visualize_CIFAR10.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "visualize_CIFAR10.ipynb",
      "version": "0.3.2",
      "provenance": [],
      "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/piyushsoni27/CNN_web/blob/master/visualize_CIFAR10.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FJRaxW5xSey_",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Y9XM8vu7StKR",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "f5881cf4-754f-4636-ef6e-872949b6fb5c"
      },
      "source": [
        "\"\"\"\n",
        "import os\n",
        "\n",
        "os.chdir(\"drive/My Drive/Google colab projects/CNN_web\")\n",
        "\"\"\""
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'\\nimport os\\n\\nos.chdir(\"drive/My Drive/Google colab projects/CNN_web\")\\n'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "My82QoirS3zY",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import numpy as np\n",
        "import pickle\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "%matplotlib inline\n",
        "%config InlineBackend.figure_format = 'retina'"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PFvTl-NgS-up",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# The names of the classes in the dataset.\n",
        "CLASS_NAMES = [\n",
        "    'airplane',\n",
        "    'automobile',\n",
        "    'bird',\n",
        "    'cat',\n",
        "    'deer',\n",
        "    'dog',\n",
        "    'frog',\n",
        "    'horse',\n",
        "    'ship',\n",
        "    'truck',\n",
        "]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "S-fsv1txTUUW",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def load_cfar10_batch(batch_id):\n",
        "\n",
        "    with open(os.path.join('cifar-10-batches-py','data_batch_' \n",
        "              + str(batch_id)), mode='rb') as file:\n",
        "        batch = pickle.load(file, encoding='latin1')\n",
        "\n",
        "    features = batch['data'].reshape((len(batch['data']), 3, 32, 32)).transpose(0, 2, 3, 1)\n",
        "    labels = batch['labels']\n",
        "\n",
        "    return features, labels"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zyb9OXk4UFnV",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def display_stats(features, labels, sample_id):\n",
        "\n",
        "    if not (0 <= sample_id < len(features)):\n",
        "        print('{} samples in batch {}.  {} is out of range.'\n",
        "              .format(len(features), batch_id, sample_id))\n",
        "        return None\n",
        "\n",
        "    print('\\nStats of batch {}:'.format(batch_id))\n",
        "    print('Samples: {}'.format(len(features)))\n",
        "    print('Label Counts: {}'.format(dict(zip(*np.unique(labels, return_counts=True)))))\n",
        "    print('First 20 Labels: {}'.format(labels[:20]))\n",
        "\n",
        "    sample_image = features[sample_id]\n",
        "    sample_label = labels[sample_id]\n",
        "\n",
        "    print('\\nExample of Image {}:'.format(sample_id))\n",
        "    print('Image - Min Value: {} Max Value: {}'.format(sample_image.min(), sample_image.max()))\n",
        "    print('Image - Shape: {}'.format(sample_image.shape))\n",
        "    print('Label - Label Id: {} Name: {}'.format(sample_label, CLASS_NAMES[sample_label]))\n",
        "    plt.axis('off')\n",
        "    plt.imshow(sample_image)\n",
        "    plt.show()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4nCF8BOIUD--",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 4197
        },
        "outputId": "c824fff8-5148-4858-8fdc-e6554f8ae6e7"
      },
      "source": [
        "\n",
        "for batch_id in range(1,6):\n",
        "    features, labels = load_cfar10_batch(batch_id)\n",
        "    for image_id in range(0,2):\n",
        "        display_stats(features, labels, image_id)\n",
