spectrograms_logwind.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='spectrograms3/', 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",
      "0.652592        0.657948        0.705833        0.860222        31      31      \n",
      "0.652619        0.657979        0.705818        0.860204        111     75      \n",
      "0.652577        0.657978        0.705855        0.860206        96      63      \n",
      "0.652586        0.657972        0.705860        0.860199        93      60      \n",
      "0.652620        0.658147        0.705594        0.859889        148     116     \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": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.652977238104\n",
      "0.70532746309\n"
     ]
    },
    {
     "data": {
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IUJ7IZBMjCLXkmFkTQnOW2WbWsLBZSrS9BzA9Wt89ejXgVOCHKK+RwLnRKCyH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\nSmZRLkmSJJXs/wPUhA4IAV3whwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ff0d34584d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "burnin=20\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": [
      "0.666861867551\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",
      "74.26\t72.83\t62.82\t0.51\t0.49\t0.12\t0.26\t0.12\n",
      "AvgP\tP\tR\tAcc\tETot\tESub\tEmiss\tEfa\n",
      "35.18\t53.39\t18.94\t0.16\t0.89\t0.08\t0.73\t0.08\n",
      "AvgP\tP\tR\tAcc\tETot\tESub\tEmiss\tEfa\n",
      "76.68\t79.03\t57.17\t0.50\t0.52\t0.06\t0.36\t0.09\n",
      "0.389288493589 0.632908011925\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": [
      "0.663947096654\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",
      "74.26\t72.83\t62.82\t0.51\t0.49\t0.12\t0.26\t0.12\n",
      "AvgP\tP\tR\tAcc\tETot\tESub\tEmiss\tEfa\n",
      "35.18\t53.39\t18.94\t0.16\t0.89\t0.08\t0.73\t0.08\n",
      "AvgP\tP\tR\tAcc\tETot\tESub\tEmiss\tEfa\n",
      "76.68\t79.03\t57.17\t0.50\t0.52\t0.06\t0.36\t0.09\n",
      "AvgP\tP\tR\tAcc\tETot\tESub\tEmiss\tEfa\n",
      "66.43\t68.06\t57.59\t0.45\t0.57\t0.13\t0.30\t0.14\n",
      "AvgP\tP\tR\tAcc\tETot\tESub\tEmiss\tEfa\n",
      "64.05\t49.58\t88.24\t0.47\t0.90\t0.11\t0.01\t0.79\n",
      "AvgP\tP\tR\tAcc\tETot\tESub\tEmiss\tEfa\n",
      "72.81\t75.69\t58.37\t0.49\t0.49\t0.11\t0.30\t0.07\n",
      "AvgP\tP\tR\tAcc\tETot\tESub\tEmiss\tEfa\n",
      "64.87\t61.50\t67.15\t0.47\t0.68\t0.07\t0.26\t0.35\n",
      "AvgP\tP\tR\tAcc\tETot\tESub\tEmiss\tEfa\n",
      "69.49\t69.83\t59.46\t0.47\t0.54\t0.13\t0.28\t0.13\n",
      "AvgP\tP\tR\tAcc\tETot\tESub\tEmiss\tEfa\n",
      "69.22\t70.83\t59.32\t0.48\t0.53\t0.12\t0.29\t0.12\n",
      "AvgP\tP\tR\tAcc\tETot\tESub\tEmiss\tEfa\n",
      "65.56\t70.58\t53.36\t0.44\t0.57\t0.12\t0.35\t0.10\n",
      "0.443693154829 0.61768037674\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": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
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