GMM.ipynb

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  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/ArghyaPal/Adversarial_Data_Programming/blob/master/GMM.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "M4766BhA31SP"
      },
      "source": [
        "import matplotlib\n",
        "matplotlib.use('Agg')\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "\n",
        "import numpy as np\n",
        "import torch\n",
        "from math import sqrt\n",
        "\n",
        "# set random seed\n",
        "np.random.seed(123)"
      ],
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "L8bBzneG38CJ"
      },
      "source": [
        "import torch\n",
        "import numpy as np\n",
        "\n",
        "from math import pi\n",
        "from scipy.special import logsumexp\n",
        "\n",
        "\n",
        "class GaussianMixture(torch.nn.Module):\n",
        "    \"\"\"\n",
        "    Fits a mixture of k=1,..,K Gaussians to the input data (K is supplied via n_components). Input tensors are expected to be flat with dimensions (n: number of samples, d: number of features).\n",
        "    The model then extends them to (n, 1, d).\n",
        "    The model parametrization (mu, sigma) is stored as (1, k, d), and probabilities are shaped (n, k, 1) if they relate to an individual sample, or (1, k, 1) if they assign membership probabilities to one of the mixture components.\n",
        "    \"\"\"\n",
        "    def __init__(self, n_components, n_features, mu_init=None, var_init=None, eps=1.e-6):\n",
        "        \"\"\"\n",
        "        Initializes the model and brings all tensors into their required shape.\n",
        "        The class expects data to be fed as a flat tensor in (n, d).\n",
        "        The class owns:\n",
        "            x:              torch.Tensor (n, 1, d)\n",
        "            mu:             torch.Tensor (1, k, d)\n",
        "            var:            torch.Tensor (1, k, d)\n",
        "            pi:             torch.Tensor (1, k, 1)\n",
        "            eps:            float\n",
        "            n_components:   int\n",
        "            n_features:     int\n",
        "            log_likelihood: float\n",
        "        args:\n",
        "            n_components:   int\n",
        "            n_features:     int\n",
        "        options:\n",
        "            mu_init:        torch.Tensor (1, k, d)\n",
        "            var_init:       torch.Tensor (1, k, d)\n",
        "            eps:            float\n",
        "        \"\"\"\n",
        "        super(GaussianMixture, self).__init__()\n",
        "\n",
        "        self.n_components = n_components\n",
        "        self.n_features = n_features\n",
        "\n",
        "        self.mu_init = mu_init\n",
        "        self.var_init = var_init\n",
        "        self.eps = eps\n",
        "\n",
        "        self.log_likelihood = -np.inf\n",
        "\n",
        "        self._init_params()\n",
        "\n",
        "\n",
        "    def _init_params(self):\n",
        "        if self.mu_init is not None:\n",
        "            assert self.mu_init.size() == (1, self.n_components, self.n_features), \"Input mu_init does not have required tensor dimensions (1, %i, %i)\" % (self.n_components, self.n_features)\n",
        "            # (1, k, d)\n",
        "            self.mu = torch.nn.Parameter(self.mu_init, requires_grad=False)\n",
        "        else:\n",
        "            self.mu = torch.nn.Parameter(torch.randn(1, self.n_components, self.n_features), requires_grad=False)\n",
        "\n",
        "        if self.var_init is not None:\n",
        "            assert self.var_init.size() == (1, self.n_components, self.n_features), \"Input var_init does not have required tensor dimensions (1, %i, %i)\" % (self.n_components, self.n_features)\n",
        "            # (1, k, d)\n",
        "            self.var = torch.nn.Parameter(self.var_init, requires_grad=False)\n",
        "        else:\n",
        "            self.var = torch.nn.Parameter(torch.ones(1, self.n_components, self.n_features), requires_grad=False)\n",
