examples/training_md_gan_two_gaussions.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true,
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "# Imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "outputs": [],
   "source": [
    "import torch\n",
    "from torch import optim\n",
    "from tqdm import tqdm\n",
    "# Imports for this repository\n",
    "from simplex_generator import simplex_params\n",
    "from md_gan_modules import LambdaNetwork, Discriminator, Generator\n",
    "from gmm_data_generator import load_mixture_of_two_gaussian\n",
    "from numpy_dataset import NumpyDataset\n",
    "from md_gan_training import MDGANTraining,gaussian_likelihood_sum\n",
    "from common.clusters_metric import ClustersMetric"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n",
     "is_executing": false
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "# Parameters"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "outputs": [],
   "source": [
    "PARAMS = {'batch_size': 500,\n",
    "          'zdim': 2,\n",
    "          'gamma':0.5,\n",
    "          'beta1':0.5,\n",
    "          'beta2':0.999,\n",
    "          'n_lr_steps':3,\n",
    "          'lambda_training_iterations':10001,\n",
    "          'epochs':2000,\n",
    "          'eta_lambda': 0.01,\n",
    "          'e_dim': 2,\n",
    "          'lr_d': 1e-3,\n",
    "          'lr_g': 1e-3,\n",
    "          'epsilon': 1e-8,  # for avoiding numerical instabilities\n",
    "          'samp_num_gen': 2500}"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n",
     "is_executing": false
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "# Init Simplex and Working Device"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "outputs": [
    {
     "name": "stdout",
     "text": [
      "Current Working Device is set to:cuda\n"
     ],
     "output_type": "stream"
    }
   ],
   "source": [
    "working_device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\") # Find working device\n",
    "simplex = simplex_params(PARAMS['e_dim'], working_device) # Create Simplex\n",
    "print(\"Current Working Device is set to:\" + str(working_device))"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n",
     "is_executing": false
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "# Search for $\\lambda$"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "outputs": [
    {
     "name": "stderr",
     "text": [
      "100%|██████████| 10001/10001 [00:12<00:00, 805.41it/s]\n"
     ],
     "output_type": "stream"
    },
    {
     "name": "stdout",
     "text": [
      "Lambda Loss:1.5327969789505005\n",
      "Lambda Loss:1.5327969789505005\n",
      "Lambda Loss:1.5327969789505005\n",
      "Lambda Loss:1.5327969789505005\n",
      "Lambda Loss:1.5327969789505005\n",
      "Lambda Loss:1.5327969789505005\n",
      "Lambda Loss:1.5327969789505005\n",
      "Lambda Loss:1.5327969789505005\n",
      "Lambda Loss:1.5327969789505005\n",
      "Lambda Loss:1.5327969789505005\n",
      "0.21593084931373596\n"
     ],
     "output_type": "stream"
    }
   ],
   "source": [
    "lambda_net = LambdaNetwork(PARAMS['e_dim']).to(working_device)\n",
    "lambda_training_data = torch.tensor([1.0], device=working_device, dtype=torch.float32, requires_grad=False)\n",
    "optimizer_lambda = optim.Adam(lambda_net.parameters(), lr=PARAMS['eta_lambda'])\n",
    "\n",
    "for i in tqdm(range(PARAMS['lambda_training_iterations'])):\n",
    "    optimizer_lambda.zero_grad()\n",
    "    e = lambda_net(lambda_training_data)\n",
    "    lambda_lk = gaussian_likelihood_sum(e, simplex)\n",
    "    lambda_loss = -torch.log(PARAMS['epsilon'] + lambda_lk).mean()\n",
    "    if i % 1000 == 0 and i > 0:\n",
    "        print(\"Lambda Loss:\" + str(lambda_loss.item()))\n",
    "        for group in optimizer_lambda.param_groups:\n",
    "            group['lr'] = group['lr'] * PARAMS['gamma']\n",
    "    lambda_loss.backward()\n",
    "    optimizer_lambda.step()\n",
    "e = lambda_net(lambda_training_data)\n",
