cifar_lasagne.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using gpu device 0: GRID K520 (CNMeM is enabled)\n"
     ]
    }
   ],
   "source": [
    "from __future__ import print_function\n",
    "import time\n",
    "import numpy as np\n",
    "import theano\n",
    "import theano.tensor as T\n",
    "import lasagne\n",
    "\n",
    "from cifar10_data import load_cifar10\n",
    "import lasagne_trainer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3002, 3002)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# verify that cuDNN is enabled\n",
    "theano.sandbox.cuda.dnn.version()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# set up plots\n",
    "\n",
    "%matplotlib inline\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "# reload external libs during development\n",
    "%load_ext autoreload\n",
    "%autoreload 2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# load data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train data shape:  (49000, 3, 32, 32)\n",
      "Train labels shape:  (49000,)\n",
      "Validation data shape:  (1000, 3, 32, 32)\n",
      "Validation labels shape:  (1000,)\n",
      "Test data shape:  (1000, 32, 32, 3)\n",
      "Test labels shape:  (1000,)\n"
     ]
    }
   ],
   "source": [
    "# get data\n",
    "X_train, y_train, X_val, y_val, X_test, y_test = load_cifar10()\n",
    "print('Train data shape: ', X_train.shape)\n",
    "print('Train labels shape: ', y_train.shape)\n",
    "print('Validation data shape: ', X_val.shape)\n",
    "print('Validation labels shape: ', y_val.shape)\n",
    "print('Test data shape: ', X_test.shape)\n",
    "print('Test labels shape: ', y_test.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# theano input_var"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "input_var = T.tensor4('inputs')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## two-layer network"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def create_twolayer(input_var, input_shape=(3, 32, 32),\n",
    "              num_hidden_units=100, num_classes=10,\n",
    "              **junk):\n",
    "    # input layer\n",
    "    network = lasagne.layers.InputLayer(shape=(None,) + input_shape,\n",
    "                                        input_var=input_var)\n",
    "    # fc-relu\n",
    "    network = lasagne.layers.DenseLayer(\n",
    "        network, num_units=num_hidden_units,\n",
    "        nonlinearity=lasagne.nonlinearities.rectify)\n",
    "    # output layer\n",
    "    network = lasagne.layers.DenseLayer(\n",
    "        lasagne.layers.dropout(network, p=.5),\n",
    "        num_units=num_classes,\n",
    "        nonlinearity=lasagne.nonlinearities.softmax)\n",
    "    return network"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# defaults\n",
    "param=dict(reg=1e-1,\n",
    "           update='momentum', momentum=0.9,\n",
    "           learning_rate=1e-4, learning_rate_decay = 0.95,\n",
    "           num_epochs=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# [loss] train_acc val_acc\n",
    "\n",
    "# 1.049 0.650 0.551\n",
    "# 1.106 0.667 0.561\n",
    "# => test accuracy: 0.548\n",
    "param.update(reg=1e0, hidden_size=200, num_epochs=40, learning_rate=1e-4)\n",
    "\n",
    "# 1.649 0.424 0.426\n",
    "param.update(reg=1e1, hidden_size=200, num_epochs=40, learning_rate=1e-4)\n",
    "\n",
    "# ????? 0.868 0.555\n",
    "# 0.477 0.860 0.546\n",
    "param.update(reg=1e-1, hidden_size=200, num_epochs=40, learning_rate=1e-4)\n",
    "\n",
    "# ????? 0.578 0.419\n",
    "param.update(reg=1e-1, hidden_size=200, num_epochs=40, learning_rate=1e-3)\n",
    "\n",
    "# 1.054 0.566 0.505\n",
    "param.update(reg=1e-1, hidden_size=200, num_epochs=40, learning_rate=1e-5)\n",
    "\n",
    "# 0.724 0.537\n",
    "param.update(reg=1e-1, hidden_size=200, num_epochs=20, learning_rate=1e-4)\n",
    "\n",
    "# 0.606 0.530\n",
    "param.update(reg=1.0, hidden_size=200, num_epochs=20, learning_rate=1e-4)\n",
    "\n",
    "# 0.534 0.474\n",
    "param.update(reg=1e-1, hidden_size=200, num_epochs=20, learning_rate=1e-5)\n",
    "\n",
    "# 0.504 0.469\n",
    "param.update(reg=1.0, hidden_size=200, num_epochs=20, learning_rate=1e-5)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# two-layer\n",
    "\n",
    "network = create_twolayer(input_var, num_hidden_units=128, num_classes=10)\n",
    "# overfit check\n",
    "xxx, loss_history, train_acc_history, val_acc_history = lasagne_trainer.train(\n",
    "          network, input_var, X_train[:50], y_train[:50], X_val, y_val,\n",
    "          learning_rate=1e-4, batch_size=10, num_epochs=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Compiling...\n",
      "Training...\n",
      "epoch 1 / 10 in 0.9s: cost 19.949498, train: 0.262000, val 0.229000, lr 1.000000e-04\n",