        "\n",
        "del features, labels # free memory  "
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Stats of batch 1:\n",
            "Samples: 10000\n",
            "Label Counts: {0: 1005, 1: 974, 2: 1032, 3: 1016, 4: 999, 5: 937, 6: 1030, 7: 1001, 8: 1025, 9: 981}\n",
            "First 20 Labels: [6, 9, 9, 4, 1, 1, 2, 7, 8, 3, 4, 7, 7, 2, 9, 9, 9, 3, 2, 6]\n",
            "\n",
            "Example of Image 0:\n",
            "Image - Min Value: 0 Max Value: 255\n",
            "Image - Shape: (32, 32, 3)\n",
            "Label - Label Id: 6 Name: frog\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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XSnN3Hr6TnplfHpd2DQtj653r0q7BsPY8XS7zz53Bal3aNdjm51arX27cenMHgGaEOwA0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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "image/png": {
              "width": 251,
              "height": 248
            }
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Stats of batch 1:\n",
            "Samples: 10000\n",
            "Label Counts: {0: 1005, 1: 974, 2: 1032, 3: 1016, 4: 999, 5: 937, 6: 1030, 7: 1001, 8: 1025, 9: 981}\n",
            "First 20 Labels: [6, 9, 9, 4, 1, 1, 2, 7, 8, 3, 4, 7, 7, 2, 9, 9, 9, 3, 2, 6]\n",
            "\n",
            "Example of Image 1:\n",
            "Image - Min Value: 5 Max Value: 254\n",
            "Image - Shape: (32, 32, 3)\n",
            "Label - Label Id: 9 Name: truck\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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8L/493EtkWmutdRK52d/us7QndwAoRrkDQDHKHQCKUe4AUIxyB4BilDsAFKPc\nAaAY5Q4AxSh3AChGuQNAMcodAIpR7gBQjHIHgGI6s9ns7/pnAAD+BnlyB4BilDsAFKPcAaAY5Q4A\nxSh3AChGuQNAMcodAIpR7gBQjHIHgGKUOwAUo9wBoBjlDgDFKHcAKEa5A0Axyh0AilHuAFCMcgeA\nYpQ7ABSj3AGgGOUOAMUodwAoRrkDQDHKHQCKUe4AUIxyB4BilDsAFKPcAaAY5Q4AxSh3AChGuQNA\nMcodAIpR7gBQjHIHgGKUOwAUo9wBoBjlDgDFKHcAKEa5A0Axyh0AilHuAFCMcgeAYpQ7ABSj3AGg\nGOUOAMUodwAoRrkDQDH/F4C8+iCLWCHcAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "image/png": {
              "width": 251,
              "height": 248
            }
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Stats of batch 2:\n",
            "Samples: 10000\n",
            "Label Counts: {0: 984, 1: 1007, 2: 1010, 3: 995, 4: 1010, 5: 988, 6: 1008, 7: 1026, 8: 987, 9: 985}\n",
            "First 20 Labels: [1, 6, 6, 8, 8, 3, 4, 6, 0, 6, 0, 3, 6, 6, 5, 4, 8, 3, 2, 6]\n",
            "\n",
            "Example of Image 0:\n",
            "Image - Min Value: 5 Max Value: 225\n",
            "Image - Shape: (32, 32, 3)\n",
            "Label - Label Id: 1 Name: automobile\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "image/png": {
              "width": 251,
              "height": 248
            }
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Stats of batch 2:\n",
            "Samples: 10000\n",
            "Label Counts: {0: 984, 1: 1007, 2: 1010, 3: 995, 4: 1010, 5: 988, 6: 1008, 7: 1026, 8: 987, 9: 985}\n",
            "First 20 Labels: [1, 6, 6, 8, 8, 3, 4, 6, 0, 6, 0, 3, 6, 6, 5, 4, 8, 3, 2, 6]\n",
            "\n",
            "Example of Image 1:\n",
            "Image - Min Value: 2 Max Value: 247\n",
            "Image - Shape: (32, 32, 3)\n",
            "Label - Label Id: 6 Name: frog\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "image/png": {
              "width": 251,
              "height": 248
            }
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Stats of batch 3:\n",
            "Samples: 10000\n",
            "Label Counts: {0: 994, 1: 1042, 2: 965, 3: 997, 4: 990, 5: 1029, 6: 978, 7: 1015, 8: 961, 9: 1029}\n",
            "First 20 Labels: [8, 5, 0, 6, 9, 2, 8, 3, 6, 2, 7, 4, 6, 9, 0, 0, 7, 3, 7, 2]\n",
            "\n",
            "Example of Image 0:\n",
            "Image - Min Value: 0 Max Value: 254\n",
            "Image - Shape: (32, 32, 3)\n",
