14_recurrent_neural_networks.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Chapter 14 – Recurrent Neural Networks**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "_This notebook contains all the sample code and solutions to the exercises in chapter 14._"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Setup"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First, let's make sure this notebook works well in both python 2 and 3, import a few common modules, ensure MatplotLib plots figures inline and prepare a function to save the figures:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# To support both python 2 and python 3\n",
    "from __future__ import division, print_function, unicode_literals\n",
    "\n",
    "# Common imports\n",
    "import numpy as np\n",
    "import os\n",
    "\n",
    "# to make this notebook's output stable across runs\n",
    "def reset_graph(seed=42):\n",
    "    tf.reset_default_graph()\n",
    "    tf.set_random_seed(seed)\n",
    "    np.random.seed(seed)\n",
    "\n",
    "# To plot pretty figures\n",
    "%matplotlib inline\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "plt.rcParams['axes.labelsize'] = 14\n",
    "plt.rcParams['xtick.labelsize'] = 12\n",
    "plt.rcParams['ytick.labelsize'] = 12\n",
    "\n",
    "# Where to save the figures\n",
    "PROJECT_ROOT_DIR = \".\"\n",
    "CHAPTER_ID = \"rnn\"\n",
    "\n",
    "def save_fig(fig_id, tight_layout=True):\n",
    "    path = os.path.join(PROJECT_ROOT_DIR, \"images\", CHAPTER_ID, fig_id + \".png\")\n",
    "    print(\"Saving figure\", fig_id)\n",
    "    if tight_layout:\n",
    "        plt.tight_layout()\n",
    "    plt.savefig(path, format='png', dpi=300)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Then of course we will need TensorFlow:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Basic RNNs\n",
    "\n",
    " A recurrent neural network looks very much like a feedforward neural network, except it also has connections pointing backward. This is called unrolling the network through time.\n",
    " \n",
    " Each recurrent neuron has two sets of weights: one for the inputs x(t) and the other for the outputs of the previous time step, y(t–1). Let’s call these weight vectors wx and wy. \n",
    " \n",
    " ### memory cell\n",
    " A part of a neural network that preserves some state across time steps is called a memory cell (or simply a cell)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Manual RNN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "reset_graph()\n",
    "\n",
    "n_inputs = 3\n",
    "n_neurons = 5\n",
    "\n",
    "X0 = tf.placeholder(tf.float32, [None, n_inputs])\n",
    "X1 = tf.placeholder(tf.float32, [None, n_inputs])\n",
    "\n",
    "Wx = tf.Variable(tf.random_normal(shape=[n_inputs, n_neurons],dtype=tf.float32))\n",
    "Wy = tf.Variable(tf.random_normal(shape=[n_neurons,n_neurons],dtype=tf.float32))\n",
    "b = tf.Variable(tf.zeros([1, n_neurons], dtype=tf.float32))\n",
    "\n",
    "Y0 = tf.tanh(tf.matmul(X0, Wx) + b)\n",
    "Y1 = tf.tanh(tf.matmul(Y0, Wy) + tf.matmul(X1, Wx) + b)\n",
    "\n",
    "init = tf.global_variables_initializer()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "\n",
    "X0_batch = np.array([[0, 1, 2], [3, 4, 5], [6, 7, 8], [9, 0, 1]]) # t = 0\n",
    "X1_batch = np.array([[9, 8, 7], [0, 0, 0], [6, 5, 4], [3, 2, 1]]) # t = 1\n",
    "\n",
    "with tf.Session() as sess:\n",
    "    init.run()\n",
    "    Y0_val, Y1_val = sess.run([Y0, Y1], feed_dict={X0: X0_batch, X1: X1_batch})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[-0.0664006   0.96257669  0.68105793  0.70918542 -0.89821601]\n",
      " [ 0.9977755  -0.71978903 -0.99657607  0.96739239 -0.99989718]\n",
      " [ 0.99999774 -0.99898803 -0.99999893  0.99677622 -0.99999988]\n",
      " [ 1.         -1.         -1.         -0.99818915  0.99950868]]\n"
     ]
    }
   ],
   "source": [
    "print(Y0_val)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 1.         -1.         -1.          0.40200251 -0.99999982]\n",
      " [-0.12210421  0.62805271  0.96718431 -0.99371219 -0.25839362]\n",
      " [ 0.99999827 -0.9999994  -0.9999975  -0.85943311 -0.99998808]\n",
      " [ 0.99928284 -0.99999815 -0.99990582  0.98579627 -0.92205757]]\n"
     ]
    }
   ],
   "source": [
    "print(Y1_val)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Using `static_rnn()`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "n_inputs = 3\n",
    "n_neurons = 5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "reset_graph()\n",
    "\n",
    "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",
    "Y0, Y1 = output_seqs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "init = tf.global_variables_initializer()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "X0_batch = np.array([[0, 1, 2], [3, 4, 5], [6, 7, 8], [9, 0, 1]])\n",
    "X1_batch = np.array([[9, 8, 7], [0, 0, 0], [6, 5, 4], [3, 2, 1]])\n",
    "\n",
    "with tf.Session() as sess:\n",
    "    init.run()\n",
    "    Y0_val, Y1_val = sess.run([Y0, Y1], feed_dict={X0: X0_batch, X1: X1_batch})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.30741334, -0.32884315, -0.65428472, -0.93850589,  0.52089024],\n",
       "       [ 0.99122757, -0.95425421, -0.75180793, -0.99952078,  0.98202348],\n",
       "       [ 0.99992681, -0.99783254, -0.82473528, -0.9999963 ,  0.99947774],\n",
       "       [ 0.99677098, -0.68750614,  0.84199691,  0.93039107,  0.8120684 ]], dtype=float32)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y0_val"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.99998885, -0.99976051, -0.06679298, -0.99998039,  0.99982214],\n",
       "       [-0.65249437, -0.51520866, -0.37968954, -0.59225935, -0.08968385],\n",
       "       [ 0.99862403, -0.99715197, -0.03308626, -0.99915648,  0.99329019],\n",
       "       [ 0.99681675, -0.95981938,  0.39660636, -0.83076048,  0.79671967]], dtype=float32)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y1_val"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "from IPython.display import clear_output, Image, display, HTML\n",
    "\n",
    "def strip_consts(graph_def, max_const_size=32):\n",
    "    \"\"\"Strip large constant values from graph_def.\"\"\"\n",
    "    strip_def = tf.GraphDef()\n",
    "    for n0 in graph_def.node:\n",
    "        n = strip_def.node.add() \n",
    "        n.MergeFrom(n0)\n",
    "        if n.op == 'Const':\n",
    "            tensor = n.attr['value'].tensor\n",
    "            size = len(tensor.tensor_content)\n",
    "            if size > max_const_size:\n",
    "                tensor.tensor_content = \"b<stripped %d bytes>\"%size\n",
    "    return strip_def\n",
    "\n",
    "def show_graph(graph_def, max_const_size=32):\n",
    "    \"\"\"Visualize TensorFlow graph.\"\"\"\n",
    "    if hasattr(graph_def, 'as_graph_def'):\n",
    "        graph_def = graph_def.as_graph_def()\n",
    "    strip_def = strip_consts(graph_def, max_const_size=max_const_size)\n",
    "    code = \"\"\"\n",
    "        <script>\n",
    "          function load() {{\n",
    "            document.getElementById(\"{id}\").pbtxt = {data};\n",
    "          }}\n",
    "        </script>\n",
    "        <link rel=\"import\" href=\"https://tensorboard.appspot.com/tf-graph-basic.build.html\" onload=load()>\n",
    "        <div style=\"height:600px\">\n",
    "          <tf-graph-basic id=\"{id}\"></tf-graph-basic>\n",
    "        </div>\n",
    "    \"\"\".format(data=repr(str(strip_def)), id='graph'+str(np.random.rand()))\n",
    "\n",
    "    iframe = \"\"\"\n",
    "        <iframe seamless style=\"width:1200px;height:620px;border:0\" srcdoc=\"{}\"></iframe>\n",
    "    \"\"\".format(code.replace('\"', '&quot;'))\n",
    "    display(HTML(iframe))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
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 attr {\\n    key: &quot;use_locking&quot;\\n    value {\\n      b: true\\n    }\\n  }\\n  attr {\\n    key: &quot;validate_shape&quot;\\n    value {\\n      b: true\\n    }\\n  }\\n}\\nnode {\\n  name: &quot;rnn/basic_rnn_cell/bias/read&quot;\\n  op: &quot;Identity&quot;\\n  input: &quot;rnn/basic_rnn_cell/bias&quot;\\n  attr {\\n    key: &quot;T&quot;\\n    value {\\n      type: DT_FLOAT\\n    }\\n  }\\n}\\nnode {\\n  name: &quot;rnn/rnn/basic_rnn_cell/basic_rnn_cell/BiasAdd&quot;\\n  op: &quot;BiasAdd&quot;\\n  input: &quot;rnn/rnn/basic_rnn_cell/basic_rnn_cell/MatMul&quot;\\n  input: &quot;rnn/basic_rnn_cell/bias/read&quot;\\n  attr {\\n    key: &quot;T&quot;\\n    value {\\n      type: DT_FLOAT\\n    }\\n  }\\n  attr {\\n    key: &quot;data_format&quot;\\n    value {\\n      s: &quot;NHWC&quot;\\n    }\\n  }\\n}\\nnode {\\n  name: &quot;rnn/rnn/basic_rnn_cell/Tanh&quot;\\n  op: &quot;Tanh&quot;\\n  input: &quot;rnn/rnn/basic_rnn_cell/basic_rnn_cell/BiasAdd&quot;\\n  attr {\\n    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input: &quot;rnn/rnn/basic_rnn_cell/basic_rnn_cell_1/BiasAdd&quot;\\n  attr {\\n    key: &quot;T&quot;\\n    value {\\n      type: DT_FLOAT\\n    }\\n  }\\n}\\nnode {\\n  name: &quot;init&quot;\\n  op: &quot;NoOp&quot;\\n  input: &quot;^rnn/basic_rnn_cell/kernel/Assign&quot;\\n  input: &quot;^rnn/basic_rnn_cell/bias/Assign&quot;\\n}\\n';\n",
