Categorical VAE.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from torchvision import datasets, transforms"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\n",
    "import torch.nn as nn\n",
    "from torch.optim import Adam\n",
    "import torch.nn.functional as F\n",
    "from torch.distributions import kl_divergence\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "device = 'cuda' if torch.cuda.is_available() else 'cpu'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_loader = torch.utils.data.DataLoader(\n",
    "    datasets.MNIST('../data', train=True, download=False,\n",
    "                   transform=transforms.Compose([\n",
    "                       transforms.ToTensor(),\n",
    "#                        transforms.Normalize((0.1307,), (0.3081,))\n",
    "                   ])),\n",
    "        batch_size=128, shuffle=True)\n",
    "\n",
    "\n",
    "test_loader = torch.utils.data.DataLoader(\n",
    "    datasets.MNIST(root='../data', train=False, download=False,\n",
    "                   transform=transforms.Compose([\n",
    "                       transforms.ToTensor(),\n",
    "#                        transforms.Normalize((0.1307,), (0.3081,))\n",
    "                       ])),\n",
    "    batch_size=1, shuffle=True)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Network"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def sample_gumbel(shape, eps=1e-20):\n",
    "    unif = torch.rand(*shape).to(device)\n",
    "    g = -torch.log(-torch.log(unif + eps))\n",
    "    return g\n",
    "\n",
    "def sample_gumbel_softmax(logits, temperature):\n",
    "    \"\"\"\n",
    "        Input:\n",
    "        logits: Tensor of log probs, shape = BS x k\n",
    "        temperature = scalar\n",
    "        \n",
    "        Output: Tensor of values sampled from Gumbel softmax.\n",
    "                These will tend towards a one-hot representation in the limit of temp -> 0\n",
    "                shape = BS x k\n",
    "    \"\"\"\n",
    "    g = sample_gumbel(logits.shape)\n",
    "    h = (g + logits)/temperature\n",
    "    h_max = h.max(dim=-1, keepdim=True)[0]\n",
    "    h = h - h_max\n",
    "    cache = torch.exp(h)\n",
    "    y = cache / cache.sum(dim=-1, keepdim=True)\n",
    "    return y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "class VAE(nn.Module):\n",
    "    def __init__(self):\n",
    "        super().__init__()\n",
    "        self.temperature = 1.\n",
    "        self.K = 10\n",
    "        self.N = 30\n",
    "        self.create_encoder()\n",
    "        self.create_decoder()\n",
    "        \n",
    "    def create_encoder(self):\n",
    "        \"\"\"\n",
    "            Input for the encoder is a BS x 784 tensor\n",
    "            Output from the encoder are the log probabilities of the categorical distribution\n",
    "        \"\"\"\n",
    "        self.encoder = nn.Sequential(\n",
    "            nn.Linear(784, 512),\n",
    "            nn.ReLU(),\n",
    "            nn.Linear(512, 256),\n",
    "            nn.ReLU(),\n",
    "            nn.Linear(256, self.K*self.N)\n",
    "        )\n",
    "        \n",
    "    def create_decoder(self):\n",
    "        \"\"\"\n",
    "            Input for the decoder is a BS x N*K tensor\n",
    "            Output from the decoder are the log probabilities of the bernoulli pixels\n",
    "        \"\"\"\n",
    "        self.decoder = nn.Sequential(\n",
    "            nn.Linear(self.N*self.K, 256),\n",
    "            nn.ReLU(),\n",
    "            nn.Linear(256, 512),\n",
    "            nn.ReLU(),\n",
    "            nn.Linear(512, 784),\n",
    "            nn.LogSigmoid()\n",
    "        )\n",
    "        \n",
    "    def sample(self, img):\n",
    "        with torch.no_grad():\n",
    "            logits_nz = self.encoder(img)\n",
    "            logits_z = F.log_softmax(logits_nz.view(-1, self.N, self.K), dim=-1)\n",
    "            latent_vars = sample_gumbel_softmax(logits_z, self.temperature)\n",
    "            logits_x = self.decoder(latent_vars)\n",
    "            dist_x = torch.distributions.Bernoulli(logits=logits_x)\n",
    "            sampled_img = dist_x.sample((1,))\n",
    "            \n",
    "        return sampled_img.cpu().numpy()\n",
    "        \n",
    "    def forward(self, img, anneal=1.):\n",
    "        \"\"\"\n",
    "            Input: \n",
    "            img: Tensor of shape BS x 784\n",
    "        \"\"\"\n",
    "        # Encoding\n",
    "        logits_nz = self.encoder(img)\n",
    "        logits_z = F.log_softmax(logits_nz.view(-1, self.N, self.K), dim=-1)\n",
    "        posterior_dist = torch.distributions.Categorical(logits=logits_z)\n",
    "        prior_dist = torch.distributions.Categorical(probs=torch.ones_like(logits_z)/self.K)\n",
    "        \n",
    "        # Sampling\n",
    "        latent_vars = sample_gumbel_softmax(logits_z, self.temperature).view(-1, self.N*self.K)\n",
    "        \n",
    "        # Decoding\n",
    "        logits_x = self.decoder(latent_vars)\n",
    "        dist_x = torch.distributions.Bernoulli(logits=logits_x)\n",
    "\n",
    "        # Losses\n",
    "        ll = dist_x.log_prob(img).sum(dim=-1)\n",
    "#         kl1 = posterior_dist.probs * (logits_z - torch.log(torch.ones_like(logits_z)/self.K))\n",
    "        kl = kl_divergence(posterior_dist, prior_dist).sum(-1)\n",
    "        assert torch.all(kl > 0)\n",
    "        assert torch.all(ll < 0)\n",
    "        elbo = ll - kl\n",
    "        loss = -elbo.mean()\n",
    "        return loss"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "def sample(model, img):\n",
    "    with torch.no_grad():\n",
    "        logits_nz = model.encoder(img)\n",
    "        logits_z = F.log_softmax(logits_nz.view(-1, model.N, model.K), dim=-1)\n",
    "        latent_vars = sample_gumbel_softmax(logits_z, model.temperature).view(-1, model.N*model.K)\n",
    "        logits_x = model.decoder(latent_vars)\n",
    "        dist_x = torch.distributions.Bernoulli(logits=logits_x)\n",
    "        sampled_img = dist_x.sample((1,))\n",
    "\n",
    "    return sampled_img.cpu().numpy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_img(model):\n",
    "    for batch_idx, (data, target) in enumerate(test_loader):\n",
    "        img_flat = sample(model, data.view(-1, 28*28).to(device))\n",
    "        plt.figure()\n",
    "        plt.subplot(121)\n",
    "        plt.imshow(img_flat.reshape(28,28))\n",
    "        plt.subplot(122)\n",
    "        plt.imshow(data.reshape(28,28))\n",
    "        plt.show()\n",
    "        break"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "def train(model, optimizer, maxiters):\n",
    "    iters = 0\n",
    "    while iters < maxiters:\n",
    "        for batch_idx, (data, target) in enumerate(train_loader):\n",
    "            iters+=1\n",
    "#             anneal = min(1., epoch*.1)\n",
    "            optimizer.zero_grad()\n",
    "            data = data.to(device)\n",
    "            loss = model(data.view(-1, 28*28))\n",
    "    #             neg_elbo = -elbo\n",
    "            loss.backward()\n",
    "            optimizer.step()\n",
    "            if batch_idx % 100 == 0:\n",
    "                model.temperature = np.maximum(model.temperature * np.exp(-ANNEAL_RATE * batch_idx), temp_min)\n",
    "                print(\"New Model Temperature: {}\".format(model.temperature))\n",
    "            if iters % 100 == 0:\n",
    "                plot_img(model)\n",
    "                print('Train Iteration: {} [{}/{} ({:.0f}%)]\\tLoss: {:.6f}'.format(\n",
    "                    iters, batch_idx * len(data), len(train_loader.dataset),\n",
    "                    100. * batch_idx / len(train_loader), loss.item()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "New Model Temperature: 1.0\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Iteration: 100 [12672/60000 (21%)]\tLoss: 205.179291\n",
      "New Model Temperature: 0.997004495503373\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Iteration: 200 [25472/60000 (42%)]\tLoss: 187.096405\n",
      "New Model Temperature: 0.9910403787728836\n"
     ]
    },
    {
     "data": {
      "image/png": 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V9KKkU53wfRr7vrHV+1anLLVNXbdv307jTFEASIIzRQEgCRo6ACRBQweAJGjoAJAEDR0AkqChA0ASNHQASIKGDgBJ/B85ja/K4MsQOgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Iteration: 300 [38272/60000 (64%)]\tLoss: 187.346329\n",
      "New Model Temperature: 0.9821610323583007\n"
     ]
    },
    {
     "data": {
