notebooks/classification/KFAC_Laplace_MNIST.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Base imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "from __future__ import print_function, division\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "# sns.set()\n",
    "\n",
    "import torch\n",
    "from torch import nn, optim\n",
    "from torch.autograd import Variable\n",
    "from torch.optim import Optimizer\n",
    "\n",
    "\n",
    "import collections\n",
    "import h5py, sys\n",
    "import gzip\n",
    "import os\n",
    "import math\n",
    "\n",
    "\n",
    "try:\n",
    "    import cPickle as pickle\n",
    "except:\n",
    "    import pickle"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Some utility functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def mkdir(paths):\n",
    "    if not isinstance(paths, (list, tuple)):\n",
    "        paths = [paths]\n",
    "    for path in paths:\n",
    "        if not os.path.isdir(path):\n",
    "            os.makedirs(path)\n",
    "\n",
    "from __future__ import print_function\n",
    "import torch\n",
    "from torch import nn, optim\n",
    "from torch.autograd import Variable\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import sys\n",
    "\n",
    "suffixes = ['B', 'KB', 'MB', 'GB', 'TB', 'PB']\n",
    "def humansize(nbytes):\n",
    "    i = 0\n",
    "    while nbytes >= 1024 and i < len(suffixes)-1:\n",
    "        nbytes /= 1024.\n",
    "        i += 1\n",
    "    f = ('%.2f' % nbytes)\n",
    "    return '%s%s' % (f, suffixes[i])\n",
    "\n",
    "\n",
    "def get_num_batches(nb_samples, batch_size, roundup=True):\n",
    "    if roundup:\n",
    "        return ((nb_samples + (-nb_samples % batch_size)) / batch_size)  # roundup division\n",
    "    else:\n",
    "        return nb_samples / batch_size\n",
    "\n",
    "def generate_ind_batch(nb_samples, batch_size, random=True, roundup=True):\n",
    "    if random:\n",
    "        ind = np.random.permutation(nb_samples)\n",
    "    else:\n",
    "        ind = range(int(nb_samples))\n",
    "    for i in range(int(get_num_batches(nb_samples, batch_size, roundup))):\n",
    "        yield ind[i * batch_size: (i + 1) * batch_size]\n",
    "\n",
    "def to_variable(var=(), cuda=True, volatile=False):\n",
    "    out = []\n",
    "    for v in var:\n",
    "        if isinstance(v, np.ndarray):\n",
    "            v = torch.from_numpy(v).type(torch.FloatTensor)\n",
    "\n",
    "        if not v.is_cuda and cuda:\n",
    "            v = v.cuda()\n",
    "\n",
    "        if not isinstance(v, Variable):\n",
    "            v = Variable(v, volatile=volatile)\n",
    "\n",
    "        out.append(v)\n",
    "    return out\n",
    "  \n",
    "def cprint(color, text, **kwargs):\n",
    "    if color[0] == '*':\n",
    "        pre_code = '1;'\n",
    "        color = color[1:]\n",
    "    else:\n",
    "        pre_code = ''\n",
    "    code = {\n",
    "        'a': '30',\n",
    "        'r': '31',\n",
    "        'g': '32',\n",
    "        'y': '33',\n",
    "        'b': '34',\n",
    "        'p': '35',\n",
    "        'c': '36',\n",
    "        'w': '37'\n",
    "    }\n",
    "    print(\"\\x1b[%s%sm%s\\x1b[0m\" % (pre_code, code[color], text), **kwargs)\n",
    "    sys.stdout.flush()\n",
    "\n",
    "def shuffle_in_unison_scary(a, b):\n",
    "    rng_state = np.random.get_state()\n",
    "    np.random.shuffle(a)\n",
    "    np.random.set_state(rng_state)\n",
    "    np.random.shuffle(b)\n",
    "    \n",
    "    \n",
    "import torch.utils.data as data\n",
    "from PIL import Image\n",
    "import numpy as np\n",
    "import h5py\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Dataloader functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "class Datafeed(data.Dataset):\n",
    "\n",
    "    def __init__(self, x_train, y_train, transform=None):\n",
    "        self.x_train = x_train\n",
    "        self.y_train = y_train\n",
    "        self.transform = transform\n",
    "\n",
    "    def __getitem__(self, index):\n",
    "        img = self.x_train[index]\n",
    "        if self.transform is not None:\n",
    "            img = self.transform(img)\n",
    "        return img, self.y_train[index]\n",
    "\n",
    "    def __len__(self):\n",
    "        return len(self.x_train)\n",
    "\n",
    "class DatafeedImage(data.Dataset):\n",
    "    def __init__(self, x_train, y_train, transform=None):\n",
    "        self.x_train = x_train\n",
    "        self.y_train = y_train\n",
    "        self.transform = transform\n",
    "\n",
    "    def __getitem__(self, index):\n",
    "        img = self.x_train[index]\n",
    "        img = Image.fromarray(np.uint8(img))\n",
    "        if self.transform is not None:\n",
    "            img = self.transform(img)\n",
    "        return img, self.y_train[index]\n",
    "\n",
    "    def __len__(self):\n",
    "        return len(self.x_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Base network wrapper"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch.nn.functional as F\n",
    "class BaseNet(object):\n",
    "    def __init__(self):\n",
    "        cprint('c', '\\nNet:')\n",
    "\n",
    "    def get_nb_parameters(self):\n",
    "        return np.sum(p.numel() for p in self.model.parameters())\n",
    "\n",
    "    def set_mode_train(self, train=True):\n",
    "        if train:\n",
    "            self.model.train()\n",
    "        else:\n",
    "            self.model.eval()\n",
    "\n",
    "    def update_lr(self, epoch, gamma=0.99):\n",
    "        self.epoch += 1\n",
    "        if self.schedule is not None:\n",
    "            if len(self.schedule) == 0 or epoch in self.schedule:\n",
    "                self.lr *= gamma\n",
    "                print('learning rate: %f  (%d)\\n' % self.lr, epoch)\n",
    "                for param_group in self.optimizer.param_groups:\n",
    "                    param_group['lr'] = self.lr\n",
    "\n",
    "    def save(self, filename):\n",
    "        cprint('c', 'Writting %s\\n' % filename)\n",
    "        torch.save({\n",
    "            'epoch': self.epoch,\n",
    "            'lr': self.lr,\n",
    "            'model': self.model,\n",
    "            'optimizer': self.optimizer}, filename)\n",
    "\n",
    "    def load(self, filename):\n",
    "        cprint('c', 'Reading %s\\n' % filename)\n",
    "        state_dict = torch.load(filename)\n",
    "        self.epoch = state_dict['epoch']\n",
    "        self.lr = state_dict['lr']\n",
    "        self.model = state_dict['model']\n",
    "        self.optimizer = state_dict['optimizer']\n",
    "        print('  restoring epoch: %d, lr: %f' % (self.epoch, self.lr))\n",
    "        return self.epoch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Our models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "class Linear_2L_KFRA(nn.Module):\n",
    "    def __init__(self, input_dim, output_dim, n_hid):\n",
    "        super(Linear_2L_KFRA, self).__init__()\n",
    "\n",
    "        self.n_hid = n_hid\n",
    "\n",
    "        self.input_dim = input_dim\n",
    "        self.output_dim = output_dim\n",
    "\n",
    "        self.fc1 = nn.Linear(input_dim, self.n_hid)\n",
    "        self.fc2 = nn.Linear(self.n_hid, self.n_hid)\n",
    "        self.fc3 = nn.Linear(self.n_hid, output_dim)\n",
    "\n",
    "        # choose your non linearity\n",
    "        self.act = nn.ReLU(inplace=True)\n",
    "\n",
    "        self.one = None\n",
    "        self.a2 = None\n",
    "        self.h2 = None\n",
    "        self.a1 = None\n",
    "        self.h1 = None\n",
    "        self.a0 = None\n",
    "\n",
    "    def forward(self, x):\n",
    "        self.one = x.new(x.shape[0], 1).fill_(1)\n",
    "\n",
    "        a0 = x.view(-1, self.input_dim)  # view(batch_size, input_dim)\n",
    "        self.a0 = torch.cat((a0.data, self.one), dim=1)\n",
    "        # -----------------\n",
    "        h1 = self.fc1(a0)\n",
    "        self.h1 = h1.data  # torch.cat((h1, self.one), dim=1)\n",
    "        # -----------------\n",
    "        a1 = self.act(h1)\n",
    "        #         a1.retain_grad()\n",
    "        self.a1 = torch.cat((a1.data, self.one), dim=1)\n",
    "        # -----------------\n",
    "        h2 = self.fc2(a1)\n",
    "        self.h2 = h2.data  # torch.cat((h2, self.one), dim=1)\n",
    "        # -----------------\n",
    "        a2 = self.act(h2)\n",
    "        #         a2.retain_grad()\n",
    "        self.a2 = torch.cat((a2.data, self.one), dim=1)\n",
    "        # -----------------\n",
    "        h3 = self.fc3(a2)\n",
    "\n",
    "        return h3\n",
    "\n",
    "    def sample_predict(self, x, Nsamples, Qinv1, HHinv1, MAP1, Qinv2, HHinv2, MAP2, Qinv3, HHinv3, MAP3):\n",
    "        # Just copies type from x, initializes new vector\n",
    "        predictions = x.data.new(Nsamples, x.shape[0], self.output_dim)\n",
    "        x = x.view(-1, self.input_dim)\n",
    "        for i in range(Nsamples):\n",
    "            # -----------------\n",
    "            w1, b1 = sample_K_laplace_MN(MAP1, Qinv1, HHinv1)\n",
    "            a = torch.matmul(x, torch.t(w1)) + b1.unsqueeze(0)\n",
    "            a = self.act(a)\n",
    "            # -----------------\n",
    "            w2, b2 = sample_K_laplace_MN(MAP2, Qinv2, HHinv2)\n",
    "            a = torch.matmul(a, torch.t(w2)) + b2.unsqueeze(0)\n",
    "            a = self.act(a)\n",
    "            # -----------------\n",
    "            w3, b3 = sample_K_laplace_MN(MAP3, Qinv3, HHinv3)\n",
    "            y = torch.matmul(a, torch.t(w3)) + b3.unsqueeze(0)\n",
    "            predictions[i] = y\n",
    "\n",
    "        return predictions\n",
    "        \n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## KFAC functions (they work batchwise if needed)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "def softmax_CE_preact_hessian(last_layer_acts):\n",
    "    side = last_layer_acts.shape[1]\n",
    "    I = torch.eye(side).type(torch.ByteTensor)\n",
    "    # for i != j    H = -ai * aj -- Note that these are activations not pre-activations\n",
    "    Hl = - last_layer_acts.unsqueeze(1) * last_layer_acts.unsqueeze(2)\n",
    "    # for i == j    H = ai * (1 - ai)\n",
    "    Hl[:, I] = last_layer_acts*(1-last_layer_acts)\n",
    "    return Hl\n",
    "\n",
    "def layer_act_hessian_recurse(prev_hessian, prev_weights, layer_pre_acts):\n",
    "    \n",
    "    newside = layer_pre_acts.shape[1]\n",
    "    batch_size = layer_pre_acts.shape[0]\n",
    "    I = torch.eye(newside).type(torch.ByteTensor) # .unsqueeze(0).expand([batch_size, -1, -1])\n",
    "    \n",
    "#     print(d_act(layer_pre_acts).unsqueeze(1).shape, I.shape)\n",
    "    B = prev_weights.data.new(batch_size, newside, newside).fill_(0)\n",
    "    B[:, I] = (layer_pre_acts > 0).type(B.type()) # d_act(layer_pre_acts)\n",
    "    D = prev_weights.data.new(batch_size, newside, newside).fill_(0) # is just 0 for a piecewise linear\n",
    "#     D[:, I] = dd_act(layer_pre_acts) * act_grads\n",
    "\n",
    "    Hl = torch.bmm(torch.t(prev_weights).unsqueeze(0).expand([batch_size, -1, -1]), prev_hessian)    \n",
    "    Hl = torch.bmm(Hl, prev_weights.unsqueeze(0).expand([batch_size, -1, -1]))\n",
    "    Hl = torch.bmm(B, Hl)\n",
    "    Hl = torch.matmul(Hl, B)\n",
    "    Hl = Hl + D\n",
    "    \n",
    "    return Hl\n",
    "\n",
    "def chol_scale_invert_kron_factor(factor, prior_scale, data_scale, upper=False):\n",
    "    \n",
    "    scaled_factor = data_scale * factor + prior_scale * torch.eye(factor.shape[0]).type(factor.type())\n",
    "    inv_factor = torch.inverse(scaled_factor)\n",
    "    chol_inv_factor = torch.cholesky(inv_factor, upper=upper)\n",
    "    return chol_inv_factor\n",
    "\n",
    "def sample_K_laplace_MN(MAP, upper_Qinv, lower_HHinv):\n",
    "    # H = Qi (kron) HHi\n",
    "    # sample isotropic unit variance mtrix normal\n",
    "    Z = MAP.data.new(MAP.size()).normal_(mean=0, std=1)\n",
    "    # AAT = HHi\n",
    "#     A = torch.cholesky(HHinv, upper=False)\n",
    "    # BTB = Qi\n",
    "#     B = torch.cholesky(Qinv, upper=True)\n",
