pytorch_expers.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import sys\n",
    "if \"/home/jogi/.local/lib/python2.7/site-packages\" in sys.path:\n",
    "    sys.path.remove(\"/home/jogi/.local/lib/python2.7/site-packages\")\n",
    "\n",
    "import scipy.stats as stats\n",
    "from scipy import special\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "# from mpl_toolkits.mplot3d import Axes3D\n",
    "from matplotlib import cm\n",
    "%matplotlib inline\n",
    "\n",
    "import torch\n",
    "from torch.autograd import Variable\n",
    "import torch.nn as nn\n",
    "import torch.nn.functional as F\n",
    "import torch.optim as optim\n",
    "from torch.nn import init\n",
    "\n",
    "import numpy as np\n",
    "import dill\n",
    "import os\n",
    "from collections import OrderedDict\n",
    "\n",
    "from models.rnn_optimizer import MetaLearner, AdaptiveMetaLearnerV1, AdaptiveMetaLearnerV2\n",
    "from models.rnn_optimizer import kl_divergence\n",
    "from utils.mlp import MLP, Net\n",
    "\n",
    "from utils.experiment import Experiment\n",
    "from utils.common import get_model, create_def_argparser, create_logger, softmax, default_mlp_architecture\n",
    "from utils.common import test40_mlp_architecture, testRELU_mlp_architecture, two_layer_mlp_architecture\n",
    "from utils.common import load_val_data\n",
    "from utils.plots import loss_plot, param_error_plot, plot_qt_probs, plot_dist_optimization_steps\n",
    "from utils.plots import plot_actsb_qts, plot_image_map_data, plot_image_map_losses, plot_gradient_stats\n",
    "from utils.probs import TimeStepsDist, ConditionalTimeStepDist\n",
    "from utils.config import config\n",
    "from val_optimizer import validate_optimizer\n",
    "from utils.regression import RegressionFunction, L2LQuadratic, RegressionWithStudentT\n",
    "from utils.helper import tensor_and, tensor_any\n",
    "from utils.batch_handler import ACTBatchHandler\n",
    "from utils.mnist_data_obj import MNISTDataSet\n",
    "from utils.epoch import Epoch\n",
    "\n",
    "import seaborn as sns\n",
    "\n",
    "%load_ext autoreload\n",
    "%autoreload 2\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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vf//7m6a97LLLKuW54447cumlly62fNKkSRxyyCHsuOOOTJw4keuvv55jjz2W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\nqf8BhudU6QGBWKoAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f225d5107d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from pylab import MaxNLocator\n",
    "\n",
    "def plot_geometric_priors(p_shape, horizon=100, fig_name=None, fig_title=None, huge=True, do_save=False,\n",
    "                          do_show=True, height=8, width=12):\n",
    "    \n",
    "    p_label = r\"$p(z|T={}, \\nu={})$\".format(horizon, p_shape)\n",
    "    bar_width = 0.3\n",
    "    \n",
    "    if fig_title is None:\n",
    "        fig_title = \"Truncated geometric prior\"\n",
    "        \n",
    "    if huge:\n",
    "        font_size = ['40', '30']\n",
    "        cbar_font = 36\n",
    "        legend_size = 16\n",
    "        tick_size = 20\n",
    "    else:\n",
    "        font_size = ['14', '12']\n",
    "        cbar_font = 25\n",
    "        legend_size = 10\n",
    "        tick_size = 10\n",
    "\n",
    "    title_font = {'fontname': 'Arial', 'size': font_size[0], 'color': 'black', 'weight': 'normal'}\n",
    "    axis_font = {'fontname': 'Arial', 'size': font_size[1], 'weight': 'normal'}\n",
    "    \n",
    "    \n",
    "    t = np.arange(1, horizon+1)\n",
    "    probs = stats.geom.pmf(t, p=p_shape)\n",
    "\n",
    "    trunc_probs = 1./np.sum(probs) * probs\n",
    "    \n",
    "    fig, ax = plt.subplots()\n",
    "    fig.set_figheight(height)\n",
    "    fig.set_figwidth(width)\n",
    "    # plt.plot(t, trunc_probs, 'o', label=p_label)\n",
    "    plt.bar(t+bar_width, trunc_probs, bar_width, color='orange', align='center', label=p_label)\n",
    "    \n",
    "    plt.ylabel(\"Probability\", **axis_font)\n",
    "    plt.legend(loc=\"best\", prop={'size': legend_size})\n",
    "    plt.xlabel(\"Time steps\", **axis_font)\n",
    "    plt.title(fig_title, **title_font)\n",
    "    plt.tick_params(axis='both', which='major', labelsize=tick_size)\n",
    "    plt.tick_params(axis='both', which='minor', labelsize=tick_size)\n",
    "    ax.xaxis.set_major_locator(MaxNLocator(integer=True))\n",
    "    \n",
    "    if do_save:\n",
    "        if fig_name is None:\n",
    "            str_shape = str.replace(str(p_shape),'.', '_')\n",
    "            fig_name = \"prior_geom_trunc_shape_\" + str_shape\n",
    "        fig_name = os.path.join(config.figure_path, fig_name + config.dflt_plot_ext)\n",
    "\n",
    "        plt.savefig(fig_name, bbox_inches='tight')\n",
    "        print(\"INFO - Successfully saved fig %s\" % fig_name)\n",
    "\n",
    "    if do_show:\n",
    "        plt.show()\n",
    "\n",
    "    plt.close()\n",
    "    \n",
    "p_shape = 0.05\n",
    "T = 50\n",
    "str_shape = str.replace(str(p_shape),'.', '_')\n",
    "fig_name = \"prior_geom_trunc_shape_\" + str_shape\n",
    "plot_geometric_priors(p_shape, T, do_save=False, fig_name=fig_name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "exp = Experiment.load(\"run_20170925_14_31_57_metaV1_100ep_50ops_lr4e05\", do_log=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO - Successfully saved fig logs/run_20170925_14_31_57_metaV1_100ep_50ops_lr4e05/loss_train_metaV1_100ep_50ops.png\n",
      "(50, 100)\n",
      "INFO - Successfully saved fig logs/run_20170925_14_31_57_metaV1_100ep_50ops_lr4e05/loss_optimizer_train_metaV1_100ep_50ops.png\n",
      "INFO - Successfully saved fig logs/run_20170925_14_31_57_metaV1_100ep_50ops_lr4e05/loss_eval_metaV1_100ep_50ops.png\n",
      "(50, 100)\n",
      "INFO - Successfully saved fig logs/run_20170925_14_31_57_metaV1_100ep_50ops_lr4e05/loss_optimizer_eval_metaV1_100ep_50ops.png\n",
      "INFO - Successfully saved fig logs/run_20170925_14_31_57_metaV1_100ep_50ops_lr4e05/train_step_loss_map_metaV1_100ep_50ops.png\n",
      "INFO - Successfully saved fig logs/run_20170925_14_31_57_metaV1_100ep_50ops_lr4e05/T_dist_train.png\n",
      "INFO - Successfully saved fig logs/run_20170925_14_31_57_metaV1_100ep_50ops_lr4e05/gradient_statistics.png\n",
      "INFO - Successfully saved fig logs/run_20170925_14_31_57_metaV1_100ep_50ops_lr4e05/eval_step_loss_map_metaV1_100ep_50ops.png\n"
     ]
    }
   ],
   "source": [
    "exp.generate_figures()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 0, final loss 2.38713312149\n",
      "Epoch: 20, final loss 2.03471589088\n",
      "Epoch: 40, final loss 1.93116784096\n",
      "Epoch: 60, final loss 1.77463662624\n",
      "Epoch: 80, final loss 1.65524721146\n",
      "Epoch: 100, final loss 1.57240653038\n",
      "Epoch: 120, final loss 1.47609651089\n",
      "Epoch: 140, final loss 1.44421005249\n",
      "Epoch: 160, final loss 1.30226016045\n",
      "Epoch: 180, final loss 1.2430986166\n",
      "Epoch: 200, final loss 1.23745930195\n",
      "Epoch: 220, final loss 1.27889370918\n",
      "Epoch: 240, final loss 1.17984735966\n",
      "Epoch: 260, final loss 1.16213309765\n",
      "Epoch: 280, final loss 0.96964263916\n",
      "Epoch: 300, final loss 1.0561439991\n",
      "Epoch: 320, final loss 0.829439401627\n",
      "Epoch: 340, final loss 1.06718730927\n",
      "Epoch: 360, final loss 0.84645408392\n",
      "Epoch: 380, final loss 0.807461440563\n",
      "Epoch: 400, final loss 0.873935103416\n",
      "Epoch: 420, final loss 0.661255121231\n",
      "Epoch: 440, final loss 0.879009604454\n",
      "Epoch: 460, final loss 0.733394384384\n",
      "Epoch: 480, final loss 0.735724687576\n",
      "Epoch: 500, final loss 0.712700486183\n",
      "Epoch: 520, final loss 0.81896007061\n",
      "Epoch: 540, final loss 0.726992845535\n",
      "Epoch: 560, final loss 0.858709514141\n",
      "Epoch: 580, final loss 0.654622614384\n",
      "Epoch: 600, final loss 0.578259348869\n",
      "Epoch: 620, final loss 0.595582664013\n",
      "Epoch: 640, final loss 0.569671630859\n",
      "Epoch: 660, final loss 0.626343667507\n",
      "Epoch: 680, final loss 0.604367733002\n",
      "Epoch: 700, final loss 0.842990040779\n",
      "Epoch: 720, final loss 0.635052204132\n",
      "Epoch: 740, final loss 0.4608104527\n",
      "Epoch: 760, final loss 0.566605508327\n",
      "Epoch: 780, final loss 0.607016205788\n",
      "0.125\n",
      "Accuracy of the network on the 10000 test images: 89 %\n",
      "Ready\n"
     ]
    }
   ],
   "source": [
    "torch.manual_seed(1234)\n",
    "torch.cuda.manual_seed(1234)\n",
    "np.random.seed(1234)\n",
    "\n",
    "use_cuda = False\n",
    "batch_size = 32\n",
    "num_of_epochs = 800\n",
    "input_size = 784\n",
    "hidden_size = 500\n",
    "num_classes = 10\n",
    "loss_function = nn.CrossEntropyLoss()\n",
    "\n",
    "dataset = MNISTDataSet(batch_size, use_cuda)\n",
    "\n",
    "\n",
    "# default_mlp_architecture[\"n_hidden_layer1\"] = 20\n",
    "# net = MLP(two_layer_mlp_architecture)\n",
    "net = MLP(default_mlp_architecture)\n",
    "# net = Net(input_size, hidden_size, num_classes)\n",
    "optimizer = optim.Adam(net.parameters(), lr=1e-3)\n",
    "# Compute initial loss of the model\n",
    "x, y = dataset.next_batch()\n",
    "f_x = net(x)\n",
    "initial_loss = loss_function(f_x, y)\n",
    "\n",
    "\n",
    "for e in np.arange(num_of_epochs):\n",
    "    sum_losses = 0.\n",
    "    num_of_opt_steps = 50\n",
    "    final_loss = 0.\n",
    "    decrease_in_loss = 0.\n",
    "    x, y = dataset.next_batch()\n",
    "    # f_x = net(x)\n",
    "    # f_x = net.evaluate(x)\n",
    "    # loss = F.nll_loss(f_x, y)\n",
    "    # loss = loss_function(f_x, y)\n",
    "    loss = net.evaluate(x, compute_loss=True, y_true=y)\n",
    "    # print(torch.sum(net.get_flat_grads()))\n",
    "    net.zero_grad()\n",
    "    loss.backward()\n",
    "    optimizer.step()\n",
    "    params = net.get_flat_grads()\n",
    "    if e % 20 == 0:\n",
    "        print(\"Epoch: {}, final loss {}\".format(e, loss.data[0]))\n",
    "    \n",
    "_, max_idx = torch.max(f_x, 1)\n",
    "print(torch.sum((y - max_idx) == 0).data[0] / float(batch_size))\n",
    "\n",
    "# Test the Model\n",
    "correct = 0\n",
    "total = 0\n",
    "i = 0\n",
    "net.set_eval_obj_parameters(net.get_flat_params())\n",
    "for images, labels in dataset.test_loader:\n",
    "    i += 1\n",
    "    images = Variable(images.view(-1, 28*28))\n",
    "    outputs = net.evaluate(images, use_copy_obj=True)\n",
    "    total += labels.size(0)\n",
    "    correct += net.accuracy(outputs, labels)\n",
    "    \n",
    "print('Accuracy of the network on the 10000 test images: %d %%' % (100 * correct / total))\n",
    "\n",
    "print(\"Ready\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO - created local logger for experiment with model metaV1_mlp_100ops\n",
      "INFO - INFO - loaded existing model from file logs/run_20170905_17_18_45_metaV1_10ep_100ops_lr5e05/metaV1_mlp_100ops.pkl\n",
      "INFO - Note: MetaLearner is running on GPU\n",
      "INFO - ['linear1.weight', 'linear1.bias', 'lstms.0.fc_i2h.weight', 'lstms.0.fc_i2h.bias', 'lstms.0.fc_h2h.weight', 'lstms.0.fc_h2h.bias', 'lstms.1.fc_i2h.weight', 'lstms.1.fc_i2h.bias', 'lstms.1.fc_h2h.weight', 'lstms.1.fc_h2h.bias', 'linear_out.weight']\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 Loading model metaV1_mlp_100ops / run_20170905_17_18_45_metaV1_10ep_100ops_lr5e05\n"
     ]
    }
   ],
   "source": [
    "expers_to_load = [(True, \"run_20170905_17_18_45_metaV1_10ep_100ops_lr5e05\")]\n",
    "MAX_EVAL_STEPS = 200\n",
    "\n",
    "without_models = False\n",
    "\n",
    "experiments = []\n",
    "models = []\n",
    "new_experiments = []\n",
    "explogger = None\n",
    "m = 0\n",
    "\n",
    "if 'logger' in vars() or 'logger' in globals():\n",
    "    pass\n",
    "else:\n",
    "    logger = create_logger(exper=None, file_handler=True, output_dir=\"/home/jogi/git/repository/meta_learner/logs\")\n",
    "\n",
    "for e, exp_path in enumerate(expers_to_load):\n",
    "    if exp_path[0]:\n",
    "        \n",
    "        exp = Experiment.load(exp_path[1], do_log=True, meta_logger=logger)\n",
    "            \n",
    "        if \"act\" in exp.args.model:\n",
    "            print(\"{} Loading model {} (q-prob {:.2f}) / {}\".format(m, exp.args.model, \n",
    "                                                                    exp.config.ptT_shape_param,\n",
    "                                                                    exp.args.log_dir))\n",
    "        else:\n",
    "            print(\"{} Loading model {} / {}\".format(m, exp.args.model, exp.args.log_dir))\n",
    "        \n",
    "        experiments.append(exp)\n",
    "        m += 1\n",
    "        if not without_models:\n",
    "            num_inputs = 1\n",
    "            if exp.args.problem == 'mlp':\n",
    "                num_inputs = 3\n",
    "            models.append(get_model(exp, num_inputs, retrain=True))\n",
    "        new_experiments.append(Experiment(exp.args, exp.config))\n",
    "        new_experiments[-1].batch_handler_class = experiments[-1].batch_handler_class\n",
    "        new_experiments[-1].output_dir = experiments[-1].output_dir\n",
    "        new_experiments[-1].reset_val_stats()\n",
    "        new_experiments[-1].meta_logger = logger\n",
    "        new_experiments[-1].validation_handler_class = \"ValidateMLPOnMetaLearner\"\n",
    "        new_experiments[-1].dta_set =  MNISTDataSet(experiments[-1].args.batch_size, \n",
