ImageClassifierAssignment.ipynb

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  "metadata": {
    "colab": {
      "name": "ImageClassifierAssignment.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/hasanahmedfaisal/imageClassifier/blob/master/ImageClassifierAssignment.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sQXyLq178AYE",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "!pip install -q torch==1.3.1 torchvision"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cOYf37SLKgcq",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 578
        },
        "outputId": "03f338b8-a497-4b4c-c14f-10ec2277e872"
      },
      "source": [
        "%cd /content/\n",
        "!if ! test -d aipnd-project/; then git clone https://github.com/udacity/aipnd-project.git ; fi  \n",
        "%cd aipnd-project/\n",
        "!mkdir -p flowers/\n",
        "%cd flowers/\n",
        "!wget -nc https://s3.amazonaws.com/content.udacity-data.com/nd089/flower_data.tar.gz\n",
        "!if ! test -d test/; then tar -xzf flower_data.tar.gz; fi  \n",
        "!ls -l\n",
        "%cd /content/aipnd-project/\n",
        "!ls -l"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/content\n",
            "Cloning into 'aipnd-project'...\n",
            "remote: Enumerating objects: 35, done.\u001b[K\n",
            "remote: Total 35 (delta 0), reused 0 (delta 0), pack-reused 35\u001b[K\n",
            "Unpacking objects: 100% (35/35), done.\n",
            "/content/aipnd-project\n",
            "/content/aipnd-project/flowers\n",
            "--2019-12-08 17:49:53--  https://s3.amazonaws.com/content.udacity-data.com/nd089/flower_data.tar.gz\n",
            "Resolving s3.amazonaws.com (s3.amazonaws.com)... 52.216.104.189\n",
            "Connecting to s3.amazonaws.com (s3.amazonaws.com)|52.216.104.189|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 344873452 (329M) [application/x-gzip]\n",
            "Saving to: ‘flower_data.tar.gz’\n",
            "\n",
            "flower_data.tar.gz  100%[===================>] 328.90M  45.4MB/s    in 7.7s    \n",
            "\n",
            "2019-12-08 17:50:01 (42.8 MB/s) - ‘flower_data.tar.gz’ saved [344873452/344873452]\n",
            "\n",
            "total 336808\n",
            "-rw-r--r--   1 root root 344873452 Jan 26  2018 flower_data.tar.gz\n",
            "drwxrwxr-x 104 1000 1000      4096 Jan 15  2018 test\n",
            "drwxrwxr-x 104 1000 1000      4096 Jan 15  2018 train\n",
            "drwxrwxr-x 104 1000 1000      4096 Jan 15  2018 valid\n",
            "/content/aipnd-project\n",
            "total 40\n",
            "drwxr-xr-x 2 root root  4096 Dec  8 17:49  assets\n",
            "-rw-r--r-- 1 root root  2218 Dec  8 17:49  cat_to_name.json\n",
            "drwxr-xr-x 5 root root  4096 Dec  8 17:50  flowers\n",
            "-rw-r--r-- 1 root root 17748 Dec  8 17:49 'Image Classifier Project.ipynb'\n",
            "-rw-r--r-- 1 root root  1064 Dec  8 17:49  LICENSE\n",
            "-rw-r--r-- 1 root root     0 Dec  8 17:49  predict.py\n",
            "-rw-r--r-- 1 root root   250 Dec  8 17:49  README.md\n",
            "-rw-r--r-- 1 root root     0 Dec  8 17:49  train.py\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Z9XWGpVjKrJL",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 255
        },
        "outputId": "acace978-f8eb-4dd4-de92-e3faa3da6c23"
      },
      "source": [
        "%cd /content/aipnd-project/\n",
        "!ls -l\n",
        "%cd /content/\n",
        "!ls -l"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/content/aipnd-project\n",
            "total 40\n",
            "drwxr-xr-x 2 root root  4096 Dec  8 17:49  assets\n",
            "-rw-r--r-- 1 root root  2218 Dec  8 17:49  cat_to_name.json\n",
            "drwxr-xr-x 5 root root  4096 Dec  8 17:50  flowers\n",
            "-rw-r--r-- 1 root root 17748 Dec  8 17:49 'Image Classifier Project.ipynb'\n",
            "-rw-r--r-- 1 root root  1064 Dec  8 17:49  LICENSE\n",
            "-rw-r--r-- 1 root root     0 Dec  8 17:49  predict.py\n",
            "-rw-r--r-- 1 root root   250 Dec  8 17:49  README.md\n",
            "-rw-r--r-- 1 root root     0 Dec  8 17:49  train.py\n",
            "/content\n",
            "total 8\n",
            "drwxr-xr-x 5 root root 4096 Dec  8 17:49 aipnd-project\n",
