examples/notebooks/FashionMNIST.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "lMov-LUSabxx"
   },
   "source": [
    "# Convolutional Neural Networks for Classifying Fashion-MNIST Dataset using Ignite\n",
    "This is a tutorial on using Ignite to train neural network models, setup experiments and validate models.\n",
    "\n",
    "In this notebook, we will be doing classification of images using Convolutional Neural Networks \n",
    "\n",
    "We will be using the [Fashion-MNIST dataset](https://github.com/zalandoresearch/fashion-mnist) Fashion-MNIST is a set of 28x28 greyscale images of clothes.\n",
    "\n",
    "![Fashion MNIST dataset](assets/fashion-mnist.png)\n",
    "\n",
    "Lets get started!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "7PiU02-fabxz"
   },
   "source": [
    "### Importing libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# !pip install pytorch-ignite"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "mGjzG-Obabx0"
   },
   "source": [
    "General Data-Science Libraries like numpy, matplotlib and seaborn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "xp_4isvHabx5"
   },
   "source": [
    "We import `torch`, `nn` and `functional` modules to create our models.\n",
    "\n",
    "We also import `datasets` and `transforms` from torchvision for loading the dataset and applying transforms to the images in the dataset.\n",
    "\n",
    "We import `Dataloader` for making train and validation loader for loading data into our model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\n",
    "from torch import nn, optim\n",
    "import torch.nn.functional as F\n",
    "from torch.utils.data import DataLoader\n",
    "from torchvision import datasets, transforms"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "Pj7oLY36abx8"
   },
   "source": [
    "`Ignite` is a High-level library to help with training neural networks in PyTorch. It comes with an `Engine` to setup a training loop, various metrics, handlers and a helpful contrib section! \n",
    "\n",
    "Below we import the following:\n",
    "* **Engine**: Runs a given process_function over each batch of a dataset, emitting events as it goes.\n",
    "* **Events**: Allows users to attach functions to an `Engine` to fire functions at a specific event. Eg: `EPOCH_COMPLETED`, `ITERATION_STARTED`, etc.\n",
    "* **Accuracy**: Metric to calculate accuracy over a dataset, for binary, multiclass, multilabel cases. \n",
    "* **Loss**: General metric that takes a loss function as a parameter, calculate loss over a dataset.\n",
    "* **RunningAverage**: General metric to attach to Engine during training. \n",
    "* **ModelCheckpoint**: Handler to checkpoint models. \n",
    "* **EarlyStopping**: Handler to stop training based on a score function. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "from ignite.engine import Events, create_supervised_trainer, create_supervised_evaluator\n",
    "from ignite.metrics import Accuracy, Loss, RunningAverage, ConfusionMatrix\n",
    "from ignite.handlers import ModelCheckpoint, EarlyStopping"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "vO53-X98abx-"
   },
   "source": [
    "The code below first sets up transform using `torhvision transfroms` for converting images to pytorch tensors and normalizing the images.\n",
    "\n",
    "Next, We use `torchvision datasets` for dowloading the fashion mnist dataset and applying transforms which we defined above.\n",
    "\n",
    "* `trainset` contains the training data.\n",
    "* `validationset` contains the validation data\n",
    "\n",
    "Next, We use `pytorch dataloader` for making dataloader from the train and validation sets."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# transform to normalize the data\n",
    "transform = transforms.Compose([transforms.ToTensor(),\n",
    "                                transforms.Normalize((0.5,), (0.5,))])\n",
    "\n",
    "# Download and load the training data\n",
    "trainset = datasets.FashionMNIST('./data', download=True, train=True, transform=transform)\n",
    "train_loader = DataLoader(trainset, batch_size=64, shuffle=True)\n",
    "\n",
    "# Download and load the test data\n",
    "validationset = datasets.FashionMNIST('./data', download=True, train=False, transform=transform)\n",
