DNN for CMPOC_CMVN supervised.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import os\n",
    "import tensorflow as tf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "feat_size = 90\n",
    "context = 5\n",
    "\n",
    "input_x = tf.placeholder(dtype = tf.float32, shape = [None, feat_size*(2*context +1)])\n",
    "output_y = tf.placeholder(dtype = tf.float32, shape = [None, 2])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Tensor(\"Relu:0\", shape=(?, 512), dtype=float32)\n"
     ]
    }
   ],
   "source": [
    "#hidden layer 1\n",
    "\n",
    "hidden_1 = 512\n",
    "hidden_1_weights = tf.get_variable( \"hidden_1_weights\",\n",
    "                                  shape = [ feat_size*(2*context +1), hidden_1],\n",
    "                                  dtype = tf.float32,\n",
    "                                  initializer = tf.contrib.layers.xavier_initializer())\n",
    "\n",
    "hidden_1_bias = tf.get_variable( \"hidden_1_bias\",\n",
    "                                    shape = [hidden_1],\n",
    "                                    dtype = tf.float32,\n",
    "                                   initializer = tf.contrib.layers.xavier_initializer())\n",
    "\n",
    "hidden_1_dnn = tf.matmul( input_x, hidden_1_weights) + hidden_1_bias\n",
    "hidden_1_out = tf.nn.relu(hidden_1_dnn)\n",
    "print(hidden_1_out)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Tensor(\"Relu_1:0\", shape=(?, 512), dtype=float32)\n"
     ]
    }
   ],
   "source": [
    "#hidden layer 2\n",
    "\n",
    "hidden_2 = 512\n",
    "hidden_2_weights = tf.get_variable( \"hidden_2_weights\",\n",
    "                                  shape = [ hidden_1, hidden_2],\n",
    "                                  dtype = tf.float32,\n",
    "                                  initializer = tf.contrib.layers.xavier_initializer())\n",
    "\n",
    "hidden_2_bias = tf.get_variable( \"hidden_2_bias\",\n",
    "                                    shape = [hidden_2],\n",
    "                                    dtype = tf.float32,\n",
    "                                   initializer = tf.contrib.layers.xavier_initializer())\n",
    "\n",
    "hidden_2_dnn = tf.matmul( hidden_1_out, hidden_2_weights) + hidden_2_bias\n",
    "hidden_2_out = tf.nn.relu(hidden_2_dnn)\n",
    "print(hidden_2_out)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Tensor(\"Relu_2:0\", shape=(?, 512), dtype=float32)\n"
     ]
    }
   ],
   "source": [
    "#hidden layer 3\n",
    "\n",
    "hidden_3 = 512\n",
    "hidden_3_weights = tf.get_variable( \"hidden_3_weights\",\n",
    "                                  shape = [ hidden_2, hidden_3],\n",
    "                                  dtype = tf.float32,\n",
    "                                  initializer = tf.contrib.layers.xavier_initializer())\n",
    "\n",
    "hidden_3_bias = tf.get_variable( \"hidden_3_bias\",\n",
    "                                    shape = [hidden_3],\n",
    "                                    dtype = tf.float32,\n",
    "                                 initializer = tf.contrib.layers.xavier_initializer())\n",
    "\n",
    "hidden_3_dnn = tf.matmul( hidden_2_out, hidden_3_weights) + hidden_3_bias\n",
    "hidden_3_out = tf.nn.relu(hidden_3_dnn)\n",
    "print(hidden_3_out)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Tensor(\"Relu_3:0\", shape=(?, 256), dtype=float32)\n"
     ]
    }
   ],
   "source": [
    "#hidden layer 4\n",
    "\n",
    "hidden_4 = 256\n",
    "hidden_4_weights = tf.get_variable( \"hidden_4_weights\",\n",
    "                                  shape = [ hidden_3, hidden_4],\n",
    "                                  dtype = tf.float32,\n",
    "                                  initializer = tf.contrib.layers.xavier_initializer())\n",
    "\n",
    "hidden_4_bias = tf.get_variable( \"hidden_4_bias\",\n",
    "                                    shape = [hidden_4],\n",
