src/demo.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Demo for InfoGANJL Training Module\n",
    "\n",
    "Christian Landeros<br>\n",
    "Harvard-MIT Program in Health Sciences and Technology"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "┌ Info: Recompiling stale cache file /home/landeros/.julia/compiled/v1.0/InfoGAN_Fns.ji for InfoGAN_Fns [top-level]\n",
      "└ @ Base loading.jl:1187\n",
      "WARNING: Method definition gcnode(AutoGrad.Node) in module AutoGrad at /home/landeros/.julia/packages/AutoGrad/eAmjh/src/core.jl:38 overwritten in module Knet at /home/landeros/.julia/packages/Knet/3lzCR/src/gcnode.jl:18.\n",
      "WARNING: Method definition gcnode(AutoGrad.Node) in module AutoGrad at /home/landeros/.julia/packages/AutoGrad/eAmjh/src/core.jl:38 overwritten in module Knet at /home/landeros/.julia/packages/Knet/3lzCR/src/gcnode.jl:18.\n"
     ]
    }
   ],
   "source": [
    "include(\"InfoGAN.jl\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Setup InfoGAN Module"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This package can be run as a terminal script using `julia InfoGAN.jl [args]`, or it can be loaded into a jupyter notebook / REPL environment. If loading into a notebook, a dictionary with necessary hyperparemeters can be created by using `InfoGAN.define_params(\"args\")`.\n",
    "Below we see the arguments that can be included to manipulate the training architecture's hyperparameters:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "usage: InfoGAN.jl [--epochs EPOCHS] [--batchsize BATCHSIZE]\n",
      "                  [--lr LR LR LR LR] [--clip CLIP] [--dreps DREPS]\n",
      "                  [--greps GREPS] [--mdl MDL] [--load] [--z Z]\n",
      "                  [--c_SS C_SS [C_SS...]] [--c_disc [C_DISC...]]\n",
      "                  [--c_cont C_CONT] [--discCoeffSS DISCCOEFFSS]\n",
      "                  [--discCoeff DISCCOEFF] [--datadir DATADIR] [--N N]\n",
      "                  [--atype ATYPE] [--printNum PRINTNUM] [-v]\n",
      "\n",
      "Implementation of InfoGAN model described in\n",
      "[https://arxiv.org/abs/1606.03657] with semi-supervised\n",
      "training capacity for auxiliary networks, and updated Discriminator\n",
      "loss function according to [https://arxiv.org/abs/1701.07875]\n",
      "\n",
      "Training Hyperparameters:\n",
      "  --epochs EPOCHS       Number of times to iterate through dataset\n",
      "                        (type: Int64, default: 100)\n",
      "  --batchsize BATCHSIZE\n",
      "                        Size of data minibatches (type: Int64,\n",
      "                        default: 64)\n",
      "  --lr LR LR LR LR      Learning rates for AdamGrad optimizers (Front\n",
      "                        End, Discriminator, Generator, Auxiliary)\n",
      "                        (type: Float64, default: [1.0e-5, 1.0e-5,\n",
      "                        0.0001, 0.0001])\n",
      "  --clip CLIP           WGAN weight-clipping coefficient (type:\n",
      "                        Float64, default: 0.5)\n",
      "  --dreps DREPS         Number of Discriminator-side training repeats\n",
      "                        per minibatch (type: Int64, default: 5)\n",
      "  --greps GREPS         Number of Generator-side training repeats per\n",
      "                        minibatch (type: Int64, default: 1)\n",
      "  --mdl MDL             File where trained model is saved/should be\n",
      "                        saved (default: \"../trained/model.jld2\")\n",
      "  --load                Load previous model before training\n",
      "\n",
      "Latent Code Hyperparameters:\n",
      "  --z Z                 Size of noise component, z (type: Int64,\n",
      "                        default: 74)\n",
      "  --c_SS C_SS [C_SS...]\n",
      "                        Size of each semisupervised discrete code\n",
      "                        (array) (type: Int64, default: [2, 2])\n",
      "  --c_disc [C_DISC...]  Size of each unsupervised discrete code\n",
