JK-Net.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle as pkl\n",
    "import scipy.sparse as sp\n",
    "from scipy.sparse.linalg.eigen.arpack import eigsh\n",
    "import sys\n",
    "\n",
    "import random\n",
    "import matplotlib.pyplot as plt\n",
    "import networkx as nx\n",
    "import numpy as np\n",
    "import tensorflow as tf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Anaconda3\\lib\\site-packages\\networkx\\drawing\\nx_pylab.py:611: MatplotlibDeprecationWarning: isinstance(..., numbers.Number)\n",
      "  if cb.is_numlike(alpha):\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "g = nx.read_graphml('D:/bigdata/Python/Graph Neural Net/R/karate.graphml')\n",
    "\n",
    "m=list(nx.get_node_attributes(g , 'membership').values())\n",
    "plt.figure(figsize=(15,8))\n",
    "pos = nx.spring_layout(g)\n",
    "nx.draw_networkx_nodes(g, pos ,nodelist=['n11'],node_color=[m[0]], node_shape='s')\n",
    "nx.draw_networkx_nodes(g, pos ,nodelist=['n'+str(i) for i in range(34) if i!=11],node_color=m[1:], node_shape='o')\n",
    "nx.draw_networkx_edges(g,pos)\n",
    "# Get important parameters of adjacency matrix\n",
    "adj = nx.adj_matrix(g)\n",
    "n_nodes = adj.shape[0]\n",
    "adj_tilde = adj + np.identity(n=adj.shape[0], dtype=np.float32)\n",
    "numpy_adj=adj_tilde-np.identity(n=adj.shape[0], dtype=np.float32)\n",
    "d_tilde_diag = np.squeeze(np.sum(np.array(adj_tilde), axis=1))\n",
    "d_tilde_inv_sqrt_diag = np.power(d_tilde_diag, -1/2, dtype=np.float32)\n",
    "d_tilde_inv_sqrt = np.diag(d_tilde_inv_sqrt_diag)\n",
    "adj_norm = np.dot(np.dot(d_tilde_inv_sqrt, adj_tilde), d_tilde_inv_sqrt)\n",
    "adj_norm=adj_norm.astype(np.float32)\n",
    "feat_x = np.identity(n=adj.shape[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Semi-supervised\n",
    "memberships = [m - 1 for m in nx.get_node_attributes(g, 'membership').values()]\n",
    "\n",
    "nb_classes = len(set(memberships))\n",
    "targets = np.array([memberships], dtype=np.int32).reshape(-1)\n",
    "one_hot_targets = np.eye(4)[targets]\n",
    "\n",
    "# Pick 3 from each class\n",
    "labels_to_keep=[17,2,3,  4,6,5,  15,9,20,  27,24,31]\n",
    "\n",
    "y_train = np.zeros(shape=one_hot_targets.shape,\n",
    "                   dtype=np.float32)\n",
    "y_test = one_hot_targets.copy()\n",
    "\n",
    "train_mask = np.zeros(shape=(n_nodes,), dtype=np.bool)\n",
    "test_mask = np.ones(shape=(n_nodes,), dtype=np.bool)\n",
    "\n",
    "for l in labels_to_keep:\n",
    "    y_train[l, :] = one_hot_targets[l, :]\n",
    "    y_test[l, :] = np.zeros(shape=(nb_classes,))\n",
    "    train_mask[l] = True\n",
    "    test_mask[l] = False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "WARNING: The TensorFlow contrib module will not be included in TensorFlow 2.0.\n",
      "For more information, please see:\n",
      "  * https://github.com/tensorflow/community/blob/master/rfcs/20180907-contrib-sunset.md\n",
      "  * https://github.com/tensorflow/addons\n",
      "If you depend on functionality not listed there, please file an issue.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "class JKNet:\n",
    "    def __init__(self, layer, n_nodes, f_dimension, nb_classes, initializer=tf.contrib.layers.xavier_initializer(uniform=False)):\n",
    "        self.X = tf.placeholder('float32',shape=(n_nodes*f_dimension))\n",
    "        self.y = tf.placeholder('float32', shape=(n_nodes, nb_classes))\n",
    "        self.mask=tf.placeholder('float32')\n",
    "        self.nodes=n_nodes\n",
    "        self.output_dim = nb_classes\n",
    "        self.layer = layer\n",
    "        self.is_train=tf.placeholder(tf.bool)\n",
    "        self.adj=tf.placeholder('float32',shape=(n_nodes, n_nodes))\n",
    "        self.dropout=tf.placeholder(tf.float32)\n",
    "        self.init=initializer\n",
    "        \n",
    "    def loss(self, lr, active=tf.nn.relu, optim=tf.train.AdamOptimizer, JK_Model='concat'):\n",
    "        self.L=tf.identity(tf.reshape(self.X, [self.nodes, -1]))\n",
    "        _layers= []\n",
    "        for i in range(len(self.layer)):\n",
    "            self.L=self.graph_conv(self.L, self.adj, self.layer[i], activation=active)\n",
    "            self.L= tf.nn.dropout(self.L, self.dropout)\n",
    "            _layers.append(self.L)\n",
    "            \n",
    "        if JK_Model=='concat':\n",
    "            hypo= tf.concat(_layers, axis=1)\n",
    "\n",
    "            \n",
    "        elif JK_Model=='MaxPooling':\n",
    "            hypo= tf.stack(_layers, axis=0)   \n",
    "            hypo = tf.reduce_max(hypo, axis=0)\n",
    "\n",
    "            \n",
    "        elif JK_Model=='Bi-LSTM Attention':\n",
    "            self.Attention=self.Bi_directional_lstm_attention(_layers)\n",
    "            hypo=0\n",
    "            for i in range(len(_layers)):\n",
    "                hypo+=self.Attention[i]*_layers[i]\n",
    "\n",
    "            \n",
    "        elif JK_Model=='Only_GCN':\n",
    "            hypo=self.L\n",
    "\n",
    "        elif JK_Model=='Only_GAT':\n",
    "            self.hidden_layer=tf.identity(self.L)\n",
    "            for i, hidden_layer in enumerate(self.layer):\n",
    "                self.hidden_layer = tf.layers.dense(self.hidden_layer, hidden_layer, kernel_initializer=self.init)\n",
    "                self.coeffs = self.attn_coeffs_DGL(self.hidden_layer, self.adj)\n",
    "                self.hidden_layer = tf.matmul(self.coeffs, self.hidden_layer) # [batch_size, num_nodes, num_nodes]  \n",
    "                self.hidden_layer = active(self.hidden_layer)\n",
    "                self.hidden_layer = tf.nn.dropout(self.hidden_layer, self.dropout)\n",
    "            hypo=self.hidden_layer\n",
    "\n",
    "        else:\n",
    "            raise ValueError(\"You should assign JK_model\")\n",
    "        \n",
    "        \n",
    "        \n",
    "        hypo=tf.matmul(self.adj, hypo)\n",
    "        self.influence=self.Influence_x_y(11, hypo)\n",
    "        self.hypothesis = tf.layers.dense(hypo, self.output_dim, activation=tf.nn.softmax, kernel_initializer=self.init)\n",
    "        \n",
    "        var   = tf.trainable_variables()     \n",
    "        lossL2 = tf.add_n([ tf.nn.l2_loss(v) for v in var if 'bias' not in v.name ]) *  0.0005  \n",
    "        \n",
    "        self.cost=tf.nn.softmax_cross_entropy_with_logits_v2(labels=self.y, logits=self.hypothesis)+lossL2\n",
    "        mask = tf.cast(self.mask, dtype=tf.float32)\n",
    "        mask /= tf.reduce_mean(mask)\n",
    "        \n",
    "\n",
    "        self.cost *= mask       \n",
    "        self.cost=tf.reduce_mean(self.cost)\n",
    "        \n",
    "        self.optimizer = optim(learning_rate=lr)\n",
    "        self.trains = self.optimizer.minimize(self.cost)\n",
    "        \n",
    "        self.sess = tf.Session()\n",
    "        self.sess.run(tf.global_variables_initializer())\n",
    "        \n",
    "    def graph_conv(self, _X, _A, output_dim, activation=tf.nn.relu):\n",
    "        output = tf.layers.dense(_X, units=output_dim, kernel_initializer=self.init)\n",
    "        output = tf.matmul(_A, output)\n",
    "        output = activation(output)\n",
    "        return output\n",
    "    \n",
    "    def Bi_directional_lstm_attention(self, Layer):\n",
    "        l=[]\n",
    "        for layer in Layer:\n",
    "            l.append(tf.reshape(layer, [1,-1]))\n",