            "drwxr-xr-x 1 root root 4096 Nov 27 22:38 sample_data\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HogMtINfKyEq",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# import depencencies\n",
        "\n",
        "import torch \n",
        "from torch import nn, optim\n",
        "import torch.nn.functional as F\n",
        "from torchvision import datasets, transforms, models\n",
        "\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "%matplotlib inline\n",
        "\n",
        "from PIL import Image\n",
        "\n",
        "import json"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CIeGm6zsAcN8",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "data_dir = '/content/aipnd-project/flowers'\n",
        "train_dir = data_dir + '/train'\n",
        "valid_dir = data_dir + '/valid'\n",
        "test_dir = data_dir + '/test'"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "G1EnIVoVkbXZ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# transform operations training, validation, and testing sets\n",
        "train_transforms = transforms.Compose([transforms.RandomResizedCrop(224),\n",
        "                                       transforms.RandomRotation(30),\n",
        "                                       transforms.RandomHorizontalFlip(),\n",
        "                                       transforms.ToTensor(),\n",
        "                                       transforms.Normalize([0.485, 0.456, 0.406],[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],[0.229, 0.224, 0.225])])\n",
        "\n",
        "# datasets for training, testing, validation\n",
        "train_datasets = datasets.ImageFolder(train_dir,train_transforms)\n",
        "valid_datasets = datasets.ImageFolder(valid_dir,test_transforms)\n",
        "test_datasets = datasets.ImageFolder(test_dir,test_transforms)\n",
        "\n",
        "\n",
        "\n",
        "# dataloaders\n",
        "train_dataloaders = torch.utils.data.DataLoader(train_datasets, batch_size=64, shuffle= True )\n",
        "valid_dataloaders = torch.utils.data.DataLoader(valid_datasets, batch_size=64, shuffle= True )\n",
        "test_dataloaders = torch.utils.data.DataLoader(test_datasets, batch_size=32 )"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zkKta_IzerLJ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# mapping integer encoded category to names\n",
        "with open('/content/aipnd-project/cat_to_name.json', 'r') as f:\n",
        "    cat_to_name = json.load(f)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "I4wW5fhOiCFZ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "rsnet_model = models.resnet50(pretrained = True)\n",
        "rsnet_model"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "P-y0WbhRiCvB",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#freezing parameters of model\n",
        "for param in rsnet_model.parameters():\n",
        "    param.requires_grad = False"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Vp9SyhWKiGzh",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 136
        },
        "outputId": "6dee6ea4-8775-49cd-af12-665477ddc3ac"
      },
      "source": [
        "#Defining Classifier\n",
        "class Network(nn.Module):\n",
        "    def __init__(self,input_size, hiddenlayers, output_size, drop_p = 0.5):\n",
        "        \n",
        "        super().__init__()\n",
        "        \n",
        "        self.hidden_layers = nn.ModuleList([nn.Linear(input_size, hiddenlayers[0])])\n",
        "        \n",
        "        layersizes = zip(hiddenlayers[:-1],hiddenlayers[1:])\n",
        "        \n",
        "        self.hidden_layers.extend([nn.Linear(h1,h2) for h1,h2 in layersizes])\n",
        "        \n",
        "        self.output = nn.Linear(hiddenlayers[-1],output_size)\n",
        "                \n",
        "        self.dropout = nn.Dropout(p = drop_p)\n",
        "        \n",
        "    def forward(self , x):\n",
        "        \n",
        "        for lin in self.hidden_layers:\n",
        "            x = F.relu(lin(x))\n",
        "            x = self.dropout(x)\n",
        "            \n",
        "        x = self.output(x)\n",
        "        \n",
        "        return F.log_softmax(x, dim= 1)\n",
        "\n",
        "#set calssifer for model\n",
        "fcclassifier = Network(2048,[512],102)\n",
        "\n",
        "fcclassifier"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Network(\n",
