CNN_CMPOC_CMVN 3conv+2pool+2dense.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "import numpy as np\n",
    "import os"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "#in and out data\n",
    "dimension = 90\n",
    "context_frames = 100\n",
    "\n",
    "x = tf.placeholder(tf.float32,[None,dimension,context_frames]) #inputs\n",
    "y = tf.placeholder(tf.float32,[None,2]) #labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Tensor(\"Reshape:0\", shape=(?, 90, 100, 1), dtype=float32)\n",
      "Tensor(\"Relu:0\", shape=(?, 44, 49, 128), dtype=float32)\n"
     ]
    }
   ],
   "source": [
    "#conv layer 1\n",
    "#[filter_height, filter_width, in-channels, out-channels]\n",
    "weights_conv1 = tf.Variable(tf.truncated_normal(shape = [3,3,1,128], stddev = 0.01, dtype = tf.float32))\n",
    "bias_conv1 = tf.Variable(tf.constant(0.1,shape = [128]))\n",
    "\n",
    "# reshape of input -1 dynamically calculates the batch_size,in_height,in_width,in_channels\n",
    "input_x = tf.reshape(x,[-1,dimension,context_frames,1])\n",
    "print(input_x)\n",
    "conv1 = tf.nn.conv2d(input_x,weights_conv1, strides = [1,2,2,1],padding = 'VALID')\n",
    "conv1_out = tf.nn.relu(conv1+bias_conv1)\n",
    "print(conv1_out)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Tensor(\"Relu_1:0\", shape=(?, 21, 24, 64), dtype=float32)\n"
     ]
    }
   ],
   "source": [
    "#conv layer 2\n",
    "weights_conv2 = tf.Variable(tf.truncated_normal(shape = [3,3,128,64], stddev = 0.01, dtype = tf.float32))\n",
    "bias_conv2 = tf.Variable(tf.constant(0.1,shape = [64]))\n",
    "\n",
    "conv2 = tf.nn.conv2d(conv1_out,weights_conv2,strides = [1,2,2,1], padding = 'VALID')\n",
    "conv2_out = tf.nn.relu(conv2+bias_conv2)\n",
    "print(conv2_out)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Tensor(\"Relu_2:0\", shape=(?, 17, 20, 64), dtype=float32)\n"
     ]
    }
   ],
   "source": [
    "#conv layer 3\n",
    "weights_conv3 = tf.Variable(tf.truncated_normal(shape = [5,5,64,64], stddev = 0.01, dtype = tf.float32))\n",
    "bias_conv3 = tf.Variable(tf.constant(0.1,shape = [64]))\n",
    "\n",
    "conv3 = tf.nn.conv2d(conv2_out,weights_conv3,strides = [1,1,1,1], padding = 'VALID')\n",
    "conv3_out = tf.nn.relu(conv3+bias_conv3)\n",
    "print(conv3_out)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Tensor(\"max_pooling2d/MaxPool:0\", shape=(?, 14, 17, 64), dtype=float32)\n"
     ]
    }
   ],
   "source": [
    "#max pool 1\n",
    "pool1_out = tf.layers.max_pooling2d(conv3_out, pool_size = [4,4], strides = [1,1], padding = 'VALID')\n",
    "#conv4_out = tf.nn.relu(conv4+bias_conv4)\n",
    "print(pool1_out)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Tensor(\"max_pooling2d_2/MaxPool:0\", shape=(?, 9, 12, 64), dtype=float32)\n"
     ]
    }
   ],
   "source": [
    "#max pool 2\n",
    "pool2_out = tf.layers.max_pooling2d(pool1_out, pool_size = [6,6], strides = [1,1], padding = 'VALID')\n",
    "#conv4_out = tf.nn.relu(conv4+bias_conv4)\n",
    "print(pool2_out)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'tensorflow.python.framework.tensor_shape.Dimension'> 6912\n",
      "Tensor(\"Relu_3:0\", shape=(?, 256), dtype=float32)\n"
     ]
    }
   ],
   "source": [
    "#print(pool_out.shape[1])\n",
    "reduced_dim = pool2_out.shape[1] * pool2_out.shape[2] * pool2_out.shape[3]\n",
    "print(type(reduced_dim),int(reduced_dim))\n",
    "\n",
    "#dense layer 1\n",
    "weights_dense1 = tf.Variable(tf.truncated_normal([int(reduced_dim),256], stddev = 0.01, dtype = tf.float32))\n",
    "bias_dense1 = tf.Variable(tf.constant(0.1,shape = [256]))\n",
    "\n",
    "input_dense = tf.reshape(pool2_out, shape = [-1,int(reduced_dim)])\n",
    "#print(input_dense)\n",
    "dense1_sum = tf.matmul(input_dense,weights_dense1)+bias_dense1\n",
    "dense1_output = tf.nn.relu(dense1_sum)\n",
    "print(dense1_output)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Tensor(\"dropout/mul:0\", shape=(?, 256), dtype=float32)\n"
     ]
    }
   ],
   "source": [
    "#adding dropout to dense layer 1\n",
    "keep_prob = tf.placeholder(dtype = tf.float32)\n",
    "dense1_dropout = tf.nn.dropout(dense1_output,keep_prob)\n",
    "print(dense1_dropout)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Tensor(\"Relu_4:0\", shape=(?, 256), dtype=float32)\n"
     ]
    }
   ],
   "source": [
    "# dense layer 2\n",
    "weights_dense2 = tf.Variable(tf.truncated_normal([256,256], stddev = 0.01, dtype = tf.float32))\n",
    "bias_dense2 = tf.Variable(tf.constant(0.1,shape = [256]))\n",
    "\n",
    "dense2_sum = tf.matmul(dense1_dropout,weights_dense2)+ bias_dense2\n",
    "dense2_output = tf.nn.relu(dense2_sum)\n",
    "print(dense2_output)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Tensor(\"dropout_1/mul:0\", shape=(?, 256), dtype=float32)\n"
     ]
    }
   ],
   "source": [
    "dense2_dropout = tf.nn.dropout(dense2_output,keep_prob)\n",
    "print(dense2_dropout)"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "# dense layer 3\n",
    "weights_dense3 = tf.Variable(tf.truncated_normal([512,512], stddev = 0.01, dtype = tf.float32))\n",
    "bias_dense3 = tf.Variable(tf.constant(0.1,shape = [512]))\n",
    "\n",
    "dense3_sum = tf.matmul(dense2_dropout,weights_dense3)+ bias_dense3\n",
    "dense3_output = tf.nn.relu(dense3_sum)\n",
    "print(dense3_output)"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "dense3_dropout = tf.nn.dropout(dense3_output,keep_prob)\n",
    "print(dense3_dropout)"
   ]
  },
  {
   "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",
    "output_weights = tf.Variable(tf.truncated_normal(shape = [256,2], stddev = 0.01, dtype = tf.float32))\n",
    "output_bias = tf.Variable(tf.constant(0.1,shape = [2]))\n",
    "\n",
    "output_sum = tf.matmul(dense2_dropout,output_weights)+output_bias\n",
    "final_output = tf.nn.softmax(output_sum)\n",
    "print(final_output)"
   ]
  },
  {
   "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_output))\n",
    "optimizer = tf.train.AdamOptimizer(1e-4)\n",
    "train_step = optimizer.minimize(cross_entropy)\n",
    "\n",
    "correct_predictions = tf.equal( tf.argmax(final_output, 1), tf.argmax(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 session\n",
    "\n",
    "session = tf.InteractiveSession()\n",
    "\n",
    "init = tf.global_variables_initializer()\n",
    "#saver = tf.train.Saver(max_to_keep = None)\n",
    "\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",
    "    dev_num = len(list(os.listdir(test_path)))//2\n",
    "    \n",
    "    for j in range(1, dev_num+1):\n",
    "        filename = \"features_\"+str(j)+\".npy\"\n",
    "        labelsname= \"labels_\"+str(j)+\".npy\"\n",
    "        \n",
    "        features = np.load(os.path.join(test_path, filename))\n",
    "        labels = np.load(os.path.join(test_path, labelsname))\n",
    "        patches = labels.shape[0]\n",
    "        patch_prob = session.run(final_output, feed_dict = {x:features, keep_prob : 1.0})\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",