    "    all_mtx_sample = MAP + torch.matmul(torch.matmul(lower_HHinv, Z), upper_Qinv)\n",
    "    \n",
    "    weight_mtx_sample = all_mtx_sample[:, :-1]\n",
    "    bias_mtx_sample = all_mtx_sample[:, -1]\n",
    "    \n",
    "    return weight_mtx_sample, bias_mtx_sample\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## quick define FGSM function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "def fgsm_attack(image, epsilon, data_grad):\n",
    "    # Collect the element-wise sign of the data gradient\n",
    "    sign_data_grad = data_grad.sign()\n",
    "    # Create the perturbed image by adjusting each pixel of the input image\n",
    "    perturbed_image = image + epsilon*sign_data_grad\n",
    "    # Adding clipping to maintain [0,1] range\n",
    "#     perturbed_image = torch.clamp(perturbed_image, 0, 1)\n",
    "    # Return the perturbed image\n",
    "    return perturbed_image"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Network wrapper"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "from __future__ import division\n",
    "\n",
    "class KBayes_Net(BaseNet):\n",
    "    eps = 1e-6\n",
    "\n",
    "    def __init__(self, lr=1e-3, channels_in=3, side_in=28, cuda=True, classes=10, n_hid=1200, batch_size=128, prior_sig=0):\n",
    "        super(KBayes_Net, self).__init__()\n",
    "        cprint('y', ' Creating Net!! ')\n",
    "        self.lr = lr\n",
    "        self.schedule = None  # [] #[50,200,400,600]\n",
    "        self.cuda = cuda\n",
    "        self.n_hid=n_hid\n",
    "        self.channels_in = channels_in\n",
    "        self.prior_sig = prior_sig\n",
    "        self.classes = classes\n",
    "        self.batch_size = batch_size\n",
    "        self.side_in=side_in\n",
    "        self.create_net()\n",
    "        self.create_opt()\n",
    "        self.epoch = 0\n",
    "\n",
    "        self.test=False\n",
    "\n",
    "    def create_net(self):\n",
    "        torch.manual_seed(42)\n",
    "        if self.cuda:\n",
    "            torch.cuda.manual_seed(42)\n",
    "\n",
    "        self.model = Linear_2L_KFRA(input_dim=self.channels_in*self.side_in*self.side_in, output_dim=self.classes, n_hid=self.n_hid)\n",
    "        if self.cuda:\n",
    "            self.model.cuda()\n",
    "#             cudnn.benchmark = True\n",
    "\n",
    "        print('    Total params: %.2fM' % (self.get_nb_parameters() / 1000000.0))\n",
    "\n",
    "    def create_opt(self):\n",
    "#         self.optimizer = torch.optim.Adam(self.model.parameters(), lr=self.lr, betas=(0.9, 0.999), eps=1e-08,\n",
    "#                                           weight_decay=0)\n",
    "        self.optimizer = torch.optim.SGD(self.model.parameters(), lr=self.lr, momentum=0.5, weight_decay=1/self.prior_sig**2)\n",
    "\n",
    "    #         self.optimizer = torch.optim.SGD(self.model.parameters(), lr=self.lr, momentum=0.9)\n",
    "#         self.sched = torch.optim.lr_scheduler.StepLR(self.optimizer, step_size=1, gamma=10, last_epoch=-1)\n",
    "\n",
    "    def fit(self, x, y):\n",
    "        x, y = to_variable(var=(x, y.long()), cuda=self.cuda)\n",
    "\n",
    "        self.optimizer.zero_grad()\n",
    "\n",
    "        out = self.model(x)\n",
    "        loss = F.cross_entropy(out, y, reduction='sum')\n",
    "            \n",
    "        loss.backward()\n",
    "        self.optimizer.step()\n",
    "\n",
    "        # out: (batch_size, out_channels, out_caps_dims)\n",
    "        pred = out.data.max(dim=1, keepdim=False)[1]  # get the index of the max log-probability\n",
    "        err = pred.ne(y.data).sum()\n",
    "\n",
    "        return loss.data, err\n",
    "\n",
    "    def eval(self, x, y, train=False):\n",
    "        x, y = to_variable(var=(x, y.long()), cuda=self.cuda)\n",
    "\n",
    "        out = self.model(x)\n",
    "\n",
    "        loss = F.cross_entropy(out, y, reduction='sum')\n",
    "\n",
    "        probs = F.softmax(out, dim=1).data.cpu()\n",
    "\n",
    "        pred = out.data.max(dim=1, keepdim=False)[1]  # get the index of the max log-probability\n",
    "        err = pred.ne(y.data).sum()\n",
    "\n",
    "        return loss.data, err, probs\n",
    "    \n",
    "    \n",
    "    def get_K_laplace_params(self, trainloader):\n",
    "        self.model.eval()\n",
    "\n",
    "        it_counter = 0\n",
    "        cum_HH1 = self.model.fc1.weight.data.new(self.model.n_hid, self.model.n_hid).fill_(0)\n",
    "        cum_HH2 = self.model.fc1.weight.data.new(self.model.n_hid, self.model.n_hid).fill_(0)\n",
    "        cum_HH3 = self.model.fc1.weight.data.new(self.model.output_dim, self.model.output_dim).fill_(0)\n",
    "\n",
    "        cum_Q1 = self.model.fc1.weight.data.new(self.model.input_dim+1, self.model.input_dim+1).fill_(0)\n",
    "        cum_Q2 = self.model.fc1.weight.data.new(self.model.n_hid+1, self.model.n_hid+1).fill_(0)\n",
    "        cum_Q3 = self.model.fc1.weight.data.new(self.model.n_hid+1, self.model.n_hid+1).fill_(0)\n",
    "\n",
    "        # Forward pass\n",
    "\n",
    "        for x, y in trainloader:\n",
    "\n",
    "            x, y = to_variable(var=(x, y.long()), cuda=use_cuda)\n",
    "\n",
    "            self.optimizer.zero_grad()\n",
    "\n",
    "            out = self.model(x)\n",
    "            out_act = F.softmax(out, dim=1)\n",
    "            loss = F.cross_entropy(out, y, reduction='sum')\n",
    "\n",
    "            loss.backward()\n",
    "\n",
    "            #     ------------------------------------------------------------------\n",
    "            HH3 = softmax_CE_preact_hessian(out_act.data)\n",
    "            cum_HH3 += HH3.sum(dim=0)\n",
    "        #     print(model.a2.data.shape)\n",
    "            Q3 = torch.bmm(self.model.a2.data.unsqueeze(2), self.model.a2.data.unsqueeze(1))\n",
    "            cum_Q3 += Q3.sum(dim=0)\n",
    "            #     ------------------------------------------------------------------\n",
    "            HH2 = layer_act_hessian_recurse(prev_hessian=HH3, prev_weights=self.model.fc3.weight.data,\n",
    "                                        layer_pre_acts=self.model.h2.data)\n",
    "            cum_HH2 += HH2.sum(dim=0)\n",
    "            Q2 = torch.bmm(self.model.a1.data.unsqueeze(2), self.model.a1.data.unsqueeze(1))\n",
    "            cum_Q2 += Q2.sum(dim=0)\n",
    "            #     ------------------------------------------------------------------\n",
    "            HH1 = layer_act_hessian_recurse(prev_hessian=HH2, prev_weights=self.model.fc2.weight.data,\n",
    "                                   layer_pre_acts=self.model.h1.data)\n",
    "            cum_HH1 += HH1.sum(dim=0)\n",
    "            Q1 = torch.bmm(self.model.a0.data.unsqueeze(2), self.model.a0.data.unsqueeze(1))\n",
    "            cum_Q1 += Q1.sum(dim=0)\n",
    "            #     ------------------------------------------------------------------\n",
    "            it_counter += x.shape[0]\n",
    "            print(it_counter)\n",
    "\n",
    "        EHH3 = cum_HH3 / it_counter\n",
    "        EHH2 = cum_HH2 / it_counter\n",
    "        EHH1 = cum_HH1 / it_counter\n",
    "\n",
    "        EQ3 = cum_Q3 / it_counter\n",
    "        EQ2 = cum_Q2 / it_counter\n",
    "        EQ1 = cum_Q1 / it_counter\n",
    "\n",
    "        MAP3 = torch.cat((self.model.fc3.weight.data, self.model.fc3.bias.data.unsqueeze(1)), dim=1)\n",
    "        MAP2 = torch.cat((self.model.fc2.weight.data, self.model.fc2.bias.data.unsqueeze(1)), dim=1)\n",
    "        MAP1 = torch.cat((self.model.fc1.weight.data, self.model.fc1.bias.data.unsqueeze(1)), dim=1)\n",
    "        \n",
    "        return EQ1, EHH1, MAP1, EQ2, EHH2, MAP2, EQ3, EHH3, MAP3\n",
    "    \n",
    "    def sample_eval(self, x, y, Nsamples, scale_inv_EQ1, scale_inv_EHH1, MAP1, scale_inv_EQ2, scale_inv_EHH2, MAP2, scale_inv_EQ3, scale_inv_EHH3, MAP3, logits=False):\n",
    "        x, y = to_variable(var=(x, y.long()), cuda=self.cuda)\n",
    "\n",
    "        out = self.model.sample_predict(x, Nsamples, scale_inv_EQ1, scale_inv_EHH1, MAP1, scale_inv_EQ2, scale_inv_EHH2, MAP2, scale_inv_EQ3, scale_inv_EHH3, MAP3)\n",
    "        \n",
    "        if logits:\n",
    "            mean_out = out.mean(dim=0, keepdim=False)\n",
    "            loss = F.cross_entropy(mean_out, y, reduction='sum')\n",
    "            probs = F.softmax(mean_out, dim=1).data.cpu()\n",
    "            \n",
    "        else:\n",
    "            mean_out =  F.softmax(out, dim=2).mean(dim=0, keepdim=False)\n",
    "            probs = mean_out.data.cpu()\n",
    "            \n",
    "            log_mean_probs_out = torch.log(mean_out)\n",
    "            loss = F.nll_loss(log_mean_probs_out, y, reduction='sum')\n",
    "\n",
    "        pred = mean_out.data.max(dim=1, keepdim=False)[1]  # get the index of the max log-probability\n",
    "        err = pred.ne(y.data).sum()\n",
    "\n",
    "        return loss.data, err, probs\n",
    "    \n",
    "    def all_sample_eval(self, x, y, Nsamples, scale_inv_EQ1, scale_inv_EHH1, MAP1, scale_inv_EQ2, scale_inv_EHH2, MAP2, scale_inv_EQ3, scale_inv_EHH3, MAP3):\n",
    "        x, y = to_variable(var=(x, y.long()), cuda=self.cuda)\n",
    "\n",
    "        out = self.model.sample_predict(x, Nsamples, scale_inv_EQ1, scale_inv_EHH1, MAP1, scale_inv_EQ2, scale_inv_EHH2, MAP2, scale_inv_EQ3, scale_inv_EHH3, MAP3)\n",
    "        \n",
    "        prob_out =  F.softmax(out, dim=2)\n",
    "        prob_out = prob_out.data\n",
    "\n",
    "        return prob_out\n",
    "    \n",
    "    def get_weight_samples(self, Nsamples, scale_inv_EQ1, scale_inv_EHH1, MAP1, scale_inv_EQ2, scale_inv_EHH2, MAP2, scale_inv_EQ3, scale_inv_EHH3, MAP3):\n",
    "        weight_vec = []\n",
    "        \n",
    "        for i in range(Nsamples):\n",
    "            \n",
    "            w1, b1 = sample_K_laplace_MN(MAP1, scale_inv_EQ1, scale_inv_EHH1)\n",
    "            w2, b2 = sample_K_laplace_MN(MAP2, scale_inv_EQ2, scale_inv_EHH2)\n",
    "            w3, b3 = sample_K_laplace_MN(MAP3, scale_inv_EQ3, scale_inv_EHH3)\n",
    "        \n",
    "            for weight in w1.cpu().numpy().flatten():\n",
    "                weight_vec.append(weight)\n",
    "            for weight in w2.cpu().numpy().flatten():\n",
    "                weight_vec.append(weight)\n",
    "            for weight in w3.cpu().numpy().flatten():\n",
    "                weight_vec.append(weight)\n",
    "            \n",
    "        return np.array(weight_vec)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[36m\n",
      "Data:\u001b[0m\n",
      "Downloading http://yann.lecun.com/exdb/mnist/train-images-idx3-ubyte.gz\n",
      "Downloading http://yann.lecun.com/exdb/mnist/train-labels-idx1-ubyte.gz\n",
      "Downloading http://yann.lecun.com/exdb/mnist/t10k-images-idx3-ubyte.gz\n",
      "Downloading http://yann.lecun.com/exdb/mnist/t10k-labels-idx1-ubyte.gz\n",
      "Processing...\n",
      "Done!\n",
      "\u001b[36m\n",
      "Network:\u001b[0m\n",
      "\u001b[36m\n",
      "Net:\u001b[0m\n",
      "\u001b[33m Creating Net!! \u001b[0m\n",
      "    Total params: 2.40M\n",
      "\u001b[36m\n",
      "Train:\u001b[0m\n",
      "  init cost variables:\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/homes/ja666/anaconda2/lib/python2.7/site-packages/ipykernel_launcher.py:7: DeprecationWarning: Calling np.sum(generator) is deprecated, and in the future will give a different result. Use np.sum(np.from_iter(generator)) or the python sum builtin instead.\n",
      "  import sys\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "it 0/10, Jtr_pred = 0.234791, err = 0.070350, \u001b[31m   time: 4.017100 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.120098, err = 0.038600\n",
      "\u001b[0m\n",
      "\u001b[36mWritting models_KLaplace_NN_MNIST/theta_best.dat\n",
      "\u001b[0m\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/homes/ja666/anaconda2/lib/python2.7/site-packages/torch/serialization.py:251: UserWarning: Couldn't retrieve source code for container of type Linear_2L_KFRA. It won't be checked for correctness upon loading.\n",
