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": [
    {
     "data": {
      "text/plain": [
       "'C:\\\\Flower_Species_Prediction'"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Show Current Working Directory\n",
    "import os\n",
    "cwd = os.getcwd()\n",
    "cwd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Imports here\n",
    "%matplotlib inline\n",
    "%config InlineBackend.figure_format = 'retina'\n",
    "\n",
    "import numpy as np\n",
    "import torch\n",
    "import torchvision\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "import torch.nn.functional as F\n",
    "from torchvision import  datasets, models, transforms\n",
    "from torch import nn, optim\n",
    "from PIL import Image\n",
    "from collections import OrderedDict\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)). You can [download the data here](https://s3.amazonaws.com/content.udacity-data.com/courses/nd188/flower_data.zip). The dataset is split into two parts, training and validation. 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. If you use a pre-trained network, you'll also need to make sure the input data is resized to 224x224 pixels as required by the networks.\n",
    "\n",
    "The validation set is 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 available from `torchvision` were trained on the ImageNet dataset where each color channel was normalized separately. For both 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."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_dir = 'C:/_GITHUB/Flower_Species_Prediction/flower_data'\n",
    "train_dir = data_dir + '/train'\n",
    "valid_dir = data_dir + '/valid'\n",
    "#test_dir = data_dir + '/test' # because don't has a test data, i make one with some images from google"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "C:/_GITHUB/Flower_Species_Prediction/flower_data\n",
      "C:/_GITHUB/Flower_Species_Prediction/flower_data/train\n",
      "C:/_GITHUB/Flower_Species_Prediction/flower_data/valid\n"
     ]
    }
   ],
   "source": [
    "#Check directories\n",
    "print(data_dir)\n",
    "print(train_dir)\n",
    "print(valid_dir)\n",
    "#print(test_dir)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# TODO: Define your transforms for the training and validation sets\n",
    "\n",
    "train_transforms = transforms.Compose([transforms.RandomRotation(30),\n",
    "                                       transforms.RandomResizedCrop(224),\n",
    "                                       transforms.RandomHorizontalFlip(),\n",
    "                                       transforms.RandomVerticalFlip(),\n",
    "                                       transforms.ToTensor(),\n",
    "                                       transforms.Normalize([0.485, 0.456, 0.406],\n",
    "                                                            [0.229, 0.224, 0.225])])\n",
    "\n",
    "valid_transforms = transforms.Compose([transforms.Resize(256),\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",
    "'''test_transforms = transforms.Compose([transforms.Resize(256),\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 = {}\n",
    "image_datasets['train_data'] = datasets.ImageFolder(train_dir, transform = train_transforms)\n",
    "image_datasets['valid_data'] = datasets.ImageFolder(valid_dir, transform = valid_transforms)\n",
    "###image_datasets['test_data'] = datasets.ImageFolder(valid_dir, transform = test_transforms)\n",
    "\n",
    "# TODO: Using the image datasets and the trainforms, define the dataloaders\n",
    "dataloaders = {}\n",
    "dataloaders['train_loader'] = torch.utils.data.DataLoader(image_datasets['train_data'], batch_size=64, shuffle=True)\n",
    "dataloaders['valid_loader'] = torch.utils.data.DataLoader(image_datasets['valid_data'] , batch_size=32, shuffle=True)\n",
    "###dataloaders['test_loader'] = torch.utils.data.DataLoader(image_datasets['test_data'] , 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": 6,
   "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. If you want to talk through it with someone, chat with your fellow students! You can also ask questions on the forums or join the instructors in office hours.\n",
    "\n",
    "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."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Importing a model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\dmiyake\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\torchvision\\models\\densenet.py:212: UserWarning: nn.init.kaiming_normal is now deprecated in favor of nn.init.kaiming_normal_.\n",
      "  nn.init.kaiming_normal(m.weight.data)\n"
     ]
    },
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     "name": "stdout",
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      "DenseNet(\n",
      "  (features): Sequential(\n",
      "    (conv0): Conv2d(3, 64, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3), bias=False)\n",
      "    (norm0): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "    (relu0): ReLU(inplace)\n",
      "    (pool0): MaxPool2d(kernel_size=3, stride=2, padding=1, dilation=1, ceil_mode=False)\n",
      "    (denseblock1): _DenseBlock(\n",
      "      (denselayer1): _DenseLayer(\n",
      "        (norm1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "        (relu1): ReLU(inplace)\n",
      "        (conv1): Conv2d(64, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "        (relu2): ReLU(inplace)\n",
      "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      )\n",
      "      (denselayer2): _DenseLayer(\n",
      "        (norm1): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "        (relu1): ReLU(inplace)\n",
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      "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "        (relu2): ReLU(inplace)\n",
      "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      )\n",
      "      (denselayer3): _DenseLayer(\n",
      "        (norm1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "        (relu1): ReLU(inplace)\n",
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      "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "        (relu2): ReLU(inplace)\n",
      "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      )\n",
      "      (denselayer4): _DenseLayer(\n",
      "        (norm1): BatchNorm2d(160, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "        (relu1): ReLU(inplace)\n",
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      "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "        (relu2): ReLU(inplace)\n",
      "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      )\n",
      "      (denselayer5): _DenseLayer(\n",
      "        (norm1): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "        (relu1): ReLU(inplace)\n",
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      "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "        (relu2): ReLU(inplace)\n",
      "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      )\n",
      "      (denselayer6): _DenseLayer(\n",
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      "        (relu1): ReLU(inplace)\n",
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      "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "        (relu2): ReLU(inplace)\n",
      "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      )\n",
      "    )\n",
      "    (transition1): _Transition(\n",
      "      (norm): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (relu): ReLU(inplace)\n",
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      "      (pool): AvgPool2d(kernel_size=2, stride=2, padding=0)\n",
      "    )\n",
      "    (denseblock2): _DenseBlock(\n",
      "      (denselayer1): _DenseLayer(\n",
      "        (norm1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "        (relu1): ReLU(inplace)\n",
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      "        (relu2): ReLU(inplace)\n",
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      "      )\n",
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      "      )\n",
      "      (denselayer4): _DenseLayer(\n",
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      "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "        (relu2): ReLU(inplace)\n",
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      "      )\n",
      "      (denselayer5): _DenseLayer(\n",
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      "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "        (relu2): ReLU(inplace)\n",
      "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      )\n",
      "      (denselayer6): _DenseLayer(\n",
      "        (norm1): BatchNorm2d(288, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "        (relu1): ReLU(inplace)\n",
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      "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "        (relu2): ReLU(inplace)\n",
      "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      )\n",
