gan_imaging_cifar-10_classifier.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "# Training of the recognition network that forms the feature extractor of the encoding model\n",
    "---"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import sys\n",
    "import os\n",
    "import struct\n",
    "import time\n",
    "import numpy as np\n",
    "import h5py\n",
    "import scipy.io as sio\n",
    "from scipy import ndimage as nd\n",
    "from scipy import misc\n",
    "from tqdm import tqdm\n",
    "import pickle\n",
    "import math\n",
    "import matplotlib.pyplot as plt\n",
    "import PIL.Image as pim\n",
    "import seaborn as sns\n",
    "%matplotlib inline\n",
    "\n",
    "import theano\n",
    "import theano.tensor as T\n",
    "\n",
    "import lasagne\n",
    "import lasagne.layers as L\n",
    "import lasagne.regularization as R\n",
    "import lasagne.nonlinearities as NL\n",
    "import lasagne.objectives as O\n",
    "import lasagne.init as I\n",
    "\n",
    "import fwrf.src.numpy_utility as pnu\n",
    "import fwrf.src.lasagne_utility as plu\n",
    "from fwrf.src.load_data import load_stimuli, load_voxels\n",
    "from src.load_images import load_cifar10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "theano floatX: float32\n",
      "numpy floatX: <type 'numpy.float32'>\n"
     ]
    }
   ],
   "source": [
    "fpX = np.float32\n",
    "print \"theano floatX: %s\" % theano.config.floatX\n",
    "print \"numpy floatX: %s\" % fpX\n",
    "\n",
    "sns.axes_style()\n",
    "sns.set_style(\"whitegrid\", {\"axes.facecolor\": '.95'})\n",
    "sns.set_context(\"notebook\", rc={'axes.labelsize': 18.0, 'axes.titlesize': 24.0, 'legend.fontsize': 18.0, 'xtick.labelsize': 18.0, 'ytick.labelsize': 18.0})\n",
    "sns.set_palette(\"deep\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Current dir: /home/styvesg/Documents/PostDoc/GitRepo/gan-imaging/\n",
      "Time Stamp: Jul-05-2018_1727\n"
     ]
    }
   ],
   "source": [
    "cwd = os.getcwd() + '/'\n",
    "output_dir  = cwd + \"output/\"\n",
    "if not os.path.exists(output_dir):\n",
    "    os.makedirs(output_dir)\n",
    "timestamp = time.strftime('%b-%d-%Y_%H%M', time.localtime())\n",
    "\n",
    "print \"Current dir: %s\" % cwd\n",
    "print \"Time Stamp: %s\" % timestamp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "saveext = \".pdf\"\n",
    "savearg = {'format':'pdf', 'dpi': 200}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 1) Load CIFAR-10 dataset "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "cifar_root = \"/home/styvesg/Documents/PostDoc/Datasets/cifar-10/\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "loading data_batch_1...\n",
      "loading data_batch_2...\n",
      "loading data_batch_3...\n",
      "loading data_batch_4...\n",
      "loading data_batch_5...\n",
      "loading test_batch...\n"
     ]
    }
   ],
   "source": [
    "input_data, label_data, label_map, input_trn_size = load_cifar10(cifar_root)\n",
    "\n",
    "input_data_size = len(input_data)\n",
    "input_trn_size = input_data_size - 500\n",
    "val_size = input_data_size - input_trn_size\n",
    "\n",
    "input_data -= 0.5\n",
    "trn_input_data = input_data[:input_trn_size]\n",
    "val_input_data = input_data[input_trn_size:] \n",
    "\n",
    "trn_label_data = label_data[:input_trn_size]\n",
    "val_label_data = label_data[input_trn_size:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-0.5\n",
      "(59500, 3, 32, 32)\n",
      "59500\n"
     ]
    }
   ],
   "source": [
    "print np.min(trn_input_data)\n",
    "print trn_input_data.shape\n",
    "print input_trn_size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['airplane', 'automobile', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse', 'ship', 'truck']\n"
     ]
    }
   ],
   "source": [
    "print label_map"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3 32 3072 10\n"
     ]
    }
   ],
   "source": [
    "npc = input_data.shape[1]         # # of channels in image\n",
    "npx = input_data.shape[2]         # # of pixels width/height of images \n",
    "nx = npx*npx*npc   # # of dimensions in X\n",
    "ny = len(label_map)   # # of classes\n",
    "print npc, npx, nx, ny"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
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c6DzI5Xh+jlUVsjMkrAFAqiZU0kMyFPG4YGVjM1ao+pKRsjWmd0ko73SgCfRa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PAAAg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\ng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4WLDAvFXRRdNgKgQEIw1hoAHSbm2iCcmB5O9kD4yNRGOSOVUGMEbseEgXOHbRBcFZJT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\nFy5cuHBxrsP9kLlw4cKFi3Md/x9RrjgpfwSLuQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f11df7db2d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,6))\n",
