translation_invariant_morelvl3.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": [
    "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/44100.\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",
    "        d2_x = 1          # lvl2 input dims_x\n",
    "        d2_y = 128          # lvl2 input dims_y\n",
    "        k2 = 128        # num lvl2 filters\n",
    "        stride_y = 2    # lvl2 stride\n",
    "        \n",
    "        d3_x = 25 # lvl3 input dims_x\n",
    "        d3_y = 1 # lvl3 input dims_y (fully connected)\n",
    "        k3 = 4096 # 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",
    "        wsin,wcos = create_filters(self.d,self.k)\n",
    "\n",
    "        print '---- Weights ----'\n",
    "        wscale = .0001\n",
    "        with tf.variable_scope('parameters'):\n",
    "            w = tf.Variable(wscale*tf.random_normal([d2_x,d2_y,1,k2],seed=999))\n",
    "            print 'w',w\n",
    "            wavg = self.register_weights(w,'w',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.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",
    "            print 'zx',zx\n",
    "            z2 = tf.nn.relu(tf.nn.conv2d(tf.log(zx+10e-15),w,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",
    "            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.z2 = tf.nn.relu(tf.nn.conv2d(tf.log(self.zx+10e-15),wavg,strides=[1,1,1,stride_y],padding='VALID',data_format='NCHW'))\n",
    "            self.z3 = tf.nn.relu(tf.nn.conv2d(self.z2,w2avg,strides=[1,1,1,1],padding='VALID',data_format='NCHW'))\n",
    "            self.y_direct = tf.matmul(tf.reshape(self.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": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "---- Weights ----\n",
      "w <tf.Variable 'parameters/Variable:0' shape=(1, 128, 1, 128) dtype=float32_ref>\n",
      "w2 <tf.Variable 'parameters/Variable_2:0' shape=(25, 1, 128, 4096) dtype=float32_ref>\n",
      "beta <tf.Variable 'parameters/Variable_4:0' shape=(790528, 128) dtype=float32_ref>\n",
      "---- Layers ----\n",
      "zx Tensor(\"queued_model/add:0\", shape=(150, 1, 25, 512), dtype=float32)\n",
      "z2 Tensor(\"queued_model/Relu:0\", shape=(150, 128, 25, 193), dtype=float32)\n",
      "z3 Tensor(\"queued_model/Relu_1:0\", shape=(150, 4096, 1, 193), dtype=float32)\n",
      "y Tensor(\"queued_model/MatMul:0\", shape=(150, 128), dtype=float32)\n"
     ]
    }
   ],
   "source": [
    "try: model.stop()\n",
    "except NameError: pass\n",
    "model = Spectrograms(labels,checkpoint_path='convnet_experimental2_morelvl3/', outputs=1, window=16384, mmap=True,\n",
    "                     normalize=True, extended_test_set=True, use_mirex=True, init=False, pitch_transforms=5, jitter=.1,\n",
    "                     restrict=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "avp_test\tavp_train\tmse_test\tmse_train\ttime\tutime\n",
      "0.787188        0.822874        0.475221        0.535489        201     201     \n",
      "0.787208        0.823182        0.475425        0.534982        649     200     \n",
      "0.787281        0.823480        0.475593        0.534573        649     200     \n",
      "0.787384        0.823754        0.475776        0.534175        649     200     \n",
      "0.787435        0.824063        0.475903        0.533799        650     201     \n",
      "0.787426        0.824299        0.476040        0.533439        650     201     \n",
      "0.787421        0.824572        0.476106        0.533070        650     201     \n",
      "0.787506        0.824855        0.476152        0.532736        650     201     \n",
      "0.787626        0.825037        0.476146        0.532465        649     201     \n",
      "0.787711        0.825271        0.476123        0.532179        648     200     \n",
      "0.787889        0.825486        0.476045        0.531894        649     200     \n",
      "0.788003        0.825657        0.475998        0.531648        648     200     \n",
      "0.788048        0.825812        0.476032        0.531443        648     199     \n",
      "0.788090        0.825987        0.476037        0.531197        648     199     \n",
      "0.788025        0.826158        0.476111        0.530980        648     199     \n",
      "0.787996        0.826334        0.476119        0.530741        648     200     \n",
      "Graceful Exit\n"
     ]
    }
   ],
   "source": [
    "lr = .00001/3/3\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 % 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",
    "    print 'Graceful Exit'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.788090216473\n",
      "0.474247319284\n"
     ]
    },
    {
     "data": {
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DG6iDG3jp0GquLiLJwP3AZGAYMF1EhjUqdiPwrDFmNDANeCDwfjFwkjHmAOBc4PFoNdwL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\nRLwhcvcfhi5dmnRZW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6JXr37846Vl6Pqxmy8f/Msj5OVt49m/z+fvz8xHSrj2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MRDwOOg9zL76226nSVgyEOvjBaHMY6bm6h0oy6mGobXYI/t4YOfTnDD8Ig0MYXsmsDorvhKGt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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f297038cb50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "burnin=10\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": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.773352751467\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",
      "83.15\t78.19\t81.75\t0.67\t0.33\t0.08\t0.10\t0.15\n",
      "AvgP\tP\tR\tAcc\tETot\tESub\tEmiss\tEfa\n",
      "56.33\t60.32\t52.71\t0.39\t0.68\t0.14\t0.34\t0.21\n",
      "AvgP\tP\tR\tAcc\tETot\tESub\tEmiss\tEfa\n",
      "80.86\t76.84\t73.37\t0.60\t0.41\t0.08\t0.19\t0.14\n",
      "0.552600558572 0.473992341199\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": [
      "0.798667349757\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",
      "83.15\t78.19\t81.75\t0.67\t0.33\t0.08\t0.10\t0.15\n",
      "AvgP\tP\tR\tAcc\tETot\tESub\tEmiss\tEfa\n",
      "56.33\t60.32\t52.71\t0.39\t0.68\t0.14\t0.34\t0.21\n",
      "AvgP\tP\tR\tAcc\tETot\tESub\tEmiss\tEfa\n",
      "80.86\t76.84\t73.37\t0.60\t0.41\t0.08\t0.19\t0.14\n",
      "AvgP\tP\tR\tAcc\tETot\tESub\tEmiss\tEfa\n",
      "86.09\t75.81\t82.79\t0.65\t0.32\t0.11\t0.06\t0.15\n",
      "AvgP\tP\tR\tAcc\tETot\tESub\tEmiss\tEfa\n",
      "86.53\t74.14\t88.02\t0.67\t0.33\t0.10\t0.02\t0.21\n",
      "AvgP\tP\tR\tAcc\tETot\tESub\tEmiss\tEfa\n",
      "82.69\t76.94\t81.08\t0.65\t0.35\t0.09\t0.10\t0.16\n",
      "AvgP\tP\tR\tAcc\tETot\tESub\tEmiss\tEfa\n",
      "76.05\t64.73\t81.63\t0.57\t0.57\t0.06\t0.12\t0.38\n",
      "AvgP\tP\tR\tAcc\tETot\tESub\tEmiss\tEfa\n",
      "77.71\t70.63\t78.52\t0.59\t0.44\t0.10\t0.12\t0.23\n",
      "AvgP\tP\tR\tAcc\tETot\tESub\tEmiss\tEfa\n",
      "78.98\t72.30\t80.92\t0.62\t0.40\t0.10\t0.09\t0.21\n",
      "AvgP\tP\tR\tAcc\tETot\tESub\tEmiss\tEfa\n",
      "76.30\t75.76\t70.52\t0.58\t0.40\t0.12\t0.18\t0.11\n",
      "0.598842562939 0.423021639721\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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