      "epoch 2 / 10 in 0.9s: cost 3.706224, train: 0.279000, val 0.283000, lr 1.000000e-04\n",
      "epoch 3 / 10 in 0.9s: cost 3.098231, train: 0.287000, val 0.298000, lr 1.000000e-04\n",
      "epoch 4 / 10 in 0.9s: cost 2.809862, train: 0.321000, val 0.312000, lr 1.000000e-04\n",
      "epoch 5 / 10 in 0.9s: cost 2.680390, train: 0.340000, val 0.306000, lr 1.000000e-04\n",
      "epoch 6 / 10 in 0.9s: cost 2.550387, train: 0.336000, val 0.315000, lr 1.000000e-04\n",
      "epoch 7 / 10 in 0.9s: cost 2.479211, train: 0.313000, val 0.321000, lr 1.000000e-04\n",
      "epoch 8 / 10 in 0.9s: cost 2.420109, train: 0.372000, val 0.332000, lr 1.000000e-04\n",
      "epoch 9 / 10 in 0.9s: cost 2.367520, train: 0.359000, val 0.355000, lr 1.000000e-04\n",
      "epoch 10 / 10 in 0.9s: cost 2.320339, train: 0.364000, val 0.345000, lr 1.000000e-04\n",
      "('1.633', 0.37199999690055846, 0.35499999970197677, 'crp_filter_size=7 num_fc=1 learning_rate=0.0001 batch_size=100 fc_num_units=32 num_epochs=5 crp_num_filters=32 reg=0.001 num_crp=1')\n"
     ]
    }
   ],
   "source": [
    "\n",
    "\n",
    "model, loss_history, train_acc_history, val_acc_history = lasagne_trainer.train(\n",
    "    network, input_var, X_train, y_train, X_val, y_val,\n",
    "    learning_rate=1e-4, num_epochs=10, batch_size=50)\n",
    "print('%.3f' % min(loss_history), max(train_acc_history), max(val_acc_history), \\\n",
    "    ' '.join('%s=%s' % (k,param[k]) for k in param))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## v1: [conv-relu-pool]xN - conv - relu - [affine]xM - [softmax or SVM]\n",
    "## v2: [conv-relu-pool]XN - [affine]XM - [softmax or SVM]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def create_v1(input_var, input_shape=(3, 32, 32),\n",
    "              num_crp=1, crp_num_filters=32, crp_filter_size=5,\n",
    "              num_cr=1,\n",
    "              num_fc=1, fc_num_units=64,\n",
    "              output_type='softmax', num_classes=10,\n",
    "              **junk):\n",
    "    # input layer\n",
    "    network = lasagne.layers.InputLayer(shape=(None,) + input_shape,\n",
    "                                        input_var=input_var)\n",
    "    # conv-relu-pool layers\n",
    "    for i in range(num_crp):\n",
    "        network = lasagne.layers.Conv2DLayer(\n",
    "            network, num_filters=crp_num_filters,\n",
    "            filter_size=(crp_filter_size, crp_filter_size),\n",
    "            pad='same',\n",
    "            nonlinearity=lasagne.nonlinearities.rectify,\n",
    "            W=lasagne.init.GlorotUniform(gain='relu'))\n",
    "        network = lasagne.layers.MaxPool2DLayer(network, pool_size=(2, 2))\n",
    "    # conv-relu\n",
    "    for i in range(num_cr):\n",
    "        network = lasagne.layers.Conv2DLayer(\n",
    "            network, num_filters=crp_num_filters,\n",
    "            filter_size=(crp_filter_size, crp_filter_size),\n",
    "            pad='same',\n",
    "            nonlinearity=lasagne.nonlinearities.rectify,\n",
    "            W=lasagne.init.GlorotUniform(gain='relu'))\n",
    "    # fc-relu\n",
    "    for i in range(num_fc - 1):\n",
    "        network = lasagne.layers.DenseLayer(\n",
    "            lasagne.layers.dropout(network, p=.5),\n",
    "            num_units=fc_num_units,\n",
    "            nonlinearity=lasagne.nonlinearities.rectify)\n",
    "    # output layer\n",
    "    assert output_type=='softmax'\n",
    "    network = lasagne.layers.DenseLayer(\n",
    "        lasagne.layers.dropout(network, p=.5),\n",
    "        num_units=num_classes,\n",
    "        nonlinearity=lasagne.nonlinearities.softmax)\n",
    "    return network\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# defaut params\n",
    "param = dict(num_crp=1, crp_filter_size=7, crp_num_filters=32,\n",
    "             num_fc=1, fc_num_units=32,\n",
    "             reg=1e-3,\n",
    "             learning_rate=1e-4, learning_rate_decay=0.95,\n",
    "             momentum=0.9, momentum_decay=0.9,\n",
    "             batch_size=100, num_epochs=20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "param.update(num_crp=3, crp_filter_size=3, crp_num_filters=128,\n",
    "             num_fc=3, fc_num_units=256, num_epochs=100,\n",
    "             learning_rate=1e-4, batch_size=128)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Compiling...\n",
      "Training...\n",
      "epoch 1 / 20 in 0.9s: loss 71.401446, train: 0.140, val 0.116, lr 9.500000e-05 mom 9.100000e-01\n",
      "epoch 2 / 20 in 0.9s: loss 33.159449, train: 0.100, val 0.112, lr 9.025000e-05 mom 9.190000e-01\n",
      "epoch 3 / 20 in 0.9s: loss 14.697625, train: 0.220, val 0.128, lr 8.573750e-05 mom 9.270999e-01\n",
