190314_3layer-trans-inv-sigmoid-noreg.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Version SIGMOID-NOREG: \n",
    "- 44,100 Hz sample rate \n",
    "- NO regularization\n",
    "- 300,000 iterations\n",
    "- 0.95 learning rate decay\n",
    "- sigmoid output function\n",
    "- add a BIAS to output layer\n",
    "- learning rete 0.005\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 0. Import the packages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os,sys,shutil,mmap\n",
    "import pickle as pickle\n",
    "import numpy as np                                       # fast vectors and matrices\n",
    "import matplotlib.pyplot as plt                          # plotting\n",
    "from scipy.fftpack import fft\n",
    "\n",
    "import time\n",
    "\n",
    "sys.path.insert(0,'lib/')\n",
    "import config\n",
    "\n",
    "from sklearn.metrics import average_precision_score\n",
    "\n",
    "import tensorflow as tf\n",
    "\n",
    "import mir_eval\n",
    "\n",
    "%matplotlib inline\n",
    "\n",
    "notebook_reference = str(190314)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "debugging = False\n",
    "delete_ids = [2560, 1792, 2562, 2506, 2564, 2566, 1793, 2568, 2570, 2571, 2572, 2573, 2575, 2576, 2302, 2581, 2582, \n",
    "              2586, 2075, 2076, 2077, 2590, 2079, 2080, 2081, 2594, 2595, 2596, 2603, 2678, 2397, 2608, 2611, 2614, \n",
    "              2104, 2105, 2619, 2620, 2621, 2622, 2112, 2113, 2114, 2627, 2116, 2629, 2118, 2119, 2632, 2633, 2127, \n",
    "              2131, 2138, 2319, 2140, 2405, 2659, 2148, 2149, 2150, 2151, 2154, 2155, 2156, 2157, 2158, 2159, 2160, \n",
    "              2161, 2677, 2166, 2167, 2168, 2169, 2239, 2325, 2177, 2178, 2179, 2180, 1729, 2186, 2242, 2194, 2195, \n",
    "              2196, 1829, 2198, 2200, 2201, 2202, 2203, 2204, 2330, 2207, 2208, 2209, 2210, 2211, 2212, 2213, 2214, \n",
    "              2215, 2588, 2218, 2219, 2220, 2221, 2222, 2224, 2225, 2227, 2228, 2229, 2230, 2231, 2232, 2234, 2591, \n",
    "              2237, 2238, 1727, 1728, 2241, 1730, 2243, 2244, 1733, 1734, 2593, 2248, 1739, 2240, 2082, 1742, 2083, \n",
    "              1749, 1750, 1751, 1752, 1755, 1756, 1757, 1758, 1760, 1763, 1764, 1765, 1766, 1768, 2282, 1771, 1772, \n",
    "              1773, 1775, 1776, 1777, 2292, 2293, 2294, 2295, 2296, 2297, 1919, 1788, 1789, 1790, 2304, 2305, 2307, \n",
    "              2308, 2310, 2313, 2314, 2315, 1805, 2318, 1807, 2320, 2322, 1811, 1812, 1813, 1817, 1818, 2334, 2335, \n",
    "              2336, 1828, 2341, 2342, 2343, 2567, 2345, 2346, 1835, 2348, 2350, 2491, 2357, 2358, 2359, 2364, 2365, \n",
    "              2366, 2368, 2371, 2372, 2373, 2374, 2529, 2376, 2377, 2379, 2443, 2381, 2383, 2384, 1873, 2388, 2389, \n",
    "              2390, 2391, 2392, 2393, 2618, 2398, 2403, 2404, 1893, 2406, 2410, 2411, 2415, 2417, 2147, 2451, 2420, \n",
    "              2422, 2423, 2424, 1916, 2538, 1918, 2431, 2432, 2433, 1922, 1923, 2436, 2441, 2442, 1931, 1932, 2626, \n",
    "              1735, 2285, 1859, 2247, 2117, 2288, 2466, 2444, 2289, 2472, 2473, 2476, 2477, 2478, 2480, 2481, 2482, \n",
    "              2483, 2462, 2486, 2487, 2488, 2490, 2463, 2492, 2494, 2555, 2497, 1822, 2501, 2502, 2504, 2505, 2078, 2507]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1. Import the data\n",
    "Import the recording and labels of the 331 recordings from the data base.\n",
    "\n",
    "#### 1.1 Import the labels of the data set.\n",
    "The 331 labels are saved in a dictionary whose\n",
    "- keys are integers (the ids of the recordings)\n",
    "- values are intervaltrees (for the different intervals of the recording it gives the played notes between 1 and 128)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open(config.labels_path, 'rb') as f:\n",
    "    labels = pickle.load(f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "331 labels\n"
     ]
    }
   ],
   "source": [
    "print(len(labels), 'labels')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2239\n",
      "<class 'intervaltree.intervaltree.IntervalTree'>\n"
     ]
    }
   ],
   "source": [
    "for key, value in labels.items() :\n",
    "    if key == 2239:\n",
    "        # id of the recording\n",
    "        print (key)\n",
    "        # intervaltree containing for each note intervals in which it is played\n",
    "        print (type(value))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 1.2 Import recordings\n",
    "\n",
    "Create a dictionary containing the data (recordings). Use Memory-mapped file objects.\n",
    "For each recording data[rec_id] is a tuple:\n",
    "1. data[rec_id][0] is the memory-mapped file object to the recording. It can be accesed by \n",
    "~~~~~~\n",
    "np.frombuffer(data[rec_id][0], dtype=np.float32).copy()\n",
    "~~~~~~\n",
    "2. data[rec_id][1] is its length (float)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "sz_float = 4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = dict()\n",
    "for record in os.listdir(config.records_path):\n",
    "    fd = os.open(config.records_path + record, os.O_RDONLY)\n",
    "    buff = mmap.mmap(fd, 0, mmap.MAP_SHARED, mmap.PROT_READ)\n",
    "    data[int(record[:-4])] = (buff, len(buff)/sz_float)\n",
    "    os.close(fd)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Key: 2494 , Format: <class 'int'>\n",
      "Value: (<mmap.mmap object at 0x7f11a245c228>, 13100544.0)\n",
      "1. Entry: Memory-mapped file objects , <mmap.mmap object at 0x7f11a24575d0>\n",
      "For example:\n",
      "[ 0.00036621  0.00039673  0.00036621  0.00036621  0.00033569  0.00033569\n",
      "  0.00036621  0.00036621  0.00036621  0.00033569  0.00036621  0.00039673]\n",
      "2. Entry: length of recording 5293440.0 , Type: <class 'float'>\n"
     ]
    }
   ],
   "source": [
    "for key, value in data.items() :\n",
    "    if key == 2494:\n",
    "        # id of the recording\n",
    "        print ('Key:',key,', Format:',type(key))\n",
    "        # its value is a tuple\n",
    "        print ('Value:',value)\n",
    "        # 1. value: reference to recording\n",
    "        print (\"1. Entry: Memory-mapped file objects ,\", data[2239][0])\n",
    "        # to access content use:\n",
    "        x = np.frombuffer(data[2239][0], dtype=np.float32).copy()\n",
    "        print('For example:')\n",
    "        print(x[3005:3017])\n",
    "        print (\"2. Entry: length of recording\",str(data[2239][1]),\", Type:\",type(data[2239][1]))\n",
    "        #print(len(x))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 1.3 Split up in the test and train set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There are 11 samples in the test set\n",
      "There are 320 samples in the train set\n"
     ]
    }
   ],
   "source": [
    "extended_test_set = True\n",
    "use_mirex = True\n",
    "    \n",
    "# use the small or big test set\n",
    "if extended_test_set:\n",
    "    test_ids = config.test_ids_ext\n",
    "else:\n",
    "    test_ids = config.test_ids\n",
    "\n",
    "# include the mirex dev set in test results\n",
    "if use_mirex:\n",
    "    test_ids = test_ids + config.mirex_id\n",
    "\n",
    "train_ids = [rec_id for rec_id in labels.keys() if rec_id not in test_ids]\n",
    "\n",
    "if debugging == True:\n",
    "    train_ids = [rec_id for rec_id in train_ids if rec_id not in delete_ids]\n",
    "\n",
    "\n",
    "print('There are {} samples in the test set'.format(len(test_ids)))\n",
    "print('There are {} samples in the train set'.format(len(train_ids)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2. Preprocessing\n",
    "\n",
    "The input data (recordings) are normalized and the possible note range is defines to 128 notes. The lowest note is ..., the highest ... ."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Normalize the recordings\n",
    "normalize = True\n",
    "# Possible notes are from range [base_note, base_note + m)\n",
    "base_note = 0\n",
    "mm = 128"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 2.1 Data augmentation\n",
    "\n",
    "Always make two small changes to input data by randomly stretching or shrinking our input audio that are not noticable by human hearing, but augment the data set:\n",
    "1. Pitch-shift in the frequency domain: Randomly  shifting  each data points in a minibatch by an integral number of semitones. This reinforces the architectural structure of the translation-invariant network.\n",
    "2. Jittering: continuous shift to each data point. This makes the models more robust to tuning variation between recordings --> regularization\n",
    "\n",
    "Define a function for extracting a sequence and its label as well as applying the data augmentation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "# pitching: stretching or shrinking our input audio with linear interpolation\n",
    "pitch_transforms=5 # =/- 5 semitones\n",
    "jitter=.1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Pitch-shifting corresponds to a multiplication by $2^{1/12}$ (an octave is equal to 12 semitones and corresponds to a factor 2 in the frequency domain) for each semirone.\n",
    "\n",
    "A scaling factor in the frequency domain implies also a scaling factor in the time domain. (speeding up the recording $\\to$ higher frequency $\\to$ sounds higher.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "# function to obtain one data sample from recordings\n",
    "# input:\n",
    "# - rec_id: the id of the recording\n",
    "# - s: frame-number to extract\n",
    "# - window: windowsize\n",
    "# - pitchshift: shift the label by pitchshift halftones\n",