    "plt.imshow(pnu.mosaic_vis(input_data[:20,...].transpose(0,2,3,1), pad=2), interpolation='None')\n",
    "plt.gca().get_xaxis().set_visible(False)\n",
    "plt.gca().get_yaxis().set_visible(False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2)  Define and train the classifier\n",
    "This may not be entirely necessary. There are dozens of trained classification networks out there and we don't need this net to be of the same input size or trained on the same set as we would the GAN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Layer\t: <lasagne.layers.input.InputLayer object at 0x7f11c4109310>\n",
      "Name\t: X\n",
      "Type\t: <class 'lasagne.layers.input.InputLayer'>\n",
      "Shape\t: (None, 3, 32, 32)\n",
      "\n",
      "\n",
      "Layer\t: <lasagne.layers.conv.Conv2DLayer object at 0x7f11c40ff990>\n",
      "Name\t: None\n",
      "Type\t: <class 'lasagne.layers.conv.Conv2DLayer'>\n",
      "Shape\t: (None, 128, 16, 16)\n",
      "Params\n",
      "        |-- W         : (128, 3, 4, 4)\n",
      "        |-- b         : (128,)\n",
      "\n",
      "\n",
      "Layer\t: <lasagne.layers.conv.Conv2DLayer object at 0x7f11c40f57d0>\n",
      "Name\t: None\n",
      "Type\t: <class 'lasagne.layers.conv.Conv2DLayer'>\n",
      "Shape\t: (None, 256, 8, 8)\n",
      "Params\n",
      "        |-- W         : (256, 128, 4, 4)\n",
      "\n",
      "\n",
      "Layer\t: <lasagne.layers.normalization.BatchNormLayer object at 0x7f11c475c6d0>\n",
      "Name\t: None\n",
      "Type\t: <class 'lasagne.layers.normalization.BatchNormLayer'>\n",
      "Shape\t: (None, 256, 8, 8)\n",
      "Params\n",
      "        |-- mean      : (256,)\n",
      "        |-- inv_std   : (256,)\n",
      "\n",
      "\n",
      "Layer\t: <lasagne.layers.conv.Conv2DLayer object at 0x7f11c4003550>\n",
      "Name\t: None\n",
      "Type\t: <class 'lasagne.layers.conv.Conv2DLayer'>\n",
      "Shape\t: (None, 384, 6, 6)\n",
      "Params\n",
      "        |-- W         : (384, 256, 3, 3)\n",
      "\n",
      "\n",
      "Layer\t: <lasagne.layers.normalization.BatchNormLayer object at 0x7f11c4003510>\n",
      "Name\t: None\n",
      "Type\t: <class 'lasagne.layers.normalization.BatchNormLayer'>\n",
      "Shape\t: (None, 384, 6, 6)\n",
      "Params\n",
      "        |-- mean      : (384,)\n",
      "        |-- inv_std   : (384,)\n",
      "\n",
      "\n",
      "Layer\t: <lasagne.layers.conv.Conv2DLayer object at 0x7f11c4003b50>\n",
      "Name\t: None\n",
      "Type\t: <class 'lasagne.layers.conv.Conv2DLayer'>\n",
      "Shape\t: (None, 512, 4, 4)\n",
      "Params\n",
      "        |-- W         : (512, 384, 3, 3)\n",
      "\n",
      "\n",
      "Layer\t: <lasagne.layers.normalization.BatchNormLayer object at 0x7f11c4003b10>\n",
      "Name\t: None\n",
      "Type\t: <class 'lasagne.layers.normalization.BatchNormLayer'>\n",
      "Shape\t: (None, 512, 4, 4)\n",
      "Params\n",
      "        |-- mean      : (512,)\n",
      "        |-- inv_std   : (512,)\n",
      "\n",
      "\n",
      "Layer\t: <lasagne.layers.conv.Conv2DLayer object at 0x7f11c400a190>\n",
      "Name\t: None\n",
      "Type\t: <class 'lasagne.layers.conv.Conv2DLayer'>\n",
      "Shape\t: (None, 1024, 4, 4)\n",
      "Params\n",
      "        |-- W         : (1024, 512, 1, 1)\n",
      "\n",
      "\n",
      "Layer\t: <lasagne.layers.normalization.BatchNormLayer object at 0x7f11c400a150>\n",
      "Name\t: None\n",
      "Type\t: <class 'lasagne.layers.normalization.BatchNormLayer'>\n",
      "Shape\t: (None, 1024, 4, 4)\n",
      "Params\n",
      "        |-- mean      : (1024,)\n",
      "        |-- inv_std   : (1024,)\n",
      "\n",
      "\n",
      "Layer\t: <lasagne.layers.dense.DenseLayer object at 0x7f11c400a810>\n",
      "Name\t: None\n",
      "Type\t: <class 'lasagne.layers.dense.DenseLayer'>\n",
      "Shape\t: (None, 10)\n",
      "Params\n",
      "        |-- W         : (1024, 10)\n",
      "        |-- b         : (10,)\n",
      "\n",
      "\n",
      "128c4s2_256c4s2_384c3_512c3_1024c1_gp_10f\n"
     ]
    }
   ],
   "source": [
    "from net.cifar10_definitions import cls_net, cls_net_str\n",
    "\n",
    "_X = T.tensor4()\n",
    "_Y = T.matrix()\n",
    "\n",
    "cls_input = L.InputLayer(shape=(None, npc, npx, npx), input_var=_X, name='X')\n",
    "aux, cls = cls_net(cls_input)\n",
    "\n",
    "plu.print_lasagne_network(cls, skipnoparam=True)\n",
    "print cls_net_str"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[W, b, W, W, W, W, W, b]\n",
