05_3layer-LSTM-norm_results.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 0. Import the packages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "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",
    "from sklearn.metrics import average_precision_score\n",
    "import tensorflow as tf\n",
    "import mir_eval\n",
    "\n",
    "sys.path.insert(0,'lib/')\n",
    "import config\n",
    "import model_functions_norm2 as model_functions_norm # contains self-written functions\n",
    "\n",
    "from versions_norm import version_dicc\n",
    "\n",
    "%matplotlib inline\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1. Pick the version"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "graph                 :  <function LSTM_PT_graph_AdamOpt_noReg at 0x7f6747b83950>\n",
      "pretrained            :  190319_11HZ\n",
      "window                :  4096\n",
      "timesteps             :  9\n",
      "stride                :  256\n",
      "kk                    :  256\n",
      "dd                    :  1024\n",
      "d2_x                  :  1\n",
      "d2_y                  :  128\n",
      "k2                    :  128\n",
      "stride_y              :  2\n",
      "d3_x                  :  13\n",
      "d3_y                  :  1\n",
      "k3                    :  1024\n",
      "num_units             :  1024\n",
      "decay_steps           :  -1\n",
      "lr_decay              :  -1\n",
      "mom                   :  -1\n",
      "starter_learning_rate :  0.001\n",
      "beta1                 :  0.9\n",
      "beta2                 :  0.999\n",
      "max_steps             :  99999\n",
      "comment               :  use pretrained weights, try more timesteps and bigger LSTM-Unit. Adam Optimizer\n",
      "\n",
      "Use pretrained weights\n"
     ]
    }
   ],
   "source": [
    "vers = \"190322_LSTM_adam_pt_2\"\n",
    "\n",
    "for key, value in version_dicc[vers].items():\n",
    "    print(f'{key:21}', \": \", value)\n",
    "\n",
    "if \"pt\" in vers:\n",
    "    PRETRAIN = True\n",
    "    pretrain = version_dicc[vers][\"pretrained\"]\n",
    "    print(\"\\nUse pretrained weights\")\n",
    "else:\n",
    "    PRETRAIN = False\n",
    "#model_functions.test_import(\"Import successful.\")\n",
    "debugging = False"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2. Import the data\n",
    "Import the recording and labels of the 331 recordings from the data base.\n",
    "\n",
    "#### 2.1 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": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "with open(config.labels_path_11, 'rb') as f:\n",
    "    labels = pickle.load(f)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 2.2 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": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "data = dict()\n",
    "for record in os.listdir(config.records_path_11_new):\n",
    "    fd = os.open(config.records_path_11_new + record, os.O_RDONLY)\n",
    "    buff = mmap.mmap(fd, 0, mmap.MAP_SHARED, mmap.PROT_READ)\n",
    "    data[int(record[:-4])] = (buff, len(buff)/model_functions_norm.sz_float)\n",
    "    os.close(fd)\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 2.3 Split up in the test and train set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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 model_functions_norm.delete_ids]\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)))\n",
    "    \n",