    "    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": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Training and testing accuracy\n",
    "saver = tf.train.Saver(max_to_keep=None)\n",
    "session.run(init)\n",
    "\n",
    "features_path = \"C:\\\\Users\\\\HIMANSHU\\\\Desktop\\\\Aditya\\\\ASVspoof2017_CMPOC_CMVN\\\\overlapData_90D\"\n",
    "development_path = \"C:\\\\Users\\\\HIMANSHU\\\\Desktop\\\\Aditya\\\\ASVspoof2017_CMPOC_CMVN\\\\devoverlapData_90D\"\n",
    "#evaluation_path = \"C:\\\\Users\\\\HIMANSHU\\\\Desktop\\\\Aditya\\\\ASVspoof2017_CMPOC_CMVN\\\\evaloverlapData_90D\"\n",
    "model_path = \"C:\\\\Users\\\\HIMANSHU\\\\Desktop\\\\Aditya\\\\ASVspoof2017_CMPOC_CMVN\\\\Models_90D_CNN_3c2p2d\"\n",
    "    \n",
    "dev_sp_path = \"C:\\\\Users\\\\HIMANSHU\\\\Desktop\\\\Aditya\\\\ASVspoof2017_CMPOC_CMVN\\\\dev_sp_90D_CNN_3c2p2d\"\n",
    "#eva_sp_path = \"C:\\\\Users\\\\HIMANSHU\\\\Desktop\\\\Aditya\\\\ASVspoof2017_CMPOC_CMVN\\\\eval_sp_90D_CNN_3c2p2d\" \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",
    "\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "255\n"
     ]
    }
   ],
   "source": [
    "print(batches)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:C:\\Users\\HIMANSHU\\Desktop\\Aditya\\ASVspoof2017_CMPOC_CMVN\\Models_90D_CNN_3c2p2d\\Model_2 is not in all_model_checkpoint_paths. Manually adding it.\n",
      "iteration   2\n",
      "FRR   0.4  FAR    0.4    mid    0.170349825793\n",
      " develop eer   0.4\n",
      "\n",
      "INFO:tensorflow:C:\\Users\\HIMANSHU\\Desktop\\Aditya\\ASVspoof2017_CMPOC_CMVN\\Models_90D_CNN_3c2p2d\\Model_3 is not in all_model_checkpoint_paths. Manually adding it.\n",
      "iteration   3\n",
      "FRR   0.36578947368421055  FAR    0.3663157894736842    mid    0.189528179665\n",
      " develop eer   0.36578947368421055\n",
      "\n",
      "INFO:tensorflow:C:\\Users\\HIMANSHU\\Desktop\\Aditya\\ASVspoof2017_CMPOC_CMVN\\Models_90D_CNN_3c2p2d\\Model_4 is not in all_model_checkpoint_paths. Manually adding it.\n",
      "iteration   4\n",
      "FRR   0.3592105263157895  FAR    0.36    mid    0.147019771969\n",
      " develop eer   0.3592105263157895\n",
      "\n",
      "INFO:tensorflow:C:\\Users\\HIMANSHU\\Desktop\\Aditya\\ASVspoof2017_CMPOC_CMVN\\Models_90D_CNN_3c2p2d\\Model_5 is not in all_model_checkpoint_paths. Manually adding it.\n",
      "iteration   5\n",
      "FRR   0.36578947368421055  FAR    0.3663157894736842    mid    -0.0693749548758\n",
      " develop eer   0.36578947368421055\n",
      "\n",
      "INFO:tensorflow:C:\\Users\\HIMANSHU\\Desktop\\Aditya\\ASVspoof2017_CMPOC_CMVN\\Models_90D_CNN_3c2p2d\\Model_6 is not in all_model_checkpoint_paths. Manually adding it.\n",
      "iteration   6\n",
      "FRR   0.3605263157894737  FAR    0.36105263157894735    mid    0.014467689124\n",
      " develop eer   0.3605263157894737\n",
      "\n",
      "INFO:tensorflow:C:\\Users\\HIMANSHU\\Desktop\\Aditya\\ASVspoof2017_CMPOC_CMVN\\Models_90D_CNN_3c2p2d\\Model_7 is not in all_model_checkpoint_paths. Manually adding it.\n",
      "iteration   7\n",
      "FRR   0.3605263157894737  FAR    0.36105263157894735    mid    -0.0522773719352\n",
      " develop eer   0.3605263157894737\n",
      "\n",
      "INFO:tensorflow:C:\\Users\\HIMANSHU\\Desktop\\Aditya\\ASVspoof2017_CMPOC_CMVN\\Models_90D_CNN_3c2p2d\\Model_8 is not in all_model_checkpoint_paths. Manually adding it.\n",
      "iteration   8\n",
      "FRR   0.3526315789473684  FAR    0.35368421052631577    mid    -0.0771955837096\n",
      " develop eer   0.3526315789473684\n",
      "\n",
      "INFO:tensorflow:C:\\Users\\HIMANSHU\\Desktop\\Aditya\\ASVspoof2017_CMPOC_CMVN\\Models_90D_CNN_3c2p2d\\Model_9 is not in all_model_checkpoint_paths. Manually adding it.\n",
      "iteration   9\n",
      "FRR   0.3513157894736842  FAR    0.35157894736842105    mid    -0.0296047319969\n",
      " develop eer   0.3513157894736842\n",
      "\n",
      "INFO:tensorflow:C:\\Users\\HIMANSHU\\Desktop\\Aditya\\ASVspoof2017_CMPOC_CMVN\\Models_90D_CNN_3c2p2d\\Model_10 is not in all_model_checkpoint_paths. Manually adding it.\n",
      "iteration   10\n",
      "FRR   0.3473684210526316  FAR    0.3473684210526316    mid    -0.17934233269\n",
      " develop eer   0.3473684210526316\n",
      "\n",
      "INFO:tensorflow:C:\\Users\\HIMANSHU\\Desktop\\Aditya\\ASVspoof2017_CMPOC_CMVN\\Models_90D_CNN_3c2p2d\\Model_11 is not in all_model_checkpoint_paths. Manually adding it.\n",
      "iteration   11\n",
      "FRR   0.3368421052631579  FAR    0.3368421052631579    mid    -0.264019255671\n",
      " develop eer   0.3368421052631579\n",
      "\n",
      "INFO:tensorflow:C:\\Users\\HIMANSHU\\Desktop\\Aditya\\ASVspoof2017_CMPOC_CMVN\\Models_90D_CNN_3c2p2d\\Model_12 is not in all_model_checkpoint_paths. Manually adding it.\n",
      "iteration   12\n",
      "FRR   0.3171052631578947  FAR    0.3178947368421053    mid    -0.709280803662\n",
      " develop eer   0.3171052631578947\n",
      "\n",
      "INFO:tensorflow:C:\\Users\\HIMANSHU\\Desktop\\Aditya\\ASVspoof2017_CMPOC_CMVN\\Models_90D_CNN_3c2p2d\\Model_13 is not in all_model_checkpoint_paths. Manually adding it.\n",
      "iteration   13\n",
      "FRR   0.24736842105263157  FAR    0.24736842105263157    mid    -0.811046438665\n",
      " develop eer   0.24736842105263157\n",
      "\n",
      "INFO:tensorflow:C:\\Users\\HIMANSHU\\Desktop\\Aditya\\ASVspoof2017_CMPOC_CMVN\\Models_90D_CNN_3c2p2d\\Model_14 is not in all_model_checkpoint_paths. Manually adding it.\n",
      "iteration   14\n",
      "FRR   0.21052631578947367  FAR    0.21052631578947367    mid    -0.264263093032\n",
      " develop eer   0.21052631578947367\n",
      "\n",
      "INFO:tensorflow:C:\\Users\\HIMANSHU\\Desktop\\Aditya\\ASVspoof2017_CMPOC_CMVN\\Models_90D_CNN_3c2p2d\\Model_15 is not in all_model_checkpoint_paths. Manually adding it.\n",
      "iteration   15\n",
      "FRR   0.1736842105263158  FAR    0.1736842105263158    mid    -0.359223573975\n",
      " develop eer   0.1736842105263158\n",
      "\n",
      "INFO:tensorflow:C:\\Users\\HIMANSHU\\Desktop\\Aditya\\ASVspoof2017_CMPOC_CMVN\\Models_90D_CNN_3c2p2d\\Model_16 is not in all_model_checkpoint_paths. Manually adding it.\n",
      "iteration   16\n",
      "FRR   0.1618421052631579  FAR    0.16210526315789472    mid    0.260050109044\n",
      " develop eer   0.1618421052631579\n",
      "\n",
      "INFO:tensorflow:C:\\Users\\HIMANSHU\\Desktop\\Aditya\\ASVspoof2017_CMPOC_CMVN\\Models_90D_CNN_3c2p2d\\Model_17 is not in all_model_checkpoint_paths. Manually adding it.\n",
      "iteration   17\n",
      "FRR   0.15789473684210525  FAR    0.15789473684210525    mid    -0.0285234742465\n",
      " develop eer   0.15789473684210525\n",
      "\n",
      "INFO:tensorflow:C:\\Users\\HIMANSHU\\Desktop\\Aditya\\ASVspoof2017_CMPOC_CMVN\\Models_90D_CNN_3c2p2d\\Model_18 is not in all_model_checkpoint_paths. Manually adding it.\n",
      "iteration   18\n",
      "FRR   0.15526315789473685  FAR    0.15578947368421053    mid    0.292486150484\n",
      " develop eer   0.15526315789473685\n",
