{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Conditional Denoising Diffusion Probabilistic Models (DDPMs) with fastai\n",
    "By Tanishq Abraham and Thomas Capelle\n",
    "\n",
    "In this notebook, we will implement [Denoising Diffusion Probabilistic Models](https://arxiv.org/abs/2006.11239), a seminal paper in the diffusion model literature.\n",
    "\n",
    "A one-sentence summary: Train a denoising model conditioned on the amount of noise present in the image, and generate samples by iteratively denoising from pure noise to a final sample conditioned to the label of the image.\n",
    "\n",
    "The final model is capable to generate an image form a label!\n",
    "\n",
    "Let's get started with the implementation!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Imports\n",
    "\n",
    "Here are all our imports. The unet file is taken from [lucidrains' DDPM implementation](https://github.com/lucidrains/denoising-diffusion-pytorch) just to focus on implementing the training process rather than architectural details."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "id": "rCPJR6JRrqUp"
   },
   "outputs": [],
   "source": [
    "from fastai.vision.all import *\n",
    "from fastai.vision.gan import *\n",
    "from unet import Unet\n",
    "from copy import deepcopy\n",
    "from data import *"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Dataloading\n",
    "\n",
    "Let's load our data. We'll work with the famous MNIST dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "id": "okr8kAqSvIUD"
   },
   "outputs": [],
   "source": [
    "bs = 512 # batch size\n",
    "size = 32 # image size\n",
    "epochs = 100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "id": "B8nsEdKXvKim"
   },
   "outputs": [],
   "source": [
    "path = untar_data(URLs.CIFAR)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We use the highly flexible DataBlock API in fastai to create our DataLoaders.\n",
    "\n",
    "~~Note that we start with pure noise, generated with the obviously named `generate_noise` function.~~"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's use a labelled dataset and train a conditional model on the label"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "id": "DQF8UdVvvP4R"
   },
   "outputs": [],
   "source": [
    "dblock = DataBlock(blocks = (ImageBlock, CategoryBlock()),\n",
    "                   get_items = get_image_files,\n",
    "                   get_y = lambda p: p.parent.name,\n",
    "                   splitter = IndexSplitter(range(bs)),\n",
    "                   item_tfms=Resize(size), \n",
    "                   batch_tfms = Normalize.from_stats(0.5, 0.5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "id": "L6iHHHFRvRPx",
    "tags": []
   },
   "outputs": [],
   "source": [
    "dls = dblock.dataloaders(path, bs=bs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 900x900 with 9 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "xb, yb = dls.one_batch()\n",
    "dls.show_batch()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(TensorImage(1., device='cuda:0'),\n",
       " TensorImage(-1., device='cuda:0'),\n",
       " TensorImage(-0.0532, device='cuda:0'),\n",
       " TensorImage(0.4992, device='cuda:0'))"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xb.max(), xb.min(), xb.mean(), xb.std()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Conditional DDPM Training as a Callback\n",
    "\n",
    "Same as DDPM callback but:\n",
    "- We invert the x,y structure of the original callback\n",
    "- Generates noise on the callback for the whole batch\n",
    "- The sampling \"before_batch_sampling\" is done with labels that are fixed to [0,1,2,3,4...9,0,1,2,3....]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "id": "N-IV1WO0wyMT"
   },
   "outputs": [],
   "source": [
    "class ConditionalDDPMCallback(Callback):\n",
    "    def __init__(self, n_steps, beta_min, beta_max, cfg_scale=0):\n",
    "        store_attr()\n",
    "        self.tensor_type=TensorImage\n",
    "\n",
    "    def before_fit(self):\n",
    "        self.beta = torch.linspace(self.beta_min, self.beta_max, self.n_steps).to(self.dls.device) # variance schedule, linearly increased with timestep\n",
    "        self.alpha = 1. - self.beta \n",
    "        self.alpha_bar = torch.cumprod(self.alpha, dim=0)\n",
    "        self.sigma = torch.sqrt(self.beta)\n",
    "    \n",
    "    def sample_timesteps(self, x, dtype=torch.long):\n",
    "        return torch.randint(self.n_steps, (x.shape[0],), device=x.device, dtype=dtype)\n",
    "    \n",
    "    def generate_noise(self, x):\n",
    "        return self.tensor_type(torch.randn_like(x))\n",
    "    \n",
    "    def noise_image(self, x, eps, t):\n",
    "        alpha_bar_t = self.alpha_bar[t][:, None, None, None]\n",
    "        return torch.sqrt(alpha_bar_t)*x + torch.sqrt(1-alpha_bar_t)*eps # noisify the image\n",
    "    \n",
    "    def before_batch_training(self):\n",
    "        x0 = self.xb[0] # original images and labels\n",
    "        y0 =  self.yb[0] if np.random.random() > 0.1 else None\n",
    "        \n",
    "        # y0 = None\n",
    "        \n",
    "        eps = self.generate_noise(x0) # noise same shape as x0\n",
    "        t =  self.sample_timesteps(x0) # select random timesteps\n",
    "        xt =  self.noise_image(x0, eps, t)  # add noise to the image\n",
    "        # print(x0.shape, y0.shape, t.shape, xt.shape, eps.shape)\n",
    "        \n",
    "        self.learn.xb = (xt, t, y0) # input to our model is noisy image, timestep and label\n",
    "        self.learn.yb = (eps,) # ground truth is the noise \n",
    "\n",
    "    def sampling_algo(self, xt, t, label=None):\n",
    "        t_batch = torch.full((xt.shape[0],), t, device=xt.device, dtype=torch.long)\n",
    "        z = self.generate_noise(xt) if t > 0 else torch.zeros_like(xt)\n",
    "        alpha_t = self.alpha[t] # get noise level at current timestep\n",
    "        alpha_bar_t = self.alpha_bar[t]\n",
    "        sigma_t = self.sigma[t]\n",
    "        alpha_bar_t_1 = self.alpha_bar[t-1]  if t > 0 else torch.tensor(1, device=xt.device)\n",
    "        beta_bar_t = 1 - alpha_bar_t\n",
    "        beta_bar_t_1 = 1 - alpha_bar_t_1\n",
    "        predicted_noise = self.model(xt, t_batch, label=label)\n",
    "        if self.cfg_scale>0:\n",
    "            uncond_predicted_noise = self.model(xt, t_batch, label=None)\n",
    "            predicted_noise = torch.lerp(uncond_predicted_noise, predicted_noise, self.cfg_scale)\n",
    "        x0hat = (xt - torch.sqrt(beta_bar_t) * predicted_noise)/torch.sqrt(alpha_bar_t)\n",
    "        x0hat = torch.clamp(x0hat, -1, 1)\n",
    "        xt = x0hat * torch.sqrt(alpha_bar_t_1)*(1-alpha_t)/beta_bar_t + xt * torch.sqrt(alpha_t)*beta_bar_t_1/beta_bar_t + sigma_t*z \n",
    "\n",
    "        return xt\n",
    "    \n",
    "    # def sampling_algo_old(self, xt, t, label=None):\n",
    "    #     t_batch = torch.full((xt.shape[0],), t, device=xt.device, dtype=torch.long)\n",
    "    #     z = self.generate_noise(xt) if t > 0 else torch.zeros_like(xt)\n",
    "    #     alpha_t = self.alpha[t] # get noise level at current timestep\n",
    "    #     alpha_bar_t = self.alpha_bar[t]\n",
    "    #     sigma_t = self.sigma[t]\n",
    "    #     xt = 1/torch.sqrt(alpha_t) * (xt - (1-alpha_t)/torch.sqrt(1-alpha_bar_t) * self.model(xt, t_batch, label=label)) + sigma_t*z \n",
    "    #          1 / torch.sqrt(alpha) * (x - ((1 - alpha) / (torch.sqrt(1 - alpha_hat))) * predicted_noise) + torch.sqrt(beta) * noise\n",
    "    #     # predict x_(t-1) in accordance to Algorithm 2 in paper\n",
    "    #     return xt\n",
    "    \n",
    "    def sample(self):\n",
    "        xt = self.generate_noise(self.xb[0]) # a full batch at once!\n",
    "        label = torch.arange(10, dtype=torch.long, device=xt.device).repeat(xt.shape[0]//10 + 1).flatten()[0:xt.shape[0]]\n",
    "        for t in progress_bar(reversed(range(self.n_steps)), total=self.n_steps, leave=False):\n",
    "            xt = self.sampling_algo(xt, t, label) \n",
    "        return xt\n",
    "    \n",
    "    def before_batch_sampling(self):\n",
    "        xt = self.sample()\n",
    "        self.learn.pred = (xt,)\n",
    "        raise CancelBatchException\n",
    "    \n",
    "    def after_validate(self):\n",
    "        if (self.epoch+1) % 4 == 0:\n",
    "            with torch.no_grad():\n",
    "                xt = self.sample()\n",
    "                wandb.log({\"preds\": [wandb.Image(torch.tensor(im)) for im in xt[0:36]]})\n",
    "    \n",
    "    def before_batch(self):\n",
    "        if not hasattr(self, 'gather_preds'): self.before_batch_training()\n",
    "        else: self.before_batch_sampling()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "class EMA(Callback):\n",
    "    \"Exponential Moving average CB\"\n",
    "    def __init__(self, beta=0.995, pct_start=0.3):\n",
    "        store_attr()\n",
    "        \n",
    "    \n",
    "    def before_fit(self):\n",
    "        self.ema_model = deepcopy(self.model).eval().requires_grad_(False)\n",
    "        self.step_start_ema = int(self.pct_start*self.n_epoch)  #start EMA at 30% of epochs\n",
    "        \n",
    "    def update_model_average(self):\n",
    "        for current_params, ma_params in zip(self.model.parameters(), self.ema_model.parameters()):\n",
    "            old_weight, up_weight = ma_params.data, current_params.data\n",
    "            ma_params.data = self.update_average(old_weight, up_weight)\n",
    "\n",
    "    def update_average(self, old, new):\n",
    "        return old * self.beta + (1 - self.beta) * new\n",
    "\n",
    "    def step_ema(self):\n",
    "        if self.epoch < self.step_start_ema:\n",
    "            self.reset_parameters()\n",
    "            self.step += 1\n",
    "            return\n",
    "        self.update_model_average()\n",
    "        self.step += 1\n",
    "\n",
    "    def reset_parameters(self):\n",
    "        self.ema_model.load_state_dict(self.model.state_dict())\n",
    "    \n",
    "    def after_batch(self):\n",
    "        if hasattr(self, 'pred'): return\n",
    "        self.step_ema()\n",
    "    \n",
    "    def after_training(self):\n",