      "COMPILING\n",
      "297.90 seconds to compile theano functions\n"
     ]
    }
   ],
   "source": [
    "_lr = theano.shared(fpX(0.0))\n",
    "_l2 = theano.shared(fpX(0.0))\n",
    "\n",
    "_cls_reg = _l2 * R.regularize_layer_params(cls, R.l2)\n",
    "cls_params = L.get_all_params(cls, trainable=True)\n",
    "print cls_params\n",
    "###\n",
    "_cls_trn_pred = L.get_output(cls, deterministic=False)\n",
    "#_cls_trn_loss = O.squared_error(_cls_trn_pred, Y).mean() + _cls_reg\n",
    "_cls_trn_loss = O.categorical_crossentropy(_cls_trn_pred, _Y).mean() + _cls_reg\n",
    "\n",
    "_cls_val_pred = L.get_output(cls, deterministic=True)\n",
    "_cls_val_loss = O.categorical_crossentropy(_cls_val_pred, _Y).mean()\n",
    "_cls_val_acc = O.categorical_accuracy(_cls_val_pred, _Y, top_k=1).mean()\n",
    "#T.mean(T.eq(T.argmax(_cls_val_pred, axis=1), T.argmax(Y, axis=1)), dtype=theano.config.floatX)\n",
    "\n",
    "_class = T.extra_ops.to_one_hot(T.argmax(_cls_val_pred, axis=1), ny)\n",
    "_fmaps = [L.get_output(a, deterministic=True) for a in aux]\n",
    "\n",
    "cls_updates = lasagne.updates.adam(_cls_trn_loss, cls_params, learning_rate=_lr, beta1=0.5, epsilon=1e-12) \n",
    "\n",
    "print 'COMPILING'\n",
    "t = time.time()\n",
    "cls_trn_fn = theano.function([_X, _Y], [_cls_trn_loss, _cls_val_acc], updates=cls_updates)\n",
    "cls_val_fn = theano.function([_X, _Y], [_cls_val_loss, _cls_val_acc])\n",
    "cls_pred_fn = theano.function([_X], _fmaps+[_class,])\n",
    "print '%.2f seconds to compile theano functions'%(time.time()-t)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Classifier network training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100it [00:26,  3.76it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  Epoch 1 of 40 took 26.911s\n",
      "  training loss:       0.480837\n",
      "  validation loss:     0.585736\n",
      "  training accuracy:   88.14 %\n",
      "  validation accuracy: 80.34 %\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "100it [00:26,  3.88it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  Epoch 2 of 40 took 26.806s\n",
      "  training loss:       0.472993\n",
      "  validation loss:     0.608267\n",
      "  training accuracy:   89.80 %\n",
      "  validation accuracy: 79.94 %\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "100it [00:26,  3.84it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  Epoch 3 of 40 took 26.989s\n",
      "  training loss:       0.459238\n",
      "  validation loss:     0.616793\n",
      "  training accuracy:   90.79 %\n",
      "  validation accuracy: 79.85 %\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "100it [00:26,  3.87it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  Epoch 4 of 40 took 26.836s\n",
      "  training loss:       0.458018\n",
      "  validation loss:     0.541495\n",
      "  training accuracy:   90.86 %\n",
      "  validation accuracy: 82.01 %\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "100it [00:26,  3.84it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  Epoch 5 of 40 took 26.779s\n",
      "  training loss:       0.454392\n",
      "  validation loss:     0.557194\n",
      "  training accuracy:   90.99 %\n",
      "  validation accuracy: 81.24 %\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "100it [00:26,  3.84it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  Epoch 6 of 40 took 26.812s\n",
      "  training loss:       0.442408\n",
      "  validation loss:     0.567626\n",
      "  training accuracy:   91.45 %\n",
      "  validation accuracy: 80.83 %\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "100it [00:26,  3.85it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  Epoch 7 of 40 took 26.843s\n",
      "  training loss:       0.435897\n",
      "  validation loss:     0.566084\n",
      "  training accuracy:   91.68 %\n",
      "  validation accuracy: 81.09 %\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "100it [00:26,  3.86it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  Epoch 8 of 40 took 26.921s\n",
      "  training loss:       0.427200\n",
      "  validation loss:     0.565376\n",
      "  training accuracy:   92.09 %\n",
      "  validation accuracy: 80.85 %\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "100it [00:26,  3.85it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  Epoch 9 of 40 took 27.020s\n",
      "  training loss:       0.422682\n",
      "  validation loss:     0.594782\n",
      "  training accuracy:   92.47 %\n",
      "  validation accuracy: 80.72 %\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "100it [00:26,  3.78it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  Epoch 10 of 40 took 26.937s\n",
