Image Classifier Project.ipynb

{
 "cells": [
  {
   "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 numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "from torch import nn\n",
    "import torch\n",
    "from torch import optim\n",
    "import torch.nn.functional as F\n",
    "from torchvision import datasets, transforms, models\n",
    "from torch.autograd import Variable"
   ]
  },
  {
   "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",
    " "
   ]
  },
  {
   "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": [
    "# TODO: Define your transforms for the training, validation, and testing sets\n",
    "data_transforms = 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",
    "\n",
    "perfom_transforms = 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",
    "# TODO: Load the datasets with ImageFolder\n",
    "image_datasets = datasets.ImageFolder(train_dir, transform=data_transforms)\n",
    "valid_datasets = datasets.ImageFolder(valid_dir, transform=perfom_transforms)\n",
    "test_datasets = datasets.ImageFolder(test_dir, transform=perfom_transforms)\n",
    "\n",
    "# TODO: Using the image datasets and the trainforms, define the dataloaders\n",
    "dataloaders = torch.utils.data.DataLoader(image_datasets, batch_size=128, shuffle=True)\n",
    "validloaders = torch.utils.data.DataLoader(valid_datasets, batch_size=32, shuffle=True)\n",
    "testloaders = torch.utils.data.DataLoader(test_datasets, batch_size=32, shuffle=True)"
   ]
  },
  {
   "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": [
    "import json\n",
    "\n",
    "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",
    "**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."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Downloading: \"https://download.pytorch.org/models/vgg16-397923af.pth\" to /root/.torch/models/vgg16-397923af.pth\n",
      "100%|██████████| 553433881/553433881 [00:10<00:00, 51933402.51it/s]\n"
     ]
    }
   ],
   "source": [
    "# TODO: Build and train your network\n",
    "model = models.vgg16(pretrained=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "for param in model.parameters():\n",
    "    param.requires_grad = False\n",
    "\n",
    "from collections import OrderedDict\n",
    "classifier = nn.Sequential(nn.Linear(25088, 1000),\n",
    "                           nn.ReLU(),\n",
    "                           nn.Dropout(0.2),\n",
    "                           \n",
    "                           nn.Linear(1000, 102),\n",
    "                           nn.LogSoftmax(dim=1))\n",
    "    \n",
    "model.classifier = classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "criterion = nn.NLLLoss()\n",
    "optimizer = optim.Adam(model.classifier.parameters(), lr=0.001)\n",
    "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
    "model.to(device);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/3.. Train loss: 5.567.. Valid loss: 4.255.. Valid accuracy: 0.237\n",
      "Epoch 1/3.. Train loss: 4.268.. Valid loss: 2.987.. Valid accuracy: 0.314\n",
      "Epoch 1/3.. Train loss: 3.151.. Valid loss: 2.248.. Valid accuracy: 0.493\n",
      "Epoch 1/3.. Train loss: 2.635.. Valid loss: 1.802.. Valid accuracy: 0.546\n",
      "Epoch 1/3.. Train loss: 2.225.. Valid loss: 1.350.. Valid accuracy: 0.671\n",
      "Epoch 1/3.. Train loss: 1.917.. Valid loss: 1.164.. Valid accuracy: 0.714\n",
      "Epoch 1/3.. Train loss: 1.690.. Valid loss: 0.980.. Valid accuracy: 0.732\n",
      "Epoch 1/3.. Train loss: 1.520.. Valid loss: 0.931.. Valid accuracy: 0.738\n",
      "Epoch 1/3.. Train loss: 1.514.. Valid loss: 0.806.. Valid accuracy: 0.791\n",
      "Epoch 1/3.. Train loss: 1.373.. Valid loss: 0.783.. Valid accuracy: 0.779\n",
      "Epoch 2/3.. Train loss: 1.424.. Valid loss: 0.799.. Valid accuracy: 0.780\n",
      "Epoch 2/3.. Train loss: 1.282.. Valid loss: 0.696.. Valid accuracy: 0.800\n",
      "Epoch 2/3.. Train loss: 1.067.. Valid loss: 0.611.. Valid accuracy: 0.843\n",
      "Epoch 2/3.. Train loss: 1.171.. Valid loss: 0.666.. Valid accuracy: 0.819\n",
      "Epoch 2/3.. Train loss: 1.061.. Valid loss: 0.581.. Valid accuracy: 0.849\n",
      "Epoch 2/3.. Train loss: 0.964.. Valid loss: 0.583.. Valid accuracy: 0.849\n",
      "Epoch 2/3.. Train loss: 1.052.. Valid loss: 0.534.. Valid accuracy: 0.854\n",
      "Epoch 2/3.. Train loss: 0.980.. Valid loss: 0.487.. Valid accuracy: 0.864\n",
      "Epoch 2/3.. Train loss: 0.909.. Valid loss: 0.476.. Valid accuracy: 0.876\n",
      "Epoch 2/3.. Train loss: 0.921.. Valid loss: 0.506.. Valid accuracy: 0.853\n",
      "Epoch 3/3.. Train loss: 0.895.. Valid loss: 0.458.. Valid accuracy: 0.880\n",
      "Epoch 3/3.. Train loss: 0.830.. Valid loss: 0.438.. Valid accuracy: 0.880\n",
      "Epoch 3/3.. Train loss: 0.800.. Valid loss: 0.447.. Valid accuracy: 0.888\n",
      "Epoch 3/3.. Train loss: 0.725.. Valid loss: 0.452.. Valid accuracy: 0.881\n",
      "Epoch 3/3.. Train loss: 0.795.. Valid loss: 0.427.. Valid accuracy: 0.892\n",
      "Epoch 3/3.. Train loss: 0.759.. Valid loss: 0.448.. Valid accuracy: 0.864\n",
      "Epoch 3/3.. Train loss: 0.867.. Valid loss: 0.385.. Valid accuracy: 0.894\n",
      "Epoch 3/3.. Train loss: 0.793.. Valid loss: 0.387.. Valid accuracy: 0.894\n",
      "Epoch 3/3.. Train loss: 0.827.. Valid loss: 0.388.. Valid accuracy: 0.890\n",
      "Epoch 3/3.. Train loss: 0.680.. Valid loss: 0.384.. Valid accuracy: 0.899\n",
      "Epoch 3/3.. Train loss: 0.778.. Valid loss: 0.396.. Valid accuracy: 0.899\n"
     ]
    }
   ],
   "source": [
    "epochs = 3\n",
    "steps = 0\n",
    "running_loss = 0\n",
    "print_every = 5\n",
    "\n",
    "for epoch in range(epochs):\n",
    "    for inputs, labels in dataloaders:\n",
    "        steps += 1\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",
    "        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 validloaders:\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",
    "                    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\"Valid loss: {test_loss/len(validloaders):.3f}.. \"\n",
    "                  f\"Valid accuracy: {accuracy/len(validloaders):.3f}\")\n",
    "            running_loss = 0\n",
    "            model.train()"
   ]
  },
  {
   "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": [
      "Test loss: 0.471.. Test accuracy: 0.872\n"
     ]
    }
   ],
   "source": [
    "# TODO: Do validation on the test set\n",
    "test_loss = 0\n",
    "accuracy = 0\n",
    "model.eval()\n",
    "\n",
    "with torch.no_grad():\n",
    "    for inputs, labels in testloaders:\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",
    "        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\"Test loss: {test_loss/len(testloaders):.3f}.. \"\n",
    "          f\"Test accuracy: {accuracy/len(testloaders):.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": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# TODO: Save the checkpoint \n",
    "checkpoint = {'input_size': 25088,\n",
    "              'output_size': 102,\n",
    "              'epochs' : 3,\n",
    "              'optimizer_state' : optimizer.state_dict(),\n",
    "              'class_to_index' : image_datasets.class_to_idx,\n",
    "              'hidden_layers': [4096,1000],\n",
    "              'state_dict': model.classifier.state_dict()}\n",
    "\n",
    "torch.save(checkpoint, 'checkpoint.pth')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'input_size': 25088,\n",
       " 'output_size': 102,\n",
       " 'epochs': 3,\n",
       " 'optimizer_state': {'state': {140097683175968: {'step': 156,\n",
       "    'exp_avg': tensor(1.00000e-02 *\n",
       "           [[ 0.0000,  0.0000,  0.0000,  ...,  0.0000,  0.0000, -0.0000],\n",
       "            [-0.0000, -0.0000, -0.0000,  ...,  0.0000,  0.0000, -0.0000],\n",
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       "            [-0.0013, -0.0003,  0.0076,  ...,  0.0005,  0.0011,  0.0089]], device='cuda:0'),\n",
       "    'exp_avg_sq': tensor(1.00000e-05 *\n",
       "           [[ 0.0000,  0.0000,  0.0000,  ...,  0.0000,  0.0000,  0.0000],\n",