    "        self.model = self.ema_model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We have to add the conditioning to the Unet, to do so, we just subclass it and inject the encoded label on the `forward` pass."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "@delegates(Unet)\n",
    "class ConditionalUnet(Unet):\n",
    "    def __init__(self, dim, num_classes=None, **kwargs):\n",
    "        super().__init__(dim=dim, **kwargs)\n",
    "        if num_classes is not None:\n",
    "            self.label_emb = nn.Embedding(num_classes, dim * 4)\n",
    "    \n",
    "    def forward(self, x, time, label=None):\n",
    "        x = self.init_conv(x)\n",
    "        t = self.time_mlp(time)\n",
    "        if label is not None:\n",
    "            t += self.label_emb(label)\n",
    "            \n",
    "        return super().forward_blocks(x, t)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's now initialize our model:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = ConditionalUnet(dim=32, channels=3, num_classes=10).cuda()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we can create a fastai Learner with our DataLoaders, Callback (with the appropriate number of timesteps and noise schedule) and the simple MSE loss that we use to train DDPM."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "import wandb\n",
    "from fastai.callback.wandb import WandbCallback"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "id": "cGfe6KqaH0iP"
   },
   "outputs": [],
   "source": [
    "ddpm_learner = Learner(dls, model, \n",
    "                       cbs=[ConditionalDDPMCallback(n_steps=1000, beta_min=0.0001, beta_max=0.02, cfg_scale=3),\n",
    "                            EMA()], \n",
    "                       loss_func=nn.L1Loss())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's use fastai's amazing LR finder to select a good LR for training:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 300
    },
    "id": "i1N52U5QLEOp",
    "outputId": "6b691a2c-81ad-481f-d2b9-aa051cdbd947",
    "tags": []
   },
   "outputs": [],
   "source": [
    "# ddpm_learner.lr_find()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And now let's train with one-cycle LR schedule:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
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    },
    "id": "gXKdZ3mRR_4G",
    "outputId": "8b128c9b-a2d8-490e-ce11-1ba064f66877"
   },
   "outputs": [
    {
     "name": "stderr",
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      "Failed to detect the name of this notebook, you can set it manually with the WANDB_NOTEBOOK_NAME environment variable to enable code saving.\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m: Currently logged in as: \u001b[33mcapecape\u001b[0m. Use \u001b[1m`wandb login --relogin`\u001b[0m to force relogin\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "wandb version 0.13.4 is available!  To upgrade, please run:\n",
       " $ pip install wandb --upgrade"
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      "text/html": [
       "Syncing run <strong><a href=\"https://wandb.ai/capecape/ddpm_fastai/runs/9xdzrfdb\" target=\"_blank\">classic-wind-75</a></strong> to <a href=\"https://wandb.ai/capecape/ddpm_fastai\" target=\"_blank\">Weights & Biases</a> (<a href=\"https://wandb.me/run\" target=\"_blank\">docs</a>)<br/>"
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    "wandb.init(project=\"ddpm_fastai\", group=\"cifar10\", tags=[\"fp\", \"ema\"])"
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  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 538
    },
    "id": "gXKdZ3mRR_4G",
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   "outputs": [
    {
     "data": {
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: left;\">\n",
       "      <th>epoch</th>\n",
       "      <th>train_loss</th>\n",
       "      <th>valid_loss</th>\n",
       "      <th>time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>0.750259</td>\n",
       "      <td>0.709923</td>\n",
       "      <td>00:42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>0.632055</td>\n",
       "      <td>0.573980</td>\n",
       "      <td>00:40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>0.490789</td>\n",
       "      <td>0.438364</td>\n",
       "      <td>00:40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>0.356443</td>\n",
       "      <td>0.307267</td>\n",
       "      <td>03:25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>0.270513</td>\n",
       "      <td>0.249062</td>\n",
       "      <td>00:40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>0.221594</td>\n",
       "      <td>0.210910</td>\n",
       "      <td>00:40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>0.190886</td>\n",
       "      <td>0.186583</td>\n",
       "      <td>00:40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>0.168048</td>\n",
       "      <td>0.167804</td>\n",
       "      <td>03:25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>0.152539</td>\n",
       "      <td>0.157809</td>\n",
       "      <td>00:40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>0.144119</td>\n",
       "      <td>0.152306</td>\n",
       "      <td>00:40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>10</td>\n",
       "      <td>0.137331</td>\n",
       "      <td>0.146878</td>\n",
       "      <td>00:40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>11</td>\n",
       "      <td>0.133565</td>\n",
       "      <td>0.139846</td>\n",
       "      <td>03:25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>12</td>\n",
       "      <td>0.131783</td>\n",
       "      <td>0.137660</td>\n",
       "      <td>00:40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>13</td>\n",
       "      <td>0.128458</td>\n",
       "      <td>0.145122</td>\n",
       "      <td>00:40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>14</td>\n",
       "      <td>0.125062</td>\n",
       "      <td>0.137213</td>\n",
       "      <td>00:40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>15</td>\n",
       "      <td>0.122997</td>\n",
       "      <td>0.133304</td>\n",
       "      <td>03:25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>16</td>\n",
       "      <td>0.121221</td>\n",
       "      <td>0.124890</td>\n",
       "      <td>00:40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>17</td>\n",
       "      <td>0.118911</td>\n",
       "      <td>0.134336</td>\n",
       "      <td>00:40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>18</td>\n",
       "      <td>0.118563</td>\n",
       "      <td>0.137339</td>\n",
       "      <td>00:40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>19</td>\n",
       "      <td>0.117204</td>\n",
       "      <td>0.123301</td>\n",
       "      <td>03:25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>20</td>\n",
       "      <td>0.115335</td>\n",
       "      <td>0.125933</td>\n",
       "      <td>00:40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>21</td>\n",
       "      <td>0.112230</td>\n",
       "      <td>0.115233</td>\n",
       "      <td>00:40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>22</td>\n",
       "      <td>0.111855</td>\n",
       "      <td>0.123500</td>\n",
       "      <td>00:40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>23</td>\n",
       "      <td>0.110091</td>\n",
       "      <td>0.133139</td>\n",
       "      <td>03:25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>24</td>\n",
       "      <td>0.109199</td>\n",
       "      <td>0.125465</td>\n",
       "      <td>00:40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>25</td>\n",
       "      <td>0.109592</td>\n",
       "      <td>0.100159</td>\n",
       "      <td>00:40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>26</td>\n",
       "      <td>0.108045</td>\n",
       "      <td>0.126357</td>\n",
       "      <td>00:40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>27</td>\n",
       "      <td>0.105945</td>\n",
       "      <td>0.115808</td>\n",
       "      <td>03:25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>28</td>\n",
       "      <td>0.105502</td>\n",
       "      <td>0.105228</td>\n",
       "      <td>00:40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>29</td>\n",
       "      <td>0.105925</td>\n",
       "      <td>0.119752</td>\n",
       "      <td>00:40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>30</td>\n",
       "      <td>0.106559</td>\n",
       "      <td>0.114725</td>\n",
       "      <td>00:40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>31</td>\n",
       "      <td>0.104310</td>\n",
       "      <td>0.114205</td>\n",
       "      <td>03:25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>32</td>\n",
       "      <td>0.104533</td>\n",
       "      <td>0.118345</td>\n",
       "      <td>00:40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>33</td>\n",
       "      <td>0.102721</td>\n",
       "      <td>0.120694</td>\n",
       "      <td>00:40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>34</td>\n",
       "      <td>0.102772</td>\n",
       "      <td>0.115649</td>\n",
       "      <td>00:40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>35</td>\n",
       "      <td>0.102187</td>\n",
       "      <td>0.115252</td>\n",
       "      <td>03:25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>36</td>\n",
       "      <td>0.102728</td>\n",
       "      <td>0.111810</td>\n",
       "      <td>00:40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>37</td>\n",
       "      <td>0.100627</td>\n",
       "      <td>0.097210</td>\n",
       "      <td>00:40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>38</td>\n",
       "      <td>0.100899</td>\n",
       "      <td>0.109537</td>\n",
       "      <td>00:40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>39</td>\n",
       "      <td>0.100359</td>\n",
       "      <td>0.121335</td>\n",
       "      <td>03:25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>40</td>\n",
       "      <td>0.101219</td>\n",
       "      <td>0.107469</td>\n",
       "      <td>00:40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>41</td>\n",