      "  training loss:       0.412512\n",
      "  validation loss:     0.535231\n",
      "  training accuracy:   92.91 %\n",
      "  validation accuracy: 82.05 %\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "100it [00:26,  3.81it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  Epoch 11 of 40 took 26.964s\n",
      "  training loss:       0.404839\n",
      "  validation loss:     0.551433\n",
      "  training accuracy:   92.96 %\n",
      "  validation accuracy: 81.96 %\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "100it [00:26,  3.83it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  Epoch 12 of 40 took 27.142s\n",
      "  training loss:       0.404173\n",
      "  validation loss:     0.568194\n",
      "  training accuracy:   93.42 %\n",
      "  validation accuracy: 81.11 %\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "100it [00:26,  3.79it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  Epoch 13 of 40 took 27.270s\n",
      "  training loss:       0.385208\n",
      "  validation loss:     0.539126\n",
      "  training accuracy:   93.61 %\n",
      "  validation accuracy: 82.27 %\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "100it [00:26,  3.79it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  Epoch 14 of 40 took 27.038s\n",
      "  training loss:       0.368383\n",
      "  validation loss:     0.553293\n",
      "  training accuracy:   94.76 %\n",
      "  validation accuracy: 82.08 %\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "100it [00:26,  3.78it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  Epoch 15 of 40 took 26.964s\n",
      "  training loss:       0.343115\n",
      "  validation loss:     0.527688\n",
      "  training accuracy:   95.21 %\n",
      "  validation accuracy: 82.52 %\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "100it [00:26,  3.86it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  Epoch 16 of 40 took 26.943s\n",
      "  training loss:       0.331046\n",
      "  validation loss:     0.514987\n",
      "  training accuracy:   95.81 %\n",
      "  validation accuracy: 83.18 %\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "100it [00:26,  3.85it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  Epoch 17 of 40 took 26.918s\n",
      "  training loss:       0.318485\n",
      "  validation loss:     0.522257\n",
      "  training accuracy:   96.26 %\n",
      "  validation accuracy: 83.07 %\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "100it [00:26,  3.84it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  Epoch 18 of 40 took 26.925s\n",
      "  training loss:       0.303181\n",
      "  validation loss:     0.503879\n",
      "  training accuracy:   96.40 %\n",
      "  validation accuracy: 83.79 %\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "100it [00:26,  3.72it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  Epoch 19 of 40 took 27.013s\n",
      "  training loss:       0.296602\n",
      "  validation loss:     0.533449\n",
      "  training accuracy:   96.93 %\n",
      "  validation accuracy: 82.95 %\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "100it [00:26,  3.78it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  Epoch 20 of 40 took 26.981s\n",
      "  training loss:       0.288337\n",
      "  validation loss:     0.504990\n",
      "  training accuracy:   97.19 %\n",
      "  validation accuracy: 83.88 %\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "100it [00:26,  3.82it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  Epoch 21 of 40 took 26.957s\n",
      "  training loss:       0.278838\n",
      "  validation loss:     0.491878\n",
      "  training accuracy:   97.53 %\n",
      "  validation accuracy: 83.96 %\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "100it [00:26,  3.79it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  Epoch 22 of 40 took 27.343s\n",
      "  training loss:       0.266951\n",
      "  validation loss:     0.501672\n",
      "  training accuracy:   97.80 %\n",
      "  validation accuracy: 84.02 %\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "100it [00:26,  3.85it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  Epoch 23 of 40 took 26.909s\n",
      "  training loss:       0.263210\n",
      "  validation loss:     0.505271\n",
      "  training accuracy:   97.92 %\n",
      "  validation accuracy: 83.88 %\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "100it [00:26,  3.81it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  Epoch 24 of 40 took 26.989s\n",
      "  training loss:       0.259075\n",
