spectrograms_wnd.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os,sys\n",
    "import cPickle 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",
    "from time import time\n",
    "\n",
    "sys.path.insert(0,'lib/')\n",
    "import config\n",
    "import diagnostics\n",
    "import base_model\n",
    "\n",
    "from sklearn.metrics import average_precision_score\n",
    "\n",
    "os.environ['CUDA_VISIBLE_DEVICES']=''\n",
    "\n",
    "import tensorflow as tf\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open(config.labels_path) as f:\n",
    "    labels = pickle.load(f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# windowed filters between 10Hz and 22kHz\n",
    "def create_filters(d,k):\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/44000.\n",
    "    scaling_ind = (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((ind*scaling_ind)*num_cycles*x)\n",
    "        wcos[0,:,0,ind] = window_mask*np.cos((ind*scaling_ind)*num_cycles*x)\n",
    "            \n",
    "    return wsin,wcos\n",
    "\n",
    "class Spectrograms(base_model.Model):\n",
    "    def __init__(self, *args, **kwargs):\n",
    "        super(Spectrograms, self).__init__(*args, **kwargs)\n",
    "\n",
    "    def define_graph(self):\n",
    "        super(Spectrograms, self).define_graph()\n",
    "        \n",
    "        # lvl1 convolutions are shared between regions\n",
    "        self.k = 512              # lvl1 nodes\n",
    "        self.d = 4096              # lvl1 receptive field\n",
    "\n",
    "        # number of lvl1 features\n",
    "        regions = 1 + (self.window - self.d)/self.stride\n",
    "        print 'Number of V1 feature regions: {}'.format(regions)\n",
    "\n",
    "        # preconditionable weights\n",
    "        wsin,wcos = create_filters(self.d,self.k)\n",
    "\n",
    "        with tf.variable_scope('parameters'):\n",
    "            beta = tf.Variable(tf.zeros([regions*self.k,self.m]))\n",
    "            betaavg = self.register_weights(beta,'beta',average=.9998)\n",
    "\n",
    "        with tf.variable_scope('queued_model'):\n",
    "            zx = tf.square(tf.nn.conv2d(self.xq,wsin,strides=[1,1,self.stride,1],padding='VALID')) \\\n",
    "               + tf.square(tf.nn.conv2d(self.xq,wcos,strides=[1,1,self.stride,1],padding='VALID'))\n",
    "            y = tf.matmul(tf.reshape(tf.log(zx + 10e-12),[self.batch_size,regions*self.k]),beta)\n",
    "            self.loss = tf.reduce_mean(tf.nn.l2_loss(y-tf.reshape(self.yq,[self.batch_size,self.m])))\n",
    "\n",
    "        with tf.variable_scope('direct_model'):\n",
    "            self.zx = tf.square(tf.nn.conv2d(self.xd,wsin,strides=[1,1,self.stride,1],padding='VALID')) \\\n",
    "                    + tf.square(tf.nn.conv2d(self.xd,wcos,strides=[1,1,self.stride,1],padding='VALID'))\n",
    "            self.y_direct = tf.matmul(tf.reshape(tf.log(self.zx + 10e-12),[tf.shape(self.xd)[0],regions*self.k]),betaavg)\n",
    "            self.loss_direct = tf.reduce_mean(tf.nn.l2_loss(self.y_direct-self.yd))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of V1 feature regions: 25\n"
     ]
    }
   ],
   "source": [
    "try: model.stop()\n",
    "except NameError: pass\n",
    "model = Spectrograms(labels,checkpoint_path='spectrograms_wnd/', outputs=1, window=16384, mmap=True,\n",
    "                    normalize=True, extended_test_set=True, use_mirex=False, init=False, pitch_transforms=5, jitter=.1,\n",
    "                    restrict=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of V1 feature regions: 25\n",
      "avp_test\tavp_train\tmse_test\tmse_train\ttime\tutime\n",
      "0.649501        0.657211        0.711790        0.865690        29      29      \n",
      "0.649487        0.657201        0.711833        0.865706        98      28      \n",
      "0.649523        0.657217        0.711797        0.865716        90      25      \n",
