INN Autoencoder_mnist-glow.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Import Packages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\n",
    "from functionalities import dataloader as dl\n",
    "from functionalities import tracker as tk\n",
    "from architecture import INN as inn\n",
    "from functionalities import MMD_autoencoder_loss as mmd_loss\n",
    "from functionalities import trainer as tr\n",
    "from functionalities import filemanager as fm\n",
    "from functionalities import plot as pl\n",
    "from functionalities import gpu "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Pretraining Setup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "num_epoch = 10\n",
    "batch_size = 128\n",
    "latent_dim_lst = [1, 2, 3, 4, 6, 8, 12, 16, 24, 32, 48, 64]\n",
    "number_dev = 0\n",
    "lr_init = 1e-3\n",
    "l2_reg  = 1e-6\n",
    "milestones = [8, 10]\n",
    "modelname = 'mnist_INN_glow_com_bottleneck'\n",
    "get_model = inn.mnist_inn_com\n",
    "\n",
    "device = gpu.get_device(number_dev)\n",
    "print(device)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "trainset, testset, classes = dl.load_mnist()\n",
    "trainloader, validloader, testloader = dl.make_dataloaders(trainset, testset, batch_size)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "model = tr.train_bottleneck(num_epoch, get_model, 'l1', modelname, milestones, latent_dim_lst, trainloader, None, \n",
    "                            testloader, a_distr=0, a_disen=0, lr_init=lr_init, l2_reg=l2_reg, device=device, save_model=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Plot Reconstruction and Difference Images Examples"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "pl.plot_diff_all(get_model, modelname, num_epoch, testloader, latent_dim_lst, device='cpu', num_img=1, grid_row_size=10, figsize=(30, 30), \n",
    "              filename=None, conditional=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Plot Recontruction Loss against Bottleneck Size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "_, l1_rec_test, _, _, _ = fm.load_variable('bottleneck_test_loss_{}'.format(modelname), modelname)\n",
    "_, l1_rec_train, _, _, _ = fm.load_variable('bottleneck_train_loss_{}'.format(modelname), modelname)\n",
    "\n",
    "pl.plot(latent_dim_lst, [l1_rec_train, l1_rec_test], 'bottleneck size', 'loss', ['train', 'test'], 'Test Reconstruction Loss History', '{}_bottleneck_History'.format(modelname)) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}