      "  validation loss:     0.495569\n",
      "  training accuracy:   98.08 %\n",
      "  validation accuracy: 84.17 %\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "100it [00:26,  3.76it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  Epoch 25 of 40 took 27.076s\n",
      "  training loss:       0.251973\n",
      "  validation loss:     0.513756\n",
      "  training accuracy:   98.21 %\n",
      "  validation accuracy: 83.72 %\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "100it [00:26,  3.76it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  Epoch 26 of 40 took 27.040s\n",
      "  training loss:       0.247583\n",
      "  validation loss:     0.489776\n",
      "  training accuracy:   98.38 %\n",
      "  validation accuracy: 84.24 %\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "100it [00:26,  3.83it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  Epoch 27 of 40 took 27.286s\n",
      "  training loss:       0.242590\n",
      "  validation loss:     0.482062\n",
      "  training accuracy:   98.49 %\n",
      "  validation accuracy: 84.36 %\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "100it [00:26,  3.80it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  Epoch 28 of 40 took 26.926s\n",
      "  training loss:       0.240342\n",
      "  validation loss:     0.492320\n",
      "  training accuracy:   98.55 %\n",
      "  validation accuracy: 84.27 %\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "100it [00:26,  3.85it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  Epoch 29 of 40 took 27.123s\n",
      "  training loss:       0.229794\n",
      "  validation loss:     0.496075\n",
      "  training accuracy:   98.59 %\n",
      "  validation accuracy: 84.27 %\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "100it [00:26,  3.82it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  Epoch 30 of 40 took 26.892s\n",
      "  training loss:       0.227848\n",
      "  validation loss:     0.487707\n",
      "  training accuracy:   98.71 %\n",
      "  validation accuracy: 84.42 %\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "100it [00:26,  3.79it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  Epoch 31 of 40 took 27.020s\n",
      "  training loss:       0.224621\n",
      "  validation loss:     0.490056\n",
      "  training accuracy:   98.73 %\n",
      "  validation accuracy: 84.38 %\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "100it [00:26,  3.77it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  Epoch 32 of 40 took 26.995s\n",
      "  training loss:       0.218905\n",
      "  validation loss:     0.488314\n",
      "  training accuracy:   98.82 %\n",
      "  validation accuracy: 84.54 %\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "100it [00:26,  3.81it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  Epoch 33 of 40 took 26.976s\n",
      "  training loss:       0.219201\n",
      "  validation loss:     0.488742\n",
      "  training accuracy:   98.80 %\n",
      "  validation accuracy: 84.58 %\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "100it [00:26,  3.77it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  Epoch 34 of 40 took 26.972s\n",
      "  training loss:       0.218396\n",
      "  validation loss:     0.482264\n",
      "  training accuracy:   98.84 %\n",
      "  validation accuracy: 84.51 %\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "100it [00:26,  3.66it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  Epoch 35 of 40 took 26.993s\n",
      "  training loss:       0.213757\n",
      "  validation loss:     0.485328\n",
      "  training accuracy:   98.97 %\n",
      "  validation accuracy: 84.66 %\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "100it [00:26,  3.72it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  Epoch 36 of 40 took 27.362s\n",
      "  training loss:       0.216468\n",
      "  validation loss:     0.482741\n",
      "  training accuracy:   98.95 %\n",
      "  validation accuracy: 84.68 %\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "100it [00:26,  3.81it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  Epoch 37 of 40 took 27.217s\n",
      "  training loss:       0.209858\n",
      "  validation loss:     0.487955\n",
      "  training accuracy:   98.97 %\n",
      "  validation accuracy: 84.57 %\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "100it [00:27,  3.77it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  Epoch 38 of 40 took 27.912s\n",
      "  training loss:       0.210997\n",
      "  validation loss:     0.484565\n",
      "  training accuracy:   99.01 %\n",