       "            [ 0.0000,  0.0000,  0.0000,  ...,  0.0000,  0.0000,  0.0000],\n",
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       "            ...,\n",
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       "            [ 0.0008,  0.0005,  0.0001,  ...,  0.0005,  0.0003,  0.0005],\n",
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       "   140097683176112: {'step': 156,\n",
       "    'exp_avg': tensor([ 1.5192e-11,  2.2703e-11,  9.4086e-12, -1.0682e-07, -7.1052e-04,\n",
       "             1.8469e-10,  1.2813e-11,  1.3174e-10,  2.4483e-11,  1.5653e-06,\n",
       "             4.9871e-11,  9.8608e-12,  6.4255e-11,  6.6237e-11,  1.0970e-04,\n",
       "             4.2203e-05,  1.8697e-11, -7.9075e-09,  8.4661e-11,  2.7593e-11,\n",
       "            -2.6860e-04, -1.9825e-04,  7.9136e-09,  2.2418e-11,  9.7524e-11,\n",
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       "             1.3963e-04,  2.8005e-08,  1.1583e-11,  2.3314e-11,  1.9389e-05,\n",
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       "             1.6201e-10,  8.5719e-12,  3.4921e-04,  2.0534e-04,  1.8191e-11,\n",
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       "             2.5504e-11,  1.4298e-04,  4.3143e-04,  3.3902e-11,  2.7451e-11,\n",
       "             1.4199e-10,  3.3853e-10,  2.5698e-05,  6.7315e-12,  6.8588e-06,\n",
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       "             1.9505e-04,  5.5297e-11, -4.7171e-06,  5.9294e-11,  2.0378e-11,\n",
       "             3.3232e-06,  9.7409e-05,  5.4514e-11,  2.3920e-04, -4.3402e-05,\n",
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       "            -7.8949e-06,  1.9610e-11,  7.5825e-12,  8.8143e-11,  8.2569e-11,\n",
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       "             6.8298e-11,  8.6782e-12, -2.3849e-09,  1.8424e-04,  9.3144e-12,\n",
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       "             7.0348e-11,  4.5819e-04, -3.0096e-04, -2.7486e-04, -7.4432e-11,\n",
       "             1.3926e-10,  1.6600e-11,  4.8778e-11,  6.7822e-11,  3.6201e-11,\n",
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       "            -2.7025e-08,  1.5473e-04,  6.1330e-10,  2.9850e-04,  2.6263e-07,\n",
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       "             1.1507e-10,  3.6283e-11,  5.5786e-11,  1.0095e-11,  2.8484e-11,\n",
       "             8.5098e-05, -1.0250e-04,  1.0272e-10,  8.7793e-11,  1.3123e-11,\n",
       "             8.1632e-11,  1.9535e-11, -1.4453e-07,  3.8116e-11, -5.9331e-05,\n",
       "             2.3720e-04,  6.0306e-11,  9.5919e-11,  1.2446e-11,  5.1309e-06,\n",
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       "             8.6751e-12,  3.5574e-11,  1.2183e-04,  8.0426e-12,  6.7958e-05,\n",
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       "             5.5372e-11,  1.8224e-11,  2.9348e-11,  2.0532e-05,  1.7551e-06,\n",
       "             5.8418e-11,  5.5816e-08,  4.2057e-11,  3.2124e-11,  2.6950e-11,\n",
       "             1.2949e-10,  1.2198e-04,  5.5518e-11, -2.3146e-04,  1.1029e-10,\n",
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       "             2.8544e-04,  1.1050e-10,  1.2798e-08,  1.8236e-11,  4.0118e-05,\n",
       "             1.2958e-11,  7.4121e-11,  2.5022e-10, -5.4054e-05,  1.2284e-10,\n",
       "             1.0307e-04,  1.9370e-11,  9.0385e-11,  1.9608e-04,  3.2543e-11,\n",
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       "            -7.8253e-09, -3.7298e-05,  9.9824e-11,  1.3897e-11, -6.1449e-06,\n",
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       "             9.7219e-07,  1.8274e-04,  4.8728e-11,  1.3816e-04,  1.2472e-11,\n",
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       "             2.8198e-10,  2.0428e-05, -4.6414e-04,  4.1101e-11,  8.2663e-11,\n",
       "             3.6413e-11,  6.0785e-11, -5.7304e-05,  5.5136e-11,  5.9309e-07,\n",
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       "             2.7104e-04,  9.3671e-11,  1.8762e-11, -3.8686e-05,  8.2729e-05,\n",
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       "             8.0265e-05, -4.3390e-04,  5.9898e-05, -2.7208e-04,  1.6785e-11,\n",
       "             6.8181e-11,  2.7878e-04, -2.9608e-05,  1.1273e-10,  4.1807e-11,\n",
       "             2.6838e-04,  1.7878e-11,  1.2089e-11, -7.1780e-05, -3.9522e-07,\n",
       "             1.3609e-10, -1.1212e-10, -1.5947e-04,  4.9261e-06,  1.4936e-06,\n",
       "             3.7471e-11,  6.4642e-11,  9.5403e-11,  7.7460e-12,  7.9808e-11,\n",
       "             1.7160e-05,  1.2401e-10,  1.2871e-11,  5.0200e-05, -1.5370e-04,\n",
       "             2.8185e-11,  2.0635e-08,  9.0367e-05,  7.2033e-11,  3.8458e-04,\n",
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       "             2.3581e-04,  3.3535e-11,  1.1010e-05,  4.9152e-04, -1.2524e-04,\n",
       "             9.7147e-05,  2.3074e-11,  1.1632e-10,  2.0675e-11,  2.2875e-04,\n",
       "             4.7428e-11,  4.8506e-11, -2.6984e-06,  1.2174e-11, -3.0909e-04,\n",
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       "             5.3224e-11, -3.4627e-05,  1.0612e-11,  4.7516e-04,  6.2652e-04,\n",
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       "             1.1540e-10,  4.0500e-05,  7.4259e-11,  3.7057e-11,  1.7315e-10,\n",
       "             1.0126e-10,  5.8694e-06,  3.4248e-04, -1.2688e-04,  3.9842e-11,\n",
       "             1.2232e-09, -3.5276e-09,  1.5666e-11, -2.0823e-04,  3.4972e-11,\n",
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       "             2.2777e-04,  9.3634e-11,  2.0541e-04, -4.2437e-09,  8.3063e-11,\n",
       "            -8.6188e-05, -3.3088e-04,  1.7245e-04,  1.4771e-10,  8.4236e-12,\n",
       "             1.2334e-10,  9.2041e-07,  9.3211e-12, -5.2814e-04,  1.1993e-11,\n",
       "             4.9615e-04,  1.6840e-11,  2.3486e-04,  2.2282e-11, -2.7042e-09,\n",
       "             1.1670e-11,  9.2323e-12,  3.1352e-04,  2.5711e-11,  2.4983e-04,\n",
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       "            -2.7329e-04,  7.3997e-11,  4.9353e-11,  1.0301e-10,  1.2128e-10,\n",
       "             1.5515e-04,  1.6750e-11,  1.1463e-11,  7.4424e-11,  7.5376e-11,\n",
       "             1.5675e-11,  8.9300e-11, -1.0555e-04,  1.8582e-04,  5.6166e-11,\n",
       "             2.3964e-11,  1.4828e-11,  8.1741e-11,  2.1838e-04,  1.3875e-11,\n",
       "             7.8222e-05,  3.4155e-05, -8.7578e-08,  1.9407e-05,  2.3391e-11,\n",
       "             5.0327e-11,  3.3806e-11, -8.1913e-05,  2.8147e-11,  6.9771e-11,\n",
       "             7.0567e-11,  8.0285e-11, -2.1656e-04, -9.1479e-05,  1.8016e-11,\n",
       "             6.1088e-11,  2.4512e-04,  2.1404e-11,  1.6110e-11,  8.7591e-05,\n",
       "             1.0658e-04,  1.9457e-08, -4.3777e-09, -1.2884e-06,  1.4225e-11,\n",
       "             2.9968e-11, -3.9877e-04,  1.8488e-11,  6.0579e-12,  1.0526e-11,\n",
       "             2.2280e-11,  6.2795e-04,  1.2148e-10,  5.2259e-06,  3.4746e-11,\n",
       "            -1.7157e-04,  4.2855e-04,  1.0356e-10,  1.5932e-06, -5.5768e-04,\n",
       "             9.2599e-12,  3.4618e-11, -7.7450e-05,  2.2177e-11,  4.6662e-11,\n",
       "             7.1331e-11,  7.3893e-11,  5.5693e-04,  5.4309e-11, -6.9291e-04,\n",
       "             9.0953e-12, -2.3544e-04, -4.0411e-05,  8.1665e-12, -1.2096e-05,\n",
       "             1.5823e-04, -9.2539e-10,  1.6216e-08,  1.1681e-04,  4.5024e-11,\n",
       "             4.6340e-11,  9.2516e-11,  1.9017e-04,  1.2597e-04,  3.4375e-11,\n",
       "             8.4033e-11,  5.2229e-04,  1.9072e-11,  9.9791e-11,  5.4534e-11,\n",
       "             8.4793e-12, -2.0309e-04, -1.7816e-10, -1.3251e-06,  2.8082e-11,\n",
       "             7.5573e-05,  1.3374e-11,  3.7070e-04,  7.4879e-05,  1.1266e-06,\n",
       "             8.0669e-10,  9.7069e-12,  2.6353e-05,  2.2708e-11,  1.3175e-11,\n",
       "             7.3083e-11,  4.0265e-11,  3.2560e-11, -1.0516e-06,  4.8443e-12,\n",
       "            -3.0550e-11,  9.4231e-11, -3.5963e-04,  5.1759e-04, -7.2017e-05,\n",