       "      <td>0.099695</td>\n",
       "      <td>0.118630</td>\n",
       "      <td>00:41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>42</td>\n",
       "      <td>0.099988</td>\n",
       "      <td>0.112241</td>\n",
       "      <td>00:40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>43</td>\n",
       "      <td>0.099661</td>\n",
       "      <td>0.107124</td>\n",
       "      <td>03:25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>44</td>\n",
       "      <td>0.100341</td>\n",
       "      <td>0.106861</td>\n",
       "      <td>00:40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>45</td>\n",
       "      <td>0.098759</td>\n",
       "      <td>0.111692</td>\n",
       "      <td>00:40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>46</td>\n",
       "      <td>0.098534</td>\n",
       "      <td>0.104519</td>\n",
       "      <td>00:40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>47</td>\n",
       "      <td>0.098645</td>\n",
       "      <td>0.107390</td>\n",
       "      <td>03:25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>48</td>\n",
       "      <td>0.099272</td>\n",
       "      <td>0.121547</td>\n",
       "      <td>00:40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>49</td>\n",
       "      <td>0.097441</td>\n",
       "      <td>0.102439</td>\n",
       "      <td>00:40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>50</td>\n",
       "      <td>0.098245</td>\n",
       "      <td>0.116344</td>\n",
       "      <td>00:40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>51</td>\n",
       "      <td>0.098801</td>\n",
       "      <td>0.112504</td>\n",
       "      <td>03:25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>52</td>\n",
       "      <td>0.097479</td>\n",
       "      <td>0.104128</td>\n",
       "      <td>00:40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>53</td>\n",
       "      <td>0.098048</td>\n",
       "      <td>0.105118</td>\n",
       "      <td>00:40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>54</td>\n",
       "      <td>0.097966</td>\n",
       "      <td>0.101353</td>\n",
       "      <td>00:40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>55</td>\n",
       "      <td>0.097155</td>\n",
       "      <td>0.098459</td>\n",
       "      <td>03:25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>56</td>\n",
       "      <td>0.097588</td>\n",
       "      <td>0.096302</td>\n",
       "      <td>00:40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>57</td>\n",
       "      <td>0.096654</td>\n",
       "      <td>0.093959</td>\n",
       "      <td>00:40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>58</td>\n",
       "      <td>0.096842</td>\n",
       "      <td>0.118672</td>\n",
       "      <td>00:40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>59</td>\n",
       "      <td>0.096856</td>\n",
       "      <td>0.109159</td>\n",
       "      <td>03:25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>60</td>\n",
       "      <td>0.096999</td>\n",
       "      <td>0.107374</td>\n",
       "      <td>00:40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>61</td>\n",
       "      <td>0.097314</td>\n",
       "      <td>0.105345</td>\n",
       "      <td>00:40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>62</td>\n",
       "      <td>0.096345</td>\n",
       "      <td>0.101585</td>\n",
       "      <td>00:40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>63</td>\n",
       "      <td>0.097237</td>\n",
       "      <td>0.107552</td>\n",
       "      <td>03:26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>64</td>\n",
       "      <td>0.096387</td>\n",
       "      <td>0.110463</td>\n",
       "      <td>00:40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>65</td>\n",
       "      <td>0.096522</td>\n",
       "      <td>0.107351</td>\n",
       "      <td>00:40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>66</td>\n",
       "      <td>0.096289</td>\n",
       "      <td>0.103139</td>\n",
       "      <td>00:40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>67</td>\n",
       "      <td>0.095761</td>\n",
       "      <td>0.108441</td>\n",
       "      <td>03:26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>68</td>\n",
       "      <td>0.096019</td>\n",
       "      <td>0.113256</td>\n",
       "      <td>00:40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>69</td>\n",
       "      <td>0.095945</td>\n",
       "      <td>0.107207</td>\n",
       "      <td>00:40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>70</td>\n",
       "      <td>0.095901</td>\n",
       "      <td>0.101774</td>\n",
       "      <td>00:40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>71</td>\n",
       "      <td>0.096231</td>\n",
       "      <td>0.101389</td>\n",
       "      <td>03:25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>72</td>\n",
       "      <td>0.096459</td>\n",
       "      <td>0.095912</td>\n",
       "      <td>00:41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>73</td>\n",
       "      <td>0.095942</td>\n",
       "      <td>0.115093</td>\n",
       "      <td>00:41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>74</td>\n",
       "      <td>0.095968</td>\n",
       "      <td>0.099037</td>\n",
       "      <td>00:40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>75</td>\n",
       "      <td>0.095558</td>\n",
       "      <td>0.099857</td>\n",
       "      <td>03:25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>76</td>\n",
       "      <td>0.095226</td>\n",
       "      <td>0.101043</td>\n",
       "      <td>00:40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>77</td>\n",
       "      <td>0.095414</td>\n",
       "      <td>0.106881</td>\n",
       "      <td>00:40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>78</td>\n",
       "      <td>0.095608</td>\n",
       "      <td>0.104538</td>\n",
       "      <td>00:40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>79</td>\n",
       "      <td>0.095830</td>\n",
       "      <td>0.102535</td>\n",
       "      <td>03:26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>80</td>\n",
       "      <td>0.095843</td>\n",
       "      <td>0.106510</td>\n",
       "      <td>00:40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>81</td>\n",
       "      <td>0.094907</td>\n",
       "      <td>0.106933</td>\n",
       "      <td>00:40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>82</td>\n",
       "      <td>0.095509</td>\n",
       "      <td>0.102437</td>\n",
       "      <td>00:40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>83</td>\n",
       "      <td>0.096072</td>\n",
       "      <td>0.108544</td>\n",
       "      <td>03:25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>84</td>\n",
       "      <td>0.095380</td>\n",
       "      <td>0.105316</td>\n",
       "      <td>00:41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>85</td>\n",
       "      <td>0.094950</td>\n",
       "      <td>0.100542</td>\n",
       "      <td>00:40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>86</td>\n",
       "      <td>0.095622</td>\n",
       "      <td>0.103235</td>\n",
       "      <td>00:40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>87</td>\n",
       "      <td>0.094806</td>\n",
       "      <td>0.106054</td>\n",
       "      <td>03:25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>88</td>\n",
       "      <td>0.094979</td>\n",
       "      <td>0.099068</td>\n",
       "      <td>00:40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>89</td>\n",
       "      <td>0.095287</td>\n",
       "      <td>0.099797</td>\n",
       "      <td>00:40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>90</td>\n",
       "      <td>0.095047</td>\n",
       "      <td>0.094034</td>\n",
       "      <td>00:40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>91</td>\n",
       "      <td>0.095369</td>\n",
       "      <td>0.109976</td>\n",
       "      <td>03:26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>92</td>\n",
       "      <td>0.094981</td>\n",
       "      <td>0.099022</td>\n",
       "      <td>00:40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>93</td>\n",
       "      <td>0.095371</td>\n",
       "      <td>0.110711</td>\n",
       "      <td>00:40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>94</td>\n",
       "      <td>0.095437</td>\n",
       "      <td>0.106877</td>\n",
       "      <td>00:40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>95</td>\n",
       "      <td>0.094820</td>\n",
       "      <td>0.099612</td>\n",
       "      <td>03:25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>96</td>\n",
       "      <td>0.094549</td>\n",
       "      <td>0.099947</td>\n",
       "      <td>00:41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>97</td>\n",
       "      <td>0.095064</td>\n",
       "      <td>0.107513</td>\n",
       "      <td>00:40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>98</td>\n",
       "      <td>0.094772</td>\n",
       "      <td>0.104572</td>\n",
       "      <td>00:40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>99</td>\n",
       "      <td>0.094053</td>\n",
       "      <td>0.104388</td>\n",
       "      <td>03:25</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Better model found at epoch 0 with train_loss value: 0.7502593398094177.\n",
      "Better model found at epoch 1 with train_loss value: 0.6320550441741943.\n",
      "Better model found at epoch 2 with train_loss value: 0.4907890260219574.\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "\n",
       "<style>\n",
       "    /* Turns off some styling */\n",
       "    progress {\n",
       "        /* gets rid of default border in Firefox and Opera. */\n",
       "        border: none;\n",
       "        /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
       "        background-size: auto;\n",
       "    }\n",
       "    .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
       "        background: #F44336;\n",
       "    }\n",
       "</style>\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Better model found at epoch 3 with train_loss value: 0.3564431965351105.\n",