        "\n",
        "        # (1, k, 1)\n",
        "        self.pi = torch.nn.Parameter(torch.Tensor(1, self.n_components, 1), requires_grad=False).fill_(1./self.n_components)\n",
        "\n",
        "        self.params_fitted = False\n",
        "\n",
        "\n",
        "    def check_size(self, x):\n",
        "        if len(x.size()) == 2:\n",
        "            # (n, d) --> (n, 1, d)\n",
        "            x = x.unsqueeze(1)\n",
        "\n",
        "        return x\n",
        "\n",
        "\n",
        "    def bic(self, x):\n",
        "        \"\"\"\n",
        "        Bayesian information criterion for a batch of samples.\n",
        "        args:\n",
        "            x:      torch.Tensor (n, d) or (n, 1, d)\n",
        "        returns:\n",
        "            bic:    float\n",
        "        \"\"\"\n",
        "        x = self.check_size(x)\n",
        "        n = x.shape[0]\n",
        "\n",
        "        # Free parameters for covariance, means and mixture components\n",
        "        free_params = self.n_features * self.n_components + self.n_features + self.n_components - 1\n",
        "\n",
        "        bic = -2. * self.__score(x, sum_data=False).mean() * n + free_params * np.log(n)\n",
        "\n",
        "        return bic\n",
        "\n",
        "\n",
        "    def fit(self, x, delta=1e-3, n_iter=100, warm_start=False):\n",
        "        \"\"\"\n",
        "        Fits model to the data.\n",
        "        args:\n",
        "            x:          torch.Tensor (n, d) or (n, k, d)\n",
        "        options:\n",
        "            delta:      float\n",
        "            n_iter:     int\n",
        "            warm_start: bool\n",
        "        \"\"\"\n",
        "        if not warm_start and self.params_fitted:\n",
        "            self._init_params()\n",
        "\n",
        "        x = self.check_size(x)\n",
        "\n",
        "        i = 0\n",
        "        j = np.inf\n",
        "\n",
        "        while (i <= n_iter) and (j >= delta):\n",
        "\n",
        "            log_likelihood_old = self.log_likelihood\n",
        "            mu_old = self.mu\n",
        "            var_old = self.var\n",
        "\n",
        "            self.__em(x)\n",
        "            self.log_likelihood = self.__score(x)\n",
        "\n",
        "            if (self.log_likelihood.abs() == float(\"Inf\")) or (self.log_likelihood == float(\"nan\")):\n",
        "                # When the log-likelihood assumes inane values, reinitialize model\n",
        "                self.__init__(self.n_components,\n",
        "                    self.n_features,\n",
        "                    mu_init=self.mu_init,\n",
        "                    var_init=self.var_init,\n",
        "                    eps=self.eps)\n",
        "\n",
        "            i += 1\n",
        "            j = self.log_likelihood - log_likelihood_old\n",
        "\n",
        "            if j <= delta:\n",
        "                # When score decreases, revert to old parameters\n",
        "                self.__update_mu(mu_old)\n",
        "                self.__update_var(var_old)\n",
        "\n",
        "        self.params_fitted = True\n",
        "\n",
        "\n",
        "    def predict(self, x, probs=False):\n",
        "        \"\"\"\n",
        "        Assigns input data to one of the mixture components by evaluating the likelihood under each.\n",
        "        If probs=True returns normalized probabilities of class membership.\n",
        "        args:\n",
        "            x:          torch.Tensor (n, d) or (n, 1, d)\n",
        "            probs:      bool\n",
        "        returns:\n",
        "            p_k:        torch.Tensor (n, k)\n",
        "            (or)\n",
        "            y:          torch.LongTensor (n)\n",
        "        \"\"\"\n",
        "        x = self.check_size(x)\n",
        "\n",
        "        weighted_log_prob = self._estimate_log_prob(x) + torch.log(self.pi)\n",
        "\n",
        "        if probs:\n",