    "val_loader = DataLoader(validationset, batch_size=64, shuffle=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "Cvzm-Oe1abyB"
   },
   "source": [
    "### CNN Model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "uf4AVJ44abyC"
   },
   "source": [
    "Explanation of Model Architecture\n",
    "\n",
    "* [Convolutional layers](https://pytorch.org/docs/stable/nn.html#conv2d), the Convolutional layer is used to create a convolution kernel that is convolved with the layer input to produce a tensor of outputs.\n",
    "* [Maxpooling layers](https://pytorch.org/docs/stable/nn.html#maxpool2d), the Maxpooling layer is used to downsample an input representation keeping the most active pixels from the previous layer.\n",
    "* The usual [Linear](https://pytorch.org/docs/stable/nn.html#linear) + [Dropout](https://pytorch.org/docs/stable/nn.html#dropout2d) layers to avoid overfitting and produce a 10-dim output.\n",
    "* We had used [Relu](https://pytorch.org/docs/stable/nn.html#id27) Non Linearity for the model and [logsoftmax](https://pytorch.org/docs/stable/nn.html#log-softmax) at the last layer because we are going to use the [NLLL loss](https://pytorch.org/docs/stable/nn.html#nllloss).\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "class CNN(nn.Module):\n",
    "    \n",
    "    def __init__(self):\n",
    "        super(CNN, self).__init__()\n",
    "        \n",
    "        self.convlayer1 = nn.Sequential(\n",
    "            nn.Conv2d(1, 32, 3,padding=1),\n",
    "            nn.BatchNorm2d(32),\n",
    "            nn.ReLU(),\n",
    "            nn.MaxPool2d(kernel_size=2, stride=2)\n",
    "        )\n",
    "        \n",
    "        self.convlayer2 = nn.Sequential(\n",
    "            nn.Conv2d(32,64,3),\n",
    "            nn.BatchNorm2d(64),\n",
    "            nn.ReLU(),\n",
    "            nn.MaxPool2d(2)\n",
    "        )\n",
    "        \n",
    "        self.fc1 = nn.Linear(64*6*6,600)\n",
    "        self.drop = nn.Dropout2d(0.25)\n",
    "        self.fc2 = nn.Linear(600, 120)\n",
    "        self.fc3 = nn.Linear(120, 10)\n",
    "        \n",
    "    def forward(self, x):\n",
    "        x = self.convlayer1(x)\n",
    "        x = self.convlayer2(x)\n",
    "        x = x.view(-1,64*6*6)\n",
    "        x = self.fc1(x)\n",
    "        x = self.drop(x)\n",
    "        x = self.fc2(x)\n",
    "        x = self.fc3(x)\n",
    "        \n",
    "        return F.log_softmax(x,dim=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "oqvY1QLBabyE"
   },
   "source": [
    "### Creating Model, Optimizer and Loss"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "8z2zUS5zabyF"
   },
   "source": [
    "Below we create an instance of the CNN model. The model is placed on a device and then a loss function of `negative log likelihood loss` and `Adam optimizer` with learning rate of 0.001 are setup. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# creating model,and defining optimizer and loss\n",
    "model = CNN()\n",
    "# moving model to gpu if available\n",
    "device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\n",
    "model.to(device)\n",
    "optimizer = optim.Adam(model.parameters(), lr=0.001)\n",
    "criterion = nn.NLLLoss()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "o69S4bEvabyI"
   },
   "source": [
    "### Training and Evaluating using Ignite"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "PALFpjYoabyJ"
   },
   "source": [
    "### Instantiating Training and Evaluating Engines\n",
    "\n",
    "Below we create 2 engines, a trainer, and a evaluator. By using the `create_supervised_trainer` and `create_supervised_evaluator` by passing the required arguments.\n",
    "\n",
    "We import the metrics from `ignite.metrics` which we want to calculate for the model. Like `Accuracy`, `ConfusionMatrix`, and `Loss` and we pass them to `evaluator` engine which will calculate these metrics for each iteration.\n",
    "\n",
    "* `training_history`: it stores the training loss and accuracy\n",
    "* `validation_history`:it stores the validation loss and accuracy\n",
    "* `last_epoch`: it stores the last epoch untill the model is trained\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# defining the number of epochs\n",
    "epochs = 12\n",
    "# creating trainer,evaluator\n",
    "trainer = create_supervised_trainer(model, optimizer, criterion, device=device)\n",
