Image Classifier Project.ipynb

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  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Developing an AI application\n",
    "\n",
    "Going forward, AI algorithms will be incorporated into more and more everyday applications. For example, you might want to include an image classifier in a smart phone app. To do this, you'd use a deep learning model trained on hundreds of thousands of images as part of the overall application architecture. A large part of software development in the future will be using these types of models as common parts of applications. \n",
    "\n",
    "In this project, you'll train an image classifier to recognize different species of flowers. You can imagine using something like this in a phone app that tells you the name of the flower your camera is looking at. In practice you'd train this classifier, then export it for use in your application. We'll be using [this dataset](http://www.robots.ox.ac.uk/~vgg/data/flowers/102/index.html) of 102 flower categories, you can see a few examples below. \n",
    "\n",
    "<img src='assets/Flowers.png' width=500px>\n",
    "\n",
    "The project is broken down into multiple steps:\n",
    "\n",
    "* Load and preprocess the image dataset\n",
    "* Train the image classifier on your dataset\n",
    "* Use the trained classifier to predict image content\n",
    "\n",
    "We'll lead you through each part which you'll implement in Python.\n",
    "\n",
    "When you've completed this project, you'll have an application that can be trained on any set of labeled images. Here your network will be learning about flowers and end up as a command line application. But, what you do with your new skills depends on your imagination and effort in building a dataset. For example, imagine an app where you take a picture of a car, it tells you what the make and model is, then looks up information about it. Go build your own dataset and make something new.\n",
    "\n",
    "First up is importing the packages you'll need. It's good practice to keep all the imports at the beginning of your code. As you work through this notebook and find you need to import a package, make sure to add the import up here."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Imports here\n",
    "%matplotlib inline\n",
    "%config InlineBackend.figure_format = 'retina'\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "import torch\n",
    "import json\n",
    "import numpy as np\n",
    "from torch import nn\n",
    "from torch import optim\n",
    "import torch.nn.functional as F\n",
    "from PIL import Image\n",
    "\n",
    "from torchvision import datasets, transforms, models"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load the data\n",
    "\n",
    "Here you'll use `torchvision` to load the data ([documentation](http://pytorch.org/docs/0.3.0/torchvision/index.html)). The data should be included alongside this notebook, otherwise you can [download it here](https://s3.amazonaws.com/content.udacity-data.com/nd089/flower_data.tar.gz). The dataset is split into three parts, training, validation, and testing. For the training, you'll want to apply transformations such as random scaling, cropping, and flipping. This will help the network generalize leading to better performance. You'll also need to make sure the input data is resized to 224x224 pixels as required by the pre-trained networks.\n",
    "\n",
    "The validation and testing sets are used to measure the model's performance on data it hasn't seen yet. For this you don't want any scaling or rotation transformations, but you'll need to resize then crop the images to the appropriate size.\n",
    "\n",
    "The pre-trained networks you'll use were trained on the ImageNet dataset where each color channel was normalized separately. For all three sets you'll need to normalize the means and standard deviations of the images to what the network expects. For the means, it's `[0.485, 0.456, 0.406]` and for the standard deviations `[0.229, 0.224, 0.225]`, calculated from the ImageNet images.  These values will shift each color channel to be centered at 0 and range from -1 to 1.\n",
    " "
   ]
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  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_dir = 'flowers'\n",
    "train_dir = data_dir + '/train'\n",
    "valid_dir = data_dir + '/valid'\n",
    "test_dir = data_dir + '/test'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_transform_train = transforms.Compose([transforms.RandomRotation(30),\n",
    "                                       transforms.RandomResizedCrop(224),\n",
    "                                       transforms.RandomHorizontalFlip(),\n",
    "                                       transforms.ToTensor(),\n",
    "                                     transforms.Normalize([0.485, 0.456, 0.406],\n",
    "                                                            [0.229, 0.224, 0.225])])\n",
    "data_transfor_test = transforms.Compose([transforms.Resize(255),\n",
    "                                 transforms.CenterCrop(224),\n",
    "                                 transforms.ToTensor(),\n",
    "                                     transforms.Normalize([0.485, 0.456, 0.406],\n",
    "                                                            [0.229, 0.224, 0.225])])\n",
    "\n",
    "# Load the datasets with ImageFolder\n",
    "image_dataset_train = datasets.ImageFolder(train_dir, transform=data_transform_train)\n",
    "image_dataset_test = datasets.ImageFolder(test_dir, transform=data_transfor_test)\n",
    "image_dataset_valid = datasets.ImageFolder(valid_dir,transform=data_transfor_test)\n",
    "\n",
    "# Using the image datasets and the trainforms, define the dataloaders\n",
    "testloader = torch.utils.data.DataLoader(image_dataset_test, batch_size=32, shuffle=True)\n",
    "trainloader = torch.utils.data.DataLoader(image_dataset_train, batch_size=32, shuffle=True)\n",
    "validloader = torch.utils.data.DataLoader(image_dataset_valid, batch_size=32, shuffle=True)\n"
   ]
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Label mapping\n",
    "\n",
    "You'll also need to load in a mapping from category label to category name. You can find this in the file `cat_to_name.json`. It's a JSON object which you can read in with the [`json` module](https://docs.python.org/2/library/json.html). This will give you a dictionary mapping the integer encoded categories to the actual names of the flowers."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open('cat_to_name.json', 'r') as f:\n",
    "    cat_to_name = json.load(f)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Building and training the classifier\n",
    "\n",
    "Now that the data is ready, it's time to build and train the classifier. As usual, you should use one of the pretrained models from `torchvision.models` to get the image features. Build and train a new feed-forward classifier using those features.\n",
    "\n",
    "We're going to leave this part up to you. Refer to [the rubric](https://review.udacity.com/#!/rubrics/1663/view) for guidance on successfully completing this section. Things you'll need to do:\n",
    "\n",
    "* Load a [pre-trained network](http://pytorch.org/docs/master/torchvision/models.html) (If you need a starting point, the VGG networks work great and are straightforward to use)\n",
    "* Define a new, untrained feed-forward network as a classifier, using ReLU activations and dropout\n",
    "* Train the classifier layers using backpropagation using the pre-trained network to get the features\n",
    "* Track the loss and accuracy on the validation set to determine the best hyperparameters\n",
    "\n",
    "We've left a cell open for you below, but use as many as you need. Our advice is to break the problem up into smaller parts you can run separately. Check that each part is doing what you expect, then move on to the next. You'll likely find that as you work through each part, you'll need to go back and modify your previous code. This is totally normal!\n",
    "\n",
    "When training make sure you're updating only the weights of the feed-forward network. You should be able to get the validation accuracy above 70% if you build everything right. Make sure to try different hyperparameters (learning rate, units in the classifier, epochs, etc) to find the best model. Save those hyperparameters to use as default values in the next part of the project.\n",
    "\n",
    "One last important tip if you're using the workspace to run your code: To avoid having your workspace disconnect during the long-running tasks in this notebook, please read in the earlier page in this lesson called Intro to\n",
    "GPU Workspaces about Keeping Your Session Active. You'll want to include code from the workspace_utils.py module.\n",
    "\n",
    "<font color='red'>**Note for Workspace users:** If your network is over 1 GB when saved as a checkpoint, there might be issues with saving backups in your workspace. Typically this happens with wide dense layers after the convolutional layers. If your saved checkpoint is larger than 1 GB (you can open a terminal and check with `ls -lh`), you should reduce the size of your hidden layers and train again.</font>"
   ]
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  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def create_classifier():\n",
    "    from collections import OrderedDict\n",
    "    classifier = nn.Sequential(OrderedDict([\n",
    "                              ('fc1', nn.Linear(1024, 500)),\n",
    "                              ('relu', nn.ReLU()),\n",
    "                              ('dropout', nn.Dropout(0.2)),\n",
    "                              ('fc2', nn.Linear(500, 102)),\n",
    "                              ('output', nn.LogSoftmax(dim=1))\n",
    "                              ]))\n",
    "    return classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.6/site-packages/torchvision-0.2.1-py3.6.egg/torchvision/models/densenet.py:212: UserWarning: nn.init.kaiming_normal is now deprecated in favor of nn.init.kaiming_normal_.\n",
      "Downloading: \"https://download.pytorch.org/models/densenet121-a639ec97.pth\" to /root/.torch/models/densenet121-a639ec97.pth\n",
      "100%|██████████| 32342954/32342954 [00:00<00:00, 43514956.99it/s]\n"
     ]
    }
   ],
   "source": [
    "# Build network\n",