      "  \"type \" + obj.__name__ + \". It won't be checked \"\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "it 1/10, Jtr_pred = 0.083141, err = 0.025900, \u001b[31m   time: 3.853954 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.073463, err = 0.023600\n",
      "\u001b[0m\n",
      "\u001b[36mWritting models_KLaplace_NN_MNIST/theta_best.dat\n",
      "\u001b[0m\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/homes/ja666/anaconda2/lib/python2.7/site-packages/torch/serialization.py:251: UserWarning: Couldn't retrieve source code for container of type Linear_2L_KFRA. It won't be checked for correctness upon loading.\n",
      "  \"type \" + obj.__name__ + \". It won't be checked \"\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "it 2/10, Jtr_pred = 0.049683, err = 0.015183, \u001b[31m   time: 3.893805 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.067570, err = 0.021700\n",
      "\u001b[0m\n",
      "\u001b[36mWritting models_KLaplace_NN_MNIST/theta_best.dat\n",
      "\u001b[0m\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/homes/ja666/anaconda2/lib/python2.7/site-packages/torch/serialization.py:251: UserWarning: Couldn't retrieve source code for container of type Linear_2L_KFRA. It won't be checked for correctness upon loading.\n",
      "  \"type \" + obj.__name__ + \". It won't be checked \"\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "it 3/10, Jtr_pred = 0.032112, err = 0.009883, \u001b[31m   time: 3.824996 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.067652, err = 0.019800\n",
      "\u001b[0m\n",
      "\u001b[36mWritting models_KLaplace_NN_MNIST/theta_best.dat\n",
      "\u001b[0m\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/homes/ja666/anaconda2/lib/python2.7/site-packages/torch/serialization.py:251: UserWarning: Couldn't retrieve source code for container of type Linear_2L_KFRA. It won't be checked for correctness upon loading.\n",
      "  \"type \" + obj.__name__ + \". It won't be checked \"\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "it 4/10, Jtr_pred = 0.020714, err = 0.006283, \u001b[31m   time: 3.752062 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.068369, err = 0.020700\n",
      "\u001b[0m\n",
      "it 5/10, Jtr_pred = 0.011983, err = 0.003183, \u001b[31m   time: 3.540784 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.060624, err = 0.017300\n",
      "\u001b[0m\n",
      "\u001b[36mWritting models_KLaplace_NN_MNIST/theta_best.dat\n",
      "\u001b[0m\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/homes/ja666/anaconda2/lib/python2.7/site-packages/torch/serialization.py:251: UserWarning: Couldn't retrieve source code for container of type Linear_2L_KFRA. It won't be checked for correctness upon loading.\n",
      "  \"type \" + obj.__name__ + \". It won't be checked \"\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "it 6/10, Jtr_pred = 0.007593, err = 0.001817, \u001b[31m   time: 4.032684 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.059090, err = 0.016800\n",
      "\u001b[0m\n",
      "\u001b[36mWritting models_KLaplace_NN_MNIST/theta_best.dat\n",
      "\u001b[0m\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/homes/ja666/anaconda2/lib/python2.7/site-packages/torch/serialization.py:251: UserWarning: Couldn't retrieve source code for container of type Linear_2L_KFRA. It won't be checked for correctness upon loading.\n",
      "  \"type \" + obj.__name__ + \". It won't be checked \"\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "it 7/10, Jtr_pred = 0.003573, err = 0.000567, \u001b[31m   time: 4.040138 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.058612, err = 0.016400\n",
      "\u001b[0m\n",
      "\u001b[36mWritting models_KLaplace_NN_MNIST/theta_best.dat\n",
      "\u001b[0m\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/homes/ja666/anaconda2/lib/python2.7/site-packages/torch/serialization.py:251: UserWarning: Couldn't retrieve source code for container of type Linear_2L_KFRA. It won't be checked for correctness upon loading.\n",
      "  \"type \" + obj.__name__ + \". It won't be checked \"\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "it 8/10, Jtr_pred = 0.002457, err = 0.000350, \u001b[31m   time: 3.580702 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.058136, err = 0.015000\n",
      "\u001b[0m\n",
      "\u001b[36mWritting models_KLaplace_NN_MNIST/theta_best.dat\n",
      "\u001b[0m\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/homes/ja666/anaconda2/lib/python2.7/site-packages/torch/serialization.py:251: UserWarning: Couldn't retrieve source code for container of type Linear_2L_KFRA. It won't be checked for correctness upon loading.\n",
      "  \"type \" + obj.__name__ + \". It won't be checked \"\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "it 9/10, Jtr_pred = 0.001214, err = 0.000100, \u001b[31m   time: 3.862284 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.056455, err = 0.014700\n",
      "\u001b[0m\n",
      "\u001b[36mWritting models_KLaplace_NN_MNIST/theta_best.dat\n",
      "\u001b[0m\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/homes/ja666/anaconda2/lib/python2.7/site-packages/torch/serialization.py:251: UserWarning: Couldn't retrieve source code for container of type Linear_2L_KFRA. It won't be checked for correctness upon loading.\n",
      "  \"type \" + obj.__name__ + \". It won't be checked \"\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[31m   average time: 5.912436 seconds\n",
      "\u001b[0m\n",
      "\u001b[36m\n",
      "RESULTS:\u001b[0m\n",
      "  cost_dev: 0.056455 (cost_train 0.001214)\n",
      "  err_dev: 0.014700\n",
      "  nb_parameters: 2395210 (2.28MB)\n",
      "  time_per_it: 5.912436s\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 600x400 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 600x400 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from __future__ import print_function\n",
    "from __future__ import division\n",
    "import time\n",
    "import torch.utils.data\n",
    "from torchvision import transforms, datasets\n",
    "import matplotlib\n",
    "\n",
    "\n",
    "models_dir = 'models_KLaplace_NN_MNIST'\n",
    "results_dir = 'results_KLaplace_NN_MNIST'\n",
    "\n",
    "mkdir(models_dir)\n",
    "mkdir(results_dir)\n",
    "# ------------------------------------------------------------------------------------------------------\n",
    "# train config\n",
    "NTrainPointsMNIST = 60000\n",
    "batch_size = 100\n",
    "nb_epochs = 10 # We can do less iterations as this method has faster convergence\n",
    "log_interval = 1\n",
    "\n",
    "# ------------------------------------------------------------------------------------------------------\n",
    "# dataset\n",
    "cprint('c', '\\nData:')\n",
    "\n",
    "\n",
    "# load data\n",
    "\n",
    "# data augmentation\n",
    "transform_train = transforms.Compose([\n",
    "    transforms.ToTensor(),\n",
    "    transforms.Normalize(mean=(0.1307,), std=(0.3081,))\n",
    "])\n",
    "\n",
    "transform_test = transforms.Compose([\n",
    "    transforms.ToTensor(),\n",
    "    transforms.Normalize(mean=(0.1307,), std=(0.3081,))\n",
    "])\n",
    "\n",
    "use_cuda = torch.cuda.is_available()\n",
    "\n",
    "trainset = datasets.MNIST(root='../data', train=True, download=True, transform=transform_train)\n",
    "valset = datasets.MNIST(root='../data', train=False, download=True, transform=transform_test)\n",
    "\n",
    "if use_cuda:\n",
    "    trainloader = torch.utils.data.DataLoader(trainset, batch_size=batch_size, shuffle=True, pin_memory=True, num_workers=3)\n",
    "    valloader = torch.utils.data.DataLoader(valset, batch_size=batch_size, shuffle=False, pin_memory=True, num_workers=3)\n",
    "\n",
    "else:\n",
    "    trainloader = torch.utils.data.DataLoader(trainset, batch_size=batch_size, shuffle=True, pin_memory=False,\n",
    "                                              num_workers=3)\n",
    "    valloader = torch.utils.data.DataLoader(valset, batch_size=batch_size, shuffle=False, pin_memory=False,\n",
    "                                            num_workers=3)\n",
    "\n",
    "\n",
    "## ---------------------------------------------------------------------------------------------------------------------\n",
    "# net dims\n",
    "cprint('c', '\\nNetwork:')\n",
    "\n",
    "lr = 1e-3\n",
    "prior_sig = 10000\n",
    "########################################################################################\n",
    "net = KBayes_Net(lr=lr, channels_in=1, side_in=28, cuda=use_cuda, classes=10, batch_size=batch_size, prior_sig=prior_sig)\n",
    "\n",
    "epoch = 0\n",
    "\n",
    "## ---------------------------------------------------------------------------------------------------------------------\n",
    "# train\n",
    "cprint('c', '\\nTrain:')\n",
    "\n",
    "print('  init cost variables:')\n",
    "pred_cost_train = np.zeros(nb_epochs)\n",
    "err_train = np.zeros(nb_epochs)\n",
    "\n",
    "cost_dev = np.zeros(nb_epochs)\n",
    "err_dev = np.zeros(nb_epochs)\n",
    "# best_cost = np.inf\n",
    "best_err = np.inf\n",
    "\n",
    "\n",
    "nb_its_dev = 1\n",
    "\n",
    "tic0 = time.time()\n",
    "for i in range(epoch, nb_epochs):\n",
    "    \n",
    "#     if i in [1]:\n",
    "#         print('updating lr')\n",
    "#         net.sched.step()\n",
    "    \n",
    "    net.set_mode_train(True)\n",
    "\n",
    "    tic = time.time()\n",
    "    nb_samples = 0\n",
    "\n",
    "    for x, y in trainloader:\n",
    "        cost_pred, err = net.fit(x, y)\n",
    "\n",
    "        err_train[i] += err\n",
    "        pred_cost_train[i] += cost_pred\n",
    "        nb_samples += len(x)\n",
    "\n",
    "    pred_cost_train[i] /= nb_samples\n",
    "    err_train[i] /= nb_samples\n",
    "\n",
    "    toc = time.time()\n",
    "    net.epoch = i\n",
    "    # ---- print\n",
    "    print(\"it %d/%d, Jtr_pred = %f, err = %f, \" % (i, nb_epochs, pred_cost_train[i], err_train[i]), end=\"\")\n",
    "    cprint('r', '   time: %f seconds\\n' % (toc - tic))\n",
    "\n",
    "    # ---- dev\n",
    "    if i % nb_its_dev == 0:\n",
    "        net.set_mode_train(False)\n",
    "        nb_samples = 0\n",
    "        for j, (x, y) in enumerate(valloader):\n",
    "\n",
    "            cost, err, probs = net.eval(x, y)\n",
    "\n",
    "            cost_dev[i] += cost\n",
    "            err_dev[i] += err\n",
    "            nb_samples += len(x)\n",
    "\n",
    "        cost_dev[i] /= nb_samples\n",
    "        err_dev[i] /= nb_samples\n",
    "\n",
    "        cprint('g', '    Jdev = %f, err = %f\\n' % (cost_dev[i], err_dev[i]))\n",
    "\n",
    "        if err_dev[i] < best_err:\n",
    "            best_err = err_dev[i]\n",
    "#             cprint('b', 'best test error')\n",
    "            net.save(models_dir+'/theta_best.dat')\n",
    "\n",
    "toc0 = time.time()\n",
    "runtime_per_it = (toc0 - tic0) / float(nb_epochs)\n",
    "cprint('r', '   average time: %f seconds\\n' % runtime_per_it)\n",
    "\n",
    "\n",
    "\n",
    "## ---------------------------------------------------------------------------------------------------------------------\n",
    "# results\n",
    "cprint('c', '\\nRESULTS:')\n",
    "nb_parameters = net.get_nb_parameters()\n",
    "best_cost_dev = np.min(cost_dev)\n",
    "best_cost_train = np.min(pred_cost_train)\n",
    "err_dev_min = err_dev[::nb_its_dev].min()\n",
    "\n",
    "print('  cost_dev: %f (cost_train %f)' % (best_cost_dev, best_cost_train))\n",
    "print('  err_dev: %f' % (err_dev_min))\n",
    "print('  nb_parameters: %d (%s)' % (nb_parameters, humansize(nb_parameters)))\n",
    "print('  time_per_it: %fs\\n' % (runtime_per_it))\n",
    "\n",
    "\n",
    "\n",
    "## Save results for plots\n",
    "# np.save('results/test_predictions.npy', test_predictions)\n",
    "np.save(results_dir + '/cost_train.npy', pred_cost_train)\n",
    "np.save(results_dir + '/cost_dev.npy', cost_dev)\n",
    "np.save(results_dir + '/err_train.npy', err_train)\n",
    "np.save(results_dir + '/err_dev.npy', err_dev)\n",
    "\n",
    "## ---------------------------------------------------------------------------------------------------------------------\n",