            "Label - Label Id: 8 Name: ship\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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FKHcAKEa5A0Axyh0AilHuAFCMcgeAYpQ7ABSj3AGgGOUOAMUodwAoRrkDQDHK\nHQCKUe4AUIxyB4BilDsAFPO/AZZK7GJo0mz2AAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "image/png": {
              "width": 251,
              "height": 248
            }
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Stats of batch 3:\n",
            "Samples: 10000\n",
            "Label Counts: {0: 994, 1: 1042, 2: 965, 3: 997, 4: 990, 5: 1029, 6: 978, 7: 1015, 8: 961, 9: 1029}\n",
            "First 20 Labels: [8, 5, 0, 6, 9, 2, 8, 3, 6, 2, 7, 4, 6, 9, 0, 0, 7, 3, 7, 2]\n",
            "\n",
            "Example of Image 1:\n",
            "Image - Min Value: 15 Max Value: 249\n",
            "Image - Shape: (32, 32, 3)\n",
            "Label - Label Id: 5 Name: dog\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "image/png": {
              "width": 251,
              "height": 248
            }
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Stats of batch 4:\n",
            "Samples: 10000\n",
            "Label Counts: {0: 1003, 1: 963, 2: 1041, 3: 976, 4: 1004, 5: 1021, 6: 1004, 7: 981, 8: 1024, 9: 983}\n",
            "First 20 Labels: [0, 6, 0, 2, 7, 2, 1, 2, 4, 1, 5, 6, 6, 3, 1, 3, 5, 5, 8, 1]\n",
            "\n",
            "Example of Image 0:\n",
            "Image - Min Value: 34 Max Value: 203\n",
            "Image - Shape: (32, 32, 3)\n",
            "Label - Label Id: 0 Name: airplane\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "image/png": {
              "width": 251,
              "height": 248
            }
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Stats of batch 4:\n",
            "Samples: 10000\n",
            "Label Counts: {0: 1003, 1: 963, 2: 1041, 3: 976, 4: 1004, 5: 1021, 6: 1004, 7: 981, 8: 1024, 9: 983}\n",
            "First 20 Labels: [0, 6, 0, 2, 7, 2, 1, 2, 4, 1, 5, 6, 6, 3, 1, 3, 5, 5, 8, 1]\n",
            "\n",
            "Example of Image 1:\n",
            "Image - Min Value: 0 Max Value: 246\n",
            "Image - Shape: (32, 32, 3)\n",
            "Label - Label Id: 6 Name: frog\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "image/png": {
              "width": 251,
              "height": 248
            }
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Stats of batch 5:\n",
            "Samples: 10000\n",
            "Label Counts: {0: 1014, 1: 1014, 2: 952, 3: 1016, 4: 997, 5: 1025, 6: 980, 7: 977, 8: 1003, 9: 1022}\n",
            "First 20 Labels: [1, 8, 5, 1, 5, 7, 4, 3, 8, 2, 7, 2, 0, 1, 5, 9, 6, 2, 0, 8]\n",
            "\n",
            "Example of Image 0:\n",
            "Image - Min Value: 2 Max Value: 255\n",
            "Image - Shape: (32, 32, 3)\n",
            "Label - Label Id: 1 Name: automobile\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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HgGKUOwAUo9wBoBjlDgDFKHcAKEa5A0Axyh0AilHuAFCMcgeAYpQ7ABSj3AGg\nGOUOAMX8P/mI9juDwUE+AAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "image/png": {
              "width": 251,
              "height": 248
            }
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Stats of batch 5:\n",
            "Samples: 10000\n",
            "Label Counts: {0: 1014, 1: 1014, 2: 952, 3: 1016, 4: 997, 5: 1025, 6: 980, 7: 977, 8: 1003, 9: 1022}\n",
            "First 20 Labels: [1, 8, 5, 1, 5, 7, 4, 3, 8, 2, 7, 2, 0, 1, 5, 9, 6, 2, 0, 8]\n",
            "\n",
            "Example of Image 1:\n",
            "Image - Min Value: 1 Max Value: 244\n",
            "Image - Shape: (32, 32, 3)\n",
            "Label - Label Id: 8 Name: ship\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "image/png": {
              "width": 251,
              "height": 248
            }
          }
        }
      ]
    }
  ]
}