      "image/png": 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BIAkaOgAkQUMHgCT+H+WqjMVURHWjAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Iteration: 400 [51072/60000 (85%)]\tLoss: 189.956055\n",
      "New Model Temperature: 0.970445533548508\n",
      "New Model Temperature: 0.970445533548508\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Iteration: 500 [3840/60000 (6%)]\tLoss: 193.834396\n",
      "New Model Temperature: 0.9675385595890319\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Iteration: 600 [16640/60000 (28%)]\tLoss: 178.103271\n",
      "New Model Temperature: 0.9617507091463666\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Iteration: 700 [29440/60000 (49%)]\tLoss: 172.169342\n",
      "New Model Temperature: 0.9531337870775046\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Iteration: 800 [42240/60000 (70%)]\tLoss: 172.517700\n",
      "New Model Temperature: 0.9417645335842486\n"
     ]
    },
    {
     "data": {
      "image/png": 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SHulgLi2xbUn3SBqMiDUjvlX7fWuj1Gu79j/78VDXlZ9YZHuppM9L6pG0LiL+sdIESmT7AUkLNXy1toOSPi3p25K+JWmOpJckXRsRZ04wdTXb75X0lKTnJZ1qhO/Q8OeNtd63dkqltqnr+u3baZwpCgCJ4ExRAEgEDR0AEkFDB4BE0NABIBE0dABIBA0dABJBQweARNDQASAR/wffd7beuQ66vAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Iteration: 900 [55040/60000 (92%)]\tLoss: 165.176849\n",
      "New Model Temperature: 0.9417645335842486\n"
     ]
    },
    {
     "data": {
      "image/png": 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q8fJESW9WtvLqsF3t80fu/p52rHhQbdfhc2pV1G2rw3Zlqu1KG/rvrdhsrbt3t2XlJWK7jmyRP6eo2xZpuzjkAgBB0NABIIh2NvRFbVx3mdiuI1vkzynqtoXZrrYdQwcAFItDLgAQROUN3cwuM7OXzOxlM5tf9fqL1Hgi/E4z2zAoNtbMVpnZ5sbX2j0x3swmmtkTZtZrZhvN7PpGvPbbVqYotU1d12/bDqi0oZvZcEnfkfQJSWdr4FFfZ1eZQ8GWSrrsoNh8SavdfZKk1Y3XdbNf0pfd/SxJ50v6UuP3FGHbShGstpeKuq6lqvfQp0h62d23uPu7kh6QNL3iHArj7k9K2nVQeLqkZY3vl0maUWlSBXD37e6+vvH9Hkm9kk5RgG0rUZjapq7rt20HVN3QT5H02qDX2xqxSMa5+3ZpoIAkndTmfHIxs1MlnSdpjYJtW8Gi13ao333Uuq66oac9kJdpNh3KzI6V9LCkG9x9d7vz6XDUdk1EruuqG/o2SRMHvX6vpDcqzqFsO8xsvCQ1vu5scz4tMbMuDRT9fe6+vBEOsW0liV7bIX730eu66ob+nKRJZnaamY2Q9GlJKyrOoWwrJM1pfD9H0qNtzKUlZmaSFkvqdffbBv2o9ttWoui1Xfvf/ZFQ15VfWGRml0v6lqThkpa4+z9WmkCBzOx+SRdr4G5tOyTdLOlHkh6U9D5Jr0q62t0PPsHU0czsIklPSXpRUn8jfJMGjjfWetvKFKW2qev6bdsBXCkKAEFwpSgABEFDB4AgaOgAEAQNHQCCoKEDQBA0dAAIgoYOAEHQ0AEgiP8D7lSPui9W0ZoAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Iteration: 1000 [7808/60000 (13%)]\tLoss: 164.834839\n",
      "New Model Temperature: 0.9389434736891331\n"
     ]
    },
    {
     "data": {
      "image/png": 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Gc6jbTtvzJanzuGuC8xkX20MaLfq7IuLeTjjFvtUke22n+N1nr+umG/rjkk62fZLt6ZIukXR/wznU7X5JyzrPl0m6bwJzGRfblrRK0raIuHHMj1q/bzXKXtut/91Phbpu/MQi2xdIulnSNEmrI+IrjSZQIdt3Szpbo1dr2ynpWknfkfQtSb8u6VVJn4qIQyeYJjXbZ0n6vqRnJB3ohK/R6PeNrd63OmWpbeq6fft2EGeKAkASnCkKAEnQ0AEgCRo6ACRBQweAJGjoAJAEDR0AkqChA0ASNHQASOJ/ASLt7duZsZnqAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Iteration: 1100 [20608/60000 (34%)]\tLoss: 159.287628\n",
      "New Model Temperature: 0.9333266800782019\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Iteration: 1200 [33408/60000 (56%)]\tLoss: 158.914444\n",
      "New Model Temperature: 0.9249644265435392\n"
     ]
    },
    {
     "data": {
      "image/png": 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rhr5b+p2r3Z8q6ZUe59Bte23Pl6TG474+5zMptoc1XvT3RMSDjXCKfeuS7LWd4u8+e133uqE/IWmh7Q/bPlbSlZLW9ziHblsvaUXj+QpJD/Uxl0mxbUl3SdoREbdO+FHt962Lstd27f/up0Jd9/zEItuXSvpnSUOS1kTE13uaQIVs3yfpAo1frW2vpBslfV/S/ZJOk/SipMsj4sgJpoFm+88l/VTS05LGGuEbNP59Y633rZuy1DZ1Xb99O4wzRQEgCc4UBYAkaOgAkAQNHQCSoKEDQBI0dABIgoYOAEnQ0AEgCRo6ACTx/wUhuksbFhwEAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Iteration: 1300 [46208/60000 (77%)]\tLoss: 156.598373\n",
      "New Model Temperature: 0.9139311852712281\n"
     ]
    },
    {
     "data": {
      "image/png": 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lKAAkwZWiAJAEgQ4ASRDoAJAEgQ4ASRDoAJAEgQ4ASRDoAJAEgQ4ASfwfXVyl+q50xvgAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Iteration: 1400 [59008/60000 (98%)]\tLoss: 157.220688\n",
      "New Model Temperature: 0.9139311852712281\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Iteration: 1500 [11776/60000 (20%)]\tLoss: 157.921890\n",
      "New Model Temperature: 0.9111935002961404\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Iteration: 1600 [24576/60000 (41%)]\tLoss: 155.480621\n",
      "New Model Temperature: 0.9057427080235484\n"
     ]
    },
    {
     "data": {
      "image/png": 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g6TuSflPSzyV9JiLOH2Aaabb/QNKPJb0u6WzR/JDmrjc2+tgGKUtuk9fNO7ZzWCkKAEmwUhQAkqCgA0ASFHQASIKCDgBJUNABIAkKOgAkQUEHgCQo6ACQxP8BrdLwuSz4J6oAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Iteration: 1700 [37376/60000 (62%)]\tLoss: 155.028183\n",
      "New Model Temperature: 0.8976275964304348\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Iteration: 1800 [50176/60000 (84%)]\tLoss: 152.712982\n",
      "New Model Temperature: 0.8869204367171574\n",
      "New Model Temperature: 0.8869204367171574\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Iteration: 1900 [2944/60000 (5%)]\tLoss: 155.766235\n",
      "New Model Temperature: 0.8842636625608208\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Iteration: 2000 [15744/60000 (26%)]\tLoss: 148.800446\n",
      "New Model Temperature: 0.8789739655455832\n"
     ]
    },
    {
     "data": {
      "image/png": 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2WNInJP2jpK9JulzSy5LeHxFnTjD1Ndu/Kenbkl6QNNII36/R7xtrvW+dlKW2qev67dtpnCkKAElwpigAJEFDB4AkaOgAkAQNHQCSoKEDQBI0dABIgoYOAEnQ0AEgif8DwoOEUB5jvCUAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Iteration: 2100 [28544/60000 (48%)]\tLoss: 145.919617\n",
      "New Model Temperature: 0.8710986917457983\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Iteration: 2200 [41344/60000 (69%)]\tLoss: 151.076508\n",
      "New Model Temperature: 0.8607079764250578\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Iteration: 2300 [54144/60000 (90%)]\tLoss: 149.218933\n",
      "New Model Temperature: 0.8607079764250578\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Iteration: 2400 [6912/60000 (12%)]\tLoss: 145.433746\n",
      "New Model Temperature: 0.8581297218113938\n"
     ]
    },
    {
     "data": {
      "image/png": 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JukvS+yS9LOmKiDh8gmmo2f6IpP+U9LSkA63wDTr4fWOj962XstQ2dd28fTuEM0UBIAnOFAWAJGjoAJAEDR0AkqChA0ASNHQASIKGDgBJ0NABIAkaOgAk8X9Ed5TC63R1MAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Iteration: 2500 [19712/60000 (33%)]\tLoss: 151.533615\n",
      "New Model Temperature: 0.8529963589691315\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXQAAAC7CAYAAAB1qmWGAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDMuMC4zLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvnQurowAADp9JREFUeJzt3X+MHPV5x/HPx+awMYQIMHaNcUJCTVqrSk242qloIipCC6gpRBVRKEKGRnVaQQUSlXBppCCitvxBIFVoEl0CspEICBUSuxJN6jq0xIIYbEKDzQXsEjc4Ptlx3MRAivGPp394nV49c3j3dmZ297n3S7J299nv7jxz99zDMPOdGUeEAACDb1qvEwAAVIOGDgBJ0NABIAkaOgAkQUMHgCRo6ACQBA0dAJKgoQNAEl01dNuX2n7J9jbbK6pKCug1ahuDyJM9U9T2dEkvS7pE0g5Jz0q6OiJerC49oHnUNgbVCV18domkbRHxiiTZfljSFZImLPoTPSNm6uQuFglM7E29obdivyv4KmobfaXd2u6moc+X9Oq41zskLX27D8zUyVrqi7tYJDCxDbGuqq+ittFX2q3tbhp62X8tCvtvbC+XtFySZmpWF4sDGkNtYyB1c1B0h6QF416fLWnnsYMiYiQihiNieEgzulgc0BhqGwOpm4b+rKSFtt9j+0RJn5C0ppq0gJ6itjGQJr3LJSIO2r5R0rckTZd0f0RsqSwzoEeobQyqbvahKyIel/R4RbkAfYPaxiDiTFEASIKGDgBJ0NABIAkaOgAkQUMHgCRo6ACQBA0dAJKgoQNAEl2dWIR6fWvn86Xx3z9rccOZANWatnhRafyLq0cKsT9+cVnp2FMv+89Kc8qALXQASIKGDgBJ0NABIAkaOgAkQUMHgCSY5dInJprR0uSymD2DpsSWbaXxp958dyH2b+9/uHTsr335hkLsvD97prvEBhxb6ACQBA0dAJKgoQNAEjR0AEiiq4OitrdLek3SIUkHI2K4iqSmok4OSDZ5AHWqorbrFQfeKo2vuv4PCrEzHig/KLrto18uxC7YcmPp2LlfeKqD7AZXFbNcfjci9lTwPUC/obYxUNjlAgBJdNvQQ9K/2N5ke3kVCQF9gtrGwOl2l8uFEbHT9hxJa23/ICKeHD+g9cewXJJmalaXiwMaQ21j4HS1hR4RO1uPuyV9XdKSkjEjETEcEcNDmtHN4oDGUNsYRJPeQrd9sqRpEfFa6/nvSbqjsszQEWbJVIfa7h0/9R+F2Kf/7k9Kx75x64OF2EO33FU69i+/8fFC7OCrOzrMrv91s8tlrqSv2z76PV+LiG9WkhXQW9Q2BtKkG3pEvCLpNyvMBegL1DYGFdMWASAJGjoAJMH10PsY1y0HpDPue7o0ftt7rynEPnvV10rH/vC6dxViCz6b76AoW+gAkAQNHQCSoKEDQBI0dABIgoYOAEkwy6VPlM1oqeJ0/m6/gxk16FeHZkQhdsb010vHnrRkalzWni10AEiChg4ASdDQASAJGjoAJMFB0T7R7cHHiT7PgU5kdeamYuzvf+uS0rEfOuuVQmy06oT6AFvoAJAEDR0AkqChA0ASNHQASOK4Dd32/bZ32948Lna67bW2t7YeT6s3TaB61DayaWeWy0pJ90p6YFxshaR1EXGn7RWt17dWn15OvZ55MtFlAsoknxGzUtT2wJqx71Ahds85/1g69gt7Lqo5m/5w3C30iHhS0t5jwldIWtV6vkrSlRXnBdSO2kY2k92HPjcixiSp9TinupSAnqK2MbBqP7HI9nJJyyVppmbVvTigMdQ2+s1kt9B32Z4nSa3H3RMNjIiRiBiOiOEhzZjk4oDGUNsYWJPdQl8jaZmkO1uPqyvLaAro9YHGXi+/z1HbA2LX8FAh9rPDJ5aOXfPdCwqxhdpQeU691s60xYckPS3pfbZ32P6kjhT7Jba3Srqk9RoYKNQ2sjnuFnpEXD3BWxdXnAvQKGob2XCmKAAkQUMHgCRo6ACQBDe46GMTnaJfNkulk7FABocXvV6IXTCjfJbLH36