      "\n",
      "INFO:tensorflow:C:\\Users\\HIMANSHU\\Desktop\\Aditya\\ASVspoof2017_CMPOC_CMVN\\Models_90D_CNN_3c2p2d\\Model_19 is not in all_model_checkpoint_paths. Manually adding it.\n",
      "iteration   19\n",
      "FRR   0.15  FAR    0.15052631578947367    mid    0.728228767946\n",
      " develop eer   0.15\n",
      "\n",
      "INFO:tensorflow:C:\\Users\\HIMANSHU\\Desktop\\Aditya\\ASVspoof2017_CMPOC_CMVN\\Models_90D_CNN_3c2p2d\\Model_20 is not in all_model_checkpoint_paths. Manually adding it.\n",
      "iteration   20\n",
      "FRR   0.14736842105263157  FAR    0.14736842105263157    mid    1.7253459971\n",
      " develop eer   0.14736842105263157\n",
      "\n",
      "INFO:tensorflow:C:\\Users\\HIMANSHU\\Desktop\\Aditya\\ASVspoof2017_CMPOC_CMVN\\Models_90D_CNN_3c2p2d\\Model_21 is not in all_model_checkpoint_paths. Manually adding it.\n",
      "iteration   21\n",
      "FRR   0.1513157894736842  FAR    0.15052631578947367    mid    1.56582926759\n",
      " develop eer   0.1513157894736842\n",
      "\n",
      "INFO:tensorflow:C:\\Users\\HIMANSHU\\Desktop\\Aditya\\ASVspoof2017_CMPOC_CMVN\\Models_90D_CNN_3c2p2d\\Model_22 is not in all_model_checkpoint_paths. Manually adding it.\n",
      "iteration   22\n",
      "FRR   0.14605263157894738  FAR    0.1463157894736842    mid    1.31164548206\n",
      " develop eer   0.14605263157894738\n",
      "\n",
      "INFO:tensorflow:C:\\Users\\HIMANSHU\\Desktop\\Aditya\\ASVspoof2017_CMPOC_CMVN\\Models_90D_CNN_3c2p2d\\Model_23 is not in all_model_checkpoint_paths. Manually adding it.\n",
      "iteration   23\n",
      "FRR   0.14868421052631578  FAR    0.14947368421052631    mid    1.0835348342\n",
      " develop eer   0.14868421052631578\n",
      "\n",
      "INFO:tensorflow:C:\\Users\\HIMANSHU\\Desktop\\Aditya\\ASVspoof2017_CMPOC_CMVN\\Models_90D_CNN_3c2p2d\\Model_24 is not in all_model_checkpoint_paths. Manually adding it.\n",
      "iteration   24\n",
      "FRR   0.14736842105263157  FAR    0.14736842105263157    mid    1.71663793054\n",
      " develop eer   0.14736842105263157\n",
      "\n",
      "INFO:tensorflow:C:\\Users\\HIMANSHU\\Desktop\\Aditya\\ASVspoof2017_CMPOC_CMVN\\Models_90D_CNN_3c2p2d\\Model_25 is not in all_model_checkpoint_paths. Manually adding it.\n",
      "iteration   25\n",
      "FRR   0.14868421052631578  FAR    0.14947368421052631    mid    1.67791925743\n",
      " develop eer   0.14868421052631578\n",
      "\n",
      "INFO:tensorflow:C:\\Users\\HIMANSHU\\Desktop\\Aditya\\ASVspoof2017_CMPOC_CMVN\\Models_90D_CNN_3c2p2d\\Model_26 is not in all_model_checkpoint_paths. Manually adding it.\n",
      "iteration   26\n",
      "FRR   0.14473684210526316  FAR    0.14526315789473684    mid    1.6957790948\n",
      " develop eer   0.14473684210526316\n",
      "\n",
      "INFO:tensorflow:C:\\Users\\HIMANSHU\\Desktop\\Aditya\\ASVspoof2017_CMPOC_CMVN\\Models_90D_CNN_3c2p2d\\Model_27 is not in all_model_checkpoint_paths. Manually adding it.\n",
      "iteration   27\n",
      "FRR   0.14210526315789473  FAR    0.14210526315789473    mid    2.83934917443\n",
      " develop eer   0.14210526315789473\n",
      "\n",
      "INFO:tensorflow:C:\\Users\\HIMANSHU\\Desktop\\Aditya\\ASVspoof2017_CMPOC_CMVN\\Models_90D_CNN_3c2p2d\\Model_28 is not in all_model_checkpoint_paths. Manually adding it.\n",
      "iteration   28\n",
      "FRR   0.14473684210526316  FAR    0.14526315789473684    mid    2.36275577289\n",
      " develop eer   0.14473684210526316\n",
      "\n",
      "INFO:tensorflow:C:\\Users\\HIMANSHU\\Desktop\\Aditya\\ASVspoof2017_CMPOC_CMVN\\Models_90D_CNN_3c2p2d\\Model_29 is not in all_model_checkpoint_paths. Manually adding it.\n",
      "iteration   29\n",
      "FRR   0.14736842105263157  FAR    0.14736842105263157    mid    3.17975594613\n",
      " develop eer   0.14736842105263157\n",
      "\n",
      "INFO:tensorflow:C:\\Users\\HIMANSHU\\Desktop\\Aditya\\ASVspoof2017_CMPOC_CMVN\\Models_90D_CNN_3c2p2d\\Model_30 is not in all_model_checkpoint_paths. Manually adding it.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "iteration   30\n",
      "FRR   0.14736842105263157  FAR    0.14736842105263157    mid    1.03887076601\n",
      " develop eer   0.14736842105263157\n",
      "\n",
      "INFO:tensorflow:C:\\Users\\HIMANSHU\\Desktop\\Aditya\\ASVspoof2017_CMPOC_CMVN\\Models_90D_CNN_3c2p2d\\Model_31 is not in all_model_checkpoint_paths. Manually adding it.\n",
      "iteration   31\n",
      "FRR   0.15394736842105264  FAR    0.15473684210526314    mid    -0.939066932229\n",
      " develop eer   0.15394736842105264\n",
      "\n",
      "INFO:tensorflow:C:\\Users\\HIMANSHU\\Desktop\\Aditya\\ASVspoof2017_CMPOC_CMVN\\Models_90D_CNN_3c2p2d\\Model_32 is not in all_model_checkpoint_paths. Manually adding it.\n",
      "iteration   32\n",
      "FRR   0.14868421052631578  FAR    0.14947368421052631    mid    -1.10337876423\n",
      " develop eer   0.14868421052631578\n",
      "\n",
      "INFO:tensorflow:C:\\Users\\HIMANSHU\\Desktop\\Aditya\\ASVspoof2017_CMPOC_CMVN\\Models_90D_CNN_3c2p2d\\Model_33 is not in all_model_checkpoint_paths. Manually adding it.\n",
      "iteration   33\n",
      "FRR   0.14868421052631578  FAR    0.14947368421052631    mid    -1.23602609049\n",
      " develop eer   0.14868421052631578\n",
      "\n",
      "INFO:tensorflow:C:\\Users\\HIMANSHU\\Desktop\\Aditya\\ASVspoof2017_CMPOC_CMVN\\Models_90D_CNN_3c2p2d\\Model_34 is not in all_model_checkpoint_paths. Manually adding it.\n",
      "iteration   34\n",
      "FRR   0.14736842105263157  FAR    0.14736842105263157    mid    -3.25473657262\n",
      " develop eer   0.14736842105263157\n",
      "\n",
      "INFO:tensorflow:C:\\Users\\HIMANSHU\\Desktop\\Aditya\\ASVspoof2017_CMPOC_CMVN\\Models_90D_CNN_3c2p2d\\Model_35 is not in all_model_checkpoint_paths. Manually adding it.\n",
      "iteration   35\n",
      "FRR   0.14736842105263157  FAR    0.14842105263157895    mid    -1.45703737348\n",
      " develop eer   0.14736842105263157\n",
      "\n",
      "INFO:tensorflow:C:\\Users\\HIMANSHU\\Desktop\\Aditya\\ASVspoof2017_CMPOC_CMVN\\Models_90D_CNN_3c2p2d\\Model_36 is not in all_model_checkpoint_paths. Manually adding it.\n",
      "iteration   36\n",
      "FRR   0.14342105263157895  FAR    0.14421052631578948    mid    -0.461021127217\n",
      " develop eer   0.14342105263157895\n",
      "\n",
      "INFO:tensorflow:C:\\Users\\HIMANSHU\\Desktop\\Aditya\\ASVspoof2017_CMPOC_CMVN\\Models_90D_CNN_3c2p2d\\Model_37 is not in all_model_checkpoint_paths. Manually adding it.\n",
      "iteration   37\n",
      "FRR   0.14736842105263157  FAR    0.14736842105263157    mid    0.126538155365\n",
      " develop eer   0.14736842105263157\n",
      "\n",
      "INFO:tensorflow:C:\\Users\\HIMANSHU\\Desktop\\Aditya\\ASVspoof2017_CMPOC_CMVN\\Models_90D_CNN_3c2p2d\\Model_38 is not in all_model_checkpoint_paths. Manually adding it.\n",
      "iteration   38\n",
      "FRR   0.14605263157894738  FAR    0.14526315789473684    mid    2.03024423204\n",
      " develop eer   0.14605263157894738\n",
      "\n",
