BayesBackdrop.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "BayesBackdrop.ipynb",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "b5eEVN1u6imv",
        "colab_type": "code",
        "colab": {}
      },
      "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"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HIofJVvx6tPb",
        "colab_type": "text"
      },
      "source": [
        "Some Utility Function"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bLTHRJAW6rmz",
        "colab_type": "code",
        "colab": {}
      },
      "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"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OF-qn10B62bt",
        "colab_type": "code",
        "colab": {}
      },
      "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)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4CiP3voo65eU",
        "colab_type": "code",
        "colab": {}
      },
      "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"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rc-usCVP68Sp",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "class laplace_prior(object):\n",
        "    def __init__(self, mu, b):\n",
        "        self.mu = mu\n",
        "        self.b = b\n",
        "\n",
        "    def loglike(self, x, do_sum=True):\n",
        "        if do_sum:\n",
        "            return (-np.log(2*self.b) - torch.abs(x - self.mu)/self.b).sum()\n",
        "        else:\n",
        "            return (-np.log(2*self.b) - torch.abs(x - self.mu)/self.b)\n",
        "\n",
        "class isotropic_gauss_prior(object):\n",
        "    def __init__(self, mu, sigma):\n",
        "        self.mu = mu\n",
        "        self.sigma = sigma\n",
        "        \n",
        "        self.cte_term = -(0.5)*np.log(2*np.pi)\n",
        "        self.det_sig_term = -np.log(self.sigma)\n",
        "\n",
        "    def loglike(self, x, do_sum=True):\n",
        "        \n",
        "        dist_term = -(0.5)*((x - self.mu)/self.sigma)**2\n",
        "        if do_sum:\n",
        "            return (self.cte_term + self.det_sig_term + dist_term).sum()\n",
        "        else:\n",
        "            return (self.cte_term + self.det_sig_term + dist_term)\n",
        "    \n",
        "\n",
        "# TODO: adapt so can be done without sum\n",
        "class spike_slab_2GMM(object):\n",
        "    def __init__(self, mu1, mu2, sigma1, sigma2, pi):\n",
        "        \n",
        "        self.N1 = isotropic_gauss_prior(mu1, sigma1)\n",
        "        self.N2 = isotropic_gauss_prior(mu2, sigma2)\n",
        "        \n",
        "        self.pi1 = pi \n",
        "        self.pi2 = (1-pi)\n",
        "\n",
        "    def loglike(self, x):\n",
        "        \n",
        "        N1_ll = self.N1.loglike(x)\n",
        "        N2_ll = self.N2.loglike(x)\n",
        "        \n",
        "        # Numerical stability trick -> unnormalising logprobs will underflow otherwise\n",
        "        max_loglike = torch.max(N1_ll, N2_ll)\n",
        "        normalised_like = self.pi1 + torch.exp(N1_ll - max_loglike) + self.pi2 + torch.exp(N2_ll - max_loglike)\n",
        "        loglike = torch.log(normalised_like) + max_loglike\n",
        "    \n",
        "        return loglike\n",
        "    "
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uwjCcF_P6_lL",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def isnan(tensor):\n",
        "  # Gross: https://github.com/pytorch/pytorch/issues/4767\n",
        "    return (tensor != tensor)\n",
        "\n",
        "def hasnan(tensor):\n",
        "    return isnan(tensor).any()\n",
        "\n",
        "def isotropic_gauss_loglike(x, mu, sigma, do_sum=True):\n",
        "    cte_term = -(0.5)*np.log(2*np.pi)\n",
        "    det_sig_term = -torch.log(sigma)\n",
        "    inner = (x - mu)/sigma\n",
        "    dist_term = -(0.5)*(inner**2)\n",
        "    \n",
        "    if do_sum:\n",
        "        out = (cte_term + det_sig_term + dist_term).sum() # sum over all weights\n",
        "    else:\n",
        "        out = (cte_term + det_sig_term + dist_term)\n",
        "#     print(hasnan(x.data), hasnan(mu.data), hasnan(sigma.data), out)\n",
        "#     if isnan(out) or hasnan(out):\n",
        "#         print('NAaaanNNN')\n",
        "#         print('mu max', mu.max())\n",
        "#         print('mu min', mu.min())\n",
        "#         print('sigma max', sigma.max())\n",
        "#         print('sigma min', sigma.min())\n",
        "#         print('x max', x.max())\n",
        "#         print('x min', x.min())\n",
        "#         print((x - mu).max())\n",
        "#         print((x - mu).min())\n",
        "#         print(hasnan(inner))\n",
        "#         print(dist_term)\n",
        "#         print(hasnan(dist_term))\n",
        "    return out \n",
        "\n",
        "\n",
        "\n",
        "class BayesLinear_Normalq(nn.Module):\n",
        "    def __init__(self, n_in, n_out, prior_class):\n",
        "        super(BayesLinear_Normalq, self).__init__()\n",
        "        self.n_in = n_in\n",
        "        self.n_out = n_out\n",
        "        self.prior = prior_class\n",
        "        \n",
        "        # Learnable parameters\n",
        "        self.W_mu = nn.Parameter(torch.Tensor(self.n_in, self.n_out).uniform_(-0.2, 0.2))\n",
        "        self.W_p = nn.Parameter(torch.Tensor(self.n_in, self.n_out).uniform_(-3, -2))\n",
        "        \n",
        "        self.b_mu = nn.Parameter(torch.Tensor(self.n_out).uniform_(-0.2, 0.2))\n",
        "        self.b_p = nn.Parameter(torch.Tensor(self.n_out).uniform_(-3, -2))\n",
        "        \n",
        "       \n",
        "        self.lpw = 0\n",
        "        self.lqw = 0\n",
        "        \n",
        "                                   \n",
        "    def forward(self, X, sample=False):\n",
        "#         print(self.training)\n",
        "\n",
        "        if not self.training and not sample: # This is just a placeholder function\n",
        "            output = torch.mm(X, self.W_mu) + self.b_mu.expand(X.size()[0], self.n_out)\n",
        "            return output, 0, 0\n",
        "                                       \n",
        "        else:\n",
        "                              \n",
        "            # Tensor.new()  Constructs a new tensor of the same data type as self tensor. \n",
        "            # the same random sample is used for every element in the minibatch\n",
        "            eps_W = Variable(self.W_mu.data.new(self.W_mu.size()).normal_())\n",
        "            eps_b = Variable(self.b_mu.data.new(self.b_mu.size()).normal_())\n",
        "                                       \n",
        "            # sample parameters         \n",
        "            \n",
        "            std_w = 1e-6 + F.softplus(self.W_p, beta=1, threshold=20)\n",
        "            std_b = 1e-6 + F.softplus(self.b_p, beta=1, threshold=20)                      \n",
        "                                   \n",
        "            W = self.W_mu + 1 * std_w * eps_W\n",
        "            b = self.b_mu + 1 * std_b * eps_b          \n",
        "    \n",
        "            \n",
        "            output = torch.mm(X, W) + b.unsqueeze(0).expand(X.shape[0], -1) # (batch_size, n_output)\n",
        "            \n",
        "            lqw = isotropic_gauss_loglike(W, self.W_mu, std_w) + isotropic_gauss_loglike(b, self.b_mu, std_b)\n",
        "            lpw = self.prior.loglike(W) + self.prior.loglike(b)\n",
        "            return output, lqw, lpw\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZpWT9VfT7DUj",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def sample_weights(W_mu, b_mu, W_p, b_p):\n",
        "    \n",
        "    eps_W = W_mu.data.new(W_mu.size()).normal_()\n",
        "    # sample parameters         \n",
        "    std_w = 1e-6 + F.softplus(W_p, beta=1, threshold=20)     \n",
        "    W = W_mu + 1 * std_w * eps_W\n",
        "    \n",
        "    if b_mu is not None:\n",
        "        std_b = 1e-6 + F.softplus(b_p, beta=1, threshold=20)  \n",
        "        eps_b = b_mu.data.new(b_mu.size()).normal_()\n",
        "        b = b_mu + 1 * std_b * eps_b\n",
        "    else:\n",
        "        b = None\n",
        "    \n",
        "    return W, b"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UpTfGdDF7FJM",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "class bayes_linear_2L(nn.Module):\n",
        "    def __init__(self, input_dim, output_dim):\n",
        "        super(bayes_linear_2L, self).__init__()\n",
        "        \n",
        "        n_hid = 400\n",
        "#         prior_instance = isotropic_gauss_prior(mu=0, sigma=0.1)\n",
        "#         prior_instance = spike_slab_2GMM(mu1=0, mu2=0, sigma1=0.135, sigma2=0.001, pi=0.5)\n",
        "        #self.prior_instance = isotropic_gauss_prior(mu=0, sigma=0.1)\n",
        "        self.prior_instance = spike_slab_2GMM(0,1,0.5,1,0.5)\n",
        "        self.input_dim = input_dim\n",
        "        self.output_dim = output_dim\n",
        "        \n",
        "        self.bfc1 = BayesLinear_Normalq(input_dim, n_hid, self.prior_instance)\n",
        "        self.bfc2 = BayesLinear_Normalq(n_hid, n_hid, self.prior_instance)\n",
        "        self.bfc3 = BayesLinear_Normalq(n_hid, output_dim, self.prior_instance)\n",
        "        \n",
        "        # choose your non linearity\n",
        "        #self.act = nn.Tanh()\n",
        "        #self.act = nn.Sigmoid()\n",
        "        self.act = nn.ReLU(inplace=True)\n",
        "        #self.act = nn.ELU(inplace=True)\n",
        "        #self.act = nn.SELU(inplace=True)\n",
        "\n",
        "    def forward(self, x, sample=False):\n",
        "        \n",
        "        tlqw = 0\n",
        "        tlpw = 0\n",
        "        \n",
        "        x = x.view(-1, self.input_dim) # view(batch_size, input_dim)\n",
        "        # -----------------\n",
        "        x, lqw, lpw = self.bfc1(x, sample)\n",
        "        tlqw = tlqw + lqw\n",
        "        tlpw = tlpw + lpw\n",
        "        # -----------------\n",
        "        x = self.act(x)\n",
        "        # -----------------\n",
        "        x, lqw, lpw = self.bfc2(x, sample)\n",
        "        tlqw = tlqw + lqw\n",
        "        tlpw = tlpw + lpw\n",
        "        # -----------------\n",
        "        x = self.act(x)\n",
        "        # -----------------\n",
        "        y, lqw, lpw = self.bfc3(x, sample)\n",
        "        tlqw = tlqw + lqw\n",
        "        tlpw = tlpw + lpw\n",
        "        \n",
        "        return y, tlqw, tlpw\n",
        "    \n",
        "    def sample_predict(self, x, Nsamples):\n",
        "        \n",
        "        # Just copies type from x, initializes new vector\n",
        "        predictions = x.data.new(Nsamples, x.shape[0], self.output_dim)\n",
        "        tlqw_vec = np.zeros(Nsamples)\n",
        "        tlpw_vec = np.zeros(Nsamples)\n",
        "        \n",
        "        for i in range(Nsamples):\n",
        "            \n",
        "            y, tlqw, tlpw = self.forward(x, sample=True)\n",
        "            predictions[i] = y\n",
        "            tlqw_vec[i] = tlqw\n",
        "            tlpw_vec[i] = tlpw \n",
        "            \n",
        "        return predictions, tlqw_vec, tlpw_vec"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YYJYSUPcrTQv",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9k-hmelx7HwE",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from __future__ import division\n",
        "import copy\n",
        "\n",
        "class Bayes_Net(BaseNet):\n",
        "    eps = 1e-6\n",
        "\n",
        "    def __init__(self, lr=1e-3, channels_in=1, side_in=28, cuda=True, classes=10, batch_size=128, Nbatches=0):\n",
        "        super(Bayes_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.channels_in = channels_in\n",
        "        self.classes = classes\n",
        "        self.batch_size = batch_size\n",
