task1/hw1_bmm_boeva.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "KhW_20LfuEEK",
    "outputId": "3561e67e-76d7-4c7e-ec8f-8465d3dfcb87",
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "No GPU/TPU found, falling back to CPU. (Set TF_CPP_MIN_LOG_LEVEL=0 and rerun for more info.)\n"
     ]
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "%config InlineBackend.figure_format = 'retina'\n",
    "\n",
    "import numpy as onp\n",
    "import jax.numpy as np\n",
    "import numpy as num\n",
    "from jax import grad, jit, vmap, value_and_grad\n",
    "from jax import random\n",
    "import jax\n",
    "\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Generate key which is used to generate random numbers\n",
    "key = random.PRNGKey(1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Работа делится на три части. Нужно различным способом получить матрицу W в нейронной модели. В нашем случае, это простой MLP:\n",
    "\n",
    "1. Просемплировать матрицу W из нормального распредления для каждого мини-батча размера $m \\times n$.\n",
    "2. Просемплировать матрицу W из нормального распредления для каждого элемента мини-батча размера $base\\_size \\times m \\times n$.\n",
    "3. Рассмотрим один из слоев в нашей нейронной сети: входная матрица признаков A размером $M \\times 512$, которая умножается на весовую матрицу W размером $512 \\times 512$, прежде чем будет применена нелинейность, мы получаем результат $\\textbf{B} = \\textbf{A}\\textbf{W}$. Затем мы задаем апостериорную аппроксимацию весов как полностью факторизованную гауссову, т.е. $q_\\phi(w_{i, j}) = \\mathcal{N}(\\mu_{i, j}, \\sigma_{i, j}^2) \\; \\forall w_{i, j} \\in \\textbf{W}$, что означает, что веса выбираются как $w_{i, j} = \\mu_{i, j} + \\sigma_{i, j} \\epsilon_{i, j}, \\text{ with } \\epsilon_{i, j} \\sim \\mathcal{N}(0, 1)$\n",
    " \n",
    "\n",
    "Тогда будет работать с $\\textbf{B}$(семплировать случайные активации)\n",
    "\t\n",
    "\\begin{equation}\n",
    "  \\begin{split}\n",
    "    q_\\psi(w_{i, j}) &= \\mathcal{N}(\\mu_{i, j}, \\sigma_{i, j}^2) \\; \\forall w_{i, j} \\in \\textbf{B} \\Longrightarrow q_\\phi(b_{m, j} | A) = \\mathcal{N}(\\gamma_{im, j}, \\delta_{m, j}), \\text{ with} \\\\\n",
    "    \\gamma_{m, j} &= \\sum\\limits_{i=1}^{512} a_{m, i} \\mu_{i, j}, \\text{ and } \\delta_{m, j} = \\sum\\limits_{i=1}^{512} a_{m, i}^2 \\sigma_{i, j}^2\n",
    "  \\end{split}\n",
    "\\end{equation}\n",
    "\n",
    "\\begin{equation*}\n",
    "  b_{m,j} = \\gamma_{m,j} + \\sqrt{\\delta_{m,j}} \\zeta_{m, j}, \\text{ with } \\zeta_{m, j} \\sim \\mathcal{N}(0, 1), \\; \\zeta \\in \\mathbb{R}^{M \\times 512}.\n",
    "\\end{equation*}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "sns.set(context='poster', style='white',\n",
    "        font='sans-serif', font_scale=1, color_codes=True, rc=None)\n",
    "\n",
    "\n",
    "def plot_mnist_performance(train_loss, train_acc, test_acc,\n",
    "                           sup_title=\"Loss Curve\"):\n",
    "    \"\"\" Visualize the learning performance of a classifier on MNIST \"\"\"\n",
    "    fig, axs = plt.subplots(1, 1, figsize=(10, 4))\n",
    "    axs.plot(train_loss)\n",
    "    axs.set_xlabel(\"# Batch Updates\")\n",
    "    axs.set_ylabel(\"Batch Loss\")\n",
    "    axs.set_title(\"Training Loss\")\n",
    "    #for i in range(1):\n",
    "    axs.spines['top'].set_visible(False)\n",
    "    axs.spines['right'].set_visible(False)\n",
    "\n",
    "    fig.suptitle(sup_title, fontsize=25)\n",
    "    fig.tight_layout(rect=[0, 0.03, 1, 0.925])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "id": "Zu6UHFODuEEO",
    "tags": []
   },
   "outputs": [],
   "source": [
    "def ReLU(x):\n",
    "    return np.maximum(0, x)\n",
    "\n",
    "jit_ReLU = jit(ReLU)\n",
    "\n",
    "def FiniteDiffGrad(x):\n",
    "    return np.array((ReLU(x + 1e-3) - ReLU(x - 1e-3)) / (2 * 1e-3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 245
    },
    "id": "SlBj7FwQuEER",
    "outputId": "7d4ad353-defa-4bcd-b25a-912dbd37ebfd",
    "tags": []
   },
   "outputs": [],
   "source": [
    "def scan(init, xs, b,length=None):\n",
    "    if xs is None:\n",
    "        xs = [None] * length\n",
    "    carry = init\n",
    "    ys = []\n",
    "    for i in range(len(xs.shape)): \n",
    "        y = np.dot(xs[i].T, carry[i, :, :]) + b[i] \n",
    "        ys.append(y) \n",
    "    return np.stack(ys)[0].sum(axis=-1)\n",
    "\n",
    "def relu_layer(params, x):\n",
    "    \"\"\" Simple ReLu layer for single sample \"\"\"\n",
    "    return ReLU(np.dot(params[0], x) + params[1])\n",
    "\n",
    "def relu_layer_sep(params, x): \n",
    "    return ReLU(scan(params[0], x, params[1]))\n",
    "\n",
    "def batch_version_relu_layer(params, x):\n",