      "epoch 4 / 20 in 0.9s: loss 7.475414, train: 0.220, val 0.130, lr 8.145062e-05 mom 9.343899e-01\n",
      "epoch 5 / 20 in 0.9s: loss 4.678493, train: 0.260, val 0.148, lr 7.737809e-05 mom 9.409509e-01\n",
      "epoch 6 / 20 in 0.9s: loss 3.727562, train: 0.200, val 0.127, lr 7.350919e-05 mom 9.468558e-01\n",
      "epoch 7 / 20 in 0.9s: loss 3.771379, train: 0.220, val 0.108, lr 6.983373e-05 mom 9.521703e-01\n",
      "epoch 8 / 20 in 0.9s: loss 3.426452, train: 0.260, val 0.112, lr 6.634204e-05 mom 9.569532e-01\n",
      "epoch 9 / 20 in 0.9s: loss 3.173775, train: 0.240, val 0.111, lr 6.302494e-05 mom 9.612579e-01\n",
      "epoch 10 / 20 in 0.9s: loss 2.937779, train: 0.200, val 0.113, lr 5.987370e-05 mom 9.651321e-01\n",
      "epoch 11 / 20 in 0.9s: loss 2.691136, train: 0.260, val 0.111, lr 5.688001e-05 mom 9.686189e-01\n",
      "epoch 12 / 20 in 0.9s: loss 2.438592, train: 0.300, val 0.111, lr 5.403601e-05 mom 9.717571e-01\n",
      "epoch 13 / 20 in 0.9s: loss 2.427982, train: 0.240, val 0.113, lr 5.133421e-05 mom 9.745814e-01\n",
      "epoch 14 / 20 in 0.9s: loss 2.301744, train: 0.300, val 0.120, lr 4.876750e-05 mom 9.771232e-01\n",
      "epoch 15 / 20 in 0.9s: loss 2.302263, train: 0.320, val 0.124, lr 4.632913e-05 mom 9.794109e-01\n",
      "epoch 16 / 20 in 0.9s: loss 2.260264, train: 0.300, val 0.117, lr 4.401267e-05 mom 9.814698e-01\n",
      "epoch 17 / 20 in 0.9s: loss 2.365086, train: 0.260, val 0.118, lr 4.181203e-05 mom 9.833229e-01\n",
      "epoch 18 / 20 in 0.9s: loss 2.178240, train: 0.320, val 0.123, lr 3.972143e-05 mom 9.849906e-01\n",
      "epoch 19 / 20 in 0.9s: loss 2.342404, train: 0.340, val 0.119, lr 3.773536e-05 mom 9.864916e-01\n",
      "epoch 20 / 20 in 0.9s: loss 2.191207, train: 0.400, val 0.110, lr 3.584859e-05 mom 9.878424e-01\n"
     ]
    }
   ],
   "source": [
    "network = create_v1(input_var, **param)\n",
    "# overfit\n",
    "xxx, loss_history, train_acc_history, val_acc_history = lasagne_trainer.train(\n",
    "          network, input_var, X_train[:50], y_train[:50], X_val, y_val,\n",
    "          learning_rate=1e-4, batch_size=10, num_epochs=20)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Compiling...\n",
      "Training...\n",
      "epoch 1 / 100 in 119.0s: loss 2.318504, train: 0.134, val 0.157, lr 9.500000e-05 mom 9.100000e-01\n",
      "epoch 2 / 100 in 118.9s: loss 2.300581, train: 0.166, val 0.146, lr 9.025000e-05 mom 9.190000e-01\n",
      "epoch 3 / 100 in 119.0s: loss 2.292756, train: 0.206, val 0.191, lr 8.573750e-05 mom 9.270999e-01\n",
      "epoch 4 / 100 in 119.0s: loss 2.275991, train: 0.248, val 0.232, lr 8.145062e-05 mom 9.343899e-01\n",
      "epoch 5 / 100 in 119.0s: loss 2.255572, train: 0.265, val 0.253, lr 7.737809e-05 mom 9.409509e-01\n",
      "epoch 6 / 100 in 119.0s: loss 2.229092, train: 0.292, val 0.258, lr 7.350919e-05 mom 9.468558e-01\n",
      "epoch 7 / 100 in 119.0s: loss 2.200565, train: 0.289, val 0.285, lr 6.983373e-05 mom 9.521703e-01\n",
      "epoch 8 / 100 in 119.0s: loss 2.173981, train: 0.307, val 0.299, lr 6.634204e-05 mom 9.569532e-01\n",
      "epoch 9 / 100 in 119.0s: loss 2.142996, train: 0.318, val 0.311, lr 6.302494e-05 mom 9.612579e-01\n",
      "epoch 10 / 100 in 118.9s: loss 2.109364, train: 0.335, val 0.338, lr 5.987370e-05 mom 9.651321e-01\n",
      "epoch 11 / 100 in 118.9s: loss 2.075525, train: 0.367, val 0.372, lr 5.688001e-05 mom 9.686189e-01\n",
      "epoch 12 / 100 in 118.9s: loss 2.042083, train: 0.369, val 0.379, lr 5.403601e-05 mom 9.717571e-01\n",
      "epoch 13 / 100 in 119.0s: loss 2.003143, train: 0.355, val 0.374, lr 5.133421e-05 mom 9.745814e-01\n",
      "epoch 14 / 100 in 119.0s: loss 1.968326, train: 0.382, val 0.401, lr 4.876750e-05 mom 9.771232e-01\n",
      "epoch 15 / 100 in 118.9s: loss 1.935849, train: 0.400, val 0.416, lr 4.632913e-05 mom 9.794109e-01\n",
      "epoch 16 / 100 in 119.0s: loss 1.896845, train: 0.394, val 0.410, lr 4.401267e-05 mom 9.814698e-01\n",
      "epoch 17 / 100 in 118.9s: loss 1.870250, train: 0.405, val 0.429, lr 4.181203e-05 mom 9.833229e-01\n",
      "epoch 18 / 100 in 118.9s: loss 1.837316, train: 0.415, val 0.439, lr 3.972143e-05 mom 9.849906e-01\n",
      "epoch 19 / 100 in 118.9s: loss 1.807514, train: 0.432, val 0.434, lr 3.773536e-05 mom 9.864916e-01\n",
      "epoch 20 / 100 in 118.9s: loss 1.771687, train: 0.423, val 0.464, lr 3.584859e-05 mom 9.878424e-01\n",
      "epoch 21 / 100 in 118.9s: loss 1.737611, train: 0.438, val 0.467, lr 3.405616e-05 mom 9.890581e-01\n",
      "epoch 22 / 100 in 118.9s: loss 1.711730, train: 0.448, val 0.477, lr 3.235336e-05 mom 9.901523e-01\n",
      "epoch 23 / 100 in 119.0s: loss 1.673019, train: 0.477, val 0.499, lr 3.073569e-05 mom 9.911371e-01\n",
      "epoch 24 / 100 in 118.9s: loss 1.644735, train: 0.460, val 0.488, lr 2.919891e-05 mom 9.920233e-01\n",
