Part 4 - Fashion-MNIST (Exercises).ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Classifying Fashion-MNIST\n",
    "\n",
    "Now it's your turn to build and train a neural network. You'll be using the [Fashion-MNIST dataset](https://github.com/zalandoresearch/fashion-mnist), a drop-in replacement for the MNIST dataset. MNIST is actually quite trivial with neural networks where you can easily achieve better than 97% accuracy. Fashion-MNIST is a set of 28x28 greyscale images of clothes. It's more complex than MNIST, so it's a better representation of the actual performance of your network, and a better representation of datasets you'll use in the real world.\n",
    "\n",
    "<img src='assets/fashion-mnist-sprite.png' width=500px>\n",
    "\n",
    "In this notebook, you'll build your own neural network. For the most part, you could just copy and paste the code from Part 3, but you wouldn't be learning. It's important for you to write the code yourself and get it to work. Feel free to consult the previous notebooks though as you work through this.\n",
    "\n",
    "First off, let's load the dataset through torchvision."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\n",
    "from torchvision import datasets, transforms\n",
    "import helper\n",
    "\n",
    "# Define a transform to normalize the data\n",
    "transform = transforms.Compose([transforms.ToTensor(),\n",
    "                                transforms.Normalize((0.5,), (0.5,))])\n",
    "# Download and load the training data\n",
    "trainset = datasets.FashionMNIST('~/.pytorch/F_MNIST_data/', download=True, train=True, transform=transform)\n",
    "trainloader = torch.utils.data.DataLoader(trainset, batch_size=64, shuffle=True)\n",
    "\n",
    "# Download and load the test data\n",
    "testset = datasets.FashionMNIST('~/.pytorch/F_MNIST_data/', download=True, train=False, transform=transform)\n",
    "testloader = torch.utils.data.DataLoader(testset, batch_size=64, shuffle=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here we can see one of the images."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "image, label = next(iter(trainloader))\n",
    "helper.imshow(image[0,:]);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Building the network\n",
    "\n",
    "Here you should define your network. As with MNIST, each image is 28x28 which is a total of 784 pixels, and there are 10 classes. You should include at least one hidden layer. We suggest you use ReLU activations for the layers and to return the logits or log-softmax from the forward pass. It's up to you how many layers you add and the size of those layers."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [],
   "source": [
    "# TODO: Define your network architecture here\n",
    "from torch import nn\n",
    "\n",
    "input_n = image.view(image.shape[0],-1)\n",
    "\n",
    "\n",
    "\n",
    "model = nn.Sequential(nn.Linear(input_n.shape[1],256),\n",
    "                      nn.ReLU(),\n",
    "                      nn.Linear(256,64),\n",
    "                      nn.ReLU(),\n",
    "                      nn.Linear(64,10),\n",
    "                      nn.LogSoftmax(dim=1))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Train the network\n",
    "\n",
    "Now you should create your network and train it. First you'll want to define [the criterion](http://pytorch.org/docs/master/nn.html#loss-functions) ( something like `nn.CrossEntropyLoss`) and [the optimizer](http://pytorch.org/docs/master/optim.html) (typically `optim.SGD` or `optim.Adam`).\n",