    "    \"\"\" Error prone batch version \"\"\"\n",
    "    return ReLU(np.dot(X, params[0].T) + params[1])\n",
    "\n",
    "def vmap_relu_layer(params, x):\n",
    "    \"\"\" vmap version of the ReLU layer \"\"\"\n",
    "    return jit(vmap(relu_layer, in_axes=(None, 0), out_axes=0))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "mX7LkOK8uEER"
   },
   "source": [
    "## Работа с MNIST"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "id": "S96MVECXuEER",
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Import some additional JAX and dataloader helpers\n",
    "from jax.scipy.special import logsumexp\n",
    "from jax.example_libraries import optimizers\n",
    "\n",
    "import torch\n",
    "from torchvision import datasets, transforms\n",
    "\n",
    "import time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "#!pip install seaborn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 283
    },
    "id": "QJ41XkNGuEER",
    "outputId": "f172521e-b2d4-4f66-fe6a-f59145516bfb",
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Set the PyTorch Data Loader for the training & test set\n",
    "batch_size = 100\n",
    "\n",
    "train_loader = torch.utils.data.DataLoader(\n",
    "    datasets.MNIST('../data', train=True, download=True,\n",
    "                   transform=transforms.Compose([\n",
    "                       transforms.ToTensor(),\n",
    "                       transforms.Normalize((0.1307,), (0.3081,))\n",
    "                   ])),\n",
    "    batch_size=batch_size, shuffle=True)\n",
    "\n",
    "test_loader = torch.utils.data.DataLoader(\n",
    "    datasets.MNIST('../data', train=False, transform=transforms.Compose([\n",
    "                       transforms.ToTensor(),\n",
    "                       transforms.Normalize((0.1307,), (0.3081,))\n",
    "                   ])),\n",
    "    batch_size=batch_size, shuffle=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Общие функции"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "rng = num.random.RandomState(42)\n",
    "def salimans_sample(shape):\n",
    "    w = rng.normal(0,1,(shape[0],shape[1]))\n",
    "    w -= np.mean(w, axis=0, keepdims=True)\n",
    "    w /= np.sqrt(np.sum(np.square(w), axis=0, keepdims=True))\n",
    "    return w.astype('float64')\n",
    "\n",
    "def _logit(x):\n",
    "    \"\"\"\n",
    "    Logit function in Numpy. Useful for parameterizing alpha.\n",
    "    \"\"\"\n",
    "    return np.log(x/(1. - x))\n",
    "\n",
    "def one_hot(x, k, dtype=np.float32):\n",
    "    \"\"\"Create a one-hot encoding of x of size k \"\"\"\n",
    "    return np.array(x[:, None] == np.arange(k), dtype)\n",
    "\n",
    "def loss(params, in_arrays, targets):\n",
    "    \"\"\" Compute the multi-class cross-entropy loss \"\"\"\n",
    "    preds, mean, logvar = batch_forward(params, in_arrays)\n",
    "    return -np.sum(preds * targets)\n",
    " \n",
    "def kl_divergence(params, in_arrays, targets):\n",
    "    preds, mean, logvar = batch_forward(params, in_arrays) \n",
    "    loss_total = -0.5 * np.sum(1 + logvar - mean ** 2 - np.exp(logvar)) - np.sum(preds * targets)\n",
    "    return loss_total"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "@jit\n",
    "def update(params, x, y, opt_state):\n",
    "    \"\"\" Compute the gradient for a batch and update the parameters \"\"\" \n",
    "    value, grads = value_and_grad(kl_divergence)(params, x, y)\n",
    "    opt_state = opt_update(0, grads, opt_state) \n",
    "    return get_params(opt_state), opt_state, value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "def run_mnist_training_loop(num_epochs, opt_state, net_type=\"MLP\", sampling=\"1\"):\n",
    "    \"\"\" Implements a learning loop over epochs. \"\"\"\n",
    "    # Initialize placeholder for loggin\n",
    "    log_acc_train, log_acc_test, train_loss, train_loss_kl = [], [], [], []\n",
    "\n",
    "    # Get the initial set of parameters\n",
    "    params = get_params(opt_state)\n",
    "\n",
    "    # Loop over the training epochs\n",
    "    for epoch in range(num_epochs):\n",
    "        start_time = time.time()\n",
    "        for batch_idx, (data, target) in enumerate(train_loader):\n",
    "            x = np.array(data).reshape(data.size(0), 28*28)\n",
    " \n",
    "            y = one_hot(np.array(target), num_classes)\n",
    " \n",
    "            params, opt_state, kl_loss = update(params, x, y, opt_state) \n",
    " \n",
    "            if batch_idx % 100 == 0 and sampling==\"2\":\n",
    "                print(kl_loss)\n",
    "            \n",
    "            train_loss_kl.append(kl_loss)\n",
    "\n",
    "        epoch_time = time.time() - start_time\n",