      "epoch 25 / 100 in 119.0s: loss 1.610350, train: 0.485, val 0.493, lr 2.773896e-05 mom 9.928210e-01\n",
      "epoch 26 / 100 in 119.0s: loss 1.587085, train: 0.500, val 0.503, lr 2.635201e-05 mom 9.935389e-01\n",
      "epoch 27 / 100 in 118.9s: loss 1.553481, train: 0.500, val 0.513, lr 2.503441e-05 mom 9.941850e-01\n",
      "epoch 28 / 100 in 118.9s: loss 1.533502, train: 0.517, val 0.536, lr 2.378269e-05 mom 9.947665e-01\n",
      "epoch 29 / 100 in 118.9s: loss 1.506634, train: 0.499, val 0.532, lr 2.259355e-05 mom 9.952899e-01\n",
      "epoch 30 / 100 in 118.9s: loss 1.482524, train: 0.516, val 0.526, lr 2.146388e-05 mom 9.957609e-01\n",
      "epoch 31 / 100 in 118.9s: loss 1.463194, train: 0.539, val 0.535, lr 2.039068e-05 mom 9.961848e-01\n",
      "epoch 32 / 100 in 119.0s: loss 1.448789, train: 0.546, val 0.535, lr 1.937115e-05 mom 9.965663e-01\n",
      "epoch 33 / 100 in 118.9s: loss 1.430224, train: 0.544, val 0.549, lr 1.840259e-05 mom 9.969097e-01\n",
      "epoch 34 / 100 in 119.0s: loss 1.404319, train: 0.567, val 0.552, lr 1.748246e-05 mom 9.972187e-01\n",
      "epoch 35 / 100 in 118.9s: loss 1.385945, train: 0.540, val 0.545, lr 1.660834e-05 mom 9.974968e-01\n",
      "epoch 36 / 100 in 119.0s: loss 1.367607, train: 0.561, val 0.569, lr 1.577792e-05 mom 9.977472e-01\n",
      "epoch 37 / 100 in 118.9s: loss 1.341838, train: 0.583, val 0.562, lr 1.498903e-05 mom 9.979725e-01\n",
      "epoch 38 / 100 in 118.9s: loss 1.329648, train: 0.580, val 0.552, lr 1.423957e-05 mom 9.981753e-01\n",
      "epoch 39 / 100 in 119.0s: loss 1.310204, train: 0.592, val 0.573, lr 1.352760e-05 mom 9.983577e-01\n",
      "epoch 40 / 100 in 118.9s: loss 1.289379, train: 0.593, val 0.589, lr 1.285122e-05 mom 9.985219e-01\n",
      "epoch 41 / 100 in 118.9s: loss 1.267939, train: 0.597, val 0.587, lr 1.220866e-05 mom 9.986697e-01\n",
      "epoch 42 / 100 in 119.0s: loss 1.255105, train: 0.622, val 0.590, lr 1.159822e-05 mom 9.988028e-01\n",
      "epoch 43 / 100 in 119.0s: loss 1.239694, train: 0.602, val 0.590, lr 1.101831e-05 mom 9.989225e-01\n",
      "epoch 44 / 100 in 119.0s: loss 1.210926, train: 0.612, val 0.600, lr 1.046740e-05 mom 9.990303e-01\n",
      "epoch 45 / 100 in 118.9s: loss 1.196522, train: 0.637, val 0.637, lr 9.944027e-06 mom 9.991273e-01\n",
      "epoch 46 / 100 in 118.9s: loss 1.179403, train: 0.641, val 0.621, lr 9.446825e-06 mom 9.992145e-01\n",
      "epoch 47 / 100 in 118.9s: loss 1.173354, train: 0.625, val 0.617, lr 8.974484e-06 mom 9.992931e-01\n",
      "epoch 48 / 100 in 118.9s: loss 1.148145, train: 0.671, val 0.635, lr 8.525760e-06 mom 9.993638e-01\n",
      "epoch 49 / 100 in 119.0s: loss 1.130034, train: 0.665, val 0.654, lr 8.099471e-06 mom 9.994274e-01\n",
      "epoch 50 / 100 in 118.9s: loss 1.127878, train: 0.662, val 0.651, lr 7.694498e-06 mom 9.994847e-01\n",
      "epoch 51 / 100 in 118.9s: loss 1.110297, train: 0.696, val 0.661, lr 7.309773e-06 mom 9.995362e-01\n",
      "epoch 52 / 100 in 119.0s: loss 1.080579, train: 0.682, val 0.647, lr 6.944284e-06 mom 9.995826e-01\n",
      "epoch 53 / 100 in 118.9s: loss 1.067140, train: 0.696, val 0.660, lr 6.597070e-06 mom 9.996243e-01\n",
      "epoch 54 / 100 in 119.0s: loss 1.057995, train: 0.698, val 0.663, lr 6.267217e-06 mom 9.996619e-01\n",
      "epoch 55 / 100 in 119.0s: loss 1.042966, train: 0.722, val 0.667, lr 5.953856e-06 mom 9.996957e-01\n",
      "epoch 56 / 100 in 118.9s: loss 1.027454, train: 0.711, val 0.677, lr 5.656163e-06 mom 9.997261e-01\n",
      "epoch 57 / 100 in 119.0s: loss 1.011541, train: 0.715, val 0.686, lr 5.373355e-06 mom 9.997535e-01\n",
      "epoch 58 / 100 in 119.0s: loss 0.995829, train: 0.734, val 0.679, lr 5.104687e-06 mom 9.997782e-01\n",
      "epoch 59 / 100 in 118.9s: loss 0.993285, train: 0.708, val 0.690, lr 4.849453e-06 mom 9.998003e-01\n",
      "epoch 60 / 100 in 118.9s: loss 0.984133, train: 0.732, val 0.686, lr 4.606980e-06 mom 9.998203e-01\n",
      "epoch 61 / 100 in 118.9s: loss 0.974277, train: 0.720, val 0.651, lr 4.376631e-06 mom 9.998382e-01\n",
      "epoch 62 / 100 in 118.9s: loss 0.967135, train: 0.715, val 0.675, lr 4.157799e-06 mom 9.998544e-01\n",
      "epoch 63 / 100 in 119.0s: loss 0.959195, train: 0.709, val 0.710, lr 3.949909e-06 mom 9.998689e-01\n",
      "epoch 64 / 100 in 119.0s: loss 0.958395, train: 0.717, val 0.645, lr 3.752414e-06 mom 9.998820e-01\n",
      "epoch 65 / 100 in 118.9s: loss 0.961230, train: 0.749, val 0.667, lr 3.564793e-06 mom 9.998938e-01\n",
      "epoch 66 / 100 in 119.0s: loss 0.956396, train: 0.728, val 0.682, lr 3.386554e-06 mom 9.999000e-01\n",
      "epoch 67 / 100 in 118.9s: loss 0.943995, train: 0.748, val 0.684, lr 3.217226e-06 mom 9.999000e-01\n",
