Deep_learning_Image_Classification.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/MudassirABBASSi/Age-Prediction/blob/main/Deep_learning_Image_Classification.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "qrS5GpxkAJuk"
      },
      "outputs": [],
      "source": [
        "import tensorflow as tf\n",
        "from tensorflow import keras\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "x9V4f9UuAnYB",
        "outputId": "956e0023-6917-45d2-f887-400ba1d7293d"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading data from https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz\n",
            "\u001b[1m170498071/170498071\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 0us/step\n"
          ]
        }
      ],
      "source": [
        "(x_train ,y_train),(x_test , y_test)  = tf.keras.datasets.cifar10.load_data()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xtXBP5IbBOse",
        "outputId": "4b5d9419-6e9a-409e-859d-d9358b849520"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(50000, 32, 32, 3)"
            ]
          },
          "metadata": {},
          "execution_count": 3
        }
      ],
      "source": [
        "x_train.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EgBXZSZ3BUfb",
        "outputId": "5b6df584-a579-4b41-8128-6b09879a50d2"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(50000, 1)"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ],
      "source": [
        "y_train.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "id": "DJsvN1DQFDJe"
      },
      "outputs": [],
      "source": [
        "\n",
        "\n",
        "def plot_sample(index):\n",
        "    plt.figure(figsize = (10,1))\n",
        "    plt.imshow(x_train[index])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 142
        },
        "id": "lHSdyuEbBu-J",
        "outputId": "3d37861d-443a-422b-adae-505d3211be82"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x100 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "plot_sample(3)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "metadata": {
        "id": "sACUgCHaByf9"
      },
      "outputs": [],
      "source": [
        "def plot_sample(index):\n",
        " plt.figure(figsize=(10,1))\n",
        " plt.imshow(x_train[index])\n",
        " plt.show() # added plt.show()\n",
        "\n",
        "# plot_sample(3)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 29,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 142
        },
        "id": "iVEh7NkzCFgz",
        "outputId": "c429ede2-39a6-44db-87e4-3613ed5de09f"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x100 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "plot_sample(1)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "\n",
        "def plot_label(index):\n",
        "    plt.figure(figsize=(10,1))\n",
        "    plt.title(f\"Label: {y_train[index][0]}\")  # Access the label value and show it as title.\n",
        "    plt.axis('off') # Hide axes for cleaner view.\n",
        "    plt.imshow(x_train[index]) # display the image\n",
        "    plt.show() # show the plot\n",
        "\n",
        "plot_label(1)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 136
        },
        "id": "GQ_dizqZMxVj",
        "outputId": "60942760-b57d-4d6c-997b-8f03ff828882"
      },
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x100 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2o4g48owE5ni"
      },
      "source": [
        "# Scaling the Dataset"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 33,
      "metadata": {
        "id": "UBS_JVt-CHZU"
      },
      "outputs": [],
      "source": [
        "x_train_scaled =x_train /255\n",
        "x_test_scaled = x_test /255"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 34,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nT3E7pLaEueo",
        "outputId": "eba13b8c-eb68-4ed0-9261-da76423946a3"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(50000, 32, 32, 3)"
            ]
          },
          "metadata": {},
          "execution_count": 34
        }
      ],
      "source": [