        "        self.Nbatches = Nbatches\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 = bayes_linear_2L(input_dim=self.channels_in*self.side_in*self.side_in, output_dim=self.classes)\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)\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, samples=1):\n",
        "        x, y = to_variable(var=(x, y.long()), cuda=self.cuda)\n",
        "\n",
        "        self.optimizer.zero_grad()\n",
        "\n",
        "        if samples == 1:\n",
        "            out, tlqw, tlpw = self.model(x)\n",
        "            mlpdw = F.cross_entropy(out, y, reduction='sum')\n",
        "            Edkl = (tlqw - tlpw)/self.Nbatches\n",
        "            \n",
        "        elif samples > 1:\n",
        "            mlpdw_cum = 0\n",
        "            Edkl_cum = 0\n",
        "            \n",
        "            for i in range(samples):\n",
        "                out, tlqw, tlpw = self.model(x, sample=True)\n",
        "                mlpdw_i = F.cross_entropy(out, y, reduction='sum')\n",
        "                Edkl_i = (tlqw - tlpw)/self.Nbatches\n",
        "                mlpdw_cum = mlpdw_cum + mlpdw_i\n",
        "                Edkl_cum = Edkl_cum + Edkl_i \n",
        "            \n",
        "            mlpdw = mlpdw_cum/samples\n",
        "            Edkl = Edkl_cum/samples\n",
        "        \n",
        "        loss = Edkl + mlpdw\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 Edkl.data, mlpdw.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",
        "    def sample_eval(self, x, y, Nsamples, logits=True, train=False):\n",
        "        x, y = to_variable(var=(x, y.long()), cuda=self.cuda)\n",
        "\n",
        "        out, _, _ = self.model.sample_predict(x, Nsamples)\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):\n",
        "        x, y = to_variable(var=(x, y.long()), cuda=self.cuda)\n",
        "\n",
        "        out, _, _ = self.model.sample_predict(x, Nsamples)\n",
        "        \n",
        "        prob_out =  F.softmax(out, dim=2)\n",
        "        prob_out = prob_out.data\n",
        "\n",
        "        return prob_out\n",
        "    \n",
        "    \n",
        "    def get_weight_samples(self, Nsamples=10):\n",
        "        state_dict = self.model.state_dict()\n",
        "        weight_vec = []\n",
        "        \n",
        "        for i in range(Nsamples):\n",
        "            previous_layer_name = ''\n",
        "            for key in state_dict.keys():\n",
        "                layer_name = key.split('.')[0]\n",
        "                if layer_name != previous_layer_name:\n",
        "                    previous_layer_name = layer_name\n",
        "\n",
        "                    W_mu = state_dict[layer_name+'.W_mu'].data\n",
        "                    W_p = state_dict[layer_name+'.W_p'].data\n",
        "\n",
        "    #                 b_mu = state_dict[layer_name+'.b_mu'].cpu().data\n",
        "    #                 b_p = state_dict[layer_name+'.b_p'].cpu().data\n",
        "\n",
        "                    W, b = sample_weights(W_mu=W_mu, b_mu=None, W_p=W_p, b_p=None)\n",
        "\n",
        "                    for weight in W.cpu().view(-1):\n",
        "                        weight_vec.append(weight)\n",
        "            \n",
        "        return np.array(weight_vec)\n",
        "    \n",
        "    def get_weight_SNR(self, thresh=None):\n",
        "        state_dict = self.model.state_dict()\n",
        "        weight_SNR_vec = []\n",
        "        \n",
        "        if thresh is not None:\n",
        "            mask_dict = {}\n",
        "        \n",
        "        previous_layer_name = ''\n",
        "        for key in state_dict.keys():\n",
        "            layer_name = key.split('.')[0]\n",
        "            if layer_name != previous_layer_name:\n",
        "                previous_layer_name = layer_name\n",
        "\n",
        "                W_mu = state_dict[layer_name+'.W_mu'].data\n",
        "                W_p = state_dict[layer_name+'.W_p'].data\n",
        "                sig_W = 1e-6 + F.softplus(W_p, beta=1, threshold=20)\n",
        "\n",
        "                b_mu = state_dict[layer_name+'.b_mu'].data\n",
        "                b_p = state_dict[layer_name+'.b_p'].data\n",
        "                sig_b = 1e-6 + F.softplus(b_p, beta=1, threshold=20)\n",
        "\n",
        "                W_snr = (torch.abs(W_mu)/sig_W)\n",
        "                b_snr = (torch.abs(b_mu)/sig_b)\n",
        "                \n",
        "                if thresh is not None:\n",
        "                    mask_dict[layer_name+'.W'] = W_snr > thresh\n",
        "                    mask_dict[layer_name+'.b'] = b_snr > thresh\n",
        "                    \n",
        "                else:\n",
        "                \n",
        "                    for weight_SNR in W_snr.cpu().view(-1):\n",
        "                        weight_SNR_vec.append(weight_SNR)\n",
        "\n",
        "                    for weight_SNR in b_snr.cpu().view(-1):\n",
        "                        weight_SNR_vec.append(weight_SNR)\n",
        "        \n",
        "        if thresh is not None:\n",
        "            return mask_dict\n",
        "        else:\n",
        "            return np.array(weight_SNR_vec)\n",
        "    \n",
        "    \n",
        "    def get_weight_KLD(self, Nsamples=20, thresh=None):\n",
        "        state_dict = self.model.state_dict()\n",
        "        weight_KLD_vec = []\n",
        "        \n",
        "        if thresh is not None:\n",
        "            mask_dict = {}\n",
        "        \n",
        "        previous_layer_name = ''\n",
        "        for key in state_dict.keys():\n",
        "            layer_name = key.split('.')[0]\n",
        "            if layer_name != previous_layer_name:\n",
        "                previous_layer_name = layer_name\n",
        "\n",
        "                W_mu = state_dict[layer_name+'.W_mu'].data\n",
        "                W_p = state_dict[layer_name+'.W_p'].data\n",
        "                b_mu = state_dict[layer_name+'.b_mu'].data\n",
        "                b_p = state_dict[layer_name+'.b_p'].data\n",
        "                \n",
        "                std_w = 1e-6 + F.softplus(W_p, beta=1, threshold=20)  \n",
        "                std_b = 1e-6 + F.softplus(b_p, beta=1, threshold=20)\n",
        "\n",
        "                KL_W = W_mu.new(W_mu.size()).zero_()\n",
        "                KL_b = b_mu.new(b_mu.size()).zero_()\n",
        "                for i in range(Nsamples):\n",
        "                    W, b = sample_weights(W_mu=W_mu, b_mu=b_mu, W_p=W_p, b_p=b_p)  \n",
        "                    # Note that this will currently not work with slab and spike prior\n",
        "                    KL_W += isotropic_gauss_loglike(W, W_mu, std_w, do_sum=False) - self.model.prior_instance.loglike(W, do_sum=False)\n",
        "                    KL_b += isotropic_gauss_loglike(b, b_mu, std_b, do_sum=False) - self.model.prior_instance.loglike(b, do_sum=False)\n",
        "\n",
        "                KL_W /= Nsamples\n",
        "                KL_b /= Nsamples\n",
        "                \n",
        "                if thresh is not None:\n",
        "                    mask_dict[layer_name+'.W'] = KL_W > thresh\n",
        "                    mask_dict[layer_name+'.b'] = KL_b > thresh\n",
        "                    \n",
        "                else:\n",
        "\n",
        "                    for weight_KLD in KL_W.cpu().view(-1):\n",
        "                        weight_KLD_vec.append(weight_KLD)\n",
        "\n",
        "                    for weight_KLD in KL_b.cpu().view(-1):\n",
        "                        weight_KLD_vec.append(weight_KLD)\n",
        "        \n",
        "        if thresh is not None:\n",
        "            return mask_dict\n",
        "        else:    \n",
        "            return np.array(weight_KLD_vec)\n",
        "        \n",
        "        \n",
        "    def mask_model(self, Nsamples=0, thresh=0):\n",
        "        '''\n",
        "        Nsamples is used to select SNR (0) or KLD (>0) based masking\n",
        "        '''\n",
        "        original_state_dict = copy.deepcopy(self.model.state_dict())\n",
        "        state_dict = self.model.state_dict()\n",
        "        \n",
        "        if Nsamples == 0:\n",
        "            mask_dict = self.get_weight_SNR(thresh=thresh)\n",
        "        else:\n",
        "            mask_dict = self.get_weight_KLD(Nsamples=Nsamples, thresh=thresh)\n",
        "        \n",
        "        n_unmasked = 0\n",
        "        \n",
        "        previous_layer_name = ''\n",
        "        for key in state_dict.keys():\n",
        "            layer_name = key.split('.')[0]\n",
        "            if layer_name != previous_layer_name:\n",
        "                previous_layer_name = layer_name\n",
        "                \n",
        "                state_dict[layer_name+'.W_mu'][1-mask_dict[layer_name+'.W']] = 0\n",
        "                state_dict[layer_name+'.W_p'][1-mask_dict[layer_name+'.W']] = -1000\n",
        "                \n",
        "                state_dict[layer_name+'.b_mu'][1-mask_dict[layer_name+'.b']] = 0\n",
        "                state_dict[layer_name+'.b_p'][1-mask_dict[layer_name+'.b']] = -1000\n",
        "                \n",
        "                \n",
        "                n_unmasked += mask_dict[layer_name+'.W'].sum()\n",
        "                n_unmasked += mask_dict[layer_name+'.b'].sum()\n",
        "                \n",
        "        return original_state_dict, n_unmasked\n",
        "            "
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "G8JO0VDD7Mh4",
        "colab_type": "code",
        "outputId": "426d3b82-060e-41c7-fe84-8b374052abe9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "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",
        "import copy\n",
        "\n",
        "\n",
        "#models_dir = 'models_weight_uncertainty_MC_MNIST_gaussian'\n",
        "#results_dir = 'results_weight_uncertainty_MC_MNIST_gaussian'\n",
        "\n",
        "#mkdir(models_dir)\n",
        "#mkdir(results_dir)\n",
        "# ------------------------------------------------------------------------------------------------------\n",
        "# train config\n",
        "NTrainPointsMNIST = 60000\n",
        "batch_size = 128\n",
        "nb_epochs = 100\n",
        "log_interval = 1\n",
        "\n",
        "savemodel_its = [20, 50, 80, 120]\n",
        "save_dicts = []\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",
        "nsamples = 3\n",
        "########################################################################################\n",
        "net = Bayes_Net(lr=lr, channels_in=1, side_in=28, cuda=use_cuda, classes=10, batch_size=batch_size,\n",
        "          Nbatches=(NTrainPointsMNIST/batch_size))\n",
        "\n",
        "epoch = 0\n",
        "\n",
        "## ---------------------------------------------------------------------------------------------------------------------\n",
        "# train\n",
        "cprint('c', '\\nTrain:')\n",
        "\n",
        "print('  init cost variables:')\n",
        "kl_cost_train = np.zeros(nb_epochs)\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_dkl, cost_pred, err = net.fit(x, y, samples=nsamples)\n",
        "\n",
        "        err_train[i] += err\n",
        "        kl_cost_train[i] += cost_dkl\n",
        "        pred_cost_train[i] += cost_pred\n",
        "        nb_samples += len(x)\n",
        "\n",
        "    kl_cost_train[i] /= nb_samples\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_KL = %f, Jtr_pred = %f, err = %f, \" % (i, nb_epochs, kl_cost_train[i], pred_cost_train[i], err_train[i]), end=\"\")\n",
        "    cprint('r', '   time: %f seconds\\n' % (toc - tic))\n",
        "\n",
        "    # Save state dict\n",
        "    \n",
        "    #if i in savemodel_its:\n",
        "    #    save_dicts.append(copy.deepcopy(net.model.state_dict()))\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",