        "            p_k = torch.exp(weighted_log_prob)\n",
        "            return torch.squeeze(p_k / (p_k.sum(1, keepdim=True)))\n",
        "        else:\n",
        "            return torch.squeeze(torch.max(weighted_log_prob, 1)[1].type(torch.LongTensor))\n",
        "\n",
        "\n",
        "    def predict_proba(self, x):\n",
        "        \"\"\"\n",
        "        Returns normalized probabilities of class membership.\n",
        "        args:\n",
        "            x:          torch.Tensor (n, d) or (n, 1, d)\n",
        "        returns:\n",
        "            y:          torch.LongTensor (n)\n",
        "        \"\"\"\n",
        "        return self.predict(x, probs=True)\n",
        "\n",
        "\n",
        "    def score_samples(self, x):\n",
        "        \"\"\"\n",
        "        Computes log-likelihood of samples under the current model.\n",
        "        args:\n",
        "            x:          torch.Tensor (n, d) or (n, 1, d)\n",
        "        returns:\n",
        "            score:      torch.LongTensor (n)\n",
        "        \"\"\"\n",
        "        x = self.check_size(x)\n",
        "\n",
        "        score = self.__score(x, sum_data=False)\n",
        "        return score\n",
        "\n",
        "\n",
        "    def _estimate_log_prob(self, x):\n",
        "        \"\"\"\n",
        "        Returns a tensor with dimensions (n, k, 1), which indicates the log-likelihood that samples belong to the k-th Gaussian.\n",
        "        args:\n",
        "            x:            torch.Tensor (n, d) or (n, 1, d)\n",
        "        returns:\n",
        "            log_prob:     torch.Tensor (n, k, 1)\n",
        "        \"\"\"\n",
        "        x = self.check_size(x)\n",
        "\n",
        "        mu = self.mu\n",
        "        prec = torch.rsqrt(self.var)\n",
        "\n",
        "        log_p = torch.sum((mu * mu + x * x - 2 * x * mu) * (prec ** 2), dim=2, keepdim=True)\n",
        "        log_det = torch.sum(torch.log(prec), dim=2, keepdim=True)\n",
        "\n",
        "        return -.5 * (self.n_features * np.log(2. * pi) + log_p) + log_det\n",
        "\n",
        "\n",
        "    def _e_step(self, x):\n",
        "        \"\"\"\n",
        "        Computes log-responses that indicate the (logarithmic) posterior belief (sometimes called responsibilities) that a data point was generated by one of the k mixture components.\n",
        "        Also returns the mean of the mean of the logarithms of the probabilities (as is done in sklearn).\n",
        "        This is the so-called expectation step of the EM-algorithm.\n",
        "        args:\n",
        "            x:              torch.Tensor (n,d) or (n, 1, d)\n",
        "        returns:\n",
        "            log_prob_norm:  torch.Tensor (1)\n",
        "            log_resp:       torch.Tensor (n, k, 1)\n",
        "        \"\"\"\n",
        "        x = self.check_size(x)\n",
        "\n",
        "        weighted_log_prob = self._estimate_log_prob(x) + torch.log(self.pi)\n",
        "\n",
        "        log_prob_norm = torch.logsumexp(weighted_log_prob, dim=1, keepdim=True)\n",
        "        log_resp = weighted_log_prob - log_prob_norm\n",
        "\n",
        "        return torch.mean(log_prob_norm), log_resp\n",
        "\n",
        "\n",
        "    def _m_step(self, x, log_resp):\n",
        "        \"\"\"\n",
        "        From the log-probabilities, computes new parameters pi, mu, var (that maximize the log-likelihood). This is the maximization step of the EM-algorithm.\n",
        "        args:\n",
        "            x:          torch.Tensor (n, d) or (n, 1, d)\n",
        "            log_resp:   torch.Tensor (n, k, 1)\n",
        "        returns:\n",
        "            pi:         torch.Tensor (1, k, 1)\n",
        "            mu:         torch.Tensor (1, k, d)\n",
        "            var:        torch.Tensor (1, k, d)\n",
        "        \"\"\"\n",
        "        x = self.check_size(x)\n",
        "\n",
        "        resp = torch.exp(log_resp)\n",
        "\n",