       "             9.1462e-11, -4.6407e-04, -2.4503e-04, -6.3986e-05,  2.1148e-04,\n",
       "            -7.5894e-05,  5.3903e-04,  1.0751e-11,  4.2672e-11, -1.1054e-04,\n",
       "            -1.3991e-04,  1.0312e-07,  2.3744e-11,  1.2367e-11, -2.4354e-09,\n",
       "             7.0421e-11,  2.0538e-04,  1.3335e-10, -1.1976e-04,  6.2927e-11,\n",
       "             4.7223e-04,  7.0823e-11,  3.5954e-11,  8.4227e-12,  8.1572e-05,\n",
       "             1.3648e-11,  1.6879e-11,  1.0027e-11, -4.4474e-05,  1.8913e-11,\n",
       "             3.7240e-11,  2.2850e-05,  1.7069e-11,  7.2466e-04,  1.2781e-11,\n",
       "             8.8886e-11,  2.3830e-04, -1.7763e-05,  2.2342e-10, -1.8599e-04,\n",
       "             9.6395e-11,  1.8017e-10,  6.9583e-12,  6.6430e-11, -1.4460e-04], device='cuda:0'),\n",
       "    'exp_avg_sq': tensor(1.00000e-07 *\n",
       "           [ 0.0302,  0.0676,  0.0116,  0.1022,  2.7572,  0.5231,  0.0115,\n",
       "             1.0129,  0.0655,  2.2287,  0.2947,  0.0127,  1.7127,  0.2701,\n",
       "             3.0766,  0.1238,  0.0402,  0.5017,  0.7954,  0.0559,  1.2792,\n",
       "             1.2017,  0.0075,  0.0426,  1.2312,  1.2901,  0.7244,  2.9111,\n",
       "             0.0634,  0.4902,  0.4990,  0.0293,  2.5736,  0.0393,  0.2683,\n",
       "             0.9132,  0.0250,  0.0082,  2.3695,  0.6104,  0.0215,  0.0805,\n",
       "             2.6464,  3.6271,  0.3514,  0.2261,  2.7156,  0.0075,  0.6704,\n",
       "             0.0064,  0.2828,  0.0085,  1.2716,  0.0086,  1.7437,  0.2064,\n",
       "             4.4392,  0.7672,  1.6703,  0.0325,  2.2222,  1.6474,  1.0445,\n",
       "             2.1870,  0.0042,  0.0086,  0.0567,  1.6582,  1.1299,  0.0086,\n",
       "             1.0110,  0.0144,  1.7502,  1.8790,  0.1020,  0.0230,  0.0639,\n",
       "             0.0707,  0.4780,  0.2275,  0.0097,  0.1212,  0.0380,  1.6027,\n",
       "             0.0871,  1.0530,  0.0112,  0.3188,  0.0681,  0.0061,  0.3462,\n",
       "             0.1993,  0.4650,  0.3117,  0.3062,  0.8549,  4.4190,  0.0038,\n",
       "             2.0929,  2.1720,  0.3195,  0.8601,  1.6640,  3.1505,  2.0496,\n",
       "             0.2179,  1.0932,  3.3597,  0.1003,  0.1841,  0.2928,  1.1192,\n",
       "             3.2680,  0.3990,  0.0315,  0.0136,  0.0104,  0.0263,  0.0128,\n",
       "             1.9205,  0.0226,  0.3073,  0.0313,  0.6097,  0.3376,  0.9000,\n",
       "             1.2923,  0.4416,  0.0515,  2.0501,  0.2175,  1.9012,  0.0089,\n",
       "             1.8313,  0.7346,  0.0277,  2.0799,  2.2917,  2.3221,  1.0680,\n",
       "             0.9852,  0.1569,  1.9696,  0.0122,  1.2906,  2.5026,  0.1055,\n",
       "             0.0521,  0.5697,  2.2287,  0.0942,  0.3681,  3.6245,  4.0367,\n",
       "             1.9018,  0.0029,  2.5438,  0.2769,  0.0691,  1.8111,  0.4012,\n",
       "             1.7618,  1.7321,  1.9378,  0.0225,  0.0266,  0.8598,  0.5306,\n",
       "             0.5237,  0.0137,  0.0351,  0.2411,  1.9080,  2.1705,  1.0361,\n",
       "             0.9880,  2.8671,  3.9453,  3.6963,  2.1865,  0.0557,  0.0136,\n",
       "             1.3670,  1.1930,  1.8892,  1.9272,  2.1094,  0.2197,  3.2575,\n",
       "             0.1769,  0.8182,  0.3289,  0.0924,  2.8075,  0.0227,  0.0166,\n",
       "             0.0197,  2.4113,  3.0983,  1.2750,  0.0119,  0.0357,  0.0013,\n",
       "             2.5139,  0.6817,  2.9450,  0.2303,  1.1377,  0.1022,  0.2450,\n",
       "             0.0323,  0.0162,  0.1488,  2.7559,  2.2513,  0.0138,  2.1974,\n",
       "             2.6022,  2.0017,  0.4170,  2.5388,  1.4049,  1.2808,  0.0527,\n",
       "             0.0320,  0.0226,  0.4534,  0.8131,  0.4058,  1.9405,  0.0615,\n",
       "             0.0369,  0.2095,  2.3273,  0.0179,  0.0165,  1.5622,  3.7271,\n",
       "             2.5329,  1.2671,  0.0461,  0.0384,  2.8002,  2.9237,  0.8180,\n",
       "             0.0347,  0.9185,  0.2411,  0.0658,  2.9191,  0.2553,  3.0856,\n",
       "             0.4858,  0.0718,  4.3432,  0.0014,  0.4723,  2.4095,  2.6811,\n",
       "             3.2635,  1.9810,  0.0470,  0.0143,  0.0712,  1.8136,  3.7968,\n",
       "             1.7907,  0.3983,  2.8604,  0.0612,  2.4772,  0.0056,  3.2095,\n",
       "             1.0206,  0.0125,  0.0287,  3.0505,  1.5881,  0.1215,  0.3515,\n",
       "             0.0686,  0.0119,  1.8948,  3.2074,  1.4069,  0.1399,  2.7252,\n",
       "             3.2965,  0.0243,  0.2692,  1.1553,  0.0036,  0.8186,  1.6015,\n",
       "             0.0110,  0.0852,  1.5250,  3.2900,  0.0426,  0.0538,  0.8168,\n",
       "             1.5634,  3.5531,  0.0043,  0.3236,  0.0679,  0.0379,  1.6006,\n",
       "             0.1224,  0.4856,  0.4092,  2.0249,  0.0403,  0.5967,  2.3823,\n",
       "             3.3236,  0.2913,  3.1107,  0.9413,  0.0369,  0.0832,  1.3543,\n",
       "             0.0312,  1.9531,  1.6981,  0.7160,  0.0797,  1.2428,  0.1383,\n",
       "             0.0272,  0.0614,  0.0504,  0.0053,  0.4459,  2.5691,  3.1978,\n",
       "             0.2935,  2.8506,  1.2649,  2.2157,  0.0084,  3.0536,  0.5930,\n",
       "             6.3046,  0.1904,  0.6465,  0.0097,  2.2469,  1.6011,  0.0069,\n",
       "             0.1307,  2.5506,  0.0190,  2.3926,  0.0012,  0.4262,  0.0044,\n",
       "             3.6630,  0.0201,  0.0202,  0.5912,  1.8244,  0.2327,  0.3927,\n",
       "             0.5030,  0.9211,  3.6295,  2.9669,  2.2452,  0.0083,  2.0710,\n",
       "             0.0361,  0.2329,  1.0128,  0.0966,  0.0092,  2.6032,  0.0184,\n",
       "             0.3492,  0.0148,  1.5753,  4.5823,  1.8261,  2.8658,  0.0036,\n",
       "             2.4536,  0.0766,  4.2976,  0.2545,  3.1477,  3.0230,  2.7187,\n",
       "             0.0301,  4.4897,  2.7682,  1.0834,  0.1774,  0.5309,  0.0134,\n",
       "             0.0354,  0.5610,  1.8584,  1.1921,  0.5206,  0.0226,  0.7823,\n",
       "             0.0500,  0.0150,  0.1834,  1.8617,  2.8445,  0.4418,  1.4497,\n",
       "             0.0203,  0.0062,  0.5429,  0.0433,  2.6284,  0.7181,  0.4356,\n",
       "             0.6775,  2.4071,  0.1016,  0.0329,  2.7653,  0.0600,  1.8330,\n",
       "             0.0154,  2.9057,  0.2845,  0.4989,  1.2012,  3.0299,  4.4978,\n",
       "             0.9748,  0.3357,  2.3525,  1.1783,  2.7017,  0.0136,  0.0574,\n",
       "             0.3999,  1.2196,  1.6032,  0.0779,  2.4533,  0.3794,  0.8558,\n",
       "             0.0949,  0.3569,  0.0323,  0.8525,  2.0591,  0.0045,  0.0087,\n",
       "             1.8467,  1.3850,  0.0515,  2.4728,  0.4399,  0.0876,  2.9004,\n",
       "             0.5595,  0.0166,  1.8320,  2.1649,  0.8511,  0.0047,  2.4960,\n",
       "             1.3654,  2.1872,  0.8628,  0.4843,  2.3874,  0.3381,  0.0099,\n",
       "             0.1203,  1.6798,  0.0048,  2.3406,  0.5059,  1.0510,  0.9578,\n",
       "             0.7664,  0.0381,  1.1345,  0.0139,  2.9121,  3.1924,  2.6213,\n",
       "             0.0416,  3.7202,  0.0470,  0.0096,  1.4492,  0.4609,  1.3177,\n",
       "             1.4503,  0.0995,  0.0218,  3.6455,  1.5769,  0.0060,  2.4617,\n",
       "             1.0486,  0.3629,  0.0435,  0.1129,  0.1909,  0.0498,  0.3973,\n",
       "             0.0914,  0.2350,  0.1281,  0.0872,  2.0859,  1.0468,  0.3783,\n",
       "             1.9752,  0.9953,  0.0223,  2.1078,  0.0117,  3.3847,  0.3112,\n",
       "             2.1224,  1.4448,  0.0756,  0.3531,  2.5879,  2.7467,  0.8647,\n",
       "             0.0457,  0.0436,  2.5648,  0.0220,  1.2841,  1.6406,  2.1292,\n",
       "             2.0644,  4.5685,  0.0492,  0.7839,  2.4777,  0.1388,  2.8738,\n",
       "             1.7683,  0.3666,  4.0760,  2.7477,  0.0582,  2.1864,  2.4254,\n",
       "             0.2785,  0.6997,  0.0620,  0.0827,  3.0936,  0.0094,  0.2696,\n",
       "             0.0073,  0.0956,  1.2017,  0.0253,  0.6228,  0.2535,  0.0346,\n",
       "             0.6902,  4.5774,  1.4599,  1.4113,  0.1532,  0.0070,  2.8993,\n",
       "             0.2641,  0.3978,  0.8738,  0.8139,  4.6736,  2.8070,  0.5735,\n",
       "             0.0755,  0.2275,  0.5900,  0.0042,  1.2639,  0.3395,  2.7655,\n",