        "x_train_scaled.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 35,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "30d4eaBtEwtK",
        "outputId": "0f60a97a-7477-4f48-a427-a18fd272b4dc"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(10000, 32, 32, 3)"
            ]
          },
          "metadata": {},
          "execution_count": 35
        }
      ],
      "source": [
        "x_test.shape"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KWW6DUB1E8rg"
      },
      "source": [
        "# One Hot Encoading on Target Values\n",
        "because we have image on target as we have scale the X values therefore we have to Encode the Target column"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 36,
      "metadata": {
        "id": "DdiaRoRtEzo8"
      },
      "outputs": [],
      "source": [
        "y_train_categorical = keras.utils.to_categorical(y_train , num_classes=10)\n",
        "y_test_categorical = keras.utils.to_categorical(y_test , num_classes=10)\n",
        "\n",
        "# # One Hot Encoding on Target Values\n",
        "# y_train_categorical = keras.utils.to_categorical(y_train, num_classes=10)\n",
        "# y_test_categorical = keras.utils.to_categorical(y_test, num_classes=10)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 37,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JDN73kK-Gn6p",
        "outputId": "4a682ea9-1bcf-47df-e662-1be138a74d32"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[6],\n",
              "       [9],\n",
              "       [9],\n",
              "       [4],\n",
              "       [1]], dtype=uint8)"
            ]
          },
          "metadata": {},
          "execution_count": 37
        }
      ],
      "source": [
        "y_train[:5]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 38,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0sC0xgSXFzm5",
        "outputId": "40ef9853-f1bf-4c61-b1af-ef02a67ff262"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[0., 0., 0., 0., 0., 0., 1., 0., 0., 0.],\n",
              "       [0., 0., 0., 0., 0., 0., 0., 0., 0., 1.],\n",
              "       [0., 0., 0., 0., 0., 0., 0., 0., 0., 1.],\n",
              "       [0., 0., 0., 0., 1., 0., 0., 0., 0., 0.],\n",
              "       [0., 1., 0., 0., 0., 0., 0., 0., 0., 0.]])"
            ]
          },
          "metadata": {},
          "execution_count": 38
        }
      ],
      "source": [
        "y_train_categorical[:5]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "60ENwxEYGzZN"
      },
      "source": [
        "# Neural Network\n",
        "without Hidden Leyers"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 39,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PikjQWrLFzaK",
        "outputId": "ebd1590b-3ac7-4a3f-93a8-510af1e571d6"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.11/dist-packages/keras/src/layers/reshaping/flatten.py:37: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n",
            "  super().__init__(**kwargs)\n",
            "/usr/local/lib/python3.11/dist-packages/keras/src/layers/core/dense.py:87: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n",
            "  super().__init__(activity_regularizer=activity_regularizer, **kwargs)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/5\n",
            "\u001b[1m1563/1563\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 3ms/step - accuracy: 0.2823 - loss: 2.0494\n",
            "Epoch 2/5\n",
            "\u001b[1m1563/1563\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 3ms/step - accuracy: 0.3467 - loss: 1.8852\n",
            "Epoch 3/5\n",
            "\u001b[1m1563/1563\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 4ms/step - accuracy: 0.3608 - loss: 1.8576\n",
            "Epoch 4/5\n",
            "\u001b[1m1563/1563\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 4ms/step - accuracy: 0.3677 - loss: 1.8377\n",
            "Epoch 5/5\n",
            "\u001b[1m1563/1563\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 4ms/step - accuracy: 0.3688 - loss: 1.8287\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.src.callbacks.history.History at 0x7c062b823990>"