    "                                    dtype = tf.float32,\n",
    "                                   initializer = tf.contrib.layers.xavier_initializer())\n",
    "\n",
    "hidden_4_dnn = tf.matmul( hidden_3_out, hidden_4_weights) + hidden_4_bias\n",
    "hidden_4_out = tf.nn.relu(hidden_4_dnn)\n",
    "print(hidden_4_out)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Tensor(\"Relu_4:0\", shape=(?, 256), dtype=float32)\n"
     ]
    }
   ],
   "source": [
    "#hidden layer 5\n",
    "\n",
    "hidden_5 = 256\n",
    "hidden_5_weights = tf.get_variable( \"hidden_5_weights\",\n",
    "                                  shape = [ hidden_4, hidden_5],\n",
    "                                  dtype = tf.float32,\n",
    "                                  initializer = tf.contrib.layers.xavier_initializer())\n",
    "\n",
    "hidden_5_bias = tf.get_variable( \"hidden_5_bias\",\n",
    "                                    shape = [hidden_5],\n",
    "                                    dtype = tf.float32,\n",
    "                                   initializer = tf.contrib.layers.xavier_initializer())\n",
    "\n",
    "hidden_5_dnn = tf.matmul( hidden_4_out, hidden_5_weights) + hidden_5_bias\n",
    "hidden_5_out = tf.nn.relu(hidden_5_dnn)\n",
    "print(hidden_5_out)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Tensor(\"Softmax:0\", shape=(?, 2), dtype=float32)\n"
     ]
    }
   ],
   "source": [
    "#output layer \n",
    "\n",
    "output_dim = 2\n",
    "output_layer_weights = tf.get_variable(\"output_layer_weights\",\n",
    "                                      shape = [hidden_5 ,output_dim],\n",
    "                                      dtype = tf.float32,\n",
    "                                      initializer = tf.contrib.layers.xavier_initializer())\n",
    "output_layer_bias = tf.get_variable(\"output_layer_bias\",\n",
    "                                   shape = [output_dim],\n",
    "                                   dtype = tf.float32,\n",
    "                                   initializer = tf.contrib.layers.xavier_initializer())\n",
    "\n",
    "output_layer_sum = tf.matmul(hidden_5_out, output_layer_weights) + output_layer_bias\n",
    "final_output = tf.nn.softmax(output_layer_sum)\n",
    "print(final_output)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "#losses and optimizers\n",
    "\n",
    "cross_entropy = tf.reduce_mean( tf.nn.softmax_cross_entropy_with_logits(labels = output_y, logits = output_layer_sum))\n",
    "optimizer = tf.train.AdamOptimizer(1e-2)\n",
    "train_step = optimizer.minimize(cross_entropy)\n",
    "\n",
    "correct_predictions = tf.equal(tf.argmax(final_output,1),tf.argmax(output_y,1))\n",
    "accuracy = tf.reduce_mean(tf.cast(correct_predictions,tf.float32))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# initializing tensors\n",
    "\n",
    "session = tf.InteractiveSession()\n",
    "\n",
    "init = tf.global_variables_initializer()\n",
    "saver = tf.train.Saver(max_to_keep = None)\n",
    "\n",
    "features_path = 'C:\\\\Users\\\\HIMANSHU\\\\Desktop\\\\Aditya\\\\ASVspoof2017_CMPOC_CMVN\\\\contextData_90D'\n",
    "dev_features_path = 'C:\\\\Users\\\\HIMANSHU\\\\Desktop\\\\Aditya\\\\ASVspoof2017_CMPOC_CMVN\\\\devcontextData_90D'\n",
    "#eval_features_path = 'C:\\\\Users\\\\HIMANSHU\\\\Desktop\\\\Aditya\\\\ASVspoof2017_CMPOC_CMVN\\\\evalcontextData'\n",
    "model_path = 'C:\\\\Users\\\\HIMANSHU\\\\Desktop\\\\Aditya\\\\ASVspoof2017_CMPOC_CMVN\\\\Models_90D'\n",
    "\n",
    "dev_sp_path = 'C:\\\\Users\\\\HIMANSHU\\\\Desktop\\\\Aditya\\\\ASVspoof2017_CMPOC_CMVN\\\\Dev_sp_90D'\n",
    "#eval_sp_path = 'C:\\\\Users\\\\HIMANSHU\\\\Desktop\\\\Aditya\\\\ASVspoof2017_CMPOC_CMVN\\\\Eval_sp'\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "def cal_eer(scores_true, scores_false):\n",
    "    \n",
    "    \"\"\" Calculate EER\n",
    "        Args:\n",
    "            scores_true: A score list of positive samples\n",