    "# - scaling_factor: scaling factor in frequency domain (includes corrections because of pitch shift and jitter)\n",
    "# - normalize: boolean\n",
    "# output\n",
    "# - x: audio segment of length window\n",
    "# - y: labels, 0-1-vector of length mm\n",
    "def get_data(rec_id,s,window,pitch_shift,scaling_factor, normalize=True):\n",
    "   \n",
    "    x = np.frombuffer(data[rec_id][0][s*sz_float:int(\n",
    "        s+scaling_factor*window)*sz_float], dtype=np.float32).copy()\n",
    "    \n",
    "    if normalize: \n",
    "        x /= np.linalg.norm(x) + config.epsilon\n",
    "\n",
    "    xp = np.arange(window,dtype=np.float32)\n",
    "    # interpolate the discrete function (np.arange(len(x)), x) at the points (scaling_factor*xp)\n",
    "    x = np.interp(scaling_factor*xp,np.arange(len(x),dtype=np.float32),x).astype(np.float32)\n",
    "    # real-valued audio frame with values in the range [−1,1], sampled at 44.1 kHz\n",
    "    # -> x has length window\n",
    "    \n",
    "    y = np.zeros(mm)\n",
    "    # select center of frame\n",
    "    for label in labels[rec_id][s+scaling_factor*window/2]:\n",
    "        y[label.data[1]+pitch_shift-base_note] = 1 # apply pitch shift here (in frequency domain)\n",
    "    \n",
    "    return x,y"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 2.1 Batch-selection\n",
    "Create the training batch applying the above explained methods for Data augmentation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "# function to create an input batch for one training step\n",
    "# input:\n",
    "# - model_stats: model_results-class containing training_ids\n",
    "# - batch_size (integer)\n",
    "# - window: window-length\n",
    "# - pitch_transforms: integer in [-5,5], number of maximum semitones shifted\n",
    "# - jitter: float in [-0.1,0.1], tuning variation\n",
    "# output:\n",
    "# - xmb: batch for a training step, size: (batch_size,1,window,1)\n",
    "# - ymb: corresponding labels to the batch xmb, size: (batch_size,1,mm)\n",
    "def get_training_batch(model_stats, batch_size, window, pitch_transforms=0, jitter=0):\n",
    "    # arrays for input data (batch)\n",
    "    xmbatch = np.empty([batch_size,1,window,1],dtype=np.float32)\n",
    "    ymbatch = np.empty([batch_size,1,mm],dtype=np.float32)\n",
    "        \n",
    "    # picking randomly batch_size recordings\n",
    "    rec_ids = [model_stats.train_ids[r] for r in \\\n",
    "               np.random.randint(0,len(model_stats.train_ids),batch_size)]\n",
    "        \n",
    "    if pitch_transforms > 0:\n",
    "        transform_type = np.random.randint(-pitch_transforms,\n",
    "                                           pitch_transforms,batch_size)\n",
    "\n",
    "    if jitter > 0:\n",
    "        jitter_amount = np.random.uniform(-jitter,jitter,batch_size)\n",
    "\n",
    "    for i in range(batch_size):\n",
    "        scaling_factor = 1\n",
    "        shift = 0\n",
    "        if pitch_transforms > 0:\n",
    "            shift = transform_type[i]\n",
    "            # half-step has a frequency ratio of 2^(1/12)\n",
    "            # (shift + jitter_amount) halfsteps are shifted\n",
    "            if jitter > 0:\n",
    "                scaling_factor = (2.**((shift+jitter_amount[i])/12.))\n",
    "            else:\n",
    "                # only shift the halftones from pitch_transform\n",
    "                scaling_factor = (2.**(shift/12.))\n",
    "        \n",
    "        # multiply in frequency domain by scaling_factor\n",
    "        \n",
    "        # pick a frame for training randomly\n",
    "        s = np.random.randint(0,data[rec_ids[i]][1]-scaling_factor*window)\n",
    "        xmbatch[i,0,:,0],ymbatch[i,0,:] = get_data(rec_ids[i],s,window,pitch_shift=shift,scaling_factor=scaling_factor)\n",
    "        \n",
    "    return xmbatch, ymbatch"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 2.3 Creation of Training and Test set for protocolling\n",
    "\n",
    "Routines that create sample for the protcoling:\n",
    "1. Routine to obtain the training samples from numpy-file.\n",
    "2. Routine to obtain test samples given the recording IDs of the test samples."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "# function to obtain training sample which is already saved in \n",
    "# numpy fromat in file. only normalization is necessary.\n",
    "# input:\n",
    "# - d: window size (integer)\n",
    "# - normalize: boolean\n",
    "# output:\n",
    "# - Xtrain: training sample of size (15000, 1, d, 1)\n",
    "# - Ytrain: corresponding labels of size (15000, 128)\n",
    "def get_training_sample(d=16384, normalize=True):\n",
    "    cachex = config.tmp + 'Xtrain_ext{}.npy'.format(d)\n",
    "    cachey = config.tmp + 'Ytrain_ext{}.npy'.format(d)\n",
    "    #shape Xtrain: (15000, 1, 16384, 1)\n",
    "    #shape Ytrain: (15000, 128)\n",
    "    if os.path.exists(cachex) and os.path.exists(cachey):\n",
    "        Xtrain = np.load(cachex)\n",
    "        Ytrain = np.load(cachey)\n",
    "    else:\n",
    "        print(cachex)\n",
    "        print(cachey)\n",
    "        raise Exception('Unimplemented')\n",
    "\n",
    "    if normalize:\n",
    "        Xtrain = Xtrain.reshape(len(Xtrain),1,d,1)\n",
    "        for i in range(len(Xtrain)):\n",
    "            Xtrain[i] /= np.linalg.norm(Xtrain[i]) + config.epsilon\n",
    "\n",
    "    return Xtrain,Ytrain\n",
    "\n",
    "\n",
    "# function to obtain test sample using get_data(). every sample is split \n",
    "# up in count sequences.\n",
    "# input:\n",
    "# - rec_ids: ids of test samples\n",
    "# - window: size of the input frame\n",
    "# - count: number of sequences to be created for each sample\n",
    "# - fixed_stride: -1 (stride is adapted to sample) or nonnegative integer\n",
    "# - pitch_shift: value by which recording is stretched or shrinked\n",
    "# output:\n",
    "# - Xtest: test sample of size (count*len(rec_ids),1,window,1)\n",
    "# - Ytest: corresponding labels of size (count*len(rec_ids),mm)\n",
    "def get_test_sample(rec_ids, window, count, fixed_stride=-1, pitch_shift=0):\n",
    "    Xtest = np.zeros([count*len(rec_ids),1,window,1])\n",
    "    Ytest = np.zeros([count*len(rec_ids),mm])\n",
    "    offset = 44100\n",
    "    # calculate scaling factor\n",
    "    sf = 2.**(pitch_shift/12.)\n",
    "    # for every sample create 1000 sequences\n",
    "    for i in range(len(rec_ids)):\n",
    "        X = np.zeros([count,1,window,1])\n",
    "        Y = np.zeros([count,mm])\n",
    "        # calculate teh stride in case it is not fixed\n",
    "        stride = (data[rec_ids[i]][1]-offset-int(sf*window))/count if fixed_stride==-1 else fixed_stride\n",
    "        for j in range(count):\n",
    "            X[j,0,:,0],Y[j] = get_data(rec_ids[i],int(offset+j*stride),window,pitch_shift=int(round(pitch_shift)),scaling_factor=sf)\n",
    "        \n",
    "        Xtest[i*count:(i+1)*count] = X\n",
    "        Ytest[i*count:(i+1)*count] = Y\n",
    "    return Xtest, Ytest"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Save the results, losses, precicions and weights at each protocol point inside a class."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "class model_results():\n",
    "    def __init__(self, test_ids, train_ids):\n",
    "        # weights\n",
    "        self.weights = dict()\n",
    "        # step\n",
    "        self.iter = 0\n",
    "        # result statistics\n",
    "        self.stats = dict()\n",
    "        self.stats['iter'] = [False,'{:<8}',[]]\n",
    "        # time that has passed since last record\n",
    "        self.stats['time'] = [True,'{:<8.0f}',[]]\n",
    "        self.stats['lr'] = [False,'{:<8.6f}',[]]\n",
    "        # mean squared errors\n",
    "        self.stats['mse_train_1'] = [True,'{:<16.6f}',[]]\n",
    "        self.stats['mse_train'] = [True,'{:<16.6f}',[]]\n",
    "        self.stats['mse_test'] = [True,'{:<16.6f}',[]]\n",
    "        # precicions\n",
    "        self.stats['avp_train'] = [True,'{:<16.6f}',[]]\n",
    "        self.stats['avp_test'] = [True,'{:<16.6f}',[]]\n",
    "        \n",
    "        self.averages = []\n",
    "        \n",
    "        self.test_ids = test_ids\n",
    "        self.train_ids = train_ids\n",
    "\n",
    "    # Register the weights inside the list w_list. \n",
    "    # The list name_list contains the corresponding names of the wariables in w_list\n",
    "    def register_weights(self, w_list, name_list):\n",
    "        if len(w_list) != len(name_list):\n",
    "            print(\"error: input lists need to have same length\")\n",
    "            \n",
    "        for ii in range(len(w_list)):\n",
    "            w = w_list[ii]\n",
    "            name = name_list[ii]\n",
    "            # register the norm of the weights\n",
    "            self.stats['n'+name] = [False,'{:<8.3f}',[]] \n",
    "            # register the weights\n",
    "            self.weights[name] = w\n",
    "            \n",
    "        return None\n",
    "        \n",
    "model_stats = model_results(test_ids, train_ids)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4. Error measures\n",
    "Define the error measures for comparing the correct labeling Y with the extimated labeling Y_hat.\n",
    "Both labelings are of size (mm) for each segment. The errors are calculated at each protocol point for the training and the test set.\n",
    "1. Mean Squared Error: When applying the model to a sequence or sequences the mean squared error is calculated directly by applying the tensorflow function.\n",
    "~~~~~~~~~~~~~~~~~~~~~\n",
    "tf.reduce_mean(tf.nn.l2_loss( Y_hat - Y ))\n",
