Number_Recognition.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "authorship_tag": "ABX9TyNzTVkqOuhszfIWKmmh36XW",
      "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/Jeelanihansha/Number-Recognition/blob/main/Number_Recognition.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "EITQ0jlvKZFY"
      },
      "outputs": [],
      "source": [
        "# Import necessary libraries\n",
        "import tensorflow as tf\n",
        "from tensorflow import keras\n",
        "import matplotlib.pyplot as plt\n",
        "import numpy as np"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Load and Preprocess the MNIST dataset\n",
        "(x_train, y_train), (x_test, y_test) = keras.datasets.mnist.load_data()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KI_PBPJdOYax",
        "outputId": "c02dcfe8-19a4-4636-c7e0-771484ade163"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/mnist.npz\n",
            "11490434/11490434 [==============================] - 0s 0us/step\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "x_train[0]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RTp5R-tAOdAY",
        "outputId": "ef74a522-3ddb-49c6-8232-1ebe4b12c19c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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            ]
          },
          "metadata": {},
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Display dataset information\n",
        "print(\"Number of training samples:\", len(x_train))\n",
        "print(\"Number of testing samples:\", len(x_test))\n",
        "print(\"Shape of an individual sample:\", x_train[0].shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "q766bA0tOkux",
        "outputId": "07d6d03d-4c52-4401-c3ce-aa9f94d1bdde"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Number of training samples: 60000\n",
            "Number of testing samples: 10000\n",
            "Shape of an individual sample: (28, 28)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Visualize a Sample Image\n",
        "plt.figure(figsize=(5, 5))\n",
        "plt.imshow(x_train[0], cmap='gray')\n",
        "plt.title(f\"Label: {y_train[0]}\")\n",
        "plt.axis('off')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 444
        },
        "id": "bXCIagr1OoBc",
        "outputId": "c90cf214-2859-48b7-bbe8-2178986354ab"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 500x500 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Scale the data so that the values are from 0 - 1\n",
        "x_train = x_train / 255\n",
        "x_test = x_test / 255"
      ],
      "metadata": {
        "id": "XyLPl6EvOptA"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "x_train[0]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1CvzZycXOtQ8",
        "outputId": "23c73712-85ef-4029-87a5-287e6062a509"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Flatten the Data\n",
        "x_train_flattened = x_train.reshape(len(x_train), -1)\n",
        "x_test_flattened = x_test.reshape(len(x_test), -1)"
      ],
      "metadata": {
        "id": "Rc7jnwtsOw4E"
      },
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Build and Compile the Simple Model\n",
        "model = keras.Sequential([\n",
        "    keras.layers.Dense(10, input_shape=(784,), activation='sigmoid')\n",
        "])\n",
        "\n",
        "model.compile(\n",
        "    optimizer='adam',\n",
        "    loss='sparse_categorical_crossentropy',\n",
        "    metrics=['accuracy']\n",
        ")\n",
        "# Train the Model\n",
        "model.fit(x_train_flattened, y_train, epochs=5)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bFQ0fZ2GPWNk",
        "outputId": "2ae2702e-068e-40ee-a109-ce200e474f26"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/5\n",
            "1875/1875 [==============================] - 7s 3ms/step - loss: 0.4771 - accuracy: 0.8737\n",
            "Epoch 2/5\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 0.3043 - accuracy: 0.9148\n",
            "Epoch 3/5\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 0.2833 - accuracy: 0.9208\n",
            "Epoch 4/5\n",
            "1875/1875 [==============================] - 3s 2ms/step - loss: 0.2734 - accuracy: 0.9243\n",
            "Epoch 5/5\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 0.2663 - accuracy: 0.9258\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.src.callbacks.History at 0x7e12d2b812a0>"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Evaluate the Model on Test Data\n",
        "test_loss, test_accuracy = model.evaluate(x_test_flattened, y_test)\n",
        "print(f\"Test loss: {test_loss:.4f}, Test accuracy: {test_accuracy:.4f}\")\n",
        "model.fit(x_train_flattened, y_train, epochs=5)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5O8uiuLvPjUG",
        "outputId": "8a10ad75-fd41-4472-95d2-f70527b75a4f"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "313/313 [==============================] - 1s 2ms/step - loss: 0.2667 - accuracy: 0.9263\n",
            "Test loss: 0.2667, Test accuracy: 0.9263\n",
            "Epoch 1/5\n",
            "1875/1875 [==============================] - 3s 2ms/step - loss: 0.2617 - accuracy: 0.9278\n",
            "Epoch 2/5\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.2582 - accuracy: 0.9287\n",
            "Epoch 3/5\n",
