CNN_Model_for_Object_Recognition_on_CIFAR10_dataset.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "CNN Model for Object Recognition on CIFAR10 dataset.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "authorship_tag": "ABX9TyOoGHiTOmNrB28oZkYK4Nhf",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/panimesh14/Deep-Learning-/blob/main/CNN_Model_for_Object_Recognition_on_CIFAR10_dataset.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#CIFAR 10 Dataset\n",
        "####Airplane:0, Car:1, Bird:2, Cat:3, Deer:4, Dog:5, Frog:6, Horse:7, Ship:8, Truck:9\n",
        "#####50,000 Color images (RGB) of 50000x32x32(x3)"
      ],
      "metadata": {
        "id": "UneCoF1y17if"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 115,
      "metadata": {
        "id": "HPA7sIBW1gc5"
      },
      "outputs": [],
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "%matplotlib inline\n",
        "import seaborn as sns"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Importing and Loading CIFAR10 dataset\n",
        "from tensorflow.keras.datasets import cifar10\n",
        "(Xtrain,Ytrain),(Xtest,Ytest)=cifar10.load_data()"
      ],
      "metadata": {
        "id": "Kt46QhKf13Ha"
      },
      "execution_count": 116,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "Xtrain.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mG3Lok4t3qQK",
        "outputId": "5ce80584-ef27-49bc-9b4b-d0d6722cca3d"
      },
      "execution_count": 117,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(50000, 32, 32, 3)"
            ]
          },
          "metadata": {},
          "execution_count": 117
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "#Data Exploration and Preparation\n",
        "plt.imshow(Xtrain[12])#Horse"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 283
        },
        "id": "sScKhSAm3tjI",
        "outputId": "f1b1a0e1-3733-4bcb-d8c2-6969cf3e9e80"
      },
      "execution_count": 118,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7f84a153b590>"
            ]
          },
          "metadata": {},
          "execution_count": 118
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "Xtrain[0].max()#pixel value"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pLI-cM9s4GJF",
        "outputId": "90f04d51-8ef5-4c87-92e6-81c3378ab7bb"
      },
      "execution_count": 119,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "255"
            ]
          },
          "metadata": {},
          "execution_count": 119
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "np.unique(Ytrain)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9QTwZjsa4YT8",
        "outputId": "5eeee42e-5134-4b6d-b9ca-5c8a7f656be1"
      },
      "execution_count": 120,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9], dtype=uint8)"
            ]
          },
          "metadata": {},
          "execution_count": 120
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Data Preparation\n",
        "#One Hot Encoding for categorical variablea\n",
        "from tensorflow.keras.utils import to_categorical\n",
        "y_cat_train=to_categorical(Ytrain,10);\n",
        "y_cat_test=to_categorical(Ytest,10);"
      ],
      "metadata": {
        "id": "SqqdRyG44gN7"
      },
      "execution_count": 121,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Model Building\n",
        "from tensorflow.keras.models import Sequential \n",
        "from tensorflow.keras.layers import Conv2D,MaxPool2D,Flatten,Dense\n",
        "cifar_model=Sequential()\n",
        "#Covolutional Layer\n",
        "cifar_model.add(Conv2D(filters=32,kernel_size=(4,4),input_shape=(32,32,3),activation='relu'))\n",
        "#Pooling Layer\n",
        "cifar_model.add(MaxPool2D(pool_size=(2,2)))\n",
        "\n",
        "#Covolutional Layer\n",
        "cifar_model.add(Conv2D(filters=32,kernel_size=(4,4),input_shape=(32,32,3),activation='relu'))\n",
        "#Pooling Layer\n",
        "cifar_model.add(MaxPool2D(pool_size=(2,2)))\n",
        "#Flatten layer\n",
        "cifar_model.add(Flatten())\n",
        "\n",
        "#MLP\n",
        "cifar_model.add(Dense(256,activation='relu'))#32×32×3=3072\n",
        "cifar_model.add(Dense(10,activation='softmax'))#for 10 classes\n",
        "cifar_model.compile(optimizer='adam',loss='categorical_crossentropy',metrics=['accuracy'])\n",
        "cifar_model.summary()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JZroQO1h6ilU",
        "outputId": "ce75f8ba-5e0e-4332-ce44-6cbe185d4179"
      },