    "~~~~~~~~~~~~~~~~~~~~~\n",
    "2. Average Precision: The average precision is calculated with the metric function from the sklear package.\n",
    "~~~~~~~~~~~~~~~~~~~~~\n",
    "sklearn.metrics.average_precision_score\n",
    "~~~~~~~~~~~~~~~~~~~~~\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5. Define the graph\n",
    "\n",
    "Define batch size (batch_size segments at each training step). Also define a small factor wscale to scale the weights. Regularization can be avoided."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "batch_size=150\n",
    "if debugging == True:\n",
    "    batch_size = 4\n",
    "out = 1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 5.1 Define layers"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### First layer: \n",
    "\n",
    "Lenth of the input (segment) is window=16384. Compute a strided convolution with a d=4096-sample recptive field and a stride=512 sample stride. Since every segment has 16384 samples there are 25 regions for each frame.\n",
    "\n",
    "After the convolution for each region a filterbank computation is calculated. \n",
    "As a filter a log=spaced filterbank is chosen (k=512 sine and cosine filters with logarithmically spaced frequencies from 50 hz to 6000 hz) which is going to be applied to all the regions of the convolution of size d=4096"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "# variables for input layer\n",
    "\n",
    "# input size of segment, predict notes played at midpoint of segment\n",
    "window=16384\n",
    "# size of regions that outputs the convolution from layer one (receptive field)\n",
    "dd = 4096\n",
    "#stride for first layer convolution\n",
    "stride=512\n",
    "# --> 25 regions\n",
    "\n",
    "# variables for filterbank (= first layer)\n",
    "# number of nodes in first layer (by filterbank get from dd nodes to kk nodes)\n",
    "kk = 512 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "def create_filters(d,k):\n",
    "    # create k filters\n",
    "    # d is the length of the receptive field (input)\n",
    "    x = np.linspace(0, 2*np.pi, d, endpoint=False)\n",
    "    wsin = np.empty((1,d,1,k), dtype=np.float32)\n",
    "    wcos = np.empty((1,d,1,k), dtype=np.float32)\n",
    "    start_freq = 50.\n",
    "    end_freq = 6000.\n",
    "    num_cycles = start_freq*d/44100.                                          \n",
    "    scaling_ind = np.log(end_freq/start_freq)/k\n",
    "    window_mask = 1.0-1.0*np.cos(x)\n",
    "    for ind in range(k):\n",
    "        wsin[0,:,0,ind] = window_mask*np.sin(np.exp(ind*scaling_ind)*num_cycles*x)\n",
    "        wcos[0,:,0,ind] = window_mask*np.cos(np.exp(ind*scaling_ind)*num_cycles*x)\n",
    "            \n",
    "    return wsin,wcos"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Second layer:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "# variables for second layer\n",
    "d2_x = 1          # lvl2 input dims_x\n",
    "d2_y = 128          # lvl2 input dims_y\n",
    "k2 = 128        # num lvl2 filters\n",
    "stride_y = 2    # lvl2 stride"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Third layer:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "# variables for third layer\n",
    "d3_x = 25 # lvl3 input dims_x\n",
    "d3_y = 1 # lvl3 input dims_y (fully connected)\n",
    "k3 = 4096 # num lvl3 filters"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Calculate the sizes of nodes at each layer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "First layer regions: (25,512)\n",
      "Second layer regions: (25,193)\n",
      "Third layer regions: (1,193)\n"
     ]
    }
   ],
   "source": [
    "num_regions  = int(1 + (window-dd)/stride)\n",
    "print ('First layer regions: ({},{})'.format(num_regions,kk))\n",
    "num_regions2_x  = int(1 + (num_regions-d2_x)/1)\n",
    "num_regions2_y = int(1 + (kk-d2_y)/stride_y)\n",
    "print ('Second layer regions: ({},{})'.format(num_regions2_x,num_regions2_y))\n",
    "num_regions3_x = int(1 + (num_regions2_x - d3_x)/1)\n",
    "num_regions3_y = int(1 + (num_regions2_y - d3_y)/1)\n",
    "print ('Third layer regions: ({},{})'.format(num_regions3_x,num_regions3_y))\n",
    "\n",
    "# create filters (shape: (1, 4096, 1, 512)) = number of regions x size of region\n",
    "wsin,wcos = create_filters(dd,kk)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 5.2 Define tf.Graph()\n",
    "Set it all together and define the graph.\n",
    "\n",
    "For evaluation not the trained weights, but its moving averages with decay 0.9998 are utilized. The shadow variables are maintained by tf.train.ExponentialMovingAverage. In the block for evaluating the direct model their values are retrieved."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "---- Weights ----\n",
      "w <tf.Variable 'parameters/w:0' shape=(1, 128, 1, 128) dtype=float32_ref>\n",
      "w2 <tf.Variable 'parameters/w2:0' shape=(25, 1, 128, 4096) dtype=float32_ref>\n",
      "beta <tf.Variable 'parameters/beta:0' shape=(790529, 128) dtype=float32_ref>\n",
      "---- Layers ----\n",
      "zx Tensor(\"queued_model/add:0\", shape=(?, 1, 25, 512), dtype=float32)\n",
      "z2 Tensor(\"queued_model/Relu:0\", shape=(?, 128, 25, 193), dtype=float32)\n",
      "z3 Tensor(\"queued_model/Relu_1:0\", shape=(?, 4096, 1, 193), dtype=float32)\n",
      "y Tensor(\"queued_model/MatMul:0\", shape=(150, 128), dtype=float32)\n",
      "loss Tensor(\"queued_model/logistic_loss:0\", shape=(150, 128), dtype=float32)\n"
     ]
    }
   ],
   "source": [
    "tf.set_random_seed(999)\n",
    "\n",
    "#end_learning_rate = .00001/3/3/3\n",
    "starter_learning_rate = .005\n",
    "mom = .95\n",
    "\n",
    "beta_reg = 0.0\n",
    "\n",
    "graph = tf.Graph()\n",
    "\n",
    "with graph.as_default():\n",
    "     \n",
    "    # if the input is a batch\n",
    "    with tf.variable_scope('data_queue'):\n",
    "        xb = tf.placeholder(tf.float32, shape=[None, 1, window+(out-1)*stride, 1])\n",
    "        yb = tf.placeholder(tf.float32, shape=[None, out, mm])\n",
    "\n",
    "        xq, yq = xb, yb\n",
    "        \n",
    "    # if the input is just one segment\n",
    "    with tf.variable_scope('direct_data'):\n",
    "        xd = tf.placeholder(tf.float32, shape=[None,1,window,1])\n",
    "        yd = tf.placeholder(tf.float32, shape=[None, mm])\n",
    "    \n",
    "    print ('---- Weights ----')\n",
    "    with tf.variable_scope('parameters'): #glorot  \n",
    "        w = tf.get_variable('w', [d2_x,d2_y,1,k2]) # 1 x 128 x 1 x 128 (16.384 values)\n",
    "        print ('w',w)\n",
    "        w2 = tf.get_variable('w2', [d3_x,d3_y,k2,k3]) # 25 x 1 x 128 x 4096 (13.107.200 values)\n",
    "        print ('w2',w2)\n",
    "        beta = tf.get_variable('beta', [int(num_regions3_x*num_regions3_y*k3)+1,mm]) \n",
    "        # (1*193*4096+1) x 128 (101.187.584 + 128 values) BIAS!!\n",
    "        print ('beta',beta)\n",
    "        model_stats.register_weights([w,w2,beta],['w','w2','beta'])\n",
    "\n",
    "    print ('---- Layers ----')\n",
    "    with tf.variable_scope('queued_model'):\n",
    "        zx = tf.square(tf.nn.conv2d(xq,wsin,strides=[1,1,stride,1],padding='VALID')) \\\n",
    "           + tf.square(tf.nn.conv2d(xq,wcos,strides=[1,1,stride,1],padding='VALID'))\n",
    "        print ('zx',zx)\n",
    "        z2 = tf.nn.relu(tf.nn.conv2d(tf.log(zx+10e-15),w,strides=[1,1,1,stride_y],padding='VALID',data_format='NCHW'))\n",
    "        print ('z2',z2)\n",
    "        z3 = tf.nn.relu(tf.nn.conv2d(z2,w2,strides=[1,1,1,1],padding='VALID',data_format='NCHW'))\n",
    "        print ('z3',z3)\n",
    "        y = tf.matmul(\n",
    "            tf.concat([tf.reshape(z3,[batch_size,int(num_regions3_x*num_regions3_y*k3)]),\n",
    "                      tf.ones([batch_size,1], tf.float32)],1),beta)\n",
    "        print ('y',y)\n",
    "        #loss = tf.reduce_mean(tf.nn.l2_loss(y-tf.reshape(yq,[batch_size,mm])))\n",
    "        loss = tf.nn.sigmoid_cross_entropy_with_logits(labels=tf.reshape(yq,[batch_size,mm]), logits=y) \n",
    "        print ('loss',loss)\n",
    "            \n",
    "    # learning rate decay\n",
    "    global_step = tf.Variable(0, trainable=False)\n",
    "    lr = tf.train.exponential_decay(starter_learning_rate, global_step, 10000, 0.95, staircase=True)\n",
    "\n",
    "    reg = tf.nn.l2_loss(w) + tf.nn.l2_loss(w2) + tf.nn.l2_loss(beta)\n",
    "    \n",
    "    #opt_op = tf.train.MomentumOptimizer(lr,mom).minimize(tf.reduce_mean(loss + beta_reg*reg), global_step=global_step)\n",
    "    \n",
    "    opt_op = tf.train.MomentumOptimizer(lr,mom).minimize(tf.reduce_mean(loss), global_step=global_step)\n",
    "\n",
    "    \n",
    "    # Create an ExponentialMovingAverage object\n",
    "    ema = tf.train.ExponentialMovingAverage(decay=0.9998)\n",
    "    \n",
    "    with tf.control_dependencies([opt_op]):\n",
    "        training_op = ema.apply([w, w2, beta])\n",
    "    \n",
    "    with tf.variable_scope('direct_model'):\n",
    "        # use ema.average(var) to return the average of variable var\n",
    "        wavg = ema.average(w)\n",
    "        w2avg = ema.average(w2)\n",
    "        betaavg = ema.average(beta)\n",
    "\n",
    "        direct_zx = tf.square(tf.nn.conv2d(xd,wsin,strides=[1,1,stride,1],padding='VALID')) \\\n",
    "                + tf.square(tf.nn.conv2d(xd,wcos,strides=[1,1,stride,1],padding='VALID'))\n",
    "        direct_z2 = tf.nn.relu(tf.nn.conv2d(tf.log(direct_zx+10e-15),wavg,strides=[1,1,1,stride_y],padding='VALID',data_format='NCHW'))\n",
    "        direct_z3 = tf.nn.relu(tf.nn.conv2d(direct_z2,w2avg,strides=[1,1,1,1],padding='VALID',data_format='NCHW'))\n",