    "        print(\"Epoch {} | T: {:0.2f} | Elbo loss: {:0.4f} |\".format(epoch+1, epoch_time, num.mean(train_loss_kl)))\n",
    "\n",
    "    return train_loss_kl, log_acc_train, log_acc_test"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "jp-MarkdownHeadingCollapsed": true,
    "tags": []
   },
   "source": [
    "## Реализация метода 1(сэмплирование весов для каждого батча)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_1969077/2348627112.py:15: UserWarning: Explicitly requested dtype float64 requested in astype is not available, and will be truncated to dtype float32. To enable more dtypes, set the jax_enable_x64 configuration option or the JAX_ENABLE_X64 shell environment variable. See https://github.com/google/jax#current-gotchas for more.\n",
      "  logitalpha = np.array(_logit(np.sqrt(p/(1.-p)))).astype('float64')\n",
      "/tmp/ipykernel_1969077/2348627112.py:11: UserWarning: Explicitly requested dtype float64 requested in astype is not available, and will be truncated to dtype float32. To enable more dtypes, set the jax_enable_x64 configuration option or the JAX_ENABLE_X64 shell environment variable. See https://github.com/google/jax#current-gotchas for more.\n",
      "  return w.astype('float64')\n"
     ]
    }
   ],
   "source": [
    "def initialize_mlp(sizes, key):\n",
    "    \"\"\" Initialize the weights of all layers of a linear layer network \"\"\"\n",
    "    keys = random.split(key, len(sizes))\n",
    "\n",
    "    def initialize_layer(m, n, key, scale=1e-2):\n",
    "        rng = num.random.RandomState(42)\n",
    "        def salimans_sample(shape):\n",
    "            w = rng.normal(0,1,(shape[0],shape[1]))\n",
    "            w -= np.mean(w, axis=0, keepdims=True)\n",
    "            w /= np.sqrt(np.sum(np.square(w), axis=0, keepdims=True))\n",
    "            return w.astype('float64')\n",
    "        w_key, b_key = random.split(key)\n",
    "        #print(w_key)\n",
    "        p = 0.5\n",
    "        logitalpha = np.array(_logit(np.sqrt(p/(1.-p)))).astype('float64')\n",
    "        alpha = jax.nn.sigmoid(logitalpha)\n",
    "        W = salimans_sample((n, m))\n",
    "        W_noised = W*(1. + random.normal(w_key, W.shape)*np.sqrt(alpha))\n",
    "\n",
    "        #print((scale *W_noised).shape)\n",
    "        return  scale *W_noised, scale * random.normal(b_key, (n,))\n",
    "\n",
    "    return [initialize_layer(m, n, k) for m, n, k in zip(sizes[:-1], sizes[1:], keys)]\n",
    "\n",
    "layer_sizes = [784, 512, 10]\n",
    "params = initialize_mlp(layer_sizes, key)\n",
    "#print(len(params))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "def forward_pass(params, in_array):\n",
    "    \"\"\" Compute the forward pass for each example individually \"\"\"\n",
    "    activations = in_array\n",
    "    #print(params[0][0], params[0][1])\n",
    "    for w, b in params[:-1]:\n",
    "        activations = relu_layer([w, b], activations)\n",
    "\n",
    "    final_w_mean, final_b_mean = params[-1]\n",
    "    final_w_logvar, final_b_logvar = params[-1]\n",
    "    final_w, final_b = params[-1]\n",
    "    \n",
    "    mean = np.dot(final_w_mean, activations) + final_b_mean\n",
    "    logvar = np.dot(final_w_logvar, activations) + final_b_logvar\n",
    "    logits = np.dot(final_w, activations) + final_b\n",
    "    return logits - logsumexp(logits), mean, logvar #logits - logsumexp(logits) #- logsumexp(logits)\n",
    "\n",
    "# Make a batched version of the `predict` function\n",
    "batch_forward = vmap(forward_pass, in_axes=(None, 0), out_axes=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "def kl_divergence(params, in_arrays, targets):\n",
    "    preds, mean, logvar = batch_forward(params, in_arrays) \n",
    "    loss_total = -0.5 * np.sum(1 + logvar - mean ** 2 - np.exp(logvar)) - np.sum(preds * targets) \n",
    "    return loss_total"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1 | T: 11.55 | Elbo loss: 208.0315 |\n",
      "Epoch 2 | T: 11.26 | Elbo loss: 207.1632 |\n",
      "Epoch 3 | T: 11.17 | Elbo loss: 206.7462 |\n",
      "Epoch 4 | T: 11.22 | Elbo loss: 206.4849 |\n",
      "Epoch 5 | T: 11.06 | Elbo loss: 206.3009 |\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x400 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 364,
       "width": 946
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "step_size = 1e-3\n",
    "opt_init, opt_update, get_params = optimizers.adam(step_size)\n",
    "opt_state = opt_init(params) \n",
    "\n",
    "num_epochs = 5\n",
    "num_classes = 10\n",
    "train_loss, train_log, test_log = run_mnist_training_loop(num_epochs,\n",
    "                                                          opt_state,\n",
    "                                                          net_type=\"MLP\")\n",