      "      (denselayer7): _DenseLayer(\n",
      "        (norm1): BatchNorm2d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "        (relu1): ReLU(inplace)\n",
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      "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "        (relu2): ReLU(inplace)\n",
      "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      )\n",
      "      (denselayer8): _DenseLayer(\n",
      "        (norm1): BatchNorm2d(352, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "        (relu1): ReLU(inplace)\n",
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      "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "        (relu2): ReLU(inplace)\n",
      "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      )\n",
      "      (denselayer9): _DenseLayer(\n",
      "        (norm1): BatchNorm2d(384, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "        (relu1): ReLU(inplace)\n",
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      "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "        (relu2): ReLU(inplace)\n",
      "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      )\n",
      "      (denselayer10): _DenseLayer(\n",
      "        (norm1): BatchNorm2d(416, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "        (relu1): ReLU(inplace)\n",
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      "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "        (relu2): ReLU(inplace)\n",
      "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      )\n",
      "      (denselayer11): _DenseLayer(\n",
      "        (norm1): BatchNorm2d(448, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "        (relu1): ReLU(inplace)\n",
      "        (conv1): Conv2d(448, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "        (relu2): ReLU(inplace)\n",
      "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      )\n",
      "      (denselayer12): _DenseLayer(\n",
      "        (norm1): BatchNorm2d(480, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "        (relu1): ReLU(inplace)\n",
      "        (conv1): Conv2d(480, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "        (relu2): ReLU(inplace)\n",
      "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      )\n",
      "    )\n",
      "    (transition2): _Transition(\n",
      "      (norm): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (relu): ReLU(inplace)\n",
      "      (conv): Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (pool): AvgPool2d(kernel_size=2, stride=2, padding=0)\n",
      "    )\n",
      "    (denseblock3): _DenseBlock(\n",
      "      (denselayer1): _DenseLayer(\n",
      "        (norm1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "        (relu1): ReLU(inplace)\n",
      "        (conv1): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "        (relu2): ReLU(inplace)\n",
      "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      )\n",
      "      (denselayer2): _DenseLayer(\n",
      "        (norm1): BatchNorm2d(288, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "        (relu1): ReLU(inplace)\n",
      "        (conv1): Conv2d(288, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "        (relu2): ReLU(inplace)\n",
      "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      )\n",
      "      (denselayer3): _DenseLayer(\n",
      "        (norm1): BatchNorm2d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "        (relu1): ReLU(inplace)\n",
      "        (conv1): Conv2d(320, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "        (relu2): ReLU(inplace)\n",
      "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      )\n",
      "      (denselayer4): _DenseLayer(\n",
      "        (norm1): BatchNorm2d(352, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "        (relu1): ReLU(inplace)\n",
      "        (conv1): Conv2d(352, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "        (relu2): ReLU(inplace)\n",
      "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      )\n",
      "      (denselayer5): _DenseLayer(\n",
      "        (norm1): BatchNorm2d(384, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "        (relu1): ReLU(inplace)\n",
      "        (conv1): Conv2d(384, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "        (relu2): ReLU(inplace)\n",
      "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      )\n",
      "      (denselayer6): _DenseLayer(\n",
      "        (norm1): BatchNorm2d(416, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "        (relu1): ReLU(inplace)\n",
      "        (conv1): Conv2d(416, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "        (relu2): ReLU(inplace)\n",
      "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      )\n",
      "      (denselayer7): _DenseLayer(\n",
      "        (norm1): BatchNorm2d(448, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "        (relu1): ReLU(inplace)\n",
      "        (conv1): Conv2d(448, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "        (relu2): ReLU(inplace)\n",
      "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      )\n",
      "      (denselayer8): _DenseLayer(\n",
      "        (norm1): BatchNorm2d(480, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "        (relu1): ReLU(inplace)\n",
      "        (conv1): Conv2d(480, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "        (relu2): ReLU(inplace)\n",
      "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      )\n",
      "      (denselayer9): _DenseLayer(\n",
      "        (norm1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "        (relu1): ReLU(inplace)\n",
      "        (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "        (relu2): ReLU(inplace)\n",
      "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      )\n",
      "      (denselayer10): _DenseLayer(\n",
      "        (norm1): BatchNorm2d(544, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "        (relu1): ReLU(inplace)\n",
      "        (conv1): Conv2d(544, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "        (relu2): ReLU(inplace)\n",
      "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      )\n",
      "      (denselayer11): _DenseLayer(\n",
      "        (norm1): BatchNorm2d(576, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "        (relu1): ReLU(inplace)\n",
      "        (conv1): Conv2d(576, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "        (relu2): ReLU(inplace)\n",
      "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      )\n",
      "      (denselayer12): _DenseLayer(\n",
      "        (norm1): BatchNorm2d(608, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "        (relu1): ReLU(inplace)\n",
      "        (conv1): Conv2d(608, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "        (relu2): ReLU(inplace)\n",
      "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      )\n",
      "      (denselayer13): _DenseLayer(\n",
      "        (norm1): BatchNorm2d(640, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "        (relu1): ReLU(inplace)\n",
      "        (conv1): Conv2d(640, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "        (relu2): ReLU(inplace)\n",
      "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      )\n",
      "      (denselayer14): _DenseLayer(\n",
      "        (norm1): BatchNorm2d(672, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "        (relu1): ReLU(inplace)\n",
      "        (conv1): Conv2d(672, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "        (relu2): ReLU(inplace)\n",
      "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      )\n",
      "      (denselayer15): _DenseLayer(\n",
      "        (norm1): BatchNorm2d(704, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "        (relu1): ReLU(inplace)\n",
      "        (conv1): Conv2d(704, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "        (relu2): ReLU(inplace)\n",
      "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      )\n",
      "      (denselayer16): _DenseLayer(\n",
      "        (norm1): BatchNorm2d(736, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "        (relu1): ReLU(inplace)\n",
      "        (conv1): Conv2d(736, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "        (relu2): ReLU(inplace)\n",
      "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      )\n",
      "      (denselayer17): _DenseLayer(\n",
      "        (norm1): BatchNorm2d(768, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "        (relu1): ReLU(inplace)\n",
      "        (conv1): Conv2d(768, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "        (relu2): ReLU(inplace)\n",
      "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      )\n",
      "      (denselayer18): _DenseLayer(\n",
      "        (norm1): BatchNorm2d(800, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "        (relu1): ReLU(inplace)\n",