      "Better model found at epoch 4 with train_loss value: 0.27051302790641785.\n",
      "Better model found at epoch 5 with train_loss value: 0.22159354388713837.\n",
      "Better model found at epoch 6 with train_loss value: 0.19088611006736755.\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "\n",
       "<style>\n",
       "    /* Turns off some styling */\n",
       "    progress {\n",
       "        /* gets rid of default border in Firefox and Opera. */\n",
       "        border: none;\n",
       "        /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
       "        background-size: auto;\n",
       "    }\n",
       "    .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
       "        background: #F44336;\n",
       "    }\n",
       "</style>\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Better model found at epoch 7 with train_loss value: 0.16804789006710052.\n",
      "Better model found at epoch 8 with train_loss value: 0.1525385081768036.\n",
      "Better model found at epoch 9 with train_loss value: 0.14411911368370056.\n",
      "Better model found at epoch 10 with train_loss value: 0.13733148574829102.\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "\n",
       "<style>\n",
       "    /* Turns off some styling */\n",
       "    progress {\n",
       "        /* gets rid of default border in Firefox and Opera. */\n",
       "        border: none;\n",
       "        /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
       "        background-size: auto;\n",
       "    }\n",
       "    .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
       "        background: #F44336;\n",
       "    }\n",
       "</style>\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Better model found at epoch 11 with train_loss value: 0.13356511294841766.\n",
      "Better model found at epoch 12 with train_loss value: 0.13178279995918274.\n",
      "Better model found at epoch 13 with train_loss value: 0.1284581422805786.\n",
      "Better model found at epoch 14 with train_loss value: 0.12506239116191864.\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "\n",
       "<style>\n",
       "    /* Turns off some styling */\n",
       "    progress {\n",
       "        /* gets rid of default border in Firefox and Opera. */\n",
       "        border: none;\n",
       "        /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
       "        background-size: auto;\n",
       "    }\n",
       "    .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
       "        background: #F44336;\n",
       "    }\n",
       "</style>\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Better model found at epoch 15 with train_loss value: 0.12299712002277374.\n",
      "Better model found at epoch 16 with train_loss value: 0.12122058123350143.\n",
      "Better model found at epoch 17 with train_loss value: 0.11891119927167892.\n",
      "Better model found at epoch 18 with train_loss value: 0.1185627207159996.\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "\n",
       "<style>\n",
       "    /* Turns off some styling */\n",
       "    progress {\n",
       "        /* gets rid of default border in Firefox and Opera. */\n",
       "        border: none;\n",
       "        /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
       "        background-size: auto;\n",
       "    }\n",
       "    .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
       "        background: #F44336;\n",
       "    }\n",
       "</style>\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Better model found at epoch 19 with train_loss value: 0.11720366775989532.\n",
      "Better model found at epoch 20 with train_loss value: 0.11533458530902863.\n",
      "Better model found at epoch 21 with train_loss value: 0.11222967505455017.\n",
      "Better model found at epoch 22 with train_loss value: 0.11185462772846222.\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "\n",
       "<style>\n",
       "    /* Turns off some styling */\n",
       "    progress {\n",
       "        /* gets rid of default border in Firefox and Opera. */\n",
       "        border: none;\n",
       "        /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
       "        background-size: auto;\n",
       "    }\n",
       "    .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
       "        background: #F44336;\n",
       "    }\n",
       "</style>\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Better model found at epoch 23 with train_loss value: 0.11009092628955841.\n",
      "Better model found at epoch 24 with train_loss value: 0.10919912904500961.\n",
      "Better model found at epoch 26 with train_loss value: 0.10804483294487.\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "\n",
       "<style>\n",
       "    /* Turns off some styling */\n",
       "    progress {\n",
       "        /* gets rid of default border in Firefox and Opera. */\n",
       "        border: none;\n",
       "        /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
       "        background-size: auto;\n",
       "    }\n",
       "    .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
       "        background: #F44336;\n",
       "    }\n",
       "</style>\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Better model found at epoch 27 with train_loss value: 0.10594470798969269.\n",
      "Better model found at epoch 28 with train_loss value: 0.10550212115049362.\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "\n",
       "<style>\n",
       "    /* Turns off some styling */\n",
       "    progress {\n",
       "        /* gets rid of default border in Firefox and Opera. */\n",
       "        border: none;\n",
       "        /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
       "        background-size: auto;\n",
       "    }\n",
       "    .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
       "        background: #F44336;\n",
       "    }\n",
       "</style>\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Better model found at epoch 31 with train_loss value: 0.10430967062711716.\n",
      "Better model found at epoch 33 with train_loss value: 0.1027207300066948.\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "\n",
       "<style>\n",
       "    /* Turns off some styling */\n",
       "    progress {\n",
       "        /* gets rid of default border in Firefox and Opera. */\n",
       "        border: none;\n",
       "        /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
       "        background-size: auto;\n",
       "    }\n",
       "    .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
       "        background: #F44336;\n",
       "    }\n",
       "</style>\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Better model found at epoch 35 with train_loss value: 0.10218702256679535.\n",
      "Better model found at epoch 37 with train_loss value: 0.10062697529792786.\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "\n",
       "<style>\n",
       "    /* Turns off some styling */\n",
       "    progress {\n",
       "        /* gets rid of default border in Firefox and Opera. */\n",
       "        border: none;\n",
       "        /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
       "        background-size: auto;\n",
       "    }\n",
       "    .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
       "        background: #F44336;\n",
       "    }\n",
       "</style>\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Better model found at epoch 39 with train_loss value: 0.10035884380340576.\n",
      "Better model found at epoch 41 with train_loss value: 0.09969516843557358.\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "\n",
       "<style>\n",
       "    /* Turns off some styling */\n",
       "    progress {\n",
       "        /* gets rid of default border in Firefox and Opera. */\n",
       "        border: none;\n",
       "        /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
       "        background-size: auto;\n",
       "    }\n",
       "    .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
       "        background: #F44336;\n",
       "    }\n",
       "</style>\n"
      ],
      "text/plain": [
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     "data": {
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       "<IPython.core.display.HTML object>"
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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Better model found at epoch 43 with train_loss value: 0.09966123104095459.\n",
      "Better model found at epoch 45 with train_loss value: 0.09875939041376114.\n",
      "Better model found at epoch 46 with train_loss value: 0.09853431582450867.\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "\n",
       "<style>\n",
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       "        border: none;\n",
       "        /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
       "        background-size: auto;\n",
       "    }\n",
       "    .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
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     "metadata": {},
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Better model found at epoch 49 with train_loss value: 0.09744056314229965.\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "\n",
       "<style>\n",
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       "    progress {\n",
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       "        border: none;\n",
       "        /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
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       "    .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
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    {
     "data": {
      "text/html": [],
      "text/plain": [
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     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "\n",
       "<style>\n",
       "    /* Turns off some styling */\n",
       "    progress {\n",
       "        /* gets rid of default border in Firefox and Opera. */\n",
       "        border: none;\n",