    "#model_functions_norm.get_mean_std(train_ids, data)\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 2.4 Calculate mean and Standard Deviation\n",
    "\n",
    "The mean is approximatly 0 and standard deviation 1. Therefore, no normalization is applied."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#model_functions_norm.mean_std(data, train_ids)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3. 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": 7,
   "metadata": {
    "collapsed": true
   },
   "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": [
    "#### 3.1 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": 8,
   "metadata": {
    "collapsed": true
   },
   "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",
    "\n",
    "All these methods are applied in the function \n",
    "~~~~~~\n",
    "model_functions.get_data(rec_id, timesteps, s, window, pitch_shift, scaling_factor, normalize=True)\n",
    "~~~~~~\n",
    "\n",
    "#### 3.2 Batch-selection\n",
    "Create the training batch applying the above explained methods for Data augmentation utilizing the function\n",
    "~~~~~~\n",
    "model_functions.get_training_batch(model_stats, batch_size, timesteps, window, pitch_transforms=0, jitter=0, normalize=True)\n",
    "~~~~~~\n",
    "\n",
    "#### 3.3 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 given the recording IDs of the training samples.\n",
    "   -> If sample with same settings already got created and was saved to file it is loaded from numpy.\n",
    "   -> Else: create the sample by taking at most 100 samples for each recording. Save it to file.\n",
    "   \n",
    "  ~~~~~~\n",
    "  model_functions.get_training_sample(rec_ids, timesteps, d=16384, normalize=True)\n",
    "  ~~~~~~\n",
    "   \n",
    "2. Routine to obtain test samples given the recording IDs of the test samples.\n",
    "   -> If sample with same settings already got created and was saved to file it is loaded from numpy.\n",
    "   -> Else: create the sample by taking at most 1000 samples for each recording. Save it to file.\n",
    "   ~~~~~~\n",
    "   model_functions.get_test_sample(rec_ids, window, count, timesteps, fixed_stride=-1, pitch_shift=0, normalize=True)\n",
    "   ~~~~~~\n",
    "   \n",
    "### 4. Prepare Protocolling\n",
    "Save the results, losses, precicions and weights at each protocol point in a diccionary, that is created and updated using the class \n",
    "~~~~~~\n",
    "model_results\n",
    "~~~~~~\n",
    "\n",
    "### 5. 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",
    "\n",
    "### 6. 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": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "batch_size=150\n",
    "if debugging == True:\n",
    "    batch_size = 2\n",
    "out = 1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 6.1 Define layers\n",
    "\n",
    "#### 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.\n",
    "The filterbank is created by \n",
    "~~~~~~\n",
    "model_functions.create_filters(d,k)\n",
    "~~~~~~\n",
    "\n",
    "#### Second layer:\n",
    "A convolution is applied along the log-frequency axis opening up a third channel dimension.\n",
    "\n",
    "#### Third layer:\n",
    "Convolution along the log-frequency axis. Filters of height 1 are used that fully connect along the time and channel axes of Layer 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "##### variables for input layer\n",
    "\n",
    "# input size of segment, predict notes played at midpoint of segment\n",
    "window = version_dicc[vers][\"window\"]\n",