        "net.save(models_dir+'/theta_last.dat')\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', kl_cost_train)\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",
        "plt.figure()\n",
        "fig, ax1 = plt.subplots()\n",
        "ax1.plot(kl_cost_train, 'r')\n",
        "ax1.set_ylabel('nats?')\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",
        "ax = plt.gca()\n",
        "plt.title('DKL (per sample)')\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",
        "\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')"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "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: 0.96M\n",
            "\u001b[36m\n",
            "Train:\u001b[0m\n",
            "  init cost variables:\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-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.fromiter(generator)) or the python sum builtin instead.\n",
            "  import sys\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "it 0/100, Jtr_KL = 11.090269, Jtr_pred = 1.284238, err = 0.220350, \u001b[31m   time: 31.613770 seconds\n",
            "\u001b[0m\n",
            "\u001b[32m    Jdev = 0.255116, err = 0.075900\n",
            "\u001b[0m\n",
            "it 1/100, Jtr_KL = 11.084159, Jtr_pred = 0.391667, err = 0.113083, \u001b[31m   time: 31.715090 seconds\n",
            "\u001b[0m\n",
            "\u001b[32m    Jdev = 0.187477, err = 0.054800\n",
            "\u001b[0m\n",
            "it 2/100, Jtr_KL = 11.076904, Jtr_pred = 0.310054, err = 0.088417, \u001b[31m   time: 31.645343 seconds\n",
            "\u001b[0m\n",
            "\u001b[32m    Jdev = 0.162079, err = 0.047800\n",
            "\u001b[0m\n",
            "it 3/100, Jtr_KL = 11.069387, Jtr_pred = 0.265415, err = 0.077083, \u001b[31m   time: 31.684786 seconds\n",
            "\u001b[0m\n",
            "\u001b[32m    Jdev = 0.141215, err = 0.041000\n",
            "\u001b[0m\n",
            "it 4/100, Jtr_KL = 11.061975, Jtr_pred = 0.236580, err = 0.069733, \u001b[31m   time: 31.536874 seconds\n",
            "\u001b[0m\n",
            "\u001b[32m    Jdev = 0.132129, err = 0.038500\n",
            "\u001b[0m\n",
            "it 5/100, Jtr_KL = 11.054317, Jtr_pred = 0.218825, err = 0.064633, \u001b[31m   time: 31.617740 seconds\n",
            "\u001b[0m\n",
            "\u001b[32m    Jdev = 0.122440, err = 0.036400\n",
            "\u001b[0m\n",
            "it 6/100, Jtr_KL = 11.046373, Jtr_pred = 0.200730, err = 0.057667, \u001b[31m   time: 31.543131 seconds\n",
            "\u001b[0m\n",
            "\u001b[32m    Jdev = 0.120792, err = 0.036600\n",
            "\u001b[0m\n",
            "it 7/100, Jtr_KL = 11.038732, Jtr_pred = 0.185125, err = 0.054083, \u001b[31m   time: 31.459277 seconds\n",
            "\u001b[0m\n",
            "\u001b[32m    Jdev = 0.109994, err = 0.031300\n",
            "\u001b[0m\n",
            "it 8/100, Jtr_KL = 11.031074, Jtr_pred = 0.175025, err = 0.050750, \u001b[31m   time: 31.639469 seconds\n",
            "\u001b[0m\n",
            "\u001b[32m    Jdev = 0.106104, err = 0.031500\n",
            "\u001b[0m\n",
            "it 9/100, Jtr_KL = 11.023521, Jtr_pred = 0.164924, err = 0.048033, \u001b[31m   time: 31.454700 seconds\n",
            "\u001b[0m\n",
            "\u001b[32m    Jdev = 0.100693, err = 0.029000\n",
            "\u001b[0m\n",
            "it 10/100, Jtr_KL = 11.015604, Jtr_pred = 0.157741, err = 0.046250, \u001b[31m   time: 31.488678 seconds\n",
            "\u001b[0m\n",
            "\u001b[32m    Jdev = 0.099777, err = 0.029500\n",
            "\u001b[0m\n",
            "it 11/100, Jtr_KL = 11.008201, Jtr_pred = 0.150306, err = 0.043217, \u001b[31m   time: 31.419805 seconds\n",
            "\u001b[0m\n",
            "\u001b[32m    Jdev = 0.096968, err = 0.028900\n",
            "\u001b[0m\n",
            "it 12/100, Jtr_KL = 10.999620, Jtr_pred = 0.142011, err = 0.041800, \u001b[31m   time: 31.498070 seconds\n",
            "\u001b[0m\n",
            "\u001b[32m    Jdev = 0.091034, err = 0.026500\n",
            "\u001b[0m\n",
            "it 13/100, Jtr_KL = 10.992236, Jtr_pred = 0.137194, err = 0.039567, \u001b[31m   time: 31.293853 seconds\n",
            "\u001b[0m\n",
            "\u001b[32m    Jdev = 0.088695, err = 0.025800\n",
            "\u001b[0m\n",
            "it 14/100, Jtr_KL = 10.984553, Jtr_pred = 0.130744, err = 0.038667, \u001b[31m   time: 31.566066 seconds\n",
            "\u001b[0m\n",
            "\u001b[32m    Jdev = 0.091609, err = 0.026600\n",
            "\u001b[0m\n",
            "it 15/100, Jtr_KL = 10.976879, Jtr_pred = 0.127658, err = 0.036967, \u001b[31m   time: 31.538027 seconds\n",
            "\u001b[0m\n",
            "\u001b[32m    Jdev = 0.083755, err = 0.024900\n",
            "\u001b[0m\n",
            "it 16/100, Jtr_KL = 10.969160, Jtr_pred = 0.120958, err = 0.034683, \u001b[31m   time: 31.349965 seconds\n",
            "\u001b[0m\n",
            "\u001b[32m    Jdev = 0.087141, err = 0.024100\n",
            "\u001b[0m\n",
            "it 17/100, Jtr_KL = 10.960801, Jtr_pred = 0.118708, err = 0.035933, \u001b[31m   time: 31.403790 seconds\n",
            "\u001b[0m\n",
            "\u001b[32m    Jdev = 0.081883, err = 0.022600\n",
            "\u001b[0m\n",
            "it 18/100, Jtr_KL = 10.952630, Jtr_pred = 0.115492, err = 0.033883, \u001b[31m   time: 31.514982 seconds\n",
            "\u001b[0m\n",
            "\u001b[32m    Jdev = 0.080554, err = 0.022700\n",
            "\u001b[0m\n",
            "it 19/100, Jtr_KL = 10.945129, Jtr_pred = 0.111606, err = 0.033217, \u001b[31m   time: 31.467605 seconds\n",
            "\u001b[0m\n",
            "\u001b[32m    Jdev = 0.078182, err = 0.023100\n",
            "\u001b[0m\n",
            "it 20/100, Jtr_KL = 10.937970, Jtr_pred = 0.110457, err = 0.032700, \u001b[31m   time: 31.376319 seconds\n",
            "\u001b[0m\n",
            "\u001b[32m    Jdev = 0.078769, err = 0.022900\n",
            "\u001b[0m\n",
            "it 21/100, Jtr_KL = 10.929667, Jtr_pred = 0.105131, err = 0.030933, \u001b[31m   time: 31.280900 seconds\n",
            "\u001b[0m\n",
            "\u001b[32m    Jdev = 0.076418, err = 0.022200\n",
            "\u001b[0m\n",
            "it 22/100, Jtr_KL = 10.922269, Jtr_pred = 0.100998, err = 0.030050, \u001b[31m   time: 31.403610 seconds\n",
            "\u001b[0m\n",
            "\u001b[32m    Jdev = 0.076329, err = 0.021100\n",
            "\u001b[0m\n",
            "it 23/100, Jtr_KL = 10.913620, Jtr_pred = 0.099078, err = 0.028833, \u001b[31m   time: 31.437565 seconds\n",
            "\u001b[0m\n",
            "\u001b[32m    Jdev = 0.076859, err = 0.021400\n",
            "\u001b[0m\n",
            "it 24/100, Jtr_KL = 10.906011, Jtr_pred = 0.097182, err = 0.029317, \u001b[31m   time: 31.381815 seconds\n",
            "\u001b[0m\n",
            "\u001b[32m    Jdev = 0.075390, err = 0.021200\n",
            "\u001b[0m\n",
            "it 25/100, Jtr_KL = 10.898178, Jtr_pred = 0.094028, err = 0.027733, \u001b[31m   time: 31.294467 seconds\n",
            "\u001b[0m\n",
            "\u001b[32m    Jdev = 0.074932, err = 0.020200\n",
            "\u001b[0m\n",
            "it 26/100, Jtr_KL = 10.890565, Jtr_pred = 0.092605, err = 0.027417, \u001b[31m   time: 31.320055 seconds\n",
            "\u001b[0m\n",
            "\u001b[32m    Jdev = 0.075077, err = 0.021000\n",
            "\u001b[0m\n",
            "it 27/100, Jtr_KL = 10.883080, Jtr_pred = 0.090458, err = 0.027200, \u001b[31m   time: 31.496585 seconds\n",
            "\u001b[0m\n",
            "\u001b[32m    Jdev = 0.073528, err = 0.020200\n",
            "\u001b[0m\n",
            "it 28/100, Jtr_KL = 10.875279, Jtr_pred = 0.089364, err = 0.027333, \u001b[31m   time: 31.226586 seconds\n",
            "\u001b[0m\n",
            "\u001b[32m    Jdev = 0.072638, err = 0.019900\n",
            "\u001b[0m\n",
            "it 29/100, Jtr_KL = 10.867162, Jtr_pred = 0.085417, err = 0.025600, \u001b[31m   time: 31.311790 seconds\n",
            "\u001b[0m\n",
            "\u001b[32m    Jdev = 0.071840, err = 0.019700\n",
            "\u001b[0m\n",
            "it 30/100, Jtr_KL = 10.859294, Jtr_pred = 0.083871, err = 0.024933, \u001b[31m   time: 31.334698 seconds\n",
            "\u001b[0m\n",
            "\u001b[32m    Jdev = 0.073881, err = 0.020300\n",
            "\u001b[0m\n",
            "it 31/100, Jtr_KL = 10.851647, Jtr_pred = 0.083094, err = 0.025367, \u001b[31m   time: 31.305428 seconds\n",
            "\u001b[0m\n",
            "\u001b[32m    Jdev = 0.071496, err = 0.019500\n",
            "\u001b[0m\n",
            "it 32/100, Jtr_KL = 10.844175, Jtr_pred = 0.079818, err = 0.024167, \u001b[31m   time: 31.189332 seconds\n",
            "\u001b[0m\n",
            "\u001b[32m    Jdev = 0.073814, err = 0.019900\n",
            "\u001b[0m\n",
            "it 33/100, Jtr_KL = 10.836180, Jtr_pred = 0.078553, err = 0.024217, \u001b[31m   time: 31.309185 seconds\n",
            "\u001b[0m\n",
            "\u001b[32m    Jdev = 0.074139, err = 0.018900\n",
            "\u001b[0m\n",
            "it 34/100, Jtr_KL = 10.828183, Jtr_pred = 0.077726, err = 0.022467, \u001b[31m   time: 31.222015 seconds\n",
            "\u001b[0m\n",
            "\u001b[32m    Jdev = 0.070634, err = 0.018600\n",
            "\u001b[0m\n",
            "it 35/100, Jtr_KL = 10.820701, Jtr_pred = 0.075556, err = 0.023067, \u001b[31m   time: 31.076359 seconds\n",
            "\u001b[0m\n",
            "\u001b[32m    Jdev = 0.069142, err = 0.018700\n",
            "\u001b[0m\n",
            "it 36/100, Jtr_KL = 10.812544, Jtr_pred = 0.075429, err = 0.024367, \u001b[31m   time: 31.134650 seconds\n",
            "\u001b[0m\n",
            "\u001b[32m    Jdev = 0.071774, err = 0.018500\n",
            "\u001b[0m\n",
            "it 37/100, Jtr_KL = 10.805061, Jtr_pred = 0.073830, err = 0.021900, \u001b[31m   time: 31.243255 seconds\n",
            "\u001b[0m\n",
            "\u001b[32m    Jdev = 0.069924, err = 0.018600\n",
            "\u001b[0m\n",
            "it 38/100, Jtr_KL = 10.797588, Jtr_pred = 0.073280, err = 0.021433, \u001b[31m   time: 31.191305 seconds\n",
            "\u001b[0m\n",
            "\u001b[32m    Jdev = 0.071160, err = 0.018800\n",
            "\u001b[0m\n",
            "it 39/100, Jtr_KL = 10.789582, Jtr_pred = 0.071317, err = 0.022183, \u001b[31m   time: 31.204792 seconds\n",
            "\u001b[0m\n",
            "\u001b[32m    Jdev = 0.068714, err = 0.018200\n",
            "\u001b[0m\n",
            "it 40/100, Jtr_KL = 10.782214, Jtr_pred = 0.069689, err = 0.022250, \u001b[31m   time: 31.250674 seconds\n",
            "\u001b[0m\n",
            "\u001b[32m    Jdev = 0.070610, err = 0.017800\n",
            "\u001b[0m\n",
            "it 41/100, Jtr_KL = 10.773838, Jtr_pred = 0.067387, err = 0.020833, \u001b[31m   time: 31.082679 seconds\n",
            "\u001b[0m\n",
            "\u001b[32m    Jdev = 0.070722, err = 0.018700\n",
            "\u001b[0m\n",
            "it 42/100, Jtr_KL = 10.766415, Jtr_pred = 0.068188, err = 0.020550, \u001b[31m   time: 31.106851 seconds\n",
            "\u001b[0m\n",
            "\u001b[32m    Jdev = 0.068506, err = 0.017700\n",
            "\u001b[0m\n",
            "it 43/100, Jtr_KL = 10.758926, Jtr_pred = 0.066671, err = 0.020317, \u001b[31m   time: 31.090608 seconds\n",
            "\u001b[0m\n",
            "\u001b[32m    Jdev = 0.070337, err = 0.018900\n",
            "\u001b[0m\n",
            "it 44/100, Jtr_KL = 10.750920, Jtr_pred = 0.066198, err = 0.020167, \u001b[31m   time: 31.068906 seconds\n",
            "\u001b[0m\n",
            "\u001b[32m    Jdev = 0.067477, err = 0.018100\n",
            "\u001b[0m\n",
            "it 45/100, Jtr_KL = 10.743494, Jtr_pred = 0.063465, err = 0.020067, \u001b[31m   time: 31.198556 seconds\n",
            "\u001b[0m\n",
            "\u001b[32m    Jdev = 0.068495, err = 0.018100\n",
            "\u001b[0m\n",
            "it 46/100, Jtr_KL = 10.735526, Jtr_pred = 0.064890, err = 0.020100, \u001b[31m   time: 31.505543 seconds\n",
            "\u001b[0m\n",
            "\u001b[32m    Jdev = 0.067722, err = 0.018300\n",
            "\u001b[0m\n",