    "            scores_false: A score list of negative samples\n",
    "        Return:\n",
    "            (EER, threshold)\n",
    "        Note:\n",
    "            Here, the threshold is set, and if the score is higher than or equal to the threshold,\n",
    "            the decision is passed, and the decision is rejected as below.\n",
    "    \"\"\"\n",
    "    min1 = min(scores_true)\n",
    "    min2 = min(scores_false)\n",
    "    low = min(min1, min2)\n",
    "    max1 = max(scores_true)\n",
    "    max2 = max(scores_false)\n",
    "    high = max(max1, max2)\n",
    "    FAR = -55\n",
    "    FRR = -55\n",
    "    mid = -100\n",
    "    # Bisection find threshold\n",
    "    while True:\n",
    "        bef_FAR = FAR \n",
    "        bef_FRR = FRR\n",
    "        bef_mid = mid\n",
    "        mid = (high + low) / 2\n",
    "        FRR = sum([1 for s in scores_true if s < mid]) / len(scores_true)\n",
    "        FAR = sum([1 for s in scores_false if s >= mid]) / len(scores_false)\n",
    "        #print(\"FRR  \",FRR,\" FAR   \",FAR,\"   mid   \",mid)\n",
    "\n",
    "        if abs(FRR - FAR) < 1e-7:\n",
    "            print(\"FRR  \",FRR,\" FAR   \",FAR,\"   mid   \",mid)\n",
    "            break\n",
    "\n",
    "        if FRR < FAR:\n",
    "            low = mid\n",
    "        else:\n",
    "            high = mid\n",
    "            \n",
    "        if bef_FAR == FAR :\n",
    "            if bef_FRR == FRR :\n",
    "                if bef_mid == mid :\n",
    "                    print(\"FRR  \",FRR,\" FAR   \",FAR,\"   mid   \",mid)\n",
    "                    break\n",
    "\n",
    "    EER  =  FRR\n",
    "    return EER, (high + low) / 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "from scipy.io import savemat\n",
    "\n",
    "def get_EER(test_path, index, sp_path ):\n",
    "    scores = []\n",
    "    development_labels = []\n",
    "    probabilities = []\n",
    "\n",
    "    #test_path = \"C:\\\\Users\\\\project1\\\\Desktop\\\\Siva\\\\HT_IFCC_hemlata\\\\CNN_input_testing_dev\"\n",
    "    indexs = (len(list(os.listdir(test_path))))//2\n",
    "    \n",
    "    for folders in range(1, indexs+1):\n",
    "        features = np.load(os.path.join(test_path, \"features\"+str(folders)+\".npy\"))\n",
    "        labels = np.load(os.path.join(test_path, \"labels\"+str(folders)+\".npy\"))\n",
    "        patches = labels.shape[0]\n",
    "        patch_prob = session.run(final_output, feed_dict = {input_x:features})\n",
    "        final_prob = np.zeros([2])\n",
    "        \n",
    "        for i in range(patches):\n",
    "            if(patch_prob[i][0] == 0):\n",
    "                patch_prob[i][0] = 1e-25\n",
    "            if(patch_prob[i][1] == 0):\n",
    "                patch_prob[i][1] = 1e-25\n",
    "                \n",
    "            final_prob[0] = final_prob[0] + np.log(patch_prob[i][0])\n",
    "            final_prob[1] = final_prob[1] + np.log(patch_prob[i][1])\n",
    "\n",
    "        final_prob = final_prob / patches\n",
    "        score = (final_prob[0]) - (final_prob[1])\n",
    "        audio_label = labels[0]\n",
    "\n",
    "        scores.append(score)\n",
    "        probabilities.append( np.mean(patch_prob, axis=0) )\n",
    "        development_labels.append( audio_label )\n",
    "\n",
    "    scores = np.array(scores)\n",
    "    development_labels = np.array(development_labels)\n",
    "    probabilities = np.array(probabilities)\n",
    "\n",
    "    scores_geniue = []\n",
    "    scores_spoof = []\n",
    "    prob_genuine = []\n",
    "    prob_spoof = []\n",
    "\n",
    "    for i in range(development_labels.shape[0]):\n",
    "        if development_labels[i][0] == 1 :\n",
    "            scores_geniue.append(scores[i])\n",
    "            prob_genuine.append(probabilities[i])\n",
    "        elif development_labels[i][1] == 1:\n",
    "            scores_spoof.append(scores[i])\n",