    "\n",
    "# number of timesteps to be considered for memory cell\n",
    "timesteps = version_dicc[vers][\"timesteps\"]\n",
    "\n",
    "# size of regions that outputs the convolution from layer one (receptive field)\n",
    "dd = version_dicc[vers][\"dd\"]\n",
    "\n",
    "#stride for first layer convolution --> 13 regions\n",
    "stride = version_dicc[vers][\"stride\"]\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 = version_dicc[vers][\"kk\"]\n",
    "wsin,wcos = model_functions_norm.create_filters(dd,kk)\n",
    "\n",
    "##### variables for second layer\n",
    "d2_x = version_dicc[vers][\"d2_x\"]           # lvl2 input dims_x\n",
    "d2_y = version_dicc[vers][\"d2_y\"]           # lvl2 input dims_y\n",
    "k2 = version_dicc[vers][\"k2\"]               # num lvl2 filters\n",
    "stride_y = version_dicc[vers][\"stride_y\"]   # lvl2 stride\n",
    "\n",
    "##### variables for third layer\n",
    "d3_x = version_dicc[vers][\"d3_x\"]           # lvl3 input dims_x\n",
    "d3_y = version_dicc[vers][\"d3_y\"]           # lvl3 input dims_y (fully connected)\n",
    "k3 = version_dicc[vers][\"k3\"]               # num lvl3 filters\n",
    "\n",
    "#### variables for LSTM-cell\n",
    "num_units = version_dicc[vers][\"num_units\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Calculate the sizes of nodes at each layer and the first layer filterbank."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "First layer regions: (13,256)\n",
      "Second layer regions: (13,65)\n",
      "Third layer regions: (1,65)\n"
     ]
    }
   ],
   "source": [
    "num_regions, num_regions2_x, num_regions2_y, num_regions3_x, num_regions3_y = \\\n",
    "model_functions_norm.calc_region_sizes(window, dd, stride, kk, d2_x, d2_y, stride_y, d3_x, d3_y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 6.2 Define tf.Graph()\n",
    "Set it all together and define the graph.\n",
    "\n",
    "Define:\n",
    "- the starter leraning rate\n",
    "- learning rate decay\n",
    "- number of steps after which learning rate decay is applied\n",
    "- the L2-regularization weight\n",
    "- the Momentum for the tf.train.MomentumOptimizer\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": 12,
   "metadata": {
    "scrolled": false
   },
   "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=(13, 1, 128, 1024) dtype=float32_ref>\n",
      "beta <tf.Variable 'parameters/beta:0' shape=(66560, 128) dtype=float32_ref>\n",
      "w_proj <tf.Variable 'parameters_train/w_proj:0' shape=(2048, 128) dtype=float32_ref>\n",
      "\n",
      "---- Layers ----\n",
      "zx Tensor(\"model/add:0\", shape=(?, 1, 13, 256), dtype=float32)\n",
      "z2 Tensor(\"model/Relu:0\", shape=(?, 128, 13, 65), dtype=float32)\n",
      "z3 Tensor(\"model/Relu_1:0\", shape=(?, 1024, 1, 65), dtype=float32)\n",
      "y_feed_forward Tensor(\"model/MatMul:0\", shape=(1350, 128), dtype=float32)\n",
      "\n",
      "---- LSTM-Cell ----\n",
      "Number of units: 1024\n",
      "Shape of input: (150, 9, 128)\n",
      "Shape of output: 2 x (150, 1024)\n",
      "y_output: Tensor(\"model/MatMul_1:0\", shape=(150, 128), dtype=float32)\n"
     ]
    }
   ],
   "source": [
    "# initialize diccionary to save results\n",
    "model_stats = model_functions_norm.model_results(test_ids, train_ids, labels, data, base_note, mm)\n",
    "\n",
    "tf.set_random_seed(999)\n",
    "\n",
    "starter_learning_rate = version_dicc[vers][\"starter_learning_rate\"]  # starter leraning rate\n",