        "        pi = torch.sum(resp, dim=0, keepdim=True) + self.eps\n",
        "        mu = torch.sum(resp * x, dim=0, keepdim=True) / pi\n",
        "\n",
        "        x2 = (resp * x * x).sum(0, keepdim=True) / pi\n",
        "        mu2 = mu * mu\n",
        "        xmu = (resp * mu * x).sum(0, keepdim=True) / pi\n",
        "        var = x2 - 2 * xmu + mu2 + self.eps\n",
        "\n",
        "        pi = pi / x.shape[0]\n",
        "\n",
        "        return pi, mu, var\n",
        "\n",
        "\n",
        "    def __em(self, x):\n",
        "        \"\"\"\n",
        "        Performs one iteration of the expectation-maximization algorithm by calling the respective subroutines.\n",
        "        args:\n",
        "            x:          torch.Tensor (n, 1, d)\n",
        "        \"\"\"\n",
        "        _, log_resp = self._e_step(x)\n",
        "        pi, mu, var = self._m_step(x, log_resp)\n",
        "\n",
        "        self.__update_pi(pi)\n",
        "        self.__update_mu(mu)\n",
        "        self.__update_var(var)\n",
        "\n",
        "\n",
        "    def __score(self, x, sum_data=True):\n",
        "        \"\"\"\n",
        "        Computes the log-likelihood of the data under the model.\n",
        "        args:\n",
        "            x:                  torch.Tensor (n, 1, d)\n",
        "            sum_data:           bool\n",
        "        returns:\n",
        "            score:              torch.Tensor (1)\n",
        "            (or)\n",
        "            per_sample_score:   torch.Tensor (n)\n",
        "\n",
        "        \"\"\"\n",
        "        weighted_log_prob = self._estimate_log_prob(x) + torch.log(self.pi)\n",
        "        per_sample_score = torch.logsumexp(weighted_log_prob, dim=1)\n",
        "\n",
        "        if sum_data:\n",
        "            return per_sample_score.sum()\n",
        "        else:\n",
        "            return torch.squeeze(per_sample_score)\n",
        "\n",
        "\n",
        "    def __update_mu(self, mu):\n",
        "        \"\"\"\n",
        "        Updates mean to the provided value.\n",
        "        args:\n",
        "            mu:         torch.FloatTensor\n",
        "        \"\"\"\n",
        "\n",
        "        assert mu.size() in [(self.n_components, self.n_features), (1, self.n_components, self.n_features)], \"Input mu does not have required tensor dimensions (%i, %i) or (1, %i, %i)\" % (self.n_components, self.n_features, self.n_components, self.n_features)\n",
        "\n",
        "        if mu.size() == (self.n_components, self.n_features):\n",
        "            self.mu = mu.unsqueeze(0)\n",
        "        elif mu.size() == (1, self.n_components, self.n_features):\n",
        "            self.mu.data = mu\n",
        "\n",
        "\n",
        "    def __update_var(self, var):\n",
        "        \"\"\"\n",
        "        Updates variance to the provided value.\n",
        "        args:\n",
        "            var:        torch.FloatTensor\n",
        "        \"\"\"\n",
        "\n",
        "        assert var.size() in [(self.n_components, self.n_features), (1, self.n_components, self.n_features)], \"Input var does not have required tensor dimensions (%i, %i) or (1, %i, %i)\" % (self.n_components, self.n_features, self.n_components, self.n_features)\n",
        "\n",
        "        if var.size() == (self.n_components, self.n_features):\n",
        "            self.var = var.unsqueeze(0)\n",
        "        elif var.size() == (1, self.n_components, self.n_features):\n",
        "            self.var.data = var\n",
        "\n",
        "\n",
        "    def __update_pi(self, pi):\n",
        "        \"\"\"\n",
        "        Updates pi to the provided value.\n",
        "        args:\n",
        "            pi:         torch.FloatTensor\n",
        "        \"\"\"\n",
        "\n",
        "        assert pi.size() in [(1, self.n_components, 1)], \"Input pi does not have required tensor dimensions (%i, %i, %i)\" % (1, self.n_components, 1)\n",
        "\n",
        "        self.pi.data = pi"
      ],