    "model = models.densenet121(pretrained=True)\n",
    "for param in model.parameters():\n",
    "        param.requires_grad = False\n",
    "\n",
    "    \n",
    "model.classifier = create_classifier()\n",
    "# Use GPU if it's available\n",
    "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
    "# Define loss\n",
    "criterion = nn.NLLLoss()\n",
    "\n",
    "# Only train the classifier parameters, feature parameters are frozen\n",
    "optimizer = optim.Adam(model.classifier.parameters(), lr=0.003)\n",
    "# Move model to device\n",
    "model.to(device);\n",
    "\n",
    "\n"
   ]
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      "loss :  1.6176304817199707\n",
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      "        [ 0.7881],\n",
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      "        [ 0.9451]], device='cuda:0'), tensor([[  14],\n",
      "        [  96],\n",
      "        [  50],\n",
      "        [  25],\n",
      "        [  49],\n",
      "        [  66],\n",
      "        [  85],\n",
      "        [  73],\n",
      "        [  83],\n",
      "        [  44],\n",
      "        [  38],\n",
      "        [  11],\n",
      "        [  15],\n",
      "        [  44],\n",
      "        [  90],\n",
      "        [  15],\n",
      "        [  77],\n",
      "        [  32],\n",
      "        [  43],\n",
      "        [  55],\n",
      "        [ 100],\n",
      "        [  77],\n",
      "        [  12],\n",
      "        [  57],\n",
      "        [  74],\n",
      "        [  75],\n",
      "        [ 100],\n",
      "        [  30],\n",
      "        [  44],\n",
      "        [  13],\n",
      "        [  73],\n",
      "        [  55]], device='cuda:0'))\n",
      "loss :  1.8907990455627441\n",
      "Prediction :  (tensor([[ 0.6638],\n",
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      "        [ 0.6980],\n",
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      "        [ 0.7714],\n",
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      "        [ 0.9950],\n",
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      "        [ 14],\n",
      "        [ 71],\n",
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      "        [ 40],\n",
      "        [ 49],\n",
      "        [ 99],\n",
      "        [ 73],\n",
      "        [ 55],\n",
      "        [ 19],\n",
      "        [ 57],\n",
      "        [ 48],\n",
      "        [ 88],\n",
      "        [ 41],\n",
      "        [ 27],\n",
      "        [ 78],\n",
      "        [ 75],\n",
      "        [ 11],\n",
      "        [  9],\n",
      "        [ 98],\n",
      "        [ 43],\n",
      "        [ 71],\n",
      "        [  8],\n",
      "        [ 96],\n",
      "        [ 24],\n",
      "        [ 52],\n",
      "        [ 89],\n",
      "        [ 38],\n",
      "        [ 75],\n",
      "        [ 32]], device='cuda:0'))\n",
      "loss :  0.9697158932685852\n",
      "Prediction :  (tensor([[ 0.8020],\n",
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      "        [ 0.5997],\n",
      "        [ 0.9947],\n",
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      "        [ 0.7471],\n",
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      "        [ 0.9933],\n",
      "        [ 0.9987],\n",
      "        [ 0.7681],\n",
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      "        [ 0.3517],\n",
      "        [ 0.9880],\n",
      "        [ 0.6911],\n",
      "        [ 0.4491],\n",
      "        [ 0.2997],\n",
      "        [ 0.4232],\n",
      "        [ 0.5296],\n",
      "        [ 0.6720],\n",
      "        [ 0.3042],\n",
      "        [ 0.7420],\n",
      "        [ 0.9911]], device='cuda:0'), tensor([[ 48],\n",
      "        [ 97],\n",
      "        [ 71],\n",
      "        [ 54],\n",
      "        [ 81],\n",
      "        [ 23],\n",
      "        [ 88],\n",
      "        [ 96],\n",
      "        [ 44],\n",
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      "        [ 49],\n",
      "        [ 13],\n",
      "        [ 59],\n",
      "        [ 84],\n",
      "        [ 76],\n",
      "        [  4],\n",
      "        [ 46],\n",
      "        [ 55],\n",
      "        [ 56],\n",
      "        [ 15],\n",
      "        [ 77],\n",
      "        [ 89],\n",
      "        [ 96],\n",
      "        [ 68],\n",
      "        [ 43],\n",
      "        [ 68],\n",
      "        [ 67],\n",
      "        [  4],\n",
      "        [ 77],\n",
      "        [ 93],\n",
      "        [ 18],\n",
      "        [ 74]], device='cuda:0'))\n",
      "loss :  1.1053481101989746\n",
      "Prediction :  (tensor([[ 0.2202],\n",
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      "        [ 0.2967],\n",
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      "        [ 0.9708],\n",
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      "        [ 0.4309],\n",
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      "        [ 83],\n",
      "        [ 41],\n",
      "        [ 96],\n",
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      "        [ 13],\n",
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      "        [ 60],\n",
      "        [ 25],\n",
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      "        [ 81],\n",
      "        [ 80],\n",
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      "        [ 91],\n",
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      "        [ 61],\n",
      "        [ 65],\n",
      "        [ 14],\n",
      "        [ 55],\n",
      "        [ 74],\n",
      "        [ 19],\n",
      "        [ 40]], device='cuda:0'))\n",
      "Epoch 1/2.. Train loss: 1.363.. Test loss: 0.783.. Test accuracy: 0.777\n",
      "loss :  1.4614784717559814\n",
      "Prediction :  (tensor([[ 0.7564],\n",
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      "        [ 0.3271]], device='cuda:0'), tensor([[ 74],\n",
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      "        [ 60],\n",
      "        [ 67],\n",
      "        [ 90],\n",
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      "        [ 47],\n",
      "        [ 80],\n",
      "        [ 99],\n",
      "        [ 74],\n",
      "        [ 96],\n",
      "        [  3],\n",
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      "        [ 12],\n",
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      "        [  5],\n",
      "        [ 15],\n",
      "        [ 33],\n",
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      "        [ 75],\n",
      "        [ 53],\n",
      "        [ 33],\n",
      "        [ 40],\n",
      "        [ 77]], device='cuda:0'))\n",
      "loss :  1.71833074092865\n",
      "Prediction :  (tensor([[ 0.5032],\n",
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      "        [   8],\n",
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      "        [  96],\n",
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      "        [ 100],\n",
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      "        [  23],\n",
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      "        [   7],\n",
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      "        [  49],\n",
      "        [  66],\n",
      "        [  48],\n",
      "        [  65]], device='cuda:0'))\n",
      "loss :  1.0140783786773682\n",
      "Prediction :  (tensor([[ 0.3672],\n",
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      "        [   8],\n",
      "        [  77],\n",
      "        [  81],\n",
      "        [  15]], device='cuda:0'))\n",
      "loss :  1.0573424100875854\n",
      "Prediction :  (tensor([[ 0.1516],\n",
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      "        [ 0.7270],\n",
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      "        [ 0.4775]], device='cuda:0'), tensor([[ 83],\n",
      "        [ 89],\n",
      "        [  1],\n",
      "        [  6],\n",
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      "        [ 83],\n",
      "        [ 18],\n",
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      "        [ 37],\n",
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      "        [ 33],\n",
      "        [  1],\n",
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      "        [ 39],\n",
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      "        [ 16],\n",
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      "        [ 20],\n",
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      "        [ 99],\n",
      "        [ 77],\n",
      "        [ 59],\n",
      "        [ 55],\n",
      "        [ 40]], device='cuda:0'))\n",
      "loss :  1.3682539463043213\n",
      "Prediction :  (tensor([[ 0.8346],\n",
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      "        [ 0.9980]], device='cuda:0'), tensor([[  49],\n",
      "        [  90],\n",
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      "        [  18],\n",
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      "        [  34],\n",
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      "        [  73],\n",
      "        [   9],\n",
      "        [   0],\n",
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      "        [  67],\n",
      "        [   6],\n",
      "        [  40],\n",
      "        [  43]], device='cuda:0'))\n",
      "loss :  1.1708678007125854\n",
      "Prediction :  (tensor([[ 0.3195],\n",
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      "        [  75],\n",
      "        [  73],\n",
      "        [  70],\n",
      "        [  54]], device='cuda:0'))\n",
      "loss :  1.0713778734207153\n",
      "Prediction :  (tensor([[ 0.2234],\n",
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      "        [ 90],\n",
      "        [ 10],\n",
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      "        [ 23],\n",
      "        [ 27],\n",
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      "        [ 0.6937],\n",
      "        [ 0.3751]], device='cuda:0'), tensor([[ 62],\n",
      "        [ 81],\n",
      "        [ 40],\n",
      "        [ 73],\n",
      "        [  6],\n",
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      "        [ 49],\n",
      "        [ 68],\n",
      "        [ 97],\n",
      "        [ 88],\n",
      "        [ 20],\n",
      "        [ 24],\n",
      "        [ 40],\n",
      "        [ 33],\n",