    "decay_steps = version_dicc[vers][\"decay_steps\"]                      # steps until learning rate decay\n",
    "lr_decay = version_dicc[vers][\"lr_decay\"]                            # learning rate decay\n",
    "mom = version_dicc[vers][\"mom\"]                                      # Momentum for Optimizer\n",
    "beta_reg = 0                                                         # L2-regularization weight\n",
    "\n",
    "\n",
    "if PRETRAIN:\n",
    "    graph, training_op, loss, global_step,lr,reg,y_pd,direct_loss,xb,yb,xd,yd,saver,w,wavg,w2,w2avg,beta,betaavg = \\\n",
    "                                    version_dicc[vers][\"graph\"] (\n",
    "                                                model_stats, batch_size, window, timesteps, \n",
    "                                                stride, out, wsin, wcos, \n",
    "                                                d2_x, d2_y, k2, stride_y, \n",
    "                                                d3_x, d3_y, k3, num_regions3_x, num_regions3_y, num_units,\n",
    "                                                starter_learning_rate, decay_steps, lr_decay)\n",
    "else:   \n",
    "    graph, training_op, loss, global_step, lr, reg, y_pd, direct_loss, xb, yb, xd, yd, saver = \\\n",
    "                                    version_dicc[vers][\"graph\"] (\n",
    "                                                model_stats, batch_size, window, timesteps, \n",
    "                                                stride, out, wsin, wcos, \n",
    "                                                d2_x, d2_y, k2, stride_y, \n",
    "                                                d3_x, d3_y, k3, num_regions3_x, num_regions3_y, num_units,\n",
    "                                                starter_learning_rate, decay_steps, lr_decay)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 6.3 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 \n",
    "~~~~~~\n",
    "model_functions.predict_direct_model(sess, X, Y) \n",
    "~~~~~~\n",
    "the mean squared error is calculated directly.\n",
    "\n",
    "### 7. Training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "190322_LSTM_adam_pt_2\n",
      "iter\t test_squ_loss\t test_avg_prec\t train_squ_loss\t train_avg_prec\t time (in s)\n",
      "1 \t 15.06593312 \t 0.04364124 \t 334.9203965 \t 5.891e-05 \t 124.29121852\n",
      "1001 \t 10.50626939 \t 0.48885641 \t 0.42479296 \t 0.55875285 \t 378.84675336\n",
      "2001 \t 10.16240281 \t 0.5255025 \t 0.26481599 \t 0.57241801 \t 359.27101755\n",
      "3001 \t 9.69871205 \t 0.53850706 \t 0.25562882 \t 0.57226647 \t 358.23579001\n",
      "4001 \t 9.28599909 \t 0.55160845 \t 0.97450787 \t 0.58789269 \t 361.26115727\n",
      "5001 \t 8.95101354 \t 0.55831766 \t 1.7137483 \t 0.58955119 \t 353.13246393\n",
      "6001 \t 8.54414339 \t 0.57037779 \t 1.37697656 \t 0.58710632 \t 352.92531776\n",
      "7001 \t 8.24267478 \t 0.57658788 \t 1.12207203 \t 0.60612359 \t 352.46146607\n",
      "8001 \t 8.09781784 \t 0.58327455 \t 0.94608789 \t 0.59198414 \t 354.65601635\n",
      "9001 \t 7.9764964 \t 0.58673148 \t 0.85908725 \t 0.59383595 \t 352.93279362\n",
      "10001 \t 7.89054579 \t 0.58734118 \t 0.62846077 \t 0.60381686 \t 350.88658214\n",
      "11001 \t 7.81480282 \t 0.58934846 \t 0.55044445 \t 0.62202916 \t 353.21442103\n",
      "12001 \t 7.67790862 \t 0.59950454 \t 0.56065249 \t 0.60805186 \t 359.80939198\n",
      "13001 \t 7.61040074 \t 0.60038069 \t 0.4888611 \t 0.63045497 \t 356.15750647\n",
      "14001 \t 7.6126511 \t 0.60028685 \t 0.36051315 \t 0.63086824 \t 355.12145996\n",