      "epoch 68 / 100 in 119.0s: loss 0.928266, train: 0.702, val 0.675, lr 3.056365e-06 mom 9.999000e-01\n",
      "epoch 69 / 100 in 119.0s: loss 0.921137, train: 0.727, val 0.706, lr 2.903546e-06 mom 9.999000e-01\n",
      "epoch 70 / 100 in 119.0s: loss 0.916508, train: 0.750, val 0.692, lr 2.758369e-06 mom 9.999000e-01\n",
      "epoch 71 / 100 in 119.0s: loss 0.915388, train: 0.710, val 0.688, lr 2.620450e-06 mom 9.999000e-01\n",
      "epoch 72 / 100 in 119.0s: loss 0.929153, train: 0.728, val 0.682, lr 2.489428e-06 mom 9.999000e-01\n",
      "epoch 73 / 100 in 119.0s: loss 0.922188, train: 0.733, val 0.665, lr 2.364957e-06 mom 9.999000e-01\n",
      "epoch 74 / 100 in 119.0s: loss 0.906357, train: 0.729, val 0.658, lr 2.246709e-06 mom 9.999000e-01\n",
      "epoch 75 / 100 in 119.0s: loss 0.911330, train: 0.729, val 0.658, lr 2.134374e-06 mom 9.999000e-01\n",
      "epoch 76 / 100 in 119.0s: loss 0.903888, train: 0.731, val 0.673, lr 2.027655e-06 mom 9.999000e-01\n",
      "epoch 77 / 100 in 118.9s: loss 0.911090, train: 0.721, val 0.674, lr 1.926272e-06 mom 9.999000e-01\n",
      "epoch 78 / 100 in 119.0s: loss 0.898858, train: 0.728, val 0.665, lr 1.829958e-06 mom 9.999000e-01\n",
      "epoch 79 / 100 in 119.0s: loss 0.903461, train: 0.723, val 0.684, lr 1.738461e-06 mom 9.999000e-01\n",
      "epoch 80 / 100 in 119.0s: loss 0.899644, train: 0.738, val 0.685, lr 1.651538e-06 mom 9.999000e-01\n",
      "epoch 81 / 100 in 119.0s: loss 0.892099, train: 0.741, val 0.670, lr 1.568961e-06 mom 9.999000e-01\n",
      "epoch 82 / 100 in 119.0s: loss 0.888836, train: 0.753, val 0.693, lr 1.490513e-06 mom 9.999000e-01\n",
      "epoch 83 / 100 in 119.0s: loss 0.887223, train: 0.717, val 0.652, lr 1.415987e-06 mom 9.999000e-01\n",
      "epoch 84 / 100 in 119.0s: loss 0.878957, train: 0.739, val 0.667, lr 1.345188e-06 mom 9.999000e-01\n",
      "epoch 85 / 100 in 118.9s: loss 0.872763, train: 0.772, val 0.703, lr 1.277928e-06 mom 9.999000e-01\n",
      "epoch 86 / 100 in 119.0s: loss 0.861406, train: 0.748, val 0.669, lr 1.214032e-06 mom 9.999000e-01\n",
      "epoch 87 / 100 in 119.0s: loss 0.864802, train: 0.741, val 0.677, lr 1.153330e-06 mom 9.999000e-01\n",
      "epoch 88 / 100 in 119.0s: loss 0.859530, train: 0.752, val 0.700, lr 1.095664e-06 mom 9.999000e-01\n",
      "epoch 89 / 100 in 119.0s: loss 0.857563, train: 0.746, val 0.694, lr 1.040881e-06 mom 9.999000e-01\n",
      "epoch 90 / 100 in 119.0s: loss 0.844187, train: 0.750, val 0.699, lr 9.888365e-07 mom 9.999000e-01\n",
      "epoch 91 / 100 in 119.0s: loss 0.840757, train: 0.747, val 0.679, lr 9.393947e-07 mom 9.999000e-01\n",
      "epoch 92 / 100 in 119.0s: loss 0.832068, train: 0.754, val 0.688, lr 8.924250e-07 mom 9.999000e-01\n",
      "epoch 93 / 100 in 119.0s: loss 0.827147, train: 0.749, val 0.696, lr 8.478037e-07 mom 9.999000e-01\n",
      "epoch 94 / 100 in 119.0s: loss 0.817345, train: 0.748, val 0.683, lr 8.054135e-07 mom 9.999000e-01\n",
      "epoch 95 / 100 in 119.0s: loss 0.820755, train: 0.754, val 0.684, lr 7.651429e-07 mom 9.999000e-01\n",
      "epoch 96 / 100 in 119.0s: loss 0.816936, train: 0.775, val 0.698, lr 7.268857e-07 mom 9.999000e-01\n",
      "epoch 97 / 100 in 119.0s: loss 0.815445, train: 0.759, val 0.701, lr 6.905415e-07 mom 9.999000e-01\n",
      "epoch 98 / 100 in 119.0s: loss 0.795261, train: 0.779, val 0.708, lr 6.560144e-07 mom 9.999000e-01\n",
      "epoch 99 / 100 in 119.0s: loss 0.803294, train: 0.765, val 0.701, lr 6.232137e-07 mom 9.999000e-01\n",
      "epoch 100 / 100 in 119.0s: loss 0.804475, train: 0.767, val 0.703, lr 5.920530e-07 mom 9.999000e-01\n",
      "0.470 0.779017857143 0.709821428571 crp_filter_size=3 momentum_decay=0.9 num_fc=3 learning_rate=0.0001 batch_size=128 fc_num_units=256 learning_rate_decay=0.95 num_epochs=100 num_crp=3 reg=0.001 momentum=0.9 crp_num_filters=128\n"
     ]
    }
   ],
   "source": [
    "model, loss_history, train_acc_history, val_acc_history = lasagne_trainer.train(\n",
    "    network, input_var, X_train, y_train, X_val, y_val,\n",
    "    learning_rate=param['learning_rate'], num_epochs=param['num_epochs'],\n",
    "    batch_size=param['batch_size'])\n",
    "print('%.3f' % min(loss_history), max(train_acc_history), max(val_acc_history), \\\n",
    "    ' '.join('%s=%s' % (k,param[k]) for k in param))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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MVCpG9vPkQGVlF+TvSuTljDbOceuqWmyo9vTB1VvOMqKfp96xV746jLWFKcv+PLRFPjsn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\nAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f3bbdc905d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.subplot(2, 1, 1)\n",
    "plt.plot(np.array(loss_history).clip(max=3))\n",
    "plt.xlabel('iteration')\n",
    "plt.ylabel('loss')\n",
    "plt.subplot(2, 1, 2)\n",