    "\n",
    "# Plot the loss curve over time\n",
    "#from helpers import plot_mnist_performance\n",
    "plot_mnist_performance(train_loss, train_log, test_log,\n",
    "                       \"Mnist For Batch sampling\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "## лосс ~ 0.21 * 10^3, время ~ 11sec"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "jp-MarkdownHeadingCollapsed": true,
    "tags": []
   },
   "source": [
    "## Реализация метода 2(сэмплирование весов для каждого элемента батча)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 245
    },
    "id": "rD5xu1GuuEES",
    "outputId": "0ef8a6a7-fbad-4096-eb48-4481b7339e62",
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_1969077/1670349637.py:17: UserWarning: Explicitly requested dtype float64 requested in astype is not available, and will be truncated to dtype float32. To enable more dtypes, set the jax_enable_x64 configuration option or the JAX_ENABLE_X64 shell environment variable. See https://github.com/google/jax#current-gotchas for more.\n",
      "  logitalpha = np.array(_logit(np.sqrt(p/(1.-p)))).astype('float64')\n",
      "/tmp/ipykernel_1969077/1670349637.py:13: UserWarning: Explicitly requested dtype float64 requested in astype is not available, and will be truncated to dtype float32. To enable more dtypes, set the jax_enable_x64 configuration option or the JAX_ENABLE_X64 shell environment variable. See https://github.com/google/jax#current-gotchas for more.\n",
      "  return w.astype('float64')\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3\n"
     ]
    }
   ],
   "source": [
    "input_shape = 100\n",
    "def initialize_mlp(sizes, key):\n",
    "    \"\"\" Initialize the weights of all layers of a linear layer network \"\"\"\n",
    "    keys = random.split(key, len(sizes))\n",
    "\n",
    "    def initialize_layer(m, n, key, scale=1e-2):\n",
    "        rng = num.random.RandomState(42)\n",
    "        input_shape\n",
    "        def salimans_sample(shape):\n",
    "            w = rng.normal(0,1,(shape[0],shape[1]))\n",
    "            w -= np.mean(w, axis=0, keepdims=True)\n",
    "            w /= np.sqrt(np.sum(np.square(w), axis=0, keepdims=True))\n",
    "            return w.astype('float64')\n",
    "        w_key, b_key = random.split(key)\n",
    "        #print(w_key)\n",
    "        p = 0.5\n",
    "        logitalpha = np.array(_logit(np.sqrt(p/(1.-p)))).astype('float64')\n",
    "        alpha = jax.nn.sigmoid(logitalpha)\n",
    "        W = salimans_sample((n, m))\n",
    "        #W_noised = W*(1. + random.normal(w_key, W.shape)*np.sqrt(alpha))\n",
    "\n",
    "        W_noised = W*(1 + random.normal(w_key, (100,\n",
    "                        W.shape[0],\n",
    "                        W.shape[1]))*np.sqrt(alpha))\n",
    "\n",
    "        #print((scale *W_noised).shape)\n",
    "        return  scale *W_noised, scale * random.normal(b_key, (n,))\n",
    "\n",
    "    return [initialize_layer(m, n, k) for m, n, k in zip(sizes[:-1], sizes[1:], keys)]\n",
    "\n",
    "layer_sizes = [784, 512, 512, 10]\n",
    "params = initialize_mlp(layer_sizes, key)\n",
    "print(len(params))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "id": "nPjaonNZwepM",
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Array([[ 869452973, 4133157646],\n",
       "       [ 261504626, 4112007671],\n",
       "       [3597360905,  253918841],\n",
       "       [  98387565,  678776088]], dtype=uint32)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "random.split(key, len(layer_sizes))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "id": "Sh4lj7VRuEES",
    "tags": []
   },
   "outputs": [],
   "source": [
    "def forward_pass(params, in_array):\n",
    "    \"\"\" Compute the forward pass for each example individually \"\"\"\n",
    "    activations = in_array\n",
    "\n",
    "    # Loop over the ReLU hidden layers\n",
    "    for w, b in params[:-1]:\n",
    "        activations = relu_layer_sep([w, b], activations)\n",
    "        \n",
    "    final_w_mean, final_b_mean = params[-1]\n",
    "    final_w_logvar, final_b_logvar = params[-1]\n",
    "    final_w, final_b = params[-1]\n",
    "    \n",
    "    mean = (np.dot(final_w_mean, activations) + final_b_mean).sum(axis=0)\n",
    "    logvar = (np.dot(final_w_logvar, activations) + final_b_logvar).sum(axis=0)\n",
    "    logits = (np.dot(final_w, activations) + final_b).sum(axis=0)\n",
    "    return logits - logsumexp(logits), mean, logvar\n",
    "\n",
    "# Make a batched version of the `predict` function\n",
    "batch_forward = vmap(forward_pass, in_axes=(None, 0), out_axes=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "id": "PpYjOD7Z1YGX",