      "0.649559        0.657216        0.711783        0.865721        94      29      \n",
      "0.649615        0.657222        0.711753        0.865721        92      27      \n",
      "0.649644        0.657224        0.711724        0.865716        94      26      \n",
      "0.649659        0.657235        0.711727        0.865716        99      31      \n",
      "0.649681        0.657242        0.711678        0.865711        99      29      \n",
      "0.649675        0.657239        0.711696        0.865709        96      28      \n",
      "0.649711        0.657255        0.711685        0.865696        95      25      \n",
      "0.649720        0.657253        0.711679        0.865680        96      25      \n",
      "0.649733        0.657247        0.711652        0.865671        98      27      \n",
      "0.649718        0.657241        0.711653        0.865667        97      27      \n",
      "0.649661        0.657226        0.711709        0.865671        93      23      \n",
      "0.649607        0.657224        0.711733        0.865661        98      27      \n",
      "0.649670        0.657251        0.711680        0.865637        97      27      \n",
      "0.649726        0.657280        0.711607        0.865599        96      27      \n",
      "0.649740        0.657283        0.711611        0.865583        94      27      \n",
      "0.649778        0.657274        0.711622        0.865587        95      26      \n",
      "0.649796        0.657280        0.711618        0.865592        96      26      \n",
      "0.649789        0.657282        0.711645        0.865588        96      28      \n",
      "0.649750        0.657273        0.711680        0.865594        91      24      \n",
      "0.649695        0.657266        0.711732        0.865607        97      27      \n",
      "0.649666        0.657256        0.711736        0.865618        98      27      \n",
      "0.649637        0.657248        0.711743        0.865625        97      28      \n",
      "0.649617        0.657257        0.711756        0.865624        97      27      \n",
      "0.649642        0.657264        0.711776        0.865618        92      25      \n",
      "0.649675        0.657273        0.711740        0.865609        96      27      \n",
      "Graceful Exit\n"
     ]
    }
   ],
   "source": [
    "lr = .000000001/3\n",
    "mom = .95\n",
    "\n",
    "# stop the model if it is already running\n",
    "model.stop()\n",
    "\n",
    "# we have to rebuild the graph every time because input queues can't be reopened\n",
    "model.define_graph()\n",
    "\n",
    "learning_rate = tf.placeholder(tf.float32, shape=[])\n",
    "opt_op = tf.train.MomentumOptimizer(learning_rate,mom).minimize(model.loss)\n",
    "with tf.control_dependencies([opt_op]):\n",
    "    train_step = tf.group(*model.averages)\n",
    "    \n",
    "# start up the session, kick off the worker threads, restore checkpoint, etc.\n",
    "model.start()\n",
    "\n",
    "try:\n",
    "    ptime = time()\n",
    "    print model.status_header()\n",
    "    while True:\n",
    "        if model.iter % 1000 == 0:\n",
    "            model.update_status(ptime,time(),lr)\n",
    "            model.checkpoint()\n",
    "            print model.status()\n",
    "            ptime = time()\n",
    "\n",
    "        model.sess.run(train_step, feed_dict={learning_rate: lr})\n",
    "        model.iter += 1\n",
    "\n",
    "except KeyboardInterrupt:\n",
    "    model.checkpoint()\n",
    "    print 'Graceful Exit'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.649796254565\n",
      "0.71160680632\n"
     ]
    },
    {
     "data": {
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ZRERGlJndAzwFzDazRjP7hJl90sw+GTa5EagD/tPMnjezVZmuSSFbRPJFtp4u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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fc520bd7110>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "burnin=1\n",
