Validation_and_inference.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Validation and inference.ipynb",
      "version": "0.3.2",
      "provenance": [],
      "collapsed_sections": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/Vishwaak/Time-for-Machine/blob/master/Validation_and_inference.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TW_K2Fv4jrAX",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import torch\n",
        "from torchvision import datasets, transforms\n",
        "import helper\n",
        "import numpy as np\n",
        "\n",
        "# Define a transform to normalize the data\n",
        "transform = transforms.Compose([transforms.ToTensor(),\n",
        "                                transforms.Normalize((0.5,), (0.5,))])\n",
        "# Download and load the training data\n",
        "trainset = datasets.FashionMNIST('~/.pytorch/F_MNIST_data/', download=True, train=True, transform=transform)\n",
        "trainloader = torch.utils.data.DataLoader(trainset, batch_size=64, shuffle=True)\n",
        "\n",
        "# Download and load the training data\n",
        "testset = datasets.FashionMNIST('~/.pytorch/F_MNIST_data/', download=True, train=False, transform=transform)\n",
        "testloader = torch.utils.data.DataLoader(testset, batch_size=64, shuffle=True)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wOyhA4vsIDvT",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nRbD7iE6IJ3P",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from torch import nn, optim\n",
        "import torch.nn.functional as F\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9TWp7lRBSugR",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FaM_hoNgIRTL",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#Network model.\n",
        "class Classifier(nn.Module):\n",
        "  def __init__(self):\n",
        "    super().__init__()\n",
        "    self.fc1 = nn.Linear(784 , 256)\n",
        "    self.fc2 = nn.Linear(256 , 128)\n",
        "    self.fc3 = nn.Linear(128 , 64)\n",
        "    self.fc4 = nn.Linear(64 , 10)\n",
        "    \n",
        "  def forward(self ,x):\n",
        "    x = x.view(x.shape[0] , -1)\n",
        "    \n",
        "    x = F.relu(self.fc1(x))\n",
        "    x = F.relu(self.fc2(x))\n",
        "    x = F.relu(self.fc3(x))\n",
        "    x = F.log_softmax(self.fc4(x) , dim =1)\n",
        "    \n",
        "    return x\n",
        "     \n",
        "    \n",
        "   \n",
        "  "
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9hxw8xFzRJA3",
        "colab_type": "code",
        "outputId": "d0797149-ac3f-4c7d-f465-a0c99e95cbb0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "model = Classifier()\n",
        "\n",
        "images, labels = next(iter(testloader))\n",
        "# Get the class probabilities\n",
        "ps = torch.exp(model(images))\n",
        "# Make sure the shape is appropriate, we should get 10 class probabilities for 64 examples\n",
        "print(ps.shape)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "torch.Size([64, 10])\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gNr5CJvyUDtj",
        "colab_type": "code",
        "outputId": "5feb283a-2713-4a73-94d9-7ec6ab4d147a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 191
        }
      },
      "source": [
        "top_p, top_class = ps.topk(1, dim=1)\n",
        "# Look at the most likely classes for the first 10 examples\n",
        "print(top_class[:10,:])"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "tensor([[8],\n",
            "        [8],\n",
            "        [8],\n",
            "        [8],\n",
            "        [2],\n",
            "        [2],\n",
            "        [2],\n",
            "        [2],\n",
            "        [8],\n",
            "        [8]])\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_edI-onUxfJi",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "equals = top_class == labels.view(*top_class.shape)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rOJFIrUSxlCG",
        "colab_type": "code",
