digit.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "CrSWNMhPMD_a"
      },
      "outputs": [],
      "source": [
        "import cv2\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import tensorflow as tf"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "mnist = tf.keras.datasets.mnist"
      ],
      "metadata": {
        "id": "jwkaI50IMZ6m"
      },
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "(X_train, y_train), (X_test, y_test) = mnist.load_data()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IN7hE1q-MleI",
        "outputId": "cb67ee1a-d870-48af-94af-7c9702e71fb2"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/mnist.npz\n",
            "11490434/11490434 [==============================] - 1s 0us/step\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X_train = tf.keras.utils.normalize(X_train, axis = 1)\n",
        "X_test = tf.keras.utils.normalize(X_test, axis = 1)"
      ],
      "metadata": {
        "id": "zv3kWhgAQX3K"
      },
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model = tf.keras.models.Sequential()\n",
        "model.add(tf.keras.layers.Flatten(input_shape=(28, 28)))\n",
        "model.add(tf.keras.layers.Dense(128, activation='relu'))\n",
        "model.add(tf.keras.layers.Dense(128, activation='relu'))\n",
        "model.add(tf.keras.layers.Dense(128, activation='relu'))\n",
        "model.add(tf.keras.layers.Dense(10, activation='softmax'))\n",
        "\n",
        "model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy'])"
      ],
      "metadata": {
        "id": "PdUkW9aVW4V5"
      },
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model.fit(X_train, y_train, epochs = 3)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SpzJHjX5YWmT",
        "outputId": "0a387093-882f-435b-8dfa-768948314a42"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/3\n",
            "1875/1875 [==============================] - 8s 4ms/step - loss: 0.2512 - accuracy: 0.9243\n",
            "Epoch 2/3\n",
            "1875/1875 [==============================] - 8s 4ms/step - loss: 0.1047 - accuracy: 0.9668\n",
            "Epoch 3/3\n",
            "1875/1875 [==============================] - 9s 5ms/step - loss: 0.0735 - accuracy: 0.9768\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.src.callbacks.History at 0x7d057afc39d0>"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model.save('digitAI.model')"
      ],
      "metadata": {
        "id": "3bh8WT33Y6da"
      },
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "loss, acc = model.evaluate(X_test, y_test)\n",
        "\n",
        "loss, acc"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_UuC4nL2ZExJ",
        "outputId": "0f6f3943-ff6a-4537-eff0-925427b8fec4"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "313/313 [==============================] - 1s 2ms/step - loss: 0.1036 - accuracy: 0.9692\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(0.10361675173044205, 0.9692000150680542)"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "len(X_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "S99pDaG_di1B",
        "outputId": "634e57cc-d457-406b-ccf3-8535d4521a95"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "10000"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# prompt: for this notebook code generate graphs to show the digits\n",
        "\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# Get the first 10 images from the test set\n",
        "images = X_test[90:110]\n",
        "\n",
        "# Predict the labels for these images\n",
        "predictions = model.predict(images)\n",
        "\n",
        "# Get the indices of the predicted labels\n",
        "predicted_labels = np.argmax(predictions, axis=1)\n",
        "\n",
        "# Get the true labels\n",
        "true_labels = y_test[90:110]\n",
        "\n",
        "# Create a figure with 10 subplots\n",
        "fig, axes = plt.subplots(2, 10, figsize=(20, 5))\n",
        "\n",
        "# Plot each image and its predicted and true labels\n",
        "for i, ax in enumerate(axes.flat):\n",
        "    ax.imshow(images[i], cmap='viridis')\n",
        "    ax.set_title(f\"Predicted: {predicted_labels[i]}\\nTrue: {true_labels[i]}\")\n",
        "    ax.axis('off')\n",
        "\n",
        "# Show the plot\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 401
        },
        "id": "yPhY0MyCbJYf",
        "outputId": "3d306e3a-c758-4e45-ab62-54b9e499b985"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1/1 [==============================] - 0s 23ms/step\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 2000x500 with 20 Axes>"
            ],
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