    "                                                    experiments[-1].args.cuda)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO - INFO - validation set loaded from data/val_mlp_5.dll\n"
     ]
    }
   ],
   "source": [
    "optimizee = load_val_data(logger=logger, exper=new_experiments[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO - Epoch: 400 - Evaluating 5 test MLPs\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Validating model metaV1_mlp_100ops \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO - INFO - Epoch 400: Final step losses: [ 2.3087  2.2801  2.2217  2.1446  2.0606  2.0059  1.9251  1.8859  1.8422\n",
      "  1.8225  1.7853  1.7581  1.7451  1.7484  1.7109  1.6976  1.6907  1.681\n",
      "  1.6741  1.6952  1.6703  1.6496  1.6595  1.6486  1.6616  1.6426  1.6501\n",
      "  1.645   1.6422  1.6388  1.6292  1.6314  1.6311  1.6135  1.6236  1.6277\n",
      "  1.6505  1.6296  1.6396  1.623   1.5998  1.6356  1.6237  1.6158  1.6078\n",
      "  1.6396  1.629   1.6047  1.5905  1.6086  1.6077]\n",
      "INFO - INFO - Epoch 400, elapsed time 4.55 seconds: \n",
      "INFO - INFO - Epoch 400: Final validation stats: total-step-losses / final-step loss / : 88.25/1.608\n"
     ]
    }
   ],
   "source": [
    "exp_idx = 0\n",
    "model = models[exp_idx]\n",
    "\n",
    "max_steps = 50\n",
    "# new_experiments[exp_idx].model_path\n",
    "\n",
    "new_experiments[exp_idx].epoch = 400\n",
    "new_experiments[exp_idx].config.max_val_opt_steps = max_steps\n",
    "new_experiments[exp_idx].config.T = max_steps\n",
    "new_experiments[exp_idx].type_prior = \"geometric\"\n",
    "\n",
    "\n",
    "epoch_obj = Epoch()\n",
    "epoch_obj.start(new_experiments[exp_idx])\n",
    "print(\"Validating model {} \".format(model.name))\n",
    "new_experiments[exp_idx].eval(epoch_obj, model, functions=optimizee, save_run=None, save_model=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "\n",
    "# Test the Model\n",
    "correct = 0\n",
    "total = 0\n",
    "i = 0\n",
    "net.set_parameters(net.get_flat_params(), use_copy_obj=True)\n",
    "for images, labels in dataset.test_loader:\n",
    "    i += 1\n",
    "    images = Variable(images.view(-1, 28*28))\n",
    "    outputs = net.evaluate(images, use_copy_obj=True)\n",
    "    total += labels.size(0)\n",
    "    correct += net.accuracy(outputs, labels)\n",
    "    \n",
    "print('Accuracy of the network on the 10000 test images: %d %%' % (100 * correct / total))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('input_layer', <class 'torch.nn.modules.linear.Linear'>)\n",
      "('weight', 20)\n",
      "('bias', 20)\n",
      "('hidden_layer1', <class 'torch.nn.modules.linear.Linear'>)\n",
      "('weight', 10)\n",
      "('bias', 10)\n",
      "('act_output_layer', <class 'torch.nn.modules.activation.Sigmoid'>)\n",
      "('loss_function', <class 'torch.nn.modules.loss.CrossEntropyLoss'>)\n",
      "('eval_copy', <class 'torch.nn.modules.container.Sequential'>)\n",
      "('input_layer.weight', 20)\n",
      "('input_layer.bias', 20)\n",
      "('hidden_layer1.weight', 10)\n",
      "('hidden_layer1.bias', 10)\n"
     ]
    }
   ],
   "source": [
    "for name, module in net.named_children():\n",
    "    print(name, type(module))\n",
    "    for name, p in module.named_parameters():\n",
    "        print(name, p.squeeze().size(0))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "num_of_epochs = 10\n",
    "num_of_batches = 100\n",
    "\n",
    "for e in np.arange(num_of_epochs):\n",
    "    train_iter = iter(train_loader)\n",
    "    sum_losses = 0.\n",
    "    num_of_opt_steps = 50\n",
    "    final_loss = 0.\n",
    "    decrease_in_loss = 0.\n",
    "    \n",
    "    for i in np.arange(num_of_batches):\n",
    "        print(\"Processing batch {}\".format(i+1))\n",
    "        net = MLP(default_mlp_architecture)\n",
    "        optimizer = optim.Adam(net.parameters(), lr=1e-3)\n",
    "        x, y = next_batch(train_iter)\n",
    "        \n",
    "        # Compute initial loss of the model\n",
    "        f_x = net(x)\n",
    "        initial_loss = F.nll_loss(f_x, y)\n",
    "        for step in np.arange(num_of_opt_steps):\n",
    "            x, y = next_batch(train_iter)\n",
    "            f_x = net(x)\n",
    "            loss = F.nll_loss(f_x, y)\n",
    "            sum_losses += loss\n",
    "            net.zero_grad()\n",
    "            loss.backward()\n",
    "            optimizer.step()\n",
    "            \n",
    "        final_loss += loss.data[0]  \n",
    "        decrease_in_loss += loss.data[0] / initial_loss.data[0]\n",
    "        \n",
    "    print(\"Epoch: {}, final loss {}, average final/initial loss ratio: {}\".format(e, final_loss / num_of_batches,\n",
    "                                                                       decrease_in_loss / num_of_batches))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "exp = Experiment.load(\"run_20170921_19_26_38_act_sbV3.2_40ep_nu0.6kls0.05_lr5e05\", do_log=False)\n",
    "# print(exp.epoch_stats[\"step_losses\"])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#    \n",
    "plot_gradient_stats(exp, do_show=True, do_save=True, offset=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from scipy.stats import geom\n",
    "\n",
    "def kl_div(q_probs, prior_probs):\n",
    "    eps = 1e-90\n",
    "    kl_div = torch.sum(q_probs * (torch.log(q_probs + eps) - torch.log(prior_probs + eps)), 1)\n",
    "    return kl_div"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "20\n"
     ]
    },
    {
     "data": {
      "image/png": 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cAL2T4zinf2cy0xPJTE/klJR49ScVEb8o3FtIfb3ji4OV5BaVs6WwjBW+MM/b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\niEgYUriLiISh/weYmDrnmWsYrQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f66bbeca310>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 0.00669285  0.01098694  0.01798621  0.02931223  0.04742587  0.07585818\n",
      "  0.11920292  0.18242552  0.26894142  0.37754067  0.5         0.62245933\n",
      "  0.73105858  0.81757448  0.88079708  0.92414182  0.95257413  0.97068777\n",
      "  0.98201379  0.98901306]\n"
     ]
    }
   ],
   "source": [
    "def sigmoid_loc(arange, step):\n",
    "    x = np.arange(arange[0], arange[1], step)\n",
    "    sigmoid = 1/(1 + np.exp(-x-arange[0]))\n",
    "    return x, sigmoid\n",
    "\n",
    "epochs = 20\n",
    "r_min = -5\n",
    "r_max = 5\n",
    "step_size = (r_max - r_min) / float(epochs)\n",
    "t = np.arange(r_min, r_max, step_size)\n",
    "print(t.shape[0])\n",
    "# priors = stats.geom.pmf(t, p=1-p)\n",
    "priors = special.expit(t)\n",
    "plt.plot(t, priors, label=r\"original\")\n",
    "# x, probs = sigmoid_loc(arange, step)\n",