      "INFO:tensorflow:C:\\Users\\HIMANSHU\\Desktop\\Aditya\\ASVspoof2017_CMPOC_CMVN\\Models_90D_CNN_3c2p2d\\Model_39 is not in all_model_checkpoint_paths. Manually adding it.\n",
      "iteration   39\n",
      "FRR   0.14868421052631578  FAR    0.14947368421052631    mid    0.816605448839\n",
      " develop eer   0.14868421052631578\n",
      "\n",
      "INFO:tensorflow:C:\\Users\\HIMANSHU\\Desktop\\Aditya\\ASVspoof2017_CMPOC_CMVN\\Models_90D_CNN_3c2p2d\\Model_40 is not in all_model_checkpoint_paths. Manually adding it.\n",
      "iteration   40\n",
      "FRR   0.14210526315789473  FAR    0.14210526315789473    mid    1.53709675757\n",
      " develop eer   0.14210526315789473\n",
      "\n",
      "INFO:tensorflow:C:\\Users\\HIMANSHU\\Desktop\\Aditya\\ASVspoof2017_CMPOC_CMVN\\Models_90D_CNN_3c2p2d\\Model_41 is not in all_model_checkpoint_paths. Manually adding it.\n",
      "iteration   41\n",
      "FRR   0.1394736842105263  FAR    0.14    mid    1.3623199415\n",
      " develop eer   0.1394736842105263\n",
      "\n",
      "INFO:tensorflow:C:\\Users\\HIMANSHU\\Desktop\\Aditya\\ASVspoof2017_CMPOC_CMVN\\Models_90D_CNN_3c2p2d\\Model_42 is not in all_model_checkpoint_paths. Manually adding it.\n",
      "iteration   42\n",
      "FRR   0.14736842105263157  FAR    0.14736842105263157    mid    2.18034439568\n",
      " develop eer   0.14736842105263157\n",
      "\n",
      "INFO:tensorflow:C:\\Users\\HIMANSHU\\Desktop\\Aditya\\ASVspoof2017_CMPOC_CMVN\\Models_90D_CNN_3c2p2d\\Model_43 is not in all_model_checkpoint_paths. Manually adding it.\n",
      "iteration   43\n",
      "FRR   0.14342105263157895  FAR    0.14421052631578948    mid    1.3764810221\n",
      " develop eer   0.14342105263157895\n",
      "\n",
      "INFO:tensorflow:C:\\Users\\HIMANSHU\\Desktop\\Aditya\\ASVspoof2017_CMPOC_CMVN\\Models_90D_CNN_3c2p2d\\Model_44 is not in all_model_checkpoint_paths. Manually adding it.\n",
      "iteration   44\n",
      "FRR   0.14473684210526316  FAR    0.14421052631578948    mid    2.32733770438\n",
      " develop eer   0.14473684210526316\n",
      "\n",
      "INFO:tensorflow:C:\\Users\\HIMANSHU\\Desktop\\Aditya\\ASVspoof2017_CMPOC_CMVN\\Models_90D_CNN_3c2p2d\\Model_45 is not in all_model_checkpoint_paths. Manually adding it.\n",
      "iteration   45\n",
      "FRR   0.14342105263157895  FAR    0.14421052631578948    mid    1.99483530384\n",
      " develop eer   0.14342105263157895\n",
      "\n",
      "INFO:tensorflow:C:\\Users\\HIMANSHU\\Desktop\\Aditya\\ASVspoof2017_CMPOC_CMVN\\Models_90D_CNN_3c2p2d\\Model_46 is not in all_model_checkpoint_paths. Manually adding it.\n",
      "iteration   46\n",
      "FRR   0.14473684210526316  FAR    0.14526315789473684    mid    0.945964471325\n",
      " develop eer   0.14473684210526316\n",
      "\n",
      "INFO:tensorflow:C:\\Users\\HIMANSHU\\Desktop\\Aditya\\ASVspoof2017_CMPOC_CMVN\\Models_90D_CNN_3c2p2d\\Model_47 is not in all_model_checkpoint_paths. Manually adding it.\n",
      "iteration   47\n",
      "FRR   0.14605263157894738  FAR    0.1463157894736842    mid    2.28394500331\n",
      " develop eer   0.14605263157894738\n",
      "\n",
      "INFO:tensorflow:C:\\Users\\HIMANSHU\\Desktop\\Aditya\\ASVspoof2017_CMPOC_CMVN\\Models_90D_CNN_3c2p2d\\Model_48 is not in all_model_checkpoint_paths. Manually adding it.\n",
      "iteration   48\n",
      "FRR   0.15  FAR    0.15052631578947367    mid    0.912832627065\n",
      " develop eer   0.15\n",
      "\n",
      "INFO:tensorflow:C:\\Users\\HIMANSHU\\Desktop\\Aditya\\ASVspoof2017_CMPOC_CMVN\\Models_90D_CNN_3c2p2d\\Model_49 is not in all_model_checkpoint_paths. Manually adding it.\n",
      "iteration   49\n",
      "FRR   0.14736842105263157  FAR    0.14736842105263157    mid    0.786940562605\n",
      " develop eer   0.14736842105263157\n",
      "\n",
      "INFO:tensorflow:C:\\Users\\HIMANSHU\\Desktop\\Aditya\\ASVspoof2017_CMPOC_CMVN\\Models_90D_CNN_3c2p2d\\Model_50 is not in all_model_checkpoint_paths. Manually adding it.\n",
      "iteration   50\n",
      "FRR   0.15263157894736842  FAR    0.15263157894736842    mid    0.492812611817\n",
      " develop eer   0.15263157894736842\n",
      "\n"
     ]
    }
   ],
   "source": [
    "for i in range(1,50):\n",
    "    \n",
    "    for j in range(1,batches+1):  #number of batches\n",
    "\n",
    "\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",
    "        #print(\"before  \",str(j),\"   \",accuracy.eval(feed_dict = {x:features, y:labels, keep_prob:1.0}))\n",
    "        train_step.run(session = session,feed_dict={ x:features, y:labels, keep_prob:0.5})\n",
    "        #print(\"after   \",str(j),\"   \",accuracy.eval(feed_dict = {x:features, y:labels, keep_prob:1.0}))\n",
    "\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()\n",
    "    \n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Restoring parameters from C:\\Users\\HIMANSHU\\Desktop\\Aditya\\ASVspoof2017_CMPOC_CMVN\\Models_90D_CNN_3c2p2d\\Model_50\n",
      "INFO:tensorflow:C:\\Users\\HIMANSHU\\Desktop\\Aditya\\ASVspoof2017_CMPOC_CMVN\\Models_90D_CNN_3c2p2d\\Model_51 is not in all_model_checkpoint_paths. Manually adding it.\n",
      " iteration    51\n",
      "FRR   0.14210526315789473  FAR    0.1431578947368421    mid    0.346311419948\n",
      " develop eer 0.14210526315789473\n",
      "\n",
      "INFO:tensorflow:C:\\Users\\HIMANSHU\\Desktop\\Aditya\\ASVspoof2017_CMPOC_CMVN\\Models_90D_CNN_3c2p2d\\Model_52 is not in all_model_checkpoint_paths. Manually adding it.\n",
      " iteration    52\n",
      "FRR   0.14736842105263157  FAR    0.14736842105263157    mid    -0.724397556622\n",
      " develop eer 0.14736842105263157\n",
      "\n",
      "INFO:tensorflow:C:\\Users\\HIMANSHU\\Desktop\\Aditya\\ASVspoof2017_CMPOC_CMVN\\Models_90D_CNN_3c2p2d\\Model_53 is not in all_model_checkpoint_paths. Manually adding it.\n",
      " iteration    53\n",
      "FRR   0.14473684210526316  FAR    0.14526315789473684    mid    -1.08741538689\n",
      " develop eer 0.14473684210526316\n",
      "\n",
      "INFO:tensorflow:C:\\Users\\HIMANSHU\\Desktop\\Aditya\\ASVspoof2017_CMPOC_CMVN\\Models_90D_CNN_3c2p2d\\Model_54 is not in all_model_checkpoint_paths. Manually adding it.\n",
      " iteration    54\n",
      "FRR   0.1394736842105263  FAR    0.14    mid    -2.6922461309\n",
      " develop eer 0.1394736842105263\n",
      "\n",
      "INFO:tensorflow:C:\\Users\\HIMANSHU\\Desktop\\Aditya\\ASVspoof2017_CMPOC_CMVN\\Models_90D_CNN_3c2p2d\\Model_55 is not in all_model_checkpoint_paths. Manually adding it.\n",
      " iteration    55\n",
      "FRR   0.14736842105263157  FAR    0.14736842105263157    mid    -2.1579606501\n",
      " develop eer 0.14736842105263157\n",
      "\n",
      "INFO:tensorflow:C:\\Users\\HIMANSHU\\Desktop\\Aditya\\ASVspoof2017_CMPOC_CMVN\\Models_90D_CNN_3c2p2d\\Model_56 is not in all_model_checkpoint_paths. Manually adding it.\n",
      " iteration    56\n",
      "FRR   0.14473684210526316  FAR    0.14526315789473684    mid    -0.865294813531\n",
      " develop eer 0.14473684210526316\n",
      "\n",