    "\n",
    "Then write the training code. Remember the training pass is a fairly straightforward process:\n",
    "\n",
    "* Make a forward pass through the network to get the logits \n",
    "* Use the logits to calculate the loss\n",
    "* Perform a backward pass through the network with `loss.backward()` to calculate the gradients\n",
    "* Take a step with the optimizer to update the weights\n",
    "\n",
    "By adjusting the hyperparameters (hidden units, learning rate, etc), you should be able to get the training loss below 0.4."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "# TODO: Create the network, define the criterion and optimizer\n",
    "from torch import optim\n",
    "\n",
    "criterion = nn.NLLLoss()\n",
    "optimizer = optim.SGD(model.parameters(),lr=0.1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Taining loss: 0.5764109064807007\n",
      "Taining loss: 0.40090511836159204\n",
      "Taining loss: 0.3558306175826201\n",
      "Taining loss: 0.32859929818223155\n",
      "Taining loss: 0.3092665624485087\n",
      "Taining loss: 0.29166202743583397\n",
      "Taining loss: 0.27800013918850597\n",
      "Taining loss: 0.2670024842865813\n",
      "Taining loss: 0.2540879010486959\n",
      "Taining loss: 0.24545015251315605\n"
     ]
    }
   ],
   "source": [
    "# TODO: Train the network here\n",
    "epochs = 10\n",
    "for e in range(epochs):\n",
    "    running_loss = 0\n",
    "    for images,labels in trainloader:\n",
    "        images = images.view(images.shape[0],-1)\n",
    "        \n",
    "        optimizer.zero_grad()\n",
    "        \n",
    "        output = model(images)\n",
    "        loss = criterion(output,labels)\n",
    "        loss.backward()\n",
    "        optimizer.step()\n",
    "        \n",
    "        running_loss += loss.item()\n",
    "    \n",
    "    else:\n",
    "        print(f\"Training loss: {running_loss/len(trainloader)}\")\n",
    "        \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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KkiRJkhpYREmSJElSA4soSZIkSWpgESVJkiRJDSyiJEmSJKnB1IkewKYmU6Y0t6nly5vyc15zZFP+OS/9flMe4MBpZzTlb1q2c3MfP120X1N+/63mNvfR+muAeSu2bspvlRVtHQBTqKb8RYsOaO5jpykLm/Knv/Wfmvv45d/u2pR/3Zl/1pQ/8HUXNOUBSOMfeLX/+UmSJI3FmShJkiRJamARJUkCIMlrklSSy9bBuU5Pcs84cjOTzLyv/Q31WwOP5Ul+neSLSQ5ZV/2spu9tkpyS5Kj12Y8kaeK5nE+SNOLF/fOhSR5eVRdP6GjW3iLgif3XU4EDgbcA5yU5pKpuWk/9bgO8tf965nrqQ5K0EXAmSpJEkkcAhwLf7A+9ZAKHc1+trKoL+sf/VNXpwJ8DOwLHTezQJEmTgUWUJAlWFU0nAxcAf5Jk+mAgyYx+idxfJ3l9kuuS3JPk/CSPGauDJI9NMjfJmUm2XUNuhyTv78+/NMnsJB9cU5txuLt/vtdOP0kekuTrSe5MsjjJZUleNMqY9kvyuSS3JlmS5Mr+e7BF//4M4LY+/taB5YSn3IcxS5I2UhZRkrSZ64ulPwHOr6qrgE8BOwHPXk2TVwHHAH8JvADYFjgryY5r6OO5wPeALwPPrKoFq8ltA/wQeBHwYeCpwHuBE4FvJMk4P9PU/rF1kocA7wPuBM4ayBwEnAccAry6/7z/B5ye5OSB3O597li6ZYF/CJwDvB/4SB+7GXhK//WngCP6xyfHM15J0qbFa6IkSc+hW+p2Wv/6S8AH6WanPj9Kfj7w9KpuD/kkNwMX0hU8XxwOJ3kD8C7gb6vqH8YYy2uAhwKPrqqf9Me+l2Q2cAZdofLtMc6xLbBs6NjNwDOqas7AsVOALYGjq+rG/thZSXaim036eFXdDfwVsC/wyIExfSfJFOAvknywqq5JMnIN2Y1VNa49/AfaDHvQeNpLkiaGM1GSpJcAC+mKJ6pqPt2M0VFJRruR2bdGCqje5f3z/kO5JPk48DbghHEUUABPB64ALhuYTZoKfAco4KhxnGMR8Mj+8Wi6GaZr6AqkIwZyTwS+P1BAjTidbpOIIwZy/zdQQA3mwqpNLCRJmwlnoiRpM5bkQODxwH8BW/SzMNDN+pzUP/5uqNntgy+qanG/ym76UG4r4HnAzxl79mjEnnS76Q3PJI3YbRznWDlc8CT5DvBr4J9YVRztSjdDNeymgfdHnmeNI9esqh4+2vF+hurwtT2vJGn9soiSpM3bi+lmU57TP4admOStVbVyLc69BDiabhbpnCRPqao7x2gzl24m6cVreL9ZVS1M8ku6HQhH3A7sNUp8n6G+xpuTJG0mLKIkaTPVX9PzIuCXwEtHiTyTbvOIPwDOXps+qurSJE+g24hhZpJjq+rWNTQ5E/hb4Paqum5t+hxNku3oZrgG+/4e8Kwke1fV4IzUn9Etb7xgIPemJIdX1SVDuQJ+0L9e0j8Pz8hJkiYZi6gtpjTFa/nysUP30QHP+UVTfrspi5v7eOtlz2jKL7ttLf5NsF3b9+prR/1LcxdTqKb8H1365035qx737015gA/eOaMp/+GLntTcR60Y1wZlv7H7XnePHRryygf8sCl/0GE3NOVzwIymPMDyX81qa9D43zcAK1eMnZk8nko3m/KGqpo5/GaSnwOvpLtmaq2KKICqujLJ79MVUucmOWaU65BGfBD4oz73AbrrrbYA9qMr5j5YVeeN0eUWA1uub0G3KcRrgJ3pNpMY8Ta6a7BmJnk7cAfdboPHASf3m0oAfICuYPpWkr8Hru8zrwT+taqu6T/n/CQ3AM9I8l3gLuCm9XhzX0nSBLGIkqTN10uApazale9equq2JF+lm60Zz7VIq1VVvxoopH6U5ElV9atRcgv63BvpbpB7f7rlfTfQzQiNZ3ZqOnD+yCnpZp+uBJ5VVV8b6OvqJEcC7wb+pW93JXBSf4Pekdxtfe49/WMH4Fd099T6p6G+X0q39fm36K4Jexv3LtwkSZOARZQkbaaq6lnjyDx/4OVcuuunRstl6PWJdPd2Gjw2Gzh46NhRo5xrAd1mFsMbWoxptH7HyF9Bd9+nsXI30M1SjZX7Lve+7kqSNAm5xbkkSZIkNbCIkiRJkqQGFlGSJEmS1MAiSpIkSZIaWERJkiRJUgOLKEmSJElqYBElSZIkSQ0soiRJkiSpgUWUJEmSJDWwiJIkSZKkBlMnegATbYutpzXlVy5c2NzHr957RFP+ydMvacqfc9vBTXmAZYu2bGuwsrkLqtryv1q2W3MfH7zumKb83jvPa8qfu7gpDsCVC/Zuym9xe+OfxVqYu2iX5jY/3PWgpvySFW1/nVz3l3s25QF+9zWz2hqsXNHchyRJ0liciZIkSZKkBhZRkiRJktTAIkqSJEmSGlhESZIkSVIDiyhJkiRJamARJUmSJEkNLKIkSZIkqYFFlCRJkiQ1sIiSJG0WkpyYpIYetyb5QZKnTvT4JEmbDosoSdLm5iTgCOBI4OXASuCsJMdN6KgkSZuMqRM9AEmSNrArquonIy+SnA3cCZwAfGvCRiVJ2mRs9kXUyoUL13sf29ySpvx3f3VQU/7vD2v/f/752x7YlN99q/nNffzJThc15d/y6z9s7uOGX+zZlP/jIy9syr/89Fc25QHe+8LTm/IHPXlOcx9fm31oU37Rsi2b+zh8+xua8j+9dZ+mfG2/vCkvrUeLgaXAb34ok5wCPA04kO7/ldcC/wJ8uqpqIDcNeDfwAmAH4BLgr4AvAzOr6sQN8gkkSRvUZl9ESZI2O1OSTAUC7An8DbAt8IWBzP7Ax4CR3yY8BvhnYF/g7QO504DnAf8AfB84BDiDrqAaU5KLV/PWg8bTXpI0MSyiJEmbmwuGXi8B/l9VnT1yoKpOGvk6yRbATLqi67VJ3lFVleQQ4E+A91bVm/r4d5PM4d4FmSRpkrGIkiRtbv4MuLL/ejfgWcC/JJlSVR8BSHIs8EbgEfz2rNIewBzgCf3rLw+9fwbw7+MZSFU9fLTj/QzV4eM5hyRpw7OIkiRtbq4c3FgCODvJ/sA/JPkccBDwbbrZp5cBN9JdM3U88GZget9u1/75Xhc2VtXyJLevv+FLkiaaRZQkSXA58GTggcDzgWXA06tq8UggyfFDbUYKpT2B2QO5qawqsCRJk5D3iZIkCQ7rn2+ju2/UcmDFyJtJpgMvHGpzbv/83KHjz8FfUkrSpOZf8pKkzc1D+tki6GaMng0cC3y1qq5L8i26bcq/mORjfeav6Tag+I2q+nmSLwB/nWQl3e58DwZeD9xNV4xJkiYhiyhJ0ubmtIGv7wauA14H/CtAVX0/yYuBNwDfpFuq9wngVuBTQ+c6CbgZeEl/jsvoZqbOBu5afx9BkjSRLKIkSZuFqjodOH2c2dO4d7E14tNDuSV0M0+vHzmW5EhgR+AnSJImJYsoSZLWUpJjgEcDl9At9zuUbmv0XwBfmcChSZLWI4soSZLW3nzgqXQzUdsDc+m2R3/T4M5+kqTJxSJKkqS1VFUXAo+b6HFIkjYsi6gNYNlj5zXld9h6aVP+msV7NeUBnr7zZU35H8w/uLmPu1Zu1ZT/633Pbu7jkl1mNOUvnHdAU/6tL/hCUx7g8Gm3NuW33WLJ2KHhPg6c1ZRfQZr7+MWStp+rBYumNeV32HlhUx4gU9v+yqrly5v7kCRJGov3iZIkSZKkBhZRkiRJktTAIkqSJEmSGlhESZIkSVIDiyhJkiRJamARJUmSJEkNLKIkSZIkqYFFlCRJkiQ1sIiSJEmSpAYWUZIkSZLUwCJKkiRJkhpMnegBbGqm3m/f5ja7bb+gKb90xZSm/Lm3HtiUB/jqgoe2NfjRzs19/OcBj25rsKK5C/75aZ9pyn9/+YOa8r9askdTHuBvz3luW4OVzV2w9Zy2n5GtH3V7cx+7b9v2c7vz9gub8nfM26YpD7D8cW0/t1NmXtLchyRJ0liciZIkSZKkBhZRkiRJktTAIkqSJEmSGlhESZLWiySPTvK1JDckWZJkTpLzk/zjRI8NIMmsJGdO9DgkSZseiyhJ0jqX5OnAecD2wMnAHwCvBf4XeN4EDk2SpPvM3fkkSevDycAs4MlVtXzg+BeTnDwxQ9qwkgTYuqoWTfRYJEnrljNRkqT1YRfgtqECCoCq+s3G/iNL6pI8NcklSRYluSrJi4fbJdkryceT3JhkaZLrkrw1ydSh3ClJfpzkjiTz+vO+pC9q1ijJK5MsT/K2gWNbJXlLP64lSW5LclqS3YfajnyW5ya5HFgMvHxc3y1J0ibFmShJ0vpwHvCyJB8E/h346WgFVe9Q4B+B9wBzgJcCn0pybVWdC10BBfyY7s5qbwd+CRwBvAWYAZw0cL79gY8BN/SvHwP8M7Bv3/a39AXW+4DXAC+tqtP741sAXwd+H/iH/nPtD7wNmJnkEUMzTQ8HDgLeAdwIrPEmbUkuXs1bbTe1kyRtUBZRkqT14Y3AA+mug3otsDjJhcCZwEeravDuzLsBj62qGwCSnAscA5wAnNtnTgF2Bh48kgO+l2QR8P4k76uq/wOoqt8UVH0RNBMI8Nok76iqGhxokul0hd4xwFOr6nsDbz8XeArwR1X1lYE2PwUuAk4E/nXoszyuqn45zu+TJGkTZBElSVrnquoO4KgkDwOeBDwKOBp4AvCKJI+qqpFZmssGCiOqanGSa+hmfEY8HfgBcNPQ8r1vA+/vz/t/AEmOpSviHgHsMDS0Pehmu0bsCnyfbpbqcVV1xVD+6cBdwDeH+r0MuAU4insXUT9rKaCq6uGjHe9nqA4f73kkSRuWRZQkab2pqkuBSwGSbAmcCvwV8Aa6zSdg9CVvi4HpA6/3BJ4BLFtNV7v1fTyarrCaCbyMbkndUuB44M1D54Rutmxn4BOjFFAj/e7Un2O1/Q64eTU5SdIkYhHVaMXuOzW3mbLFPU35qTXmtc/3Mm3K6i4zWL35t2zflN/ucXc193Hknjc15S/63sHNffz9lX/YlF+2YkpT/pd37tqUB9hu33lN+d23W9Dcx/VL92nKL7l7m+Y+7rpu56b8lN0XN+V32mHh2KEhy7fdsSnf9qet9a2qliV5O10R9ZDG5nOBy+kKodGM/IXzfLpC6+lV9ZsfyiTHr6bd+cB/0l2DBfCKwY0v+n5vp1vSN5r5Q69r1JQkaVKxiJIkrXNJ9q6q0WZlRn5b0vZblu5aqqcBv6yqO9eQWwksB1YMjGU68MLVNaiqzyRZAPwHsG2SF1XVSPsz6QqzKVV1YeOYJUmTlEWUJGl9ODvJbOCbwFV0t9Q4DHg9cA/wocbz/T1wLHBekg8DVwNb0+3M9zTgVVV1PfAtupmuLyb5GN01T38NLFnTyavqjCQLgTOA6Un+pKqWAl8EXgCcleRDdDsELgPuR3eN15lVdUbjZ5EkbeIsoiRJ68O7gGcCrwP2BqbRXS90DvCeqrqy5WRVdXOSRwB/B/wNXREzH7gO+A79dVVV9f3+HlNvoCvgZgOfAG4FPjVGH2cleVrf7utJnl1Vi5L8Id0Ogy8E3kQ303Uj8EO6JYaSpM2MRZQkaZ2rqi8DXx5HbsZqjh81yrG5rNoyfU3nPA04bZS3Pj1W31U1E9h+6NhyuvtY/eMY/f7W+SRJk9MWEz0ASZIkSdqUWERJkiRJUgOLKEmSJElqYBElSZIkSQ0soiRJkiSpgUWUJEmSJDWwiJIkSZKkBhZRkiRJktTAm+02qmlTmtvMmrVHU/539pvblD9851835QH2OHR+U/5nt+7T3Md51zygKZ9tq7mPJ+77i6b8zYt3bMpfcN39m/IAW14zvSl/w0HTmvtg78VN8UPvN7u5izc/7ltN+Y/OeWJT/ud37NWUB7j5iLa/sma0fQRJkqRxcSZKkiRJkhpYREmSJElSA5fzSZK0Ebpi9t3MeKNrUqUNYdapx030ELSJcSZKkiRJkhpYREmSJElSA4soSZIkSWpgESVJkiRJDSyiJEmSJKmBRZQkSZIkNbCIkiRNGkkeneRrSW5IsiTJnCTnJ/nHgcysJGeO41xHJakkR42z71cmOXHtRy9J2lRYREmSJoUkTwfOA7YHTgb+AHgt8L/A89bilJcAR/TP4/FK4MS16EeStInxZnNso5EAACAASURBVLuN7jh42+Y2zzz8oqb8j246oCm/ZGX7H+O8pdOb8lt/cafmPpY+Z35TfuGiKc19fOfzRzTld3/qjU35nb6/dVMeYPk2bfn997qtuY9bvrp/U/6abX+3uY/bX9r2s37szj9vyl922z5NeYDtft3cRJuXk4FZwJOravnA8S8mObn1ZFU1D7hgrFyS6VW1qPX8kqRNlzNRkqTJYhfgtqECCoCqWjl8LMlTk1ySZFGSq5K8eOj931rOl2RmkiuSHJ3kwiSLgPckmQU8GHhC36aSzFzHn0+StJFwJkqSNFmcB7wsyQeBfwd+OlpB1TsU+EfgPcAc4KXAp5JcW1XnjtHPPsDpwKnAVcAi4DPAGcDddMv6AOaNNeAkF6/mrQeN1VaSNHEsoiRJk8UbgQfSXQf1WmBxkguBM4GPVtXCgexuwGOr6gaAJOcCxwAnAGMVUTsDz6qqHw4e7Gel5lXVmEsAJUmbNosoSdKkUFV3AEcleRjwJOBRwNHAE4BXJHlUVd3exy8bKaD6touTXAOM54LDO4cLqPsw5oePdryfoTp8XfQhSVr3vCZKkjSpVNWlVfX+qnou3dK7fwIOAN4wELt9lKaLgfHsunPzfR+lJGlTZhElSZq0qmoZ8Pb+5UPW1WnX0XkkSZsoiyhJ0qSQZO/VvHVw/3zTeh7CEsY3kyVJ2sR5TZQkabI4O8ls4Jt0u+ZtARwGvB64B/jQeu7/CuB5Sf4YuA6YX1VXr+c+JUkTwCJKkjRZvAt4JvA6YG9gGt31S+cA76mqK9dz/28F9gVOA7YFfggctZ77lCRNAIsoSdKkUFVfBr48jtyM1Rw/auj1TCBrygy9N4tum3RJ0iTnNVGSJEmS1MAiSpIkSZIauJyv0dwjlze3+eGNBzbll62Y0pTfY6v5TXmAr//40U357Z9/Z3MfD9jprqb8zxdu1dzHtseMdquXNeS3XNqUP/xVFzTlAc78Zdsuynctbt/Ma94jFjflt/7F1s193LR856b8V+a03Re0KmOHhsx7fNvn3u3jzV1IkiSNyZkoSZIkSWpgESVJkiRJDVzOJ0nSRugh++7IxaceN9HDkCSNwpkoSZIkSWpgESVJkiRJDSyiJEmSJKmBRZQkSZIkNbCIkiRJkqQGFlGSJEmS1MAiSpIkSZIaWERJkiRJUgNvtttou90WNLd5wC5zm/I3zt+pKf/xmU9sygNMXdaWP3i3Oc19XDl3z6b8ygVbNvex5373NOXnL5vWlP/RLQ9oygPstdO8pvz1s3dr7mPatkub8ssfvKK5jwO2urUp/9AdZzflt5na9hkALr5nv+Y2kiRJ65ozUZIkSZLUwCJKkiRJkhpYREmSJElSA4soSZIkSWpgESVJmnBJHp3ka0luSLIkyZwk5yf5xwkYy4wkleTEtWh7VN/2qHU/MknSxsIiSpI0oZI8HTgP2B44GfgD4LXA/wLPm8ChSZI0Krc4lyRNtJOBWcCTq2r5wPEvJjl5YoYkSdLqORMlSZpouwC3DRVQAFTVypGvkzw/yXeT3JxkUZIrk5yaZNvBNklOT3JPkt9N8u3+618n+cck04ay+yT5cpL5Se5O8iVgr+FxJHlEki8mmdX3PSvJF5Lsv+6+DZKkTYUzUZKkiXYe8LIkHwT+HfjpaAUVcCDwLeADwALgQcAbgEcBw3cd3xL4BvBJ4P3A44G/A+4G3g6QZDpwDrAP8CbgF8DTgS+N0vcM4Grgi8AdwN7AK4CLkhxSVW13Ve8luXg1bz1obc4nSdowLKIkSRPtjcAD6a6Dei2wOMmFwJnAR6tqIUBVvXOkQZLQXTN1JfDDJA+tqssHzrkV8PdV9Z/96+8leSRwAn0RBbwIOBh4ZlV9oz/2nSTbAC8eHGBVnQGcMdD/lH58c/pzfvi+fQskSZsSl/NJkiZUVd1RVUcBhwN/A3wTeDDwPuBnSXYFSHJgv4TuFmAFsAz4YX+ag4dPS1fkDLocGFx+dzQwf6CAGvG54TEm2T7J+5L8MslyYDlwD7DtKH2PW1U9fLQHcNXanlOStP45EyVJ2ihU1aXApQBJtgROBf4KeEOSdwDnAguBtwDX9F//DvAVYPrQ6RZW1aKhY4uBrQde70o3kzTs5lGOfYGu6HoHcBEwj65QO2uUviVJk5xFVKPll+3U3OaGRy1ryj9411ua8vMWbj12aMiUX+/QlJ82ZbTLE9Zs6fIpTfl9z0lzH5dvs29T/rAZv27K33Bx2/kBjj/2gqb8jRfv09xHFkwbOzRg2b7tf363LW/7GVmysu2vk2tu370pDzB9myXNbbRpqqplSd5OV0Q9hO6ap72Bo6pqZPaJJO1/Ka9yO931VMP2HnzR9/E04G1VderA8Wl0m2JIkjYzLueTJE2oJHuv5q2RZXI3ASO79C0dyrz8PnT9A2D7JH84dPxPh16vBDJK3y8F2n5bJEmaFJyJkiRNtLOTzKa7Fuoqul/wHQa8nu66ow/RFVJ3Ah9P8la666FeABx6H/r9LPA64LNJ3syq3fmOGQxV1bwk5wJ/k2QucB3wBOAlwF33oX9J0ibKmShJ0kR7F12B9Dq6bcm/DbyGbvvxR1XVz6rqduA4uqLqc8Cn+6+ft7ad9rv+PbHv51S63ff2AZ4/SvwE4PvAe+muwXoEcCzdlumSpM2MM1GSpAlVVV8GvjyO3PnAkaO8laHcicCJo7Q/BThl6Nhs4DnjOOfqcjOGcjOH20qSJh9noiRJkiSpgUWUJEmSJDWwiJIkSZKkBhZRkiRJktTAIkqSJEmSGlhESZIkSVIDiyhJkiRJauB9ohp94IWfam9z/bFN+ZsX7tCUX3TH9KY8wHaN+V/evVtzH9tMW9aUv/mZS5v7ePlh/9OUn79i66b8HQ/btikP8PWrH9qU3+7gO5v72HP7+U35687br7mPC+55QFN+0YqtmvKH7DanKQ8we8GOzW0kSZLWNWeiJEmSJKmBRZQkSZIkNbCIkiRJkqQGXhMlSdJG6IrZdzPjjd+a6GFMCrNOPW6ihyBpknEmSpIkSZIaWERJkiRJUgOLKEmSJElqYBElSZIkSQ0soiRJkiSpgUWUJEmSJDWwiJIkTTpJfi/JaUmuS7I4yT1JLknyziR7rqc+j0xySpKd1sf5JUkbj83+PlHZcqum/FfvOLy5j1/f2fb/0z994EVN+auv2bcpD3DPA5c25Xdu7gGmb7msKb/V1sub+zj7lkOa8rtPv6cpf/11uzflgeZfTWyz8/zmLm68q+1n6mFHX93cx9Xz2/6dedD2c5ryP5p9QFMe4FUH/bAp/1/s0dyHNn1JTgI+DlwDvA/4P2BL4BHAy4AHA89aD10fCbwVOB24az2cX5K0kdjsiyhJ0uSR5NHAJ4DvAsdX1ZKBt7+b5P3AUyZkcJKkScPlfJKkyeTNQAEvGyqgAKiqZVX1TYAkWyQ5OclVSZYkuTXJZ5Pcb7BNkmOTfCPJjf3SwGuTfDzJbgOZU+hmvQCuS1L9Y8Z6+pySpAnkTJQkaVJIMgV4EnBxVd04jib/Sre875+Bs4AZwDuAo5IcXlVz+9wDgPPoZrju7nN/BfxPkt+rqmXAJ4FdgFcDzwZu7tuOPK9uzBev5q0HjWP8kqQJYhElSZosdgO2Aa4bK5jkQcCfAx+pqtcOHL8UuBB4Hd2sFlX1sYH3Q1dQzQSuB54KfKOqbkxyQx+7tKpmrYPPI0naSLmcT5K0OTq6f/7s4MGq+jFwJd2MFgBJ9kzyb0luBJYDy+gKKICD78sgqurhoz2Aq+7LeSVJ65czUZKkyWIusJBuud1Ydu2fR1tudxOwP3TXTdFtUrEX3VK/nwEL6H4JeQEw/T6NWJK0SbKIkiRNClW1Isk5wNOS7FtVs9cQv71/3gsYvn5qH7qCDOD3+seJVfWZkUCSA9fRsCVJmyCX80mSJpN3AwE+nuS3bgSYZMskzwC+3x/606H3H0G3RO97/aGV/fPwzfVePkrfI7sBOjslSZOcM1GSpEmjqi5M8ud0N9v9SZJ/ZdXNdh9Gt5nEFVX1rCT/BrwmSQHfZtXufL8GPtCf8irgl8Cp/dK+ucAzgGNH6f6K/vn/Jfkc3bVTl1dV293NJUkbPYsoSdKkUlWfTvITuh323gDsTVfQXAP8B/CRPvoKugLpJcCr6LYvPxt4U1Xd3p9rWT9z9SG6LdGXA+cAxwAju/GNmEl3r6gXAn9Bt9rj/sCs9fAxJUkTyCJKkjTpVNXlwEljZFYC/9A/1pS7EviDUd7KUK6Ak/uHJGkS85ooSZIkSWrgTNRhBzXF7162ps2eRjd16oqm/CV3/05Tfpvr2/8YF85Y1pS/6Wd7Nvcxdb8FTfmVv9yuuY8HPunapvzjd7ymKX/LjB2a8gBzfrxXU/627bZv7mPZ/N+6Xn6NLry1fSOxrXZe3JTfdVrbn/cj9vp1Ux5gym+u8ZckSZo4zkRJkiRJUgOLKEmSJElqYBElSZIkSQ28JkqSpI3QQ/bdkYtPPW6ihyFJGoUzUZIkSZLUwCJKkiRJkhpYREmSJElSA4soSZIkSWpgESVJkiRJDSyiJEmSJKmBRZQkSZIkNdjs7xOVRcua8s/Y7afNfdyz7FFN+c8f8O2m/MNXPrApDzBl+7bPve2e9zT38YBd5jblf3bdgc19LFg+rSl/zeK9mvKzb92pKQ+w1yPnNOXnL277DACkmuLL7tq6uYtlt2zTlN/vQXc05W9ctHNTHuC/bjm8scVNzX1IkiSNxZkoSZIkSWpgESVJkiRJDSyiJEmSJKmBRZQkSZIkNbCIkiRJkqQGFlGSpE1CkhOT1MBjcZJbkvwgyZuS7DHRY5QkbR4soiRJm5qTgCOAY4FXAZcBbwCuTHLMRA5MkrR52OzvEyVJ2uRcUVU/GXj9X0k+APwI+EqS362qUW/YlmSbqlq4QUYpSZq0nImSJG3yquoG4PXA9sDLAZKcnuSeJIcm+X6Se4D/GGmT5Jgk30syL8nCJP+b5EmD502ye5J/S/LrJEuS3NbnjhnIHJ7kzCS39pmbknwryf02zKeXJG1ozkRJkiaLs4AVwOMHjm0FfA34KPDOkYNJ/hT4LPB14EXAMrri6ztJnlxV3+ujnwMeBrwZuAbYCTgc2LU/z3bAfwOz6JYWzgH2Ao6mK+jWKMnFq3nrQWO1lSRNHIsoSdKkUFULk8wF9hk4vCVwSlV9ZuRAkm2ADwFnVtWzBo6fBVwCvBt4dH/4SOCTVfWJgXN+feDrg+gKqpdU1eDxL6+DjyRJ2khZREmSJpOMcuyrQ6+PBHYBPpNk+P+DZwMnJ9m2qhYAPwZOTHI73YzTpVW1bCB/LXAn8N4kewE/rKqrxjvYqnr4qB+im6E6fLznkSRtWJt9EbXFXfOb8tcu3rO5j5vnj7mi415OnXtoU376rdWUB9hxl3lN+SP3uK65jx/d8oCm/HHHXNTcx6IVWzblf3FP2w7Ijz5gVlMeYO+t727KX3DbjOY+Dtj5jqb8XbtMb+7jhpt3aW7T4ud37NXc5tUP+EFT/vMHHd3cx4qrr21uo41Dkm3pZoV+NnB4YVUN/4U38hf5GWs43S7AAuB5wFuAlwLvAOYn+Qrwxqq6paruTvIEuuV+7wF2TnIT8AngXUMFlyRpktjsiyhJ0qRxHDAFmDlwbLTfMs3tn18NXLCac80BqKq5wF8Cf5lkX+B44L101z09pc/8DHg+QJKHAC8B3gosBk5d608jSdpoWURJkjZ5SfYD3g/MA/5tjPj/AncBh1TVR8bbR1XNBv6l35nvsavJXAG8LsmJuBxPkiYtiyhJ0qbmIf21TFOBPYDfp7sB7wrg+Kq6dU2Nq+qeJK+muyZqF7plfbcCuwOHAntW1cuT7Aj8gG5b9KuA+cAj6WagvgKQ5OnAK+l2APwV3TVZz6bbxe+76/JDS5I2HhZRkqRNzWn981K6GaUr6ZbYfbKqbhvPCarqc0luAE4GPk63HfmtwGV0W59DtxzvQuCFwAy6nf6up1ui974+84t+DCfT7Qq4lK7gOnFwR0BJ0uRiESVJ2iRU1enA6Q35E4ET1/D+ucC5a3h/CfCKMfq4GjhhvGOSJE0OW0z0ACRJkiRpU2IRJUmSJEkNLKIkSZIkqYFFlCRJkiQ1sIiSJEmSpAYWUZIkSZLUYLPf4vy2J+3XlH/Jzl9q7uMHcx7YlN9x6sKm/F0PaooD8Of3+2lT/odz2z4DwH473NmU//rlhzb3sdvu85vyJx5wflP+0788oikPsHjHtv+s9tym7TMA/Hrezk35naYvau7jMQde15TfcUpbH7fdvn1THmCvg+5qys8/ZNfmPra5+trmNpIkafPiTJQkSZIkNbCIkiRJkqQGFlGSJEmS1MAiSpIkSZIabPYbS0iStDG6YvbdzHjjt8adn3XqcetxNJKkQc5ESZIkSVIDiyhJkiRJamARJUmSJEkNLKIkSZIkqYFFlCRJkiQ1sIiSJEmSpAYWUZKk9SbJiUlq4LE4yTVJPpJkz7U438wkMwdez+jPe+K6HLckSWuy2d8natl2acq/5BfPb+5jwdKtmvJ3L9+mKb/9dU1xAM6+5cFN+V/N2qO5j732vbMpv9VNbd8ngCcedk1T/s7l2zblH7r7zU15gJmXHtyU33P/O5r7mHvTjk35rfZb3tzHj2+Z0ZR/7MOubcpXtf23B/CLJXs15e/Zd0pzH23/9anBScBVwHTg8cCbgKcl+b2qWjChI5MkqdFmX0RJkjaIK6rqJ/3XP0gyBfg74Hjg8xM3rPUryTZVtXCixyFJWrdczidJmggX9M/7JzklSQ0HBpYCzmg9eZI/THJ+koVJ5if5bpIjBt4/vj/3k0Zp+4r+vQcPHHtEkm8kuaNfknhpkueuZrxPTvKZJLcDs1vHLkna+DkTJUmaCA/on28D9l2XJ05yAt3s1neAPwGmAScDM5M8qar+B/hW3/dJwPeGTnEScFFV/bw/39HA2cCFwF8AdwPPB77UzzSdPtT+U8DXgROA7cYY68WreetBY39SSdJEsYiSJG0IU5JMBbYGfh94CzAf+AbwinXVSZItgPcBlwNPq6qV/fGzgF8C7wUeW1XLknwO+IskO1TVvD53CPBI4JUDp/0o8HPgiVU1coHhd5LsBrw7yWdH+ul9t6peta4+kyRp4+NyPknShnABsIyucDoLmAM8tarmrON+DgL2AT43WNhU1T3AfwGPSTKyf8hpdBtdPG+g/UnAYuALAEkOpJsV+nz/eurIo/8ce/d9DvrqeAdbVQ8f7UG3CYckaSPlTJQkaUP4M+BKYDkwp6rat74cn13759HOfxPdLw93BhZW1c/65XQnAp/oC6MXAl+rqrv6NiPbsL+/f4xmt6HX6+uzSZI2EhZRkqQN4cqB3fmGLQZIMq2qlgwcHy5OxuP2/nm0/fD3AVYCg/dfOA34SJIH0s0o7dkfGzG3f34P8JXV9Hn10Ovf2iRDkjS5uJxPkjTRZvXPDx06/oy1ONfVdDvivSDJb25GlmRb4NnA+UNbjv8HsIRuNuok4EbgnJE3q+pq4BfAoVX1k9U85q/FOCVJmzBnoiRJE+0s4A7gU0n+nm7J34nA77SeqKpWJjmZ7hqmM5N8nG53vr+hW8b3xqH8nUm+DrwY2AX4h6FNIgBeDnw7yXeA0+mKtF2Ag4FHVtWzW8cpSdq0ORMlSZpQ/c54T6HbdOJzwMeAK4B3reX5/oPuJr67Al+iW543Dzi639582Gl0y/i2pCuShs/3A+BRwF3AB+lmqv4VOAb47tqMUZK0aXMmSpK03vT3UDp9HLmLgMeO8tanhnJHDb2eBYQhVfV1uns1jWeMZ492jqHM5dx7F7/RMqczjs8qSdr0bfZF1Pz7D6/aWLPjdr2+uY+r5u85dmjAnKU7NOW3vrP9GuYtt1jRlN/invYflbsXTG/KL91zWXMfX77wUU35Fzzm/Kb8ub84sCkPsOXdU5ryO05b3NzHnC3bfm5vvaPtZwpg5dxpTfklK7dsyr/4sPOa8gC7T2279GS72W0/55IkSePhcj5JkiRJamARJUmSJEkNLKIkSZIkqYFFlCRJkiQ12Ow3lpAkaWP0kH135OJTj5voYUiSRuFMlCRJkiQ1sIiSJEmSpAYWUZIkSZLUwCJKkiRJkhpYREmSJElSA4soSZIkSWqw2W9xvt31bXXkzYt3bO7jziXbNOUvvvL+Tfkd92yvhWdMW9iU33KfBc19/M7OdzXl79i67fsE8ICd5za3afHsB1/W3OZ7Oz6wKZ9Ucx9PPPjqpvyv5u/a3Mf1S3dryk/bYllT/mf37NuUB9h5atvP4S1HtP+3ccBXm5tIkqTNjDNRkiRJktTAIkqSJEmSGlhESZIkSVIDiyhJkiRJamARJUmSJEkNLKIkSZIkqYFFlCRpvUtS43wcNdFjlSRpLJv9faIkSRvEEUOvXw88Z5Tj/7dhhiNJ0tqziJIkrXdVdcHg6yRzRju+OkmmA4urqv3u1BMsyfSqWjTR45AkrTsu55MkbVSSPKVf2vf8JP/SF1wLgGn9+4cmOTPJXUkWJbkkyQlD5/iL/hx7rebcjxk49qgk305yW5IlSWYn+eZg2yRbJHltksuTLE7y/9u79yA7qjqB498fAWIIkBfvZ8RCI4KwCaLIKxBAXFegQHQXcWUtwAcPa8EtEbdERdbVUgShVkRElIcgvnAVCEIMQpACgohCCCAkBHknIQmQhIT89o/ucS+XmcycmTszd5Lvp6qrc7t/fc65Z3om93dP9+kFEXF1RGzfVP4dEXF3RBwcEXdFxDLgzH7oJknSIHIkSpLUrr4JTAOOBTYEVkTELsAMYB7wKWBRvf+KiNgkM79dUkFEjAZuBGYBnwCeA7YEDgBGNoReCnwI+BbwGWBTquTotojYLTPnN8RuD3wPOBt4mCoB7Kr+mV3smlDyPiRJA8skSpLUru7LzI80boiIL9f/nJyZz9T//k1E3AycFRHfz8wuk5ZOvA0YBXwxM6c2bL+6oc7JwEeAEzPzfxq23w48CJzCa0ebNgH27OmlipKkoWetT6JyysKi+HWi/HL8zUcsKYp/+rFtiuKzFxdlbvmGRUXxG22waXEdr6waVhT/4p2bFNex9WGPFMU/vnRMUfzwdV4tigfYbtQLRfF/emTb4jrmPbJ990ENphx5V3EdS67aqij+vOEHFMWPG/1iUTzA/fO36D6owfovRHEdaiu/6GTbAcDUhgSqww/rfe8AphfU8SCwGPhmRGwH3JKZDzXF/BPwKnBlRDT+vzmPaiKMyU3xT/U0gcrMSZ1tr0eoJvakDEnSwPOeKElSu3qq8UVEDAM2bt5ee7JejyupoL4Mbz+qy/m+DsyOiHkR8Z91fQCbA8OAhcCKpmU3qpGnLtstSVrzrPUjUZKktvWaof/MfDUiFgOdDUl2DJ0+X6+X1evhTXGvG/LOzHuBoyIigF2AE4CzgBeBc+syVwJ7U41INWueeW/IzSAoSSrjSJQkaSi5GXhPRDRfY/yvVJfl3V2/nlOv394Ud2hXBWflvsw8iSox6ric7tdUXzpunpl3d7Lc34f3I0kaghyJkiQNJWcCBwPTI+Js4AXgo8AU4NMNk0rMAB4DzqufMbUEOArYvbGwiDiSana/a+v4YcAHgRHAbwEy8+aI+BHVDIAXALcBL1ONfu0D3JOZF/XXG5YktR+TKEnSkJGZf46IvammD/8O1eV6DwDHZOYVDXErIuJ9wPnAxVQjS5cDp/LaCSsepJqC/HNUSdEyqvujXlMeVaI1AziOajY+qO7DmgGUz9wiSRrSTKIkSQOuvmTupC723QB0ObVifQ/T+3pQxyzgwE52RUPM/cA/96CsBC6ql9XFvWt1+yVJawbviZIkSZKkAiZRkiRJklTAJEqSJEmSCphESZIkSVIBkyhJkiRJKrDWz863+OmNiuJnj9isuI4ly4YXxa/3Uvcxr4lfkmUHAL/8/R5F8SPfuKi4jrEjXi2KH7a8uAp+PnNSUfx7/+HPRfFPvDy6KB5g3PCyH+AGo5cW17Fi1HpF8XNeGldex8ZdTo7WqWGzRxbF733ofUXxANc9tlNR/NIty85BSZKknnAkSpIkSZIKmERJkiRJUgGTKEmSJEkqYBIlSZIkSQVMoiRJkiSpgEmUJEmSJBUwiZIkSZKkAiZRkiRJklTAJEqSJEmSCphESZIkSVIBkyhJkiRJKmASJUmSJEkF1h3sBgy2YUuGFcVvuP7y4jqGr7uyKP7JkWOL4je76+WieIDPn/Gzovgvz3pfcR3zFo4uip942APFdew+ak5R/DXzJhbFb7vRC0XxAPOXjyyK32D4iuI62Hl+UfivdryhuIrdl3yyKH6rmxcWxe/4oWeK4gH22HrDovhbnt6puI61WURkD0P3z8zpfajnK8DngTGZudpfsoh4ArghM4/rQbl7AwcC52Tm4i5iTgVOB7YE3gp8ALgkMx8vexeSpLXZWp9ESZL+bs+m16dRJRnN28u/8ei99wOLehi7N3AmcDHQaRIFHAn8MjNfjYid6/ibAJMoSVKPmURJkgDIzDsaX0fEM51tH0iZ+cfuYiJig8zsdkg+IraiSgi/3Iq2SZLWXt4TJUlqmYgYFhFfiIjZEbE0Il6IiPsi4qROwreMiKsjYnFEPB0RF0fExk3lPRERFze8Pi4iMiIOiojLImI+MLe+RPCrddi8OiYjYpuG4o6gGtWaFhHHAT+ut9/aEL93w/s4vX4fyyPimYj4YZ2INbbvtoi4NyL2j4i7ImJZRMyNiP/oU0dKktqaI1GSpFY6A/gC1WjPbcD6wARgTCexPweuAr4H7AqcDawCTuhBPT+oj/8XYEPgzrqOTwGHAs/Vcc82HHMk8KvMXBER1wJbAGcBHwfuq2Pur9cXAf8GnAdcD+xQx+4bEZMyc0FDuVsDlwFfAeYARwFfj4jhmfmVHrwXSdIQYxIlSWqldwN/zMzGS+a6zNMEhAAADLdJREFUmtnkosz8Vv3vmyLiLcDR9CyJuj4zT2ncEBHz6n/+MTOfaNq3GbAP1WgUmflcRDxS736g8ZLFiHgb8DHg3Mz894bt9wEzgE9T3UvVYRPgkMyc2vF+I2JT4HMR8e2uJrmoy5zZxa4JXR0jSRp8Xs4nSSoWEes2LVHvuhOYFBEXRMSBEbHRaor5VdPr+4CRETGuB034RWGTDweWAjf2IPaAev2jxo2ZeTvwMDClKX5hQwLV4UpgA2CPwnZKkoYAR6IkSUUiYl2geW7+jwCXU13StgT4MPBJ4NWImA58tpNJIprn6l9Wr0f0oBlPlbSZapbB32Tmsm4joSOJ66yOJ4HNm7Z1Nl//001ldSozJ3W2vR6hKnsmgyRpwJhESZKKZObKiHhH0+ZH630rgG8A34iIUcBBwH8DN0bEtj1MYnrUjJ4GRsQYYH+qSwV7oiO524L/T4Y6bMXrk6bmpKrj2MayJElrEC/nkyQVy8y7m5YFncQsysyfAt+hum9ou35uVsfT0JtHsg6jGjm7rofx0+r1MY0bI+JdwI7AzU3xYyLiPU3bjgZeprq8UZK0hnEkSpLUMhFxHXAvMJNqhrw3AidTjVQ92s/V/6VenxwRVwIrgT9RXco3NTNfaorvmInv+Ih4CXgFeDAz74+IS4BT63u9ptbv4yxgLvDtpnKeB75fT7P+GPBBqocEn7m6SSUkSUOXSZQkqZV+RzWJw/HAxlSXw00FzsrMlf1c903AOVT3Y51IdbXF9sCBwHHNwZn5UER8FjgJuLWO34dqavYTgEeopjk/mer5UtcDp3cy6vY34DTg68DOVNOqfw74WmvfniSpXURmjy8rf42D1jmqdwe2mec/vmdR/LZHl3+RuvOoJ4vib7hg76L4cRf/oShea59Yt/z7klxZ9nl33e23LYpfcOH6RfEAR23bPC/B6p1/24HFdbz5E+139dVvV10T3UepMxFxNNUzpTbLzEX9UP5twIaZuVuLy505ceLEiTNndjUDuiSpryZNmsQ999xzT1eT/KyO90RJktZYmXllZg7vjwRKkrT2MomSJEmSpALeEyVJUi9lZtn115KkNYIjUZIkSZJUwCRKkiRJkgqYREmSJElSAZMoSZIkSSpgEiVJkiRJBUyiJEmSJKmASZQkSZIkFTCJkiRJkqQCJlGSJEmSVGDdwW7AYBu+KIviH1s4triOvy4YVxS/3bSniuJXFkVrbZQrB+AsWf5KUfhzC8t+LwDOf3xKUfz4X6wqrkOSJKk7jkRJkiRJUgGTKEmSJEkqYBIlSZIkSQVMoiRJkiSpgEmUJEmSJBUwiZIkSZKkAiZRkiRJklRgrX9OlCRJbWj8rFmzmDRp0mC3Q5LWWLNmzQIY35tjTaIkSWo/Gy5duvTVe+6550+D3ZAhaEK9fnBQWzE02Xe9Z9/13mD23XhgcW8ONImSJKn9/AUgMx2KKhQRM8G+6w37rvfsu94bqn3nPVGSJEmSVKDXI1G/XXVNtLIhanDYYDdAWkMcP9gNkCRJayJHoiRJkiSpgEmUJEmSJBUwiZIkSZKkApGZg90GSZIkSRoyHImSJEmSpAImUZIkSZJUwCRKkiRJkgqYREmSJElSAZMoSZIkSSpgEiVJkiRJBUyiJEmSJKmASZQkSZIkFTCJkiSpRSJim4i4JCKejIjlETEnIs6NiDGF5Yytj5tTl/NkXe42/V33YOlr+yNiZER8OCKujIgHI+KliFgSEXdHxGkRsX4Xx+Vqljta+y77Ryt+9hExvZu+eEMXx+0UET+JiGcjYllEzI6IL0XEiNa9w/7TgvNucjf91rFs23TckD3vIuIDEXF+RNwaEYvrNl