CNN_RNN_3c1mp_2blstm_3d.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import numpy as np\n",
    "import tensorflow as tf\n",
    "from scipy.io import loadmat,savemat\n",
    "\n",
    "feature_dimension = 36\n",
    "frames = 100\n",
    "fc_neurons = 256\n",
    "tf.set_random_seed(3456)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(?, 36, 100)\n"
     ]
    }
   ],
   "source": [
    "x = tf.placeholder(shape = [None, feature_dimension, frames], dtype = tf.float32) #inputs\n",
    "print(x.shape)\n",
    "y = tf.placeholder(shape = [None, 2], dtype = tf.float32) #labels\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Tensor(\"Reshape:0\", shape=(?, 60, 100, 1), dtype=float32)\n",
      "Tensor(\"Relu:0\", shape=(?, 29, 49, 256), dtype=float32)\n"
     ]
    }
   ],
   "source": [
    "#conv layer 1\n",
    "weights_conv1 = tf.get_variable(\"conv1_weights\",\n",
    "                                shape = [3,3,1,256],  \n",
    "                                dtype = tf.float32,\n",
    "                               initializer = tf.contrib.layers.xavier_initializer())\n",
    "bias_conv1 = tf.get_variable(\"conv1_bias\",\n",
    "                             shape = [256],\n",
    "                              dtype = tf.float32,\n",
    "                              initializer = tf.contrib.layers.xavier_initializer())\n",
    "\n",
    "#reshaping of input(batch_size, feature_width, feature_height,input_channels)\n",
    "input_x = tf.reshape(x,[-1,feature_dimension, frames,1])\n",
    "print(input_x)\n",
    "\n",
    "conv1 = tf.nn.conv2d(input_x, weights_conv1, strides = [1,1,1,1], padding = 'VALID')\n",
    "conv1_out = tf.nn.relu(conv1)\n",
    "print(conv1_out)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Tensor(\"Relu_1:0\", shape=(?, 14, 24, 128), dtype=float32)\n"
     ]
    }
   ],
   "source": [
    "# conv layer 2\n",
    "weights_conv2 = tf.get_variable(\"conv2_weights\",\n",
    "                               shape = [3,3,256,128],\n",
    "                               dtype = tf.float32,\n",
    "                                initializer = tf.contrib.layers.xavier_initializer())\n",
    "bias_conv2 = tf.get_variable(\"conv2_bias\",\n",
    "                             shape = [128],\n",
    "                            dtype = tf.float32,\n",
    "                            initializer = tf.contrib.layers.xavier_initializer())\n",
    "\n",
    "conv2 = tf.nn.conv2d(conv1_out,weights_conv2, strides = [1,2,2,1], padding = 'VALID')\n",
    "conv2_out = tf.nn.relu(conv2)\n",
    "print(conv2_out)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# conv layer 3\n",
    "weights_conv3 = tf.get_variable(\"conv3_weights\",\n",
    "                               shape = [5,5,128,128],\n",
    "                               dtype = tf.float32,\n",
    "                                initializer = tf.contrib.layers.xavier_initializer())\n",
    "bias_conv3 = tf.get_variable(\"conv3_bias\",\n",
    "                             shape = [128],\n",
    "                            dtype = tf.float32,\n",
    "                            initializer = tf.contrib.layers.xavier_initializer())\n",
    "\n",
    "conv3 = tf.nn.conv2d(conv2_out,weights_conv3, strides = [1,2,2,1], padding = 'VALID')\n",
    "conv3_out = tf.nn.relu(conv3)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Tensor(\"max_pooling2d/MaxPool:0\", shape=(?, 2, 7, 128), dtype=float32)\n"
     ]
    }
   ],
   "source": [
    "#max pool1\n",
    "pool1_out = tf.layers.max_pooling2d(conv3_out,pool_size = [4,4], strides = [1,1], padding = 'VALID')\n",
    "print(pool1_out)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "#unstacking the input\n",
    "reshape_input = tf.reshape(conv3_out,[-1,int(conv3_out.shape[1])*int(conv3_out.shape[2]),int(conv3_out.shape[3])])\n",
    "input_lstm = tf.unstack(reshape_input,reshape_input.shape[2],2)\n",
    "#print(input_lstm)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "lstm_fw1 = tf.contrib.rnn.LSTMCell( 64, use_peepholes = True,\n",
    "                    initializer = tf.contrib.layers.xavier_initializer(uniform=True, seed=3456, dtype=tf.float32))\n",
    "lstm_fw2 = tf.contrib.rnn.LSTMCell( 64, use_peepholes = True,\n",
    "                    initializer = tf.contrib.layers.xavier_initializer(uniform=True, seed=3457, dtype=tf.float32))\n",
    "\n",
    "lstm_fw = tf.contrib.rnn.MultiRNNCell((lstm_fw1, lstm_fw2))\n",
    "\n",