      "  validation accuracy: 84.76 %\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "100it [00:27,  3.79it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  Epoch 39 of 40 took 27.787s\n",
      "  training loss:       0.206184\n",
      "  validation loss:     0.482615\n",
      "  training accuracy:   99.05 %\n",
      "  validation accuracy: 84.70 %\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "100it [00:26,  3.72it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  Epoch 40 of 40 took 27.333s\n",
      "  training loss:       0.207877\n",
      "  validation loss:     0.484416\n",
      "  training accuracy:   99.05 %\n",
      "  validation accuracy: 84.69 %\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "##### train this shit\n",
    "lr = fpX(1e-3)      # initial learning rate for adam\n",
    "l2 = fpX(4.0e-3)       # l2 weight decay\n",
    "\n",
    "niter = 10       # # of iter at starting learning rate\n",
    "perc_decay = 10.0 # # of iter to linearly decay learning rate to zero\n",
    "num_epochs = 40\n",
    "batch_size = 500\n",
    "\n",
    "_lr.set_value(fpX(lr))\n",
    "_l2.set_value(fpX(l2))\n",
    "\n",
    "trn_hist = []\n",
    "val_hist = []\n",
    "\n",
    "for epoch in range(num_epochs):\n",
    "    # In each epoch, we do a full pass over the training data:\n",
    "    trn_err = 0\n",
    "    trn_acc = 0\n",
    "    trn_batches = 0\n",
    "    start_time = time.time()\n",
    "    for xb, yb in tqdm(fwrf.iterate_minibatches(trn_input_data, trn_label_data, batch_size)):\n",
    "        err, acc = cls_trn_fn(xb, yb)\n",
    "        trn_err += err\n",
    "        trn_acc += acc\n",
    "        trn_batches += 1\n",
    "\n",
    "    # And a full pass over the validation data:\n",
    "    val_err = 0\n",
    "    val_acc = 0\n",
    "    val_batches = 0\n",
    "    for xb, yb in fwrf.iterate_minibatches(val_input_data, val_label_data, batch_size):\n",
    "        err, acc = cls_val_fn(xb, yb)\n",
    "        val_err += err\n",
    "        val_acc += acc\n",
    "        val_batches += 1\n",
    "\n",
    "    trn_hist += [trn_acc / trn_batches,]\n",
    "    val_hist += [val_acc / val_batches,]\n",
    "    # Then we print the results for this epoch:\n",
    "    print(\"\\n  Epoch {} of {} took {:.3f}s\".format(epoch + 1, num_epochs, time.time() - start_time))\n",
    "    print(\"  training loss:       {:.6f}\".format(trn_err / trn_batches))\n",
    "    print(\"  validation loss:     {:.6f}\".format(val_err / val_batches))    \n",
    "    print(\"  training accuracy:   {:.2f} %\".format(trn_acc / trn_batches * 100))    \n",
    "    print(\"  validation accuracy: {:.2f} %\".format(val_acc / val_batches * 100))\n",
    "    \n",
    "    if epoch > niter:\n",
    "        _lr.set_value(fpX(_lr.get_value() * (1.0 - perc_decay / 100.0)))     \n",
    "    sys.stdout.flush()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 3) Training results\n",
    "## History"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7feefc4841d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20,5))\n",
    "plt.plot(trn_hist, 'b', label='trn', lw=2)\n",
    "plt.plot(val_hist, 'r', label='val', lw=2)\n",
    "plt.axhline(1./ny, color='k', linestyle='--', label='chance')\n",
    "plt.xlabel('epoch')\n",
    "plt.ylabel('accuracy')\n",
    "plt.ylim(0.0, 1.0)\n",
    "plt.legend(bbox_to_anchor=(1., 0.3))\n",
    "plt.grid()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## First layer filters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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RkXe/xq19u9PxsClNZhpuIW0I01vlIpx4YF2/B7fnavaux22nWzvr1TVj7Ntg\nHpU/2dLehx3xV5i/99VP6pqkFPz5HJE0ztLeMXPw178jSv++ZMXh72WnfgomNL4DtzEP9OqDGcMd\n+PG2pJZa2vuLHfh+apJb1DX+QTxAMKZDH/aGjOyZA/PtJb+qa3I68WvVE0yxtLcIr/CJiIzBgk9E\nZAgWfCIiQ7DgExEZggWfiMgQtmAwqLZgFBYWSkFBwR/5eIiI6Hfwer2Sn58P/8YrfCIiQ7DgExEZ\nggWfiMgQLPhERIZgwSciMsSQs3QmLf5nSHZsKj6mS0RkfAKerVK3Hx9dtuzhG2H+57lXwdyTrM83\nOe8CPJck0FgD879ccTPMrx0zFuZx5+odSyMmXQHznlp8LOI1150I85GxF8L88sv0Y9wmnjER5s3d\n+HlfetI9ML/1nIthXhKFj3cTERkYrIV5ZASeCfTOSx/DfMnf/g7zjBOPVPeODeKP78o3voT5jm/e\ngPnP3/4A88df/kTdu6cBz2+ZM2MEzG96+EmYL7gQP+/mdv2ovxOODz12VESkvgnPVFp6L97jmusW\nwtw5kKnuPWUant/y2248++fxJ1+D+UMvLoP5Z//cre59ww2XwvzHb1bDfOWq0NolIrLkpOtgXtnm\nUvceFYWPGayOxvlrH6+E+fNPL4V5c5U+r6rej+douZr110rDK3wiIkOw4BMRGYIFn4jIECz4RESG\nYMEnIjLEkF06F48N7bbw+Pept+8vx6fG+Mo3WXhYImdeNBvmL/7jfXXNMw/8C+YXXn22pb3HFlwN\n84nZp6lrXi/GpwTtKdllae+5xzpgXjc5R11T24zfj86+Qkt7Zx19A8ybHHvUNa7gAMyHO/AJVlqX\nTpMPv35rbqtU9375Bdxtcc3fz4D51UqXzp3Lcd7agE/UEhF54D+WwHxfGO5a0rQ0dsH8+uWXq2tK\nSnBe+OOblvZuLcankk04zK2uSQ7DHXoVvmpLezsSEmHe3qW/5jlZyTDv8+ude8ipc46BeVq63n3o\nS+mGeX8bPqFL69JZ/y3+LvnbetS9M2Nxh17mZNxNeCi8wiciMgQLPhGRIVjwiYgMwYJPRGQIFnwi\nIkMM2aXjaSoOyQZ6mtXbB5rx3zrDcDeCJiIG//q94nw8F0RE5LG3H4T5R+9ssbR32KRxMN8/qHeM\n1H72GcztHn1GBnLmEXhez4c/bVXX2JJwB09buv4+QQHccdPRjufiiIjk1nbCPC4fv3+aKxafB/MH\nr39KXXOorpsYAAAUnklEQVTDjXgO09+uW2Fp7x5PFcxX/g3PRxIR6YzBc2u+/Oh7S3sfceWfYd60\noVFdc//r98J8uvK51Qy68Kwle8oR6pqBTPx+9zVae7/D6vD3olf0uU0xLXjvA844S3t/UPsOzH99\nV2l/EpHB4XiP9KQ2S3tPn3USzKcdqXfcOJxJMN+yX3+8Gl7hExEZggWfiMgQLPhERIZgwSciMgQL\nPhGRIVjwiYgMMWRbZnm/PSRzlOKhSyIiEhcB4273VGXBRpiu/elnmI+dho/7EhE58cozYb73myJ1\nDZJxDB78tfpRPPhLRKS0D7dNFmRPgzl+1iJpjhiYVxTpLaHBDNzyOnP0XGUFHuhWtuVhmHvq8OAo\nEZHwGbiNrnq1PgwK2bABt0YuWnGHuuajl1+G+a9FX1ja+7J5J8C82F+n7/3hSzAfNiXP0t49O/Ex\ndc9sWK+uOfmEY2D+p5nzYP7eG7hlOCIRH5Vob9ffu8YD+LsRnzRSWYEH+PlrcTvj2Gx9SOC2dvx+\njHDqRxMitlHDYD7tJHxUqIhISj8+2vFguzIc8aXtMO4Mw5/z5176Ud27OQq3vNoH8Htx9JiH1Pvi\nFT4RkSFY8ImIDMGCT0RkCBZ8IiJDsOATERnCFgwG1elehYWFUlCAh3kREdH/Pl6vV/Lz8+HfeIVP\nRGQIFnwiIkOw4BMRGYIFn4jIECz4RESGGHKWzr13PxOSdVTpxxVGRbpx7g6dySMicvNDC2D+3Ct3\nw/znrfq/UQ7HKJiX1OHH9OPbp8P8mplzYJ45Z7a695i542H+5Qf4GLJXXr4L5leejecBJSivn4hI\nX7gD5oNu3ID11Mo3Yf7XW5+F+bwKPNdIRKQgbQPMo1vxY8p7fR/Mlz50GszDOvGxdiIifXb8mpd4\nomD+4RJ8BOYjTy3B99Onv+Y1NXiGSkxtBszfegfP/bnn8Q9g3le7Vt27tKoF5tNy4mF+64PPwfy6\n2/BRkJ3OaHXv+sFBmAcd+P3+8k78/V72xOMwz7UnqHvXBfB3v78Fz/a6676FMP/4hstgHsiPVPf+\nrRLP/iny4tf8/Y9C66aIyC1L8fGKPXH6MY1Ztl9gXrff2tGtIrzCJyIyBgs+EZEhWPCJiAzBgk9E\nZAgWfCIiQwzZpTMxKRCSuWL17oXefnyXfbZ+Cw9LZOpgDcx3VYQ+nn9bn4BPyxnoabC0d8T04TCf\nfcqJ6po1a/Av6ZVl+kk2yIioNJh3hqkjj6TCXwtzf6S+BilPmYzz/nJ1zbBm/JrbIqydMpa9Tznp\nqHGvuqZkFO4QOjrpOJh/qNxPRTHOJ3U0qnu7o8fBvC0Td7Fo0kbik6JqD36vrklNxJ+Ril341DWN\nfxCfFHUgGne1iYiECf7+NQ7g7iRNUl07zLf58IlQIiJ9I3wwj3Ba2lp6kvFJUa0B/J6KiJSuxa+t\nPxx372icHg/Mu7oOqmvqf/sJ5hGjrH2/RXiFT0RkDBZ8IiJDsOATERmCBZ+IyBAs+EREhhiyS+er\nda0hWX/br+rt93XjvC8ZdwRoohLwr99pGRPUNTY7/tW6J0Xv7EHmZM+C+fb9u9U1T3/4BszPX4zn\n8nz7PZ6VEnH0CTCfNGyEuvekAO5CGkzEt//imY9h7guLhfn2bHx6joiIu3cA5sFBPFtFZA1MUwR3\nbZSG6bNVJnTg2Sd14bgTQjOxEs9DiRqBcxGRqHrcGpI+PsnS3ps6NsJ8wDNaXXNUP+5g25MeY2nv\nhEH8vQi0Nqlr/Cl4bs1hPbiTa4dyP2sLvTCvjSpT984rwR/ojhTlg65oGMC1padC/36XJeOOrdHx\nuCNMExWO37u6Zr1LJ3wm/o5l9Fi/XucVPhGRIVjwiYgMwYJPRGQIFnwiIkOw4BMRGYIFn4jIEEO2\nZcaPDD3O0N55mHr7QCserDZ8Dh5IVvjJRzDvqm+GeUS6PkzrbFsuzCvDcPtZqXI/5YO4zfGn1fre\ns8bjNroz8vBrhQ8TFNn92TqY73Tj4WwiIuVd1TA/fGyyugZpcePWsM6eXHXNtvR6mHtq8YAqrS2z\nMA2/fomRuA1QRKR1ALf6+ur1YxGR+jb8+rnW6cO03C4bvi97j6W9o7/Hw9Zih2mfTpE2B16TnKsM\noFOMjtwO8zgXHs4mIhLlw9+N5ihrz3vGnCNgnpN3obpmcAAfrdrUhAeuffo2bj+ObMGtsx3u0Bb0\nfzsmeSzMu6Zam9xW0YmHSIYN6q9fWB0+/jDGmW5pbxFe4RMRGYMFn4jIECz4RESGYMEnIjIECz4R\nkSFswWBQPSersLBQCgoK/sjHQ0REv4PX65X8fDzwkFf4RESGYMEnIjIECz4RkSFY8ImIDMGCT0Rk\niCFn6Sw4d0FINm7UNPX25QmVMK/dsh/m77z1DsxXLr8U5o5s/ci7HS34yDvZgue3PPsWPqrxunOu\nxnfTre+dMBHPMZkwBR/198g5l8H8qTcXwrynwa3unZpQAfPuQXw02oJLP4T5adefCfOYbH22Sp57\nJt47iGcqPbTwApjfe94T+H4a9fkwLjc+qi4mvRjmi194EOa333UJzEvD9OddX4fnM01MxzOj/rEM\nv6+fv/QBzCsO6Ndi+1p3wXxnHZ5r9MMHz8D85Oe/h3ns6i/UvWeNw3OKJrvwPJtjHvkc5rc/fB/M\nnTZ9/lNMcRHMi3wHYP7Se3hO14tLV8L849LP1L2HuabDPH8GroV/X3gOzJc/+DbM61v0OT41a7+F\n+UUTrM0vEuEVPhGRMVjwiYgMwYJPRGQIFnwiIkOw4BMRGWLILp0p8aGnCiV7tNOMRJJicWeDPzsV\n5u8I7tIp6o6HefEO/Vf89Br8S3d5V56yAnfpzM3DnSG2shJ176ItuDOkKepYdQ1S+wN+nYLj8Ck9\nIiLBhkyYe3J6lRW4S6clLBvmEf256t5FQfx+nIAPZVK1+/HpPe0xendSR38EzMObrO1d5sIdRcWl\n+DMoIjKYhj9rTpf+eJGGbzbC3HN0jLpmcsxkmNud3TD/QbmfCX24g2ydU3/zCvbj5z387BZ1DTLg\niIV5QgR+TCIipWH4jbUP4vvS2MfgbqYr5vxNXfPAHd/gx1SPu5M0cRWNMHeedpq6piER14SXv3ka\n5vccYn9e4RMRGYIFn4jIECz4RESGYMEnIjIECz4RkSGG7NLpSgud17Bm3259QVs1jA9zTvrvPyoR\nGRA8B+awWXPUNZX7fTCf1Yt/Gd/w6VswT0vHM1qOjR6p7t2xYTP+Q/E2dQ2yoxQ/B+nEz0FEZDAS\nv42H1+uPF3EPKh0m7eqhaJLqwe9Tr0fvKkJyHHg2zfZkj7omvAd/1mJ81lqEhkUMg3nCtBnqmtjG\nfTDPcXZZ2ruptAbmqSP1bpX06GaYdzqVWVKKwBh8+9Yvx6prGrPx4+0bxK+hJsWGP1NdPjyrR0Sk\nx47XBKOtXbduXoM/N/PPyFLXPLzkRJg/sfxLS3tvL34I5mnFeAaZiMgR+Uth3tOl1wQNr/CJiAzB\ngk9EZAgWfCIiQ7DgExEZggWfiMgQLPhERIYYsi0zwhE6UGtwFG7FExFpasAtcQNHjrbwsES61+Pj\nzFLS9QFmmQkZMI/tH29p7529eOhYTqJTXXP01FEwL67tsLT34bl4cFttJz4iUkSk29cP874k3H6m\n8bTh4VRue4W6pqMW/606Dr8emiY7Hk41tvEIdY09Kwrm3SmFlvYOa8Ltfgld+AhAEZGkaNy66yrS\n20iRumQ8fCtQXKeuaRqHWz+TE/yW9vbu8cJ8XA4ewiYi4mkcAfNdXmuveddB/HnuTM9V17iceDBj\nWFa7pb1rY/HRgA+twm3aIiJnHnE8zK+9/2SYf3o6vq+/jMHtqz+uXavuvacF16/2tNDBliIi89R7\n4hU+EZExWPCJiAzBgk9EZAgWfCIiQ7DgExEZwhYMBtXJWIWFhVJQUPBHPh4iIvodvF6v5Ofnw7/x\nCp+IyBAs+EREhmDBJyIyBAs+EZEhWPCJiAwx5Cyd0fc+GJLFOKartz8yWAvz3I4NMF+84iWYX3/P\n3TDfu26XuvekqXkwP24uPurvlNOvhPlll1wD87Ej9aP+unqTYd4QPxfmzy7B+ee3RsO8qDhe3btl\nxE0w35mRivdYcg7ML1/wJMzDPq9Q9+4fkwhzx2j8PF589gaYH3XGMpjXDejH9qXV43kzXXH4vdi6\nBh8Vt+Se82FeE2VX93aXlMI8rAfP2HnptT0wn7PkKphfcupR6t4tlQ0wP7ilGOZPPvMizJfdiY/b\nm2jHM1pERDbsxUc7VrvwzKjVr74B81uWvgnz+hp9HlB3JD7aMcGdAvPnH70C5g//9XqYx+bmqnu7\noyNgXnwAzw5b/vIimJ999WMwTxsdq+6dWr0e