      "        (conv1): Conv2d(800, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "        (relu2): ReLU(inplace)\n",
      "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      )\n",
      "      (denselayer19): _DenseLayer(\n",
      "        (norm1): BatchNorm2d(832, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "        (relu1): ReLU(inplace)\n",
      "        (conv1): Conv2d(832, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "        (relu2): ReLU(inplace)\n",
      "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      )\n",
      "      (denselayer20): _DenseLayer(\n",
      "        (norm1): BatchNorm2d(864, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "        (relu1): ReLU(inplace)\n",
      "        (conv1): Conv2d(864, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "        (relu2): ReLU(inplace)\n",
      "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      )\n",
      "      (denselayer21): _DenseLayer(\n",
      "        (norm1): BatchNorm2d(896, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "        (relu1): ReLU(inplace)\n",
      "        (conv1): Conv2d(896, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "        (relu2): ReLU(inplace)\n",
      "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      )\n",
      "      (denselayer22): _DenseLayer(\n",
      "        (norm1): BatchNorm2d(928, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "        (relu1): ReLU(inplace)\n",
      "        (conv1): Conv2d(928, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "        (relu2): ReLU(inplace)\n",
      "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      )\n",
      "      (denselayer23): _DenseLayer(\n",
      "        (norm1): BatchNorm2d(960, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "        (relu1): ReLU(inplace)\n",
      "        (conv1): Conv2d(960, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "        (relu2): ReLU(inplace)\n",
      "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      )\n",
      "      (denselayer24): _DenseLayer(\n",
      "        (norm1): BatchNorm2d(992, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "        (relu1): ReLU(inplace)\n",
      "        (conv1): Conv2d(992, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "        (relu2): ReLU(inplace)\n",
      "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      )\n",
      "    )\n",
      "    (transition3): _Transition(\n",
      "      (norm): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (relu): ReLU(inplace)\n",
      "      (conv): Conv2d(1024, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (pool): AvgPool2d(kernel_size=2, stride=2, padding=0)\n",
      "    )\n",
      "    (denseblock4): _DenseBlock(\n",
      "      (denselayer1): _DenseLayer(\n",
      "        (norm1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "        (relu1): ReLU(inplace)\n",
      "        (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "        (relu2): ReLU(inplace)\n",
      "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      )\n",
      "      (denselayer2): _DenseLayer(\n",
      "        (norm1): BatchNorm2d(544, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "        (relu1): ReLU(inplace)\n",
      "        (conv1): Conv2d(544, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "        (relu2): ReLU(inplace)\n",
      "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      )\n",
      "      (denselayer3): _DenseLayer(\n",
      "        (norm1): BatchNorm2d(576, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "        (relu1): ReLU(inplace)\n",
      "        (conv1): Conv2d(576, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "        (relu2): ReLU(inplace)\n",
      "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      )\n",
      "      (denselayer4): _DenseLayer(\n",
      "        (norm1): BatchNorm2d(608, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "        (relu1): ReLU(inplace)\n",
      "        (conv1): Conv2d(608, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "        (relu2): ReLU(inplace)\n",
      "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      )\n",
      "      (denselayer5): _DenseLayer(\n",
      "        (norm1): BatchNorm2d(640, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "        (relu1): ReLU(inplace)\n",
      "        (conv1): Conv2d(640, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "        (relu2): ReLU(inplace)\n",
      "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      )\n",
      "      (denselayer6): _DenseLayer(\n",
      "        (norm1): BatchNorm2d(672, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "        (relu1): ReLU(inplace)\n",
      "        (conv1): Conv2d(672, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "        (relu2): ReLU(inplace)\n",
      "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      )\n",
      "      (denselayer7): _DenseLayer(\n",
      "        (norm1): BatchNorm2d(704, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "        (relu1): ReLU(inplace)\n",
      "        (conv1): Conv2d(704, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "        (relu2): ReLU(inplace)\n",
      "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      )\n",
      "      (denselayer8): _DenseLayer(\n",
      "        (norm1): BatchNorm2d(736, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "        (relu1): ReLU(inplace)\n",
      "        (conv1): Conv2d(736, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "        (relu2): ReLU(inplace)\n",
      "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      )\n",
      "      (denselayer9): _DenseLayer(\n",
      "        (norm1): BatchNorm2d(768, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "        (relu1): ReLU(inplace)\n",
      "        (conv1): Conv2d(768, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "        (relu2): ReLU(inplace)\n",
      "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      )\n",
      "      (denselayer10): _DenseLayer(\n",
      "        (norm1): BatchNorm2d(800, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "        (relu1): ReLU(inplace)\n",
      "        (conv1): Conv2d(800, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "        (relu2): ReLU(inplace)\n",
      "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      )\n",
      "      (denselayer11): _DenseLayer(\n",
      "        (norm1): BatchNorm2d(832, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "        (relu1): ReLU(inplace)\n",
      "        (conv1): Conv2d(832, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "        (relu2): ReLU(inplace)\n",
      "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      )\n",
      "      (denselayer12): _DenseLayer(\n",
      "        (norm1): BatchNorm2d(864, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "        (relu1): ReLU(inplace)\n",
      "        (conv1): Conv2d(864, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "        (relu2): ReLU(inplace)\n",
      "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      )\n",
      "      (denselayer13): _DenseLayer(\n",
      "        (norm1): BatchNorm2d(896, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "        (relu1): ReLU(inplace)\n",
      "        (conv1): Conv2d(896, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "        (relu2): ReLU(inplace)\n",
      "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      )\n",
      "      (denselayer14): _DenseLayer(\n",
      "        (norm1): BatchNorm2d(928, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "        (relu1): ReLU(inplace)\n",
      "        (conv1): Conv2d(928, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "        (relu2): ReLU(inplace)\n",
      "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      )\n",
      "      (denselayer15): _DenseLayer(\n",
      "        (norm1): BatchNorm2d(960, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "        (relu1): ReLU(inplace)\n",
      "        (conv1): Conv2d(960, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "        (relu2): ReLU(inplace)\n",
      "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      )\n",
      "      (denselayer16): _DenseLayer(\n",
      "        (norm1): BatchNorm2d(992, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "        (relu1): ReLU(inplace)\n",
      "        (conv1): Conv2d(992, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "        (relu2): ReLU(inplace)\n",
      "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      )\n",
      "    )\n",
      "    (norm5): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "  )\n",
      "  (classifier): Linear(in_features=1024, out_features=1000, bias=True)\n",
      ")\n"
     ]
    }
   ],
   "source": [
    "model = models.densenet121(pretrained=True)\n",
    "print(model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# TODO: Build and train your network\n",
    "\n",
    "# Defining a New networks and classsifier, with ReLU activation and dropout to less overfitting\n",