      "                        (array) (type: Int64)\n",
      "  --c_cont C_CONT       Number of real-valued codes (int) (type:\n",
      "                        Int64, default: 0)\n",
      "  --discCoeffSS DISCCOEFFSS\n",
      "                        Loss Coefficient for semisupervised Discrete\n",
      "                        Variables (type: Float64, default: 1.0)\n",
      "  --discCoeff DISCCOEFF\n",
      "                        Loss Coefficient for unsupervised Discrete\n",
      "                        Variables (type: Float64, default: 1.0)\n",
      "\n",
      "Data Set Hyperparameters:\n",
      "  --datadir DATADIR     Directory location holding holography dataset.\n",
      "                        Use 'MNIST' to load MNIST dataset instead\n",
      "                        (default: \"../resources/training_data\")\n",
      "  --N N                 Number of dataset samples to use for training\n",
      "                        (type: Int64)\n",
      "  --atype ATYPE         Array type for training data: Array for cpu,\n",
      "                        KnetArray for gpu (default:\n",
      "                        \"KnetArray{Float32}\")\n",
      "\n",
      "Other parameters:\n",
      "  --printNum PRINTNUM   Number of images that generator function\n",
      "                        produces (default folder ../outputs/). Set to\n",
      "                        0 for no printing (type: Int64, default: 100)\n",
      "  -v                    verbose flag, use to print updates every 2\n",
      "                        epochs\n",
      "\n"
     ]
    }
   ],
   "source": [
    "InfoGAN.define_params(\"--help\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Demo with MNIST Dataset:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Set up necessary parameters:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Dict{Symbol,Any} with 19 entries:\n",
       "  :atype       => KnetArray{Float32,N} where N\n",
       "  :c_disc      => Int64[]\n",
       "  :greps       => 1\n",
       "  :mdl         => \"../trained/model.jld2\"\n",
       "  :printNum    => 100\n",
       "  :clip        => 0.01\n",
       "  :c_cont      => 2\n",
       "  :batchsize   => 64\n",
       "  :N           => nothing\n",
       "  :v           => true\n",
       "  :discCoeff   => 1.0\n",
       "  :c_SS        => [10]\n",
       "  :z           => 62\n",
       "  :load        => false\n",
       "  :datadir     => \"MNIST\"\n",
       "  :dreps       => 5\n",
       "  :discCoeffSS => 1.0\n",
       "  :epochs      => 200\n",
       "  :lr          => [0.0001, 0.0001, 0.0001, 0.0001]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "args = \"--epochs 200 --lr 1e-4 1e-4 1e-4 1e-4 --clip 0.01 --z 62 --c_SS 10 --c_cont 2 --dreps 5 -v --datadir MNIST\"\n",
    "o = InfoGAN.define_params(args);\n",
    "display(o)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Set up Relevant Directories:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\"../tests/outputs\""
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "~ispath(\"../tests\") && mkpath(\"../tests/\")\n",
    "LOGFILE = \"../tests/mnist-log.txt\"\n",
    "MDLFILE = \"../tests/mnist-model.jld2\"\n",
    "PRINTFOLDER = \"../tests/outputs\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Load Our Example Dataset:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "┌ Info: Size of Datasets: \n",
      "│   size(xtrn) = (28, 28, 1, 60000)\n",
      "│   size(xtst) = (28, 28, 1, 10000)\n",
      "│   size(ytrn) = (1, 60000)\n",
      "│   size(ytst) = (1, 10000)\n",
      "└ @ Main In[8]:2\n"
     ]
    }
   ],
   "source": [
    "(xtrn,xtst,ytrn,ytst) = InfoGAN.loaddata(o; drop_p = 0.8)\n",
    "@info \"Size of Datasets: \" size(xtrn) size(xtst) size(ytrn) size(ytst)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Visualizing MNIST Dataset:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
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       "</g>\n",