    "# plt.plot(x, probs, label=r\"shifted\")\n",
    "plt.legend(loc=\"best\")\n",
    "plt.show()\n",
    "print(priors[0:20])\n",
    "# print(priors[-10:])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.JointGrid at 0x7f4eb4067510>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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QFCjaQyrAgkCLQHCgBSGBFoQEBiAkyILQjvedHwu0dBzz748HX/C5fx8f3MOfu74PsAhY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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f4eb4067550>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "m_gaussian = np.random.multivariate_normal([5,5], [[1, 0.9], [0.9, 1]], 200)\n",
    "sns.jointplot(m_gaussian[:, 0], m_gaussian[:, 1], kind=\"kde\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "\n",
    "exper = get_experiment(\"run_20170810_13_05_59_act_sbV1_25ep_nu0.9_lr5e05\")\n",
    "print(hasattribute(exper, ))\n",
    "\n",
    "# print(exper.val_stats[\"loss\"])\n",
    "# loss_plot(exper, loss_type=\"loss\", save=False, validation=True, show=True)\n",
    "# plot_actsb_qts(exper, data_set=\"val\", show=True, plot_prior=True, epoch=None)\n",
    "# plot_image_map_losses(exper, data_set=\"eval\", do_show=True)\n",
    "# plot_image_training_data(exper, data=\"halting_step\", do_save=False, do_show=True)\n",
    "# plot_dist_optimization_steps(exper, data_set=\"eval\", show=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Rest1=0.4559642584 / Rest2=0.4559642584\n",
      "Rest1=0.4225023902 / Rest2=0.4225023902\n",
      "Rest1=0.1924583571 / Rest2=0.1924583571\n",
      "Rest1=0.1638096644 / Rest2=0.1638096644\n",
      "Rest1=0.0627206648 / Rest2=0.0627206648\n",
      "Rest1=0.0373422571 / Rest2=0.0373422571\n",
      "Rest1=0.0308639525 / Rest2=0.0308639525\n",
      "Rest1=0.0296295225 / Rest2=0.0296295225\n",
      "Rest1=0.0100686135 / Rest2=0.0100686135\n",
      "Rest1=0.0026871904 / Rest2=0.0026871904\n",
      "Rest1=0.0009717060 / Rest2=0.0009717060\n",
      "Rest1=0.0008266473 / Rest2=0.0008266473\n",
      "Rest1=0.0001129760 / Rest2=0.0001129760\n",
      "Rest1=0.0000713150 / Rest2=0.0000713150\n",
      "Rest1=0.0000356434 / Rest2=0.0000356434\n",
      "[  5.44035742e-01   3.34618682e-02   2.30044033e-01   2.86486927e-02\n",
      "   1.01089000e-01   2.53784078e-02   6.47830456e-03   1.23443001e-03\n",
      "   1.95609090e-02   7.38142307e-03   1.71548439e-03   1.45058780e-04\n",
      "   7.13671259e-04   4.16609563e-05   3.56716539e-05]\n",
      "0.999964356607\n"
     ]
    }
   ],
   "source": [
    "\"\"\"\n",
    "    Basic stick-breaking approach\n",
    "\"\"\"\n",
    "T = 15\n",
    "qt = np.zeros(T)\n",
    "rho = np.zeros(T)\n",
    "\n",
    "for t in np.arange(T):\n",
    "    rho[t] = np.random.uniform()\n",
    "    if t > 0:\n",
    "        qt[t] = np.prod((1. - rho[0:t])) * rho[t]\n",
    "    else:\n",
    "        qt[t] = rho[t]\n",
    "    rest1 = np.prod((1. - rho))\n",
    "    rest2 = 1. - np.sum(qt)\n",
    "    print(\"Rest1={:.10f} / Rest2={:.10f}\".format(rest1, rest2))\n",
    "print(np.array_str(qt))\n",
    "print(np.sum(qt))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def nll_with_t_dist(true_y, est_y, N, shape_p=1., scale_p=1., avg_batch=False):\n",
    "    \"\"\"\n",
    "\n",
    "    :param true_y:\n",
    "    :param est_y:\n",
    "    :param N:\n",
    "    :param shape_p: degrees of freedom (nu)\n",
    "    :param scale_p: scale parameter lambda\n",
    "    :param avg_batch: average over batch dimension (dim0)\n",
    "    :return: negative log-likelihood based on student-t distribution\n",
    "    \"\"\"\n",
    "    # source of NLL is http://web.itu.edu.tr/etaner/wseasJ05.pdf eq(7)\n",
    "    C = (-1) * N * (gammaln((shape_p + 1)/2.) - gammaln(shape_p/2.) - 0.5 * np.log(np.pi * shape_p) -\\\n",
    "                    np.log(scale_p))\n",
    "    nll = (shape_p + 1)/2. * torch.sum(torch.log(1. + (1./(scale_p * np.sqrt(shape_p)) * true_y - est_y)**2), 1)\n",
    "    nll += C\n",
    "    if avg_batch:\n",
    "        nll = torch.mean(nll, 0).squeeze()\n",
    "\n",
    "    return nll"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "shape parameter pT 0.930\n",
      "shape parameter ptT 0.930\n"
     ]
    }
   ],
   "source": [
    "exper = get_experiment(\"run_20170707_09_02_13_actV1_20ep_21ops_lr5e06_adam\")\n",
    "print(\"shape parameter pT {:.3f}\".format(exper.config.pT_shape_param))\n",
    "print(\"shape parameter ptT {:.3f}\".format(exper.config.ptT_shape_param))     "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'utils.regression.RegressionWithStudentT'>\n"
     ]
    }
   ],
   "source": [
    "print(r_funcs.__class__)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('Initial: ', array([-1.64124417,  0.24064669, -0.4123731 , -1.13879979, -0.99921674,\n",
      "        0.26206735, -1.52250254,  0.21178144, -0.25012246, -0.33881077], dtype=float32))\n",
      "('True: ', array([ 0.75546157,  0.80159581,  0.6876142 ,  0.76666152,  0.44648236,\n",
      "        0.48281795,  0.71662086,  0.29410532,  0.08327106,  0.45288184], dtype=float32))\n",
      "Iters: 10000, loss 102.42600\n",
      "NLL init versus last 37.570 / 22.154\n",
      "(' Final params ', array([-93.97291565,  25.33129501, -27.49786568,  23.7045517 ,\n",
      "        28.71099091,  56.06012344,  95.63793945, -93.16131592,\n",
      "        -7.79728222,  -5.27361822], dtype=float32))\n"
     ]
    }
   ],
   "source": [
    "batch_size = 20\n",
    "use_cuda = True\n",
    "# r_funcs = L2LQuadratic(batch_size=batch_size, num_dims=10, stddev=0.01, use_cuda=use_cuda)\n",
    "r_funcs = RegressionWithStudentT(n_funcs=batch_size, n_samples=10, x_dim=10, scale_p=1., shape_p=1, \n",
    "                                 use_cuda=use_cuda)\n",
    "lr = 1e-3\n",
    "\n",
    "print(\"Initial: \" , r_funcs.params.data.cpu().numpy()[0, :])\n",
    "print(\"True: \" , r_funcs.true_params.data.cpu().numpy()[0, :])\n",
    "\n",
    "init_nll = r_funcs.compute_neg_ll(average_over_funcs=True)\n",
    "losses = []\n",
    "for i in range(10000):\n",
    "    loss = r_funcs.compute_loss(average=False)\n",
    "    losses.append(loss)\n",
    "    if use_cuda:\n",
    "        loss.backward(torch.ones(batch_size).cuda())\n",
    "    else:\n",
    "        loss.backward(torch.ones(batch_size))\n",
    "        \n",
    "    # print(\"Sum gradients \", torch.sum(r_funcs.params.grad.data))\n",
    "    new_params = r_funcs.params - lr * r_funcs.params.grad\n",
    "    r_funcs.set_parameters(new_params)\n",
    "    neg_ll = r_funcs.compute_neg_ll(average_over_funcs=True)\n",
    "    # print(\"neg ll {:.4f}\".format(torch.mean(neg_ll).data.cpu().numpy()[0]))\n",
    "    post_loss = r_funcs.compute_loss(average=True, params=None)\n",