              "  (hidden_layers): ModuleList(\n",
              "    (0): Linear(in_features=2048, out_features=512, bias=True)\n",
              "  )\n",
              "  (output): Linear(in_features=512, out_features=102, bias=True)\n",
              "  (dropout): Dropout(p=0.5, inplace=False)\n",
              ")"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ep2cf_o3jIzT",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "\n",
        "rsnet_model.fc = fcclassifier\n",
        "rsnet_model"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "r-qKMx85ji8J",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#Set device\n",
        "device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\" )"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nsV2j-FBjo72",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#Validation \n",
        "def Validation(model,criterion,valid_dataloaders):\n",
        "    testloss = 0\n",
        "    accuracy = 0\n",
        "    totaldatasetlen = 0\n",
        "    model.to(device)\n",
        "\n",
        "    for images , labels in valid_dataloaders:\n",
        "        images,labels = images.to(device),labels.to(device)\n",
        "        output = model.forward(images)\n",
        "        testloss += criterion(output, labels).item()\n",
        "        \n",
        "        ps = torch.exp(output)\n",
        "        \n",
        "        equity = (labels == ps.max(dim = 1)[1])\n",
        "        totaldatasetlen +=labels.size(0)\n",
        "        accuracy += equity.type(torch.FloatTensor).mean()\n",
        "    \n",
        "    \n",
        "    return testloss,accuracy,totaldatasetlen"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2Fba4_KYj4-i",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#train the network\n",
        "def trainthemodel(model,optimizer,criterion,train_dataloaders,valid_dataloaders,epochs = 10,print_every = 40,):\n",
        "    \n",
        "\n",
        "    running_loss = 0 \n",
        "    steps = 0\n",
        "    \n",
        "    model.to(device)\n",
        "\n",
        "    print(\"The training about to start and print every {} steps...\".format(printevery))\n",
        "    print(\"The divice its running on is : {}. ..\".format(device))\n",
        "\n",
        "\n",
        "    for e in range(epochs):\n",
        "        model.train()\n",
        "        \n",
        "        for images,labels in train_dataloaders:\n",
        "            steps += 1\n",
        "            images,labels = images.to(device),labels.to(device)\n",
        "            optimizer.zero_grad()\n",
        "            output = model.forward(images)\n",
        "            Loss = criterion(output,labels)\n",
        "            \n",
        "            Loss.backward()\n",
        "            optimizer.step()\n",
        "            \n",
        "            running_loss += Loss.item()\n",
        "            \n",
        "            \n",
        "            if steps%printevery == 0:\n",
        "                model.eval()\n",
        "                with torch.no_grad():\n",
        "                    test_loss,accuracy,totaldatalen = Validation(model,criterion,valid_dataloaders)\n",
        "                \n",
        "                print(\"The no of steps is {}...\".format(steps),\n",
        "                      \"The epoch is {}/{}....\".format(e+1 ,epochs),\n",
        "                      \"The Loss of model is {:.4f}...\".format(running_loss/printevery),\n",
        "                      \"Test Loss: {:.3f}.. \".format(test_loss/len(valid_dataloaders)),\n",
        "                      \"Test Accuracy: {:.3f}%\".format(accuracy/len(valid_dataloaders) * 100)\n",
        "                 )\n",
        "                \n",
        "                running_loss = 0\n",
        "                model.train()    \n",
        "            \n",
        "    print(\"Training Complete\")"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iqLbx0RukDah",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Do validation on testset\n",
        "\n",
        "def Acuuracyontestset(model,criterion,test_dataloaders):\n",
        "    testloss = 0\n",
        "    accuracy = 0\n",
        "    totaldatasetlen = 0\n",
        "    \n",
        "    model.to(device)\n",
        "    \n",
        "    model.eval()\n",
        "    for images , labels in test_dataloaders:\n",