weDcMbnABAOhbNHQASIKGDgBJ0NABIAkOivaxiQ5odnI9815fex2oy8K5PynE/nbP+0rHrn7mA4XYeXqm8px6jS10AEiChg4ASdDQASAJGjoAJMFB0R7o9kBlJ2eKtrv8TnMAmjJ90Xml8evP+mYhtmDop6Vj1396fiFWvMX04GMLHQCSoKEDQBI0dABIgoYOAEm0c0/R+23vtr15XOx22z+2/Xzr3+X1pglUj9pGNu3Mclkp6V5JDxwTvyci7qo8owHVySyTbmepdDsjBr+0UtT2L00/79zS+Et/fmYhdurW8m3BuU//vBCb9vr/FGJxUvl1y/Xy9kLolJHymStXnvyzQuz9995UOvbsnz5VvrxkjruFHhFPStrbQC5Ao6htZNPNPvQbbX+/9b+tp1WWEdB71DYG0mQb+pcknStpsaQxSZ+baKDt5bY32t54QPsnuTigMdQ2BtakGnpE7IqIQxFxWNJXJC15m7EjETEcEcNDmjHZPIFGUNsYZJM69d/2vIgYa738mKTNbzd+Kujk4GO7p/53cqAU1chW29NmzSqN/+Cu3yjE/uLD/1o69p9O29ZVDi8feLMQW3BC+bbk5/cW/w6ueWd5XlJx3YaK942eUo7b0G0/JOkiSbNt75D0GUkX2V4sKSRtl/SpGnMEakFtI5vjNvSIuLokfF8NuQCNoraRDWeKAkASNHQASIKGDgBJcIOLGjV5IwlO8UfZjJbpj7+zdOwPzxspxA7F4baX9d+Hi6fzS9Jfj32kmIOjELvjV9aVfv6vznixJFo+UwdFbKEDQBI0dABIgoYOAEnQ0AEgCQ6KdqiK65a3+x0c6EQn9jxydiH23YUPl47dtP9gIbbi2uWlY6et7+QSFMWDpXuv/+1CbPod3277G2/aWfy8JG1/4/T205oi2EIHgCRo6ACQBA0dAJKgoQNAEjR0AEiCWS4d6mTmSien/nf7+U40eUkCNOdw8Qz7CX18ffEy77+6/nsVZvN/9nzoQCF26rSZpWOX3n5DITZ75abSsXFgrBCbN+vnpWPbv6jBYGMLHQCSoKEDQBI0dABI4rgN3fYC20/YHrW9xfZNrfjpttfa3tp6PK3+dIHqUNvIpp2Dogcl3RIRz9l+h6RNttdKuk7Suoi40/YKSSsk3VpfqvXp5HT+ifT6gGIV6zAFpa/tiXx44bZCbOyE8nbgkvi0M2eXjj346o5CbMHq4nbj5SPXlX5+9veeK8TiwFulY8sc/sUv2h6b0XG30CNiLCKeaz1/TdKopPmSrpC0qjVslaQr60oSqAO1jWw62odu+xxJ50vaIGluRIxJR/4wJM2pOjmgKdQ2Mmi7ods+RdKjkm6OiH0dfG657Y22Nx7Q/snkCNSK2kYWbTV020M6UvAPRsRjrfAu2/Na78+TtLvssxExEhHDETE8pBlV5AxUhtpGJu3McrGk+ySNRsTd495aI2lZ6/kySaurTw+oD7WNbNqZ5XKhpGslvWD76FSK2yTdKekR25+U9CNJV9WTYv06maEy0WySKm580e64smXVNcsm+WUCUtX24X8uzjzZt/jN0rFfXfDvhdgfrbusdOzo+vcWYofOKf/ec68pznI5afUzpWPLdHD1ApQ4bkOPiPWSPMHbF1ebDtAcahvZcKYoACRBQweAJGjoAJAE10PvUCcHKqv43m7HlunkQGeSg59TwpwvPlWIfeTgLaVjl/5p8drnB6N8+27ZR79diH3n2g+Ujp0q1x3vV2yhA0ASNHQASIKGDgBJ0NABIAkaOgAk4YjmTrY91afHUnMCHuqxIdZpX+yd6MzPWlHbqFO7tc0WOgAkQUMHgCRo6ACQBA0dAJKgoQNAEjR0AEiChg4ASdDQASCJdm4SvcD2E7ZHbW+xfVMrfrvtH9t+vvXv8vrTBapDbSObdq6HflDSLRHxnO13SNpke23rvXsi4q760gNqRW0jlXZuEj0maaz1/DXbo5Lm150YUDdqG9l0tA/d9jmSzpe0oRW60fb3bd9v+7SKcwMaQ20jg7Ybuu1TJD0q6eaI2CfpS5LOlbRYR7ZyPjfB55bb3mh74wHtryBloFrUNrJoq6HbHtKRgn8wIh6TpIjYFRGHIuKwpK9IWlL22YgYiYjhiBge0oyq8gYqQW0jk3ZmuVjSfZJGI+LucfF544Z9TNLm6tMD6kNtI5t2ZrlcKOlaSS/YPnq7+NskXW17saSQtF3Sp2rJEKgPtY1U2pnlsl5S2YXVH68+HaA51Day4UxRAEiChg4ASdDQASAJGjoAJEFDB4AkaOgAkAQNHQCSoKEDQBI0dABIwhHR3MLsn0j6r9bL2ZL2NLbw5rBevfPuiDizFwseV9uD8HOarKzrNgjr1VZtN9rQ/9+C7Y0RMdyThdeI9ZraMv+csq5bpvVilwsAJEFDB4AketnQR3q47DqxXlNb5p9T1nVLs14924cOAKgWu1wAIInGG7rtS22/ZHub7RVNL79KrTvC77a9eVzsdNtrbW9tPQ7cHeNtL7D9hO1R21ts39SKD/y61SlLbVPXg7duRzXa0G1Pl/QPki6TtEhHbvW1qMkcKrZS0qXHxFZIWhcRCyWta70eNAcl3RIRvy7pg5JuaP2eMqxbLZLV9kpR1wOp6S30JZK2RcQrEfGWpIclXdFwDpWJiCcl7T0mfIWkVa3nqyRd2WhSFYiIsYh4rvX8NUmjkuYrwbrVKE1tU9eDt25HNd3Q50t6ddzrHa1YJnMjYkw6UkCS5vQ4n67YPkfS+ZI2KNm6VSx7baf63Wet66YbetkNeZlm06dsnyLpUUk3R8S+XufT56jtAZG5rptu6DskLRj3+mxJOxvOoW67bM+TpNbj7h7nMym2h3Sk6B+MiMda4RTrVpPstZ3id5+9rptu6M9KWmj7PbZPlPQJSWsazqFuayQtaz1fJml1D3OZFNuWdJ+k0Yi4e9xbA79uNcpe2wP/u58Kdd34iUW2L5f0eUnTJd0fEX/TaAIVsv2QpIt05GptuyR9RtI3JD0i6V2SfiTpqog49gBTX7P9O5K+I+kFSYdb4dt0ZH/jQK9bnbLUNnU9eOt2FGeKAkASnCkKAEnQ0AEgCRo6ACRBQweAJGjoAJAEDR0AkqChA0ASNHQASOJ/AUeosKpbZPDvAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Iteration: 2600 [32512/60000 (54%)]\tLoss: 152.944214\n",
      "New Model Temperature: 0.8453538346846587\n"
     ]
    },
    {
     "data": {
      "image/png": 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S6/XwFPdnUmz3aSzpH46Ix1vhJI6tIqnndhK/+9Tzuu6C/oKky2y/2/ZMSZ+QtKnmPlRtk6QVra9XSHpyCvsyKbYt6SFJeyJi7bhvNf7YKpR6bjf+dz8d8rr2hUW2b5F0n6QZktZFxF/W2oES2X5E0vUau1vbIUlfkPRPkr4l6RJJP5T08Yg4dYCpp9m+TtK/S9op6UQrvFpj1xsbfWxVSiW3yevmHdtJrBQFgESwUhQAEkFBB4BEUNABIBEUdABIBAUdABJBQQeARFDQASARFHQASMT/AZVs6nk5MUiLAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Iteration: 2700 [45312/60000 (75%)]\tLoss: 148.645416\n",
      "New Model Temperature: 0.835270211411272\n"
     ]
    },
    {
     "data": {
      "image/png": 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6VRFxRyecYt8akr22U/zus9d1vxv6E5IW2j7C9n6SPi3pnj7n0LR7JC3uPF8s6e4B5jIpti3pJkkbIuK6cW+1ft8alL22W/+73xvquu8nFtk+U9I/SZoiaXlE/H1fE6iR7ZslnaqdV2vbLOlKSXdJulXSByX9UNKnIqJV9+uy/VuS/l3SM5Le7YSv0M7vG1u9b03KUtvUdfv2bRfOFAWAJDhTFACSoKEDQBI0dABIgoYOAEnQ0AEgCRo6ACRBQweAJGjoAJDE/wN0xZUrsgebEwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Iteration: 2800 [58112/60000 (97%)]\tLoss: 147.439590\n",
      "New Model Temperature: 0.835270211411272\n"
     ]
    },
    {
     "data": {
      "image/png": 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SxlrhWzT+eWOj961OWWqbum7evh3GmaIAkARnigJAEjR0AEiChg4ASdDQASAJGjoAJEFDB4AkaOgAkAQNHQCS+D8MeXVc6EuS2gAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Iteration: 2900 [10880/60000 (18%)]\tLoss: 149.624756\n",
      "New Model Temperature: 0.832768155737091\n"
     ]
    },
    {
     "data": {
      "image/png": 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Z67ruhl72hbwss5mgbB8uabWkpRGxu9/5THDUdkNkruu6G/o2SfNGvD5W0qs159BrO2zPlaTW484+5zMutgc0XPS3RcRdrXCKbeuR7LWd4nefva7rbugbJB1v+wO2p0q6UNKamnPotTWSLm49v1jS3X3MZVxsW9KtkrZExA0jftT4beuh7LXd+N/9ZKjr2i8ssn2epJskTZG0IiKurTWBCtm+XdJZGr5b2w5JV0v6oaQ7Jf2GpJckfToiDj7BNKHZ/j1JD0h6XNL+VvgqDR9vbPS29VKW2qaum7dtB3ClKAAkwZWiAJAEDR0AkqChA0ASNHQASIKGDgBJ0NABIAkaOgAkQUMHgCT+H05R5Kyy1rJ9AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Iteration: 3000 [23680/60000 (39%)]\tLoss: 153.763428\n",
      "New Model Temperature: 0.8277865066947336\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Iteration: 3100 [36480/60000 (61%)]\tLoss: 151.079147\n",
      "New Model Temperature: 0.8203698531378311\n"
     ]
    },
    {
     "data": {
      "image/png": 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BIAkKOgAkQUEHgCQo6ACQxF8ATVglQqnKoYkAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Iteration: 3200 [49280/60000 (82%)]\tLoss: 143.718567\n",
      "New Model Temperature: 0.8105842459701871\n",
      "New Model Temperature: 0.8105842459701871\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Iteration: 3300 [2048/60000 (3%)]\tLoss: 141.524811\n",
      "New Model Temperature: 0.8081561372164884\n"
     ]
    },
    {
     "data": {
      "image/png": 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kTkQMSkcKSNLsLudTiu1zJV0iaZMS27aKpV7bSf3uU63rpht63gN5Oc1mgrLdJ+lhSbdFxP5u5zPBUdstkXJdN93Qd0uaN+LzRyTtaTiHuu21PVeSOq/7upzPuNju1ZGifyAiHumEk9i2mqRe20n87lOv66Yb+jOSFtieb3uKpBslrW84h7qtl7Sk836JpHVdzGVcbFvS/ZK2RcTdI37U+m2rUeq13frf/WSo68YvLLJ9naR7JPVIWh0Rf9NoAhWy/aCkK3Tkbm17JX1R0mOSvi3pHEmvS/p0RBw7wTSh2f64pP+S9IKkw53wHTpyvLHV21anVGqbum7fth3FlaIAkAiuFAWARNDQASARNHQASAQNHQASQUMHgETQ0AEgETR0AEgEDR0AEvH/U1jsXxyYSy8AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Iteration: 3400 [14848/60000 (25%)]\tLoss: 150.185028\n",
      "New Model Temperature: 0.8033217181536265\n"
     ]
    },
    {
     "data": {
      "image/png": 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2qevy7dtJRTf0qZJ293u9pxKLZLK790h9BSRpUoPzqYuZTZN0iaT1CrZvGYte26F+91HruuiGnvaFvCyzaVJmNk7SKkm3uvvBRufT5Kjtkohc10U39D2S2vu9PlfS3oJzyNs+M5siSZXH/Q3OZ1jMrE19Rf+Iuz9eCYfYt5xEr+0Qv/vodV10Q98gaYaZTTez0yXdIGl1wTnkbbWkBZXnCyQ92cBchsXMTNIySd3ufn+/H5V+33IUvbZL/7tvhbou/MIiM5sj6fuSRkpa7u5/X2gCGTKzRyVdqb67te2T9C1J/ybpMUl/IOkNSde7+6kTTE3NzD4h6TlJL0s6UQkvUt/5xlLvW56i1DZ1Xb59O4krRQEgCK4UBYAgaOgAEAQNHQCCoKEDQBA0dAAIgoYOAEHQ0AEgCBo6AATx/9H94x41YtAIAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Iteration: 3500 [27648/60000 (46%)]\tLoss: 148.096329\n",
      "New Model Temperature: 0.7961242598354537\n"
     ]
    },
    {
     "data": {
      "image/png": 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nb5e0s8851G237bmS1Pq6Z8D5jIvtIR0s+tsj4u5WOMW+1SR7baf43Wev63439AclLbB9mu2pki6XtLHPOdRto6SlrcdLJd0zwFzGxbYlrZU0EhE3j/lW4/etRtlru/G/+8lQ130/scj2JZJWSZoiaV1E/GVfE6iQ7TskLdbBq7XtlnS9pG9I+qqkn5X0vKQPR8ThE0wTmu0PSPpXSY9JOtAKX6eDnzc2et/qlKW2qevm7dshnCkKAElwpigAJEFDB4AkaOgAkAQNHQCSoKEDQBI0dABIgoYOAEnQ0AEgif8F8hjHt48kp/EAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Iteration: 3600 [40448/60000 (67%)]\tLoss: 146.985870\n",
      "New Model Temperature: 0.7866278610665534\n"
     ]
    },
    {
     "data": {
      "image/png": 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53b59O42VogCQBCtFASAJCjoAJEFBB4AkKOgAkAQFHQCSoKADQBIUdABIgoIOAEn8Dx57oAhgRL71AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Iteration: 3700 [53248/60000 (89%)]\tLoss: 143.400253\n",
      "New Model Temperature: 0.7866278610665534\n"
     ]
    },
    {
     "data": {
      "image/png": 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/UtIptk+yPUnS5ZLWdjmHTlsraWnr+VJJD/YwlzGxbUl3StoSEbcM+VHjj62Dstd243/3R0Ndd/3CItsXSfqapImSVkXEX3Y1gRrZvkfSeTr4bW27Jd0o6fuS7pV0oqSfSvpERBw+wTSu2f6IpMclbZJ0oBVeoYOfNzb62DopS21T1807tkO4UhQAkuBKUQBIgoYOAEnQ0AEgCRo6ACRBQweAJGjoAJAEDR0AkqChA0AS/wPjIsgVfCpFsQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Iteration: 3800 [6016/60000 (10%)]\tLoss: 137.799576\n",