       "             0.0132,  1.9558,  0.3963,  0.0129,  0.0355,  0.0171,  1.0599,\n",
       "             0.0290,  1.8767,  0.3167,  2.4028,  0.0204,  0.0473,  3.1063,\n",
       "             0.1489,  1.3746,  1.2837,  1.0983,  0.0838,  2.5061,  0.0366,\n",
       "             0.9728,  0.1419,  0.4736,  3.5923,  0.1735,  0.0502,  0.8137,\n",
       "             0.0466,  0.0193,  0.0091,  0.4965,  2.4058,  0.7414,  1.1728,\n",
       "             3.4024,  1.8006,  3.3590,  2.1208,  0.3749,  1.2915,  0.0068,\n",
       "             1.4563,  0.0552,  2.5412,  2.7683,  0.5326,  0.2785,  0.5651,\n",
       "             0.0403,  3.7232,  2.5746,  0.0205,  0.1033,  0.4574,  0.6427,\n",
       "             1.8607,  0.6779,  0.5635,  0.2288,  0.2034,  1.2260,  0.3182,\n",
       "             0.0104,  1.1668,  0.0183,  2.5469,  2.1150,  0.4813,  1.0228,\n",
       "             1.9592,  0.3514,  0.4384,  0.4987,  0.0428,  0.0276,  1.1090,\n",
       "             0.0150,  3.2333,  1.8575,  3.4598,  1.4054,  1.5721,  0.9674,\n",
       "             0.0319,  1.6344,  2.1798,  1.8588,  4.0977,  0.8025,  0.0165,\n",
       "             0.9623,  0.0228,  0.4271,  0.7691,  2.4846,  2.4155,  2.8371,\n",
       "             0.5978,  2.8362,  0.4058,  0.0313,  2.8016,  5.6485,  3.1710,\n",
       "             4.6351,  0.0369,  0.7679,  2.9946,  0.1886,  1.1838,  0.1844,\n",
       "             2.4908,  0.0029,  0.0192,  2.7575,  0.7208,  0.8249,  0.0396,\n",
       "             0.1290,  0.2796,  0.3296,  0.1486,  0.5867,  0.4268,  0.0079,\n",
       "             0.4842,  2.1392,  0.5901,  0.0220,  0.5436,  2.5364,  0.0896,\n",
       "             0.0105,  0.0413,  0.7920,  2.2639,  0.7699,  2.6096,  2.1503,\n",
       "             1.0190,  0.1047,  2.5561,  0.1175,  2.0728,  2.9389,  3.6214,\n",
       "             1.4546,  0.0698,  1.6231,  0.0560,  1.4094,  0.3309,  0.1692,\n",
       "             2.8855,  0.0039,  0.8131,  2.2946,  1.5670,  0.5249,  0.0048,\n",
       "             0.0075,  0.2886,  1.8632,  0.0148,  3.5196,  2.3400,  0.6543,\n",
       "             0.0168,  1.5506,  3.4748,  0.7043,  0.0285,  3.4402,  0.0139,\n",
       "             0.2651,  1.3721,  0.0167,  1.1967,  0.0604,  2.7289,  1.0473,\n",
       "             0.0743,  4.3369,  0.3021,  4.0136,  0.0147,  3.0800,  0.0317,\n",
       "             0.0029,  0.0228,  2.2176,  0.5938,  2.4501,  0.0127,  0.9989,\n",
       "             0.0978,  0.6450,  2.4585,  0.6090,  0.0785,  2.2913,  0.7164,\n",
       "             1.6917,  2.3489,  1.2295,  0.2016,  0.0216,  0.0440,  0.0322,\n",
       "             1.8490,  0.1436,  0.0078,  0.0598,  0.7611,  1.1856,  0.0099,\n",
       "             3.9311,  1.7071,  2.8111,  3.9688,  2.5162,  0.1406,  0.5955,\n",
       "             2.9784,  0.0136,  0.6875,  2.0074,  2.0017,  5.2841,  0.8519,\n",
       "             0.0093,  1.8276,  0.0269,  0.0057,  2.6179,  0.0189,  2.8046,\n",
       "             0.0316,  1.8788,  0.0651,  0.0787,  0.0178,  0.0112,  2.4264,\n",
       "             0.0866,  3.7505,  0.0590,  0.1539,  0.2678,  0.0274,  0.1425,\n",
       "             2.3331,  0.4072,  0.5061,  1.0666,  0.8717,  4.1916,  0.0368,\n",
       "             0.0172,  0.8887,  0.6857,  0.0322,  0.9941,  3.2660,  0.4043,\n",
       "             0.2904,  0.0753,  0.0245,  0.8254,  3.1301,  0.0252,  1.8760,\n",
       "             3.1982,  1.0364,  2.7617,  0.0622,  0.3054,  0.0914,  1.5266,\n",
       "             0.0562,  0.5349,  0.6396,  0.7478,  3.8472,  2.3637,  0.0425,\n",
       "             0.4872,  2.5929,  0.0600,  0.0340,  3.1890,  1.9739,  0.0091,\n",
       "             0.0178,  1.0199,  0.0265,  0.0744,  2.4000,  0.0205,  0.0048,\n",
       "             0.0145,  0.0651,  3.5047,  1.8983,  2.1878,  0.1623,  2.8768,\n",
       "             4.9994,  0.7893,  0.0184,  2.5838,  0.0056,  0.1571,  1.1570,\n",
       "             0.0645,  1.2669,  1.1471,  0.7405,  3.2624,  0.2313,  2.4494,\n",
       "             0.0088,  2.2151,  2.3777,  0.0039,  0.9029,  1.5839,  0.0263,\n",
       "             0.0256,  3.3194,  0.1515,  0.2355,  0.6422,  1.2112,  2.3750,\n",
       "             0.1304,  0.3871,  2.2755,  0.0477,  1.1652,  0.2282,  0.0094,\n",
       "             2.9001,  0.0157,  1.1209,  0.4925,  0.9802,  0.0234,  2.2849,\n",
       "             0.0446,  0.8354,  0.0182,  0.0096,  0.3177,  0.0297,  0.0104,\n",
       "             0.6929,  0.1516,  0.2741,  1.9029,  0.0031,  0.0181,  1.2234,\n",
       "             1.1457,  1.4151,  2.1012,  0.7684,  3.2152,  2.6512,  2.6490,\n",
       "             3.3678,  2.6128,  3.3468,  0.0151,  0.1934,  1.2858,  2.5350,\n",
       "             0.0101,  0.0739,  0.0200,  0.0254,  0.4943,  1.5565,  2.2406,\n",
       "             1.7214,  0.5045,  3.6813,  0.4273,  0.2398,  0.0085,  1.3575,\n",
       "             0.0114,  0.0373,  0.0132,  2.6754,  0.0469,  0.1500,  0.3629,\n",
       "             0.0401,  2.2953,  0.0214,  0.9552,  2.5232,  0.5349,  1.2164,\n",
       "             2.8358,  1.0798,  0.0241,  0.0063,  0.1846,  2.6042], device='cuda:0')},\n",
       "   140097683176256: {'step': 156,\n",
       "    'exp_avg': tensor([[ 9.6740e-12,  1.3564e-11,  6.4534e-12,  ..., -8.4359e-12,\n",
       "              1.9687e-11,  1.2241e-03],\n",
       "            [ 8.7791e-12,  1.3057e-11,  6.3840e-12,  ..., -2.0413e-11,\n",
       "             -7.1047e-12, -6.3135e-03],\n",
       "            [ 8.5863e-12,  1.2813e-11,  7.0437e-12,  ...,  9.8711e-12,\n",
       "              2.6794e-10, -6.3347e-05],\n",
       "            ...,\n",
       "            [ 1.3339e-11,  1.8732e-11,  1.1166e-11,  ...,  1.3547e-11,\n",
       "              4.5708e-10,  3.0059e-03],\n",
       "            [ 8.4352e-12, -1.8859e-11,  6.2391e-12,  ..., -8.5970e-11,\n",
       "              8.7728e-11, -4.8638e-03],\n",
       "            [ 1.1102e-11, -3.4336e-11, -3.1744e-11,  ...,  1.0933e-11,\n",
       "             -9.4428e-10,  1.6633e-04]], device='cuda:0'),\n",
       "    'exp_avg_sq': tensor([[ 1.2265e-09,  2.4112e-09,  5.4583e-10,  ...,  9.3269e-10,\n",
       "              2.3203e-07,  1.3535e-04],\n",
       "            [ 1.0101e-09,  2.2345e-09,  5.3415e-10,  ...,  5.4615e-09,\n",
       "              5.5459e-07,  6.0092e-04],\n",
       "            [ 9.6624e-10,  2.1516e-09,  6.5024e-10,  ...,  1.2771e-09,\n",
       "              4.9313e-07,  5.7116e-04],\n",
       "            ...,\n",
       "            [ 2.3319e-09,  4.5987e-09,  1.6342e-09,  ...,  2.4053e-09,\n",
       "              4.3475e-07,  1.0924e-04],\n",
       "            [ 9.3253e-10,  4.6613e-09,  5.1017e-10,  ...,  9.6867e-08,\n",
       "              2.0998e-07,  3.5049e-04],\n",
       "            [ 1.6155e-09,  1.5452e-08,  1.3207e-08,  ...,  1.5665e-09,\n",
       "              3.8004e-06,  3.2028e-05]], device='cuda:0')},\n",
       "   140097683176328: {'step': 156, 'exp_avg': tensor(1.00000e-03 *\n",
       "           [-0.1952, -0.8640,  0.2779, -0.8676,  0.0365,  0.1013,  1.3902,\n",
       "             0.2270,  0.1111, -0.6092,  1.4095, -1.1762,  2.4196,  1.1885,\n",
       "             1.0510,  0.9590, -0.4444,  0.0787, -1.0617,  1.4763,  0.0108,\n",
       "             0.8076,  0.1368,  0.5802,  0.4874,  0.3067,  1.9165,  1.2407,\n",
       "             1.4437, -0.4430, -0.0803,  0.4979, -0.0333,  0.5668,  2.1811,\n",
       "             0.5810,  0.5983,  1.7486, -3.9135,  0.4752,  1.6057, -0.3702,\n",
       "            -0.2914, -4.9901,  0.0864, -0.6912, -2.0146,  0.0251, -0.6629,\n",
       "            -0.3263,  0.7496, -1.3417, -0.8697,  0.7724,  0.6409,  0.3224,\n",
       "             1.1598,  0.2401, -0.3404,  0.4632,  0.2844, -3.6372,  0.2889,\n",
       "             0.7823, -0.8174,  4.6833, -0.0638, -0.3178,  0.2673, -0.6493,\n",
       "             0.2611,  0.2152, -0.2446,  0.7893,  0.5228,  0.0026,  0.5849,\n",
       "             0.9186, -0.8124, -0.3579,  0.2915,  0.6639,  0.4648, -0.2774,\n",
       "             0.2465, -0.1847,  1.0437,  0.1374, -0.0160,  0.2082, -1.9402,\n",