    "            prob_spoof.append(probabilities[i])\n",
    "\n",
    "    scores_geniue = np.array(scores_geniue)\n",
    "    scores_spoof = np.array(scores_spoof)\n",
    "    prob_genuine = np.array(prob_genuine)\n",
    "    prob_spoof = np.array(prob_spoof)\n",
    "    \n",
    "    a,b = cal_eer(scores_geniue, scores_spoof)\n",
    "    \n",
    "    score_path = os.path.join(sp_path, \"scores\")\n",
    "    prob_path = os.path.join(sp_path, \"probabilities\")\n",
    "    \n",
    "    if not os.path.exists(score_path):\n",
    "        os.makedirs(score_path)\n",
    "        \n",
    "    if not os.path.exists(prob_path):\n",
    "        os.makedirs(prob_path)\n",
    "        \n",
    "    prob_file = \"probabilities_\"+str(index)\n",
    "    sc_file = \"scores_\"+str(index)\n",
    "    savemat(os.path.join(score_path, sc_file), { 'genuine':scores_geniue, 'spoof':scores_spoof, 'eer' : a*100 })\n",
    "    savemat(os.path.join(prob_path, prob_file), { 'genuine':prob_genuine, 'spoof':prob_spoof })\n",
    "    \n",
    "    return a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:C:\\Users\\HIMANSHU\\Desktop\\Aditya\\ASVspoof2017_CMPOC_CMVN\\Models_90D\\Model1 is not in all_model_checkpoint_paths. Manually adding it.\n",
      "iteration   1\n",
      "FRR   0.15921052631578947  FAR    0.16    mid    0.439770764611\n",
      " develop eer   0.15921052631578947\n",
      "\n",
      "INFO:tensorflow:C:\\Users\\HIMANSHU\\Desktop\\Aditya\\ASVspoof2017_CMPOC_CMVN\\Models_90D\\Model2 is not in all_model_checkpoint_paths. Manually adding it.\n",
      "iteration   2\n",
      "FRR   0.15921052631578947  FAR    0.15894736842105264    mid    0.84233767562\n",
      " develop eer   0.15921052631578947\n",
      "\n",
      "INFO:tensorflow:C:\\Users\\HIMANSHU\\Desktop\\Aditya\\ASVspoof2017_CMPOC_CMVN\\Models_90D\\Model3 is not in all_model_checkpoint_paths. Manually adding it.\n",
      "iteration   3\n",
      "FRR   0.14210526315789473  FAR    0.14210526315789473    mid    0.428668602799\n",
      " develop eer   0.14210526315789473\n",
      "\n",
      "INFO:tensorflow:C:\\Users\\HIMANSHU\\Desktop\\Aditya\\ASVspoof2017_CMPOC_CMVN\\Models_90D\\Model4 is not in all_model_checkpoint_paths. Manually adding it.\n",
      "iteration   4\n",
      "FRR   0.16052631578947368  FAR    0.16    mid    0.826277007032\n",
      " develop eer   0.16052631578947368\n",
      "\n",
      "INFO:tensorflow:C:\\Users\\HIMANSHU\\Desktop\\Aditya\\ASVspoof2017_CMPOC_CMVN\\Models_90D\\Model5 is not in all_model_checkpoint_paths. Manually adding it.\n",
      "iteration   5\n",
      "FRR   0.15789473684210525  FAR    0.15894736842105264    mid    0.955334887412\n",
      " develop eer   0.15789473684210525\n",
      "\n",
      "INFO:tensorflow:C:\\Users\\HIMANSHU\\Desktop\\Aditya\\ASVspoof2017_CMPOC_CMVN\\Models_90D\\Model6 is not in all_model_checkpoint_paths. Manually adding it.\n",
      "iteration   6\n",
      "FRR   0.14736842105263157  FAR    0.14736842105263157    mid    0.218046440305\n",
      " develop eer   0.14736842105263157\n",
      "\n",
      "INFO:tensorflow:C:\\Users\\HIMANSHU\\Desktop\\Aditya\\ASVspoof2017_CMPOC_CMVN\\Models_90D\\Model7 is not in all_model_checkpoint_paths. Manually adding it.\n",
      "iteration   7\n",
      "FRR   0.15526315789473685  FAR    0.15578947368421053    mid    0.593224293444\n",
      " develop eer   0.15526315789473685\n",
      "\n",
      "INFO:tensorflow:C:\\Users\\HIMANSHU\\Desktop\\Aditya\\ASVspoof2017_CMPOC_CMVN\\Models_90D\\Model8 is not in all_model_checkpoint_paths. Manually adding it.\n",
      "iteration   8\n",
      "FRR   0.15263157894736842  FAR    0.15263157894736842    mid    0.51939929218\n",
      " develop eer   0.15263157894736842\n",
      "\n",
      "INFO:tensorflow:C:\\Users\\HIMANSHU\\Desktop\\Aditya\\ASVspoof2017_CMPOC_CMVN\\Models_90D\\Model9 is not in all_model_checkpoint_paths. Manually adding it.\n",