      "15001 \t 7.52874 \t 0.60713909 \t 0.33911629 \t 0.6320515 \t 354.60004163\n",
      "16001 \t 7.55546887 \t 0.60483499 \t 0.26734427 \t 0.63304309 \t 353.10662699\n",
      "17001 \t 7.52969178 \t 0.60465426 \t 0.19608353 \t 0.63588956 \t 351.86163926\n",
      "18001 \t 7.51606645 \t 0.60562314 \t 0.18618044 \t 0.63641176 \t 355.56797886\n",
      "19001 \t 7.47219285 \t 0.6091113 \t 0.23977603 \t 0.64629056 \t 353.91993308\n",
      "20001 \t 7.46143894 \t 0.61172805 \t 0.14556822 \t 0.64572919 \t 357.54879951\n",
      "21001 \t 7.41417679 \t 0.61426847 \t 0.37177729 \t 0.63974565 \t 355.7189126\n",
      "22001 \t 7.43232186 \t 0.61089798 \t 0.20693511 \t 0.65095779 \t 354.23345566\n",
      "23001 \t 7.34772817 \t 0.62046349 \t 0.17822672 \t 0.64613316 \t 345.61175489\n",
      "24001 \t 7.31507868 \t 0.62143923 \t 0.1529036 \t 0.66049306 \t 360.24470305\n",
      "25001 \t 7.35456687 \t 0.6159453 \t 0.37494537 \t 0.6433953 \t 352.00042057\n",
      "26001 \t 7.36002615 \t 0.61652027 \t 0.30477208 \t 0.62949143 \t 350.58102727\n",
      "27001 \t 7.35134863 \t 0.61734039 \t 0.14817854 \t 0.64126874 \t 358.19043112\n",
      "28001 \t 7.39708311 \t 0.61314493 \t 0.22009884 \t 0.63845401 \t 355.71231651\n",
      "29001 \t 7.36724575 \t 0.61437457 \t 0.17733583 \t 0.645093 \t 360.90175986\n",
      "30001 \t 7.35721146 \t 0.61579822 \t 0.20203731 \t 0.65671423 \t 349.38411212\n",
      "31001 \t 7.34974268 \t 0.61709185 \t 0.14332408 \t 0.65543404 \t 355.62150908\n",
      "32001 \t 7.31633717 \t 0.62128347 \t 0.22145524 \t 0.64394013 \t 354.02298379\n",
      "33001 \t 7.34111199 \t 0.61642393 \t 0.17518961 \t 0.63838662 \t 353.16064906\n",
      "34001 \t 7.32933679 \t 0.61696124 \t 0.19679522 \t 0.65348109 \t 353.05534172\n",
      "35001 \t 7.3515674 \t 0.61643885 \t 0.12316699 \t 0.65038504 \t 353.32618546\n",
      "36001 \t 7.26952205 \t 0.62133477 \t 0.21929254 \t 0.65495847 \t 353.24531198\n",
      "37001 \t 7.26789363 \t 0.6226161 \t 0.18946589 \t 0.66305143 \t 353.6788466\n",
      "38001 \t 7.27606429 \t 0.62244476 \t 0.19860859 \t 0.6661383 \t 352.75837994\n",
      "39001 \t 7.2885994 \t 0.62351448 \t 0.13796126 \t 0.67174801 \t 355.16447949\n",
      "40001 \t 7.23488664 \t 0.62740292 \t 0.19862568 \t 0.67469803 \t 352.57382965\n",
      "41001 \t 7.23926855 \t 0.62702753 \t 0.25791797 \t 0.67628585 \t 351.43701959\n",
      "42001 \t 7.27575515 \t 0.62334912 \t 0.2520977 \t 0.67948528 \t 354.17272139\n",
      "43001 \t 7.24541457 \t 0.62387918 \t 0.14607298 \t 0.66484119 \t 356.92501879\n",
      "44001 \t 7.22994752 \t 0.62648237 \t 0.21591589 \t 0.67335844 \t 355.32623029\n",
      "45001 \t 7.22094429 \t 0.62500864 \t 0.12724227 \t 0.67738084 \t 351.74977183\n",
      "46001 \t 7.22901289 \t 0.6253706 \t 0.16313159 \t 0.67580297 \t 354.0817008\n",
      "47001 \t 7.2468267 \t 0.62692936 \t 0.11561535 \t 0.66807288 \t 354.70294046\n",
      "48001 \t 7.2085477 \t 0.62968779 \t 0.16289406 \t 0.6677147 \t 353.92570233\n",
      "49001 \t 7.21565049 \t 0.62760824 \t 0.11452639 \t 0.67618521 \t 356.07259059\n",
      "50001 \t 7.23922521 \t 0.62669106 \t 0.11719513 \t 0.67748001 \t 358.45817161\n",
      "51001 \t 7.20916732 \t 0.62926239 \t 0.15331165 \t 0.6689857 \t 357.11978817\n",
      "52001 \t 7.18008848 \t 0.6308582 \t 0.16359845 \t 0.67102851 \t 358.19200802\n",
      "53001 \t 7.21779574 \t 0.6297495 \t 0.1108215 \t 0.6653192 \t 351.2738831\n",
      "54001 \t 7.18060919 \t 0.63152817 \t 0.14328507 \t 0.66710322 \t 357.2640872\n",