        "outputId": "75338c9e-bda6-49a5-85f8-39cc10901e2c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "accuracy = torch.mean(equals.type(torch.FloatTensor))\n",
        "print(f'Accuracy: {accuracy.item()*100}%')"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Accuracy: 9.375%\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "suu6hZml2RiR",
        "colab_type": "code",
        "outputId": "925a25a3-f1a3-4828-d6f7-e36832af381e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 558
        }
      },
      "source": [
        "model = Classifier()\n",
        "criterion = nn.NLLLoss()\n",
        "optimizer = optim.Adam(model.parameters(), lr = 0.003)\n",
        "\n",
        "epochs = 30\n",
        "steps = 0\n",
        "\n",
        "train_losses, test_losses = [] , []\n",
        "for e in range(epochs):\n",
        "  running_loss = 0\n",
        "  for images , labels in trainloader:\n",
        "    \n",
        "    optimizer.zero_grad()\n",
        "    \n",
        "    log_ps = model(images)\n",
        "    loss = criterion(log_ps , labels)\n",
        "    loss.backward()\n",
        "    optimizer.step()\n",
        "    running_loss += loss.item()\n",
        "    \n",
        "  else:\n",
        "      test_loss = 0\n",
        "      accuracy = 0\n",
        "      \n",
        "      with torch.no_grad():\n",
        "        for images , labels in testloader:\n",
        "          log_ps = model(images)\n",
        "          test_loss += criterion(log_ps , labels)\n",
        "          \n",
        "          ps = torch.exp(log_ps)\n",
        "          top_p , top_class = ps.topk(1 , dim=1)\n",
        "          equals = top_class == labels.view(*top_class.shape)\n",
        "          accuracy += torch.mean(equals.type(torch.FloatTensor))\n",
        "    \n",
        "  train_losses.append(running_loss/len(trainloader))\n",
        "  test_losses.append(test_loss/len(testloader))\n",
        "    \n",
        "  print(\"Epoch: {}/{}.. \".format(e+1, epochs),\n",
        "              \"Training Loss: {:.3f}.. \".format(running_loss/len(trainloader)),\n",
        "              \"Test Loss: {:.3f}.. \".format(test_loss/len(testloader)),\n",
        "              \"Test Accuracy: {:.3f}\".format(accuracy/len(testloader)))\n",
        "    \n",
        "    \n",
        "    "
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch: 1/30..  Training Loss: 0.520..  Test Loss: 0.443..  Test Accuracy: 0.841\n",
            "Epoch: 2/30..  Training Loss: 0.393..  Test Loss: 0.448..  Test Accuracy: 0.836\n",
            "Epoch: 3/30..  Training Loss: 0.359..  Test Loss: 0.408..  Test Accuracy: 0.852\n",
            "Epoch: 4/30..  Training Loss: 0.335..  Test Loss: 0.415..  Test Accuracy: 0.856\n",
            "Epoch: 5/30..  Training Loss: 0.316..  Test Loss: 0.376..  Test Accuracy: 0.861\n",
            "Epoch: 6/30..  Training Loss: 0.302..  Test Loss: 0.374..  Test Accuracy: 0.869\n",
            "Epoch: 7/30..  Training Loss: 0.292..  Test Loss: 0.362..  Test Accuracy: 0.876\n",
            "Epoch: 8/30..  Training Loss: 0.284..  Test Loss: 0.366..  Test Accuracy: 0.874\n",
            "Epoch: 9/30..  Training Loss: 0.275..  Test Loss: 0.378..  Test Accuracy: 0.868\n",
            "Epoch: 10/30..  Training Loss: 0.266..  Test Loss: 0.357..  Test Accuracy: 0.876\n",
            "Epoch: 11/30..  Training Loss: 0.261..  Test Loss: 0.358..  Test Accuracy: 0.873\n",
            "Epoch: 12/30..  Training Loss: 0.253..  Test Loss: 0.365..  Test Accuracy: 0.878\n",
            "Epoch: 13/30..  Training Loss: 0.247..  Test Loss: 0.364..  Test Accuracy: 0.879\n",
            "Epoch: 14/30..  Training Loss: 0.244..  Test Loss: 0.401..  Test Accuracy: 0.873\n",
            "Epoch: 15/30..  Training Loss: 0.239..  Test Loss: 0.379..  Test Accuracy: 0.880\n",
            "Epoch: 16/30..  Training Loss: 0.236..  Test Loss: 0.370..  Test Accuracy: 0.880\n",
            "Epoch: 17/30..  Training Loss: 0.224..  Test Loss: 0.392..  Test Accuracy: 0.876\n",
            "Epoch: 18/30..  Training Loss: 0.222..  Test Loss: 0.379..  Test Accuracy: 0.880\n",
            "Epoch: 19/30..  Training Loss: 0.217..  Test Loss: 0.369..  Test Accuracy: 0.886\n",