      "        [ 44],\n",
      "        [ 24],\n",
      "        [ 62],\n",
      "        [ 54],\n",
      "        [ 74],\n",
      "        [ 54],\n",
      "        [ 86],\n",
      "        [ 49],\n",
      "        [ 38],\n",
      "        [ 37],\n",
      "        [ 70],\n",
      "        [ 13],\n",
      "        [ 63],\n",
      "        [  6],\n",
      "        [ 73],\n",
      "        [ 90],\n",
      "        [ 47],\n",
      "        [ 11]], device='cuda:0'))\n",
      "loss :  1.0380303859710693\n",
      "Prediction :  (tensor([[ 0.5305],\n",
      "        [ 0.9703],\n",
      "        [ 0.1966],\n",
      "        [ 0.7772],\n",
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      "        [ 0.3517],\n",
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      "        [ 0.2134],\n",
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      "        [ 0.2816],\n",
      "        [ 0.5574],\n",
      "        [ 0.2249]], device='cuda:0'), tensor([[ 47],\n",
      "        [ 80],\n",
      "        [ 34],\n",
      "        [ 96],\n",
      "        [ 90],\n",
      "        [ 78],\n",
      "        [ 96],\n",
      "        [ 75],\n",
      "        [ 88],\n",
      "        [ 64],\n",
      "        [ 48],\n",
      "        [ 92],\n",
      "        [ 19],\n",
      "        [ 27],\n",
      "        [ 49],\n",
      "        [ 50],\n",
      "        [ 82],\n",
      "        [ 34],\n",
      "        [ 17],\n",
      "        [ 74],\n",
      "        [ 77],\n",
      "        [ 64],\n",
      "        [ 83],\n",
      "        [ 64],\n",
      "        [ 40],\n",
      "        [ 41],\n",
      "        [ 24],\n",
      "        [  2],\n",
      "        [ 32],\n",
      "        [ 84],\n",
      "        [ 97],\n",
      "        [ 60]], device='cuda:0'))\n",
      "loss :  1.2398701906204224\n",
      "Prediction :  (tensor([[ 0.0850],\n",
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      "        [ 0.9957],\n",
      "        [ 0.2428],\n",
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      "        [ 0.3797],\n",
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      "        [ 0.9810],\n",
      "        [ 0.6345],\n",
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      "        [  86],\n",
      "        [  77],\n",
      "        [  39],\n",
      "        [  80],\n",
      "        [  72],\n",
      "        [  13],\n",
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      "        [  56],\n",
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      "        [  40],\n",
      "        [  72],\n",
      "        [  83],\n",
      "        [  38],\n",
      "        [  69],\n",
      "        [  34],\n",
      "        [  53],\n",
      "        [ 101],\n",
      "        [  97],\n",
      "        [  32],\n",
      "        [  90],\n",
      "        [  51],\n",
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      "        [  80],\n",
      "        [  79],\n",
      "        [  64],\n",
      "        [  64],\n",
      "        [  75],\n",
      "        [  83],\n",
      "        [  86]], device='cuda:0'))\n",
      "Epoch 1/2.. Train loss: 1.292.. Test loss: 0.747.. Test accuracy: 0.808\n",
      "loss :  1.409358263015747\n",
      "Prediction :  (tensor([[ 0.3320],\n",
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      "        [ 0.8423]], device='cuda:0'), tensor([[ 47],\n",
      "        [ 89],\n",
      "        [ 38],\n",
      "        [  6],\n",
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      "        [ 74],\n",
      "        [ 97],\n",
      "        [ 48],\n",
      "        [  5],\n",
      "        [ 64],\n",
      "        [ 51],\n",
      "        [ 84],\n",
      "        [ 91],\n",
      "        [ 56],\n",
      "        [ 48],\n",
      "        [ 32],\n",
      "        [ 13],\n",
      "        [ 38],\n",
      "        [ 16],\n",
      "        [ 90],\n",
      "        [ 54],\n",
      "        [ 78],\n",
      "        [ 41],\n",
      "        [ 23],\n",
      "        [ 72],\n",
      "        [ 17],\n",
      "        [ 41],\n",
      "        [ 97],\n",
      "        [ 80],\n",
      "        [ 64],\n",
      "        [  8]], device='cuda:0'))\n",
      "loss :  1.7888526916503906\n",
      "Prediction :  (tensor([[ 0.8730],\n",
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      "        [ 0.4768]], device='cuda:0'), tensor([[  82],\n",
      "        [  63],\n",
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      "        [  77],\n",
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      "        [ 100],\n",
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      "        [  84],\n",
      "        [  86],\n",
      "        [  84],\n",
      "        [   6]], device='cuda:0'))\n",
      "loss :  1.4787890911102295\n",
      "Prediction :  (tensor([[ 0.4328],\n",
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      "        [ 81],\n",
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      "        [ 49],\n",
      "        [ 90],\n",
      "        [ 45],\n",
      "        [ 38],\n",
      "        [ 70]], device='cuda:0'))\n",
      "loss :  0.9767770171165466\n",
      "Prediction :  (tensor([[ 0.9924],\n",
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      "        [  42],\n",
      "        [   6],\n",
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      "        [ 101],\n",
      "        [  29],\n",
      "        [  81],\n",
      "        [  32],\n",
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      "        [  10],\n",
      "        [  41],\n",
      "        [  77],\n",
      "        [  71]], device='cuda:0'))\n",
      "loss :  1.2859941720962524\n",
      "Prediction :  (tensor([[ 0.6140],\n",
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      "        [ 0.7245]], device='cuda:0'), tensor([[  7],\n",
      "        [ 40],\n",
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      "        [ 56],\n",
      "        [ 56],\n",
      "        [ 90],\n",
      "        [ 17],\n",
      "        [ 52],\n",
      "        [ 50],\n",
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      "        [ 41],\n",
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      "        [ 94],\n",
      "        [ 84],\n",
      "        [ 24],\n",
      "        [  5],\n",
      "        [ 41]], device='cuda:0'))\n",
      "loss :  1.7496882677078247\n",
      "Prediction :  (tensor([[ 0.3037],\n",
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      "        [  90],\n",
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      "        [  14],\n",
      "        [  77],\n",
      "        [  74],\n",
      "        [  11]], device='cuda:0'))\n",
      "loss :  1.425485610961914\n",
      "Prediction :  (tensor([[ 0.2858],\n",
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      "        [ 0.9382]], device='cuda:0'), tensor([[ 94],\n",
      "        [ 56],\n",
      "        [  1],\n",
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      "        [  5],\n",
      "        [ 32],\n",
      "        [ 40],\n",
      "        [ 55],\n",
      "        [ 82]], device='cuda:0'))\n",
      "loss :  1.1536529064178467\n",
      "Prediction :  (tensor([[ 0.8034],\n",
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      "        [  1],\n",
      "        [ 51],\n",
      "        [ 77],\n",
      "        [ 85],\n",
      "        [ 40],\n",
      "        [ 10],\n",
      "        [ 13],\n",
      "        [ 16],\n",
      "        [ 84]], device='cuda:0'))\n",
      "loss :  1.6365201473236084\n",
      "Prediction :  (tensor([[ 0.6800],\n",
      "        [ 0.5775],\n",
      "        [ 0.7704],\n",
      "        [ 0.9112],\n",
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      "        [ 0.4787]], device='cuda:0'), tensor([[  47],\n",
      "        [  74],\n",
      "        [   4],\n",
      "        [  71],\n",
      "        [ 101],\n",
      "        [  79],\n",
      "        [  71],\n",
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      "        [  52],\n",
      "        [  59],\n",
      "        [  76],\n",
      "        [  15],\n",
      "        [  41],\n",
      "        [  74],\n",
      "        [  31],\n",
      "        [  93],\n",
      "        [  44],\n",
      "        [  91],\n",
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      "        [  74],\n",
      "        [  78],\n",
      "        [  84],\n",
      "        [   0],\n",
      "        [  78]], device='cuda:0'))\n",
      "loss :  1.791523814201355\n",
      "Prediction :  (tensor([[ 0.6616],\n",
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      "        [ 0.1036],\n",
      "        [ 0.7898],\n",
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      "        [ 0.7091],\n",
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      "        [ 89],\n",
      "        [ 93],\n",
      "        [ 54],\n",
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      "        [ 75],\n",
      "        [ 22],\n",
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      "        [ 97],\n",
      "        [ 83],\n",
      "        [ 20],\n",
      "        [ 13],\n",
      "        [ 71],\n",
      "        [ 13],\n",
      "        [ 70],\n",
      "        [ 90],\n",
      "        [ 99],\n",
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      "        [ 39],\n",
      "        [ 93],\n",
      "        [ 54],\n",
      "        [ 50],\n",
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      "        [ 43],\n",
      "        [ 71],\n",
      "        [ 25],\n",
      "        [ 85],\n",
      "        [ 77],\n",
      "        [ 39]], device='cuda:0'))\n",
      "Epoch 1/2.. Train loss: 1.472.. Test loss: 0.652.. Test accuracy: 0.836\n",
      "loss :  1.1253520250320435\n",
      "Prediction :  (tensor([[ 0.9209],\n",
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      "        [  77],\n",
      "        [  21],\n",
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      "        [  21],\n",
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      "        [  95],\n",
      "        [  78],\n",
      "        [  96],\n",
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      "        [  75],\n",
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      "        [  98],\n",
      "        [  84],\n",
      "        [  26],\n",
      "        [  40],\n",