      "INFO:tensorflow:C:\\Users\\HIMANSHU\\Desktop\\Aditya\\ASVspoof2017_CMPOC_CMVN\\Models_90D_CNN_3c2p2d\\Model_57 is not in all_model_checkpoint_paths. Manually adding it.\n",
      " iteration    57\n",
      "FRR   0.1394736842105263  FAR    0.14    mid    -1.34754359158\n",
      " develop eer 0.1394736842105263\n",
      "\n",
      "INFO:tensorflow:C:\\Users\\HIMANSHU\\Desktop\\Aditya\\ASVspoof2017_CMPOC_CMVN\\Models_90D_CNN_3c2p2d\\Model_58 is not in all_model_checkpoint_paths. Manually adding it.\n",
      " iteration    58\n",
      "FRR   0.13815789473684212  FAR    0.13894736842105262    mid    -1.78030886756\n",
      " develop eer 0.13815789473684212\n",
      "\n",
      "INFO:tensorflow:C:\\Users\\HIMANSHU\\Desktop\\Aditya\\ASVspoof2017_CMPOC_CMVN\\Models_90D_CNN_3c2p2d\\Model_59 is not in all_model_checkpoint_paths. Manually adding it.\n",
      " iteration    59\n",
      "FRR   0.14736842105263157  FAR    0.14842105263157895    mid    -0.97953630843\n",
      " develop eer 0.14736842105263157\n",
      "\n",
      "INFO:tensorflow:C:\\Users\\HIMANSHU\\Desktop\\Aditya\\ASVspoof2017_CMPOC_CMVN\\Models_90D_CNN_3c2p2d\\Model_60 is not in all_model_checkpoint_paths. Manually adding it.\n",
      " iteration    60\n",
      "FRR   0.14736842105263157  FAR    0.14736842105263157    mid    2.48774818511\n",
      " develop eer 0.14736842105263157\n",
      "\n",
      "INFO:tensorflow:C:\\Users\\HIMANSHU\\Desktop\\Aditya\\ASVspoof2017_CMPOC_CMVN\\Models_90D_CNN_3c2p2d\\Model_61 is not in all_model_checkpoint_paths. Manually adding it.\n",
      " iteration    61\n",
      "FRR   0.14342105263157895  FAR    0.14421052631578948    mid    1.66355083016\n",
      " develop eer 0.14342105263157895\n",
      "\n",
      "INFO:tensorflow:C:\\Users\\HIMANSHU\\Desktop\\Aditya\\ASVspoof2017_CMPOC_CMVN\\Models_90D_CNN_3c2p2d\\Model_62 is not in all_model_checkpoint_paths. Manually adding it.\n",
      " iteration    62\n",
      "FRR   0.14078947368421052  FAR    0.14105263157894737    mid    -0.458565651197\n",
      " develop eer 0.14078947368421052\n",
      "\n",
      "INFO:tensorflow:C:\\Users\\HIMANSHU\\Desktop\\Aditya\\ASVspoof2017_CMPOC_CMVN\\Models_90D_CNN_3c2p2d\\Model_63 is not in all_model_checkpoint_paths. Manually adding it.\n",
      " iteration    63\n",
      "FRR   0.14868421052631578  FAR    0.14842105263157895    mid    4.64939461206\n",
      " develop eer 0.14868421052631578\n",
      "\n",
      "INFO:tensorflow:C:\\Users\\HIMANSHU\\Desktop\\Aditya\\ASVspoof2017_CMPOC_CMVN\\Models_90D_CNN_3c2p2d\\Model_64 is not in all_model_checkpoint_paths. Manually adding it.\n",
      " iteration    64\n",
      "FRR   0.14210526315789473  FAR    0.14210526315789473    mid    3.1095528262\n",
      " develop eer 0.14210526315789473\n",
      "\n",
      "INFO:tensorflow:C:\\Users\\HIMANSHU\\Desktop\\Aditya\\ASVspoof2017_CMPOC_CMVN\\Models_90D_CNN_3c2p2d\\Model_65 is not in all_model_checkpoint_paths. Manually adding it.\n",
      " iteration    65\n",
      "FRR   0.14473684210526316  FAR    0.14526315789473684    mid    3.13461923587\n",
      " develop eer 0.14473684210526316\n",
      "\n",
      "INFO:tensorflow:C:\\Users\\HIMANSHU\\Desktop\\Aditya\\ASVspoof2017_CMPOC_CMVN\\Models_90D_CNN_3c2p2d\\Model_66 is not in all_model_checkpoint_paths. Manually adding it.\n",
      " iteration    66\n",
      "FRR   0.14473684210526316  FAR    0.14421052631578948    mid    3.0917825624\n",
      " develop eer 0.14473684210526316\n",
      "\n",
      "INFO:tensorflow:C:\\Users\\HIMANSHU\\Desktop\\Aditya\\ASVspoof2017_CMPOC_CMVN\\Models_90D_CNN_3c2p2d\\Model_67 is not in all_model_checkpoint_paths. Manually adding it.\n",
      " iteration    67\n",
      "FRR   0.14078947368421052  FAR    0.14105263157894737    mid    4.55995427419\n",
      " develop eer 0.14078947368421052\n",
      "\n",
      "INFO:tensorflow:C:\\Users\\HIMANSHU\\Desktop\\Aditya\\ASVspoof2017_CMPOC_CMVN\\Models_90D_CNN_3c2p2d\\Model_68 is not in all_model_checkpoint_paths. Manually adding it.\n",
      " iteration    68\n",
      "FRR   0.14210526315789473  FAR    0.1431578947368421    mid    5.5103670799\n",
      " develop eer 0.14210526315789473\n",
      "\n",
      "INFO:tensorflow:C:\\Users\\HIMANSHU\\Desktop\\Aditya\\ASVspoof2017_CMPOC_CMVN\\Models_90D_CNN_3c2p2d\\Model_69 is not in all_model_checkpoint_paths. Manually adding it.\n",
      " iteration    69\n",
      "FRR   0.1394736842105263  FAR    0.14    mid    6.90466891733\n",
      " develop eer 0.1394736842105263\n",
      "\n",
      "INFO:tensorflow:C:\\Users\\HIMANSHU\\Desktop\\Aditya\\ASVspoof2017_CMPOC_CMVN\\Models_90D_CNN_3c2p2d\\Model_70 is not in all_model_checkpoint_paths. Manually adding it.\n",
      " iteration    70\n",
      "FRR   0.14473684210526316  FAR    0.14526315789473684    mid    5.00449504833\n",
      " develop eer 0.14473684210526316\n",
      "\n",
      "INFO:tensorflow:C:\\Users\\HIMANSHU\\Desktop\\Aditya\\ASVspoof2017_CMPOC_CMVN\\Models_90D_CNN_3c2p2d\\Model_71 is not in all_model_checkpoint_paths. Manually adding it.\n",
      " iteration    71\n",
      "FRR   0.1513157894736842  FAR    0.15157894736842106    mid    3.73798019272\n",
      " develop eer 0.1513157894736842\n",
      "\n",
      "INFO:tensorflow:C:\\Users\\HIMANSHU\\Desktop\\Aditya\\ASVspoof2017_CMPOC_CMVN\\Models_90D_CNN_3c2p2d\\Model_72 is not in all_model_checkpoint_paths. Manually adding it.\n",
      " iteration    72\n",
      "FRR   0.14342105263157895  FAR    0.14421052631578948    mid    4.68063517735\n",
      " develop eer 0.14342105263157895\n",
      "\n",
      "INFO:tensorflow:C:\\Users\\HIMANSHU\\Desktop\\Aditya\\ASVspoof2017_CMPOC_CMVN\\Models_90D_CNN_3c2p2d\\Model_73 is not in all_model_checkpoint_paths. Manually adding it.\n",
      " iteration    73\n",
      "FRR   0.14605263157894738  FAR    0.14526315789473684    mid    5.47655071186\n",
      " develop eer 0.14605263157894738\n",
      "\n",
      "INFO:tensorflow:C:\\Users\\HIMANSHU\\Desktop\\Aditya\\ASVspoof2017_CMPOC_CMVN\\Models_90D_CNN_3c2p2d\\Model_74 is not in all_model_checkpoint_paths. Manually adding it.\n",
      " iteration    74\n",
      "FRR   0.14473684210526316  FAR    0.14526315789473684    mid    5.36991415495\n",
      " develop eer 0.14473684210526316\n",
      "\n",
      "INFO:tensorflow:C:\\Users\\HIMANSHU\\Desktop\\Aditya\\ASVspoof2017_CMPOC_CMVN\\Models_90D_CNN_3c2p2d\\Model_75 is not in all_model_checkpoint_paths. Manually adding it.\n",
      " iteration    75\n",
      "FRR   0.15  FAR    0.15052631578947367    mid    4.36328586545\n",
      " develop eer 0.15\n",
      "\n"
     ]
    }
   ],
   "source": [
    "\n",
    "with tf.Session() as session:\n",
    "    saver.restore(session,os.path.join(model_path,\"Model_\"+str(50)))\n",
    "    \n",
    "    for i in range(50,75):\n",
    "        for j in range(1,batches+1):\n",
    "            \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, keep_prob:0.5})\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()\n",