       "             1.0835,  1.9808, -1.0659, -3.7906, -1.1336, -0.0807, -2.8574,\n",
       "            -2.0394, -0.1892, -4.8591,  0.8290], device='cuda:0'), 'exp_avg_sq': tensor(1.00000e-05 *\n",
       "           [ 0.4491,  0.3456,  0.5345,  0.7175,  0.3021,  1.4764,  0.6145,\n",
       "             0.2564,  0.4955,  0.3860,  0.5627,  0.6419,  1.4556,  0.6598,\n",
       "             0.4102,  0.6408,  0.3476,  0.6342,  0.5936,  0.3744,  0.3441,\n",
       "             0.4526,  0.3461,  0.8034,  0.7681,  0.5093,  0.8865,  0.6963,\n",
       "             0.6254,  0.3633,  0.3702,  0.2697,  0.6309,  0.9012,  0.5016,\n",
       "             0.5030,  0.7952,  0.9266,  1.4375,  0.6815,  1.4897,  0.5174,\n",
       "             0.5364,  1.5571,  0.2826,  0.4692,  0.4111,  0.9240,  0.7105,\n",
       "             5.4686,  0.6507,  0.9612,  0.3777,  0.8588,  0.5311,  0.5425,\n",
       "             0.6286,  0.4986,  0.2515,  0.4735,  0.4236,  0.7222,  0.4760,\n",
       "             0.5432,  1.3790,  0.7539,  0.7807,  0.6322,  0.3734,  0.4017,\n",
       "             0.3699,  0.6068,  1.8633,  3.6221,  2.0758,  1.0067,  0.8356,\n",
       "             8.4454,  2.1640,  0.3068,  0.4109,  2.0325,  1.5210,  1.7540,\n",
       "             1.2818,  1.3567,  0.8168,  0.5070,  0.8753,  1.8520,  2.0002,\n",
       "             0.9530,  1.1467,  0.6977,  0.6110,  0.5694,  6.4765,  1.4561,\n",
       "             1.7481,  0.8486,  1.1945,  0.3810], device='cuda:0')}},\n",
       "  'param_groups': [{'lr': 0.001,\n",
       "    'betas': (0.9, 0.999),\n",
       "    'eps': 1e-08,\n",
       "    'weight_decay': 0,\n",
       "    'amsgrad': False,\n",
       "    'params': [140097683175968,\n",
       "     140097683176112,\n",
       "     140097683176256,\n",
       "     140097683176328]}]},\n",
       " 'class_to_index': {'1': 0,\n",
       "  '10': 1,\n",
       "  '100': 2,\n",
       "  '101': 3,\n",
       "  '102': 4,\n",
       "  '11': 5,\n",
       "  '12': 6,\n",
       "  '13': 7,\n",
       "  '14': 8,\n",
       "  '15': 9,\n",
       "  '16': 10,\n",
       "  '17': 11,\n",
       "  '18': 12,\n",
       "  '19': 13,\n",
       "  '2': 14,\n",
       "  '20': 15,\n",
       "  '21': 16,\n",
       "  '22': 17,\n",
       "  '23': 18,\n",
       "  '24': 19,\n",
       "  '25': 20,\n",
       "  '26': 21,\n",
       "  '27': 22,\n",
       "  '28': 23,\n",
       "  '29': 24,\n",
       "  '3': 25,\n",
       "  '30': 26,\n",
       "  '31': 27,\n",
       "  '32': 28,\n",
       "  '33': 29,\n",
       "  '34': 30,\n",
       "  '35': 31,\n",
       "  '36': 32,\n",
       "  '37': 33,\n",
       "  '38': 34,\n",
       "  '39': 35,\n",
       "  '4': 36,\n",
       "  '40': 37,\n",
       "  '41': 38,\n",
       "  '42': 39,\n",
       "  '43': 40,\n",
       "  '44': 41,\n",
       "  '45': 42,\n",
       "  '46': 43,\n",
       "  '47': 44,\n",
       "  '48': 45,\n",
       "  '49': 46,\n",
       "  '5': 47,\n",
       "  '50': 48,\n",
       "  '51': 49,\n",
       "  '52': 50,\n",
       "  '53': 51,\n",
       "  '54': 52,\n",
       "  '55': 53,\n",
       "  '56': 54,\n",
       "  '57': 55,\n",
       "  '58': 56,\n",
       "  '59': 57,\n",
       "  '6': 58,\n",
       "  '60': 59,\n",
       "  '61': 60,\n",
       "  '62': 61,\n",
       "  '63': 62,\n",
       "  '64': 63,\n",
       "  '65': 64,\n",
       "  '66': 65,\n",
       "  '67': 66,\n",
       "  '68': 67,\n",
       "  '69': 68,\n",
       "  '7': 69,\n",
       "  '70': 70,\n",
       "  '71': 71,\n",
       "  '72': 72,\n",
       "  '73': 73,\n",
       "  '74': 74,\n",
       "  '75': 75,\n",
       "  '76': 76,\n",
       "  '77': 77,\n",
       "  '78': 78,\n",
       "  '79': 79,\n",
       "  '8': 80,\n",
       "  '80': 81,\n",
       "  '81': 82,\n",
       "  '82': 83,\n",
       "  '83': 84,\n",
       "  '84': 85,\n",
       "  '85': 86,\n",
       "  '86': 87,\n",
       "  '87': 88,\n",
       "  '88': 89,\n",
       "  '89': 90,\n",
       "  '9': 91,\n",
       "  '90': 92,\n",
       "  '91': 93,\n",
       "  '92': 94,\n",
       "  '93': 95,\n",
       "  '94': 96,\n",
       "  '95': 97,\n",
       "  '96': 98,\n",
       "  '97': 99,\n",
       "  '98': 100,\n",
       "  '99': 101},\n",
       " 'hidden_layers': [4096, 1000],\n",
       " 'state_dict': OrderedDict([('0.weight',\n",
       "               tensor([[-1.1822e-02, -1.0295e-02, -6.7424e-03,  ..., -6.9935e-03,\n",
       "                        -8.1729e-03,  6.9257e-03],\n",
       "                       [ 2.6702e-03,  4.4645e-03,  1.9212e-03,  ..., -1.1499e-02,\n",
       "                        -6.5435e-03,  1.1330e-02],\n",
       "                       [-1.0115e-02, -5.9839e-03, -1.0862e-02,  ...,  4.0069e-03,\n",
       "                         8.6546e-03, -3.0782e-03],\n",
       "                       ...,\n",
       "                       [-1.2759e-04, -9.8369e-04,  3.5231e-03,  ..., -1.1564e-02,\n",
       "                        -1.4341e-03,  5.0586e-03],\n",
       "                       [ 1.0116e-03,  9.2845e-04, -1.2537e-02,  ..., -9.5747e-03,\n",
       "                        -1.6557e-03, -4.6251e-03],\n",
       "                       [ 1.6387e-02,  1.1887e-02,  1.8715e-04,  ...,  5.7090e-03,\n",
       "                        -4.1832e-03, -9.7542e-03]], device='cuda:0')),\n",
       "              ('0.bias',\n",
       "               tensor([-2.1694e-03, -1.9392e-03, -6.1089e-04,  1.2632e-03,  1.7730e-04,\n",
       "                       -5.4793e-03, -1.8112e-03, -8.3598e-03,  4.6409e-04, -6.2751e-03,\n",
       "                       -6.8815e-04, -6.8532e-03,  4.7062e-03, -5.2319e-03,  2.4997e-03,\n",
       "                       -7.2102e-03, -6.0882e-03,  1.6543e-03, -9.6309e-03, -2.5030e-03,\n",
       "                        1.5626e-03, -1.3909e-04, -7.5387e-03, -9.0901e-03,  8.8717e-04,\n",
       "                       -6.1903e-03, -5.1195e-05, -2.4525e-03, -3.5217e-03, -9.3178e-03,\n",
       "                        2.3438e-04, -1.1713e-02,  4.4247e-03, -2.2663e-03, -1.0969e-02,\n",
       "                       -8.6432e-03, -9.6777e-03, -1.0478e-02,  7.1238e-03, -3.0364e-03,\n",
       "                       -4.4938e-03, -5.9265e-03,  1.7549e-03,  2.2549e-03, -9.1794e-03,\n",
       "                       -2.1453e-03, -4.1874e-03, -7.3434e-03, -4.2038e-03, -1.9906e-03,\n",
       "                       -1.2937e-02, -5.0725e-03,  3.4956e-03, -1.7185e-03, -7.8584e-03,\n",
       "                       -1.1622e-02,  3.3633e-03, -9.1447e-03, -2.2871e-03, -8.6584e-03,\n",
       "                       -9.7632e-03,  5.0178e-03, -9.1457e-03,  1.9720e-03, -4.8408e-03,\n",
       "                       -1.2929e-02, -3.8518e-03, -6.9210e-03, -2.6493e-03, -4.1632e-03,\n",
       "                       -2.8544e-03, -1.6240e-03, -6.7337e-04, -3.7466e-03, -8.2429e-03,\n",
       "                       -2.7814e-03, -4.9059e-03, -1.2793e-02, -3.8605e-03, -1.5113e-03,\n",
       "                       -5.1599e-03, -2.1926e-03, -1.0954e-02, -1.2095e-03, -3.9540e-03,\n",
       "                       -3.8226e-03, -5.0250e-03, -5.7443e-03, -1.0153e-02, -1.4010e-02,\n",
       "                       -3.2631e-03, -5.3947e-03, -7.4604e-03, -3.3973e-03,  1.9741e-03,\n",
       "                       -4.4000e-03,  2.8857e-03, -1.1541e-03,  3.5070e-03, -7.4498e-03,\n",
       "                       -3.1368e-04, -5.2518e-03, -5.9489e-04,  5.9403e-03, -1.2192e-03,\n",
       "                       -4.7734e-03, -5.8623e-04, -7.9943e-03, -7.6207e-03, -7.0246e-03,\n",
       "                       -8.8356e-03,  1.7230e-04,  5.8667e-03, -8.0102e-03, -1.0235e-03,\n",
       "                       -4.8124e-03, -1.1095e-02, -1.1958e-03, -1.1235e-03, -1.4928e-03,\n",
       "                       -1.6660e-02, -3.0486e-04, -3.4568e-03,  1.5555e-03, -4.6763e-03,\n",
       "                        6.5472e-03, -9.4575e-03,  1.2012e-03, -9.0603e-03, -1.8172e-03,\n",
       "                        2.3168e-03, -5.4415e-03, -9.2583e-03,  4.0807e-03, -6.0323e-03,\n",
       "                       -4.8540e-03,  4.4819e-03,  3.9951e-03, -5.7022e-03,  5.5111e-03,\n",