       "        /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
       "        background-size: auto;\n",
       "    }\n",
       "    .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
       "        background: #F44336;\n",
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       "</style>\n"
      ],
      "text/plain": [
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    {
     "data": {
      "text/html": [],
      "text/plain": [
       "<IPython.core.display.HTML object>"
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     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Better model found at epoch 55 with train_loss value: 0.09715472906827927.\n",
      "Better model found at epoch 57 with train_loss value: 0.09665428847074509.\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "\n",
       "<style>\n",
       "    /* Turns off some styling */\n",
       "    progress {\n",
       "        /* gets rid of default border in Firefox and Opera. */\n",
       "        border: none;\n",
       "        /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
       "        background-size: auto;\n",
       "    }\n",
       "    .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
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       "</style>\n"
      ],
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    {
     "data": {
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       "<IPython.core.display.HTML object>"
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     "metadata": {},
     "output_type": "display_data"
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Better model found at epoch 62 with train_loss value: 0.09634526073932648.\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "\n",
       "<style>\n",
       "    /* Turns off some styling */\n",
       "    progress {\n",
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       "        border: none;\n",
       "        /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
       "        background-size: auto;\n",
       "    }\n",
       "    .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
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       "</style>\n"
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    {
     "data": {
      "text/html": [],
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       "<IPython.core.display.HTML object>"
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     "metadata": {},
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Better model found at epoch 66 with train_loss value: 0.0962885320186615.\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "\n",
       "<style>\n",
       "    /* Turns off some styling */\n",
       "    progress {\n",
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       "        border: none;\n",
       "        /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
       "        background-size: auto;\n",
       "    }\n",
       "    .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
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    {
     "data": {
      "text/html": [],
      "text/plain": [
       "<IPython.core.display.HTML object>"
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     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Better model found at epoch 67 with train_loss value: 0.09576054662466049.\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "\n",
       "<style>\n",
       "    /* Turns off some styling */\n",
       "    progress {\n",
       "        /* gets rid of default border in Firefox and Opera. */\n",
       "        border: none;\n",
       "        /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
       "        background-size: auto;\n",
       "    }\n",
       "    .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
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    },
    {
     "data": {
      "text/html": [],
      "text/plain": [
       "<IPython.core.display.HTML object>"
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     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "\n",
       "<style>\n",
       "    /* Turns off some styling */\n",
       "    progress {\n",
       "        /* gets rid of default border in Firefox and Opera. */\n",
       "        border: none;\n",
       "        /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
       "        background-size: auto;\n",
       "    }\n",
       "    .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
       "        background: #F44336;\n",
       "    }\n",
       "</style>\n"
      ],
      "text/plain": [
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    {
     "data": {
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       "<IPython.core.display.HTML object>"
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     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Better model found at epoch 75 with train_loss value: 0.09555771201848984.\n",
      "Better model found at epoch 76 with train_loss value: 0.09522632509469986.\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "\n",
       "<style>\n",
       "    /* Turns off some styling */\n",
       "    progress {\n",
       "        /* gets rid of default border in Firefox and Opera. */\n",
       "        border: none;\n",
       "        /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
       "        background-size: auto;\n",
       "    }\n",
       "    .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
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     "data": {
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     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Better model found at epoch 81 with train_loss value: 0.09490688145160675.\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "\n",
       "<style>\n",
       "    /* Turns off some styling */\n",
       "    progress {\n",
       "        /* gets rid of default border in Firefox and Opera. */\n",
       "        border: none;\n",
       "        /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
       "        background-size: auto;\n",
       "    }\n",
       "    .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
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    {
     "data": {
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       "<IPython.core.display.HTML object>"
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     "metadata": {},
     "output_type": "display_data"
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    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "IOPub message rate exceeded.\n",
      "The Jupyter server will temporarily stop sending output\n",
      "to the client in order to avoid crashing it.\n",
      "To change this limit, set the config variable\n",
      "`--ServerApp.iopub_msg_rate_limit`.\n",
      "\n",
      "Current values:\n",
      "ServerApp.iopub_msg_rate_limit=1000.0 (msgs/sec)\n",
      "ServerApp.rate_limit_window=3.0 (secs)\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "\n",
       "<style>\n",
       "    /* Turns off some styling */\n",
       "    progress {\n",
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       "        border: none;\n",
       "        /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
       "        background-size: auto;\n",
       "    }\n",
       "    .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
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    {
     "data": {
      "text/html": [],
      "text/plain": [
       "<IPython.core.display.HTML object>"
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     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "\n",
       "<style>\n",
       "    /* Turns off some styling */\n",
       "    progress {\n",
       "        /* gets rid of default border in Firefox and Opera. */\n",
       "        border: none;\n",
       "        /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
       "        background-size: auto;\n",
       "    }\n",
       "    .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
       "        background: #F44336;\n",
       "    }\n",
       "</style>\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
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     "metadata": {},
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    },
    {
     "data": {
      "text/html": [],
      "text/plain": [
       "<IPython.core.display.HTML object>"
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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Better model found at epoch 96 with train_loss value: 0.09454851597547531.\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "\n",
       "<style>\n",
       "    /* Turns off some styling */\n",
       "    progress {\n",
       "        /* gets rid of default border in Firefox and Opera. */\n",
       "        border: none;\n",
       "        /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
       "        background-size: auto;\n",
       "    }\n",
       "    .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
       "        background: #F44336;\n",
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       "</style>\n"
      ],
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       "<IPython.core.display.HTML object>"
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     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [],
      "text/plain": [
       "<IPython.core.display.HTML object>"
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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Better model found at epoch 99 with train_loss value: 0.09405282139778137.\n"
     ]
    }
   ],
   "source": [
    "ddpm_learner.fit_one_cycle(epochs, 3e-4, cbs =[SaveModelCallback(monitor=\"train_loss\", fname=\"cifar10\"), \n",
    "                                               WandbCallback(log_preds=False, log_model=True)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ddpm_learner.recorder.plot_loss()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Sample generation\n",
    "\n",
    "Since we implemented sampling in the Callback, we simply can call fastai's built-in `get_preds` function to get our predictions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "<style>\n",