            "Epoch: 20/30..  Training Loss: 0.216..  Test Loss: 0.379..  Test Accuracy: 0.887\n",
            "Epoch: 21/30..  Training Loss: 0.213..  Test Loss: 0.387..  Test Accuracy: 0.877\n",
            "Epoch: 22/30..  Training Loss: 0.207..  Test Loss: 0.381..  Test Accuracy: 0.885\n",
            "Epoch: 23/30..  Training Loss: 0.206..  Test Loss: 0.404..  Test Accuracy: 0.881\n",
            "Epoch: 24/30..  Training Loss: 0.201..  Test Loss: 0.408..  Test Accuracy: 0.881\n",
            "Epoch: 25/30..  Training Loss: 0.204..  Test Loss: 0.447..  Test Accuracy: 0.878\n",
            "Epoch: 26/30..  Training Loss: 0.198..  Test Loss: 0.429..  Test Accuracy: 0.880\n",
            "Epoch: 27/30..  Training Loss: 0.197..  Test Loss: 0.434..  Test Accuracy: 0.877\n",
            "Epoch: 28/30..  Training Loss: 0.187..  Test Loss: 0.403..  Test Accuracy: 0.884\n",
            "Epoch: 29/30..  Training Loss: 0.183..  Test Loss: 0.427..  Test Accuracy: 0.876\n",
            "Epoch: 30/30..  Training Loss: 0.182..  Test Loss: 0.468..  Test Accuracy: 0.881\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gjw6GgRORAa8",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "%matplotlib inline\n",
        "%config InlineBackend.figure_format = 'retina'\n",
        "\n",
        "import matplotlib.pyplot as plt"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PV5qouXRSAWE",
        "colab_type": "code",
        "outputId": "d286033c-dae7-4674-83b1-bb634a58adc4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 284
        }
      },
      "source": [
        "plt.plot(train_losses , label = 'Training Loss')\n",
        "plt.plot(test_losses , label='Test  Loss')\n",
        "plt.legend(frameon = False)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7f7ffc0b6c18>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAvgAAAH0CAYAAABICFkFAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nklrEBl+SJElqkf8BblPhRJDsfQoAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "image/png": {
              "width": 380,
              "height": 250
            }
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_3S8Ha25CijT",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RpodYD9Tl4dg",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "class Classifier(nn.Module):\n",
        "  def __init__(self):\n",
        "    super().__init__()\n",
        "    self.fc1 = nn.Linear(784 , 256)\n",
        "    self.fc2 = nn.Linear(256 , 128)\n",
        "    self.fc3 = nn.Linear(128 , 64)\n",
        "    self.fc4 = nn.Linear(64,10)\n",
        "    \n",
        "    self.dropout = nn.Dropout(p=0.2)\n",
        "    \n",
        "  def forward(self ,x):\n",
        "    x = x.view(x.shape[0] , -1)\n",
        "  \n",
        "    x = self.dropout(F.relu(self.fc1(x)))\n",
        "    x = self.dropout(F.relu(self.fc2(x)))\n",
        "    x = self.dropout(F.relu(self.fc3(x)))\n",
        "  \n",
        "    x = F.log_softmax(self.fc4(x) , dim =1)\n",
        "  \n",
        "    return x\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "r-2h5yXjnWlr",
        "colab_type": "code",
        "outputId": "40bdbe4f-f6bb-480e-e48a-adb73932d8d6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 732
        }
      },
      "source": [
        "model = Classifier()\n",
        "criterion = nn.NLLLoss()\n",
        "optimizer = optim.Adam(model.parameters(), lr=0.003)\n",
        "\n",
        "epochs = 40\n",
        "steps = 0\n",
        "\n",
        "train_losses, test_losses = [], []\n",
        "for e in range(epochs):\n",
        "    running_loss = 0\n",
        "    for images, labels in trainloader:\n",
        "        \n",
        "        optimizer.zero_grad()\n",
        "        \n",
        "        log_ps = model(images)\n",
        "        loss = criterion(log_ps, labels)\n",
        "        loss.backward()\n",
        "        optimizer.step()\n",
        "        \n",
        "        running_loss += loss.item()\n",
        "        \n",
        "    else:\n",
        "        test_loss = 0\n",
        "        accuracy = 0\n",
        "        \n",
        "        \n",
        "        with torch.no_grad():\n",