            "it 47/100, Jtr_KL = 10.727845, Jtr_pred = 0.064334, err = 0.020200, \u001b[31m   time: 31.193488 seconds\n",
            "\u001b[0m\n",
            "\u001b[32m    Jdev = 0.070281, err = 0.018600\n",
            "\u001b[0m\n",
            "it 48/100, Jtr_KL = 10.720303, Jtr_pred = 0.062787, err = 0.018467, \u001b[31m   time: 31.304299 seconds\n",
            "\u001b[0m\n",
            "\u001b[32m    Jdev = 0.070105, err = 0.017800\n",
            "\u001b[0m\n",
            "it 49/100, Jtr_KL = 10.712255, Jtr_pred = 0.061579, err = 0.018533, \u001b[31m   time: 31.269334 seconds\n",
            "\u001b[0m\n",
            "\u001b[32m    Jdev = 0.066106, err = 0.016700\n",
            "\u001b[0m\n",
            "it 50/100, Jtr_KL = 10.704665, Jtr_pred = 0.059725, err = 0.017917, \u001b[31m   time: 31.186824 seconds\n",
            "\u001b[0m\n",
            "\u001b[32m    Jdev = 0.067317, err = 0.017400\n",
            "\u001b[0m\n",
            "it 51/100, Jtr_KL = 10.697069, Jtr_pred = 0.058720, err = 0.018017, \u001b[31m   time: 31.363923 seconds\n",
            "\u001b[0m\n",
            "\u001b[32m    Jdev = 0.067442, err = 0.016800\n",
            "\u001b[0m\n",
            "it 52/100, Jtr_KL = 10.689744, Jtr_pred = 0.058454, err = 0.018033, \u001b[31m   time: 31.203228 seconds\n",
            "\u001b[0m\n",
            "\u001b[32m    Jdev = 0.067653, err = 0.017300\n",
            "\u001b[0m\n",
            "it 53/100, Jtr_KL = 10.681924, Jtr_pred = 0.058455, err = 0.017950, \u001b[31m   time: 31.031705 seconds\n",
            "\u001b[0m\n",
            "\u001b[32m    Jdev = 0.070665, err = 0.017300\n",
            "\u001b[0m\n",
            "it 54/100, Jtr_KL = 10.674140, Jtr_pred = 0.057700, err = 0.017400, \u001b[31m   time: 31.068012 seconds\n",
            "\u001b[0m\n",
            "\u001b[32m    Jdev = 0.068305, err = 0.016200\n",
            "\u001b[0m\n",
            "it 55/100, Jtr_KL = 10.666769, Jtr_pred = 0.056939, err = 0.018167, \u001b[31m   time: 31.080238 seconds\n",
            "\u001b[0m\n",
            "\u001b[32m    Jdev = 0.069133, err = 0.018000\n",
            "\u001b[0m\n",
            "it 56/100, Jtr_KL = 10.658954, Jtr_pred = 0.056221, err = 0.017150, \u001b[31m   time: 31.073549 seconds\n",
            "\u001b[0m\n",
            "\u001b[32m    Jdev = 0.066655, err = 0.016500\n",
            "\u001b[0m\n",
            "it 57/100, Jtr_KL = 10.651369, Jtr_pred = 0.054064, err = 0.017450, \u001b[31m   time: 31.202544 seconds\n",
            "\u001b[0m\n",
            "\u001b[32m    Jdev = 0.069046, err = 0.016100\n",
            "\u001b[0m\n",
            "it 58/100, Jtr_KL = 10.643776, Jtr_pred = 0.054780, err = 0.016667, \u001b[31m   time: 31.064372 seconds\n",
            "\u001b[0m\n",
            "\u001b[32m    Jdev = 0.068884, err = 0.016700\n",
            "\u001b[0m\n",
            "it 59/100, Jtr_KL = 10.636407, Jtr_pred = 0.053484, err = 0.016517, \u001b[31m   time: 30.845379 seconds\n",
            "\u001b[0m\n",
            "\u001b[32m    Jdev = 0.068579, err = 0.016700\n",
            "\u001b[0m\n",
            "it 60/100, Jtr_KL = 10.628856, Jtr_pred = 0.053129, err = 0.016433, \u001b[31m   time: 31.040154 seconds\n",
            "\u001b[0m\n",
            "\u001b[32m    Jdev = 0.067380, err = 0.016300\n",
            "\u001b[0m\n",
            "it 61/100, Jtr_KL = 10.621040, Jtr_pred = 0.052571, err = 0.016550, \u001b[31m   time: 30.953000 seconds\n",
            "\u001b[0m\n",
            "\u001b[32m    Jdev = 0.067834, err = 0.016200\n",
            "\u001b[0m\n",
            "it 62/100, Jtr_KL = 10.613975, Jtr_pred = 0.053887, err = 0.016133, \u001b[31m   time: 31.009326 seconds\n",
            "\u001b[0m\n",
            "\u001b[32m    Jdev = 0.068143, err = 0.016500\n",
            "\u001b[0m\n",
            "it 63/100, Jtr_KL = 10.605874, Jtr_pred = 0.052812, err = 0.016333, \u001b[31m   time: 30.976777 seconds\n",
            "\u001b[0m\n",
            "\u001b[32m    Jdev = 0.069690, err = 0.017000\n",
            "\u001b[0m\n",
            "it 64/100, Jtr_KL = 10.598365, Jtr_pred = 0.051288, err = 0.015550, \u001b[31m   time: 31.114046 seconds\n",
            "\u001b[0m\n",
            "\u001b[32m    Jdev = 0.067994, err = 0.016600\n",
            "\u001b[0m\n",
            "it 65/100, Jtr_KL = 10.590742, Jtr_pred = 0.050845, err = 0.015317, \u001b[31m   time: 31.214865 seconds\n",
            "\u001b[0m\n",
            "\u001b[32m    Jdev = 0.069337, err = 0.016700\n",
            "\u001b[0m\n",
            "it 66/100, Jtr_KL = 10.583261, Jtr_pred = 0.050261, err = 0.015383, \u001b[31m   time: 31.088038 seconds\n",
            "\u001b[0m\n",
            "\u001b[32m    Jdev = 0.066516, err = 0.015900\n",
            "\u001b[0m\n",
            "it 67/100, Jtr_KL = 10.575525, Jtr_pred = 0.048780, err = 0.014650, \u001b[31m   time: 31.036906 seconds\n",
            "\u001b[0m\n",
            "\u001b[32m    Jdev = 0.066325, err = 0.016800\n",
            "\u001b[0m\n",
            "it 68/100, Jtr_KL = 10.568391, Jtr_pred = 0.049501, err = 0.015383, \u001b[31m   time: 30.930282 seconds\n",
            "\u001b[0m\n",
            "\u001b[32m    Jdev = 0.068549, err = 0.015800\n",
            "\u001b[0m\n",
            "it 69/100, Jtr_KL = 10.560995, Jtr_pred = 0.048724, err = 0.014483, \u001b[31m   time: 31.051502 seconds\n",
            "\u001b[0m\n",
            "\u001b[32m    Jdev = 0.068965, err = 0.016800\n",
            "\u001b[0m\n",
            "it 70/100, Jtr_KL = 10.553562, Jtr_pred = 0.048145, err = 0.014783, \u001b[31m   time: 31.129305 seconds\n",
            "\u001b[0m\n",
            "\u001b[32m    Jdev = 0.070245, err = 0.015400\n",
            "\u001b[0m\n",
            "it 71/100, Jtr_KL = 10.546060, Jtr_pred = 0.048425, err = 0.015117, \u001b[31m   time: 30.780700 seconds\n",
            "\u001b[0m\n",
            "\u001b[32m    Jdev = 0.069252, err = 0.015800\n",
            "\u001b[0m\n",
            "it 72/100, Jtr_KL = 10.538036, Jtr_pred = 0.047365, err = 0.015283, \u001b[31m   time: 30.893604 seconds\n",
            "\u001b[0m\n",
            "\u001b[32m    Jdev = 0.069459, err = 0.016200\n",
            "\u001b[0m\n",
            "it 73/100, Jtr_KL = 10.530812, Jtr_pred = 0.047740, err = 0.014833, \u001b[31m   time: 30.892484 seconds\n",
            "\u001b[0m\n",
            "\u001b[32m    Jdev = 0.068898, err = 0.014800\n",
            "\u001b[0m\n",
            "it 74/100, Jtr_KL = 10.522714, Jtr_pred = 0.046691, err = 0.014617, \u001b[31m   time: 31.019726 seconds\n",
            "\u001b[0m\n",
            "\u001b[32m    Jdev = 0.069636, err = 0.015000\n",
            "\u001b[0m\n",
            "it 75/100, Jtr_KL = 10.515179, Jtr_pred = 0.049390, err = 0.014117, \u001b[31m   time: 30.939444 seconds\n",
            "\u001b[0m\n",
            "\u001b[32m    Jdev = 0.069868, err = 0.016800\n",
            "\u001b[0m\n",
            "it 76/100, Jtr_KL = 10.508379, Jtr_pred = 0.047056, err = 0.014933, \u001b[31m   time: 31.086975 seconds\n",
            "\u001b[0m\n",
            "\u001b[32m    Jdev = 0.068891, err = 0.016200\n",
            "\u001b[0m\n",
            "it 77/100, Jtr_KL = 10.500489, Jtr_pred = 0.045840, err = 0.014067, \u001b[31m   time: 31.000275 seconds\n",
            "\u001b[0m\n",
            "\u001b[32m    Jdev = 0.068607, err = 0.015600\n",
            "\u001b[0m\n",
            "it 78/100, Jtr_KL = 10.493160, Jtr_pred = 0.045111, err = 0.013767, \u001b[31m   time: 30.900056 seconds\n",
            "\u001b[0m\n",
            "\u001b[32m    Jdev = 0.067691, err = 0.015600\n",
            "\u001b[0m\n",
            "it 79/100, Jtr_KL = 10.485785, Jtr_pred = 0.045768, err = 0.014250, \u001b[31m   time: 30.919079 seconds\n",
            "\u001b[0m\n",
            "\u001b[32m    Jdev = 0.068498, err = 0.016200\n",
            "\u001b[0m\n",
            "it 80/100, Jtr_KL = 10.478507, Jtr_pred = 0.043153, err = 0.013400, \u001b[31m   time: 30.883101 seconds\n",
            "\u001b[0m\n",
            "\u001b[32m    Jdev = 0.069369, err = 0.015800\n",
            "\u001b[0m\n",
            "it 81/100, Jtr_KL = 10.470844, Jtr_pred = 0.044630, err = 0.013400, \u001b[31m   time: 30.949302 seconds\n",
            "\u001b[0m\n",
            "\u001b[32m    Jdev = 0.069924, err = 0.015000\n",
            "\u001b[0m\n",
            "it 82/100, Jtr_KL = 10.463392, Jtr_pred = 0.045773, err = 0.014000, \u001b[31m   time: 30.872614 seconds\n",
            "\u001b[0m\n",
            "\u001b[32m    Jdev = 0.068956, err = 0.016100\n",
            "\u001b[0m\n",
            "it 83/100, Jtr_KL = 10.455680, Jtr_pred = 0.043209, err = 0.013783, \u001b[31m   time: 31.029034 seconds\n",
            "\u001b[0m\n",
            "\u001b[32m    Jdev = 0.070216, err = 0.016200\n",
            "\u001b[0m\n",
            "it 84/100, Jtr_KL = 10.448350, Jtr_pred = 0.042736, err = 0.012650, \u001b[31m   time: 31.112946 seconds\n",
            "\u001b[0m\n",
            "\u001b[32m    Jdev = 0.065884, err = 0.015200\n",
            "\u001b[0m\n",
            "it 85/100, Jtr_KL = 10.441329, Jtr_pred = 0.044985, err = 0.013650, \u001b[31m   time: 31.049189 seconds\n",
            "\u001b[0m\n",
            "\u001b[32m    Jdev = 0.068331, err = 0.015300\n",
            "\u001b[0m\n",
            "it 86/100, Jtr_KL = 10.433658, Jtr_pred = 0.041576, err = 0.013683, \u001b[31m   time: 30.994079 seconds\n",
            "\u001b[0m\n",
            "\u001b[32m    Jdev = 0.070373, err = 0.015800\n",
            "\u001b[0m\n",
            "it 87/100, Jtr_KL = 10.425959, Jtr_pred = 0.042680, err = 0.013233, \u001b[31m   time: 30.949860 seconds\n",
            "\u001b[0m\n",
            "\u001b[32m    Jdev = 0.070028, err = 0.015500\n",
            "\u001b[0m\n",
            "it 88/100, Jtr_KL = 10.418761, Jtr_pred = 0.041755, err = 0.013033, \u001b[31m   time: 31.023240 seconds\n",
            "\u001b[0m\n",
            "\u001b[32m    Jdev = 0.070416, err = 0.015300\n",
            "\u001b[0m\n",
            "it 89/100, Jtr_KL = 10.411282, Jtr_pred = 0.040250, err = 0.013150, \u001b[31m   time: 30.982298 seconds\n",
            "\u001b[0m\n",
            "\u001b[32m    Jdev = 0.068745, err = 0.015800\n",
            "\u001b[0m\n",
            "it 90/100, Jtr_KL = 10.403936, Jtr_pred = 0.041747, err = 0.013917, \u001b[31m   time: 30.973559 seconds\n",
            "\u001b[0m\n",
            "\u001b[32m    Jdev = 0.069857, err = 0.015800\n",
            "\u001b[0m\n",
            "it 91/100, Jtr_KL = 10.397108, Jtr_pred = 0.042296, err = 0.012550, \u001b[31m   time: 31.135344 seconds\n",
            "\u001b[0m\n",
            "\u001b[32m    Jdev = 0.069616, err = 0.015200\n",
            "\u001b[0m\n",
            "it 92/100, Jtr_KL = 10.389676, Jtr_pred = 0.040019, err = 0.012367, \u001b[31m   time: 31.523345 seconds\n",
            "\u001b[0m\n",
            "\u001b[32m    Jdev = 0.068510, err = 0.015100\n",
            "\u001b[0m\n",
            "it 93/100, Jtr_KL = 10.381630, Jtr_pred = 0.041968, err = 0.013150, \u001b[31m   time: 31.319065 seconds\n",
            "\u001b[0m\n",
            "\u001b[32m    Jdev = 0.068909, err = 0.015000\n",
            "\u001b[0m\n",
            "it 94/100, Jtr_KL = 10.374634, Jtr_pred = 0.040400, err = 0.013000, \u001b[31m   time: 31.372485 seconds\n",
            "\u001b[0m\n",
            "\u001b[32m    Jdev = 0.069364, err = 0.015000\n",
            "\u001b[0m\n",
            "it 95/100, Jtr_KL = 10.366898, Jtr_pred = 0.041293, err = 0.012717, \u001b[31m   time: 31.316434 seconds\n",
            "\u001b[0m\n",
            "\u001b[32m    Jdev = 0.070212, err = 0.015700\n",
            "\u001b[0m\n",
            "it 96/100, Jtr_KL = 10.359752, Jtr_pred = 0.040124, err = 0.012667, \u001b[31m   time: 31.186347 seconds\n",
            "\u001b[0m\n",
            "\u001b[32m    Jdev = 0.068343, err = 0.015000\n",
            "\u001b[0m\n",
            "it 97/100, Jtr_KL = 10.352428, Jtr_pred = 0.039483, err = 0.013017, \u001b[31m   time: 31.214013 seconds\n",
            "\u001b[0m\n",
            "\u001b[32m    Jdev = 0.069085, err = 0.014900\n",
            "\u001b[0m\n",
            "it 98/100, Jtr_KL = 10.344852, Jtr_pred = 0.038226, err = 0.012433, \u001b[31m   time: 31.315853 seconds\n",