    "\n",
    "lstm_back1 = tf.contrib.rnn.LSTMCell( 64, use_peepholes = True,\n",
    "                    initializer = tf.contrib.layers.xavier_initializer(uniform=True, seed=3458, dtype=tf.float32))\n",
    "lstm_back2 = tf.contrib.rnn.LSTMCell( 64, use_peepholes = True,\n",
    "                    initializer = tf.contrib.layers.xavier_initializer(uniform=True, seed=3459, dtype=tf.float32))\n",
    "\n",
    "lstm_back = tf.contrib.rnn.MultiRNNCell((lstm_back1, lstm_back2))\n",
    "\n",
    "blstm_out, state_fw, state_bw = tf.contrib.rnn.stack_bidirectional_rnn( [lstm_fw], [lstm_back], input_lstm, dtype = tf.float32)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Tensor(\"Relu_3:0\", shape=(?, 512), dtype=float32)\n"
     ]
    }
   ],
   "source": [
    "#print(blstm_out[-1])\n",
    "#dense layer 1\n",
    "\n",
    "dense_1 = 512\n",
    "dense_1_weights = tf.get_variable( \"dense_1_weights\",\n",
    "                                  shape = [ 128, dense_1],\n",
    "                                  dtype = tf.float32,\n",
    "                                  initializer = tf.contrib.layers.xavier_initializer())\n",
    "\n",
    "dense_1_bias = tf.get_variable( \"dense_1_bias\",\n",
    "                                    shape = [dense_1],\n",
    "                                    dtype = tf.float32,\n",
    "                                   initializer = tf.contrib.layers.xavier_initializer())\n",
    "\n",
    "dense_1_dnn = tf.matmul( blstm_out[-1],dense_1_weights) + dense_1_bias\n",
    "dense_1_out = tf.nn.relu(dense_1_dnn)\n",
    "print(dense_1_out)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Tensor(\"Relu_4:0\", shape=(?, 512), dtype=float32)\n"
     ]
    }
   ],
   "source": [
    "#dense layer 2\n",
    "\n",
    "dense_2 = 512\n",
    "dense_2_weights = tf.get_variable( \"dense_2_weights\",\n",
    "                                  shape = [ dense_1, dense_2],\n",
    "                                  dtype = tf.float32,\n",
    "                                  initializer = tf.contrib.layers.xavier_initializer())\n",
    "\n",
    "dense_2_bias = tf.get_variable( \"dense_2_bias\",\n",
    "                                    shape = [dense_2],\n",
    "                                    dtype = tf.float32,\n",
    "                                   initializer = tf.contrib.layers.xavier_initializer())\n",
    "\n",
    "dense_2_dnn = tf.matmul(dense_1_out,dense_2_weights) + dense_2_bias\n",
    "dense_2_out = tf.nn.relu(dense_2_dnn)\n",
    "print(dense_2_out)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Tensor(\"Relu_5:0\", shape=(?, 256), dtype=float32)\n"
     ]
    }
   ],
   "source": [
    "dense_3 = 256\n",
    "dense_3_weights = tf.get_variable(\"dense_3_weights\",\n",
    "                                 shape = [dense_2,dense_3],\n",
    "                                 dtype = tf.float32,\n",
    "                                 initializer = tf.contrib.layers.xavier_initializer())\n",
    "dense_3_bias = tf.get_variable( \"dense_3_bias\",\n",
    "                                    shape = [dense_3],\n",
    "                                    dtype = tf.float32,\n",
    "                                   initializer = tf.contrib.layers.xavier_initializer())\n",
    "\n",
    "dense_3_dnn = tf.matmul(dense_2_out,dense_3_weights) + dense_3_bias\n",
    "dense_3_out = tf.nn.relu(dense_3_dnn)\n",
    "print(dense_3_out)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Tensor(\"Softmax:0\", shape=(?, 2), dtype=float32)\n"
     ]
    }
   ],
   "source": [
    "#output layer\n",
    "\n",
    "final = 2\n",
    "final_weights = tf.get_variable( \"final_weights\",\n",
    "                                  shape = [ dense_3, final],\n",
    "                                  dtype = tf.float32,\n",
    "                                  initializer = tf.contrib.layers.xavier_initializer())\n",
    "\n",
    "final_bias = tf.get_variable( \"final_bias\",\n",
    "                                    shape = [final],\n",
    "                                    dtype = tf.float32,\n",
    "                                   initializer = tf.contrib.layers.xavier_initializer())\n",
    "\n",
    "final_dnn = tf.matmul( dense_3_out, final_weights) + final_bias\n",
    "final_out = tf.nn.softmax(final_dnn)\n",
    "print(final_out)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# losses and optimizers\n",
    "\n",
