channel_convolution.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",
    "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": [
    "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",
    "        num_regions  = 1 + (self.window-self.d)/self.stride\n",
    "        \n",
    "        print 'Lvl1 regions: {}'.format(num_regions)\n",
    "        \n",
    "        d2_x = 1\n",
    "        d2_y = 128\n",
    "        k2 = 128\n",
    "        stride_y = 2\n",
    "        \n",
    "        d3_x = 25 # lvl3 input dims_x\n",
    "        d3_y = 1 # lvl3 input dims_y (fully connected)\n",
    "        k3 = 256 # num lvl3 filters\n",
    "        \n",
    "        num_regions  = 1 + (self.window-self.d)/self.stride\n",
    "        #print 'First layer regions: ({},{})'.format(num_regions,self.k)\n",
    "        num_regions2_x  = 1 + (num_regions-d2_x)/1\n",
    "        num_regions2_y = 1 + (self.k-d2_y)/stride_y\n",
    "        #print 'Second layer regions: ({},{})'.format(num_regions2_x,num_regions2_y)\n",
    "        num_regions3_x = 1 + (num_regions2_x - d3_x)/1\n",
    "        num_regions3_y = 1 + (num_regions2_y - d3_y)/1\n",
    "\n",
    "        print '---- Weights ----'\n",
    "        wscale = 10e-3\n",
    "        with tf.variable_scope('parameters'):\n",
    "            w = tf.Variable(wscale*tf.random_normal([1,self.d,1,self.k],seed=999))\n",
    "            print 'w',w\n",
    "            wavg = self.register_weights(w,'w',average=.9998)\n",
    "            w1 = tf.Variable(wscale*tf.random_normal([d2_x,d2_y,1,k2],seed=999))\n",
    "            print 'w1',w1\n",
    "            w1avg = self.register_weights(w1,'w1',average=.9998)\n",
    "            w2 = tf.Variable(wscale*tf.random_normal([d3_x,d3_y,k2,k3],seed=999))\n",
    "            print 'w2',w2\n",
    "            w2avg = self.register_weights(w2,'w2',average=.9998)\n",
    "            beta = tf.Variable(wscale*tf.random_normal([num_regions3_x*num_regions3_y*k3,self.m],seed=999))\n",
    "            print 'beta',beta\n",
    "            betaavg = self.register_weights(beta,'beta',average=.9998)\n",
    "\n",
    "        print '---- Layers ----'\n",
    "        with tf.variable_scope('queued_model'):\n",
    "            zx = tf.log(tf.constant(1.) + tf.nn.relu(tf.nn.conv2d(self.xq,w,strides=[1,1,self.stride,1],padding='VALID')))\n",
    "            print 'zx',zx\n",
    "            z2 = tf.nn.relu(tf.nn.conv2d(zx,w1,strides=[1,1,1,stride_y],padding='VALID',data_format='NCHW'))\n",
    "            print 'z2',z2\n",
    "            z3 = tf.nn.relu(tf.nn.conv2d(z2,w2,strides=[1,1,1,1],padding='VALID',data_format='NCHW'))\n",
    "            print 'z3',z3\n",
    "            y = tf.matmul(tf.reshape(z3,[self.batch_size,num_regions3_x*num_regions3_y*k3]),beta)\n",
    "            print 'y',y\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.log(tf.constant(1.) + tf.nn.relu(tf.nn.conv2d(self.xd,wavg,strides=[1,1,self.stride,1],padding='VALID')))\n",
    "            z2 = tf.nn.relu(tf.nn.conv2d(zx,w1avg,strides=[1,1,1,stride_y],padding='VALID',data_format='NCHW'))\n",
    "            z3 = tf.nn.relu(tf.nn.conv2d(z2,w2avg,strides=[1,1,1,1],padding='VALID',data_format='NCHW'))\n",
    "            self.y_direct = tf.matmul(tf.reshape(z3,[tf.shape(self.xd)[0],num_regions3_x*num_regions3_y*k3]),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": [
      "Lvl1 regions: 25\n",
      "---- Weights ----\n",
      "w <tf.Variable 'parameters/Variable:0' shape=(1, 4096, 1, 512) dtype=float32_ref>\n",