    "# fig cost vs its\n",
    "\n",
    "textsize = 15\n",
    "marker=5\n",
    "\n",
    "plt.figure(dpi=100)\n",
    "fig, ax1 = plt.subplots()\n",
    "ax1.plot(pred_cost_train, 'r--')\n",
    "ax1.plot(range(0, nb_epochs, nb_its_dev), cost_dev[::nb_its_dev], 'b-')\n",
    "ax1.set_ylabel('Cross Entropy')\n",
    "plt.xlabel('epoch')\n",
    "plt.grid(b=True, which='major', color='k', linestyle='-')\n",
    "plt.grid(b=True, which='minor', color='k', linestyle='--')\n",
    "lgd = plt.legend(['test error', 'train error'], markerscale=marker, prop={'size': textsize, 'weight': 'normal'})\n",
    "ax = plt.gca()\n",
    "plt.title('classification costs')\n",
    "for item in ([ax.title, ax.xaxis.label, ax.yaxis.label] +\n",
    "    ax.get_xticklabels() + ax.get_yticklabels()):\n",
    "    item.set_fontsize(textsize)\n",
    "    item.set_weight('normal')\n",
    "plt.savefig(results_dir + '/cost.png', bbox_extra_artists=(lgd,), bbox_inches='tight')\n",
    "\n",
    "\n",
    "\n",
    "plt.figure(dpi=100)\n",
    "fig2, ax2 = plt.subplots()\n",
    "ax2.set_ylabel('% error')\n",
    "ax2.semilogy(range(0, nb_epochs, nb_its_dev), 100 * err_dev[::nb_its_dev], 'b-')\n",
    "ax2.semilogy(100 * err_train, 'r--')\n",
    "plt.xlabel('epoch')\n",
    "plt.grid(b=True, which='major', color='k', linestyle='-')\n",
    "plt.grid(b=True, which='minor', color='k', linestyle='--')\n",
    "ax2.get_yaxis().set_minor_formatter(matplotlib.ticker.ScalarFormatter())\n",
    "ax2.get_yaxis().set_major_formatter(matplotlib.ticker.ScalarFormatter())\n",
    "lgd = plt.legend(['test error', 'train error'], markerscale=marker, prop={'size': textsize, 'weight': 'normal'})\n",
    "ax = plt.gca()\n",
    "for item in ([ax.title, ax.xaxis.label, ax.yaxis.label] +\n",
    "    ax.get_xticklabels() + ax.get_yticklabels()):\n",
    "    item.set_fontsize(textsize)\n",
    "    item.set_weight('normal')\n",
    "plt.savefig(results_dir + '/err.png',  bbox_extra_artists=(lgd,), box_inches='tight')\n",
    "\n",
    " \n",
    " \n",
    " "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 600x400 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 600x400 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "textsize = 15\n",
    "marker=5\n",
    "\n",
    "plt.figure(dpi=100)\n",
    "fig, ax1 = plt.subplots()\n",
    "ax1.plot(pred_cost_train, 'r--')\n",
    "ax1.plot(range(0, nb_epochs, nb_its_dev), cost_dev[::nb_its_dev], 'b-')\n",
    "ax1.set_ylabel('Cross Entropy')\n",
    "plt.xlabel('epoch')\n",
    "plt.grid(b=True, which='major', color='k', linestyle='-')\n",
    "plt.grid(b=True, which='minor', color='k', linestyle='--')\n",
    "lgd = plt.legend(['test error', 'train error'], markerscale=marker, prop={'size': textsize, 'weight': 'normal'})\n",
    "ax = plt.gca()\n",
    "plt.title('classification costs')\n",
    "for item in ([ax.title, ax.xaxis.label, ax.yaxis.label] +\n",
    "    ax.get_xticklabels() + ax.get_yticklabels()):\n",
    "    item.set_fontsize(textsize)\n",
    "    item.set_weight('normal')\n",
    "plt.savefig(results_dir + '/cost.png', bbox_extra_artists=(lgd,), bbox_inches='tight')\n",
    "\n",
    "\n",
    "\n",
    "plt.figure(dpi=100)\n",
    "fig2, ax2 = plt.subplots()\n",
    "ax2.set_ylabel('% error')\n",
    "ax2.semilogy(range(0, nb_epochs, nb_its_dev), 100 * err_dev[::nb_its_dev], 'b-')\n",
    "ax2.semilogy(100 * err_train, 'r--')\n",
    "plt.xlabel('epoch')\n",
    "plt.grid(b=True, which='major', color='k', linestyle='-')\n",
    "plt.grid(b=True, which='minor', color='k', linestyle='--')\n",
    "ax2.get_yaxis().set_minor_formatter(matplotlib.ticker.ScalarFormatter())\n",
    "ax2.get_yaxis().set_major_formatter(matplotlib.ticker.ScalarFormatter())\n",
    "lgd = plt.legend(['test error', 'train error'], markerscale=marker, prop={'size': textsize, 'weight': 'normal'})\n",
    "ax = plt.gca()\n",
    "for item in ([ax.title, ax.xaxis.label, ax.yaxis.label] +\n",
    "    ax.get_xticklabels() + ax.get_yticklabels()):\n",
    "    item.set_fontsize(textsize)\n",
    "    item.set_weight('normal')\n",
    "plt.savefig(results_dir + '/err.png',  bbox_extra_artists=(lgd,), bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "torch.cuda.empty_cache()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Get Kron hessian approx (takes a little bit)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "trainloader = torch.utils.data.DataLoader(trainset, batch_size=128, shuffle=True, pin_memory=True, num_workers=3)\n",
    "EQ1, EHH1, MAP1, EQ2, EHH2, MAP2, EQ3, EHH3, MAP3 = net.get_K_laplace_params(trainloader)\n",
    "\n",
    "\n",
    "def save_object(obj, filename):\n",
    "    with open(filename, 'wb') as output:  # Overwrites any existing file.\n",
    "        pickle.dump(obj, output, pickle.HIGHEST_PROTOCOL)\n",
    "        \n",
    "\n",
    "\n",
    "\n",
    "h_params = [EQ1, EHH1, MAP1, EQ2, EHH2, MAP2, EQ3, EHH3, MAP3]\n",
    "save_object(h_params, models_dir+'/block_hessian_params.pkl')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## load up net and hessian params"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[36m\n",
      "Data:\u001b[0m\n",
      "\u001b[36m\n",
      "Network:\u001b[0m\n",
      "\u001b[36m\n",
      "Net:\u001b[0m\n",
      "\u001b[33m Creating Net!! \u001b[0m\n",
      "    Total params: 2.40M\n",
      "\u001b[36mReading models_KLaplace_NN_MNIST/theta_best.dat\n",
      "\u001b[0m\n",
      "  restoring epoch: 9, lr: 0.001000\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/homes/ja666/anaconda2/lib/python2.7/site-packages/ipykernel_launcher.py:7: DeprecationWarning: Calling np.sum(generator) is deprecated, and in the future will give a different result. Use np.sum(np.from_iter(generator)) or the python sum builtin instead.\n",
      "  import sys\n"
     ]
    }
   ],
   "source": [
    "from __future__ import print_function\n",
    "from __future__ import division\n",
    "import time\n",
    "import torch.utils.data\n",
    "from torchvision import transforms, datasets\n",
    "import matplotlib\n",
    "\n",
    "\n",
    "models_dir = 'models_KLaplace_NN_MNIST'\n",
    "results_dir = 'results_KLaplace_NN_MNIST'\n",
    "\n",
    "mkdir(models_dir)\n",
    "mkdir(results_dir)\n",
    "# ------------------------------------------------------------------------------------------------------\n",
    "# train config\n",
    "NTrainPointsMNIST = 60000\n",
    "batch_size = 100\n",
    "nb_epochs = 15 # We can do less iterations as this method has faster convergence\n",
    "log_interval = 1\n",
    "\n",
    "# ------------------------------------------------------------------------------------------------------\n",
    "# dataset\n",
    "cprint('c', '\\nData:')\n",
    "\n",
    "\n",
    "# load data\n",
    "\n",
    "# data augmentation\n",
    "transform_train = transforms.Compose([\n",
    "    transforms.ToTensor(),\n",
    "    transforms.Normalize(mean=(0.1307,), std=(0.3081,))\n",
    "])\n",
    "\n",
    "transform_test = transforms.Compose([\n",
    "    transforms.ToTensor(),\n",
    "    transforms.Normalize(mean=(0.1307,), std=(0.3081,))\n",
    "])\n",
    "\n",
    "use_cuda = torch.cuda.is_available()\n",
    "\n",
    "trainset = datasets.MNIST(root='../data', train=True, download=True, transform=transform_train)\n",
    "valset = datasets.MNIST(root='../data', train=False, download=True, transform=transform_test)\n",
    "\n",
    "if use_cuda:\n",
    "    trainloader = torch.utils.data.DataLoader(trainset, batch_size=batch_size, shuffle=True, pin_memory=True, num_workers=3)\n",
    "    valloader = torch.utils.data.DataLoader(valset, batch_size=batch_size, shuffle=False, pin_memory=True, num_workers=3)\n",
    "\n",
    "else:\n",
    "    trainloader = torch.utils.data.DataLoader(trainset, batch_size=batch_size, shuffle=True, pin_memory=False,\n",
    "                                              num_workers=3)\n",
    "    valloader = torch.utils.data.DataLoader(valset, batch_size=batch_size, shuffle=False, pin_memory=False,\n",
    "                                            num_workers=3)\n",
    "\n",
    "\n",
    "## ---------------------------------------------------------------------------------------------------------------------\n",
    "# net dims\n",
    "cprint('c', '\\nNetwork:')\n",
    "\n",
    "lr = 1e-3\n",
    "prior_sig = 2\n",
    "########################################################################################\n",
    "net = KBayes_Net(lr=lr, channels_in=1, side_in=28, cuda=use_cuda, classes=10, batch_size=batch_size, prior_sig=prior_sig)\n",
    "\n",
    "net.load(models_dir+'/theta_best.dat')\n",
    "\n",
    "\n",
    "\n",
    "with open(models_dir+'/block_hessian_params.pkl', 'rb') as input:\n",
    "    [EQ1, EHH1, MAP1, EQ2, EHH2, MAP2, EQ3, EHH3, MAP3] = pickle.load(input)\n",
    "    \n",
    "    \n",
    "models_dir = 'models_KLaplace_NN_MNIST2'\n",
    "results_dir = 'results_KLaplace_NN_MNIST2'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## do scaling and get inverse"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_scale = np.sqrt(60000)\n",
    "\n",
    "prior_sig = 0.15\n",
    "\n",
    "prior_prec = 1/prior_sig**2\n",
    "prior_scale = np.sqrt(prior_prec)\n",
    "\n",
    "# upper_Qinv, lower_HHinv\n",
    "\n",
    "scale_inv_EQ1 = chol_scale_invert_kron_factor(EQ1, prior_scale, data_scale, upper=True)\n",
    "scale_inv_EHH1 = chol_scale_invert_kron_factor(EHH1, prior_scale, data_scale, upper=False)\n",
    "\n",
    "scale_inv_EQ2 = chol_scale_invert_kron_factor(EQ2, prior_scale, data_scale, upper=True)\n",
    "scale_inv_EHH2 = chol_scale_invert_kron_factor(EHH2, prior_scale, data_scale, upper=False)\n",
    "\n",
    "scale_inv_EQ3 = chol_scale_invert_kron_factor(EQ3, prior_scale, data_scale, upper=True)\n",
    "scale_inv_EHH3 = chol_scale_invert_kron_factor(EHH3, prior_scale, data_scale, upper=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## MAP inference on test set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[34m    Loglike = -564.552979, err = 0.014700\n",
      "\u001b[0m\n"
     ]
    }
   ],
   "source": [
    "batch_size = 100\n",
    "\n",
    "if use_cuda:\n",
    "    valloader = torch.utils.data.DataLoader(valset, batch_size=batch_size, shuffle=False, pin_memory=True, num_workers=4)\n",
    "\n",
    "else:\n",
    "    valloader = torch.utils.data.DataLoader(valset, batch_size=batch_size, shuffle=False, pin_memory=False,\n",
    "                                            num_workers=4)\n",
    "\n",
    "nb_samples = 0    \n",
    "\n",
    "test_cost = 0  # Note that these are per sample\n",
    "test_err = 0\n",
    "test_predictions = np.zeros((10000, 10))\n",
    "\n",
    "net.set_mode_train(False)\n",
    "\n",
    "for j, (x, y) in enumerate(valloader):\n",
    "    cost, err, probs = net.eval(x, y) # \n",
    "    \n",
    "\n",
    "    test_cost += cost\n",
    "    test_err += err.cpu().numpy()\n",
    "    test_predictions[nb_samples:nb_samples+len(x), :] = probs.numpy()\n",
    "    nb_samples += len(x)\n",
    "\n",
    "test_err /= nb_samples\n",
    "cprint('b', '    Loglike = %5.6f, err = %1.6f\\n' % (-test_cost, test_err))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Laplace inference"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[34m    Loglike = -1387.241211, err = 0.020800\n",
      "\u001b[0m\n"
     ]
    }
   ],
   "source": [
    "batch_size = 100\n",
    "\n",
    "if use_cuda:\n",
    "    valloader = torch.utils.data.DataLoader(valset, batch_size=batch_size, shuffle=False, pin_memory=True, num_workers=4)\n",
    "\n",
    "else:\n",
    "    valloader = torch.utils.data.DataLoader(valset, batch_size=batch_size, shuffle=False, pin_memory=False,\n",
    "                                            num_workers=4)\n",
    "test_cost = 0  # Note that these are per sample\n",
    "test_err = 0\n",
    "nb_samples = 0\n",
    "test_predictions = np.zeros((10000, 10))\n",
    "\n",
    "Nsamples = 100\n",
    "\n",
    "net.set_mode_train(False)\n",
    "\n",
    "for j, (x, y) in enumerate(valloader):\n",
    "    \n",
    "    cost, err, probs = net.sample_eval(x, y, Nsamples, scale_inv_EQ1, scale_inv_EHH1, MAP1, scale_inv_EQ2, scale_inv_EHH2, MAP2, scale_inv_EQ3, scale_inv_EHH3, MAP3, logits=False)\n",