            "\u001b[0m\n",
            "\u001b[32m    Jdev = 0.070422, err = 0.015800\n",
            "\u001b[0m\n",
            "it 99/100, Jtr_KL = 10.337449, Jtr_pred = 0.039129, err = 0.012167, \u001b[31m   time: 31.220793 seconds\n",
            "\u001b[0m\n",
            "\u001b[32m    Jdev = 0.070328, err = 0.015600\n",
            "\u001b[0m\n",
            "\u001b[31m   average time: 32.967322 seconds\n",
            "\u001b[0m\n",
            "\u001b[36mWritting models_weight_uncertainty_MC_MNIST_gaussian/theta_last.dat\n",
            "\u001b[0m\n",
            "\u001b[36m\n",
            "RESULTS:\u001b[0m\n",
            "  cost_dev: 0.065884 (cost_train 0.038226)\n",
            "  err_dev: 0.014800\n",
            "  nb_parameters: 956820 (934.39KB)\n",
            "  time_per_it: 32.967322s\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/torch/serialization.py:292: UserWarning: Couldn't retrieve source code for container of type bayes_linear_2L. It won't be checked for correctness upon loading.\n",
            "  \"type \" + obj.__name__ + \". It won't be checked \"\n",
            "/usr/local/lib/python3.6/dist-packages/torch/serialization.py:292: UserWarning: Couldn't retrieve source code for container of type BayesLinear_Normalq. It won't be checked for correctness upon loading.\n",
            "  \"type \" + obj.__name__ + \". It won't be checked \"\n",
            "/usr/local/lib/python3.6/dist-packages/torch/serialization.py:292: UserWarning: Couldn't retrieve source code for container of type ReLU. It won't be checked for correctness upon loading.\n",
            "  \"type \" + obj.__name__ + \". It won't be checked \"\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 600x400 with 0 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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2Nj4+PnTr1p3hw18t9d73MhqNrF27mm+/3cz169fw86vFoEFDefrpv5mvGT16BB4eHrRp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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 0 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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FxwIc/f3psCia1v94kJIzP5O6dBF5n38mox4hRLMnRcdCFCoV7nePxm/ZKhz8/MncvpW0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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 600x400 with 0 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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HT8DJydmwaqoxiKJjBAUxMeT+dRTnR/4PuaX5Lr0FQQAvL2/s7e3R6/UEB7cu935OTi6r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\nAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ShZS6rc8MFtE",
        "colab_type": "code",
        "outputId": "9c7a4b2f-d73a-4bff-e095-ccbc77cdc83f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "len(trainset)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "60000"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JYgJ-xfgMbE-",
        "colab_type": "code",
        "outputId": "ea2e5f9e-a256-422c-9958-efee2ecf388d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 102
        }
      },
      "source": [
        "pip install mnist"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Collecting mnist\n",
            "  Downloading https://files.pythonhosted.org/packages/c6/c4/5db3bfe009f8d71f1d532bbadbd0ec203764bba3a469e4703a889db8e5e0/mnist-0.2.2-py2.py3-none-any.whl\n",
            "Requirement already satisfied: numpy in /usr/local/lib/python3.6/dist-packages (from mnist) (1.17.4)\n",
            "Installing collected packages: mnist\n",
            "Successfully installed mnist-0.2.2\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "v_hs9R3EGMcS",
        "colab_type": "code",
        "outputId": "ee2649b6-9855-42dd-8463-52507198ac8b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 476
        }
      },
      "source": [
        "from keras.optimizers import SGD\n",
        "import numpy as np\n",
        "import mnist\n",
        "from keras.models import Sequential\n",
        "from keras.layers import Dense\n",
        "from keras.utils import to_categorical\n",
        "\n",
        "train_images = mnist.train_images()\n",
        "train_labels = mnist.train_labels()\n",
        "test_images = mnist.test_images()\n",
        "test_labels = mnist.test_labels()\n",
        "\n",
        "# Normalize the images.\n",
        "train_images = (train_images / 126) - 0.5\n",
        "test_images = (test_images / 126) - 0.5\n",
        "\n",
        "# Flatten the images.\n",
        "train_images = train_images.reshape((-1, 784))\n",
        "test_images = test_images.reshape((-1, 784))\n",
        "\n",
        "baseline_model = Sequential([\n",
        "  Dense(400, activation='relu', input_shape=(784,)),\n",
        "  Dense(400, activation='relu'),  \n",
        "  Dense(10, activation='softmax'),\n",
        "])\n",
        "\n",
        "baseline_model.compile(optimizer=SGD(lr=0.001),\n",
        "                       loss='binary_crossentropy',\n",
        "                       metrics=['accuracy'])\n",
        "\n",
        "baseline_model.summary()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:66: The name tf.get_default_graph is deprecated. Please use tf.compat.v1.get_default_graph instead.\n",
            "\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:541: The name tf.placeholder is deprecated. Please use tf.compat.v1.placeholder instead.\n",
            "\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:4432: The name tf.random_uniform is deprecated. Please use tf.random.uniform instead.\n",
            "\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/optimizers.py:793: The name tf.train.Optimizer is deprecated. Please use tf.compat.v1.train.Optimizer instead.\n",
            "\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:3657: The name tf.log is deprecated. Please use tf.math.log instead.\n",
            "\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow_core/python/ops/nn_impl.py:183: where (from tensorflow.python.ops.array_ops) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Use tf.where in 2.0, which has the same broadcast rule as np.where\n",
            "Model: \"sequential_1\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "dense_1 (Dense)              (None, 400)               314000    \n",
            "_________________________________________________________________\n",
            "dense_2 (Dense)              (None, 400)               160400    \n",
            "_________________________________________________________________\n",
            "dense_3 (Dense)              (None, 10)                4010      \n",
            "=================================================================\n",
            "Total params: 478,410\n",
            "Trainable params: 478,410\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bolwVfefMlzR",
        "colab_type": "code",
        "outputId": "ea1292a6-e9e7-413a-ce67-6690d5534ede",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "baseline_history = baseline_model.fit(train_images,\n",
        "                                      to_categorical(train_labels),\n",
        "                                      epochs=100,\n",
        "                                      batch_size=128,\n",
        "                                      validation_data=(test_images,\n",
        "                                                      to_categorical(test_labels)))\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:1033: The name tf.assign_add is deprecated. Please use tf.compat.v1.assign_add instead.\n",
            "\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:1020: The name tf.assign is deprecated. Please use tf.compat.v1.assign instead.\n",
            "\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:3005: The name tf.Session is deprecated. Please use tf.compat.v1.Session instead.\n",
            "\n",
            "Train on 60000 samples, validate on 10000 samples\n",
            "Epoch 1/100\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:190: The name tf.get_default_session is deprecated. Please use tf.compat.v1.get_default_session instead.\n",
            "\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:197: The name tf.ConfigProto is deprecated. Please use tf.compat.v1.ConfigProto instead.\n",
            "\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:207: The name tf.global_variables is deprecated. Please use tf.compat.v1.global_variables instead.\n",
            "\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:216: The name tf.is_variable_initialized is deprecated. Please use tf.compat.v1.is_variable_initialized instead.\n",
            "\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:223: The name tf.variables_initializer is deprecated. Please use tf.compat.v1.variables_initializer instead.\n",
            "\n",
            "60000/60000 [==============================] - 6s 107us/step - loss: 0.3256 - acc: 0.9000 - val_loss: 0.3151 - val_acc: 0.9000\n",
            "Epoch 2/100\n",
            "60000/60000 [==============================] - 3s 43us/step - loss: 0.3067 - acc: 0.9000 - val_loss: 0.2976 - val_acc: 0.9000\n",
            "Epoch 3/100\n",
            "60000/60000 [==============================] - 3s 44us/step - loss: 0.2907 - acc: 0.9000 - val_loss: 0.2818 - val_acc: 0.9000\n",
            "Epoch 4/100\n",
            "60000/60000 [==============================] - 3s 44us/step - loss: 0.2757 - acc: 0.9000 - val_loss: 0.2668 - val_acc: 0.9002\n",
            "Epoch 5/100\n",
            "60000/60000 [==============================] - 3s 46us/step - loss: 0.2613 - acc: 0.9005 - val_loss: 0.2523 - val_acc: 0.9010\n",
            "Epoch 6/100\n",
            "60000/60000 [==============================] - 3s 45us/step - loss: 0.2474 - acc: 0.9020 - val_loss: 0.2382 - val_acc: 0.9037\n",
            "Epoch 7/100\n",
            "60000/60000 [==============================] - 3s 43us/step - loss: 0.2338 - acc: 0.9059 - val_loss: 0.2247 - val_acc: 0.9083\n",
            "Epoch 8/100\n",
            "60000/60000 [==============================] - 3s 43us/step - loss: 0.2208 - acc: 0.9107 - val_loss: 0.2117 - val_acc: 0.9136\n",
            "Epoch 9/100\n",
            "60000/60000 [==============================] - 3s 43us/step - loss: 0.2084 - acc: 0.9155 - val_loss: 0.1996 - val_acc: 0.9188\n",
            "Epoch 10/100\n",
            "60000/60000 [==============================] - 3s 43us/step - loss: 0.1968 - acc: 0.9205 - val_loss: 0.1882 - val_acc: 0.9240\n",
            "Epoch 11/100\n",
            "60000/60000 [==============================] - 3s 42us/step - loss: 0.1861 - acc: 0.9255 - val_loss: 0.1777 - val_acc: 0.9290\n",
            "Epoch 12/100\n",
            "60000/60000 [==============================] - 3s 44us/step - loss: 0.1761 - acc: 0.9305 - val_loss: 0.1682 - val_acc: 0.9340\n",
            "Epoch 13/100\n",
            "60000/60000 [==============================] - 3s 43us/step - loss: 0.1671 - acc: 0.9351 - val_loss: 0.1595 - val_acc: 0.9384\n",
            "Epoch 14/100\n",
            "60000/60000 [==============================] - 3s 43us/step - loss: 0.1589 - acc: 0.9392 - val_loss: 0.1516 - val_acc: 0.9419\n",
            "Epoch 15/100\n",
            "60000/60000 [==============================] - 3s 43us/step - loss: 0.1514 - acc: 0.9430 - val_loss: 0.1445 - val_acc: 0.9452\n",
            "Epoch 16/100\n",
            "60000/60000 [==============================] - 3s 43us/step - loss: 0.1447 - acc: 0.9462 - val_loss: 0.1380 - val_acc: 0.9488\n",
            "Epoch 17/100\n",
            "60000/60000 [==============================] - 3s 42us/step - loss: 0.1386 - acc: 0.9493 - val_loss: 0.1322 - val_acc: 0.9520\n",
            "Epoch 18/100\n",
            "60000/60000 [==============================] - 3s 42us/step - loss: 0.1330 - acc: 0.9522 - val_loss: 0.1270 - val_acc: 0.9547\n",
            "Epoch 19/100\n",
            "60000/60000 [==============================] - 3s 43us/step - loss: 0.1280 - acc: 0.9545 - val_loss: 0.1222 - val_acc: 0.9571\n",
            "Epoch 20/100\n",
            "60000/60000 [==============================] - 3s 43us/step - loss: 0.1235 - acc: 0.9568 - val_loss: 0.1178 - val_acc: 0.9593\n",