       "          }\n",
       "        </script>\n",
       "        <link rel=&quot;import&quot; href=&quot;https://tensorboard.appspot.com/tf-graph-basic.build.html&quot; onload=load()>\n",
       "        <div style=&quot;height:600px&quot;>\n",
       "          <tf-graph-basic id=&quot;graph0.374540118847&quot;></tf-graph-basic>\n",
       "        </div>\n",
       "    \"></iframe>\n",
       "    "
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "show_graph(tf.get_default_graph())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Packing sequences"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "n_steps = 2\n",
    "n_inputs = 3\n",
    "n_neurons = 5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "reset_graph()\n",
    "\n",
    "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",
    "outputs = tf.transpose(tf.stack(output_seqs), perm=[1, 0, 2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "init = tf.global_variables_initializer()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_batch = np.array([\n",
    "        # t = 0      t = 1 \n",
    "        [[0, 1, 2], [9, 8, 7]], # instance 1\n",
    "        [[3, 4, 5], [0, 0, 0]], # instance 2\n",
    "        [[6, 7, 8], [6, 5, 4]], # instance 3\n",
    "        [[9, 0, 1], [3, 2, 1]], # instance 4\n",
    "    ])\n",
    "\n",
    "with tf.Session() as sess:\n",
    "    init.run()\n",
    "    outputs_val = outputs.eval(feed_dict={X: X_batch})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[[-0.45652324 -0.68064123  0.40938237  0.63104504 -0.45732826]\n",
      "  [-0.94288003 -0.99988687  0.94055814  0.99999851 -0.9999997 ]]\n",
      "\n",
      " [[-0.80015349 -0.99218267  0.78177971  0.9971031  -0.99646091]\n",
      "  [-0.63711601  0.11300932  0.5798437   0.43105593 -0.63716984]]\n",
      "\n",
      " [[-0.93605185 -0.99983788  0.93088669  0.99998152 -0.99998295]\n",
      "  [-0.9165386  -0.99456042  0.89605415  0.99987197 -0.99997509]]\n",
      "\n",
      " [[ 0.99273688 -0.99819332 -0.55543643  0.9989031  -0.9953323 ]\n",
      "  [-0.02746334 -0.73191994  0.7827872   0.95256817 -0.97817713]]]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(4, 2, 5)"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(outputs_val)\n",
    "outputs_val.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[-0.94288003 -0.99988687  0.94055814  0.99999851 -0.9999997 ]\n",
      " [-0.63711601  0.11300932  0.5798437   0.43105593 -0.63716984]\n",
      " [-0.9165386  -0.99456042  0.89605415  0.99987197 -0.99997509]\n",
      " [-0.02746334 -0.73191994  0.7827872   0.95256817 -0.97817713]]\n"
     ]
    }
   ],
   "source": [
    "print(np.transpose(outputs_val, axes=[1, 0, 2])[1])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "However, this approach still builds a graph containing one cell per time step. If there were 50 time steps, the graph would look pretty ugly.\n",
    "\n",
    "Fortunately, there is a better solution: the dynamic_rnn() function.\n",
    "\n",
    "## Using `dynamic_rnn()`\n",
    "\n",
    "there is no need to stack, unstack, or transpose. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "n_steps = 2\n",
    "n_inputs = 3\n",
    "n_neurons = 5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "reset_graph()\n",
    "\n",
    "X = tf.placeholder(tf.float32, [None, n_steps, n_inputs])\n",
    "\n",
    "basic_cell = tf.contrib.rnn.BasicRNNCell(num_units=n_neurons)\n",
    "outputs, states = tf.nn.dynamic_rnn(basic_cell, X, dtype=tf.float32)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "init = tf.global_variables_initializer()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_batch = np.array([\n",
    "        [[0, 1, 2], [9, 8, 7]], # instance 1\n",
    "        [[3, 4, 5], [0, 0, 0]], # instance 2\n",
    "        [[6, 7, 8], [6, 5, 4]], # instance 3\n",
    "        [[9, 0, 1], [3, 2, 1]], # instance 4\n",
    "    ])\n",
    "\n",
    "with tf.Session() as sess:\n",
    "    init.run()\n",
    "    outputs_val = outputs.eval(feed_dict={X: X_batch})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[[-0.45652324 -0.68064123  0.40938237  0.63104504 -0.45732826]\n",
      "  [-0.94288003 -0.99988687  0.94055814  0.99999851 -0.9999997 ]]\n",
      "\n",
      " [[-0.80015349 -0.99218267  0.78177971  0.9971031  -0.99646091]\n",
      "  [-0.63711601  0.11300932  0.5798437   0.43105593 -0.63716984]]\n",
      "\n",
      " [[-0.93605185 -0.99983788  0.93088669  0.99998152 -0.99998295]\n",
      "  [-0.9165386  -0.99456042  0.89605415  0.99987197 -0.99997509]]\n",
      "\n",
      " [[ 0.99273688 -0.99819332 -0.55543643  0.9989031  -0.9953323 ]\n",
      "  [-0.02746334 -0.73191994  0.7827872   0.95256817 -0.97817713]]]\n"
     ]
    }
   ],
   "source": [
    "print(outputs_val)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "          }\n",
       "        </script>\n",
       "        <link rel=&quot;import&quot; href=&quot;https://tensorboard.appspot.com/tf-graph-basic.build.html&quot; onload=load()>\n",
       "        <div style=&quot;height:600px&quot;>\n",
       "          <tf-graph-basic id=&quot;graph0.374540118847&quot;></tf-graph-basic>\n",
       "        </div>\n",
       "    \"></iframe>\n",
       "    "
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "show_graph(tf.get_default_graph())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Setting the sequence lengths\n",
    "\n",
    "### Handling Variable Length Input Sequences\n",
    "It must be padded with a zero vector in order to fit in the input tensor X "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [],
   "source": [
    "n_steps = 2\n",
    "n_inputs = 3\n",
    "n_neurons = 5\n",
    "\n",
    "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)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [],
   "source": [
    "seq_length = tf.placeholder(tf.int32, [None])\n",
    "outputs, states = tf.nn.dynamic_rnn(basic_cell, X, dtype=tf.float32,\n",
    "                                    sequence_length=seq_length)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [],
   "source": [
    "init = tf.global_variables_initializer()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_batch = np.array([\n",
    "        # step 0     step 1\n",
    "        [[0, 1, 2], [9, 8, 7]], # instance 1\n",
    "        [[3, 4, 5], [0, 0, 0]], # instance 2 (padded with zero vectors)\n",
    "        [[6, 7, 8], [6, 5, 4]], # instance 3\n",
    "        [[9, 0, 1], [3, 2, 1]], # instance 4\n",
    "    ])\n",
    "seq_length_batch = np.array([2, 1, 2, 1])  # list of x size "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [],
   "source": [
    "with tf.Session() as sess:\n",
    "    init.run()\n",
    "    outputs_val, states_val = sess.run(\n",
    "        [outputs, states], feed_dict={X: X_batch, seq_length: seq_length_batch})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[-0.61832315  0.99999994  0.86584806  0.97933769  0.9902246 ]\n",
      " [ 0.8438704   0.99697548  0.84789461  0.4303219   0.09275495]\n",
      " [-0.77545464  0.99987745  0.97847283  0.73172009  0.888776  ]\n",
      " [-0.99952269  0.99977839  0.59744656  0.99309695  0.99847412]]\n"
     ]
    }
   ],
   "source": [
    "print(states_val)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Handling Variable-Length Output Sequences\n",
    "\n",
    "the most common solution is to define a special output called an end-of-sequence token (EOS token). Any output past the EOS should be ignored (we will discuss this later in this chapter).\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Training a sequence classifier\n",
    "\n",
    "To train an RNN, the trick is to unroll it through time (like we just did) and then simply use regular backpropagation (see Figure 14-5). This strategy is called backpropagation through time (`BPTT`).\n",
    "\n",
    "Note that the gradients flow backward through all the outputs used by the cost function, not just through the final output "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note: the book uses `tensorflow.contrib.layers.fully_connected()` rather than `tf.layers.dense()` (which did not exist when this chapter was written). It is now preferable to use `tf.layers.dense()`, because anything in the contrib module may change or be deleted without notice. The `dense()` function is almost identical to the `fully_connected()` function. The main differences relevant to this chapter are:\n",
    "* several parameters are renamed: `scope` becomes `name`, `activation_fn` becomes `activation` (and similarly the `_fn` suffix is removed from other parameters such as `normalizer_fn`), `weights_initializer` becomes `kernel_initializer`, etc.\n",
    "* the default `activation` is now `None` rather than `tf.nn.relu`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [],
   "source": [
    "reset_graph()\n",
    "\n",
    "n_steps = 28\n",
    "n_inputs = 28\n",
    "n_neurons = 150\n",
    "n_outputs = 10\n",
    "\n",
    "learning_rate = 0.001\n",
    "\n",
    "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",
    "outputs, states = tf.nn.dynamic_rnn(basic_cell, X, dtype=tf.float32)\n",
    "\n",
    "# Note that the fully connected layer is connected to the states tensor, \n",