      "execution_count": 133,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential_5\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " conv2d_10 (Conv2D)          (None, 29, 29, 32)        1568      \n",
            "                                                                 \n",
            " max_pooling2d_10 (MaxPoolin  (None, 14, 14, 32)       0         \n",
            " g2D)                                                            \n",
            "                                                                 \n",
            " conv2d_11 (Conv2D)          (None, 11, 11, 32)        16416     \n",
            "                                                                 \n",
            " max_pooling2d_11 (MaxPoolin  (None, 5, 5, 32)         0         \n",
            " g2D)                                                            \n",
            "                                                                 \n",
            " flatten_5 (Flatten)         (None, 800)               0         \n",
            "                                                                 \n",
            " dense_10 (Dense)            (None, 256)               205056    \n",
            "                                                                 \n",
            " dense_11 (Dense)            (None, 10)                2570      \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 225,610\n",
            "Trainable params: 225,610\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "#Early Stopping \n",
        "from tensorflow.keras.callbacks import EarlyStopping\n",
        "early_stop=EarlyStopping(monitor='val_loss',patience=2)"
      ],
      "metadata": {
        "id": "oaDy1QBb8sDn"
      },
      "execution_count": 134,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "cifar_model.fit(Xtrain,y_cat_train,epochs=15,validation_split=0.2,callbacks=[early_stop])\n",
        "#20% training dataset used for validation"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iO7ky_uK-4vK",
        "outputId": "9f9424f0-5503-4ecb-f666-fb485fd5109f"
      },
      "execution_count": 135,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/15\n",
            "1250/1250 [==============================] - 56s 44ms/step - loss: 2.4732 - accuracy: 0.1070 - val_loss: 2.3019 - val_accuracy: 0.1024\n",
            "Epoch 2/15\n",
            "1250/1250 [==============================] - 53s 43ms/step - loss: 2.2873 - accuracy: 0.1275 - val_loss: 2.2681 - val_accuracy: 0.1773\n",
            "Epoch 3/15\n",
            "1250/1250 [==============================] - 54s 43ms/step - loss: 1.9179 - accuracy: 0.3043 - val_loss: 1.7302 - val_accuracy: 0.3692\n",
            "Epoch 4/15\n",
            "1250/1250 [==============================] - 53s 42ms/step - loss: 1.5850 - accuracy: 0.4291 - val_loss: 1.5212 - val_accuracy: 0.4512\n",
            "Epoch 5/15\n",
            "1250/1250 [==============================] - 54s 43ms/step - loss: 1.4214 - accuracy: 0.4947 - val_loss: 1.4867 - val_accuracy: 0.4698\n",
            "Epoch 6/15\n",
            "1250/1250 [==============================] - 53s 42ms/step - loss: 1.3010 - accuracy: 0.5397 - val_loss: 1.3930 - val_accuracy: 0.5168\n",
            "Epoch 7/15\n",
            "1250/1250 [==============================] - 54s 43ms/step - loss: 1.1955 - accuracy: 0.5770 - val_loss: 1.4324 - val_accuracy: 0.5095\n",
            "Epoch 8/15\n",
            "1250/1250 [==============================] - 52s 42ms/step - loss: 1.0901 - accuracy: 0.6144 - val_loss: 1.4216 - val_accuracy: 0.5270\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.callbacks.History at 0x7f849fa50dd0>"
            ]
          },
          "metadata": {},
          "execution_count": 135
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#MOdel Evaluation\n",
        "cifar_model_loss=pd.DataFrame(cifar_model.history.history)\n",
        "cifar_model_loss.columns"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kDTo9JZuDIrR",
        "outputId": "764db640-ed54-4648-f987-5758fbb4acf4"
      },
      "execution_count": 136,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['loss', 'accuracy', 'val_loss', 'val_accuracy'], dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 136
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#loss metrics plot \n",
        "cifar_model_loss[['loss','val_loss']].plot()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "id": "64DI8WdqDfNH",
        "outputId": "2e81c8fe-d9f6-42f9-bd75-04e5efd9f1e0"