       "</svg>\n"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "using Plots\n",
    "xtrn .-= minimum(xtrn; dims =(1,2))\n",
    "xtrn ./= maximum(xtrn; dims=(1,2))\n",
    "\n",
    "num2show = 10\n",
    "idx = rand(1:size(xtrn,4), num2show)\n",
    "imgs = [xtrn[:,:,1,i]' for i = idx]; imgarr = hcat(imgs...)\n",
    "plot(Gray.(imgarr), size = (num2show*120, 120), axis=false)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Set Up Model Functions & Weights:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Model architectures can be manually defined by providing \n",
    "- initialized weights\n",
    "- forward function\n",
    "- array type for weights\n",
    "- optimization techniques for each weight\n",
    "\n",
    "Below we show the documentation for the default implemented Generator stored as InfoGAN.defaultG"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/latex": [
       "\\begin{verbatim}\n",
       "defaultG(wG, Z; mode=true)\n",
       "\\end{verbatim}\n",
       "Default Generator Network to create image samples from latent code.\n",
       "\n",
       "<b> Arguments </b>\n",
       "\n",
       "\\begin{itemize}\n",
       "\\item \\texttt{wG::Array}: Array of neural network weights stored as KnetArray or Array.\n",
       "\n",
       "\n",
       "\\item \\texttt{Z::Array}: latent code.\n",
       "\n",
       "\n",
       "\\item \\texttt{mode::bool}:  batch normalization train/test mode.\n",
       "\n",
       "\\end{itemize}\n"
      ],
      "text/markdown": [
       "```\n",
       "defaultG(wG, Z; mode=true)\n",
       "```\n",
       "\n",
       "Default Generator Network to create image samples from latent code.\n",
       "\n",
       "<b> Arguments </b>\n",
       "\n",
       "  * `wG::Array`: Array of neural network weights stored as KnetArray or Array.\n",
       "  * `Z::Array`: latent code.\n",
       "  * `mode::bool`:  batch normalization train/test mode.\n"
      ],
      "text/plain": [
       "\u001b[36m  defaultG(wG, Z; mode=true)\u001b[39m\n",
       "\n",
       "  Default Generator Network to create image samples from latent code.\n",
       "\n",
       "  <b> Arguments </b>\n",
       "\n",
       "    •    \u001b[36mwG::Array\u001b[39m: Array of neural network weights stored as KnetArray or\n",
       "        Array.\n",
       "\n",
       "    •    \u001b[36mZ::Array\u001b[39m: latent code.\n",
       "\n",
       "    •    \u001b[36mmode::bool\u001b[39m: batch normalization train/test mode."
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "? InfoGAN.defaultG"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Below we define our own architectures using Knet functions and we define our InfoModel using these functions and weights."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "using Knet\n",
    "bn(h, mode) = batchnorm(h, bnmoments(); training=mode)\n",
    "bn_relu(h, mode) = relu.(bn(h, mode))\n",
    "prelu(x, p) = max.(p .* x, x)\n",
    "bn_prelu(h, mode, p) = prelu(bn(h, mode), p)\n",
    "\n",
    "# Leaky ReLU parameter\n",
    "α = 0.1;"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Front End Network"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "fe_fun (generic function with 1 method)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wFE = [randn(4,4,1,64) * sqrt(2/(1*4*4)), zeros(1,1,64,1),\n",
    "        randn(4,4,64,128) * sqrt(2/(64*4*4)), zeros(1,1,128,1),\n",
    "        randn(1024, 5*5*128) * sqrt(2/5*5*128), zeros(1024,1)]\n",
    "wFE = map(wi->convert(o[:atype], wi), wFE)\n",
    "\n",
    "function fe_fun(w, x; atype=Array{Float32}, mode=true)\n",
    "    a = prelu(conv4(w[1], x; stride=2) .+ w[2], α)\n",
    "    b = bn_prelu(conv4(w[3], a; stride=2) .+ w[4], mode, α)\n",
    "    c = bn_prelu((w[5] * mat(b)) .+ w[6], mode, α)\n",
    "    return c\n",
    "end"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Discriminator Network"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "d_fun (generic function with 1 method)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wD = [randn(1, 1024) * sqrt(2/1024), zeros(1,1)]\n",