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rivUM8Dl4sOm"
      },
      "source": [
        "# set device\n",
        "if torch.cuda.is_available():\n",
        "    device = torch.device('cuda:0')\n",
        "else:\n",
        "    device = torch.device('cpu')"
      ],
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 845,
          "referenced_widgets": [
            "40124f2148474051936623e7bb9359ed",
            "9b3913ee379341369de05c1dd4acc6a3",
            "93809dce2a3a4f6b912be9e7477db61b",
            "d2abad2656394219beb4048328593a36",
            "9aa1eb4c24d0470eb6bf7ea1f432f579",
            "d93397b3edb7451abda847dc951c143b",
            "3da640083a374af88520a394f3ad7ed3",
            "dae700ba798c42f9ad84c010e91f6790",
            "6d01ef739c554c01a4c65a7da46f1b60",
            "6edef9b1a3eb4609a3b805c0915ad45b",
            "f41e244f69e841ecb547148efb77ad64",
            "36a2d70911eb4a2e97f4536a1c4b7bba",
            "8d294112605b4be4b49c7b6f7b1a8c05",
            "bccac558af7a4bcc88a02e5402916716",
            "9027f50c5166420c9172520b85388f52",
            "b7952d6474394c8694e704500737225c",
            "740a13214910474d84c159f20b03a03a",
            "e7577be73d4c4fbda8f7c9374ca52172",
            "ddc95aa02dc74c5e8e92dc0af0d5bef6",
            "21f4ee6238bf46eab3df760050028309",
            "f7f93335a7a942e2ab7f1bbdcddbae36",
            "2095ffe38c68406089c73b786fb7a7a8",
            "1db82c678c07452eb8e2ef86ed8dfcb9",
            "cfc9f66ba8334a7fb3f1c895b25ad252",
            "2a5a81f43d284bd0838497b814db4330",
            "7e61735eea2a4054982cc9dc94cc7deb",
            "3cb561c4944d453b95d93d8dcd538f69",
            "14d281a660cf4c3fb22ddce06d8ed5e6",
            "899ffb240e574e4ebb829ab0328fc5ad",
            "59c883c581134f318c79f981e2288d7a",
            "b664944e8d6c4b8492068476d03b1cf8",
            "1aec2a2b09a84d978e49f81800dc76ad"
          ]
        },
        "id": "Asd4McQ-4wHF",
        "outputId": "dbc023e6-e8a4-4d54-906a-eaf46c358257"
      },
      "source": [
        "import torchvision\n",
        "import torch.nn as nn\n",
        "from torchvision import transforms\n",
        "\n",
        "\n",
        "# Image processing\n",
        "transform = transforms.Compose([\n",
        "                transforms.ToTensor(),\n",
        "                transforms.Normalize(mean=(0.5),   # 3 for RGB channels\n",
        "                                     std=(0.5))])\n",
        "\n",
        "# MNIST dataset\n",
        "mnist = torchvision.datasets.MNIST(root='./data/',\n",
        "                                   train=True,\n",
        "                                   transform=transform,\n",
        "                                   download=True)\n",
        "\n",
        "# Data loader\n",
        "data_loader = torch.utils.data.DataLoader(dataset=mnist,\n",
        "                                          batch_size=60000, \n",
        "                                          shuffle=True)"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Downloading http://yann.lecun.com/exdb/mnist/train-images-idx3-ubyte.gz\n",
            "Failed to download (trying next):\n",
            "HTTP Error 503: Service Unavailable\n",
            "\n",
            "Downloading https://ossci-datasets.s3.amazonaws.com/mnist/train-images-idx3-ubyte.gz\n",
            "Downloading https://ossci-datasets.s3.amazonaws.com/mnist/train-images-idx3-ubyte.gz to ./data/MNIST/raw/train-images-idx3-ubyte.gz\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "40124f2148474051936623e7bb9359ed",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, max=9912422.0), HTML(value='')))"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Extracting ./data/MNIST/raw/train-images-idx3-ubyte.gz to ./data/MNIST/raw\n",
            "\n",
            "Downloading http://yann.lecun.com/exdb/mnist/train-labels-idx1-ubyte.gz\n",
            "Failed to download (trying next):\n",
            "HTTP Error 503: Service Unavailable\n",
            "\n",
            "Downloading https://ossci-datasets.s3.amazonaws.com/mnist/train-labels-idx1-ubyte.gz\n",