    "        X=tf.stack(l, axis=1)\n",
    "        \n",
    "        lstm_fw_cell = tf.nn.rnn_cell.LSTMCell(num_units = self.layer[0], state_is_tuple = True)\n",
    "        lstm_fw_cell = tf.nn.rnn_cell.DropoutWrapper(lstm_fw_cell, output_keep_prob=self.dropout)\n",
    "        \n",
    "        lstm_bw_cell = tf.nn.rnn_cell.LSTMCell(num_units = self.layer[0], state_is_tuple = True)\n",
    "        lstm_bw_cell = tf.nn.rnn_cell.DropoutWrapper(lstm_bw_cell, output_keep_prob=self.dropout)\n",
    "        \n",
    "        outputs,_ = tf.nn.bidirectional_dynamic_rnn(lstm_fw_cell,lstm_bw_cell, X, dtype = tf.float32)\n",
    "\n",
    "        outputs_fw = tf.reshape(outputs[0], [len(Layer),self.layer[0]])\n",
    "        outputs_bw = tf.reshape(outputs[1], [len(Layer),self.layer[0]])\n",
    "        outputs_concat = tf.concat([outputs_fw, outputs_bw], axis=1)\n",
    "        concatenated=tf.layers.dense(outputs_concat, units=1, use_bias=True, kernel_initializer=self.init)\n",
    "        s=tf.nn.softmax(tf.reshape(concatenated,[-1]))\n",
    "        return s\n",
    "                    \n",
    "    def Influence_x_y(self, Node_x, hypo):\n",
    "        abs_grad=tf.math.abs(tf.gradients(tf.reshape(hypo[Node_x], [-1]), self.X)[0])\n",
    "        abs_grad=tf.reshape(abs_grad,[self.nodes,-1])\n",
    "        jacobian=tf.reduce_sum(abs_grad,axis=1)\n",
    "        Influence_of_Y_ON_X=jacobian/tf.reduce_sum(jacobian)\n",
    "        return Influence_of_Y_ON_X\n",
    "        \n",
    "    def attn_coeffs_DGL(self, H, A):\n",
    "        H=tf.matmul(A,H)\n",
    "        logits = tf.layers.dense(H, self.nodes,activation=tf.nn.leaky_relu)\n",
    "        zero_vec = -9e15*tf.ones_like(logits)\n",
    "        attention = tf.where(A > 0, logits, zero_vec)\n",
    "        coefs = tf.nn.softmax(attention, axis=1)\n",
    "        return coefs\n",
    "    \n",
    "    def accuracy(self, X, Y, mask, adj):\n",
    "        \"\"\"Accuracy with masking.\"\"\"\n",
    "        preds, influence=self.sess.run([self.hypothesis, self.influence], feed_dict={self.X:X, self.y:Y, self.adj:adj,\n",
    "                                                                              self.mask:mask, self.dropout:1})\n",
    "        correct_prediction = tf.equal(tf.argmax(preds, 1), tf.argmax(Y, 1))\n",
    "        accuracy_all = tf.cast(correct_prediction, tf.float32)\n",
    "        mask = tf.cast(mask, dtype=tf.float32)\n",
    "        mask /= tf.reduce_mean(mask)\n",
    "        accuracy_all *= mask\n",
    "        return self.sess.run(tf.reduce_mean(accuracy_all)), influence\n",
    "    \n",
    "        \n",
    "    \n",
    "    def train(self, batch_xs, batch_ys, mask, avg_cost, adj):\n",
    "        _, c = self.sess.run([self.trains, self.cost], feed_dict={self.X: batch_xs, self.y: batch_ys,  self.adj:adj,\n",
    "                                                                  self.mask:mask, self.dropout:0.5})\n",
    "        avg_cost += np.mean(c)\n",
    "        return avg_cost\n",
    "    \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From <ipython-input-4-b36accad768f>:78: dense (from tensorflow.python.layers.core) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Use keras.layers.dense instead.\n",
      "WARNING:tensorflow:From C:\\Anaconda3\\lib\\site-packages\\tensorflow\\python\\framework\\op_def_library.py:263: colocate_with (from tensorflow.python.framework.ops) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Colocations handled automatically by placer.\n",
      "WARNING:tensorflow:From <ipython-input-4-b36accad768f>:19: calling dropout (from tensorflow.python.ops.nn_ops) with keep_prob is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Please use `rate` instead of `keep_prob`. Rate should be set to `rate = 1 - keep_prob`.\n",
      "WARNING:tensorflow:From C:\\Anaconda3\\lib\\site-packages\\tensorflow\\python\\ops\\math_ops.py:3066: to_int32 (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Use tf.cast instead.\n",
      "Epoch: 0001 cost= 1.3245 train_acc:  0.41666666   test_acc:  0.40909088\n",
      "Epoch: 0002 cost= 1.05236 train_acc:  0.75   test_acc:  0.5454545\n",
      "Epoch: 0003 cost= 1.0059 train_acc:  1.0   test_acc:  0.6818182\n",
      "Epoch: 0004 cost= 0.8984 train_acc:  0.9166666   test_acc:  0.77272725\n",
      "Epoch: 0005 cost= 1.09423 train_acc:  1.0   test_acc:  0.7272727\n"
     ]
    }
   ],
   "source": [
    "tf.reset_default_graph()\n",
    "model=JKNet([16,16,16,16], 34, 34, 4)\n",
    "model.loss(1e-2, active=tf.nn.relu, JK_Model='Only_GCN')\n",
    "for epoch in range(5):\n",
    "    avg_cost=0\n",
    "    for i in range(50):\n",
    "        batch_xs, batch_ys, adj = feat_x.reshape(-1),  y_train, np.array(adj_norm)\n",
    "        avg_cost=model.train(batch_xs, batch_ys, train_mask, avg_cost/50, adj)\n",
    "    train_acc, influe=model.accuracy(batch_xs, batch_ys, train_mask, adj)\n",
    "    test_acc, _=model.accuracy(batch_xs, y_test, test_mask, adj)\n",
    "    print('Epoch:','%04d' %(epoch+1),'cost=', np.round(avg_cost,5),'train_acc: ',train_acc, '  test_acc: ',test_acc)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Influence distribution of 4-Layer GCN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Anaconda3\\lib\\site-packages\\networkx\\drawing\\nx_pylab.py:611: MatplotlibDeprecationWarning: isinstance(..., numbers.Number)\n",
      "  if cb.is_numlike(alpha):\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15,8))\n",
    "plt.title('4-layer GCN')\n",
    "pos = nx.spring_layout(g)\n",
    "cmap = plt.cm.coolwarm\n",
    "nx.draw_networkx_nodes(g, pos ,nodelist=['n11'],node_color=[influe[0]], node_shape='s',  cmap=cmap,vmin=0, vmax=influe.max())\n",
    "nx.draw_networkx_nodes(g, pos ,nodelist=['n'+str(i) for i in range(34) if i!=11],node_color=influe[1:], node_shape='o', cmap=cmap, vmin=0, vmax=influe.max())\n",
    "nx.draw_networkx_edges(g,pos)\n",
    "sm = plt.cm.ScalarMappable(cmap=cmap, norm=plt.Normalize(vmin=0, vmax=influe.max()))\n",
    "sm.set_array([])\n",
    "cbar = plt.colorbar(sm)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 0001 cost= 1.3985 train_acc:  0.25   test_acc:  0.36363634\n",
      "Epoch: 0002 cost= 1.36889 train_acc:  0.41666666   test_acc:  0.36363634\n",
      "Epoch: 0003 cost= 1.37062 train_acc:  0.5   test_acc:  0.45454544\n",
      "Epoch: 0004 cost= 1.37434 train_acc:  0.41666666   test_acc:  0.36363634\n",
      "Epoch: 0005 cost= 1.33081 train_acc:  0.41666666   test_acc:  0.36363634\n"
     ]
    }
   ],
   "source": [
    "tf.reset_default_graph()\n",
    "model=JKNet([16,16,16,16], 34, 34, 4)\n",
    "model.loss(1e-2, active=tf.nn.relu, JK_Model='Only_GAT')\n",
    "for epoch in range(5):\n",
    "    avg_cost=0\n",
    "    for i in range(50):\n",
    "        batch_xs, batch_ys, adj = feat_x.reshape(-1),  y_train, np.array(adj_norm)\n",
    "        avg_cost=model.train(batch_xs, batch_ys, train_mask, avg_cost/50, adj)\n",
    "    train_acc, influe=model.accuracy(batch_xs, batch_ys, train_mask, adj)\n",
    "    test_acc, _=model.accuracy(batch_xs, y_test, test_mask, adj)\n",
    "    print('Epoch:','%04d' %(epoch+1),'cost=', np.round(avg_cost,5),'train_acc: ',train_acc, '  test_acc: ',test_acc)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Influence distribution of 4-Layer GAT"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15,8))\n",
    "plt.title('4-layer GAT')\n",