5q5Um7pGwyt8IiJDsOATERmCBZ+IyBAs+EREhmDB\nJyIyxJBdOvZJl4Vkye5O9fbDvsMnF7lL8WlKmtzIOJjv7jigrony4FOW6gYdlvYeG1kJ89oGfNqP\niEhnNO4uSIu0dgpRsBV3SIwfP0Vd83PuNJi31jVa2js+HL+v/Vn6+x2ThN+ndrt+KhpyxyX4JKC+\nYny6k4hIIAV3i+3d2wrzrWvw/Xiyx8A8ag/uOBMR8TuTYN49oL1PuEtn2dQzYX5w32517+ceewHm\ns8cf6qyjULsO4Mc0/5xx6prkHNwBtXav29LeP23GJ9q1ROonxGUl4+9SfrLeVYQMT8mEedwI/fv9\n7aZ1MP9ij/7dQJIn4L0dQdztIyLSGIa7A8evV2rhcfr+vMInIjIECz4RkSFY8ImIDMGCT0RkCBZ8\nIiJDDNmlk/btFyHZONE7COKrX4H5pMght/ovGir2w/y6+59S19RV4nk9993wgKW963vxPJSocQF1\njXMX7gwJz9W7DpBETxbMt7txJ4mIyFYfzvf36x0uyOyp6TDf0vW1uibeifcoaMVdN9q7t6fqJ5gH\n+vR5IfjRiiROwN1amo4ePA8o3K93GvmTcVdFcjh+/zTeth9h/ugK/D0SEZl7JJ6zc9UJZ8B81SfP\nwnzXwX6YP/kpnlEkInLuFNydlJ+Qoa5B5p+KO+fmTZivrknvx7N0vt74vqW9C06dCfO33/5QXfPJ\nT7jLcOrRuKNptxffT1ivE+YJbuVLLCKljbjbLpDYq67R8AqfiMgQLPhERIZgwSciMgQLPhGRIVjw\niYgMwYJPRGSIIXslC5JDj01L69bbgXJSJsM8GXdziQhux+vrwQ+toWavuveuCty+dPyc42H+4uvK\nfYXjI9N6a3Ebm4iIOwavCavCbViaykE8fKuqHR/fKCLS3IuPncvv8cD8S+V+OifjQVBZNv2Ywert\nNTCvcOvD3pCqRjwAq71RH7rnUz6+gc7NlvbuLcbtlxlH6u2dBR2nwrxpALf0amq24hbIc8/GR2CK\niMw/9WaYf/b5d5b2nn7a6TD/Zcun6hqPtwLmBWn6EZxIrA8f0/jNR7eoa2wd+P2u6LO297UP/QPm\n3l0V6prr77wd5nNPToD5u/hkR7FH4u/YwQP16t6jI3Frcq/NWguwCK/wiYiMwYJPRGQIFnwiIkOw\n4BMRGYIFn4jIELZgMIjPzxKRwsJCKSgo+CMfDxER/Q5er1fy8/Ph33iFT0RkCBZ8IiJDsOATERmC\nBZ+IyBAs+EREhhhyls5rC28MyZyT9WUJQTxLpLWvCubnXI8nu3y4+VGYF36kTYIR2dmHH1dYEOcf\nP4Hv69bLFsA8dupwde8UZwzM39n0Pcy/e+09mB951l0wj6tZq+594QknwDyQMQLmF111EczvufU+\nmO+vKlL3TulOg3ldFp7z8eZTN8B8/lknwdwfpc8vinbgQw4T7Xg2zkvPvw3z8gUnwrx2UL8e2mXP\nhLlX8JF3Lz4b+j0SEVn5ykKYO3v1IwO7Bsph3h+Fj5W8+bJ71Psis/AKn4jIECz4RESGYMEnIjIE\nCz4RkSFY8ImIDDF0l87e0C6JGMFdECIiY1z4lKUJ2fOUFbhTpnt36ElbIiIykKPunRVTAXNPTrS6\nBmkadMO8auMGdc1ldz0I8083fWRp755qL8zHzZmvrplxLD5l7NXX3re0d1lND8z3VY5X1/Sm4TUj\nfHGW9r7g4rNgnpuq30/LAP5beRXuFNO6dCLduIPsYDvuxBERqUtOhnmFC3drafqdeA9HMKCu2dGA\nT1GLbPJZ2pvMwyt8IiJDsOATERmCBZ+IyBAs+EREhmDBJyIyxJBdOskpuSHZgbIy9fZFg00w31ne\n+t9/VCLy0kNbYJ4+K0JdM3bqHJgPdmtdOh/ANHP8KJi/tvwTde9fz94I88MnzIT5p/INzG84/UyY\nFxxmU/de+Cies2OTg+oaJMc9G+aJuaXqmlZfNcyL4los7b15+zaYf1DXp67p2FsD8xFpeZb23hCO\n59+UT8GdOCIiXwVGw7zSn2hp79Zm3FmzpdyhromMKYF5b4/+3SAS4RU+EZExWPCJiAzBgk9EZAgW\nfCIiQ7DgExEZggWfiMgQQ7Zlnnla6FFr3Z0p6u1LeiJhPtDwE8y//BDfz4xLb4V5cpw+PK2yBh+H\nF9ytD6JCJnhwa92Ek6eqa9Z8g9sKc8fhYwY1vTHdMH/nwzp1jbsPt+klTsVD1eTTr/H9uP0wr0zF\nA/FERCK9+PlNbMNtuFpjq60FtxRmdeptjr3ZuJ0yJqZdXYNUuONh/ks1Pr5RRKQiEX8HepTPv6Yr\niAfARSUH1TWDYcfCvN2On4fIF5YeE/3fxSt8IiJDsOATERmCBZ+IyBAs+EREhhjyR9uYmNATfJwO\n/cSr1F58Gs+gLfTHXxGRqVPxD6FpyfjkoHiPS9074MRPJ5iJH6+2d0wC3nt4tv6DcUZ0AsyTEpMs\n7R2fgO+nN0P/ES9noBPmcWn4R0dt7+R0PHPIH6V/TFzD8Q/iMZF2S3unpafjDaL0H4z7bfhvUcpn\nRNs7KQWfOpXbpzcn+GOjYN7rxHt3KHunJ+J5PdHuQ/1oi+cqdfXj90973mQeWzAYVD9ZhYWFf+Rj\nISKi/wH5+fkwP2TBJyKi/zv43/CJiAzBgk9EZAgWfCIiQ7DgExEZggWfiMgQ/wkB3Hw7h4E3qgAA\nAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1175630650>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,6))\n",
    "plt.imshow(pnu.mosaic_vis(cls_params[0].get_value().transpose(0, 2, 3, 1), pad=1), interpolation='None')\n",
    "plt.gca().get_xaxis().set_visible(False)\n",
    "plt.gca().get_yaxis().set_visible(False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Partial test accuracy (for future reference)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Classification accuracy = 0.864000\n"
     ]
    }
   ],
   "source": [
    "sample_size = 500\n",
    "        \n",
    "real_X = val_input_data[:sample_size,...]\n",
    "real_Y = val_label_data[:sample_size,...]\n",
    "\n",
    "inf = cls_pred_fn(real_X)\n",
    "inf_fmaps, inf_Y = inf[:-1], inf[-1]\n",
    "\n",
    "print \"Classification accuracy = %f\" % (np.mean(np.argmax(inf_Y, axis=1)==np.argmax(real_Y, axis=1)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Save the results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "ename": "SyntaxError",
     "evalue": "invalid syntax (<ipython-input-17-b7af32b18515>, line 1)",
     "output_type": "error",
     "traceback": [
      "\u001b[1;36m  File \u001b[1;32m\"<ipython-input-17-b7af32b18515>\"\u001b[1;36m, line \u001b[1;32m1\u001b[0m\n\u001b[1;33m    ***\u001b[0m\n\u001b[1;37m     ^\u001b[0m\n\u001b[1;31mSyntaxError\u001b[0m\u001b[1;31m:\u001b[0m invalid syntax\n"
     ]
    }
   ],
   "source": [
    "cls_param_file_name = \"cifar10-cls-%s-%s.pkl\" % (cls_net_str, timestamp)\n",
    "print cls_param_file_name\n",
    "cls_param_file = open(output_dir + cls_param_file_name, 'wb')\n",
    "cls_param_values = L.get_all_param_values(_cls)\n",
    "pickle.dump(cls_param_values, cls_param_file)\n",
    "cls_param_file.close()"
   ]
  }
 ],
 "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": "2.7.15+"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}