      },
      "execution_count": 137,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f849f8c9750>"
            ]
          },
          "metadata": {},
          "execution_count": 137
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#accuracy metrics\n",
        "cifar_model_loss[['accuracy','val_accuracy']].plot()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "id": "LPdUpTg9D43R",
        "outputId": "1256f7a0-24bd-43c4-a14a-c2f11bb37655"
      },
      "execution_count": 138,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f849f819cd0>"
            ]
          },
          "metadata": {},
          "execution_count": 138
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "cifar_model.evaluate(Xtest,y_cat_test,verbose=0)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3aeVKaTQEJnH",
        "outputId": "590476c6-c694-419b-fbe7-cb7fd9515e47"
      },
      "execution_count": 139,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[1.4042084217071533, 0.5238000154495239]"
            ]
          },
          "metadata": {},
          "execution_count": 139
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Multi-class classificaton\n",
        "(cifar_model.predict(Xtest))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4AFBQvBKcDrH",
        "outputId": "9725f52b-e109-498c-81d6-7dbb18aac167"
      },
      "execution_count": 140,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[1.26259355e-02, 1.82236033e-03, 3.53008471e-02, ...,\n",
              "        4.06053998e-02, 8.35010409e-03, 5.31614432e-03],\n",
              "       [5.77345490e-02, 5.97936690e-01, 1.12176337e-03, ...,\n",
              "        8.08334560e-04, 1.56852901e-01, 1.84676006e-01],\n",
              "       [2.24907741e-01, 6.84393272e-02, 3.77047881e-02, ...,\n",
              "        1.85266286e-02, 3.50001425e-01, 1.62240967e-01],\n",
              "       ...,\n",
              "       [5.38533186e-07, 1.10156137e-07, 8.73908214e-03, ...,\n",
              "        6.71207847e-04, 1.31508193e-06, 6.99388139e-08],\n",
              "       [7.44575486e-02, 2.89564468e-02, 2.11808071e-01, ...,\n",
              "        2.13150635e-01, 5.56171080e-03, 2.97220331e-02],\n",
              "       [4.26179584e-04, 9.23789994e-05, 1.36998901e-02, ...,\n",
              "        5.55945575e-01, 3.06792674e-04, 1.46261635e-04]], dtype=float32)"
            ]
          },
          "metadata": {},
          "execution_count": 140
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Classification Report and Confusion Matrix\n",
        "from sklearn.metrics import classification_report, confusion_matrix\n",
        "y_hat=np.argmax(cifar_model.predict(Xtest),axis=-1);\n",
        "print(classification_report(Ytest,y_hat));"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uKqCzqgaEi9i",
        "outputId": "42729777-986e-4d75-ed5a-a36001e61d5c"
      },
      "execution_count": 141,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.57      0.53      0.55      1000\n",
            "           1       0.63      0.70      0.67      1000\n",
            "           2       0.45      0.42      0.44      1000\n",
            "           3       0.32      0.50      0.39      1000\n",
            "           4       0.48      0.46      0.47      1000\n",
            "           5       0.42      0.40      0.41      1000\n",
            "           6       0.66      0.54      0.60      1000\n",
            "           7       0.61      0.57      0.59      1000\n",
            "           8       0.65      0.53      0.58      1000\n",
            "           9       0.60      0.57      0.58      1000\n",
            "\n",
            "    accuracy                           0.52     10000\n",
            "   macro avg       0.54      0.52      0.53     10000\n",
            "weighted avg       0.54      0.52      0.53     10000\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(12,6))\n",
        "sns.heatmap((confusion_matrix(Ytest,y_hat)),annot=True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 393
        },
        "id": "5iBbsfnRFejI",
        "outputId": "4df8a45e-72a3-449c-bea8-b103a269f1f1"
      },
      "execution_count": 142,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f849f776750>"
            ]
          },
          "metadata": {},
          "execution_count": 142
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 864x432 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    }
  ]
}