    "lambda_value = gaussian_likelihood_sum(e, simplex).sum().item()\n",
    "print(lambda_value)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n",
     "is_executing": false
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "# Train Generator and Discriminator"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "outputs": [
    {
     "name": "stderr",
     "text": [
      "100%|██████████| 2000/2000 [05:53<00:00,  5.65it/s]\n"
     ],
     "output_type": "stream"
    },
    {
     "name": "stdout",
     "text": [
      "Learning rate steps: [500, 1000, 1500]\n"
     ],
     "output_type": "stream"
    }
   ],
   "source": [
    "net_g = Generator(PARAMS['zdim']).to(working_device) # Init generator function\n",
    "net_d = Discriminator(PARAMS['e_dim']).to(working_device) # Init discriminator function\n",
    "optimizer_g = optim.Adam(net_g.parameters(), lr=PARAMS['lr_g'], betas=(PARAMS['beta1'], PARAMS['beta2']))\n",
    "optimizer_d = optim.Adam(net_d.parameters(), lr=PARAMS['lr_d'], betas=(PARAMS['beta1'], PARAMS['beta2']))\n",
    "\n",
    "epoch_step_size=PARAMS['epochs']/(PARAMS['n_lr_steps']+1) # calculate learning rate decay step size\n",
    "lr_steps=[int((i+1)*epoch_step_size) for i in range(PARAMS['n_lr_steps'])] \n",
    "print(\"Learning rate steps:\",lr_steps)\n",
    "lr_g = optim.lr_scheduler.MultiStepLR(optimizer_g, lr_steps, gamma=PARAMS['gamma'])\n",
    "lr_d = optim.lr_scheduler.MultiStepLR(optimizer_d, lr_steps, gamma=PARAMS['gamma'])\n",
    "\n",
    "training_data = load_mixture_of_two_gaussian()\n",
    "train_dataset = NumpyDataset(training_data)\n",
    "train_loader = torch.utils.data.DataLoader(dataset=train_dataset,\n",
    "                                           batch_size=PARAMS['batch_size'],\n",
    "                                           shuffle=True)\n",
    "cm=ClustersMetric(training_data)\n",
    "md_gan_training=MDGANTraining(net_d, net_g, optimizer_d,optimizer_g,PARAMS['batch_size'], PARAMS['zdim'], simplex,\n",
    "                     PARAMS['epsilon'],\n",
    "                     lambda_value,\n",
    "                     working_device)\n",
    "# Start Training Loop\n",
    "for epoch in tqdm(range(PARAMS['epochs'])):\n",
    "    for d in train_loader:\n",
    "        d = d.to(working_device)\n",
    "        md_gan_training.update_discriminator(d)\n",
    "        md_gan_training.update_generator()\n",
    "\n",
    "    lr_g.step(epoch) # update Generator learning rate\n",
    "    lr_d.step(epoch) # update Discriminator learning rate\n",
    "    cm.evaluate(md_gan_training.get_generator_function())"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n",
     "is_executing": false
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "# Plot Samples from the Generator"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib import pyplot as plt\n",
    "\n",
    "g_samples = net_g(torch.randn(PARAMS['batch_size'], PARAMS['zdim']).to(working_device))\n",
    "g_samples = g_samples.cpu().detach().numpy()\n",
    "\n",
    "plt.plot(training_data[:, 0], training_data[:, 1], 'o',label='real data')\n",
    "plt.plot(g_samples[:, 0], g_samples[:, 1], '^',label='MD-GAN')\n",
    "plt.legend()\n",
    "plt.grid()\n",
    "plt.xlabel(r'$x_o$')\n",
    "plt.ylabel(r'$x_1$')\n",
    "# plt.savefig('images/gmm_2.png', bbox_inches='tight')\n",
    "plt.show() "
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n",
     "is_executing": false
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "# Save Generator"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "outputs": [],
   "source": [
    "if False:\n",
    "    torch.save(net_g.state_dict(), 'models/generator_2.pt')\n",
    " "
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n",
     "is_executing": false
    }
   }
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.6"
  },
  "pycharm": {
   "stem_cell": {
    "cell_type": "raw",
    "source": [],
    "metadata": {
     "collapsed": false
    }
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}