    "        direct_y = tf.nn.sigmoid(tf.matmul(\n",
    "            tf.concat([tf.reshape(direct_z3,[tf.shape(xd)[0],int(num_regions3_x*num_regions3_y*k3)]),\n",
    "                     tf.ones([tf.shape(xd)[0],1], tf.float32)],1),betaavg))\n",
    "        direct_loss = tf.reduce_mean(tf.nn.l2_loss(direct_y-yd))\n",
    "        "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 5.3 Evaluation of the graph\n",
    "\n",
    "For the aplication of the neuronal network a moving average of the weights is used. Therefore, a different function for evaluation is used.\n",
    "\n",
    "Inside the function predict_direct_model the mean squared error is calculated directly."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "# function for prediction without training that splits up the whole set\n",
    "# in small subsets to prevent errors of type \"ResourceExhaustedError\"\n",
    "# input: \n",
    "# - sess: the actual session\n",
    "# - X: sample, size: (n,1,window,1)\n",
    "# - Y: corresponding labels, size: (n,mm)\n",
    "# output:\n",
    "#  Yhat: prediction, size: (n,mm)\n",
    "#  mse: Mean Squared Error when comparing Y and Yhat\n",
    "def predict_direct_model(sess, X, Y):\n",
    "    Yhat = np.empty((len(X),mm))\n",
    "    subdiv = 100\n",
    "    subset = X.shape[0]/subdiv\n",
    "    mse = 0\n",
    "    for j in range(subdiv):\n",
    "        Yhat[int(subset*j):int(subset*(j+1))], se = sess.run([direct_y,direct_loss], \n",
    "                                                         feed_dict={xd: X[int(subset*j):int(subset*(j+1))],\n",
    "                                                                    yd: Y[int(subset*j):int(subset*(j+1))]})\n",
    "        mse += se\n",
    "\n",
    "    return Yhat, mse/float(subdiv)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 6. Training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "if debugging == True:\n",
    "    num_steps = 5\n",
    "    control_step = 1\n",
    "else:\n",
    "    num_steps = 300000\n",
    "    control_step = 1000\n",
    "\n",
    "gpu_memory_growth = False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "# arrays for input data (batch)\n",
    "xmb = np.empty([batch_size,1,window,1],dtype=np.float32)\n",
    "ymb = np.empty([batch_size,1,mm],dtype=np.float32)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "# train saver\n",
    "saver = tf.train.Saver({'wavg': wavg, 'w2avg': w2avg, 'betaavg': betaavg})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Initialized\n",
      "iter\t test_squ_loss\t test_avg_prec\t train_squ_loss\t train_avg_prec\t regularization\t time (in s)\n",
      "1 \t 1774.52914307 \t 0.01498712 \t 2416.64270508 \t 0.01848589 \t 253.081 \t 33.02560878\n",
      "1001 \t 578.23144928 \t 0.30070088 \t 842.30477356 \t 0.33441585 \t 255.605 \t 539.47744656\n",
      "2001 \t 274.99009262 \t 0.51037048 \t 415.84526978 \t 0.53977823 \t 256.26 \t 539.64069295\n",
      "3001 \t 166.65559174 \t 0.58904409 \t 257.69496399 \t 0.60996324 \t 256.769 \t 540.547086\n",
      "4001 \t 117.08202866 \t 0.62653261 \t 184.68053101 \t 0.64098866 \t 257.225 \t 540.39966464\n",
      "5001 \t 92.53661472 \t 0.64850733 \t 148.56236099 \t 0.65818463 \t 257.666 \t 540.74291611\n",
      "6001 \t 79.80252346 \t 0.66259854 \t 129.90293915 \t 0.66924019 \t 258.092 \t 539.92923379\n",
      "7001 \t 72.7537734 \t 0.6728635 \t 119.68309624 \t 0.67703952 \t 258.491 \t 539.64271832\n",
      "8001 \t 68.66226709 \t 0.6800237 \t 113.77709145 \t 0.68285832 \t 258.89 \t 540.57047725\n",
      "9001 \t 66.10314537 \t 0.68578882 \t 110.13908272 \t 0.68755093 \t 259.276 \t 541.15296125\n",
      "10001 \t 64.42218225 \t 0.69035438 \t 107.76964935 \t 0.69145613 \t 259.651 \t 540.18022823\n",
      "11001 \t 63.29391108 \t 0.69398893 \t 106.14735985 \t 0.69483449 \t 260.006 \t 541.34598732\n",
      "12001 \t 62.44233969 \t 0.69737295 \t 104.94629044 \t 0.69779967 \t 260.354 \t 540.43050432\n",
      "13001 \t 61.77987957 \t 0.70041169 \t 104.01638626 \t 0.70055136 \t 260.7 \t 540.40870905\n",
      "14001 \t 61.27462349 \t 0.70295769 \t 103.27167809 \t 0.70299561 \t 261.039 \t 540.55995727\n",
      "15001 \t 60.84394413 \t 0.70539994 \t 102.63597084 \t 0.70531412 \t 261.391 \t 540.53055072\n",
      "16001 \t 60.47211887 \t 0.70755707 \t 102.08539474 \t 0.70745535 \t 261.727 \t 540.79481792\n",
      "17001 \t 60.1382266 \t 0.70966414 \t 101.58635918 \t 0.70948059 \t 262.057 \t 544.98197532\n",
      "18001 \t 59.85805574 \t 0.71162681 \t 101.1354261 \t 0.71137307 \t 262.39 \t 542.40320587\n",
      "19001 \t 59.5810787 \t 0.71355122 \t 100.7212442 \t 0.71318698 \t 262.721 \t 541.33249402\n",
      "20001 \t 59.34314499 \t 0.7152147 \t 100.33241043 \t 0.71490718 \t 263.046 \t 540.59422565\n",
      "21001 \t 59.12807957 \t 0.7168073 \t 99.96804726 \t 0.71654151 \t 263.354 \t 542.20445371\n",
      "22001 \t 58.9342954 \t 0.71827834 \t 99.63176636 \t 0.71804424 \t 263.66 \t 541.81462908\n",
      "23001 \t 58.74432475 \t 0.71965576 \t 99.30572365 \t 0.71953513 \t 263.96 \t 541.73903823\n",
      "24001 \t 58.55677143 \t 0.72103578 \t 98.9967765 \t 0.72093437 \t 264.265 \t 540.3459208\n",
      "25001 \t 58.38091175 \t 0.72238812 \t 98.69210312 \t 0.72234744 \t 264.565 \t 542.20470953\n",
      "26001 \t 58.22307706 \t 0.72360927 \t 98.40570061 \t 0.72366017 \t 264.868 \t 540.9165771\n",
      "27001 \t 58.07049112 \t 0.72474875 \t 98.12118622 \t 0.72493241 \t 265.175 \t 540.91544247\n",
      "28001 \t 57.91359034 \t 0.72595344 \t 97.86122612 \t 0.72614307 \t 265.471 \t 540.95711803\n",
      "29001 \t 57.78123141 \t 0.72694731 \t 97.60386032 \t 0.72728899 \t 265.767 \t 540.70202804\n",
      "30001 \t 57.65876324 \t 0.72793943 \t 97.35744675 \t 0.72841508 \t 266.066 \t 540.52684498\n",
      "31001 \t 57.53976736 \t 0.72890316 \t 97.12541191 \t 0.72950789 \t 266.349 \t 541.10824084\n",
      "32001 \t 57.42562749 \t 0.72994181 \t 96.91977486 \t 0.73050187 \t 266.633 \t 541.26264191\n",
      "33001 \t 57.31959646 \t 0.73079072 \t 96.71318733 \t 0.73143446 \t 266.91 \t 540.97974586\n",
      "34001 \t 57.22890318 \t 0.73149362 \t 96.51259064 \t 0.73235226 \t 267.192 \t 540.82241702\n",
      "35001 \t 57.14608978 \t 0.73217334 \t 96.30849167 \t 0.73330513 \t 267.469 \t 541.86023498\n",
      "36001 \t 57.04136425 \t 0.7329767 \t 96.12535881 \t 0.73419843 \t 267.76 \t 540.11166978\n",
      "37001 \t 56.93824591 \t 0.73379573 \t 95.93661591 \t 0.73505048 \t 268.034 \t 540.8008523\n",
      "38001 \t 56.84985857 \t 0.73451267 \t 95.74503365 \t 0.73594321 \t 268.313 \t 540.94249105\n",
      "39001 \t 56.74960202 \t 0.73528442 \t 95.56635483 \t 0.73678711 \t 268.594 \t 540.49168515\n",
      "40001 \t 56.65878645 \t 0.73595975 \t 95.39760208 \t 0.73758677 \t 268.873 \t 539.93048477\n",
      "41001 \t 56.58245953 \t 0.7365691 \t 95.23913551 \t 0.73834549 \t 269.135 \t 541.7227087\n",
      "42001 \t 56.49276138 \t 0.73742591 \t 95.08463768 \t 0.73908113 \t 269.402 \t 541.07685757\n",
      "43001 \t 56.42614026 \t 0.73803911 \t 94.92380455 \t 0.73985641 \t 269.665 \t 540.71722937\n",
      "44001 \t 56.33999895 \t 0.73879056 \t 94.77252823 \t 0.74056407 \t 269.936 \t 540.90988612\n",
      "45001 \t 56.26774686 \t 0.73929209 \t 94.61957817 \t 0.74127601 \t 270.199 \t 540.90478849\n",
      "46001 \t 56.18559032 \t 0.73991645 \t 94.46992577 \t 0.74195266 \t 270.465 \t 542.84052825\n",
      "47001 \t 56.09326799 \t 0.74062627 \t 94.3257534 \t 0.74264204 \t 270.727 \t 540.41365933\n",
      "48001 \t 56.01314498 \t 0.74118672 \t 94.1807415 \t 0.7432457 \t 270.992 \t 540.38033962\n",
      "49001 \t 55.93223853 \t 0.74191092 \t 94.03184547 \t 0.74391367 \t 271.255 \t 541.38323236\n",
      "50001 \t 55.84261702 \t 0.74272871 \t 93.89902077 \t 0.7445276 \t 271.521 \t 541.34919047\n",
      "51001 \t 55.78385941 \t 0.74330409 \t 93.76406929 \t 0.74513985 \t 271.766 \t 539.72867489\n",
      "52001 \t 55.7202323 \t 0.74377513 \t 93.62915169 \t 0.74576375 \t 272.02 \t 539.66190338\n",
      "53001 \t 55.6420047 \t 0.74439267 \t 93.4985553 \t 0.74634055 \t 272.276 \t 539.56390953\n",
      "54001 \t 55.57802991 \t 0.7449136 \t 93.37452034 \t 0.7469102 \t 272.521 \t 539.12692595\n",
      "55001 \t 55.52402272 \t 0.74530329 \t 93.25223877 \t 0.7474781 \t 272.776 \t 542.88027596\n",
      "56001 \t 55.46089458 \t 0.74576171 \t 93.13149475 \t 0.74799911 \t 273.017 \t 541.4623723\n",
      "57001 \t 55.39111471 \t 0.74627676 \t 93.00863861 \t 0.7485085 \t 273.269 \t 542.17823315\n",
      "58001 \t 55.3390983 \t 0.74674795 \t 92.88427956 \t 0.74904942 \t 273.521 \t 539.78823757\n",
      "59001 \t 55.29981988 \t 0.74708532 \t 92.76474358 \t 0.74962168 \t 273.768 \t 540.96400714\n",
      "60001 \t 55.25848799 \t 0.747418 \t 92.63215836 \t 0.75023885 \t 274.02 \t 544.43914366\n",
      "61001 \t 55.20391315 \t 0.74781599 \t 92.50519867 \t 0.7508115 \t 274.263 \t 544.22121549\n",
      "62001 \t 55.16212881 \t 0.74811717 \t 92.38719742 \t 0.75138095 \t 274.499 \t 544.09637094\n",
      "63001 \t 55.1083013 \t 0.74851808 \t 92.26482452 \t 0.75196371 \t 274.738 \t 544.19977903\n",
      "64001 \t 55.05354225 \t 0.7489957 \t 92.14372353 \t 0.75252276 \t 274.974 \t 544.12702775\n",
      "65001 \t 55.00788391 \t 0.74937043 \t 92.03463676 \t 0.75304295 \t 275.212 \t 544.46937966\n",
      "66001 \t 54.97463598 \t 0.74964364 \t 91.92182838 \t 0.75355451 \t 275.458 \t 545.02908707\n",
      "67001 \t 54.9170148 \t 0.75016369 \t 91.81747688 \t 0.75400051 \t 275.687 \t 543.24355602\n",
      "68001 \t 54.85931019 \t 0.75052499 \t 91.72175079 \t 0.75443246 \t 275.922 \t 543.65747356\n",