       "    /* Turns off some styling */\n",
       "    progress {\n",
       "        /* gets rid of default border in Firefox and Opera. */\n",
       "        border: none;\n",
       "        /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
       "        background-size: auto;\n",
       "    }\n",
       "    .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
       "        background: #F44336;\n",
       "    }\n",
       "</style>\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "\n",
       "<style>\n",
       "    /* Turns off some styling */\n",
       "    progress {\n",
       "        /* gets rid of default border in Firefox and Opera. */\n",
       "        border: none;\n",
       "        /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
       "        background-size: auto;\n",
       "    }\n",
       "    .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
       "        background: #F44336;\n",
       "    }\n",
       "</style>\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "preds = ddpm_learner.get_preds()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "we are passing a labels vector that looks like `[0,1,2,3,4,5,6,7,8,9,0,1,2,3,4.....]`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<PIL.PngImagePlugin.PngImageFile image mode=RGB size=32x32>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wandb.Image(torch.tensor(0.5*preds[0][0]+0.5)).image"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "p = preds[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([512, 3, 32, 32])"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "p.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "TensorImage([-0.0303, -0.0196, -0.0900])"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "p.mean(dim=(0,2,3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 3000x1500 with 50 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "nrows=5\n",
    "ncols = int(math.ceil(25/10))\n",
    "axs = subplots(nrows, 10)[1].flat\n",
    "for i, (pred, ax) in enumerate(zip(preds[0], axs)): \n",
    "    ((pred+1)/2).show(ax=ax, title=dls.vocab[i] if i<10 else None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "def log_table(rows=10):\n",
    "    table = wandb.Table(columns=list(dls.vocab))\n",
    "    for i, row in enumerate(preds[0].split(len(dls.vocab))):\n",
    "        if i<rows:\n",
    "            table.add_data(*[wandb.Image(img.permute(1,2,0).squeeze().cpu().numpy()) for img in row])\n",
    "    wandb.log({\"pred_table\":table})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "Waiting for W&B process to finish... <strong style=\"color:green\">(success).</strong>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "VBox(children=(Label(value='37.925 MB of 37.925 MB uploaded (0.000 MB deduped)\\r'), FloatProgress(value=1.0, m…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<style>\n",
       "    table.wandb td:nth-child(1) { padding: 0 10px; text-align: left ; width: auto;} td:nth-child(2) {text-align: left ; width: 100%}\n",
       "    .wandb-row { display: flex; flex-direction: row; flex-wrap: wrap; justify-content: flex-start; width: 100% }\n",
       "    .wandb-col { display: flex; flex-direction: column; flex-basis: 100%; flex: 1; padding: 10px; }\n",
       "    </style>\n",
       "<div class=\"wandb-row\"><div class=\"wandb-col\"><h3>Run history:</h3><br/><table class=\"wandb\"><tr><td>epoch</td><td>▁▁▁▁▂▂▂▂▂▃▃▃▃▃▃▄▄▄▄▄▅▅▅▅▅▅▆▆▆▆▆▇▇▇▇▇▇███</td></tr><tr><td>eps_0</td><td>▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁</td></tr><tr><td>lr_0</td><td>▁▂▂▃▄▅▆▇███████▇▇▇▇▆▆▆▅▅▅▄▄▄▃▃▃▂▂▂▂▁▁▁▁▁</td></tr><tr><td>mom_0</td><td>██▇▆▅▄▃▂▁▁▁▁▁▁▁▂▂▂▂▃▃▃▄▄▄▅▅▅▆▆▆▇▇▇▇█████</td></tr><tr><td>raw_loss</td><td>█▄▃▂▂▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁</td></tr><tr><td>sqr_mom_0</td><td>▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁</td></tr><tr><td>train_loss</td><td>█▅▃▂▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁</td></tr><tr><td>train_samples_per_sec</td><td>████▁███████████████████████████████████</td></tr><tr><td>valid_loss</td><td>█▆▃▂▂▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁</td></tr><tr><td>wd_0</td><td>▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁</td></tr></table><br/></div><div class=\"wandb-col\"><h3>Run summary:</h3><br/><table class=\"wandb\"><tr><td>epoch</td><td>100.0</td></tr><tr><td>eps_0</td><td>1e-05</td></tr><tr><td>lr_0</td><td>0.0</td></tr><tr><td>mom_0</td><td>0.95</td></tr><tr><td>raw_loss</td><td>0.09004</td></tr><tr><td>sqr_mom_0</td><td>0.99</td></tr><tr><td>train_loss</td><td>0.09405</td></tr><tr><td>train_samples_per_sec</td><td>1659.48473</td></tr><tr><td>valid_loss</td><td>0.10457</td></tr><tr><td>wd_0</td><td>0.01</td></tr></table><br/></div></div>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "Synced <strong style=\"color:#cdcd00\">classic-wind-75</strong>: <a href=\"https://wandb.ai/capecape/ddpm_fastai/runs/9xdzrfdb\" target=\"_blank\">https://wandb.ai/capecape/ddpm_fastai/runs/9xdzrfdb</a><br/>Synced 5 W&B file(s), 901 media file(s), 102 artifact file(s) and 0 other file(s)"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "Find logs at: <code>./wandb/run-20221007_132515-9xdzrfdb/logs</code>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "log_table()\n",
    "wandb.finish()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Another useful thing to check is the prediction of the completely denoised image at some timestep. Our sampling takes our prediction of noise in the image but takes only a fraction of it to remove from the noisy image during the iterative process. But we can also try to see the full denoising prediction by fully subtracting out the prediction. Of course, at higher noise levels this will be inaccurate, but at lower noise levels it should be quite accurate."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "eps = TensorImage(torch.randn(xb.shape, device=xb.device))\n",
    "x0 = xb # original images\n",
    "batch_size = x0.shape[0]\n",
    "with torch.no_grad():\n",
    "    t = torch.randint(0, ddpm_learner.conditional_ddpm.n_steps, (batch_size,), device=x0.device, dtype=torch.long)\n",
    "    alpha_bar_t = ddpm_learner.conditional_ddpm.alpha_bar[t].reshape(-1, 1, 1, 1)\n",
    "    xt =  torch.sqrt(alpha_bar_t)*x0 + torch.sqrt(1-alpha_bar_t)*eps # noisy images\n",
    "    x0hat = (xt - torch.sqrt(1-alpha_bar_t)*ddpm_learner.model(xt,t))/torch.sqrt(alpha_bar_t) # predicted denoised images"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Timestep 73 is closer to 0 so less noisy but noise is still visible."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([], dtype=int64)"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.where((t==73).cpu())[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we can see the original clean image (x0), the noisy image (xt), and the model's attempt to remove the noise (x0hat)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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BGIMrr7zyF499yCGHAPhxFe9PPfXUU81WzP6ntofU/3RSHw6H8fDDD++2fRD9X3D44Ydj2LBhmDlzJhoaGnbUtycHPPLII81+/umnn0ZjY+OO7XYyMjIwfPhwTJs2DbFYDF988cWObT6fD+eccw4efPBBzJkzB71791ZjLol+CyNHjkRlZSWSyST233//nf63feHX9vSYRx99tFn/k08+afvs69q1K7p06YIFCxbslBL1Uz//9nW7I488Ei6XCz/88IN4jNt/WejWrRtKSkqwaNGiZilTa9euxbJly2zPxS95jSNHjsTnn3+OLl26iMfzSyfN3bp1Q5s2bfDYY481O/bGxkY8/fTTOxI17FiWhV69euG2225DdnY2Pvroox3Ha4zBxo0bxePdZ599APz4Zz4+n2+nc7Bs2TLxH4z6LfGb5t1kwIABmDt3Li6++GIMHDgQF154Idq3b49169Zh3rx5eP/99zF37twW/Y3Tz/Xs2RMnn3wybr31VjidThx66KH44osvcOuttyIrK6tFq3NbYsSIEZgzZw7GjRuHc845B5WVlZg9e7a6mp+IdDfffDP69u2LrVu37vjPp8OGDcORRx6JqVOnoq6uDgMGDNiRnrHffvvh1FNPVcc7++yz4ff7MWDAAJSUlKC8vBw33ngjsrKydvqG+vzzz8esWbPw4Ycf4r777vuvvk6iX+qkk07Co48+iqOPPhqTJ09Gv3794Ha7sWHDBixduhTHHXccRo0ahR49euCPf/wj5s6dC7fbjcMPPxyff/45Zs+evdOffEjmzZuHY445BgcddBAuueSSHc/kV199dccEbfvE7fbbb8fpp58Ot9uNbt26oWPHjpg5cyamTZuGVatW4aijjkJOTg62bNmC5cuXIyMjAzNmzIDD4cB1112HCRMmYNSoUTj77LNRU1ODa6+9tkV/WvBLXuPMmTPx+uuvo3///rjooovQrVs3RCIRrFmzBi+99BLuuuuuX/TnKA6HA7NmzcIpp5yCkSNHYuLEiYhGo7jllltQU1OzIyJX8sILL2D+/Pn4wx/+gM6dO8MYg2eeeQY1NTUYNmwYgB/nReeccw7Gjx+PDz74AIcccggyMjKwefNm/Otf/8I+++yD8847Dzk5Obj88stx/fXXY8KECTjhhBOwfv36Fp/DX9VvtQLxf1VZWZkZM2aMKSoqMi6XyxQWFprjjz/eLFu2rNnPbU/IqKio2GmMn6dnGGNMJBIxl156qSksLDQ+n88cdNBBpqyszGRlZTVbdaulZ2RkZLRoPwsWLDDdunUzXq/XdO7c2dx4443m/vvv32l1MdMziH700/SMnxs3bpwBsCM9wxhjwuGwmTp1qunQoYNxu92mpKTEnHfeeaa6urpZ788/Yw8++KAZOnSoKSoqMh6Px5SWlpqxY8ealStXisc1ZMgQk5uba5qamnbL6yTaLt01/1Pas8cYY+LxuJk9e7bp1auX8fl8JhgMmu7du5uJEyea7777bsfPRaNRc9lll+307OvQoYNteoYxPz6Thw8fbrKysozX6zVdunTZKaniyiuvNKWlpcbhcOw0xuLFi83QoUNNZmam8Xq9pkOHDmbMmDHmn//8Z7Mx7rvvPrPnnnsaj8djunbtahYsWGBOP/102/SMX/IajTGmoqLCXHTRRaZTp07G7Xab3Nxc07dvXzNt2jTT0NBgjPl3esYtt9yy074AmOnTpzerLV682Bx44IHG5/OZjIwMc9hhh5l333232c/8PD3j66+/NieffLLp0qWL8fv9Jisry/Tr188sXLhwp30uWLDAHHjggSYjI8P4/X7TpUsXc9ppp5kPPvhgx8+kUilz4403mnbt2hmPx2P23Xdf8/zzz7e6uYZlDP/Vit+rZcuWYcCAAXj00Ucxbty43/pwiKiV2Lp1Kzp06IBJkyZh1qxZv/XhEBH9T+Ck+Xfi9ddfR1lZGfr27Qu/349PP/0UN910E7KysrBy5cpditQhov8tGzZswKpVq3DLLbfgjTfewLfffttscREREe06/k3z70RmZiZee+01zJ07F/X19cjPz8fw4cNx4403csJMRACA++67DzNnzkTHjh3x6KOPcsJMRLQb8ZtmIiIiIiIbjJwjIiIiIrLBSTMRERERkQ1OmomIiIiIbHDSTERERERko8XpGWecPkysp5Iptcfxk3+OuVnd6D2AvC4xBbfakbLkl9EUiSl70PefTCXlDWnWS7rd8rG55JcPALCU8dKtyjQp+bhTyn6caU6z01jyWJZ+BFHlvJnkL19Lasm7h2Xpv8dZSfl9Tnc5WQ55R440+4ElDxhVLg0AME752J57/CW9qTVoM1EsT1qyTm25I/mGWJ95gfx5A4C3zhot1pfc+rTa84+X5fqRM56Uj+ta5aICMOK0E8T6Wa+rLfjXWWVi/bnjXlB7sp+5Xqyf5P+T2rPyk9li/S97yCdg7sNz1bFQ9KpYfsb7udoyevJYsb6022S1Z0inc8S65f1QrPd076+O9fnLq8X6rIeOV3tuWvaRWK8+U37PAODc6w4W6x1fXqr2XGF9JtbNmRepPa2CcoO97gA9z798oPwvr+U16PeChlx5P75krdoT9vnFurVRvu9WeorUsbxGfvbmdW1Se1LlNWLdndCfCVlh+d72g0v/Z6uzioNivTxDPmfhSv08t6uTHz7fpfR7brZXfia5V+nTPqunfGyxH+rFerRgD3UsT3SzWA8F9USf7IR8DuqTHdSeTbH35XoipPa891hvsW4it6g92/GbZiIiIiIiG5w0ExERERHZ4KSZiIiIiMgGJ81ERERERDZavBAw5ZL/SD+pLJoCADiVhTkmnmZH8qKypNFX1SWS8txfWyBoHGF9/450ixRlSUdC3mDSLGpT1s6ZNEsBtW2plLwYIh5XjguAU1vwqCxqBICUQ7lcnGneT0VSWdTodOjnzKEsXky7DFFZEJNMs+DRKIsrkmnez5TxpDuKVuv2sr3l+mX91J6s3vICtW1364u9Hur+gFg/tu4AteeSB9aK9cvPKBTrl5a8q451fu4Isb5oVZr3rZO8aGrwyfLCNQDIOedTsb7v+/ICOQAoyJGP4dRvjhLrE44fro5139Nbxfroo59Re/DDl2L503E91JaXS06UN2zqL5af/1x//ad4O4r1CaXyYj8AqIZ8DTyxQF8YdWKHGnlD76FqD459St/Wil3657PFeuVm/b5