        "        images,labels = images.to(device),labels.to(device)\n",
        "        output = model.forward(images)        \n",
        "        ps = torch.exp(output)\n",
        "        \n",
        "        equity = (labels == ps.max(dim = 1)[1])\n",
        "        totaldatasetlen +=labels.size(0)\n",
        "        accuracy += equity.type(torch.FloatTensor).sum().item()\n",
        "    \n",
        "    \n",
        "    return accuracy,totaldatasetlen"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xea1GmXdkcND",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def Acurracy(model):\n",
        "    print(\"Calculating Acuracy:\")\n",
        "    with torch.no_grad():\n",
        "        accuracy,total = Acuuracyontestset(model,criterion,test_dataloaders)\n",
        "    \n",
        "    print('Accuracy of the network on the {} test images: {}%...'.format(total,(100 * accuracy / total)))\n",
        "    \n",
        "    return (100 * accuracy / total)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TUwQw29nkc6D",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Save the checkpoint \n",
        "def savecheckpoint(filename):\n",
        "    modelcheckpoint = { 'input_size':2048,\n",
        "                  'output_size':102,\n",
        "                   'epochs': epochs,\n",
        "                    'learningrate': learningrate,\n",
        "                   'model' : models.resnet50(pretrained= True),\n",
        "                   'classifier' : rsnet_model.fc,\n",
        "                   'state_Dict': rsnet_model.state_dict(),\n",
        "                   'class_to_idx': train_datasets.class_to_idx\n",
        "                   }\n",
        "\n",
        "    torch.save(modelcheckpoint, filename)\n",
        "    print(\"The model is saved in {} file..\".format(filename))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Vd5VIFCGkt54",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#  function that loads a checkpoint and rebuilds the model\n",
        "def loadCheckpoint(filepath):\n",
        "    \n",
        "    if device == 'cuda:0':\n",
        "        checkpoint = torch.load(filepath)\n",
        "    else:\n",
        "        checkpoint = torch.load(filepath, map_location=lambda storage, loc: storage)\n",
        "    \n",
        "    model = checkpoint['model']\n",
        "    for param in model.parameters():\n",
        "        param.requires_grad = False\n",
        "    \n",
        "    model.fc = checkpoint['classifier']\n",
        "    model.load_state_dict(checkpoint['state_Dict'])\n",
        "    epochs = checkpoint['epochs']\n",
        "    model.class_to_idx = checkpoint['class_to_idx']\n",
        "    \n",
        "    print(\"The model is loaded from {} file..\".format(filepath))\n",
        "    return model"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yqk3ItpUlL72",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "printevery = 40\n",
        "epochs = 10\n",
        "\n",
        "learningrate = 0.0005\n",
        "\n",
        "criterion = nn.NLLLoss()\n",
        "optimizer = optim.Adam(rsnet_model.fc.parameters(), lr = learningrate)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Um4aD4KIlTjZ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 527
        },
        "outputId": "51bb2880-d04e-4631-e2d3-bd88c3cdc006"
      },
      "source": [
        "    \n",
        "trainthemodel(rsnet_model,optimizer,criterion,train_dataloaders,valid_dataloaders,epochs,printevery)\n",
        "old_acuraccy = Acurracy(rsnet_model)"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "The training about to start and print every 40 steps...\n",
            "The divice its running on is : cuda:0. ..\n",
            "The no of steps is 40... The epoch is 1/10.... The Loss of model is 4.4312... Test Loss: 4.075..  Test Accuracy: 13.611%\n",
            "The no of steps is 80... The epoch is 1/10.... The Loss of model is 4.0116... Test Loss: 3.518..  Test Accuracy: 25.385%\n",
            "The no of steps is 120... The epoch is 2/10.... The Loss of model is 3.5604... Test Loss: 2.943..  Test Accuracy: 34.947%\n",
            "The no of steps is 160... The epoch is 2/10.... The Loss of model is 3.1141... Test Loss: 2.465..  Test Accuracy: 44.062%\n",
            "The no of steps is 200... The epoch is 2/10.... The Loss of model is 2.7606... Test Loss: 2.081..  Test Accuracy: 52.558%\n",