    "    # print(\"Loss {:.4f}\".format(post_loss.data.cpu().numpy()[0]))\n",
    "    r_funcs.params.grad.data.zero_()\n",
    "    if post_loss.data.cpu().squeeze().numpy()[0] < 0.1:\n",
    "        print(\"Final loss {:.5f}\".format(post_loss.data.cpu().numpy()[0]))\n",
    "        break\n",
    "# print(\"Function {}\".format(r_funcs.poly_desc(idx=0)))\n",
    "print(\"Iters: {}, loss {:.5f}\".format(i+1, torch.mean(loss).data.cpu().squeeze().numpy()[0]))\n",
    "print(\"NLL init versus last {:.3f} / {:.3f}\".format(init_nll.data.cpu().squeeze().numpy()[0],\n",
    "                                                   neg_ll.data.cpu().squeeze().numpy()[0]))\n",
    "print(\" Final params \", r_funcs.params.data.cpu().numpy()[0, :])\n",
    "\n",
    "# print(\"Loss: {:.5f}\".format(post_loss.data.cpu().squeeze().numpy()[0]))\n",
    "# print(\" Final params \", r_funcs.params.data.cpu().numpy()[0, :])\n",
    "# print(\"Loss with true parameters {:.14f}\".format(r_funcs.min_loss.data.cpu().numpy()[0]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "r_funcs = RegressionFunction(n_funcs=100, n_samples=100, noise_sigma=1., poly_degree=2, non_linear=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "E[T] 26.0\n",
      "Sample mean 25.81\n",
      "Sum of probs 1.00\n"
     ]
    },
    {
     "data": {
      "image/png": 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OZ+afjXF4X2BpZj5YXusaYD5QG8TNB67IzARuiYgtImK7zFyRmf8eEbPbKIPU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\nSZKkCjKIkyRJqqD/B8BcOOGJ24GuAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f66bbbb5cd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Median 22.0\n"
     ]
    },
    {
     "data": {
      "image/png": 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re+4OUiWNotaHu4NUSaOo9W0ZB6mSRlGrw91BqqRR1dpwd5AqaZS1tufuIFXSKGttuDtI\nlTTKWtuWcZAqaZS1MtwdpEoada0LdwepktTCnruDVElqYbg7SJWklrVlur32LVvgyBEHqZJGV2vC\n3V67JD2pNW0Ze+2S9KTWhLu9dkl6UivaMvbaJempGh/u9tol6eka35ax1y5JT9fYlXu3FXPiicWK\nvbtyt9cuSX2Ge0SsBz4JLAauzcyP9ZyPzvk3Ar8A3p2Z36m41if0tmLstUvSU80Y7hGxGLgaOA84\nCOyOiB2Zua902fnA2s7H2cCnOp8rNzEBV1wBjz0Gx48XAX/kiBuESVJZPyv3s4ADmXkvQERsBzYA\n5XDfANyQmQncGhHLI+LkzHywymK7K/ZusC9aZCtGkibTz0B1JfBA6fXBzrHZXjNv3eFpN9jPPde7\nYyRpMgs6UI2ITcAmgNWrV8/613ffqNTttV9xRTOC/YwXndHHRX1cI0l96ifcDwGnlF6v6hyb7TVk\n5jZgG8DY2FjOqlKa+3SlLeu39HFRH9dIUp/6CffdwNqIOJUisDcCF/RcswO4tNOPPxt4pOp+e9e6\ndc0JdUkalhnDPTOPRcSlwM0Ut0Jel5l7I+LizvmtwE6K2yAPUNwKedHgSpYkzaSvnntm7qQI8PKx\nraWvE7ik2tIkSXPV+O0HJElPZ7hLUgsZ7pLUQoa7JLWQ4S5JLRTFjS5D+MYRh4EfzfGXnwT8tMJy\nBsEaq2GN1bDG+atLfS/JzBUzXTS0cJ+PiNiTmWPDrmM61lgNa6yGNc5f3evrZVtGklrIcJekFmpq\nuG8bdgF9sMZqWGM1rHH+6l7fUzSy5y5Jml5TV+6SpGk0LtwjYn1E7I+IAxFx2bDrAYiIUyLiPyNi\nX0TsjYgPdI4/PyK+FhE/6Hx+3pDrXBwR342IL9W0vuUR8bmI+H5E3B0R62pY4593/o7viojPRsSv\nDbvGiLguIh6KiLtKx6asKSIu7/z87I+INwyxxo93/q7vjIh/i4jldauxdO6DEZERcdIwa5yNRoV7\n6WHd5wOnAW+PiNOGWxUAx4APZuZpwGuBSzp1XQbckplrgVs6r4fpA8Ddpdd1q++TwFcy8xXA6RS1\n1qbGiFgJ/BkwlpmvpNgCe2MNarweWN9zbNKaOv9dbgR+s/Nrrun8XA2jxq8Br8zMVwH3AJfXsEYi\n4hTg9cD9pWPDqrFvjQp3Sg/rzsyjQPdh3UOVmQ9m5nc6X/8fRSitpKjtM53LPgO8dTgVQkSsAt4E\nXFs6XKf6ngv8HvCPAJl5NDMfpkY1diwBfj0ilgDPAH7MkGvMzG8AP+s5PFVNG4DtmflYZt5H8QyG\ns4ZRY2Z+NTOPdV7eSvEEt1rV2PEJ4C+A8oByKDXORtPCfUEexD0fEbEGOBP4FvDC0hOpfgK8cEhl\nAWyh+A/0eOlYneo7FTgMfLrTOro2Ip5JjWrMzEPA31Gs4B6keOLYV6lRjSVT1VTXn6H3AF/ufF2b\nGiNiA3AoM+/oOVWbGqfStHCvtYh4FvB5YHNmPlo+13mgyVBuTYqINwMPZeZtU10zzPo6lgCvBj6V\nmWcCP6envTHsGjt96w0U/xC9GHhmRLyzfM2wa5xMHWsqi4gPU7Q2bxx2LWUR8QzgL4GPDLuWuWha\nuPf1IO5hiIgTKIL9xsy8qXP4fyLi5M75k4GHhlTe7wBviYgfUrSy/iAi/rlG9UGx8jmYmd/qvP4c\nRdjXqcZzgfsy83BmPg7cBPx2zWrsmqqmWv0MRcS7gTcD78gn78uuS42/QfEP+R2dn51VwHci4kXU\np8YpNS3cn3hYd0QspRho7BhyTUREUPSK787Mvy+d2gG8q/P1u4AvLnRtAJl5eWauysw1FH9m/5GZ\n76xLfQCZ+RPggYh4eefQOcA+alQjRTvmtRHxjM7f+TkU85U61dg1VU07gI0RsSyKh96vBb49hPqI\niPUUrcK3ZOYvSqdqUWNmfi8zX5CZazo/OweBV3f+W61FjdPKzEZ9UDyI+x7gv4EPD7ueTk2/S/G/\nvXcCt3c+3gicSHGnwg+ArwPPr0Gt48CXOl/Xqj7gDGBP58/xC8Dzaljj3wDfB+4C/glYNuwagc9S\nzAAepwig905XE/Dhzs/PfuD8IdZ4gKJv3f2Z2Vq3GnvO/xA4aZg1zubDd6hKUgs1rS0jSeqD4S5J\nLWS4S1ILGe6S1EKGuyS1kOEuSS1kuEtSCxnuktRC/w+lQY4nJAbYuAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f66bbb65dd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def find_nearest(array,value):\n",
    "    idx = (np.abs(array-value)).argmin()\n",
    "    return idx, array[idx]\n",
    "\n",
    "T = 150\n",
    "q = 0.945\n",
    "pmfT = TimeStepsDist(q_prob=q, T=T) # 14/0.8\n",
    "\n",
    "print(\"E[T] {}\".format(pmfT.mean))\n",
    "ts = pmfT.rvs(n=1500)\n",
    "# print(ts)\n",
    "print(\"Sample mean {:.2f}\".format(np.mean(ts)))\n",
    "# print(\"Get prob \", pmfT.pmfunc(np.array([3, 4, 5, 6])))\n",
    "pmf = pmfT.pmfunc(normalize=True, T=T)\n",
    "cdf = np.cumsum(pmf)\n",
    "\n",
    "# choice = np.random.uniform(size=10)\n",
    "# print(choice)\n",
    "# index = np.zeros(len(choice)).astype(int)\n",
    "# for i in range(len(choice)):\n",
    "#    index[i], _ = find_nearest(cdf, choice[i])\n",
    "\n",
    "# print(\"Mean \", np.mean(index))\n",
    "# choice = [0.6]\n",
    "print(\"Sum of probs {:.2f}\".format(np.sum(pmfT.pdf)))\n",
    "\n",
    "pmfT.plot_pmf(T, do_save=False, do_show=True)\n",
    "\n",
    "_ = plt.plot(pmfT.cdf[0:T], 'bo', markersize=3)\n",
    "y = np.arange(0, 1, 0.01)\n",
    "mean = np.empty(100)\n",
    "mean.fill(pmfT.mean)\n",
    "median = np.empty(100)\n",
    "median.fill(pmfT.median)\n",
    "print(\"Median {}\".format(pmfT.median))\n",
    "plt.plot(mean, y, 'r')\n",
    "plt.plot(median, y, 'g')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f509a99c5d0>]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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aW9u5smgMS2/6IB89L/99TVaSupRERKTXtLa18/u3qvn5S9tZu+MQQ7Iz+Jvi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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f50a256ab50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = np.arange(1,10)\n",
    "# probs = stats.nbinom.pmf(x, 50, p=0.3)\n",
    "probs = stats.poisson.pmf(x, mu=25)\n",
    "# probs = stats.geom.pmf(x, p=0.1)\n",
    "# trunc_probs = 1./np.sum(probs) * probs\n",
    "plt.plot(x, probs, 'o-')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 3.2295312   1.74654868  1.22379161  0.95348323  0.78738459  0.67462117\n",
      "  0.59290931  0.53091027  0.48223158  0.44298896  0.41068301  0.38363095\n",
      "  0.36065737  0.34091595  0.32378147  0.30878189  0.295554    0.28381375\n",
      "  0.27333573  0.26393876  0.25547559  0.24782528  0.24088761  0.23457883\n",
      "  0.22882845  0.22357673  0.21877274  0.2143728   0.21033931  0.20663972\n",
      "  0.20324574  0.20013275  0.1972792   0.19466622  0.19227725  0.19009773\n",
      "  0.18811486  0.18631739  0.18469544  0.18324032  0.18194448  0.18080132\n",
      "  0.17980517  0.17895116  0.1782352   0.17765389  0.17720452  0.17688501\n",