    "# which contains only the final state of the RNN\n",
    "logits = tf.layers.dense(states, n_outputs)\n",
    "xentropy = tf.nn.sparse_softmax_cross_entropy_with_logits(labels=y,\n",
    "                                                          logits=logits)\n",
    "loss = tf.reduce_mean(xentropy)\n",
    "optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate)\n",
    "training_op = optimizer.minimize(loss)\n",
    "correct = tf.nn.in_top_k(logits, y, 1)\n",
    "accuracy = tf.reduce_mean(tf.cast(correct, tf.float32))\n",
    "\n",
    "init = tf.global_variables_initializer()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "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"
     ]
    }
   ],
   "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"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 Train accuracy: 0.946667 Test accuracy: 0.9366\n",
      "1 Train accuracy: 0.966667 Test accuracy: 0.9488\n",
      "2 Train accuracy: 0.96 Test accuracy: 0.9614\n",
      "3 Train accuracy: 0.966667 Test accuracy: 0.9572\n",
      "4 Train accuracy: 0.946667 Test accuracy: 0.9624\n",
      "5 Train accuracy: 0.96 Test accuracy: 0.9634\n",
      "6 Train accuracy: 0.973333 Test accuracy: 0.9714\n",
      "7 Train accuracy: 0.98 Test accuracy: 0.9686\n",
      "8 Train accuracy: 0.953333 Test accuracy: 0.9647\n",
      "9 Train accuracy: 0.973333 Test accuracy: 0.9706\n",
      "10 Train accuracy: 0.973333 Test accuracy: 0.9697\n",
      "11 Train accuracy: 0.966667 Test accuracy: 0.9692\n",
      "12 Train accuracy: 0.986667 Test accuracy: 0.9751\n",
      "13 Train accuracy: 0.993333 Test accuracy: 0.9738\n",
      "14 Train accuracy: 0.986667 Test accuracy: 0.9721\n",
      "15 Train accuracy: 0.98 Test accuracy: 0.9752\n",
      "16 Train accuracy: 0.993333 Test accuracy: 0.9779\n",
      "17 Train accuracy: 0.98 Test accuracy: 0.976\n",
      "18 Train accuracy: 0.986667 Test accuracy: 0.976\n",
      "19 Train accuracy: 0.98 Test accuracy: 0.9709\n",
      "20 Train accuracy: 0.986667 Test accuracy: 0.9758\n",
      "21 Train accuracy: 0.98 Test accuracy: 0.9751\n",
      "22 Train accuracy: 0.986667 Test accuracy: 0.9748\n",
      "23 Train accuracy: 0.98 Test accuracy: 0.9767\n",
      "24 Train accuracy: 0.98 Test accuracy: 0.9696\n",
      "25 Train accuracy: 1.0 Test accuracy: 0.9779\n",
      "26 Train accuracy: 0.98 Test accuracy: 0.979\n",
      "27 Train accuracy: 0.98 Test accuracy: 0.9783\n",
      "28 Train accuracy: 0.966667 Test accuracy: 0.9793\n",
      "29 Train accuracy: 0.986667 Test accuracy: 0.9756\n",
      "30 Train accuracy: 0.98 Test accuracy: 0.9765\n",
      "31 Train accuracy: 0.986667 Test accuracy: 0.9784\n",
      "32 Train accuracy: 0.986667 Test accuracy: 0.9753\n",
      "33 Train accuracy: 0.98 Test accuracy: 0.9769\n",
      "34 Train accuracy: 0.993333 Test accuracy: 0.9785\n",
      "35 Train accuracy: 1.0 Test accuracy: 0.9787\n",
      "36 Train accuracy: 1.0 Test accuracy: 0.971\n",
      "37 Train accuracy: 0.993333 Test accuracy: 0.9782\n",
      "38 Train accuracy: 0.993333 Test accuracy: 0.9755\n",
      "39 Train accuracy: 0.986667 Test accuracy: 0.9732\n",
      "40 Train accuracy: 0.986667 Test accuracy: 0.9695\n",
      "41 Train accuracy: 0.986667 Test accuracy: 0.9812\n",
      "42 Train accuracy: 0.993333 Test accuracy: 0.9785\n",
      "43 Train accuracy: 0.993333 Test accuracy: 0.9768\n",
      "44 Train accuracy: 0.98 Test accuracy: 0.979\n",
      "45 Train accuracy: 0.986667 Test accuracy: 0.981\n",
      "46 Train accuracy: 0.993333 Test accuracy: 0.9792\n",
      "47 Train accuracy: 1.0 Test accuracy: 0.9812\n",
      "48 Train accuracy: 0.993333 Test accuracy: 0.9775\n",
      "49 Train accuracy: 1.0 Test accuracy: 0.9747\n",
      "50 Train accuracy: 1.0 Test accuracy: 0.9815\n",
      "51 Train accuracy: 0.986667 Test accuracy: 0.9805\n",
      "52 Train accuracy: 0.986667 Test accuracy: 0.9798\n",
      "53 Train accuracy: 0.986667 Test accuracy: 0.9791\n",
      "54 Train accuracy: 1.0 Test accuracy: 0.9771\n",
      "55 Train accuracy: 0.986667 Test accuracy: 0.98\n",
      "56 Train accuracy: 0.993333 Test accuracy: 0.978\n",
      "57 Train accuracy: 0.986667 Test accuracy: 0.9794\n",
      "58 Train accuracy: 0.993333 Test accuracy: 0.9784\n",
      "59 Train accuracy: 0.993333 Test accuracy: 0.9826\n",
      "60 Train accuracy: 0.986667 Test accuracy: 0.9746\n",
      "61 Train accuracy: 1.0 Test accuracy: 0.978\n",
      "62 Train accuracy: 1.0 Test accuracy: 0.9757\n",
      "63 Train accuracy: 1.0 Test accuracy: 0.9792\n",
      "64 Train accuracy: 0.986667 Test accuracy: 0.9758\n",
      "65 Train accuracy: 1.0 Test accuracy: 0.9823\n",
      "66 Train accuracy: 0.986667 Test accuracy: 0.9752\n",
      "67 Train accuracy: 0.986667 Test accuracy: 0.9794\n",
      "68 Train accuracy: 0.993333 Test accuracy: 0.9798\n",
      "69 Train accuracy: 0.993333 Test accuracy: 0.9762\n",
      "70 Train accuracy: 0.993333 Test accuracy: 0.9799\n",
      "71 Train accuracy: 1.0 Test accuracy: 0.9804\n",
      "72 Train accuracy: 1.0 Test accuracy: 0.9777\n",
      "73 Train accuracy: 0.993333 Test accuracy: 0.9771\n",
      "74 Train accuracy: 0.986667 Test accuracy: 0.9764\n",
      "75 Train accuracy: 0.993333 Test accuracy: 0.9749\n",
      "76 Train accuracy: 0.993333 Test accuracy: 0.9804\n",
      "77 Train accuracy: 0.993333 Test accuracy: 0.9776\n",
      "78 Train accuracy: 0.986667 Test accuracy: 0.9749\n",
      "79 Train accuracy: 0.993333 Test accuracy: 0.976\n",
      "80 Train accuracy: 0.986667 Test accuracy: 0.9754\n",
      "81 Train accuracy: 0.98 Test accuracy: 0.9783\n",
      "82 Train accuracy: 1.0 Test accuracy: 0.9787\n",
      "83 Train accuracy: 0.993333 Test accuracy: 0.9793\n",
      "84 Train accuracy: 0.986667 Test accuracy: 0.9739\n",
      "85 Train accuracy: 1.0 Test accuracy: 0.9776\n",
      "86 Train accuracy: 0.986667 Test accuracy: 0.9761\n",
      "87 Train accuracy: 0.98 Test accuracy: 0.9781\n",
      "88 Train accuracy: 0.98 Test accuracy: 0.9549\n",
      "89 Train accuracy: 0.98 Test accuracy: 0.9767\n",
      "90 Train accuracy: 1.0 Test accuracy: 0.9809\n",
      "91 Train accuracy: 0.98 Test accuracy: 0.9809\n",
      "92 Train accuracy: 0.986667 Test accuracy: 0.9754\n",
      "93 Train accuracy: 0.993333 Test accuracy: 0.979\n",
      "94 Train accuracy: 1.0 Test accuracy: 0.9777\n",
      "95 Train accuracy: 1.0 Test accuracy: 0.9771\n",
      "96 Train accuracy: 1.0 Test accuracy: 0.9814\n",
      "97 Train accuracy: 0.993333 Test accuracy: 0.9673\n",
      "98 Train accuracy: 0.993333 Test accuracy: 0.9788\n",
      "99 Train accuracy: 1.0 Test accuracy: 0.9792\n"
     ]
    }
   ],
   "source": [
    "n_epochs = 100\n",
    "batch_size = 150\n",
    "\n",
    "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((-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_test = accuracy.eval(feed_dict={X: X_test, y: y_test})\n",
    "        print(epoch, \"Train accuracy:\", acc_train, \"Test accuracy:\", acc_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Multi-layer RNN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [],
   "source": [
    "reset_graph()\n",
    "\n",
    "n_steps = 28\n",
    "n_inputs = 28\n",
    "n_outputs = 10\n",
    "\n",
    "learning_rate = 0.001\n",
    "\n",
    "X = tf.placeholder(tf.float32, [None, n_steps, n_inputs])\n",
    "y = tf.placeholder(tf.int32, [None])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [],
   "source": [
    "n_neurons = 100\n",
    "n_layers = 3\n",
    "\n",
    "layers = [tf.contrib.rnn.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",
    "outputs, states = tf.nn.dynamic_rnn(multi_layer_cell, X, dtype=tf.float32)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [],
   "source": [
    "states_concat = tf.concat(axis=1, values=states)\n",
    "logits = tf.layers.dense(states_concat, n_outputs)\n",
    "xentropy = tf.nn.sparse_softmax_cross_entropy_with_logits(labels=y, logits=logits)\n",
    "loss = tf.reduce_mean(xentropy)\n",
    "optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate)\n",
    "training_op = optimizer.minimize(loss)\n",
    "correct = tf.nn.in_top_k(logits, y, 1)\n",
    "accuracy = tf.reduce_mean(tf.cast(correct, tf.float32))\n",
    "\n",
    "init = tf.global_variables_initializer()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 Train accuracy: 0.946667 Test accuracy: 0.9495\n",
      "1 Train accuracy: 0.973333 Test accuracy: 0.9625\n",
      "2 Train accuracy: 0.953333 Test accuracy: 0.9648\n",
      "3 Train accuracy: 0.986667 Test accuracy: 0.9761\n",
      "4 Train accuracy: 0.993333 Test accuracy: 0.9719\n",
      "5 Train accuracy: 0.993333 Test accuracy: 0.9739\n",
      "6 Train accuracy: 0.98 Test accuracy: 0.9746\n",
      "7 Train accuracy: 0.98 Test accuracy: 0.9782\n",
      "8 Train accuracy: 0.986667 Test accuracy: 0.9768\n",
      "9 Train accuracy: 0.986667 Test accuracy: 0.9769\n"
     ]
    }
   ],
   "source": [
    "n_epochs = 10\n",
    "batch_size = 150\n",
    "\n",
    "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((-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_test = accuracy.eval(feed_dict={X: X_test, y: y_test})\n",