      "iteration   9\n",
      "FRR   0.17105263157894737  FAR    0.17157894736842105    mid    0.476884858982\n",
      " develop eer   0.17105263157894737\n",
      "\n",
      "INFO:tensorflow:C:\\Users\\HIMANSHU\\Desktop\\Aditya\\ASVspoof2017_CMPOC_CMVN\\Models_90D\\Model10 is not in all_model_checkpoint_paths. Manually adding it.\n",
      "iteration   10\n",
      "FRR   0.18421052631578946  FAR    0.18421052631578946    mid    0.416439459752\n",
      " develop eer   0.18421052631578946\n",
      "\n",
      "INFO:tensorflow:C:\\Users\\HIMANSHU\\Desktop\\Aditya\\ASVspoof2017_CMPOC_CMVN\\Models_90D\\Model11 is not in all_model_checkpoint_paths. Manually adding it.\n",
      "iteration   11\n",
      "FRR   0.16973684210526316  FAR    0.1705263157894737    mid    0.21617128443\n",
      " develop eer   0.16973684210526316\n",
      "\n",
      "INFO:tensorflow:C:\\Users\\HIMANSHU\\Desktop\\Aditya\\ASVspoof2017_CMPOC_CMVN\\Models_90D\\Model12 is not in all_model_checkpoint_paths. Manually adding it.\n",
      "iteration   12\n",
      "FRR   0.16842105263157894  FAR    0.16842105263157894    mid    0.121963888882\n",
      " develop eer   0.16842105263157894\n",
      "\n",
      "INFO:tensorflow:C:\\Users\\HIMANSHU\\Desktop\\Aditya\\ASVspoof2017_CMPOC_CMVN\\Models_90D\\Model13 is not in all_model_checkpoint_paths. Manually adding it.\n",
      "iteration   13\n",
      "FRR   0.15263157894736842  FAR    0.15263157894736842    mid    0.164923517197\n",
      " develop eer   0.15263157894736842\n",
      "\n",
      "INFO:tensorflow:C:\\Users\\HIMANSHU\\Desktop\\Aditya\\ASVspoof2017_CMPOC_CMVN\\Models_90D\\Model14 is not in all_model_checkpoint_paths. Manually adding it.\n",
      "iteration   14\n",
      "FRR   0.1736842105263158  FAR    0.17473684210526316    mid    0.1160943979\n",
      " develop eer   0.1736842105263158\n",
      "\n",
      "INFO:tensorflow:C:\\Users\\HIMANSHU\\Desktop\\Aditya\\ASVspoof2017_CMPOC_CMVN\\Models_90D\\Model15 is not in all_model_checkpoint_paths. Manually adding it.\n",
      "iteration   15\n",
      "FRR   0.1631578947368421  FAR    0.1631578947368421    mid    0.196458798941\n",
      " develop eer   0.1631578947368421\n",
      "\n",
      "INFO:tensorflow:C:\\Users\\HIMANSHU\\Desktop\\Aditya\\ASVspoof2017_CMPOC_CMVN\\Models_90D\\Model16 is not in all_model_checkpoint_paths. Manually adding it.\n",
      "iteration   16\n",
      "FRR   0.16973684210526316  FAR    0.1705263157894737    mid    0.190162068289\n",
      " develop eer   0.16973684210526316\n",
      "\n",
      "INFO:tensorflow:C:\\Users\\HIMANSHU\\Desktop\\Aditya\\ASVspoof2017_CMPOC_CMVN\\Models_90D\\Model17 is not in all_model_checkpoint_paths. Manually adding it.\n",
      "iteration   17\n",
      "FRR   0.16710526315789473  FAR    0.16736842105263158    mid    0.123861076187\n",
      " develop eer   0.16710526315789473\n",
      "\n",
      "INFO:tensorflow:C:\\Users\\HIMANSHU\\Desktop\\Aditya\\ASVspoof2017_CMPOC_CMVN\\Models_90D\\Model18 is not in all_model_checkpoint_paths. Manually adding it.\n",
      "iteration   18\n",
      "FRR   0.16710526315789473  FAR    0.16736842105263158    mid    0.0628989588746\n",
      " develop eer   0.16710526315789473\n",
      "\n",
      "INFO:tensorflow:C:\\Users\\HIMANSHU\\Desktop\\Aditya\\ASVspoof2017_CMPOC_CMVN\\Models_90D\\Model19 is not in all_model_checkpoint_paths. Manually adding it.\n",
      "iteration   19\n",
      "FRR   0.16447368421052633  FAR    0.16526315789473683    mid    0.197719293636\n",
      " develop eer   0.16447368421052633\n",
      "\n",
      "INFO:tensorflow:C:\\Users\\HIMANSHU\\Desktop\\Aditya\\ASVspoof2017_CMPOC_CMVN\\Models_90D\\Model20 is not in all_model_checkpoint_paths. Manually adding it.\n",
      "iteration   20\n",
      "FRR   0.15789473684210525  FAR    0.15894736842105264    mid    0.221031911002\n",
      " develop eer   0.15789473684210525\n",
      "\n",