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1348.2806\n",
      "230.45642\n",
      "230.43025\n",
      "230.45282\n",
      "230.3172\n",
      "230.12933\n",
      "Epoch 1 | T: 500.63 | Elbo loss: 240.3019 |\n",
      "230.45065\n",
      "230.49217\n",
      "230.87167\n",
      "230.01723\n",
      "230.27951\n",
      "230.46162\n",
      "Epoch 2 | T: 506.17 | Elbo loss: 235.3364 |\n",
      "230.4071\n",
      "230.3141\n",
      "230.30899\n",
      "230.20265\n",
      "230.21472\n",
      "230.38559\n",
      "Epoch 3 | T: 408.26 | Elbo loss: 233.6836 |\n",
      "230.41663\n",
      "230.27388\n",
      "230.27673\n",
      "230.19528\n",
      "230.44974\n",
      "230.40903\n",
      "Epoch 4 | T: 415.33 | Elbo loss: 232.8603 |\n",
      "230.60883\n",
      "230.50023\n",
      "230.49857\n",
      "230.56998\n",
      "230.27295\n",
      "230.49377\n",
      "Epoch 5 | T: 421.21 | Elbo loss: 232.3699 |\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1000x400 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 364,
       "width": 947
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "step_size = 1e-3\n",
    "opt_init, opt_update, get_params = optimizers.adam(step_size)\n",
    "opt_state = opt_init(params)\n",
    "\n",
    "num_epochs = 5\n",
    "num_classes = 10\n",
    "train_loss, train_log, test_log = run_mnist_training_loop(num_epochs,\n",
    "                                                          opt_state,\n",
    "                                                          net_type=\"MLP\",\n",
    "                                                          sampling =\"2\")\n",
    "\n",
    "# Plot the loss curve over time\n",
    "#from helpers import plot_mnist_performance\n",
    "plot_mnist_performance(train_loss, train_log, test_log,\n",
    "                       \"Mnist sampling for each element from batch\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "## лосс ~ 0.24 * 10^3, время ~ 500 сек почти в 50 раз медленнее 1 метода"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Local Reparametrization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "from flax import linen as nn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "#!pip install flax"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 181,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_475989/444684596.py:8: UserWarning: Explicitly requested dtype float64 requested in astype is not available, and will be truncated to dtype float32. To enable more dtypes, set the jax_enable_x64 configuration option or the JAX_ENABLE_X64 shell environment variable. See https://github.com/google/jax#current-gotchas for more.\n",
      "  logitalpha = np.array(_logit(np.sqrt(p/(1.-p)))).astype('float64')\n"
     ]
    }
   ],
   "source": [
    "from typing import Sequence, Callable\n",
    "\n",
    "class MultiLayerPerceptron(nn.Module):\n",
    "    p = 0.5\n",
    "    hidden_neurons: Sequence[int]\n",
    "    kernel_init: Callable = nn.initializers.lecun_normal()\n",
    "    activation: Callable = nn.tanh\n",
    "    logitalpha = np.array(_logit(np.sqrt(p/(1.-p)))).astype('float64')\n",
    "    alpha = jax.nn.sigmoid(logitalpha)\n",
    "    llayer = nn.Dense(features=10)\n",
    "    \n",
    "    def new_linear(self,x, neurons, scale=1e-2):\n",
    "        theta = self.kernel_init(jax.random.PRNGKey(42), x.shape, np.float64)\n",
    "        mu_z = x #nn.Dense(neurons, kernel_init=self.kernel_init)(x)\n",
    "        sigma_z = np.sqrt(np.dot(np.square(x[1]), self.alpha*np.square(theta)[0]))\n",
    "        randn = num.random.normal(size=x[0].shape) \n",
    "        return mu_z + sigma_z*randn\n",
    "    \n",
    "    @nn.compact\n",
    "    def __call__(self, x): \n",
    "        for k, neurons in enumerate(self.hidden_neurons):\n",
    "            x = nn.Dense(neurons, kernel_init=self.kernel_init)(x)\n",
    "            x = self.new_linear(x, neurons)\n",
    "            #print(x.shape)\n",
    "            if k != len(self.hidden_neurons) - 1:\n",
    "                x = self.activation(x)\n",
    "            #x = self.new_linear(x, neurons)\n",
    "        return x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 182,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "class MLP(nn.Module): \n",
    "    @nn.compact\n",
    "    def __call__(self, x):\n",
    "        x = MultiLayerPerceptron([748, 512, 256], activation=nn.tanh)(x)\n",
    "        #print(x.shape)\n",
    "        mean = nn.Dense(features=10)(x)\n",
    "        logvar = nn.Dense(features=10)(x)\n",
    "        logits = nn.Dense(features=10)(x)\n",
    "        #print(mean.shape)\n",
    "        return logits, mean, logvar"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 183,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "import optax\n",