    "pos = nx.spring_layout(g)\n",
    "cmap = plt.cm.coolwarm\n",
    "nx.draw_networkx_nodes(g, pos ,nodelist=['n11'],node_color=[influe[0]], node_shape='s',  cmap=cmap,vmin=0, vmax=influe.max())\n",
    "nx.draw_networkx_nodes(g, pos ,nodelist=['n'+str(i) for i in range(34) if i!=11],node_color=influe[1:], node_shape='o', cmap=cmap, vmin=0, vmax=influe.max())\n",
    "nx.draw_networkx_edges(g,pos)\n",
    "sm = plt.cm.ScalarMappable(cmap=cmap, norm=plt.Normalize(vmin=0, vmax=influe.max()))\n",
    "sm.set_array([])\n",
    "cbar = plt.colorbar(sm)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 0001 cost= 0.9282 train_acc:  1.0   test_acc:  0.9090908\n",
      "Epoch: 0002 cost= 0.82147 train_acc:  1.0   test_acc:  0.9090908\n",
      "Epoch: 0003 cost= 0.83026 train_acc:  1.0   test_acc:  0.9090908\n",
      "Epoch: 0004 cost= 0.88604 train_acc:  1.0   test_acc:  0.9090908\n",
      "Epoch: 0005 cost= 0.84369 train_acc:  1.0   test_acc:  0.95454544\n"
     ]
    }
   ],
   "source": [
    "tf.reset_default_graph()\n",
    "model=JKNet([16,16,16,16], 34, 34, 4)\n",
    "model.loss(1e-2, active=tf.nn.relu, JK_Model='concat')\n",
    "for epoch in range(5):\n",
    "    avg_cost=0\n",
    "    for i in range(50):\n",
    "        batch_xs, batch_ys, adj = feat_x.reshape(-1),  y_train, np.array(adj_norm)\n",
    "        avg_cost=model.train(batch_xs, batch_ys, train_mask, avg_cost/50, adj)\n",
    "    train_acc, influe=model.accuracy(batch_xs, batch_ys, train_mask, adj)\n",
    "    test_acc, _=model.accuracy(batch_xs, y_test, test_mask, adj)\n",
    "    print('Epoch:','%04d' %(epoch+1),'cost=', np.round(avg_cost,5),'train_acc: ',train_acc, '  test_acc: ',test_acc)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Influence distribution of 4-Layer CONCATENATION"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15,8))\n",
    "plt.title('4-layer CONCAT')\n",
    "pos = nx.spring_layout(g)\n",
    "cmap = plt.cm.coolwarm\n",
    "nx.draw_networkx_nodes(g, pos ,nodelist=['n11'],node_color=[influe[0]], node_shape='s',  cmap=cmap,vmin=0, vmax=influe.max())\n",
    "nx.draw_networkx_nodes(g, pos ,nodelist=['n'+str(i) for i in range(34) if i!=11],node_color=influe[1:], node_shape='o', cmap=cmap, vmin=0, vmax=influe.max())\n",
    "nx.draw_networkx_edges(g,pos)\n",
    "sm = plt.cm.ScalarMappable(cmap=cmap, norm=plt.Normalize(vmin=0, vmax=influe.max()))\n",
    "sm.set_array([])\n",
    "cbar = plt.colorbar(sm)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From C:\\Anaconda3\\lib\\site-packages\\tensorflow\\python\\ops\\math_grad.py:102: div (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Deprecated in favor of operator or tf.math.divide.\n",
      "Epoch: 0001 cost= 0.98986 train_acc:  1.0   test_acc:  0.7272727\n",
      "Epoch: 0002 cost= 0.83257 train_acc:  1.0   test_acc:  0.95454544\n",
      "Epoch: 0003 cost= 0.81961 train_acc:  1.0   test_acc:  0.95454544\n",
      "Epoch: 0004 cost= 0.81141 train_acc:  1.0   test_acc:  0.95454544\n",
      "Epoch: 0005 cost= 0.80572 train_acc:  1.0   test_acc:  1.0\n"
     ]
    }
   ],
   "source": [
    "tf.reset_default_graph()\n",
    "model=JKNet([16,16,16,16], 34, 34, 4)\n",
    "model.loss(1e-2, active=tf.nn.relu, JK_Model='MaxPooling')\n",
    "for epoch in range(5):\n",
    "    avg_cost=0\n",
    "    for i in range(50):\n",
    "        batch_xs, batch_ys, adj = feat_x.reshape(-1),  y_train, np.array(adj_norm)\n",
    "        avg_cost=model.train(batch_xs, batch_ys, train_mask, avg_cost/50, adj)\n",
    "    train_acc, influe=model.accuracy(batch_xs, batch_ys, train_mask, adj)\n",
    "    test_acc, _=model.accuracy(batch_xs, y_test, test_mask, adj)\n",
    "    print('Epoch:','%04d' %(epoch+1),'cost=', np.round(avg_cost,5),'train_acc: ',train_acc, '  test_acc: ',test_acc)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Influence distribution of 4-Layer MAXPOOLING"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15,8))\n",
    "plt.title('4-layer MAXPOOL')\n",
    "pos = nx.spring_layout(g)\n",
    "cmap = plt.cm.coolwarm\n",
    "nx.draw_networkx_nodes(g, pos ,nodelist=['n11'],node_color=[influe[0]], node_shape='s',  cmap=cmap,vmin=0, vmax=influe.max())\n",
    "nx.draw_networkx_nodes(g, pos ,nodelist=['n'+str(i) for i in range(34) if i!=11],node_color=influe[1:], node_shape='o', cmap=cmap, vmin=0, vmax=influe.max())\n",
    "nx.draw_networkx_edges(g,pos)\n",
    "sm = plt.cm.ScalarMappable(cmap=cmap, norm=plt.Normalize(vmin=0, vmax=influe.max()))\n",
    "sm.set_array([])\n",
    "cbar = plt.colorbar(sm)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From <ipython-input-4-b36accad768f>:89: LSTMCell.__init__ (from tensorflow.python.ops.rnn_cell_impl) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "This class is equivalent as tf.keras.layers.LSTMCell, and will be replaced by that in Tensorflow 2.0.\n",
      "WARNING:tensorflow:From <ipython-input-4-b36accad768f>:95: bidirectional_dynamic_rnn (from tensorflow.python.ops.rnn) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Please use `keras.layers.Bidirectional(keras.layers.RNN(cell))`, which is equivalent to this API\n",
      "WARNING:tensorflow:From C:\\Anaconda3\\lib\\site-packages\\tensorflow\\python\\ops\\rnn.py:443: dynamic_rnn (from tensorflow.python.ops.rnn) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Please use `keras.layers.RNN(cell)`, which is equivalent to this API\n",
      "Epoch: 0001 cost= 1.10196 train_acc:  0.9166666   test_acc:  0.5454545\n",
      "Epoch: 0002 cost= 0.88588 train_acc:  1.0   test_acc:  0.9090908\n",
      "Epoch: 0003 cost= 0.8806 train_acc:  1.0   test_acc:  1.0\n",
      "Epoch: 0004 cost= 0.87165 train_acc:  1.0   test_acc:  1.0\n",
      "Epoch: 0005 cost= 0.83702 train_acc:  1.0   test_acc:  1.0\n"
     ]
    }
   ],
   "source": [
    "tf.reset_default_graph()\n",
    "model=JKNet([16,16,16,16], 34, 34, 4)\n",
    "model.loss(1e-2, active=tf.nn.relu, JK_Model='Bi-LSTM Attention')\n",
    "for epoch in range(5):\n",
    "    avg_cost=0\n",
    "    for i in range(50):\n",
    "        batch_xs, batch_ys, adj = feat_x.reshape(-1),  y_train, np.array(adj_norm)\n",
    "        avg_cost=model.train(batch_xs, batch_ys, train_mask, avg_cost/50, adj)\n",
    "    train_acc, influe=model.accuracy(batch_xs, batch_ys, train_mask, adj)\n",
    "    test_acc, _=model.accuracy(batch_xs, y_test, test_mask, adj)\n",
    "    print('Epoch:','%04d' %(epoch+1),'cost=', np.round(avg_cost,5),'train_acc: ',train_acc, '  test_acc: ',test_acc)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Influence distribution of 4-Layer BI-LSTM ATTENTION"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15,8))\n",
    "plt.title('4-layer LSTM')\n",
    "pos = nx.spring_layout(g)\n",
    "cmap = plt.cm.coolwarm\n",
    "nx.draw_networkx_nodes(g, pos ,nodelist=['n11'],node_color=[influe[0]], node_shape='s',  cmap=cmap,vmin=0, vmax=influe.max())\n",
    "nx.draw_networkx_nodes(g, pos ,nodelist=['n'+str(i) for i in range(34) if i!=11],node_color=influe[1:], node_shape='o', cmap=cmap, vmin=0, vmax=influe.max())\n",
    "nx.draw_networkx_edges(g,pos)\n",
    "sm = plt.cm.ScalarMappable(cmap=cmap, norm=plt.Normalize(vmin=0, vmax=influe.max()))\n",
    "sm.set_array([])\n",
    "cbar = plt.colorbar(sm)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Cora Datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "def parse_index_file(filename):\n",
    "    \"\"\"Parse index file.\"\"\"\n",
    "    index = []\n",
