Number_Recognition.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "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/vandanac17/Number_Recognition_System/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 tensorflow as tf\n",
        "from tensorflow import keras\n",
        "import matplotlib.pyplot as plt\n",
        "import numpy as np"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "(x_train, y_train), (x_test, y_test) = keras.datasets.mnist.load_data()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KI_PBPJdOYax",
        "outputId": "dc95baff-8222-46db-f0e9-e0a65bc2cac0"
      },
      "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": "b2c5478c-1b50-47da-b565-3a50930eff15"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "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": "5900d81f-f48b-4fbf-c148-f6fe596f1403"
      },
      "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": [
        "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": "2a4f9f47-3f55-48f4-ac6f-9adb2b4743ce"
      },
      "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": [
        "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/"
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        "id": "1CvzZycXOtQ8",
        "outputId": "16fba83d-06f2-4d59-af44-5e2171c8811d"
      },
      "execution_count": null,
      "outputs": [
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            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "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": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "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",
        "model.fit(x_train_flattened, y_train, epochs=5)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bFQ0fZ2GPWNk",
        "outputId": "49d4de31-5395-49e8-b826-d5a8a4395666"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/5\n",
            "1875/1875 [==============================] - 9s 4ms/step - loss: 0.4678 - accuracy: 0.8768\n",
            "Epoch 2/5\n",
            "1875/1875 [==============================] - 9s 5ms/step - loss: 0.3035 - accuracy: 0.9150\n",
            "Epoch 3/5\n",
            "1875/1875 [==============================] - 7s 4ms/step - loss: 0.2831 - accuracy: 0.9209\n",
            "Epoch 4/5\n",
            "1875/1875 [==============================] - 8s 4ms/step - loss: 0.2730 - accuracy: 0.9232\n",
            "Epoch 5/5\n",
            "1875/1875 [==============================] - 3s 2ms/step - loss: 0.2662 - accuracy: 0.9253\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.src.callbacks.History at 0x7b19a3666950>"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "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": "508d45cc-29b9-4a1b-871e-f9833dc85a34"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "313/313 [==============================] - 1s 3ms/step - loss: 0.2656 - accuracy: 0.9266\n",
            "Test loss: 0.2656, Test accuracy: 0.9266\n",
            "Epoch 1/5\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 0.2618 - accuracy: 0.9265\n",
            "Epoch 2/5\n",
            "1875/1875 [==============================] - 3s 2ms/step - loss: 0.2577 - accuracy: 0.9283\n",
            "Epoch 3/5\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 0.2556 - accuracy: 0.9293\n",
            "Epoch 4/5\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 0.2529 - accuracy: 0.9299\n",
            "Epoch 5/5\n",
            "1875/1875 [==============================] - 3s 2ms/step - loss: 0.2506 - accuracy: 0.9308\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.src.callbacks.History at 0x7b19a04f49a0>"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "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": "eacda439-5362-497e-b8ab-d83fd315ca70"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "313/313 [==============================] - 1s 2ms/step\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "cm = tf.math.confusion_matrix(labels=y_test, predictions=y_predicted_labels)"
      ],
      "metadata": {
        "id": "GJS64BbAPxwj"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "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": "c35ec7a9-697b-4382-aae7-f4a507085ba2"
      },
      "execution_count": null,
      "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": [
        "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",
        "model_with_hidden.fit(x_train_flattened, y_train, epochs=5)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4IzI9TsKP6ft",
        "outputId": "322dec04-58da-4ad7-ffac-0bf63b497bd0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/5\n",
            "1875/1875 [==============================] - 7s 3ms/step - loss: 0.2700 - accuracy: 0.9223\n",
            "Epoch 2/5\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.1225 - accuracy: 0.9641\n",
            "Epoch 3/5\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.0875 - accuracy: 0.9732\n",
            "Epoch 4/5\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.0659 - accuracy: 0.9805\n",
            "Epoch 5/5\n",
            "1875/1875 [==============================] - 7s 3ms/step - loss: 0.0521 - accuracy: 0.9844\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.src.callbacks.History at 0x7b199944b880>"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "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": "a8888bd9-2642-4b4f-a62e-00cafa2f1785"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "313/313 [==============================] - 1s 2ms/step - loss: 0.0873 - accuracy: 0.9720\n",
            "Test accuracy with hidden layer: 0.9720\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "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",
        "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": "0031875a-d616-4ae4-ce53-0b471067fbfb"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1/1 [==============================] - 0s 48ms/step\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "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": 208
        },
        "id": "7hAKb9iXQQPY",
        "outputId": "082a9ab8-6572-4775-aeeb-94bf9dfdabb6"
      },
      "execution_count": null,
      "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": []
    }
  ]
}