      "        [   5],\n",
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      "        [  72],\n",
      "        [  79],\n",
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      "        [  23],\n",
      "        [  64],\n",
      "        [  49],\n",
      "        [   6],\n",
      "        [  49]], device='cuda:0'))\n",
      "loss :  1.6837998628616333\n",
      "Prediction :  (tensor([[ 0.3502],\n",
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      "        [  59],\n",
      "        [  43],\n",
      "        [  32],\n",
      "        [ 100],\n",
      "        [ 100],\n",
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      "        [  49],\n",
      "        [   4],\n",
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      "        [  97]], device='cuda:0'))\n",
      "loss :  1.2112650871276855\n",
      "Prediction :  (tensor([[ 0.4706],\n",
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      "        [ 49],\n",
      "        [ 80],\n",
      "        [ 49],\n",
      "        [ 90],\n",
      "        [ 26]], device='cuda:0'))\n",
      "loss :  1.5402878522872925\n",
      "Prediction :  (tensor([[ 0.4035],\n",
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      "        [  21],\n",
      "        [   8],\n",
      "        [  80],\n",
      "        [  56]], device='cuda:0'))\n",
      "loss :  1.9827240705490112\n",
      "Prediction :  (tensor([[ 0.4546],\n",
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      "        [  80],\n",
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      "        [  41],\n",
      "        [  23],\n",
      "        [  32],\n",
      "        [  97]], device='cuda:0'))\n",
      "loss :  1.477003812789917\n",
      "Prediction :  (tensor([[ 0.8495],\n",
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      "        [  64],\n",
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      "        [ 100],\n",
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      "        [  39],\n",
      "        [  40],\n",
      "        [  43],\n",
      "        [  93]], device='cuda:0'))\n",
      "loss :  1.380552053451538\n",
      "Prediction :  (tensor([[ 0.1283],\n",
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      "        [ 13],\n",
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      "        [ 89],\n",
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      "        [  3],\n",
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      "        [ 35],\n",
      "        [ 54],\n",
      "        [ 78],\n",
      "        [ 94],\n",
      "        [ 97]], device='cuda:0'))\n",
      "loss :  1.1310451030731201\n",
      "Prediction :  (tensor([[ 0.9508],\n",
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      "        [ 0.9996]], device='cuda:0'), tensor([[ 83],\n",
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      "        [ 96],\n",
      "        [  9],\n",
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      "        [ 51],\n",
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      "        [ 90],\n",
      "        [ 20],\n",
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      "        [ 76],\n",
      "        [  0],\n",
      "        [ 27],\n",
      "        [  9],\n",
      "        [ 77]], device='cuda:0'))\n",
      "loss :  1.539685845375061\n",
      "Prediction :  (tensor([[ 0.3622],\n",
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      "        [ 0.4103],\n",
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      "        [  2],\n",
      "        [ 83],\n",
      "        [ 38],\n",
      "        [ 63],\n",
      "        [ 47],\n",
      "        [ 33],\n",
      "        [ 96],\n",
      "        [ 36],\n",
      "        [ 59],\n",
      "        [ 66],\n",
      "        [ 49],\n",
      "        [ 74],\n",
      "        [ 39],\n",
      "        [ 21],\n",
      "        [ 82],\n",
      "        [ 41],\n",
      "        [ 75],\n",
      "        [ 46],\n",
      "        [ 90],\n",
      "        [ 88],\n",
      "        [ 61],\n",
      "        [ 51],\n",
      "        [ 45],\n",
      "        [ 95]], device='cuda:0'))\n",
      "loss :  0.8166617751121521\n",
      "Prediction :  (tensor([[ 0.9908],\n",
      "        [ 0.5797],\n",
      "        [ 0.5126],\n",
      "        [ 0.9382],\n",
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      "        [ 0.5234],\n",
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      "        [ 0.2434],\n",
      "        [ 0.7073],\n",
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      "        [ 0.9537],\n",
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      "        [  94],\n",
      "        [  89],\n",
      "        [  51],\n",
      "        [  81],\n",
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      "        [  72],\n",
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      "        [  14],\n",
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      "        [  36],\n",
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      "        [  49],\n",
      "        [  34],\n",
      "        [  69],\n",
      "        [  69],\n",
      "        [  73],\n",
      "        [  43],\n",
      "        [  71],\n",
      "        [ 101],\n",
      "        [  37],\n",
      "        [  83],\n",
      "        [  79],\n",
      "        [  94],\n",
      "        [  71]], device='cuda:0'))\n",
      "Epoch 1/2.. Train loss: 1.387.. Test loss: 0.747.. Test accuracy: 0.809\n",
      "loss :  1.3856632709503174\n",
      "Prediction :  (tensor([[ 0.7658],\n",
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      "        [ 0.9643]], device='cuda:0'), tensor([[  50],\n",
      "        [  63],\n",
      "        [  47],\n",
      "        [  89],\n",
      "        [  65],\n",
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      "        [  72],\n",
      "        [  74],\n",
      "        [  89],\n",
      "        [   4],\n",
      "        [  25],\n",
      "        [  20],\n",
      "        [  48],\n",
      "        [  77],\n",
      "        [  92],\n",
      "        [  96],\n",
      "        [  93],\n",
      "        [  74],\n",
      "        [  38],\n",
      "        [  55],\n",
      "        [  80],\n",
      "        [  59],\n",
      "        [  48],\n",
      "        [  83],\n",
      "        [  26],\n",
      "        [  77],\n",
      "        [   7],\n",
      "        [   1],\n",
      "        [ 101],\n",
      "        [  77],\n",
      "        [  65],\n",
      "        [  71]], device='cuda:0'))\n",
      "loss :  1.394319772720337\n",
      "Prediction :  (tensor([[ 0.5075],\n",
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      "        [ 0.2020],\n",
      "        [ 0.2249],\n",
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      "        [  74],\n",
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      "        [  61],\n",
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      "        [  33],\n",
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      "        [  65],\n",
      "        [  38],\n",
      "        [  61],\n",
      "        [  59]], device='cuda:0'))\n",
      "loss :  1.4837985038757324\n",
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      "        [ 64],\n",
      "        [ 48],\n",
      "        [ 98],\n",
      "        [  6],\n",
      "        [ 82]], device='cuda:0'))\n",
      "loss :  1.0138911008834839\n",
      "Prediction :  (tensor([[ 0.2055],\n",
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      "        [  48],\n",
      "        [  93],\n",
      "        [  43],\n",
      "        [  97]], device='cuda:0'))\n",
      "loss :  1.0560412406921387\n",
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      "        [  72],\n",
      "        [  94],\n",
      "        [  66],\n",
      "        [  65]], device='cuda:0'))\n",
      "loss :  1.368752360343933\n",
      "Prediction :  (tensor([[ 0.5727],\n",
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      "        [ 10],\n",
      "        [ 52],\n",
      "        [ 96],\n",
      "        [ 57],\n",
      "        [ 12]], device='cuda:0'))\n",
      "loss :  1.1618144512176514\n",
      "Prediction :  (tensor([[ 0.3826],\n",
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      "        [ 0.7954],\n",
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      "        [ 94],\n",
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      "        [  5],\n",
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      "        [ 22],\n",
      "        [ 77],\n",
      "        [ 66],\n",
      "        [ 26],\n",
      "        [ 38]], device='cuda:0'))\n",
      "loss :  1.363133192062378\n",
      "Prediction :  (tensor([[ 0.8968],\n",
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      "        [   9],\n",
      "        [  38],\n",
      "        [  49],\n",
      "        [  47]], device='cuda:0'))\n",
      "loss :  1.2228121757507324\n",
      "Prediction :  (tensor([[ 0.2703],\n",
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      "        [ 76],\n",
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      "loss :  1.5353456735610962\n",
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      "        [ 0.8430],\n",
      "        [ 0.3392],\n",
      "        [ 0.4208],\n",
      "        [ 0.6457],\n",
      "        [ 0.9867],\n",
      "        [ 0.8898],\n",
      "        [ 0.2439],\n",
      "        [ 0.7082],\n",
      "        [ 0.1365],\n",
      "        [ 0.9987],\n",
      "        [ 0.9957],\n",
      "        [ 0.4023]], device='cuda:0'), tensor([[  73],\n",
      "        [  77],\n",
      "        [  99],\n",
      "        [  20],\n",
      "        [  78],\n",
      "        [  50],\n",
      "        [  69],\n",
      "        [  96],\n",
      "        [  23],\n",
      "        [  80],\n",
      "        [   7],\n",
      "        [  66],\n",
      "        [  60],\n",
      "        [  99],\n",
      "        [  44],\n",
      "        [  48],\n",
      "        [  49],\n",
      "        [  75],\n",
      "        [  38],\n",
      "        [  43],\n",
      "        [  78],\n",
      "        [   3],\n",
      "        [  79],\n",
      "        [  84],\n",
      "        [  59],\n",
      "        [  54],\n",
      "        [ 100],\n",
      "        [   3],\n",
      "        [  37],\n",