    "cross_entropy = tf.reduce_mean( tf.nn.softmax_cross_entropy_with_logits(labels = y, logits = final_dnn))\n",
    "optimizer = tf.train.AdamOptimizer(1e-4)\n",
    "train_step = optimizer.minimize(cross_entropy)\n",
    "\n",
    "#correct_predictions = tf.equal( tf.argmax(final_dnn, 1), tf.argmax(output_y, 1))\n",
    "#accuracy = tf.reduce_mean( tf.cast( correct_predictions, tf.float32 ))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "#initializing tensors using sessoion\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\\\\overlapData'\n",
    "development_path = 'C:\\\\Users\\\\HIMANSHU\\\\Desktop\\\\Aditya\\\\ASVspoof2017_CMPOC_CMVN\\\\devoverlapData'\n",
    "eval_features_path = 'C:\\\\Users\\\\HIMANSHU\\\\Desktop\\\\Aditya\\\\ASVspoof2017_CMPOC_CMVN\\\\evaloverlapData'\n",
    "model_path = 'C:\\\\Users\\\\HIMANSHU\\\\Desktop\\\\Aditya\\\\ASVspoof2017_CMPOC_CMVN\\\\Models_60D_CNNRNN_3c_1p_2lstm_3d'\n",
    "\n",
    "dev_sp_path = 'C:\\\\Users\\\\HIMANSHU\\\\Desktop\\\\Aditya\\\\ASVspoof2017_CMPOC_CMVN\\\\Dev_sp_60D_CNNRNN_3c_1p_2lstm_3d'\n",
    "eval_sp_path = 'C:\\\\Users\\\\HIMANSHU\\\\Desktop\\\\Aditya\\\\ASVspoof2017_CMPOC_CMVN\\\\Eval_sp_60D_CNNRNN_3c_1p_2lstm_3d'\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "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": 16,
   "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_out, feed_dict = {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_genuine = []\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_genuine.append(scores[i])\n",
    "            prob_genuine.append(probabilities[i])\n",
    "            \n",
    "        elif development_labels[i][1] == 1:\n",
    "            scores_spoof.append(scores[i])\n",
    "            prob_spoof.append(probabilities[i])\n",
    "\n",
    "    scores_genuine = np.array(scores_genuine)\n",
    "    scores_spoof = np.array(scores_spoof)\n",
    "    \n",
    "    prob_genuine = np.array(prob_genuine)\n",
    "    prob_spoof = np.array(prob_spoof)\n",
    "    \n",
    "    #print(scores_genuine.shape,scores_spoof.shape)\n",
    "    \n",
    "    a,b = cal_eer(scores_genuine, 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_genuine, '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": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:C:\\Users\\HIMANSHU\\Desktop\\Aditya\\ASVspoof2017_CMPOC_CMVN\\Models_60D_CNNRNN_3c_1p_2lstm_3d\\Model1 is not in all_model_checkpoint_paths. Manually adding it.\n",
      "iteration   1\n",
      "FRR   0.22894736842105262  FAR    0.2294736842105263    mid    1.02137106508\n",
      " develop eer   0.22894736842105262\n",
      "\n",
      "INFO:tensorflow:C:\\Users\\HIMANSHU\\Desktop\\Aditya\\ASVspoof2017_CMPOC_CMVN\\Models_60D_CNNRNN_3c_1p_2lstm_3d\\Model2 is not in all_model_checkpoint_paths. Manually adding it.\n",
      "iteration   2\n",
      "FRR   0.16842105263157894  FAR    0.16842105263157894    mid    1.65744674633\n",
      " develop eer   0.16842105263157894\n",
      "\n",
      "INFO:tensorflow:C:\\Users\\HIMANSHU\\Desktop\\Aditya\\ASVspoof2017_CMPOC_CMVN\\Models_60D_CNNRNN_3c_1p_2lstm_3d\\Model3 is not in all_model_checkpoint_paths. Manually adding it.\n",
      "iteration   3\n",
      "FRR   0.15263157894736842  FAR    0.15157894736842106    mid    1.47487166247\n",
      " develop eer   0.15263157894736842\n",
      "\n",
      "INFO:tensorflow:C:\\Users\\HIMANSHU\\Desktop\\Aditya\\ASVspoof2017_CMPOC_CMVN\\Models_60D_CNNRNN_3c_1p_2lstm_3d\\Model4 is not in all_model_checkpoint_paths. Manually adding it.\n",
      "iteration   4\n",
      "FRR   0.14210526315789473  FAR    0.1431578947368421    mid    1.19873034948\n",
      " develop eer   0.14210526315789473\n",
      "\n",
      "INFO:tensorflow:C:\\Users\\HIMANSHU\\Desktop\\Aditya\\ASVspoof2017_CMPOC_CMVN\\Models_60D_CNNRNN_3c_1p_2lstm_3d\\Model5 is not in all_model_checkpoint_paths. Manually adding it.\n",
      "iteration   5\n",
      "FRR   0.1368421052631579  FAR    0.1368421052631579    mid    1.05006892216\n",
      " develop eer   0.1368421052631579\n",
      "\n",
      "INFO:tensorflow:C:\\Users\\HIMANSHU\\Desktop\\Aditya\\ASVspoof2017_CMPOC_CMVN\\Models_60D_CNNRNN_3c_1p_2lstm_3d\\Model6 is not in all_model_checkpoint_paths. Manually adding it.\n",
      "iteration   6\n",
      "FRR   0.1355263157894737  FAR    0.13473684210526315    mid    0.938149004217\n",
      " develop eer   0.1355263157894737\n",
      "\n",