      "INFO:tensorflow:C:\\Users\\HIMANSHU\\Desktop\\Aditya\\ASVspoof2017_CMPOC_CMVN\\Models_90D\\Model21 is not in all_model_checkpoint_paths. Manually adding it.\n",
      "iteration   21\n",
      "FRR   0.15657894736842104  FAR    0.1568421052631579    mid    0.18404543824\n",
      " develop eer   0.15657894736842104\n",
      "\n",
      "INFO:tensorflow:C:\\Users\\HIMANSHU\\Desktop\\Aditya\\ASVspoof2017_CMPOC_CMVN\\Models_90D\\Model22 is not in all_model_checkpoint_paths. Manually adding it.\n",
      "iteration   22\n",
      "FRR   0.16052631578947368  FAR    0.16105263157894736    mid    0.106826923916\n",
      " develop eer   0.16052631578947368\n",
      "\n",
      "INFO:tensorflow:C:\\Users\\HIMANSHU\\Desktop\\Aditya\\ASVspoof2017_CMPOC_CMVN\\Models_90D\\Model23 is not in all_model_checkpoint_paths. Manually adding it.\n",
      "iteration   23\n",
      "FRR   0.18026315789473685  FAR    0.18105263157894738    mid    0.115544763315\n",
      " develop eer   0.18026315789473685\n",
      "\n",
      "INFO:tensorflow:C:\\Users\\HIMANSHU\\Desktop\\Aditya\\ASVspoof2017_CMPOC_CMVN\\Models_90D\\Model24 is not in all_model_checkpoint_paths. Manually adding it.\n",
      "iteration   24\n",
      "FRR   0.18552631578947368  FAR    0.1863157894736842    mid    0.159562543536\n",
      " develop eer   0.18552631578947368\n",
      "\n",
      "INFO:tensorflow:C:\\Users\\HIMANSHU\\Desktop\\Aditya\\ASVspoof2017_CMPOC_CMVN\\Models_90D\\Model25 is not in all_model_checkpoint_paths. Manually adding it.\n",
      "iteration   25\n",
      "FRR   0.25394736842105264  FAR    0.25473684210526315    mid    0.221301056357\n",
      " develop eer   0.25394736842105264\n",
      "\n",
      "INFO:tensorflow:C:\\Users\\HIMANSHU\\Desktop\\Aditya\\ASVspoof2017_CMPOC_CMVN\\Models_90D\\Model26 is not in all_model_checkpoint_paths. Manually adding it.\n",
      "iteration   26\n",
      "FRR   0.19078947368421054  FAR    0.19263157894736843    mid    0.131007703081\n",
      " develop eer   0.19078947368421054\n",
      "\n",
      "INFO:tensorflow:C:\\Users\\HIMANSHU\\Desktop\\Aditya\\ASVspoof2017_CMPOC_CMVN\\Models_90D\\Model27 is not in all_model_checkpoint_paths. Manually adding it.\n",
      "iteration   27\n",
      "FRR   0.18157894736842106  FAR    0.18210526315789474    mid    0.165309530824\n",
      " develop eer   0.18157894736842106\n",
      "\n",
      "INFO:tensorflow:C:\\Users\\HIMANSHU\\Desktop\\Aditya\\ASVspoof2017_CMPOC_CMVN\\Models_90D\\Model28 is not in all_model_checkpoint_paths. Manually adding it.\n",
      "iteration   28\n",
      "FRR   0.18947368421052632  FAR    0.18947368421052632    mid    0.182279027196\n",
      " develop eer   0.18947368421052632\n",
      "\n",
      "INFO:tensorflow:C:\\Users\\HIMANSHU\\Desktop\\Aditya\\ASVspoof2017_CMPOC_CMVN\\Models_90D\\Model29 is not in all_model_checkpoint_paths. Manually adding it.\n",
      "iteration   29\n",
      "FRR   0.20789473684210527  FAR    0.20842105263157895    mid    0.144590313714\n",
      " develop eer   0.20789473684210527\n",
      "\n",
      "INFO:tensorflow:C:\\Users\\HIMANSHU\\Desktop\\Aditya\\ASVspoof2017_CMPOC_CMVN\\Models_90D\\Model30 is not in all_model_checkpoint_paths. Manually adding it.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "iteration   30\n",
      "FRR   0.2565789473684211  FAR    0.25684210526315787    mid    0.138122451993\n",
      " develop eer   0.2565789473684211\n",
      "\n",
      "INFO:tensorflow:C:\\Users\\HIMANSHU\\Desktop\\Aditya\\ASVspoof2017_CMPOC_CMVN\\Models_90D\\Model31 is not in all_model_checkpoint_paths. Manually adding it.\n",
      "iteration   31\n",
      "FRR   0.2  FAR    0.20105263157894737    mid    0.150610314101\n",
      " develop eer   0.2\n",
      "\n",
      "INFO:tensorflow:C:\\Users\\HIMANSHU\\Desktop\\Aditya\\ASVspoof2017_CMPOC_CMVN\\Models_90D\\Model32 is not in all_model_checkpoint_paths. Manually adding it.\n",
      "iteration   32\n",