        "            model.eval()\n",
        "            for images, labels in testloader:\n",
        "                log_ps = model(images)\n",
        "                test_loss += criterion(log_ps, labels)\n",
        "                \n",
        "                ps = torch.exp(log_ps)\n",
        "                top_p, top_class = ps.topk(1, dim=1)\n",
        "                equals = top_class == labels.view(*top_class.shape)\n",
        "                accuracy += torch.mean(equals.type(torch.FloatTensor))\n",
        "        \n",
        "        model.train()\n",
        "        \n",
        "        train_losses.append(running_loss/len(trainloader))\n",
        "        test_losses.append(test_loss/len(testloader))\n",
        "\n",
        "        print(\"Epoch: {}/{}.. \".format(e+1, epochs),\n",
        "              \"Training Loss: {:.3f}.. \".format(train_losses[-1]),\n",
        "              \"Test Loss: {:.3f}.. \".format(test_losses[-1]),\n",
        "              \"Test Accuracy: {:.3f}\".format(accuracy/len(testloader)))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch: 1/40..  Training Loss: 0.601..  Test Loss: 0.479..  Test Accuracy: 0.826\n",
            "Epoch: 2/40..  Training Loss: 0.488..  Test Loss: 0.458..  Test Accuracy: 0.835\n",
            "Epoch: 3/40..  Training Loss: 0.450..  Test Loss: 0.440..  Test Accuracy: 0.841\n",
            "Epoch: 4/40..  Training Loss: 0.438..  Test Loss: 0.426..  Test Accuracy: 0.851\n",
            "Epoch: 5/40..  Training Loss: 0.424..  Test Loss: 0.422..  Test Accuracy: 0.851\n",
            "Epoch: 6/40..  Training Loss: 0.411..  Test Loss: 0.384..  Test Accuracy: 0.860\n",
            "Epoch: 7/40..  Training Loss: 0.408..  Test Loss: 0.384..  Test Accuracy: 0.864\n",
            "Epoch: 8/40..  Training Loss: 0.397..  Test Loss: 0.392..  Test Accuracy: 0.862\n",
            "Epoch: 9/40..  Training Loss: 0.393..  Test Loss: 0.388..  Test Accuracy: 0.866\n",
            "Epoch: 10/40..  Training Loss: 0.391..  Test Loss: 0.386..  Test Accuracy: 0.862\n",
            "Epoch: 11/40..  Training Loss: 0.385..  Test Loss: 0.384..  Test Accuracy: 0.868\n",
            "Epoch: 12/40..  Training Loss: 0.374..  Test Loss: 0.401..  Test Accuracy: 0.865\n",
            "Epoch: 13/40..  Training Loss: 0.382..  Test Loss: 0.413..  Test Accuracy: 0.857\n",
            "Epoch: 14/40..  Training Loss: 0.376..  Test Loss: 0.378..  Test Accuracy: 0.870\n",
            "Epoch: 15/40..  Training Loss: 0.366..  Test Loss: 0.383..  Test Accuracy: 0.868\n",
            "Epoch: 16/40..  Training Loss: 0.374..  Test Loss: 0.377..  Test Accuracy: 0.869\n",
            "Epoch: 17/40..  Training Loss: 0.359..  Test Loss: 0.420..  Test Accuracy: 0.853\n",
            "Epoch: 18/40..  Training Loss: 0.371..  Test Loss: 0.397..  Test Accuracy: 0.864\n",
            "Epoch: 19/40..  Training Loss: 0.366..  Test Loss: 0.381..  Test Accuracy: 0.872\n",
            "Epoch: 20/40..  Training Loss: 0.354..  Test Loss: 0.389..  Test Accuracy: 0.874\n",
            "Epoch: 21/40..  Training Loss: 0.364..  Test Loss: 0.372..  Test Accuracy: 0.876\n",
            "Epoch: 22/40..  Training Loss: 0.355..  Test Loss: 0.378..  Test Accuracy: 0.876\n",
            "Epoch: 23/40..  Training Loss: 0.347..  Test Loss: 0.386..  Test Accuracy: 0.868\n",
            "Epoch: 24/40..  Training Loss: 0.346..  Test Loss: 0.378..  Test Accuracy: 0.874\n",
            "Epoch: 25/40..  Training Loss: 0.362..  Test Loss: 0.367..  Test Accuracy: 0.875\n",
            "Epoch: 26/40..  Training Loss: 0.347..  Test Loss: 0.384..  Test Accuracy: 0.874\n",
            "Epoch: 27/40..  Training Loss: 0.341..  Test Loss: 0.382..  Test Accuracy: 0.875\n",
            "Epoch: 28/40..  Training Loss: 0.339..  Test Loss: 0.405..  Test Accuracy: 0.870\n",
            "Epoch: 29/40..  Training Loss: 0.345..  Test Loss: 0.367..  Test Accuracy: 0.875\n",
            "Epoch: 30/40..  Training Loss: 0.341..  Test Loss: 0.381..  Test Accuracy: 0.874\n",
            "Epoch: 31/40..  Training Loss: 0.337..  Test Loss: 0.389..  Test Accuracy: 0.874\n",
