three-layer.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "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",
    "import tensorflow as tf\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open(config.labels_path) as f:\n",
    "    labels = pickle.load(f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# windowed, exponentially distributed filters between 50Hz 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",
    "        self.k2 = 512              # lvl2 nodes\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",
    "        wsin,wcos = create_filters(self.d,self.k)\n",
    "        \n",
    "        wscale = 10e-5\n",
    "        with tf.variable_scope('parameters'):\n",
    "            w1 = tf.Variable(wscale*tf.random_normal([regions*self.k,self.k2],seed=999))\n",
    "            w1avg = self.register_weights(w1,'w1',average=.9998)\n",
    "            beta = tf.Variable(wscale*tf.random_normal([self.k2,self.m],seed=999))\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",
    "            z2 = tf.nn.relu(tf.matmul(tf.reshape(tf.log(zx + 10e-12),[self.batch_size,regions*self.k]),w1))\n",
    "            y = tf.matmul(z2,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",
    "            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",
    "            z2 = tf.nn.relu(tf.matmul(tf.reshape(tf.log(zx + 10e-12),[tf.shape(self.xd)[0],regions*self.k]),w1avg))\n",
    "            self.y_direct = tf.matmul(z2,betaavg)\n",
    "            self.loss_direct = tf.reduce_mean(tf.nn.l2_loss(self.y_direct-self.yd))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of V1 feature regions: 25\n"
     ]
    }
   ],
   "source": [
    "model = Spectrograms(labels,checkpoint_path='mlp/', outputs=1, window=16384, mmap=True, batch_size=100,\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": {
    "scrolled": false
   },
   "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",
      "0.746182        0.742859        0.496267        0.636604        221     126     \n",
      "0.745702        0.743012        0.496507        0.636451        340     116     \n",
      "0.746068        0.743000        0.496023        0.636317        593     337     \n",
      "0.746270        0.743069        0.495992        0.635978        381     155     \n",
      "0.746216        0.743203        0.496024        0.635921        459     205     \n",
      "0.746018        0.743306        0.496374        0.635823        475     218     \n",
      "Graceful Exit\n"
     ]
    }
   ],
   "source": [
    "lr = .000001/3/3\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 % 10000 == 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'\n",
    "finally:\n",
    "    model.stop()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.746269900191\n",
      "0.495928858278\n"
     ]
    },
    {
     "data": {
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d6QsvwD33HHmu2Fi44w64/no3ONAYkzsirqQpJibv50hNdcm3L7net+/o96mp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+ZVdNm9YO0CHKdji46MzB1JQW4Bww8ZTocWHeIOVTKIkmkRERFJWxs+31BRdWNq4VS27NxCI2vE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\nRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f530d250310>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "burnin=100\n",
    "fig, ((ax1, ax2),(ax3,ax4),(ax5,ax6)) = plt.subplots(3,2)\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 top')\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",
    "ax6.set_title('weights middle')\n",
    "ax6.plot(model.stats['iter'][2][burnin:],model.stats['nw1'][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": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.738604619935\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",
      "79.96\t72.03\t79.28\t0.61\t0.42\t0.10\t0.11\t0.21\n",
      "AvgP\tP\tR\tAcc\tETot\tESub\tEmiss\tEfa\n",
      "51.37\t53.32\t52.76\t0.36\t0.76\t0.18\t0.30\t0.29\n",
      "AvgP\tP\tR\tAcc\tETot\tESub\tEmiss\tEfa\n",
      "78.34\t72.87\t73.34\t0.58\t0.45\t0.09\t0.17\t0.18\n",
      "0.514374022417 0.540598749024\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": [
      "0.762895810652\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",
      "79.96\t72.03\t79.28\t0.61\t0.42\t0.10\t0.11\t0.21\n",
      "AvgP\tP\tR\tAcc\tETot\tESub\tEmiss\tEfa\n",
      "51.37\t53.32\t52.76\t0.36\t0.76\t0.18\t0.30\t0.29\n",
      "AvgP\tP\tR\tAcc\tETot\tESub\tEmiss\tEfa\n",
      "78.34\t72.87\t73.34\t0.58\t0.45\t0.09\t0.17\t0.18\n",
      "AvgP\tP\tR\tAcc\tETot\tESub\tEmiss\tEfa\n",
      "79.59\t71.70\t75.91\t0.58\t0.42\t0.12\t0.12\t0.18\n",
      "AvgP\tP\tR\tAcc\tETot\tESub\tEmiss\tEfa\n",
      "82.43\t66.40\t87.70\t0.61\t0.47\t0.10\t0.02\t0.35\n",
      "AvgP\tP\tR\tAcc\tETot\tESub\tEmiss\tEfa\n",
      "79.11\t74.36\t79.13\t0.62\t0.38\t0.10\t0.11\t0.17\n",
      "AvgP\tP\tR\tAcc\tETot\tESub\tEmiss\tEfa\n",
      "71.92\t61.17\t79.87\t0.53\t0.63\t0.08\t0.12\t0.43\n",
      "AvgP\tP\tR\tAcc\tETot\tESub\tEmiss\tEfa\n",
      "75.01\t67.55\t72.20\t0.54\t0.50\t0.12\t0.16\t0.23\n",
      "AvgP\tP\tR\tAcc\tETot\tESub\tEmiss\tEfa\n",
      "76.51\t69.26\t78.77\t0.58\t0.45\t0.11\t0.10\t0.24\n",
      "AvgP\tP\tR\tAcc\tETot\tESub\tEmiss\tEfa\n",
      "72.20\t72.11\t67.60\t0.54\t0.45\t0.13\t0.19\t0.13\n",
      "0.554188517326 0.492134505874\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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