            ]
          },
          "metadata": {},
          "execution_count": 39
        }
      ],
      "source": [
        "model = keras.Sequential([\n",
        "    keras.layers.Flatten(input_shape = (32,32,3)),  #converting the Multidimension Array in to Single Array\n",
        "    keras.layers.Dense(10,input_shape = (32,32,3,),activation='sigmoid')\n",
        "\n",
        "])\n",
        "model.compile(\n",
        "    optimizer ='adam',\n",
        "    loss = 'categorical_crossentropy',\n",
        "    metrics = ['accuracy']\n",
        ")\n",
        "model.fit(x_train_scaled,y_train_categorical,epochs=5)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7FFAq7zMMeNl"
      },
      "source": [
        "Single Hidden Layers"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "i7kzxS7VKTKO",
        "outputId": "df48fef5-93ea-4edb-840d-15325318e69e"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/5\n",
            "\u001b[1m 254/1563\u001b[0m \u001b[32m━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m14:02\u001b[0m 644ms/step - accuracy: 0.1897 - loss: 5.8870"
          ]
        }
      ],
      "source": [
        "model = keras.Sequential([\n",
        "    keras.layers.Flatten(input_shape = (32,32,3)),\n",
        "     keras.layers.Dense(10000, input_shape=(784,), activation='relu'),\n",
        "    keras.layers.Dense(10, activation='sigmoid')\n",
        "\n",
        "    ])\n",
        "\n",
        "\n",
        "model.compile(\n",
        "    optimizer ='adam',\n",
        "    loss = 'categorical_crossentropy',\n",
        "    metrics = ['accuracy']\n",
        ")\n",
        "model.fit(x_train_scaled,y_train_categorical,epochs=5)\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "s6i_CN_YjhJ-"
      },
      "source": [
        "Number of hidden Layer is small in number"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "LEl5sA9lM7YR"
      },
      "outputs": [],
      "source": [
        "model = keras.Sequential([\n",
        "    keras.layers.Flatten(input_shape = (32,32,3)),\n",
        "    keras.layers.Dense(100,input_shape = (32,32,3,),activation = 'relu'),\n",
        "    keras.layers.Dense(200,input_shape = (32,32,3,),activation = 'relu'),\n",
        "    keras.layers.Dense(300,input_shape = (32,32,3,),activation = 'relu'),\n",
        "    keras.layers.Dense(10,activation = 'sigmoid')\n",
        "\n",
        "])\n",
        "model.compile(\n",
        "    optimizer = 'adam',\n",
        "    loss = 'categorical_crossentropy',\n",
        "    metrics = ['accuracy']\n",
        "\n",
        ")\n",
        "model.fit(x_train_scaled,y_train_categorical,epochs = 5)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DGVn2QGqlFpc"
      },
      "source": [
        "With two Hidden Layers"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "PhgXJwfnky46"
      },
      "outputs": [],
      "source": [
        "model = keras.Sequential([\n",
        "    keras.layers.Flatten(input_shape =(32,32,3)),\n",
        "    keras.layers.Dense(300,input_shape = (32,32,3,),activation  = 'relu'),\n",
        "  #  keras.layers.Dense(300,input_shape = (32,32,3,),activation  = 'relu'),\n",
        "    keras.layers.Dense(10,activation = 'sigmoid')\n",
        "\n",
        "])\n",
        "\n",
        "model.compile(\n",
        "    optimizer = 'adam',\n",
        "    loss = 'categorical_crossentropy',\n",
        "    # matrics = ['accuracy']\n",
        "     metrics = ['accuracy']\n",
        ")\n",
        "model.fit(x_train_scaled,y_train_categorical,epochs = 5)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ND_Tw0sEmF0A"
      },
      "outputs": [],
      "source": [
        "model = keras.Sequential([\n",
        "    keras.layers.Flatten(input_shape =(32,32,3)),\n",
        "    keras.layers.Dense(300,input_shape = (32,32,3,),activation  = 'relu'),\n",
        "    keras.layers.Dense(300,input_shape = (32,32,3,),activation  = 'relu'),\n",
        "    keras.layers.Dense(300,input_shape = (32,32,3,),activation  = 'relu'),\n",
        "    keras.layers.Dense(10,activation = 'sigmoid')\n",