      "  0.17669389  0.17663028  0.17669389  0.17688501  0.17720452  0.17765389\n",
      "  0.1782352   0.17895116  0.17980517  0.18080132  0.18194448  0.18324032\n",
      "  0.18469544  0.18631739  0.18811486  0.19009773  0.19227725  0.19466622\n",
      "  0.1972792   0.20013275  0.20324574  0.20663972  0.21033931  0.2143728\n",
      "  0.21877274  0.22357673  0.22882845  0.23457883  0.24088761  0.24782528\n",
      "  0.25547559  0.26393876  0.27333573  0.28381375  0.295554    0.30878189\n",
      "  0.32378147  0.34091595  0.36065737  0.38363095  0.41068301  0.44298896\n",
      "  0.48223158  0.53091027  0.59290931  0.67462117  0.78738459  0.95348323\n",
      "  1.22379161  1.74654868  3.2295312 ]\n",
      "1.0\n"
     ]
    },
    {
     "data": {
      "image/png": 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1kOLv+psk/ZmkbyXHfWcz+llLkh6R9KqkF+fYX59ci4jMfwEF4LvAzwHdwLeA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\nZhz8ZmZt5v8DKXkBZI+BzpMAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0010d1e990>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = np.arange(0.01, 1, 0.01)\n",
    "probs = stats.beta.pdf(x, a=0.1, b=0.1)\n",
    "print(probs)\n",
    "trunc_probs = 1./np.sum(probs) * probs\n",
    "plt.plot(x, trunc_probs, 'o-')\n",
    "print(np.sum(trunc_probs))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "E[t|10] 5.0\n"
     ]
    },
    {
     "data": {
      "image/png": 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Fw68kSVNUs0fW9gdWZ2ZfZj4JXAwcUdsgM+/JzFuArFv3TcCVmflQZj4IXAkc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\n7P8Hqx7bkc1Cz4QAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f14f4067510>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "q = 0.9\n",
    "T=10\n",
    "pmf_t_T = ConditionalTimeStepDist(T, q)\n",
    "print(\"E[t|{}] {}\".format(T, pmf_t_T.mean))\n",
    "priors = Variable(torch.from_numpy(pmf_t_T.pmfunc(T=T, normalize=True)).float())\n",
    "\n",
    "samples = pmf_t_T.rvs(n=1000000)\n",
    "# plt.hist(samples, bins=T, normed=True)\n",
    "pmf_t_T.plot_pmf(do_save=False, do_show=True, marker_size=5)\n",
    "# print(\"{}\".format(np.array_str(priors.data.numpy())))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.99999988"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.sum(priors.data.numpy())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "qt = np.array([  3.5374e-57,   1.0894e-56,   2.9237e-56,   8.1168e-56,   2.4303e-55,\n",
    "   7.8928e-55,   2.7671e-54,   1.0375e-53,   4.1141e-53,   1.7077e-52,\n",
    "   7.3567e-52,   3.2699e-51,   1.4881e-50,   6.8986e-50,   3.2434e-49,\n",
    "   1.5402e-48,   7.3734e-48,   3.5472e-47,   1.7114e-46,   8.2555e-46,\n",
    "   1.8072e-45,   3.5995e-45,   7.6743e-45,   1.8595e-44,   5.2279e-44,\n",
    "   1.6857e-43,   6.0178e-43,   2.3144e-42,   9.3980e-42,   3.9703e-41,\n",
    "   1.7292e-40,   7.7205e-40,   3.5106e-39,   1.6162e-38,   7.4983e-38,\n",
    "   3.4950e-37,   1.6351e-36,   7.6630e-36,   3.5991e-35,   1.6895e-34,\n",
    "   3.6486e-34,   7.1267e-34,   1.4746e-33,   3.4694e-33,   9.5001e-33,\n",
    "   2.9994e-32,   1.0618e-31,   4.0581e-31,   1.6410e-30,   6.9501e-30,\n",
    "   3.0378e-29,   1.3630e-28,   6.2531e-28,   2.9126e-27,   1.3689e-26,\n",
    "   6.4614e-26,   3.0598e-25,   1.4492e-24,   6.8716e-24,   3.2535e-23,\n",
    "   7.0361e-23,   1.3707e-22,   2.8222e-22,   6.6022e-22,   1.8029e-21,\n",
    "   5.6797e-21,   2.0004e-20,   7.6115e-20,   3.0600e-19,   1.2873e-18,\n",
    "   5.5859e-18,   2.4871e-17,   1.1317e-16,   5.2226e-16,   2.4347e-15,\n",
    "   1.1413e-14,   5.3754e-14,   2.5340e-13,   1.1977e-12,   5.6558e-12,\n",
    "   1.2228e-11,   2.3864e-11,   4.9239e-11,   1.1532e-10,   3.1436e-10,\n",
    "   9.8838e-10,   3.4828e-09,   1.3241e-08,   5.3262e-08,   2.2427e-07,\n",
    "   9.7413e-07,   4.3450e-06,   1.9813e-05,   9.1626e-05,   4.2797e-04,\n",
    "   2.0097e-03,   9.4783e-03,   4.4737e-02,   2.1160e-01,   1.0000e+00])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('Mean ', 4.0)\n",
      "Sum priors 1.22\n",
      "Length prior 10\n",
      "Priors [ 0.2545  0.2087  0.1711  0.1403  0.1151  0.0944  0.0774  0.0634  0.052\n",
      "  0.0427]\n",
      "Samples [ 4  5  3  3  6  6 10  3  2  3]\n"
     ]
    }
   ],
   "source": [
    "steps = 10\n",
    "pmf_pt_T = ConditionalTimeStepDist(steps, 0.82)\n",
    "print(\"Mean \", pmf_pt_T.mean)\n",
    "priors = Variable(torch.from_numpy(pmf_pt_T.pmfunc(np.arange(0, steps), normalize=True)).float())\n",
    "print(\"Sum priors {:.3}\".format(torch.sum(priors).data.squeeze().numpy()[0]))\n",
    "print(\"Length prior {}\".format(priors.size(0)))\n",
    "print(\"Priors {}\".format(np.array_str(priors.data.squeeze().numpy(), precision=4)))\n",
    "print(\"Samples {}\".format(pmf_pt_T.rvs(n=10)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "T = 15\n",
      "('Cumsum ', 1.0)\n",
      "Mean 4.00\n",
      "CDF prior probs [ 0.25911686  0.46641034  0.63224512  0.76491296  0.87104726  0.95595467\n",
      "  1.0238806   1.07822132  1.12169397  1.15647209  1.18429458  1.20655251\n",
      "  1.22435892  1.23860407  1.25000012]\n",
      "Sum prior_dist 1.25\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f3d26db4050>]"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Jjh07Tj8fGxtjbGysv7+BtExGRzdy993bmy5DOm16eprp6ekzeo1Fh2x2Q/wpOu30zwAP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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f3d2490a790>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "continue_prob = 0.8\n",
    "pt_dist = TimeStepsDist(config.T, continue_prob)\n",
    "# optimizer_steps = pt_dist.rvs(n=1)\n",
    "optimizer_steps = 15\n",
    "print(\"T = {}\".format(optimizer_steps))\n",
    "\n",
    "prior_dist = ConditionalTimeStepDist(optimizer_steps, q_prob=continue_prob)\n",
    "print(\"Cumsum \", prior_dist.cdf[-1])\n",
    "print(\"Mean {:.2f}\".format(prior_dist.mean))\n",
    "prior_probs = Variable(torch.from_numpy(prior_dist.pmfunc(np.arange(0, optimizer_steps), normalize=True)).float())\n",
    "print(\"CDF prior probs {}\".format(np.cumsum(prior_probs.data.squeeze().numpy())))\n",
    "# t = np.arange(optimizer_steps)\n",
    "# prior_probs = pmf_ptT(t, optimizer_steps, config.continue_prob)\n",
    "# print(torch.sum(prior_probs.data.squeeze()))\n",
    "print(\"Sum prior_dist {:.2f}\".format(torch.sum(prior_probs.data.squeeze())))\n",
    "# print(\"Sum prior_dist {:.8f}\".format(np.sum(prior_probs)))\n",
    "plt.plot(prior_probs.data.squeeze().numpy(), 'bo')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "p(t) 0.000 += 1.000 p(1|1) * p(1)\n",
      "p(t) 1.000 += 0.267 p(1|2) * p(2)\n",
      "p(t) 1.267 += 0.131 p(1|3) * p(3)\n",
      "p(t) 0.000 += 0.231 p(2|2) * p(2)\n",