/ey7KK+79dzrnIzIGsT5KkNVJEvAm4HdgMuBZ4ENgD2B+YDeyVmfN7UM64upw3A9OAu4AJwGHAs8Cemflof9Q9WFrR/og4BLgeWAD8DngEGAu8H9iiLn9KZi5rOi6BucClnRT7RGZe3Os3NgBaeN5NB/YDvtRFyFcyc2XTMe+kOkfXA34KzAMOAHYHZlD19/LydzUwWnTejQeO7WL3LsARwP2ZuXPTcUP2vIuIe4FdgReBJ6j+Pl2RmccUllPc/211zmWmi4uLi4uLSx8XYCqQwMlN28+pt1/Yw3K+W8ef07T9lHr7Df1V91DuO2A34MPA+k3bNwJm1uWc1slxCUwf7D5og/NuevWxsMf1DgMeqOs4tGH7OlQfbhM4fbD7ZyD6bjXl/7gu55RO9g3Z844qydkRCGBy/V4u7+/+b7dzzpEoSZL6KCJ2AP4KzAHelJmrGvZtBDxF9YFjs8x8aTXljASeA1YBW2bmkoZ969R1jK/reLSVdQ+WgWh/RBwNXAH8OjPf37QvgVsyc3Kv3sAgamXfdYxEZWb0sO4DgJuB32fmfl20ay7wxmzDD5v9fd7VI8p/o/pd3jozFzbtH7LnXaOImEw18ls0EtWb/m+3c857oiRJ6rsD6vWNjR8GAOpEaAawAfCubsrZExgBzGhMoOpyVgE31i/374e6B8tAtH9FvV7Zxf7REfGxiDgjIk6MiHbtq2Yt77uI+FBEnB4Rp0bEeyNieDd139C8o07wHwK2B3boad0DrL/Pu2OB4cA1zQlUg6F63rVCb/q/rc45kyhJkvruLfX6oS72P1yv39wP5bSq7sEyEO3/WL1+3Yev2q7A94GzgQuAP0TEvRGxSx/qHAj90XdXAV8FvglcBzweER8YoLoHUn+3/7h6/d3VxAzV864VhvzfOpMoSZL6blS9XtTF/o7to/uhnFbVPVj6tf0RcRJwCHAvcEknIecAewGbUt0/9Q6q+yt2BaZFxNa9qXeAtLLvrqWahGMbqtHQCVTJ1Gjg6oh4bz/WPRj6rf0RsR9V/92fmbd3ETaUz7tWGPJ/60yiJEnqfx33mfT1Ov3elNOqugdLr9sfEUcA5wJPA0dm5ormmMw8LTNvz8znM/PFzLw7M48CfgZsAnymD20fbD3uu8z8Vmb+OjP/lpnLMnN2Zp4BnEb1efG/+qvuNtWX9p9Qr7schVrDz7tWaPu/dSZRkiT1Xcc3oKO62L9xU1wry2lV3YOlX9ofEYdTXZr2LDA5m6aF74EL6/W+hccNpIH42V9MdS/ZbvUN/wNZd3/qr/NuLHAksBS4rBftGgrnXSsM+b91JlGSJPXd7Hrd1bX4O9brrq7l70s5rap7sLS8/RFxFHAN8AzVjHOzuzmkM8/V65G9OHag9PvPPqvnanVMctLYF553nfso1YQSP8nMF3rRrqFw3rXCkP9bZxIlSVLf/a5eH1xPRf539bf3e1F9M31HN+XcUcft1fStf8cU5wc31dfKugdLS9tfT2f+Y+BJqgTq4W4O6UrHrGClI1gDqd9/9hHxFmAMVSL1fMOuafX6kE6O2YHqg+5c2rf/+qvvjq/XF/WyXUPhvGuF3vR/W51zJlGSJPVRZv6Vavrx8cCJTbu/RPWt8o8anzcTERMiYkJTOS9SXQI0EvhiUzkn1eVPbbw0rTd1t5NW9V29/aNU/fc4sG93l/BFxMT62VzN299ONWMawOU9fzcDq1V9FxE7dDaRQURsAvygfnlVZjZOEX8LMAvYNyIObThmHeBr9csL2/EZUdDa865h/z7AW4G/rGZCiSF/3pWIiPXqfntT4/Ze/t1qq3POh+1KktQC9YeE24HNqGY6mwW8k+qZTg8B787M+Q3xCdD8cNP6IZ23U32rOg24k+qD2WFU9/e8u/4A0uu6200r+i4i9gduovqC+BJgXidVvZCZ5zYccylwBFU/zwOWU82qdggwDPge8PF2TQSgZX13LNW9T7dQPbB0AbAd8I9U95/cDRzUfHlaRLyTqu/Wo5pZ7nFgCrA71XN+pmTm8la/51Zp1e9sw/7LgGOAUzLz/NXUeylD+Lyr7zc8vH65BfAeqtGfW+ttz2fmZ+rY8cBjwNzMHN9UTvHfrbY65zLTxcXFxcXFpQULsC3VN/dPAa9QXVpyHjC2k9is/hvutJyx9XFz63KeokoMtmlF3e249LXvqB5umt0sc5qOORz4OfAIsLihr/8XOHSw+2QA+24X4FLgz8B8qocTL6D6UHwysP5q6t6J6v6z56mSgYeoRhJGDHa/DETfNewbQ3X52cvA6G7qHNLnHdUoeY9+z6hGml73u9eb/m+3c86RKEmSJEkq4D1RkiRJklTAJEqSJEmSCphESZIkSVIBkyhJkiRJKmASJUmSJEkFTKIkSZIkqYBJlCRJkiQVMImSJEmSpAImUZIkSZJUwCRKkiRJkgqYREmSJElSAZMoSZIkSSpgEiVJkiRJBUyiJEmSJKmASZQkSZIkFTCJkiRJkqQC/wc/U9AvZICfkwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x648 with 2 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 216,
       "width": 424
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "%config InlineBackend.figure_format = 'retina'\n",
    "\n",
    "import helper\n",
    "\n",
    "# Test out your network!\n",
    "\n",
    "dataiter = iter(testloader)\n",
    "images, labels = dataiter.next()\n",
    "img = images[8]\n",
    "\n",
    "# Convert 2D image to 1D vector\n",
    "img = img.resize_(1, 784)\n",
    "\n",
    "# TODO: Calculate the class probabilities (softmax) for img\n",
    "with torch.no_grad():\n",
    "    logps = model(img)\n",
    "    \n",
    "ps = torch.exp(logps)\n",
    "\n",
    "# Plot the image and probabilities\n",
    "helper.view_classify(img.resize_(1, 28, 28), ps, version='Fashion')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "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": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}