      "55001 \t 7.19223686 \t 0.63009723 \t 0.08456552 \t 0.67349565 \t 353.25560427\n",
      "56001 \t 7.1742664 \t 0.62954194 \t 0.11303681 \t 0.67420191 \t 359.12691402\n",
      "57001 \t 7.17139687 \t 0.6311122 \t 0.08022054 \t 0.66746082 \t 352.17368746\n",
      "58001 \t 7.19976011 \t 0.63010302 \t 0.10920841 \t 0.67096317 \t 352.00469065\n",
      "59001 \t 7.19115988 \t 0.6293155 \t 0.11205972 \t 0.67621423 \t 351.27098155\n",
      "60001 \t 7.12451311 \t 0.63611936 \t 0.14346778 \t 0.66723556 \t 354.1009593\n",
      "61001 \t 7.20188138 \t 0.62950721 \t 0.0890157 \t 0.68333254 \t 357.16548729\n",
      "62001 \t 7.15962622 \t 0.63332304 \t 0.07733861 \t 0.69306561 \t 356.77929306\n",
      "63001 \t 7.16881222 \t 0.63115111 \t 0.14266316 \t 0.67487161 \t 354.63075447\n",
      "64001 \t 7.2032143 \t 0.62811706 \t 0.10613228 \t 0.68982737 \t 367.44586682\n",
      "65001 \t 7.181658 \t 0.62942857 \t 0.07545596 \t 0.68431095 \t 359.71618176\n",
      "66001 \t 7.17660425 \t 0.6305184 \t 0.15972579 \t 0.6799158 \t 356.06145287\n",
      "67001 \t 7.19459412 \t 0.62703614 \t 0.08405809 \t 0.67532406 \t 352.01579404\n",
      "68001 \t 7.14429468 \t 0.63342801 \t 0.08543086 \t 0.67925002 \t 364.46368647\n",
      "69001 \t 7.19918858 \t 0.62825988 \t 0.08160885 \t 0.68861806 \t 352.32140493\n",
      "70001 \t 7.14545309 \t 0.63393483 \t 0.09698353 \t 0.67613792 \t 353.83429027\n",
      "71001 \t 7.14089227 \t 0.63808177 \t 0.08525657 \t 0.68731424 \t 354.42973685\n",
      "72001 \t 7.18810775 \t 0.63213884 \t 0.08340636 \t 0.69578228 \t 359.43593216\n",
      "73001 \t 7.14649572 \t 0.63428988 \t 0.1192667 \t 0.68431293 \t 352.44079995\n",
      "74001 \t 7.1292502 \t 0.63418716 \t 0.07518154 \t 0.69584438 \t 361.59216833\n",
      "75001 \t 7.15539079 \t 0.63463644 \t 0.07762179 \t 0.6915392 \t 353.95148468\n",
      "76001 \t 7.16234096 \t 0.63168381 \t 0.06743523 \t 0.69803144 \t 356.15246773\n",
      "77001 \t 7.12979347 \t 0.63308377 \t 0.11319365 \t 0.70463815 \t 357.96008348\n",
      "78001 \t 7.17911289 \t 0.63020095 \t 0.08276906 \t 0.69524228 \t 362.4618659\n",
      "79001 \t 7.20782139 \t 0.63162045 \t 0.08101489 \t 0.70663099 \t 353.23502851\n",
      "80001 \t 7.14345697 \t 0.6338812 \t 0.09094362 \t 0.70049269 \t 357.5634613\n",
      "81001 \t 7.10632433 \t 0.63628729 \t 0.0833455 \t 0.70308769 \t 350.01656008\n",
      "82001 \t 7.15817545 \t 0.63131037 \t 0.15468626 \t 0.70192889 \t 351.73457432\n",
      "83001 \t 7.15758413 \t 0.63084084 \t 0.09370178 \t 0.70492894 \t 368.66420197\n",
      "84001 \t 7.12235709 \t 0.6363775 \t 0.07775174 \t 0.70568764 \t 352.243788\n",
      "85001 \t 7.16362765 \t 0.6341386 \t 0.07923419 \t 0.69255099 \t 356.5972414\n",
      "86001 \t 7.11508726 \t 0.63767194 \t 0.13409737 \t 0.69683773 \t 348.01421905\n",
      "87001 \t 7.08304121 \t 0.63963282 \t 0.07732697 \t 0.68754115 \t 353.4924233\n",
      "88001 \t 7.13184455 \t 0.6351111 \t 0.06944465 \t 0.71442219 \t 357.99468279\n",
      "89001 \t 7.12607682 \t 0.63646319 \t 0.09159295 \t 0.68970183 \t 353.21388674\n",
      "90001 \t 7.13163037 \t 0.6344476 \t 0.11107505 \t 0.67918633 \t 352.83979082\n",
      "91001 \t 7.1226678 \t 0.63606411 \t 0.07657961 \t 0.68780348 \t 360.54539299\n",
      "92001 \t 7.09488132 \t 0.6390243 \t 0.10845098 \t 0.69862444 \t 360.73144746\n",
      "93001 \t 7.10244522 \t 0.63801998 \t 0.2575077 \t 0.69379273 \t 355.49988842\n",
      "94001 \t 7.12603571 \t 0.63574024 \t 0.08466893 \t 0.6890109 \t 352.90309525\n",