      "New Model Temperature: 0.7842715137715565\n"
     ]
    },
    {
     "data": {
      "image/png": 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kZa87+5xPT2wPaX/R3xcRD2bhJI6tJqnXdhK/+9TruumG/qSk02yfbHuypM9LWtlwDnVbKWlh9n6hpIf7mEtPbFvSPZI2RsStY37U+mOrUeq13frf/USo68ZvLLJ9qaTbJU2StDQivt5oAhWyfb+kC7X/29p2SLpR0vckPSDpw5J+LOmKiDh4gGmg2b5A0g8kPStpNAvfoP3XG1t9bHVKpbap6/Yd2wHcKQoAieBOUQBIBA0dABJBQweARNDQASARNHQASAQNHQASQUMHgETQ0AEgEf8L4wS/I9pLZ1AAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Iteration: 3900 [18816/60000 (31%)]\tLoss: 147.202454\n",
      "New Model Temperature: 0.7795799733847004\n"
     ]
    },
    {
     "data": {
      "image/png": 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cACAIGjoABNHOhr64jfsuEsc1sUX+OUU9tjDH1bb30AEA+eItFwAIovSGbmYXmNkmM9tiZgvK3n+eak+E32VmG0fFppvZajPbXPtYuSfGm9mxZvaYmfWb2Qtmdl0tXvljK1KU2qauq3dse5Xa0M2sS9Jdkn5f0ukaedTX6WXmkLNlki7YJ7ZA0hp375G0pvZ11QxKusHdT5P0CUlX135PEY6tEMFqe5mo60oq+wx9jqQt7v6yu38g6fuS5pecQ27c/XFJb+0Tni9pee3z5ZIuLDWpHLj7gLs/W/t8t6R+SbMV4NgKFKa2qevqHdteZTf02ZJ+Merr7bVYJDPdfUAaKSBJM9qcT0vM7HhJZ0laq2DHlrPotR3qdx+1rstu6KkH8jLNpkOZ2VRJKyRd7+7vtDufDkdtV0Tkui67oW+XdOyor4+RtKPkHIq208xmSVLt46425zMuZtatkaK/190frIVDHFtBotd2iN999Louu6Gvk9RjZieY2YGSPidpZck5FG2lpEtrn18q6ZE25jIuZmaS7pHU7+63jfpW5Y+tQNFru/K/+4lQ16XfWGRm8yR9U1KXpKXu/vVSE8iRmd0v6RyNrNa2U9LNkh6W9ICkX5G0TdLF7r7vBaaOZmZnS/pPSc9LGq6Fb9LI+42VPrYiRalt6rp6x7YXd4oCQBDcKQoAQdDQASAIGjoABEFDB4AgaOgAEAQNHQCCoKEDQBA0dAAI4v8AOixBEc8RJPUAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Iteration: 4000 [31616/60000 (53%)]\tLoss: 145.507874\n",
      "New Model Temperature: 0.772595232106928\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXQAAAC7CAYAAAB1qmWGAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDMuMC4zLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvnQurowAADYxJREFUeJzt3X+MHPV5x/HPx+Z8Lj9SmVAsYxxsfqRN1AhTXUxVBwlK+RH+qKFSpPBH5EpUF6VBApVWWPQPokqVaBMIVI6oHGzZUSgkqiFYAjV1XVQa0RpsQsBwEFPkBMeWXURbu1Xxz6d/3Fi6eufw3s7s7M5z75dk7e53Z2ee8T3+MOzM98YRIQBA+80ZdAEAgHoQ6ACQBIEOAEkQ6ACQBIEOAEkQ6ACQBIEOAEkQ6ACQRKVAt32z7bdtv2N7TV1FAYNGb6ON3OtMUdtzJf1U0g2S9kp6WdLtEfFmfeUBzaO30VZnVfjsCknvRMS7kmT7SUmrJE3b9PM8GvN1ToVNAtP7UP+jo3HENayK3sZQ6ba3qwT6YknvTXm9V9LVH/WB+TpHV/v6CpsEprc9ttW1KnobQ6Xb3q4S6GX/tej4/sb2uKRxSZqvsytsDmgMvY1WqnJSdK+kJVNeXyxp3+kLRcS6iBiLiLERjVbYHNAYehutVCXQX5Z0he1ltudJ+qKkLfWUBQwUvY1W6vkrl4g4bvtOST+UNFfShoh4o7bKgAGht9FWVb5DV0Q8J+m5mmoBhga9jTZipigAJEGgA0ASBDoAJEGgA0ASBDoAJEGgA0ASBDoAJEGgA0ASBDoAJEGgA0ASBDoAJEGgA0ASBDoAJEGgA0ASBDoAJEGgA0ASBDoAJEGgA0ASBDoAJFHpnqK290g6LOmEpOMRMVZHUZi5H+57tXT8pouWV1p2tqK3B+OsxRd1jL31FwtLl33rusc6xj75zFdKl/3kH75UrbCWqBTohesi4v0a1gMMG3obrcJXLgCQRNVAD0l/b3un7fE6CgKGBL2N1qn6lcvKiNhn+0JJW22/FREvTF2g+McwLknzdXbFzQGNobfROpWO0CNiX/F4UNLTklaULLMuIsYiYmxEo1U2BzSG3kYb9XyEbvscSXMi4nDx/EZJf1ZbZZhW2VUq012hMpNlMYnebsCcuaXDux+8oGPszWvWlS57smRs7Y3fKV127ZW3dX7+JxPT19dSVb5yWSjpadun1vM3EfF3tVQFDBa9jVbqOdAj4l1JV9ZYCzAU6G20FZctAkASBDoAJFHHTFHUoOrJy+mm889kWU6WoilzF/xy6fjr16yvtN7PzCuf2Hv0452XlWYMP47QASAJAh0AkiDQASAJAh0AkiDQASCJjCd602j6phX8mgA05tjx0uHtR0Y6xq4ePdb1au/f9/nS8bP+cWfX62gzjtABIAkCHQCSINABIAkCHQCS4KTokGhymj8nOjFoJw4dKh1f/U93dIy9deNf97ucNDhCB4AkCHQASIJAB4AkCHQASOKMgW57g+2DtndNGTvf9lbbu4vHBf0tE6gfvY1surnKZaOktZKm3k57jaRtEfGA7TXF63vrLw9l+jX1fxZe/bJR9PZAzP3Yx0rHH73mu5XWu2PzZ0rHL9KLldbbFmc8Qo+IFyR9cNrwKkmbiuebJN1ac11A39HbyKbX79AXRsR+SSoeL6yvJGCg6G20Vt8nFtkelzQuSfPVeV8/oK3obQybXo/QD9heJEnF48HpFoyIdRExFhFjIxrtcXNAY+httFavR+hbJK2W9EDx+ExtFaHv6jiBmhi93YAPV1xROn7dL22rtN5z956s9Pm26+ayxSck/YukX7W91/Ydmmz2G2zvlnRD8RpoFXob2ZzxCD0ibp/mretrrgVoFL2NbJgpCgBJEOgAkASBDgBJcIOLFuIGF2i7hx/71jTvzO16HX/1H7/WMbbg2TdLlz3R9VrbjSN0AEiCQAeAJAh0AEiCQAeAJDgpOiRmcqKzzHQnOquuF+iHT42MlI6fVOfU/Ymj5dP5n733tzvGRg+9XK2wluMIHQCSINABIAkCHQCSINABIAlOig6JqrM3OfmJYXXkls+WjO7s+vMvfbisdHz02dl9ArQMR+gAkASBDgBJEOgAkASBDgBJdHNP0Q22D9reNWXsa7Z/YfvV4s8t/S0TqB+9jWy6ucplo6S1kr5z2vg3I+IbtVc0S83kKpWyK2KY+t+TjaK3azVn/vyOsfPX7OkYG3H57z0/Fp1jX//xjaXLXip6+3RnPEKPiBckfdBALUCj6G1kU+U79Dttv1b8b+uC2ioCBo/eRiv1GuiPSrpM0nJJ+yU9ON2Ctsdt77C945iO9Lg5oDH0Nlqrp0CPiAMRcSIiTkr6tqQVH7HsuogYi4ixEY32WifQCHobbdbT1H/biyJif/HyNkm7Pmp59KaOE53cEHpm6O1qfvYnv9Ex9uPLHukYOxblx5I/PXa0Y+zSh2fLLZ6rO2Og235C0rWSLrC9V9L9kq61vVxSSNoj6ct9rBHoC3ob2Zwx0CPi9pLh9X2oBWgUvY1smCkKAEkQ6ACQBIEOAElwg4sWKrtyhSn+aNLcCz5eOv7rN71dab23vviVjrFLX6K3u8UROgAkQaADQBIEOgAkQaADQBKcFB2AshOYVafoM8UfTTry5Dml408v29zV598/8b+l45f/ZefU/5PdlzXrcYQOAEkQ6ACQBIEOAEkQ6ACQBIEOAElwlcuQ4KYVGFZnLbm4Y+yrl/xDpXV+7m//uHT88lf/tdJ6ZzuO0AEgCQIdAJIg0AEgiTMGuu0ltp+3PWH7Ddt3FePn295qe3fxuKD/5QL1obeRTTcnRY9LuiciXrF9nqSdtrdK+n1J2yLiAdtrJK2RdG//Sh1uM5nOz0nNoUFvT1F28lOSFnzvvzvGbjr7v7pe73cPLekYu/yPOPnZD2c8Qo+I/RHxSvH8sKQJSYslrZK0qVhsk6Rb+1Uk0A/0NrKZ0XfotpdKukrSdkkLI2K/NPkPQ9KFdRcHNIXeRgZdB7rtcyVtlnR3RByawefGbe+wveOYjvRSI9BX9Day6CrQbY9osuEfj4iniuEDthcV7y+SdLDssxGxLiLGImJsRKN11AzUht5GJt1c5WJJ6yVNRMRDU97aIml18Xy1pGfqLw/oH3ob2XRzlctKSV+S9LrtU5dy3CfpAUnft32HpJ9L+kJ/Ssyn22n+XA3Td/T2FP/2B58oHf/JJY90vY5Nhy7pGPvB760sWXJ31+tE984Y6BHxI0me5u3r6y0HaA69jWyYKQoASRDoAJAEgQ4ASfD70PtoupOfnOzEMFp502uV17H2sc5JtYsmXqy8XnSHI3QASIJAB4AkCHQASIJAB4AkCHQASIKrXGpSduVKt1P8gaa996e/1TH2+OKvT7P0vI6RKx+7q3TJpY+81DEWM6oMVXCEDgBJEOgAkASBDgBJEOgAkAQnRT8CJzWR1dLf2dMxdt6czpOf01n29H+Wjp88frzXklADjtABIAkCHQCSINABIIlubhK9xPbztidsv2H7rmL8a7Z/YfvV4s8t/S8XqA+9jWy6OSl6XNI9EfGK7fMk7bS9tXjvmxHxjf6VB/QVvY1UurlJ9H5J+4vnh21PSFrc78KGATeiyG029/aJ6/Z1jP2uPjuDNbxZXzGozYy+Q7e9VNJVkrYXQ3fafs32BtsLaq4NaAy9jQy6DnTb50raLOnuiDgk6VFJl0larsmjnAen+dy47R22dxzTkRpKBupFbyOLrgLd9ogmG/7xiHhKkiLiQESciIiTkr4taUXZZyNiXUSMRcTYiEbrqhuoBb2NTLq5ysWS1kuaiIiHpowvmrLYbZJ21V8e0D/0NrLp5iqXlZK+JOl126fmwt8n6XbbyzX56473SPpyXyoE+ofeRirdXOXyI0kueeu5+ssBmkNvIxtmigJAEgQ6ACRBoANAEgQ6ACRBoANAEgQ6ACRBoANAEgQ6ACRBoANAEo6I5jZm/7uknxUvL5D0fmMbbw77NTiXRMSvDGLDU3q7DX9Pvcq6b23Yr656u9FA/38btndExNhANt5H7NfslvnvKeu+ZdovvnIBgCQIdABIYpCBvm6A2+4n9mt2y/z3lHXf0uzXwL5DBwDUi69cACCJxgPd9s2237b9ju01TW+/TsUd4Q/a3jVl7HzbW23vLh5bd8d420tsP297wvYbtu8qxlu/b/2Upbfp6/bt2ymNBrrtuZK+Jenzkj6tyVt9fbrJGmq2UdLNp42tkbQtIq6QtK143TbHJd0TEZ+S9JuSvlr8nDLsW18k6+2Noq9bqekj9BWS3omIdyPiqKQnJa1quIbaRMQLkj44bXiVpE3F802Sbm20qBpExP6IeKV4fljShKTFSrBvfZSmt+nr9u3bKU0H+mJJ7015vbcYy2RhROyXJhtI0oUDrqcS20slXSVpu5LtW82y93aqn33Wvm460MtuyMtlNkPK9rmSNku6OyIODbqeIUdvt0Tmvm460PdKWjLl9cWS9jVcQ78dsL1IkorHgwOupye2RzTZ9I9HxFPFcIp965PsvZ3iZ5+9r5sO9JclXWF7me15kr4oaUvDNfTbFkmri+erJT0zwFp6YtuS1kuaiIiHprzV+n3ro+y93fqf/Wzo68YnFtm+RdLDkuZK2hARf95oATWy/YSkazX529oOSLpf0g8kfV/SJyT9XNIXIuL0E0xDzfbnJP2zpNclnSyG79Pk942t3rd+ytLb9HX79u0UZooCQBLMFAWAJAh0AEiCQAeAJAh0AEiCQAeAJAh0AEiCQAeAJAh0AEji/wB8QUjgUtlIFAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Iteration: 4100 [44416/60000 (74%)]\tLoss: 152.225143\n",
      "New Model Temperature: 0.7633794943368531\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Iteration: 4200 [57216/60000 (95%)]\tLoss: 144.191559\n",
      "New Model Temperature: 0.7633794943368531\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Iteration: 4300 [9984/60000 (17%)]\tLoss: 144.429718\n",
      "New Model Temperature: 0.7610927876289343\n"
     ]
    },
    {
     "data": {
      "image/png": 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oFU6xbzXJXtsp/uyz13W/G/oWSWfbfrftGZKulPRIn3Oo2yOSrmo9v0rSwwPMZUpsW9JaSTsj4vZJP2r8vtUoe203/s/+RKjrvp9YZPtSSX8rabqkdRHx131NoEK275d0gSau1rZP0i2SviXpG5J+TdKPJV0REcdOMA012+dL+ndJz0oab4Vv1sT3jY3etzplqW3qunn7dhRnigJAEpwpCgBJ0NABIAkaOgAkQUMHgCRo6ACQBA0dAJKgoQNAEjR0AEjifwEKZc9EaH1aGQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Iteration: 4400 [22784/60000 (38%)]\tLoss: 139.791718\n",
      "New Model Temperature: 0.7565399032150474\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Iteration: 4500 [35584/60000 (59%)]\tLoss: 144.005341\n",
      "New Model Temperature: 0.7497615922390413\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Iteration: 4600 [48384/60000 (81%)]\tLoss: 144.864227\n",
      "New Model Temperature: 0.7408182206817178\n",
      "New Model Temperature: 0.7408182206817178\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Iteration: 4700 [1152/60000 (2%)]\tLoss: 143.582977\n",
      "New Model Temperature: 0.7385990963704825\n"
     ]
    },
    {
     "data": {
      "image/png": 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pzq0h2Ws7xe8+e1233dBflLTY9rm250i6SdK6lnNo2jpJyzvPl0t6coC5zIhtS3pI0vaIeGDaj0b+3BqUvbZH/nd/ItR16wuLbF8v6fOSZktaHRGfbTWBGtl+RNJVOvxtbbsl3SPpXyU9Juk3Jf1I0kci4ugBpqFm+0pJz0l6RdKhTvhuHf68caTPrUlZapu6Hr1zO4KVogCQBCtFASAJGjoAJEFDB4AkaOgAkAQNHQCSoKEDQBI0dABIgoYOAEn8LzdE9epcYOCmAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Iteration: 4800 [13952/60000 (23%)]\tLoss: 142.958679\n",
      "New Model Temperature: 0.7341807700262634\n"
     ]
