Linear Model.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "from tensorflow.examples.tutorials.mnist import input_data\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import random\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Extracting data/MNIST/train-images-idx3-ubyte.gz\n",
      "Extracting data/MNIST/train-labels-idx1-ubyte.gz\n",
      "Extracting data/MNIST/t10k-images-idx3-ubyte.gz\n",
      "Extracting data/MNIST/t10k-labels-idx1-ubyte.gz\n"
     ]
    }
   ],
   "source": [
    "mnist = input_data.read_data_sets('data/MNIST/', one_hot=True) #onhot=true -> y=5 ise => on tane eleman olan vektore cevirdi -> y=[0,0,0,0,1,0,0,0,0,0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def show_digit(pixels):\n",
    "    img = pixels.reshape(28, 28)\n",
    "    plt.axis('off')\n",
    "    plt.imshow(img, cmap='gray_r')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "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": [
    "sample = random.choice(mnist.train.images)\n",
    "\n",
    "show_digit(sample)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training data set: 55000\n",
      "Test data set: 10000\n"
     ]
    }
   ],
   "source": [
    "print(u\"Training data set: %d\" % len(mnist.train.images))\n",
    "print(u\"Test data set: %d\" % len(mnist.test.images))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "#veri setini iclerine koymak icin\n",
    "X = tf.placeholder(tf.float32, [None, 784]) #yertutucu tersorfow degiskeni, yertutucunun veri tipi float32, matrix formatinda kac satir oldugunu bilmiyoruz o yuzden none, 784 deger\n",
    "y = tf.placeholder(tf.float32, [None, 10]) #onhot=true -> y=5 ise => on tane eleman olan vektore cevirdi -> y=[0,0,0,0,1,0,0,0,0,0]\n",
    "\n",
    "W = tf.Variable(tf.truncated_normal(shape=[784, 10], stddev=0.1)) #rastgele sayilar olustur, boyutu 784x4. 0.1 -> elemanlar arasi sicrama\n",
    "b = tf.Variable(tf.constant(shape=[10], value=0.1)) #basta sabit bir deger=0.1, boyutu 10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = tf.nn.softmax(tf.matmul(X, W) + b) #y_pred=[0.01, 0.2.., 0.75, ...,0.1] en yuksek 5inci index, y ile karsilastirmasi kolay eger onehot true ise\n",
    "\n",
    "loss = tf.reduce_mean(-tf.reduce_sum(y * tf.log(y_pred),\n",
    "                                    reduction_indices=[1])) # probleme gore maliyet fonksiyonlari vardir, bu fonksiyon cross entropy. fonksiyona gore deger ne kadar buyuk ise o kadar kotu\n",
    "# loss un minimum oldugu noktayi ariyoruz\n",
    "\n",
    "optimizer = tf.train.GradientDescentOptimizer(0.05).minimize(loss) # 0.05 -> learning rate \n",
    "\n",
    "correct_predictions = tf.equal(tf.argmax(y, 1), tf.argmax(y_pred, 1)) # liste halinde true false cikaracak\n",
    "accuracy = tf.reduce_mean(tf.cast(correct_predictions, tf.float32)) # dogrular 1.0, yanlislar 0. reduce_mean -> listenin icindeki m tane sayiyi topla ve m ye bol (yani ortalama al)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[*] Step: 0, test accuracy: 6.90%\n",
      "[*] Step: 500, test accuracy: 88.45%\n",
      "[*] Step: 1000, test accuracy: 89.79%\n",
      "[*] Step: 1500, test accuracy: 90.39%\n",
      "[*] Step: 2000, test accuracy: 90.73%\n",
      "[*] Step: 2500, test accuracy: 90.99%\n",
      "[*] Step: 3000, test accuracy: 91.14%\n",
      "[*] Step: 3500, test accuracy: 91.29%\n",
      "[*] Step: 4000, test accuracy: 91.29%\n",
      "[*] Step: 4500, test accuracy: 91.47%\n",
      "[*] Step: 5000, test accuracy: 91.61%\n",
      "[*] Step: 5500, test accuracy: 91.63%\n",
      "[*] Step: 6000, test accuracy: 91.67%\n",
      "[*] Step: 6500, test accuracy: 91.60%\n",
      "[*] Step: 7000, test accuracy: 91.79%\n",
      "[*] Step: 7500, test accuracy: 91.84%\n",
      "[*] Step: 8000, test accuracy: 91.88%\n",
      "[*] Step: 8500, test accuracy: 91.77%\n",
      "[*] Step: 9000, test accuracy: 92.03%\n",
      "[*] Step: 9500, test accuracy: 91.88%\n"
     ]
    }
   ],
   "source": [
    "sess = tf.Session() # calisirabilmek icin session olusturduk\n",
    "sess.run(tf.global_variables_initializer())\n",
    "\n",
    "for i in range(10000):\n",
    "    xs, ys = mnist.train.next_batch(150) # 128 resim. tek seferde tek resim gondermek maliyeti yukseltir o yuzden 128 tane resmi paket halinde gonder. sistem gucune gore sayi artabilir azaltilabilir\n",
    "    \n",
    "    sess.run(optimizer, feed_dict={X: xs, y: ys})\n",
    "    \n",
    "    if i % 500 == 0: #her 500 adimda accuracy calistir, test veri setinin hepsini gonder, modelin basarisini olc\n",
    "        acc = sess.run(accuracy, feed_dict={X: mnist.test.images,\n",
    "                                           y: mnist.test.labels})\n",
    "        print(\"[*] Step: %d, test accuracy: %.2f%%\" % (i, acc * 100))\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "weig = tf.Print(W,[W])\n",
    "bia = tf.Print(b,[b])\n",
    "# print sess.run(weig), sess.run(bia)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Probability of being 0: 0.00%\n",
      "Probability of being 1: 0.00%\n",
      "Probability of being 2: 0.00%\n",
      "Probability of being 3: 0.00%\n",
      "Probability of being 4: 99.73%\n",
      "Probability of being 5: 0.00%\n",
      "Probability of being 6: 0.12%\n",
      "Probability of being 7: 0.01%\n",
      "Probability of being 8: 0.01%\n",
      "Probability of being 9: 0.11%\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": [
    "sample = random.choice(mnist.test.images)\n",
    "\n",
    "predict = sess.run(y_pred, feed_dict={X: [sample]})[0]\n",
    "\n",
    "for i, v in enumerate(predict):\n",
    "    print(\"Probability of being %d: %.2f%%\" % (i, v * 100))\n",
    "    \n",
    "show_digit(sample)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Probability of being 0: 0.00%\n",
      "Probability of being 1: 0.00%\n",
      "Probability of being 2: 31.93%\n",
      "Probability of being 3: 64.93%\n",
      "Probability of being 4: 0.00%\n",
      "Probability of being 5: 3.00%\n",
      "Probability of being 6: 0.00%\n",
      "Probability of being 7: 0.00%\n",
      "Probability of being 8: 0.14%\n",
      "Probability of being 9: 0.00%\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": [
    "random_img = np.random.rand(784)\n",
    "\n",
    "predict = sess.run(y_pred, feed_dict={X: [random_img]})[0]\n",
    "\n",
    "for i, v in enumerate(predict):\n",
    "    print(\"Probability of being %d: %.2f%%\" % (i, v * 100))\n",
    "    \n",
    "show_digit(random_img)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.6.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}