      "        [  96],\n",
      "        [  82],\n",
      "        [  21]], device='cuda:0'))\n",
      "Epoch 1/2.. Train loss: 1.280.. Test loss: 0.722.. Test accuracy: 0.783\n",
      "loss :  1.7947962284088135\n",
      "Prediction :  (tensor([[ 0.7999],\n",
      "        [ 0.3393],\n",
      "        [ 0.8092],\n",
      "        [ 0.7726],\n",
      "        [ 0.9941],\n",
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      "        [ 0.7264],\n",
      "        [ 0.3640],\n",
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      "        [ 0.4500],\n",
      "        [ 0.9859],\n",
      "        [ 0.5841],\n",
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      "        [ 0.3743],\n",
      "        [ 0.9852],\n",
      "        [ 0.9136],\n",
      "        [ 0.7425],\n",
      "        [ 0.9933],\n",
      "        [ 0.9781],\n",
      "        [ 0.4698],\n",
      "        [ 0.2191],\n",
      "        [ 0.6514],\n",
      "        [ 0.4869]], device='cuda:0'), tensor([[ 101],\n",
      "        [  12],\n",
      "        [  11],\n",
      "        [  84],\n",
      "        [  38],\n",
      "        [  55],\n",
      "        [  45],\n",
      "        [  15],\n",
      "        [  96],\n",
      "        [  51],\n",
      "        [  18],\n",
      "        [   3],\n",
      "        [  71],\n",
      "        [  53],\n",
      "        [  63],\n",
      "        [  69],\n",
      "        [  85],\n",
      "        [  97],\n",
      "        [  49],\n",
      "        [  49],\n",
      "        [  74],\n",
      "        [  81],\n",
      "        [  45],\n",
      "        [  56],\n",
      "        [   6],\n",
      "        [  83],\n",
      "        [   2],\n",
      "        [  15],\n",
      "        [  19],\n",
      "        [  45],\n",
      "        [  17],\n",
      "        [  82]], device='cuda:0'))\n",
      "loss :  1.4069257974624634\n",
      "Prediction :  (tensor([[ 0.9935],\n",
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      "        [ 0.2932],\n",
      "        [ 0.3704],\n",
      "        [ 0.7819],\n",
      "        [ 0.8785],\n",
      "        [ 0.7776],\n",
      "        [ 0.9908],\n",
      "        [ 0.4606],\n",
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      "        [ 0.9181],\n",
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      "        [ 0.1911],\n",
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      "        [ 0.8029],\n",
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      "        [ 0.3838],\n",
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      "        [ 0.5306],\n",
      "        [ 0.7854],\n",
      "        [ 0.9999],\n",
      "        [ 0.9271]], device='cuda:0'), tensor([[  68],\n",
      "        [  59],\n",
      "        [  49],\n",
      "        [  49],\n",
      "        [  83],\n",
      "        [  81],\n",
      "        [  73],\n",
      "        [  56],\n",
      "        [ 101],\n",
      "        [  43],\n",
      "        [  54],\n",
      "        [  57],\n",
      "        [   9],\n",
      "        [  94],\n",
      "        [  91],\n",
      "        [  38],\n",
      "        [  34],\n",
      "        [  29],\n",
      "        [  70],\n",
      "        [   9],\n",
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      "        [  55],\n",
      "        [  97],\n",
      "        [  74],\n",
      "        [  90],\n",
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      "        [   0],\n",
      "        [  11],\n",
      "        [  37],\n",
      "        [  77],\n",
      "        [  19]], device='cuda:0'))\n",
      "loss :  1.4293224811553955\n",
      "Prediction :  (tensor([[ 0.3771],\n",
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      "        [ 0.3993],\n",
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      "        [ 43],\n",
      "        [ 38],\n",
      "        [ 99],\n",
      "        [ 90],\n",
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      "        [ 74],\n",
      "        [ 49],\n",
      "        [ 59],\n",
      "        [ 31],\n",
      "        [ 48],\n",
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      "        [ 85],\n",
      "        [ 54],\n",
      "        [ 83],\n",
      "        [ 52],\n",
      "        [ 27],\n",
      "        [ 18],\n",
      "        [ 64],\n",
      "        [  5],\n",
      "        [ 90],\n",
      "        [  0],\n",
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      "        [ 97],\n",
      "        [  6],\n",
      "        [ 11],\n",
      "        [ 77],\n",
      "        [ 30],\n",
      "        [ 78],\n",
      "        [ 49],\n",
      "        [ 71],\n",
      "        [ 40]], device='cuda:0'))\n",
      "loss :  1.3304625749588013\n",
      "Prediction :  (tensor([[ 0.7911],\n",
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      "        [ 0.8369],\n",
      "        [ 0.9508],\n",
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      "        [ 0.8353]], device='cuda:0'), tensor([[ 46],\n",
      "        [  5],\n",
      "        [ 47],\n",
      "        [ 77],\n",
      "        [ 66],\n",
      "        [ 77],\n",
      "        [ 96],\n",
      "        [  3],\n",
      "        [ 82],\n",
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      "        [ 73],\n",
      "        [ 40],\n",
      "        [ 24],\n",
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      "        [ 64],\n",
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      "        [ 96],\n",
      "        [ 26],\n",
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      "        [ 96],\n",
      "        [ 99],\n",
      "        [ 77],\n",
      "        [ 16],\n",
      "        [ 50]], device='cuda:0'))\n",
      "loss :  0.9712023735046387\n",
      "Prediction :  (tensor([[ 0.4928],\n",
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      "        [ 0.9471]], device='cuda:0'), tensor([[ 50],\n",
      "        [ 43],\n",
      "        [ 94],\n",
      "        [ 15],\n",
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      "        [ 85],\n",
      "        [ 54],\n",
      "        [ 32],\n",
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      "        [ 14],\n",
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      "        [ 64],\n",
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      "        [ 90],\n",
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      "        [ 77],\n",
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      "        [ 38],\n",
      "        [  6],\n",
      "        [ 38],\n",
      "        [ 96],\n",
      "        [ 92],\n",
      "        [ 80]], device='cuda:0'))\n",
      "loss :  1.4136369228363037\n",
      "Prediction :  (tensor([[ 0.1443],\n",
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      "        [  9],\n",
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      "        [ 86],\n",
      "        [ 84],\n",
      "        [ 97],\n",
      "        [ 18],\n",
      "        [ 68]], device='cuda:0'))\n",
      "loss :  1.5619852542877197\n",
      "Prediction :  (tensor([[ 0.9389],\n",
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      "        [  31],\n",
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      "        [  17],\n",
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      "        [ 101],\n",
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      "        [ 100],\n",
      "        [  11],\n",
      "        [  26],\n",
      "        [  85],\n",
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      "        [  49],\n",
      "        [  31],\n",
      "        [  43],\n",
      "        [  97]], device='cuda:0'))\n",
      "loss :  1.5760927200317383\n",
      "Prediction :  (tensor([[ 0.2311],\n",
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      "        [  98],\n",
      "        [  35],\n",
      "        [  17],\n",
      "        [  83]], device='cuda:0'))\n",
      "loss :  0.5961431264877319\n",
      "Prediction :  (tensor([[ 0.7222],\n",
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      "        [  83]], device='cuda:0'))\n",
      "loss :  1.1448991298675537\n",
      "Prediction :  (tensor([[ 0.8929],\n",
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      "        [  70],\n",
      "        [  50],\n",
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      "        [  5],\n",
      "        [ 59]], device='cuda:0'))\n",
      "Epoch 1/2.. Train loss: 1.301.. Test loss: 0.632.. Test accuracy: 0.830\n",
      "loss :  1.4480499029159546\n",
      "Prediction :  (tensor([[ 0.1751],\n",
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      "        [ 0.8434]], device='cuda:0'), tensor([[  18],\n",
      "        [  82],\n",
      "        [ 100],\n",
      "        [   2],\n",
      "        [  73],\n",
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      "        [  27],\n",
      "        [  96],\n",
      "        [  30],\n",
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      "        [  43],\n",
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      "        [  74],\n",
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      "        [  93],\n",
      "        [  54],\n",
      "        [  59],\n",
      "        [  90],\n",
      "        [  12],\n",
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      "        [  82],\n",
      "        [  75],\n",
      "        [  84],\n",
      "        [  43]], device='cuda:0'))\n",
      "loss :  1.4463489055633545\n",
      "Prediction :  (tensor([[ 0.4737],\n",
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      "        [  35],\n",
      "        [  84],\n",
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      "        [   3],\n",
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      "        [  12],\n",
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      "        [  81],\n",
      "        [  23],\n",
      "        [  73],\n",
      "        [  51],\n",
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      "        [  90],\n",
      "        [   0],\n",
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      "        [  77],\n",
      "        [  87],\n",
      "        [  93],\n",
      "        [  82]], device='cuda:0'))\n",
      "loss :  1.3902671337127686\n",
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      "        [  50],\n",