    },
    {
     "data": {
      "image/png": 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GeJ6k1Y3XqyXdWWlSBXD3fnff2nh9WFKfpCkKsG0lClPb1HX9tu20qhv6FEk/G/J+XyMWySR375cGC0jSxBbnk4uZXSbpekkbFWzbCha9tkP97qPWddUNPe2BvFxm06bMbIKk5yQ97O6HWp1Pm6O2ayJyXVfd0PdJmjbk/VRJByrOoWwHzWyyJDW+DrQ4nxExsy4NFv2z7v58Ixxi20oSvbZD/O6j13XVDX2TpCvN7HIzGyPpXknrKs6hbOskLWi8XiDphRbmMiJmZpJWSOpz92VDvlX7bStR9Nqu/e++E+q68olFZjZX0jckjZK00t2/WmkCBTKzNZJu0eDd2g5K+rKk70laK+lSSe9JusfdzzzB1NbM7GZJP5T0lqRTjfASDR5vrPW2lSlKbVPX9du205gpCgBBMFMUAIKgoQNAEDR0AAiChg4AQdDQASAIGjoABEFDB4AgaOgAEMT/AdfrGkYEKRQvAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Iteration: 4900 [26752/60000 (45%)]\tLoss: 142.349976\n",
      "New Model Temperature: 0.7276027884145955\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Iteration: 5000 [39552/60000 (66%)]\tLoss: 137.947296\n",
      "New Model Temperature: 0.7189237334319262\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Iteration: 5100 [52352/60000 (87%)]\tLoss: 148.914597\n",
      "New Model Temperature: 0.7189237334319262\n"
     ]
    },
    {
     "data": {
      "image/png": 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2CoWpbeq6ecd2Vt0NfY6kA6NeH0xikcx290FppIAkXd7hfAoxs/mSrpO0UcGOrWTRazvU7z5qXdfd0LNuyMsymy5lZtMlPSVpubu/1ul8uhy13RCR67ruhn5Q0rxRr+dKOlxzDlU7YmZ9kpQ8Hu1wPm0xs16NFP0T7v50Eg5xbBWJXtshfvfR67ruhr5J0tVmdqWZXSjpg5LW1pxD1dZKWpY8Xybp2Q7m0hYzM0mrJO1090dG/ajxx1ah6LXd+N/9RKjr2i8sMrMlkv5SUo+k1e7+J7UmUCIze1LSIo18W9sRSZ+R9I+SvibpFyTtl3Snu587wdTVzOxXJf2bpJckDSfhBzTyeWOjj61KUWqbum7esZ3FlaIAEARXigJAEDR0AAiChg4AQdDQASAIGjoABEFDB4AgaOgAEAQNHQCC+H/vvAOSvOoYagAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Iteration: 5200 [5120/60000 (9%)]\tLoss: 141.459991\n",
      "New Model Temperature: 0.716770194155699\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXQAAAC7CAYAAAB1qmWGAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDMuMC4zLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvnQurowAAEE9JREFUeJzt3XuQVvV9x/HPd5cFRPBCFQaBiHXUam3UZAN2dOJtbNVmirZ1GsfJmDEptsaKjnFCmDaaxkTTqLk0qRZHK854Gadek3GaUEpirAZF4gWzMRKrgiBESQRvCLvf/rEP03XPb+F59lye53x5v2aY3f3u7zm/39n98uXw/H6/c8zdBQCov652DwAAUAwKOgAEQUEHgCAo6AAQBAUdAIKgoANAEBR0AAiCgg4AQeQq6GZ2mpk9b2arzWxBUYMC2o3cRh3ZaHeKmlm3pF9JOlXSWklPSDrH3X9R3PCA6pHbqKsxOV47W9Jqd39RkszsLklzJY2Y9GNtnI/Xnjm6BEb2nt7W+77VCjgUuY2O0mxu5yno0yWtGfL1WklzdvaC8dpTc+yUHF0CI1vuS4s6FLmNjtJsbucp6Kl/LTLv35jZPEnzJGm8JuToDqgMuY1ayjMpulbSzCFfz5C0bngjd1/k7r3u3tujcTm6AypDbqOW8hT0JyQdYmYHmdlYSZ+U9GAxwwLaitxGLY36LRd3325mF0n6oaRuSbe4+3OFjQxoE3IbdZXnPXS5+0OSHipoLEDHILdRR+wUBYAgKOgAEAQFHQCCoKADQBAUdAAIgoIOAEFQ0AEgCAo6AARBQQeAICjoABAEBR0AgqCgA0AQFHQACIKCDgBB5Lp9Lurph+ueSsb/9ICjKx4JgCJxhQ4AQVDQASAICjoABEFBB4Agck2KmtlLkrZI6pe03d17ixgUytXK5GcRE6ipY3T6BCy5XU9jph+Q/sbYnkzIf/u7ZNP+371Z5JAqVcQql5Pc/fUCjgN0GnIbtcJbLgAQRN6C7pJ+ZGZPmtm8IgYEdAhyG7WT9y2X49x9nZlNkbTEzH7p7g8PbdD4yzBPksZrQs7ugMqQ26idXFfo7r6u8XGjpPskzU60WeTuve7e26NxeboDKkNuo45GfYVuZntK6nL3LY3P/0TSPxU2MrRkpNUondBfp69oGY7c7izdhx+SjL8+e79MbNGV30q2PXxs9tr1wjUnJdu+cvkxmVjPL15Otu1/Y1My3i553nKZKuk+M9txnDvc/T8LGRXQXuQ2amnUBd3dX5R0VIFjAToCuY26YtkiAARBQQeAILgfeg11woRkK2Oo49Z/lOuds+Yk4/tf+mIm9rcH3J9se8Ie7ySizV+j3jjzJ8n4wF3LMrEvbfxYsu2jX8mex4R7lzc9hqJxhQ4AQVDQASAICjoABEFBB4AgKOgAEIS5e2Wd7WWTfY6dUll/dZJ3JUgRW/+rXHlSxIMzhlvuS7XZN9moD5ADuT3orbOzqz6mz1+dif3bgd9Pvn5iV/aeOAMayD+whK4Rrmdb6a/v/Wzb+Rf/fbLt+O8/3vRxh2s2t7lCB4AgKOgAEAQFHQCCoKADQBBs/W+DvBOYrUygdkJb1Fv3PntnYn1XH5Zsu/wT12die3eNTbTsyTusjpC6z/orf5Zue2h6HrhQXKEDQBAUdAAIgoIOAEFQ0AEgiF0WdDO7xcw2mtmqIbHJZrbEzF5ofNy33GECxSO3Ec0ut/6b2cclvSXpNnc/shH7Z0mb3P0aM1sgaV93/8KuOmN7dGs6eTt/J65yaXXrP7n9QW+ee2wyfsLnf5aJXTXlyVx9re9/Nxk/9bbLM7FZP3g72bZ7y9ZM7NfnTG56DGPeSafK9GXZ/u64+1+TbffuGp+JLdzQm2z7zEdGf5uVwrb+u/vDkjYNC8+VtLjx+WJJZ7Y8QqDNyG1EM9r30Ke6+3pJanycUtyQgLYit1FbpW8sMrN5kuZJ0nhNKLs7oDLkNjrNaK/QN5jZNElqfNw4UkN3X+Tuve7e26PsrTGBDkNuo7ZGe4X+oKTzJF3T+PhAYSOqqVYmMJudPGz3JGOrWrnHeRn3Qy/IbpHbm87/40zsviu/kWw7tXuPTKyVe4ZfuOakTOzlLx6abDtr2WNNH7c/9fp/aPrlI+qaNCkTu/K1k5NtrzvgkUzsqqnp+57/uT6Wb2BNaGbZ4p2SHpN0mJmtNbPPaDDZTzWzFySd2vgaqBVyG9Hs8grd3c8Z4Vv1XqOF3R65jWjYKQoAQVDQASAICjoABMEDLnYi7/b2Vtp24lZ6qZjbD6R0wrntDt74bHY1iyTd8Y/XZmL7d+dfennC09lpib2u2jMT6350Ze6+yvLGXxyZid1/wHeafv19b7VvLxpX6AAQBAUdAIKgoANAEBR0AAiCSdGdKGsCtIzXV62V7fydOuEbjX30DzOx1OSnJB04Zmyuvk5+9q+T8cmfGn43Yqn/jdW5+irL9pM/mowfdeEzuY77xWVnJ+OHKn1LgCJxhQ4AQVDQASAICjoABEFBB4AgdrtJ0VYm6No9mVfWPcPLOi4TndXo3mfvZHza917OxPJOfkrS41uzzybe6y83JNv2v51+oHMnmv7VF5Lx7874cdPH+M5v/yATO+Lq9cm225s+6uhxhQ4AQVDQASAICjoABEFBB4Agmnmm6C1mttHMVg2JXWlmr5rZU40/Z5Q7TKB45DaiaWaVy62SvivptmHxb7p7el9xB+vUFSJVHbPV4+a9p3urx6jYraphbvd9/dBk/IGZNyai+f8T3p84xkCHrmbpmjQpGb/i6R9nYseO70623ebZ873q9Q8n2674xEGZ2PY1a3YywnLt8rft7g9Lyt6gAag5chvR5Pnn+yIze6bx39Z9CxsR0H7kNmpptAX9BkkHSzpa0npJ143U0MzmmdkKM1uxTVtH2R1QGXIbtTWqgu7uG9y9390HJN0kafZO2i5y91537+1R/mcWAmUit1Fno9r6b2bT3H3H/tazJK3aWft2aGWCrqwHIXcqHvw8sjrk9vzjlyTjAxoopb8B7ynluHn5cdl8m3F9ejv/MeOyP5sfvTM+2fbGdadmYls/nX3QtSRtX/PSTkZYvV0WdDO7U9KJkvYzs7WSrpB0opkdLcklvSTpghLHCJSC3EY0uyzo7n5OInxzCWMBKkVuIxp2igJAEBR0AAiCgg4AQex2D7hIqdvqjCpX5dTtZ7M7+Nw+v07GW1njcvGrH8/EvjLtv5Jtjxz7fib29l/NSbbd8z+WtzCKrK4JEzKx185P5+Dpn3kkE7tiypNN97Xw659Nxn/vpscS0d80fdx24godAIKgoANAEBR0AAiCgg4AQYSYFG1lkjDVdqSJv1aOW8bkYRH9t/sc0Jn+99LDMrE55/5Rsu2v5t6QiV36tTuTba/e+9xMbOJr2efdj92UnWiVpFc+n2278thvJ9u24sP/fnEmNis5+VlvXKEDQBAUdAAIgoIOAEFQ0AEgCAo6AAQRYpVL3odWFPG0+lZWz7QyhqpeDxy+8PlkfOGxvZnYVVMfT7b9yJe+kYn1WLbdO54ISjpoTPahE63c0uCo/zk/GT/4ul9mYv0tHLcuuEIHgCAo6AAQBAUdAILYZUE3s5lmtszM+szsOTOb34hPNrMlZvZC4+O+5Q8XKA65jWiamRTdLukyd19pZpMkPWlmSyR9WtJSd7/GzBZIWiDpC+UNdWR5t7cXMaFY5QRoK+fAdv6d6vjcTumx7mR8mzd/jKP+5elM7GtTVzTd3zZPXwvOGLNH84NIWLv93Uzs9OV/l2z7+1/O3j7gwOeeTbaNOAGasssrdHdf7+4rG59vkdQnabqkuZIWN5otlnRmWYMEykBuI5qW3kM3s1mSjpG0XNJUd18vDf7FkDSl6MEBVSG3EUHTBd3MJkq6R9Il7r65hdfNM7MVZrZim7aOZoxAqchtRNFUQTezHg0m/O3ufm8jvMHMpjW+P03SxtRr3X2Ru/e6e2+PxhUxZqAw5DYiaWaVi0m6WVKfu18/5FsPSjqv8fl5kh4ofnhAechtRGPuO58aN7PjJf1U0rP6/124CzX4XuPdkj4k6RVJZ7v7pp0day+b7HPslLxjrkyV2+lZjZLfcl+qzb4pvac8oa65bf89PRl/4LD7S+mvK3HdN9DChvxV72drzA82p/P90QuytxnQz55puq+oms3tXS5bdPdHJI10oPpUZ2AYchvRsFMUAIKgoANAEBR0AAgixP3Q82pl23xZbYFm9X85vc9p2c0TM7GT9nir7OF8wM+3Zq8RL7/swkxswn3LRzgCE6B5cIUOAEFQ0AEgCAo6AARBQQeAICjoABAEq1xa1MoKFVazoAxdP/l5Mn7t35ybiV12Uf67QL737thM7OAb0rcM6X7zvUxswqqRVrSgaFyhA0AQFHQACIKCDgBBUNABIAgmRcXkJWLoXrYyE5uxrNoxNH+XdJSBK3QACIKCDgBBUNABIIhmHhI908yWmVmfmT1nZvMb8SvN7FUze6rx54zyhwsUh9xGNM1Mim6XdJm7rzSzSZKeNLMlje99092vLW94QKnIbYTSzEOi10ta3/h8i5n1SUo/dhyoEXIb0bT0HrqZzZJ0jKQdN2e4yMyeMbNbzGzfgscGVIbcRgRNF3QzmyjpHkmXuPtmSTdIOljS0Rq8yrluhNfNM7MVZrZim/LfKAgoGrmNKJoq6GbWo8GEv93d75Ukd9/g7v3uPiDpJkmzU69190Xu3uvuvT0aV9S4gUKQ24ikmVUuJulmSX3ufv2Q+LQhzc6StKr44QHlIbcRTTOrXI6T9ClJz5rZjsfYL5R0jpkdLcklvSTpglJGCJSH3EYozaxyeUSSJb71UPHDAapDbiMadooCQBAUdAAIgoIOAEFQ0AEgCAo6AARBQQeAICjoABAEBR0AgqCgA0AQ5u7VdWb2G0kvN77cT9LrlXVeHc6rfQ509/3b0fGQ3K7Dz2m0op5bHc6rqdyutKB/oGOzFe7e25bOS8R57d4i/5yinluk8+ItFwAIgoIOAEG0s6AvamPfZeK8dm+Rf05Rzy3MebXtPXQAQLF4ywUAgqi8oJvZaWb2vJmtNrMFVfdfpMYT4Tea2aohsclmtsTMXmh8rN0T481sppktM7M+M3vOzOY34rU/tzJFyW3yun7ntkOlBd3MuiV9T9Lpko7Q4KO+jqhyDAW7VdJpw2ILJC1190MkLW18XTfbJV3m7odLOlbS5xq/pwjnVopguX2ryOtaqvoKfbak1e7+oru/L+kuSXMrHkNh3P1hSZuGhedKWtz4fLGkMysdVAHcfb27r2x8vkVSn6TpCnBuJQqT2+R1/c5th6oL+nRJa4Z8vbYRi2Squ6+XBhNI0pQ2jycXM5sl6RhJyxXs3AoWPbdD/e6j5nXVBT31QF6W2XQoM5so6R5Jl7j75naPp8OR2zUROa+rLuhrJc0c8vUMSesqHkPZNpjZNElqfNzY5vGMipn1aDDpb3f3exvhEOdWkui5HeJ3Hz2vqy7oT0g6xMwOMrOxkj4p6cGKx1C2ByWd1/j8PEkPtHEso2JmJulmSX3ufv2Qb9X+3EoUPbdr/7vfHfK68o1FZnaGpG9J6pZ0i7t/tdIBFMjM7pR0ogbv1rZB0hWS7pd0t6QPSXpF0tnuPnyCqaOZ2fGSfirpWUkDjfBCDb7fWOtzK1OU3Cav63duO7BTFACCYKcoAARBQQeAICjoABAEBR0AgqCgA0AQFHQACIKCDgBBUNABIIj/A6s1YWfrIuUvAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Iteration: 5300 [17920/60000 (30%)]\tLoss: 145.471039\n",
      "New Model Temperature: 0.7124824490891918\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Iteration: 5400 [30720/60000 (51%)]\tLoss: 144.424728\n",
      "New Model Temperature: 0.7060988762143844\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Iteration: 5500 [43520/60000 (72%)]\tLoss: 144.384521\n",
      "New Model Temperature: 0.697676326071031\n"
     ]
    },
    {
     "data": {
      "image/png": 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MZVRsW9IKSdsi4q5hX6r9unVQ9tqu/c9+LNR1108ssn2FpC9JGidpZUTc1tUEKmT7fkmXauhqbXskfUHS9yV9R9K7JP1c0tURcewEU1+z/SFJP5S0RdKRRvgWDe1vrPW6dVKW2qau67duR3GmKAAkwZmiAJAEDR0AkqChA0ASNHQASIKGDgBJ0NABIAkaOgAkQUMHgCT+H3w1yk97ZadAAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Iteration: 5600 [56320/60000 (94%)]\tLoss: 140.859543\n",
      "New Model Temperature: 0.697676326071031\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Iteration: 5700 [9088/60000 (15%)]\tLoss: 138.985260\n",
      "New Model Temperature: 0.6955864334990951\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Iteration: 5800 [21888/60000 (36%)]\tLoss: 141.904556\n",