    "    for line in open(filename):\n",
    "        index.append(int(line.strip()))\n",
    "    return index\n",
    "\n",
    "\n",
    "def sample_mask(idx, l):\n",
    "    \"\"\"Create mask.\"\"\"\n",
    "    mask = np.zeros(l)\n",
    "    mask[idx] = 1\n",
    "    return np.array(mask, dtype=np.bool)\n",
    "\n",
    "\n",
    "def load_data(dataset_str):\n",
    "    \"\"\"\n",
    "    Loads input data from gcn/data directory\n",
    "\n",
    "    ind.dataset_str.x => the feature vectors of the training instances as scipy.sparse.csr.csr_matrix object;\n",
    "    ind.dataset_str.tx => the feature vectors of the test instances as scipy.sparse.csr.csr_matrix object;\n",
    "    ind.dataset_str.allx => the feature vectors of both labeled and unlabeled training instances\n",
    "        (a superset of ind.dataset_str.x) as scipy.sparse.csr.csr_matrix object;\n",
    "    ind.dataset_str.y => the one-hot labels of the labeled training instances as numpy.ndarray object;\n",
    "    ind.dataset_str.ty => the one-hot labels of the test instances as numpy.ndarray object;\n",
    "    ind.dataset_str.ally => the labels for instances in ind.dataset_str.allx as numpy.ndarray object;\n",
    "    ind.dataset_str.graph => a dict in the format {index: [index_of_neighbor_nodes]} as collections.defaultdict\n",
    "        object;\n",
    "    ind.dataset_str.test.index => the indices of test instances in graph, for the inductive setting as list object.\n",
    "\n",
    "    All objects above must be saved using python pickle module.\n",
    "\n",
    "    :param dataset_str: Dataset name\n",
    "    :return: All data input files loaded (as well the training/test data).\n",
    "    \"\"\"\n",
    "    names = ['x', 'y', 'tx', 'ty', 'allx', 'ally', 'graph']\n",
    "    objects = []\n",
    "    for i in range(len(names)):\n",
    "        with open(\"D:/bigdata/Python/Graph Neural Net/Graph-neural-networks-master/tutorials/data/ind.{}.{}\".format(dataset_str, names[i]), 'rb') as f:\n",
    "            if sys.version_info > (3, 0):\n",
    "                objects.append(pkl.load(f, encoding='latin1'))\n",
    "            else:\n",
    "                objects.append(pkl.load(f))\n",
    "\n",
    "    x, y, tx, ty, allx, ally, graph = tuple(objects)\n",
    "    test_idx_reorder = parse_index_file(\"D:/bigdata/Python/Graph Neural Net/Graph-neural-networks-master/tutorials/data/ind.{}.test.index\".format(dataset_str))\n",
    "    test_idx_range = np.sort(test_idx_reorder)\n",
    "    if dataset_str == 'citeseer':\n",
    "        # Fix citeseer dataset (there are some isolated nodes in the graph)\n",
    "        # Find isolated nodes, add them as zero-vecs into the right position\n",
    "        test_idx_range_full = range(min(test_idx_reorder), max(test_idx_reorder)+1)\n",
    "        tx_extended = sp.lil_matrix((len(test_idx_range_full), x.shape[1]))\n",
    "        tx_extended[test_idx_range-min(test_idx_range), :] = tx\n",
    "        tx = tx_extended\n",
    "        ty_extended = np.zeros((len(test_idx_range_full), y.shape[1]))\n",
    "        ty_extended[test_idx_range-min(test_idx_range), :] = ty\n",
    "        ty = ty_extended\n",
    "\n",
    "    features = sp.vstack((allx, tx)).tolil()\n",
    "    features[test_idx_reorder, :] = features[test_idx_range, :]\n",
    "    \n",
    "    adj = nx.adjacency_matrix(nx.from_dict_of_lists(graph))\n",
    "\n",
    "    labels = np.vstack((ally, ty))\n",
    "    labels[test_idx_reorder, :] = labels[test_idx_range, :]\n",
    "\n",
    "    idx_test = np.random.choice(2708, int(2708*0.2), replace=False)\n",
    "    without_test=np.array([i for i in range(2708) if i not in idx_test ])\n",
    "    idx_train = without_test[np.random.choice(np.arange(len(without_test)),int(2708*0.6), replace=False)]\n",
    "    idx_val = np.array([i for i in range(2708) if i not in idx_test if i not in idx_train])\n",
    "\n",
    "    train_mask = sample_mask(idx_train, labels.shape[0])\n",
    "    val_mask = sample_mask(idx_val, labels.shape[0])\n",
    "    test_mask = sample_mask(idx_test, labels.shape[0])\n",
    "\n",
    "    y_train = np.zeros(labels.shape)\n",
    "    y_val = np.zeros(labels.shape)\n",
    "    y_test = np.zeros(labels.shape)\n",
    "    y_train[train_mask, :] = labels[train_mask, :]\n",
    "    y_val[val_mask, :] = labels[val_mask, :]\n",
    "    y_test[test_mask, :] = labels[test_mask, :]\n",
    "\n",
    "    return adj, features, np.array(y_train), np.array(y_val), np.array(y_test), np.array(train_mask), np.array(val_mask), np.array(test_mask), graph\n",
    "\n",
    "\n",
    "def sparse_to_tuple(sparse_mx):\n",
    "    \"\"\"Convert sparse matrix to tuple representation.\"\"\"\n",
    "    def to_tuple(mx):\n",
    "        if not sp.isspmatrix_coo(mx):\n",
    "            mx = mx.tocoo()\n",
    "        coords = np.vstack((mx.row, mx.col)).transpose()\n",
    "        values = mx.data\n",
    "        shape = mx.shape\n",
    "        return coords, values, shape\n",
    "\n",
    "    if isinstance(sparse_mx, list):\n",
    "        for i in range(len(sparse_mx)):\n",
    "            sparse_mx[i] = to_tuple(sparse_mx[i])\n",
    "    else:\n",
    "        sparse_mx = to_tuple(sparse_mx)\n",
    "\n",
    "    return sparse_mx\n",
    "\n",
    "\n",
    "def preprocess_features(features):\n",
    "    \"\"\"Row-normalize feature matrix and convert to tuple representation\"\"\"\n",
    "    rowsum = np.array(features.sum(1))\n",
    "    r_inv = np.power(rowsum, -1).flatten()\n",
    "    r_inv[np.isinf(r_inv)] = 0.\n",
    "    r_mat_inv = sp.diags(r_inv)\n",
    "    features = r_mat_inv.dot(features)\n",
    "    return sparse_to_tuple(features)\n",
    "\n",
    "\n",
    "def normalize_adj(adj):\n",
    "    \"\"\"Symmetrically normalize adjacency matrix.\"\"\"\n",
    "    adj = sp.coo_matrix(adj)\n",
    "    rowsum = np.array(adj.sum(1))\n",
    "    d_inv_sqrt = np.power(rowsum, -0.5).flatten()\n",
    "    d_inv_sqrt[np.isinf(d_inv_sqrt)] = 0.\n",
    "    d_mat_inv_sqrt = sp.diags(d_inv_sqrt)\n",
    "    return adj.dot(d_mat_inv_sqrt).transpose().dot(d_mat_inv_sqrt).tocoo()\n",
    "\n",
    "\n",
    "def preprocess_adj(adj):\n",
    "    \"\"\"Preprocessing of adjacency matrix for simple GCN model and conversion to tuple representation.\"\"\"\n",
    "    adj_normalized = normalize_adj(adj + sp.eye(adj.shape[0]))\n",
    "    return sparse_to_tuple(adj_normalized)\n",
    "\n",
    "\n",
    "def construct_feed_dict(features, support, labels, labels_mask, placeholders):\n",
    "    \"\"\"Construct feed dictionary.\"\"\"\n",
    "    feed_dict = dict()\n",
    "    feed_dict.update({placeholders['labels']: labels})\n",
    "    feed_dict.update({placeholders['labels_mask']: labels_mask})\n",
    "    feed_dict.update({placeholders['features']: features})\n",
    "    feed_dict.update({placeholders['support'][i]: support[i] for i in range(len(support))})\n",
    "    feed_dict.update({placeholders['num_features_nonzero']: features[1].shape})\n",
    "    return feed_dict\n",
    "\n",
    "\n",
    "def chebyshev_polynomials(adj, k):\n",
    "    \"\"\"Calculate Chebyshev polynomials up to order k. Return a list of sparse matrices (tuple representation).\"\"\"\n",
    "    print(\"Calculating Chebyshev polynomials up to order {}...\".format(k))\n",
    "\n",
    "    adj_normalized = normalize_adj(adj)\n",
