Build_a_simple_image_classifier.ipynb

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        "<a href=\"https://colab.research.google.com/github/hussain0048/Projects-/blob/master/Build_a_simple_image_classifier.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "PKIk5iSfqvoS"
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      "source": []
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      "cell_type": "markdown",
      "source": [
        "# **Table of Content**\n",
        "\n",
        "\n",
        "1.   Introduction\n",
        "2.   Import Libraries\n",
        "3.   Load Dataset\n",
        "4.   Hot Encoding\n",
        "5.   Create Model\n",
        "5.   Fit The model\n",
        "6.   Improve Accuracy\n",
        "7.   Source\n",
        "\n"
      ],
      "metadata": {
        "id": "UER8fSt13gBE"
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    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Introduction**"
      ],
      "metadata": {
        "id": "2CngjLQ2rTtL"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "This is a very simple fashion classifier.It uses a data set called \"fashion mnist\", a set of small b&w images of apparel This training script classifies with around 15% accuracy. See if you can get it to 80% accuracy by normalizing the input data andusing a softmax activation function."
      ],
      "metadata": {
        "id": "6L389pO6rQFr"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Import Libraries**"
      ],
      "metadata": {
        "id": "x11CA7ERrqih"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install wandb"
      ],
      "metadata": {
        "id": "CB7UOUAksBpa"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from tensorflow.keras.utils import to_categorical\n",
        "from tensorflow.keras.datasets import fashion_mnist\n",
        "from tensorflow.keras.models import Sequential\n",
        "from tensorflow.keras.layers import Dense, Flatten\n",
        "import wandb\n",
        "from wandb.keras import WandbCallback"
      ],
      "metadata": {
        "id": "F5YtpX3Pryqw"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Load Dataset**"
      ],
      "metadata": {
        "id": "3VK1jGPdsH6A"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# load data\n",
        "(X_train, y_train), (X_test, y_test) = fashion_mnist.load_data()\n",
        "img_width = X_train.shape[1]\n",
        "img_height = X_train.shape[2]"
      ],
      "metadata": {
        "id": "9e1ES5jfsQLB"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Hot encoding**"
      ],
      "metadata": {
        "id": "QBI8XJOcsh2E"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# one hot encode outputs\n",
        "y_train = to_categorical(y_train)\n",
        "y_test = to_categorical(y_test)\n",
        "labels = [\"T-shirt/top\", \"Trouser\", \"Pullover\", \"Dress\",\n",
        "          \"Coat\", \"Sandal\", \"Shirt\", \"Sneaker\", \"Bag\", \"Ankle boot\"]\n"
      ],
      "metadata": {
        "id": "KCfBuwUdsp5J"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "num_classes = y_train.shape[1]"
      ],
      "metadata": {
        "id": "gctbDDzcs2Qo"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Create model**"
      ],
      "metadata": {
        "id": "ODai5bROso_g"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# create model\n",
        "model = Sequential()\n",
        "model.add(Flatten(input_shape=(img_width, img_height)))\n",
        "model.add(Dense(num_classes))\n",
        "model.compile(loss='categorical_crossentropy', optimizer='adam',\n",
        "              metrics=['accuracy'])"
      ],
      "metadata": {
        "id": "ePhA_hcttEZC"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "7t5ZkviLtKE7"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Fit the model**"
      ],
      "metadata": {
        "id": "I5NPht3vtLI4"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model.fit(X_train, y_train, epochs=10, validation_data=(X_test, y_test))\n"
      ],
      "metadata": {
        "id": "egRzmSM-wB2C"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Improve Accuracy**"
      ],
      "metadata": {
        "id": "WDHsJwSK4UKk"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **import libraries**"
      ],
      "metadata": {
        "id": "nx68sNSo4bKM"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install wandb"
      ],
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    },
    {
      "cell_type": "code",
      "source": [
        "from tensorflow.keras.utils import to_categorical\n",
        "from tensorflow.keras.datasets import fashion_mnist\n",
        "from tensorflow.keras.models import Sequential\n",
        "from tensorflow.keras.layers import Dense, Flatten\n",
        "import wandb"
      ],
      "metadata": {
        "id": "69tIHILD49W1"
      },
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **Load Data**"
      ],
      "metadata": {
        "id": "05SuCJWj5h9F"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# load data\n",
        "(X_train, y_train), (X_test, y_test) = fashion_mnist.load_data()\n",
        "img_width = X_train.shape[1]\n",
        "img_height = X_train.shape[2]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Jytxfw-L5jRG",
        "outputId": "45374dda-4c1a-4e48-e564-68c2a71ab7d1"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/train-labels-idx1-ubyte.gz\n",
            "29515/29515 [==============================] - 0s 0us/step\n",
            "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/train-images-idx3-ubyte.gz\n",
            "26421880/26421880 [==============================] - 0s 0us/step\n",
            "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/t10k-labels-idx1-ubyte.gz\n",
            "5148/5148 [==============================] - 0s 0us/step\n",
            "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/t10k-images-idx3-ubyte.gz\n",
            "4422102/4422102 [==============================] - 0s 0us/step\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **One hot encode outputs**"
      ],
      "metadata": {
        "id": "cIVXpWdh52TE"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "y_train = to_categorical(y_train)\n",
        "y_test = to_categorical(y_test)\n",
        "labels = [\"T-shirt/top\", \"Trouser\", \"Pullover\", \"Dress\",\n",
        "          \"Coat\", \"Sandal\", \"Shirt\", \"Sneaker\", \"Bag\", \"Ankle boot\"]"
      ],
      "metadata": {
        "id": "4xUxF5C75_k_"
      },
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_train = X_train / 255.\n",
        "X_test = X_test / 255."