      "New Model Temperature: 0.6914254104503086\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Iteration: 5900 [34688/60000 (58%)]\tLoss: 141.898712\n",
      "New Model Temperature: 0.6852305006658703\n"
     ]
    },
    {
     "data": {
      "image/png": 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p25ImS1oeEbf2NIEK2X5Q0rnaf7e2nZJukfSYpIckfUTSq5I+GxEHTzANNNsLJT0laZOkkVb4Zu0/39jofatTltqmrpu3bwdwpSgAJMGVogCQBA0dAJKgoQNAEjR0AEiChg4ASdDQASAJGjoAJEFDB4Ak/h8o0LTMAanioAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Iteration: 6000 [47488/60000 (79%)]\tLoss: 145.109451\n",
      "New Model Temperature: 0.6770568744981648\n",
      "New Model Temperature: 0.6770568744981648\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Iteration: 6100 [256/60000 (0%)]\tLoss: 146.778992\n",
      "New Model Temperature: 0.6750287475861333\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Iteration: 6200 [13056/60000 (22%)]\tLoss: 132.485992\n",
      "New Model Temperature: 0.670990701353446\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Iteration: 6300 [25856/60000 (43%)]\tLoss: 141.221588\n",
      "New Model Temperature: 0.6649788788224019\n"
     ]
    },
    {
     "data": {
      "image/png": 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2dV2/czuk6oY+IOnVEV9va8RSMiMidkjDBSTp+C7n0xHbcySdLmmDEju3gqVe20n97lOt66obet4NeVlm06NsT5V0p6TLI2JPt/PpcdR2TaRc11U39G2SZo34+gRJ2yvOoWw7bc+UpMbHXV3Opy22+zVc9Gsi4q5GOIlzK0nqtZ3E7z71uq66oT8haa7tk2xPkvRJSfdWnEPZ7pW0pPH5Ekn3dDGXtti2pFWStkTEdSO+VftzK1HqtV373/14qOvKNxbZPkfSDZL6JN0cEV+uNIEC2b5N0pkafre2nZKukfRdSXdIOlHSK5IujIjDJ5h6mu0PS/pPSU9LeqcRvlrDrzfW+tzKlEptU9f1O7dD2CkKAIlgpygAJIKGDgCJoKEDQCJo6ACQCBo6ACSChg4AiaChA0AiaOgAkIj/B5IF9pLcnPq+AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Iteration: 6400 [38656/60000 (64%)]\tLoss: 145.177170\n",
      "New Model Temperature: 0.6570468198150567\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Iteration: 6500 [51456/60000 (86%)]\tLoss: 145.159485\n",
      "New Model Temperature: 0.6570468198150567\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Iteration: 6600 [4224/60000 (7%)]\tLoss: 138.717545\n",
      "New Model Temperature: 0.6550786331118063\n"
     ]
    },
    {
     "data": {
      "image/png": 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Jncd9fc6nJ7aHNFH090fEg51wEvtWk9RrO4mffep13XRD3y7pXNsftD1H0qclPdJwDnV7RNINnec3SHq4j7n0xLYlrZe0MyLunPRW6/etRqnXdut/9jOhrhufWGT7Gkl/J2mWpA0R8fVGE6iQ7QckXaaJq7XtlfRlSf8k6YeSzpD0sqRVEXH0ANNAs32ppJ9KekbS4U74Vk1839jqfatTKrVNXbdv345gpigAJIKZogCQCBo6ACSChg4AiaChA0AiaOgAkAgaOgAkgoYOAImgoQNAIv4fZNLhRCsUDH8AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Iteration: 6700 [17024/60000 (28%)]\tLoss: 142.876038\n",
      "New Model Temperature: 0.6511599291810325\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Iteration: 6800 [29824/60000 (50%)]\tLoss: 139.523865\n",
      "New Model Temperature: 0.6453257828572946\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Iteration: 6900 [42624/60000 (71%)]\tLoss: 142.520966\n",
      "New Model Temperature: 0.6376281516217733\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Iteration: 7000 [55424/60000 (92%)]\tLoss: 142.814026\n",
      "New Model Temperature: 0.6376281516217733\n"
     ]
    },
    {
     "data": {
      "image/png": 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SSVdKekHB9q1g0Ws71O8+al1X3dCzbsjLMpsOZWZTJD0u6W53P9bufDoctV0Tkeu66oZ+QNLsEc8vlnSo4hzKdtjMZkpS8vVIm/MZFTPr1nDRr3f3J5JwiH0rSfTaDvG7j17XVTf0FyXNNbM5ZjZB0hJJGyvOoWwbJS1PHi+X9FQbcxkVMzNJD0vqc/dVI75V+30rUfTarv3vfizUdeUnFpnZTZL+UVKXpDXu/veVJlAgM3tU0rUavlrbYUlflvSkpA2SPirpTUmL3f30CaaOZmaflvRTSTslDSXh+zT8eWOt961MUWqbuq7fvp3CmaIAEARnigJAEDR0AAiChg4AQdDQASAIGjoABEFDB4AgaOgAEAQNHQCC+D+E4dB9BWuGUgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Iteration: 7100 [8192/60000 (14%)]\tLoss: 139.095139\n",
      "New Model Temperature: 0.6357181336264144\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Iteration: 7200 [20992/60000 (35%)]\tLoss: 145.355469\n",
      "New Model Temperature: 0.631915244899496\n"
     ]
    },
    {
     "data": {
      "image/png": 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WkKQZLc4nFzObK+laSdsUbN8KFr22Q/3uo9Z11Q0964a8TLNpU2Z2rqQnJd3j7sdanU+bo7ZrInJdV93QeyXNGfL6vZIOVZxD2Q6b2SxJSh6PtDifUTGzDg0W/Rp3fyoJh9i3kkSv7RC/++h1XXVDf0HSFWZ2qZlNkPRZSRsrzqFsGyUtSp4vkrShhbmMipmZpMcl7XH35UPeqv2+lSh6bdf+dz8W6rryE4vM7FZJj0gaL2mlu/9FpQkUyMzWSrpRg1drOyzpfkk/kfRDSRdL+pWk2939zAGmtmZmH5X0M0m7JJ1Kwss0+H1jrfetTFFqm7qu376dxpmiABAEZ4oCQBA0dAAIgoYOAEHQ0AEgCBo6AARBQweAIGjoABAEDR0Agvg/nXXjgpuyEZYAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Iteration: 7300 [33792/60000 (56%)]\tLoss: 147.061340\n",
      "New Model Temperature: 0.626253523657556\n"
     ]
    },
    {
     "data": {
      "image/png": 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hT5O0t9/n3iQWyRR3PyD1FZCkyS3OpxAzmyHpSkkbFOzYSha9tkP93Uet62Y39KwH8jLNpk2Z2ThJqyQtcvcjrc6nzVHbNRG5rpvd0HslTe/3+UJJ+5ucQ9UOmtlUSUpeD7U4nyExs071Ff2T7v5sEg5xbBWJXtsh/u6j13WzG/pGSTPN7L1mNlLS7ZJWNzmHqq2WtDB5v1DS8y3MZUjMzCQtk7TN3R/u96PaH1uFotd27f/uh0NdN/3GIjObL+lLkjokLXf3v21qAiUys6ckXau+b2s7KOl+Sf8q6WlJF0n6qaTb3P3sAaa2ZmbXSHpJ0huSTifh+9R3vbHWx1alKLVNXdfv2M7gTlEACII7RQEgCBo6AARBQweAIGjoABAEDR0AgqChA0AQNHQACIKGDgBB/C8dNvAvLpEH5wAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Iteration: 7400 [46592/60000 (78%)]\tLoss: 141.911896\n",
      "New Model Temperature: 0.618783391806141\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Iteration: 7500 [59392/60000 (99%)]\tLoss: 137.207153\n",
      "New Model Temperature: 0.618783391806141\n"
     ]
    },
    {
     "data": {
      "image/png": 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e6WMubbFtSWslbYuIuyd8q/b71kXZa7v2v/tjoa57fmKR7ask3SNpSNK6iPhyTxOokO0HJF2q8au17ZH0RUn/IOm7kk6T9IakT0XEkRNMA832RyX9q6SXJY01wqs1/nljrfetm7LUNnVdv307jDNFASAJzhQFgCRo6ACQBA0dAJKgoQNAEjR0AEiChg4ASdDQASAJGjoAJPH/Ba2W8jP0yoEAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Iteration: 7600 [12160/60000 (20%)]\tLoss: 140.803589\n",
      "New Model Temperature: 0.6169298233735475\n"
     ]
    },
    {
     "data": {
      "image/png": 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bkm6NiFMnmGY12x+V9KyknZJOtJrv1tTnjY0+typlyW3yunnndhI7RQEgCXaKAkASFHQASIKCDgBJUNABIAkKOgAkQUEHgCQo6ACQBAUdAJL4fzhmip/tXS9WAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Iteration: 7700 [24960/60000 (42%)]\tLoss: 136.106064\n",
      "New Model Temperature: 0.6132393269939276\n"
     ]
    },
    {
     "data": {
      "image/png": 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+RtJC2yfYPkTSpZJWNJxD3VZIWtx5v1jSQ33MZUpsW9KdkjZGxC3jftT6batR9tpu/e9+OtR14xcW2b5A0lckDUm6KyK+1GgCFbJ9r6SzNHa3tu2SPi/pQUn3SXqPpJ9J+nhEvPME00Czfaak70t6RtJoJ3y9xo43tnrb6pSltqnr9m3bPlwpCgBJcKUoACRBQweAJGjoAJAEDR0AkqChA0ASNHQASIKGDgBJ0NABIIn/A0wrzlSG9G+2AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Iteration: 7800 [37760/60000 (63%)]\tLoss: 141.176559\n",
      "New Model Temperature: 0.6077449349024903\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Iteration: 7900 [50560/60000 (84%)]\tLoss: 141.987350\n",
      "New Model Temperature: 0.600495578812266\n",
      "New Model Temperature: 0.600495578812266\n"
     ]
    },
    {
     "data": {
      "image/png": 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xSRopIEmH9zifttieL2mhpLVKtm8Vy17bqf7ss9Z1txt62Q15WWbTp2wfIulBSUsjYnuv8+lz1HZNZK7rbjf0jZLmjXp9tKQ3u5xDp222PUeSGo9bepzPhNge0EjR3xsRDzXCKfatQ7LXdoo/++x13e2G/oyk420fa3u6pPMkre5yDp22WtJFjecXSXqkh7lMiG1LukvScETcPOpHtd+3Dspe27X/s58Mdd31E4tsnyVpuaSpklZGxN90NYEK2b5f0qkauVrbZknXSvonSd+R9GFJr0s6NyL2n2Dqa7Z/U9JTkl6QtLcRvloj3zfWet86KUttU9f127d9OFMUAJLgTFEASIKGDgBJ0NABIAkaOgAkQUMHgCRo6ACQBA0dAJKgoQNAEv8LC2r6xaHN01UAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Iteration: 8000 [3328/60000 (6%)]\tLoss: 136.093628\n",
      "New Model Temperature: 0.5986967916057292\n"
     ]
    },
    {
     "data": {
      "image/png": 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C\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Iteration: 8100 [16128/60000 (27%)]\tLoss: 142.974243\n",
      "New Model Temperature: 0.5951153658775501\n"
     ]
    },
    {
     "data": {
      "image/png": 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TYtuSVksajog7xrzV+H2rUfbabvzvfjrUdddPLLJ9maS/kzRD0pqI+JuuJlAh2/dLukijV2vbI2mlpH+T9D1JZ0h6U9IXIuLoAaa+ZvvTkp6S9JKkkVb4Vo1+39jofatTltqmrpu3b0dwpigAJMGZogCQBA0dAJKgoQNAEjR0AEiChg4ASdDQASAJGjoAJEFDB4Ak/hdmVZI0Qo9e1gAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Iteration: 8200 [28928/60000 (48%)]\tLoss: 141.733719\n",
      "New Model Temperature: 0.5897833576128505\n"
     ]
    },
    {
     "data": {