      "p(t) 0.231 += 0.113 p(2|3) * p(3)\n",
      "p(t) 0.000 += 0.088 p(3|3) * p(3)\n",
      "Ready len(pt)=3\n",
      "[ 1.39729359  0.34467426  0.08784334]\n"
     ]
    }
   ],
   "source": [
    "def pmf_pt(T_horizon, q_prob):\n",
    "    \n",
    "    PT_dists = {}\n",
    "    pt_T_dists = {}\n",
    "    for T in range(1, T_horizon+1):\n",
    "        dist = TimeStepsDist(T, q_prob)\n",
    "        PT_dists[T] = dist.pmfunc(np.arange(1, T+1), normalize=True)\n",
    "    for t in range(1, T_horizon+1):\n",
    "        dist = ConditionalTimeStepDist(t, q_prob)\n",
    "        pt_T_dists[t] = dist.pmfunc(np.arange(1, t+1), normalize=True)\n",
    "        \n",
    "    probs = []\n",
    "    t_values = range(1, T_horizon+1)\n",
    "    \n",
    "    for t in t_values:\n",
    "        T_range = range(t, T_horizon+1)\n",
    "        T_sum = 0.\n",
    "        for T in T_range:\n",
    "            \n",
    "            prob = pt_T_dists[T][t-1] * PT_dists[T][t-1]\n",
    "            print(\"p(t) {:.3f} += {:.3f} p({}|{}) * p({})\".format(T_sum, prob, t, T, T))\n",
    "            T_sum += prob\n",
    "        probs.append(T_sum)\n",
    "        \n",
    "    return np.array(probs)\n",
    "\n",
    "pt = pmf_pt(3, 0.8)    \n",
    "print(\"Ready len(pt)={}\".format(len(pt)))\n",
    "print(pt)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def softmax(x):\n",
    "    \"\"\"Compute softmax values for each sets of scores in x.\"\"\"\n",
    "    e_x = np.exp(x - np.max(x))\n",
    "    return e_x / e_x.sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import models.rnn_optimizer\n",
    "myclass = getattr(models.rnn_optimizer, \"AdaptiveMetaLearner\"+\"V2\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 179,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def pmf_ptT(t, T, q_prob):\n",
    "    p = 1 - q_prob\n",
    "    return (p * q_prob**t) / (1 - q_prob**(T+1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "input_num = 10\n",
    "hidden_num = 20\n",
    "\n",
    "lstms = nn.ModuleList()\n",
    "for i in range(num_layers):\n",
    "    self.lstms.append(LayerNormLSTMCell(num_hidden, num_hidden))\n",
    "\n",
    "input = Variable(torch.randn(6, 3, 10))\n",
    "hx = Variable(torch.randn(3, 20))\n",
    "output = []\n",
    "for i in range(6):\n",
    "    hx = rnn(input[i], hx)\n",
    "    output.append(hx)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7fa915a702d0>]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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PiB8GXgY+lJk/eNVPOMAi4nrg+sx8PiKuBT4LbM/MYzWXVouIeF1mfrNzM/0fgXszcyj/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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa915b23110>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(x, stats.geom.pmf(x, p), 'bo', ms=8, label='geom pmf')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "x = np.linspace(-2, 2, 1000)\n",
    "y = np.linspace(-2, 2, 1000)\n",
    "\n",
    "X, Y = np.meshgrid(x,y)\n",
    "Z = np.exp(-0.5 * (X**2 + Y**2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1000, 1000)"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.array(Z).shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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AjB06oQD3vDTJGqTLPQ/h1DTsSlk99N/+ZlbcJdPT0/rcXXnllXjd616HN73p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\nUAq98X0fQgjcf//92LZtW1cJY3lhN7t378batWt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ZFtlENpVNkE1B1KdEo0/eM0bRgxJF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\nkJO86QscAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f292ee32250>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from mpl_toolkits.mplot3d import Axes3D\n",
    "from matplotlib import cm\n",
    "\n",
    "fig = plt.figure()\n",
    "ax = fig.gca(projection='3d')\n",
    "ax.plot_surface(X, Y, Z, cmap=cm.coolwarm,\n",
    "                       linewidth=0, antialiased=False)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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gb1WFl6TCc/dbW/oa9HMqwiVYD7LVsI4Ul3nWtDk/OXqSbk197r1kl2Ov+ZkM0rlgzsw1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\nNzLPWs8+PoBAERpIvxfaXli3hralw/nJ0dPGTcdO/Vr6nH8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oe/XaCe41gAcK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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd052642b90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from cycler import cycler\n",
    "MAP = 'summer'\n",
    "cm = plt.get_cmap(MAP)\n",
    "\n",
    "plt.figure(figsize=(8,6))\n",
    "ax = plt.gca()\n",
    "plt.contourf(X, Y, Z, 300)\n",
    "x_point = [9, 8.5, 8, 6.4, 5, 4.3, 3, 0]\n",
    "y_point = [-9, -8.2, -6.9, -6, -5.3, -4, -1, 0]\n",
    "NPOINTS = len(x_point)\n",
    "# ax.set_color_cycle([cm(1.*i/(NPOINTS-1)) for i in range(NPOINTS-1)])\n",
    "ax.set_prop_cycle(cycler('color', plt.cm.Accent(np.linspace(0, 1, NPOINTS))))\n",
    "ax.set_prop_cycle(cycler('color', [cm(1.*i/(NPOINTS-1)) for i in range(NPOINTS-1)]))\n",
    "\n",
    "for i in range(NPOINTS-1):\n",
    "    ax.plot(x_point[i:i+2],y_point[i:i+2], 'o-')\n",
    "# plt.axis('off')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "minimum at (4.334/7.437) with value 123.9278\n"
     ]
    }
   ],
   "source": [
    "\n",
    "print(\"minimum at (%.3f/%.3f) with value %.4f\" % (X.ravel()[np.argmin(Z)], Y.ravel()[np.argmin(Z)], np.min(Z)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "      fun: 123.92781597315845\n",
       " hess_inv: array([[ 3.84370028,  4.17917771],\n",
       "       [ 4.17917771,  7.14183093]])\n",
       "      jac: array([ 0.,  0.])\n",
       "  message: 'Optimization terminated successfully.'\n",
       "     nfev: 20\n",
       "      nit: 4\n",
       "     njev: 5\n",
       "   status: 0\n",
       "  success: True\n",
       "        x: array([ 4.33719279,  7.43479978])"
      ]
     },
     "execution_count": 128,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from scipy.optimize import minimize\n",
    "minimize(quad_2D, [2,4], args=(W), method='BFGS')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1000, 1000)"
      ]
     },