      ],
      "metadata": {
        "id": "MqgDtwyM6EbV"
      },
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "num_classes = y_train.shape[1]"
      ],
      "metadata": {
        "id": "SmnTYC1g6H0u"
      },
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **Create model**"
      ],
      "metadata": {
        "id": "hDEoCJzM6O8M"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# create model\n",
        "model = Sequential()\n",
        "model.add(Flatten(input_shape=(img_width, img_height)))\n",
        "model.add(Dense(num_classes, activation=\"softmax\"))\n",
        "model.compile(loss='categorical_crossentropy', optimizer='adam',\n",
        "              metrics=['accuracy'])"
      ],
      "metadata": {
        "id": "B4x2HvGu6MD8"
      },
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **Fit the model**"
      ],
      "metadata": {
        "id": "8-bVfBy46eF8"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model.fit(X_train, y_train, epochs=10, validation_data=(X_test, y_test))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3sDzCFf06obG",
        "outputId": "04bb2441-b547-43a3-8971-c84ff4be3ae6"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/10\n",
            "1875/1875 [==============================] - 5s 2ms/step - loss: 0.5971 - accuracy: 0.7986 - val_loss: 0.5115 - val_accuracy: 0.8235\n",
            "Epoch 2/10\n",
            "1875/1875 [==============================] - 3s 2ms/step - loss: 0.4613 - accuracy: 0.8428 - val_loss: 0.5009 - val_accuracy: 0.8208\n",
            "Epoch 3/10\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 0.4364 - accuracy: 0.8501 - val_loss: 0.4658 - val_accuracy: 0.8388\n",
            "Epoch 4/10\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 0.4224 - accuracy: 0.8551 - val_loss: 0.4715 - val_accuracy: 0.8359\n",
            "Epoch 5/10\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 0.4123 - accuracy: 0.8580 - val_loss: 0.4604 - val_accuracy: 0.8411\n",
            "Epoch 6/10\n",
            "1875/1875 [==============================] - 3s 2ms/step - loss: 0.4076 - accuracy: 0.8575 - val_loss: 0.4633 - val_accuracy: 0.8353\n",
            "Epoch 7/10\n",
            "1875/1875 [==============================] - 3s 2ms/step - loss: 0.4015 - accuracy: 0.8607 - val_loss: 0.4718 - val_accuracy: 0.8381\n",
            "Epoch 8/10\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 0.3981 - accuracy: 0.8620 - val_loss: 0.4449 - val_accuracy: 0.8447\n",
            "Epoch 9/10\n",
            "1875/1875 [==============================] - 3s 2ms/step - loss: 0.3958 - accuracy: 0.8620 - val_loss: 0.4608 - val_accuracy: 0.8352\n",
            "Epoch 10/10\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 0.3913 - accuracy: 0.8643 - val_loss: 0.4640 - val_accuracy: 0.8389\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.src.callbacks.History at 0x7c8e0df27a60>"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Source**\n",
        "1- [Projects](https://github.com/lukas/ml-class)"
      ],
      "metadata": {
        "id": "B1ckuxnO0UJr"
      }
    }
  ]
}