       "                       -8.8894e-04, -7.9470e-04,  4.7539e-03, -9.4113e-03, -9.3166e-05,\n",
       "                        4.0328e-03,  4.9571e-04, -3.6113e-04,  1.1193e-03,  4.2122e-03,\n",
       "                       -5.7766e-03, -3.3383e-03,  5.7228e-03,  3.4215e-03,  9.3054e-03,\n",
       "                       -1.2579e-02,  4.9699e-03, -4.3213e-03, -3.5292e-03, -1.5182e-03,\n",
       "                       -1.5644e-03,  1.7066e-03, -4.9661e-03,  5.1991e-03, -6.3951e-03,\n",
       "                       -1.2645e-03, -8.8776e-03, -4.2367e-03, -7.1318e-03, -1.4099e-02,\n",
       "                       -3.0983e-03, -8.0405e-03,  3.5327e-03,  6.4267e-03, -5.6619e-04,\n",
       "                       -3.4093e-03, -8.5335e-03,  8.0038e-03, -2.3450e-03,  1.3124e-03,\n",
       "                       -7.7166e-03, -1.0080e-02, -9.5088e-03, -1.9333e-03,  1.6316e-03,\n",
       "                        4.7422e-03,  1.7382e-03, -8.1461e-03, -4.4202e-03, -3.1464e-03,\n",
       "                       -1.0181e-02, -2.3311e-03, -4.8108e-03, -3.6747e-03, -7.2327e-03,\n",
       "                       -3.0022e-04, -2.7708e-03,  1.9541e-03,  4.5305e-03, -4.4584e-03,\n",
       "                        1.9977e-04, -7.0706e-03, -8.8853e-03, -3.8093e-03, -9.0129e-03,\n",
       "                        3.4620e-03, -1.6832e-02, -9.3759e-03, -1.0929e-02, -9.9706e-03,\n",
       "                       -5.2995e-03, -1.2014e-02, -3.6052e-03, -5.7966e-03,  1.2954e-03,\n",
       "                       -1.0305e-02, -1.4120e-03, -1.5041e-03, -8.6566e-03, -1.2921e-03,\n",
       "                        1.7921e-03, -2.3561e-03, -5.0128e-03, -7.3161e-03, -4.2437e-03,\n",
       "                       -1.1767e-02, -1.1184e-03, -7.9061e-03, -1.0991e-02,  1.2043e-05,\n",
       "                       -7.6619e-04, -6.5145e-03, -7.0399e-03, -1.4515e-03, -1.0771e-02,\n",
       "                       -8.7385e-03, -1.9218e-03, -1.1770e-03, -2.7429e-03, -4.7307e-03,\n",
       "                       -4.6196e-03, -4.0836e-03, -7.3493e-03,  1.3717e-02, -3.6995e-03,\n",
       "                       -1.1811e-02, -7.7629e-03,  1.2637e-03, -3.7968e-03,  4.9238e-03,\n",
       "                       -8.4105e-03,  1.0804e-03,  1.3728e-03, -6.3659e-03, -7.3895e-03,\n",
       "                       -9.8192e-03, -7.6866e-03, -3.5635e-03, -1.9371e-03, -2.0768e-03,\n",
       "                       -6.2851e-03, -1.5260e-02, -1.0218e-02, -1.0873e-02, -7.5287e-03,\n",
       "                        9.7626e-03, -1.6254e-04,  3.7552e-03,  3.3350e-03, -1.4514e-02,\n",
       "                       -6.2748e-03, -1.9868e-03, -5.0647e-03, -8.9159e-04, -1.0073e-02,\n",
       "                       -3.8762e-03,  1.0433e-04,  7.9713e-03, -1.2307e-02, -7.3286e-03,\n",
       "                       -1.0162e-04, -1.3218e-02,  1.0355e-03,  6.7171e-04, -3.0677e-03,\n",
       "                       -8.1992e-03, -3.9755e-03,  7.6345e-03, -6.5672e-03,  6.4262e-04,\n",
       "                        1.1011e-03, -5.7156e-03, -4.4922e-03, -2.4176e-03, -8.8383e-03,\n",
       "                       -3.5903e-03,  3.9102e-03,  8.8843e-03, -8.8937e-04, -1.3642e-02,\n",
       "                       -3.7432e-03, -1.0670e-02,  1.0135e-02, -4.8228e-03, -3.0875e-03,\n",
       "                       -6.4334e-04, -3.3106e-04,  2.9568e-03, -9.6867e-03, -9.8428e-03,\n",
       "                        2.5294e-03, -4.0260e-03, -8.7274e-03,  1.0822e-03, -5.1905e-03,\n",
       "                       -3.9151e-03,  6.0154e-04,  6.0369e-03, -1.6480e-03, -1.7650e-03,\n",
       "                       -1.2370e-02,  3.1018e-04, -7.5419e-03,  3.9821e-03, -4.1678e-03,\n",
       "                       -6.3025e-03, -6.1230e-04, -2.5982e-03, -2.8449e-03, -8.3067e-03,\n",
       "                       -9.1595e-03, -3.0679e-03, -4.0362e-03, -9.3646e-03, -4.9139e-03,\n",
       "                       -1.4644e-03, -6.5757e-03,  1.3921e-04, -4.1186e-03,  1.1683e-02,\n",
       "                       -1.2547e-02,  5.2783e-03, -2.0234e-04, -2.0488e-03,  3.7230e-04,\n",
       "                       -9.6640e-03, -1.0038e-02, -5.8666e-03,  7.6795e-03, -3.9628e-03,\n",
       "                       -8.4799e-03,  7.3775e-03, -7.6273e-03, -3.2546e-04, -1.2208e-02,\n",
       "                       -7.3609e-03, -1.2224e-02,  4.8736e-03, -6.0454e-03, -4.7685e-03,\n",
       "                       -1.0744e-02, -9.7289e-03, -8.3293e-03, -4.6850e-03,  1.4748e-03,\n",
       "                       -7.0567e-03, -3.1068e-03,  2.4248e-03,  8.8425e-03, -2.7831e-03,\n",
       "                       -2.4611e-03, -9.7512e-04, -1.9244e-03,  2.0211e-06, -2.4120e-03,\n",
       "                       -1.2419e-02,  3.2346e-03, -9.1846e-03, -7.2111e-03, -1.2470e-02,\n",
       "                       -6.1474e-03, -1.7401e-03,  2.5331e-04,  6.0977e-03, -1.8748e-02,\n",
       "                       -7.1498e-04, -4.7214e-03, -4.1679e-03,  1.2278e-03,  7.4984e-03,\n",
       "                        3.0396e-03,  3.8969e-03, -2.6594e-03, -3.1293e-04, -6.7869e-03,\n",
       "                       -1.0912e-02,  6.0846e-04, -4.7245e-03, -3.3670e-03, -5.2190e-03,\n",
       "                        2.7578e-04,  4.5528e-03, -8.9495e-03,  5.1435e-04, -9.2492e-03,\n",
       "                       -8.4023e-03, -3.5025e-03, -4.2057e-03, -4.6432e-03, -6.1932e-03,\n",
       "                        9.6829e-04, -2.0299e-03, -9.2211e-03, -8.5992e-03, -4.0778e-03,\n",
       "                       -8.0209e-03, -2.4780e-03, -5.0845e-03, -3.9846e-03,  3.1505e-04,\n",
       "                       -5.3893e-03,  5.6259e-03, -1.2796e-02, -9.6417e-03,  6.6436e-03,\n",
       "                       -3.4122e-03, -3.3476e-04, -8.0907e-05,  3.9637e-03, -7.0085e-03,\n",
       "                       -1.7865e-03,  1.9141e-04,  1.3458e-04, -4.6764e-03, -8.1916e-03,\n",
       "                       -6.0975e-03,  4.9335e-03, -7.1506e-03,  5.5856e-03, -6.6149e-03,\n",
       "                       -1.1047e-02, -5.4029e-03,  1.8647e-03,  4.2047e-03, -4.6406e-03,\n",
       "                       -1.0053e-03, -8.5361e-03, -4.8509e-03, -1.1284e-02, -8.2414e-03,\n",
       "                       -6.4205e-03, -9.0428e-03,  4.0795e-03, -6.1961e-03, -1.1499e-02,\n",
       "                        6.0022e-03,  6.2187e-03, -8.6397e-03, -9.6295e-03, -9.3158e-03,\n",
       "                       -7.8217e-03, -4.3768e-03, -1.9063e-03, -1.7932e-03, -1.2446e-04,\n",
       "                        2.2421e-03,  4.4508e-03, -1.1034e-02, -6.3614e-03, -9.2247e-03,\n",
       "                        2.4039e-03,  1.3501e-03, -2.4496e-03,  6.2764e-03, -9.8338e-03,\n",
       "                       -7.6828e-03, -3.4676e-03, -2.4284e-04, -6.2173e-03,  3.3940e-03,\n",
       "                       -1.1431e-03, -5.9927e-03, -4.2217e-03,  4.7053e-04, -1.2802e-03,\n",
       "                       -1.1404e-02, -6.7274e-03,  1.4099e-03, -2.0033e-03,  4.2155e-03,\n",
       "                       -1.7555e-02, -7.9666e-03, -9.5352e-03, -1.8986e-02, -9.9364e-03,\n",
       "                        2.9492e-03,  8.8292e-04, -2.4721e-03, -8.1906e-03, -3.1354e-03,\n",
       "                       -5.6522e-03, -1.3161e-03, -9.6050e-03, -2.5725e-03, -5.6955e-03,\n",
       "                        1.1176e-03, -5.8393e-03, -7.8742e-03, -2.0566e-02, -1.3697e-02,\n",
       "                       -3.0274e-03, -5.9080e-03, -5.5856e-03, -1.3124e-04, -1.9615e-03,\n",
       "                       -1.5549e-03, -6.9480e-04,  5.0124e-04, -3.3009e-04,  2.3708e-03,\n",
       "                       -7.9865e-03,  6.7605e-03, -1.1858e-03, -5.8237e-04, -3.7618e-03,\n",
       "                       -1.4252e-03, -7.9453e-03, -8.1642e-03, -7.4002e-04,  5.0434e-04,\n",
       "                        9.7651e-04, -1.1322e-04, -1.6918e-03, -9.1078e-03,  3.0840e-04,\n",
       "                       -1.5624e-03, -1.3573e-03, -9.1318e-03, -5.1710e-03, -4.2971e-03,\n",
       "                       -2.6241e-03,  1.1017e-04, -1.0285e-04,  5.6318e-04, -2.3592e-03,\n",