    "metrics = {\n",
    "    'accuracy':Accuracy(),\n",
    "    'nll':Loss(criterion),\n",
    "    'cm':ConfusionMatrix(num_classes=10)\n",
    "}\n",
    "evaluator = create_supervised_evaluator(model, metrics=metrics, device=device)\n",
    "training_history = {'accuracy':[],'loss':[]}\n",
    "validation_history = {'accuracy':[],'loss':[]}\n",
    "last_epoch = []"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "aonym06RabyL"
   },
   "source": [
    "### Metrics - RunningAverage\n",
    "\n",
    "To start, we will attach a metric of `RunningAverage` to track a running average of the scalar loss output for each batch. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "RunningAverage(output_transform=lambda x: x).attach(trainer, 'loss')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "2vDAID6BabyP"
   },
   "source": [
    "### EarlyStopping - Tracking Validation Loss\n",
    "\n",
    "Now we will setup a `EarlyStopping` handler for this training process. EarlyStopping requires a score_function that allows the user to define whatever criteria to stop trainig. In this case, if the loss of the validation set does not decrease in 10 epochs, the training process will stop early.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": "<ignite.engine.engine.RemovableEventHandle at 0x7f3689feae80>"
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def score_function(engine):\n",
    "    val_loss = engine.state.metrics['nll']\n",
    "    return -val_loss\n",
    "\n",
    "handler = EarlyStopping(patience=10, score_function=score_function, trainer=trainer)\n",
    "evaluator.add_event_handler(Events.COMPLETED, handler)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "GZzf0wtLabyR"
   },
   "source": [
    "### Attaching Custom Functions to Engine at specific Events\n",
    "\n",
    "Below you will see ways to define your own custom functions and attaching them to various `Events` of the training process.\n",
    "\n",
    "The functions below both achieve similar tasks, they print the results of the evaluator run on a dataset. One function does that on the training evaluator and dataset, while the other on the validation. Another difference is how these functions are attached in the trainer engine.\n",
    "\n",
    "The first method involves using a decorator, the syntax is simple - `@` `trainer.on(Events.EPOCH_COMPLETED)`, means that the decorated function will be attached to the trainer and called at the end of each epoch. \n",
    "\n",
    "The second method involves using the add_event_handler method of trainer - `trainer.add_event_handler(Events.EPOCH_COMPLETED, custom_function)`. This achieves the same result as the above. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": "<ignite.engine.engine.RemovableEventHandle at 0x7f368a0289b0>"
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "@trainer.on(Events.EPOCH_COMPLETED)\n",
    "def log_training_results(trainer):\n",
    "    evaluator.run(train_loader)\n",
    "    metrics = evaluator.state.metrics\n",
    "    accuracy = metrics['accuracy']*100\n",
    "    loss = metrics['nll']\n",
    "    last_epoch.append(0)\n",
    "    training_history['accuracy'].append(accuracy)\n",
    "    training_history['loss'].append(loss)\n",
    "    print(\"Training Results - Epoch: {}  Avg accuracy: {:.2f} Avg loss: {:.2f}\"\n",
    "          .format(trainer.state.epoch, accuracy, loss))\n",
    "\n",
    "def log_validation_results(trainer):\n",
    "    evaluator.run(val_loader)\n",
    "    metrics = evaluator.state.metrics\n",
    "    accuracy = metrics['accuracy']*100\n",
    "    loss = metrics['nll']\n",
    "    validation_history['accuracy'].append(accuracy)\n",
    "    validation_history['loss'].append(loss)\n",
    "    print(\"Validation Results - Epoch: {}  Avg accuracy: {:.2f} Avg loss: {:.2f}\"\n",
    "          .format(trainer.state.epoch, accuracy, loss))\n",
    "    \n",
    "trainer.add_event_handler(Events.EPOCH_COMPLETED, log_validation_results)    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "z4ZHDOs1abyT"
   },
   "source": [
    "### Confusion Matrix"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "j5NOsTQxabyU"
   },
   "source": [
    "Confusion matrix gives us a better idea of what our classification model is getting right and what types of errors it is making.\n",
    "\n",
