MNIST_BILSTM.ipynb

{
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  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
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   "source": [
    "from tensorflow.examples.tutorials.mnist import input_data\n",
    "import tensorflow as tf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Extracting MNIST_data/train-images-idx3-ubyte.gz\n",
      "Extracting MNIST_data/train-labels-idx1-ubyte.gz\n",
      "Extracting MNIST_data/t10k-images-idx3-ubyte.gz\n",
      "Extracting MNIST_data/t10k-labels-idx1-ubyte.gz\n"
     ]
    }
   ],
   "source": [
    "mnist = input_data.read_data_sets('MNIST_data/', one_hot=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "HIDDEN_UNITS = 256\n",
    "N_CLASSES = 10\n",
    "LEARNING_RATE = 1e-2\n",
    "TRAINING_ITER = 1000\n",
    "BATCH_SIZE = 100\n",
    "\n",
    "def BiRNN(x, weights, biases):\n",
    "    x = tf.transpose(x, [1,0,2])\n",
    "    x = tf.reshape(x, [-1, 28])\n",
    "    x = tf.split(x, 28)\n",
    "    \n",
    "    lstm_fw_cell = tf.contrib.rnn.BasicLSTMCell(num_units=HIDDEN_UNITS)\n",
    "    lstm_bw_cell = tf.contrib.rnn.BasicLSTMCell(num_units=HIDDEN_UNITS)\n",
    "    \n",
    "    outputs, _, _ = tf.contrib.rnn.static_bidirectional_rnn(lstm_fw_cell, lstm_bw_cell, x, dtype=tf.float32)\n",
    "    \n",
    "    return tf.matmul(outputs[-1], weights) + biases\n",
    "\n",
    "\n",
    "x = tf.placeholder(tf.float32, [None, 784])\n",
    "y = tf.placeholder(tf.float32, [None, 10])\n",
    "\n",
    "x_2d = tf.reshape(x, [-1, 28, 28])\n",
    "\n",
    "weights = {'output': tf.Variable(tf.random_normal([2 * HIDDEN_UNITS, N_CLASSES]))}\n",
    "biases = {'output': tf.Variable(tf.zeros([N_CLASSES]) + 0.1)}\n",
    "\n",
    "predictions = BiRNN(x_2d, weights['output'], biases['output'])\n",
    "\n",
    "loss = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(labels=y, logits=predictions))\n",
    "train_step = tf.train.AdamOptimizer(LEARNING_RATE).minimize(loss)\n",
    "\n",
    "accuracy = tf.reduce_mean(tf.cast(tf.equal(tf.arg_max(y, 1), tf.arg_max(predictions, 1)), tf.float32))\n",
    "\n",
    "\n",
    "init = tf.global_variables_initializer()\n"
   ]
  },
  {
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     "text": [
      "0.15\n",
      "0.73\n",
      "0.84\n",
      "0.92\n",
      "0.95\n",
      "0.91\n",
      "0.95\n",
      "0.98\n",
      "0.99\n",
      "0.97\n",
      "0.97\n",
      "0.99\n",
      "0.99\n",
      "0.98\n",
      "0.99\n",
      "0.99\n"
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    {
     "ename": "KeyboardInterrupt",
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     "output_type": "error",
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      "\u001b[0;32m/Users/miaofan/anaconda/lib/python2.7/site-packages/tensorflow/python/client/session.pyc\u001b[0m in \u001b[0;36mrun\u001b[0;34m(self, fetches, feed_dict, options, run_metadata)\u001b[0m\n\u001b[1;32m    765\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    766\u001b[0m       result = self._run(None, fetches, feed_dict, options_ptr,\n\u001b[0;32m--> 767\u001b[0;31m                          run_metadata_ptr)\n\u001b[0m\u001b[1;32m    768\u001b[0m       \u001b[0;32mif\u001b[0m \u001b[0mrun_metadata\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    769\u001b[0m         \u001b[0mproto_data\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtf_session\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mTF_GetBuffer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrun_metadata_ptr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
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      "\u001b[0;32m/Users/miaofan/anaconda/lib/python2.7/site-packages/tensorflow/python/client/session.pyc\u001b[0m in \u001b[0;36m_do_run\u001b[0;34m(self, handle, target_list, fetch_list, feed_dict, options, run_metadata)\u001b[0m\n\u001b[1;32m   1013\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mhandle\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1014\u001b[0m       return self._do_call(_run_fn, self._session, feed_dict, fetch_list,\n\u001b[0;32m-> 1015\u001b[0;31m                            target_list, options, run_metadata)\n\u001b[0m\u001b[1;32m   1016\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1017\u001b[0m       return self._do_call(_prun_fn, self._session, handle, feed_dict,\n",
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      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
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   "source": [
    "with tf.Session() as sess:\n",
    "    sess.run(init)\n",
    "    for i in range(TRAINING_ITER):\n",
    "        batch_xs, batch_ys = mnist.train.next_batch(BATCH_SIZE)\n",
    "        sess.run(train_step, feed_dict = {x: batch_xs, y: batch_ys})\n",
    "        if i % 50 == 0:\n",
    "            print sess.run(accuracy, feed_dict = {x: batch_xs, y: batch_ys})\n",
    "            print sess.run(accuracy, feed_dict = {x: mnist.validation.images, y: mnist.validation.labels})"
   ]
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