    "        print(epoch, \"Train accuracy:\", acc_train, \"Test accuracy:\", acc_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Time series"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {},
   "outputs": [],
   "source": [
    "t_min, t_max = 0, 30\n",
    "resolution = 0.1\n",
    "\n",
    "def time_series(t):\n",
    "    return t * np.sin(t) / 3 + 2 * np.sin(t*5)\n",
    "\n",
    "def next_batch(batch_size, n_steps):\n",
    "    t0 = np.random.rand(batch_size, 1) * (t_max - t_min - n_steps * resolution)\n",
    "    Ts = t0 + np.arange(0., n_steps + 1) * resolution\n",
    "    ys = time_series(Ts)\n",
    "    return ys[:, :-1].reshape(-1, n_steps, 1), ys[:, 1:].reshape(-1, n_steps, 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saving figure time_series_plot\n"
     ]
    },
    {
     "data": {
      "image/png": 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IdEDu3Tu5dk1HNIRJUZRJ4WBNG/Ny3YhI/7L5eZY3oqwm+iox9fkMx5s6R6zA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\nD04p1TEFBwlR4R3pu5S2UzcbtY6DUEop5Ua7JRAikiUixsWy3oOHKAY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3387q1atbfA6U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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11c975d90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "t = np.linspace(t_min, t_max, int((t_max - t_min) / resolution))\n",
    "\n",
    "n_steps = 20\n",
    "t_instance = np.linspace(12.2, 12.2 + resolution * (n_steps + 1), n_steps + 1)\n",
    "\n",
    "plt.figure(figsize=(11,4))\n",
    "plt.subplot(121)\n",
    "plt.title(\"A time series (generated)\", fontsize=14)\n",
    "plt.plot(t, time_series(t), label=r\"$t . \\sin(t) / 3 + 2 . \\sin(5t)$\")\n",
    "plt.plot(t_instance[:-1], time_series(t_instance[:-1]), \"b-\", linewidth=3, label=\"A training instance\")\n",
    "plt.legend(loc=\"lower left\", fontsize=14)\n",
    "plt.axis([0, 30, -17, 13])\n",
    "plt.xlabel(\"Time\")\n",
    "plt.ylabel(\"Value\")\n",
    "\n",
    "plt.subplot(122)\n",
    "plt.title(\"A training instance\", fontsize=14)\n",
    "plt.plot(t_instance[:-1], time_series(t_instance[:-1]), \"bo\", markersize=10, label=\"instance\")\n",
    "plt.plot(t_instance[1:], time_series(t_instance[1:]), \"w*\", markersize=10, label=\"target\")\n",
    "plt.legend(loc=\"upper left\")\n",
    "plt.xlabel(\"Time\")\n",
    "\n",
    "\n",
    "save_fig(\"time_series_plot\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_batch, y_batch = next_batch(1, n_steps)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-2.84420859, -2.25797719],\n",
       "       [-2.25797719, -1.55860615],\n",
       "       [-1.55860615, -0.98486829],\n",
       "       [-0.98486829, -0.74091726],\n",
       "       [-0.74091726, -0.94556233],\n",
       "       [-0.94556233, -1.60247334],\n",
       "       [-1.60247334, -2.59861906],\n",
       "       [-2.59861906, -3.73132895],\n",
       "       [-3.73132895, -4.75736043],\n",
       "       [-4.75736043, -5.45196489],\n",
       "       [-5.45196489, -5.66349768],\n",
       "       [-5.66349768, -5.35020711],\n",
       "       [-5.35020711, -4.59020027],\n",
       "       [-4.59020027, -3.56214977],\n",
       "       [-3.56214977, -2.50146837],\n",
       "       [-2.50146837, -1.64268375],\n",
       "       [-1.64268375, -1.1621235 ],\n",
       "       [-1.1621235 , -1.1349435 ],\n",
       "       [-1.1349435 , -1.51701993],\n",
       "       [-1.51701993, -2.15613685]])"
      ]
     },
     "execution_count": 156,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.c_[X_batch[0], y_batch[0]]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Using an `OuputProjectionWrapper`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's create the RNN. It will contain 100 recurrent neurons and we will unroll it over 20 time steps since each traiing instance will be 20 inputs long. Each input will contain only one feature (the value at that time). The targets are also sequences of 20 inputs, each containing a sigle value:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "At each time step we now have an output vector of size 100. But what we actually want is a single output value at each time step. The simplest solution is to wrap the cell in an `OutputProjectionWrapper`."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Without using an `OutputProjectionWrapper`\n",
    "\n",
    "Although using an OutputProjectionWrapper is the simplest solution to reduce the dimensionality of the RNN’s output sequences down to just one value per time step (per instance), it is not the most efficient. There is a trickier but more efficient solution: you can reshape the RNN outputs from [batch_size, n_steps, n_neurons] to [batch_size * n_steps, n_neurons], then apply a single fully connected layer with the appropriate output size (in our case just 1), which will result in an output tensor of shape [batch_size * n_steps, n_outputs], and then reshape this tensor to [batch_size, n_steps, n_outputs]. \n",
    "\n",
    "The rest of the code is the same as earlier. This can provide a significant speed boost since there is just one fully connected layer instead of one per time step."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {},
   "outputs": [],
   "source": [
    "reset_graph()\n",
    "\n",
    "n_neurons = 100\n",
    "n_steps = 20\n",
    "n_inputs = 1\n",
    "n_outputs = 1\n",
    "learning_rate = 0.001\n",
    "\n",
    "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",
    "rnn_outputs, states = tf.nn.dynamic_rnn(cell, X, dtype=tf.float32)\n",
    "\n",
    "stacked_rnn_outputs = tf.reshape(rnn_outputs, [-1, n_neurons])\n",
    "stacked_outputs = tf.layers.dense(stacked_rnn_outputs, n_outputs)\n",
    "outputs = tf.reshape(stacked_outputs, [-1, n_steps, n_outputs])\n",
    "\n",
    "loss = tf.reduce_mean(tf.square(outputs - y))\n",
    "optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate)\n",
    "training_op = optimizer.minimize(loss)\n",
    "\n",
    "init = tf.global_variables_initializer()\n",
    "saver = tf.train.Saver()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 \tMSE: 13.798524\n",
      "100 \tMSE: 0.5184861\n",
      "200 \tMSE: 0.18258934\n",
      "300 \tMSE: 0.084822476\n",
      "400 \tMSE: 0.062449113\n",
      "500 \tMSE: 0.060697965\n",
      "600 \tMSE: 0.055861413\n",
      "700 \tMSE: 0.050936103\n",
      "800 \tMSE: 0.05196387\n",
      "900 \tMSE: 0.05027977\n",
      "1000 \tMSE: 0.048902925\n",
      "1100 \tMSE: 0.05085831\n",
      "1200 \tMSE: 0.04179183\n",
      "1300 \tMSE: 0.048203375\n",
      "1400 \tMSE: 0.04349015\n"
     ]
    }
   ],
   "source": [
    "n_iterations = 1500\n",
    "batch_size = 50\n",
    "\n",
    "t_instance = np.linspace(12.2, 12.2 + resolution * (n_steps + 1), n_steps + 1)\n",
    "\n",
    "with tf.Session() as sess:\n",
    "    init.run()\n",
    "    for iteration in range(n_iterations):\n",
    "        X_batch, y_batch = next_batch(batch_size, n_steps)\n",
    "        sess.run(training_op, feed_dict={X: X_batch, y: y_batch})\n",
    "        if iteration % 100 == 0:\n",
    "            mse = loss.eval(feed_dict={X: X_batch, y: y_batch})\n",
    "            print(iteration, \"\\tMSE:\", mse)\n",
    "    \n",
    "    X_new = time_series(np.array(t_instance[:-1].reshape(-1, n_steps, n_inputs)))\n",
    "    y_pred = sess.run(outputs, feed_dict={X: X_new})\n",
    "    \n",
    "    saver.save(sess, \"./my_time_series_model\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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U5PNYoTU/dEdzn/FYDKZOpfjvZsHixWE+dSrEYuozLjlJQT5PJTc/xBpjXD0k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\nTVv7MLNSYAbwT+5e6+5PEwJ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      "text/plain": [
       "<matplotlib.figure.Figure at 0x11c7abbd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# y_pred\n",
    "def show_predict(t_instance):\n",
    "    plt.title(\"Testing the model\", fontsize=14)\n",
    "    plt.plot(t_instance[:-1], time_series(t_instance[:-1]), \"bo\", markersize=10, label=\"instance\")\n",
    "    plt.plot(t_instance[1:], time_series(t_instance[1:]), \"w*\", markersize=10, label=\"target\")\n",
    "    plt.plot(t_instance[1:], y_pred[0,:,0], \"r.\", markersize=10, label=\"prediction\")\n",
    "    plt.legend(loc=\"upper left\")\n",
    "    plt.xlabel(\"Time\")\n",
    "\n",
    "    plt.show()\n",
    "\n",
    "show_predict(t_instance)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Generating a creative new sequence\n",
    "\n",
    "All we need is to provide it a seed sequence containing n_steps values (e.g., full of zeros), use the model to predict the next value, append this predicted value to the sequence, \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Restoring parameters from ./my_time_series_model\n"
     ]
    }
   ],
   "source": [
    "with tf.Session() as sess:                        # not shown in the book\n",
    "    saver.restore(sess, \"./my_time_series_model\") # not shown\n",
    "\n",
    "    sequence = [0.] * n_steps\n",
    "    for iteration in range(300):\n",
    "        X_batch = np.array(sequence[-n_steps:]).reshape(1, n_steps, 1)\n",