    "We visualize the `confusion matrix` using the `seaborn.heatmap` from `seaborn` library."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "@trainer.on(Events.COMPLETED)\n",
    "def log_confusion_matrix(trainer):\n",
    "    evaluator.run(val_loader)\n",
    "    metrics = evaluator.state.metrics\n",
    "    cm = metrics['cm']\n",
    "    cm = cm.numpy()\n",
    "    cm = cm.astype(int)\n",
    "    classes = ['T-shirt/top','Trouser','Pullover','Dress','Coat','Sandal','Shirt','Sneaker','Bag','Ankle Boot']\n",
    "    fig, ax = plt.subplots(figsize=(10,10))  \n",
    "    ax= plt.subplot()\n",
    "    sns.heatmap(cm, annot=True, ax = ax,fmt=\"d\")\n",
    "    # labels, title and ticks\n",
    "    ax.set_xlabel('Predicted labels')\n",
    "    ax.set_ylabel('True labels') \n",
    "    ax.set_title('Confusion Matrix') \n",
    "    ax.xaxis.set_ticklabels(classes,rotation=90)\n",
    "    ax.yaxis.set_ticklabels(classes,rotation=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "HX-NImoSabyW"
   },
   "source": [
    "### ModelCheckpoint\n",
    "\n",
    "Lastly, we want to checkpoint this model. It's important to do so, as training processes can be time consuming and if for some reason something goes wrong during training, a model checkpoint can be helpful to restart training from the point of failure.\n",
    "\n",
    "Below we will use Ignite's `ModelCheckpoint` handler to checkpoint models at the end of each epoch."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": "<ignite.engine.engine.RemovableEventHandle at 0x7f368a009898>"
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     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "checkpointer = ModelCheckpoint('./saved_models', 'fashionMNIST', n_saved=2, create_dir=True, save_as_state_dict=True, require_empty=False)\n",
    "trainer.add_event_handler(Events.EPOCH_COMPLETED, checkpointer, {'fashionMNIST': model})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "4YK6lGPHabyY"
   },
   "source": [
    "### Run Engine\n",
    "\n",
    "Next, we will run the trainer for 12 epochs and monitor results. Below we can see that custom functions defined above helps prints the `loss` and `accuracy` per epoch.  \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": "Training Results - Epoch: 1  Avg accuracy: 89.26 Avg loss: 0.29\nValidation Results - Epoch: 1  Avg accuracy: 88.04 Avg loss: 0.33\nTraining Results - Epoch: 2  Avg accuracy: 90.81 Avg loss: 0.26\nValidation Results - Epoch: 2  Avg accuracy: 89.37 Avg loss: 0.30\nTraining Results - Epoch: 3  Avg accuracy: 91.63 Avg loss: 0.23\nValidation Results - Epoch: 3  Avg accuracy: 89.31 Avg loss: 0.29\nTraining Results - Epoch: 4  Avg accuracy: 91.21 Avg loss: 0.23\nValidation Results - Epoch: 4  Avg accuracy: 88.86 Avg loss: 0.31\nTraining Results - Epoch: 5  Avg accuracy: 92.90 Avg loss: 0.19\nValidation Results - Epoch: 5  Avg accuracy: 90.39 Avg loss: 0.27\nTraining Results - Epoch: 6  Avg accuracy: 94.75 Avg loss: 0.15\nValidation Results - Epoch: 6  Avg accuracy: 91.85 Avg loss: 0.24\nTraining Results - Epoch: 7  Avg accuracy: 94.75 Avg loss: 0.15\nValidation Results - Epoch: 7  Avg accuracy: 91.60 Avg loss: 0.25\nTraining Results - Epoch: 8  Avg accuracy: 94.87 Avg loss: 0.14\nValidation Results - Epoch: 8  Avg accuracy: 91.69 Avg loss: 0.26\nTraining Results - Epoch: 9  Avg accuracy: 95.17 Avg loss: 0.13\nValidation Results - Epoch: 9  Avg accuracy: 91.29 Avg loss: 0.26\nTraining Results - Epoch: 10  Avg accuracy: 95.66 Avg loss: 0.12\nValidation Results - Epoch: 10  Avg accuracy: 91.38 Avg loss: 0.26\nTraining Results - Epoch: 11  Avg accuracy: 95.84 Avg loss: 0.11\nValidation Results - Epoch: 11  Avg accuracy: 91.18 Avg loss: 0.27\nTraining Results - Epoch: 12  Avg accuracy: 96.82 Avg loss: 0.09\nValidation Results - Epoch: 12  Avg accuracy: 91.91 Avg loss: 0.26\n"
    },
    {
     "data": {
      "text/plain": "State:\n\titeration: 11256\n\tepoch: 12\n\tepoch_length: 938\n\tmax_epochs: 12\n\toutput: 0.09215123951435089\n\tbatch: <class 'list'>\n\tmetrics: <class 'dict'>\n\tdataloader: <class 'torch.utils.data.dataloader.DataLoader'>\n\tseed: 12"
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": "<Figure size 720x720 with 2 Axes>"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "trainer.run(train_loader, max_epochs=epochs)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