      "69001 \t 54.81877964 \t 0.75078547 \t 91.63443626 \t 0.75485681 \t 276.16 \t 542.88929248\n",
      "70001 \t 54.79171882 \t 0.75100866 \t 91.54189468 \t 0.75529084 \t 276.397 \t 543.31292415\n",
      "71001 \t 54.74842249 \t 0.75128855 \t 91.45947708 \t 0.75570293 \t 276.62 \t 541.83060908\n",
      "72001 \t 54.69367384 \t 0.75171274 \t 91.36530014 \t 0.75613162 \t 276.845 \t 543.25910378\n",
      "73001 \t 54.62990354 \t 0.75221739 \t 91.27000298 \t 0.75655419 \t 277.07 \t 542.86429\n",
      "74001 \t 54.58446749 \t 0.75254909 \t 91.17726608 \t 0.75699017 \t 277.301 \t 543.18669176\n",
      "75001 \t 54.55399489 \t 0.75272388 \t 91.08984779 \t 0.75739976 \t 277.522 \t 542.47903466\n",
      "76001 \t 54.53896537 \t 0.75289029 \t 90.99661179 \t 0.75783236 \t 277.746 \t 542.81550455\n",
      "77001 \t 54.51450095 \t 0.75308444 \t 90.91349525 \t 0.75821437 \t 277.975 \t 542.37352967\n",
      "78001 \t 54.47837948 \t 0.75331046 \t 90.82536064 \t 0.75859753 \t 278.198 \t 543.2253108\n",
      "79001 \t 54.43139574 \t 0.75370993 \t 90.74278534 \t 0.75892979 \t 278.425 \t 542.20006037\n",
      "80001 \t 54.37762755 \t 0.7541348 \t 90.64051926 \t 0.75933996 \t 278.662 \t 541.74122834\n",
      "81001 \t 54.34933754 \t 0.75436589 \t 90.54762993 \t 0.75975196 \t 278.874 \t 544.56531882\n",
      "82001 \t 54.32688683 \t 0.75443168 \t 90.45785202 \t 0.76014752 \t 279.083 \t 543.25131297\n",
      "83001 \t 54.28104729 \t 0.75457709 \t 90.37932312 \t 0.76047534 \t 279.301 \t 543.02984667\n",
      "84001 \t 54.25789 \t 0.75474536 \t 90.29233978 \t 0.76085251 \t 279.52 \t 542.88569927\n",
      "85001 \t 54.23360082 \t 0.75491394 \t 90.21094635 \t 0.76121226 \t 279.732 \t 543.34947991\n",
      "86001 \t 54.218118 \t 0.755066 \t 90.14264191 \t 0.76153674 \t 279.949 \t 543.29933238\n",
      "87001 \t 54.18703151 \t 0.75535964 \t 90.07787148 \t 0.7618782 \t 280.166 \t 543.06503725\n",
      "88001 \t 54.14265232 \t 0.75569536 \t 90.00641983 \t 0.76223648 \t 280.384 \t 544.230268\n",
      "89001 \t 54.10728415 \t 0.75595224 \t 89.93985016 \t 0.76254587 \t 280.599 \t 544.85157943\n",
      "90001 \t 54.08829486 \t 0.75612107 \t 89.86801659 \t 0.76289379 \t 280.812 \t 544.06670618\n",
      "91001 \t 54.04929948 \t 0.75635793 \t 89.79776772 \t 0.76321092 \t 281.022 \t 543.62513232\n",
      "92001 \t 54.01121901 \t 0.75664489 \t 89.7326033 \t 0.76353226 \t 281.214 \t 543.57416391\n",
      "93001 \t 53.99297585 \t 0.75678355 \t 89.65594681 \t 0.76389129 \t 281.421 \t 544.01177168\n",
      "94001 \t 53.98389692 \t 0.75684117 \t 89.56788185 \t 0.7643069 \t 281.625 \t 544.19867826\n",
      "95001 \t 53.96308453 \t 0.75698005 \t 89.49353188 \t 0.76463507 \t 281.83 \t 543.82910037\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "96001 \t 53.93884791 \t 0.75711118 \t 89.42696259 \t 0.76488715 \t 282.037 \t 542.72797942\n",
      "97001 \t 53.92445641 \t 0.75712161 \t 89.34864563 \t 0.76521107 \t 282.241 \t 543.367239\n",
      "98001 \t 53.92228288 \t 0.75698285 \t 89.27488953 \t 0.76549912 \t 282.456 \t 543.83230114\n",
      "99001 \t 53.91007485 \t 0.75704587 \t 89.19512329 \t 0.76580248 \t 282.664 \t 543.25806022\n",
      "100001 \t 53.91439196 \t 0.75699625 \t 89.12168991 \t 0.76614486 \t 282.866 \t 543.61598444\n",
      "101001 \t 53.89024576 \t 0.7570456 \t 89.04675453 \t 0.76649416 \t 283.062 \t 543.75285077\n",
      "102001 \t 53.88730269 \t 0.75703264 \t 88.97792404 \t 0.76679335 \t 283.25 \t 544.53056288\n",
      "103001 \t 53.87390511 \t 0.75719776 \t 88.91288391 \t 0.76709974 \t 283.448 \t 543.85879254\n",
      "104001 \t 53.84498598 \t 0.75751179 \t 88.84247406 \t 0.76740011 \t 283.641 \t 543.54112339\n",
      "105001 \t 53.81261708 \t 0.75769326 \t 88.77803246 \t 0.76769923 \t 283.841 \t 543.92980742\n",
      "106001 \t 53.77793971 \t 0.75796432 \t 88.71398163 \t 0.76796274 \t 284.038 \t 544.009197\n",
      "107001 \t 53.76585995 \t 0.75811677 \t 88.65605141 \t 0.76820133 \t 284.233 \t 544.45186377\n",
      "108001 \t 53.7548971 \t 0.7583108 \t 88.60127472 \t 0.76846084 \t 284.432 \t 545.03779316\n",
      "109001 \t 53.74023001 \t 0.75836121 \t 88.53661507 \t 0.76872301 \t 284.627 \t 545.15876365\n",
      "110001 \t 53.70810237 \t 0.75850366 \t 88.47414795 \t 0.76898984 \t 284.823 \t 546.09827113\n",
      "111001 \t 53.6852482 \t 0.7587504 \t 88.41336967 \t 0.76927198 \t 285.013 \t 544.52502203\n",
      "112001 \t 53.64012045 \t 0.75909884 \t 88.3586377 \t 0.76952572 \t 285.201 \t 543.928931\n",
      "113001 \t 53.6016849 \t 0.75935529 \t 88.29260674 \t 0.76977239 \t 285.381 \t 544.5286653\n",
      "114001 \t 53.569436 \t 0.75949592 \t 88.24072975 \t 0.77003026 \t 285.572 \t 544.1967752\n",
      "115001 \t 53.54012385 \t 0.75971672 \t 88.17129013 \t 0.77031561 \t 285.753 \t 544.12131977\n",
      "116001 \t 53.52221 \t 0.75985026 \t 88.10213799 \t 0.77058041 \t 285.939 \t 545.21517396\n",
      "117001 \t 53.49918122 \t 0.76001828 \t 88.03945915 \t 0.77085649 \t 286.129 \t 544.62166595\n",
      "118001 \t 53.46290028 \t 0.76025492 \t 87.98193909 \t 0.77111422 \t 286.323 \t 544.26639748\n",
      "119001 \t 53.44610063 \t 0.76037405 \t 87.91927467 \t 0.77142212 \t 286.51 \t 543.25262022\n",
      "120001 \t 53.41217779 \t 0.76071634 \t 87.85201538 \t 0.77172261 \t 286.692 \t 544.31424856\n",
      "121001 \t 53.38342101 \t 0.76092606 \t 87.79640709 \t 0.77196484 \t 286.867 \t 543.20922661\n",
      "122001 \t 53.3786393 \t 0.76093491 \t 87.74577209 \t 0.77219 \t 287.052 \t 544.23450708\n",
      "123001 \t 53.3608449 \t 0.76107046 \t 87.68789505 \t 0.77245005 \t 287.222 \t 545.71923566\n",
      "124001 \t 53.33877292 \t 0.76128332 \t 87.62705002 \t 0.77275102 \t 287.4 \t 543.51517916\n",
      "125001 \t 53.32116939 \t 0.76138595 \t 87.57517082 \t 0.77298925 \t 287.577 \t 544.67203474\n",
      "126001 \t 53.31146559 \t 0.76150272 \t 87.51429131 \t 0.77327581 \t 287.755 \t 543.49744749\n",
      "127001 \t 53.2909896 \t 0.76170251 \t 87.46276878 \t 0.77349817 \t 287.937 \t 544.81111836\n",
      "128001 \t 53.26903491 \t 0.76178978 \t 87.40487282 \t 0.77372805 \t 288.115 \t 543.07830048\n",
      "129001 \t 53.25234132 \t 0.76181514 \t 87.35356758 \t 0.77393067 \t 288.294 \t 544.65356421\n",
      "130001 \t 53.25512621 \t 0.76175197 \t 87.30519867 \t 0.77415125 \t 288.465 \t 546.16553617\n",
      "131001 \t 53.25638548 \t 0.76170361 \t 87.25243782 \t 0.77438947 \t 288.636 \t 545.19738865\n",
      "132001 \t 53.25181129 \t 0.76169059 \t 87.1894352 \t 0.77469352 \t 288.809 \t 544.69405437\n",
      "133001 \t 53.24612968 \t 0.76171177 \t 87.1311174 \t 0.77491322 \t 288.972 \t 543.52076983\n",
      "134001 \t 53.2593155 \t 0.76155062 \t 87.07583923 \t 0.77510717 \t 289.141 \t 545.82165813\n",
      "135001 \t 53.24059334 \t 0.7616146 \t 87.01388802 \t 0.77537822 \t 289.308 \t 543.75417614\n",
      "136001 \t 53.23629502 \t 0.76155461 \t 86.96221977 \t 0.77557733 \t 289.478 \t 546.49152923\n",
      "137001 \t 53.22275632 \t 0.76157586 \t 86.90859138 \t 0.77576343 \t 289.649 \t 544.24621296\n",
      "138001 \t 53.1969243 \t 0.76176022 \t 86.85209038 \t 0.77601411 \t 289.819 \t 543.85433865\n",
      "139001 \t 53.16582297 \t 0.76200584 \t 86.79530273 \t 0.77624978 \t 289.99 \t 544.46981525\n",
      "140001 \t 53.13616977 \t 0.76222265 \t 86.74248726 \t 0.77645154 \t 290.161 \t 544.7228992\n",
      "141001 \t 53.12094827 \t 0.76232812 \t 86.68864944 \t 0.77669418 \t 290.317 \t 545.01016736\n",
      "142001 \t 53.10185701 \t 0.76248619 \t 86.64043755 \t 0.77690804 \t 290.478 \t 543.69714212\n",
      "143001 \t 53.09271317 \t 0.76260574 \t 86.60188812 \t 0.77710142 \t 290.635 \t 545.10473108\n",
      "144001 \t 53.06635511 \t 0.76281606 \t 86.57048744 \t 0.77724912 \t 290.796 \t 544.60154724\n",
      "145001 \t 53.05450166 \t 0.76291684 \t 86.52534294 \t 0.77746612 \t 290.952 \t 545.48391247\n",
      "146001 \t 53.060418 \t 0.7629265 \t 86.49409584 \t 0.77761519 \t 291.113 \t 548.80211949\n",
      "147001 \t 53.04714192 \t 0.76311089 \t 86.46191154 \t 0.77776605 \t 291.273 \t 546.6341095\n",
      "148001 \t 53.01936617 \t 0.76331196 \t 86.42311775 \t 0.77794749 \t 291.431 \t 543.80045915\n",
      "149001 \t 52.98992229 \t 0.76357829 \t 86.38521797 \t 0.77811296 \t 291.595 \t 542.92735004\n",
      "150001 \t 52.98397917 \t 0.76360044 \t 86.34559364 \t 0.77828476 \t 291.756 \t 543.60664773\n",
      "151001 \t 52.97162431 \t 0.76362445 \t 86.30105431 \t 0.7784949 \t 291.907 \t 548.72787166\n",
      "152001 \t 52.9623962 \t 0.76369977 \t 86.26167221 \t 0.77867123 \t 292.06 \t 544.52949905\n",
      "153001 \t 52.94183039 \t 0.7638208 \t 86.216763 \t 0.77890277 \t 292.213 \t 544.82081556\n",
      "154001 \t 52.91739235 \t 0.7640022 \t 86.1752282 \t 0.77910301 \t 292.37 \t 546.25188732\n",
      "155001 \t 52.91479805 \t 0.76398811 \t 86.13125847 \t 0.77926649 \t 292.517 \t 544.23704481\n",
      "156001 \t 52.92185795 \t 0.76399669 \t 86.09584671 \t 0.77940292 \t 292.671 \t 545.05360007\n",
      "157001 \t 52.91423643 \t 0.76414921 \t 86.0444976 \t 0.77963725 \t 292.825 \t 545.21872282\n",
      "158001 \t 52.90878762 \t 0.76418378 \t 85.99185562 \t 0.77984349 \t 292.98 \t 547.27177882\n",
      "159001 \t 52.89866508 \t 0.76418224 \t 85.94858162 \t 0.78001304 \t 293.133 \t 545.52895951\n",
      "160001 \t 52.88624932 \t 0.76426074 \t 85.8986219 \t 0.78022515 \t 293.284 \t 543.3704288\n",
      "161001 \t 52.85490173 \t 0.76450412 \t 85.85731651 \t 0.7804173 \t 293.433 \t 544.44873691\n",
      "162001 \t 52.84292234 \t 0.76460717 \t 85.82228577 \t 0.7806169 \t 293.58 \t 544.47905588\n",
      "163001 \t 52.83290038 \t 0.76462808 \t 85.78327614 \t 0.78079224 \t 293.725 \t 545.71104479\n",
      "164001 \t 52.83340914 \t 0.76458458 \t 85.75235565 \t 0.7809381 \t 293.872 \t 543.71526861\n",
