2.Logistic_Regression/TensorFlow.ipynb

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   "source": [
    "# Reference.\n",
    "# (Eng) https://www.tensorflow.org/versions/r0.8/tutorials/mnist/beginners/index.html\n",
    "# (Kor) https://gist.github.com/haje01/202ac276bace4b25dd3f"
   ]
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     "text": [
      "Successfully downloaded train-images-idx3-ubyte.gz 9912422 bytes.\n",
      "Extracting ./../data/MNIST/train-images-idx3-ubyte.gz\n",
      "Successfully downloaded train-labels-idx1-ubyte.gz 28881 bytes.\n",
      "Extracting ./../data/MNIST/train-labels-idx1-ubyte.gz\n",
      "Successfully downloaded t10k-images-idx3-ubyte.gz 1648877 bytes.\n",
      "Extracting ./../data/MNIST/t10k-images-idx3-ubyte.gz\n",
      "Successfully downloaded t10k-labels-idx1-ubyte.gz 4542 bytes.\n",
      "Extracting ./../data/MNIST/t10k-labels-idx1-ubyte.gz\n"
     ]
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   "source": [
    "# Load Mnist DataSet\n",
    "import os\n",
    "from tensorflow.examples.tutorials.mnist import input_data\n",
    "\n",
    "data_path = \"./../data/MNIST/\"\n",
    "if not os.path.exists(data_path):\n",
    "    os.makedirs(data_path)\n",
    "\n",
    "mnist = input_data.read_data_sets(data_path, one_hot=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
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   "outputs": [],
   "source": [
    "import tensorflow as tf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
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   "outputs": [],
   "source": [
    "# Construct Model\n",
    "X = tf.placeholder(\"float\", [None, 784])\n",
    "W = tf.Variable(tf.zeros([784, 10]))\n",
    "b = tf.Variable(tf.zeros([10]))\n",
    "\n",
    "y = tf.nn.softmax(tf.matmul(X,W) + b)\n",
    "\n",
    "# For Answer\n",
    "y_ = tf.placeholder(\"float\", [None, 10])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
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   "outputs": [],
   "source": [
    "# Loss\n",
    "cross_entropy = -tf.reduce_sum(y_ * tf.log(y))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
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   "source": [
    "# Optimizer\n",
    "learning_rate = 0.01\n",
    "train_step = tf.train.GradientDescentOptimizer(learning_rate).minimize(cross_entropy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
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   "source": [
    "# Training\n",
    "\n",
    "init = tf.global_variables_initializer()\n",
    "\n",
    "sess = tf.Session()\n",
    "sess.run(init)\n",
    "\n",
    "batch_size = 100\n",
    "# Sampling 100 case\n",
    "for i in range(1000):\n",
    "    batch_xs, batch_ys = mnist.train.next_batch(batch_size)\n",
    "    sess.run(train_step, feed_dict={X: batch_xs, y_: batch_ys})"
   ]
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   "cell_type": "code",
   "execution_count": 8,
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     "text": [
      "0.9097\n"
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   "source": [
    "# Test\n",
    "correct_prediction = tf.equal(tf.argmax(y, 1), tf.argmax(y_, 1))\n",
    "accuracy = tf.reduce_mean(tf.cast(correct_prediction, \"float\"))\n",
    "\n",
    "print(sess.run(accuracy, feed_dict={X: mnist.test.images, y_: mnist.test.labels}))"
   ]
  }
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