    "        y_pred = sess.run(outputs, feed_dict={X: X_batch})\n",
    "        sequence.append(y_pred[0, -1, 0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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nlGsv7z/4nLn2Uq5tCtAtn97/Uq49795/YCtgLnB8vrz36qJ37YFV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\ncpASvIiISA76f+PAyQEFCZ28AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1193eb790>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8,4))\n",
    "plt.plot(np.arange(len(sequence)), sequence, \"b-\")\n",
    "plt.plot(t[:n_steps], sequence[:n_steps], \"b-\", linewidth=3)\n",
    "plt.xlabel(\"Time\")\n",
    "plt.ylabel(\"Value\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Restoring parameters from ./my_time_series_model\n",
      "Saving figure creative_sequence_plot\n"
     ]
    },
    {
     "data": {
      "image/png": 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WNbe86SY45hi7MHPJJba24pVXbJ9p02DMGJtqqumWkk2STSDuAB5x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\ngg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x117848690>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "with tf.Session() as sess:\n",
    "    saver.restore(sess, \"./my_time_series_model\")\n",
    "\n",
    "    sequence1 = [0. for i in range(n_steps)]\n",
    "    for iteration in range(len(t) - n_steps):\n",
    "        X_batch = np.array(sequence1[-n_steps:]).reshape(1, n_steps, 1)\n",
    "        y_pred = sess.run(outputs, feed_dict={X: X_batch})\n",
    "        sequence1.append(y_pred[0, -1, 0])\n",
    "\n",
    "    sequence2 = [time_series(i * resolution + t_min + (t_max-t_min/3)) for i in range(n_steps)]\n",
    "    for iteration in range(len(t) - n_steps):\n",
    "        X_batch = np.array(sequence2[-n_steps:]).reshape(1, n_steps, 1)\n",
    "        y_pred = sess.run(outputs, feed_dict={X: X_batch})\n",
    "        sequence2.append(y_pred[0, -1, 0])\n",
    "\n",
    "plt.figure(figsize=(11,4))\n",
    "plt.subplot(121)\n",
    "plt.plot(t, sequence1, \"b-\")\n",
    "plt.plot(t[:n_steps], sequence1[:n_steps], \"b-\", linewidth=3)\n",
    "plt.xlabel(\"Time\")\n",
    "plt.ylabel(\"Value\")\n",
    "\n",
    "plt.subplot(122)\n",
    "plt.plot(t, sequence2, \"b-\")\n",
    "plt.plot(t[:n_steps], sequence2[:n_steps], \"b-\", linewidth=3)\n",
    "plt.xlabel(\"Time\")\n",
    "save_fig(\"creative_sequence_plot\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Deep RNN"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## MultiRNNCell"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {},
   "outputs": [],
   "source": [
    "reset_graph()\n",
    "\n",
    "n_inputs = 2\n",
    "n_steps = 5\n",
    "\n",
    "X = tf.placeholder(tf.float32, [None, n_steps, n_inputs])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "metadata": {},
   "outputs": [],
   "source": [
    "n_neurons = 100\n",
    "n_layers = 3\n",
    "\n",
    "layers = [tf.contrib.rnn.BasicRNNCell(num_units=n_neurons)\n",
    "          for layer in range(n_layers)]\n",
    "multi_layer_cell = tf.contrib.rnn.MultiRNNCell(layers)\n",
    "outputs, states = tf.nn.dynamic_rnn(multi_layer_cell, X, dtype=tf.float32)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {},
   "outputs": [],
   "source": [
    "init = tf.global_variables_initializer()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_batch = np.random.rand(2, n_steps, n_inputs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {},
   "outputs": [],
   "source": [
    "with tf.Session() as sess:\n",
    "    init.run()\n",
    "    outputs_val, states_val = sess.run([outputs, states], feed_dict={X: X_batch})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2, 5, 100)"
      ]
     },
     "execution_count": 157,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "outputs_val.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Distributing a Deep RNN Across Multiple GPUs"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Do **NOT** do this:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "with tf.device(\"/gpu:0\"):  # BAD! This is ignored.\n",
    "    layer1 = tf.contrib.rnn.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)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Instead, you need a `DeviceCellWrapper`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "\n",
    "class DeviceCellWrapper(tf.contrib.rnn.RNNCell):\n",
    "  def __init__(self, device, cell):\n",
    "    self._cell = cell\n",
    "    self._device = device\n",
    "\n",
    "  @property\n",
    "  def state_size(self):\n",
    "    return self._cell.state_size\n",
    "\n",
    "  @property\n",
    "  def output_size(self):\n",
    "    return self._cell.output_size\n",
    "\n",
    "  def __call__(self, inputs, state, scope=None):\n",
    "    with tf.device(self._device):\n",
    "        return self._cell(inputs, state, scope)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "reset_graph()\n",
    "\n",
    "n_inputs = 5\n",
    "n_steps = 20\n",
    "n_neurons = 100\n",
    "\n",
    "X = tf.placeholder(tf.float32, shape=[None, n_steps, n_inputs])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "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",
    "         for dev in devices]\n",
    "multi_layer_cell = tf.contrib.rnn.MultiRNNCell(cells)\n",
    "outputs, states = tf.nn.dynamic_rnn(multi_layer_cell, X, dtype=tf.float32)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "init = tf.global_variables_initializer()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[[-0.06683909 -0.06814943  0.12806301 ..., -0.04951219  0.0169118\n",
      "    0.09129722]\n",
      "  [-0.03898398 -0.32816607  0.25709429 ..., -0.22360352 -0.00203764\n",
      "    0.18901907]\n",
      "  [-0.14598769 -0.03324183  0.06588719 ..., -0.36336255 -0.117153\n",
      "    0.39544109]\n",
      "  ..., \n",
      "  [-0.52596134  0.04002573  0.14033252 ...,  0.18522167  0.25101244\n",
      "   -0.05308188]\n",
      "  [-0.45618156 -0.11686647 -0.09905577 ..., -0.17943858  0.27567461\n",
      "   -0.04363405]\n",
      "  [-0.55723065  0.13874871 -0.14983818 ...,  0.04673974  0.10338999\n",
      "   -0.03823486]]\n",
      "\n",
      " [[-0.0191099  -0.06458578  0.08206855 ..., -0.07772326 -0.05498064\n",
      "    0.01358664]\n",
      "  [-0.05150904 -0.36381066  0.0913103  ..., -0.12480559 -0.03924585\n",
      "    0.06585156]\n",
      "  [-0.29961377 -0.00120922  0.06789977 ..., -0.27556923 -0.15278165\n",
      "    0.21452278]\n",
      "  ..., \n",
      "  [-0.6460501   0.17479922  0.14066698 ..., -0.08995064 -0.03049678\n",
      "    0.05738082]\n",
      "  [-0.61097401 -0.17900243 -0.23193845 ..., -0.2500132   0.25146627\n",
      "    0.36902413]\n",
      "  [-0.25920284 -0.08149087  0.19740498 ..., -0.32611009 -0.02686078\n",
      "    0.11232778]]]\n"
     ]
    }
   ],
   "source": [
    "with tf.Session() as sess:\n",
    "    init.run()\n",
    "    print(sess.run(outputs, feed_dict={X: np.random.rand(2, n_steps, n_inputs)}))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Dropout"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Unfortunately, this code is only usable for training, because the `DropoutWrapper` class has no `training` parameter, so it always applies dropout, even when the model is not being trained, so we must first train the model, then create a different model for testing, without the `DropoutWrapper`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "metadata": {},
   "outputs": [],
   "source": [
    "n_inputs = 1\n",
    "n_neurons = 100\n",
    "n_layers = 3\n",
    "n_steps = 20\n",
    "n_outputs = 1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Another option is to write a script with a command line argument to specify whether you want to train the mode or use it for making predictions:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Restoring parameters from /tmp/my_model.ckpt\n"
     ]
    }
   ],
   "source": [
    "reset_graph()\n",
    "\n",
    "import sys\n",
    "training = False  # in a script, this would be (sys.argv[-1] == \"train\") instead\n",
    "\n",
    "n_iterations = 1000\n",
    "batch_size = 50\n",
    "\n",
    "X = tf.placeholder(tf.float32, [None, n_steps, n_inputs])\n",
    "y = tf.placeholder(tf.float32, [None, n_steps, n_outputs])\n",
    "\n",
    "cells = [tf.contrib.rnn.BasicRNNCell(num_units=n_neurons)\n",
    "         for layer in range(n_layers)]\n",
    "if training:\n",
    "    cells = [tf.contrib.rnn.DropoutWrapper(cell, input_keep_prob=keep_prob)\n",
    "             for cell in cells]\n",
    "multi_layer_cell = tf.contrib.rnn.MultiRNNCell(cells)\n",
    "rnn_outputs, states = tf.nn.dynamic_rnn(multi_layer_cell, X, dtype=tf.float32)\n",
    "\n",
    "stacked_rnn_outputs = tf.reshape(rnn_outputs, [-1, n_neurons])    # not shown in the book\n",
    "stacked_outputs = tf.layers.dense(stacked_rnn_outputs, n_outputs) # not shown\n",
    "outputs = tf.reshape(stacked_outputs, [-1, n_steps, n_outputs])   # not shown\n",
    "loss = tf.reduce_mean(tf.square(outputs - y))                     # not shown\n",
    "optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate)   # not shown\n",
    "training_op = optimizer.minimize(loss)                            # not shown\n",