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   },
   "source": [
    "### Plotting the loss and accuracy\n",
    "Next, we will plot the loss and accuracy which we have stored in the `training_history` and `validation_history` dictionary to see how loss and accuracy are changing with each epoch."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": "<Figure size 432x288 with 1 Axes>"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(training_history['accuracy'],label=\"Training Accuracy\")\n",
    "plt.plot(validation_history['accuracy'],label=\"Validation Accuracy\")\n",
    "plt.xlabel('No. of Epochs')\n",
    "plt.ylabel('Accuracy')\n",
    "plt.legend(frameon=False)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": "<Figure size 432x288 with 1 Axes>"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(training_history['loss'],label=\"Training Loss\")\n",
    "plt.plot(validation_history['loss'],label=\"Validation Loss\")\n",
    "plt.xlabel('No. of Epochs')\n",
    "plt.ylabel('Loss')\n",
    "plt.legend(frameon=False)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "NW9ze2HCabyp"
   },
   "source": [
    "### Loading the saved model from the disk\n",
    "Loading the saved pytorch model from the disk for inferencing."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": "Model Loaded\n"
    }
   ],
   "source": [
    "# loading the saved model\n",
    "def fetch_last_checkpoint_model_filename(model_save_path):\n",
    "    import os\n",
    "    checkpoint_files = os.listdir(model_save_path)\n",
    "    checkpoint_files = [f for f in checkpoint_files if '.pth' in f]\n",
    "    checkpoint_iter = [\n",
    "        int(x.split('_')[2].split('.')[0])\n",
    "        for x in checkpoint_files]\n",
    "    last_idx = np.array(checkpoint_iter).argmax()\n",
    "    return os.path.join(model_save_path, checkpoint_files[last_idx])\n",
    "\n",
    "model.load_state_dict(torch.load(fetch_last_checkpoint_model_filename('./saved_models')))\n",
    "print(\"Model Loaded\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "QNRWgV0Kabys"
   },
   "source": [
    "### Inferencing the model \n",
    "Below code will be used for inferencing from the model and visualizing the results.\n",
    "\n",
    "Here we do iteration from the `val_loader` and then select the class with highest probability and then compare it with actul class."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": "<Figure size 1800x288 with 10 Axes>"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# classes of fashion mnist dataset\n",
    "classes = ['T-shirt/top','Trouser','Pullover','Dress','Coat','Sandal','Shirt','Sneaker','Bag','Ankle Boot']\n",
    "# creating iterator for iterating the dataset\n",
    "dataiter = iter(val_loader)\n",
    "images, labels = dataiter.next()\n",
    "images_arr = []\n",
    "labels_arr = []\n",
    "pred_arr = []\n",
    "# moving model to cpu for inference \n",
    "model.to(\"cpu\")\n",
    "# iterating on the dataset to predict the output\n",
    "for i in range(0,10):\n",
    "    images_arr.append(images[i].unsqueeze(0))\n",
    "    labels_arr.append(labels[i].item())\n",
    "    ps = torch.exp(model(images_arr[i]))\n",
    "    ps = ps.data.numpy().squeeze()\n",
    "    pred_arr.append(np.argmax(ps))\n",
    "# plotting the results\n",
    "fig = plt.figure(figsize=(25,4))\n",
    "for i in range(10):\n",
    "    ax = fig.add_subplot(2, 20/2, i+1, xticks=[], yticks=[])\n",
    "    ax.imshow(images_arr[i].resize_(1, 28, 28).numpy().squeeze())\n",
    "    ax.set_title(\"{} ({})\".format(classes[pred_arr[i]], classes[labels_arr[i]]),\n",
    "                 color=(\"green\" if pred_arr[i]==labels_arr[i] else \"red\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "cWXOEpQ4abyv"
   },
   "source": [
    "### Refrences \n",
    "* [Pytorch Ignite Text CNN example notebook](https://github.com/pytorch/ignite/blob/master/examples/notebooks/TextCNN.ipynb)\n",
    "* [Pytorch Ignite MNIST example](https://github.com/pytorch/ignite/blob/master/examples/mnist/mnist.py)"
   ]
  }
 ],
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  "accelerator": "GPU",
  "colab": {
   "name": "FashionMNIST.ipynb",
   "provenance": [],
   "version": "0.3.2"
  },
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
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