dig_class.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": 3,
      "metadata": {
        "id": "4EGBH9jeJBdX"
      },
      "outputs": [],
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "from tensorflow.keras.datasets import mnist\n",
        "from tensorflow.keras.models import Sequential\n",
        "from tensorflow.keras.layers import Dense, Flatten, Dropout, BatchNormalization\n",
        "from tensorflow.keras.utils import to_categorical\n",
        "from tensorflow.keras.optimizers import Adam"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Load dataset\n",
        "(x_train, y_train), (x_test, y_test) = mnist.load_data()\n",
        "\n",
        "# Normalize the images\n",
        "x_train = x_train.astype('float32') / 255.0\n",
        "x_test = x_test.astype('float32') / 255.0\n",
        "\n",
        "# Convert labels to categorical\n",
        "y_train = to_categorical(y_train, 10)\n",
        "y_test = to_categorical(y_test, 10)"
      ],
      "metadata": {
        "id": "5H7gaDq4LYfG"
      },
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model = Sequential([\n",
        "      Flatten(input_shape=(28, 28)),\n",
        "      Dense(128, activation='relu'),\n",
        "      BatchNormalization(),\n",
        "      Dense(64, activation='relu'),\n",
        "      BatchNormalization(),\n",
        "      Dense(10, activation='softmax')\n",
        "  ])\n",
        "model.compile(optimizer=Adam(learning_rate=0.001),\n",
        "              loss='categorical_crossentropy',\n",
        "              metrics=['accuracy'])"
      ],
      "metadata": {
        "id": "9s3KmAVWLeuM"
      },
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "history = model.fit(x_train, y_train, epochs=20, batch_size=32,\n",
        "                    validation_data=(x_test, y_test), verbose=0)"
      ],
      "metadata": {
        "id": "2K3zFrINPRz6"
      },
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "test_loss, test_accuracy = model.evaluate(x_test, y_test, verbose=0)\n",
        "print(f'Model Test Loss: {test_loss}')\n",
        "print(f'Model Test Loss: {test_accuracy}')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PBG0NCZsS5LH",