       "                       -3.2934e-03, -5.0172e-03, -1.1102e-03,  2.9734e-04, -7.7176e-04,\n",
       "                       -2.6781e-03,  2.2660e-03,  1.2948e-03, -3.0562e-03, -2.9607e-03,\n",
       "                       -7.6771e-03, -4.8921e-03, -7.6780e-03, -8.6470e-03, -3.4632e-03,\n",
       "                       -1.1795e-02, -1.4339e-02, -8.3023e-03, -7.6367e-03, -4.1787e-03,\n",
       "                       -3.8710e-03, -1.5245e-03, -8.0579e-03, -2.4033e-03, -4.0995e-03,\n",
       "                        1.4220e-03, -5.3048e-03, -4.0956e-03, -9.2636e-04, -5.1102e-03,\n",
       "                       -7.3512e-03,  1.5611e-03, -2.0563e-03, -1.0531e-03,  8.0584e-03,\n",
       "                       -7.3104e-03, -2.2258e-03, -4.8061e-03,  6.5398e-04, -7.6532e-03,\n",
       "                       -2.0754e-03, -6.4213e-03, -2.9906e-03, -1.5088e-02, -1.0087e-02,\n",
       "                       -5.0849e-03, -5.6397e-03, -9.3327e-03, -2.2450e-03, -1.3455e-03,\n",
       "                       -6.2363e-03, -4.4427e-03, -5.6221e-03, -6.7519e-03, -9.9828e-03,\n",
       "                       -3.7574e-03, -1.4005e-03, -1.0070e-02,  1.1160e-02,  6.2148e-03,\n",
       "                       -9.3210e-03, -8.7173e-03,  3.7556e-04, -9.5342e-03,  1.2682e-03,\n",
       "                       -3.3243e-03, -3.7158e-03, -2.8325e-03, -1.9950e-03, -1.7830e-03,\n",
       "                        4.8527e-04, -1.2561e-02, -1.2824e-03, -6.0464e-03, -2.5703e-04,\n",
       "                        9.8003e-05, -1.8606e-03,  2.2059e-03,  2.8900e-03,  6.2660e-04,\n",
       "                        6.5536e-04,  1.9421e-04, -1.8898e-04,  4.9782e-03, -1.6340e-03,\n",
       "                       -2.7655e-03, -6.0870e-03, -9.8294e-03,  9.0336e-03, -8.0091e-03,\n",
       "                        1.4404e-03, -2.7330e-03, -3.2822e-03, -1.4278e-03, -8.3985e-03,\n",
       "                       -1.1574e-02, -6.4912e-03, -6.2557e-03, -1.1100e-02,  7.2374e-03,\n",
       "                        2.4941e-03, -1.2916e-02, -2.5200e-03, -2.5997e-03, -9.1422e-04,\n",
       "                       -6.0768e-03, -2.2438e-03,  6.4590e-03, -1.1053e-02,  8.1951e-03,\n",
       "                       -3.2036e-03, -5.6006e-03, -7.3986e-03,  1.2551e-03,  2.9314e-03,\n",
       "                       -7.6914e-03, -5.8665e-03, -1.3496e-02, -3.9129e-03, -5.4664e-03,\n",
       "                       -6.0666e-04,  4.4790e-03,  6.8644e-05, -1.0361e-03,  3.4093e-03,\n",
       "                        5.2473e-03, -3.7840e-03, -1.0765e-02, -3.6651e-03,  7.8100e-03,\n",
       "                       -3.5053e-03,  4.4909e-03,  2.2747e-03, -1.3386e-02, -6.4305e-03,\n",
       "                        9.0451e-05,  1.6207e-03, -8.9111e-03,  1.0434e-03, -5.7436e-04,\n",
       "                       -3.6375e-03, -6.3236e-03,  2.8397e-03, -3.9472e-03, -1.4887e-02,\n",
       "                        4.8114e-03,  5.5267e-03,  6.3582e-03, -1.7541e-03, -5.5689e-03,\n",
       "                       -2.7514e-03,  6.5984e-03, -6.3141e-04,  3.8323e-04, -2.2281e-03,\n",
       "                        8.0528e-04, -1.8672e-03, -3.1862e-03,  4.9751e-03,  4.1982e-03,\n",
       "                       -7.9088e-03, -9.8341e-03,  7.7871e-04, -1.3506e-02, -1.6494e-03,\n",
       "                       -6.9237e-03, -5.8141e-03, -5.1814e-03, -1.1474e-02,  3.2171e-03,\n",
       "                       -2.5656e-03, -1.2709e-03, -1.6127e-03, -1.4635e-02,  4.3031e-03,\n",
       "                       -2.9624e-03, -1.0114e-02, -1.3903e-02,  6.7276e-04, -6.7629e-04,\n",
       "                       -3.1110e-03,  9.9586e-03, -1.5323e-02, -1.1332e-03,  1.3115e-04,\n",
       "                        2.8002e-03, -4.6394e-03,  2.1966e-03,  2.9601e-03, -6.8890e-03,\n",
       "                        6.1239e-05, -1.2248e-02, -5.7094e-03, -2.7809e-04,  5.0197e-03,\n",
       "                        2.8098e-07, -5.8714e-04,  3.2854e-03, -2.5889e-03, -2.4377e-03,\n",
       "                        9.0302e-03, -6.0064e-04, -2.0378e-03, -1.1081e-02, -1.0883e-02,\n",
       "                       -8.8526e-03, -1.0719e-03, -2.4959e-03,  3.7141e-03, -3.6231e-03,\n",
       "                       -6.8032e-03, -3.7313e-03, -9.2652e-03,  5.1056e-03, -9.5611e-03,\n",
       "                       -7.8016e-03,  1.8337e-04, -1.1763e-02, -7.4172e-03,  9.5961e-04,\n",
       "                       -1.0600e-02, -3.0037e-04, -3.2435e-03,  3.0387e-03, -4.4817e-03,\n",
       "                       -3.5621e-03, -4.2394e-03, -5.8810e-03,  5.3211e-03, -5.9376e-03,\n",
       "                        2.2585e-03, -9.2979e-03, -2.3946e-03, -3.4130e-03, -2.8672e-03,\n",
       "                        1.5207e-03,  7.2064e-03, -3.4964e-03,  6.0876e-04,  2.3492e-03,\n",
       "                       -9.4370e-04,  3.9948e-03, -8.9343e-04, -1.8085e-03, -1.1311e-02,\n",
       "                        6.8425e-04, -5.0281e-03, -1.1739e-02, -6.3462e-03, -5.0720e-03,\n",
       "                       -8.7481e-03, -1.2615e-02, -1.0993e-02,  2.6130e-03, -2.7875e-03,\n",
       "                       -5.6599e-03, -6.8077e-03,  5.2150e-03, -6.0160e-03, -4.1853e-03,\n",
       "                        6.8323e-03, -1.5439e-03, -6.8730e-03, -4.0822e-04,  6.2285e-04,\n",
       "                       -1.4497e-02, -1.7166e-03, -5.0758e-03, -8.9805e-03,  1.5984e-04,\n",
       "                       -7.1254e-03, -6.9331e-03,  6.5197e-03, -5.0274e-03, -4.2250e-03,\n",
       "                       -3.6203e-03, -2.7282e-03, -1.1826e-02, -4.5586e-03, -8.5824e-03,\n",
       "                        8.9084e-04, -6.8027e-03, -5.6603e-04, -8.6125e-03, -4.8871e-03,\n",
       "                       -1.1016e-02, -6.6368e-03, -1.0080e-03, -1.1440e-02,  7.4765e-04,\n",
       "                       -9.2713e-03, -5.0220e-03, -6.2528e-03, -1.2796e-02, -9.4926e-03,\n",
       "                        3.6958e-04, -6.1120e-03, -3.2513e-03, -6.0267e-03, -6.9605e-03,\n",
       "                        6.3579e-03, -6.4391e-03, -5.2715e-03,  2.6553e-04, -7.7057e-03,\n",
       "                       -2.0513e-03, -5.8667e-03, -3.4150e-03,  2.8414e-03, -1.9328e-03,\n",
       "                       -3.0402e-03, -7.1636e-03, -4.2131e-03,  4.6578e-03, -3.4385e-03,\n",
       "                        2.4998e-03,  3.6728e-04,  1.5474e-03, -4.6476e-03, -3.9445e-03,\n",
       "                       -1.9449e-03,  5.6989e-06,  5.4469e-03, -9.9577e-03, -6.4098e-03,\n",
       "                       -4.4901e-03,  8.4439e-04,  5.4332e-03,  4.7726e-03, -7.9535e-03,\n",
       "                       -6.3281e-03,  5.4348e-03, -3.7403e-03, -9.3765e-03, -4.7952e-03,\n",
       "                       -3.0557e-03, -1.4288e-02, -6.1269e-03,  1.4037e-03, -1.0691e-02,\n",
       "                       -3.1865e-03, -2.6077e-03, -1.3260e-02, -5.9797e-04, -6.0906e-03,\n",
       "                       -5.5467e-03, -1.0789e-03, -3.5204e-03,  6.8309e-03, -5.5038e-03,\n",
       "                        1.0986e-04, -2.9344e-03, -3.3257e-03, -1.1810e-02,  3.0797e-03,\n",
       "                       -9.8455e-03, -6.9916e-03,  5.6207e-03, -2.2297e-03, -7.0055e-03,\n",
       "                        2.0748e-03, -9.5382e-03,  7.6533e-03, -8.8776e-03,  9.0385e-03,\n",
       "                       -6.4567e-03,  1.9903e-03,  5.4620e-03, -7.8943e-03,  2.0495e-03,\n",
       "                        5.4934e-03, -2.0868e-03, -3.8974e-03, -5.7152e-03, -2.2903e-03,\n",
       "                       -2.9099e-03, -3.5666e-04,  8.7809e-04,  3.4245e-03, -1.6253e-04,\n",
       "                       -2.4708e-03,  4.1996e-03, -1.1816e-02, -2.2185e-03, -3.3920e-03,\n",
       "                       -6.0372e-03,  3.9026e-03, -2.3631e-03,  1.1553e-03, -1.9467e-03,\n",
       "                       -1.7003e-03, -3.1565e-03, -5.9607e-03, -4.7948e-03, -4.8242e-03,\n",
       "                       -8.2518e-03, -2.7311e-03, -5.9507e-03, -7.6113e-03, -2.1669e-03,\n",
       "                       -2.0099e-03, -5.4035e-03, -8.4866e-03, -5.1842e-03, -7.4356e-03,\n",
       "                       -6.6196e-04, -8.0027e-03,  1.5963e-03, -1.2231e-02, -8.0191e-03,\n",