    "    \n",
    "    test_cost += cost\n",
    "    test_err += err.cpu().numpy()\n",
    "    test_predictions[nb_samples:nb_samples+len(x), :] = probs.numpy()\n",
    "    nb_samples += len(x)\n",
    "\n",
    "# test_cost /= nb_samples\n",
    "test_err /= nb_samples\n",
    "cprint('b', '    Loglike = %5.6f, err = %1.6f\\n' % (-test_cost, test_err))\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## MAP inference  on train set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[34m    Loglike = -42.213203, err = 0.00000000\n",
      "\u001b[0m\n"
     ]
    }
   ],
   "source": [
    "batch_size = 100\n",
    "\n",
    "trainset = datasets.MNIST(root='../data', train=True, download=True, transform=transform_train)\n",
    "\n",
    "if use_cuda:\n",
    "    trainloader = torch.utils.data.DataLoader(trainset, batch_size=batch_size, shuffle=True, pin_memory=True, num_workers=3)\n",
    "else:\n",
    "    trainloader = torch.utils.data.DataLoader(trainset, batch_size=batch_size, shuffle=True, pin_memory=False,\n",
    "                                              num_workers=3)\n",
    "nb_samples = 0    \n",
    "\n",
    "test_cost = 0  # Note that these are per sample\n",
    "test_err = 0\n",
    "test_predictions = np.zeros((60000, 10))\n",
    "\n",
    "net.set_mode_train(False)\n",
    "\n",
    "for j, (x, y) in enumerate(trainloader):\n",
    "    cost, err, probs = net.eval(x, y) # \n",
    "    \n",
    "\n",
    "    test_cost += cost\n",
    "    test_err += err.cpu().numpy()\n",
    "    test_predictions[nb_samples:nb_samples+len(x), :] = probs.numpy()\n",
    "    nb_samples += len(x)\n",
    "\n",
    "test_err /= nb_samples\n",
    "cprint('b', '    Loglike = %5.6f, err = %1.8f\\n' % (-test_cost, test_err))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##  Laplace on train set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[34m    Loglike = -7313.162598, err = 0.00488333\n",
      "\u001b[0m\n"
     ]
    }
   ],
   "source": [
    "batch_size = 100\n",
    "\n",
    "trainset = datasets.MNIST(root='../data', train=True, download=True, transform=transform_train)\n",
    "\n",
    "if use_cuda:\n",
    "    trainloader = torch.utils.data.DataLoader(trainset, batch_size=batch_size, shuffle=True, pin_memory=True, num_workers=3)\n",
    "else:\n",
    "    trainloader = torch.utils.data.DataLoader(trainset, batch_size=batch_size, shuffle=True, pin_memory=False,\n",
    "                                              num_workers=3)\n",
    "nb_samples = 0    \n",
    "\n",
    "test_cost = 0  # Note that these are per sample\n",
    "test_err = 0\n",
    "test_predictions = np.zeros((60000, 10))\n",
    "\n",
    "net.set_mode_train(False)\n",
    "\n",
    "Nsamples = 100\n",
    "\n",
    "for j, (x, y) in enumerate(trainloader):\n",
    "    cost, err, probs = net.sample_eval(x, y, Nsamples, scale_inv_EQ1, scale_inv_EHH1, MAP1, scale_inv_EQ2, scale_inv_EHH2, MAP2, scale_inv_EQ3, scale_inv_EHH3, MAP3, logits=False)\n",
    "\n",
    "    test_cost += cost\n",
    "    test_err += err.cpu().numpy()\n",
    "    test_predictions[nb_samples:nb_samples+len(x), :] = probs.numpy()\n",
    "    nb_samples += len(x)\n",
    "\n",
    "test_err /= nb_samples\n",
    "cprint('b', '    Loglike = %5.6f, err = %1.8f\\n' % (-test_cost, test_err))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Kuzushiji-MNIST Validation set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "class KMNIST(datasets.MNIST):\n",
    "    \"\"\"`Kuzushiji-MNIST <https://github.com/rois-codh/kmnist>`_ Dataset.\n",
    "\n",
    "    Args:\n",
    "        root (string): Root directory of dataset where ``KMNIST/processed/training.pt``\n",
    "            and  ``KMNIST/processed/test.pt`` exist.\n",
    "        train (bool, optional): If True, creates dataset from ``training.pt``,\n",
    "            otherwise from ``test.pt``.\n",
    "        download (bool, optional): If true, downloads the dataset from the internet and\n",
    "            puts it in root directory. If dataset is already downloaded, it is not\n",
    "            downloaded again.\n",
    "        transform (callable, optional): A function/transform that  takes in an PIL image\n",
    "            and returns a transformed version. E.g, ``transforms.RandomCrop``\n",
    "        target_transform (callable, optional): A function/transform that takes in the\n",
    "            target and transforms it.\n",
    "    \"\"\"\n",
    "    urls = [\n",
    "        'http://codh.rois.ac.jp/kmnist/dataset/kmnist/train-images-idx3-ubyte.gz',\n",
    "        'http://codh.rois.ac.jp/kmnist/dataset/kmnist/train-labels-idx1-ubyte.gz',\n",
    "        'http://codh.rois.ac.jp/kmnist/dataset/kmnist/t10k-images-idx3-ubyte.gz',\n",
    "        'http://codh.rois.ac.jp/kmnist/dataset/kmnist/t10k-labels-idx1-ubyte.gz',\n",
    "    ]\n",
    "    classes = ['o', 'ki', 'su', 'tsu', 'na', 'ha', 'ma', 'ya', 're', 'wo']\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[34m    Loglike = -37333.945312, err = 0.89530000\n",
      "\u001b[0m\n"
     ]
    },
    {
     "ename": "NameError",
     "evalue": "name 'test_targets' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-27-e81ae3c00af0>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     33\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msave\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mresults_dir\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;34m'/KMNIST_testpreds.npy'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtest_predictions\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     34\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 35\u001b[0;31m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msave\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'KMNIST_test_targets.npy'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtest_targets\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m: name 'test_targets' is not defined"
     ]
    }
   ],
   "source": [
    "batch_size = 100\n",
    "\n",
    "valset = datasets.MNIST(root='../KMNIST_data', train=False, download=True, transform=transform_train)\n",
    "\n",
    "if use_cuda:\n",
    "    valloader = torch.utils.data.DataLoader(valset, batch_size=batch_size, shuffle=False, pin_memory=True, num_workers=3)\n",
    "else:\n",
    "    valloader = torch.utils.data.DataLoader(valset, batch_size=batch_size, shuffle=False, pin_memory=False,\n",
    "                                              num_workers=3)\n",
    "nb_samples = 0    \n",
    "\n",
    "test_cost = 0  # Note that these are per sample\n",
    "test_err = 0\n",
    "\n",
    "net.set_mode_train(False)\n",
    "Nsamples = 100\n",
    "\n",
    "test_predictions = np.zeros((Nsamples, 10000, 10))\n",
    "\n",
    "for j, (x, y) in enumerate(valloader):\n",
    "    cost, err, probs = net.sample_eval(x, y, Nsamples, scale_inv_EQ1, scale_inv_EHH1, MAP1, scale_inv_EQ2, scale_inv_EHH2, MAP2, scale_inv_EQ3, scale_inv_EHH3, MAP3, logits=False)\n",
    "    sample_probs = net.all_sample_eval(x, y, Nsamples, scale_inv_EQ1, scale_inv_EHH1, MAP1, scale_inv_EQ2, scale_inv_EHH2, MAP2, scale_inv_EQ3, scale_inv_EHH3, MAP3)\n",
    "    \n",
    "    test_cost += cost\n",
    "    test_err += err.cpu().numpy()\n",
    "#     print(sample_probs.shape)\n",
    "    test_predictions[:, nb_samples:nb_samples+len(x), :] = sample_probs.cpu().numpy()\n",
    "    nb_samples += len(x)\n",
    "\n",
    "test_err /= nb_samples\n",
    "cprint('b', '    Loglike = %5.6f, err = %1.8f\\n' % (-test_cost, test_err))\n",
    "\n",
    "np.save(results_dir + '/KMNIST_testpreds.npy', test_predictions)\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## rotations, Bayesian"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### First load data into numpy format"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(10000, 1, 28, 28)\n",
      "(10000,)\n"
     ]
    }
   ],
   "source": [
    "x_dev = []\n",
    "y_dev = []\n",
    "for x, y in valloader:\n",
    "    x_dev.append(x.cpu().numpy())\n",
    "    y_dev.append(y.cpu().numpy())\n",
    "\n",
    "x_dev = np.concatenate(x_dev)\n",
    "y_dev = np.concatenate(y_dev)\n",
    "print(x_dev.shape)\n",
    "print(y_dev.shape)\n",
    "\n",
    "# np.save('MNIST_test_targets.npy', y_dev)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(10, 1, 28, 28)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/homes/ja666/anaconda2/lib/python2.7/site-packages/ipykernel_launcher.py:83: MatplotlibDeprecationWarning: The set_color_cycle function was deprecated in version 1.5. Use `.set_prop_cycle` instead.\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 800x640 with 10 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "## ROTATIONS marginloss percentile distance\n",
    "import matplotlib\n",
    "from torch.autograd import Variable\n",
    "\n",
    "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()\n",
    "        \n",
    "###########################################\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import scipy.ndimage as ndim\n",
    "import matplotlib.colors as mcolors\n",
    "conv = mcolors.ColorConverter().to_rgb\n",
    "\n",
    "#############\n",
    "im_ind = 90\n",
    "Nsamples = 100\n",
    "#############\n",
    "\n",
    "angle = 0\n",
    "plt.figure()\n",
    "plt.imshow( ndim.interpolation.rotate(x_dev[im_ind,0,:,:], 0, reshape=False))\n",
    "plt.title('original image')\n",
    "# plt.savefig('original_digit.png')\n",
    "\n",
    "\n",
    "s_rot = 0\n",
    "end_rot = 179\n",
    "steps = 10\n",
    "rotations = (np.linspace(s_rot, end_rot, steps)).astype(int)            \n",
    "  \n",
    "ims = []\n",
    "predictions = []\n",
    "# percentile_dist_confidence = []\n",
    "x, y = x_dev[im_ind], y_dev[im_ind]\n",
    "\n",
    "fig = plt.figure(figsize=(steps, 8), dpi=80)\n",
    "\n",
    "# DO ROTATIONS ON OUR IMAGE\n",
    "\n",
    "for i in range(len(rotations)):\n",
    "    \n",
    "    angle = rotations[i]\n",
    "    x_rot = np.expand_dims(ndim.interpolation.rotate(x[0, :, :], angle, reshape=False, cval=-0.42421296), 0)\n",
    "    \n",
    "    \n",
    "    ax = fig.add_subplot(3, (steps-1), 2*(steps-1)+i)\n",
    "    ax.imshow(x_rot[0,:,:])\n",
    "    ax.axis('off')\n",
    "    ax.set_xticklabels([])\n",
    "    ax.set_yticklabels([])\n",
    "    ims.append(x_rot[:,:,:])\n",
    "    \n",
    "ims = np.concatenate(ims)\n",
    "net.set_mode_train(False)\n",
    "y = np.ones(ims.shape[0])*y\n",
    "ims = np.expand_dims(ims, axis=1)\n",
    "cost, err, probs = net.sample_eval(torch.from_numpy(ims), torch.from_numpy(y), Nsamples, scale_inv_EQ1, scale_inv_EHH1, MAP1, scale_inv_EQ2, scale_inv_EHH2, MAP2, scale_inv_EQ3, scale_inv_EHH3, MAP3, False) # , logits=True\n",
    "\n",
    "predictions = probs.numpy()\n",
    "    \n",
    "    \n",
    "    \n",
    "textsize = 20\n",
    "lw = 5\n",
    "    \n",
    "print(ims.shape)\n",
    "ims = ims[:,0,:,:]\n",
    "# predictions = np.concatenate(predictions)\n",
    "#print(percentile_dist_confidence)\n",
    "\n",
    "c = ['#1f77b4', '#ff7f0e', '#2ca02c', '#d62728', '#9467bd',\n",
    "     '#8c564b', '#e377c2', '#7f7f7f', '#bcbd22', '#17becf']  \n",
    "                                         \n",
    "\n",
    "# c = ['#ff0000', '#ffff00', '#00ff00', '#00ffff', '#0000ff',\n",
    "#      '#ff00ff', '#990000', '#999900', '#009900', '#009999']\n",
    "\n",
    "ax0 = plt.subplot2grid((3, steps-1), (0, 0), rowspan=2, colspan=steps-1)\n",
    "#ax0 = fig.add_subplot(2, 1, 1)\n",
    "plt.gca().set_color_cycle(c)\n",
    "ax0.plot(rotations, predictions, linewidth=lw)\n",
    "\n",
    "\n",
    "##########################\n",
    "# Dots at max\n",
    "\n",
    "for i in range(predictions.shape[1]):\n",
    "  \n",
    "    selections = (predictions[:,i] == predictions.max(axis=1))\n",
    "    for n in range(len(selections)):\n",
    "        if selections[n]:\n",
    "            ax0.plot(rotations[n], predictions[n, i], 'o', c=c[i], markersize=15.0)\n",
    "##########################  \n",
    "\n",
    "lgd = ax0.legend(['prob 0', 'prob 1', 'prob 2',\n",