      "165001 \t 52.82822174 \t 0.76468018 \t 85.71379684 \t 0.78113786 \t 294.016 \t 545.12000871\n",
      "166001 \t 52.83576386 \t 0.76472333 \t 85.68328102 \t 0.78129572 \t 294.16 \t 545.76481867\n",
      "167001 \t 52.82827697 \t 0.76481052 \t 85.64536697 \t 0.78143648 \t 294.302 \t 544.61050367\n",
      "168001 \t 52.82303593 \t 0.76484988 \t 85.59808395 \t 0.78162323 \t 294.449 \t 544.3236289\n",
      "169001 \t 52.82420254 \t 0.76486175 \t 85.55853294 \t 0.78181304 \t 294.595 \t 545.45295238\n",
      "170001 \t 52.81967472 \t 0.76483174 \t 85.52186188 \t 0.78194635 \t 294.74 \t 544.61441159\n",
      "171001 \t 52.81371405 \t 0.7648767 \t 85.48969856 \t 0.78211658 \t 294.877 \t 544.60217071\n",
      "172001 \t 52.80768222 \t 0.7648868 \t 85.45619331 \t 0.78222773 \t 295.015 \t 544.0100987\n",
      "173001 \t 52.78915722 \t 0.7650019 \t 85.41414726 \t 0.78236678 \t 295.161 \t 544.4192102\n",
      "174001 \t 52.76791019 \t 0.76515458 \t 85.37552689 \t 0.78253656 \t 295.297 \t 544.26933837\n",
      "175001 \t 52.75121889 \t 0.76523425 \t 85.34083252 \t 0.782719 \t 295.439 \t 545.61226845\n",
      "176001 \t 52.7193274 \t 0.76541253 \t 85.30143166 \t 0.78291632 \t 295.579 \t 544.93913436\n",
      "177001 \t 52.70117387 \t 0.76561071 \t 85.2613176 \t 0.7831129 \t 295.715 \t 544.06206465\n",
      "178001 \t 52.6898582 \t 0.76570191 \t 85.22084923 \t 0.78327916 \t 295.856 \t 545.41730118\n",
      "179001 \t 52.69873834 \t 0.76562622 \t 85.19181168 \t 0.7834051 \t 295.996 \t 545.09674311\n",
      "180001 \t 52.67450015 \t 0.76584544 \t 85.15279259 \t 0.78355242 \t 296.136 \t 545.00798607\n",
      "181001 \t 52.65364211 \t 0.76599851 \t 85.11431808 \t 0.78367848 \t 296.268 \t 546.13549805\n",
      "182001 \t 52.624558 \t 0.76619473 \t 85.08345039 \t 0.78381032 \t 296.399 \t 544.8191433\n",
      "183001 \t 52.60886099 \t 0.76637623 \t 85.05100914 \t 0.78398503 \t 296.533 \t 544.40545034\n",
      "184001 \t 52.60029857 \t 0.76648981 \t 85.01012825 \t 0.78419606 \t 296.664 \t 542.89994812\n",
      "185001 \t 52.59394553 \t 0.76649681 \t 84.97537621 \t 0.78434109 \t 296.796 \t 544.38717961\n",
      "186001 \t 52.56165701 \t 0.7667237 \t 84.94328842 \t 0.78448888 \t 296.927 \t 543.95520067\n",
      "187001 \t 52.53329007 \t 0.76696492 \t 84.9051368 \t 0.78466212 \t 297.059 \t 544.7193594\n",
      "188001 \t 52.52016737 \t 0.76714323 \t 84.87215187 \t 0.7848006 \t 297.194 \t 545.19444489\n",
      "189001 \t 52.51898001 \t 0.76725115 \t 84.83177567 \t 0.78496034 \t 297.331 \t 543.9372921\n",
      "190001 \t 52.50735486 \t 0.76727302 \t 84.79191757 \t 0.78511502 \t 297.46 \t 544.02258015\n",
      "191001 \t 52.49900902 \t 0.76726682 \t 84.75188644 \t 0.78523381 \t 297.585 \t 545.33780169\n",
      "192001 \t 52.50101212 \t 0.7672249 \t 84.71767647 \t 0.78539778 \t 297.712 \t 544.99471521\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "193001 \t 52.50549292 \t 0.76717559 \t 84.6801757 \t 0.78558207 \t 297.84 \t 545.73823476\n",
      "194001 \t 52.50354667 \t 0.76712394 \t 84.64663811 \t 0.7857553 \t 297.962 \t 545.56683612\n",
      "195001 \t 52.49135493 \t 0.76725681 \t 84.61023705 \t 0.7859513 \t 298.087 \t 545.65556455\n",
      "196001 \t 52.50268991 \t 0.76726516 \t 84.58877968 \t 0.78606543 \t 298.211 \t 543.93644834\n",
      "197001 \t 52.48839072 \t 0.76747873 \t 84.56082962 \t 0.78619573 \t 298.34 \t 544.36750484\n",
      "198001 \t 52.47195947 \t 0.76772294 \t 84.5284407 \t 0.78632505 \t 298.468 \t 546.60080934\n",
      "199001 \t 52.44718534 \t 0.76799188 \t 84.49225288 \t 0.7864379 \t 298.591 \t 544.74734259\n",
      "200001 \t 52.45407136 \t 0.76799541 \t 84.45696152 \t 0.78661387 \t 298.714 \t 543.73006058\n",
      "201001 \t 52.45497668 \t 0.76798376 \t 84.42780228 \t 0.78675623 \t 298.84 \t 545.07489347\n",
      "202001 \t 52.45970142 \t 0.7679161 \t 84.39693649 \t 0.7868847 \t 298.958 \t 546.934582\n",
      "203001 \t 52.45579891 \t 0.76794991 \t 84.35802055 \t 0.78701763 \t 299.08 \t 544.13563418\n",
      "204001 \t 52.457171 \t 0.76795291 \t 84.33637154 \t 0.78715281 \t 299.2 \t 544.65468955\n",
      "205001 \t 52.45183228 \t 0.76799954 \t 84.30073677 \t 0.78730327 \t 299.319 \t 545.38923788\n",
      "206001 \t 52.45138155 \t 0.76798567 \t 84.28093933 \t 0.78738654 \t 299.441 \t 544.22328639\n",
      "207001 \t 52.4612189 \t 0.76782589 \t 84.25362991 \t 0.78749261 \t 299.566 \t 544.4602809\n",
      "208001 \t 52.46343956 \t 0.76771781 \t 84.22727028 \t 0.78758142 \t 299.685 \t 545.22097993\n",
      "209001 \t 52.45194711 \t 0.76784085 \t 84.20199341 \t 0.78766247 \t 299.803 \t 545.2102437\n",
      "210001 \t 52.45680028 \t 0.76776654 \t 84.1789183 \t 0.78776162 \t 299.919 \t 545.79040956\n",
      "211001 \t 52.44871449 \t 0.7677538 \t 84.14727203 \t 0.787873 \t 300.033 \t 546.458673\n",
      "212001 \t 52.43905159 \t 0.76779236 \t 84.11892311 \t 0.78798537 \t 300.149 \t 544.90574241\n",
      "213001 \t 52.45403687 \t 0.76758966 \t 84.08440208 \t 0.78810158 \t 300.26 \t 544.67622375\n",
      "214001 \t 52.43588325 \t 0.76760263 \t 84.05563347 \t 0.78821324 \t 300.378 \t 540.77307773\n",
      "215001 \t 52.43204757 \t 0.76764854 \t 84.01982811 \t 0.788384 \t 300.491 \t 537.56817317\n",
      "216001 \t 52.41943207 \t 0.76767038 \t 83.99534599 \t 0.78852359 \t 300.607 \t 538.22761488\n",
      "217001 \t 52.41651287 \t 0.76770157 \t 83.96407761 \t 0.78869944 \t 300.724 \t 537.68693304\n",
      "218001 \t 52.41744773 \t 0.76769488 \t 83.93008789 \t 0.78885965 \t 300.843 \t 537.39950228\n",
      "219001 \t 52.39574637 \t 0.76777722 \t 83.89754135 \t 0.78896539 \t 300.954 \t 538.52607536\n",
      "220001 \t 52.39207434 \t 0.76768731 \t 83.87024651 \t 0.78907318 \t 301.069 \t 537.98213053\n",
      "221001 \t 52.37799054 \t 0.76774065 \t 83.83604462 \t 0.78917337 \t 301.179 \t 537.1650362\n",
      "222001 \t 52.37062616 \t 0.76780423 \t 83.80444695 \t 0.78927726 \t 301.292 \t 537.55795169\n",
      "223001 \t 52.36466096 \t 0.76782672 \t 83.77916229 \t 0.78935822 \t 301.398 \t 538.79209757\n",
      "224001 \t 52.36785213 \t 0.76777883 \t 83.7450872 \t 0.78948633 \t 301.512 \t 538.12178397\n",
      "225001 \t 52.35701402 \t 0.76788076 \t 83.70873322 \t 0.78964927 \t 301.621 \t 536.98553491\n",
      "226001 \t 52.36588825 \t 0.76790223 \t 83.67574272 \t 0.78980571 \t 301.729 \t 537.5359416\n",
      "227001 \t 52.36418633 \t 0.76784627 \t 83.64609535 \t 0.78990272 \t 301.839 \t 537.78228688\n",
      "228001 \t 52.34685803 \t 0.7680245 \t 83.61865974 \t 0.79004448 \t 301.947 \t 536.05537701\n",
      "229001 \t 52.34115495 \t 0.76807921 \t 83.58747681 \t 0.79022275 \t 302.06 \t 537.58093905\n",
      "230001 \t 52.34630929 \t 0.76811321 \t 83.56470154 \t 0.79034233 \t 302.17 \t 537.09627485\n",
      "231001 \t 52.34387466 \t 0.76814961 \t 83.54436722 \t 0.79045262 \t 302.271 \t 537.49209905\n",
      "232001 \t 52.35358511 \t 0.76809279 \t 83.52489502 \t 0.79053567 \t 302.377 \t 538.80930877\n",
      "233001 \t 52.35854702 \t 0.76803172 \t 83.51269089 \t 0.79062553 \t 302.478 \t 539.10725951\n",
      "234001 \t 52.35106754 \t 0.76807188 \t 83.50451759 \t 0.79071241 \t 302.584 \t 539.30562758\n",
      "235001 \t 52.35155892 \t 0.76804524 \t 83.48075394 \t 0.79082213 \t 302.684 \t 536.71606708\n",
      "236001 \t 52.34177238 \t 0.7680892 \t 83.45205078 \t 0.79093699 \t 302.787 \t 538.24390721\n",
      "237001 \t 52.33054149 \t 0.76816186 \t 83.41059639 \t 0.7911199 \t 302.893 \t 537.92360854\n",
      "238001 \t 52.32309315 \t 0.76823208 \t 83.37031891 \t 0.79132468 \t 302.998 \t 537.60200477\n",
      "239001 \t 52.30949525 \t 0.76826752 \t 83.3387944 \t 0.79147244 \t 303.1 \t 538.12027717\n",
      "240001 \t 52.30200544 \t 0.76835564 \t 83.31420013 \t 0.79156738 \t 303.202 \t 537.92963743\n",
      "241001 \t 52.28680764 \t 0.7684529 \t 83.28278519 \t 0.79169628 \t 303.297 \t 541.24874449\n",
      "242001 \t 52.27336554 \t 0.76840852 \t 83.24645012 \t 0.79183453 \t 303.398 \t 539.5676055\n",
      "243001 \t 52.28656231 \t 0.7682103 \t 83.21571144 \t 0.79198572 \t 303.497 \t 537.91759777\n",
      "244001 \t 52.28057796 \t 0.76819101 \t 83.18900414 \t 0.79209274 \t 303.597 \t 541.19058681\n",
      "245001 \t 52.27086694 \t 0.76816434 \t 83.16352829 \t 0.79211819 \t 303.695 \t 538.30034661\n",
      "246001 \t 52.27104408 \t 0.76816794 \t 83.14126663 \t 0.79216344 \t 303.796 \t 539.33087516\n",
      "247001 \t 52.27742033 \t 0.76809387 \t 83.11236542 \t 0.79225419 \t 303.897 \t 538.54350972\n",
      "248001 \t 52.28424348 \t 0.76801073 \t 83.08300827 \t 0.79237446 \t 304.0 \t 537.62771893\n",
      "249001 \t 52.28500292 \t 0.76802182 \t 83.05335876 \t 0.79247451 \t 304.098 \t 538.07076597\n",
      "250001 \t 52.28462452 \t 0.76797523 \t 83.0281321 \t 0.79256532 \t 304.199 \t 538.25656939\n",
      "251001 \t 52.28743058 \t 0.76797058 \t 83.01492767 \t 0.79261271 \t 304.29 \t 539.00641036\n",
      "252001 \t 52.26370089 \t 0.76817482 \t 82.99487152 \t 0.79269259 \t 304.383 \t 539.16636872\n",
      "253001 \t 52.24155573 \t 0.76836356 \t 82.97619911 \t 0.7927729 \t 304.479 \t 537.85079813\n",
      "254001 \t 52.22761402 \t 0.76848847 \t 82.95582527 \t 0.79287768 \t 304.574 \t 538.69693995\n",
      "255001 \t 52.23000296 \t 0.76847427 \t 82.93813705 \t 0.79293671 \t 304.665 \t 539.59152484\n",
      "256001 \t 52.21579327 \t 0.76864468 \t 82.91941238 \t 0.79301984 \t 304.759 \t 539.01699591\n",
      "257001 \t 52.19712109 \t 0.76874162 \t 82.89816139 \t 0.79310034 \t 304.856 \t 542.35956073\n",
      "258001 \t 52.21298852 \t 0.76864371 \t 82.86863319 \t 0.79325382 \t 304.947 \t 538.74865198\n",