       "                       -6.5387e-03,  4.8138e-03, -2.8813e-03,  5.4744e-03,  6.7630e-03,\n",
       "                       -6.2155e-03,  2.9882e-03, -6.2671e-03, -1.1461e-03, -7.9638e-04,\n",
       "                       -7.1390e-04, -1.1338e-02, -1.1476e-02, -2.0094e-03, -1.2834e-02,\n",
       "                       -7.8915e-03, -1.4203e-03, -1.7710e-03, -5.0910e-03, -4.3116e-03,\n",
       "                        6.3623e-04, -7.5021e-03, -7.6987e-03, -4.4537e-03,  5.9741e-03,\n",
       "                       -5.9206e-03, -1.4597e-03, -7.2625e-03, -9.9806e-03, -5.9218e-03,\n",
       "                       -1.9674e-03, -5.9016e-03, -4.0381e-03, -6.7402e-03, -1.1586e-02,\n",
       "                       -3.6809e-03,  3.1727e-03,  1.3730e-03, -4.1264e-03,  3.2920e-03,\n",
       "                       -2.6867e-04, -8.5886e-03, -9.0108e-03, -8.5267e-03,  6.0090e-03], device='cuda:0')),\n",
       "              ('3.weight',\n",
       "               tensor([[ 1.3954e-02,  3.5424e-04,  1.3667e-02,  ..., -1.5850e-02,\n",
       "                        -5.9325e-03,  1.7013e-02],\n",
       "                       [ 1.1694e-03, -3.7579e-02,  2.3470e-02,  ..., -1.2885e-02,\n",
       "                        -2.0836e-02,  2.4029e-02],\n",
       "                       [ 8.0521e-04, -5.4255e-03,  2.3810e-02,  ...,  1.6723e-02,\n",
       "                         1.8670e-02, -4.1545e-04],\n",
       "                       ...,\n",
       "                       [-2.1545e-02,  6.7181e-03,  1.7654e-02,  ...,  1.4317e-03,\n",
       "                         2.0679e-02, -3.0900e-02],\n",
       "                       [ 2.2008e-02, -8.1166e-03,  8.1024e-03,  ...,  9.3445e-03,\n",
       "                         4.2562e-03, -1.6314e-04],\n",
       "                       [-2.6317e-02, -1.9496e-04,  6.7455e-03,  ..., -3.5576e-02,\n",
       "                         1.8831e-02, -4.4589e-02]], device='cuda:0')),\n",
       "              ('3.bias', tensor(1.00000e-02 *\n",
       "                      [-2.5125, -1.8410, -2.3434, -0.1940, -2.8660, -0.9259,  0.0810,\n",
       "                       -0.3039, -0.2044, -0.3823,  0.9625,  1.2017, -2.2005,  1.3554,\n",
       "                        0.0502, -2.7444, -0.9889, -1.0388, -1.4196,  0.3312, -2.5298,\n",
       "                       -0.8420, -3.4565,  1.5857, -1.7262,  2.5299,  1.0131,  1.4373,\n",
       "                        2.5061,  0.3333, -2.2688, -1.5878, -2.1224, -1.0733, -1.5853,\n",
       "                        1.7248,  1.1086,  2.7666,  0.7602, -1.4753, -2.5681, -0.0283,\n",
       "                        0.0416, -2.4536,  1.1867,  1.3430, -0.1284,  1.5082, -1.4322,\n",
       "                       -1.4522, -0.3397, -3.0427, -0.2061, -0.0722,  0.0638,  2.1970,\n",
       "                        2.4400, -0.9039, -1.7387,  0.0139,  0.3809,  0.3959, -3.0124,\n",
       "                       -1.7284,  2.3558,  2.1358,  0.0764,  2.4898,  1.7376,  1.9919,\n",
       "                        3.6429,  1.1764,  2.7133,  0.8991,  0.0357,  0.8806,  1.7378,\n",
       "                        1.1782, -0.1490,  1.1045,  1.9747,  1.8644,  1.3527, -1.1264,\n",
       "                       -0.0417,  0.6227, -1.8144,  0.8848,  2.9340,  1.7834, -1.3878,\n",
       "                        2.0457, -3.1130, -3.2941, -0.7495, -0.4215,  2.9951, -1.5195,\n",
       "                        3.0889,  0.6980, -1.2930, -2.0704], device='cuda:0'))])}"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "torch.load('checkpoint.pth')"
   ]
  },
  {
   "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": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# TODO: Write a function that loads a checkpoint and rebuilds the model\n",
    "def load_checkpoint(filepath = 'checkpoint.pth'):\n",
    "    \n",
    "    checkpoint = torch.load(filepath)\n",
    "    model = models.vgg16(pretrained=True)\n",
    "    \n",
    "    # Freeze the feature parameters\n",
    "    for params in model.parameters():\n",
    "        params.requires_grad = False\n",
    "    \n",
    "\n",
    "\n",
    "    classifier.load_state_dict(checkpoint['state_dict'])\n",
    "    \n",
    "    model.classifier = classifier\n",
    "    class_to_idx = model.class_to_idx = checkpoint['class_to_index']\n",
    "    model.idx_to_class = inv_map = {v: k for k, v in class_to_idx.items()}\n",
    "    \n",
    "    for i in model.idx_to_class:\n",
    "        model.idx_to_class[i] = cat_to_name[model.idx_to_class[i]]\n",
    "    \n",
    "\n",
    "    return model"
   ]
  },
  {
   "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": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "from PIL import Image\n",
    "def process_image(image):\n",
    "    ''' Scales, crops, and normalizes a PIL image for a PyTorch model,\n",
    "        returns an Numpy array\n",
    "    '''\n",
    "    \n",
    "    # TODO: Process a PIL image for use in a PyTorch model\n",
    "    perform_transforms = 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",
    "    image = perform_transforms(Image.open(image))\n",
    "    \n",
    "    return image"
   ]
  },
  {
   "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": 14,
   "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.numpy().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",
    "    \n",
    "    ax.imshow(image)\n",
    "    \n",
    "    return ax"
   ]
  },
  {
   "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": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "def predict(image_path, model, topk=5):\n",
    "    ''' Predict the class (or classes) of an image using a trained deep learning model.\n",
    "    '''\n",
    "    \n",
    "    # TODO: Implement the code to predict the class from an image file\n",
    "    image = process_image(image_path)\n",
    "    device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
    "    model.to(device)\n",
    "    model = model.eval()\n",
    "    inputs = Variable(image.unsqueeze(0))\n",
    "    inputs = inputs.to(device)\n",
    "    output = model(inputs)\n",
    "    \n",
    "    ps = torch.exp(output).data\n",
    "    ps_top = ps.topk(topk)\n",
    "    index_to_class = model.idx_to_class\n",
    "    probs = ps_top[0].tolist()[0]\n",
    "    classes = [index_to_class[i] for i in ps_top[1].tolist()[0]]\n",
    "    \n",
    "    return probs, classes"
   ]
  },
  {
   "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": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "([0.9533689022064209,\n",
       "  0.042615607380867004,\n",
       "  0.001225132029503584,\n",
       "  0.000528129399754107,\n",
       "  0.0003861241275444627],\n",
       " ['trumpet creeper', 'rose', 'camellia', 'bolero deep blue', 'anthurium'])"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# TODO: Display an image along with the top 5 classes\n",
    "predict('flowers/test/101/image_07952.jpg', load_checkpoint())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f6b26b6aa20>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 364,
       "width": 297
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "img_path = 'flowers/test/101/image_07952.jpg'\n",
    "probs, classes = predict(img_path, load_checkpoint())\n",
    "max_index = np.argmax(probs)\n",
    "max_probability = probs[max_index]\n",
    "label = classes[max_index]\n",
    "\n",
    "fig = plt.figure(figsize=(6,6))\n",
    "ax1 = plt.subplot2grid((15,9), (0,0), colspan=9, rowspan=9)\n",
    "ax2 = plt.subplot2grid((15,9), (9,2), colspan=5, rowspan=5)\n",
    "\n",
    "image = Image.open(img_path)\n",
    "ax1.axis('off')\n",
    "ax1.set_title(label)\n",
    "ax1.imshow(image)\n",
    "\n",
    "labels = []\n",
    "for cl in classes:\n",
    "    labels.append(cl)\n",
    "\n",
    "y_pos = np.arange(5)\n",
    "ax2.set_yticks(y_pos)\n",
    "ax2.set_yticklabels(labels)\n",
    "ax2.set_xlabel('Probability')\n",
    "ax2.invert_yaxis()\n",
    "ax2.barh(y_pos, probs, xerr=0, align='center')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "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
}