      "95001 \t 7.10880879 \t 0.63566417 \t 0.07297903 \t 0.68811915 \t 351.52291632\n",
      "96001 \t 7.19256409 \t 0.63136906 \t 0.08511454 \t 0.71614348 \t 356.6774745\n",
      "97001 \t 7.11499831 \t 0.63680226 \t 0.10001742 \t 0.69664391 \t 352.83639836\n",
      "98001 \t 7.09078994 \t 0.64033243 \t 0.07247734 \t 0.70065333 \t 352.14363837\n",
      "99001 \t 7.07624773 \t 0.63953332 \t 0.07724069 \t 0.70273746 \t 356.78688693\n"
     ]
    }
   ],
   "source": [
    "print(vers)\n",
    "results_stats = model_functions_norm.load_obj('results/'+vers+'_stats')\n",
    "iters_res = results_stats['iter'][2]\n",
    "mse_test_res = results_stats['mse_test'][2]\n",
    "avp_test_res = results_stats['avp_test'][2]\n",
    "mse_train_res = results_stats['mse_train'][2]\n",
    "avp_train_res = results_stats['avp_train'][2]\n",
    "time_res = results_stats['time'][2]\n",
    "print ('iter\\t test_squ_loss\\t test_avg_prec\\t train_squ_loss\\t train_avg_prec\\t time (in s)')\n",
    "for i in range(len(iters_res)):\n",
    "    print (iters_res[i],'\\t', round(mse_test_res[i],8),\n",
    "            '\\t', round(avp_test_res[i],8),\n",
    "            '\\t', round(mse_train_res[i],8),\n",
    "            '\\t', round(avp_train_res[i],8),\n",
    "            '\\t', round(time_res[i],8))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 8. Post Processing\n",
    "#### 8.1 Plots\n",
    "\n",
    "Plot leraning rate, possibly runtime, errors, precision and norms"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "results_stats = model_functions_norm.load_obj('results/'+vers+'_stats')\n",
    "\n",
    "# size of plots\n",
    "plot_characteristics = {'width': 20, 'height': 13, 'fontsize': 24, 'control_step': 1000, 'savefig': False}\n",
    "\n",
    "plt.rcParams['xtick.labelsize']=plot_characteristics['fontsize']\n",
    "plt.rcParams['ytick.labelsize']=plot_characteristics['fontsize']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "################################################################\n",
    "# learning rate\n",
    "model_functions_norm.plot_lr(results_stats, plot_characteristics, vers)\n",
    "\n",
    "################################################################\n",
    "# error and precision\n",
    "model_functions_norm.plot_err_prec(results_stats, plot_characteristics, vers)\n",
    "\n",
    "################################################################\n",
    "# norms of the weights\n",
    "if \"LSTM\" not in vers:\n",
    "    model_functions_norm.plot_norms(results_stats, plot_characteristics, vers)\n",
    "else:\n",
    "    model_functions_norm.plot_norms_lstm(results_stats, plot_characteristics, vers)\n",
    "    \n",
    "################################################################\n",
    "# save only precision and plot of norm output layer\n",
    "#plot_characteristics['savefig'] = True\n",
    "model_functions_norm.plot_prec(results_stats, plot_characteristics, vers)\n",
    "if \"LSTM\" in vers:\n",
    "    model_functions_norm.plot_out_norms(results_stats, plot_characteristics, vers)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "#### 8.2 Mirex Statistics\n",
    "\n",
    "Statistics that are necesarry for evaluation. The autuor used a threshold of 0.4 for calculation of error etc...\n",
    "\n",
    "First, restore the model and calculate the output to the test sample:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Restoring parameters from models/model_190322_LSTM_adam_pt_2/model-99999\n",
      "Model restored.\n"
     ]
    }
   ],