    "# Freeze parameters to not backpropagation them\n",
    "for param in model.parameters():\n",
    "    param.requires_grad = False\n",
    "\n",
    "classifier = nn.Sequential(OrderedDict([\n",
    "                               ('fc1', nn.Linear(1024,512)),\n",
    "                               ('relu', nn.ReLU()),\n",
    "                               ('dropout1', nn.Dropout(0.1)),\n",
    "                               ('fc2', nn.Linear(512,256)),\n",
    "                               ('dropout2', nn.Dropout(0.1)),\n",
    "                               ('relu', nn.ReLU()),\n",
    "                               ('fc3', nn.Linear(256,102)),\n",
    "                               ('output', nn.LogSoftmax(dim=1)) \n",
    "                               ]))\n",
    "\n",
    "model.classifier = classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Measuring of Validation Loss and Accuracy\n",
    "def validation(model, dataloader, criterion, device):\n",
    "    loss = 0\n",
    "    accuracy = 0\n",
    "    with torch.no_grad():\n",
    "        for images, labels in iter(dataloader):\n",
    "            images, labels = images.to(device), labels.to(device) # moving to GPU\n",
    "            output = model.forward(images)\n",
    "            loss += criterion(output, labels).item()\n",
    "            prob_class = torch.exp(output)\n",
    "            equality = (labels.data == prob_class.max(dim =1)[1])\n",
    "            accuracy += equality.type(torch.FloatTensor).mean()\n",
    "        return loss, accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cuda\n"
     ]
    }
   ],
   "source": [
    "# Use GPU if it's available\n",
    "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
    "print(device)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# specify loss function (categorical cross-entropy)\n",
    "criterion = nn.CrossEntropyLoss()\n",
    "# specify optimizer (stochastic gradient descent) and learning rate = 0.001\n",
    "optimizer = optim.Adam(model.classifier.parameters(), lr=0.001)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 1/40..  Training Loss: 3.864..  Training Accuracy: 0.179 Validation Loss: 2.610..  Validation Accuracy: 0.374\n",
      "Epoch: 1/40..  Training Loss: 2.183..  Training Accuracy: 0.468 Validation Loss: 1.382..  Validation Accuracy: 0.646\n",
      "Epoch: 2/40..  Training Loss: 1.512..  Training Accuracy: 0.606 Validation Loss: 0.945..  Validation Accuracy: 0.743\n",
      "Epoch: 2/40..  Training Loss: 1.193..  Training Accuracy: 0.677 Validation Loss: 0.736..  Validation Accuracy: 0.793\n",
      "Epoch: 3/40..  Training Loss: 1.038..  Training Accuracy: 0.713 Validation Loss: 0.594..  Validation Accuracy: 0.837\n",
      "Epoch: 3/40..  Training Loss: 0.888..  Training Accuracy: 0.761 Validation Loss: 0.499..  Validation Accuracy: 0.866\n",
      "Epoch: 4/40..  Training Loss: 0.790..  Training Accuracy: 0.788 Validation Loss: 0.463..  Validation Accuracy: 0.862\n",
      "Epoch: 4/40..  Training Loss: 0.814..  Training Accuracy: 0.774 Validation Loss: 0.463..  Validation Accuracy: 0.876\n",
      "Epoch: 5/40..  Training Loss: 0.718..  Training Accuracy: 0.804 Validation Loss: 0.470..  Validation Accuracy: 0.878\n",
      "Epoch: 5/40..  Training Loss: 0.693..  Training Accuracy: 0.804 Validation Loss: 0.402..  Validation Accuracy: 0.889\n",
      "Epoch: 6/40..  Training Loss: 0.624..  Training Accuracy: 0.822 Validation Loss: 0.331..  Validation Accuracy: 0.917\n",
      "Epoch: 6/40..  Training Loss: 0.703..  Training Accuracy: 0.808 Validation Loss: 0.410..  Validation Accuracy: 0.891\n",
      "Epoch: 7/40..  Training Loss: 0.612..  Training Accuracy: 0.826 Validation Loss: 0.338..  Validation Accuracy: 0.904\n",
      "Epoch: 7/40..  Training Loss: 0.612..  Training Accuracy: 0.830 Validation Loss: 0.329..  Validation Accuracy: 0.913\n",
      "Epoch: 8/40..  Training Loss: 0.598..  Training Accuracy: 0.834 Validation Loss: 0.361..  Validation Accuracy: 0.904\n",
      "Epoch: 8/40..  Training Loss: 0.575..  Training Accuracy: 0.840 Validation Loss: 0.311..  Validation Accuracy: 0.922\n",
      "Epoch: 9/40..  Training Loss: 0.563..  Training Accuracy: 0.843 Validation Loss: 0.318..  Validation Accuracy: 0.904\n",
      "Epoch: 9/40..  Training Loss: 0.548..  Training Accuracy: 0.845 Validation Loss: 0.345..  Validation Accuracy: 0.912\n",
      "Epoch: 10/40..  Training Loss: 0.563..  Training Accuracy: 0.832 Validation Loss: 0.301..  Validation Accuracy: 0.926\n",
      "Epoch: 10/40..  Training Loss: 0.536..  Training Accuracy: 0.848 Validation Loss: 0.321..  Validation Accuracy: 0.910\n",
      "Epoch: 11/40..  Training Loss: 0.544..  Training Accuracy: 0.847 Validation Loss: 0.336..  Validation Accuracy: 0.902\n",
      "Epoch: 11/40..  Training Loss: 0.528..  Training Accuracy: 0.850 Validation Loss: 0.331..  Validation Accuracy: 0.916\n",
      "Epoch: 12/40..  Training Loss: 0.497..  Training Accuracy: 0.864 Validation Loss: 0.269..  Validation Accuracy: 0.933\n",
      "Epoch: 12/40..  Training Loss: 0.524..  Training Accuracy: 0.847 Validation Loss: 0.315..  Validation Accuracy: 0.910\n",
      "Epoch: 13/40..  Training Loss: 0.515..  Training Accuracy: 0.856 Validation Loss: 0.355..  Validation Accuracy: 0.916\n",
      "Epoch: 13/40..  Training Loss: 0.459..  Training Accuracy: 0.874 Validation Loss: 0.292..  Validation Accuracy: 0.920\n",
      "Epoch: 14/40..  Training Loss: 0.493..  Training Accuracy: 0.856 Validation Loss: 0.300..  Validation Accuracy: 0.925\n",
      "Epoch: 14/40..  Training Loss: 0.455..  Training Accuracy: 0.876 Validation Loss: 0.268..  Validation Accuracy: 0.939\n",
      "Epoch: 15/40..  Training Loss: 0.530..  Training Accuracy: 0.857 Validation Loss: 0.339..  Validation Accuracy: 0.910\n",
      "Epoch: 15/40..  Training Loss: 0.513..  Training Accuracy: 0.852 Validation Loss: 0.304..  Validation Accuracy: 0.927\n",
      "Epoch: 16/40..  Training Loss: 0.459..  Training Accuracy: 0.868 Validation Loss: 0.301..  Validation Accuracy: 0.925\n",
      "Epoch: 16/40..  Training Loss: 0.460..  Training Accuracy: 0.871 Validation Loss: 0.305..  Validation Accuracy: 0.926\n",
      "Epoch: 17/40..  Training Loss: 0.487..  Training Accuracy: 0.864 Validation Loss: 0.277..  Validation Accuracy: 0.934\n",
      "Epoch: 17/40..  Training Loss: 0.407..  Training Accuracy: 0.881 Validation Loss: 0.296..  Validation Accuracy: 0.934\n",
      "Epoch: 17/40..  Training Loss: 0.435..  Training Accuracy: 0.881 Validation Loss: 0.306..  Validation Accuracy: 0.923\n",
      "Epoch: 18/40..  Training Loss: 0.454..  Training Accuracy: 0.874 Validation Loss: 0.265..  Validation Accuracy: 0.943\n",
      "Epoch: 18/40..  Training Loss: 0.438..  Training Accuracy: 0.872 Validation Loss: 0.290..  Validation Accuracy: 0.931\n",
      "Epoch: 19/40..  Training Loss: 0.473..  Training Accuracy: 0.861 Validation Loss: 0.258..  Validation Accuracy: 0.938\n",
      "Epoch: 19/40..  Training Loss: 0.440..  Training Accuracy: 0.871 Validation Loss: 0.282..  Validation Accuracy: 0.932\n",
      "Epoch: 20/40..  Training Loss: 0.442..  Training Accuracy: 0.872 Validation Loss: 0.313..  Validation Accuracy: 0.925\n",
      "Epoch: 20/40..  Training Loss: 0.444..  Training Accuracy: 0.872 Validation Loss: 0.275..  Validation Accuracy: 0.936\n",
      "Epoch: 21/40..  Training Loss: 0.394..  Training Accuracy: 0.890 Validation Loss: 0.292..  Validation Accuracy: 0.928\n",
      "Epoch: 21/40..  Training Loss: 0.407..  Training Accuracy: 0.881 Validation Loss: 0.288..  Validation Accuracy: 0.928\n",
      "Epoch: 22/40..  Training Loss: 0.391..  Training Accuracy: 0.888 Validation Loss: 0.268..  Validation Accuracy: 0.939\n",
      "Epoch: 22/40..  Training Loss: 0.416..  Training Accuracy: 0.887 Validation Loss: 0.263..  Validation Accuracy: 0.934\n",
      "Epoch: 23/40..  Training Loss: 0.422..  Training Accuracy: 0.879 Validation Loss: 0.268..  Validation Accuracy: 0.939\n",
      "Epoch: 23/40..  Training Loss: 0.392..  Training Accuracy: 0.888 Validation Loss: 0.286..  Validation Accuracy: 0.936\n",
      "Epoch: 24/40..  Training Loss: 0.427..  Training Accuracy: 0.877 Validation Loss: 0.264..  Validation Accuracy: 0.939\n",
      "Epoch: 24/40..  Training Loss: 0.346..  Training Accuracy: 0.905 Validation Loss: 0.241..  Validation Accuracy: 0.947\n",
      "Epoch: 25/40..  Training Loss: 0.385..  Training Accuracy: 0.886 Validation Loss: 0.269..  Validation Accuracy: 0.935\n",
      "Epoch: 25/40..  Training Loss: 0.387..  Training Accuracy: 0.884 Validation Loss: 0.285..  Validation Accuracy: 0.925\n",
      "Epoch: 26/40..  Training Loss: 0.386..  Training Accuracy: 0.890 Validation Loss: 0.261..  Validation Accuracy: 0.935\n",
      "Epoch: 26/40..  Training Loss: 0.432..  Training Accuracy: 0.877 Validation Loss: 0.267..  Validation Accuracy: 0.933\n",
      "Epoch: 27/40..  Training Loss: 0.404..  Training Accuracy: 0.887 Validation Loss: 0.238..  Validation Accuracy: 0.942\n",