    "    laplacian = sp.eye(adj.shape[0]) - adj_normalized\n",
    "    largest_eigval, _ = eigsh(laplacian, 1, which='LM')\n",
    "    scaled_laplacian = (2. / largest_eigval[0]) * laplacian - sp.eye(adj.shape[0])\n",
    "\n",
    "    t_k = list()\n",
    "    t_k.append(sp.eye(adj.shape[0]))\n",
    "    t_k.append(scaled_laplacian)\n",
    "\n",
    "    def chebyshev_recurrence(t_k_minus_one, t_k_minus_two, scaled_lap):\n",
    "        s_lap = sp.csr_matrix(scaled_lap, copy=True)\n",
    "        return 2 * s_lap.dot(t_k_minus_one) - t_k_minus_two\n",
    "\n",
    "    for i in range(2, k+1):\n",
    "        t_k.append(chebyshev_recurrence(t_k[-1], t_k[-2], scaled_laplacian))\n",
    "\n",
    "    return sparse_to_tuple(t_k)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "adj_cora, features_cora, y_train_cora, y_val_cora, y_test_cora, train_mask_cora, val_mask_cora, test_mask_cora, cora_graph = load_data('cora')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "features_cora=np.array(features_cora.todense())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "adj_norm_cora = normalize_adj(adj_cora + sp.eye(adj_cora.shape[0]))\n",
    "adj_norm_cora=adj_norm_cora.todense()\n",
    "adj_norm_cora=np.array(adj_norm_cora.astype(np.float32))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "feature shape:  (2708, 1433)\n",
      "adj_norm_cora shape:  (2708, 2708)\n",
      "y_train_cora shape:  (2708, 7)\n"
     ]
    }
   ],
   "source": [
    "print('feature shape: ',features_cora.shape)\n",
    "print('adj_norm_cora shape: ',adj_norm_cora.shape)\n",
    "print('y_train_cora shape: ',y_train_cora.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# GCN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 0001 train_acc:  0.8965496\n",
      "Epoch: 0002 train_acc:  0.93657404\n",
      "Epoch: 0003 train_acc:  0.95012075\n",
      "Epoch: 0004 train_acc:  0.9562784\n",
      "Epoch: 0005 train_acc:  0.96120447\n",
      "Test_acc:  0.8909425\n"
     ]
    }
   ],
   "source": [
    "tf.reset_default_graph()\n",
    "model_cora=JKNet([32], 2708, 1433, 7)\n",
    "model_cora.loss(5e-3, active=tf.nn.relu, JK_Model='Only_GCN')\n",
    "for epoch in range(5):\n",
    "    avg_cost=0\n",
    "    for i in range(40):\n",
    "        batch_xs, batch_ys, adj=features_cora.reshape(-1),  y_train_cora, adj_norm_cora\n",
    "        avg_cost=model_cora.train(batch_xs, batch_ys, train_mask_cora, avg_cost/100, adj)\n",
    "    train_acc, _=model_cora.accuracy(batch_xs, batch_ys, train_mask_cora, adj)\n",
    "    print('Epoch:','%04d' %(epoch+1),'train_acc: ',train_acc)\n",
    "test_acc, _=model_cora.accuracy(batch_xs, y_test_cora, test_mask_cora, adj)\n",
    "print('Test_acc: ',test_acc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 0001 train_acc:  0.7980278\n",
      "Epoch: 0002 train_acc:  0.88115555\n",
      "Epoch: 0003 train_acc:  0.93657404\n",
      "Epoch: 0004 train_acc:  0.9464262\n",
      "Epoch: 0005 train_acc:  0.949505\n",
      "Test_acc:  0.8927909\n"
     ]
    }
   ],
   "source": [
    "tf.reset_default_graph()\n",
    "model_cora=JKNet([32,32], 2708, 1433, 7)\n",
    "model_cora.loss(5e-3, active=tf.nn.relu, JK_Model='Only_GCN')\n",
    "for epoch in range(5):\n",
    "    avg_cost=0\n",
    "    for i in range(40):\n",
    "        batch_xs, batch_ys, adj=features_cora.reshape(-1),  y_train_cora, adj_norm_cora\n",
    "        avg_cost=model_cora.train(batch_xs, batch_ys, train_mask_cora, avg_cost/100, adj)\n",
    "    train_acc, _=model_cora.accuracy(batch_xs, batch_ys, train_mask_cora, adj)\n",
    "    print('Epoch:','%04d' %(epoch+1),'train_acc: ',train_acc)\n",
    "test_acc, _=model_cora.accuracy(batch_xs, y_test_cora, test_mask_cora, adj)\n",
    "print('Test_acc: ',test_acc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 0001 train_acc:  0.79125446\n",
      "Epoch: 0002 train_acc:  0.86021966\n",
      "Epoch: 0003 train_acc:  0.92364305\n",
      "Epoch: 0004 train_acc:  0.9334952\n",
      "Epoch: 0005 train_acc:  0.9353425\n",
      "Test_acc:  0.8872456\n"
     ]
    }
   ],
   "source": [
    "tf.reset_default_graph()\n",
    "model_cora=JKNet([32,32,32], 2708, 1433, 7)\n",
    "model_cora.loss(5e-3, active=tf.nn.relu, JK_Model='Only_GCN')\n",
    "for epoch in range(5):\n",
    "    avg_cost=0\n",
    "    for i in range(40):\n",
    "        batch_xs, batch_ys, adj=features_cora.reshape(-1),  y_train_cora, adj_norm_cora\n",
    "        avg_cost=model_cora.train(batch_xs, batch_ys, train_mask_cora, avg_cost/100, adj)\n",
    "    train_acc, _=model_cora.accuracy(batch_xs, batch_ys, train_mask_cora, adj)\n",
    "    print('Epoch:','%04d' %(epoch+1),'train_acc: ',train_acc)\n",
    "test_acc, _=model_cora.accuracy(batch_xs, y_test_cora, test_mask_cora, adj)\n",
    "print('Test_acc: ',test_acc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 0001 train_acc:  0.6373142\n",
      "Epoch: 0002 train_acc:  0.7998751\n",
      "Epoch: 0003 train_acc:  0.86391425\n",
      "Epoch: 0004 train_acc:  0.9217957\n",
      "Epoch: 0005 train_acc:  0.9291849\n",
      "Test_acc:  0.8872456\n"
     ]
    }
   ],
   "source": [
    "tf.reset_default_graph()\n",
    "model_cora=JKNet([32,32,32, 32], 2708, 1433, 7)\n",
    "model_cora.loss(5e-3, active=tf.nn.relu, JK_Model='Only_GCN')\n",
    "for epoch in range(5):\n",
    "    avg_cost=0\n",
    "    for i in range(40):\n",
    "        batch_xs, batch_ys, adj=features_cora.reshape(-1),  y_train_cora, adj_norm_cora\n",
    "        avg_cost=model_cora.train(batch_xs, batch_ys, train_mask_cora, avg_cost/100, adj)\n",
    "    train_acc, _=model_cora.accuracy(batch_xs, batch_ys, train_mask_cora, adj)\n",
    "    print('Epoch:','%04d' %(epoch+1),'train_acc: ',train_acc)\n",
    "test_acc, _=model_cora.accuracy(batch_xs, y_test_cora, test_mask_cora, adj)\n",
    "print('Test_acc: ',test_acc)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Best performance GCN Layer : 2\n",
    "### Test Acc : 89.2%"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# GAT"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 0001 train_acc:  0.8331262\n",
      "Epoch: 0002 train_acc:  0.93287945\n",
      "Epoch: 0003 train_acc:  0.949505\n",
      "Epoch: 0004 train_acc:  0.9568941\n",
      "Epoch: 0005 train_acc:  0.9587414\n",
      "Test_acc:  0.88354874\n"
     ]
    }
   ],
   "source": [
    "tf.reset_default_graph()\n",
    "model_cora=JKNet([32], 2708, 1433, 7)\n",
    "model_cora.loss(5e-3, active=tf.nn.relu, JK_Model='Only_GAT')\n",
    "for epoch in range(5):\n",
    "    avg_cost=0\n",
    "    for i in range(40):\n",
    "        batch_xs, batch_ys, adj=features_cora.reshape(-1),  y_train_cora, adj_norm_cora\n",
    "        avg_cost=model_cora.train(batch_xs, batch_ys, train_mask_cora, avg_cost/100, adj)\n",
    "    train_acc, _=model_cora.accuracy(batch_xs, batch_ys, train_mask_cora, adj)\n",
    "    print('Epoch:','%04d' %(epoch+1),'train_acc: ',train_acc)\n",
    "test_acc, _=model_cora.accuracy(batch_xs, y_test_cora, test_mask_cora, adj)\n",
    "print('Test_acc: ',test_acc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 0001 train_acc:  0.6847278\n",
      "Epoch: 0002 train_acc:  0.8706876\n",
      "Epoch: 0003 train_acc:  0.8866974\n",
      "Epoch: 0004 train_acc:  0.9261061\n",
      "Epoch: 0005 train_acc:  0.9396528\n",
      "Test_acc:  0.88354874\n"
     ]
    }
   ],
   "source": [
    "tf.reset_default_graph()\n",
    "model_cora=JKNet([32,32], 2708, 1433, 7)\n",