    "fig, ((ax1, ax2),(ax3,ax4)) = plt.subplots(2,2)\n",
    "fig.set_figwidth(12)\n",
    "fig.set_figheight(10)\n",
    "ax1.set_title('average precision')\n",
    "ax1.plot(model.stats['iter'][2][burnin:],model.stats['avp_test'][2][burnin:],color='g')\n",
    "ax1.plot(model.stats['iter'][2][burnin:],model.stats['avp_train'][2][burnin:],color='b')\n",
    "ax2.set_title('square loss')\n",
    "ax2.plot(model.stats['iter'][2][burnin:],model.stats['mse_test'][2][burnin:],color='g')\n",
    "ax2.plot(model.stats['iter'][2][burnin:],model.stats['mse_train'][2][burnin:],color='b')\n",
    "ax3.set_title('weights')\n",
    "ax3.plot(model.stats['iter'][2][burnin:],model.stats['nbeta'][2][burnin:],color='g')\n",
    "ax4.set_title('learning rate')\n",
    "ax4.plot(model.stats['iter'][2][burnin:],model.stats['lr'][2][burnin:],color='g')\n",
    "\n",
    "print max(model.stats['avp_test'][2])\n",
    "print min(model.stats['mse_test'][2])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "# Mirex Stats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.661303091004\n"
     ]
    }
   ],
   "source": [
    "mse_test, Yhat, Y, mse_breakdown, avp_breakdown = model.sample_records(config.test_ids, 7500, fixed_stride=512)\n",
    "avp_test = average_precision_score(Y.flatten(),Yhat.flatten())\n",
    "print avp_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AvgP\tP\tR\tAcc\tETot\tESub\tEmiss\tEfa\n",
      "73.10\t73.90\t60.90\t0.50\t0.50\t0.10\t0.29\t0.11\n",
      "AvgP\tP\tR\tAcc\tETot\tESub\tEmiss\tEfa\n",
      "35.18\t52.93\t19.49\t0.17\t0.89\t0.09\t0.72\t0.09\n",
      "AvgP\tP\tR\tAcc\tETot\tESub\tEmiss\tEfa\n",
      "76.50\t79.56\t56.69\t0.49\t0.52\t0.06\t0.37\t0.08\n",
      "0.387377157886 0.637071188342\n"
     ]
    }
   ],
   "source": [
    "Accavg = Etotavg = 0\n",
    "for i in range(3):\n",
    "    _,_,_,Acc,Etot = diagnostics.mirex_statistics(model,i,threshold=.4)\n",
    "    Accavg += Acc\n",
    "    Etotavg += Etot\n",
    "\n",
    "print Accavg/3.,Etotavg/3."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "# Extended test set stats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.661310280439\n"
     ]
    }
   ],
   "source": [
    "mse_test, Yhat, Y, mse_breakdown, avp_breakdown = model.sample_records(config.test_ids_ext, 7500, fixed_stride=512)\n",
    "avp_test = average_precision_score(Y.flatten(),Yhat.flatten())\n",
    "print avp_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AvgP\tP\tR\tAcc\tETot\tESub\tEmiss\tEfa\n",
      "73.10\t73.90\t60.90\t0.50\t0.50\t0.10\t0.29\t0.11\n",
      "AvgP\tP\tR\tAcc\tETot\tESub\tEmiss\tEfa\n",
      "35.18\t52.93\t19.49\t0.17\t0.89\t0.09\t0.72\t0.09\n",
      "AvgP\tP\tR\tAcc\tETot\tESub\tEmiss\tEfa\n",
      "76.50\t79.56\t56.69\t0.49\t0.52\t0.06\t0.37\t0.08\n",
      "AvgP\tP\tR\tAcc\tETot\tESub\tEmiss\tEfa\n",
      "66.92\t67.05\t58.30\t0.45\t0.58\t0.13\t0.29\t0.16\n",
      "AvgP\tP\tR\tAcc\tETot\tESub\tEmiss\tEfa\n",
      "64.69\t50.09\t87.91\t0.47\t0.89\t0.11\t0.01\t0.76\n",
      "AvgP\tP\tR\tAcc\tETot\tESub\tEmiss\tEfa\n",
      "72.62\t75.39\t57.74\t0.49\t0.50\t0.11\t0.31\t0.07\n",
      "AvgP\tP\tR\tAcc\tETot\tESub\tEmiss\tEfa\n",
      "64.66\t62.50\t63.83\t0.46\t0.67\t0.07\t0.29\t0.31\n",
      "AvgP\tP\tR\tAcc\tETot\tESub\tEmiss\tEfa\n",
      "69.33\t69.06\t60.79\t0.48\t0.54\t0.13\t0.26\t0.14\n",
      "AvgP\tP\tR\tAcc\tETot\tESub\tEmiss\tEfa\n",
      "68.85\t71.44\t56.82\t0.46\t0.54\t0.12\t0.31\t0.11\n",
      "AvgP\tP\tR\tAcc\tETot\tESub\tEmiss\tEfa\n",
      "65.43\t70.27\t53.75\t0.44\t0.57\t0.12\t0.34\t0.10\n",
      "0.441001374707 0.618267377226\n"
     ]
    }
   ],
   "source": [
    "Accavg = Etotavg = 0\n",
    "for i in range(10):\n",
    "    _,_,_,Acc,Etot = diagnostics.mirex_statistics(model,i,threshold=.4)\n",
    "    Accavg += Acc\n",
    "    Etotavg += Etot\n",
    "\n",
    "print Accavg/10.,Etotavg/10."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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