      "Epoch: 27/40..  Training Loss: 0.391..  Training Accuracy: 0.888 Validation Loss: 0.279..  Validation Accuracy: 0.927\n",
      "Epoch: 28/40..  Training Loss: 0.420..  Training Accuracy: 0.878 Validation Loss: 0.298..  Validation Accuracy: 0.918\n",
      "Epoch: 28/40..  Training Loss: 0.431..  Training Accuracy: 0.881 Validation Loss: 0.288..  Validation Accuracy: 0.935\n",
      "Epoch: 29/40..  Training Loss: 0.395..  Training Accuracy: 0.883 Validation Loss: 0.277..  Validation Accuracy: 0.939\n",
      "Epoch: 29/40..  Training Loss: 0.405..  Training Accuracy: 0.890 Validation Loss: 0.260..  Validation Accuracy: 0.932\n",
      "Epoch: 30/40..  Training Loss: 0.393..  Training Accuracy: 0.889 Validation Loss: 0.242..  Validation Accuracy: 0.946\n",
      "Epoch: 30/40..  Training Loss: 0.353..  Training Accuracy: 0.892 Validation Loss: 0.273..  Validation Accuracy: 0.939\n",
      "Epoch: 31/40..  Training Loss: 0.386..  Training Accuracy: 0.891 Validation Loss: 0.278..  Validation Accuracy: 0.939\n",
      "Epoch: 31/40..  Training Loss: 0.391..  Training Accuracy: 0.895 Validation Loss: 0.280..  Validation Accuracy: 0.940\n",
      "Epoch: 32/40..  Training Loss: 0.397..  Training Accuracy: 0.882 Validation Loss: 0.324..  Validation Accuracy: 0.928\n",
      "Epoch: 32/40..  Training Loss: 0.413..  Training Accuracy: 0.883 Validation Loss: 0.297..  Validation Accuracy: 0.932\n",
      "Epoch: 33/40..  Training Loss: 0.338..  Training Accuracy: 0.906 Validation Loss: 0.292..  Validation Accuracy: 0.929\n",
      "Epoch: 33/40..  Training Loss: 0.375..  Training Accuracy: 0.889 Validation Loss: 0.270..  Validation Accuracy: 0.934\n",
      "Epoch: 34/40..  Training Loss: 0.406..  Training Accuracy: 0.883 Validation Loss: 0.257..  Validation Accuracy: 0.937\n",
      "Epoch: 34/40..  Training Loss: 0.357..  Training Accuracy: 0.901 Validation Loss: 0.290..  Validation Accuracy: 0.931\n",
      "Epoch: 34/40..  Training Loss: 0.371..  Training Accuracy: 0.899 Validation Loss: 0.229..  Validation Accuracy: 0.947\n",
      "Epoch: 35/40..  Training Loss: 0.356..  Training Accuracy: 0.893 Validation Loss: 0.226..  Validation Accuracy: 0.948\n",
      "Epoch: 35/40..  Training Loss: 0.356..  Training Accuracy: 0.898 Validation Loss: 0.272..  Validation Accuracy: 0.947\n",
      "Epoch: 36/40..  Training Loss: 0.373..  Training Accuracy: 0.891 Validation Loss: 0.336..  Validation Accuracy: 0.921\n",
      "Epoch: 36/40..  Training Loss: 0.402..  Training Accuracy: 0.887 Validation Loss: 0.313..  Validation Accuracy: 0.921\n",
      "Epoch: 37/40..  Training Loss: 0.388..  Training Accuracy: 0.888 Validation Loss: 0.288..  Validation Accuracy: 0.938\n",
      "Epoch: 37/40..  Training Loss: 0.372..  Training Accuracy: 0.899 Validation Loss: 0.280..  Validation Accuracy: 0.934\n",
      "Epoch: 38/40..  Training Loss: 0.381..  Training Accuracy: 0.892 Validation Loss: 0.275..  Validation Accuracy: 0.945\n",
      "Epoch: 38/40..  Training Loss: 0.349..  Training Accuracy: 0.902 Validation Loss: 0.261..  Validation Accuracy: 0.938\n",
      "Epoch: 39/40..  Training Loss: 0.366..  Training Accuracy: 0.896 Validation Loss: 0.299..  Validation Accuracy: 0.936\n",
      "Epoch: 39/40..  Training Loss: 0.364..  Training Accuracy: 0.896 Validation Loss: 0.299..  Validation Accuracy: 0.933\n",
      "Epoch: 40/40..  Training Loss: 0.371..  Training Accuracy: 0.892 Validation Loss: 0.236..  Validation Accuracy: 0.946\n",
      "Epoch: 40/40..  Training Loss: 0.335..  Training Accuracy: 0.908 Validation Loss: 0.334..  Validation Accuracy: 0.927\n",
      "\n",
      " Training is completed!\n"
     ]
    }
   ],
   "source": [
    "model.to(device)\n",
    "epochs = 40\n",
    "steps = 0\n",
    "running_loss = 0\n",
    "print_every = 50\n",
    "train_accuracy = 0\n",
    "\n",
    "for epoch in range(epochs):\n",
    "    model.train()  # to turn on Dropout\n",
    "    \n",
    "    for images, labels in iter(dataloaders['train_loader']):\n",
    "        # Move input and label tensors to the default device\n",
    "        images, labels = images.to(device), labels.to(device)\n",
    "        \n",
    "        steps += 1\n",
    "                \n",
    "        optimizer.zero_grad()\n",
    "        output = model.forward(images)\n",
    "        loss = criterion(output, labels)\n",
    "        loss.backward()\n",
    "        optimizer.step()\n",
    "        running_loss += loss.item()\n",
    "        \n",
    "        # Important get loss probs for log-softmax function\n",
    "        \n",
    "        prob_class = torch.exp(output)\n",
    "        equality = (labels.data == prob_class.max(dim =1)[1])\n",
    "        train_accuracy += equality.type(torch.FloatTensor).mean()\n",
    "        \n",
    "        \n",
    "        if steps % print_every == 0:\n",
    "            model.eval()\n",
    "            \n",
    "            with torch.no_grad():\n",
    "                valid_loss, valid_accuracy = validation(model, dataloaders['valid_loader'], criterion, device)\n",
    "\n",
    "            print(\"Epoch: {}/{}.. \".format(epoch+1, epochs),\n",
    "                  \"Training Loss: {:.3f}.. \".format(running_loss/print_every),\n",
    "                  \"Training Accuracy: {:.3f}\".format(train_accuracy/print_every),\n",
    "                  \"Validation Loss: {:.3f}.. \".format(valid_loss/len(dataloaders['valid_loader'])),\n",
    "                  \"Validation Accuracy: {:.3f}\".format(valid_accuracy/len(dataloaders['valid_loader'])))\n",
    "\n",
    "            running_loss = 0\n",
    "            train_accuracy = 0\n",
    "            model.train() \n",
    "            \n",
    "print(\"\\n Training is completed!\")"
   ]
  },
  {
   "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": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# TODO: Save the checkpoint \n",
    "model.to('cpu')\n",
    "model.class_to_idx = image_datasets['train_data'].class_to_idx\n",
    "model_check = {'input_size': 1024,\n",
    "               'output_size': 102,\n",
    "               'model': model,\n",
    "               'state_dict': model.state_dict(),\n",
    "               'optimizer_state_dict': optimizer.state_dict,\n",
    "               'criterion': criterion,\n",
    "               'epochs': epochs,\n",
    "               'class_to_idx': model.class_to_idx}\n",
    "\n",
    "torch.save(model_check, 'model_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": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# TODO: Write a function that loads a checkpoint and rebuilds the model\n",
    "def load_check(filepath):\n",
    "    model_check = torch.load(filepath)\n",
    "    model = models.densenet121(pretrained=True)\n",
    "    \n",
    "    classifier = nn.Sequential(OrderedDict([\n",
    "                               ('fc1', nn.Linear(1024,512)),\n",
    "                               ('relu', nn.ReLU()),\n",
    "                               ('dropout1', nn.Dropout(0.5)),\n",
    "                               ('fc2', nn.Linear(512,256)),\n",
    "                               ('dropout2', nn.Dropout(0.1)),\n",
    "                               ('relu', nn.ReLU()),\n",
    "                               ('fc3', nn.Linear(256,102)),\n",
    "                               ('output', nn.LogSoftmax(dim=1)) \n",
    "                               ]))\n",
    "    \n",
    "    model.classifier = classifier\n",
    "    model.load_state_dict(model_check['state_dict'])\n",
    "    \n",
    "    \n",
    "    return model, model_check['class_to_idx']\n",
    "\n",
    "# get index to class mapping\n",
    "model_load, class_to_idx = load_check('model_checkpoint.pth')\n",
    "idx_to_class = { v : k for k,v in class_to_idx.items()}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DenseNet(\n",
      "  (features): Sequential(\n",
      "    (conv0): Conv2d(3, 64, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3), bias=False)\n",
      "    (norm0): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "    (relu0): ReLU(inplace)\n",
      "    (pool0): MaxPool2d(kernel_size=3, stride=2, padding=1, dilation=1, ceil_mode=False)\n",
      "    (denseblock1): _DenseBlock(\n",
      "      (denselayer1): _DenseLayer(\n",
      "        (norm1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "        (relu1): ReLU(inplace)\n",
      "        (conv1): Conv2d(64, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "        (relu2): ReLU(inplace)\n",
      "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      )\n",
      "      (denselayer2): _DenseLayer(\n",
      "        (norm1): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "        (relu1): ReLU(inplace)\n",
      "        (conv1): Conv2d(96, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "        (relu2): ReLU(inplace)\n",
      "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      )\n",
      "      (denselayer3): _DenseLayer(\n",
      "        (norm1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "        (relu1): ReLU(inplace)\n",