            "The no of steps is 240... The epoch is 3/10.... The Loss of model is 2.4761... Test Loss: 1.848..  Test Accuracy: 58.274%\n",
            "The no of steps is 280... The epoch is 3/10.... The Loss of model is 2.2240... Test Loss: 1.558..  Test Accuracy: 67.798%\n",
            "The no of steps is 320... The epoch is 4/10.... The Loss of model is 2.1230... Test Loss: 1.429..  Test Accuracy: 69.462%\n",
            "The no of steps is 360... The epoch is 4/10.... The Loss of model is 1.9397... Test Loss: 1.276..  Test Accuracy: 72.587%\n",
            "The no of steps is 400... The epoch is 4/10.... The Loss of model is 1.8471... Test Loss: 1.207..  Test Accuracy: 73.308%\n",
            "The no of steps is 440... The epoch is 5/10.... The Loss of model is 1.7492... Test Loss: 1.092..  Test Accuracy: 77.019%\n",
            "The no of steps is 480... The epoch is 5/10.... The Loss of model is 1.6472... Test Loss: 1.025..  Test Accuracy: 77.120%\n",
            "The no of steps is 520... The epoch is 6/10.... The Loss of model is 1.5845... Test Loss: 1.007..  Test Accuracy: 76.019%\n",
            "The no of steps is 560... The epoch is 6/10.... The Loss of model is 1.5379... Test Loss: 0.946..  Test Accuracy: 77.635%\n",
            "The no of steps is 600... The epoch is 6/10.... The Loss of model is 1.4306... Test Loss: 0.839..  Test Accuracy: 79.899%\n",
            "The no of steps is 640... The epoch is 7/10.... The Loss of model is 1.4500... Test Loss: 0.825..  Test Accuracy: 79.231%\n",
            "The no of steps is 680... The epoch is 7/10.... The Loss of model is 1.4233... Test Loss: 0.761..  Test Accuracy: 82.115%\n",
            "The no of steps is 720... The epoch is 7/10.... The Loss of model is 1.3921... Test Loss: 0.828..  Test Accuracy: 78.942%\n",
            "The no of steps is 760... The epoch is 8/10.... The Loss of model is 1.3390... Test Loss: 0.740..  Test Accuracy: 82.543%\n",
            "The no of steps is 800... The epoch is 8/10.... The Loss of model is 1.3108... Test Loss: 0.698..  Test Accuracy: 84.139%\n",
            "The no of steps is 840... The epoch is 9/10.... The Loss of model is 1.2794... Test Loss: 0.707..  Test Accuracy: 81.928%\n",
            "The no of steps is 880... The epoch is 9/10.... The Loss of model is 1.2434... Test Loss: 0.681..  Test Accuracy: 83.572%\n",
            "The no of steps is 920... The epoch is 9/10.... The Loss of model is 1.2463... Test Loss: 0.660..  Test Accuracy: 83.865%\n",
            "The no of steps is 960... The epoch is 10/10.... The Loss of model is 1.2013... Test Loss: 0.650..  Test Accuracy: 83.404%\n",
            "The no of steps is 1000... The epoch is 10/10.... The Loss of model is 1.2107... Test Loss: 0.598..  Test Accuracy: 86.543%\n",
            "Training Complete\n",
            "Calculating Acuracy:\n",
            "Accuracy of the network on the 819 test images: 85.47008547008546%...\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HWvloGtLlWSI",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 479
        },
        "outputId": "ca0242e0-9f2c-49a0-b58b-86b2ada3f508"
      },
      "source": [
        "  \n",
        "filename = 'checkpointresnet.pth'\n",
        "savecheckpoint(filename)\n",
        "rsnet_model = loadCheckpoint(filename) "
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/torch/serialization.py:292: UserWarning: Couldn't retrieve source code for container of type ResNet. It won't be checked for correctness upon loading.\n",
            "  \"type \" + obj.__name__ + \". It won't be checked \"\n",
            "/usr/local/lib/python3.6/dist-packages/torch/serialization.py:292: UserWarning: Couldn't retrieve source code for container of type Conv2d. It won't be checked for correctness upon loading.\n",
            "  \"type \" + obj.__name__ + \". It won't be checked \"\n",
            "/usr/local/lib/python3.6/dist-packages/torch/serialization.py:292: UserWarning: Couldn't retrieve source code for container of type BatchNorm2d. It won't be checked for correctness upon loading.\n",
            "  \"type \" + obj.__name__ + \". It won't be checked \"\n",
            "/usr/local/lib/python3.6/dist-packages/torch/serialization.py:292: UserWarning: Couldn't retrieve source code for container of type ReLU. It won't be checked for correctness upon loading.\n",