      "w1 <tf.Variable 'parameters/Variable_2:0' shape=(1, 128, 1, 128) dtype=float32_ref>\n",
      "w2 <tf.Variable 'parameters/Variable_4:0' shape=(25, 1, 128, 256) dtype=float32_ref>\n",
      "beta <tf.Variable 'parameters/Variable_6:0' shape=(49408, 93) dtype=float32_ref>\n",
      "---- Layers ----\n",
      "zx Tensor(\"queued_model/Log:0\", shape=(100, 1, 25, 512), dtype=float32)\n",
      "z2 Tensor(\"queued_model/Relu_1:0\", shape=(100, 128, 25, 193), dtype=float32)\n",
      "z3 Tensor(\"queued_model/Relu_2:0\", shape=(100, 256, 1, 193), dtype=float32)\n",
      "y Tensor(\"queued_model/MatMul:0\", shape=(100, 93), dtype=float32)\n"
     ]
    }
   ],
   "source": [
    "try: model.stop()\n",
    "except NameError: pass\n",
    "model = Spectrograms(labels,checkpoint_path='convnet_experimental2_raw/', outputs=1, window=16384, mmap=True, batch_size=100,\n",
    "                     normalize=True, extended_test_set=True, use_mirex=True, init=False, pitch_transforms=5, jitter=.1,\n",
    "                     restrict=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Lvl1 regions: 25\n",
      "---- Weights ----\n",
      "w <tf.Variable 'parameters/Variable:0' shape=(1, 4096, 1, 512) dtype=float32_ref>\n",
      "w1 <tf.Variable 'parameters/Variable_2:0' shape=(1, 128, 1, 128) dtype=float32_ref>\n",
      "w2 <tf.Variable 'parameters/Variable_4:0' shape=(25, 1, 128, 256) dtype=float32_ref>\n",
      "beta <tf.Variable 'parameters/Variable_6:0' shape=(49408, 93) dtype=float32_ref>\n",
      "---- Layers ----\n",
      "zx Tensor(\"queued_model/Log:0\", shape=(100, 1, 25, 512), dtype=float32)\n",
      "z2 Tensor(\"queued_model/Relu_1:0\", shape=(100, 128, 25, 193), dtype=float32)\n",
      "z3 Tensor(\"queued_model/Relu_2:0\", shape=(100, 256, 1, 193), dtype=float32)\n",
      "y Tensor(\"queued_model/MatMul:0\", shape=(100, 93), dtype=float32)\n",
      "avp_test\tavp_train\tmse_test\tmse_train\ttime\tutime\n",
      "0.731866        0.742900        0.552159        0.662046        24      24      \n",
      "0.731535        0.742970        0.552241        0.662027        477     24      \n",
      "0.731191        0.742966        0.552544        0.662166        477     24      \n",
      "0.731286        0.743162        0.552480        0.661990        477     24      \n",
      "0.730898        0.743040        0.552665        0.662012        477     24      \n",
      "0.731478        0.743192        0.552579        0.661960        475     23      \n",
      "0.731673        0.743051        0.552505        0.661997        475     24      \n",
      "0.731882        0.743376        0.552316        0.661657        476     24      \n",
      "0.732052        0.743275        0.551978        0.661719        477     24      \n",
      "0.731744        0.743306        0.552161        0.661528        476     24      \n",
      "0.731902        0.743257        0.552004        0.661666        477     24      \n",
      "0.731450        0.743176        0.552379        0.661691        478     25      \n",
      "0.731205        0.743118        0.552515        0.661744        472     25      \n",
      "0.731388        0.742939        0.552357        0.661951        472     25      \n",
      "0.730872        0.742876        0.552736        0.661935        473     24      \n",
      "0.731405        0.743097        0.552485        0.661818        475     24      \n",
      "Graceful Exit\n"
     ]
    }
   ],
   "source": [