      "259001 \t 52.22607266 \t 0.76862249 \t 82.84543617 \t 0.79339048 \t 305.042 \t 538.7681334\n",
      "260001 \t 52.22944023 \t 0.76859749 \t 82.82033279 \t 0.79352987 \t 305.136 \t 538.27477407\n",
      "261001 \t 52.24182695 \t 0.76856261 \t 82.79787201 \t 0.79362045 \t 305.226 \t 540.12084031\n",
      "262001 \t 52.24339625 \t 0.76868792 \t 82.77815987 \t 0.79373009 \t 305.313 \t 538.63578486\n",
      "263001 \t 52.23605743 \t 0.76883878 \t 82.75281563 \t 0.79383007 \t 305.408 \t 538.98154664\n",
      "264001 \t 52.23193096 \t 0.76894708 \t 82.71913254 \t 0.79395146 \t 305.497 \t 539.69571185\n",
      "265001 \t 52.21880035 \t 0.76906211 \t 82.69075432 \t 0.79408268 \t 305.586 \t 539.65826321\n",
      "266001 \t 52.21258219 \t 0.76916146 \t 82.67534615 \t 0.79412991 \t 305.673 \t 538.78685451\n",
      "267001 \t 52.1936034 \t 0.76930861 \t 82.65075729 \t 0.79421693 \t 305.763 \t 548.45967722\n",
      "268001 \t 52.1928609 \t 0.769394 \t 82.6282457 \t 0.79432727 \t 305.856 \t 538.35901499\n",
      "269001 \t 52.20602077 \t 0.76929844 \t 82.60729301 \t 0.79442652 \t 305.945 \t 538.25529885\n",
      "270001 \t 52.22057854 \t 0.7691567 \t 82.59039001 \t 0.79447517 \t 306.038 \t 538.76874352\n",
      "271001 \t 52.21544613 \t 0.76925684 \t 82.56677727 \t 0.79459588 \t 306.124 \t 539.37664771\n",
      "272001 \t 52.21291247 \t 0.76931539 \t 82.54613358 \t 0.79472722 \t 306.207 \t 540.24441838\n",
      "273001 \t 52.21708638 \t 0.76916778 \t 82.53040009 \t 0.7947841 \t 306.292 \t 539.22117352\n",
      "274001 \t 52.21682448 \t 0.76907726 \t 82.51085526 \t 0.79484202 \t 306.375 \t 539.51825142\n",
      "275001 \t 52.22026384 \t 0.76897578 \t 82.4842189 \t 0.79494059 \t 306.46 \t 539.48031044\n",
      "276001 \t 52.21921372 \t 0.76903091 \t 82.4643248 \t 0.79502041 \t 306.546 \t 539.01623607\n",
      "277001 \t 52.2171154 \t 0.7690132 \t 82.44050575 \t 0.79515318 \t 306.633 \t 548.21073008\n",
      "278001 \t 52.20252122 \t 0.76905381 \t 82.41647003 \t 0.79528969 \t 306.719 \t 555.88697219\n",
      "279001 \t 52.20283525 \t 0.76906259 \t 82.39409409 \t 0.79542078 \t 306.807 \t 557.45969868\n",
      "280001 \t 52.18691652 \t 0.76909774 \t 82.37207924 \t 0.79550215 \t 306.891 \t 545.86230707\n",
      "281001 \t 52.20211825 \t 0.7689877 \t 82.35130241 \t 0.79559249 \t 306.976 \t 547.2646687\n",
      "282001 \t 52.21579658 \t 0.76888852 \t 82.3392276 \t 0.79563222 \t 307.06 \t 545.47554755\n",
      "283001 \t 52.23960286 \t 0.7686474 \t 82.32476082 \t 0.79567519 \t 307.14 \t 553.4569664\n",
      "284001 \t 52.25445763 \t 0.76852728 \t 82.31811668 \t 0.79568225 \t 307.222 \t 558.39359331\n",
      "285001 \t 52.24314612 \t 0.76863205 \t 82.30828865 \t 0.79575529 \t 307.302 \t 558.7978723\n",
      "286001 \t 52.24194393 \t 0.76864937 \t 82.28700714 \t 0.7958257 \t 307.388 \t 555.53910589\n",
      "287001 \t 52.22778294 \t 0.76879244 \t 82.26450279 \t 0.79590108 \t 307.466 \t 551.96339202\n",
      "288001 \t 52.19574447 \t 0.76902546 \t 82.2393576 \t 0.79599887 \t 307.546 \t 551.67809629\n",
      "289001 \t 52.17008634 \t 0.76912601 \t 82.22021629 \t 0.79610261 \t 307.628 \t 551.9005177\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "290001 \t 52.14327092 \t 0.76931919 \t 82.20002213 \t 0.79621456 \t 307.711 \t 552.25188303\n",
      "291001 \t 52.13030952 \t 0.76938447 \t 82.17811432 \t 0.79631236 \t 307.787 \t 552.51575136\n",
      "292001 \t 52.11388554 \t 0.76946612 \t 82.15575478 \t 0.7964117 \t 307.867 \t 549.1782186\n",
      "293001 \t 52.11423985 \t 0.7694318 \t 82.13356018 \t 0.79651815 \t 307.943 \t 549.82873058\n",
      "294001 \t 52.11190169 \t 0.7694991 \t 82.11470734 \t 0.79660096 \t 308.021 \t 548.69490504\n",
      "295001 \t 52.12566043 \t 0.76950067 \t 82.09516319 \t 0.79668718 \t 308.099 \t 548.55338788\n",
      "296001 \t 52.12222643 \t 0.76962583 \t 82.08033104 \t 0.79670093 \t 308.178 \t 550.2971735\n",
      "297001 \t 52.13036561 \t 0.76956032 \t 82.05902252 \t 0.79680362 \t 308.257 \t 547.90722537\n",
      "298001 \t 52.14432141 \t 0.76944742 \t 82.03444244 \t 0.79692766 \t 308.335 \t 549.46138787\n",
      "299001 \t 52.15547127 \t 0.76935897 \t 82.00892288 \t 0.79701882 \t 308.412 \t 548.15608764\n",
      "\n",
      "TRAINING DONE\n"
     ]
    }
   ],
   "source": [
    "with tf.Session(graph=graph) as session:\n",
    "    tf.global_variables_initializer().run()\n",
    "    print(\"Initialized\")\n",
    "    ptime = time.time()\n",
    "    tfconfig = tf.ConfigProto()\n",
    "    tfconfig.gpu_options.allow_growth=gpu_memory_growth\n",
    "    init_op = tf.global_variables_initializer()    \n",
    "    sess = tf.Session(config=tfconfig)\n",
    "    sess.run(init_op)\n",
    "    print ('iter\\t test_squ_loss\\t test_avg_prec\\t',\n",
    "           'train_squ_loss\\t train_avg_prec\\t regularization\\t time (in s)')\n",
    "    for step in range(num_steps):        \n",
    "        #print('Step',step)\n",
    "        #print('There are {} samples in the test set'.format(len(model_stats.test_ids)))\n",
    "        #print('There are {} samples in the train set'.format(len(model_stats.train_ids)))\n",
    "       \n",
    "        xmb, ymb = get_training_batch(model_stats, batch_size, window, \n",
    "                                      pitch_transforms, jitter)\n",
    "        \n",
    "        # every 1000-nd step save the results           \n",
    "        if step%control_step==0:\n",
    "            # training step \n",
    "            # to check output the loss (save the loss but you cannot compare it with the mse_train)\n",
    "            _, np_loss, np_global_step, np_lr, reg_np = \\\n",
    "            sess.run([training_op, loss, global_step, lr, reg], \n",
    "                                     feed_dict={xb: xmb, yb: ymb})\n",
    "            \n",
    "            # A) run model on test set\n",
    "                        \n",
    "            # 1. get the test set\n",
    "            X,Y = get_test_sample(model_stats.test_ids, window, 1000)\n",
    "            Y = Y[:,base_note:base_note+mm] # in general without effect\n",
    "            # shape Xtest: (1000*len(test_ids), 1, 16384, 1)\n",
    "            # shape Ytest: (1000*len(test_ids), 128)\n",
    "            # extended test-set: 11 samples\n",
    "\n",
    "            \n",
    "            # 2. prediction of output and calculation of\n",
    "            # - MSE\n",
    "            # - average precision\n",
    "            Y_p, mse_test = predict_direct_model(sess, X, Y)\n",
    "            avp_test = average_precision_score(Y.flatten(), Y_p.flatten())\n",
    "            \n",
    "            del X,Y,Y_p\n",
    "            \n",
    "            # B) run model on training set\n",
    "            \n",
    "            # 1. get training sample\n",
    "            X,Y = get_training_sample(window)\n",
    "            Y = Y[:,base_note:base_note+mm] # in general without effect\n",
    "            # shape Xtrain: (15000, 1, 16384, 1)\n",
    "            # shape Ytrain: (15000, 128)\n",
    "            \n",
    "            # 2. prediction of output and calculation of\n",
    "            # - MSE\n",
    "            # - average precision\n",
    "            Y_p, mse_train = predict_direct_model(sess, X, Y)\n",
    "            avp_train = average_precision_score(Y.flatten(), Y_p.flatten())\n",
    "            \n",
    "            del X,Y,Y_p\n",
    "            \n",
    "            # c) Print the temporary results\n",
    "            print (np_global_step,'\\t', round(mse_test,8),\n",
    "                    '\\t', round(avp_test,8),\n",
    "                    '\\t', round(mse_train,8),\n",
    "                    '\\t', round(avp_train,8),\n",
    "                    '\\t', round(reg_np,8),\n",
    "                    '\\t', round(time.time() - ptime,8))\n",
    "            \n",
    "            #D) Save the temporary results\n",
    "            model_stats.stats['iter'][2].append(np_global_step)\n",
    "            # protocol learning rate\n",
    "            model_stats.stats['lr'][2].append(np_lr)\n",
    "            # protocol runtime\n",
    "            model_stats.stats['time'][2].append(time.time() - ptime)\n",
    "            # protocol weights and its norms\n",
    "            weight_norms = dict()\n",
    "            for name, w in model_stats.weights.items():\n",
    "                weight_norms[name] = np.mean(np.linalg.norm(w.eval(session=sess),axis=0))\n",
    "            for name, norm in weight_norms.items():\n",
    "                model_stats.stats['n'+name][2].append(norm)\n",
    "            # protocol training errors     \n",
    "            track = []\n",
    "            model_stats.stats['mse_train_1'][2].append(np_loss)\n",
    "            model_stats.stats['mse_train'][2].append(mse_train)\n",
    "            track += [tf.summary.scalar('mse_train', mse_train)]\n",
    "            model_stats.stats['avp_train'][2].append(avp_train)\n",
    "            track += [tf.summary.scalar('avp_train', avp_train)]\n",
    "            # protocol test errors\n",
    "            model_stats.stats['mse_test'][2].append(mse_test)\n",
    "            track += [tf.summary.scalar('mse_test', mse_test)]\n",
    "            model_stats.stats['avp_test'][2].append(avp_test)\n",
    "            track += [tf.summary.scalar('avp_test', avp_test)]\n",
    "            ptime = time.time()\n",
    "            \n",
    "                   \n",
    "        else:\n",
    "            #training step \n",
    "            sess.run([training_op], feed_dict={xb: xmb, yb: ymb})\n",
    "        \n",
    "        if step/num_steps==0.5 or (step+1)/num_steps==1:\n",