      "        [  76],\n",
      "        [  84],\n",
      "        [ 100]], device='cuda:0'))\n",
      "loss :  1.3465662002563477\n",
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      "        [ 90],\n",
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      "        [ 75],\n",
      "        [ 73],\n",
      "        [ 52],\n",
      "        [ 33]], device='cuda:0'))\n",
      "loss :  0.678244948387146\n",
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      "        [ 92],\n",
      "        [ 72],\n",
      "        [ 68],\n",
      "        [ 76],\n",
      "        [ 68]], device='cuda:0'))\n",
      "loss :  1.5493648052215576\n",
      "Prediction :  (tensor([[ 0.2096],\n",
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      "        [ 60],\n",
      "        [ 84],\n",
      "        [ 50]], device='cuda:0'))\n",
      "loss :  1.5824487209320068\n",
      "Prediction :  (tensor([[ 0.8924],\n",
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      "        [ 77]], device='cuda:0'))\n",
      "loss :  1.445554256439209\n",
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      "        [  32]], device='cuda:0'))\n",
      "loss :  1.177791714668274\n",
      "Prediction :  (tensor([[ 0.7536],\n",
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      "loss :  1.2387882471084595\n",
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      "loss :  1.0882296562194824\n",
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      "        [  82],\n",
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      "loss :  1.1870536804199219\n",
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      "        [ 73],\n",
      "        [ 81],\n",
      "        [ 70]], device='cuda:0'))\n",
      "loss :  1.0784846544265747\n",
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      "        [ 89],\n",
      "        [ 81],\n",
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      "        [ 65]], device='cuda:0'))\n",
      "loss :  1.1168161630630493\n",
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      "        [ 90],\n",
      "        [ 68],\n",
      "        [ 17],\n",
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      "loss :  1.3760011196136475\n",
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      "loss :  1.2682069540023804\n",
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      "        [  84],\n",
      "        [  86]], device='cuda:0'))\n",
      "loss :  1.5335309505462646\n",
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      "        [ 53],\n",
      "        [ 26],\n",
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      "        [ 89],\n",
      "        [ 84]], device='cuda:0'))\n",
      "loss :  0.9167723655700684\n",
      "Prediction :  (tensor([[ 0.2544],\n",
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      "        [ 59],\n",
      "        [ 76],\n",
      "        [ 70],\n",
      "        [ 27],\n",
      "        [ 59]], device='cuda:0'))\n",
      "loss :  0.8625147342681885\n",
      "Prediction :  (tensor([[ 0.5915],\n",
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      "        [ 0.9953]], device='cuda:0'), tensor([[ 89],\n",
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      "        [ 54],\n",
      "        [ 82],\n",
      "        [ 50],\n",
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      "        [ 21],\n",
      "        [ 77],\n",
      "        [ 18],\n",
      "        [ 90],\n",
      "        [  7],\n",
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      "        [ 53],\n",
      "        [ 96],\n",
      "        [ 64],\n",
      "        [ 31],\n",
      "        [  4],\n",
      "        [ 73],\n",
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      "        [ 56],\n",
      "        [ 82],\n",
      "        [ 67],\n",
      "        [ 24],\n",
      "        [ 38],\n",
      "        [ 97],\n",
      "        [ 88],\n",
      "        [ 36],\n",
      "        [ 80]], device='cuda:0'))\n",
      "loss :  1.191957950592041\n",
      "Prediction :  (tensor([[ 0.1805],\n",
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      "        [  96],\n",
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      "        [  86],\n",
      "        [  92],\n",
      "        [  24],\n",
      "        [  26],\n",
      "        [  59],\n",
      "        [  50],\n",
      "        [  82],\n",
      "        [  71],\n",
      "        [  76],\n",
      "        [  74],\n",
      "        [   3],\n",
      "        [  90],\n",
      "        [  17],\n",
      "        [  49],\n",
      "        [  77],\n",
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      "        [  97],\n",
      "        [  24],\n",
      "        [  77],\n",
      "        [  56],\n",
      "        [  97]], device='cuda:0'))\n",
      "Epoch 1/2.. Train loss: 1.246.. Test loss: 0.613.. Test accuracy: 0.820\n",
      "loss :  1.4985671043395996\n",
      "Prediction :  (tensor([[ 0.4743],\n",
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      "        [ 0.8724]], device='cuda:0'), tensor([[ 55],\n",
      "        [ 73],\n",
      "        [ 89],\n",
      "        [ 84],\n",
      "        [  1],\n",
      "        [ 61],\n",
      "        [ 72],\n",
      "        [ 12],\n",
      "        [ 70],\n",
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      "        [ 0.9767]], device='cuda:0'), tensor([[  5],\n",
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      "        [ 36],\n",
      "        [ 89],\n",
      "        [ 61],\n",
      "        [ 89],\n",
      "        [ 77]], device='cuda:0'))\n",
      "loss :  1.4042527675628662\n",
      "Prediction :  (tensor([[ 0.7424],\n",
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      "        [ 0.5397]], device='cuda:0'), tensor([[  89],\n",
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      "        [  82],\n",
      "        [  96],\n",
      "        [  36],\n",
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      "        [  89],\n",
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      "        [  59],\n",
      "        [  42],\n",
      "        [  78],\n",
      "        [   2],\n",
      "        [  49],\n",
      "        [  55]], device='cuda:0'))\n",
      "loss :  1.1717463731765747\n",
      "Prediction :  (tensor([[ 1.0000],\n",
      "        [ 0.5876],\n",
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      "        [ 0.2100],\n",
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      "        [ 0.9786],\n",
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      "        [ 0.5284]], device='cuda:0'), tensor([[  59],\n",
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      "        [  51],\n",
      "        [  92],\n",
      "        [   5],\n",
      "        [  37],\n",
      "        [  33]], device='cuda:0'))\n",
      "loss :  0.7801001071929932\n",
      "Prediction :  (tensor([[ 0.5033],\n",
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      "        [ 0.5440]], device='cuda:0'), tensor([[  12],\n",
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      "        [   2],\n",
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      "        [  13],\n",
      "        [  45],\n",
      "        [  18],\n",
      "        [  83]], device='cuda:0'))\n",
      "loss :  0.728368878364563\n",
      "Prediction :  (tensor([[ 0.4150],\n",
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      "        [ 0.5764],\n",
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      "        [ 0.9961],\n",
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      "        [ 0.5217]], device='cuda:0'), tensor([[ 65],\n",
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      "        [ 18],\n",
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      "        [ 92],\n",
      "        [  4],\n",
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      "        [ 43],\n",
      "        [ 59],\n",
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      "        [  8],\n",
      "        [ 98],\n",
      "        [ 63],\n",
      "        [ 92],\n",
      "        [ 77]], device='cuda:0'))\n",
      "loss :  0.9439607858657837\n",
      "Prediction :  (tensor([[ 0.8263],\n",
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      "        [ 0.3779]], device='cuda:0'), tensor([[   8],\n",
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      "        [  59],\n",
      "        [  16],\n",
      "        [  42],\n",
      "        [ 100]], device='cuda:0'))\n",
      "loss :  1.8382399082183838\n",
      "Prediction :  (tensor([[ 0.3637],\n",
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      "        [ 0.6273]], device='cuda:0'), tensor([[ 71],\n",
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      "        [ 77],\n",
      "        [ 75],\n",
      "        [ 77],\n",
      "        [ 40]], device='cuda:0'))\n",
      "loss :  1.2985421419143677\n",
      "Prediction :  (tensor([[ 0.9786],\n",
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      "        [ 75],\n",
      "        [ 97],\n",
      "        [ 90],\n",
      "        [  6],\n",
      "        [ 89]], device='cuda:0'))\n",
      "loss :  1.081231951713562\n",
      "Prediction :  (tensor([[ 0.5576],\n",
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      "        [ 0.9667],\n",
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      "        [ 0.3933],\n",
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      "        [ 0.9999]], device='cuda:0'), tensor([[ 94],\n",
      "        [ 25],\n",
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      "        [ 44],\n",
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      "        [ 59],\n",
      "        [ 62],\n",
      "        [ 16],\n",
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      "        [ 81],\n",
      "        [ 69],\n",
      "        [ 93],\n",
      "        [ 69],\n",
      "        [ 52],\n",
      "        [ 82],\n",
      "        [ 43],\n",
      "        [ 49],\n",
      "        [  1],\n",
      "        [ 11],\n",
      "        [ 63],\n",
      "        [ 64],\n",
      "        [ 72],\n",
      "        [ 70],\n",
      "        [ 10],\n",
      "        [ 75],\n",
      "        [ 77]], device='cuda:0'))\n"
     ]
    }
   ],
   "source": [
    "# Train Network\n",
    "epochs = 2\n",