      "        (conv1): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "        (relu2): ReLU(inplace)\n",
      "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      )\n",
      "      (denselayer4): _DenseLayer(\n",
      "        (norm1): BatchNorm2d(160, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "        (relu1): ReLU(inplace)\n",
      "        (conv1): Conv2d(160, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "        (relu2): ReLU(inplace)\n",
      "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      )\n",
      "      (denselayer5): _DenseLayer(\n",
      "        (norm1): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "        (relu1): ReLU(inplace)\n",
      "        (conv1): Conv2d(192, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "        (relu2): ReLU(inplace)\n",
      "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      )\n",
      "      (denselayer6): _DenseLayer(\n",
      "        (norm1): BatchNorm2d(224, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "        (relu1): ReLU(inplace)\n",
      "        (conv1): Conv2d(224, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "        (relu2): ReLU(inplace)\n",
      "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      )\n",
      "    )\n",
      "    (transition1): _Transition(\n",
      "      (norm): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (relu): ReLU(inplace)\n",
      "      (conv): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (pool): AvgPool2d(kernel_size=2, stride=2, padding=0)\n",
      "    )\n",
      "    (denseblock2): _DenseBlock(\n",
      "      (denselayer1): _DenseLayer(\n",
      "        (norm1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "        (relu1): ReLU(inplace)\n",
      "        (conv1): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "        (relu2): ReLU(inplace)\n",
      "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      )\n",
      "      (denselayer2): _DenseLayer(\n",
      "        (norm1): BatchNorm2d(160, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "        (relu1): ReLU(inplace)\n",
      "        (conv1): Conv2d(160, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "        (relu2): ReLU(inplace)\n",
      "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      )\n",
      "      (denselayer3): _DenseLayer(\n",
      "        (norm1): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "        (relu1): ReLU(inplace)\n",
      "        (conv1): Conv2d(192, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "        (relu2): ReLU(inplace)\n",
      "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      )\n",
      "      (denselayer4): _DenseLayer(\n",
      "        (norm1): BatchNorm2d(224, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "        (relu1): ReLU(inplace)\n",
      "        (conv1): Conv2d(224, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "        (relu2): ReLU(inplace)\n",
      "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      )\n",
      "      (denselayer5): _DenseLayer(\n",
      "        (norm1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "        (relu1): ReLU(inplace)\n",
      "        (conv1): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "        (relu2): ReLU(inplace)\n",
      "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      )\n",
      "      (denselayer6): _DenseLayer(\n",
      "        (norm1): BatchNorm2d(288, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "        (relu1): ReLU(inplace)\n",
      "        (conv1): Conv2d(288, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "        (relu2): ReLU(inplace)\n",
      "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      )\n",
      "      (denselayer7): _DenseLayer(\n",
      "        (norm1): BatchNorm2d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "        (relu1): ReLU(inplace)\n",
      "        (conv1): Conv2d(320, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "        (relu2): ReLU(inplace)\n",
      "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      )\n",
      "      (denselayer8): _DenseLayer(\n",
      "        (norm1): BatchNorm2d(352, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "        (relu1): ReLU(inplace)\n",
      "        (conv1): Conv2d(352, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "        (relu2): ReLU(inplace)\n",
      "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      )\n",
      "      (denselayer9): _DenseLayer(\n",
      "        (norm1): BatchNorm2d(384, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "        (relu1): ReLU(inplace)\n",
      "        (conv1): Conv2d(384, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "        (relu2): ReLU(inplace)\n",
      "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      )\n",
      "      (denselayer10): _DenseLayer(\n",
      "        (norm1): BatchNorm2d(416, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "        (relu1): ReLU(inplace)\n",
      "        (conv1): Conv2d(416, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "        (relu2): ReLU(inplace)\n",
      "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      )\n",
      "      (denselayer11): _DenseLayer(\n",
      "        (norm1): BatchNorm2d(448, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "        (relu1): ReLU(inplace)\n",
      "        (conv1): Conv2d(448, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "        (relu2): ReLU(inplace)\n",
      "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      )\n",
      "      (denselayer12): _DenseLayer(\n",
      "        (norm1): BatchNorm2d(480, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "        (relu1): ReLU(inplace)\n",
      "        (conv1): Conv2d(480, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "        (relu2): ReLU(inplace)\n",
      "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      )\n",
      "    )\n",
      "    (transition2): _Transition(\n",
      "      (norm): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (relu): ReLU(inplace)\n",
      "      (conv): Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (pool): AvgPool2d(kernel_size=2, stride=2, padding=0)\n",
      "    )\n",
      "    (denseblock3): _DenseBlock(\n",
      "      (denselayer1): _DenseLayer(\n",
      "        (norm1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "        (relu1): ReLU(inplace)\n",
      "        (conv1): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "        (relu2): ReLU(inplace)\n",
      "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      )\n",
      "      (denselayer2): _DenseLayer(\n",
      "        (norm1): BatchNorm2d(288, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "        (relu1): ReLU(inplace)\n",
      "        (conv1): Conv2d(288, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "        (relu2): ReLU(inplace)\n",
      "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      )\n",
      "      (denselayer3): _DenseLayer(\n",
      "        (norm1): BatchNorm2d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "        (relu1): ReLU(inplace)\n",
      "        (conv1): Conv2d(320, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "        (relu2): ReLU(inplace)\n",
      "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      )\n",
      "      (denselayer4): _DenseLayer(\n",
      "        (norm1): BatchNorm2d(352, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "        (relu1): ReLU(inplace)\n",
      "        (conv1): Conv2d(352, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "        (relu2): ReLU(inplace)\n",
      "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      )\n",
      "      (denselayer5): _DenseLayer(\n",
      "        (norm1): BatchNorm2d(384, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "        (relu1): ReLU(inplace)\n",
      "        (conv1): Conv2d(384, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "        (relu2): ReLU(inplace)\n",
      "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      )\n",
      "      (denselayer6): _DenseLayer(\n",
      "        (norm1): BatchNorm2d(416, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "        (relu1): ReLU(inplace)\n",
      "        (conv1): Conv2d(416, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "        (relu2): ReLU(inplace)\n",
      "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      )\n",
      "      (denselayer7): _DenseLayer(\n",
      "        (norm1): BatchNorm2d(448, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "        (relu1): ReLU(inplace)\n",
      "        (conv1): Conv2d(448, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "        (relu2): ReLU(inplace)\n",
      "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      )\n",
      "      (denselayer8): _DenseLayer(\n",
      "        (norm1): BatchNorm2d(480, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "        (relu1): ReLU(inplace)\n",