XkNUo1r+u1Be75dTVifVyh76fZLX8HPGUyou9/E79M+oIVIn1bWv0hWOWp51Yr62UXwsAdM2TF/U5/fpcYv2aCrnH10tuiNSoY9VnyIva/Ou3qD3xgPweNPn155i3xivWCwrlz1s15PMPAEGHvLA0tjlD7Qm7u4j1pspVak+jT14o2qut/D4DwCHjd/15zW+aiYiIiIhscNJMRERERGSDk2YiIiIiIhucNBMRERER2eCkmYiIiIjIBifNREREREQ2Whw5Fwtr8WVKrByAuBLdVVoUUHtScTn2prq2Qe3RZv6OpBy5Eo/rsXLaq7GU6DIASBl5PKPE5wGAQznqNLuBUSLXHEpKXDzWpI6l/LP0aY85kZBj/6xd+N3LaSljpUn8c1jy9eTU0wjV1xOP6zF5BblyJM62mnq1pymeNviu1cpv31mszxih3xquf2SAvOGTkWqPNWhfeUOmHt82c+0/xfo/Zy0T67lZ09SxZmfJ9XGZj6g9fynbJtbbu/+u9uS/JMfuXXVLVO3BfDn+6Hrl0K7++3fqUEPGytFQd5Tr8ZOjXjpXrN9zzkFqz9c4RN7wryfE8gKvfp6HLesr1r/cqn+mnrhIvgZO3Lin2oMi+f00W55TW6wnjpA3KIl7rUXTVvkeZsWVqFEA275bI9a7x3LUnq9q5XuyJ1/vKcmRn/GxRjlWLN6kj+XxypFvNUX6QyFra7VY37M0U+2pr5fnH4XKcwwAsi05Cq0hJH9+vZn6w7+uZp1YL+qkR8Q2hbPFel6OHCsHAE0JOcKuLi5H7uVacqwcAKQichydlaXH1CHgF8sJlz5nLG6Qz1uqSb/WN62Sz2dL8JtmIiIiIiIbnDQTEREREdngpJmIiIiIyAYnzURERERENjhpJiIiIiKy0eL0jLaZPrGeTOorFCMRecV4txJ9NaxJyqtBNzlr1J6MoLyyc1uVvEo3GpNXaKaTk6Mfs5asEfTL5wwAGurkJIZ0GQxOp/w7jscvr4atqdZXzOfnyMfm9WerPduq5cQJy6FfRk2N8ntgWfJraWysU8eyTESsBwL6amB/QF5FXlGxVe3Zv2tHsb5xi766eUOVfGyt3SmfHylvWP6W2mOd8a5YL+hUo/YUlbUV6+Xv6Of0YyWk4vs7Oor1hd17qmOdeOGfxHoHK1/t6Vn1jli3HtITP1Y9K9dXt9Gv0UuTcgLB8+/IqRpv5uWpY732d3msfn31z+gFj58n7+eeJWrPwGsPE+sjfF3FekWefC4BoPF+OeVn7KGPqz3ZJz8j1s3ECrVn3OoCecNNckoLABy1QH6drd26sJwUklGvP5MCxSGxvqlcv7eVBJXndUBPbtrUKD/7ssLyvSA3WqOO1ZCUnyOulWoLQoXyZ9FRoz97AgE5fidcKX/eACCSLz/NkxH5s+h0y88qAAgp20xTudqT8spRVE0NckIGAKSUKKpAvfy83OTSU6jau+TXb/L1hJtoRE4p8RSkiRXLkxNUIjH9O+F4wdH6eDb4TTMRERERkQ1OmomIiIiIbHDSTERERERkg5NmIiIiIiIbnDQTEREREdngpJmIiIiIyEaLI+eO6N9JrMdieuRIIiHH0WXISUoAgGRU7umQK8fKAYDXL8exNIblCJ14Qv9dQYt8C/gDao/LpUTBpTm7jQ3ynpwuOfIFAFxOeUCXR379tWki51yWHAUXzMpUe+Id5bgiAz12p7FRfrNdLvm1RKP6+5xQIgwdSnwdAAQCcrxgRa7ekwk5eqjDXiVqT1UsXVhg62UdLb8PC9e/rDfdKcdJfRTZoLa8OPD8X3RcALDf5XI957I1Yj2rix7FVm+dLtaDA3uoPc/dLNfN7WPUnk2Ri8X6ZjNW7elyoRz5lhgtX1NnX3GmOtYBa+QIMDNSfv0AYDnke1vD3UepPcGJz4v1S3GEWHfhNHWse86cJNYvsE5We3DxXXK9UImVA/DYVXL9kgv1eLSXpygb9LegVchyy/cql1t/KBVUrhXrMY8e95XyyHFfe4f0Z8/Gr6rFemHuHvLP5+txjR3i8nOsvKhS7alLys/YYr/+7Kmqk/eDbP157fbLc5nEWvn54nfLsXYA4MqSP9frU/q5QUKO9isOyfMiAPi+Sv4sFCjTn7xq+b0EgOqCQrnHsU7tcSNX3mD0aGMrIj/jq+rkaxMA8oP6Njv8ppmIiIiIyAYnzURERERENjhpJiIiIiKywUkzEREREZENTpqJiIiIiGy0OD2jfbG8sjMe11fJqpRUDQBASl7Z6XDqq1TVoYy8+jyR0vcfV47N6AuIYZBStkTUngyf/HrcLnmVLAA4lINIGjnBJOjRkzAckJMw4NDPs1GyRZIp/RrI8sure7UkEGP0aJVkQr4GE3H9/dSOOcMrr+wFACfk1+P26sfmzfjl12drcPD668T66dATBc6AvJR6iU+/dmfMlusd6/VVzDXX9hHrcw/8SqzftfxxdazgnCVi3QrkqD17TDxWrB93s34z+AYPiPU/Dv5M7Vnc/X55w8jjxPLyz/RzlnnWG2L9L+Fn1J5Pj/lYrAdv0F9n3zseFOt3XSgnAyyLbVHHmvW2nHpyxXl6msEZufLr7KGcSgDQztrcDXrixtzH5eM2KNJ31Arkx+SEAteeSjoBgNot8v3VpSQqAEBGjZzc1BDQr9FEkfxdXZVf3k+oWkmuAFDfWZ4vuL7Rrx1nUH5eb0xUqD05RcVi3RfWX2d9nfz5ycyQz3N1Qn+Omm3yfrJy9fSMWF22WG/K2Kr2eErlY/Nsk9+D7PzO6ljJCnk/TY35ak+0ZqNYz8rVEz9i9fJndOtm+doEANdBLypb7GNx+E0zEREREZENTpqJiIiIiGxw0kxEREREZIOTZiIiIiIiG5w0ExERERHZ4KSZiIiIiMhGiyPnPC45vsMyuxA555ZjwADAspRtSnwcAKSUyDGXkpjkdehRSpZTjs2Kp8mcMw75dw/LoZ8bK6XE1CW0+DrAUjZZTvncpIxfHSuV0s5zmmw9hcutR7skk/J7k9DidSx9/ym3fJ49Snrej8PJ45k015PTJX8s4jE52g8A/L/TXz/PvK+rWN9/gh4ldDbkWKDwfRPUnm1nLRLrR5bob96ia78U6/OXy+9ptvlIHcvqNVWsX/CMfh3MG3S8WF96S0e159A/yTFLs666Wu35/KhvxfqxZk+x/ula/R7xXeQwsd6u9DS1Z8rmD+UNf1Vb8OSkE8X67a/OEOt3nfapOtbMsS+L9WH5B6k9SyueEOtnrj9S7dnnhzPEuml7odqDdtPkun7ZtArV+d3EerJcj/5zbpM/ixn+tWpPMlOJAS1PM7XIUO79q+XIuVSamDzUylFsnfz6c6TCknsK0zwTHE01yhb9GZtp5Di4+kz5dQbjenxckxLrmgo0qD1Jq16ue/X4tpxt8rG53XIsZI0jpo7lteQox2BEjxAMFMrH1lSlvzfbsuV7bp+ifdSejXX6/cjO7/RRT0RERET06+GkmYiIiIjIBifNREREREQ2OGkmIiIiIrLBSTMRERERkY0Wp2ck4vLqzXhcTxTQkgssh776G8o2K92hujzKsck/HmnUj7m2rlqsV9fLq0oBoLZeXsHaFJZX6QJAwCendLRvU6j2lBTKK1gdRk6oSKbkOgAYLT0jjZSynw0b9RXZPp+8IjsrS151bSlJJECa6ylN4oaWkpGuJx6TVwSnS9wwv9PfPydMOEmsp/72sdoTmyRfB5/XX6v27LnxDbE+6tmD1Z6TrUvE+r1XLBHrd+fo1/tNMw4V65l7PKj2jHvzK7E+Y/BqteeBP8nX1fijctQenCmnZFxtPSXW3wqfrg51SLGyoUl/nV+svUqsL+igr7KfDnml/3UN8nt2VdFMdazamw8Q6zNflpM4ACBRPEesP/nFpWrP6df2FOvWm3eqPce2HynWn4P++WgNMjevFOtBv/wMAYCqgJyKk/Lr6RVWVL7v1RTon8WsBjltqXZPOYmifVVUHSuSIb+ezdCTq4K+TLGeHdfv79VGnpc0uvVnfKZfnkvUJeXXn+eW0y4AIBSU5ywOU6r2ZHvllIrKJv1zHU2Ui/UaZ0SsByN6ekeVV56XISvNs7JanhcV5+kpS+54B7FeF6lVe3w1ae7HNn6fT3oiIiIiol8RJ81ERERERDY4aSYiIiIissFJMxERERGRDU6aiYiIiIhstDg9w+GU59eOpD7vdjicct2VJtXBUsZz6P/Ge2WVvIL1/fc+EesVm6rUsZqicnJCRZXes61SXqXp8erHHArKq0FLCuVUCQDo2aOzXO8u17MyA+pYKaOsLrb0ZJNYTO7ZulX+t98BoE2bNmLd65VX3yeT+v7VawN6EkYqJY/n2IWUjnTpGQ6Hfgyt2ddvLRfr3efpr/VL3CPW511yt9rzbrWckPDRpFPUno2z5MSNjzco+6l+XB2rH+SEhG83ySkDALD+qafF+huD1RZguHze5twqnzMA+PtoeZX5xeddKNYPGTZZHeutwI1ifcgVagueeOUGsX7llXIdAO674QuxvtfIzWL9q0P0NIMOg+UkimfxV7XnsPPkczA2/rzag3Pl/bQ9/hO15R9nfqOP14rVheTEi7zGGrWnPiI/r6yEnhbjzs4W64Go/oz3uOTkgtwKeTriCOr3ooyQ/OyvbsxXe1IB+d6/oUJ/jnWB/Bmt8urPq5pGOQmirUd+nZ58PdEh8Yn8+akKyQkdAOCFnHgRkMM7AADukJze5UjKn+uGpJ72la8kblTG9XNWGpDPTQXkuSQAZAfl96Zpi55GsiJTT2Sxw2+aiYiIiIhscNJMRERERGSDk2YiIiIiIhucNBMRERER2eCkmYiIiIjIBifNREREREQ2Whw5Z1Jy7Eu66C6nElOXTOlxNJZLjg+prNYjQha/+C+x/tGKz8R6hhJ3BgCBUKZY31rRqPZU1TSJdb9fjyELR+Rz0NQkx7QAwObyCrG+rWKbWB8yqJ86VihTjhcy0ONgtJi4vffeW+1xu+XYHS2+Ld31pEXOWWoUXfrxdmcP0sTRtWbdX5Sj4Oa1n6/2rEzIkW+ru+n76dRrpFgftl9/tef1I+X9LNxPOa6/fquOdeLkDmI9+ecT1J5JN8vv6Q13LFR7zprbSawv6HGO2nNxr/3F+okD5fdmrL57PFgiH/Nw+WMIAHC9KN+nVv1zutpzKPYV6w+8cppY7zD7eHWsKgwX6xGXHgH2yntXinXryVvUnhfPGSbWFxwzS+2Zjme1Da1aaKt8f98W1qNTszPle3+mu1TtaUzJUWjxcJqpRWGNWHYl5RjW7/z6fCGzXI6cKy7S5wu+hPwcq/fqsYgVRo5Wq20oV3tCIfkcJJRY24Zt+rwkWSx/rr21+gfbWyDvp36dPi/xZ20R66ZRiXzLks8/AHwTl89nm1Rc7amBfMzJqP58rc4oEuvOtnJMHgB0/bqLus0Ov2kmIiIiIrLBSTMRERERkQ1OmomIiIiIbHDSTERERERkg5NmIiIiIiIbLU7PSCTllZApJVUDAFJJJYnBqa/erNxaL9b/8dIytefNZV+JdYdDTuII+OU6AETi8jHXNeqrcSNJ+RxYSb3Ha8mn3nLLq54BoLquQax//NGXYr0wR04CAYDefXqKdY9Xfz+1kIpAIKD2JJP6ymfx59Mkq8CSrxulnFYqpaeEWOp+9B1pSTGt3cPfHivWVyyWrykAuHq2nPbQcOUf1J7Xjn9XrE+fs1jtGbyHnAqz/9pjxPoFz12sjnWqGSvWV0JeFQ8AB+0hv9/31eqfkQUX5ckb4nupPd0WyekRWpLNtg83qGOl5tSK9Zc236b23GS+EeuXXTBQ7elWtUasv3m1fI8af7WebLIND8sb6grUHuuxdmJ9UJoQm6dHrpLrB/XWe7bI18AEtO60nPxO8muNb9XTmTaG5OditrVG7XF55bSYICrVnso6+bmY5ZOTEzpU6s+x7Ez5OVqdKFF71tfIqQo9i5XPLoDGenle4klkqD1a2lQgFJT34dDTM6rXrxHroZCe+OHwy+fGVaj3JB25Yj0cll+/leb1901uEusbY9lqT1Q5B7GEfG0AQKRKvucVlhSrPWXeXzYv+anf55OeiIiIiOhXxEkzEREREZENTpqJiIiIiGxw0kxEREREZIOTZiIiIiIiG5w0ExERERHZaHHknNvtE+uJpB4FAocc11PXqMd9vPjKO2L9gw/lWCQACPiyxXpTU1isb6usUsdyeeU4mOoGPaoHlhw/FNQy2gBk+OXzaVJp4mDi8n6iym4+//J7dSxfhhwTt1dPOUIIAJxK3UB/P40SSeh0KaOZNBGGyjaHI01M3i7k0RllP+ni8ywlQrC1e2vN82L9fdNd7TkHchTY7dajas913w0Q6/3T3D7uPl6Olut2jhwTN3E/OT4PAMqefFusbz2xq9pj3l0j1l9qo19T102aK9bH4WK1p8tzT4v10OIssX7K1lHqWAt73SXWrfPk+w0AGKNEY/or1J7nhslRUw965fu3GXekOlaHBcrnd8W+ao/JXiLWz3BOUnsmolGszy5bofa8efM6dVtr5m6QP1i+sBwpBgA5kSaxXussUntMtnzthPPaqz0lcflZ6qp2i/VUUH8mNuTLDz/TtFXffxv5M7/V0p/xOR75fJqQfgMrVx4JJVZcrLs3lqtjdSyRI/TKI/qzLzsqv58xtx4H6LPk2L2tWfK8yB3V51LuQLZYTxl5XgYAcad8DWSkeU5kbZNj6lyFepzoXu1q9AFt8JtmIiIiIiIbnDQTEREREdngpJmIiIiIyAYnzURERERENjhpJiIiIiKy0eIl/0b5UcupL2u0/PKqxhXvf6n2fPTFGrGeSnOoVkJeweuBvErVcupj1YTllfH1EX3FfCggJ0EEnHJCxY/HJnNAP59+n7JSWEluqG7Q0x7eW7FSrPv8frWnZ88uYj2Zks8zAKRMSqy7XMp7YOnHrKV3OBy//He/dKkaiYS2WlvvcaRJSmnN7nvqYrH+rqWvPv9h+sNi3TS+rvZcdJm8yntCUD+n07rJyR41M+WV3O3/LK8wB4DaP24U6+Gy+WrPhWUPiPXOf1FbcMlZfxDr57f7Qe0ZdaG8yv2UveWECKviTnWsH56REwDalF2h9qyrl9+DDh/frPaU958qb5gxSCy//MD5+v7nykktpt/Xas+Dd54p1oPn6ffcftdki/XHZmh3Y+C7jdeIdT2/pHXwVYfE+iq/npwQdcvJCb7serXHGPmzWJzapPbUfJct1tt0V55j0JNfssNyT42SBAIARTF5vhAPKCkyAJyZ8nM5WqFfO52jclrLBpf8fPF55PkSAASj8rEVhPXEjwpnrViPp/RnVdy7Sqy7YvKxpTL092ZLfZ1YD0RK1Z5SX7VY3xqTUzUAoM7IPY31a9SenuGB6jY7v88nPRERERHRr4iTZiIiIiIiG5w0ExERERHZ4KSZiIiIiMgGJ81ERERERDY4aSYiIiIistHiyLmUEitmLDlSDACqquTYl2XLPlF7kgk5WCwY1KOErKQcZ9XYIEe7xFx6TMy2bXK8joH+OtuVFor14kw5jgcALEuJikkTuebxyG9XMqmEsSlRdABQUyPHwXz55VdqT+fObcW6U0+DUaPdkkn5fO5KfFw6WnxcKqW/n06nFm6nSxdh15rNK5Cj5b7q8Kjac1Tne8S6LyDHGAJA27vk87P+4y/UnvsX9hTrrj/LF1zoof3VsYZ9XSzWyzN7qD0fXjJDrC+8VL7fAMClSkrbQ9anas9Hky4R699Me1Osr4ufoY5VNus4sX51msvzoHGfiXUz4xm1xxoon4Or7vyzWB8+Xo6VA4A2Ifn9tNJEPGLEs2LZ4A9qyyz57UTXR/R73rVD5WOYDv0aaA3W5cr397Zu+R4OAFu2KFGbjhy1JxySo9ACublqj6O4Rqw3pbrKP795nToW2svzknbubLUlqTwTsrP0ZwJi8nM54m9QW+r8chybR4lv82dmqmPFK+X7dJNHj3wL5sj7ccf0eUkqIZ8Dv1OeM1Ul9WjBnEQnsd6Yt0XtKd8QFuvZ2fpcJuiReyq27an2vBCR73nXqh3/xm+aiYiIiIhscNJMRERERGSDk2YiIiIiIhucNBMRERER2eCkmYiIiIjIRovTMyynvHrUGH2F83ffbBDrFVsa1Z6AT17Z6XHq+4lEasV6Y1he2botrqcjNETk1cB9enZWe44ZdqBY/+rzj9Wemho5PSOVUlYwA/B45HOQSMjvjdOlr5LNy5ETP6qqKtWeDRvk9zO/UF9draVKaCkZ6ZIrtG3pkiuSSfncRKPy+2x3DBorTbpKa+abJqdknLmqRO1Z9v1msT4UIbVn5DsfifX7RssJGQAw4z75erv7ttPE+sRhD6pjWXdPEetzL9HTBD5cdaRY/8ycre/n/nvFeukBd6s97VadKNYPO7BarH9yjH69j8VJYt1brl+fY+9+UqxfM0i/f33YXT6G00+S389EqXz+AeD6p26Xe8zf1J7eJaPEunWL/t68bMmv8/UbytWeMVe27pQMVe0+Yvk7s15tKSnJEutN2+TkBgBwO+RUhY1r9PtrSUmBWE9skZN0XEV6PFPF+jyxXlyk38PdRk4WaazVp0P+hJxSEUjzvaMnLH9+jUNOAvM2yfMYAGjKUJ6xKTmhDADCFXJ6RlOhPseI1cuv0+WV308LHdSxotFvxLoj0VHtycuUj60+oc8xEJPTVRI+fS5zaHc9ackOv2kmIiIiIrLBSTMRERERkQ1OmomIiIiIbHDSTERERERkg5NmIiIiIiIbnDQTEREREdloceRcU6Mc32almXZv3ihHfpiYHpnk9ssDul16TEpBezl2puOepWJ9+Rd67E5jkxytMqi/HlGy7957iPWaKjkKBQDiSXk/ibgeDdUUlqP6mhrlsQrz5WgfAMjMzBTrdTXy+wwAsXhcrKeLaDPml0U2pYuCc7vl6CGPR449AvRoO5dLv/S1Y04Xbfc7DabCBPdIsX7IhGy155oH5LjCD/P1aKorhpXJG1bdpvYke8hxcLnXyT//7A3y9QkAfz1illgPvXax2nPL+1eI9Yz3XlF7upfME+uZK65We253DhLr8R6XivUR8/Vrd9jrr4v1/ovPUHseWXuIWO96snKiAfQ9T66fdG0bsX7K+UoDgH/UXyPW791zktoz7onB8oZD5Mg/ABh+gByHN/gIv9oz+PgV8oZuux5Z9WsIur6W65Z+7YTD9WLd7dCfvZFEjVhPejLUnuqtMXlDdrF8XE16RG1pe7meiq1Ve1zubLGe4ZQj2gAgki2ft4QSHQsAMWe+WDfVYbFe69CfIqESuccK6xOwiEt+Lsej+jM+7JGPwReW6y6XPsdx+XLFep1/m9qTlSu/B556OToWADKy5efENuhRhe6Afn3Y4TfNREREREQ2OGkmIiIiIrLBSTMRERERkQ1OmomIiIiIbHDSTERERERko8XpGYC8EjOV0tMOojF5lWy6RAW3U06PCOgLnNF/oLwqunOndmJ9r8/19IzX3/pIrHdok6P25ObIK4WPOvpItWflyh/E+ldfyHUAqF71jVhPKAtL06ZaKHUtoQIAHEp6RLokimRSfj+1IIpUSk8P0bYlEvrqbs2u9ITD8gpmAAiku0BbMWN9ItZPe2CC2vMnjBDrw2/S93PCSPmKO+yKS9SesqEXivW/KKk0ZukSdSxr6HCxPtycq/ac/tFEsf5k4h9qT5fRchrJ9+YWteeEz7LE+iPzThDrH90tnxcAuHvi02L94g/+pPa86ZZX03/T/ni157Qn5e9bHs7tIdafbnpVHeutgHwzmFysPyce7CPf20KQzxkAvDn4erHeJ3mG2nNutwOULa07LyfWJJ9Tv0e/V9dA7vFG9e/W3L5suSes38fhllM6vKkisd4mR07rAYDaJvna9TTIxwUA7sKQWLdcaZ49jfK9P9Orn5t4VEl1ypLnCyYup40BQDwiH7MjqSdh+Bvla7TRoSd+dArJ85xayO9ZlkNOKAOADcrc0N+kX4PVypzRFdPfG1c3+fW4PtLnMk2Fv/z5vx2/aSYiIiIissFJMxERERGRDU6aiYiIiIhscNJMRERERGSDk2YiIiIiIhstTs+wlPQMh1Ofd2cE5NWLeXkBtSc/T/63xwvylZWoAEpL5NXnmfIiVfTuqq/4XL92g1hvqtP/vfR4rFisG0s/vd//ICcArFql/1vutdXyCtZQ0CcfV1xPNqmqlLe5HPLqVQBIJOUVp/F4uh452sPrkd9nS4vVgJ4Gki6NJansf1d6IhF91bEzzeegVeu3USwnHp6htlSfKifMrDjraLUnar0m1uu67qX2LKz6m1h/8IPDxfqI/S5Wx/oE8sr0XoMz1Z52