    "steps = 0\n",
    "running_loss = 0\n",
    "print_every = 20\n",
    "for epoch in range(epochs):\n",
    "    for inputs, labels in trainloader:\n",
    "        steps += 1\n",
    "        # Move input and label tensors to the default device\n",
    "        inputs, labels = inputs.to(device), labels.to(device)\n",
    "        \n",
    "        optimizer.zero_grad()\n",
    "        \n",
    "        logps = model.forward(inputs)\n",
    "        loss = criterion(logps, labels)\n",
    "        #print(\"loss : \", loss.item())\n",
    "        #top_prob, top_cl = torch.exp(logps).topk(1,dim=1)\n",
    "        #print(\"Prediction : \" , labels.view(*top_cl.shape) , \" Probility : \" )\n",
    "        loss.backward()\n",
    "        optimizer.step()\n",
    "\n",
    "        running_loss += loss.item()\n",
    "        \n",
    "        if steps % print_every == 0:\n",
    "            test_loss = 0\n",
    "            accuracy = 0\n",
    "            model.eval()\n",
    "            with torch.no_grad():\n",
    "                for inputs, labels in validloader:\n",
    "                    inputs, labels = inputs.to(device), labels.to(device)\n",
    "                    logps = model.forward(inputs)\n",
    "                    batch_loss = criterion(logps, labels)\n",
    "                    \n",
    "                    test_loss += batch_loss.item()\n",
    "                    \n",
    "                    # Calculate accuracy\n",
    "                    ps = torch.exp(logps)\n",
    "                    top_p, top_class = ps.topk(1, dim=1)\n",
    "                    equals = top_class == labels.view(*top_class.shape)\n",
    "                    accuracy += torch.mean(equals.type(torch.FloatTensor)).item()\n",
    "                    \n",
    "            print(f\"Epoch {epoch+1}/{epochs}.. \"\n",
    "                  f\"Train loss: {running_loss/print_every:.3f}.. \"\n",
    "                  f\"Test loss: {test_loss/len(validloader):.3f}.. \"\n",
    "                  f\"Test accuracy: {accuracy/len(validloader):.3f}\")\n",
    "            running_loss = 0\n",
    "            model.train()\n",
    "print(\"Training Complete!\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Testing your network\n",
    "\n",
    "It's good practice to test your trained network on test data, images the network has never seen either in training or validation. This will give you a good estimate for the model's performance on completely new images. Run the test images through the network and measure the accuracy, the same way you did validation. You should be able to reach around 70% accuracy on the test set if the model has been trained well."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train loss: 0.627.. Test loss: 0.654.. Test accuracy: 0.819\n"
     ]
    }
   ],
   "source": [
    "test_loss = 0\n",
    "accuracy = 0\n",
    "model.eval()\n",
    "with torch.no_grad():\n",
    "    for inputs, labels in testloader:\n",
    "        inputs, labels = inputs.to(device), labels.to(device)\n",
    "        logps = model.forward(inputs)\n",
    "        batch_loss = criterion(logps, labels)\n",
    "\n",
    "        test_loss += batch_loss.item()\n",
    "\n",
    "        # Calculate accuracy\n",
    "        ps = torch.exp(logps)\n",
    "        top_p, top_class = ps.topk(1, dim=1)\n",
    "        equals = top_class == labels.view(*top_class.shape)\n",
    "        accuracy += torch.mean(equals.type(torch.FloatTensor)).item()\n",
    "\n",
    "print(f\"Train loss: {running_loss/print_every:.3f}.. \"\n",
    "      f\"Test loss: {test_loss/len(testloader):.3f}.. \"\n",
    "      f\"Test accuracy: {accuracy/len(testloader):.3f}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Save the checkpoint\n",
    "\n",
    "Now that your network is trained, save the model so you can load it later for making predictions. You probably want to save other things such as the mapping of classes to indices which you get from one of the image datasets: `image_datasets['train'].class_to_idx`. You can attach this to the model as an attribute which makes inference easier later on.\n",
    "\n",
    "```model.class_to_idx = image_datasets['train'].class_to_idx```\n",
    "\n",
    "Remember that you'll want to completely rebuild the model later so you can use it for inference. Make sure to include any information you need in the checkpoint. If you want to load the model and keep training, you'll want to save the number of epochs as well as the optimizer state, `optimizer.state_dict`. You'll likely want to use this trained model in the next part of the project, so best to save it now."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sequential(\n",
      "  (fc1): Linear(in_features=1024, out_features=500, bias=True)\n",
      "  (relu): ReLU()\n",
      "  (dropout): Dropout(p=0.2)\n",
      "  (fc2): Linear(in_features=500, out_features=102, bias=True)\n",
      "  (output): LogSoftmax()\n",
      ")\n",
      "Linear(in_features=1024, out_features=500, bias=True)\n",
      "ReLU()\n",
      "Dropout(p=0.2)\n",
      "Linear(in_features=500, out_features=102, bias=True)\n",
      "LogSoftmax()\n"
     ]
    }
   ],
   "source": [
    "# Save the checkpoint\n",
    "def save_model(model):\n",
    "    for layer in model.classifier.modules():\n",
    "        print(layer)\n",
    "    checkpoint = {'class_to_idx': image_dataset_train.class_to_idx,\n",
    "                  'epoch': epoch,\n",
    "                  'model_state_dict': model.classifier.state_dict(),\n",
    "                  'optimizer_state_dict': optimizer.state_dict(),\n",
    "                  'loss': loss}\n",
    "\n",
    "    torch.save(checkpoint, 'checkpoint.pth')\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "save_model(model)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Loading the checkpoint\n",
    "\n",
    "At this point it's good to write a function that can load a checkpoint and rebuild the model. That way you can come back to this project and keep working on it without having to retrain the network."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def load_checkpoint(filepath):\n",
    "    checkpoint = torch.load(filepath)\n",
    "    # Download base model\n",
    "    model = models.densenet121(pretrained=True)\n",
    "    # Fix base model parameters\n",
    "    for param in model.parameters():\n",
    "        param.requires_grad = False\n",
    "    classifier = create_classifier()\n",
    "    # Load classifier weights\n",
    "    classifier.load_state_dict(checkpoint['model_state_dict'])\n",
    "    model.classifier = classifier\n",
    "    # Only train the classifier parameters, feature parameters are frozen\n",
    "    optimizer = optim.Adam(model.classifier.parameters(), lr=0.003)\n",
    "    optimizer.load_state_dict(checkpoint['optimizer_state_dict'])\n",
    "    # Load training checkpoint information\n",
    "    epoch = checkpoint['epoch']\n",
    "    loss = checkpoint['loss']\n",
    "    class_to_idx = checkpoint['class_to_idx']\n",
    "    # Move all tensors and model to device\n",
    "    device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
    "    model.to(device)\n",
    "    loss.to(device)\n",
    "    return model,optimizer,epoch,loss,class_to_idx\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.6/site-packages/torchvision-0.2.1-py3.6.egg/torchvision/models/densenet.py:212: UserWarning: nn.init.kaiming_normal is now deprecated in favor of nn.init.kaiming_normal_.\n",
      "Downloading: \"https://download.pytorch.org/models/densenet121-a639ec97.pth\" to /root/.torch/models/densenet121-a639ec97.pth\n",
      "100%|██████████| 32342954/32342954 [00:00<00:00, 91361730.40it/s]\n"
     ]
    }
   ],
   "source": [
    "loaded_model,optimizer,epoch,loss,class_to_idx = load_checkpoint('checkpoint.pth')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Inference for classification\n",
    "\n",
    "Now you'll write a function to use a trained network for inference. That is, you'll pass an image into the network and predict the class of the flower in the image. Write a function called `predict` that takes an image and a model, then returns the top $K$ most likely classes along with the probabilities. It should look like \n",
    "\n",
    "```python\n",
    "probs, classes = predict(image_path, model)\n",
    "print(probs)\n",
    "print(classes)\n",
    "> [ 0.01558163  0.01541934  0.01452626  0.01443549  0.01407339]\n",
    "> ['70', '3', '45', '62', '55']\n",
    "```\n",
    "\n",
    "First you'll need to handle processing the input image such that it can be used in your network. \n",
    "\n",
    "## Image Preprocessing\n",
    "\n",
    "You'll want to use `PIL` to load the image ([documentation](https://pillow.readthedocs.io/en/latest/reference/Image.html)). It's best to write a function that preprocesses the image so it can be used as input for the model. This function should process the images in the same manner used for training. \n",
    "\n",
    "First, resize the images where the shortest side is 256 pixels, keeping the aspect ratio. This can be done with the [`thumbnail`](http://pillow.readthedocs.io/en/3.1.x/reference/Image.html#PIL.Image.Image.thumbnail) or [`resize`](http://pillow.readthedocs.io/en/3.1.x/reference/Image.html#PIL.Image.Image.thumbnail) methods. Then you'll need to crop out the center 224x224 portion of the image.\n",
    "\n",
    "Color channels of images are typically encoded as integers 0-255, but the model expected floats 0-1. You'll need to convert the values. It's easiest with a Numpy array, which you can get from a PIL image like so `np_image = np.array(pil_image)`.\n",
    "\n",
    "As before, the network expects the images to be normalized in a specific way. For the means, it's `[0.485, 0.456, 0.406]` and for the standard deviations `[0.229, 0.224, 0.225]`. You'll want to subtract the means from each color channel, then divide by the standard deviation. \n",
    "\n",
    "And finally, PyTorch expects the color channel to be the first dimension but it's the third dimension in the PIL image and Numpy array. You can reorder dimensions using [`ndarray.transpose`](https://docs.scipy.org/doc/numpy-1.13.0/reference/generated/numpy.ndarray.transpose.html). The color channel needs to be first and retain the order of the other two dimensions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "def process_image(image):\n",