            "Epoch: 32/40..  Training Loss: 0.344..  Test Loss: 0.383..  Test Accuracy: 0.869\n",
            "Epoch: 33/40..  Training Loss: 0.346..  Test Loss: 0.373..  Test Accuracy: 0.874\n",
            "Epoch: 34/40..  Training Loss: 0.332..  Test Loss: 0.400..  Test Accuracy: 0.871\n",
            "Epoch: 35/40..  Training Loss: 0.332..  Test Loss: 0.380..  Test Accuracy: 0.878\n",
            "Epoch: 36/40..  Training Loss: 0.339..  Test Loss: 0.385..  Test Accuracy: 0.871\n",
            "Epoch: 37/40..  Training Loss: 0.328..  Test Loss: 0.393..  Test Accuracy: 0.867\n",
            "Epoch: 38/40..  Training Loss: 0.334..  Test Loss: 0.387..  Test Accuracy: 0.875\n",
            "Epoch: 39/40..  Training Loss: 0.325..  Test Loss: 0.377..  Test Accuracy: 0.873\n",
            "Epoch: 40/40..  Training Loss: 0.331..  Test Loss: 0.384..  Test Accuracy: 0.875\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RFk_x-VVCkSK",
        "colab_type": "code",
        "outputId": "53fab423-84cc-4cb4-82c2-9042dd3a93de",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 284
        }
      },
      "source": [
        "plt.plot(train_losses , label = 'Training Loss')\n",
        "plt.plot(test_losses , label='Test  Loss')\n",
        "plt.legend(frameon = False)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7f7ff8c35eb8>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 20
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAvgAAAH0CAYAAABICFkFAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nSZIKxAZfkiRJKpD/B0OalLJ6UrxFAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "image/png": {
              "width": 380,
              "height": 250
            }
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gDMYwOB1Ct9T",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "model.eval()\n",
        "\n",
        "detaiter = iter(testloader)\n",
        "\n",
        "images , labels = detaiter.next()\n",
        "img = images[1]\n",
        "\n",
        "img = img.view(1,784)\n",
        "\n",
        "with torch.no_grad():\n",
        "  output = model.forward(img)\n",
        "  \n",
        "ps = torch.exp(output)\n",
        "\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "R66D0X4GDrd9",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def view_classify(img, ps, version=\"MNIST\"):\n",
        "    ''' Function for viewing an image and it's predicted classes.\n",
        "    '''\n",
        "    ps = ps.data.numpy().squeeze()\n",
        "\n",
        "    fig, (ax1, ax2) = plt.subplots(figsize=(6,9), ncols=2)\n",
        "    ax1.imshow(img.resize_(1, 28, 28).numpy().squeeze())\n",
        "    ax1.axis('off')\n",
        "    ax2.barh(np.arange(10), ps)\n",
        "    ax2.set_aspect(0.1)\n",
        "    ax2.set_yticks(np.arange(10))\n",
        "    if version == \"MNIST\":\n",
        "        ax2.set_yticklabels(np.arange(10))\n",
        "    elif version == \"Fashion\":\n",
        "        ax2.set_yticklabels(['T-shirt/top',\n",
        "                            'Trouser',\n",
        "                            'Pullover',\n",
        "                            'Dress',\n",
        "                            'Coat',\n",
        "                            'Sandal',\n",
        "                            'Shirt',\n",
        "                            'Sneaker',\n",
        "                            'Bag',\n",
        "                            'Ankle Boot'], size='small');\n",
        "    ax2.set_title('Class Probability')\n",
        "    ax2.set_xlim(0, 1.1)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BXMaSBrVD8ek",
        "colab_type": "code",
        "outputId": "c2d49ed1-1660-44fc-a819-4973da25e5c0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 214
        }
      },
      "source": [
        "view_classify(img.view(1 , 28 , 28) , ps , version='Fashion')"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAuUAAAGKCAYAAACrcD/sAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x648 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "image/png": {
              "width": 370,
              "height": 197
            }
          }
        }
      ]
    }
  ]
}