      "INFO:tensorflow:C:\\Users\\HIMANSHU\\Desktop\\Aditya\\ASVspoof2017_CMPOC_CMVN\\Models_60D_CNNRNN_3c_1p_2lstm_3d\\Model7 is not in all_model_checkpoint_paths. Manually adding it.\n",
      "iteration   7\n",
      "FRR   0.1355263157894737  FAR    0.13473684210526315    mid    0.912223152816\n",
      " develop eer   0.1355263157894737\n",
      "\n",
      "INFO:tensorflow:C:\\Users\\HIMANSHU\\Desktop\\Aditya\\ASVspoof2017_CMPOC_CMVN\\Models_60D_CNNRNN_3c_1p_2lstm_3d\\Model8 is not in all_model_checkpoint_paths. Manually adding it.\n",
      "iteration   8\n",
      "FRR   0.13421052631578947  FAR    0.1336842105263158    mid    0.922463666958\n",
      " develop eer   0.13421052631578947\n",
      "\n",
      "INFO:tensorflow:C:\\Users\\HIMANSHU\\Desktop\\Aditya\\ASVspoof2017_CMPOC_CMVN\\Models_60D_CNNRNN_3c_1p_2lstm_3d\\Model9 is not in all_model_checkpoint_paths. Manually adding it.\n",
      "iteration   9\n",
      "FRR   0.13157894736842105  FAR    0.13052631578947368    mid    0.766615894047\n",
      " develop eer   0.13157894736842105\n",
      "\n",
      "INFO:tensorflow:C:\\Users\\HIMANSHU\\Desktop\\Aditya\\ASVspoof2017_CMPOC_CMVN\\Models_60D_CNNRNN_3c_1p_2lstm_3d\\Model10 is not in all_model_checkpoint_paths. Manually adding it.\n",
      "iteration   10\n",
      "FRR   0.13026315789473683  FAR    0.13052631578947368    mid    0.732818812807\n",
      " develop eer   0.13026315789473683\n",
      "\n",
      "INFO:tensorflow:C:\\Users\\HIMANSHU\\Desktop\\Aditya\\ASVspoof2017_CMPOC_CMVN\\Models_60D_CNNRNN_3c_1p_2lstm_3d\\Model11 is not in all_model_checkpoint_paths. Manually adding it.\n",
      "iteration   11\n",
      "FRR   0.12894736842105264  FAR    0.12947368421052632    mid    0.723748000339\n",
      " develop eer   0.12894736842105264\n",
      "\n",
      "INFO:tensorflow:C:\\Users\\HIMANSHU\\Desktop\\Aditya\\ASVspoof2017_CMPOC_CMVN\\Models_60D_CNNRNN_3c_1p_2lstm_3d\\Model12 is not in all_model_checkpoint_paths. Manually adding it.\n",
      "iteration   12\n",
      "FRR   0.12894736842105264  FAR    0.12842105263157894    mid    1.28694460564\n",
      " develop eer   0.12894736842105264\n",
      "\n",
      "INFO:tensorflow:C:\\Users\\HIMANSHU\\Desktop\\Aditya\\ASVspoof2017_CMPOC_CMVN\\Models_60D_CNNRNN_3c_1p_2lstm_3d\\Model13 is not in all_model_checkpoint_paths. Manually adding it.\n",
      "iteration   13\n",
      "FRR   0.12368421052631579  FAR    0.1231578947368421    mid    2.71329510159\n",
      " develop eer   0.12368421052631579\n",
      "\n",
      "INFO:tensorflow:C:\\Users\\HIMANSHU\\Desktop\\Aditya\\ASVspoof2017_CMPOC_CMVN\\Models_60D_CNNRNN_3c_1p_2lstm_3d\\Model14 is not in all_model_checkpoint_paths. Manually adding it.\n",
      "iteration   14\n",
      "FRR   0.12631578947368421  FAR    0.12631578947368421    mid    3.81839482661\n",
      " develop eer   0.12631578947368421\n",
      "\n",
      "INFO:tensorflow:C:\\Users\\HIMANSHU\\Desktop\\Aditya\\ASVspoof2017_CMPOC_CMVN\\Models_60D_CNNRNN_3c_1p_2lstm_3d\\Model15 is not in all_model_checkpoint_paths. Manually adding it.\n",
      "iteration   15\n",
      "FRR   0.13157894736842105  FAR    0.13263157894736843    mid    4.9100763301\n",
      " develop eer   0.13157894736842105\n",
      "\n",
      "INFO:tensorflow:C:\\Users\\HIMANSHU\\Desktop\\Aditya\\ASVspoof2017_CMPOC_CMVN\\Models_60D_CNNRNN_3c_1p_2lstm_3d\\Model16 is not in all_model_checkpoint_paths. Manually adding it.\n",
      "iteration   16\n",
      "FRR   0.14210526315789473  FAR    0.14105263157894737    mid    4.00790657188\n",
      " develop eer   0.14210526315789473\n",
      "\n",
      "INFO:tensorflow:C:\\Users\\HIMANSHU\\Desktop\\Aditya\\ASVspoof2017_CMPOC_CMVN\\Models_60D_CNNRNN_3c_1p_2lstm_3d\\Model17 is not in all_model_checkpoint_paths. Manually adding it.\n",
      "iteration   17\n",
      "FRR   0.1355263157894737  FAR    0.13578947368421052    mid    3.48445594081\n",
      " develop eer   0.1355263157894737\n",
      "\n",
      "INFO:tensorflow:C:\\Users\\HIMANSHU\\Desktop\\Aditya\\ASVspoof2017_CMPOC_CMVN\\Models_60D_CNNRNN_3c_1p_2lstm_3d\\Model18 is not in all_model_checkpoint_paths. Manually adding it.\n",
      "iteration   18\n",
      "FRR   0.13157894736842105  FAR    0.13157894736842105    mid    3.54065124859\n",
      " develop eer   0.13157894736842105\n",
      "\n",
      "INFO:tensorflow:C:\\Users\\HIMANSHU\\Desktop\\Aditya\\ASVspoof2017_CMPOC_CMVN\\Models_60D_CNNRNN_3c_1p_2lstm_3d\\Model19 is not in all_model_checkpoint_paths. Manually adding it.\n",