    "from flax.training import train_state\n",
    "\n",
    "\n",
    "def create_train_state(rng):\n",
    "    \"\"\"Creates initial `TrainState`.\"\"\"\n",
    "    param_rng, z_rng = jax.random.split(rng)\n",
    "\n",
    "    mlp = MLP()\n",
    "    params = mlp.init(param_rng, np.ones([1, 28 * 28]))['params']\n",
    "    tx = optax.adam(0.001)\n",
    "    return train_state.TrainState.create(\n",
    "    apply_fn=mlp.apply, params=params, tx=tx)\n",
    "\n",
    "rng = jax.random.PRNGKey(42)\n",
    "state = create_train_state(rng)\n",
    "def get_mlp(params, x):\n",
    "    logits, mean, logvar = state.apply_fn({'params': params['params']}, x)\n",
    "    return logits, mean, logvar"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 184,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "mlp = MLP()\n",
    "#batch_forward = vmap(get_mlp, in_axes=(None, 0), out_axes=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 185,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "#!pip install jax_metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 186,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "def kl_divergence(params, in_arrays, targets, loss='1'):\n",
    "    #print('ok')\n",
    "    preds, mean, logvar = get_mlp(params, in_arrays)  \n",
    "    loss_total = -0.5 * np.sum(1 + logvar - mean ** 2 - np.exp(logvar)) - np.sum(preds * targets) #- np.sum(preds * targets)\n",
    "    #print(loss_total)\n",
    "    return loss_total"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 187,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "def update(params, x, y, opt_state):\n",
    "    \"\"\" Compute the gradient for a batch and update the parameters \"\"\"\n",
    "    #grad_fn = jax.value_and_grad(kl_divergence, has_aux=True)\n",
    "    #value, grads = grad_fn(params)\n",
    "    value, grads = value_and_grad(kl_divergence)(params, x, y) #loss\n",
    "    opt_state = opt_update(0, grads, opt_state) \n",
    "    return get_params(opt_state), opt_state, value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 188,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1 | T: 46.61 | Elbo loss: -1196.9543 |\n",
      "Epoch 2 | T: 46.53 | Elbo loss: -25282.0410 |\n",
      "Epoch 3 | T: 46.34 | Elbo loss: -58645.6719 |\n",
      "Epoch 4 | T: 46.45 | Elbo loss: -95697.0703 |\n",
      "Epoch 5 | T: 46.93 | Elbo loss: -135310.5469 |\n"
     ]
    },
    {
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bFOvFF19UtWrV9Oqrryb9nkYJUFj2ySef6Pbt2xbrChcurGnTpumJJ55IMU6ZMmU0a9YsffzxxxbPC5I0fvx4NWnSxKZ4MTExWrt2rcW6oUOH6vXXX08xxqN8fHzUtGlTNW3aVNeuXdNvv/1mU3Jx3759unz5crLyrFmz6tdff1XFihXtbkuBAgX02muv6bXXXtPRo0f1559/2h0DAAAArse17gZNtnHjRn3zzTeGvROlf5MprVu3Vs+ePe3qyYeMqUiRIknTcyUmJ/38/B56DT0z/xUZGam+fftanO61UaNG+uKLL5QjRw67Yvr4+KhVq1Zq1aqVw+vzAAAAGClWrJimT59u2pSiEyZMUNGiRR2O4+XlpREjRigqKkp///13svrZs2erZ8+eLpe0e9CgQYPUqVMnu7YpWbKkBg0apP79+yeru3HjhtatW6fGjRub1EJz+fn56ffff7f779+pUyf9888/2rRpU7K6v//+WwMGDLD575yQkKDx48dbrPPx8dHUqVNtTlImKlq0qKZOnar27duneh3Ex9WuXbu0evVqi3W5c+fWlClTbEoqJsqSJYuGDx+usLAwi2vARkdH66uvvtJXX32VYqw9e/ZYTDoHBwenKkn5qHz58tncmWLDhg0Wy7t3756qJOWjAgMDNWTIEIfjAAAAIP25zB3w7du3NXjw4GRTp1SrVk2dO3e2O96YMWM0bdo0STKcjkX6d7qhqVOnasWKFRo7dqwqVapk977g+ipUqKBt27YpV65c6d2UDOOLL77QmTNnkpU3btxYX3/9tTw9PR2K74rTmgEAgMxh5MiRpq57aEaSMpGbm5uGDx+uzZs3JxtheOPGDW3ZskV16tQxbX9mqlevnt1JykQtWrTQ5MmTFRISkqxu69atLpuoHDFiRKr//v3797eYqLx586aOHTumcuXK2RRn06ZNOnXqlMW6Xr16qVSpUqlq35NPPqm3335bX3zxRaq2f1wZTesr/ft5KVmypN0x3d3dNWbMGO3fv9/iKMTly5fr3LlzKSZAL126ZLG8WbNmdrfJUUZtadq0qZNbAgAAAFfnMnOerlixQitXrtTq1au1evVqrVq1SqtXr07VRf6nn36qn3/+WQkJCUpISJCbm5vV/xISEnT+/Hl16dJFhw8fToPfDuktR44cJCntcPToUc2ePTtZedmyZfXVV185nKQEAABIK82aNVPNmjXTuxlW+fj4qEuXLhbrNm/e7OTW2Mbd3V1Dhw51KEaLFi0slh85csShuGklODjYoaRKuXLlDJOI9tx3Whp9K0nFixd3eJRcx44d7Rr997i7evWq/vnnH4t1tWvX1vPPP5/q2NmzZ9eAAQMs1sXFxWnu3Lkpxnh0muZE9o64NYNRW4oUKeLklgAAAMDVuUyicunSpUn/ThwBWaJECdWrV8+uOKtXr9b06dOTJSKtSXxdRESEunbt+tB6JMDj6Ntvv1VcXFyy8pEjR8rLyysdWgQAAGCb9u3bp3cTbNKoUSOL5Xv37nVuQ2zUoEEDh0eWGo0UPXr0aIr3bOmhY8eODsd49tlnLZbbmpyNj4/XunXrLNa1adPG4WmCPT091aZNG4diPE5WrFhh8T5Jkt555x2H4zdt2lSlS5e2WLdkyZIUt3dzc7NYnh7T+xq1hfVQAQAA8CiXSFTGxMRo+/btD13Iurm5Gfa4NRIXF6cxY8Yk/fzgiMoHf07878HXJbp165Y+//zz1P4qgGmioqK0fPlyjRo1Sh06dFCDBg1UpUoVBQUFqVKlSqpTp45effVVffLJJ1q9erViYmJM2e/p06e1Zs2aZOX169dnamQAAODSChcurODg4PRuhk2KFy9ucXraEydOpENrUmbG1JElS5a0mFiLiIhQWFiYw/HN5O3trfr16zscp0yZMhbLL168aNP2R48etfjeuLm5qXnz5o40LUnz5s0Nk0p42MaNGy2WFypUSNWrVzdlHy1btrRYfvbs2RQ/N0azCK1fv97RZtnNldoCAAAA1+YSa1QePnxYsbGxyW6OGjRoYFecefPm6dy5c0mjKB+NV7VqVVWoUEGenp46c+aMNmzYoJiYmIden5CQoMWLF+u1115TlSpVHP7dAHudOnVKU6ZM0ZIlS3T37l2Lr7l3757u3bunq1evateuXfr111+VM2dOvf7663rzzTfl4+OT6v3/9ddfFnu0//e//011TAAAAGeoUqVKhkq45M2bVzdv3nyo7Pbt