    "init = tf.global_variables_initializer()                          # not shown\n",
    "saver = tf.train.Saver()                                          # not shown\n",
    "\n",
    "with tf.Session() as sess:\n",
    "    if training:\n",
    "        init.run()\n",
    "        for iteration in range(n_iterations):\n",
    "            X_batch, y_batch = next_batch(batch_size, n_steps)    # not shown\n",
    "            _, mse = sess.run([training_op, loss], feed_dict={X: X_batch, y: y_batch}) # not shown\n",
    "            if iteration % 100 == 0:                              # not shown\n",
    "                print(iteration, \"Training MSE:\", mse)            # not shown\n",
    "        save_path = saver.save(sess, \"/tmp/my_model.ckpt\")\n",
    "    else:\n",
    "        saver.restore(sess, \"/tmp/my_model.ckpt\")\n",
    "        X_new = time_series(np.array(t_instance[:-1].reshape(-1, n_steps, n_inputs))) # not shown\n",
    "        y_pred = sess.run(outputs, feed_dict={X: X_new})                              # not shown"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Restoring parameters from ./my_dropout_time_series_model\n"
     ]
    },
    {
     "data": {
      "image/png": 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JJ57IYYcdBsC5557L5s2bgdB9cOJoUZ9++im1UfSBLCLSiZztT76nrFixgpqaGl566SWK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\nAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11ec49dd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "with tf.Session() as sess:\n",
    "    saver.restore(sess, \"./my_dropout_time_series_model\")\n",
    "\n",
    "    X_new = time_series(np.array(t_instance[:-1].reshape(-1, n_steps, n_inputs)))\n",
    "    y_pred = sess.run(outputs, feed_dict={X: X_new})\n",
    "\n",
    "plt.title(\"Testing the model\", fontsize=14)\n",
    "plt.plot(t_instance[:-1], time_series(t_instance[:-1]), \"bo\", markersize=10, label=\"instance\")\n",
    "plt.plot(t_instance[1:], time_series(t_instance[1:]), \"w*\", markersize=10, label=\"target\")\n",
    "plt.plot(t_instance[1:], y_pred[0,:,0], \"r.\", markersize=10, label=\"prediction\")\n",
    "plt.legend(loc=\"upper left\")\n",
    "plt.xlabel(\"Time\")\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Oops, it seems that Dropout does not help at all in this particular case. :/"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# LSTM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 177,
   "metadata": {},
   "outputs": [],
   "source": [
    "reset_graph()\n",
    "\n",
    "lstm_cell = tf.contrib.rnn.BasicLSTMCell(num_units=n_neurons)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "metadata": {},
   "outputs": [],
   "source": [
    "n_steps = 28\n",
    "n_inputs = 28\n",
    "n_neurons = 150\n",
    "n_outputs = 10\n",
    "n_layers = 3\n",
    "\n",
    "learning_rate = 0.001\n",
    "\n",
    "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",
    "              for layer in range(n_layers)]\n",
    "multi_cell = tf.contrib.rnn.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",
    "xentropy = tf.nn.sparse_softmax_cross_entropy_with_logits(labels=y, logits=logits)\n",
    "loss = tf.reduce_mean(xentropy, name=\"loss\")\n",
    "optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate)\n",
    "training_op = optimizer.minimize(loss)\n",
    "correct = tf.nn.in_top_k(logits, y, 1)\n",
    "accuracy = tf.reduce_mean(tf.cast(correct, tf.float32))\n",
    "    \n",
    "init = tf.global_variables_initializer()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 179,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(LSTMStateTuple(c=<tf.Tensor 'rnn/while/Exit_2:0' shape=(?, 150) dtype=float32>, h=<tf.Tensor 'rnn/while/Exit_3:0' shape=(?, 150) dtype=float32>),\n",
       " LSTMStateTuple(c=<tf.Tensor 'rnn/while/Exit_4:0' shape=(?, 150) dtype=float32>, h=<tf.Tensor 'rnn/while/Exit_5:0' shape=(?, 150) dtype=float32>),\n",
       " LSTMStateTuple(c=<tf.Tensor 'rnn/while/Exit_6:0' shape=(?, 150) dtype=float32>, h=<tf.Tensor 'rnn/while/Exit_7:0' shape=(?, 150) dtype=float32>))"
      ]
     },
     "execution_count": 179,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "states"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<tf.Tensor 'rnn/while/Exit_7:0' shape=(?, 150) dtype=float32>"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "top_layer_h_state"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 0 Train accuracy = 0.933333 Test accuracy = 0.9505\n",
      "Epoch 1 Train accuracy = 0.973333 Test accuracy = 0.9711\n",
      "Epoch 2 Train accuracy = 0.993333 Test accuracy = 0.9751\n",
      "Epoch 3 Train accuracy = 1.0 Test accuracy = 0.9772\n",
      "Epoch 4 Train accuracy = 0.98 Test accuracy = 0.9799\n",
      "Epoch 5 Train accuracy = 0.98 Test accuracy = 0.9841\n",
      "Epoch 6 Train accuracy = 0.993333 Test accuracy = 0.9836\n",
      "Epoch 7 Train accuracy = 1.0 Test accuracy = 0.9867\n",
      "Epoch 8 Train accuracy = 1.0 Test accuracy = 0.9866\n",
      "Epoch 9 Train accuracy = 1.0 Test accuracy = 0.9879\n"
     ]
    }
   ],
   "source": [
    "n_epochs = 10\n",
    "batch_size = 150\n",
    "\n",
    "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",
    "            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_test = accuracy.eval(feed_dict={X: X_test, y: y_test})\n",
    "        print(\"Epoch\", epoch, \"Train accuracy =\", acc_train, \"Test accuracy =\", acc_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## peephole connections: \n",
    "    the previous long-term state c(t–1) is added as an input to the controllers of the forget gate and the input gate, and the current long-term state c(t) is added as input to the controller of the output gate."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "lstm_cell = tf.contrib.rnn.LSTMCell(num_units=n_neurons, use_peepholes=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## GRU cell\n",
    "\n",
    "The GRU cell is a simplified version of the LSTM cell, and it seems to perform just as well"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "gru_cell = tf.contrib.rnn.GRUCell(num_units=n_neurons)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Embeddings"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This section is based on TensorFlow's [Word2Vec tutorial](https://www.tensorflow.org/versions/r0.11/tutorials/word2vec/index.html).\n",
    "\n",
    " In practice we approximate the expectation by drawing k contrastive words from the noise distribution\n",
    " \n",
    " This objective is maximized when the model assigns high probabilities to the real words, and low probabilities to noise words. Technically, this is called Negative Sampling,\n",
    " \n",
    "computing the loss function now scales only with the number of noise words that we select (k), and not all words in the vocabulary (V). This makes it much faster to train. \n",
    "\n",
    " that skip-gram inverts contexts and targets, and tries to predict each context word from its target word, so the task becomes to predict 'the' and 'brown' from 'quick', 'quick' and 'fox' from 'brown', etc. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Fetch the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 183,
   "metadata": {},
   "outputs": [],
   "source": [
    "from six.moves import urllib\n",
    "\n",
    "import errno\n",
    "import os\n",
    "import zipfile\n",
    "\n",
    "WORDS_PATH = \"datasets/words\"\n",
    "WORDS_URL = 'http://mattmahoney.net/dc/text8.zip'\n",
    "\n",
    "def mkdir_p(path):\n",
    "    \"\"\"Create directories, ok if they already exist.\n",
    "    \n",
    "    This is for python 2 support. In python >=3.2, simply use:\n",
    "    >>> os.makedirs(path, exist_ok=True)\n",
    "    \"\"\"\n",
    "    try:\n",
    "        os.makedirs(path)\n",
    "    except OSError as exc:\n",
    "        if exc.errno == errno.EEXIST and os.path.isdir(path):\n",
    "            pass\n",
    "        else:\n",
    "            raise\n",
    "\n",
    "def fetch_words_data(words_url=WORDS_URL, words_path=WORDS_PATH):\n",
    "    os.makedirs(words_path)\n",
    "    zip_path = os.path.join(words_path, \"words.zip\")\n",
    "    if not os.path.exists(zip_path):\n",
    "        urllib.request.urlretrieve(words_url, zip_path)\n",
    "    with zipfile.ZipFile(zip_path) as f:\n",
    "        data = f.read(f.namelist()[0])\n",
    "    return data.decode(\"ascii\").split()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 184,
   "metadata": {},
   "outputs": [],
   "source": [
    "words = fetch_words_data()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 185,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[u'anarchism', u'originated', u'as', u'a', u'term']"
      ]
     },
     "execution_count": 185,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "words[:5]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Build the dictionary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 186,
   "metadata": {},
   "outputs": [],
   "source": [
    "from collections import Counter\n",
    "\n",
    "vocabulary_size = 50000\n",
    "# build vocabulary with limit size\n",
    "vocabulary = [(\"UNK\", None)] + Counter(words).most_common(vocabulary_size - 1)\n",