    "            'prob 3', 'prob 4', 'prob 5',\n",
    "            'prob 6', 'prob 7', 'prob 8',\n",
    "            'prob 9'], loc='upper right', prop={'size': textsize, 'weight': 'normal'}, bbox_to_anchor=(1.4,1))\n",
    "plt.xlabel('rotation angle')\n",
    "# plt.ylabel('probability')\n",
    "plt.title('True class: %d, Nsamples %d' % (y[0], Nsamples))\n",
    "# ax0.axis('tight')\n",
    "plt.tight_layout()\n",
    "plt.autoscale(enable=True, axis='x', tight=True)\n",
    "plt.subplots_adjust(wspace=0, hspace=0)\n",
    "\n",
    "for item in ([ax0.title, ax0.xaxis.label, ax0.yaxis.label] +\n",
    "             ax0.get_xticklabels() + ax0.get_yticklabels()):\n",
    "    item.set_fontsize(textsize)\n",
    "    item.set_weight('normal')\n",
    "\n",
    "# plt.savefig('percentile_label_probabilities.png', bbox_extra_artists=(lgd,), bbox_inches='tight')\n",
    "\n",
    "# files.download('percentile_label_probabilities.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(10, 28, 28)\n"
     ]
    }
   ],
   "source": [
    "print(ims.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
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     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "IOPub message rate exceeded.\n",
      "The notebook server will temporarily stop sending output\n",
      "to the client in order to avoid crashing it.\n",
      "To change this limit, set the config variable\n",
      "`--NotebookApp.iopub_msg_rate_limit`.\n",
      "\n",
      "Current values:\n",
      "NotebookApp.iopub_msg_rate_limit=1000.0 (msgs/sec)\n",
      "NotebookApp.rate_limit_window=3.0 (secs)\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
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    }
   ],
   "source": [
    "## ROTATIONS marginloss percentile distance\n",
    "import matplotlib\n",
    "from torch.autograd import Variable\n",
    "\n",
    "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()\n",
    "    \n",
    "        \n",
    "###########################################\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import scipy.ndimage as ndim\n",
    "import matplotlib.colors as mcolors\n",
    "conv = mcolors.ColorConverter().to_rgb\n",
    "\n",
    "Nsamples = 100\n",
    "\n",
    "s_rot = 0\n",
    "end_rot = 179\n",
    "steps = 16\n",
    "rotations = (np.linspace(s_rot, end_rot, steps)).astype(int)            \n",
    "  \n",
    "\n",
    "all_preds = np.zeros((len(x_dev), steps, 10))\n",
    "all_sample_preds = np.zeros((len(x_dev), Nsamples, steps, 10))\n",
    "\n",
    "# DO ROTATIONS ON OUR IMAGE\n",
    "for im_ind in range(len(x_dev)):\n",
    "    x, y = x_dev[im_ind], y_dev[im_ind]\n",
    "    print(im_ind)\n",
    "    \n",
    "    ims = []\n",
    "    predictions = []\n",
    "    for i in range(len(rotations)):\n",
    "\n",
    "        angle = rotations[i]\n",
    "        x_rot = np.expand_dims(ndim.interpolation.rotate(x[0, :, :], angle, reshape=False, cval=-0.42421296), 0)\n",
    "        ims.append(x_rot[:,:,:])\n",
    "    \n",
    "    ims = np.concatenate(ims)\n",
    "    net.set_mode_train(False)\n",
    "    y = np.ones(ims.shape[0])*y\n",
    "    ims = np.expand_dims(ims, axis=1)\n",
    "    \n",
    "#     cost, err, probs = net.sample_eval(torch.from_numpy(ims), torch.from_numpy(y), Nsamples=Nsamples, logits=False)\n",
    "    sample_probs = net.all_sample_eval(torch.from_numpy(ims), torch.from_numpy(y), Nsamples, scale_inv_EQ1, scale_inv_EHH1, MAP1, scale_inv_EQ2, scale_inv_EHH2, MAP2, scale_inv_EQ3, scale_inv_EHH3, MAP3) # , logits=True\n",
    "    probs = sample_probs.mean(dim=0)\n",
    "    \n",
    "    all_sample_preds[im_ind, :, :, :] = sample_probs.cpu().numpy()\n",
    "    predictions = probs.cpu().numpy()\n",
    "    all_preds[im_ind, :, :] = predictions\n",
    "   \n",
    "    \n",
    "all_preds_entropy = -(all_preds * np.log(all_preds)).sum(axis=2)\n",
    "mean_angle_entropy = all_preds_entropy.mean(axis=0)\n",
    "std_angle_entropy = all_preds_entropy.std(axis=0)\n",
    "\n",
    "correct_preds = np.zeros((len(x_dev), steps))\n",
    "for i in range(len(x_dev)):\n",
    "    correct_preds[i,:] = all_preds[i,:,y_dev[i]]\n",
    "    \n",
    "correct_mean = correct_preds.mean(axis=0)\n",
    "correct_std = correct_preds.std(axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.save(results_dir+'/correct_preds.npy', correct_preds)\n",
    "np.save(results_dir+'/all_preds.npy', all_preds)\n",
    "np.save(results_dir+'/all_sample_preds.npy', all_sample_preds) #all_sample_preds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "def errorfill(x, y, yerr, color=None, alpha_fill=0.3, ax=None):\n",
    "    ax = ax if ax is not None else plt.gca()\n",
    "    if color is None:\n",
    "        color = ax._get_lines.color_cycle.next()\n",
    "    if np.isscalar(yerr) or len(yerr) == len(y):\n",
    "        ymin = y - yerr\n",
    "        ymax = y + yerr\n",
    "    elif len(yerr) == 2:\n",
    "        ymin, ymax = yerr\n",
    "    line_ax = ax.plot(x, y, color=color)\n",
    "    ax.fill_between(x, ymax, ymin, color=color, alpha=alpha_fill)\n",
    "    \n",
    "    return line_ax"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 600x400 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(dpi=100)\n",
    "\n",
    "line_ax0 = errorfill(rotations, correct_mean, yerr=correct_std, color=c[2])\n",
    "\n",
    "ax = plt.gca()\n",
    "ax2 = ax.twinx()\n",
    "line_ax1 = errorfill(rotations, mean_angle_entropy, yerr=std_angle_entropy, color=c[3], ax=ax2)\n",
    "\n",
    "plt.xlabel('rotation angle')\n",
    "lns = line_ax0+line_ax1\n",
    "\n",
    "\n",
    "lgd = plt.legend(lns, ['correct class', 'predictive entropy'], loc='upper right',\n",
    "                 prop={'size': 15, 'weight': 'normal'}, bbox_to_anchor=(1.75,1))\n",
    "\n",
    "\n",
    "\n",
    "for item in ([ax.title, ax.xaxis.label, ax.yaxis.label] + [ax2.title, ax2.xaxis.label, ax2.yaxis.label] +\n",
    "            ax.get_xticklabels() + ax.get_yticklabels() + ax2.get_xticklabels() + ax2.get_yticklabels()):\n",
    "    item.set_fontsize(15)\n",
    "    item.set_weight('normal')\n",
    "plt.autoscale(enable=True, axis='x', tight=True)\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Weight distribution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "results_KLaplace_NN_MNIST2/weight_samples_K_laplace.npy\n"
     ]
    }
   ],
   "source": [
    "print(results_dir+'/weight_samples_'+name+'.npy')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<type 'numpy.ndarray'>\n",
      "(11964000,)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/homes/ja666/anaconda2/lib/python2.7/site-packages/scipy/stats/stats.py:1713: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
      "  return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5,1,'Total parameters: 11964000')"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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ltJQUDq8HYN68eRx88MHpjkVEREQkEWMa8qZCExERERFRUigiIiIiSgpFREREBCWFIiIiIoIKTURERNLKOUdHRwetra10d3fjnEt3SJJBzIySkhKqq6upqKjAzFJ2LyWFIiIiaeKco6mpiebmZgAikQgFBerEkz36+/tpaWmhpaWFyZMnU19fn7LEUEmhiIhImnR0dNDc3Ex5eTnTp0+nuLg43SFJBurp6WHLli00NzdTUVFBZWVlSu6jP0dERETSpLW1FUAJoQyruLiY6dOnA3t+ZlJBSaGIiEiadHd3E4lElBDKiIqLi4lEInR3d6fsHhmfFJrZF8zMmVl7gsfXm9mNZrbdzDrN7AkzOznVcYqIiIyWc05jCCVhZpbSQqSM/kk0sxnAT4HNCR5fAjwAnAx8Db8gdCNwj5ktSVWcIiIiIqmWyspjyPxCk38HHgWagY8ncPzngUXAMc65JwDM7CHgBeBK4KgUxSkiIiKS1TI2KTSzTwNLgIOAHyR42hnA62FCCOCc6zOzm4HLzWyGc25T8qMVEUkt5xzdfQOUFBWkvLVAMsety9enOwTOPGr2mM+98cYbOeuss3j66ac54ogjBrdv376dD3zgA6xcuZI77riDU045ZdjrrF27ljlz5nDDDTewbNmyMcczkosvvphLLrkkb+eKzMik0Mzqgf8LfNs5t3EUH4CLgL/F2f5i8PVgQEmhiGS8prYurvjLSl7Z3MKuzl527e6lp2+Ad+5byy1fOIrSSGG6QxQZk40bN3LKKafQ2NjI/fffz9FHH53ukCSQkUkhcC3wOvBvozxvCr6rOVZz1P64gkS0Lmbz3FHeX0Rk3Lr7+vnSfz7L8xt2vW3fs+t28qO/ruTiDx+chshExmf16tW8973vpbe3l0ceeYRDDjkk3SFJlIwrNDGzjwFLgS+6sbXfDnfOcPvOBl6Oedw5hvuLiIyZc47v/+mVwYTwHbMn8ckjZvGPS/bn4H2qAbjx8bU8tLIpnWGKjNrzzz/PcccdR1FREY899ti4E8I1a9Zw1llnMX/+fMrLy5kxYwZLly7lpZde2uu4hx9+GDPj5ptv5vzzz6ehoYGysjKWLFnCihUrRrzPb3/7W973vvcxffp0ysrKWLhwId/+9rfp6Oh427HLly9n6dKlTJkyhdLSUubOnct555231zGrV6/mzDPPpL6+npKSEhYuXMg111wzrvciWTKqpdDMKoFrgKuBzWY2KdhVHOyfBPQ6597+nfB2EL81cHLwNV4rYuha4PaYbXNRYigiE+jm5ev57TMbAFg8exIfOXwGRYX+7/eqRRHWNLXT3TfAV3+zgnPfM4+q0ggwvnFfIqn22GOPcfHFFzNr1izuu+++wYmYx2Pz5s1MmTKFH/3oR9TV1dHc3MxNN93EUUcdxYoVK1iwYMFex3/3u99l8eLF/OpXv6KlpYWLL76YE088kRUrVrD//vsPeZ/Vq1dz2mmncd5551FRUcHKlSv58Y9/zFNPPcWDDz44eNy9997L0qVLWbhwIT//+c+ZPXs2a9eu5b777hs85tVXX+WYY45h9uzZ/OxnP6OhoYF7772Xc889l+3bt3PRRReN+30Zj4xKCoGpwDTgG8Ej1k58kvaRIc5/CYj3p0e47eWhbuycawL2+tNbg7lFZCI99VYzl/z3KwDUV5Xw759+J/e/tudjaXJFMR8+bB9uf3YjHd19/PG5TXz23fvqs0oy3te//nVqamp48MEHqauLHak1NieccAInnHDC4L/7+/v54Ac/yMEHH8x1113Hz3/+872Or6ur44477hj8/3Lccccxf/58rrjiCn75y18OeZ8LLrhg8LlzjmOPPZaFCxeyZMkSXnzxRQ499FAAzjnnHGbPns3y5cspLS0dPOess84afH7++edTVVXFY489RnW1b/k/5ZRT6O7u5kc/+hHnnnsutbW143hXxifTuo+3AifFedwLdAXPLxjybLgDONDMBqeeMbMi4NPAcudcQvMdiohMtKa2Ls6+5Vn6BhzFhQX8+2feSX116duOO3zWJA6dWQPA641tPPnmjokOVWTUPvzhD9PS0sJ5551Hf39/Uq7Z19fH5ZdfzkEHHURxcTFFRUUUFxezevVqXnvttbcdf+aZZ+71B9S+++7LMcccw0MPPTTsfd58803OPPNMGhoaKCwsJBKJsGSJn/o4vM+qVat44403+PznP79XQhitq6uLBx54gDPOOIPy8nL6+voGH6eddhpdXV08+eSTY307kiKjWgqdc13Aw7HbzWwZ0O+cezhq26+BzwFznXPrgs3XA+cAt5vZt/Etf2cDC4D3pjJ2EZHxuPF/17K9vQeAS08/mMWz47cWmBmnHzaD9c2d7Ors5Z5XtvKOIY4VyRQXXnghhx9+OJdeeikDAwPcfPPNFBaOr4L+/PPP55prruFb3/oWS5Ysoba2loKCAr7whS+we/futx3f0NAQd9sLL7ww5D3a29s5/vjjKS0t5Qc/+AEHHHAA5eXlbNiwgY9+9KOD99m2bRsAM2fOHPJaO3bsoK+vj6uvvpqrr7467jHbt28f9jWnWkYlhaNUGDwG037nXHewpN2V+HGJ5cDzwKnOuUfSEqWIyAicc9z5vO/IOHzWJD515PDjA8uKC1l66D7815Pr6O13rGpsm4gwRcblkksuwcy45JJLGBgY4JZbbqGoaOxpyM0338xnP/tZLr/88r22b9++nUmTJr3t+K1bt8bdNmXKkBOT8OCDD7J582YefvjhwdZBgF279p4ZIOwS37hx45DXqq2tpbCwkM985jOcc845cY+ZM2fOkOdPhKxICp1zy4BlI20LtjfiWxBFRLLCc+t3smmXb3H4yOH7JHTO/GmVlBQV0N03wCubW1MZnkjSXHzxxRQUFHDRRRfhnOPWW28dc2JoZpSUlOy17e6772bTpk3Mmzfvbcf/5je/4fzzzx/sQl63bh2PP/44n/3sZ4e9B/C2+1x33XV7/fuAAw5g7ty5XH/99Zx//vlvOx6gvLyck046iRUrVnDooYdSXFyc2AudQFmRFIqI5LKwlbDA4IOHJpYUFhUUsKChihc3trCqsY3uvn5KijShtWS+73//+xQUFHDhhRfinOM3v/nNmBLDD33oQ9x4440ceOCBHHrooTz77LP85Cc/GbILt6mpiTPOOIMvfvGLtLS0cNFFF1FaWsp3vvOdIe9xzDHHUFtby5e//GUuuugiIpEIt9xyS9wu52uuuYalS5dy9NFH8/Wvf53Zs2ezfv167r33Xm655RYArrrqKo477jiOP/54vvKVr7DffvvR1tbGmjVr+POf/7xXNXM6KCkUEUmDcPmy/gHHH571XU5z6yr5n1cbE77GQdOreXFjC919Azz+xg5OWlCfklhFku2CCy6goKCA733vewwMDHDbbbcRiURGdY2rrrqKSCTCFVdcQXt7O4sXL+aPf/zjXtXC0S6//HKefvppzjrrLFpbWznyyCO57bbbmDt36HUqpkyZwt133803vvENPv3pT1NRUcHpp5/Ob3/7WxYvXrzXse9///t59NFHufTSSzn33HPp6upi5syZfPjDHx485qCDDuK5557jsssu44ILLqCpqYlJkyYxf/58TjvttFG9/lSwfF3fLxFmdjDw8ssvv8zBB2v1ABFJnjApXNXYxo2PrwXg44tnsnjfxItGunr7+eFfXqN/wPF/jpzNFR/V6hDZ5s033wQYdp48GZ+HH36Yk046idtvv52Pf/zj6Q5nXEb6eXnllVdYtGgRwCLn3CujvX6mTUkjIpJXwpVLigqMg4IVSxJVGilkXl0lAP/zaiMDA/ojX0TGTkmhiEia9PQN8GpQJHLg9GpKI6MfE3jQdJ9Ibm/vZsWGnUmNTyTVnHN7zdcX76EezYmjpFBEJE1Wbm2lp38AgMODCalH68DpVYPzct33SuLjEUUywSOPPEIkEhn2cdNNN43rHieeeCLOuazvOp4IKjQREUmTF4Ku49JIAQdMqxrTNapKI8yeXM665k7ufWUr3z71QC17J1njne98J08//fSwx6R77r58oqRQRCQNOnv6WNXYDsCifWooKhx7x81B+1SzrrmTtTs6Wd3UPuYEU2SiVVVVccQRR6Q7DAmo+1hEJA1e3dxKfzBW6rBZb199YTTCcYUA97789lUbRCQ3pHp8pZJCEZE0WLPNtxKWRQqZM7ViXNeaUlnCgqB18L5RzHMo6WdmDAwMpDsMyRLOuZQOD1FSKCIywQYGHG80+aRw/7oKCpLwIf/+g6cB8NKmFprausZ9PZkYJSUl9Pb20tPTk+5QJMP19PTQ29sbdwm9ZFFSKCIywV5vbKOjpx/wq5gkw3Hz6wafL3+zOSnXlNSrrvZd/1u2bFFiKEPq6elhy5YtwJ6fmVRQoYmIyAT73zXbB5/Pq09OUnjYrBpKigro7htg+Vs7WHpYYmsoS3pVVFQwefJkmpubeeONN4hEIpiZKsgF8N3Fzjl6e3sBmDx5MhUV4xtuMhwlhSIiEyxMCmvKIkypKE7KNUuKCnnnvrU8/sYOnlRLYdYwM+rr66moqKC1tZXu7m5N1iyDzIyCggLKy8uprq6moqIipX8wKCkUEZlAvf0DPPWWT9rm1lUm9QP+qDlTePyNHaxpamdbWzd1VakbeyTJY2ZUVlZSWZmcVmORsdKYQhGRCfTChl2D4wnn1Se3G+jo/ScPPg8TTxGRRCkpFBGZQP+7Zsfg8/2TVGQSOmzWJEqK/Mf6k2/uGOFoEZG9KSkUEZlA4XjC+qoSqksjSb12aaSQd8z2E2ErKRSR0VJSKCIyQTp7+lixYScAc5NUdRzr6P2nALC6qZ3t7d0puYeI5CYlhSIiE+Spt5rp7feVpfOS3HUcCpPC8H4iIolSUigiMkHCruPCAhv30nZDOXzWJIo1rlBExkBJoYjIBAmLTA6dWUNppDAl9yiNFPKOWRpXKCKjp6RQRGQCNHf08OqWVgCOnTs1pfcKu5BXNbazQ+MKRSRBmrxaRGQCPPHGnla7Y+dN5a3tHUm9/q3L1w8+b+/uG3z+s/tWsWhGzeC/zzxqdlLvKyK5Qy2FIiIT4Nl1vuq4uLBgcNqYVJk9uZzCAr9SyptJTj5FJHcpKRQRmQDPB1PRLNynOmXjCUORwgJm1ZYD8Nb29pTeS0Ryh5JCEZEU6+kb4OXNfjxhWASSamF1c2NrN509fSMcLSKipFBEJOVWbm2lp28A8FPGTIR9p5QPPt+4c/eE3FNEspuSQhGRFHthw67B5xOVFM6cVDb4fOPOzgm5p4hkNyWFIiIptiJICieVR/ZqwUul8pIiplQUA7ChWS2FIjKyjEoKzexwM7vbzNab2W4zazazJ8zs0wmcu8zM3BCPhomIX0QknueDpPCwmZMwswm778xa31q4cWcnzrkJu6+IZKdMm6dwErAB+A2wCagA/h74LzPbzzn3gwSucRawMmabpvUXkbRo6ezlzW1+WpiJ6joOzZpczgsbW+jo6WdnZy+Tg5ZDEZF4MiopdM49DDwcs/kuM5sDfAlIJCl82Tn3TJJDExEZkxc2Ro0nTPH8hLFm1kYXm3QqKRSRYWVU9/EwtgOaU0FEss7z0UUmMyc2KZxeU0ph0F29oVnFJiIyvIxMCs2swMyKzKzOzM4G3g/8OMHT7zKz/mA84h/NbFEKQxURGVaYFO43pZzaCW6pixQW0FBTCmhaGhEZWUZ1H0e5FvjH4HkPcK5z7roRztkK/BB4EmgFDgG+DTxpZsc6514Y7mQzqwfqYjbPHW3gIiIh59xgUjjR4wlDsyaXsWnXbjbt2k3/gIpNRGRomZoUXg78CqgHlgK/MLMK59xPhzrBOXcPcE/UpkfN7G7gJeBS4PQR7nk2cNG4ohYRibKheTfNHT1A+pJCP66wmb4BR2NrV1piEJHskJFJoXNuPbA++OdfgikcrjCzm5xz20ZxnbVm9hhwdAKHXwvcHrNtLnBnovcTEYm2IljvGOCwdLUURhWbbNAk1iIyjIxMCuN4CvgysD+QcFIYMGBgpIOcc01A014nTuB8YiKSG25dvn7w+V0vbgagsMB4aWMLr21pm/B4plQWUxopoKt3gI2axFpEhpGRhSZxnIRP7N4czUnBVDa3bQwtAAAgAElEQVTH4scZiohMqLDid3pNKUWF6fm4LTAbnJpGLYUiMpyMaik0s//AF4k8BTQCU4FPAJ8EfhJ2HZvZr4HPAXOdc+uCbfcDjwIvsqfQ5F8AB1w4sa9ERPJd38AAW1r8GL7oLtx0mFVbxpqmdra1ddPW1UtVaSSt8YhIZsqopBB4Ar8iyefwq5u0Ay8An3HO3Rx1XGHwiO7ffQmfPH4TKMN3BT8IXOacW5X60EVE9tja0kVfUO07a3JZWmMJWwod8NLGFo6ZNzWt8YhIZsqopNA5dwNwQwLHLQOWxWz7emqiEhEZveh5AWemuaUwXAMZ4PmNu5QUikhc2TKmUEQkq2wNpn8pKSpI+/JyVaURJpX7LuPn1+8a4WgRyVdKCkVEUmBrMJ5wWnUpBRkwk0E4rjF6LWYRkWhKCkVEkmzA7ZkoOlxmLt3CLuTG1m6aNIm1iMShpFBEJMl2dfbS3eenR22ozoykMDo5Xbl14udLFJHMp6RQRCTJwq5j8HMUZoLo5HTl1tY0RiIimUpJoYhIkm1t3VN5PC1DWgqrSiNUlPgJJ9RSKCLxKCkUEUmysKVwUnmE0khhmqPZY3qQoK5Mw3J7IpL5lBSKiCTZ1tZuIHPGE4bCcYVrmtrp7R9xSXgRyTNKCkVEkqinb4Ad7UFSmCHjCUNhktrTP8Bb2zvSHI2IZBolhSIiSdTU1oULnmdqSyHAa1tUbCIie1NSKCKSRNGVx5nWUlhXVUJhgZ9IW8UmIhJLSaGISBKFy9sVFRhTKkrSHM3eIoUFzJlaAcDrSgpFJIaSQhGRJIpe3i5slcskBzZUAbBS3cciEkNJoYhIkjjnBlsKM2V+wlgLp1cDsLmli5bO3jRHIyKZREmhiEiSbGvrprOnH8i88YShsKUQtLKJiOxNSaGISJK8FjVOL9Mqj0MHBi2FoGITEdmbkkIRkSSJHqeXqS2F+9SUUlUaLnenlkIR2UNJoYhIkoQtb1UlRVQG6wxnGjNjYYNvLVRLoYhEU1IoIpIk4YTQmdpKGFoQjCt8fWsbAwNuhKNFJF8oKRQRSYKevgHe2NYOZO54wtCB031S2NnTz4adnWmORkQyhZJCEZEkeHN7O739vtUt01sKD2zYU2zy2hZ1IYuIp6RQRCQJVkYlV5k6R2FogaalEZE4lBSKiCTB640+KSwwqK/KrOXtYlWWFDF7cjmwdzIrIvlNSaGISBKEawlPqSyhqDDzP1oHl7tTS6GIBDL/k0tEJAuESWGmdx2HwqRwXXMnnT19aY5GRDKBkkIRkXFq6+pl067dADRUZ3bXcWhBUGziHKxpak9zNCKSCZQUioiM06rGPUlVtrQULmioHHz+uiaxFhGUFIqIjNuqxsxf8zjWvlMqKA7GPkbHLyL5S0mhiMg4hS1tpZECaiuK0xxNYiKFBexfVwHA643qPhYRJYUiIuMWJoXz66soMEtzNIkL5ytcpe5jEQEyasV2Mzsc+CFwCFAH7AZeB65xzt2cwPn1wJXAh4By4AXgAufcAykLWkTymnNucI7C6EmhM9Wty9cPPt/d0w/A1tYufv23tygrLhzcd+ZRsyc8NhFJr0xrKZwEbAC+C5wGfBZYC/yXmV0w3IlmVgI8AJwMfA04HWgE7jGzJSmMWUTy2Pb2Hpo7egBYMC3zk8Jo0UUxja1daYxERDJBRrUUOuceBh6O2XyXmc0BvgT8YJjTPw8sAo5xzj0BYGYP4VsLrwSOSna8IiLRRRoLGqrYuHN3GqMZnb2SwrYu9ptakcZoRCTdMq2lcCjbgZFmVz0DeD1MCAGcc33AzcCRZjYjhfGJSJ5auXXvpDCbTCqPECn0YyAbW7vTHI2IpFtGJoVmVmBmRWZWZ2ZnA+8HfjzCaYuAF+NsD7cdnMwYRURgT5FGTVkk49c8jlVgNthaqO5jEcmo7uMo1wL/GDzvAc51zl03wjlTgOY425uj9g8pKFKpi9k8d4R7ikieWxlVZGJZVHkcmlZVysadu2ls7cI5l5WvQUSSI1OTwsuBXwH1wFLgF2ZW4Zz76QjnuTHuAzgbuCjxEEUk3w0MOFaHSWGWFZmEpgXL8nX29NPe3UdVaSTNEYlIumRkUuicWw+E8yb8JfjL9Qozu8k5t22I03YQvzVwcvA1XititGuB22O2zQXuHDliEclHm3btpjOY1iXbxhOG9q5A7lZSKJLHMjIpjOMp4MvA/sBQSeFL+PkNY4XbXh7uBs65JqApepu6UURkONlcZBKKnZZmXn3lMEeLSC7LyEKTOE4CBoA3hznmDuBAMxucesbMioBPA8udc5tTG6KI5Jvo6WgOqM/OpLCqtIiyiJ+0WsUmIvkto1oKzew/gFZ8y2AjMBX4BPBJ4Cdh17GZ/Rr4HDDXObcuOP164BzgdjP7Nr7V72xgAfDeiXwdIpIfwpbC6TWl1JRnZ7ermTGtuoS1OzqVFIrkuYxKCoEngLPwCd8koB0/+fRnYpa5Kwweg/27zrluMzsZP1H11fhl7p4HTnXOPTIx4YtIPgmnozkgS4tMQvXVpT4pbOtWBbJIHsuopNA5dwNwQwLHLQOWxdneiE8oRURSqqdvgDe2tQNwYJaOJwyF4wp7+gbYtbuX2vLiNEckIumQLWMKRUQyylvbO+gb8DNdZXtLYTgtDUCTupBF8paSQhGRMXi9Mfsrj0PTqvaelkZE8pOSQhGRMQgnrTYj66dxqSgpoqrEjyZSsYlI/lJSKCIyBqsb/XjC2ZPLKQ2mdMlmWgNZRJQUioiMweom31I4P8tbCUPhuMKmtm76B0ZaFVREcpGSQhGRUerpG2Dtjk4A5mXppNWx6oOWwr4Bx87OnjRHIyLpoKRQRGSU1u7oGGxNy5WWwvqq6ApkFZuI5CMlhSIioxSOJ4Tsn44mVB9VgdzUpnGFIvkooyavFhHJVLcuXz/4/IHXGgefP7tuJy9taklHSElVVlxIdWkRrV19NLWppVAkH6mlUERklMKkqbY8QnFR7nyM1qsCWSSv5c6nmYjIBAm7V6O7XHNBOK5wmyqQRfKSkkIRkVHoH3Bsb/PVudHFGbkgTHL7Bhwbd3amORoRmWhKCkVERqG5o4d+51vR6qtzKymMXgN5VVQxjYjkByWFIiKjED3eLte6j+uiWj7DyblFJH8oKRQRGYXoyty6HOs+Li/eswbyGrUUiuQdJYUiIqMQFpnUlEVyYs3jWGGX+Cq1FIrkHSWFIiKjsC1oKcy1IpNQ2CW+pqmdAVUgi+QVJYUiIgkacC73k8KgpbCrd4CNO3enORoRmUhKCkVEErSzo4e+oPUs14pMQtGvS8UmIvlFSaGISIKii0xybTqa0LS9KpBVbCKST5QUiogkqCmHp6MJlZcUURlUIK9qVEuhSD5RUigikqCwpbCqtIiy4tyrPA6F4yXXqKVQJK8oKRQRSVBTjheZhMKu8dWNqkAWySdKCkVEEjDg3OAchbnadRwKX9/u3n427VIFski+UFIoIpKAls5eevtzc83jWNGvT13IIvlDSaGISAIa23K/yCQ0Ler1qdhEJH8oKRQRSUBTa9R0NDk+prCipIgpFcWApqURySdKCkVEEhAWmVQUF1IRTNmSy+bVVwJKCkXyiZJCEZEEDBaZVOd213HogGlVAKxpbMM5VSCL5AMlhSIiI3DO5c10NKH503xLYUdPP5tbukY4WkRyQUYlhWb2HjO73sxWmlmHmW0yszvN7J0JnLvMzNwQj4aJiF9EctOWli56+gaAPEoK66sGn6vYRCQ/ZNrAmK8AU4CrgFeBOuAbwJNm9n7n3IMJXOMsYGXMth1JjVJE8kr0uLp86T4OWwoB1jS2c9KC+jRGIyITIdOSwnOcc03RG8zsHmAN8F0gkaTwZefcM6kITkTy0+qolrJ8aSmcUlFMbXmEnZ29rG5SS6FIPsio7uPYhDDY1o5vNZw18RGJiOyZwLksUkhlHlQeA5gZ84Nik1WNqkAWyQcZlRTGY2Y1wGLglQRPucvM+s2s2cz+aGaLUhieiOSBsPu4vroEM0tzNBNnfjAtzZqmdlUgi+SBbPiT9xqgAvjhCMdtDY55EmgFDgG+jR+PeKxz7oXhTjazevwYxmhzxxSxiOQM59xg93Gur2QSK0wK27v72NLSxT6TytIckYikUkYnhWZ2GfD3wD85554d7ljn3D3APVGbHjWzu4GXgEuB00e43dnAReMIV0RyUFNbN61dfUD+jCcMhXMVgm8tVVIoktsytvvYzC4CLgC+55z7xViu4ZxbCzwGHJ3A4dcCi2IeIyWSIpLjVjdGVx7nV1I4L6oCebWmpRHJeRnZUhgkhBcDFzvnLh/v5YCBkQ4KilxiK5/HeWsRyXbRlbf51n1cV1nCpPIIuzp790qORSQ3ZVxLoZldiE8If+Ccu2Sc15oDHIsfZygiMmphkUlJUQHVpRn5d3TKmNnguEJNSyOS+zLqE87MvoEf/3cPcLeZ7dXt65x7Mjju18DngLnOuXXBtvuBR4EX2VNo8i+AAy6cqNcgIrllTdBCVl+VX5XHoXn1VTy9dierG30Fcj6+ByL5YtxJoZkVO+d6khEMsDT4+oHg8bbbBV8Lg0f0p9NLwCeBbwJl+K7gB4HLnHOrkhSfiOQR5xyrghayfFnJJNYBwbjCtu4+Glu7aajJz/dBJB8ko6Vwk5n9Cvg359z68VzIOXdigsctA5bFbPv6eO4tIhJrR0cPuzp7AZiWZ5XHoeg1kFc3tSkpFMlhyRhT+GfgXOANM7vDzE5OwjVFRNJu78rj/EyGotdA1somIrlt3Emhc+4fgJnA94DDgPvM7DUz+6qZVQ1/tohI5lrTlH9rHseqryoZLLBZo2ITkZyWlOpj59xO59yV+BVAzgA2AFfhu5Z/YWYHJuM+IiITKaw8rigupKYskuZo0iN6DWRNSyOS25I6JY3z/hv4FvAIUIlfKeQVM/tDsJSciEhWCJOgefWVeV11GxabrGps0xrIIjksaVPSmFkR8AngHODd+NbCbwG/xa8MciHwn8SvKhYRyThhS+G8+vwbCXPr8j11g627/TJ/rV19XPfom1SX7mk1PfOo2RMem4ikRjKmpJkB/CPwRWAa8Dfg74A7nHPhSiJXm9km4Obx3k9EZCLs7Ohhe3s3sHexRT6KHk/Z1Nq9V1IoIrkjGS2Fa4E+4DbgKufc80Mc9ybQmIT7iYikXNhKCDC/vpLG1u40RpNe0ZXXTW1dzKvP7yRZJFclY0zhJcBs59xZwySEOOeed87NScL9RERSLnpZt/l52H0crbq0iJIi/+uiKY+TY5Fcl4ykcD0wEG+HmU02s88m4R4iIhMqLDIpjRQwo7YszdGkl5kxLWgtbGzrSnM0IpIqyUgKb8BPRRPPnGC/iEhWWRN0H8+tq6SwIH8rj0PhuMKm1m5VIIvkqGQkhcN9WpYC/Um4h4jIhAq7j+dr/BywZ1zh7t5+2rr70hyNiKTCmApNzGw2sF/UpneYWewaUGXAl/DdyyIiWaNld+9gYUk4cXO+m1a9pwK5saVLFcgiOWis1cdnARcBLnhcG+eYsAXxa2O8h4hIWqyJqTwWaIiqQN7a2qVkWSQHjTUp/B3wMj7x+x3wXWB1zDHdwMvOubVjjk5EJA2i1/hV8uNVlhRRXlxIZ08/ja0qNhHJRWNKCp1zrwGvAZjZWcBdzrkdyQxMRCRdwsrj4qICZuV55XHIzGioLuXN7R1sVVIokpPGXWjinLtJCaGI5JJw4ur9p1ZQVJjUJeKz2rQa34Xc1NrNgCqQRXLOWAtNvg/8yjm3OXg+HOecu2ws9xERSYdwTKG6jvcWjivsG3A0t/cwNWr5OxHJfmMdU3gxcA+wOXg+HAcoKRSRrNDe3cemXbsBFZnEmhZTbKKkUCS3jHVMYUG85yIi2U6Vx0ObFpUEbm3tYtGMmjRGIyLJpoRORCTK6sboymMlhdFKIoXUlvv5CVWBLJJ7xp0UmlmpmVXHbPs7M/uRmb13vNcXEZlIYUthUYGx75SKNEeTecJxhVtblBSK5JpktBT+F/D/wn+Y2bnAbcC/APea2WlJuIeIyIQIK4/nTK0gosrjtwkrkJs7eujpG0hzNCKSTMn4xDsSX3QSOhe4GZgE/BH4ZhLuISIyIQbXPFbXcVxhS6EDmtrUWiiSS5KRFNYBmwDMbA6wP3C1c64V+DWwKAn3EBFJuc6ePjbuDCuPNR1NPNEVyOH60CKSG5KRFHYCYQna8UA78Ezw7y5Af26LSFZ4c1sH4ZzMaimMb2plCYUFfml7FZuI5JaxzlMY7SXgHDNbB5wNPOTc4FT3s4GtSbiHiEhK3Lp8/eDzFet3Dj5f1di+1z7xCguMusoStrZ2abk7kRyTjKTwMuAu4HmgB4iuOP4g8FwS7iEiknJNbb47tMBgakVxmqPJXA01pWxt7aJRFcgiOWXcSaFz7kEzWwi8E3jeOfdm1O4H8cmiiEjGC5PCyRUlWvN4GOG4wrbuPpo7episBFokJySjpRDn3DpgXZzt1yXj+iIiE6Ep6A6t1/Jtw2qo3vP+rNzayjFzp6YxGhFJlqQkhQBmVg/sC5TF7nPOPZqs+4iIpEJv/wDNHT0A1FcrKRxOdAXy61vblBSK5IhxJ4VmNh0/gfVJ8Xbjp7MqTPBa7wE+DRwDzAJ24SuZL3XOPZvA+fXAlcCHgHLgBeAC59wDidxfRPLX9vZuwgq5+qrSYY/NdzVlEUojBXT1DrAqallAEcluyWgp/AXwDuBbwIvAeCau+gowBbgKeBU/B+I3gCfN7P3OuQeHOtHMSoAH8JNmfw1oAs4B7jGz9zrnHhlHXCKS46Ln3FP38fDMjGnVpazb0cnKrUoKRXJFMpLCJcA3nXM3JOFa5zjnmqI3mNk9wBrgu/jClaF8Hj9R9jHOuSeCcx/CtxZeCRyVhPhEJEdtbfGTVhcY1CkpHFGYFK7a2sbAgKMgmLtQRLJXMsrrHLAhCdchNiEMtrXjWw1njXD6GcDrYUIYnNuHX3LvSDObkYwYRSQ3bW4Ji0xKteZxAsLl7jp6+gdXgRGR7JaMT77b8WP4UsLMaoDFwCsjHLoI330dK9x2cDLjEpHc4Zxjyy6f2Eyv0XjCRES/T69uaU1jJCKSLMnoPv4d8EszKwD+DOyIPcA5N54JrK8BKoAfjnDcFKA5zvbmqP1DCopU6mI2z00kQBHJbq1dfXT09AMwfdLbJlCQOBqiKpBXbm3lA4sa0hiNiCRDMpLCcJzfV/GFHdFGVX0cy8wuA/4e+KdEqo+De41lH/gl+i5KNDYRyR1bWvZ0f6qlMDElkUImVxTT3NHDa2opFMkJyUgKz0rCNd7GzC4CLgC+55z7RQKn7CB+a+Dk4Gu8VsRo1+K7wqPNBe5M4N4iksU279qzXNs+NWopTFRDdSnNHT2qQBbJEclY5u6mZAQSLUgILwYuds5dnuBpLwGHxNkebnt5uJODIpfYyucEby0i2SxsKawtj1BWPKaOjbzUUFPKq1taWbejk/buPipLkrYegoikQVJL7MxsgZkda2YV47jGhfiE8AfOuUtGceodwIFmNjj1jJkV4SfDXu6c2zzWmEQkt20JKo+nq5VwVBpiVjYRkeyWlKTQzD5rZhvxU8c8CiwItv/OzL44iut8A7gUuAe428yOjn5EHfdrM+szs32jTr8eX6F8u5mdaWbvxRfBLMBPrC0i8jZdvf2Dy9tNn6TxhKMRPf5S4wpFst+4k0Iz+wRwI/Acvtgkus/1OeDvRnG5pcHXDwBPxHmECoPH4L2cc93AycBDwNX4SujpwKlazUREhhK2EoLGE45WbUUxFUF3+8qtSgpFsl0yWgq/A9zgnPsw8B8x+14DDkr0Qs65E51zNtQj6rhlwba1Mec3Ouc+55yb4pwrc8692zl3/zhem4jkuM27VHk8VgVmLGioAuC1Leo+Fsl2yUgKFwK3DbGvmRHmBxQRSaewpbC8uJCaskiao8k+B06vBvyYwoGBkWb+EpFMloyksBOoGWLfDGBnEu4hIpISYeXx9JpSzTgwBguDpLC9u0/L3YlkuWQkhf8LfNXif5ouAx5Owj1ERJKuu6+fxlbfUqjxhGOzMOg+BnhN4wpFsloyksJLgaOBp4Bz8SuHfNTM/gycwMjL04mIpMXqxnbCHk9VHo/NguikUBXIIllt3Emhc+4Z4FSgEvgZviL4u8ABwGnOuWEnjRYRSZdXN+9JYjRH4dhUlUaYNdm/dytVbCKS1ZIy/bxz7iFgoZnNBaYB251zq5JxbRGRVHk1aNkqKjCmVpakOZrstbChmg3Nu9V9LJLlxtVSaGZ1ZnaBmd1nZi/j1wm+GPiYmanqWEQy2iubWwC/XFthgYpMxiqsQF63o5OO7r40RyMiYzXmpNDMTgZW48cUnoSfemZq8PyHwCozOyEZQYqIJNvAgBvsPlaRyfgcNH3PuMKVWu5OJGuNKSk0szrgt0ALfsWSGufcdOdcA356mk8BHcDv1WIoIplofXMnHT39gIpMxuvAhurB51rZRCR7jbWl8PP4ZeaOdc793jnXGe5wznU6534HHAdEgmNFRDLKK1FFJmopHJ/Zk8spD5a7UwWySPYaa1L4PuB659zGoQ5wzq0HbsCvYywiklFe3eLHExowrVotheNRUKDl7kRywViTwoXAYwkc97fgWBGRjBK2FE6tKqG4KBlTtua3cGWTlVtatdydSJYa6yfhJKApgeOagmNFRDLKniITtRImw0FBUtjR08+65s4RjhaRTDTWpLAE6E3guD6geIz3EBFJiW1t3TS1dQOatDpZFs2oGXz+8qaWNEYiImM1nsmrF5jZSBNSHTiO64uIpEQ4PyHAPpOUFCbDgQ1VFBYY/QOOlze3sPSwfdIdkoiM0niSwhsTOMbwayGLiGSMV7dEL2+n7uNkKI0UMr++kpVb29RSKJKlxpoUnpXUKEREJlBYZDK9ppSKkqSs9pm3bl2+fvB5ebF/L59bt4tbnlyH2Z5VYs48avaExyYiozOmT0Pn3E3JDkREZKK8FiSFYXGEJMeMSaU8tx529/azq7OX2goNKRfJJpqHQUTySkd3H2/t6ADg4H2UFCZT9PjMTbt2pzESERkLJYUiklde29KKC0Y6H7RPzfAHy6hMrykj7DDerKRQJOsoKRSRvBJdZKKWwuQqLiqgrqoEgM0tSgpFso2SQhHJK69s8klhVWkRM2s1HU2yhV3Im3buxjlNPiGSTZQUikheCVsKD5pevVd1rCRHmBR29PTT2jXSVLYikkmUFIpI3ujtH+D1rW0AHKzxhCkxI6rYROMKRbKLkkIRyRtvbGunp38AgIM0njAloicDVwWySHZRUigieSMcTwgqMkmV0kghUyv9/IRqKRTJLkoKRSRvhCuZFBcWMK++Ms3R5K5wXKGSQpHsoqRQRPLGq1v8mrwHNFQSKdTHX6rsU+OTwtauPtq6etMcjYgkSp+KIpIXnHO8GrQUHjxdRSapNKNWxSYi2UhJoYjkhY07dw9OkaIik9QKWwoBNu3qSmMkIjIaGZcUmlmVmV1pZveZ2TYzc2Z2cYLnLguOj/doSHHoIpLBwvGEoCKTVCsrLqS2PAKopVAkmxSlO4A4pgBfAl4A/gR8YQzXOAtYGbNtxzjjEpEs9vyGXQAUGCycrqQw1WZMKmNnZ6+SQpEskolJ4Tqg1jnnzGwqY0sKX3bOPZPkuEQkiz23ficABzZUU1GSiR99uWXGpDJe3tzKrt29KjYRyRIZ98notFimiCRZX/8AL270LYXvmD0pzdHkh5mTywefb9yp1kKRbJBxYwqT5C4z6zezZjP7o5ktSndAIpI+K7e20dXrVzJZPLs2zdHkh5mTyghXlt7Q3JnWWEQkMRnXUjhOW4EfAk8CrcAhwLeBJ83sWOfcC0OdaGb1QF3M5rmpClREUuvW5esHnz/55p4hxZt27t5rn6RGSaSQadWlbG3tYv1OJYUi2SCnkkLn3D3APVGbHjWzu4GXgEuB04c5/WzgohSGJyJpsj5oqSqLFDIlWIJNUm/W5DK2tnaxcedu+gcchQU28kkikja52n08yDm3FngMOHqEQ68FFsU8hksiRSRLhN2XsyeXY6bEZKLMqvXjCnv6BljT1J7maERkJDnVUjgMAwaGO8A51wQ07XWSfnmIZL327j52dPQAMCuq+EFSL/r9XrF+JwsaqtIYjYiMJOdbCs1sDnAsfpyhiOSZjVFFDrOVFE6ouqoSSor8r5kV63elORoRGUlGthSa2alABRD+WXmQmX08eP4X51ynmf0a+Bww1zm3LjjvfuBR4EX2FJr8C+CACyfwJYhIhgjHExowM2pNXkm9AjNm1ZazZls7KzbsTHc4IjKCjEwKgX8D9o369yeCB8AcYC1QGDyi+3hfAj4JfBMow3cHPwhc5pxbldqQRSQThZWv9dUllEYK0xxN/pk1uYw129pZ3dROW1cvVaWRdIckIkPIyKTQObdfAscsA5bFbPt6aiISkWw04NzgxMnqOk6PsNjEOXhxYwvHzpua5ohEZCg5P6ZQRPJXY2sXPX2+xixMTmRizYwpNhGRzKWkUERy1noVmaRdZUkRkyv83JAqNhHJbEoKRSRnbWj2XcelkQKmVpWkOZr8NSso8Hl+wy60vL1I5lJSKCI5K2wpnFVbToHmHU2bcL7CHR09g4m6iGQeJYUikpM6e/rY3t4NaNLqdIvuutfUNCKZS0mhiOSk6BYpjSdMr4aaUoo1ibVIxlNSKCI5aX1zB+AnMlXlcXoVFRSwaJ9qAFZsUFIokqmUFIpITlq3Y8+k1WXFmrQ63d4xuxaAVze30NXbn+ZoRCQeJYUiknN6+wfYEKxksu/kijRHIwBH7OuTwt5+py5kkQylpFBEcs6rm1vp7fdTn+w7RV3HmeCI/SYPPn96bXMaIxGRoSgpFJGc88y6PRWu+05RS2EmqKsqYf86/71QUiiSmZQUikjOeTDjdZAAACAASURBVHadTzqqSouoLY+kORoJHRm0Fj67bid9/QNpjkZEYikpFJGc4pzj6bW+pXDfyeWYJq3OGEfO8UlhZ08/r2xuTXM0IhJLSaGI5JQNzbvZ1uYnrVbXcWZ5V9S4wqfeUheySKZRUigiOeWZdXuSDRWZZJaZtWVMrykF4CmNKxTJOEoKRSSnhEUmkUJjek1ZmqORaGY22IX8zNpmBgZcmiMSkWhKCkUkpzwTtEDNqi2nsEDjCTNN2IW8s7OXNdva0xyNiERTUigiOaOls5dVjT7RUNdxZgpbCkHjCkUyjZJCEckZz63X/ISZbl5d5eA0QZqvUCSzKCkUkZwRFpmYwezJainMRAUFNri6yVNvNeOcxhWKZIqidAcgIpIs4fyEC6ZVURopTHM0Eu3W5esHn0eCsZ5bWrq49qE3qK0oHtx35lGzJzw2EfHUUigiOaGnb4AXNuwC4Ij9atMcjQxnv6l7uvbX7uhIYyQiEk