            "Downloading https://ossci-datasets.s3.amazonaws.com/mnist/train-labels-idx1-ubyte.gz to ./data/MNIST/raw/train-labels-idx1-ubyte.gz\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "6d01ef739c554c01a4c65a7da46f1b60",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, max=28881.0), HTML(value='')))"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Extracting ./data/MNIST/raw/train-labels-idx1-ubyte.gz to ./data/MNIST/raw\n",
            "\n",
            "Downloading http://yann.lecun.com/exdb/mnist/t10k-images-idx3-ubyte.gz\n",
            "Failed to download (trying next):\n",
            "HTTP Error 503: Service Unavailable\n",
            "\n",
            "Downloading https://ossci-datasets.s3.amazonaws.com/mnist/t10k-images-idx3-ubyte.gz\n",
            "Downloading https://ossci-datasets.s3.amazonaws.com/mnist/t10k-images-idx3-ubyte.gz to ./data/MNIST/raw/t10k-images-idx3-ubyte.gz\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "740a13214910474d84c159f20b03a03a",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, max=1648877.0), HTML(value='')))"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Extracting ./data/MNIST/raw/t10k-images-idx3-ubyte.gz to ./data/MNIST/raw\n",
            "\n",
            "Downloading http://yann.lecun.com/exdb/mnist/t10k-labels-idx1-ubyte.gz\n",
            "Failed to download (trying next):\n",
            "HTTP Error 503: Service Unavailable\n",
            "\n",
            "Downloading https://ossci-datasets.s3.amazonaws.com/mnist/t10k-labels-idx1-ubyte.gz\n",
            "Downloading https://ossci-datasets.s3.amazonaws.com/mnist/t10k-labels-idx1-ubyte.gz to ./data/MNIST/raw/t10k-labels-idx1-ubyte.gz\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "2a5a81f43d284bd0838497b814db4330",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, max=4542.0), HTML(value='')))"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Extracting ./data/MNIST/raw/t10k-labels-idx1-ubyte.gz to ./data/MNIST/raw\n",
            "\n",
            "Processing...\n",
            "Done!\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/torchvision/datasets/mnist.py:502: UserWarning: The given NumPy array is not writeable, and PyTorch does not support non-writeable tensors. This means you can write to the underlying (supposedly non-writeable) NumPy array using the tensor. You may want to copy the array to protect its data or make it writeable before converting it to a tensor. This type of warning will be suppressed for the rest of this program. (Triggered internally at  /pytorch/torch/csrc/utils/tensor_numpy.cpp:143.)\n",
            "  return torch.from_numpy(parsed.astype(m[2], copy=False)).view(*s)\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3ZQ-KYKE42Hc"
      },
      "source": [
        "from torch.autograd import Variable"
      ],
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "aDPdpiPq3qnE",
        "outputId": "9aafc2b5-19ed-4a45-934a-4cfa28af4b98"
      },
      "source": [
        "def main():\n",
        "    for i, (images, original_labels) in enumerate(data_loader):\n",
        "        images = images.view(60000, -1).cuda()\n",
        "        images = Variable(images)\n",
        "\n",
        "    data = images[0:50000].to(device) #input.to(dev)\n",
        "    # Next, the Gaussian mixture is instantiated and ..\n",
        "    n_components = 10\n",
        "    model = GaussianMixture(n_components, 784)\n",
        "    model.cuda()\n",
        "    model.fit(data)\n",
        "    torch.save(model, '/content/data/gmm.pt')\n",
        "    model_1 = torch.load('/content/data/gmm.pt')\n",
        "    # .. used to predict the data points as they where shifted\n",
        "    y = images[50001:50021]\n",
        "\n",
        "\n",
        "    result = model.predict(y)\n",
        "\n",
        "    print(result)\n",
        "\n",
        "\n",
        "if __name__ == \"__main__\":\n",
        "    main()"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "tensor([8, 9, 6, 9, 0, 2, 1, 2, 1, 0, 1, 2, 9, 1, 2, 6, 4, 6, 0, 0])\n"
          ],
          "name": "stdout"
        }
      ]
    }
  ]
}