            "Epoch 21/100\n",
            "60000/60000 [==============================] - 3s 43us/step - loss: 0.1193 - acc: 0.9588 - val_loss: 0.1139 - val_acc: 0.9613\n",
            "Epoch 22/100\n",
            "60000/60000 [==============================] - 3s 43us/step - loss: 0.1155 - acc: 0.9605 - val_loss: 0.1103 - val_acc: 0.9631\n",
            "Epoch 23/100\n",
            "60000/60000 [==============================] - 3s 43us/step - loss: 0.1120 - acc: 0.9622 - val_loss: 0.1070 - val_acc: 0.9647\n",
            "Epoch 24/100\n",
            "60000/60000 [==============================] - 3s 42us/step - loss: 0.1088 - acc: 0.9637 - val_loss: 0.1039 - val_acc: 0.9660\n",
            "Epoch 25/100\n",
            "60000/60000 [==============================] - 3s 43us/step - loss: 0.1059 - acc: 0.9649 - val_loss: 0.1011 - val_acc: 0.9672\n",
            "Epoch 26/100\n",
            "60000/60000 [==============================] - 3s 43us/step - loss: 0.1032 - acc: 0.9662 - val_loss: 0.0986 - val_acc: 0.9684\n",
            "Epoch 27/100\n",
            "60000/60000 [==============================] - 3s 43us/step - loss: 0.1007 - acc: 0.9672 - val_loss: 0.0962 - val_acc: 0.9694\n",
            "Epoch 28/100\n",
            "60000/60000 [==============================] - 3s 43us/step - loss: 0.0984 - acc: 0.9681 - val_loss: 0.0940 - val_acc: 0.9701\n",
            "Epoch 29/100\n",
            "60000/60000 [==============================] - 3s 42us/step - loss: 0.0963 - acc: 0.9689 - val_loss: 0.0920 - val_acc: 0.9709\n",
            "Epoch 30/100\n",
            "60000/60000 [==============================] - 3s 43us/step - loss: 0.0943 - acc: 0.9697 - val_loss: 0.0901 - val_acc: 0.9717\n",
            "Epoch 31/100\n",
            "60000/60000 [==============================] - 3s 43us/step - loss: 0.0924 - acc: 0.9704 - val_loss: 0.0883 - val_acc: 0.9723\n",
            "Epoch 32/100\n",
            "60000/60000 [==============================] - 3s 43us/step - loss: 0.0907 - acc: 0.9710 - val_loss: 0.0866 - val_acc: 0.9730\n",
            "Epoch 33/100\n",
            "60000/60000 [==============================] - 3s 43us/step - loss: 0.0891 - acc: 0.9716 - val_loss: 0.0851 - val_acc: 0.9735\n",
            "Epoch 34/100\n",
            "60000/60000 [==============================] - 3s 42us/step - loss: 0.0875 - acc: 0.9722 - val_loss: 0.0836 - val_acc: 0.9739\n",
            "Epoch 35/100\n",
            "60000/60000 [==============================] - 3s 43us/step - loss: 0.0861 - acc: 0.9727 - val_loss: 0.0823 - val_acc: 0.9743\n",
            "Epoch 36/100\n",
            "60000/60000 [==============================] - 3s 44us/step - loss: 0.0848 - acc: 0.9732 - val_loss: 0.0810 - val_acc: 0.9748\n",
            "Epoch 37/100\n",
            "60000/60000 [==============================] - 3s 42us/step - loss: 0.0835 - acc: 0.9736 - val_loss: 0.0798 - val_acc: 0.9751\n",
            "Epoch 38/100\n",
            "60000/60000 [==============================] - 3s 43us/step - loss: 0.0823 - acc: 0.9740 - val_loss: 0.0786 - val_acc: 0.9754\n",
            "Epoch 39/100\n",
            "60000/60000 [==============================] - 3s 43us/step - loss: 0.0812 - acc: 0.9743 - val_loss: 0.0776 - val_acc: 0.9758\n",
            "Epoch 40/100\n",
            "60000/60000 [==============================] - 3s 43us/step - loss: 0.0801 - acc: 0.9746 - val_loss: 0.0765 - val_acc: 0.9759\n",
            "Epoch 41/100\n",
            "60000/60000 [==============================] - 3s 44us/step - loss: 0.0791 - acc: 0.9749 - val_loss: 0.0756 - val_acc: 0.9763\n",
            "Epoch 42/100\n",
            "60000/60000 [==============================] - 3s 43us/step - loss: 0.0781 - acc: 0.9752 - val_loss: 0.0747 - val_acc: 0.9765\n",
            "Epoch 43/100\n",
            "60000/60000 [==============================] - 3s 43us/step - loss: 0.0772 - acc: 0.9754 - val_loss: 0.0738 - val_acc: 0.9767\n",
            "Epoch 44/100\n",
            "60000/60000 [==============================] - 3s 43us/step - loss: 0.0763 - acc: 0.9756 - val_loss: 0.0730 - val_acc: 0.9770\n",
            "Epoch 45/100\n",
            "60000/60000 [==============================] - 3s 43us/step - loss: 0.0755 - acc: 0.9759 - val_loss: 0.0722 - val_acc: 0.9773\n",
            "Epoch 46/100\n",
            "60000/60000 [==============================] - 3s 43us/step - loss: 0.0747 - acc: 0.9761 - val_loss: 0.0714 - val_acc: 0.9776\n",
            "Epoch 47/100\n",
            "60000/60000 [==============================] - 3s 42us/step - loss: 0.0739 - acc: 0.9763 - val_loss: 0.0707 - val_acc: 0.9779\n",
            "Epoch 48/100\n",
            "60000/60000 [==============================] - 3s 43us/step - loss: 0.0732 - acc: 0.9765 - val_loss: 0.0700 - val_acc: 0.9782\n",
            "Epoch 49/100\n",
            "60000/60000 [==============================] - 3s 43us/step - loss: 0.0725 - acc: 0.9767 - val_loss: 0.0693 - val_acc: 0.9784\n",
            "Epoch 50/100\n",
            "60000/60000 [==============================] - 3s 42us/step - loss: 0.0718 - acc: 0.9769 - val_loss: 0.0687 - val_acc: 0.9784\n",
            "Epoch 51/100\n",
            "60000/60000 [==============================] - 3s 43us/step - loss: 0.0712 - acc: 0.9771 - val_loss: 0.0681 - val_acc: 0.9787\n",
            "Epoch 52/100\n",
            "60000/60000 [==============================] - 3s 43us/step - loss: 0.0706 - acc: 0.9772 - val_loss: 0.0675 - val_acc: 0.9789\n",
            "Epoch 53/100\n",
            "60000/60000 [==============================] - 3s 42us/step - loss: 0.0700 - acc: 0.9774 - val_loss: 0.0669 - val_acc: 0.9789\n",
            "Epoch 54/100\n",
            "60000/60000 [==============================] - 3s 44us/step - loss: 0.0694 - acc: 0.9776 - val_loss: 0.0664 - val_acc: 0.9791\n",
            "Epoch 55/100\n",
            "60000/60000 [==============================] - 3s 43us/step - loss: 0.0689 - acc: 0.9777 - val_loss: 0.0659 - val_acc: 0.9792\n",
            "Epoch 56/100\n",
            "60000/60000 [==============================] - 3s 42us/step - loss: 0.0683 - acc: 0.9779 - val_loss: 0.0654 - val_acc: 0.9794\n",
            "Epoch 57/100\n",
            "60000/60000 [==============================] - 3s 44us/step - loss: 0.0678 - acc: 0.9781 - val_loss: 0.0649 - val_acc: 0.9796\n",
            "Epoch 58/100\n",
            "60000/60000 [==============================] - 3s 43us/step - loss: 0.0673 - acc: 0.9782 - val_loss: 0.0644 - val_acc: 0.9798\n",
            "Epoch 59/100\n",
            "60000/60000 [==============================] - 3s 45us/step - loss: 0.0668 - acc: 0.9784 - val_loss: 0.0640 - val_acc: 0.9799\n",
            "Epoch 60/100\n",
            "60000/60000 [==============================] - 3s 42us/step - loss: 0.0664 - acc: 0.9785 - val_loss: 0.0635 - val_acc: 0.9800\n",
            "Epoch 61/100\n",
            "60000/60000 [==============================] - 3s 42us/step - loss: 0.0659 - acc: 0.9787 - val_loss: 0.0631 - val_acc: 0.9802\n",
            "Epoch 62/100\n",
            "60000/60000 [==============================] - 3s 43us/step - loss: 0.0655 - acc: 0.9788 - val_loss: 0.0627 - val_acc: 0.9803\n",
            "Epoch 63/100\n",
            "60000/60000 [==============================] - 3s 43us/step - loss: 0.0651 - acc: 0.9789 - val_loss: 0.0623 - val_acc: 0.9803\n",
            "Epoch 64/100\n",
            "60000/60000 [==============================] - 3s 43us/step - loss: 0.0647 - acc: 0.9790 - val_loss: 0.0619 - val_acc: 0.9804\n",
            "Epoch 65/100\n",
            "60000/60000 [==============================] - 3s 43us/step - loss: 0.0643 - acc: 0.9791 - val_loss: 0.0616 - val_acc: 0.9805\n",
            "Epoch 66/100\n",
            "60000/60000 [==============================] - 3s 43us/step - loss: 0.0639 - acc: 0.9792 - val_loss: 0.0612 - val_acc: 0.9806\n",
            "Epoch 67/100\n",
            "60000/60000 [==============================] - 3s 43us/step - loss: 0.0635 - acc: 0.9793 - val_loss: 0.0609 - val_acc: 0.9807\n",
            "Epoch 68/100\n",
            "60000/60000 [==============================] - 3s 43us/step - loss: 0.0632 - acc: 0.9795 - val_loss: 0.0605 - val_acc: 0.9808\n",
            "Epoch 69/100\n",
            "60000/60000 [==============================] - 3s 43us/step - loss: 0.0628 - acc: 0.9795 - val_loss: 0.0602 - val_acc: 0.9809\n",
            "Epoch 70/100\n",
            "60000/60000 [==============================] - 3s 42us/step - loss: 0.0625 - acc: 0.9797 - val_loss: 0.0599 - val_acc: 0.9810\n",
            "Epoch 71/100\n",
            "60000/60000 [==============================] - 3s 44us/step - loss: 0.0621 - acc: 0.9798 - val_loss: 0.0595 - val_acc: 0.9811\n",
            "Epoch 72/100\n",
            "60000/60000 [==============================] - 3s 43us/step - loss: 0.0618 - acc: 0.9799 - val_loss: 0.0592 - val_acc: 0.9812\n",
            "Epoch 73/100\n",
            "60000/60000 [==============================] - 3s 43us/step - loss: 0.0615 - acc: 0.9799 - val_loss: 0.0589 - val_acc: 0.9813\n",
            "Epoch 74/100\n",
            "60000/60000 [==============================] - 3s 42us/step - loss: 0.0612 - acc: 0.9800 - val_loss: 0.0586 - val_acc: 0.9813\n",
            "Epoch 75/100\n",
            "60000/60000 [==============================] - 3s 42us/step - loss: 0.0609 - acc: 0.9801 - val_loss: 0.0584 - val_acc: 0.9814\n",
            "Epoch 76/100\n",
            "60000/60000 [==============================] - 3s 43us/step - loss: 0.0606 - acc: 0.9802 - val_loss: 0.0581 - val_acc: 0.9815\n",
            "Epoch 77/100\n",
            "60000/60000 [==============================] - 3s 43us/step - loss: 0.0603 - acc: 0.9803 - val_loss: 0.0578 - val_acc: 0.9816\n",
            "Epoch 78/100\n",
            "60000/60000 [==============================] - 3s 42us/step - loss: 0.0600 - acc: 0.9804 - val_loss: 0.0575 - val_acc: 0.9817\n",
            "Epoch 79/100\n",
            "60000/60000 [==============================] - 3s 43us/step - loss: 0.0597 - acc: 0.9805 - val_loss: 0.0573 - val_acc: 0.9817\n",
            "Epoch 80/100\n",
            "60000/60000 [==============================] - 3s 43us/step - loss: 0.0595 - acc: 0.9805 - val_loss: 0.0570 - val_acc: 0.9818\n",
            "Epoch 81/100\n",
            "60000/60000 [==============================] - 3s 42us/step - loss: 0.0592 - acc: 0.9806 - val_loss: 0.0568 - val_acc: 0.9819\n",
            "Epoch 82/100\n",
            "60000/60000 [==============================] - 3s 42us/step - loss: 0.0590 - acc: 0.9807 - val_loss: 0.0565 - val_acc: 0.9819\n",
            "Epoch 83/100\n",
            "60000/60000 [==============================] - 3s 44us/step - loss: 0.0587 - acc: 0.9808 - val_loss: 0.0563 - val_acc: 0.9820\n",
            "Epoch 84/100\n",
            "60000/60000 [==============================] - 3s 43us/step - loss: 0.0585 - acc: 0.9809 - val_loss: 0.0561 - val_acc: 0.9820\n",
            "Epoch 85/100\n",
            "60000/60000 [==============================] - 3s 43us/step - loss: 0.0582 - acc: 0.9809 - val_loss: 0.0559 - val_acc: 0.9821\n",
            "Epoch 86/100\n",
            "60000/60000 [==============================] - 3s 43us/step - loss: 0.0580 - acc: 0.9810 - val_loss: 0.0556 - val_acc: 0.9821\n",
            "Epoch 87/100\n",
            "60000/60000 [==============================] - 3s 43us/step - loss: 0.0577 - acc: 0.9811 - val_loss: 0.0554 - val_acc: 0.9822\n",
            "Epoch 88/100\n",
            "60000/60000 [==============================] - 3s 44us/step - loss: 0.0575 - acc: 0.9811 - val_loss: 0.0552 - val_acc: 0.9822\n",
            "Epoch 89/100\n",
            "60000/60000 [==============================] - 3s 44us/step - loss: 0.0573 - acc: 0.9812 - val_loss: 0.0550 - val_acc: 0.9823\n",
            "Epoch 90/100\n",
            "60000/60000 [==============================] - 3s 42us/step - loss: 0.0571 - acc: 0.9813 - val_loss: 0.0548 - val_acc: 0.9824\n",
            "Epoch 91/100\n",
            "60000/60000 [==============================] - 3s 43us/step - loss: 0.0568 - acc: 0.9813 - val_loss: 0.0546 - val_acc: 0.9824\n",