      "image/png": 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Fstd2qt991rquu6GX3ZCXaTYDyvaxkh6QdGNEvNXvfAYctd0Qmeu67oa+VdLJ416fJGlbzTn02g7bcySp9bizz/lMiu0hjRX9PRHxYCucYt16JHttp/jdZ6/ruhv6U5Lm255n+yhJV0paVXMOvbZK0jWt59dIeqiPuUyKbUu6U9JIRNw67q3Gr1sPZa/txv/uD4e6rv3EItuXSrpN0hRJKyLir2tNoEK275N0vsau1rZD0pck/ZOk70g6RdLLkq6IiAMPMA002x+V9H1Jz0kabYVv1tj+xkavWy9lqW3qunnrth9nigJAEpwpCgBJ0NABIAkaOgAkQUMHgCRo6ACQBA0dAJKgoQNAEjR0AEji/wBWKvw14r029QAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Iteration: 8300 [41728/60000 (70%)]\tLoss: 138.764435\n",
      "New Model Temperature: 0.5827482523739898\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Iteration: 8400 [54528/60000 (91%)]\tLoss: 139.522949\n",
      "New Model Temperature: 0.5827482523739898\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXQAAAC7CAYAAAB1qmWGAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDMuMC4zLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvnQurowAADsZJREFUeJzt3X+sX3V9x/HXi3Lb0gJaxDZdW4f8iM5tsXR3hcmysRA2RGerGSozWheTyyJksJDNhrjosiwjjh9OUEhNa0vk5wIIMahrGAszaqUwBq1XaMeqlNZ2QBjdmNBe3vuj3y53PefS773nx/eed5+PpPl+v+/v53s+n3Pv+745nM/nfI8jQgCA7jtm0AMAANSDgg4ASVDQASAJCjoAJEFBB4AkKOgAkAQFHQCSoKADQBKVCrrtC2w/ZXu77dV1DQoYNHIbXeSpXilqe4akpyWdL2mnpEckXRwRP6pveED7yG101bEVPrtc0vaIeEaSbN8haYWkCZN+pmfFbM2t0CUwsZ/rv/VavOoaNkVuY1rpN7erFPRFkp4d93qnpLPe6AOzNVdn+bwKXQIT2xQP1rUpchvTSr+5XaWgl/3XonD+xvaIpBFJmq05FboDWkNuo5OqTIrulLRk3OvFknYd3igi1kTEcEQMD2lWhe6A1pDb6KQqBf0RSWfYfrvtmZI+Kun+eoYFDBS5jU6a8imXiDhg+zJJ35E0Q9K6iNha28iAASG30VVVzqErIh6Q9EBNYwGmDXIbXcSVogCQBAUdAJKgoANAEhR0AEiCgg4ASVDQASAJCjoAJEFBB4AkKOgAkAQFHQCSoKADQBIUdABIgoIOAElQ0AEgCQo6ACRBQQeAJCjoAJAEBR0AkqCgA0ASle4panuHpH2SxiQdiIjhOgYFDBq5jS6qVNB7ficinq9hO8B0Q26jUzjlAgBJVC3oIekfbD9qe6SOAQHTBLmNzql6yuWciNhle76kjbZ/HBEPj2/Q+2MYkaTZmlOxO6A15DY6p9IRekTs6j3ulXSvpOUlbdZExHBEDA9pVpXugNaQ2+iiKRd023Ntn3DouaTflbSlroEBg0Juo6uqnHJZIOle24e2c1tEfLuWUQGDRW6jk6Zc0CPiGUnvrnEswLRAbqOrWLYIAElQ0AEgiTquFMUkfWfX44XY7/3C0gGM5MjKxiqVj3cybQHUjyN0AEiCgg4ASVDQASAJCjoAJEFBB4AkWOXSoKorROpYHTLRGJrAapbpx0MzJ4g386fvUxYXYj/+43mN9FXm73//htL4r80q/zk04dR7LymNn3Hppsb75ggdAJKgoANAEhR0AEiCgg4ASTApWpOqk49NTShOZrttTqBi6p6+uXCvjQldfNYPSuN/NT/r77q8pI3F662NYNvKm0rjF166rPG+OUIHgCQo6ACQBAUdAJKgoANAEkcs6LbX2d5re8u42Em2N9re1nts71IwoCbkNrLpZ5XLekk3SrplXGy1pAcj4mrbq3uvP1P/8KafOm74MJntNtFXUytqOniDi/XqYG5vet/1pfG3HHNcyyPpz//Ea4XY9v2uvN01z/92IfbQN8tXkozNikLsmx+7prTtacdW+zk++dr+Sp+v4ohH6BHxsKQXDwuvkLSh93yDpJU1jwtoHLmNbKZ6Dn1BROyWpN7j/PqGBAwUuY3OavzCItsjkkYkabbmNN0d0BpyG9PNVI/Q99heKEm9x70TNYyINRExHBHDQ5o1xe6A1pDb6KypHqHfL2mVpKt7j/fVNqKGTYfvHe93DBNts+o+1HGJ/2TG0NTPvCHTPrc/PHJFafwvblzX9zY+vfljhdhp858vbfvUcwsKsdlb+584PG5vcULyLWu/3/fnJ/bzQuRt+l5py51XvacQqzr5OZHV7/ytCd55tZH+xutn2eLtkr4v6R22d9r+lA4m+/m2t0k6v/ca6BRyG9kc8Qg9Ii6e4K3zah4L0CpyG9lwpSgAJEFBB4AkKOgAkAQ3uFBzN3aoupKj7ZUgVb9SYDLbxdTN+tYjpfEvnParfW/jFD1RiI1N0PZ07ep7u4O27e/OLo1/e+XflkT7X+Wy48ArpfEP3Pznhdji/Zv63m7dOEIHgCQo6ACQBAUdAJKgoANAEkfdpCgTdEC3zHjzm0rjo1e/oxB74ILrSttO5jL/p/cXv1LgA3f8WWnbU/+m/KsGBoUjdABIgoIOAElQ0AEgCQo6ACRx1E2KlungzY371uaNqoGqZsybV4jtueWtpW23L7u5JDq7777Wvry4NP711e8vxE69r47vb28eR+gAkAQFHQCSoKADQBIUdABIop97iq6zvdf2lnGxz9t+zvbjvX8XNjtMoH7kNrLpZ5XLekk3SrrlsPj1EXFN7SMagMwrNpr6jvMk1it5bk9XPvOXS+M/+8sDhdgjy26v3N8NL51aiG18/7tL2x737z+s3N+gHPEIPSIelvRiC2MBWkVuI5sq59Avs/1E739bi4tHge4it9FJUy3oN0k6TdJSSbslXTtRQ9sjtjfb3rxfr06xO6A15DY6a0oFPSL2RMRYRLwu6auSlr9B2zURMRwRw0OaNdVxAq0gt9FlU7r03/bCiNjde/lBSVveqP3RoGyicTKTrZP5fNVL9DNPAldFbtfvvy46qxD70hduKG27dGa1byMZ3vyHpfGFq3YXYmMv/aRSX9PREX96tm+XdK6kk23vlPQ5SefaXiopJO2QdEmDYwQaQW4jmyMW9Ii4uCS8toGxAK0it5ENV4oCQBIUdABIgoIOAElwg4uaNLGihUv00TXbv3h2IXbXii8VYpNZzfLka/tL43907Z8WYgtvHS1tO/bSf/bdX5dxhA4ASVDQASAJCjoAJEFBB4AkmBSdJqpezs8EKpoy481vKsSe/nLx+8Ul6a731D8B+unVl5e2nX/n9wqxsb57yokjdABIgoIOAElQ0AEgCQo6ACRBQQeAJFjlUpOqN7ioiptWoKoZJ55YGn/2a4sKsaeWT/Qtw/2VlIku5y9b0XLCnT/oa5vgCB0A0qCgA0ASFHQASOKIBd32EtsP2R61vdX25b34SbY32t7We5zX/HCB+pDbyKafGYwDkq6MiMdsnyDpUdsbJX1S0oMRcbXt1ZJWS/pMc0Od3qpOSjKpORBHbW4fM3duIfbcLcXJT0l6/Ne/Xqmv33j8I4XYSZ+dWdr2hH9hArSKIx6hR8TuiHis93yfpFFJiyStkLSh12yDpJVNDRJoArmNbCZ1Dt32KZLOlLRJ0oKI2C0d/MOQNL/uwQFtIbeRQd8F3fbxku6WdEVEvDyJz43Y3mx78369OpUxAo0it5FFXwXd9pAOJvytEXFPL7zH9sLe+wsl7S37bESsiYjhiBge0qw6xgzUhtxGJv2scrGktZJGI+K6cW/dL2lV7/kqSffVPzygOeQ2sulnlcs5kj4u6Unbh65vv0rS1ZLusv0pST+VdFEzQxycyVzOX/UGE4P+6oCjVPrcPmbOnNL4z25bUog9Nnxr5f7O+dcPF2Inf+KFQmzshRcr94WiIxb0iPiuJE/w9nn1DgdoD7mNbLhSFACSoKADQBIUdABIgu9D18QTmmWTkk21BaryrOLSyb13Li5tu3nZbX1vd8eBVwqxFY9eUtp2wY2zC7GxF7b33Req4QgdAJKgoANAEhR0AEiCgg4ASVDQASAJVrmoucv5Wc2CNr30B2cWYj9c9pXK2/2nV04vxBZ9aGvl7aJ+HKEDQBIUdABIgoIOAElQ0AEgibSTopOZ0Jxo8pJJTUxHPrb8z3bmJ/ZU2m7ZJf6S9LXPrijE5mpTpb7QDI7QASAJCjoAJEFBB4Ak+rlJ9BLbD9ketb3V9uW9+OdtP2f78d6/C5sfLlAfchvZ9DMpekDSlRHxmO0TJD1qe2Pvvesj4prmhgc0itxGKv3cJHq3pN295/tsj0pa1PTAmjKZlStlK2VY+ZJHV3P7lfctK40/9Cs3972N58aKK1o+ecWVpW3n3suKlq6Y1Dl026dIOlP6vzVLl9l+wvY62/NqHhvQGnIbGfRd0G0fL+luSVdExMuSbpJ0mqSlOniUc+0Enxuxvdn25v16tYYhA/Uit5FFXwXd9pAOJvytEXGPJEXEnogYi4jXJX1V0vKyz0bEmogYjojhIRXveQgMErmNTPpZ5WJJayWNRsR14+ILxzX7oKQt9Q8PaA65jWz6WeVyjqSPS3rS9qFZwqskXWx7qaSQtENS+W3AB6SOyUsmQNPrZG7P/cfR0vjp3xopxLa/d01p249sXVWIncjkZ+f1s8rlu5Jc8tYD9Q8HaA+5jWy4UhQAkqCgA0ASFHQASIKCDgBJpL3BBZDV6/v2lcbf+Sc/KsROv758gc6cHUOF2In6t2oDw8BxhA4ASVDQASAJCjoAJEFBB4AkHBHtdWb/h6Sf9F6eLOn51jpvD/s1OL8YEW8dRMfjcrsLP6epyrpvXdivvnK71YL+/zq2N0fE8EA6bxD7dXTL/HPKum+Z9otTLgCQBAUdAJIYZEEv/17P7mO/jm6Zf05Z9y3Nfg3sHDoAoF6ccgGAJFov6LYvsP2U7e22V7fdf516d4Tfa3vLuNhJtjfa3tZ77Nwd420vsf2Q7VHbW21f3ot3ft+alCW3yevu7dshrRZ02zMkfVnSeyW9Swdv9fWuNsdQs/WSLjgstlrSgxFxhqQHe6+75oCkKyPilySdLenS3u8pw741Illurxd53UltH6Evl7Q9Ip6JiNck3SFpRctjqE1EPCzpxcPCKyRt6D3fIGllq4OqQUTsjojHes/3SRqVtEgJ9q1BaXKbvO7evh3SdkFfJOnZca939mKZLIiI3dLBBJI0f8DjqcT2KZLOlLRJyfatZtlzO9XvPmtet13Qy27IyzKbacr28ZLulnRFRLw86PFMc+R2R2TO67YL+k5JS8a9XixpV8tjaNoe2wslqfe4d8DjmRLbQzqY9LdGxD29cIp9a0j23E7xu8+e120X9EcknWH77bZnSvqopPtbHkPT7pe0qvd8laT7BjiWKbFtSWsljUbEdePe6vy+NSh7bnf+d3805HXrFxbZvlDSFyXNkLQuIv661QHUyPbtks7VwW9r2yPpc5K+IekuSW+T9FNJF0XE4RNM05rt35T0z5KelPR6L3yVDp5v7PS+NSlLbpPX3du3Q7hSFACS4EpRAEiCgg4ASVDQASAJCjoAJEFBB4AkKOgAkAQFHQCSoKADQBL/C6nOrpfP0MkUAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Iteration: 8500 [7296/60000 (12%)]\tLoss: 132.537354\n",
      "New Model Temperature: 0.581002627363602\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Iteration: 8600 [20096/60000 (33%)]\tLoss: 137.473663\n",
      "New Model Temperature: 0.5775270487619548\n"
     ]
    },
    {
     "data": {
      "image/png": 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Imjfq9fGStnU4h3bbYXuOJDUed3Y5n0mx3aeRor8jIu5phFPsW5tkr+0Uv/vsdd3phv64pJNsf8T2IZI+L2lVh3Not1WSLm88v1zSfV3MZVJsW9JySRsj4qZRP6r9vrVR9tqu/e9+KtR1xxcW2b5A0j9Jmi7ptoj4+44mUCHbd0k6RyN3a9sh6ZuSfiLpR5J+R9Irki6NiIMvMPU024skPSLpaUnDjfC1GjnfWOt9a6cstU1d12/fDmClKAAkwUpRAEiChg4ASdDQASAJGjoAJEFDB4AkaOgAkAQNHQCSoKEDQBL/Dw7n2aveY8ZCAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Iteration: 8700 [32896/60000 (55%)]\tLoss: 137.457153\n",
      "New Model Temperature: 0.5723526251566333\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Iteration: 8800 [45696/60000 (76%)]\tLoss: 146.170273\n",
      "New Model Temperature: 0.5655254386995372\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Iteration: 8900 [58496/60000 (97%)]\tLoss: 142.952362\n",
      "New Model Temperature: 0.5655254386995372\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Iteration: 9000 [11264/60000 (19%)]\tLoss: 141.329178\n",
      "New Model Temperature: 0.5638314047049557\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Iteration: 9100 [24064/60000 (40%)]\tLoss: 139.243896\n",
      "New Model Temperature: 0.5604585449744905\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Iteration: 9200 [36864/60000 (61%)]\tLoss: 140.282089\n",
      "New Model Temperature: 0.5554370486980184\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Iteration: 9300 [49664/60000 (83%)]\tLoss: 139.160370\n",
      "New Model Temperature: 0.5488116360940265\n",
      "New Model Temperature: 0.5488116360940265\n"
     ]
    },
    {
     "data": {
      "image/png": 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0uD9jYnuyhpL+/oh4pBVOsW8dkj23U/zus+d1twv6C5IW2J5ne4qkL0pa3+U+dNp6Scta75dJeqyHfRkT25Z0r6QdEXHnsK8av28dlD23G/+7Hw953fULi2xfKenvJU2UtCYibutqB2pk+wFJl2robm37Ja2S9C+SHpL0SUlvSbomIo4dYOprtn9T0tOSXpJ0pBVeqaHzjY3et07KktvkdfP27SiuFAWAJLhSFACSoKADQBIUdABIgoIOAElQ0AEgCQo6ACRBQQeAJCjoAJDE/wGQRbmnVIe4/gAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Iteration: 9400 [2432/60000 (4%)]\tLoss: 130.388916\n",
