Mnist _latest_version.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "id": "858xJ0lV-vuI"
   },
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "from tensorflow.keras import *\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import tkinter as tk\n",
    "from tkinter import messagebox\n",
    "from PIL import Image, ImageTk\n",
    "import imageio"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "id": "7hPw4wZw_OoU"
   },
   "outputs": [],
   "source": [
    "mnist = tf.keras.datasets.mnist\n",
    "(x_train, y_train), (x_test, y_test) = mnist.load_data()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 680
    },
    "id": "iR2qk20gAzPP",
    "outputId": "b2c1814a-5730-4a15-96b5-42eb6d69df0c"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 800x800 with 25 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "figure=plt.figure(figsize=(8,8))\n",
    "for i in range(1,26):\n",
    "  figure.add_subplot(5,5,i)\n",
    "  plt.imshow(x_train[i])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "id": "6kJHLqXrOil2"
   },
   "outputs": [],
   "source": [
    "# Normalize the input images\n",
    "x_train = x_train / 255.0\n",
    "x_test = x_test / 255.0\n",
    "\n",
    "# Reshape the input images to match the expected format\n",
    "x_train = x_train.reshape(-1, 28, 28, 1)\n",
    "x_test = x_test.reshape(-1, 28, 28, 1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "a8LU4nEqA9zQ",
    "outputId": "0ac64cfd-47e8-4c5b-98db-c30d21d67f3e"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/10\n",
      "469/469 [==============================] - 28s 58ms/step - loss: 0.4242 - accuracy: 0.8638 - val_loss: 0.1539 - val_accuracy: 0.9514\n",
      "Epoch 2/10\n",
      "469/469 [==============================] - 27s 57ms/step - loss: 0.1319 - accuracy: 0.9594 - val_loss: 0.0978 - val_accuracy: 0.9708\n",
      "Epoch 3/10\n",
      "469/469 [==============================] - 27s 57ms/step - loss: 0.0993 - accuracy: 0.9693 - val_loss: 0.0977 - val_accuracy: 0.9682\n",
      "Epoch 4/10\n",
      "469/469 [==============================] - 27s 57ms/step - loss: 0.0846 - accuracy: 0.9741 - val_loss: 0.0711 - val_accuracy: 0.9779\n",
      "Epoch 5/10\n",
      "469/469 [==============================] - 27s 58ms/step - loss: 0.0720 - accuracy: 0.9776 - val_loss: 0.0792 - val_accuracy: 0.9767\n",
      "Epoch 6/10\n",
      "469/469 [==============================] - 27s 58ms/step - loss: 0.0634 - accuracy: 0.9804 - val_loss: 0.0701 - val_accuracy: 0.9783\n",
      "Epoch 7/10\n",
      "469/469 [==============================] - 27s 58ms/step - loss: 0.0564 - accuracy: 0.9823 - val_loss: 0.0719 - val_accuracy: 0.9773\n",
      "Epoch 8/10\n",
      "469/469 [==============================] - 27s 57ms/step - loss: 0.0489 - accuracy: 0.9851 - val_loss: 0.0650 - val_accuracy: 0.9796\n",
      "Epoch 9/10\n",
      "469/469 [==============================] - 27s 57ms/step - loss: 0.0463 - accuracy: 0.9852 - val_loss: 0.0602 - val_accuracy: 0.9812\n",
      "Epoch 10/10\n",
      "469/469 [==============================] - 27s 57ms/step - loss: 0.0421 - accuracy: 0.9871 - val_loss: 0.0643 - val_accuracy: 0.9810\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x28e0642cb80>"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model=tf.keras.Sequential([\n",
    "    tf.keras.layers.Conv2D(64,kernel_size=(3,3),activation=tf.nn.relu,input_shape=(28,28,1)),\n",
    "    tf.keras.layers.MaxPool2D(pool_size=(2,2)),\n",
    "    tf.keras.layers.Conv2D(32,kernel_size=(3,3),activation=tf.nn.relu,input_shape=(28,28,1)),\n",
    "    tf.keras.layers.MaxPool2D(pool_size=(2,2)),\n",
    "    tf.keras.layers.Conv2D(16,kernel_size=(3,3),activation=tf.nn.relu,input_shape=(28,28,1)),\n",
    "    tf.keras.layers.MaxPool2D(pool_size=(2,2)),\n",
    "    tf.keras.layers.Flatten(),\n",
    "    tf.keras.layers.Dense(512,activation=tf.nn.relu),\n",
    "    tf.keras.layers.Dense(256,activation=tf.nn.relu),\n",
    "    tf.keras.layers.Dense(128,activation=tf.nn.relu),\n",
    "    tf.keras.layers.Dense(10,activation=tf.nn.softmax)]\n",
    "                          )\n",
    "\n",
    "model.compile(optimizer='adam',\n",
    "              loss='sparse_categorical_crossentropy',\n",
    "              metrics=['accuracy'])\n",
    "model.fit(x_train,y_train, epochs=10, batch_size=128, validation_data=(x_test, y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 466
    },
    "id": "XBWdF5UlKdlA",
    "outputId": "7108c13b-3544-479e-b566-e1a115ec1299"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "313/313 [==============================] - 2s 6ms/step\n",
      "The predicted number is  5\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "prediction=model.predict(x_test)\n",
    "prediction=np.argmax(prediction,axis=1)\n",
    "plt.imshow(x_test[15].reshape(28,28))\n",
    "print('The predicted number is  '+str(prediction[15]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 371
    },
    "id": "DLhZMq5hSJw3",
    "outputId": "55e4b59e-d6b0-48dd-9d66-81d923506427"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\abdel\\AppData\\Local\\Temp\\ipykernel_17748\\938666293.py:15: DeprecationWarning: ANTIALIAS is deprecated and will be removed in Pillow 10 (2023-07-01). Use Resampling.LANCZOS instead.\n",
      "  img = img.resize((28, 28), resample=Image.ANTIALIAS)  # Use Resampling.LANCZOS instead\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1/1 [==============================] - 0s 28ms/step\n"
     ]
    }
   ],
   "source": [
    "\n",
    "# Create the GUI application\n",
    "window = tk.Tk()\n",
    "canvas = tk.Canvas(window, width=280, height=280, bg='white')\n",
    "canvas.pack()\n",
    "\n",
    "def draw(event):\n",
    "    x = event.x\n",
    "    y = event.y\n",
    "    r = 8\n",
    "    canvas.create_oval(x-r, y-r, x+r, y+r, fill='black')\n",
    "\n",
    "def predict_number():\n",
    "    canvas.postscript(file='temp.eps')\n",
    "    img = Image.open('temp.eps')\n",
    "    img = img.resize((28, 28), resample=Image.ANTIALIAS)  # Use Resampling.LANCZOS instead\n",
    "    img_array = np.array(img)\n",
    "    img_array = img_array[:, :, 0]\n",
    "    img_array = img_array.reshape(1, 28, 28, 1) / 255.0\n",
    "    result = model.predict(img_array)\n",
    "    predicted_class = np.argmax(result[0])\n",
    "    messagebox.showinfo('Prediction', f'The predicted number is: {predicted_class}')\n",
    "\n",
    "canvas.bind('<B1-Motion>', draw)\n",
    "predict_button = tk.Button(window, text='Predict', command=predict_number)\n",
    "predict_button.pack()\n",
    "\n",
    "window.mainloop()\n"
   ]
  }
 ],
 "metadata": {
  "colab": {
   "provenance": []
  },
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.16"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}