      "iteration   19\n",
      "FRR   0.12631578947368421  FAR    0.12631578947368421    mid    3.73729246595\n",
      " develop eer   0.12631578947368421\n",
      "\n",
      "INFO:tensorflow:C:\\Users\\HIMANSHU\\Desktop\\Aditya\\ASVspoof2017_CMPOC_CMVN\\Models_60D_CNNRNN_3c_1p_2lstm_3d\\Model20 is not in all_model_checkpoint_paths. Manually adding it.\n",
      "iteration   20\n",
      "FRR   0.12631578947368421  FAR    0.12631578947368421    mid    2.32880217736\n",
      " develop eer   0.12631578947368421\n",
      "\n",
      "INFO:tensorflow:C:\\Users\\HIMANSHU\\Desktop\\Aditya\\ASVspoof2017_CMPOC_CMVN\\Models_60D_CNNRNN_3c_1p_2lstm_3d\\Model21 is not in all_model_checkpoint_paths. Manually adding it.\n",
      "iteration   21\n",
      "FRR   0.12105263157894737  FAR    0.12210526315789473    mid    3.0420538997\n",
      " develop eer   0.12105263157894737\n",
      "\n",
      "INFO:tensorflow:C:\\Users\\HIMANSHU\\Desktop\\Aditya\\ASVspoof2017_CMPOC_CMVN\\Models_60D_CNNRNN_3c_1p_2lstm_3d\\Model22 is not in all_model_checkpoint_paths. Manually adding it.\n",
      "iteration   22\n",
      "FRR   0.125  FAR    0.12526315789473685    mid    1.79407192245\n",
      " develop eer   0.125\n",
      "\n",
      "INFO:tensorflow:C:\\Users\\HIMANSHU\\Desktop\\Aditya\\ASVspoof2017_CMPOC_CMVN\\Models_60D_CNNRNN_3c_1p_2lstm_3d\\Model23 is not in all_model_checkpoint_paths. Manually adding it.\n",
      "iteration   23\n",
      "FRR   0.12631578947368421  FAR    0.12631578947368421    mid    2.0098918003\n",
      " develop eer   0.12631578947368421\n",
      "\n",
      "INFO:tensorflow:C:\\Users\\HIMANSHU\\Desktop\\Aditya\\ASVspoof2017_CMPOC_CMVN\\Models_60D_CNNRNN_3c_1p_2lstm_3d\\Model24 is not in all_model_checkpoint_paths. Manually adding it.\n",
      "iteration   24\n",
      "FRR   0.12763157894736843  FAR    0.12842105263157894    mid    0.557820437775\n",
      " develop eer   0.12763157894736843\n",
      "\n",
      "INFO:tensorflow:C:\\Users\\HIMANSHU\\Desktop\\Aditya\\ASVspoof2017_CMPOC_CMVN\\Models_60D_CNNRNN_3c_1p_2lstm_3d\\Model25 is not in all_model_checkpoint_paths. Manually adding it.\n",
      "iteration   25\n",
      "FRR   0.12631578947368421  FAR    0.12631578947368421    mid    1.35215549212\n",
      " develop eer   0.12631578947368421\n",
      "\n",
      "INFO:tensorflow:C:\\Users\\HIMANSHU\\Desktop\\Aditya\\ASVspoof2017_CMPOC_CMVN\\Models_60D_CNNRNN_3c_1p_2lstm_3d\\Model26 is not in all_model_checkpoint_paths. Manually adding it.\n",
      "iteration   26\n",
      "FRR   0.12631578947368421  FAR    0.12631578947368421    mid    0.860259326718\n",
      " develop eer   0.12631578947368421\n",
      "\n",
      "INFO:tensorflow:C:\\Users\\HIMANSHU\\Desktop\\Aditya\\ASVspoof2017_CMPOC_CMVN\\Models_60D_CNNRNN_3c_1p_2lstm_3d\\Model27 is not in all_model_checkpoint_paths. Manually adding it.\n",
      "iteration   27\n",
      "FRR   0.12763157894736843  FAR    0.12842105263157894    mid    0.225146100263\n",
      " develop eer   0.12763157894736843\n",
      "\n",
      "INFO:tensorflow:C:\\Users\\HIMANSHU\\Desktop\\Aditya\\ASVspoof2017_CMPOC_CMVN\\Models_60D_CNNRNN_3c_1p_2lstm_3d\\Model28 is not in all_model_checkpoint_paths. Manually adding it.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "iteration   28\n",
      "FRR   0.12763157894736843  FAR    0.12842105263157894    mid    0.862202889063\n",
      " develop eer   0.12763157894736843\n",
      "\n",
      "INFO:tensorflow:C:\\Users\\HIMANSHU\\Desktop\\Aditya\\ASVspoof2017_CMPOC_CMVN\\Models_60D_CNNRNN_3c_1p_2lstm_3d\\Model29 is not in all_model_checkpoint_paths. Manually adding it.\n",
      "iteration   29\n",
      "FRR   0.11973684210526316  FAR    0.12    mid    0.60560250141\n",
      " develop eer   0.11973684210526316\n",
      "\n",
      "INFO:tensorflow:C:\\Users\\HIMANSHU\\Desktop\\Aditya\\ASVspoof2017_CMPOC_CMVN\\Models_60D_CNNRNN_3c_1p_2lstm_3d\\Model30 is not in all_model_checkpoint_paths. Manually adding it.\n",
      "iteration   30\n",
      "FRR   0.125  FAR    0.12526315789473685    mid    0.343412239895\n",
      " develop eer   0.125\n",
      "\n",
      "INFO:tensorflow:C:\\Users\\HIMANSHU\\Desktop\\Aditya\\ASVspoof2017_CMPOC_CMVN\\Models_60D_CNNRNN_3c_1p_2lstm_3d\\Model31 is not in all_model_checkpoint_paths. Manually adding it.\n",
      "iteration   31\n",
      "FRR   0.12236842105263158  FAR    0.1231578947368421    mid    0.662171859841\n",
      " develop eer   0.12236842105263158\n",