    "wD = map(wi->convert(o[:atype], wi), wD)\n",
    "function d_fun(w, fc)\n",
    "    return (w[1] * fc) .+ w[2]\n",
    "end"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Auxiliary Network\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "q_fun (generic function with 1 method)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wQ = [randn(128, 1024) * sqrt(2/1024), zeros(128,1),\n",
    "        randn(10, 128) * sqrt(2/128), zeros(10,1),\n",
    "        randn(o[:c_cont]*2, 128) * sqrt(2/128), zeros(o[:c_cont]*2, 1)]\n",
    "wQ = map(wi->convert(o[:atype], wi), wQ)\n",
    "function q_fun(w, fc, o; mode=true)\n",
    "    fc2 = bn_relu((w[1] * fc) .+ w[2], mode)\n",
    "    logitsSS = [(w[3] * fc2) .+ wQ[4]]\n",
    "    logits=nothing\n",
    "\n",
    "    fc_cont = (w[5] * fc2) .+ w[6]\n",
    "    mu = fc_cont[1:o[:c_cont],:]\n",
    "    var = exp.(fc_cont[o[:c_cont]+1:end,:])\n",
    "    return logitsSS, logits, mu, var\n",
    "end"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Generator Network"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "g_fun (generic function with 1 method)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wG = [randn(1024, 74) * sqrt(2/74), zeros(1024,1),\n",
    "        randn(7*7*128, 1024) * sqrt(2/1024), zeros(7*7*128,1),\n",
    "        randn(4,4,64,128) * sqrt(2/(128*4*4)), zeros(1,1,64,1),\n",
    "        randn(3,3,64,64) * sqrt(2/(64*9)), zeros(1,1,64,1),\n",
    "        randn(4,4,1,64) * sqrt(2/(64*4*4)), zeros(1,1,1,1),\n",
    "        randn(3,3,1,1) * sqrt(2/(1*9)), zeros(1,1,1,1)]\n",
    "wG = map(wi->convert(o[:atype], wi), wG)\n",
    "function g_fun(w, Z; mode=true)\n",
    "    Z= mat(Z)\n",
    "    # #Linear Layers\n",
    "    fc1 = bn((w[1] * Z) .+ w[2], mode)\n",
    "    fc2 = bn_relu((w[3] * fc1) .+ w[4], mode)\n",
    "    \n",
    "    a = reshape(fc2, 7, 7, 128, :)\n",
    "    \n",
    "    b = deconv4(w[5], a; stride=2, padding=1) .+ w[6]\n",
    "    b = bn_relu(conv4(w[7], b; padding=1) .+ w[8], mode)\n",
    "    \n",
    "    c = deconv4(w[9], b; stride=2, padding=1) .+ w[10]\n",
    "    c = conv4(w[11], c; padding=1) .+ w[12]\n",
    "    return c\n",
    "end"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Neural Network Model Constructors\n",
    "We then define each neural network individually as is own structure. The first way we can construct our nn structure is by supplying all four parameters.  Below we see the parameters we need."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "InfoGAN_Fns.FrontEnd <: Any\n",
      "  w::AbstractArray\n",
      "  atype::Any\n",
      "  forward::Any\n",
      "  opt::AbstractArray\n"
     ]
    }
   ],
   "source": [
    "dump(InfoGAN.FrontEnd)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "┌ Info: Initialized FrontEnd weights:\n",
      "│   size(w) = (6,)\n",
      "│   typeof(w) = Array{KnetArray{Float32,N} where N,1}\n",
      "│   s = Number of Parameters: 3.410 M\n",
      "└ @ InfoGAN_Fns /home/landeros/julia-notebooks/github/infoganJL/src/InfoGAN_Fns.jl:21\n"
     ]
    }
   ],
   "source": [
    "Fopt = map(x->Rmsprop(;lr=o[:lr][1]), wFE)\n",
    "F = InfoGAN.FrontEnd(wFE, o[:atype], fe_fun, Fopt);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The defualt RMSprop optimizer will be used if only the weights, function, and parameter dictionary are supplied:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "┌ Info: Initialized Generator weights:\n",
      "│   size(w) = (12,)\n",
      "│   typeof(w) = Array{KnetArray{Float32,N} where N,1}\n",
      "│   s = Number of Parameters: 6.675 M\n",
      "└ @ InfoGAN_Fns /home/landeros/julia-notebooks/github/infoganJL/src/InfoGAN_Fns.jl:64\n",
      "┌ Info: Initialized Discriminator weights:\n",
      "│   size(w) = (2,)\n",
      "│   typeof(w) = Array{KnetArray{Float32,2},1}\n",
      "│   s = Number of Parameters: 0.001 M\n",
      "└ @ InfoGAN_Fns /home/landeros/julia-notebooks/github/infoganJL/src/InfoGAN_Fns.jl:43\n",