    "            saver.save(sess, 'models_'+notebook_reference+'/model', global_step=step)            \n",
    "        \n",
    "    print ('\\nTRAINING DONE')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "X,Y = get_test_sample(model_stats.test_ids, window, 1000)\n",
    "Y = Y[:,base_note:base_note+mm] # in general without effect\n",
    "# shape Xtest: (1000*len(test_ids), 1, 16384, 1)\n",
    "# shape Ytest: (1000*len(test_ids), 128)\n",
    "# extended test-set: 11 samples\n",
    "\n",
    "\n",
    "# 2. prediction of output and calculation of\n",
    "# - MSE\n",
    "# - average precision\n",
    "Y_p, mse_test = predict_direct_model(sess, X, Y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 7. Post Processing\n",
    "\n",
    "Define functions to save the model_results diccionary to a file and load it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "# in case diccionary should be printed\n",
    "def print_dict(dictionary):\n",
    "    for key, value in  dictionary.items():\n",
    "        print (key)\n",
    "        print (value)\n",
    "#print_dict(model_stats.stats)\n",
    "    \n",
    "#save diccionary\n",
    "def save_obj(obj, name ):\n",
    "    with open(name + '.pkl', 'wb') as f:\n",
    "        pickle.dump(obj, f, pickle.HIGHEST_PROTOCOL)\n",
    "\n",
    "# load diccionary\n",
    "def load_obj(name ):\n",
    "    with open(name + '.pkl', 'rb') as f:\n",
    "        return pickle.load(f)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 7.1 Plots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "# save diccionary to have a look at outputs at other times\n",
    "save_obj(model_stats.stats, 'results/'+notebook_reference+'_stats')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "results_stats = load_obj('results/'+notebook_reference+'_stats')\n",
    "\n",
    "# size of plots\n",
    "width = 20\n",
    "height = 13\n",
    "fontsize = 24\n",
    "control_step = 1000"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7f0bd06b73c8>"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0bd068a630>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "iters_res = results_stats['iter'][2]\n",
    "time_res = results_stats['time'][2]\n",
    "lr_res = results_stats['lr'][2]\n",
    "\n",
    "plt.figure(figsize=(width, height/2))\n",
    "plt.rcParams['xtick.labelsize']=fontsize\n",
    "plt.rcParams['ytick.labelsize']=fontsize\n",
    "#plot time needed for 'control_step' steps\n",
    "\"\"\"\n",
    "plt.subplot(2, 1, 1)\n",
    "plt.title('Runtime and Learning Rate')\n",
    "plt.plot(iters_res[1:], time_res[1:], c=\"darkblue\")\n",
    "plt.ylabel('Runtime of last '+str(control_step)+' steps (sec)')\n",
    "plt.ylim( (0.9*np.min(time_res[1:]), 1.1*np.max(time_res[1:])))\n",
    "\n",
    "plt.subplot(2, 1, 2)\n",
    "\"\"\"\n",
    "plt.subplot(1, 1, 1)\n",
    "plt.plot(iters_res[1:], lr_res[1:], c=\"darkblue\")\n",
    "plt.ylabel('Learning rate',fontsize=fontsize)\n",
    "plt.xlabel('Iteration',fontsize=fontsize)\n",
    "\n",
    "#plt.savefig('results/plots/'+notebook_reference+'_runtime_lr.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7f0bd0101a20>"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0bd01e3c50>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "mse_train_res = results_stats['mse_train'][2]\n",
    "mse_test_res = results_stats['mse_test'][2]\n",
    "\n",
    "avp_train_res = results_stats['avp_train'][2]\n",
    "avp_test_res = results_stats['avp_test'][2]\n",
    "\n",
    "plt.figure(figsize=(width, height))\n",
    "\n",
    "#plot mean squared error\n",
    "plt.subplot(2, 1, 1)\n",
    "#plt.title('Error and Precision',fontsize=fontsize)\n",
    "plt.plot(iters_res, mse_train_res, label='Training', c=\"c\")\n",
    "plt.plot(iters_res, mse_test_res, label='Test', c=\"darkblue\")\n",
    "plt.legend(fontsize=fontsize)\n",
    "plt.ylabel('Mean Square Error',fontsize=fontsize)\n",
    "\n",
    "#plot average precision\n",
    "plt.subplot(2, 1, 2)\n",
    "plt.plot(iters_res, avp_train_res, label='Training', c=\"c\")\n",
    "plt.plot(iters_res, avp_test_res, label='Test', c=\"darkblue\")\n",
    "plt.legend(fontsize=fontsize)\n",
    "plt.ylabel('Average Precision',fontsize=fontsize)\n",
    "plt.xlabel('Iteration',fontsize=fontsize)\n",
    "\n",
    "#plt.savefig('results/plots/'+notebook_reference+'_error_prec.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7f0ba83bae48>"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0bd87a4e48>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "nw = results_stats['nw'][2]\n",
    "nw2 = results_stats['nw2'][2]\n",
    "nbeta = results_stats['nbeta'][2]\n",
    "\n",
    "plt.figure(figsize=(width, height))\n",
    "\n",
    "plt.subplot(2, 1, 1)\n",
    "#plt.title('Norm of the Weights of Hidden and Output Layer',fontsize=fontsize)\n",
    "plt.plot(iters_res, nw, label='2nd layer', c=\"darkblue\")\n",
    "plt.plot(iters_res, nw2, label='3rd layer', c=\"c\")\n",
    "\n",
    "plt.legend(fontsize=fontsize)\n",
    "plt.ylabel('l2-Norm',fontsize=fontsize)\n",
    "\n",
    "plt.subplot(2, 1, 2)\n",
    "\n",
    "plt.plot(iters_res, nbeta, label='Output layer', c=\"darkblue\")\n",
    "plt.legend(fontsize=fontsize)\n",
    "plt.ylabel('l2-Norm',fontsize=fontsize)\n",
    "plt.xlabel('Iteration',fontsize=fontsize)\n",
    "\n",
    "#plt.savefig('results/plots/'+notebook_reference+'_weight_norms.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 7.2 Mirex Statistics\n",
    "\n",
    "Statistics that are necesarry for evaluation. The autuor used a threshold of 0.4 for calculation of error etc..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [],
   "source": [
    "def mirex_statistics(Y, Y_p, threshold=.5, base_note=0, mm=128):\n",
    "    \n",
    "    avp = average_precision_score(Y.flatten(), Y_p.flatten())\n",
    "\n",
    "    # we want zeros and ones\n",
    "    Yhat_p = Y_p>threshold\n",
    "    \n",
    "    # obtain frequencies\n",
    "    Yp_list = []\n",
    "    Y_list = []\n",
    "    \n",
    "    # for every sample\n",
    "    for i in range(len(Yhat_p)):\n",
    "        fhat = []\n",
    "        f = []\n",
    "        \n",
    "        for note in range(mm):\n",
    "            if Yhat_p[i][note] == 1:\n",
    "                fhat.append(440.*2**(((note+base_note) - 69.)/12.))\n",
    "            if Y[i][note] == 1:\n",
    "                f.append(440.*2**(((note+base_note) - 69.)/12.))\n",
    "\n",
    "        Yp_list.append(np.array(fhat))\n",
    "        Y_list.append(np.array(f))\n",
    "\n",
    "    # Precision, Recall, Accuracy, Substitution, Miss, False Alarm, and Total Error scores \n",
    "    # based both on raw frequency values and values mapped to a single octave (chroma)\n",
    "    \n",
    "    P,R,Acc,Esub,Emiss,Efa,Etot,cP,cR,cAcc,cEsub,cEmiss,cEfa,cEtot = \\\n",
    "    mir_eval.multipitch.metrics(np.arange(len(Y_list))/100., Y_list,\n",
    "                                np.arange(len(Yp_list))/100., Yp_list)\n",
    "\n",
    "\n",
    "    print('{:.2f}\\t{:.3f}\\t{:.3f}\\t{:.3f}\\t{:.3f}\\t{:.3f}\\t{:.3f}\\t{:.3f}\\t{:.3f}'.format(\n",
    "        threshold,100*avp,100*P,100*R,Acc,Etot,Esub,Emiss,Efa))\n",
    "\n",
    "    return avp,P,R,Acc,Etot\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Restoring parameters from models_190314/model-299999\n",
      "Model restored.\n"
     ]
    }
   ],
   "source": [
    "tf.reset_default_graph()\n",
    "\n",
    "num_steps = 300000\n",
    "\n",
    "# Add ops to save and restore all the variables.\n",
    "saver = tf.train.Saver({'wavg': wavg, 'w2avg': w2avg, 'betaavg': betaavg})\n",
    "\n",
    "# Later, launch the model, use the saver to restore variables from disk, and\n",
    "# do some work with the model.\n",
    "with tf.Session(graph=graph) as sess:\n",
    "    # Restore variables from disk.\n",
    "    saver.restore(sess, 'models_'+notebook_reference+'/model-'+str(num_steps-1))\n",
    "    print(\"Model restored.\")\n",
    "    # Check the values of the variables\n",
    "\n",
    "    # A) run model on test set\n",
    "\n",
    "    # 1. get the test set\n",
    "    X, Y = get_test_sample(model_stats.test_ids, window, 1000)\n",
    "    Y = Y[:,base_note:base_note+mm] # in general without effect\n",
    "    # shape Xtest: (1000*len(test_ids), 1, 16384, 1)\n",
    "    # shape Ytest: (1000*len(test_ids), 128)\n",
    "    # extended test-set: 11 samples\n",
    "\n",
    "\n",
    "    # 2. prediction of output and calculation of\n",
    "    # - MSE\n",
    "    # - average precision\n",
    "    Y_p, mse_test = predict_direct_model(sess, X, Y)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "With the output of the test sample predicted calculate the mirex statistics:\n",
    "\n",
    "Average Precision (not from mireval), Precision, Recall, Accuracy, Total Error, Substitution, Miss, False Alarm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Thr\tAvgP\tP\tR\tAcc\tETot\tESub\tEmiss\tEfa\n",
      "0.30\t76.937\t66.959\t79.091\t0.569\t0.490\t0.109\t0.100\t0.281\n",
      "0.35\t76.937\t69.474\t76.076\t0.570\t0.465\t0.108\t0.131\t0.226\n",
      "0.40\t76.937\t71.710\t72.898\t0.566\t0.452\t0.106\t0.165\t0.181\n",
      "0.45\t76.937\t73.737\t69.821\t0.559\t0.449\t0.102\t0.200\t0.147\n",
      "0.50\t76.937\t75.607\t66.512\t0.548\t0.454\t0.095\t0.239\t0.119\n"
     ]
    }
   ],
   "source": [
    "print('Thr\\tAvgP\\tP\\tR\\tAcc\\tETot\\tESub\\tEmiss\\tEfa')\n",
    "for th in [.3, .35,.4, .45, .5]:\n",
    "    #print(\"When using threshold\",th)\n",
    "    avp,P,R,Acc,Etot = mirex_statistics(Y, Y_p, th)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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