      "FRR   0.2513157894736842  FAR    0.2536842105263158    mid    0.159382888528\n",
      " develop eer   0.2513157894736842\n",
      "\n",
      "INFO:tensorflow:C:\\Users\\HIMANSHU\\Desktop\\Aditya\\ASVspoof2017_CMPOC_CMVN\\Models_90D\\Model33 is not in all_model_checkpoint_paths. Manually adding it.\n",
      "iteration   33\n",
      "FRR   0.18947368421052632  FAR    0.18947368421052632    mid    0.131788776078\n",
      " develop eer   0.18947368421052632\n",
      "\n",
      "INFO:tensorflow:C:\\Users\\HIMANSHU\\Desktop\\Aditya\\ASVspoof2017_CMPOC_CMVN\\Models_90D\\Model34 is not in all_model_checkpoint_paths. Manually adding it.\n",
      "iteration   34\n",
      "FRR   0.1763157894736842  FAR    0.17684210526315788    mid    0.143979654007\n",
      " develop eer   0.1763157894736842\n",
      "\n",
      "INFO:tensorflow:C:\\Users\\HIMANSHU\\Desktop\\Aditya\\ASVspoof2017_CMPOC_CMVN\\Models_90D\\Model35 is not in all_model_checkpoint_paths. Manually adding it.\n",
      "iteration   35\n",
      "FRR   0.16447368421052633  FAR    0.16526315789473683    mid    0.129596659225\n",
      " develop eer   0.16447368421052633\n",
      "\n",
      "INFO:tensorflow:C:\\Users\\HIMANSHU\\Desktop\\Aditya\\ASVspoof2017_CMPOC_CMVN\\Models_90D\\Model36 is not in all_model_checkpoint_paths. Manually adding it.\n",
      "iteration   36\n",
      "FRR   0.2631578947368421  FAR    0.2631578947368421    mid    0.147092615069\n",
      " develop eer   0.2631578947368421\n",
      "\n",
      "INFO:tensorflow:C:\\Users\\HIMANSHU\\Desktop\\Aditya\\ASVspoof2017_CMPOC_CMVN\\Models_90D\\Model37 is not in all_model_checkpoint_paths. Manually adding it.\n",
      "iteration   37\n",
      "FRR   0.18421052631578946  FAR    0.18421052631578946    mid    0.15059252254\n",
      " develop eer   0.18421052631578946\n",
      "\n",
      "INFO:tensorflow:C:\\Users\\HIMANSHU\\Desktop\\Aditya\\ASVspoof2017_CMPOC_CMVN\\Models_90D\\Model38 is not in all_model_checkpoint_paths. Manually adding it.\n",
      "iteration   38\n",
      "FRR   0.18289473684210528  FAR    0.1831578947368421    mid    0.163270453254\n",
      " develop eer   0.18289473684210528\n",
      "\n",
      "INFO:tensorflow:C:\\Users\\HIMANSHU\\Desktop\\Aditya\\ASVspoof2017_CMPOC_CMVN\\Models_90D\\Model39 is not in all_model_checkpoint_paths. Manually adding it.\n",
      "iteration   39\n",
      "FRR   0.20394736842105263  FAR    0.20421052631578948    mid    0.0829301929759\n",
      " develop eer   0.20394736842105263\n",
      "\n",
      "INFO:tensorflow:C:\\Users\\HIMANSHU\\Desktop\\Aditya\\ASVspoof2017_CMPOC_CMVN\\Models_90D\\Model40 is not in all_model_checkpoint_paths. Manually adding it.\n",
      "iteration   40\n",
      "FRR   0.23026315789473684  FAR    0.2305263157894737    mid    0.155123229908\n",
      " develop eer   0.23026315789473684\n",
      "\n",
      "INFO:tensorflow:C:\\Users\\HIMANSHU\\Desktop\\Aditya\\ASVspoof2017_CMPOC_CMVN\\Models_90D\\Model41 is not in all_model_checkpoint_paths. Manually adding it.\n",
      "iteration   41\n",
      "FRR   0.21973684210526315  FAR    0.22    mid    0.158340816612\n",
      " develop eer   0.21973684210526315\n",
      "\n",
      "INFO:tensorflow:C:\\Users\\HIMANSHU\\Desktop\\Aditya\\ASVspoof2017_CMPOC_CMVN\\Models_90D\\Model42 is not in all_model_checkpoint_paths. Manually adding it.\n",
      "iteration   42\n",
      "FRR   0.18157894736842106  FAR    0.18210526315789474    mid    0.15229977822\n",
      " develop eer   0.18157894736842106\n",
      "\n",
      "INFO:tensorflow:C:\\Users\\HIMANSHU\\Desktop\\Aditya\\ASVspoof2017_CMPOC_CMVN\\Models_90D\\Model43 is not in all_model_checkpoint_paths. Manually adding it.\n",
      "iteration   43\n",
      "FRR   0.19210526315789472  FAR    0.19263157894736843    mid    0.137694987702\n",
      " develop eer   0.19210526315789472\n",
      "\n",
      "INFO:tensorflow:C:\\Users\\HIMANSHU\\Desktop\\Aditya\\ASVspoof2017_CMPOC_CMVN\\Models_90D\\Model44 is not in all_model_checkpoint_paths. Manually adding it.\n",