    "model_cora.loss(5e-3, active=tf.nn.relu, JK_Model='Only_GAT')\n",
    "for epoch in range(5):\n",
    "    avg_cost=0\n",
    "    for i in range(40):\n",
    "        batch_xs, batch_ys, adj=features_cora.reshape(-1),  y_train_cora, adj_norm_cora\n",
    "        avg_cost=model_cora.train(batch_xs, batch_ys, train_mask_cora, avg_cost/100, adj)\n",
    "    train_acc, _=model_cora.accuracy(batch_xs, batch_ys, train_mask_cora, adj)\n",
    "    print('Epoch:','%04d' %(epoch+1),'train_acc: ',train_acc)\n",
    "test_acc, _=model_cora.accuracy(batch_xs, y_test_cora, test_mask_cora, adj)\n",
    "print('Test_acc: ',test_acc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 0001 train_acc:  0.4316501\n",
      "Epoch: 0002 train_acc:  0.57389086\n",
      "Epoch: 0003 train_acc:  0.794949\n",
      "Epoch: 0004 train_acc:  0.8060327\n",
      "Epoch: 0005 train_acc:  0.8121903\n",
      "Test_acc:  0.72643214\n"
     ]
    }
   ],
   "source": [
    "tf.reset_default_graph()\n",
    "model_cora=JKNet([32,32,32], 2708, 1433, 7)\n",
    "model_cora.loss(5e-3, active=tf.nn.relu, JK_Model='Only_GAT')\n",
    "for epoch in range(5):\n",
    "    avg_cost=0\n",
    "    for i in range(40):\n",
    "        batch_xs, batch_ys, adj=features_cora.reshape(-1),  y_train_cora, adj_norm_cora\n",
    "        avg_cost=model_cora.train(batch_xs, batch_ys, train_mask_cora, avg_cost/100, adj)\n",
    "    train_acc, _=model_cora.accuracy(batch_xs, batch_ys, train_mask_cora, adj)\n",
    "    print('Epoch:','%04d' %(epoch+1),'train_acc: ',train_acc)\n",
    "test_acc, _=model_cora.accuracy(batch_xs, y_test_cora, test_mask_cora, adj)\n",
    "print('Test_acc: ',test_acc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 0001 train_acc:  0.4107142\n",
      "Epoch: 0002 train_acc:  0.46798\n",
      "Epoch: 0003 train_acc:  0.6231517\n",
      "Epoch: 0004 train_acc:  0.6520925\n",
      "Epoch: 0005 train_acc:  0.6613289\n",
      "Test_acc:  0.5711643\n"
     ]
    }
   ],
   "source": [
    "tf.reset_default_graph()\n",
    "model_cora=JKNet([32,32,32,32], 2708, 1433, 7)\n",
    "model_cora.loss(5e-3, active=tf.nn.relu, JK_Model='Only_GAT')\n",
    "for epoch in range(5):\n",
    "    avg_cost=0\n",
    "    for i in range(40):\n",
    "        batch_xs, batch_ys, adj=features_cora.reshape(-1),  y_train_cora, adj_norm_cora\n",
    "        avg_cost=model_cora.train(batch_xs, batch_ys, train_mask_cora, avg_cost/100, adj)\n",
    "    train_acc, _=model_cora.accuracy(batch_xs, batch_ys, train_mask_cora, adj)\n",
    "    print('Epoch:','%04d' %(epoch+1),'train_acc: ',train_acc)\n",
    "test_acc, _=model_cora.accuracy(batch_xs, y_test_cora, test_mask_cora, adj)\n",
    "print('Test_acc: ',test_acc)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Best performance GAT Layer : 1,2\n",
    "### Test Acc : 88.3%"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Concat"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 0001 train_acc:  0.8706876\n",
      "Epoch: 0002 train_acc:  0.9051702\n",
      "Epoch: 0003 train_acc:  0.95381534\n",
      "Epoch: 0004 train_acc:  0.9599729\n",
      "Epoch: 0005 train_acc:  0.9605887\n",
      "Test_acc:  0.8872456\n"
     ]
    }
   ],
   "source": [
    "tf.reset_default_graph()\n",
    "model_cora=JKNet([32,32], 2708, 1433, 7)\n",
    "model_cora.loss(5e-3, active=tf.nn.relu, JK_Model='concat')\n",
    "for epoch in range(5):\n",
    "    avg_cost=0\n",
    "    for i in range(40):\n",
    "        batch_xs, batch_ys, adj=features_cora.reshape(-1),  y_train_cora, adj_norm_cora\n",
    "        avg_cost=model_cora.train(batch_xs, batch_ys, train_mask_cora, avg_cost/100, adj)\n",
    "    train_acc, _=model_cora.accuracy(batch_xs, batch_ys, train_mask_cora, adj)\n",
    "    print('Epoch:','%04d' %(epoch+1),'train_acc: ',train_acc)\n",
    "test_acc, _=model_cora.accuracy(batch_xs, y_test_cora, test_mask_cora, adj)\n",
    "print('Test_acc: ',test_acc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 0001 train_acc:  0.810343\n",
      "Epoch: 0002 train_acc:  0.9100963\n",
      "Epoch: 0003 train_acc:  0.9470419\n",
      "Epoch: 0004 train_acc:  0.9464262\n",
      "Epoch: 0005 train_acc:  0.9568941\n",
      "Test_acc:  0.88354874\n"
     ]
    }
   ],
   "source": [
    "tf.reset_default_graph()\n",
    "model_cora=JKNet([32,32,32], 2708, 1433, 7)\n",
    "model_cora.loss(5e-3, active=tf.nn.relu, JK_Model='concat')\n",
    "for epoch in range(5):\n",
    "    avg_cost=0\n",
    "    for i in range(40):\n",
    "        batch_xs, batch_ys, adj=features_cora.reshape(-1),  y_train_cora, adj_norm_cora\n",
    "        avg_cost=model_cora.train(batch_xs, batch_ys, train_mask_cora, avg_cost/100, adj)\n",
    "    train_acc, _=model_cora.accuracy(batch_xs, batch_ys, train_mask_cora, adj)\n",
    "    print('Epoch:','%04d' %(epoch+1),'train_acc: ',train_acc)\n",
    "test_acc, _=model_cora.accuracy(batch_xs, y_test_cora, test_mask_cora, adj)\n",
    "print('Test_acc: ',test_acc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 0001 train_acc:  0.8423626\n",
      "Epoch: 0002 train_acc:  0.93287945\n",
      "Epoch: 0003 train_acc:  0.9433474\n",
      "Epoch: 0004 train_acc:  0.9464262\n",
      "Epoch: 0005 train_acc:  0.949505\n",
      "Test_acc:  0.88539714\n"
     ]
    }
   ],
   "source": [
    "tf.reset_default_graph()\n",
    "model_cora=JKNet([32,32,32,32], 2708, 1433, 7)\n",
    "model_cora.loss(5e-3, active=tf.nn.relu, JK_Model='concat')\n",
    "for epoch in range(5):\n",
    "    avg_cost=0\n",
    "    for i in range(40):\n",
    "        batch_xs, batch_ys, adj=features_cora.reshape(-1),  y_train_cora, adj_norm_cora\n",
    "        avg_cost=model_cora.train(batch_xs, batch_ys, train_mask_cora, avg_cost/100, adj)\n",
    "    train_acc, _=model_cora.accuracy(batch_xs, batch_ys, train_mask_cora, adj)\n",
    "    print('Epoch:','%04d' %(epoch+1),'train_acc: ',train_acc)\n",
    "test_acc, _=model_cora.accuracy(batch_xs, y_test_cora, test_mask_cora, adj)\n",
    "print('Test_acc: ',test_acc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 0001 train_acc:  0.80110663\n",
      "Epoch: 0002 train_acc:  0.9267218\n",
      "Epoch: 0003 train_acc:  0.9390371\n",
      "Epoch: 0004 train_acc:  0.94519466\n",
      "Epoch: 0005 train_acc:  0.949505\n",
      "Test_acc:  0.8909425\n"
     ]
    }
   ],
   "source": [
    "tf.reset_default_graph()\n",
    "model_cora=JKNet([32,32,32,32,32], 2708, 1433, 7)\n",
    "model_cora.loss(5e-3, active=tf.nn.relu, JK_Model='concat')\n",
    "for epoch in range(5):\n",
    "    avg_cost=0\n",
    "    for i in range(40):\n",
    "        batch_xs, batch_ys, adj=features_cora.reshape(-1),  y_train_cora, adj_norm_cora\n",
    "        avg_cost=model_cora.train(batch_xs, batch_ys, train_mask_cora, avg_cost/100, adj)\n",
    "    train_acc, _=model_cora.accuracy(batch_xs, batch_ys, train_mask_cora, adj)\n",
    "    print('Epoch:','%04d' %(epoch+1),'train_acc: ',train_acc)\n",
    "test_acc, _=model_cora.accuracy(batch_xs, y_test_cora, test_mask_cora, adj)\n",
    "print('Test_acc: ',test_acc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 0001 train_acc:  0.8405153\n",
      "Epoch: 0002 train_acc:  0.93287945\n",
      "Epoch: 0003 train_acc:  0.9384213\n",
      "Epoch: 0004 train_acc:  0.9433474\n",
      "Epoch: 0005 train_acc:  0.94888926\n",
      "Test_acc:  0.8946394\n"
     ]
    }
   ],
   "source": [
    "tf.reset_default_graph()\n",