    "plt.plot(train_acc_history)\n",
    "plt.plot(val_acc_history)\n",
    "plt.legend(['train', 'val'], loc='upper left')\n",
    "plt.xlabel('epoch')\n",
    "plt.ylabel('accuracy')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'batch_size': 128,\n",
       " 'crp_filter_size': 3,\n",
       " 'crp_num_filters': 128,\n",
       " 'fc_num_units': 256,\n",
       " 'learning_rate': 0.0001,\n",
       " 'learning_rate_decay': 0.95,\n",
       " 'momentum': 0.9,\n",
       " 'momentum_decay': 0.9,\n",
       " 'num_crp': 3,\n",
       " 'num_epochs': 100,\n",
       " 'num_fc': 3,\n",
       " 'reg': 0.001}"
      ]
     },
     "execution_count": 121,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pickle\n",
    "with open('v1.pickle', 'wb') as f:\n",
    "    pickle.dump(model, f, -1)\n",
    "\n",
    "param"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# v3: [conv-relu-conv-relu-pool]xN - [affine]xM - [softmax or SVM]\n",
    "\n",
    "# VGG-ish\n",
    "* input: 32x32x3\n",
    "* CONV3-64: 32x32x64\n",
    "* CONV3-64: 32x32x64\n",
    "* POOL2: 16x16x64\n",
    "* CONV3-128: 16x16x128\n",
    "* CONV3-128: 16x16x128\n",
    "* POOL2: 8x8x128\n",
    "* FC: 1x1x512\n",
    "* FC: 1x1x512\n",
    "* FC: 1x1x10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def create_v3(input_var, input_shape=(3, 32, 32),\n",
    "              ccp_num_filters=[64, 128], ccp_filter_size=3,\n",
    "              fc_num_units=[128, 128], num_classes=10,\n",
    "              **junk):\n",
    "    # input layer\n",
    "    network = lasagne.layers.InputLayer(shape=(None,) + input_shape,\n",
    "                                        input_var=input_var)\n",
    "    # conv-relu-conv-relu-pool layers\n",
    "    for num_filters in ccp_num_filters:\n",
    "        network = lasagne.layers.Conv2DLayer(\n",
    "            network, num_filters=num_filters,\n",
    "            filter_size=(ccp_filter_size, ccp_filter_size),\n",
    "            pad='same',\n",
    "            nonlinearity=lasagne.nonlinearities.rectify,\n",
    "            W=lasagne.init.GlorotUniform(gain='relu'))\n",
    "        network = lasagne.layers.Conv2DLayer(\n",
    "            network, num_filters=num_filters,\n",
    "            filter_size=(ccp_filter_size, ccp_filter_size),\n",
    "            pad='same',\n",
    "            nonlinearity=lasagne.nonlinearities.rectify,\n",
    "            W=lasagne.init.GlorotUniform(gain='relu'))\n",
    "        network = lasagne.layers.MaxPool2DLayer(network, pool_size=(2, 2))\n",
    "    # fc-relu\n",
    "    for num_units in fc_num_units:\n",
    "        network = lasagne.layers.DenseLayer(\n",
    "            lasagne.layers.dropout(network, p=.5),\n",
    "            num_units=num_units,\n",
    "            nonlinearity=lasagne.nonlinearities.rectify,\n",
    "            W=lasagne.init.GlorotUniform(gain='relu'))\n",
    "    # output layer\n",
    "    network = lasagne.layers.DenseLayer(\n",
    "        lasagne.layers.dropout(network, p=.5),\n",
    "        num_units=num_classes,\n",
    "        nonlinearity=lasagne.nonlinearities.softmax)\n",
    "    return network"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "\n",
    "param = dict(ccp_num_filters=[64, 128], ccp_filter_size=3,\n",
    "             fc_num_units=[256, 256], num_classes=10,\n",
    "             learning_rate=1e-2, learning_rate_decay=0.5,\n",
    "             momentum=0.9, momentum_decay=0.5,\n",
    "             decay_after_epochs=10,\n",
    "             batch_size=128, num_epochs=50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Compiling...\n",
      "Training...\n",
      "epoch 1 / 30 in 0.8s: loss 3.247790, train: 0.160, val 0.110, lr 1.000000e-02 mom 9.000000e-01\n",
      "epoch 2 / 30 in 0.8s: loss 2.458449, train: 0.160, val 0.119, lr 1.000000e-02 mom 9.000000e-01\n",
      "epoch 3 / 30 in 0.8s: loss 2.399592, train: 0.180, val 0.088, lr 1.000000e-02 mom 9.000000e-01\n",
      "epoch 4 / 30 in 0.8s: loss 2.211803, train: 0.160, val 0.079, lr 9.500000e-03 mom 9.050000e-01\n",
      "epoch 5 / 30 in 0.8s: loss 2.236917, train: 0.200, val 0.122, lr 9.500000e-03 mom 9.050000e-01\n",
      "epoch 6 / 30 in 0.8s: loss 2.406256, train: 0.300, val 0.156, lr 9.500000e-03 mom 9.050000e-01\n",
      "epoch 7 / 30 in 0.8s: loss 2.243320, train: 0.360, val 0.162, lr 9.025000e-03 mom 9.097500e-01\n",
      "epoch 8 / 30 in 0.8s: loss 2.254551, train: 0.320, val 0.158, lr 9.025000e-03 mom 9.097500e-01\n",
      "epoch 9 / 30 in 0.8s: loss 2.170130, train: 0.320, val 0.145, lr 9.025000e-03 mom 9.097500e-01\n",
      "epoch 10 / 30 in 0.8s: loss 2.164701, train: 0.360, val 0.126, lr 8.573750e-03 mom 9.142625e-01\n",
      "epoch 11 / 30 in 0.8s: loss 2.091385, train: 0.340, val 0.153, lr 8.573750e-03 mom 9.142625e-01\n",