    "        \n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Restoring parameters from C:\\Users\\HIMANSHU\\Desktop\\Aditya\\ASVspoof2017_CMPOC_CMVN\\Models_90D_CNN_3c2p2d\\Model_2\n",
      "iteration  2\n",
      "FRR   0.44838212634822805  FAR    0.4484510326449034    mid    0.170382076786\n",
      " evaluation err   0.44838212634822805\n",
      "\n",
      "INFO:tensorflow:Restoring parameters from C:\\Users\\HIMANSHU\\Desktop\\Aditya\\ASVspoof2017_CMPOC_CMVN\\Models_90D_CNN_3c2p2d\\Model_3\n",
      "iteration  3\n",
      "FRR   0.42295839753466874  FAR    0.42371752165223187    mid    0.189829950531\n",
      " evaluation err   0.42295839753466874\n",
      "\n",
      "INFO:tensorflow:Restoring parameters from C:\\Users\\HIMANSHU\\Desktop\\Aditya\\ASVspoof2017_CMPOC_CMVN\\Models_90D_CNN_3c2p2d\\Model_4\n",
      "iteration  4\n",
      "FRR   0.42449922958397535  FAR    0.42513324450366424    mid    0.15734212368\n",
      " evaluation err   0.42449922958397535\n",
      "\n",
      "INFO:tensorflow:Restoring parameters from C:\\Users\\HIMANSHU\\Desktop\\Aditya\\ASVspoof2017_CMPOC_CMVN\\Models_90D_CNN_3c2p2d\\Model_5\n",
      "iteration  5\n",
      "FRR   0.43297380585516176  FAR    0.43312791472351764    mid    0.332366216363\n",
      " evaluation err   0.43297380585516176\n",
      "\n",
      "INFO:tensorflow:Restoring parameters from C:\\Users\\HIMANSHU\\Desktop\\Aditya\\ASVspoof2017_CMPOC_CMVN\\Models_90D_CNN_3c2p2d\\Model_6\n",
      "iteration  6\n",
      "FRR   0.4352850539291217  FAR    0.4347934710193205    mid    0.683245144137\n",
      " evaluation err   0.4352850539291217\n",
      "\n",
      "INFO:tensorflow:Restoring parameters from C:\\Users\\HIMANSHU\\Desktop\\Aditya\\ASVspoof2017_CMPOC_CMVN\\Models_90D_CNN_3c2p2d\\Model_7\n",
      "iteration  7\n",
      "FRR   0.4322033898305085  FAR    0.43287808127914723    mid    0.845233016544\n",
      " evaluation err   0.4322033898305085\n",
      "\n",
      "INFO:tensorflow:Restoring parameters from C:\\Users\\HIMANSHU\\Desktop\\Aditya\\ASVspoof2017_CMPOC_CMVN\\Models_90D_CNN_3c2p2d\\Model_8\n",
      "iteration  8\n",
      "FRR   0.4306625577812018  FAR    0.43079613590939375    mid    1.06088989807\n",
      " evaluation err   0.4306625577812018\n",
      "\n",
      "INFO:tensorflow:Restoring parameters from C:\\Users\\HIMANSHU\\Desktop\\Aditya\\ASVspoof2017_CMPOC_CMVN\\Models_90D_CNN_3c2p2d\\Model_9\n",
      "iteration  9\n",
      "FRR   0.4268104776579353  FAR    0.42663224516988674    mid    1.25104398544\n",
      " evaluation err   0.4268104776579353\n",
      "\n",
      "INFO:tensorflow:Restoring parameters from C:\\Users\\HIMANSHU\\Desktop\\Aditya\\ASVspoof2017_CMPOC_CMVN\\Models_90D_CNN_3c2p2d\\Model_10\n",
      "iteration  10\n",
      "FRR   0.4167950693374422  FAR    0.41663890739507    mid    1.33339752508\n",
      " evaluation err   0.4167950693374422\n",
      "\n",
      "INFO:tensorflow:Restoring parameters from C:\\Users\\HIMANSHU\\Desktop\\Aditya\\ASVspoof2017_CMPOC_CMVN\\Models_90D_CNN_3c2p2d\\Model_11\n",
      "iteration  11\n",
      "FRR   0.4106317411402157  FAR    0.4106429047301799    mid    1.4412343238\n",
      " evaluation err   0.4106317411402157\n",
      "\n",
      "INFO:tensorflow:Restoring parameters from C:\\Users\\HIMANSHU\\Desktop\\Aditya\\ASVspoof2017_CMPOC_CMVN\\Models_90D_CNN_3c2p2d\\Model_12\n",
      "iteration  12\n",
      "FRR   0.3913713405238829  FAR    0.39182211858760824    mid    1.16548324289\n",
      " evaluation err   0.3913713405238829\n",
      "\n",
      "INFO:tensorflow:Restoring parameters from C:\\Users\\HIMANSHU\\Desktop\\Aditya\\ASVspoof2017_CMPOC_CMVN\\Models_90D_CNN_3c2p2d\\Model_13\n",
      "iteration  13\n",
      "FRR   0.3436055469953775  FAR    0.3436875416389074    mid    0.893796288541\n",
      " evaluation err   0.3436055469953775\n",
      "\n",
      "INFO:tensorflow:Restoring parameters from C:\\Users\\HIMANSHU\\Desktop\\Aditya\\ASVspoof2017_CMPOC_CMVN\\Models_90D_CNN_3c2p2d\\Model_14\n",
      "iteration  14\n",
      "FRR   0.29583975346687214  FAR    0.2963857428381079    mid    1.3916071553\n",
      " evaluation err   0.29583975346687214\n",
      "\n",
      "INFO:tensorflow:Restoring parameters from C:\\Users\\HIMANSHU\\Desktop\\Aditya\\ASVspoof2017_CMPOC_CMVN\\Models_90D_CNN_3c2p2d\\Model_15\n",
      "iteration  15\n",
      "FRR   0.26040061633281975  FAR    0.25991005996002664    mid    1.37586045585\n",
      " evaluation err   0.26040061633281975\n",
      "\n",
      "INFO:tensorflow:Restoring parameters from C:\\Users\\HIMANSHU\\Desktop\\Aditya\\ASVspoof2017_CMPOC_CMVN\\Models_90D_CNN_3c2p2d\\Model_16\n",
      "iteration  16\n",
      "FRR   0.25115562403698  FAR    0.2504163890739507    mid    2.3345388545\n",
      " evaluation err   0.25115562403698\n",
      "\n",
      "INFO:tensorflow:Restoring parameters from C:\\Users\\HIMANSHU\\Desktop\\Aditya\\ASVspoof2017_CMPOC_CMVN\\Models_90D_CNN_3c2p2d\\Model_17\n",
      "iteration  17\n",
      "FRR   0.23728813559322035  FAR    0.23742504996668887    mid    2.0477809993\n",
      " evaluation err   0.23728813559322035\n",
      "\n",
      "INFO:tensorflow:Restoring parameters from C:\\Users\\HIMANSHU\\Desktop\\Aditya\\ASVspoof2017_CMPOC_CMVN\\Models_90D_CNN_3c2p2d\\Model_18\n",
      "iteration  18\n",
      "FRR   0.23959938366718028  FAR    0.23967355096602266    mid    2.42606395687\n",
      " evaluation err   0.23959938366718028\n",
      "\n",
      "INFO:tensorflow:Restoring parameters from C:\\Users\\HIMANSHU\\Desktop\\Aditya\\ASVspoof2017_CMPOC_CMVN\\Models_90D_CNN_3c2p2d\\Model_19\n",
      "iteration  19\n",
      "FRR   0.23651771956856701  FAR    0.23659227181878748    mid    2.88015281241\n",
      " evaluation err   0.23651771956856701\n",
      "\n",
      "INFO:tensorflow:Restoring parameters from C:\\Users\\HIMANSHU\\Desktop\\Aditya\\ASVspoof2017_CMPOC_CMVN\\Models_90D_CNN_3c2p2d\\Model_20\n",
      "iteration  20\n",
      "FRR   0.23420647149460708  FAR    0.2347601598934044    mid    4.01722129307\n",
      " evaluation err   0.23420647149460708\n",
      "\n",
      "INFO:tensorflow:Restoring parameters from C:\\Users\\HIMANSHU\\Desktop\\Aditya\\ASVspoof2017_CMPOC_CMVN\\Models_90D_CNN_3c2p2d\\Model_21\n",
      "iteration  21\n",
      "FRR   0.23189522342064714  FAR    0.23259493670886075    mid    3.97633804887\n",
      " evaluation err   0.23189522342064714\n",
      "\n",
      "INFO:tensorflow:Restoring parameters from C:\\Users\\HIMANSHU\\Desktop\\Aditya\\ASVspoof2017_CMPOC_CMVN\\Models_90D_CNN_3c2p2d\\Model_22\n",
      "iteration  22\n",
      "FRR   0.2218798151001541  FAR    0.22168554297135243    mid    3.51182076914\n",
      " evaluation err   0.2218798151001541\n",
      "\n",
      "INFO:tensorflow:Restoring parameters from C:\\Users\\HIMANSHU\\Desktop\\Aditya\\ASVspoof2017_CMPOC_CMVN\\Models_90D_CNN_3c2p2d\\Model_23\n",
      "iteration  23\n",
      "FRR   0.22650231124807396  FAR    0.22651565622918055    mid    3.61975817724\n",
      " evaluation err   0.22650231124807396\n",
      "\n",