    "model_cora=JKNet([32,32,32,32,32,32], 2708, 1433, 7)\n",
    "model_cora.loss(5e-3, active=tf.nn.relu, JK_Model='concat')\n",
    "for epoch in range(5):\n",
    "    avg_cost=0\n",
    "    for i in range(40):\n",
    "        batch_xs, batch_ys, adj=features_cora.reshape(-1),  y_train_cora, adj_norm_cora\n",
    "        avg_cost=model_cora.train(batch_xs, batch_ys, train_mask_cora, avg_cost/100, adj)\n",
    "    train_acc, _=model_cora.accuracy(batch_xs, batch_ys, train_mask_cora, adj)\n",
    "    print('Epoch:','%04d' %(epoch+1),'train_acc: ',train_acc)\n",
    "test_acc, _=model_cora.accuracy(batch_xs, y_test_cora, test_mask_cora, adj)\n",
    "print('Test_acc: ',test_acc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 0001 train_acc:  0.7943333\n",
      "Epoch: 0002 train_acc:  0.92487454\n",
      "Epoch: 0003 train_acc:  0.94211584\n",
      "Epoch: 0004 train_acc:  0.9464262\n",
      "Epoch: 0005 train_acc:  0.9464262\n",
      "Test_acc:  0.8964878\n"
     ]
    }
   ],
   "source": [
    "tf.reset_default_graph()\n",
    "model_cora=JKNet([32,32,32,32,32,32,32], 2708, 1433, 7)\n",
    "model_cora.loss(5e-3, active=tf.nn.relu, JK_Model='concat')\n",
    "for epoch in range(5):\n",
    "    avg_cost=0\n",
    "    for i in range(40):\n",
    "        batch_xs, batch_ys, adj=features_cora.reshape(-1),  y_train_cora, adj_norm_cora\n",
    "        avg_cost=model_cora.train(batch_xs, batch_ys, train_mask_cora, avg_cost/100, adj)\n",
    "    train_acc, _=model_cora.accuracy(batch_xs, batch_ys, train_mask_cora, adj)\n",
    "    print('Epoch:','%04d' %(epoch+1),'train_acc: ',train_acc)\n",
    "test_acc, _=model_cora.accuracy(batch_xs, y_test_cora, test_mask_cora, adj)\n",
    "print('Test_acc: ',test_acc)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Best performance Concatenate Layer : 7\n",
    "### Test Acc : 89.6%"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# MaxPooling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 0001 train_acc:  0.7598506\n",
      "Epoch: 0002 train_acc:  0.8719191\n",
      "Epoch: 0003 train_acc:  0.94519466\n",
      "Epoch: 0004 train_acc:  0.95381534\n",
      "Epoch: 0005 train_acc:  0.9562784\n",
      "Test_acc:  0.89094245\n"
     ]
    }
   ],
   "source": [
    "tf.reset_default_graph()\n",
    "model_cora=JKNet([32,32], 2708, 1433, 7)\n",
    "model_cora.loss(5e-3, active=tf.nn.relu, JK_Model='MaxPooling')\n",
    "for epoch in range(5):\n",
    "    avg_cost=0\n",
    "    for i in range(40):\n",
    "        batch_xs, batch_ys, adj=features_cora.reshape(-1),  y_train_cora, adj_norm_cora\n",
    "        avg_cost=model_cora.train(batch_xs, batch_ys, train_mask_cora, avg_cost/100, adj)\n",
    "    train_acc, _=model_cora.accuracy(batch_xs, batch_ys, train_mask_cora, adj)\n",
    "    print('Epoch:','%04d' %(epoch+1),'train_acc: ',train_acc)\n",
    "test_acc, _=model_cora.accuracy(batch_xs, y_test_cora, test_mask_cora, adj)\n",
    "print('Test_acc: ',test_acc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 0001 train_acc:  0.8676088\n",
      "Epoch: 0002 train_acc:  0.9008599\n",
      "Epoch: 0003 train_acc:  0.94519466\n",
      "Epoch: 0004 train_acc:  0.9507365\n",
      "Epoch: 0005 train_acc:  0.94888926\n",
      "Test_acc:  0.8964878\n"
     ]
    }
   ],
   "source": [
    "tf.reset_default_graph()\n",
    "model_cora=JKNet([32,32,32], 2708, 1433, 7)\n",
    "model_cora.loss(5e-3, active=tf.nn.relu, JK_Model='MaxPooling')\n",
    "for epoch in range(5):\n",
    "    avg_cost=0\n",
    "    for i in range(40):\n",
    "        batch_xs, batch_ys, adj=features_cora.reshape(-1),  y_train_cora, adj_norm_cora\n",
    "        avg_cost=model_cora.train(batch_xs, batch_ys, train_mask_cora, avg_cost/100, adj)\n",
    "    train_acc, _=model_cora.accuracy(batch_xs, batch_ys, train_mask_cora, adj)\n",
    "    print('Epoch:','%04d' %(epoch+1),'train_acc: ',train_acc)\n",
    "test_acc, _=model_cora.accuracy(batch_xs, y_test_cora, test_mask_cora, adj)\n",
    "print('Test_acc: ',test_acc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 0001 train_acc:  0.75492454\n",
      "Epoch: 0002 train_acc:  0.9217957\n",
      "Epoch: 0003 train_acc:  0.9396528\n",
      "Epoch: 0004 train_acc:  0.9470419\n",
      "Epoch: 0005 train_acc:  0.95012075\n",
      "Test_acc:  0.8909425\n"
     ]
    }
   ],
   "source": [
    "tf.reset_default_graph()\n",
    "model_cora=JKNet([32,32,32,32], 2708, 1433, 7)\n",
    "model_cora.loss(5e-3, active=tf.nn.relu, JK_Model='MaxPooling')\n",
    "for epoch in range(5):\n",
    "    avg_cost=0\n",
    "    for i in range(40):\n",
    "        batch_xs, batch_ys, adj=features_cora.reshape(-1),  y_train_cora, adj_norm_cora\n",
    "        avg_cost=model_cora.train(batch_xs, batch_ys, train_mask_cora, avg_cost/100, adj)\n",
    "    train_acc, _=model_cora.accuracy(batch_xs, batch_ys, train_mask_cora, adj)\n",
    "    print('Epoch:','%04d' %(epoch+1),'train_acc: ',train_acc)\n",
    "test_acc, _=model_cora.accuracy(batch_xs, y_test_cora, test_mask_cora, adj)\n",
    "print('Test_acc: ',test_acc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 0001 train_acc:  0.79618055\n",
      "Epoch: 0002 train_acc:  0.88238704\n",
      "Epoch: 0003 train_acc:  0.9322637\n",
      "Epoch: 0004 train_acc:  0.9347267\n",
      "Epoch: 0005 train_acc:  0.9482735\n",
      "Test_acc:  0.88539726\n"
     ]
    }
   ],
   "source": [
    "tf.reset_default_graph()\n",
    "model_cora=JKNet([32,32,32,32,32], 2708, 1433, 7)\n",
    "model_cora.loss(5e-3, active=tf.nn.relu, JK_Model='MaxPooling')\n",
    "for epoch in range(5):\n",
    "    avg_cost=0\n",
    "    for i in range(40):\n",
    "        batch_xs, batch_ys, adj=features_cora.reshape(-1),  y_train_cora, adj_norm_cora\n",
    "        avg_cost=model_cora.train(batch_xs, batch_ys, train_mask_cora, avg_cost/100, adj)\n",
    "    train_acc, _=model_cora.accuracy(batch_xs, batch_ys, train_mask_cora, adj)\n",
    "    print('Epoch:','%04d' %(epoch+1),'train_acc: ',train_acc)\n",
    "test_acc, _=model_cora.accuracy(batch_xs, y_test_cora, test_mask_cora, adj)\n",
    "print('Test_acc: ',test_acc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 0001 train_acc:  0.8158849\n",
      "Epoch: 0002 train_acc:  0.93164796\n",
      "Epoch: 0003 train_acc:  0.9402686\n",
      "Epoch: 0004 train_acc:  0.949505\n",
      "Epoch: 0005 train_acc:  0.9476577\n",
      "Test_acc:  0.90018463\n"
     ]
    }
   ],
   "source": [
    "tf.reset_default_graph()\n",
    "model_cora=JKNet([32,32,32,32,32,32], 2708, 1433, 7)\n",
    "model_cora.loss(5e-3, active=tf.nn.relu, JK_Model='MaxPooling')\n",
    "for epoch in range(5):\n",
    "    avg_cost=0\n",
    "    for i in range(40):\n",
    "        batch_xs, batch_ys, adj=features_cora.reshape(-1),  y_train_cora, adj_norm_cora\n",
    "        avg_cost=model_cora.train(batch_xs, batch_ys, train_mask_cora, avg_cost/100, adj)\n",
    "    train_acc, _=model_cora.accuracy(batch_xs, batch_ys, train_mask_cora, adj)\n",
    "    print('Epoch:','%04d' %(epoch+1),'train_acc: ',train_acc)\n",
    "test_acc, _=model_cora.accuracy(batch_xs, y_test_cora, test_mask_cora, adj)\n",
    "print('Test_acc: ',test_acc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 0001 train_acc:  0.8614512\n",
      "Epoch: 0002 train_acc:  0.9051702\n",
      "Epoch: 0003 train_acc:  0.9402686\n",
      "Epoch: 0004 train_acc:  0.9464262\n",
      "Epoch: 0005 train_acc:  0.9482735\n",
      "Test_acc:  0.8835488\n"