1JoYjkhFc2t9Dd55dOO2LfySMcLek0vaaM4kL/6+et7UoKRTKFkkIRyQlPvLlj8LlaCjNbYYExO6gOV0uhSOZQUigiOeGx1dsBmDO1gpm1KjLJdPsF1eHb23to2d2b5mhEBJQUikgO6Ozp45mgyOS4eVPTHI0kYl7dnnGFbzRpEmuRTKCkUESy3vK3munp9+MJj5+vpDAbzKgtpzTifwVpZRORzKCkUESy3t9W+a7jwgLj3XOnpDkaSURhgbH/1EoA1jS1a75CkQygpFBEst5ja7YBsHj2JKpKI2mORhI1r94nhe3dfWxt7UpzNCKipFBEstrWlq7B9Y6Pm1eX5mhkNMKkEDSuUCQTZFxSaGZVZnalmd1nZtvMzJnZxaM4v97MbjSz7WbWaWZPmNnJKQxZRNLob6u3DT4//gCNJ8wmUyqKmRSsg6xxhSLpl3FJITAF+BJQAvxpNCeaWQnwAHAy8DXgdKARuMfMliQ5ThHJAI+t8eMJq0uLOHRGTZqjkdEwM+bV+dbCt7Z30BcUC4lIemTi2sfrgFrnnDOzqcAXRnHu54FFwDHOuScAzOwh4AXgSuCoZAcrIukzMOAG5yc8dt5Uigoz8e9cGc68+kqeWbeT3n7HuubOdIcjktcy7hPUBcZ4+hnA62FCGFyvD7gZONLMZiQjRhHJDK9uaWVHRw8Ax2kqmqw0t64SC56v0bhCkbTKxJbC8VgE/C3O9heDrwcDmyYuHBFJtluXrx98/siqPeMJd3b07rVPskNFSRHTJ5WyeVeXkkKRNMu1pHAK0Bxne3PU/rjMrB6ILV2cm6S4RCQF1jS1Ab5gYXJFcZqjkbGaV1fF5l1dbN61m50dPdTqeymSFhnXfZwEw3U9D7fvbODlmMedSYxLRJKop2+AtTv8GLToqU0k+4TfPwc8/saO9AYjksdyLSncQfzWwMnB13itiKFr8d3P0Y/TkxqdiCTNW9s76B/wf+fNV1KY1fadUk5RgR9ZGE5ELvL/27v3+LrKOt/jn9++5J42TdI06R16oZBCuVUogkinKqCDAjoCwgBnxqOD4+XMOUc9Z/R4dNTjcc68ZtQZcFTAGaEw8gJEkZtyFYFQraVNLS2959I0bdOkuSd77+f8sXZCmqRpmr2TtfbO9/16rdfeefZaO7/1vNZe+7fXs57nkamXbc3HW4CzRykfKKs90YbOuWageWiZmZ1gbRHx25tNxwAIm3H6bCWFmSwaDnFaeSFvNXfwm7cO45zT+VfEB9l2pfBRYIWZDQ49Y2YR4GagxjnX6FtkIpI2zjnebPLuJzytvJC8aNjniCRVA03I9Ue7B2eoEZGpFcik0MyuMrMPA3+aLDrLzD6cXAqS69xtZjEzWzRk03uArcBDZnaTma0DfgqcAXxhKvdBRCbPgbYe2rr7AVhRVexzNJIOZ1XNGHz+9NYmHyMRmb4CmRQCdwEP4SV5AB9J/v0QUJEsCyeXwTYG51wv3mwmzwPfA34BVAFXOedenJLIRWTSDTQdA5xZOWOMNSVTlBXlUjkjD1BSKOKXQN5T6JxbPI51bgNuG6X8IHBr2oMSkcDYdsBrOq6ckafhS7LImVUzaDrWw9bGY9Qf7WL+rAK/QxKZVoJ6pVBEZFTHuvtpaO0GYEWlmo6zSfXct6/6PrP1oI+RiExPSgpFJKMMdDAB78qSZI+qmXnMK8kH1IQs4gclhSKSUQbuJyzKjTBvVr7P0Ug6mRnvq64EYMPeFlqS81qLyNRQUigiGaOrLzY4P+6KymJCGssu67y3eg4ACQe/3qYmZJGppKRQRDLGy28dJpacxWSFeh1npdWLSwfnsX5GTcgiU0pJoYhkjGe3eZMORUKm+Y6zVDhkrDvTG3nspbcO09kb8zkikelDSaGIZIREwvHsm15SuLSiiJyITl/ZauC+wr5Ygpd2aC5kkamis6qIZIRN9a0c7ugF1HSc7d65tJyCHG/qQvVCFpk6SgpFJCM8sfkA4E1hpPEJs1teNMy7z5gNwLNvNtMbi/sckcj0oKRQRAIvkXA8nkwKF5cXMiM/6nNEMtmuWlkFQHtPjBe3qwlZZCooKRSRwHt9bwtNx3oAOGf+TJ+jkamw7sw5g03Ij73R6HM0ItNDIOc+FhEZ6hfJpCASMlbOVVKYzdbX7B98vnxOMZvqWnm6tol7X95DbjQ8+NpNFy30IzyRrKYrhSISaP3xBE9s8ZqOL11WTmGufstOF6uSV4VjCccfDxzzORqR7KekUEQC7bc7D3O0qx+APz1nrs/RyFRaWlE82IT8Rn2rz9GIZD8lhSISaD9PNh3nREKDU6DJ9BAOGSvneVcLdzZ30KGBrEUmlZJCEQmsnv44z2z15r9de0YFxXnqdTzdrJpfAnhzIdc2tPkcjUh2U1IoIoH1wvbmwatD15yrpuPpaFFZATOTQxCpCVlkcikpFJHAGmg6LswJs3ZFhc/RiB9CZoPDEO070kVrV5/PEYlkLyWFIhJIHb0xnt3mzXX83upK8oYMRyLTy0ATMsDmejUhi0wWJYUiEkhPbDlAbywBwDWr1HQ8nVXNzGN2US4Am+pacc75HJFIdlJSKCKBNDCI8eziXC5dVu5zNOInM2PVAu9qYdOxHhpau32OSCQ7KSkUkcDZ2tjGpjqvU8FHL1xANKxT1XR3waJZhMx7XrO7xd9gRLKUpgYQkUAYOr3ZzzY1AGBAfk74uNdkepqZH2VF5Qz+eOAYmxtaaevqZ2aBhigSSSf9/BaRQOntjw9eJVw+p5hZBTk+RyRBcdHppQD0xx0Pb6z3ORqR7KOkUEQCZVN9K33JDibvOK3U52gkSJbMLqK00PuRcH/NPnU4EUkzJYUiEhjOOTbs8e4Xm5kf5YzKYp8jkiAJmXFR8ofCrkOdvKZ7C0XSSkmhiARG/dFuGtt6AFi9eBYhM58jkqA5f+EsIskeJ/fX7PM5GpHsoqRQRALj9eRVwpDBhYvUdCwjFeZGWDnPm+Hk6a1NHGrv9TkikeyhpFBEAqG7L87mBq+DyYrKGczIV89SGd1AE3J/3PHT39X5HI1I9lBSKCKBsGFvC/1xr+PARepgImNYWFrAiuT9putr9tMfT/gckUh2CFxSaGZFZvZPZtZoZj1mtsnMbhjHdreZmTvBUjkVsYvIxPTG4vx212HAm8FkSUWRzxFJkJkZt6xZBEBDazePb270OSKR7BC4pBB4BLgV+CpwFbABeMDMbhrn9rcDa4YtRyYhThFJk0c3NtDeEwPg8mWz1cFETur68+dTnpwP+a4XdpFIaHgakVQFKik0s6uB9wB3OOf+1Tn3vHPu48CvgL83s/A43qbWOffasKV/UgMXkQmLJxw/eGk34A1Dc86CmT5HJJkgLxrmLy49DYAdBzt47s1mnyMSyXyBSgqBa4EO4KFh5fcCc4GLpjwiEZlUz2xtYvfhTgAuXVpOJBS005IE1c0XL6Q4z5ut9c4Xdmowa5EUBe3suxLY5pyLDSvfPOT1k3nczOJm1mJmj5jZeLYRER845/j+i7sAyI+GuXDxLJ8jkkxSnBflz5P3Fm7c30rNHg1mLZKKoCWFZcBon+qWIa+fSBPwDeAvgSuALwOrgdfMbNXJ/rGZVZhZ9dAFWHJK0YvIKXl11xHeqG8DYM2SMnIj47lDRORtt7/zNHIj3lfZnS/s8jkakcwW8TuAUYx1/f+ErznnngKeGlL0kpn9EtgCfA344En+7x3AV8YbpIik7q7kVcK8aIg1p4/1m0/keOtr9g8+P29hCa/tbuGlHYf4f09vZ25J/uBrN1200I/wRDJS0K4UHmH0q4EDg5adUtuAc24v8DJw8ThWvxOveXrocrJEUkQm6I26Vn7zljcMzQ2rF1KYG8TfqJIJLls6m+TMd7yw45C/wYhksKAlhVuAM81s+LfD2cnH2gm8pwEnHdnUOdfsnNs6dAHUFiEyCZxzfP2XfwQgGjb+8rLTfI5IMtmswhxWzS8BoLahjbqWLp8jEslMQUsKHwWKgOuHld8KNAI1p/JmZnYa8E7gtbREJyJp8cSWJjbsPQrAbZcsZv6sAp8jkky3dkUF4eTlwsc3N5JQT2SRUxaopNA59yTemIR3mdnHzewKM/sBcCXweedcHMDM7jazmJktGtjWzH5tZv/LzD5kZmvN7LN4TccOr9OJiARAT3+cbz6xDYDSwhz+eu0ynyOSbFBWlMulS8sBqDvazaa6Vp8jEsk8gUoKk64DfoLXOeQpvLEJb3TO3T9knXByGTrtwRbgo8C/A08DnweeAy50zk2k2VlEJsHdL++hobUbgP/63uXMzI/6HJFki3cvnz04buHTW5vo7Y/7HJFIZglcUuic63DOfdY5V+Wcy3XOrXLOPThsnducc5bsSDJQ9l+cc9XOuRnOuahzbp5z7hbn3I4p3wkRGVXzsR7ufH4nACsqi/nohQt8jkiySW40zPuqvanu23ti6nQicorU3U9EJtXQoUMe3lhPZ5939eaSJeX89Hf1foUlWercBSXU7D5C3dFuXt55mH1HOllUVuh3WCIZIXBXCkUkO9W1dLFxn9e55MzKYpZWFPkckWSjkBkfOGcu4M2r/XePb/M5IpHMoaRQRCZdLJ7g4Y31OCBsxlVnV/kdkmSxBaUFnL/QG6Lm19sO8szWJp8jEskMSgpFZNI9v72Z5vZeANaeWUF5Ua7PEUm2u3JlFflRb9rEr/x8Kx29MZ8jEgk+JYUiMqkaW7t5MXnDf9XMPN61bLbPEcl0UJQb4cqVXqeTA209/OOv1OdQ5GSUFIrIpOlPNhsnHIQMrj9//uAAwyKT7YJFs1i9eBYA9/52D1vq23yOSCTYlBSKyKT5/gu7ONDWA8DlyyuYW5Lvc0QynYTM+Oa1ZxMNGwkH/+PRzcTiJ531VGTaUlIoIpNia2Mb333uLQAqinO54gw1G8vUWzanmE9evgSA2oZj/Nur+3yOSCS4lBSKSNq1dfdzx/0b6Y87DK/ZOBLW6Ub88akrlrK4zJtf+9tPvUltg5qRRUajs7SIpJVzjv/+0BvsO9IFwHurK1lQWuBzVDKd5UXDfOv6cwgZ9MYSfOInv6els8/vsEQCRzOaiEha/fA3u3nmjwcBWHdmBZctK/c5IpnOhs6o877qSp6sbaKhtZsPf/8Vbr/ktMGOTzddtNCvEEUCQ1cKRSRtanYf4f8+tR2ABaX5/MNHziVk6m0swXDp0nLOmT8TgN2HOnlag1qLHEdJoYikxYG2bj79wB+IJxw5kRB3fewCZhZE/Q5LZJCZcd1586mamQfAyzsPs6mu1eeoRIJDSaGIpOxIRy83/6hmcNaSr15Tzcp5M32OSmSknEiIj120aHC2k4c31rPrUIfPUYkEg+4pFJEJGbhXq6c/zo9e3k1jqzce4ZrTy0gk3HH3cokESWlhDje+YyE/fmUP8YTjvtf2ce158/RDRqY9XSkUkQnriyX491f3DiaE5y0o4f3nVGG6j1ACbmlFER+5YAGG1yP51nteZ7euGMo0p6RQRCYkFk+w/vV97E0OPXNW1QyuO3++OpZIxli1oIQPnFMFwJHOPm65+3WakjPwiExHSgpF5JT1xuKsf30/Ow56V1aWzC7ko6sXaF5jyThrlpSzdkUFAA2t3dz4w9d0xVCmLSWFInJKemNx/uq+jbzZ1A7AwtICbr54EVHNWCIZ6k9WVHDLxYsA2HO4k2vvfIVXdh32OSqRqaezuIiMW09/nE/+5Pc892YzAItKC7j9ksXkRsI+RyYycWbGV6+p5tNrlwLeNI1/fvfr/McGdZaS6UW9j0VkXNq6+vnMg3/gxR2HAFhUVsBtaxaTG1VCKJnvwQ11VM3M5yMXzOeRPzQQSzi+8PAWfr6pkStXVmnmE5kWlBSKyEk9VdvElx+r5VByHMJ3LC7lqpWVSggl65y3cBazCnK4r2YfXX1xfrvrCA2t3dzwjoXMyNNg7JLd1HwsIid0qL2XT92/kU/e9/vBhPCKM2Zz7+2rlRBK1lpcXsgd7146OPPJ3iNd/PNzO9l9WB1QJLvpSqGIHGd9zX6cc2yqa+XxzQfo7o8DkB8N8/5zqjhvQQmPbWr0OUqRyVVamMMnL1/Czzc18vv9R+nojXHPy3soiIb5xOVLKMzV16dkHx3VInKc1q4+frapYXC4GYDquTO4ZtVcitV8JtNINBzi+gvms6isgJ+/0Ugs4fjucztZ/3odn123jBtWL1Cve8kqSgpFBPCGmvmPDXV859m36I0lACjKjXDNqrma/kumtQsXlzK3JJ+HN9ZzoK2Hwx29fPlntdzz8h7+5j3Lef/ZVYQ0RqdkAXPO+R1DYJlZNVBbW1tLdXW13+GITIq2rn7uq9nHj1/ZO3jfIMD5C0u4+uwqCnL021EEIOEcxXkR/v7p7dQf7R4sXz6niE+vXcbVZ1dpAHfx1datW1m5ciXASufc1lPdXknhGJQUSjZ762A799fs56e/q6OrLz5YXlIQ5UPnzmP5nGIfoxMJrlg8Qc2eFl7Y3kznkM9ORXEuly4tZ+W8mfynS0/zMUKZrlJNCnUJQGQa6emP82TtAdbX7GfD3qPHvbaispiPX3Y6nX0xIiHdJyVyIpFwiHcuLWf14lJe232El946RFdfnOb2Xh75QwO/2NzIG/WtXH/+fN65tFxXDyVjKCkUyXIdvTFe3H6IH7y0i+0H2+npTxz3+tKKIi5dWs6yiiJ6YwklhCLjlBMJ8a7ls7n49DJq9hzh5Z2Hae+J0R93PLapkcc2NVJWmMN7q+dw5coq1pxeRk5Eny8JrsAlhWZWBHwd+DOgFHgT+JZz7sFxbFsBfBv4AFAAvAF8yTn37ORFLBIszjl2Nnfw0luHeWnHIV7dfYS+2PGJYGFOmAsWzWL14lLKinJ9ilQkO+REQly2bDaXLClnZ3MHG/cfZfvBdvpiCY509vHA63U88HodxXkRLl1aziVLylizpIwls4sw01VECY7AJYXAI8Bq4IvADuAm4AEzCznn1p9oIzPLBZ4FSoDPAs3Ap4CnzGydc+7FSY9cZIo556g/2s2Og+1sP9jO9qZ2ana30HSsZ8S6OZEQZ8wppnruDM6qmkFEQ2mIpFU4ZJxRWcwZlcV098XZ2tjG1sZj7DzUQTzhaO+J8WRtE0/WNgFQnBvhsuXlnL9wFuctLKF67kzyNCi8+ChQHU3M7Grgl8BNzrkHhpQ/A1QDC51z8RNsewfwL8AlzrlXk2URvKuFHc65iyYQjzqaSCD09MdpaO2m/mg3u5o7BpPAHU3tx93oPtzM/CjL5xRxVtUMlswuUiIo4oOe/jjbD7az7cAxdh3qpLM3Nup60bCxqKyQRaUFLCorZHF5AQtLC1hcVsi8WfkaE1FOKts6mlwLdAAPDSu/F1gPXAS8Msa22wcSQgDnXMzM7gO+aWbznHMNkxCzyIT1xRK0dPbR3N5D87Femtt7veftvTQf8x4bW71x0cZjRl6EuSX5LK0oYllFMeVFOWqeEvFZXjTMqvklrJpfgnOO5vZedh/qYPfhTupaujjW4yWJ/XHv1o+dzSOn0wuHjLkleVTOyKOsMJeyohzKinIpK8zxnhfmUp4sK8mPatxEmZCgJYUrgW3OueE/ozYPef1ESeFK4DejlA9sWw0oKZwGnHMkHMQTjoRzOOeNL5ZIlicSbz8fWDfhHPHEyHWdc/THHf3xBP3xBH3xBH2xxHFlvTHvsT/mvd7Tn6CrL053X4zOvjjdfXE6+2J09cXp6ovR2RunvSdGe0//4CDRpyovGqJyRh5zjltyNaagSMCZ2eBnds2ScpxztHX3s7+li/qj3Rzp6OVIZx8tnX3EEm+35MUTjrqWbupausd4d084ZMwqyKGsMIeivAj50TB50RB50TD50TD5OeFk2dvP86NhcqMhcsIhIuEQkbARCRmRUIho2AiHjOiw8kiyPGTe4j2HUMgIJ8tCIYY8V6IadEH7BikDdo9S3jLk9bG2bRmlfDzbDnRSmT2seAXAzp07x9p0wrr6Ytxyd80pbzdWi/+JbgcY8yaBsd5vgnFM6P3GeHXsfR5I7rxELhvkR8MU5kYozA1TlBOhuCDCjLwoM/KizMyPUpQbwSwOdHrLMWg5NvoHQESCrxQoLQQKvb8TLkpnb4zW7n7auvpp6/aWrr4Y3X1xuvri9PTHT3jW7AaCOEP5QMOFDf5tg88ZfO3tlWzEdnbc9qO955DNB1tKgtJgct6CWXzpA2dN2vsPyVdyJrJ90JJCOFneMHnb3gF8ZbQXPvShD51kUxEREZGxbQce/OKU/KsFwB9OdaOgJYVHGP2KXmnycawLIalsC3AnI+9lLAKWA7VA30m2H24J8BjwQWDXKW6brVQnI6lORlKdjKQ6GUl1MpLqZKTpVic5eAnhhEZcCVpSuAW40cwiw+4rPDv5WHuSbc8epXw82+Kca8Ybxma4U2/fhaE39++aSA+gbKQ6GUl1MpLqZCTVyUiqk5FUJyNN0zo55SuEA4LWv/1RvKtz1w8rvxXv9oixErRHgRVmNjj0THJImpuBGudcEG+vEBEREQmEQF0pdM49aWa/Au4ysxnATuBG4Erg5oExCs3sbrxEcYlzbl9y83vwBqt+yMy+iHfV7w7gDGDd1O6JiIiISGYJVFKYdB3wDeBrvD3N3Y3DprkLJ5fB68LOuV4z+xO8ae6+hzfN3SbgKs1mIiIiIjK2wCWFzrkOvGnqPjvGOrcBt41SfhDvCmIQHAK+mnwUj+pkJNXJSKqTkVQnI6lORlKdjKQ6OQWBmuZORERERPwRtI4mIiIiIuIDJYUiIiIioqRQRERERJQUioiIiAhKCtPGzIrM7J/MrNHMesxsk5ndcArbX2FmvzKzZjPrMLPNZvYZMwtPZtyTKdU6Sb7HB83sRTM7ZmadZrbVzP7zZMU82dJRJ0Pe6+tm5sxszNl6gi6VOjGz68zsATPbaWbdZrbXzO43s2WTHXc6pLjvFWb2YzM7bGZdZvZqcliujDbROsn0Y2Es6TpvZMs5A9LynZtV3y3pErghaTLYI8Bq4IvADuAm4AEzCznn1o+1oZmtA54GXgI+DnQC1wDfwZu38YTD8wTchOsEIDkI+TeA7wP/B+gHVuDN7ZipUqqTAWZ2LvDfgIOTEuXUSqVOvgA04R0nu/Hm/PyfwEYzuzgDprWa0L6bWS7wLFCCd35oxhu8/ykzW5fhY7NO9HjI9GNhLCmfN7LsnAGpfedm43dLejjntKS4AFcDDm+Q7aHlzwANQPgk298H9ACFw8qfBtr83j+f6uQCIA583u99CUqdDFk/gje35XeAF4Bav/fNx+OkYpSyuUAf8CO/92+y9h1vtiYHrBl2XGzFm9bT9/3zoU4y9liYrDoZdmxkxTkjDcdJ1n23pHNR83F6XAt0AA8NK78X76R00YgtjtePd+LqHlbeipcsZqJU6+SvgV682WmyRap1MuCLeLP9/G36QvNNSnXinGsepawRqMe7UhRkqez7tcB259yrAwXOuRjeD8x3mNm8NMc6VSZcJxl+LIwlHeeNbDpnQGp1ko3fLWmjpDA9VgLbkifloTYPeX0s38e7bP1dM5trZiVmdgvegf/t9IY6ZVKtk3cB24DrzWy7mcXNrN7MvmVmmXqJP9U6wczOAr4E/JXzZv/JdCnXyXBmdjqwCO+qWZClsu8rh6w32rbVKcbml7QeDxl0LIwlpTrJwnMGpFYn2fjdkjZKCtOjDGgZpbxlyOsn5JyrAdbiJYENwFG8Xzx/65z7hzTGOZVSqhNgHrAM+G5yWQf8GO+emHvTE+KUS6lOzCwE3AM84px7Is2x+SXV4+Q4ZhYB7sa7ivCPqYU26VLZ97TWW4Ckbb8y7FgYy4TrJEvPGZDacZKN3y1po44mw5jZu4Hnx7n6ec65TcnnY80XOOZcgmZ2AfAoUAN8Aq+jyVrg62aW55z7u3HGMyn8qBO8HyzFePeMPJgse97MCoHPmdlXnHM7xxlT2vlUJ3+DdzK7Zpz/d0r5VCdD/7/hJQGXAdc75+rGu62PUtn3tNRbAKW8Xxl6LIxlonUS6HNGiiZaJ4H+bvGbksKRtuP1AB6P/cnHI4z+y6Q0+TjaL5qh/gWvR9i1zrl4sux5M0sA/9vM7nfO7R5nTJPBjzo5AlTidbYZ6kngc8D5gJ8f3CmtEzNbCHwN796gPjMrSb4UAULJv3udc8PvS51KfhwnwGAS8CPgZuBW59xj44zDT6nse1rqLYBS3q8MPRbGMqE6yZBzxkSl+tkJ8neLr5QUDuOcO4B3QjkVW4AbzSwy7B6Hs5OPJxsT6lzggSEJ4YANeL9qzsQbYsEXPtXJZrwP7nCWfEycYjxp5UOdnA7k4/Ue/M4orx9Nln/uFGNKG5+Ok6FJwO3AXzjn7jvFGPySyr5vGbLeUOOut4BK6XjI4GNhLBOtk8CfM1KQynES6O8Wv+mewvR4FCgCrh9WfivQiNcsPJZG4EIbOVD1btgATwAAAe1JREFUmuRjfcoRTr1U6+Th5ONVw8qvxvvQbkg1QB+kUiebgCtGWd4A9iaf/3N6w50SKR0nySTgh3hJwCecc5l0T1Aq+/4osMLMBntZJu+huxlvSJrGNMc6VSZcJxl+LIxlonWSrecMSO2zk43fLenj95g42bLgjY/Ugtd8dgXwA7z7Gj42bL27gRiwaEjZp5PrPgF8EHgP8C28oWp+5fe++VQnUeD3eMPyfAbvZuBvJdf7nt/75kednOD9XiDzxxxL5Tj5XnLdu4GLhy3n+b1v6dj3E+x3Lt7VkP14g/auwxvMtx+43O/98qlOMvpYmIw6OcF7Zfw5I8XjJCu/W9JWr34HkC0L3q+W7wAH8MZAegO4YZT1fpw8cBcPK78O+A1wCK+3XC3eMAKFUxF/QOukFG+4nia8cRy34/UQC/m9b37VySjrZfwJPpU6wbvi4U6w7PV739Kx72N8PuYA/4Z3j1Q38Cqwzu998qtOMv1YmKzjZJT3yvhzRqp1ko3fLelaLFlBIiIiIjKN6Z5CEREREVFSKCIiIiJKCkVEREQEJYUiIiIigpJCEREREUFJoYiIiIigpFBEREREUFIoIiIiIigpFBERERGUFIqIiIgISgpFREREBCWFIiIiIoKSQhERERFBSaGIiIiIAP8f2AwwBHOVcqgAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 720x480 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "name = 'K_laplace'\n",
    "# mkdir('weight_samples')\n",
    "Nsamples = 5\n",
    "weight_vector = net.get_weight_samples(Nsamples, scale_inv_EQ1, scale_inv_EHH1, MAP1, scale_inv_EQ2, scale_inv_EHH2, MAP2, scale_inv_EQ3, scale_inv_EHH3, MAP3)\n",
    "print(type(weight_vector))\n",
    "# np.save(results_dir+'/weight_samples_'+name+'.npy', weight_vector)\n",
    "\n",
    "print(weight_vector.shape)\n",
    "\n",
    "fig = plt.figure(dpi=120)\n",
    "ax = fig.add_subplot(111)\n",
    "\n",
    "sns.distplot(weight_vector, norm_hist=False, label=name, ax=ax)\n",
    "# ax.hist(weight_vector, bins=70, density=True);\n",
    "\n",
    "ax.set_ylabel('Density')\n",
    "ax.legend()\n",
    "plt.title('Total parameters: %d' % len(weight_vector))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print('a')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.save('results_KLaplace_NN_MNIST2/weight_samples_K_laplace.npy', weight_vector)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.15"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}