            "Epoch 92/100\n",
            "60000/60000 [==============================] - 3s 43us/step - loss: 0.0566 - acc: 0.9814 - val_loss: 0.0544 - val_acc: 0.9825\n",
            "Epoch 93/100\n",
            "60000/60000 [==============================] - 3s 43us/step - loss: 0.0564 - acc: 0.9815 - val_loss: 0.0542 - val_acc: 0.9826\n",
            "Epoch 94/100\n",
            "60000/60000 [==============================] - 3s 43us/step - loss: 0.0562 - acc: 0.9816 - val_loss: 0.0540 - val_acc: 0.9826\n",
            "Epoch 95/100\n",
            "60000/60000 [==============================] - 3s 43us/step - loss: 0.0560 - acc: 0.9816 - val_loss: 0.0538 - val_acc: 0.9826\n",
            "Epoch 96/100\n",
            "60000/60000 [==============================] - 3s 43us/step - loss: 0.0558 - acc: 0.9817 - val_loss: 0.0536 - val_acc: 0.9827\n",
            "Epoch 97/100\n",
            "60000/60000 [==============================] - 3s 42us/step - loss: 0.0556 - acc: 0.9818 - val_loss: 0.0534 - val_acc: 0.9827\n",
            "Epoch 98/100\n",
            "60000/60000 [==============================] - 3s 43us/step - loss: 0.0554 - acc: 0.9819 - val_loss: 0.0532 - val_acc: 0.9828\n",
            "Epoch 99/100\n",
            "60000/60000 [==============================] - 3s 43us/step - loss: 0.0552 - acc: 0.9819 - val_loss: 0.0531 - val_acc: 0.9828\n",
            "Epoch 100/100\n",
            "60000/60000 [==============================] - 3s 42us/step - loss: 0.0550 - acc: 0.9820 - val_loss: 0.0529 - val_acc: 0.9829\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ihahSkNGGPob",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from numpy  import array\n",
        "data_acc=baseline_history.history['val_acc']\n",
        "type(data_acc)\n",
        "a = array( data_acc )\n",
        "acc_sgd= 1-a"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Fi5VYa0SOL-G",
        "colab_type": "code",
        "outputId": "c64559f2-9d06-41c9-a0fd-ff982d8e2a38",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 357
        }
      },
      "source": [
        "acc_sgd"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0.10000002, 0.10000002, 0.09999002, 0.09980002, 0.09896   ,\n",
              "       0.09629001, 0.09172002, 0.08644003, 0.08119004, 0.07600003,\n",
              "       0.07099003, 0.06602002, 0.06162001, 0.05807001, 0.05479001,\n",
              "       0.05117001, 0.04803001, 0.04533001, 0.04288001, 0.04065   ,\n",
              "       0.03865   , 0.03689   , 0.0353    , 0.034     , 0.03276   ,\n",
              "       0.03163   , 0.03062   , 0.02991   , 0.02911999, 0.02826   ,\n",
              "       0.02767999, 0.02696999, 0.02652   , 0.02606999, 0.02566999,\n",
              "       0.02520999, 0.02491999, 0.02455999, 0.02421999, 0.02407999,\n",
              "       0.02371   , 0.02354999, 0.02325999, 0.02300999, 0.02273999,\n",
              "       0.02243   , 0.02214   , 0.02184   , 0.02156999, 0.02156999,\n",
              "       0.02133999, 0.02115   , 0.02109999, 0.02090999, 0.02076   ,\n",
              "       0.02058   , 0.02045   , 0.02023   , 0.02012   , 0.01996   ,\n",
              "       0.01979   , 0.01974999, 0.01968   , 0.01959   , 0.01946   ,\n",
              "       0.01935   , 0.01929   , 0.01921   , 0.01914   , 0.01903   ,\n",
              "       0.01891   , 0.01883   , 0.01874   , 0.01868   , 0.01859   ,\n",
              "       0.01847   , 0.01839   , 0.01827   , 0.01827   , 0.01819   ,\n",
              "       0.01814   , 0.01808   , 0.01801999, 0.01798   , 0.01794   ,\n",
              "       0.01787   , 0.01785   , 0.01777   , 0.01769   , 0.01765   ,\n",
              "       0.01756   , 0.01752   , 0.01744   , 0.01738   , 0.01737   ,\n",
              "       0.01734   , 0.01726   , 0.01722   , 0.0172    , 0.01712   ])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7lnBxKLQczIc",
        "colab_type": "code",
        "outputId": "62066d17-4be3-4c28-c17f-be3c2b842c8c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 618
        }
      },
      "source": [
        "from pylab import rcParams\n",
        "rcParams['figure.figsize'] = 10, 10\n",
        "textsize = 15\n",
        "marker=20\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 * acc_sgd, '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 (Bayes By Backprop)', 'Test_Error (Vanilla SGD)'], 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')"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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ss6Msykrq3bsvFi36ErGxMfjrrz0IDm5v0bJ0P3X1/fersHr18vuqq6ZNmwIo7CYvSvRC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\nAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 720x720 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "F62IIl8I8JCX",
        "colab_type": "code",
        "outputId": "2ad97908-0720-40d9-b374-b9b725bd1e54",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "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', kl_cost_train)\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",
        "plt.figure()\n",
        "fig, ax1 = plt.subplots()\n",
        "ax1.plot(kl_cost_train, 'r')\n",
        "ax1.set_ylabel('nats?')\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",
        "ax = plt.gca()\n",
        "plt.title('DKL (per sample)')\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",
        "\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"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\u001b[36m\n",
            "RESULTS:\u001b[0m\n",
            "  cost_dev: 0.068071 (cost_train 0.038630)\n",
            "  err_dev: 0.016300\n",
            "  nb_parameters: 956820 (934.39KB)\n",
            "  time_per_it: 39.844658s\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-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.fromiter(generator)) or the python sum builtin instead.\n",
            "  import sys\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 600x400 with 0 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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Nmo+pXbsO7drd6Ed2d/cgLS2tWHylbdtb0r95pdKyfZzui2QSFBTE//73v2Ll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\nTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 0 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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oCY5ebTmbuOrSvZ5SGeEmRGOzuYEE6enpqFSqKy69BQcHk56efs197777bt5/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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 600x400 with 0 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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wLn2FUqW9A46t25K2bSsu3Xtg6eZu8BgFQRBMpUxXOv/73/+YNm0aXbt2ZfHi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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gkT9cfkL8S5g",
        "colab_type": "code",
        "outputId": "0a0561da-4644-4962-87c1-5fac059eede9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "nb_its_dev"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SFCfl8oeupW8",
        "colab_type": "code",
        "outputId": "d1c7de5f-b33c-456d-ad41-733345c36d50",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 238
        }
      },
      "source": [
        "err_dev[::nb_its_dev]"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0.0749, 0.056 , 0.0474, 0.0401, 0.0393, 0.0372, 0.0357, 0.0306,\n",
              "       0.0306, 0.0288, 0.0279, 0.0273, 0.026 , 0.0262, 0.0267, 0.0254,\n",
              "       0.0243, 0.0245, 0.0242, 0.0234, 0.0225, 0.0229, 0.0233, 0.0227,\n",
              "       0.0216, 0.0227, 0.0225, 0.0222, 0.0217, 0.0206, 0.0209, 0.0217,\n",
              "       0.0211, 0.0206, 0.02  , 0.02  , 0.0201, 0.0195, 0.0205, 0.0201,\n",
              "       0.0205, 0.0193, 0.019 , 0.0186, 0.0188, 0.0195, 0.0181, 0.0191,\n",
              "       0.0191, 0.0178, 0.0184, 0.0182, 0.0184, 0.0183, 0.019 , 0.0184,\n",
              "       0.0184, 0.0191, 0.0186, 0.0178, 0.0187, 0.0188, 0.0177, 0.0185,\n",
              "       0.018 , 0.018 , 0.0179, 0.0174, 0.018 , 0.0173, 0.0176, 0.0166,\n",
              "       0.0174, 0.0177, 0.017 , 0.0181, 0.0175, 0.0174, 0.0175, 0.0173,\n",
              "       0.0179, 0.0176, 0.018 , 0.0166, 0.0171, 0.0175, 0.0173, 0.018 ,\n",
              "       0.0171, 0.0178, 0.0163, 0.0166, 0.0168, 0.0163, 0.0171, 0.0172,\n",
              "       0.0175, 0.0174, 0.0177, 0.0178])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "K0Tnv8aLc07l",
        "colab_type": "code",
        "outputId": "839c09c1-7cce-46e7-89f0-e4241673392d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 180
        }
      },
      "source": [
        "err_dev[::nb_its_dev]\n",
        "baseline_history.history['val_loss']"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "error",
          "ename": "NameError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-27-9d0f526fcf6c>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0merr_dev\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0mnb_its_dev\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mbaseline_history\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhistory\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'val_loss'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;31mNameError\u001b[0m: name 'baseline_history' is not defined"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5oVsXyAnAax9",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "models_dir = 'models_weight_uncertainty_MC_MNIST_gaussian'\n",
        "results_dir = 'results_weight_uncertainty_MC_MNIST_gaussian'\n",
        "\n",
        "mkdir(models_dir)\n",
        "mkdir(results_dir)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uM1q3hVjPHL6",
        "colab_type": "code",
        "outputId": "31c7e8a6-ea7b-49c3-85bd-876d61831ecd",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        }
      },
      "source": [
        "name = 'Bayes_By_Backprop'\n",
        "Nsamples = 10\n",
        "weight_vector = net.get_weight_samples(Nsamples=Nsamples)\n",
        "np.save(results_dir+'/weight_samples_'+name+'.npy', weight_vector)\n",
        "\n",
        "print(weight_vector.shape)\n",
        "\n",
        "fig = plt.figure(dpi=100)\n",
        "ax = fig.add_subplot(111)\n",
        "symlim = 0.7\n",
        "\n",
        "lim_idxs = np.where(np.logical_and(weight_vector>=symlim, weight_vector<=symlim))\n",
        "\n",
        "sns.distplot(weight_vector, 500, norm_hist=False, label=name, ax=ax)\n",
        "ax.set_xlim((-symlim, symlim))\n",
        "# ax.hist(weight_vector, bins=70, density=True);\n",
        "\n",
        "ax.set_ylabel('Density')\n",
        "ax.legend()\n",
        "plt.title('Total parameters: %d, samples: %d' % (len(weight_vector)/Nsamples, Nsamples))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(4776000,)\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Total parameters: 477600, samples: 10')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 21
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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8738fGdTFl19+gcjIA9i9e3+lRxs4OTkbXAsAduz4BkeO/IKoqP9gZ2eP5s2D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\nERF5hM0EEREReeT/A31YVe9TpIqRAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 600x400 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EWQhxi1LPrXf",
        "colab_type": "code",
        "outputId": "8b4e1d4b-84ff-4eff-a7a2-dc018dde2fc9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 231
        }
      },
      "source": [
        "last_state_dict = copy.deepcopy(net.model.state_dict())\n",
        "savemodel_its = [20, 50, 80, 90]\n",
        "Nsamples = 10\n",
        "\n",
        "for idx, iteration in enumerate(savemodel_its):\n",
        "    \n",
        "    net.model.load_state_dict(save_dicts[idx])\n",
        "    weight_vector = net.get_weight_samples(Nsamples=Nsamples)\n",
        "    fig = plt.figure(dpi=120)\n",
        "    ax = fig.add_subplot(111)\n",
        "    symlim = 1\n",
        "    lim_idxs = np.where(np.logical_and(weight_vector>=symlim, weight_vector<=symlim))\n",