+8jpGT2669f7mzc8JNbnHXq12rN1tZzs8dya78X6DSfsoY6V9YSckjH1ygFqT0XR/WL9ZsxVe/Y7ub1Yf2iRfI+ajE3qWMFVC8X68yP0e8FIr/z5tfSPKBZ3let9ksvUnmv04Vq1vd3yPWx5nvysBIBSt/xZqHbrqUGJBnk/8aC+n+xwUKzXKGkPMa9+7bSLyp/FujbyMxkANtdUi/VgUE6oAABvpjz/cHj0a9TRoKQ3NMmvM5qSn4kAYDnlCzuaJm0q6pfPc1F2mgQV5dZmOeQ5xqZYXB3L5VSSMDz6MXui8n0l5ahQe1aXK3OZlH6f7pjbS91m53f6pCciIiIi+vVw0kxEREREZIOTZiIiIiIiG5w0ExERERHZ4KSZiIiIiMgGJ81ERERERDZaHDkHyNEiLocSqwKgxx75Yr2pdova43TIES5dOrdVe3KCclSOZeSYmC2V+v63VMhxNC6vkl8HYFuFHGe1abM8FgCs/PB9sd6ubaHaM3hAP7FeVSnH4bksPUJnwzo5aqxdBz0qKDNbjsRJpPRoOy3aLRaXY2csh/573K5EzmnbdiVyLhiUI3x+HE+P0WnNzj+xs1ivD56l9uQU3q5skCMJAeAKNIn1mrAc8QgAL75wgFg/ue93Yn0R3lLHWmFyxfqc199Ue+5NyIFjfS+Xo+gAoOJz+dq54BA9tuuCvtPF+sFfnivWh9z2D3Wsf07pItZPDa9Qe0ad1UOsHzZDvkcAwNQ2F8obPlos76PwOnUsx73fivXe+kcUx3R/XKxfjJPVnrX/HCTWl970ttqz5EP5eXR93zQH1wp84moU690a9IiwZXlVYj13o/6MT3nke78vrGf/pZQ0uKykfK5dFX51rE3ZBWLdqpCjDwGgKCSPZxn9dSbq5ed/Q12D2pPZRn5eNNbI8wWfU3++RJrk82lF9OdlQUC+54TX6XOZks7yOahokvcT8urXU7Jcfh44QvrrrHTIx9wmqMcUZ+bI87zVWfoz+avNX6vb7PCbZiIiIiIiG5w0ExERERHZ4KSZiIiIiMgGJ81ERERERDY4aSYiIiIistHi9IxYVFv97VN72pTIK9Y9/fZSexIJecVj2uSCpLySORqVV282KXUAcLjl1aN5efJrAYBYVF5BHIvqK4j77CefgwP6dld7OrSV00hSRl6xv/qHCnWsreWbxXrnLh3VnmBIXsGaTOrnU3s/I2F5dbPHIyd0AIBlyb/jmdTuTc+wLHkVt1YHgFSaY2jNFr7eS6x/esJ7ak9149/F+rtPLtN7rCPE+t0N+r2g77qPxfoHyBbrj10mvxYAGFtylVg/OHCj2rPH+fIy/3OH/EvtueOMm+UNDz6k9rw3+AF5P0eNF+uzTpWTdwDAfLJe3vDwIrUHPYaI5Qva5Kktd74vp6uct+gSsf7IZ/L9BgDMafJY1oej1Z5rbhkh1mfeMVXtOfu5GrE+Y6C+kv7Ed5UNfdWWVqE+KT+vvjO1ak/eFvkZ60nzvDQeedvGiJzoBABF9TliPdcvj2WU5xsAeOTgBLgSetqCPyU3hfXbOzIS8uux2ioHACDcWC7X3cq0KxlTx/IWyOfGq4d3wBWRz1udW5+XRLYqY2XIJ6e+Uj9pfre8LRnWz5m3UE7jcDTp5yYWkeemiViaxI1GPaXMDr9pJiIiIiKywUkzEREREZENTpqJiIiIiGxw0kxEREREZIOTZiIiIiIiG5w0ExERERHZaHHknIEcqZVK6XEwTqdTrGdlZ6k9DkvuSRcR5nDI0SbxuBxfUldTqY4V9MsRejnZmWqPyyHHquVkZ6g97doeINZLi9LtR349llN+/U6XHhXUtl2BXG9bpPb4/fLrMUn9dy/tPbCU39fSXU/RiBzt53Lpl3EyqY+n0a7bREI/n1A+H61d+JwTxPqwQS+qPcu7vCDWv1rTXu05a9kosR7o31bt6XaesqHDh2J59LBT1bGeeXCJWO97mx6FhmElYvmIoi5qywGPdBTrT16m7+bgt84U619Cjlna/6Vz1bE+mzxbrN/0+N1qz6NXfSfW/3Xn42rPSzefJNZrrb3F+n1v16ljYd4zcv3Ud9SWIZ+tEOsz/ry/2nPfq3J93Kdj1Z6h98qfa+3SbC32jMqfq4pO+rOvpla+V2Zl6M/rkEO+j4cj+v0ww5Kj7eLKM8GZod/fU2E5VtUbl+/hAFCuJK6ZkByFBwAJlzwv8Nbr+4kqzx5vk/wcbQjVqGM565vksfQEQRifHO3m8OvxvTHleb2tQc6iywzKMbgAUFsdEuv1MT0nr7DBL9aTCfn1A0CkWp7/mIw0McGOXz4v2I7fNBMRERER2eCkmYiIiIjIBifNREREREQ2OGkmIiIiIrLBSTMRERERkY0Wp2cEAgGxbln6vFtLIUifhKH0pPQen5J4EY8raQcpeVU6AHjd8krMWERf8Znbvo1Y37r1B7WnpChbrAf88opXAHBCPrYk5Nfp8ervTec95NXV2dnycQFAbbV8Dmpr9HNTUiqnceTmyitbm5r0scJh+X1zKSubf9wmX+KJNKkabqXHoawUB9KnfrRqxx8rlu+dc4/a8s8n5CiIcRPK1Z7JncrE+kV4Q+15O2OEWP9j09li/fj79GvngnbtxPo7p92i9pwKOT3j73sOV3s+niWvPn/9i4PVHqPcpla+8IlYf2jdiepYe8YvF+sDh9+l9lir5Ot9HW5Sez7r82ex/tHZV4n1NnJ4CQDgoRdGi/Xgi8vVnqF3y/fJYJWc0gIADc8/LNbPtvRzU/fWYfKGytadllOXK6eVNEb0R75TuYWlcvR7W1OTfH8PZm5UeyJN8nhOyFEQrpCeUJGok6+DcpcSkQHAFZfTQCykefY65VSHzHo9vsJKbhPrTR75OWKVy893AIgZ+fXEEnJyFwA0FsrPS0dMTyxKxuTrozDuFetbg/oxR71yUkteslDtCadq5A1G30+qsFis+2q+Unt8CX0OaIffNBMRERER2eCkmYiIiIjIBifNREREREQ2OGkmIiIiIrLBSTMRERERkQ1OmomIiIiIbLQ4cs7tcssbLD0KRIvocjr1uboxcoSKw5kmjiUmx7FoMXV79+ymjlUf/kas19XI8TEA4LDkyBMH9FiTZKJRrMej+ltiLPn1uP1yHI7Xq0expZLy+XQ59Z7vvv1arH/ztVwHgD777yPWu3fvLNY9HuU6AxCPy9eGtQvXYLrYw2g0Ktbdbv3YtGi71m5sv6BYX3jvGLWnW+Z6sR5aon9GDjFnivUv/vWt2tPz4L+I9WMXyGMFtkxTxxrV9g6xXvu5/F4DQOiaQWL98af1WLNe8weL9RuKXlB7al3ytZhZtkysR2YNUMdqf7xcb6i6Xu054dHPxXq7G85Se+bKH1/cOlH+LJYt3lcd6/DQvWLdg35qT7hePmf9HpGjLAEgOFm+1gd3naD2ZH7TVay37sA5wB9WIvly9HtlpErJPqyX4+sAIBJV7q91ekxdUHmOObPlsRqq9Vi1TOU5Vpslx8oBQLYyL6iNbVV7Chxy5G59QD+fSXeBWA9uVeIAXfq8qB5ynGawQI9vy6qpEesRr37MnvgWsR7Olc9nkUOP3NtaKz8T45nfqz0hp/z53dSkxw7GP/tUrOe33VPtWR3T7/t2+E0zEREREZENTpqJiIiIiGxw0kxEREREZIOTZiIiIiIiG5w0ExERERHZaPGS/2RSWQ2bJrkgFpPTI7SEDABQwg6QTCorewGkUvJ4Lqe84jI3O6SOZVLyMRcU6qtUMwLySuWc7Ay1x60tBk3pq461BJFUQl4N7HLKxwUAyaS8erSuVk4iAYCmxrhY7959L7UnP09eQZxSVjCnjP4+e5T0inTpGdo2j0c/N1qyRirNdZtK6O9ba/bESXJ969H3qT2FA+XVzy99W6P2uDsdLm9Y3V7tueD8K8T67fnXivWDHjpUHatuQplYty7TkzA+fvpJsb7fWwPVHpwvr3Jfev6taktPnCfWC2bJP3/Twe+oY81qJ491OuSEDAB4ruI7sf72DSPUnjmfvSHWb5NDSnDb2nnqWC9CTim5t+eJao//kblifcgD3dWepr/J10C3Zw9SewbUPShvyFRbWoUCyPfKLLecAgEAlRlyopPl9qo9cbd8vSeMvh+HR77HRyz5nux36ffqxpQ8lnub/FoAICNDTmhwWHpKyLa4/Ia7jX4h1PgqxXoiIKddIU9/jgXr5W2R+iZ9/0qqV3t3jdoTDrYR6wmXPC/wNuWrY0Xc1fJYFUVqT9QhP2MDefr3uz5l/rXN0ucyHWv147bDb5qJiIiIiGxw0kxEREREZIOTZiIiIiIiG5w0ExERERHZ4KSZiIiIiMhGi9MzEkoSRrr0jKSSKOByp5mrK8kFltHTCYySOJFUkhhSyopbAAj55GPLT5OEYUFOlbDSpC1Em+RVr6mAT+1JWvKxJaNhsR4K6SuYKyq2ivXvvvtC7QlmyPvfo0up2pMRlF+PpaSUpBLKdQYARl4Rbiz5mgGApHJ5upz6pe92yq8zXXpGUkkDae1mZMmpBsnJe6g9706pEOvvn3el2rPf1+eI9cb9Jqo98z55QKxPN9eI9dSHenLC4WetEuvvZH+g9hwIOb7igqeGqz13DJVX4E/fJieBAMDo8Etive6hE8R64jQ5bQIAzEIlNeDOh9Wev0zeX6zfP2CS2gPn3vL+l8mfg6cGXKUOtXU/uZ6f+7jaY32ujBfRk3zqP7lBrPcf8bza0/0m5XOtB3u0ChuDcqpFY43+HHW65W2mSk/PcFjKMz6gxUMBtTXyfTw/J0vev1P+eQCwzBaxnvLrz9GaWvkz4lT2DwBwyD21aRI3wluCYr04kCP/fJX8ngFAtlNOwqip1+cyyKgVy65IidoSya2Rj61anq+4nPocIx6WPzv5Xj0NpVaZsyXK9ed1ZXFnsV7v0J/JeZ3169MOv2kmIiIiIrLBSTMRERERkQ1OmomIiIiIbHDSTERERERkg5NmIiIiIiIbnDQTEREREdloceRcXW2NWPd49Dgap1OO9UgosXIAoCWBJZN6VE4yIceUJCHvJ81Q6NROjnYJBfRTlYjJkW+puL6jhli9WM/w63EslhKhkoScqxbICKljFeRlivVNsc1qT2FBrliPxyrVntoque5wyNdGuuvJghz5ljR67KFRrrVouvg45bpxuPTfMdMkL7Zq07+5QKw/USRHDAHAzCPka3TkCDl6EQB6Jl8X6+0/2Uft8Z8yW6zPwPdi/aVTC9SxavrKn8XJRy9Xe/rgr2K9dO8/qz03nHmzWJ+x9wy1Z2bOWrFuzvWL9SPMUHWsB/CRWF942Flqz95ysh/2ffdqtacSh4j1XDk9D6/eK8e9AcDobU+L9dV/0j9UUz7rKNYPnnyb2hNaLEf1mba3qz2Hn/SuvOHEAWpPa9Bzc1SslwfkGDQAWJGQo91KivUYUKdDiRwz8jMRAPxF8rOvpkp+xmY55GcVAGRkFor1bY36HMNh5LhVf1iPlU1G5edVICg/EwEgkpIffslcOb4uvKFcHcs07SnXQ/qzNxqTn3HrC/Vzk9Ekx9EFMuRjTqWZQeZWy+cs7NWfLV5/RKxH00TbebLkqL726/PVHl/+D+o2O/ymmYiIiIjIBifNREREREQ2OGkmIiIiIrLBSTMRERERkQ1OmomIiIiIbLQ4PSMSkVc1hsNyHdDTM7xeeZUuALjc8jze7dJ7LCgpHQl5NX80qq/yD4XkVZpOp76SO5mUV6mm69ECRIySEAEAUeU9cCmJE/V1tepYLiUJIj8/R+9xyj1aHQASSrJJNCqv7rYsfSxtP4lEmjgURVOTvoLX4/eJ9VQiTeqL4/f5+6d1uFw35g9qT/0rY8X6yDYHqz2n33OhWPfjC7XnHKwX6zfgJrEe/0r/vE398imx/lfcp/bMj0wU69+7Jqg9F7xwvVhvLJ+u9syxDhXrx0+SV7KfBjmhAwC+u3O+WH9rn7PVHqtEXpm/pewfas8ruR+L9fNHyKka/r/L+wCAlx/8TKy/1bhB7fHtK4/3DkapPUdNlpNiMLOf2rOkg5aS0brTM37oKd/HE5n6I7+4Un72mTRJGNkN8vOqwuj78Snbgtny5zea3KaO5fLKaVeuNGlG7rh8blJ6SAd8EfkZm3DIqRIAEHTL+4mvknusRj3ZxJFfIY/lzlN7tOdYIlan9tTVKElgXvkZa9zy8x0Agh75Ge8p0edfTWvl52jYKb9+APBula/BaEB/Q9cE5HtrS/w+n/RERERERL8iTpqJiIiIiGxw0kxEREREZIOTZiIiIiIiG5w0ExERERHZ4KSZiIiIiMhGiyPnPG458q2xSY+cSyblmJJUSo8I8xk5JsXpSBf5JkeBJZSIMC0iDgCMQ45DCUfk+BQACBg5qgdpYm+Mkc+B1+tRe2IxOSbN45Yj9+Jx/XValnxu3GnOM1LyeA63fhl5PPLr0eIIjUlzzMqveG6PPBYAxONyJI7box9zLCa/1y7ltQBAMs013ZodkLlKrH918Jlqz/uj5G3nPtRb39Fh8vtw2cLJasvtR44W675B8oVw08IsdazPBsmRayPX6tFDLwbai/Uvo4PUnvOnyZ+r1/ZRW/DNIPkzd8k9cnzd0Vfvp4511IGDxfrI279LcwBytNybo2Jqy4XPHiPWa8aNF+sHL3paHStzqV+sD4Ie8XiCkaPtzKwtag82thXL4fZXqy1vXqrEnZ2g76Y1qN6YL9ZLMvTn9SarRqwXefV7clODHJ/mz9V7qmvl66ooVSjWHR49bjaCBnn/Kf1eHciX7x91Efk6BICq+Fax7tuqR86FlTlTsE1Xse4p159J4Uz52FLQo1NddfIzqV7LuwUQU+ZsIZ/y7E/K8zUAcOTJ86KIW4+cKwjKz4lqn34NuD2VYt21UX7PAMC1hxxV2BL8ppmIiIiIyAYnzURERERENjhpJiIiIiKywUkzEREREZENTpqJiIiIiGy0OD3D65NXSUZj8mpHAEgm5W2WpacdwMgrybWEDCBNSoZyaBb0/Tsc8irRxsZ6tScakVcDm5R+zE6nllKhrzp2ueXfcYzyQkMBfTWwW1nZ26AeFxCLya9TS8IAAK/XK9aNsoK3qSmsjgVLPjcup34ZG+V68nr1cxNWrmmvT++x0oSOtGYbtsrXdSWWqj37pC4V6/fMPEztOSf0sFif014/cUbeDWBdJpZ7nt5dHWtzj4PE+gcH6qkS3Y+aJtZ7tJHrABAY9qVYN5NuVnss6zqxvvId+b3ZcJN+vU+euFCsf/K+nGoBAKM+k+tHXq7f26tvv005gJFiuaz9vupYF7y8UKw3DVf2AeCiqVPEurWuTO259XE5GeCyty9Se0xSfm9au1L/WrFe36h/3jKVJKyGyjRpV275nlxfrqcq5BXLz4SYks4Uqc9WxwqFMsR6RUpPTkjVy/sJ1+vnJuSR9+NO5qo9gewasZ4Iy4kfHp8+X6hVkrOKTabaU98k3z96tw2qPWur5eevO1dOwkhtq1PHCmXIr8dp9CSMyqS8zV+7Se2pb5L3s0kJNQOALuvkpJaW4DfNREREREQ2OGkmIiIiIrLBSTMRERERkQ1OmomIiIiIbHDSTERERERkg5NmIiIiIiIbLY6cczjk+bXbrQ+hbbPSzNWjUS3mSO/Rjk2NtrP0aBeXEqHj9ekxKeEm+Zj9fj12JxRS8lCUWDUA8Hjk85mIRuV6TI/Q8SljBf1yHBAA1Kfk1+l06deAFjmnBu6l9HijeFyOvIsl5ToAJJPy+bQc+vsZUN43Y+nXYCBNvF9r9t56+TPS/tYJas+g0feJ9bFt9ffhzjo5jq4b9M+iNUe+SkZAjnn6cvg6daw5h/1BrM968Ca1Z+R71WL9tTuPVXsW3/8Xsf6k0c/niSZbrK+ZdrxY9x6lx9f5X3tFrPca+6za88nT8vWejVPUHjzRWS5fvFisRwb1Uoeav/o0sW6W6LGHR9ZPFevHlY9VexZNPEPecMhXas+XL/YV63upHa3Dhjz5fhRMZak9LpdHrAcclWrPVsgRZbluOSINADZtLhHrmalGsZ6dqceQViXlZ19BZqnakxWS7+OZStwaADRVyvcpd1CO6QOAmqD8OjP8cnxdvFKPb8uNyu9nKiDfowDAVyi/zs0b9Pt0VrFybDU1Yt3K1c9ZXYPyvLb0Z29DQL4Gg9FstacgX55jlIT1c+Ms0K9PO/ymmYiIiIjIBifNREREREQ2OGkmIiIiIrLBSTMRERERkQ1OmomIiIiIbLQ4PSOZkFMNLEsfQgm1QDwWV3uiEXk1qksbDIDTK6+49LrlejKlj5WI1Yt1T5qEiPqYvOq3KaUnYfj98njxuJ4m4HTKSQdqqkRUPi4AgJIE4XIriSMA4nH5fWto0lfQKuEV8Pnk9BCHU0kVAZBULhvLoaeEOJ3yNmPpPUkjvzfG6NdNMqmP15ot/1NPsd5hfm+1p2T2AWJ9UvkKtSd5sHx+LKOft8QRcj3nNTmJIeOxWepYQ+ovFevPTztY7Tn2s0fF+pmN+mfUPKS8nrf1nsx5Ss+TclrNin89r46FP8gfkqmj5YQKAJg19DKxfgOq1J6hZfL5POcY+ec/m/iwOlZl4hF5w3VPqj3rNsgpEKmz9PvX6l5niHVzxYtqz9N5V4r1vTBC7WkN8lKbxbojliYF6ns5iaLDHnoykMOqEOv+TDl1BAB8CTnlxu1pK9a3VcnPZAAoCsjzkhplHwBgIoViPRSSXwsAOOuUbbld1R5vvfxcbgzKaSRFWdvUsSyffI/wRdurPVtcG8V6VkQ+zwCQTK4U62vi8jnb73v9eqoNKikdOXpCVygmp1rUB/T3pmOgn1h/PB5Ue85cpZ9rO/ymmYiIiIjIBifNREREREQ2OGkmIiIiIrLBSTMRERERkQ1OmomIiIiIbHDSTERERERko8WRcwklOyyhpIoAgNcnD+9Js9ekQx4wlZTjlwAgEZXjnFxK5JzLqf+ukErK29xOPVrF65L3E03oxxyPy9sM9Jg6nzckb7B8YjmW0KPg4iklii2ZJk5LScaKpYkQbGhU3k9kyA0OPY4m5ZDfg3SRcz6fHJeUTMpRRQCQUC7qZJpr0ErzOWjNxlwvn7vKG+QYMgCo6Cjnit0zTX8f+lwvX1dB62K154833inW+0GObBoYfEsd69uQ3HM69MimbpE1Yn3OCP0z+sHt8uucmXOy2pNzjXzc7ZXb81H6ywRGrxHL3afV6j19y8Tyhpn6+3nsZ/I958W9R4r1MSv0sQ7vN12sj7hNjrkCgEknzxfr518s34sB4MOxyjX9oB5PtuiS0WJdrrYef17wilg/+rRT1Z6uHeTz/UVEjgEDgGDXbLFe6V2v9ngL5ft43nfVYj3p1j9vxckisV4X0a+3bE8bsd7QUXm+AvBAfsZ9Vas/L3sXy/WKHDkucdumTHWsyGr5/mU66ZMp/5pSeaz276o9TufhYr33J3uK9co99f2nHC+J9bDJV3tiia3yWIXKfAHAd5vkaN2MHD2m7q1b5QjOM9SOf+M3zURERERENjhpJiIiIiKywUkzEREREZENTpqJiIiIiGxw0kxEREREZMMyxujLP4mIiIiIiN80ExERERHZ4aSZiIiIiMgGJ81ERERERDY4aSYiIiIissFJMxERERGRDU6aiYiIiIhscNJMRERERGSDk2YiIiIiIhucNBMRERER2fh/SyDjmZZqjQEAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 900x300 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "idx = 383\n",
    "ctxs = get_grid(3,1,3)\n",
    "ax1 = dls.after_batch.decode((x0,))[0][idx].show(ctx=ctxs[0], title='Original')\n",
    "ax2 =  dls.after_batch.decode((xt,))[0][idx].show(ctx=ctxs[1], title='Noisy')\n",
    "ax3 =  dls.after_batch.decode((x0hat,))[0][idx].show(ctx=ctxs[2], title='Predicted denoised')"
   ]
  }
 ],
 "metadata": {
  "accelerator": "GPU",
  "colab": {
   "collapsed_sections": [],
   "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": "3.10.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}