    "lr = 10e-4/2./2/2/2/2\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",
    "    print 'Graceful Exit'\n",
    "\n",
    "# avp_test  avp_train  mse_test  mse_train  time  utime\n",
    "# 0.509154  0.511357   1.171773  1.453633   11    11     "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.732052216474\n",
      "0.551977709133\n"
     ]
    },
    {
     "data": {
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rGxWGaU6m+ZhCWbaLUtCvn8y+k54OERGlcx7TrjXTfMA6uQW3OrkiYHbrnH2F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\n/B+Y92vsUzElmwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f7fe0116d50>"
      ]
     },
     "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.grid(color='b', linestyle='--', linewidth=0.5, alpha=0.3)\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.grid(color='b', linestyle='--', linewidth=0.5, alpha=0.3)\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('avp breakdown')\n",
    "ax3.grid(color='b', linestyle='--', linewidth=0.5, alpha=0.3)\n",
    "cmap = plt.cm.get_cmap('hsv',len(model.test_ids))\n",
    "plots = []\n",
    "keys = []\n",
    "for i,rec_id in enumerate(model.test_ids):\n",
    "    plot, = ax3.plot(model.stats['iter'][2][burnin:], model.stats['avp_test_' + str(rec_id)][2][burnin:], color=cmap(i))\n",
    "    plots.append(plot)\n",
    "    keys.append('avp_test_' + str(rec_id))\n",
    "leg = ax3.legend(plots,keys,loc='upper right',ncol=1,prop={'size':11})\n",
    "ax4.set_title('learning rate')\n",
    "ax4.grid(color='b', linestyle='--', linewidth=0.5, alpha=0.3)\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": [
      "0.733640288246\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",
      "80.57\t75.68\t76.69\t0.62\t0.39\t0.09\t0.15\t0.16\n",
      "AvgP\tP\tR\tAcc\tETot\tESub\tEmiss\tEfa\n",
      "49.81\t55.56\t45.29\t0.33\t0.75\t0.16\t0.38\t0.20\n",
      "AvgP\tP\tR\tAcc\tETot\tESub\tEmiss\tEfa\n",
      "78.94\t73.70\t70.68\t0.56\t0.45\t0.09\t0.20\t0.16\n",
      "0.504057951317 0.530727941417\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": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.745707285268\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": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AvgP\tP\tR\tAcc\tETot\tESub\tEmiss\tEfa\n",
      "80.57\t75.68\t76.69\t0.62\t0.39\t0.09\t0.15\t0.16\n",
      "AvgP\tP\tR\tAcc\tETot\tESub\tEmiss\tEfa\n",
      "49.81\t55.56\t45.29\t0.33\t0.75\t0.16\t0.38\t0.20\n",
      "AvgP\tP\tR\tAcc\tETot\tESub\tEmiss\tEfa\n",
      "78.94\t73.70\t70.68\t0.56\t0.45\t0.09\t0.20\t0.16\n",
      "AvgP\tP\tR\tAcc\tETot\tESub\tEmiss\tEfa\n",
      "77.51\t72.20\t70.45\t0.55\t0.42\t0.15\t0.15\t0.12\n",
      "AvgP\tP\tR\tAcc\tETot\tESub\tEmiss\tEfa\n",
      "85.48\t73.74\t84.71\t0.65\t0.34\t0.12\t0.04\t0.19\n",
      "AvgP\tP\tR\tAcc\tETot\tESub\tEmiss\tEfa\n",
      "80.14\t75.17\t76.84\t0.61\t0.38\t0.11\t0.12\t0.15\n",
      "AvgP\tP\tR\tAcc\tETot\tESub\tEmiss\tEfa\n",
      "67.78\t59.31\t75.19\t0.50\t0.67\t0.09\t0.15\t0.42\n",
      "AvgP\tP\tR\tAcc\tETot\tESub\tEmiss\tEfa\n",
      "71.41\t69.07\t66.62\t0.51\t0.50\t0.13\t0.21\t0.17\n",
      "AvgP\tP\tR\tAcc\tETot\tESub\tEmiss\tEfa\n",
      "74.90\t70.52\t74.70\t0.57\t0.44\t0.12\t0.13\t0.19\n",
      "AvgP\tP\tR\tAcc\tETot\tESub\tEmiss\tEfa\n",
      "69.05\t70.13\t62.13\t0.49\t0.49\t0.15\t0.23\t0.11\n",
      "0.539966307185 0.483388782374\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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