     "execution_count": 130,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Z.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "torch.mean received an invalid combination of arguments - got (NoneType), but expected one of:\n * (torch.FloatTensor source)\n * (torch.FloatTensor source, int dim)\n",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-13-01755aae351e>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m     22\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     23\u001b[0m \u001b[0mloss_sum\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mbackward\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 24\u001b[1;33m \u001b[1;32mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"grads {:.3f}\"\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtorch\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmean\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmeta_x\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mgrad\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mnumpy\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     25\u001b[0m \u001b[1;32mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"new x \"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mx\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mnumpy\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     26\u001b[0m \u001b[1;32mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"meta_x \"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmeta_x\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mnumpy\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mTypeError\u001b[0m: torch.mean received an invalid combination of arguments - got (NoneType), but expected one of:\n * (torch.FloatTensor source)\n * (torch.FloatTensor source, int dim)\n"
     ]
    }
   ],
   "source": [
    "myfunc = lambda x: torch.sum((x+2.45)**2 * 2.34)\n",
    "x = Variable(torch.FloatTensor([6]), requires_grad=True)\n",
    "meta_x = Variable(torch.FloatTensor([6]), requires_grad=True)\n",
    "loss = myfunc(x)\n",
    "meta_loss = myfunc(meta_x)\n",
    "loss_sum = 0\n",
    "for i in np.arange(200):\n",
    "    loss_sum = loss_sum + meta_loss\n",
    "    loss = myfunc(x)\n",
    "    loss.backward()\n",
    "    # print(\"grads {:.3f}\".format(torch.mean(x.grad).data.numpy()[0]))\n",
    "    x_new = x - 0.01 * x.grad\n",
    "    x.grad.data.zero_()\n",
    "    x.data.copy_(x_new.data)\n",
    "    if i == 1:\n",
    "        meta_x.data.copy_(x_new.data)\n",
    "    else:\n",
    "        meta_x = Variable(x_new.data, requires_grad=True)\n",
    "    # print(\"new x \", x.data.numpy())\n",
    "    loss = myfunc(x)\n",
    "    meta_loss = myfunc(meta_x)\n",
    "\n",
    "loss_sum.backward()\n",
    "print(\"grads {:.3f}\".format(torch.mean(meta_x.grad).data.numpy()[0]))\n",
    "print(\"new x \", x.data.numpy())\n",
    "print(\"meta_x \", meta_x.data.numpy())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Current x-value 5.999\n",
      "Current x-value 5.998\n",
      "Current x-value 5.997\n",
      "Current x-value 5.996\n",
      "Current x-value 5.995\n",
      "Current x-value 5.994\n",
      "Current x-value 5.993\n",
      "Current x-value 5.992\n",
      "Current x-value 5.991\n",
      "Current x-value 5.990\n"
     ]
    }
   ],
   "source": [
    "class MyFunc(object):\n",
    "    def __init__(self):\n",
    "        self.f = lambda x: torch.sum((x+2.45)**2 * 2.34)\n",
    "        self.x = Variable(torch.FloatTensor([6]), requires_grad=True)\n",
    "\n",
    "func = MyFunc()\n",
    "optimizer = optim.Adam([func.x], lr=1e-3)\n",
    "for i in np.arange(10):\n",
    "    loss = func.f(func.x)\n",
    "    loss.backward()\n",
    "    optimizer.step()\n",
    "    x.grad.data.zero_()\n",
    "    print(\"Current x-value {:.3f}\".format(func.x.data.numpy()[0]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "rnn = nn.LSTM(1, 20, 2)\n",
    "input = Variable(torch.randn(5, 3, 1))\n",
    "h0 = Variable(torch.randn(2, 3, 20))\n",
    "c0 = Variable(torch.randn(2, 3, 20))\n",
    "output, hn = rnn(input, (h0, c0))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([80, 1])\n",
      "torch.Size([80, 20])\n",
      "torch.Size([80])\n",
      "torch.Size([80])\n",
      "torch.Size([80, 20])\n",
      "torch.Size([80, 20])\n",
      "torch.Size([80])\n",
      "torch.Size([80])\n"
     ]
    }
   ],
   "source": [
    "for param_name, params in rnn._parameters.iteritems():\n",
    "    print(params.size())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['linear1.weight', 'linear1.bias', 'ln1.weight', 'ln1.bias', 'lstms.0.fc_i2h.weight', 'lstms.0.fc_i2h.bias', 'lstms.0.fc_h2h.weight', 'lstms.0.fc_h2h.bias', 'lstms.0.ln_i2h.weight', 'lstms.0.ln_i2h.bias', 'lstms.0.ln_h2h.weight', 'lstms.0.ln_h2h.bias', 'lstms.0.ln_h2o.weight', 'lstms.0.ln_h2o.bias', 'lstms.1.fc_i2h.weight', 'lstms.1.fc_i2h.bias', 'lstms.1.fc_h2h.weight', 'lstms.1.fc_h2h.bias', 'lstms.1.ln_i2h.weight', 'lstms.1.ln_i2h.bias', 'lstms.1.ln_h2h.weight', 'lstms.1.ln_h2h.bias', 'lstms.1.ln_h2o.weight', 'lstms.1.ln_h2o.bias', 'linear_out.weight', 'linear_out.bias']\n",
      "['weight', 'bias']\n",
      "['weight', 'bias']\n",
      "['0.fc_i2h.weight', '0.fc_i2h.bias', '0.fc_h2h.weight', '0.fc_h2h.bias', '0.ln_i2h.weight', '0.ln_i2h.bias', '0.ln_h2h.weight', '0.ln_h2h.bias', '0.ln_h2o.weight', '0.ln_h2o.bias', '1.fc_i2h.weight', '1.fc_i2h.bias', '1.fc_h2h.weight', '1.fc_h2h.bias', '1.ln_i2h.weight', '1.ln_i2h.bias', '1.ln_h2h.weight', '1.ln_h2h.bias', '1.ln_h2o.weight', '1.ln_h2o.bias']\n",
      "['weight', 'bias']\n"
     ]
    }
   ],
   "source": [
    "q2_func = Quadratic2D(False)\n",
    "\n",
    "meta_optimizer = MetaLearner(HelperQuadratic(q2_func), num_layers=2, num_hidden=20, use_cuda=False)\n",
    "print(meta_optimizer.state_dict().keys())\n",
    "for child in meta_optimizer.children():\n",
    "    print(child.state_dict().keys())\n",
    "\n",
    "meta_optimizer.load_state_dict(torch.load('meta_learner_v1.pkl'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "myfunc_list = []\n",
    "for i in np.arange(20):\n",
    "    f = Quadratic2D()\n",
    "    myfunc_list.append(f)\n",
    "    \n",
    "\n",
    "myfunc_file = os.path.join(\"utils\", \"val_functions.pkl\")\n",
    "with open(myfunc_file, 'wb') as f:\n",
    "    dill.dump(myfunc_list, f)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1 func: ((x1-1.50)^2/0.03^2+(x2-1.27)^2/0.03^2)+0.44\n",
      "Minimum 1.496, 1.274\n",
      "Final parameter values (1.03,23.35)\n"
     ]
    }
   ],
   "source": [
    "myfunc_file = os.path.join(\"logs/run_20170322_14_00_47\", \"diff_funcs.dll\")\n",
    "with open(myfunc_file, 'rb') as f:\n",
    "    loaded_funcs = dill.load(f)\n",
    "    \n",
    "for i in np.arange(len(loaded_funcs)):\n",
    "    myfunc = loaded_funcs[i]\n",
    "    print(\"{} func: {}\".format(i+1, loaded_funcs[i].poly_desc))\n",
    "    print(\"Minimum {:.3f}, {:.3f}\".format(loaded_funcs[i].true_opt[0].data.numpy()[0],\n",
    "                                         loaded_funcs[i].true_opt[1].data.numpy()[0]))\n",
    "    print(\"Final parameter values ({:.2f},{:.2f})\".format(myfunc.parameter[0].data.numpy()[0],\n",
    "                                                          myfunc.parameter[1].data.numpy()[0]))\n",
    "    \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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