      "epoch 12 / 30 in 0.8s: loss 2.147110, train: 0.280, val 0.151, lr 8.573750e-03 mom 9.142625e-01\n",
      "epoch 13 / 30 in 0.8s: loss 2.098304, train: 0.380, val 0.156, lr 8.145062e-03 mom 9.185494e-01\n",
      "epoch 14 / 30 in 0.8s: loss 1.919416, train: 0.460, val 0.159, lr 8.145062e-03 mom 9.185494e-01\n",
      "epoch 15 / 30 in 0.8s: loss 1.965349, train: 0.420, val 0.151, lr 8.145062e-03 mom 9.185494e-01\n",
      "epoch 16 / 30 in 0.8s: loss 2.081868, train: 0.480, val 0.167, lr 7.737809e-03 mom 9.226219e-01\n",
      "epoch 17 / 30 in 0.8s: loss 1.955984, train: 0.560, val 0.183, lr 7.737809e-03 mom 9.226219e-01\n",
      "epoch 18 / 30 in 0.8s: loss 1.939489, train: 0.580, val 0.161, lr 7.737809e-03 mom 9.226219e-01\n",
      "epoch 19 / 30 in 0.8s: loss 2.001198, train: 0.560, val 0.157, lr 7.350919e-03 mom 9.264908e-01\n",
      "epoch 20 / 30 in 0.8s: loss 1.742004, train: 0.540, val 0.179, lr 7.350919e-03 mom 9.264908e-01\n",
      "epoch 21 / 30 in 0.8s: loss 1.729675, train: 0.640, val 0.173, lr 7.350919e-03 mom 9.264908e-01\n",
      "epoch 22 / 30 in 0.8s: loss 1.593313, train: 0.560, val 0.197, lr 6.983373e-03 mom 9.301662e-01\n",
      "epoch 23 / 30 in 0.8s: loss 1.687712, train: 0.700, val 0.185, lr 6.983373e-03 mom 9.301662e-01\n",
      "epoch 24 / 30 in 0.8s: loss 1.387340, train: 0.640, val 0.172, lr 6.983373e-03 mom 9.301662e-01\n",
      "epoch 25 / 30 in 0.8s: loss 1.491013, train: 0.780, val 0.188, lr 6.634204e-03 mom 9.336579e-01\n",
      "epoch 26 / 30 in 0.8s: loss 1.406746, train: 0.820, val 0.204, lr 6.634204e-03 mom 9.336579e-01\n",
      "epoch 27 / 30 in 0.8s: loss 1.489491, train: 0.740, val 0.183, lr 6.634204e-03 mom 9.336579e-01\n",
      "epoch 28 / 30 in 0.8s: loss 1.459224, train: 0.720, val 0.154, lr 6.302494e-03 mom 9.369751e-01\n",
      "epoch 29 / 30 in 0.8s: loss 1.486630, train: 0.540, val 0.148, lr 6.302494e-03 mom 9.369751e-01\n",
      "epoch 30 / 30 in 0.8s: loss 1.628676, train: 0.680, val 0.191, lr 6.302494e-03 mom 9.369751e-01\n"
     ]
    }
   ],
   "source": [
    "network = create_v3(input_var, **param)\n",
    "# overfit\n",
    "xxx, loss_history, train_acc_history, val_acc_history = lasagne_trainer.train(\n",
    "          network, input_var, X_train[:50], y_train[:50], X_val, y_val,\n",
    "          learning_rate=1e-2, batch_size=10, num_epochs=30, decay_after_epochs=3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Compiling...\n",
      "Training...\n",
      "epoch 1 / 50 in 88.7s: loss 1.820408, train: 0.460, val 0.493, lr 1.000000e-02 mom 9.000000e-01\n",
      "epoch 2 / 50 in 88.7s: loss 1.497272, train: 0.546, val 0.562, lr 1.000000e-02 mom 9.000000e-01\n",
      "epoch 3 / 50 in 88.7s: loss 1.332127, train: 0.622, val 0.623, lr 1.000000e-02 mom 9.000000e-01\n",
      "epoch 4 / 50 in 88.7s: loss 1.191845, train: 0.634, val 0.666, lr 1.000000e-02 mom 9.000000e-01\n",
      "epoch 5 / 50 in 88.7s: loss 1.086699, train: 0.684, val 0.703, lr 1.000000e-02 mom 9.000000e-01\n",
      "epoch 6 / 50 in 88.7s: loss 1.004664, train: 0.710, val 0.721, lr 1.000000e-02 mom 9.000000e-01\n",
      "epoch 7 / 50 in 88.7s: loss 0.951341, train: 0.718, val 0.728, lr 1.000000e-02 mom 9.000000e-01\n",
      "epoch 8 / 50 in 88.7s: loss 0.906197, train: 0.734, val 0.735, lr 1.000000e-02 mom 9.000000e-01\n",
      "epoch 9 / 50 in 88.7s: loss 0.861574, train: 0.771, val 0.748, lr 1.000000e-02 mom 9.000000e-01\n",
      "epoch 10 / 50 in 88.7s: loss 0.823561, train: 0.772, val 0.748, lr 1.000000e-02 mom 9.000000e-01\n",
      "epoch 11 / 50 in 88.7s: loss 0.794733, train: 0.795, val 0.767, lr 5.000000e-03 mom 9.500000e-01\n",
      "epoch 12 / 50 in 88.7s: loss 0.759413, train: 0.791, val 0.776, lr 5.000000e-03 mom 9.500000e-01\n",
      "epoch 13 / 50 in 88.7s: loss 0.734678, train: 0.815, val 0.788, lr 5.000000e-03 mom 9.500000e-01\n",
      "epoch 14 / 50 in 88.7s: loss 0.707664, train: 0.817, val 0.795, lr 5.000000e-03 mom 9.500000e-01\n",
      "epoch 15 / 50 in 88.7s: loss 0.685139, train: 0.828, val 0.782, lr 5.000000e-03 mom 9.500000e-01\n",
      "epoch 16 / 50 in 88.7s: loss 0.667930, train: 0.846, val 0.791, lr 5.000000e-03 mom 9.500000e-01\n",
      "epoch 17 / 50 in 88.7s: loss 0.644620, train: 0.855, val 0.801, lr 5.000000e-03 mom 9.500000e-01\n",
      "epoch 18 / 50 in 88.7s: loss 0.620334, train: 0.857, val 0.795, lr 5.000000e-03 mom 9.500000e-01\n",
      "epoch 19 / 50 in 88.7s: loss 0.605781, train: 0.855, val 0.797, lr 5.000000e-03 mom 9.500000e-01\n",
      "epoch 20 / 50 in 88.8s: loss 0.592860, train: 0.864, val 0.811, lr 5.000000e-03 mom 9.500000e-01\n",
      "epoch 21 / 50 in 88.7s: loss 0.579383, train: 0.863, val 0.809, lr 2.500000e-03 mom 9.750000e-01\n",