      "\n",
      "INFO:tensorflow:C:\\Users\\HIMANSHU\\Desktop\\Aditya\\ASVspoof2017_CMPOC_CMVN\\Models_60D_CNNRNN_3c_1p_2lstm_3d\\Model32 is not in all_model_checkpoint_paths. Manually adding it.\n",
      "iteration   32\n",
      "FRR   0.12368421052631579  FAR    0.12421052631578948    mid    0.0244669060085\n",
      " develop eer   0.12368421052631579\n",
      "\n",
      "INFO:tensorflow:C:\\Users\\HIMANSHU\\Desktop\\Aditya\\ASVspoof2017_CMPOC_CMVN\\Models_60D_CNNRNN_3c_1p_2lstm_3d\\Model33 is not in all_model_checkpoint_paths. Manually adding it.\n",
      "iteration   33\n",
      "FRR   0.12894736842105264  FAR    0.12947368421052632    mid    0.2645744526\n",
      " develop eer   0.12894736842105264\n",
      "\n",
      "INFO:tensorflow:C:\\Users\\HIMANSHU\\Desktop\\Aditya\\ASVspoof2017_CMPOC_CMVN\\Models_60D_CNNRNN_3c_1p_2lstm_3d\\Model34 is not in all_model_checkpoint_paths. Manually adding it.\n",
      "iteration   34\n",
      "FRR   0.12631578947368421  FAR    0.12631578947368421    mid    0.73062119141\n",
      " develop eer   0.12631578947368421\n",
      "\n",
      "INFO:tensorflow:C:\\Users\\HIMANSHU\\Desktop\\Aditya\\ASVspoof2017_CMPOC_CMVN\\Models_60D_CNNRNN_3c_1p_2lstm_3d\\Model35 is not in all_model_checkpoint_paths. Manually adding it.\n",
      "iteration   35\n",
      "FRR   0.12631578947368421  FAR    0.12631578947368421    mid    2.7190062024\n",
      " develop eer   0.12631578947368421\n",
      "\n",
      "INFO:tensorflow:C:\\Users\\HIMANSHU\\Desktop\\Aditya\\ASVspoof2017_CMPOC_CMVN\\Models_60D_CNNRNN_3c_1p_2lstm_3d\\Model36 is not in all_model_checkpoint_paths. Manually adding it.\n",
      "iteration   36\n",
      "FRR   0.12763157894736843  FAR    0.12736842105263158    mid    2.0418805798\n",
      " develop eer   0.12763157894736843\n",
      "\n",
      "INFO:tensorflow:C:\\Users\\HIMANSHU\\Desktop\\Aditya\\ASVspoof2017_CMPOC_CMVN\\Models_60D_CNNRNN_3c_1p_2lstm_3d\\Model37 is not in all_model_checkpoint_paths. Manually adding it.\n",
      "iteration   37\n",
      "FRR   0.12631578947368421  FAR    0.12631578947368421    mid    3.76169868598\n",
      " develop eer   0.12631578947368421\n",
      "\n",
      "INFO:tensorflow:C:\\Users\\HIMANSHU\\Desktop\\Aditya\\ASVspoof2017_CMPOC_CMVN\\Models_60D_CNNRNN_3c_1p_2lstm_3d\\Model38 is not in all_model_checkpoint_paths. Manually adding it.\n",
      "iteration   38\n",
      "FRR   0.125  FAR    0.12526315789473685    mid    0.576667242479\n",
      " develop eer   0.125\n",
      "\n",
      "INFO:tensorflow:C:\\Users\\HIMANSHU\\Desktop\\Aditya\\ASVspoof2017_CMPOC_CMVN\\Models_60D_CNNRNN_3c_1p_2lstm_3d\\Model39 is not in all_model_checkpoint_paths. Manually adding it.\n",
      "iteration   39\n",
      "FRR   0.12631578947368421  FAR    0.12631578947368421    mid    1.7163189677\n",
      " develop eer   0.12631578947368421\n",
      "\n",
      "INFO:tensorflow:C:\\Users\\HIMANSHU\\Desktop\\Aditya\\ASVspoof2017_CMPOC_CMVN\\Models_60D_CNNRNN_3c_1p_2lstm_3d\\Model40 is not in all_model_checkpoint_paths. Manually adding it.\n",
      "iteration   40\n",
      "FRR   0.12631578947368421  FAR    0.12631578947368421    mid    1.17785306364\n",
      " develop eer   0.12631578947368421\n",
      "\n",
      "INFO:tensorflow:C:\\Users\\HIMANSHU\\Desktop\\Aditya\\ASVspoof2017_CMPOC_CMVN\\Models_60D_CNNRNN_3c_1p_2lstm_3d\\Model41 is not in all_model_checkpoint_paths. Manually adding it.\n",
      "iteration   41\n",
      "FRR   0.12631578947368421  FAR    0.12631578947368421    mid    5.23085758391\n",
      " develop eer   0.12631578947368421\n",
      "\n",
      "INFO:tensorflow:C:\\Users\\HIMANSHU\\Desktop\\Aditya\\ASVspoof2017_CMPOC_CMVN\\Models_60D_CNNRNN_3c_1p_2lstm_3d\\Model42 is not in all_model_checkpoint_paths. Manually adding it.\n",
      "iteration   42\n",
      "FRR   0.13026315789473683  FAR    0.13052631578947368    mid    0.777279944604\n",
      " develop eer   0.13026315789473683\n",
      "\n",
      "INFO:tensorflow:C:\\Users\\HIMANSHU\\Desktop\\Aditya\\ASVspoof2017_CMPOC_CMVN\\Models_60D_CNNRNN_3c_1p_2lstm_3d\\Model43 is not in all_model_checkpoint_paths. Manually adding it.\n",
      "iteration   43\n",
      "FRR   0.12368421052631579  FAR    0.1231578947368421    mid    14.6548561576\n",
      " develop eer   0.12368421052631579\n",
      "\n",
      "INFO:tensorflow:C:\\Users\\HIMANSHU\\Desktop\\Aditya\\ASVspoof2017_CMPOC_CMVN\\Models_60D_CNNRNN_3c_1p_2lstm_3d\\Model44 is not in all_model_checkpoint_paths. Manually adding it.\n",