27p9+7b8/PzSqVWWVa1a1eEYXl5eKlCggC5cuJCsLiIiwqXWMK9YsaLDoxUlqVixYhbLbR1VduDAAYvlJUuWVKFChVLdrgcVKVJExYsX1+nTp02Jl5nt2bPHYnmTJk1MO/c0a9ZM48aNs1i3a9cuq9O4GiXG58+fr6ZNmzp1/dsnn3zSYvm4ceMUHBycLtPRAgAAwDW5xIhKS9MbFShQQIGBgXbFeXBNvQdHUebJk0czZ87UzJkz9eGHH+r999/X999/r5UrV6p69eoWk5p//PGH/b8I4IDo6GiNGTNGLVq00F9//WWYpDQSHh6u77//Xk2bNtWmTZtS3Q5LUwrlyJFDTz/9dKpjAgAAOEP58uXTuwl2yZkzp8Xya9euObkl1uXKlUsFCxY0JZZRh7r0mJrSmnLlypkSx+j3tTVRefz4cYvl9t4rp8TseJlRaGio4cjfihUrmrafIkWKKG/evBbrjh07ZnXb8uXLW+zkEB8fr7feektjx45N1jkirRjdP169elVt27bVrFmzFB0d7ZS2AAAAwLW5xIjKB3tuJiYN7X3IcObMGe3fv/+hhGNCQoI8PDz0448/qkKFCsm2yZ8/vyZNmqT27dsnTbGUOKpy5cqVGj58uLJnz57K3wqw3eXLl/XOO+/o0KFDDscKDQ1Vt27dNHjwYL322mt2bXv8+HGL0wkFBwezNiUAAHB5pUqVcur+zp8/r3379unYsWM6duyYLl68qMjIyKT/YmNjUxXX1ZJ2Zo50zJEjh8VyV1u3zqzf2dHf99KlSxbLy5Ytm+o2WRIYGKilS5eaGjOzsTbi1KzEdqLAwECL08ymNOrVw8ND7du31+TJk5PVxcbGavLkyZo2bZoaNGigxo0bq06dOhanoDZD+fLlVblyZYsd02/evKmPPvpI48aN0/PPP69GjRopODiY5y8AAACPKZdIVFq6+bK3R+fy5csf+jkx4dmsWTOLScpE3t7eGjhwoLp27fpQkvPu3bvasmWLGjZsaFc7AHtduHBBb7zxhtX1Rjw8PFSmTBnlzZtXOXPmVGRkpK5fv66jR49afAAWFxenjz/+WN7e3nrppZdsbsu2bdsslpvZQxgAACCtOGO61Nu3b2vevHlavHix9u3blyb7MGvtcbMYjfxMDQ8PD4vlcXFxpu3DDGZ9ltzdLU9iFB8fb9P2N27csFhuNOIutfLkyWNqvMzo6tWrFss9PT31xBNPmLqvJ5980mKi0qgND3rzzTc1f/58w5HZsbGxWrFihVasWCE3NzeVLl1a1apVU5UqVVStWjUVLVrU4fYn6t+/vzp27Gh4fIeFhWnWrFmaNWuWPD09VaFCBVWvXj2pLUbrXAIAACBzcZlE5aNTr5YsWdKuGFu2bLFYbsuIsmeffVbFihXTuXPnHio/fPgwiUqkqTt37qhbt26GScrg4GB17NhRtWrVsjhtVEREhFatWqXx48fr/Pnzyeo//vhjVapUyebjyehhW+nSpZOVnT17VsuXL9e2bdt08uRJ3bp1S/Hx8fL391fu3LlVpEgR1a5dW08//bRKlChh0/4BAAAc4cg63SlJSEjQn3/+qS+//FK3bt1Ks/1Irpe0exxn1nCV39lo5KXZn3VfX19T42VGRlOm+vj4mL42rtHfw5ZpW/39/TV+/Hh17txZUVFRVl+bkJCg48eP6/jx40nL3xQpUkS1atVS3bp11aBBA4eOhRo1amjQoEH65JNPUnxtbGys9uzZk7QOqLu7uwIDA1WrVi01atRI1apVy1BrEAMAAMB2LpGoDA8PT1ZmT6/dmJgY7dmzJ9lFa8GCBVWpUiWbYtStW1e//vrrQzGOHDlicxuA1Bg2bJhOnTqVrDxXrlz69NNP1ahRI6vb+/j46MUXX1TTpk01fPhw/fXXXw/VR0VF6b333tOcOXOUJUvKh7vRGjgP9qo9deqUvvjiC61du1YJCQnJXnvlyhVduXJFR44c0cqVKyVJlSpVUp8+fVjnEgAApKm0mjbw9u3b6t27t2HnSCCtGI2uNTtRmZZJ/szCaD3FtHjvjGLauqZj5cqV9ccff6hXr146e/asXfu+cOGC/vzzT/3555/y8/NTkyZN9Oabb6a682mHDh2UN29eDR482K5prePj43X48GEdPnxYU6dOVeHChfXSSy/pjTfeMHWUNwAAANKf5XlonOzevXvJyuzp0blv376HLtgTp321ZzTko2t8JCQkJBthCZhp/fr1Wrx4cbLyvHnzas6cOSkmKR/k5eWlTz/9VK+++mqyusOHD9u83syZM2cslieuWzJjxgy1bNlSa9assZikNLJv3z517txZXbt2VVhYmM3bAQAApLeoqCh16dKFJCVciq1Tx9rK1UbxuiKjNWfTooOEt7e3XW2wpGzZslq0aJEGDhyY6jVXb9++rdmzZ6tZs2b68MMPLXYyt8ULL7ygFStW6L///W+qR2hevHhR48ePV6NGjTRp0iTTjwEAAACkH5cYUWkpUZk1a1abt9+9e7fF8lq1atkco0iRIkn/dnNzU0JCgu7cuWPz9oA9EhISNG7cuGTlnp6e+vHHH1O9xsmgQYO0b98+HTp06KHyadOmqUWLFla3vXPnju7evWuxztfXV2PGjNHUqVNT1a5EGzZsUPv27fXjjz8yHSwAAMgQRo0apf3791t9Te7cuVWpUiWVKVNGhQsXVt68eeXr6ytvb29lyZLFcF3GQYMG6eDBg2nRbGQSRvfFRlPCppY9I90eV56enhbLje6hHGE0ZatRG4x4eXnpzTff1Ouvv641a9bo77//1tatW+3+/MTFxemvv/7S1q1b9d1336l8+fJ2bS/92yF3+PDh6tOnjxYtWqSlS5dq3759diVfpX/vW7/88ktt3LhRX3/9dVKnWgAAAGRcLpGodHd3T9aD01Ly0oilRKWbm5tq1Khhc4wcOXIkKyNRibSyZcsWHT16NFn5q6++qqeeeirVcb28vNSnTx9169btofKDBw9q7969qly5suG2165dM6z7/fffDZOUBQoUUIkSJeTv76+oqCiFhobq6NGjhj1cz5w5ow4dOmjevHnKly9fyr8UAABAOjlw4ID+/PNPw/rnn39eHTt2VNWqVVO1dlq2bNkcaR4eA0YzDZmdqDQ7XmbkrKSxtZj2dOh+kJeXl5o0aaImTZooLi5OBw4c0LZt27Rz507t2bPH5mcfFy9eVJcuXTRnzpyHlgexR65cufT666/r9ddfV1RUlHbs2KEdO3Zo586dOnjwoM2Jy23btuntt9/W9OnTXWZNWQAAAKSOSyQqfX19ky0Kb+uFcnx8vHbt2pX0YCBxOspSpUrZtW6BpZ6J9iRLAXs8upak9O9nsEePHg7Hrlu3rooXL55sGtfNmzdbTVQa9dqVZHH0Z/PmzdW1a1cFBQUlq7t586b+/vtvTZgwweJUr9euXVO/fv00bdo0wxEGAAAA6W3atGkWy7NkyaJRo0bpxRdfdCj+7du3HdoemZ9Rx74rV66Yup/Lly+bGi8zMhq5FxERkbT8jFmMnoeYMXrQw8NDlStXVuXKlfW///1P8fHxOnLkiHbs2KF169Zp586dVpOFt27dUv/+/TVr1iyH2+Lt7a169eqpXr16kv59BrN3715t27ZNa9eu1ZEjR6xuv2fPHn377bfq37+/w20BAABA+nGJNSotLRRv6/qQBw8eTNbb0M3NTdWrV7erDZYeEtDDGWkhLi5O//zzT7Ly2rVrmzZtTc2aNZOVGU2RnOjBdV4fdf/+/aR/e3t7a/Lkyfryyy8tJimlf2+gO3bsqGXLlqlKlSoWX7N9+3ZNnz7dapsAAADSS0xMjFavXm2x7q233nI4SSmJtbuRogeXKHmQpdlZHGF2vMwoICDAYnlsbKzNzy9sdeLECbva4Ah3d3eVL19enTp10rRp07R582YNHjzY6ojJvXv3at26daa3JVu2bKpVq5beffddzZ8/X6tWrVLXrl0tPjNKNGPGDN24ccP0tgAAAMB5XCJRGRAQkDQSMpGtN0pr1qyxWG7PtK+SLC4Kb7SAPeCII0eOWEyM16lTx7R9VKhQIVnZ3r17rW5jNFXrgzw8PDRx4kTVrVvXpnbkzp1bU6dONVzDZOrUqYqJibEpFgAAgDMdOnTI4tpz3t7e6tq1q8Pxo6Kiks0qAzzqySeftFie0kgze5GoTFmJEiUM65z197DWBrP4+fnpjTfe0JIlS/Tqq68avm7x4sVp3paiRYvq/fff19KlS1W1alWLr7l7967hcyEAAABkDC6RqCxXrlzSv93c3JSQkKCNGzfatO3SpUstTrFiaUSZNQ/2wEtMmvr5+dkVA7DF4cOHLZaXLl3atH3kzp07WdmdO3esrp+SJUvKM0F3795dwcHB/4+9+46vsrz/P/4+K3svSCCsAGHJkOEAF06ciNU6q3W3jlq1v9Z+a1vb2mFtXVRRa21ddVXUttaBCiiy9wgBAgEyyV4nOfP+/RETCeeEnISTkwN5PR8PHobrvu/r+pwInJz7fV/X1a1aYmJi9Mc//tHvfioHDhzQe++9163+AAAAQmHPnj1+208++eSgrLyyYcOGDqtWAP5MmjTJb3txcbF27NgRlDG2b9+ukpKSoPR1LBswYICSkpL8Htu0aVPQxikpKVFFRYXfY6NHjw7aOF2JiIjQL37xC82aNcvv8RUrVoSsloyMDD377LNKS0vr81oAAAAQfGGxR6W/5SNLS0v16aef6swzz+z0umXLlmnv3r0++1OOHDlSqamp3arh0CcgTSZTp8vsAEfi0L0j