      "iteration   44\n",
      "FRR   0.20394736842105263  FAR    0.20421052631578948    mid    0.206757048766\n",
      " develop eer   0.20394736842105263\n",
      "\n",
      "INFO:tensorflow:C:\\Users\\HIMANSHU\\Desktop\\Aditya\\ASVspoof2017_CMPOC_CMVN\\Models_90D\\Model45 is not in all_model_checkpoint_paths. Manually adding it.\n",
      "iteration   45\n",
      "FRR   0.19473684210526315  FAR    0.19473684210526315    mid    0.159871546587\n",
      " develop eer   0.19473684210526315\n",
      "\n",
      "INFO:tensorflow:C:\\Users\\HIMANSHU\\Desktop\\Aditya\\ASVspoof2017_CMPOC_CMVN\\Models_90D\\Model46 is not in all_model_checkpoint_paths. Manually adding it.\n",
      "iteration   46\n",
      "FRR   0.2513157894736842  FAR    0.25157894736842107    mid    0.213177886502\n",
      " develop eer   0.2513157894736842\n",
      "\n",
      "INFO:tensorflow:C:\\Users\\HIMANSHU\\Desktop\\Aditya\\ASVspoof2017_CMPOC_CMVN\\Models_90D\\Model47 is not in all_model_checkpoint_paths. Manually adding it.\n",
      "iteration   47\n",
      "FRR   0.18421052631578946  FAR    0.18526315789473685    mid    0.123981787199\n",
      " develop eer   0.18421052631578946\n",
      "\n",
      "INFO:tensorflow:C:\\Users\\HIMANSHU\\Desktop\\Aditya\\ASVspoof2017_CMPOC_CMVN\\Models_90D\\Model48 is not in all_model_checkpoint_paths. Manually adding it.\n",
      "iteration   48\n",
      "FRR   0.20921052631578949  FAR    0.2094736842105263    mid    0.149216958317\n",
      " develop eer   0.20921052631578949\n",
      "\n",
      "INFO:tensorflow:C:\\Users\\HIMANSHU\\Desktop\\Aditya\\ASVspoof2017_CMPOC_CMVN\\Models_90D\\Model49 is not in all_model_checkpoint_paths. Manually adding it.\n",
      "iteration   49\n",
      "FRR   0.17894736842105263  FAR    0.18    mid    0.118595670771\n",
      " develop eer   0.17894736842105263\n",
      "\n",
      "INFO:tensorflow:C:\\Users\\HIMANSHU\\Desktop\\Aditya\\ASVspoof2017_CMPOC_CMVN\\Models_90D\\Model50 is not in all_model_checkpoint_paths. Manually adding it.\n",
      "iteration   50\n",
      "FRR   0.1868421052631579  FAR    0.18736842105263157    mid    0.175408389725\n",
      " develop eer   0.1868421052631579\n",
      "\n"
     ]
    }
   ],
   "source": [
    "#Training and testing accuracy\n",
    "session.run(init)\n",
    "\n",
    "if not os.path.exists(model_path):\n",
    "    os.makedirs(model_path)\n",
    "    \n",
    "if not os.path.exists(dev_sp_path):\n",
    "    os.makedirs(dev_sp_path)\n",
    "    \n",
    "batches = (len(list(os.listdir(features_path))))//2\n",
    "    \n",
    "for i in range(0,50):\n",
    "    for j in range(1,batches+1):  #number of batches\n",
    "        filename = \"batch_\"+str(j)+\".npy\"\n",
    "        labelsname= \"label_\"+str(j)+\".npy\"\n",
    "        \n",
    "        features = np.load(os.path.join(features_path, filename))\n",
    "        labels = np.load(os.path.join(features_path, labelsname))\n",
    "        \n",
    "        #if(np.sum(np.isnan(features)) > 0):\n",
    "        #    print(np.sum(np.isnan(features)))\n",
    "        \n",
    "        train_step.run(session = session,feed_dict={ input_x:features, output_y:labels})\n",
    "        \n",
    "    save_path = saver.save(session,os.path.join(model_path,\"Model\"+str(i+1)))\n",
    "    \n",
    "    print(\"iteration  \",(i+1))\n",
    "    eer_dev = get_EER(dev_features_path, (i+1), dev_sp_path)\n",
    "    print(\" develop eer  \",eer_dev)\n",
    "    print()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "h1 = session.run(hidden_layer1_weights)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.sum(np.isnan(h1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "eer_dev = get_EER(dev_features_path, (i+1), dev_sp_path)\n",
    "print(\" develop eer  \",eer_dev)\n",
    "print()"
   ]
  }
 ],
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