   "source": [
    "tf.reset_default_graph()\n",
    "\n",
    "num_steps = version_dicc[vers][\"max_steps\"]\n",
    "timesteps = version_dicc[vers][\"timesteps\"]\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/model_'+vers+'/model-'+str(num_steps))\n",
    "    print(\"Model restored.\")\n",
    "\n",
    "    # 1. get the test set\n",
    "    X, Y = model_functions_norm.get_test_sample(model_stats, timesteps, window, 1000)\n",
    "    Y = Y[:,base_note:base_note+mm] # in general without effect\n",
    "    # shape Xtest: (1000*len(test_ids), 1, window, 1)\n",
    "    # shape Ytest: (1000*len(test_ids), 128)\n",
    "    # extended test-set: 11 samples\n",
    "\n",
    "    # 2. prediction of output\n",
    "    Y_p, mse_test = model_functions_norm.predict_direct_model(sess, y_pd, direct_loss, xd, yd, 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": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Thr\tAvgP\tP\tR\tAcc\tETot\tESub\tEmiss\tEfa\n",
      "0.30\t57.235\t51.866\t62.311\t0.395\t0.731\t0.224\t0.153\t0.354\n",
      "0.35\t57.235\t56.044\t57.102\t0.394\t0.673\t0.204\t0.225\t0.244\n",
      "0.40\t57.235\t59.906\t51.657\t0.384\t0.655\t0.175\t0.309\t0.171\n",
      "0.45\t57.235\t63.586\t45.836\t0.363\t0.658\t0.146\t0.395\t0.116\n",
      "0.50\t57.235\t66.944\t39.933\t0.334\t0.681\t0.117\t0.484\t0.080\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 = model_functions_norm.mirex_statistics(Y, Y_p, th)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Restoring parameters from models/model_190322_LSTM_adam_pt_2/model-99999\n",
      "Model restored.\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 2191 sehr einfach\n",
    "# 2628 einfach\n",
    "# 2718 schwierig\n",
    "\n",
    "sample_test_ids = [2628,2718] \n",
    "#sample_test_ids = [2382, 1819, 2298, 2191, 2556, 2416, 2628, 1759, 2106, 2718]\n",
    "tf.reset_default_graph()\n",
    "with tf.Session(graph=graph) as sess:\n",
    "    num_steps = version_dicc[vers][\"max_steps\"]\n",
    "    timesteps = version_dicc[vers][\"timesteps\"]\n",
    "    # Restore variables from disk.\n",
    "    saver.restore(sess, 'models/model_'+vers+'/model-'+str(num_steps))\n",
    "    print(\"Model restored.\")\n",
    "    for i in range(len(sample_test_ids)):\n",
    "        sample_test_id = sample_test_ids[i]\n",
    "\n",
    "        lenn = data[sample_test_id][1]\n",
    "        y_sample = labels[sample_test_id]\n",
    "        times, check = model_functions_norm.scores(y_sample, sample_test_id, start=2)\n",
    "\n",
    "        # 1. get the set\n",
    "        X, Y = model_functions_norm.get_times(model_stats, sample_test_id,times,timesteps,window)\n",
    "        Y = Y[:,base_note:base_note+mm] # in general without effect\n",
    "\n",
    "        # 2. prediction of output\n",
    "        Y_p, mse_test = model_functions_norm.predict_direct_model(sess, y_pd, direct_loss, xd, yd, X, Y)\n",
    "\n",
    "        _ = model_functions_norm.scores(Y_p, str(sample_test_id)+': prediction', \n",
    "                                        start=1,downsample=False,cmap='ocean_r') \n",
    "        _ = model_functions_norm.scores(Y_p>0.4, str(sample_test_id)+': prediction, 0.4 threshold applied', start=1,downsample=False) \n",
    "        _ = model_functions_norm.compare_scores(check, 1*(Y_p>0.4), sample_test_id, cmap='CMRmap_r',\n",
    "                    savefig='results/plots/'+vers+'_score_rec_'+str(sample_test_id)+'_compare.png')\n",
    "    \n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}