handwritten machine learning project.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "2a9b1c03",
   "metadata": {},
   "outputs": [],
   "source": [
    "import cv2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "bd033bc5",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "b77be37f",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "5a750d1a",
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "a7eecfcf",
   "metadata": {},
   "outputs": [],
   "source": [
    "mnist = tf.keras.datasets.mnist"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "88bb4214",
   "metadata": {},
   "outputs": [],
   "source": [
    "(x_train,y_train), (x_test,y_test) = mnist.load_data()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "58bcce9d",
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train = tf.keras.utils.normalize(x_train,axis=1)\n",
    "x_test = tf.keras.utils.normalize(x_test,axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "e7cd1c38",
   "metadata": {},
   "outputs": [],
   "source": [
    "model = tf.keras.models.Sequential()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "4e24c3e8",
   "metadata": {},
   "outputs": [],
   "source": [
    "model.add(tf.keras.layers.Flatten(input_shape=(28,28)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "1c82b0bb",
   "metadata": {},
   "outputs": [],
   "source": [
    "model.add(tf.keras.layers.Dense(200,activation='sigmoid'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "8ee5cdf3",
   "metadata": {},
   "outputs": [],
   "source": [
    "model.add(tf.keras.layers.Dense(200,activation='sigmoid'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "6875eb93",
   "metadata": {},
   "outputs": [],
   "source": [
    "model.add(tf.keras.layers.Dense(100,activation='sigmoid'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "97cef054",
   "metadata": {},
   "outputs": [],
   "source": [
    "model.add(tf.keras.layers.Dense(10,activation='softmax'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "97918a0f",
   "metadata": {},
   "outputs": [],
   "source": [
    "model.compile(optimizer='adam',loss=\"sparse_categorical_crossentropy\",metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "395abf65",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/7\n",
      "1875/1875 [==============================] - 6s 3ms/step - loss: 0.5366 - accuracy: 0.8424\n",
      "Epoch 2/7\n",
      "1875/1875 [==============================] - 5s 3ms/step - loss: 0.2153 - accuracy: 0.9355\n",
      "Epoch 3/7\n",
      "1875/1875 [==============================] - 6s 3ms/step - loss: 0.1564 - accuracy: 0.9532\n",
      "Epoch 4/7\n",
      "1875/1875 [==============================] - 6s 3ms/step - loss: 0.1228 - accuracy: 0.9630\n",
      "Epoch 5/7\n",
      "1875/1875 [==============================] - 6s 3ms/step - loss: 0.0987 - accuracy: 0.9701\n",
      "Epoch 6/7\n",
      "1875/1875 [==============================] - 6s 3ms/step - loss: 0.0818 - accuracy: 0.9754\n",
      "Epoch 7/7\n",
      "1875/1875 [==============================] - 6s 3ms/step - loss: 0.0680 - accuracy: 0.9796\n",
      "INFO:tensorflow:Assets written to: handwritten.model\\assets\n"
     ]
    }
   ],
   "source": [
    "model.fit(x_train,y_train,epochs=7)\n",
    "model.save(\"handwritten.model\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "f0ccda00",
   "metadata": {},
   "outputs": [],
   "source": [
    "model = tf.keras.models.load_model(\"handwritten.model\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "c4e1d668",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "313/313 [==============================] - 1s 2ms/step - loss: 0.0970 - accuracy: 0.9702\n"
     ]
    }
   ],
   "source": [
    "loss,accuracy = model.evaluate(x_test,y_test)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "e17f1265",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.09698960185050964 0.9702000021934509\n"
     ]
    }
   ],
   "source": [
    "print(loss,accuracy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "a6016f5b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
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     ]
    }
   ],
   "source": [
    "TestImage = cv2.imread(\"img1.png\")[:,:,0]\n",
    "print(TestImage)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "0908e381",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
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     ]
    }
   ],
   "source": [
    "TestImage = np.invert(np.array([TestImage]))\n",
    "print(TestImage)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "34a53d97",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "This digit is probably  2\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "prediction = model.predict(TestImage)\n",
    "print(\"This digit is probably \",np.argmax(prediction))\n",
    "plt.imshow(TestImage[0],cmap=plt.cm.binary)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c2d86e94",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "TFOD",
   "language": "python",
   "name": "myenv"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}