.120124, Training Accuracy= 0.96094\n",
-      "Iter 85760, Minibatch Loss= 0.064916, Training Accuracy= 0.97656\n",
-      "Iter 87040, Minibatch Loss= 0.137179, Training Accuracy= 0.93750\n",
-      "Iter 88320, Minibatch Loss= 0.138268, Training Accuracy= 0.95312\n",
-      "Iter 89600, Minibatch Loss= 0.072827, Training Accuracy= 0.97656\n",
-      "Iter 90880, Minibatch Loss= 0.123839, Training Accuracy= 0.96875\n",
-      "Iter 92160, Minibatch Loss= 0.087194, Training Accuracy= 0.96875\n",
-      "Iter 93440, Minibatch Loss= 0.083489, Training Accuracy= 0.97656\n",
-      "Iter 94720, Minibatch Loss= 0.131827, Training Accuracy= 0.95312\n",
-      "Iter 96000, Minibatch Loss= 0.098764, Training Accuracy= 0.96875\n",
-      "Iter 97280, Minibatch Loss= 0.115553, Training Accuracy= 0.94531\n",
-      "Iter 98560, Minibatch Loss= 0.079704, Training Accuracy= 0.96875\n",
-      "Iter 99840, Minibatch Loss= 0.064562, Training Accuracy= 0.98438\n",
-      "Optimization Finished!\n",
-      "Testing Accuracy: 0.992188\n"
-     ]
-    }
-   ],
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
    "source": [
     "# Launch the graph\n",
     "with tf.Session() as sess:\n",
@@ -259,6 +169,15 @@
     "    print \"Testing Accuracy:\", \\\n",
     "        sess.run(accuracy, feed_dict={x: test_data, y: test_label})"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {
@@ -270,14 +189,14 @@
   "language_info": {
    "codemirror_mode": {
     "name": "ipython",
-    "version": 2.0
+    "version": 2
    },
    "file_extension": ".py",
    "mimetype": "text/x-python",
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython2",
-   "version": "2.7.11"
+   "version": "2.7.13"
   }
  },
  "nbformat": 4,