      "New Model Temperature: 0.5471676683703056\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXQAAAC7CAYAAAB1qmWGAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDMuMC4zLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvnQurowAAD3lJREFUeJzt3X+wFfV5x/HPI1ygSmygoCWAoAgWp63Y3mL80YyNMbFUB/wj09BMhk6TYqc1VaMTqTMOmkmnJFXSOrE2OBBI64+xg0bM+IsytsaMYfhRo9CrQiwK4Q5oIQVaBIGnf9xl5obd491zz+6es899v2aYe85zvmf3u9yHx3W/3++uubsAAPV3Wrs7AAAoBgUdAIKgoANAEBR0AAiCgg4AQVDQASAICjoABEFBB4AgWiroZnaNmb1hZtvNbFFRnQLajdxGHdlgV4qa2TBJb0q6WtIuSRskzXf3/yyue0D1yG3U1fAWvjtb0nZ3f0uSzOxRSXMlNUz6ETbSR+mMFnYJNPa+/ldH/YgVsClyGx0lb263UtAnStrZ7/0uSZd82BdG6QxdYle1sEugsfW+rqhNkdvoKHlzu5WCnvVfi9T1GzNbKGmhJI3S6S3sDqgMuY1aamVQdJekyf3eT5K0+9RG7r7M3bvdvbtLI1vYHVAZchu11EpB3yBpupmda2YjJH1O0ppiugW0FbmNWhr0JRd3P2ZmN0p6TtIwSSvcfWthPQPahNxGXbVyDV3u/rSkpwvqC9AxyG3UEStFASAICjoABEFBB4AgKOgAEAQFHQCCoKADQBAUdAAIgoIOAEFQ0AEgCAo6AARBQQeAICjoABAEBR0AgqCgA0AQLd0+F/E9t/uV3G0/87FZJfYE+EXDJ01MxT7xzJuZbW//lW25t/s/Jw6nYnP/9C8z2458ZkPu7VaBM3QACIKCDgBBUNABIAgKOgAE0dKgqJntkHRQ0nFJx9y9u4hOoThZg5rNDF4O1YFOcrvzvTN/Sip229inMtse9/zbHW0jU7HeK7JL5dRn8m+3CkXMcvk9d3+vgO0AnYbcRq1wyQUAgmi1oLuk581sk5ktLKJDQIcgt1E7rV5yudzdd5vZWZLWmtnr7v5i/wbJP4aFkjRKp7e4O6Ay5DZqp6UzdHffnfzcK+kJSbMz2ixz92537+5SerAB6ETkNupo0GfoZnaGpNPc/WDy+tOSvlZYz1CaRsv5W53RUtZ2q0Zut8++P7k0Fbv6yz/KbLt6/NKM6IiCe1QvrVxyOVvSE2Z2cjsPu/uzhfQKaC9yG7U06ILu7m9JuqjAvgAdgdxGXTFtEQCCoKADQBDcD30IKmKQstVbCmBo23nnZZnxl2+4NxXLWorfp5wB0EN+JBUb95Mm7h3QRpyhA0AQFHQACIKCDgBBUNABIAgKOgAEwSyXDlbEUvqsts1st1FbIK9j/3pOKvajC+7JbDvaRpXdnQF1P/yVVOy8x15uQ0+axxk6AARBQQeAICjoABAEBR0AgmBQtA2qHGhsZol+M/1qdbAV9TbsgvMz4+f9885U7K5ffSQVO/O0Xyq8T0UZfu6hdndh0DhDB4AgKOgAEAQFHQCCoKADQBADFnQzW2Fme81sS7/YWDNba2bbkp9jyu0mUDxyG9GY+4ffuN3MPiHpkKTvufuvJ7FvStrn7kvMbJGkMe5++0A7O9PG+iV2VQHdrocqZ7M0M5OkiKX/nThzZb2v0wHfZ3nbk9uD99Y3L82Mv/75+1va7tYPjqZiX1p8S2bbsVsOpGKHpo7ObPvv9z2Quw87jv1fKvbnU67I/f0y5M3tAc/Q3f1FSftOCc+VtCp5vUrSvKZ7CLQZuY1oBnsN/Wx375Wk5OdZxXUJaCtyG7VV+sIiM1soaaEkjdLpZe8OqAy5jU4z2DP0PWY2QZKSn3sbNXT3Ze7e7e7dXWr0sFegY5DbqK3BnqGvkbRA0pLk55OF9aimylpin6XVAcmy+hXEkM3tRsv5x373vVTsnyZn389cSi/p33T0eCr2h//2Z5nf/rW/P5yKjXkl+17kWdM5ztyVfYVs8bsXpWJ3j/9JZts6yzNt8RFJL0u6wMx2mdkX1ZfsV5vZNklXJ++BWiG3Ec2AZ+juPr/BR0NnjhZCIrcRDStFASAICjoABEFBB4AgeMBFG1S5bL6Z2TfNLP1HvQ2bMS0V67llbGbb7VMey4hmP6Aia0bLLV+9MRWb8S/rM79/IjOan4/PPoa7xz/b4pbrgTN0AAiCgg4AQVDQASAICjoABMGg6IeockCxrIHSTrxvOdrv9S+PS8W2X5f/nuFZg59S9gDo6AYDoK0a9tFfTsX+a3FXy9v91HPp+6/P0IaWt1sFztABIAgKOgAEQUEHgCAo6AAQxJAbFG1moLMsdRqobObe6XU6rqFi552XZcY3zcu6n/mo3NttdD/zRitAy/Dzz8xMxbZc9g+5v//TY+l7r0vSzHtOfcyslD0E3Hk4QweAICjoABAEBR0AgqCgA0AQeZ4pusLM9prZln6xu8zsZ2b2SvJnTrndBIpHbiOaPLNcVkr6tqTvnRL/lrs3evR3x6pyJkaEWR+Nbl8Q4dgULLeHTz0nFfv+l/42s+2Zp2XfzzzLxUvTy/lnrnw9s20Zs0Hev3Z2ZnzJ3/xj7m0c8iOp2LUP35bZ9tw3X8693U4z4Bm6u78oKT2PB6g5chvRtHIN/UYzezX539YxhfUIaD9yG7U02IL+gKRpkmZJ6pV0b6OGZrbQzDaa2cYPlP7fHqDDkNuorUEVdHff4+7H3f2EpAclZV/k6mu7zN273b27SyMH20+gEuQ26mxQS//NbIK79yZvr5e05cPad5JWH3rcCfc4r1KEY2hGHXJ7+JTJmfHuJ3+aik0bnn/wc8Hbn8yMT1rRk4od378/93azZN3LXJL2z0kv5//G17MHPy8fmf+R0rN+cFMqNuOv6jv42ciABd3MHpF0paRxZrZL0mJJV5rZLEkuaYekG0rsI1AKchvRDFjQ3X1+Rnh5CX0BKkVuIxpWigJAEBR0AAiCgg4AQQy5B1w0I2uGRycshW/m4RI8iCKebTdMyoyvGfdk7m18fsenUrG9d5+X2bZr/8bc283r3esvzIz/+Ov3597GgRPvp2K//cQtmW1nfufnqVhdHlrRDM7QASAICjoABEFBB4AgKOgAEMSQGxRtZkCQAUVE9R+70gOrU58vfvBTko78we+kYvfe+UDu72cNfkrSJQ/dmopNX5S9nD/iAGgWztABIAgKOgAEQUEHgCAo6AAQBAUdAIIIO8ulE5bol6VTb0mAoePwvOwHOe39o8Op2PLu76RiH2/iAU/dq7+SGT+/wYyWoYwzdAAIgoIOAEFQ0AEgiAELuplNNrMXzKzHzLaa2U1JfKyZrTWzbcnPMeV3FygOuY1o8gyKHpN0q7tvNrOPSNpkZmsl/bGkde6+xMwWSVok6fbyulqeZgYUq2zLgGbpapnbv/m721rexqgfj07Fhk+amNm256vp2wQ8ft19mW1/Y0RXrv0f9qOZ8fv2XZSKXfDg/sy2Q2U5fzMGPEN3915335y8PiipR9JESXMlrUqarZI0r6xOAmUgtxFNU9fQzWyqpIslrZd0trv3Sn3/MCSdVXTngKqQ24ggd0E3s9GSVku62d0PNPG9hWa20cw2fqAjg+kjUCpyG1HkKuhm1qW+hH/I3R9PwnvMbELy+QRJe7O+6+7L3L3b3bu71MRqAqAC5DYiyTPLxSQtl9Tj7kv7fbRG0oLk9QJJ+Z9QC3QAchvR5JnlcrmkL0h6zcxOTsW4Q9ISSY+Z2RclvSPps+V0sbMUMfOk0eyXVjAjZlBqmduv/nB69gfnPZ97G5tv+3Y6eFszvcg3m0WSlu5P9/fR+z6d2Xbcsqzl/G/k3tdQN2BBd/eXJFmDj68qtjtAdchtRMNKUQAIgoIOAEFQ0AEgiLD3Qy9CM8vxWbqPoeQb/z0zM77qqU+mYtNW7knFxm3jXuZl4AwdAIKgoANAEBR0AAiCgg4AQVDQASCIsLNcipiNktW2jGX7QDM+9tKxzPh1l16bij014wct7+/8ZxemYhd+LT1zRZKmvp2evcKDKKrDGToABEFBB4AgKOgAEAQFHQCCCDso2kiry/FZzo92G/n0hsz48afTsTn6rZb3N0MbU7HsYVm0G2foABAEBR0AgqCgA0AQeR4SPdnMXjCzHjPbamY3JfG7zOxnZvZK8mdO+d0FikNuI5o8g6LHJN3q7pvN7COSNpnZ2uSzb7n7PeV1DygVuY1Q8jwkuldSb/L6oJn1SJpYdsc6ATNaYhvKuY2YmrqGbmZTJV0saX0SutHMXjWzFWY2puC+AZUhtxFB7oJuZqMlrZZ0s7sfkPSApGmSZqnvLOfeBt9baGYbzWzjBzpSQJeBYpHbiCJXQTezLvUl/EPu/rgkufsedz/u7ickPShpdtZ33X2Zu3e7e3eXRhbVb6AQ5DYiyTPLxSQtl9Tj7kv7xSf0a3a9pC3Fdw8oD7mNaPLMcrlc0hckvWZmJ28Gfoek+WY2S5JL2iHphlJ6CJSH3EYoeWa5vCTJMj7KuHMEUB/kNqJhpSgABEFBB4AgKOgAEAQFHQCCoKADQBAUdAAIgoIOAEFQ0AEgCAo6AARh7l7dzszelfR28nacpPcq23l1OK72meLu49ux4365XYe/p8GKemx1OK5cuV1pQf+FHZttdPfutuy8RBzX0Bb57ynqsUU6Li65AEAQFHQACKKdBX1ZG/ddJo5raIv89xT12MIcV9uuoQMAisUlFwAIovKCbmbXmNkbZrbdzBZVvf8iJU+E32tmW/rFxprZWjPblvys3RPjzWyymb1gZj1mttXMbkritT+2MkXJbfK6fsd2UqUF3cyGSbpf0u9LulB9j/q6sMo+FGylpGtOiS2StM7dp0tal7yvm2OSbnX3mZI+Lukvkt9ThGMrRbDcXinyupaqPkOfLWm7u7/l7kclPSppbsV9KIy7vyhp3ynhuZJWJa9XSZpXaacK4O697r45eX1QUo+kiQpwbCUKk9vkdf2O7aSqC/pESTv7vd+VxCI52917pb4EknRWm/vTEjObKuliSesV7NgKFj23Q/3uo+Z11QU964G8TLPpUGY2WtJqSTe7+4F296fDkds1ETmvqy7ouyRN7vd+kqTdFfehbHvMbIIkJT/3trk/g2JmXepL+ofc/fEkHOLYShI9t0P87qPnddUFfYOk6WZ2rpmNkPQ5SWsq7kPZ1khakLxeIOnJNvZlUMzMJC2X1OPuS/t9VPtjK1H03K79734o5HXlC4vMbI6kv5M0TNIKd//rSjtQIDN7RNKV6rtb2x5JiyV9X9Jjks6R9I6kz7r7qQNMHc3MrpD0Q0mvSTqRhO9Q3/XGWh9bmaLkNnldv2M7iZWiABAEK0UBIAgKOgAEQUEHgCAo6AAQBAUdAIKgoANAEBR0AAiCgg4AQfw/zUjzsaG9oysAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Iteration: 9500 [15232/60000 (25%)]\tLoss: 135.948471\n",
      "New Model Temperature: 0.54389449170959\n"
     ]
    },
    {
     "data": {
      "image/png": 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rJd09wFwmxLYl3SppU0QsG/ejxu9bD2Wv7cb/2U+Guu77wiLbF0v6gqQpklZExKf7mkCNbN8u6Xzt/ba27ZJukvRtSXdIequkFyRdHhH7TzANNdvvlvQDSU9KGivCN2rv+cZG71svZalt6rp5+7YPK0UBIAlWigJAEjR0AEiChg4ASdDQASAJGjoAJEFDB4AkaOgAkAQNHQCS+H9mLqlZaMSgvwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Iteration: 9600 [28032/60000 (47%)]\tLoss: 135.260315\n",
      "New Model Temperature: 0.5390214030763572\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Iteration: 9700 [40832/60000 (68%)]\tLoss: 137.762253\n",
      "New Model Temperature: 0.5325918010068973\n"
     ]
    },
    {
     "data": {
      "image/png": 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xr8+StLfNOZRtn+1ZklR73N/hfMbF9hSNFv2aiHimFk5i30qSem0n8Wefel23u6G/KOl82x+1fZKkWyStbXMOZVsraXHt+WJJz3Ywl3GxbUmPSxqIiIfG/Kjy+1ai1Gu78n/2E6Gu276wyPYNkv5a0mRJKyPiq21NoEC2n5B0tUav1rZP0j2SviPpW5LmSPqppE9FxPEnmLqa7Y9L+g9J2ySN1MJ3a/T7xkrvW5lSqW3qunr7dgwrRQEgEawUBYBE0NABIBE0dABIBA0dABJBQweARNDQASARNHQASAQNHQAS8X/PTdCKx/Z0KQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Iteration: 9800 [53632/60000 (89%)]\tLoss: 142.307205\n",
      "New Model Temperature: 0.5325918010068973\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Iteration: 9900 [6400/60000 (11%)]\tLoss: 140.283524\n",
      "New Model Temperature: 0.5309964198721144\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Iteration: 10000 [19200/60000 (32%)]\tLoss: 137.325272\n",
      "New Model Temperature: 0.5278199802012078\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Iteration: 10100 [32000/60000 (53%)]\tLoss: 137.008087\n",
      "New Model Temperature: 0.5230909131025009\n"
     ]
    },
    {
     "data": {
      "image/png": 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H251Ph6O2KyJyXZfd0PdLmjvq9RxJB0rOoWgHzWyWJCVfD7U5n6aYWbdGiv4xd38qCYfYt4JEr+0Qf/fR67rshr5N0gIzm29mEyV9VtKmknMo2iZJK5PvV0p6uo25NMXMTNIaSYPu/uCo36r8vhUoem1X/u/+XKjr0i8sMrNbJH1TUpekte7+Z6UmkCMze1zSDRq5W9tBSfdK2ijpSUmXSXpb0mfc/ewJpo5mZsskfU/Sa5JOJ+FVGjnfWOl9K1KU2qauq7dvZ3ClKAAEwZWiABAEDR0AgqChA0AQNHQACIKGDgBB0NABIAgaOgAEQUMHgCD+F3pM3tH5r1YMAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Iteration: 10200 [44800/60000 (75%)]\tLoss: 134.525269\n",
      "New Model Temperature: 0.5168513344916993\n"
     ]
    },
    {
     "data": {
      "image/png": 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gAOdWojC5TV7X79xOqrqgD0h6fcrz0SwWyQXuPiZNJpCkRV3uT0fM7GJJl0napmDnVrDouR3qdx81r6su6Kkb8rLMZpYys7MlPSHpbnc/0u3+zHLkdk1EzuuqC/qopMVTnl8o6UDFfSjbQTPrl6Ts66Eu96ctZtanyaR/1N2fzMIhzq0k0XM7xO8+el5XXdBfkLTEzN5nZqdLulXSpor7ULZNktZkj9dIeqqLfWmLmZmkdZJG3P3BKd+q/bmVKHpu1/533wt5XfnGIjO7UdJfSporab27/2mlHSiQmT0m6RpNXq3toKTPSfqmpK9LukjSjyStdvdTJ5hmNTO7StJ3JO2WNJGF79Xk5421PrcyRclt8rp+53YSO0UBIAh2igJAEBR0AAiCgg4AQVDQASAICjoABEFBB4AgKOgAEAQFHQCC+D8f+SjVnD9WkQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Iteration: 10300 [57600/60000 (96%)]\tLoss: 139.928070\n"
     ]
    }
   ],
   "source": [
    "# model = VAE().to(device)\n",
    "# optimizer = Adam(model.parameters(), lr=1e-3)\n",
    "temp_min = 0.5\n",
    "ANNEAL_RATE = 0.00003\n",
    "train(model, optimizer, maxiters=10000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.7.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}