      "INFO:tensorflow:Restoring parameters from C:\\Users\\HIMANSHU\\Desktop\\Aditya\\ASVspoof2017_CMPOC_CMVN\\Models_90D_CNN_3c2p2d\\Model_24\n",
      "iteration  24\n",
      "FRR   0.22419106317411402  FAR    0.22418387741505663    mid    4.2891926969\n",
      " evaluation err   0.22419106317411402\n",
      "\n",
      "INFO:tensorflow:Restoring parameters from C:\\Users\\HIMANSHU\\Desktop\\Aditya\\ASVspoof2017_CMPOC_CMVN\\Models_90D_CNN_3c2p2d\\Model_25\n",
      "iteration  25\n",
      "FRR   0.22110939907550076  FAR    0.2203530979347102    mid    4.24603879119\n",
      " evaluation err   0.22110939907550076\n",
      "\n",
      "INFO:tensorflow:Restoring parameters from C:\\Users\\HIMANSHU\\Desktop\\Aditya\\ASVspoof2017_CMPOC_CMVN\\Models_90D_CNN_3c2p2d\\Model_26\n",
      "iteration  26\n",
      "FRR   0.21648690292758088  FAR    0.2171885409726849    mid    4.2326126454\n",
      " evaluation err   0.21648690292758088\n",
      "\n",
      "INFO:tensorflow:Restoring parameters from C:\\Users\\HIMANSHU\\Desktop\\Aditya\\ASVspoof2017_CMPOC_CMVN\\Models_90D_CNN_3c2p2d\\Model_27\n",
      "iteration  27\n",
      "FRR   0.2172573189522342  FAR    0.2176882078614257    mid    5.41742700955\n",
      " evaluation err   0.2172573189522342\n",
      "\n",
      "INFO:tensorflow:Restoring parameters from C:\\Users\\HIMANSHU\\Desktop\\Aditya\\ASVspoof2017_CMPOC_CMVN\\Models_90D_CNN_3c2p2d\\Model_28\n",
      "iteration  28\n",
      "FRR   0.2172573189522342  FAR    0.217271818787475    mid    5.19723232127\n",
      " evaluation err   0.2172573189522342\n",
      "\n",
      "INFO:tensorflow:Restoring parameters from C:\\Users\\HIMANSHU\\Desktop\\Aditya\\ASVspoof2017_CMPOC_CMVN\\Models_90D_CNN_3c2p2d\\Model_29\n",
      "iteration  29\n",
      "FRR   0.21571648690292758  FAR    0.21577281812125249    mid    6.083067139\n",
      " evaluation err   0.21571648690292758\n",
      "\n",
      "INFO:tensorflow:Restoring parameters from C:\\Users\\HIMANSHU\\Desktop\\Aditya\\ASVspoof2017_CMPOC_CMVN\\Models_90D_CNN_3c2p2d\\Model_30\n",
      "iteration  30\n",
      "FRR   0.20955315870570107  FAR    0.20961025982678214    mid    3.34318018575\n",
      " evaluation err   0.20955315870570107\n",
      "\n",
      "INFO:tensorflow:Restoring parameters from C:\\Users\\HIMANSHU\\Desktop\\Aditya\\ASVspoof2017_CMPOC_CMVN\\Models_90D_CNN_3c2p2d\\Model_31\n",
      "iteration  31\n",
      "FRR   0.20184899845916796  FAR    0.20211525649566955    mid    0.919702901509\n",
      " evaluation err   0.20184899845916796\n",
      "\n",
      "INFO:tensorflow:Restoring parameters from C:\\Users\\HIMANSHU\\Desktop\\Aditya\\ASVspoof2017_CMPOC_CMVN\\Models_90D_CNN_3c2p2d\\Model_32\n",
      "iteration  32\n",
      "FRR   0.2033898305084746  FAR    0.20411392405063292    mid    1.03460154628\n",
      " evaluation err   0.2033898305084746\n",
      "\n",
      "INFO:tensorflow:Restoring parameters from C:\\Users\\HIMANSHU\\Desktop\\Aditya\\ASVspoof2017_CMPOC_CMVN\\Models_90D_CNN_3c2p2d\\Model_33\n",
      "iteration  33\n",
      "FRR   0.2033898305084746  FAR    0.20361425716189208    mid    0.892511233363\n",
      " evaluation err   0.2033898305084746\n",
      "\n",
      "INFO:tensorflow:Restoring parameters from C:\\Users\\HIMANSHU\\Desktop\\Aditya\\ASVspoof2017_CMPOC_CMVN\\Models_90D_CNN_3c2p2d\\Model_34\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "iteration  34\n",
      "FRR   0.19645608628659475  FAR    0.1966189207195203    mid    -1.29245051382\n",
      " evaluation err   0.19645608628659475\n",
      "\n",
      "INFO:tensorflow:Restoring parameters from C:\\Users\\HIMANSHU\\Desktop\\Aditya\\ASVspoof2017_CMPOC_CMVN\\Models_90D_CNN_3c2p2d\\Model_35\n",
      "iteration  35\n",
      "FRR   0.19568567026194145  FAR    0.1963690872751499    mid    0.0913331768129\n",
      " evaluation err   0.19568567026194145\n",
      "\n",
      "INFO:tensorflow:Restoring parameters from C:\\Users\\HIMANSHU\\Desktop\\Aditya\\ASVspoof2017_CMPOC_CMVN\\Models_90D_CNN_3c2p2d\\Model_36\n",
      "iteration  36\n",
      "FRR   0.20184899845916796  FAR    0.20186542305129915    mid    1.42588151537\n",
      " evaluation err   0.20184899845916796\n",
      "\n",
      "INFO:tensorflow:Restoring parameters from C:\\Users\\HIMANSHU\\Desktop\\Aditya\\ASVspoof2017_CMPOC_CMVN\\Models_90D_CNN_3c2p2d\\Model_37\n",
      "iteration  37\n",
      "FRR   0.19799691833590138  FAR    0.19795136575616257    mid    2.01229275819\n",
      " evaluation err   0.19799691833590138\n",
      "\n",
      "INFO:tensorflow:Restoring parameters from C:\\Users\\HIMANSHU\\Desktop\\Aditya\\ASVspoof2017_CMPOC_CMVN\\Models_90D_CNN_3c2p2d\\Model_38\n",
      "iteration  38\n",
      "FRR   0.20647149460708783  FAR    0.20644570286475683    mid    4.82438580003\n",
      " evaluation err   0.20647149460708783\n",
      "\n",
      "INFO:tensorflow:Restoring parameters from C:\\Users\\HIMANSHU\\Desktop\\Aditya\\ASVspoof2017_CMPOC_CMVN\\Models_90D_CNN_3c2p2d\\Model_39\n",
      "iteration  39\n",
      "FRR   0.20570107858243453  FAR    0.2057794803464357    mid    3.62375235867\n",
      " evaluation err   0.20570107858243453\n",
      "\n",
      "INFO:tensorflow:Restoring parameters from C:\\Users\\HIMANSHU\\Desktop\\Aditya\\ASVspoof2017_CMPOC_CMVN\\Models_90D_CNN_3c2p2d\\Model_40\n",
      "iteration  40\n",
      "FRR   0.20724191063174113  FAR    0.2078614257161892    mid    4.14915930614\n",
      " evaluation err   0.20724191063174113\n",
      "\n",
      "INFO:tensorflow:Restoring parameters from C:\\Users\\HIMANSHU\\Desktop\\Aditya\\ASVspoof2017_CMPOC_CMVN\\Models_90D_CNN_3c2p2d\\Model_41\n",
      "iteration  41\n",
      "FRR   0.20647149460708783  FAR    0.20677881412391738    mid    4.37576011739\n",
      " evaluation err   0.20647149460708783\n",
      "\n",
      "INFO:tensorflow:Restoring parameters from C:\\Users\\HIMANSHU\\Desktop\\Aditya\\ASVspoof2017_CMPOC_CMVN\\Models_90D_CNN_3c2p2d\\Model_42\n",
      "iteration  42\n",
      "FRR   0.20570107858243453  FAR    0.20569620253164558    mid    4.80376352282\n",
      " evaluation err   0.20570107858243453\n",
      "\n",
      "INFO:tensorflow:Restoring parameters from C:\\Users\\HIMANSHU\\Desktop\\Aditya\\ASVspoof2017_CMPOC_CMVN\\Models_90D_CNN_3c2p2d\\Model_43\n",
      "iteration  43\n",
      "FRR   0.2049306625577812  FAR    0.20436375749500332    mid    4.17048159724\n",
      " evaluation err   0.2049306625577812\n",
      "\n",
      "INFO:tensorflow:Restoring parameters from C:\\Users\\HIMANSHU\\Desktop\\Aditya\\ASVspoof2017_CMPOC_CMVN\\Models_90D_CNN_3c2p2d\\Model_44\n",
      "iteration  44\n",
      "FRR   0.2049306625577812  FAR    0.2045303131245836    mid    4.79480383866\n",
      " evaluation err   0.2049306625577812\n",
      "\n",
      "INFO:tensorflow:Restoring parameters from C:\\Users\\HIMANSHU\\Desktop\\Aditya\\ASVspoof2017_CMPOC_CMVN\\Models_90D_CNN_3c2p2d\\Model_45\n",
      "iteration  45\n",
      "FRR   0.20955315870570107  FAR    0.20944370419720187    mid    5.05871856205\n",
      " evaluation err   0.20955315870570107\n",
      "\n",