      "┌ Info: Initialized Auxiliary weights:\n",
      "│   size(w) = (6,)\n",
      "│   typeof(w) = Array{KnetArray{Float32,2},1}\n",
      "│   s = Number of Parameters: 0.133 M\n",
      "└ @ InfoGAN_Fns /home/landeros/julia-notebooks/github/infoganJL/src/InfoGAN_Fns.jl:87\n"
     ]
    }
   ],
   "source": [
    "G = InfoGAN.Generator(wG, o, g_fun)\n",
    "D = InfoGAN.Discriminator(wD, o, d_fun)\n",
    "Q = InfoGAN.Auxiliary(wQ, o, q_fun)\n",
    "\n",
    "model = InfoGAN.InfoModel(F, D, G, Q, o);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Train our model using the specified data set and specified parameters"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally, we can use our constructed InfoModel and loaded dataset to initiate training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\tBatch Discriminator Loss: 6.638e-02 (D: 6.638e-02, GP: 0.000e+00)\n",
      "\tBatch1 L-Gen: -3.510e+00, L-DiscSS: 2.209, L-Disc: 0.000, and L-Cont: 0.927\n",
      "\n",
      "\tBatch Discriminator Loss: 3.102e-02 (D: 3.102e-02, GP: 0.000e+00)\n",
      "\tBatch1 L-Gen: -3.705e+00, L-DiscSS: 2.286, L-Disc: 0.000, and L-Cont: 0.184\n",
      "\n",
      "\tBatch Discriminator Loss: -3.703e-02 (D: -3.703e-02, GP: 0.000e+00)\n",
      "\tBatch1 L-Gen: -3.722e+00, L-DiscSS: 2.503, L-Disc: 0.000, and L-Cont: 0.184\n",
      "\n",
      "\tBatch Discriminator Loss: "
     ]
    }
   ],
   "source": [
    "@time results = InfoGAN.train(xtrn, ytrn, xtst, ytst, model; mdlfile=MDLFILE, logfile=LOGFILE, printfolder=PRINTFOLDER);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
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     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "plot(results, xlabel = \"Epoch Number\", ylabel=\"Loss\", label=[\"Discriminator\", \"Generator\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "get_results (generic function with 1 method)"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
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   "source": [
    "function get_results(epoch_init, epoch_final, interval, img_dir)\n",
    "    epochs = epoch_init:interval:epoch_final\n",
    "    p, imgs = [], []\n",
    "    for e = epochs\n",
    "        imgfile = string(img_dir, \"/epoch\", lpad(e, 3, \"0\"), \".png\")\n",
    "        img = convert(Array{Float32}, channelview(Gray.(load(imgfile))))[1:56, 1:56]\n",
    "        push!(imgs, img)\n",
    "    end\n",
    "    return imgs, epochs\n",
    "end"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
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       "\" transform=\"translate(148, 47)\"/>\n",
       "</g>\n",
       "</svg>\n"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "using Images\n",
    "IMGDIR = string(pwd(), \"/tests/outputs\");\n",
    "imgs, epochs = get_results(10,100,10, IMGDIR);\n",
    "imgsarr = hcat(imgs...);\n",
    "size(imgsarr)\n",
    "\n",
    "h = 120\n",
    "plot(Gray.(imgsarr), size = (length(epochs)*h, h), axis=false)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Viewing Categorizations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "preds = InfoGAN.get_c(xtst, model);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10×10 Array{Int64,2}:\n",
       " 0   63  0  0  0  0  0  0  917  0\n",
       " 0  273  0  0  0  0  0  0  862  0\n",
       " 0   57  0  0  0  0  0  0  975  0\n",
       " 0   72  0  0  0  0  0  0  938  0\n",
       " 0   67  0  0  0  0  0  0  915  0\n",
       " 0  110  0  0  0  0  0  0  782  0\n",
       " 0   50  0  0  0  0  0  0  908  0\n",
       " 0  113  0  0  0  0  0  0  915  0\n",
       " 0   38  0  0  0  0  0  0  936  0\n",
       " 0   68  0  0  0  0  0  0  941  0"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "using MLBase\n",
    "confusmat(10, convert(Array{Int64}, ytst[1,:]), preds[1,:])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Generated Samples"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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