            "1875/1875 [==============================] - 5s 2ms/step - loss: 0.2559 - accuracy: 0.9293\n",
            "Epoch 4/5\n",
            "1875/1875 [==============================] - 3s 2ms/step - loss: 0.2530 - accuracy: 0.9298\n",
            "Epoch 5/5\n",
            "1875/1875 [==============================] - 3s 2ms/step - loss: 0.2510 - accuracy: 0.9316\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.src.callbacks.History at 0x7e12a3d337c0>"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Make Predictions on Test Data\n",
        "y_predicted = model.predict(x_test_flattened)\n",
        "y_predicted_labels = [np.argmax(i) for i in y_predicted]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5ELQDgAJPn52",
        "outputId": "4a143ae8-2690-44e1-8449-7d848678c2c8"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "313/313 [==============================] - 1s 1ms/step\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Create Confusion Matrix\n",
        "cm = tf.math.confusion_matrix(labels=y_test, predictions=y_predicted_labels)"
      ],
      "metadata": {
        "id": "GJS64BbAPxwj"
      },
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Plot Confusion Matrix\n",
        "import seaborn as sn\n",
        "plt.figure(figsize=(10, 7))\n",
        "sn.heatmap(cm, annot=True, fmt='d')\n",
        "plt.xlabel('Predicted')\n",
        "plt.ylabel('Truth')\n",
        "plt.title('Confusion Matrix')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 641
        },
        "id": "IbVtFdwMP1k-",
        "outputId": "b8241d67-7540-4f76-8fd5-4e3b24ea9745"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x700 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Build and Compile a Model with Hidden Layer\n",
        "model_with_hidden = keras.Sequential([\n",
        "    keras.layers.Dense(100, input_shape=(784,), activation='relu'),\n",
        "    keras.layers.Dense(10, activation='sigmoid')\n",
        "])\n",
        "\n",
        "model_with_hidden.compile(\n",
        "    optimizer='adam',\n",
        "    loss='sparse_categorical_crossentropy',\n",
        "    metrics=['accuracy']\n",
        ")\n",
        "# Train the Model with Hidden Layer\n",
        "model_with_hidden.fit(x_train_flattened, y_train, epochs=5)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4IzI9TsKP6ft",
        "outputId": "ef0d7079-7397-4472-ce92-5d7965bcf5c4"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/5\n",
            "1875/1875 [==============================] - 9s 4ms/step - loss: 0.2772 - accuracy: 0.9202\n",
            "Epoch 2/5\n",
            "1875/1875 [==============================] - 8s 4ms/step - loss: 0.1298 - accuracy: 0.9613\n",
            "Epoch 3/5\n",
            "1875/1875 [==============================] - 7s 3ms/step - loss: 0.0900 - accuracy: 0.9731\n",
            "Epoch 4/5\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.0677 - accuracy: 0.9790\n",
            "Epoch 5/5\n",
            "1875/1875 [==============================] - 7s 3ms/step - loss: 0.0539 - accuracy: 0.9831\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.src.callbacks.History at 0x7e12a19a5090>"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Evaluate the Model with Hidden Layer on Test Data\n",
        "hidden_test_loss, hidden_test_accuracy = model_with_hidden.evaluate(x_test_flattened, y_test)\n",
        "print(f\"Test accuracy with hidden layer: {hidden_test_accuracy:.4f}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OjB_kLY2P-y7",
        "outputId": "db992ec8-afc7-4ccc-f9dd-ace5b878d111"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "313/313 [==============================] - 1s 2ms/step - loss: 0.0801 - accuracy: 0.9761\n",
            "Test accuracy with hidden layer: 0.9761\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Display Sample Predictions\n",
        "sample_indices = np.random.randint(0, len(x_test), size=5)\n",
        "sample_images = x_test[sample_indices]\n",
        "sample_labels = y_test[sample_indices]\n",
        "\n",
        "# Flatten the sample images\n",
        "sample_images_flattened = sample_images.reshape(len(sample_images), -1)\n",
        "\n",
        "sample_predictions = model_with_hidden.predict(sample_images_flattened)\n",
        "predicted_classes = np.argmax(sample_predictions, axis=1)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dMbh-hpTQMpg",
        "outputId": "2968168c-9d6b-476b-e049-b6815937eaf9"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1/1 [==============================] - 0s 82ms/step\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Display Sample Images, True Labels, and Predicted Labels\n",
        "plt.figure(figsize=(12, 4))\n",
        "for i in range(5):\n",
        "    plt.subplot(1, 5, i + 1)\n",
        "    plt.imshow(sample_images[i], cmap='gray')\n",
        "    plt.title(f\"True: {sample_labels[i]}, Predicted: {predicted_classes[i]}\")\n",
        "    plt.axis('off')\n",
        "\n",
        "plt.tight_layout()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 281
        },
        "id": "7hAKb9iXQQPY",
        "outputId": "1681b6d1-6ae7-4c3e-ab92-460ae4dc6093"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x400 with 5 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "SA8MJ5wGQUow"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}