            "  \"type \" + obj.__name__ + \". It won't be checked \"\n",
            "/usr/local/lib/python3.6/dist-packages/torch/serialization.py:292: UserWarning: Couldn't retrieve source code for container of type MaxPool2d. It won't be checked for correctness upon loading.\n",
            "  \"type \" + obj.__name__ + \". It won't be checked \"\n",
            "/usr/local/lib/python3.6/dist-packages/torch/serialization.py:292: UserWarning: Couldn't retrieve source code for container of type Sequential. It won't be checked for correctness upon loading.\n",
            "  \"type \" + obj.__name__ + \". It won't be checked \"\n",
            "/usr/local/lib/python3.6/dist-packages/torch/serialization.py:292: UserWarning: Couldn't retrieve source code for container of type Bottleneck. It won't be checked for correctness upon loading.\n",
            "  \"type \" + obj.__name__ + \". It won't be checked \"\n",
            "/usr/local/lib/python3.6/dist-packages/torch/serialization.py:292: UserWarning: Couldn't retrieve source code for container of type AdaptiveAvgPool2d. It won't be checked for correctness upon loading.\n",
            "  \"type \" + obj.__name__ + \". It won't be checked \"\n",
            "/usr/local/lib/python3.6/dist-packages/torch/serialization.py:292: UserWarning: Couldn't retrieve source code for container of type Linear. It won't be checked for correctness upon loading.\n",
            "  \"type \" + obj.__name__ + \". It won't be checked \"\n",
            "/usr/local/lib/python3.6/dist-packages/torch/serialization.py:292: UserWarning: Couldn't retrieve source code for container of type Network. It won't be checked for correctness upon loading.\n",
            "  \"type \" + obj.__name__ + \". It won't be checked \"\n",
            "/usr/local/lib/python3.6/dist-packages/torch/serialization.py:292: UserWarning: Couldn't retrieve source code for container of type ModuleList. It won't be checked for correctness upon loading.\n",
            "  \"type \" + obj.__name__ + \". It won't be checked \"\n",
            "/usr/local/lib/python3.6/dist-packages/torch/serialization.py:292: UserWarning: Couldn't retrieve source code for container of type Dropout. It won't be checked for correctness upon loading.\n",
            "  \"type \" + obj.__name__ + \". It won't be checked \"\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The model is saved in checkpointresnet.pth file..\n",
            "The model is loaded from checkpointresnet.pth file..\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "B8uYL4qAowbV",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def predict(image_path, model, topk=5):\n",
        "    ''' Predict the class (or classes) of an image using a trained deep learning model.\n",
        "    '''\n",
        "    \n",
        "    # TODO: Implement the code to predict the class from an image file\n",
        "    \n",
        "    image = process_image(image_path)\n",
        "    \n",
        "    #print(image.shape)\n",
        "\n",
        "    image.unsqueeze_(0)\n",
        "    \n",
        "    #print(image.shape)\n",
        "    \n",
        "    image = image\n",
        "\n",
        "    image = image.to(device)\n",
        "    \n",
        "    model.type(torch.DoubleTensor)\n",
        "    \n",
        "    model.to(device)\n",
        "    \n",
        "    model.eval()\n",
        "    \n",
        "    with torch.no_grad():\n",
        "        \n",
        "        output = model.forward(image)\n",
        "        \n",
        "        prob = torch.exp(output)\n",
        "        \n",
        "        probs , indices = torch.topk(prob , topk) \n",
        "        \n",
        "                \n",
        "        indice = indices.cpu()\n",
        "        \n",
        "        indice = indice.numpy()[0]\n",
        "        \n",
        "        # Convert indices to classes\n",
        "        idx_to_class = {val: key for key, val in model.class_to_idx.items()}  \n",
        "        \n",
        "        #print(idx_to_class)\n",
        "        \n",
        "        classes = [idx_to_class[index] for index in indice]\n",
        "        \n",
        "    \n",
        "    return  probs , classes\n",
        "        "
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EzlkTXWxo2vU",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def SanityCheck(path):\n",