      "INFO:tensorflow:Restoring parameters from C:\\Users\\HIMANSHU\\Desktop\\Aditya\\ASVspoof2017_CMPOC_CMVN\\Models_90D_CNN_3c2p2d\\Model_46\n",
      "iteration  46\n",
      "FRR   0.20724191063174113  FAR    0.20736175882744837    mid    4.30388881972\n",
      " evaluation err   0.20724191063174113\n",
      "\n",
      "INFO:tensorflow:Restoring parameters from C:\\Users\\HIMANSHU\\Desktop\\Aditya\\ASVspoof2017_CMPOC_CMVN\\Models_90D_CNN_3c2p2d\\Model_47\n",
      "iteration  47\n",
      "FRR   0.2041602465331279  FAR    0.20461359093937376    mid    5.31613654967\n",
      " evaluation err   0.2041602465331279\n",
      "\n",
      "INFO:tensorflow:Restoring parameters from C:\\Users\\HIMANSHU\\Desktop\\Aditya\\ASVspoof2017_CMPOC_CMVN\\Models_90D_CNN_3c2p2d\\Model_48\n",
      "iteration  48\n",
      "FRR   0.2041602465331279  FAR    0.20461359093937376    mid    3.78740042049\n",
      " evaluation err   0.2041602465331279\n",
      "\n",
      "INFO:tensorflow:Restoring parameters from C:\\Users\\HIMANSHU\\Desktop\\Aditya\\ASVspoof2017_CMPOC_CMVN\\Models_90D_CNN_3c2p2d\\Model_49\n",
      "iteration  49\n",
      "FRR   0.2049306625577812  FAR    0.20569620253164558    mid    3.77510101436\n",
      " evaluation err   0.2049306625577812\n",
      "\n",
      "INFO:tensorflow:Restoring parameters from C:\\Users\\HIMANSHU\\Desktop\\Aditya\\ASVspoof2017_CMPOC_CMVN\\Models_90D_CNN_3c2p2d\\Model_50\n",
      "iteration  50\n",
      "FRR   0.20570107858243453  FAR    0.20627914723517654    mid    3.73769938421\n",
      " evaluation err   0.20570107858243453\n",
      "\n"
     ]
    }
   ],
   "source": [
    "evaluation_path = \"C:\\\\Users\\\\HIMANSHU\\\\Desktop\\\\Aditya\\\\ASVspoof2017_CMPOC_CMVN\\\\evaloverlapData_90D\"\n",
    "eva_sp_path = \"C:\\\\Users\\\\HIMANSHU\\\\Desktop\\\\Aditya\\\\ASVspoof2017_CMPOC_CMVN\\\\eval_sp_90D_CNN_3c2p2d\" \n",
    "\n",
    "if not os.path.exists(eva_sp_path):\n",
    "    os.makedirs(eva_sp_path)\n",
    "    \n",
    "with tf.Session() as session:\n",
    "    for i in range(1,50):\n",
    "        saver.restore(session,os.path.join(model_path,\"Model_\"+str(i+1)))\n",
    "        print(\"iteration \", (i+1))\n",
    "        eval_err = get_EER(evaluation_path, (i+1), eva_sp_path)\n",
    "        print(\" evaluation err  \", eval_err)\n",
    "        print()"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "for i in range(5):\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",
    "        #print(\"before  \",str(j),\"   \",accuracy.eval(feed_dict = {x:features, y:labels, keep_prob:1.0}))\n",
    "        train_step.run(session = session,feed_dict={ x:features, y:labels, keep_prob:0.5})\n",
    "        #print(\"after   \",str(j),\"   \",accuracy.eval(feed_dict = {x:features, y:labels, keep_prob:1.0}))\n",
    "        \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": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import numpy as np\n",
    "development_path = \"E:\\\\Aditya\\\\ASVspoof2019_CFCC_IF_CMN\\\\78D\\\\overlapData_78D\"\n",
    "dev_num = len(os.listdir(development_path))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# features_path = \"E:\\\\Aditya\\\\ASVspoof2017_CFCC_IF_CMVN\\\\devoverlapData_117D\"\n",
    "# batches = (len(list(os.listdir(features_path))))//2\n",
    "# print(batches)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "ename": "FileNotFoundError",
     "evalue": "[Errno 2] No such file or directory: 'E:\\\\Aditya\\\\ASVspoof2019_CFCC_IF_CMN\\\\78D\\\\overlapData_78D\\\\batch_127.npy'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mFileNotFoundError\u001b[0m                         Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-13-c8b107c71d03>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mdev_num\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      2\u001b[0m     \u001b[0mfilename\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;34m'batch_'\u001b[0m\u001b[1;33m+\u001b[0m\u001b[0mstr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mi\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m+\u001b[0m\u001b[1;34m'.npy'\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 3\u001b[1;33m     \u001b[0mfile\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mload\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mos\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpath\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mjoin\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdevelopment_path\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mfilename\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      4\u001b[0m     \u001b[1;32mif\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msum\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0misnan\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfile\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m>\u001b[0m \u001b[1;36m0\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      5\u001b[0m         \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfilename\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\WinPython-64bit-3.6.3.0Qt5\\python-3.6.3.amd64\\lib\\site-packages\\numpy\\lib\\npyio.py\u001b[0m in \u001b[0;36mload\u001b[1;34m(file, mmap_mode, allow_pickle, fix_imports, encoding)\u001b[0m\n\u001b[0;32m    368\u001b[0m     \u001b[0mown_fid\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;32mFalse\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    369\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfile\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mbasestring\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 370\u001b[1;33m         \u001b[0mfid\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mopen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfile\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m\"rb\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    371\u001b[0m         \u001b[0mown_fid\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;32mTrue\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    372\u001b[0m     \u001b[1;32melif\u001b[0m \u001b[0mis_pathlib_path\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfile\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: 'E:\\\\Aditya\\\\ASVspoof2019_CFCC_IF_CMN\\\\78D\\\\overlapData_78D\\\\batch_127.npy'"
     ]
    }
   ],
   "source": [
    "for i in range(1,dev_num):\n",
    "    filename = 'batch_'+str(i)+'.npy'\n",
    "    file = np.load(os.path.join(development_path,filename))\n",
    "    if(np.sum(np.isnan(file)) > 0) :\n",
    "        print(filename)\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "True\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\WinPython-64bit-3.6.3.0Qt5\\python-3.6.3.amd64\\lib\\site-packages\\ipykernel_launcher.py:1: RuntimeWarning: invalid value encountered in log\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n"
     ]
    }
   ],
   "source": [
    "a = [1,np.log(-1),np.log(5/6)]\n",
    "print(np.isnan(a).any())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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