      "iteration   44\n",
      "FRR   0.12105263157894737  FAR    0.12105263157894737    mid    3.26658182997\n",
      " develop eer   0.12105263157894737\n",
      "\n",
      "INFO:tensorflow:C:\\Users\\HIMANSHU\\Desktop\\Aditya\\ASVspoof2017_CMPOC_CMVN\\Models_60D_CNNRNN_3c_1p_2lstm_3d\\Model45 is not in all_model_checkpoint_paths. Manually adding it.\n",
      "iteration   45\n",
      "FRR   0.12236842105263158  FAR    0.12210526315789473    mid    4.62148938395\n",
      " develop eer   0.12236842105263158\n",
      "\n",
      "INFO:tensorflow:C:\\Users\\HIMANSHU\\Desktop\\Aditya\\ASVspoof2017_CMPOC_CMVN\\Models_60D_CNNRNN_3c_1p_2lstm_3d\\Model46 is not in all_model_checkpoint_paths. Manually adding it.\n",
      "iteration   46\n",
      "FRR   0.12105263157894737  FAR    0.12105263157894737    mid    11.4711471635\n",
      " develop eer   0.12105263157894737\n",
      "\n",
      "INFO:tensorflow:C:\\Users\\HIMANSHU\\Desktop\\Aditya\\ASVspoof2017_CMPOC_CMVN\\Models_60D_CNNRNN_3c_1p_2lstm_3d\\Model47 is not in all_model_checkpoint_paths. Manually adding it.\n",
      "iteration   47\n",
      "FRR   0.12631578947368421  FAR    0.12631578947368421    mid    2.49810713459\n",
      " develop eer   0.12631578947368421\n",
      "\n",
      "INFO:tensorflow:C:\\Users\\HIMANSHU\\Desktop\\Aditya\\ASVspoof2017_CMPOC_CMVN\\Models_60D_CNNRNN_3c_1p_2lstm_3d\\Model48 is not in all_model_checkpoint_paths. Manually adding it.\n",
      "iteration   48\n",
      "FRR   0.12368421052631579  FAR    0.1231578947368421    mid    14.8783240484\n",
      " develop eer   0.12368421052631579\n",
      "\n",
      "INFO:tensorflow:C:\\Users\\HIMANSHU\\Desktop\\Aditya\\ASVspoof2017_CMPOC_CMVN\\Models_60D_CNNRNN_3c_1p_2lstm_3d\\Model49 is not in all_model_checkpoint_paths. Manually adding it.\n",
      "iteration   49\n",
      "FRR   0.12236842105263158  FAR    0.12210526315789473    mid    2.18411981443\n",
      " develop eer   0.12236842105263158\n",
      "\n",
      "INFO:tensorflow:C:\\Users\\HIMANSHU\\Desktop\\Aditya\\ASVspoof2017_CMPOC_CMVN\\Models_60D_CNNRNN_3c_1p_2lstm_3d\\Model50 is not in all_model_checkpoint_paths. Manually adding it.\n",
      "iteration   50\n",
      "FRR   0.12368421052631579  FAR    0.12421052631578948    mid    5.15575182588\n",
      " develop eer   0.12368421052631579\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",
    "        train_step.run(session = session,feed_dict={ x:features, 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(development_path, (i+1), dev_sp_path)\n",
    "    print(\" develop eer  \",eer_dev)\n",
    "    print()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Restoring parameters from C:\\Users\\HIMANSHU\\Desktop\\Aditya\\ASVspoof2017_CMPOC_CMVN\\Models_60D_CNNRNN_3c_1p_2lstm_3d\\Model36\n",
      "iteration   36\n",
      "FRR   0.1926040061633282  FAR    0.19337108594270486    mid    6.5584796362\n",
      "eval eer   0.1926040061633282\n",
      "\n",
      "INFO:tensorflow:Restoring parameters from C:\\Users\\HIMANSHU\\Desktop\\Aditya\\ASVspoof2017_CMPOC_CMVN\\Models_60D_CNNRNN_3c_1p_2lstm_3d\\Model37\n",
      "iteration   37\n",
      "FRR   0.19106317411402157  FAR    0.1911225849433711    mid    11.8590426777\n",
      "eval eer   0.19106317411402157\n",
      "\n",
      "INFO:tensorflow:Restoring parameters from C:\\Users\\HIMANSHU\\Desktop\\Aditya\\ASVspoof2017_CMPOC_CMVN\\Models_60D_CNNRNN_3c_1p_2lstm_3d\\Model38\n",
      "iteration   38\n",
      "FRR   0.19029275808936827  FAR    0.1902898067954697    mid    5.06259353814\n",
      "eval eer   0.19029275808936827\n",
      "\n",
      "INFO:tensorflow:Restoring parameters from C:\\Users\\HIMANSHU\\Desktop\\Aditya\\ASVspoof2017_CMPOC_CMVN\\Models_60D_CNNRNN_3c_1p_2lstm_3d\\Model39\n",
      "iteration   39\n",
      "FRR   0.19722650231124808  FAR    0.19711858760826115    mid    5.22788413564\n",
      "eval eer   0.19722650231124808\n",
      "\n",
      "INFO:tensorflow:Restoring parameters from C:\\Users\\HIMANSHU\\Desktop\\Aditya\\ASVspoof2017_CMPOC_CMVN\\Models_60D_CNNRNN_3c_1p_2lstm_3d\\Model40\n",
      "iteration   40\n",
      "FRR   0.19568567026194145  FAR    0.1963690872751499    mid    4.29606725558\n",
      "eval eer   0.19568567026194145\n",
      "\n",
      "INFO:tensorflow:Restoring parameters from C:\\Users\\HIMANSHU\\Desktop\\Aditya\\ASVspoof2017_CMPOC_CMVN\\Models_60D_CNNRNN_3c_1p_2lstm_3d\\Model41\n",