        "outputId": "be18b07f-9648-4848-aa4c-f59fae7b4800"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model Test Loss: 0.07622469961643219\n",
            "Model Test Loss: 0.9811999797821045\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "predictions = model.predict(x_test)\n",
        "for i in range(5):\n",
        "    plt.imshow(x_test[i], cmap='gray')\n",
        "    plt.title(f'Predicted: {np.argmax(predictions[i])}, Actual: {np.argmax(y_test[i])}')\n",
        "    plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "783AvBZuTtPp",
        "outputId": "984a5f8e-ec46-4e9d-f005-516c13f35f85"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "313/313 [==============================] - 1s 3ms/step\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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EALCCAAIAWEEAAQCsIIAAAFYQQAAAKwggAIAVBBAAwAoCCABgBQEEALCCAAIAWEEAAQCsIIAAAFYQQAAAKwIeQAsXLlRISIjf1L9//0BvBgDQxnUKxkoHDBigLVu2/P9GOgVlMwCANiwoydCpUyfFx8cHY9UAgHYiKJ8B7d+/X4mJiUpNTdWjjz6qQ4cONTu2vr5eXq/XbwIAtH8BD6DMzEzl5+ersLBQy5cvV1VVle655x6dPn26yfF5eXlyu92+KSkpKdAtAQBaoRBjjAnmBk6dOqXk5GS9/vrrmj59eqPl9fX1qq+v9732er2EEAC0Ax6PR1FRUc0uD/rdAd26dVPfvn114MCBJpe7XC65XK5gtwEAaGWC/ndAZ86cUWVlpRISEoK9KQBAGxLwAHrmmWdUUlKigwcP6o9//KO+853vqGPHjnrkkUcCvSkAQBsW8EtwR44c0SOPPKKTJ08qJiZGI0aMUFlZmWJiYgK9KQBAGxb0mxCc8nq9crvdttsAANyk692EwLPgAABWEEAAACsIIACAFQQQAMAKAggAYAUBBACwggACAFhBAAEArCCAAABWEEAAACsIIACAFQQQAMCKoH8hHW6tBx980HHNzJkzW7Sto0ePOq6pq6tzXFNQUOC4prq62nGNpGa/OBFA4HEGBACwggACAFhBAAEArCCAAABWEEAAACsIIACAFQQQAMAKAggAYAUBBACwggACAFhBAAEArCCAAABWEEAAACtCjDHGdhNX8nq9crvdtttos/761786runTp0/gG7Hs9OnTLar7y1/+EuBOEGhHjhxxXLN48eIWbWvXrl0tqsMlHo9HUVFRzS7nDAgAYAUBBACwggACAFhBAAEArCCAAABWEEAAACsIIACAFQQQAMAKAggAYAUBBACwggACAFhBAAEArOhkuwEE1syZMx3XZGRktGhbn376qeOar3/9645r7rrrLsc1I0eOdFwjSd/85jcd1xw+fNhxTVJSkuOaW+nChQuOaz7//HPHNQkJCY5rWuLQoUMtquNhpMHFGRAAwAoCCABgBQEEALCCAAIAWEEAAQCsIIAAAFYQQAAAKwggAIAVBBAAwAoCCABgBQEEALCCAAIAWMHDSNuZoqKiW1LTUoWFhbdkO927d29R3Te+8Q3HNbt373ZcM2TIEMc1t1JdXZ3jmv/+7/92XNOSB9pGR0c7rqmsrHRcg+DjDAgAYAUBBACwwnEAbdu2Tffff78SExMVEhKidevW+S03xujll19WQkKCwsPDlZ2drf379weqXwBAO+E4gGprazVo0CAtW7asyeWLFy/Wm2++qXfeeUc7d+5URESExo4d26JrygCA9svxTQg5OTnKyclpcpkxRkuXLtWLL76oBx54QJL0y1/+UnFxcVq3bp0mT558c90CANqNgH4GVFVVperqamVnZ/vmud1uZWZmqrS0tMma+vp6eb1evwkA0P4FNICqq6slSXFxcX7z4+LifMuulpeXJ7fb7ZuSkpIC2RIAoJWyfhfc/Pnz5fF4fNPhw4dttwQAuAUCGkDx8fGSpJqaGr/5NTU1vmVXc7lcioqK8psAAO1fQAMoJSVF8fHxfn9Z7/V6tXPnTg0bNiyQmwIAtHGO74I7c+aMDhw44HtdVVWljz/+WNHR0erdu7fmzp2rV199VXfeeadSUlL00ksvKTExURMmTAhk3wCANs5xAO3atUv33Xef7/W8efMkSVOnTlV+fr6ee+451dbWatasWTp16pRGjBihwsJCde7cOXBdAwDavBBjjLHdxJW8Xq/cbrftNgA4NGnSJMc17733nuOa8vJyxzVX/qfZiS+++KJFdbjE4/Fc83N963fBAQBuTwQQAMAKAggAYAUBBACwggACAFhBAAEArCCAAABWEEAAACsIIACAFQQQAMAKAggAYAUBBACwggACAFjh+OsYALR/sbGxjmt+/vOfO67p0MH5/4FfeeUVxzU81bp14gwIAGAFAQQAsIIAAgBYQQABAKwggAAAVhBAAAArCCAAgBUEEADACgIIAGAFAQQAsIIAAgBYQQABAKzgYaQAGsnNzXVcExMT47jmyy+/dFxTUVHhuAatE2dAAAArCCAAgBUEEADACgIIAGAFAQQAsIIAAgBYQQABAKwggAAAVhBAAAArCCAAgBUEEADACgIIAGAFDyMF2rHhw4e3qO75558PcCdNmzBhguOa8vLywDcCKzgDAgBYQQABAKwggAAAVhBAAAArCCAAgBUEEADACgIIAGAFAQQAsIIAAgBYQQABAKwggAAAVhBAAAAreBgp0I6NHz++RXWhoaGOa4qKihzXlJaWOq5B+8EZEADACgIIAGCF4wDatm2b7r//fiUmJiokJETr1q3zWz5t2jSFhIT4TePGjQtUvwCAdsJxANXW1mrQoEFatmxZs2PGjRunY8eO+abVq1ffVJMAgPbH8U0IOTk5ysnJueYYl8ul+Pj4FjcFAGj/gvIZUHFxsWJjY9WvXz898cQTOnnyZLNj6+vr5fV6/SYAQPsX8AAaN26cfvnLX6qoqEg/+clPVFJSopycHF28eLHJ8Xl5eXK73b4pKSkp0C0BAFqhgP8d0OTJk30/Dxw4UBkZGUpLS1NxcbFGjx7daPz8+fM1b94832uv10sIAcBtIOi3Yaempqpnz546cOBAk8tdLpeioqL8JgBA+xf0ADpy5IhOnjyphISEYG8KANCGOL4Ed+bMGb+zmaqqKn388ceKjo5WdHS0Fi1apEmTJik+Pl6VlZV67rnndMcdd2js2LEBbRwA0LY5DqBdu3bpvvvu872+/PnN1KlTtXz5cu3du1e/+MUvdOrUKSUmJmrMmDH60Y9+JJfLFbiuAQBtXogxxthu4kper1dut9t2G0CrEx4e7rhm+/btLdrWgAEDHNeMGjXKcc0f//hHxzVoOzwezzU/1+dZcAAAKwggAIAVBBAAwAoCCABgBQEEALCCAAIAWEEAAQCsIIAAAFYQQAAAKwggAIAVBBAAwAoCCABgBQEEALAi4F/JDSA4nn32Wcc1f/u3f9uibRUWFjqu4cnWcIozIACAFQQQAMAKAggAYAUBBACwggACAFhBAAEArCCAAABWEEAAACsIIACAFQQQAMAKAggAYAUBBACwgoeRAhZ8+9vfdlzz0ksvOa7xer2OayTplVdeaVEd4ARnQAAAKwggAIAVBBAAwAoCCABgBQEEALCCAAIAWEEAAQCsIIAAAFYQQAAAKwggAIAVBBAAwAoCCABgBQ8jBW5Sjx49HNe8+eabjms6duzouGbTpk2OaySprKysRXWAE5wBAQCsIIAAAFYQQAAAKwggAIAVBBAAwAoCCABgBQEEALCCAAIAWEEAAQCsIIAAAFYQQAAAKwggAIAVPIwUuEJLHvhZWFjouCYlJcVxTWVlpeOal156yXENcKtwBgQAsIIAAgBY4SiA8vLyNGTIEEVGRio2NlYTJkxQRUWF35i6ujrl5uaqR48e6tq1qyZNmqSampqANg0AaPscBVBJSYlyc3NVVlamzZs36/z58xozZoxqa2t9Y55++mlt2LBBa9asUUlJiY4ePaqJEycGvHEAQNvm6CaEqz9szc/PV2xsrHbv3q2srCx5PB7927/9m1atWqVRo0ZJklasWKGvf/3rKisr0ze/+c3AdQ4AaNNu