spectrograms_log.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, exponentially distributed 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 = 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\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_log/', 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": null,
   "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",
      "0.634015        0.649345        0.692654        0.850922        21      21      \n",
      "0.634036        0.649353        0.692631        0.850917        65      22      \n",
      "0.634056        0.649358        0.692608        0.850913        65      24      \n",
      "0.634255        0.649545        0.692511        0.850737        64      22      \n",
      "0.634267        0.649557        0.692497        0.850730        63      21      \n",
      "0.634285        0.649561        0.692475        0.850726        64      20      \n",
      "0.634312        0.649549        0.692453        0.850727        67      25      \n",
      "0.634289        0.649464        0.692438        0.850743        64      21      \n",
      "0.634276        0.649462        0.692493        0.850745        64      20      \n",
      "0.634259        0.649458        0.692508        0.850750        65      22      \n",
      "0.634259        0.649451        0.692512        0.850748        65      22      \n",
      "0.634266        0.649459        0.692496        0.850747        63      21      \n",
      "0.634275        0.649461        0.692490        0.850744        64      20      \n",
      "0.634285        0.649463        0.692472        0.850739        65      22      \n",
      "0.634281        0.649463        0.692470        0.850734        62      20      \n",
      "0.634285        0.649470        0.692467        0.850728        65      20      \n",
      "0.634293        0.649476        0.692468        0.850725        64      19      \n",
      "0.634299        0.649474        0.692472        0.850728        64      20      \n",
      "0.634312        0.649480        0.692466        0.850729        63      19      \n",
      "0.634338        0.649488        0.692459        0.850730        63      19      \n",
      "0.634341        0.649494        0.692461        0.850727        64      20      \n",
      "0.634366        0.649505        0.692446        0.850720        63      20      \n",
      "0.634349        0.649508        0.692439        0.850712        63      19      \n",
      "0.634342        0.649517        0.692443        0.850703        65      21      \n",
      "0.634331        0.649526        0.692441        0.850697        65      21      \n",
      "Graceful Exit\n"
     ]
    }
   ],
   "source": [
    "lr = .000000001/2/2/3\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": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.635170002727\n",
      "0.691946692902\n"
     ]
    },
    {
     "data": {
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/+1miIxERERGR2k7J9y4sWQJjxsDll+u53iIiIiKy95R878Ijj4TPK69MbBwi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\nMzMzM6uR/w9K/PYKCQWrWAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f31fe366f90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "burnin=100\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": {},
   "source": [
    "# Mirex Stats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.627362753789\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": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AvgP\tP\tR\tAcc\tETot\tESub\tEmiss\tEfa\n",
      "70.97\t73.28\t63.45\t0.52\t0.50\t0.10\t0.27\t0.13\n",
      "AvgP\tP\tR\tAcc\tETot\tESub\tEmiss\tEfa\n",
      "35.45\t50.38\t30.01\t0.23\t0.87\t0.13\t0.57\t0.17\n",
      "AvgP\tP\tR\tAcc\tETot\tESub\tEmiss\tEfa\n",
      "73.83\t77.20\t51.47\t0.45\t0.57\t0.06\t0.42\t0.09\n",
      "0.397895741753 0.646460766455\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": {},
   "source": [
    "# Extended test set stats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.643125241318\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",
      "70.97\t73.28\t63.45\t0.52\t0.50\t0.10\t0.27\t0.13\n",
      "AvgP\tP\tR\tAcc\tETot\tESub\tEmiss\tEfa\n",
      "35.45\t50.38\t30.01\t0.23\t0.87\t0.13\t0.57\t0.17\n",
      "AvgP\tP\tR\tAcc\tETot\tESub\tEmiss\tEfa\n",
      "73.83\t77.20\t51.47\t0.45\t0.57\t0.06\t0.42\t0.09\n",
      "AvgP\tP\tR\tAcc\tETot\tESub\tEmiss\tEfa\n",
      "63.51\t58.94\t65.38\t0.45\t0.63\t0.17\t0.18\t0.28\n",
      "AvgP\tP\tR\tAcc\tETot\tESub\tEmiss\tEfa\n",
      "66.15\t52.20\t87.70\t0.49\t0.81\t0.12\t0.00\t0.68\n",
      "AvgP\tP\tR\tAcc\tETot\tESub\tEmiss\tEfa\n",
      "70.42\t71.35\t59.06\t0.48\t0.50\t0.14\t0.27\t0.09\n",
      "AvgP\tP\tR\tAcc\tETot\tESub\tEmiss\tEfa\n",
      "61.53\t59.42\t63.83\t0.44\t0.71\t0.09\t0.28\t0.35\n",
      "AvgP\tP\tR\tAcc\tETot\tESub\tEmiss\tEfa\n",
      "68.41\t71.21\t56.34\t0.46\t0.54\t0.12\t0.31\t0.10\n",
      "AvgP\tP\tR\tAcc\tETot\tESub\tEmiss\tEfa\n",
      "67.48\t68.70\t63.98\t0.50\t0.51\t0.15\t0.22\t0.15\n",
      "AvgP\tP\tR\tAcc\tETot\tESub\tEmiss\tEfa\n",
      "64.94\t69.30\t55.10\t0.44\t0.55\t0.14\t0.31\t0.10\n",
      "0.444829138775 0.618831991132\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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