        "    \n",
        "    image = Image.open(path)\n",
        "    \n",
        "    lis = path.split(\"/\")\n",
        "    \n",
        "    OriginalClass = cat_to_name[lis[-2]]\n",
        "    \n",
        "    probs, classes = predict(path, rsnet_model)\n",
        "    \n",
        "    probs = probs.cpu()\n",
        "    probs = probs.numpy()\n",
        "    \n",
        "    TopKClasses = [cat_to_name[clas] for clas in classes]\n",
        "    \n",
        "    \n",
        "    plt.figure(figsize = [5, 10]) # larger figure size for subplots\n",
        "    \n",
        "    #Original Image\n",
        "    ax = plt.subplot(2,1,1)\n",
        "    \n",
        "    image = np.array(image)\n",
        "    \n",
        "    ax.imshow(image)\n",
        "    ax.axis('off')\n",
        "    ax.set_title(OriginalClass)\n",
        "    \n",
        "    #Image Prediction\n",
        "    ax = plt.subplot(2,1,2)\n",
        "    \n",
        "    y_pos = np.arange(len(TopKClasses))\n",
        "    \n",
        "    ax.barh(y_pos, probs[0])\n",
        "    ax.set_yticks(y_pos)\n",
        "    ax.set_yticklabels(TopKClasses)\n",
        "    ax.invert_yaxis() \n",
        "    ax.set_xlabel('Probability')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rzooptt-rSs7",
        "colab_type": "code",
        "colab": {}
      },
      "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",
        "    \n",
        "    imag = Image.open(image)\n",
        "    \n",
        "    imsize = imag.size\n",
        "    \n",
        "    #print(imsize)\n",
        "    \n",
        "    maxwidth = 256\n",
        "    maxheight = 256\n",
        "    \n",
        "    #for keeping aspect ratio\n",
        "    ratio = max(maxwidth/imsize[0],maxheight/imsize[1])\n",
        "    \n",
        "    resize_values = (int(imsize[0]*ratio),int(imsize[1]*ratio))\n",
        "    \n",
        "    imag = imag.resize(resize_values,Image.ANTIALIAS)\n",
        "    \n",
        "    #print(imag.size)\n",
        "    \n",
        "    def centercrop(im,neww,newh):\n",
        "        w,h = im.size\n",
        "        \n",
        "        left = (w-neww)/2\n",
        "        right = (w+neww)/2\n",
        "        \n",
        "        top = (h-newh)/2\n",
        "        bottom = (h+newh)/2\n",
        "        \n",
        "        im = im.crop((left,top,right,bottom))\n",
        "        \n",
        "        return im\n",
        "    \n",
        "    imag = centercrop(imag,224,224)\n",
        "    \n",
        "    #print(imag.size)\n",
        "\n",
        "    \n",
        "    np_image = np.array(imag)\n",
        "    \n",
        "    mean = np.array([0.485, 0.456, 0.406])\n",
        "    std = np.array([0.229, 0.224, 0.225])\n",
        "    \n",
        "    np_image = np_image / 255\n",
        "    \n",
        "    np_image = np_image-mean\n",
        "    \n",
        "    np_image = np_image/std\n",
        "    \n",
        "    #print(np_image.shape)\n",
        "    \n",
        "    np_image = np_image.transpose((2,0,1))\n",
        "    \n",
        "    #print(np_image.shape)\n",
        "\n",
        "    \n",
        "    torch_image = torch.from_numpy(np_image)\n",
        "    \n",
        "    return torch_image"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MOHLDxXSrT2Z",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 603
        },
        "outputId": "7be83dd0-f182-447f-baa2-3e4e7ec44173"
      },
      "source": [
        "path = test_dir + '/34/image_06941.jpg'\n",
        "\n",
        "SanityCheck(path)"
      ],
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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lJ0rKBBOCixiFiifgGPsNd3YXbLqe7B2+a+6Ri4sLymQUX6hiXB4vybki0rz8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nUDSRK+sfjWIrZwGHfKsiJqrqtYyBHjQKyArWZhrpqFgTJ04JXKHTjdeJfPU7tZx3xMTIfMeB85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wBtW9O2/4RXXn/fr/eJUckVJ1eKZsWkpZ8zzNxZhL5fZKsavmSTH/OwaeYuRxQNs48qpyVeaL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FEvFDRlx9q0Zf/9x1QVlPugL3pWpodTnwKpGSyFu2i7oM1/C30xpuw79k/2p2a51U3aM+c4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\nQmCC\n",
            "text/plain": [
              "<Figure size 360x720 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dNcTmfAssHdY",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}