6jMgj8cjSYqOjpYk7d69W+fPn1d2drZvTP/+/dW7d2+VlpY2uY76+np5vV6/CQDQ/rU4gBoaGjR37lwNHz5c6enpkqTq6mqFhYWpW7dufmPj4uJUXV3d5Hry8vLkdrt9U1JSUktbAgC0IS0OoNzcXJWXl+vdd9+9qQbmz58vj8fjmw4fPnxT6wMAtA0t+kPU2bNna+PGjdq2bZt69erlmx8fH69z587p1KlTfmdBNTU1io+Pb3JdLpdLLperJW0AANowR2dAxhjNnj1ba9eu1QcffNDor7kHDx6s0NBQFRUV+eZVVFTo0KFDGjZsWGA6BgC0C47OgHJzc7Vq1SqtX79ekZGRvs913G63wsPD5Xa7NX36dM2bN0/R0dGKiorSnDlzNGzYMO6AAwD4cRRAy5cvlySNHDnSb/6KFSs0bdo0SdKSJUvUoUMHTZo0SfX19Ro7dqx+/vOfB6RZAED7EWKMMbabuJLX65Xb7bbdBm5Tffv2dVyzb9++IHTS2AMPPOC4ZsOGDUHoBLgxHo9HUVFRzS7nWXAAACsIIACAFQQQAMAKAggAYAUBBACwggACAFhBAAEArCCAAABWEEAAACsIIACAFQQQAMAKAggAYAUBBACwokXfiAq0dsnJyS2q+8Mf/hDgTpr27LPPOq7ZuHFjEDoB7OEMCABgBQEEALCCAAIAWEEAAQCsIIAAAFYQQAAAKwggAIAVBBAAwAoCCABgBQEEALCCAAIAWEEAAQCs4GGkaJdmzZrVorrevXsHuJOmlZSUOK4xxgShE8AezoAAAFYQQAAAKwggAIAVBBAAwAoCCABgBQEEALCCAAIAWEEAAQCsIIAAAFYQQAAAKwggAIAVBBAAwAoeRopWb8SIEY5r5syZE4ROAAQSZ0AAACsIIACAFQQQAMAKAggAYAUBBACwggACAFhBAAEArCCAAABWEEAAACsIIACAFQQQAMAKAggAYAUPI0Wrd8899ziu6dq1axA6aVplZaXjmjNnzgShE6Bt4QwIAGAFAQQAsMJRAOXl5WnIkCGKjIxUbGysJkyYoIqKCr8xI0eOVEhIiN/0+OOPB7RpAEDb5yiASkpKlJubq7KyMm3evFnnz5/XmDFjVFtb6zdu5syZOnbsmG9avHhxQJsGALR9jm5CKCws9Hudn5+v2NhY7d69W1lZWb75Xbp0UXx8fGA6BAC0Szf1GZDH45EkRUdH+80vKChQz549lZ6ervnz5+vs2bPNrqO+vl5er9dvAgC0fy2+DbuhoUFz587V8OHDlZ6e7ps/ZcoUJScnKzExUXv37tUPf/hDVVRU6P33329yPXl5eVq0aFFL2wAAtFEtDqDc3FyVl5dr+/btfvNnzZrl+3ngwIFKSEjQ6NGjVVlZqbS0tEbrmT9/vubNm+d77fV6lZSU1NK2AABtRIsCaPbs2dq4caO2bdumXr16XXNsZmamJOnAgQNNBpDL5ZLL5WpJGwCANsxRABljNGfOHK1du1bFxcVKSUm5bs3HH38sSUpISGhRgwCA9slRAOXm5mrVqlVav369IiMjVV1dLUlyu90KDw9XZWWlVq1apfHjx6tHjx7au3evnn76aWVlZSkjIyMovwAAoG1yFEDLly+XdOmPTa+0YsUKTZs2TWFhYdqyZYuWLl2q2tpaJSUladKkSXrxxRcD1jAAoH1wfAnuWpKSklRSUnJTDQEAbg88DRu4wieffOK4ZvTo0Y5rvvjiC8c1QHvDw0gBAFYQQAAAKwggAIAVBBAAwAoCCABgBQEEALCCAAIAWEEAAQCsIIAAAFYQQAAAKwggAIAVBBAAwIoQc71HXN9iXq9XbrfbdhsAgJvk8XgUFRXV7HLOgAAAVhBAAAArCCAAgBUEEADACgIIAGAFAQQAsIIAAgBYQQABAKwggAAAVhBAAAArCCAAgBWtLoBa2aPpAAAtdL3381YXQKdPn7bdAgAgAK73ft7qnobd0NCgo0ePKjIyUiEhIX7LvF6vkpKSdPjw4Ws+YbW9Yz9cwn64hP1wCfvhktawH4wxOn36tBITE9WhQ/PnOZ1uYU83pEOHDurVq9c1x0RFRd3WB9hl7IdL2A+XsB8uYT9cYns/3MjX6rS6S3AAgNsDAQQAsKJNBZDL5dKCBQvkcrlst2IV++ES9sMl7IdL2A+XtKX90OpuQgAA3B7a1BkQAKD9IIAAAFYQQAAAKwggAIAVBBAAwIo2E0DLli1Tnz591LlzZ2VmZupPf/qT7ZZuuYULFyokJMRv6t+/v+22gm7btm26//77lZiYqJCQEK1bt85vuTFGL7/8shISEhQeHq7s7Gzt37/fTrNBdL39MG3atEbHx7hx4+w0GyR5eXkaMmSIIiMjFRsbqwkTJqiiosJvTF1dnXJzc9WjRw917dpVkyZNUk1NjaWOg+NG9sPIkSMbHQ+PP/64pY6b1iYC6Ne//rXmzZunBQsW6KOPPtKgQYM0duxYHT9+3HZrt9yAAQN07Ngx37R9+3bbLQVdbW2tBg0apGXLljW5fPHixXrzzTf1zjvvaOfOnYqIiNDYsWNVV1d3izsNruvtB0kaN26c3/GxevXqW9hh8JWUlCg3N1dlZWXavHmzzp8/rzFjxqi2ttY35umnn9aGDRu0Zs0alZSU6OjRo5o4caLFrgPvRvaDJM2cOdPveFi8eLGljpth2oChQ4ea3Nxc3+uLFy+axMREk5eXZ7GrW2/BggVm0KBBttuwSpJZu3at73VDQ4OJj483P/3pT33zTp06ZVwul1m9erWFDm+Nq/eDMcZMnTrVPPDAA1b6seX48eNGkikpKTHGXPq3Dw0NNWvWrPGN+fTTT40kU1paaqvNoLt6PxhjzL333mueeuope03dgFZ/BnTu3Dnt3r1b2dnZvnkdOnRQdna2SktLLXZmx/79+5WYmKjU1FQ9+uijOnTokO2WrKqqqlJ1dbXf8eF2u5WZmXlbHh/FxcWKjY1Vv3799MQTT+jkyZO2Wwoqj8cjSYqOjpYk7d69W+fPn/c7Hvr376/evXu36+Ph6v1wWUFBgXr27Kn09HTNnz9fZ8+etdFes1rd07CvduLECV28eFFxcXF+8+Pi4rRv3z5LXdmRmZmp/Px89evXT8eOHdOiRYt0zz33qLy8XJGRkbbbs6K6ulqSmjw+Li+7XYwbN04TJ05USkqKKisr9cILLygnJ0elpaXq2LGj7fYCrqGhQXPnztXw4cOVnp4u6dLxEBYWpm7duvmNbc/HQ1P7QZKmTJmi5ORkJSYmau/evfrhD3+oiooKvf/++xa79dfqAwj/Lycnx/dzRkaGMjMzlZycrPfee0/Tp0+32Blag8mTJ/t+HjhwoDIyMpSWlqbi4mKNHj3aYmfBkZubq/Ly8tvic9BraW4/zJo1y/fzwIEDlZCQoNGjR6uyslJpaWm3us0mtfpLcD179lTHjh0b3cVSU1Oj+Ph4S121Dt26dVPfvn114MAB261Yc/kY4PhoLDU1VT179myXx8fs2bO1ceNGbd261e/7w+Lj43Xu3DmdOnXKb3x7PR6a2w9NyczMlKRWdTy0+gAKCwvT4MGDVVRU5JvX0NCgoqIiDRs2zGJn9p05c0aVlZVKSEiw3Yo1KSkpio+P9zs+vF6vdu7cedsfH0eOHNHJkyfb1fFhjNHs2bO1du1affDBB0pJSfFbPnjwYIWGhvodDxUVFTp06FC7Oh6utx+a8vHHH0tS6zoebN8FcSPeffdd43K5TH5+vvmv//ovM2vWLNOtWzdTXV1tu7Vb6h/+4R9McXGxqaqqMjt27DDZ2dmmZ8+e5vjx47ZbC6rTp0+bPXv2mD179hhJ5vXXXzd79uwxn332mTHGmB//+MemW7duZv369Wbv3r3mgQceMCkpKearr76y3HlgXWs/nD592jzzzDOmtLTUVFVVmS1btpi77rrL3Hnnnaaurs526wHzxBNPGLfbbYqLi82xY8d809mzZ31jHn/8cdO7d2/zwQcfmF27dplhw4aZYcOGWew68K63Hw4cOGBeeeUVs2vXLlNVVWXWr19vUlNTTVZWluXO/bWJADLGmLfeesv07t3bhIWFmaFDh5qysjLbLd1yDz/8sElISDBhYWHma1/7mnn44YfNgQMHbLcVdFu3bjWSGk1Tp041xly6Ffull14ycXFxxuVymdGjR5uKigq7TQfBtfbD2bNnzZgxY0xMTIwJDQ01ycnJZubMme3uP2lN/f6SzIoVK3xjvvrqK/Pkk0+a7t27my5dupjvfOc75tixY/aaDoLr7YdDhw6ZrKwsEx0dbVwul7njjjvMs88+azwej93Gr8L3AQEArGj1nwEBANonAggAYAUBBACwggACAFhBAAEArCCAAABWEEAAACsIIACAFQQQAMAKAggAYAUBBACw4n8BFdcG4sUAdl8AAAAASUVORK5CYII=\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    }
  ]
}