      "epoch 22 / 50 in 88.7s: loss 0.553778, train: 0.864, val 0.802, lr 2.500000e-03 mom 9.750000e-01\n",
      "epoch 23 / 50 in 88.7s: loss 0.540664, train: 0.886, val 0.814, lr 2.500000e-03 mom 9.750000e-01\n",
      "epoch 24 / 50 in 88.8s: loss 0.520104, train: 0.894, val 0.820, lr 2.500000e-03 mom 9.750000e-01\n",
      "epoch 25 / 50 in 88.7s: loss 0.510712, train: 0.897, val 0.808, lr 2.500000e-03 mom 9.750000e-01\n",
      "epoch 26 / 50 in 88.7s: loss 0.498141, train: 0.894, val 0.809, lr 2.500000e-03 mom 9.750000e-01\n",
      "epoch 27 / 50 in 88.7s: loss 0.490399, train: 0.901, val 0.819, lr 2.500000e-03 mom 9.750000e-01\n",
      "epoch 28 / 50 in 88.8s: loss 0.468590, train: 0.900, val 0.824, lr 2.500000e-03 mom 9.750000e-01\n",
      "epoch 29 / 50 in 88.7s: loss 0.462874, train: 0.920, val 0.816, lr 2.500000e-03 mom 9.750000e-01\n",
      "epoch 30 / 50 in 88.8s: loss 0.445270, train: 0.925, val 0.827, lr 2.500000e-03 mom 9.750000e-01\n",
      "epoch 31 / 50 in 88.8s: loss 0.446132, train: 0.930, val 0.828, lr 1.250000e-03 mom 9.875000e-01\n",
      "epoch 32 / 50 in 88.8s: loss 0.423588, train: 0.919, val 0.811, lr 1.250000e-03 mom 9.875000e-01\n",
      "epoch 33 / 50 in 88.7s: loss 0.412946, train: 0.930, val 0.829, lr 1.250000e-03 mom 9.875000e-01\n",
      "epoch 34 / 50 in 88.7s: loss 0.401293, train: 0.939, val 0.821, lr 1.250000e-03 mom 9.875000e-01\n",
      "epoch 35 / 50 in 88.7s: loss 0.389699, train: 0.942, val 0.828, lr 1.250000e-03 mom 9.875000e-01\n",
      "epoch 36 / 50 in 88.8s: loss 0.386479, train: 0.943, val 0.821, lr 1.250000e-03 mom 9.875000e-01\n",
      "epoch 37 / 50 in 88.8s: loss 0.381396, train: 0.940, val 0.845, lr 1.250000e-03 mom 9.875000e-01\n",
      "epoch 38 / 50 in 88.7s: loss 0.368441, train: 0.952, val 0.838, lr 1.250000e-03 mom 9.875000e-01\n",
      "epoch 39 / 50 in 88.7s: loss 0.358264, train: 0.953, val 0.831, lr 1.250000e-03 mom 9.875000e-01\n",
      "epoch 40 / 50 in 88.7s: loss 0.358964, train: 0.961, val 0.830, lr 1.250000e-03 mom 9.875000e-01\n",
      "epoch 41 / 50 in 88.8s: loss 0.366442, train: 0.965, val 0.842, lr 6.250000e-04 mom 9.937500e-01\n",
      "epoch 42 / 50 in 88.8s: loss 0.337541, train: 0.956, val 0.830, lr 6.250000e-04 mom 9.937500e-01\n",
      "epoch 43 / 50 in 88.8s: loss 0.333747, train: 0.967, val 0.825, lr 6.250000e-04 mom 9.937500e-01\n",
      "epoch 44 / 50 in 88.8s: loss 0.329299, train: 0.965, val 0.835, lr 6.250000e-04 mom 9.937500e-01\n",
      "epoch 45 / 50 in 88.8s: loss 0.319019, train: 0.964, val 0.831, lr 6.250000e-04 mom 9.937500e-01\n",
      "epoch 46 / 50 in 88.7s: loss 0.307978, train: 0.970, val 0.848, lr 6.250000e-04 mom 9.937500e-01\n",
      "epoch 47 / 50 in 88.8s: loss 0.305475, train: 0.974, val 0.829, lr 6.250000e-04 mom 9.937500e-01\n",
      "epoch 48 / 50 in 88.7s: loss 0.299597, train: 0.979, val 0.840, lr 6.250000e-04 mom 9.937500e-01\n",
      "epoch 49 / 50 in 88.8s: loss 0.288366, train: 0.975, val 0.839, lr 6.250000e-04 mom 9.937500e-01\n",
      "epoch 50 / 50 in 88.7s: loss 0.278476, train: 0.980, val 0.836, lr 6.250000e-04 mom 9.937500e-01\n",
      "0.105 0.979910714286 0.848214285714 learning_rate=0.01 ccp_num_filters=[64, 128] batch_size=128 decay_after_epochs=10 fc_num_units=[256, 256] momentum=0.9 ccp_filter_size=3 num_epochs=50 momentum_decay=0.5 num_classes=10 learning_rate_decay=0.5\n"
     ]
    }
   ],
   "source": [
    "model, loss_history, train_acc_history, val_acc_history = lasagne_trainer.train(\n",
    "    network, input_var, X_train, y_train, X_val, y_val,\n",
    "    learning_rate=param['learning_rate'], learning_rate_decay=param['learning_rate_decay'],\n",
    "    momentum=param['momentum'], momentum_decay=param['momentum_decay'],\n",
    "    decay_after_epochs=param['decay_after_epochs'],\n",
    "    batch_size=param['batch_size'], num_epochs=param['num_epochs'],\n",
    "    save_path='net_v3')\n",
    "print('%.3f' % min(loss_history), max(train_acc_history), max(val_acc_history), \\\n",
    "    ' '.join('%s=%s' % (k,param[k]) for k in param))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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conI6B+rQqFX836s/ubiUoqMoKru8aaJra2gUw6UoNhhRFIUfDzQ9O+MzH9sC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\nP3T9MQ81GFViAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fbe50572490>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.subplot(2, 1, 1)\n",
    "plt.plot(np.array(loss_history).clip(max=3))\n",
    "plt.xlabel('iteration')\n",
    "plt.ylabel('loss')\n",
    "plt.subplot(2, 1, 2)\n",
    "plt.plot(train_acc_history)\n",
    "plt.plot(val_acc_history)\n",
    "plt.legend(['train', 'val'], loc='upper left')\n",
    "plt.xlabel('epoch')\n",
    "plt.ylabel('accuracy')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}