CAMI.ipynb

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      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\h5py\\__init__.py:34: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.\n",
      "  from ._conv import register_converters as _register_converters\n"
     ]
    }
   ],
   "source": [
    "import tensorflow as tf\n",
    "import numpy as np\n",
    "from IPython.display import clear_output, Image, display, HTML"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "sess = tf.InteractiveSession()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Helpers\n",
    "Helper functions for TF Graph visualization\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "# Helper functions for TF Graph visualization\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 = tf.compat.as_bytes(\"<stripped %d bytes>\"%size)\n",
    "    return strip_def\n",
    "  \n",
    "def rename_nodes(graph_def, rename_func):\n",
    "    res_def = tf.GraphDef()\n",
    "    for n0 in graph_def.node:\n",
    "        n = res_def.node.add() \n",
    "        n.MergeFrom(n0)\n",
    "        n.name = rename_func(n.name)\n",
    "        for i, s in enumerate(n.input):\n",
    "            n.input[i] = rename_func(s) if s[0]!='^' else '^'+rename_func(s[1:])\n",
    "    return res_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:1000px;height:620px;border:0\" srcdoc=\"{}\"></iframe>\n",
    "    \"\"\".format(code.replace('\"', '&quot;'))\n",
    "    display(HTML(iframe))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# CAMI Model as baseline (DM)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "class CAMIModel(object):\n",
    "    def __init__(self):\n",
    "        self.settings = {}\n",
    "        self.settings['filters_conv1'] = 6\n",
    "        self.settings['k-max-pool1'] = 10\n",
    "        self.settings['filters_conv2'] = 4\n",
    "        self.settings['k-max-pool2'] = 4\n",
    "    def buildModel(self, X, y, batch_size):\n",
    "\n",
    "        assert X.shape[1:] == (20, 56)  # each event has 56 features and 20 phases.\n",
    "        assert y.shape[1:] == 2\n",
    "\n",
    "        # reshape for conv layer.\n",
    "        X_reshaped = tf.reshape(X, shape=[-1, 20, 56, 1])\n",
    "\n",
    "        # conv1\n",
    "        conv1 = tf.layers.conv2d(inputs=X_reshaped, filters=self.settings['filters_conv1'],\n",
    "                                 kernel_size=[20, 7], padding='valid', activation=tf.nn.tanh)\n",
    "        tf.assert_equal(tf.shape(conv1)[1:], [1, 50, self.settings['filters_conv1']])  # (batch_size, 1, 50, 6)\n",
    "\n",
    "        # transpose for kmax pooling\n",
    "        T1 = tf.transpose(conv1, [0, 3, 1, 2])  # shape=(batch_size,6,1,50)\n",
    "\n",
    "        # first kmax pooling\n",
    "        k1 = self.settings['k-max-pool1']\n",
    "        with tf.name_scope(\"kmaxpooling1\"):\n",
    "            pool1 = self.kmax_squish_pooling_general(T1, k1, batch_size)\n",
    "            tf.assert_equal(tf.shape(pool1)[1:], [6, 1, k1]) #(batch_size, 6, 1, 10)\n",
    "\n",
    "        # transpose for second conv layer\n",
    "        pool1 = tf.transpose(pool1, [0, 2, 3, 1])  # shape=(batch_size,1,10,6)\n",
    "\n",
    "        # conv2\n",
    "        with tf.name_scope('conv2'):\n",
    "            conv2 = tf.layers.conv2d(inputs=pool1, filters=self.settings['filters_conv2'],\n",
    "                                     kernel_size=[1, 5], padding='valid', activation=tf.nn.tanh)\n",
    "            tf.assert_equal(tf.shape(conv2)[1:], [1, k1 - 5 + 1, self.settings['filters_conv2']])  # (batch_size,1,6,4)\n",
    "\n",
    "        # transpose for kmax pooling\n",
    "        T2 = tf.transpose(conv2, [0, 3, 1, 2])  # shape=(batch_size,4,1,6)\n",
    "\n",
    "        # second kmax pooling (hoi weird here!!!)\n",
    "        k2 = self.settings['k-max-pool2']\n",
    "        with tf.name_scope('k_max_pooling2'):\n",
    "            pool2 = self.kmax_squish_pooling_general(T2, k2, batch_size)\n",
    "            tf.assert_equal(pool2.get_shape()[1:], [4, 1, k2])  # (batch_size,4,1,4)\n",
    "\n",
    "        pool2 = tf.transpose(pool2, [0, 2, 3, 1])  # shape=(batch_size,4,1,4)\n",