27z22mt6//33gzJGZx9sa2trO102JyIi4rB9JiQk6NZbb+1RPTk5OZo7d67ffUw++eQTXX755T3qFwAAoLd0tpRgVlZWUPrnxjoCMWLECGVkZOjAgQM+x/7973/rvvvuO+IxgvUZpD84/vjj/c7e+/DDD/WjH/0oKPtUHm6m4tSpU4+4/+666aab/D5IfuDAAblcLtlstpDUkZCQoG9961tasGCBzzGCdgAAgKNbWASVp5xyiqxWqzwej6RvZlX+7ne/0/Tp0/3ObHQ6nXrkkUd82k0mk84666xu17BlyxaftiFDhnS7H6Ar5eXlfts/+eSTXh+7rq6u0wA+Njb2sNdecsklR7Qc8tVXX+03qFy7dq08Ho8sFkuP+wYAAAg2l8vltz0YsymdTqfefvvtI+4H/cPZZ5+tV1991af9nXfe0W233XbY/fu60tDQoIULFx5Jef3KzJkz/QaVxcXFWrduXVCCxM6C42HDhmnQoEFH3H93+dtWpE1tba3S09NDVstxxx3nt72mpiZkNQAAACD4wmLp19TUVM2cOdNnn8qioiJdeeWVWrNmTYf24uJi3X777crPz28PNQ82b968bo3vdDrb+zrYsGHDutUPEIiGhoY+G7upqanTY/6Wiz3YSSeddERjjxkzRikpKT7tjY2N7IkDAADCTmJiot92fzPbuuudd97pdMYmcKi5c+f6ba+srNRf/vKXI+r7ySefZK/Ubjj33HM7fcDy6aefPuL+P/74406X9D3//POPuP+eONyMyejo6BBW0nktR/JALQAAAPpeWASVknTttde2f31w8Lh7925dd911OvXUU3XllVfqoosu0tlnn63ly5e3n9sWVppMJp1yyinKzs7u1thLly71uz/MxIkTe/hqgM45HI4+G/vQUP9gCQkJh/0Q2tn+ON3R2d+p0tLSI+4bAAAgmPw9YCW1Ltl6uJ+pulJUVKRHH320x9ej/5k4caKmTJni99jLL7+sr776qkf9fvHFF3rttdeOpLR+Jz09XaeeeqrfY19++aXf2ZaBcjgc+v3vf+/3mNls1qWXXtrjvo9EZ1uXxMTEHNFs3p7obO/gjIyMkNYBAACA4AqboPKUU07ROeec0x44Suqw9+SBAwe0ceNG7dy5U16vt/28g2dBms1m3Xvvvd0e+3//+59PW1RUlMaNG9fDVwN0LlyXODWZTJ3uuWS1WpWWlnbEYwwYMMBvO0v1AACAcNPZZ4GysjL95z//6VGfjY2Nuvfee/t0hQ0cnX7wgx/4bXe5XLrjjju0du3abvW3Zs0a3XXXXX4f2MXh3XLLLZ0ee/DBB7Vv375u92kYhn7605+quLjY7/E5c+YEtDXNwoULVVZW1u3xD+df//qX3/bRo0cf9rq///3vstvtQavD4/Ho3Xff9Xts1KhRQRsHAAAAoRcWe1S2efDBB7VlyxaVlpb6hJXSN7PBDg4wDz7v5ptv1pgxY7o1psPh0Oeff+7T54QJE8I2UMLRrbPlcT744APl5OSEuJqOsrOztXfvXp/2YD0p62+/Wal1bxMAAIBwMnToUGVnZ2v//v0+x37zm99ozJgx3bo5XlFRoVtuuUV5eXnBLBP9xEknnaQ5c+b4fcjWbrfrO9/5jm666SZ9//vfP+w+qi0tLZo/f77+9re/yePxtLdnZmayykmApk6dqrPOOkuLFi3yOVZZWambbrpJf//73wPeT9Lj8ei3v/1tpw9AREZG6p577gmor3feeUcPPvigLrzwQn33u99Vbm5uQNd1ZtGiRX73R5Vaw9PD+d3vfqdnnnlGV199ta655pojfvD10Ucf7fTfz65qAQAAQHgLq6AyPT1d//jHP3TdddeprKzMZ8/Izn5vGIbOO+88/fCHP+z2mO+//77sdrtP37Nmzep2X0AgOvuAFg5hXW5urr788suQj3vwTRIAAIBwcfHFF/vdA7C2tlbXXHONfv7zn+uCCy7w+SxxMJfLpddee01PP/10h5/3rFarkpOTVVFR0Rul4xj0y1/+UuvXr/c7Y87tduvZZ5/Vq6++qjPPPFMnnXSSBgwYoMTERNXW1qq8vFzLly/Xp59+6rNvfVxcnO6991796Ec/CtVLCarO9nQMhsTERL+rwvzf//2fVq5c6Xd29L59+3TxxRfrJz/5iS677DKZzZ0vZLVr1y799Kc/1caNGzs954477ghoNmUbl8ulhQsXauHChcrNzdWcOXM0Z84cDRs2LOA+ampq9Pzzz+vFF1+U1+v1OR4TE6MLLrigy35qa2v19NNP69lnn9WMGTM0Z84cnX322Z0ure1PQUGB/vjHP+rzzz/3e3z8+PGshgUAAHCUC6ugUmqd0fXee+/pj3/8o/71r3/5zKJs09YeHR2t22+/Xbfddlu3xzIMQy+88ILfGwunn35694sHAtDZHqpVVVUhrsTXcccd57e9sbExKP3X19f7be9slikAAEBf+u53v6tXXnlFdXV1Psfq6up033336amnntJ5552n4447TmlpabLZbKqurlZ5eblWrlyppUuX+n0g7bbbbtPq1asJKhGwpKQkPfPMM7rmmms6XVKzsbFR7733nt57772A+jSbzfrDH/6g+Pj4To+Hu4suuqjX+r700kv97huZlZWlP/zhD7rjjjv87lnb2Nion/3sZ3riiSc0Z84cTZo0SRkZGYqOjlZFRYX27dunjz76SOvXrz/snrennnqqbr311h7Xn5+fr/z8fD3++ONKT0/XhAkTNGHCBA0ZMkSJiYlKSkpSZGSk7Ha7qqurtXv3bq1du1bLly+Xw+HotN/7779f6enpAdfh8Xi0fPlyLV++XL/4xS80dOjQ9lraAvXExESZTCY1NzerrKxMu3bt0ldffaVNmzZ1+j2yWq16+OGHu/19AQAAQHgJu6BSan1q8Te/+Y1uueUWffTRR/riiy9UUlKiqqoqmUwmJSUladSoUTr55JM1d+5cJSUl9WicRYs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",
      "text/plain": [
       "<Figure size 1000x400 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 364,
       "width": 917
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "step_size = 1e-3\n",
    "params = mlp.init(random.PRNGKey(42), np.ones([1, 28 * 28]))\n",
    "opt_init, opt_update, get_params = optimizers.adam(step_size)\n",
    "opt_state = opt_init(params)\n",
    "#params = mlp.init(random.PRNGKey(42), np.ones([1, 28 * 28]))\n",
    "num_epochs = 5\n",
    "num_classes = 10\n",
    "train_loss, train_log, test_log = run_mnist_training_loop(num_epochs,\n",
    "                                                          opt_state,\n",
    "                                                          net_type=\"MLP\",\n",
    "                                                          sampling =\"1\")\n",
    "\n",
    "# Plot the loss curve over time\n",
    "#from helpers import plot_mnist_performance\n",
    "plot_mnist_performance(train_loss, train_log, test_log,\n",
    "                       \"Mnist sampling local reparametrization\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Вывод"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Реализованы три метода по различным стратегиям по сэмплированию в нейронных моделях. Но тут можно еще дорабатывать. Но к сожалению, не успела(( Но лосс в последнем случае самый наименьший(~-1.1*10^(3))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### По времени метод 1, 3 быстрее 2, что написано и в статье, так как мы семплируем очень много параметров только для одного слоя, когда их несколько, нужно умножать на n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "colab": {
   "provenance": []
  },
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}