      "        (conv1): Conv2d(480, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "        (relu2): ReLU(inplace)\n",
      "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      )\n",
      "      (denselayer9): _DenseLayer(\n",
      "        (norm1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "        (relu1): ReLU(inplace)\n",
      "        (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "        (relu2): ReLU(inplace)\n",
      "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      )\n",
      "      (denselayer10): _DenseLayer(\n",
      "        (norm1): BatchNorm2d(544, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "        (relu1): ReLU(inplace)\n",
      "        (conv1): Conv2d(544, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "        (relu2): ReLU(inplace)\n",
      "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      )\n",
      "      (denselayer11): _DenseLayer(\n",
      "        (norm1): BatchNorm2d(576, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "        (relu1): ReLU(inplace)\n",
      "        (conv1): Conv2d(576, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "        (relu2): ReLU(inplace)\n",
      "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      )\n",
      "      (denselayer12): _DenseLayer(\n",
      "        (norm1): BatchNorm2d(608, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "        (relu1): ReLU(inplace)\n",
      "        (conv1): Conv2d(608, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "        (relu2): ReLU(inplace)\n",
      "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      )\n",
      "      (denselayer13): _DenseLayer(\n",
      "        (norm1): BatchNorm2d(640, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "        (relu1): ReLU(inplace)\n",
      "        (conv1): Conv2d(640, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "        (relu2): ReLU(inplace)\n",
      "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      )\n",
      "      (denselayer14): _DenseLayer(\n",
      "        (norm1): BatchNorm2d(672, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "        (relu1): ReLU(inplace)\n",
      "        (conv1): Conv2d(672, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "        (relu2): ReLU(inplace)\n",
      "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      )\n",
      "      (denselayer15): _DenseLayer(\n",
      "        (norm1): BatchNorm2d(704, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "        (relu1): ReLU(inplace)\n",
      "        (conv1): Conv2d(704, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "        (relu2): ReLU(inplace)\n",
      "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      )\n",
      "      (denselayer16): _DenseLayer(\n",
      "        (norm1): BatchNorm2d(736, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "        (relu1): ReLU(inplace)\n",
      "        (conv1): Conv2d(736, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "        (relu2): ReLU(inplace)\n",
      "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      )\n",
      "      (denselayer17): _DenseLayer(\n",
      "        (norm1): BatchNorm2d(768, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "        (relu1): ReLU(inplace)\n",
      "        (conv1): Conv2d(768, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "        (relu2): ReLU(inplace)\n",
      "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      )\n",
      "      (denselayer18): _DenseLayer(\n",
      "        (norm1): BatchNorm2d(800, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "        (relu1): ReLU(inplace)\n",
      "        (conv1): Conv2d(800, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "        (relu2): ReLU(inplace)\n",
      "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      )\n",
      "      (denselayer19): _DenseLayer(\n",
      "        (norm1): BatchNorm2d(832, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "        (relu1): ReLU(inplace)\n",
      "        (conv1): Conv2d(832, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "        (relu2): ReLU(inplace)\n",
      "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      )\n",
      "      (denselayer20): _DenseLayer(\n",
      "        (norm1): BatchNorm2d(864, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "        (relu1): ReLU(inplace)\n",
      "        (conv1): Conv2d(864, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "        (relu2): ReLU(inplace)\n",
      "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      )\n",
      "      (denselayer21): _DenseLayer(\n",
      "        (norm1): BatchNorm2d(896, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "        (relu1): ReLU(inplace)\n",
      "        (conv1): Conv2d(896, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "        (relu2): ReLU(inplace)\n",
      "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      )\n",
      "      (denselayer22): _DenseLayer(\n",
      "        (norm1): BatchNorm2d(928, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "        (relu1): ReLU(inplace)\n",
      "        (conv1): Conv2d(928, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "        (relu2): ReLU(inplace)\n",
      "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      )\n",
      "      (denselayer23): _DenseLayer(\n",
      "        (norm1): BatchNorm2d(960, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "        (relu1): ReLU(inplace)\n",
      "        (conv1): Conv2d(960, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "        (relu2): ReLU(inplace)\n",
      "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      )\n",
      "      (denselayer24): _DenseLayer(\n",
      "        (norm1): BatchNorm2d(992, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "        (relu1): ReLU(inplace)\n",
      "        (conv1): Conv2d(992, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "        (relu2): ReLU(inplace)\n",
      "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      )\n",
      "    )\n",
      "    (transition3): _Transition(\n",
      "      (norm): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (relu): ReLU(inplace)\n",
      "      (conv): Conv2d(1024, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (pool): AvgPool2d(kernel_size=2, stride=2, padding=0)\n",
      "    )\n",
      "    (denseblock4): _DenseBlock(\n",
      "      (denselayer1): _DenseLayer(\n",
      "        (norm1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "        (relu1): ReLU(inplace)\n",
      "        (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "        (relu2): ReLU(inplace)\n",
      "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      )\n",
      "      (denselayer2): _DenseLayer(\n",
      "        (norm1): BatchNorm2d(544, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "        (relu1): ReLU(inplace)\n",
      "        (conv1): Conv2d(544, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "        (relu2): ReLU(inplace)\n",
      "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      )\n",
      "      (denselayer3): _DenseLayer(\n",
      "        (norm1): BatchNorm2d(576, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "        (relu1): ReLU(inplace)\n",
      "        (conv1): Conv2d(576, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "        (relu2): ReLU(inplace)\n",
      "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      )\n",
      "      (denselayer4): _DenseLayer(\n",
      "        (norm1): BatchNorm2d(608, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "        (relu1): ReLU(inplace)\n",
      "        (conv1): Conv2d(608, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "        (relu2): ReLU(inplace)\n",
      "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      )\n",
      "      (denselayer5): _DenseLayer(\n",
      "        (norm1): BatchNorm2d(640, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "        (relu1): ReLU(inplace)\n",
      "        (conv1): Conv2d(640, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "        (relu2): ReLU(inplace)\n",
      "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      )\n",
      "      (denselayer6): _DenseLayer(\n",
      "        (norm1): BatchNorm2d(672, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "        (relu1): ReLU(inplace)\n",
      "        (conv1): Conv2d(672, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "        (relu2): ReLU(inplace)\n",
      "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      )\n",
      "      (denselayer7): _DenseLayer(\n",
      "        (norm1): BatchNorm2d(704, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "        (relu1): ReLU(inplace)\n",
      "        (conv1): Conv2d(704, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "        (relu2): ReLU(inplace)\n",
      "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      )\n",
      "      (denselayer8): _DenseLayer(\n",
      "        (norm1): BatchNorm2d(736, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "        (relu1): ReLU(inplace)\n",