    "        pool2 = tf.reshape(pool2, [-1, 1 * self.settings['filters_conv2'] * k2])\n",
    "\n",
    "        # fully connect layer.\n",
    "        fc = tf.layers.dense(pool2, units=2)\n",
    "        y_hat = tf.nn.softmax(fc)\n",
    "        # print(y_hat.get_shape())\n",
    "        tf.assert_equal(y_hat.get_shape()[1:], [2])\n",
    "\n",
    "        loss = -tf.reduce_mean(tf.reduce_sum(tf.multiply(y, tf.log(y_hat)), axis=1) )\n",
    "        return loss, y_hat\n",
    "\n",
    "    def fit(self):\n",
    "        X = np.random.randn(1, 20, 56)\n",
    "        y = np.random.randn(1,2)\n",
    "        X_ph = tf.placeholder(tf.float32, shape=[None, 20, 56], name=\"X\")\n",
    "        y_ph = tf.placeholder(tf.float32, shape=[None, 2], name=\"y\")\n",
    "        bs_ph = tf.placeholder(tf.int32)\n",
    "        loss, y_hat = self.buildModel(X_ph, y_ph, bs_ph)\n",
    "\n",
    "        with tf.Session() as sess:\n",
    "            sess.run(tf.global_variables_initializer())\n",
    "            lossVL = loss.eval(feed_dict={X_ph: X, y_ph: y, bs_ph: 1})\n",
    "            print(lossVL)\n",
    "    def visualize(self):\n",
    "        lap_graph = tf.Graph()\n",
    "        with lap_graph.as_default():\n",
    "            X = np.random.randn(1, 20, 56)\n",
    "            y = np.random.randn(1,2)\n",
    "            X_ph = tf.placeholder(tf.float32, shape=[None, 20, 56], name=\"X\")\n",
    "            y_ph = tf.placeholder(tf.float32, shape=[None, 2], name=\"y\")\n",
    "            bs_ph = tf.placeholder(tf.int32, name=\"batch_size\")\n",
    "            loss, y_hat = self.buildModel(X_ph, y_ph, bs_ph)\n",
    "        show_graph(lap_graph)\n",
    "    def kmax_squish_pooling_general(self, inp, k, batch_size):\n",
    "        '''\n",
    "        only keep k largest values among the last dimension of inp. The order of elements must be kept same as before.\n",
    "        Ex: if inp has shape (3,4,5) => result.shape = (3,4,k).\n",
    "        '''\n",
    "        assert inp.get_shape().ndims >= 2, \"Expected tensor at least 2D but receive %sD\" % (inp.get_shape().ndims)\n",
    "        assert inp.get_shape()[-1] >= k, \"k must less than or equal to the last dimension of the inputted tensor\"\n",
    "        values, indices = tf.nn.top_k(inp, k)\n",
    "        # for some reasons, I have to negative indices to have indices sorted based their order\n",
    "        T, _ = tf.nn.top_k(-indices, k)  # reverse order\n",
    "        T = -T\n",
    "\n",
    "        # we want to generate indices based on first ndims-1 dimensions of tensor @inp and the last dimension\n",
    "        # is k (i.e. k largest values according to the last dimension)\n",
    "        print(inp.get_shape())\n",
    "        S = inp.get_shape()\n",
    "        dimensions =  [tf.range(batch_size)] + [tf.range(S[1])] + [tf.range(e) for e in inp.get_shape()[2:-1]] + [tf.range(k)]\n",
    "        # we swap the first two dimensions (It reduces readability of the code, but it works for meshgrid.), the rest dimensions\n",
    "        # are kept same as before\n",
    "        ls = [dimensions[1]] + [dimensions[0]] + dimensions[2:]\n",
    "        # generate meshgrid\n",
    "        mesh = tf.meshgrid(*ls)\n",
    "        # again, we have to swap the first two dimension again. You may wonder why do I need to swap twice? Trust me, I tried\n",
    "        # not to swap anything but it does not show the result I want.\n",
    "        mesh = [mesh[1], mesh[0]] + mesh[2:]\n",
    "        # after swap, we will stack the indices to first ndims-1 and dimsions found by tensor @T above.\n",
    "        mesh = mesh[:-1] + [T]\n",
    "        B = tf.stack(mesh, axis=-1)\n",
    "\n",
    "        # time to collect easter eggs!!!\n",
    "        r = tf.gather_nd(inp, B)\n",
    "        return r"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(?, 6, 1, 50)\n",
      "(?, 4, 1, 6)\n",
      "-3.7158847\n"
     ]
    }
   ],
   "source": [
    "c = CAMIModel()\n",
    "c.fit()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
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     "text": [
      "(?, 6, 1, 50)\n",
      "(?, 4, 1, 6)\n"
     ]
    },
    {
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