        "\n",
        "])\n",
        "\n",
        "model.compile(\n",
        "    optimizer = 'adam',\n",
        "    loss = 'categorical_crossentropy',\n",
        "    # matrics = ['accuracy']\n",
        "     metrics = ['accuracy']\n",
        ")\n",
        "model.fit(x_train_scaled,y_train_categorical,epochs = 5)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "6kGmi5j6nq-x"
      },
      "outputs": [],
      "source": [
        "model = keras.Sequential([\n",
        "    keras.layers.Flatten(input_shape =(32,32,3)),\n",
        "    keras.layers.Dense(600,input_shape = (32,32,3,),activation  = 'relu'),\n",
        "    keras.layers.Dense(300,input_shape = (32,32,3,),activation  = 'relu'),\n",
        "    keras.layers.Dense(1000,input_shape = (32,32,3,),activation  = 'relu'),\n",
        "    keras.layers.Dense(10,activation = 'sigmoid')\n",
        "\n",
        "])\n",
        "\n",
        "model.compile(\n",
        "    optimizer = 'adam',\n",
        "    loss = 'categorical_crossentropy',\n",
        "    # matrics = ['accuracy']\n",
        "     metrics = ['accuracy']\n",
        ")\n",
        "model.fit(x_train_scaled,y_train_categorical,epochs = 5)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "MFOVcyrin05z"
      },
      "outputs": [],
      "source": [
        "model = keras.Sequential([\n",
        "    keras.layers.Flatten(input_shape =(32,32,3)),\n",
        "    keras.layers.Dense(300,input_shape = (32,32,3,),activation  = 'relu'),\n",
        "    keras.layers.Dense(600,input_shape = (32,32,3,),activation  = 'relu'),\n",
        "    keras.layers.Dense(300,input_shape = (32,32,3,),activation  = 'relu'),\n",
        "    keras.layers.Dense(10,activation = 'sigmoid')\n",
        "\n",
        "])\n",
        "\n",
        "model.compile(\n",
        "    optimizer = 'adam',\n",
        "    loss = 'categorical_crossentropy',\n",
        "    # matrics = ['accuracy']\n",
        "     metrics = ['accuracy']\n",
        ")\n",
        "model.fit(x_train_scaled,y_train_categorical,epochs = 5)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import tensorflow as tf\n",
        "import datetime\n",
        "\n",
        "# Load and preprocess the CIFAR-10 dataset\n",
        "(x_train, y_train), (x_test, y_test) = tf.keras.datasets.cifar10.load_data()\n",
        "x_train, x_test = x_train / 255.0, x_test / 255.0\n",
        "\n",
        "# Define the CNN model\n",
        "model = tf.keras.Sequential([\n",
        "    tf.keras.layers.Conv2D(32, (3, 3), activation='relu', input_shape=(32, 32, 3)),\n",
        "    tf.keras.layers.MaxPooling2D((2, 2)),\n",
        "    tf.keras.layers.Conv2D(64, (3, 3), activation='relu'),\n",
        "    tf.keras.layers.MaxPooling2D((2, 2)),\n",
        "    tf.keras.layers.Flatten(),\n",
        "    tf.keras.layers.Dense(64, activation='relu'),\n",
        "    tf.keras.layers.Dense(10, activation='softmax')\n",
        "])\n",
        "\n",
        "model.compile(optimizer='adam',\n",
        "              loss='sparse_categorical_crossentropy',\n",
        "              metrics=['accuracy'])\n",
        "\n",
        "# Set up TensorBoard logging\n",
        "log_dir = \"logs/fit/\" + datetime.datetime.now().strftime(\"%Y%m%d-%H%M%S\")\n",
        "tensorboard_callback = tf.keras.callbacks.TensorBoard(log_dir=log_dir, histogram_freq=1)\n",
        "\n",
        "# Train the model\n",
        "model.fit(x_train, y_train,\n",
        "          epochs=5,\n",
        "          validation_data=(x_test, y_test),\n",
        "          callbacks=[tensorboard_callback])\n",
        "\n",
        "# Start TensorBoard\n",
        "%load_ext tensorboard\n",
        "%tensorboard --logdir logs/fit\n"
      ],
      "metadata": {
        "id": "wvG2ThM93szX"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "vlu2zglR3xWV"
      },
      "execution_count": null,
      "outputs": []
    }
  ],
  "metadata": {
    "colab": {
      "provenance": [],
      "authorship_tag": "ABX9TyMPenE47hLiT0nnW2++D2Ki",
      "include_colab_link": true
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}