    "    print(\"Image size :\", image.size)\n",
    "    \n",
    "    # Resize image where shortest side is 256\n",
    "    if ( image.size[0] > image.size[1]):\n",
    "        resize = image.size[0], 256\n",
    "        image.thumbnail(resize)\n",
    "    elif ( image.size[1] > image.size[0]):\n",
    "        resize = 256, image.size[1]\n",
    "        image.thumbnail(resize)\n",
    "    else:\n",
    "        resize  = 256, 256\n",
    "        image.thumbnail(resize)\n",
    "    print(\"Image resized : \" , image.size)\n",
    "    \n",
    "    # Crop out 224X224 center\n",
    "    left = (image.size[0] - 224)/2\n",
    "    top = (image.size[1] - 224)/2\n",
    "    right = (image.size[0] + 224)/2\n",
    "    bottom = (image.size[1] + 224)/2\n",
    "    image = image.crop((left,top,right,bottom))\n",
    "    print(\"Image cropped size \", image.size)\n",
    "    \n",
    "    # Convert int (0-255) to float (0-1)\n",
    "    np_image = np.array(image)\n",
    "    print('Data Type: %s' % np_image.dtype)\n",
    "    print('Min: %.3f, Max: %.3f' % (np_image.min(), np_image.max()))\n",
    "    np_image = np_image.astype('float32')\n",
    "    np_image /= 255.0\n",
    "    print('Min: %.3f, Max: %.3f' % (np_image.min(), np_image.max()))\n",
    "\n",
    "    # Normalize with means and std as follows\n",
    "    mean = np.array([0.485, 0.456, 0.406]) \n",
    "    std = np.array([0.229, 0.224, 0.225])\n",
    "    np_image = (np_image - mean)/std\n",
    "    print('Min: %.3f, Max: %.3f' % (np_image.min(), np_image.max()))\n",
    "    np_image = np_image.transpose((2, 0, 1))\n",
    "    \n",
    "    return np_image\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "image = Image.open(test_dir + \"/10/image_07104.jpg\")\n",
    "processed_image = process_image(image)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To check your work, the function below converts a PyTorch tensor and displays it in the notebook. If your `process_image` function works, running the output through this function should return the original image (except for the cropped out portions)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "def imshow(image, ax=None, title=None):\n",
    "    \"\"\"Imshow for Tensor.\"\"\"\n",
    "    if ax is None:\n",
    "        fig, ax = plt.subplots()\n",
    "    \n",
    "    # PyTorch tensors assume the color channel is the first dimension\n",
    "    # but matplotlib assumes is the third dimension\n",
    "    image = image.transpose((1, 2, 0))\n",
    "    \n",
    "    # Undo preprocessing\n",
    "    mean = np.array([0.485, 0.456, 0.406])\n",
    "    std = np.array([0.229, 0.224, 0.225])\n",
    "    image = std * image + mean\n",
    "    \n",
    "    # Image needs to be clipped between 0 and 1 or it looks like noise when displayed\n",
    "    image = np.clip(image, 0, 1)\n",
    "    plt.title(title)\n",
    "    ax.imshow(image)\n",
    "    \n",
    "    return ax\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f85c3d23d30>"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f85c3d234a8>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 263,
       "width": 259
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "imshow(processed_image)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "def predict(image_path, model, top=5):\n",
    "    # Turn model to eval mode\n",
    "    model.eval()\n",
    "    # Process image\n",
    "    image = Image.open(image_path)\n",
    "    np_image = process_image(image)\n",
    "    # Convert np array to tensor\n",
    "    image_tensor = torch.from_numpy(np_image).type(torch.FloatTensor)\n",
    "    inp = image_tensor.unsqueeze(0)\n",
    "    # move model and input to device\n",
    "    device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
    "    inp = inp.to(device)\n",
    "    model.to(device)\n",
    "    # feed forward\n",
    "    ps = torch.exp(model.forward(inp))\n",
    "    # Top probability and top class\n",
    "    top_p, top_class = ps.topk(top)\n",
    "    idx_to_class = {val: key for key, val in class_to_idx.items()}\n",
    "    top_class = [idx_to_class[int(cl_index)] for cl_index in top_class[0]]\n",
    "    return top_p, top_class"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Image size : (500, 548)\n",
      "Image resized :  (256, 280)\n",
      "Image cropped size  (224, 224)\n",
      "Data Type: uint8\n",
      "Min: 0.000, Max: 255.000\n",
      "Min: 0.000, Max: 1.000\n",
      "Min: -2.118, Max: 2.640\n"
     ]
    }
   ],
   "source": [
    "top_p, top_class= predict( test_dir + \"/10/image_07104.jpg\", loaded_model)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Class Prediction\n",
    "\n",
    "Once you can get images in the correct format, it's time to write a function for making predictions with your model. A common practice is to predict the top 5 or so (usually called top-$K$) most probable classes. You'll want to calculate the class probabilities then find the $K$ largest values.\n",
    "\n",
    "To get the top $K$ largest values in a tensor use [`x.topk(k)`](http://pytorch.org/docs/master/torch.html#torch.topk). This method returns both the highest `k` probabilities and the indices of those probabilities corresponding to the classes. You need to convert from these indices to the actual class labels using `class_to_idx` which hopefully you added to the model or from an `ImageFolder` you used to load the data ([see here](#Save-the-checkpoint)). Make sure to invert the dictionary so you get a mapping from index to class as well.\n",
    "\n",
    "Again, this method should take a path to an image and a model checkpoint, then return the probabilities and classes.\n",
    "\n",
    "```python\n",
    "probs, classes = predict(image_path, model)\n",
    "print(probs)\n",
    "print(classes)\n",
    "> [ 0.01558163  0.01541934  0.01452626  0.01443549  0.01407339]\n",
    "> ['70', '3', '45', '62', '55']\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Sanity Checking\n",
    "\n",
    "Now that you can use a trained model for predictions, check to make sure it makes sense. Even if the testing accuracy is high, it's always good to check that there aren't obvious bugs. Use `matplotlib` to plot the probabilities for the top 5 classes as a bar graph, along with the input image. It should look like this:\n",
    "\n",
    "<img src='assets/inference_example.png' width=300px>\n",
    "\n",
    "You can convert from the class integer encoding to actual flower names with the `cat_to_name.json` file (should have been loaded earlier in the notebook). To show a PyTorch tensor as an image, use the `imshow` function defined above."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Image size : (500, 548)\n",
      "Image resized :  (256, 280)\n",
      "Image cropped size  (224, 224)\n",
      "Data Type: uint8\n",
      "Min: 0.000, Max: 255.000\n",
      "Min: 0.000, Max: 1.000\n",
      "Min: -2.118, Max: 2.640\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f85c534c6d8>"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f85c3607358>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 263,
       "width": 259
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Display Image\n",
    "image = Image.open(test_dir + \"/10/image_07104.jpg\")\n",
    "processed_image = process_image(image)\n",
    "imshow(processed_image, title=\"Image\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Image size : (500, 548)\n",
      "Image resized :  (256, 280)\n",
      "Image cropped size  (224, 224)\n",
      "Data Type: uint8\n",
      "Min: 0.000, Max: 255.000\n",
      "Min: 0.000, Max: 1.000\n",
      "Min: -2.118, Max: 2.640\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f85c518a630>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 250,
       "width": 454
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "probs, top_cl = predict(test_dir + \"/10/image_07104.jpg\", loaded_model, top=5)\n",
    "classes_name = [cat_to_name[class_i] for class_i in top_cl]\n",
    "probs = probs.cpu().detach().numpy().tolist()[0]\n",
    "\n",
    "plt.barh(range(len((classes_name))), probs)\n",
    "plt.yticks(range(len(classes_name)),classes_name)\n",
    "plt.gca().invert_yaxis()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font color='red'>**Reminder for Workspace users:** If your network becomes very large when saved as a checkpoint, there might be issues with saving backups in your workspace. You should reduce the size of your hidden layers and train again. \n",
    "    \n",
    "We strongly encourage you to delete these large interim files and directories before navigating to another page or closing the browser tab.</font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "def display_prediction(top_probability, top_classes,image_path, labels):\n",
    "    image = Image.open(image_path)\n",
    "    processed_image = process_image(image)\n",
    "    imshow(processed_image, title=image_path)\n",
    "    plt.subplot(212)\n",
    "    classes_name = [cat_to_name[class_i] for class_i in top_classes]\n",
    "    probs = top_probability.cpu().detach().numpy().tolist()[0]\n",
    "\n",
    "    plt.barh(range(len((classes_name))), probs)\n",
    "    plt.yticks(range(len(classes_name)),classes_name)\n",
    "    plt.gca().invert_yaxis()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Image size : (500, 548)\n",
      "Image resized :  (256, 280)\n",
      "Image cropped size  (224, 224)\n",
      "Data Type: uint8\n",
      "Min: 0.000, Max: 255.000\n",
      "Min: 0.000, Max: 1.000\n",
      "Min: -2.118, Max: 2.640\n",
      "Image size : (500, 548)\n",
      "Image resized :  (256, 280)\n",
      "Image cropped size  (224, 224)\n",
      "Data Type: uint8\n",
      "Min: 0.000, Max: 255.000\n",
      "Min: 0.000, Max: 1.000\n",
      "Min: -2.118, Max: 2.640\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7efdf14a8780>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 131,
       "width": 454
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "top_p, top_class= predict( test_dir + \"/10/image_07104.jpg\", loaded_model)\n",
    "with open('cat_to_name.json', 'r') as f:\n",
    "    cat_to_name = json.load(f)\n",
    "display_prediction(top_p, top_class,test_dir + \"/10/image_07104.jpg\", cat_to_name )\n"
   ]
  },
  {
   "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": "3.6.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}