     ]
    }
   ],
   "source": [
    "tf.reset_default_graph()\n",
    "model_cora=JKNet([32,32,32,32,32,32,32], 2708, 1433, 7)\n",
    "model_cora.loss(5e-3, active=tf.nn.relu, JK_Model='MaxPooling')\n",
    "for epoch in range(5):\n",
    "    avg_cost=0\n",
    "    for i in range(40):\n",
    "        batch_xs, batch_ys, adj=features_cora.reshape(-1),  y_train_cora, adj_norm_cora\n",
    "        avg_cost=model_cora.train(batch_xs, batch_ys, train_mask_cora, avg_cost/100, adj)\n",
    "    train_acc, _=model_cora.accuracy(batch_xs, batch_ys, train_mask_cora, adj)\n",
    "    print('Epoch:','%04d' %(epoch+1),'train_acc: ',train_acc)\n",
    "test_acc, _=model_cora.accuracy(batch_xs, y_test_cora, test_mask_cora, adj)\n",
    "print('Test_acc: ',test_acc)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Best performance MAXPOOLING Layer : 6\n",
    "### Test Acc : 90.0%"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Bi-LSTM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 0001 train_acc:  0.8312789\n",
      "Epoch: 0002 train_acc:  0.93595827\n",
      "Epoch: 0003 train_acc:  0.9507365\n",
      "Epoch: 0004 train_acc:  0.9599729\n",
      "Epoch: 0005 train_acc:  0.9618202\n",
      "Test_acc:  0.8909425\n"
     ]
    }
   ],
   "source": [
    "tf.reset_default_graph()\n",
    "model_cora=JKNet([32], 2708, 1433, 7)\n",
    "model_cora.loss(5e-3, active=tf.nn.relu, JK_Model='Bi-LSTM Attention')\n",
    "for epoch in range(5):\n",
    "    avg_cost=0\n",
    "    for i in range(40):\n",
    "        batch_xs, batch_ys, adj=features_cora.reshape(-1),  y_train_cora, adj_norm_cora\n",
    "        avg_cost=model_cora.train(batch_xs, batch_ys, train_mask_cora, avg_cost/100, adj)\n",
    "    train_acc, _=model_cora.accuracy(batch_xs, batch_ys, train_mask_cora, adj)\n",
    "    print('Epoch:','%04d' %(epoch+1),'train_acc: ',train_acc)\n",
    "test_acc, _=model_cora.accuracy(batch_xs, y_test_cora, test_mask_cora, adj)\n",
    "print('Test_acc: ',test_acc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 0001 train_acc:  0.79741204\n",
      "Epoch: 0002 train_acc:  0.86021966\n",
      "Epoch: 0003 train_acc:  0.8793082\n",
      "Epoch: 0004 train_acc:  0.9427316\n",
      "Epoch: 0005 train_acc:  0.951968\n",
      "Test_acc:  0.8909425\n"
     ]
    }
   ],
   "source": [
    "tf.reset_default_graph()\n",
    "model_cora=JKNet([32,32], 2708, 1433, 7)\n",
    "model_cora.loss(5e-3, active=tf.nn.relu, JK_Model='Bi-LSTM Attention')\n",
    "for epoch in range(5):\n",
    "    avg_cost=0\n",
    "    for i in range(40):\n",
    "        batch_xs, batch_ys, adj=features_cora.reshape(-1),  y_train_cora, adj_norm_cora\n",
    "        avg_cost=model_cora.train(batch_xs, batch_ys, train_mask_cora, avg_cost/100, adj)\n",
    "    train_acc, _=model_cora.accuracy(batch_xs, batch_ys, train_mask_cora, adj)\n",
    "    print('Epoch:','%04d' %(epoch+1),'train_acc: ',train_acc)\n",
    "test_acc, _=model_cora.accuracy(batch_xs, y_test_cora, test_mask_cora, adj)\n",
    "print('Test_acc: ',test_acc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 0001 train_acc:  0.84790444\n",
      "Epoch: 0002 train_acc:  0.866993\n",
      "Epoch: 0003 train_acc:  0.93595827\n",
      "Epoch: 0004 train_acc:  0.94519466\n",
      "Epoch: 0005 train_acc:  0.949505\n",
      "Test_acc:  0.8909425\n"
     ]
    }
   ],
   "source": [
    "tf.reset_default_graph()\n",
    "model_cora=JKNet([32,32,32], 2708, 1433, 7)\n",
    "model_cora.loss(5e-3, active=tf.nn.relu, JK_Model='Bi-LSTM Attention')\n",
    "for epoch in range(5):\n",
    "    avg_cost=0\n",
    "    for i in range(40):\n",
    "        batch_xs, batch_ys, adj=features_cora.reshape(-1),  y_train_cora, adj_norm_cora\n",
    "        avg_cost=model_cora.train(batch_xs, batch_ys, train_mask_cora, avg_cost/100, adj)\n",
    "    train_acc, _=model_cora.accuracy(batch_xs, batch_ys, train_mask_cora, adj)\n",
    "    print('Epoch:','%04d' %(epoch+1),'train_acc: ',train_acc)\n",
    "test_acc, _=model_cora.accuracy(batch_xs, y_test_cora, test_mask_cora, adj)\n",
    "print('Test_acc: ',test_acc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 0001 train_acc:  0.83989954\n",
      "Epoch: 0002 train_acc:  0.9137909\n",
      "Epoch: 0003 train_acc:  0.94088435\n",
      "Epoch: 0004 train_acc:  0.95443106\n",
      "Epoch: 0005 train_acc:  0.9587414\n",
      "Test_acc:  0.89463943\n"
     ]
    }
   ],
   "source": [
    "tf.reset_default_graph()\n",
    "model_cora=JKNet([32,32,32,32], 2708, 1433, 7)\n",
    "model_cora.loss(5e-3, active=tf.nn.relu, JK_Model='Bi-LSTM Attention')\n",
    "for epoch in range(5):\n",
    "    avg_cost=0\n",
    "    for i in range(40):\n",
    "        batch_xs, batch_ys, adj=features_cora.reshape(-1),  y_train_cora, adj_norm_cora\n",
    "        avg_cost=model_cora.train(batch_xs, batch_ys, train_mask_cora, avg_cost/100, adj)\n",
    "    train_acc, _=model_cora.accuracy(batch_xs, batch_ys, train_mask_cora, adj)\n",
    "    print('Epoch:','%04d' %(epoch+1),'train_acc: ',train_acc)\n",
    "test_acc, _=model_cora.accuracy(batch_xs, y_test_cora, test_mask_cora, adj)\n",
    "print('Test_acc: ',test_acc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 0001 train_acc:  0.7697028\n",
      "Epoch: 0002 train_acc:  0.9193327\n",
      "Epoch: 0003 train_acc:  0.9390371\n",
      "Epoch: 0004 train_acc:  0.9513523\n",
      "Epoch: 0005 train_acc:  0.95935714\n",
      "Test_acc:  0.8909425\n"
     ]
    }
   ],
   "source": [
    "tf.reset_default_graph()\n",
    "model_cora=JKNet([32,32,32,32,32], 2708, 1433, 7)\n",
    "model_cora.loss(5e-3, active=tf.nn.relu, JK_Model='Bi-LSTM Attention')\n",
    "for epoch in range(5):\n",
    "    avg_cost=0\n",
    "    for i in range(40):\n",
    "        batch_xs, batch_ys, adj=features_cora.reshape(-1),  y_train_cora, adj_norm_cora\n",
    "        avg_cost=model_cora.train(batch_xs, batch_ys, train_mask_cora, avg_cost/100, adj)\n",
    "    train_acc, _=model_cora.accuracy(batch_xs, batch_ys, train_mask_cora, adj)\n",
    "    print('Epoch:','%04d' %(epoch+1),'train_acc: ',train_acc)\n",
    "test_acc, _=model_cora.accuracy(batch_xs, y_test_cora, test_mask_cora, adj)\n",
    "print('Test_acc: ',test_acc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 0001 train_acc:  0.5825115\n",
      "Epoch: 0002 train_acc:  0.7980278\n",
      "Epoch: 0003 train_acc:  0.81280607\n",
      "Epoch: 0004 train_acc:  0.8866974\n",
      "Epoch: 0005 train_acc:  0.9137909\n",
      "Test_acc:  0.87245816\n"
     ]
    }
   ],
   "source": [
    "tf.reset_default_graph()\n",
    "model_cora=JKNet([32,32,32,32,32,32], 2708, 1433, 7)\n",
    "model_cora.loss(5e-3, active=tf.nn.relu, JK_Model='Bi-LSTM Attention')\n",
    "for epoch in range(5):\n",
    "    avg_cost=0\n",
    "    for i in range(40):\n",
    "        batch_xs, batch_ys, adj=features_cora.reshape(-1),  y_train_cora, adj_norm_cora\n",
    "        avg_cost=model_cora.train(batch_xs, batch_ys, train_mask_cora, avg_cost/100, adj)\n",
    "    train_acc, _=model_cora.accuracy(batch_xs, batch_ys, train_mask_cora, adj)\n",
    "    print('Epoch:','%04d' %(epoch+1),'train_acc: ',train_acc)\n",
    "test_acc, _=model_cora.accuracy(batch_xs, y_test_cora, test_mask_cora, adj)\n",
    "print('Test_acc: ',test_acc)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Best performance LSTM Layer : 4\n",
    "### Test Acc : 89.4%"
   ]
  }
 ],
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