      "iteration   41\n",
      "FRR   0.20107858243451462  FAR    0.20044970019986674    mid    17.3209611995\n",
      "eval eer   0.20107858243451462\n",
      "\n",
      "INFO:tensorflow:Restoring parameters from C:\\Users\\HIMANSHU\\Desktop\\Aditya\\ASVspoof2017_CMPOC_CMVN\\Models_60D_CNNRNN_3c_1p_2lstm_3d\\Model42\n",
      "iteration   42\n",
      "FRR   0.19491525423728814  FAR    0.19487008660892738    mid    2.65629799912\n",
      "eval eer   0.19491525423728814\n",
      "\n",
      "INFO:tensorflow:Restoring parameters from C:\\Users\\HIMANSHU\\Desktop\\Aditya\\ASVspoof2017_CMPOC_CMVN\\Models_60D_CNNRNN_3c_1p_2lstm_3d\\Model43\n",
      "iteration   43\n",
      "FRR   0.19029275808936827  FAR    0.1902898067954697    mid    32.999118328\n",
      "eval eer   0.19029275808936827\n",
      "\n",
      "INFO:tensorflow:Restoring parameters from C:\\Users\\HIMANSHU\\Desktop\\Aditya\\ASVspoof2017_CMPOC_CMVN\\Models_60D_CNNRNN_3c_1p_2lstm_3d\\Model44\n",
      "iteration   44\n",
      "FRR   0.1933744221879815  FAR    0.19370419720186544    mid    9.50602262628\n",
      "eval eer   0.1933744221879815\n",
      "\n",
      "INFO:tensorflow:Restoring parameters from C:\\Users\\HIMANSHU\\Desktop\\Aditya\\ASVspoof2017_CMPOC_CMVN\\Models_60D_CNNRNN_3c_1p_2lstm_3d\\Model45\n",
      "iteration   45\n",
      "FRR   0.18875192604006163  FAR    0.18870752831445703    mid    12.3122353404\n",
      "eval eer   0.18875192604006163\n",
      "\n",
      "INFO:tensorflow:Restoring parameters from C:\\Users\\HIMANSHU\\Desktop\\Aditya\\ASVspoof2017_CMPOC_CMVN\\Models_60D_CNNRNN_3c_1p_2lstm_3d\\Model46\n",
      "iteration   46\n",
      "FRR   0.19645608628659475  FAR    0.1965356429047302    mid    24.060160485\n",
      "eval eer   0.19645608628659475\n",
      "\n",
      "INFO:tensorflow:Restoring parameters from C:\\Users\\HIMANSHU\\Desktop\\Aditya\\ASVspoof2017_CMPOC_CMVN\\Models_60D_CNNRNN_3c_1p_2lstm_3d\\Model47\n",
      "iteration   47\n",
      "FRR   0.1941448382126348  FAR    0.1936209193870753    mid    8.62649143397\n",
      "eval eer   0.1941448382126348\n",
      "\n",
      "INFO:tensorflow:Restoring parameters from C:\\Users\\HIMANSHU\\Desktop\\Aditya\\ASVspoof2017_CMPOC_CMVN\\Models_60D_CNNRNN_3c_1p_2lstm_3d\\Model48\n",
      "iteration   48\n",
      "FRR   0.1941448382126348  FAR    0.19437041972018654    mid    34.5719957006\n",
      "eval eer   0.1941448382126348\n",
      "\n",
      "INFO:tensorflow:Restoring parameters from C:\\Users\\HIMANSHU\\Desktop\\Aditya\\ASVspoof2017_CMPOC_CMVN\\Models_60D_CNNRNN_3c_1p_2lstm_3d\\Model49\n",
      "iteration   49\n",
      "FRR   0.1941448382126348  FAR    0.19445369753497668    mid    8.22623504936\n",
      "eval eer   0.1941448382126348\n",
      "\n",
      "INFO:tensorflow:Restoring parameters from C:\\Users\\HIMANSHU\\Desktop\\Aditya\\ASVspoof2017_CMPOC_CMVN\\Models_60D_CNNRNN_3c_1p_2lstm_3d\\Model50\n",
      "iteration   50\n",
      "FRR   0.1933744221879815  FAR    0.1932045303131246    mid    15.5877071424\n",
      "eval eer   0.1933744221879815\n",
      "\n"
     ]
    }
   ],
   "source": [
    "eval_features_path = 'C:\\\\Users\\\\HIMANSHU\\\\Desktop\\\\Aditya\\\\ASVspoof2017_CMPOC_CMVN\\\\evaloverlapData'\n",
    "eval_sp_path = 'C:\\\\Users\\\\HIMANSHU\\\\Desktop\\\\Aditya\\\\ASVspoof2017_CMPOC_CMVN\\\\Eval_sp_60D_CNNRNN_3c_1p_2lstm_3d'\n",
    "\n",
    "if not os.path.exists(eval_sp_path):\n",
    "    os.makedirs(eval_sp_path)\n",
    "    \n",
    "with tf.Session() as session:\n",
    "    for i in range(35,50):\n",
    "        saver.restore(session,os.path.join(model_path,\"Model\"+str(i+1)))\n",
    "        print(\"iteration  \", (i+1))\n",
    "        eval_eer = get_EER(eval_features_path, (i+1), eval_sp_path)\n",
    "        print(\"eval eer  \", eval_eer)\n",
    "        print()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(16, 90, 100)\n"
     ]
    }
   ],
   "source": [
    "eval_features_path = 'C:\\\\Users\\\\HIMANSHU\\\\Desktop\\\\Aditya\\\\ASVspoof2017_CMPOC_CMVN\\\\evaloverlapData'\n",
    "no_of_files = len(list(os.listdir(eval_features_path)))//2\n",
    "for i in range(1298,1299):\n",
    "    feature_name = 'features_'+str(i+1)+'.npy'\n",
    "    matFile = np.load(os.path.join(eval_features_path,feature_name))\n",
    "    print(matFile.shape)\n",
    "#print(no_of_files)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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