      "        (conv1): Conv2d(736, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "        (relu2): ReLU(inplace)\n",
      "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      )\n",
      "      (denselayer9): _DenseLayer(\n",
      "        (norm1): BatchNorm2d(768, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "        (relu1): ReLU(inplace)\n",
      "        (conv1): Conv2d(768, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "        (relu2): ReLU(inplace)\n",
      "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      )\n",
      "      (denselayer10): _DenseLayer(\n",
      "        (norm1): BatchNorm2d(800, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "        (relu1): ReLU(inplace)\n",
      "        (conv1): Conv2d(800, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "        (relu2): ReLU(inplace)\n",
      "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      )\n",
      "      (denselayer11): _DenseLayer(\n",
      "        (norm1): BatchNorm2d(832, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "        (relu1): ReLU(inplace)\n",
      "        (conv1): Conv2d(832, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "        (relu2): ReLU(inplace)\n",
      "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      )\n",
      "      (denselayer12): _DenseLayer(\n",
      "        (norm1): BatchNorm2d(864, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "        (relu1): ReLU(inplace)\n",
      "        (conv1): Conv2d(864, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "        (relu2): ReLU(inplace)\n",
      "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      )\n",
      "      (denselayer13): _DenseLayer(\n",
      "        (norm1): BatchNorm2d(896, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "        (relu1): ReLU(inplace)\n",
      "        (conv1): Conv2d(896, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "        (relu2): ReLU(inplace)\n",
      "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      )\n",
      "      (denselayer14): _DenseLayer(\n",
      "        (norm1): BatchNorm2d(928, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "        (relu1): ReLU(inplace)\n",
      "        (conv1): Conv2d(928, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "        (relu2): ReLU(inplace)\n",
      "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      )\n",
      "      (denselayer15): _DenseLayer(\n",
      "        (norm1): BatchNorm2d(960, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "        (relu1): ReLU(inplace)\n",
      "        (conv1): Conv2d(960, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "        (relu2): ReLU(inplace)\n",
      "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      )\n",
      "      (denselayer16): _DenseLayer(\n",
      "        (norm1): BatchNorm2d(992, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "        (relu1): ReLU(inplace)\n",
      "        (conv1): Conv2d(992, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "        (relu2): ReLU(inplace)\n",
      "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      )\n",
      "    )\n",
      "    (norm5): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "  )\n",
      "  (classifier): Sequential(\n",
      "    (fc1): Linear(in_features=1024, out_features=512, bias=True)\n",
      "    (relu): ReLU()\n",
      "    (dropout1): Dropout(p=0.5)\n",
      "    (fc2): Linear(in_features=512, out_features=256, bias=True)\n",
      "    (dropout2): Dropout(p=0.1)\n",
      "    (fc3): Linear(in_features=256, out_features=102, bias=True)\n",
      "    (output): LogSoftmax()\n",
      "  )\n",
      ")\n"
     ]
    }
   ],
   "source": [
    "print(model_load)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([81, 41])\n"
     ]
    }
   ],
   "source": [
    "# Testing checkpoint\n",
    "\n",
    "model_load.eval()\n",
    "images, labels = next(iter(dataloaders['valid_loader']))\n",
    "output = model_load.forward(Variable(images[:2]))\n",
    "ps = torch.exp(output).data\n",
    "print(ps.max(1)[1])"
   ]
  },
  {
   "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": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "def process_image(image):\n",
    "    ''' Scales, crops, and normalizes a PIL image for a PyTorch model,\n",
    "        returns an Numpy array\n",
    "    '''\n",
    "    # TODO: Process a PIL image for use in a PyTorch model\n",
    "    size = 256, 256\n",
    "    half_pixels = 112\n",
    "    new_width = 256 \n",
    "    new_heigth = 256\n",
    "    image.thumbnail(size, Image.ANTIALIAS)\n",
    "    image = image.crop((new_width/2 - half_pixels, new_heigth/2 - half_pixels, new_width/2 + half_pixels, new_heigth/2 + half_pixels))\n",
    "    np_image = np.array(image)\n",
    "    np_image = np_image/255  \n",
    "    np_image = (np_image - np.array([0.485,0.456,0.406])) / np.array([0.229,0.224,0.255])\n",
    "    np_image = np.transpose(np_image, (2,0,1))\n",
    "    \n",
    "    return np_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": 24,
   "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",
    "    \n",
    "    ax.imshow(image)\n",
    "    \n",
    "    return ax"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x10a99cac278>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 251,
       "width": 259
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pil_image = Image.open(\"C:/_GITHUB/Flower_Species_Prediction/flower_data/test/24/image_2401.jpg\")\n",
    "np_image = process_image(pil_image)\n",
    "imshow(np_image)"
   ]
  },
  {
   "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": 26,
   "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",
    "    # TODO: Implement the code to predict the class from an image file\n",
    "    \n",
    "    image = torch.FloatTensor([process_image(Image.open(image_path))])\n",
    "    model_load.eval()\n",
    "    output = model_load.forward(Variable(image))\n",
    "    pobabilities = torch.exp(output).data.numpy()[0]\n",
    "    \n",
    "    top_index = np.argsort(pobabilities)[-topk:][::-1] \n",
    "    top_class = [idx_to_class[x] for x in top_index]\n",
    "    top_probability = pobabilities[top_index]\n",
    "\n",
    "    return top_probability, top_class"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1.0000000e+00 9.0809893e-10 1.9793349e-10 1.1722542e-10 2.5297578e-11]\n",
      "['77', '14', '13', '92', '91']\n"
     ]
    }
   ],
   "source": [
    "# Test the functions\n",
    "probs, classes = predict('C:/_GITHUB/Flower_Species_Prediction/flower_data/test/77/image_7701.jpg', model_load)\n",
    "print(probs)\n",
    "print(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 validation 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": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "# TODO: Display an image along with the top 5 classes\n",
    "\n",
    "def display_classify(img, probabilities, classes, mapper):\n",
    "    ''' Function for viewing an image and it's predicted classes.\n",
    "    '''\n",
    "    img_filename = img.split('/')[-2]\n",
    "    img = Image.open(img)\n",
    "\n",
    "    fig, (ax1, ax2) = plt.subplots(figsize=(6,10), ncols=1, nrows=2)\n",
    "    flower_name = mapper[img_filename]\n",
    "    \n",
    "    ax1.set_title(flower_name)\n",
    "    ax1.imshow(img)\n",
    "    ax1.axis('off')\n",
    "    \n",
    "    y_pos = np.arange(len(probabilities))\n",
    "    ax2.barh(y_pos, probabilities)\n",
    "    ax2.set_yticks(y_pos)\n",
    "    ax2.set_yticklabels([mapper[x] for x in classes])\n",
    "    ax2.invert_yaxis()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 589,
       "width": 454
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "image_path = \"C:/_GITHUB/Flower_Species_Prediction/flower_data/test/15/image_1501.jpg\"\n",
    "p, c = predict(image_path, model_load)\n",
    "display_classify(image_path, p, c, cat_to_name)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## END OF PROJECT"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### References\n",
    "I use the reference below to help me in some parts of code, principally in some functions that I had dificulty, at this moment of upload of this model, I am learning more about matplotlib, numpy and pandas.\n",
    "\n",
    "- Pytorch Documentation: https://pytorch.org/docs/stable/index.html\n",
    "- Udacity Deep Learning Repository: https://github.com/udacity/deep-learning-v2-pytorch\n",
    "- Image Data: https://s3.amazonaws.com/content.udacity-data.com/courses/nd188/flower_data.zip\n",
    "- Udacity Project Repository: https://github.com/udacity/pytorch_challenge\n",
    "- rajesh-iiith/AIPND-ImageClassifier Repository: https://github.com/rajesh-iiith/AIPND-ImageClassifier\n",
    "- angang-li/image_classifier Repository: https://github.com/angang-li/image_classifier"
   ]
  },
  {
   "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",
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