    "vocabulary = np.array([word for word, _ in vocabulary])\n",
    "# {word: id}\n",
    "dictionary = {word: code for code, word in enumerate(vocabulary)}\n",
    "data = np.array([dictionary.get(word, 0) for word in words])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 189,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(u'anarchism originated as a term of abuse first used',\n",
       " array([5239, 3084,   12,    6,  195,    2, 3137,   46,   59]))"
      ]
     },
     "execution_count": 189,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\" \".join(words[:9]), data[:9]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 252,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "u'anarchism originated as a term of abuse first used against'"
      ]
     },
     "execution_count": 252,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "' '.join([vocabulary[i] for i in data[:10]])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Generate batches"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 302,
   "metadata": {},
   "outputs": [],
   "source": [
    "def show_gram(lst):\n",
    "    print(' '.join([vocabulary[i] for i in lst]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 325,
   "metadata": {},
   "outputs": [],
   "source": [
    "import random\n",
    "from collections import deque\n",
    "\n",
    "def generate_batch(batch_size, num_skips, skip_window):\n",
    "    \"\"\"\n",
    "    These batches will consist of input words (stored in batch) and random associated context \n",
    "    words within the gram as the labels to predict (stored in context). \n",
    "    \n",
    "    num_skips: the number of words drawn randomly from the surrounding context\n",
    "    skip_window: the size of the window of context words to draw from around the input word \n",
    "    \"\"\"\n",
    "    global data_index\n",
    "    assert batch_size % num_skips == 0\n",
    "    assert num_skips <= 2 * skip_window\n",
    "    batch = np.ndarray(shape=(batch_size), dtype=np.int32)\n",
    "    context = np.ndarray(shape=(batch_size, 1), dtype=np.int32)\n",
    "    span = 2 * skip_window + 1 # [ skip_window target skip_window ]\n",
    "    # Whenever a new word index is added to the buffer, the left most element \n",
    "    # will drop out of the buffer to allow room for the new word index being added. \n",
    "    buffer = deque(maxlen=span)\n",
    "    show_gram(buffer)\n",
    "    for _ in range(span):\n",
    "        buffer.append(data[data_index])\n",
    "        data_index = (data_index + 1) % len(data)\n",
    "    for i in range(batch_size // num_skips):\n",
    "        target = skip_window  # target label at the center of the buffer\n",
    "        targets_to_avoid = [ skip_window ]\n",
    "        for j in range(num_skips):\n",
    "            while target in targets_to_avoid:\n",
    "                target = random.randint(0, span - 1)\n",
    "            targets_to_avoid.append(target)\n",
    "            # set batch by index\n",
    "            batch[i * num_skips + j] = buffer[skip_window]  # this is the input word\n",
    "            context[i * num_skips + j, 0] = buffer[target]  # these are the context words\n",
    "        buffer.append(data[data_index])\n",
    "        data_index = (data_index + 1) % len(data)\n",
    "    return batch, context"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 327,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "anarchism originated as a term of abuse first\n",
      "as as a a term term of of\n",
      "originated a of originated abuse a term abuse\n"
     ]
    }
   ],
   "source": [
    "data_index=0\n",
    "batch, labels = generate_batch(8, 2, 1)\n",
    "show_gram(data[:8])\n",
    "show_gram(batch)\n",
    "show_gram(labels[:,0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Build the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 328,
   "metadata": {},
   "outputs": [],
   "source": [
    "batch_size = 128\n",
    "embedding_size = 128  # Dimension of the embedding vector.\n",
    "skip_window = 1       # How many words to consider left and right.\n",
    "num_skips = 2         # How many times to reuse an input to generate a label.\n",
    "\n",
    "# We pick a random validation set to sample nearest neighbors. Here we limit the\n",
    "# validation samples to the words that have a low numeric ID, which by\n",
    "# construction are also the most frequent.\n",
    "valid_size = 16     # Random set of words to evaluate similarity on.\n",
    "valid_window = 100  # Only pick dev samples in the head of the distribution.\n",
    "valid_examples = np.random.choice(valid_window, valid_size, replace=False)\n",
    "num_sampled = 64    # Number of negative examples to sample.\n",
    "\n",
    "learning_rate = 0.01"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 338,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([83, 53, 70, 45, 44, 39, 22, 80, 10,  0, 18, 30, 73, 33, 90,  4])"
      ]
     },
     "execution_count": 338,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "valid_examples"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 330,
   "metadata": {},
   "outputs": [],
   "source": [
    "reset_graph()\n",
    "\n",
    "# Input data.\n",
    "train_labels = tf.placeholder(tf.int32, shape=[batch_size, 1])\n",
    "valid_dataset = tf.constant(valid_examples, dtype=tf.int32)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 331,
   "metadata": {},
   "outputs": [],
   "source": [
    "vocabulary_size = 50000\n",
    "embedding_size = 150\n",
    "\n",
    "# Look up embeddings for inputs.\n",
    "init_embeds = tf.random_uniform([vocabulary_size, embedding_size], -1.0, 1.0)\n",
    "embeddings = tf.Variable(init_embeds)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 332,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_inputs = tf.placeholder(tf.int32, shape=[None])\n",
    "embed = tf.nn.embedding_lookup(embeddings, train_inputs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 333,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Construct the variables for the NCE loss\n",
    "nce_weights = tf.Variable(\n",
    "    tf.truncated_normal([vocabulary_size, embedding_size],\n",
    "                        stddev=1.0 / np.sqrt(embedding_size)))\n",
    "nce_biases = tf.Variable(tf.zeros([vocabulary_size]))\n",
    "\n",
    "# Compute the average NCE(Noise-contrastive estimation) loss for the batch.\n",
    "# tf.nce_loss automatically draws a new sample of the negative labels each\n",
    "# time we evaluate the loss.\n",
    "loss = tf.reduce_mean(\n",
    "    tf.nn.nce_loss(nce_weights, nce_biases, train_labels, embed,\n",
    "                   num_sampled, vocabulary_size))\n",
    "\n",
    "# Construct the Adam optimizer\n",
    "optimizer = tf.train.AdamOptimizer(learning_rate)\n",
    "training_op = optimizer.minimize(loss)\n",
    "\n",
    "# Compute the cosine similarity between minibatch examples and all embeddings.\n",
    "norm = tf.sqrt(tf.reduce_sum(tf.square(embeddings), axis=1, keep_dims=True))\n",
    "normalized_embeddings = embeddings / norm\n",
    "valid_embeddings = tf.nn.embedding_lookup(normalized_embeddings, valid_dataset)\n",
    "similarity = tf.matmul(valid_embeddings, normalized_embeddings, transpose_b=True)\n",
    "\n",
    "# Add variable initializer.\n",
    "init = tf.global_variables_initializer()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Train the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 369,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(50000, 150)"
      ]
     },
     "execution_count": 369,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#tf.nn.embedding_lookup?\n",
    "normalized_embeddings, valid_embeddings, similarity\n",
    "final_embeddings.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 336,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration: 0\t\n",
      "Average loss at step  0 :  285.792297363\n",
      "Nearest to people: keane, consumption, yag, alhazred, spill, persians, constitutionality, prodigal,\n",
      "Nearest to can: connie, ptolemaic, thermochemistry, spamalot, leverett, michael, inherit, fiasco,\n",
      "Nearest to than: fiery, reunification, receives, marquise, divides, picatinny, sliced, octane,\n",
      "Nearest to its: belew, kovacs, tully, pup, boasian, iana, mechanosynthesis, cold,\n",
      "Nearest to their: horizonte, basal, ray, verbose, aversion, prowess, prudence, fairchild,\n",
      "Nearest to have: might, slough, bitrate, beale, commercialism, sponges, suzaku, ascorbic,\n",
      "Nearest to six: interpolating, batavia, madhu, chiuchow, doubling, halves, amer, mordecai,\n",
      "Nearest to over: kazaa, pregnant, vcjd, detecting, cambridge, preset, hindered, mica,\n",
      "Nearest to two: unaltered, shimbun, flocked, liberator, ogden, team, psychiatrist, instinctively,\n",
      "Nearest to UNK: rai, fi, gonzales, affirm, chekhov, correspond, correspondance, cir,\n",
      "Nearest to was: penetrated, munition, greenish, xe, finalists, eng, kilowatt, restarted,\n",
      "Nearest to his: sergei, procession, strings, sector, developmental, lussac, value, befell,\n",