        "    sns.distplot(weight_vector, 350, norm_hist=False, label='it %d' % (iteration), ax=ax)\n",
        "    ax.legend()\n",
        "\n",
        "    ax.set_xlim((-symlim, symlim))\n",
        "\n",
        "\n",
        "net.model.load_state_dict(last_state_dict)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "error",
          "ename": "IndexError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mIndexError\u001b[0m                                Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-25-39544fdf255d>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0midx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0miteration\u001b[0m \u001b[0;32min\u001b[0m \u001b[0menumerate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msavemodel_its\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      6\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 7\u001b[0;31m     \u001b[0mnet\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mload_state_dict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msave_dicts\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0midx\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      8\u001b[0m     \u001b[0mweight_vector\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnet\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_weight_samples\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mNsamples\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mNsamples\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      9\u001b[0m     \u001b[0mfig\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdpi\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m120\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mIndexError\u001b[0m: list index out of range"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Yu9VDoe1Pwfo",
        "colab_type": "code",
        "outputId": "9f52da62-9ddf-4231-c424-8528f0e54c6c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 163
        }
      },
      "source": [
        "net.model.load_state_dict(save_dicts[2])"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "error",
          "ename": "IndexError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mIndexError\u001b[0m                                Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-24-393e6bb374a7>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mnet\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mload_state_dict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msave_dicts\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;31mIndexError\u001b[0m: list index out of range"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vcKLOK-xfKZN",
        "colab_type": "code",
        "outputId": "01013a18-5aa9-4b5d-a115-cd78cad74439",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 900
        }
      },
      "source": [
        "name1 = 'Bayes_By_Backprop'\n",
        "name2 = 'Vanilla SGD'\n",
        "Nsamples = 10\n",
        "weight_vector = net.get_weight_samples(Nsamples=Nsamples)\n",
        "np.save(results_dir+'/weight_samples_'+name+'.npy', weight_vector)\n",
        "\n",
        "print(weight_vector.shape)\n",
        "\n",
        "fig = plt.figure(dpi=100)\n",
        "ax = fig.add_subplot(111)\n",
        "symlim = 0.7\n",
        "\n",
        "lim_idxs = np.where(np.logical_and(weight_vector>=symlim, weight_vector<=symlim))\n",
        "\n",
        "sns.distplot(weight_vector, 500, norm_hist=False, label=name1, ax=ax)\n",
        "sns.distplot(Weights, 500, norm_hist=False, label=name2, ax=ax)\n",
        "ax.set_xlim((-symlim, symlim))\n",
        "# ax.hist(weight_vector, bins=70, density=True);\n",
        "\n",
        "ax.set_ylabel('Density')\n",
        "ax.set_xlabel('Values of Weights in Neural Network')\n",
        "ax.legend()\n",
        "plt.title('Total parameters: %d, samples: %d' % (len(weight_vector)/Nsamples, Nsamples))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(4776000,)\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Total parameters: 477600, samples: 10')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 114
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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zmDhxLOrUqYc33ngb3t7e+Pvvc4iOvuPwvH/8cRbTpr2Grl17YOLEKTZz4ubO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iLSmfU7vaNII7NtMBpkjavpKdACnCEpLISvi6DqdoX9ZxuNEqaN9a6HW7vzODxhr4CISDsMSURE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\nQmCC\n",
            "text/plain": [
              "<Figure size 1000x1000 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SVYFJPJBlt8V",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cigV27U3luom",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CARJ9k4Ffbhj",
        "colab_type": "code",
        "outputId": "029d08f8-ebda-47ca-fd29-17210bfd6cc3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "len(weight_vector)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "4776000"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 52
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_lxmYGoFfs67",
        "colab_type": "code",
        "outputId": "9f2d56a2-4f16-41a0-8238-e09c77443ffa",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "weigh=baseline_model.layers[1].get_weights()[0][398]\n",
        "len(weigh)\n",
        "weigh[0]"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.009048584"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 65
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EOKaxejAgl5f",
        "colab_type": "code",
        "outputId": "1c263b8f-723b-4158-9794-bfabad56db86",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "new1=[]\n",
        "for i in range(0,784):\n",
        "  mylist = baseline_model.layers[0].get_weights()[0][i]\n",
        "  new1=np.append(new1, mylist)\n",
        "len(new1)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "313600"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 102
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5EO9YD1LjCH2",
        "colab_type": "code",
        "outputId": "9548e244-5efe-406b-ca46-b293988a6a87",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "new2=[]\n",
        "for i in range(0,399):\n",
        "  mylist = baseline_model.layers[1].get_weights()[0][i]\n",
        "  new2=np.append(new2, mylist)\n",
        "len(new2)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "159600"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 103
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-d7Zr-4IiqJG",
        "colab_type": "code",
        "outputId": "20ccbd74-d079-4fcd-f65b-39bff08fb54e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "Weights = np.append(new1,new2)\n",
        "len(Weights)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "473200"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 104
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qFNPNh-yi2vu",
        "colab_type": "code",
        "outputId": "2a4e604b-b8cb-4e54-bae2-ed9e43f65e92",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 594
        }
      },
      "source": [
        "import numpy as np\n",
        "import matplotlib.mlab as mlab\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "num_bins = 100\n",
        "n, bins, patches = plt.hist(Weights, num_bins, facecolor='blue', alpha=0.5)\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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BABQgwgAAChBhAAAFiDAAgAJEGABAASIMAKAAEQYAUIAIAwAoQIQBABQgwgAAChBhAAAF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\n",
            "text/plain": [
              "<Figure size 720x720 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "20_g3-u2lNmc",
        "colab_type": "code",
        "outputId": "f9eeac74-bca6-4c9d-e465-bd97da4b2364",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 859
        }
      },
      "source": [
        "fig = plt.figure(dpi=100)\n",
        "ax = fig.add_subplot(111)\n",
        "symlim = 0.7\n",
        "\n",
        "lim_idxs = np.where(np.logical_and(Weights>=symlim, Weights<=symlim))\n",
        "\n",
        "sns.distplot(Weights, 500, norm_hist=False, label=name, ax=ax)\n",
        "ax.set_xlim((-symlim, symlim))\n",
        "# ax.hist(weight_vector, bins=70, density=True);\n",
        "\n",
        "ax.set_ylabel('Density')\n",
        "ax.legend()\n",
        "plt.title('Total parameters: %d, samples: %d' % (len(Weights)/Nsamples, Nsamples))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Total parameters: 47320, samples: 10')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 106
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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8cLC++mqdunQ5R2lpaerTp68WLZofCkler1fLli3WkCHD5XQ6ZZqmpk9/Rn37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\nAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 1000x1000 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "clef-LQTlv2N",
        "colab_type": "code",
        "outputId": "f50f966b-ca66-4d53-9fc3-463f06f501b3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 696
        }
      },
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "#foo = np.random.normal(loc=1, size=100) # a normal distribution\n",
        "#bar = np.random.normal(loc=-1, size=10000) # a normal distribution\n",
        "\n",
        "_, bins, _ = plt.hist(weight_vector, bins=50, range=[-0.4, 0.4], normed=True)\n",
        "_ = plt.hist(Weights, bins=bins, alpha=0.5, normed=True)\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:7: MatplotlibDeprecationWarning: \n",
            "The 'normed' kwarg was deprecated in Matplotlib 2.1 and will be removed in 3.1. Use 'density' instead.\n",
            "  import sys\n",
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:8: MatplotlibDeprecationWarning: \n",
            "The 'normed' kwarg was deprecated in Matplotlib 2.1 and will be removed in 3.1. Use 'density' instead.\n",
            "  \n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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c6JoyKTo78pfzwPq+OuwGYPwRTDAOdHa0x8L/uSVd93/PeU0ddgMw/jgkBwCQEEwAAAnB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\n",
            "text/plain": [
              "<Figure size 720x720 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_eO5wuMzmdOp",
        "colab_type": "code",
        "outputId": "1d49a22b-6294-4444-8156-2fc7c481042c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 793
        }
      },
      "source": [
        "import seaborn as sns\n",
        "sns.distplot( weight_vector , color=\"skyblue\", label=\"Sepal Length\")\n",
        "sns.distplot( Weights , color=\"red\", label=\"Sepal Width\")\n",
        "sns.plt.legend()\n",
        " "
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "error",
          "ename": "AttributeError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-111-49ec72fa49d5>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0msns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdistplot\u001b[0m\u001b[0;34m(\u001b[0m \u001b[0mweight_vector\u001b[0m \u001b[0;34m,\u001b[0m \u001b[0mcolor\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"skyblue\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"Sepal Length\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0msns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdistplot\u001b[0m\u001b[0;34m(\u001b[0m \u001b[0mWeights\u001b[0m \u001b[0;34m,\u001b[0m \u001b[0mcolor\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"red\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"Sepal Width\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m \u001b[0msns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlegend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      5\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mAttributeError\u001b[0m: module 'seaborn' has no attribute 'plt'"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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sXf9fwIEDBzQxMaFGo6EPfOADmpub04c//OE07g8A0I4f/VFpbEz62Mck3z/5fbXa1r3t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\nAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 720x720 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    }
  ]
}