      "Nearest to b: speculative, studs, invalid, keyboards, atanasoff, styx, subjectivity, insects,\n",
      "Nearest to this: asia, nevado, empirically, birthright, posthuman, purporting, put, dhole,\n",
      "Nearest to use: ambulance, compassion, paralysis, corrode, restrained, told, isuzu, entrances,\n",
      "Nearest to one: ibo, ghola, hindrance, aleman, oppress, alan, sasquatch, habitable,\n",
      "Iteration: 1\t\n",
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      "Iteration: 9978\t\n",
      "Iteration: 9979\t\n",
      "Iteration: 9980\t\n",
      "Iteration: 9981\t\n",
      "Iteration: 9982\t\n",
      "Iteration: 9983\t\n",
      "Iteration: 9984\t\n",
      "Iteration: 9985\t\n",
      "Iteration: 9986\t\n",
      "Iteration: 9987\t\n",
      "Iteration: 9988\t\n",
      "Iteration: 9989\t\n",
      "Iteration: 9990\t\n",
      "Iteration: 9991\t\n",
      "Iteration: 9992\t\n",
      "Iteration: 9993\t\n",
      "Iteration: 9994\t\n",
      "Iteration: 9995\t\n",
      "Iteration: 9996\t\n",
      "Iteration: 9997\t\n",
      "Iteration: 9998\t\n",
      "Iteration: 9999\t\n",
      "Iteration: 10000\t\n",
      "Average loss at step  10000 :  25.5025387301\n",
      "Nearest to people: methanol, one, experts, mechanization, backspace, agincourt, two, grapheme,\n",
      "Nearest to can: waterloo, fixtures, commune, sanjaks, could, blacks, would, torsion,\n",
      "Nearest to than: galatian, switch, chloroplasts, in, sprawl, commencement, ufos, preserve,\n",
      "Nearest to its: the, columbus, his, longevity, aquarius, abscess, motorola, complaints,\n",
      "Nearest to their: the, a, his, ksc, hostage, apeiron, plata, aarhus,\n",
      "Nearest to have: been, had, aforementioned, proprietary, make, aquarius, vannevar, radiated,\n",
      "Nearest to six: eight, five, nine, two, one, zero, three, four,\n",
      "Nearest to over: backspace, zero, two, frida, with, amhr, six, four,\n",
      "Nearest to two: zero, three, six, one, four, five, nine, eight,\n",
      "Nearest to UNK: the, and, two, three, zero, one, nine, five,\n",
      "Nearest to was: by, in, manzikert, aquarius, dissociation, deuterostomes, has, to,\n",
      "Nearest to his: the, one, s, mpla, automorphism, compensation, bowl, liege,\n",
      "Nearest to b: one, aleister, and, five, nine, affidavit, eight, the,\n",
      "Nearest to this: the, a, which, deuterostomes, morpork, cleaner, illyrians, tourism,\n",
      "Nearest to use: logically, accredited, millennia, hyacinthus, study, mans, UNK, embody,\n",
      "Nearest to one: nine, seven, six, two, eight, three, five, zero,\n"
     ]
    }
   ],
   "source": [
    "num_steps = 10001\n",
    "\n",
    "with tf.Session() as session:\n",
    "    init.run()\n",
    "\n",
    "    average_loss = 0\n",
    "    for step in range(num_steps):\n",
    "        print(\"\\rIteration: {}\".format(step), end=\"\\t\")\n",
    "        batch_inputs, batch_labels = generate_batch(batch_size, num_skips, skip_window)\n",
    "        feed_dict = {train_inputs : batch_inputs, train_labels : batch_labels}\n",
    "\n",
    "        # We perform one update step by evaluating the training op (including it\n",
    "        # in the list of returned values for session.run()\n",
    "        _, loss_val = session.run([training_op, loss], feed_dict=feed_dict)\n",
    "        average_loss += loss_val\n",
    "\n",
    "        if step % 2000 == 0:\n",
    "            if step > 0:\n",
    "                average_loss /= 2000\n",
    "            # The average loss is an estimate of the loss over the last 2000 batches.\n",
    "            print(\"Average loss at step \", step, \": \", average_loss)\n",
    "            average_loss = 0\n",
    "\n",
    "        # Note that this is expensive (~20% slowdown if computed every 500 steps)\n",
    "        if step % 10000 == 0:\n",
    "            sim = similarity.eval()\n",
    "            for i in range(valid_size):\n",
    "                valid_word = vocabulary[valid_examples[i]]\n",
    "                top_k = 8 # number of nearest neighbors\n",
    "                nearest = (-sim[i, :]).argsort()[1:top_k+1]\n",
    "                log_str = \"Nearest to %s:\" % valid_word\n",
    "                for k in range(top_k):\n",
    "                    close_word = vocabulary[nearest[k]]\n",
    "                    log_str = \"%s %s,\" % (log_str, close_word)\n",
    "                print(log_str)\n",
    "\n",
    "    final_embeddings = normalized_embeddings.eval()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's save the final embeddings (of course you can use a TensorFlow `Saver` if you prefer):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 340,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.save(\"./my_final_embeddings.npy\", final_embeddings)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plot the embeddings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 341,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_with_labels(low_dim_embs, labels):\n",
    "    assert low_dim_embs.shape[0] >= len(labels), \"More labels than embeddings\"\n",
    "    plt.figure(figsize=(18, 18))  #in inches\n",
    "    for i, label in enumerate(labels):\n",
    "        x, y = low_dim_embs[i,:]\n",
    "        plt.scatter(x, y)\n",
    "        plt.annotate(label,\n",
    "                     xy=(x, y),\n",
    "                     xytext=(5, 2),\n",
    "                     textcoords='offset points',\n",
    "                     ha='right',\n",
    "                     va='bottom')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 342,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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WKgI1yF9jIdtsNp8DvgQeLcJziIjIPchkMlm7BBERERG5S4osWDCbzWeB/wAvGAwGW4PB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Bm4u7sTHR3N6NGji+38SUpK4vTp0xw8eLDCOxw2bNjA8+fPK+W49MgHJO+9Qm5yQWG/3OQXJO+9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\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uDrbmJmTNnftYtpWHp6cnoaGh2NjYoKGhwQ8//ICxsep3wNXVlf379/P222/TrFkz7t27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      "text/plain": [
       "<matplotlib.figure.Figure at 0x11b427550>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.manifold import TSNE\n",
    "\n",
    "tsne = TSNE(perplexity=30, n_components=2, init='pca', n_iter=5000)\n",
    "plot_only = 500\n",
    "low_dim_embs = tsne.fit_transform(final_embeddings[:plot_only,:])\n",
    "labels = [vocabulary[i] for i in range(plot_only)]\n",
    "plot_with_labels(low_dim_embs, labels)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Machine Translation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The `basic_rnn_seq2seq()` function creates a simple Encoder/Decoder model: it first runs an RNN to encode `encoder_inputs` into a state vector, then runs a decoder initialized with the last encoder state on `decoder_inputs`. Encoder and decoder use the same RNN cell type but they don't share parameters."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 358,
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "reset_graph()\n",
    "\n",
    "n_steps = 50\n",
    "n_neurons = 200\n",
    "n_layers = 3\n",
    "num_encoder_symbols = 20000\n",
    "num_decoder_symbols = 20000\n",
    "embedding_size = 150\n",
    "learning_rate = 0.01\n",
    "\n",
    "X = tf.placeholder(tf.int32, [None, n_steps]) # English sentences\n",
    "Y = tf.placeholder(tf.int32, [None, n_steps]) # French translations\n",
    "W = tf.placeholder(tf.float32, [None, n_steps - 1, 1])\n",
    "Y_input = Y[:, :-1]\n",
    "Y_target = Y[:, 1:]\n",
    "\n",
    "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",
    "              for layer in range(n_layers)]\n",
    "cell = tf.contrib.rnn.MultiRNNCell(lstm_cells)\n",
    "\n",
    "output_seqs, states = tf.contrib.legacy_seq2seq.embedding_rnn_seq2seq(\n",
    "    encoder_inputs,\n",
    "    decoder_inputs,\n",
    "    cell,\n",
    "    num_encoder_symbols,\n",
    "    num_decoder_symbols,\n",
    "    embedding_size)\n",
    "\n",
    "logits = tf.transpose(tf.unstack(output_seqs), perm=[1, 0, 2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 359,
   "metadata": {},
   "outputs": [],
   "source": [
    "logits_flat = tf.reshape(logits, [-1, num_decoder_symbols])\n",
    "Y_target_flat = tf.reshape(Y_target, [-1])\n",
    "W_flat = tf.reshape(W, [-1])\n",
    "xentropy = W_flat * tf.nn.sparse_softmax_cross_entropy_with_logits(labels=Y_target_flat, logits=logits_flat)\n",
    "loss = tf.reduce_mean(xentropy)\n",
    "optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate)\n",
    "training_op = optimizer.minimize(loss)\n",
    "\n",
    "init = tf.global_variables_initializer()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "# Exercise solutions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Coming soon**"
   ]
  }
 ],
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   "display_name": "Python 2",
   "language": "python",
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   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.14"
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   "nav_menu": {},
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