5_2.mnist_mlp.ipynb

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  {
   "cell_type": "code",
   "execution_count": 1,
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   "source": [
    "import tensorflow as tf\n",
    "import random\n",
    "# import matplotlib.pyplot as plt\n",
    "\n",
    "from tensorflow.examples.tutorials.mnist import input_data\n",
    "\n",
    "tf.set_random_seed(777)  # reproducibilit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Extracting data/train-images-idx3-ubyte.gz\n",
      "Extracting data/train-labels-idx1-ubyte.gz\n",
      "Extracting data/t10k-images-idx3-ubyte.gz\n",
      "Extracting data/t10k-labels-idx1-ubyte.gz\n"
     ]
    }
   ],
   "source": [
    "mnist = input_data.read_data_sets(\"../data/\", one_hot=True)\n",
    "# Check out https://www.tensorflow.org/get_started/mnist/beginners for\n",
    "# more information about the mnist dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# parameters\n",
    "learning_rate = 0.001\n",
    "training_epochs = 15\n",
    "batch_size = 100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# input place holders\n",
    "X = tf.placeholder(tf.float32, [None, 784])\n",
    "Y = tf.placeholder(tf.float32, [None, 10])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# weights & bias for nn layers\n",
    "W1 = tf.Variable(tf.random_normal([784, 256]))\n",
    "b1 = tf.Variable(tf.random_normal([256]))\n",
    "L1 = tf.nn.relu(tf.matmul(X, W1) + b1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
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   "outputs": [],
   "source": [
    "W2 = tf.Variable(tf.random_normal([256, 256]))\n",
    "b2 = tf.Variable(tf.random_normal([256]))\n",
    "L2 = tf.nn.relu(tf.matmul(L1, W2) + b2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "W3 = tf.Variable(tf.random_normal([256, 10]))\n",
    "b3 = tf.Variable(tf.random_normal([10]))\n",
    "hypothesis = tf.matmul(L2, W3) + b3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# define cost/loss & optimizer\n",
    "cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(\n",
    "    logits=hypothesis, labels=Y))\n",
    "optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate).minimize(cost)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# initialize\n",
    "sess = tf.Session()\n",
    "sess.run(tf.global_variables_initializer())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 0001 cost = 142.389119772\n",
      "Epoch: 0002 cost = 38.822415031\n",
      "Epoch: 0003 cost = 24.315938472\n",
      "Epoch: 0004 cost = 16.913954507\n",
      "Epoch: 0005 cost = 12.307805933\n",
      "Epoch: 0006 cost = 9.150873576\n",
      "Epoch: 0007 cost = 6.815710759\n",
      "Epoch: 0008 cost = 5.089267444\n",
      "Epoch: 0009 cost = 3.747570896\n",
      "Epoch: 0010 cost = 2.908875781\n",
      "Epoch: 0011 cost = 2.165973197\n",
      "Epoch: 0012 cost = 1.672954455\n",
      "Epoch: 0013 cost = 1.180632929\n",
      "Epoch: 0014 cost = 0.978651556\n",
      "Epoch: 0015 cost = 0.778702534\n",
      "Learning Finished!\n"
     ]
    }
   ],
   "source": [
    "# train my model\n",
    "for epoch in range(training_epochs):\n",
    "    avg_cost = 0\n",
    "    total_batch = int(mnist.train.num_examples / batch_size)\n",
    "\n",
    "    for i in range(total_batch):\n",
    "        batch_xs, batch_ys = mnist.train.next_batch(batch_size)\n",
    "        feed_dict = {X: batch_xs, Y: batch_ys}\n",
    "        c, _ = sess.run([cost, optimizer], feed_dict=feed_dict)\n",
    "        avg_cost += c / total_batch\n",
    "\n",
    "    print('Epoch:', '%04d' % (epoch + 1), 'cost =', '{:.9f}'.format(avg_cost))\n",
    "\n",
    "print('Learning Finished!')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.9432\n"
     ]
    }
   ],
   "source": [
    "# Test model and check accuracy\n",
    "correct_prediction = tf.equal(tf.argmax(hypothesis, 1), tf.argmax(Y, 1))\n",
    "accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))\n",
    "print('Accuracy:', sess.run(accuracy, feed_dict={\n",
    "      X: mnist.test.images, Y: mnist.test.labels}))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Label:  [9]\n",
      "Prediction:  [9]\n"
     ]
    }
   ],
   "source": [
    "# Get one and predict\n",
    "r = random.randint(0, mnist.test.num_examples - 1)\n",
    "print(\"Label: \", sess.run(tf.argmax(mnist.test.labels[r:r + 1], 1)))\n",
    "print(\"Prediction: \", sess.run(\n",
    "    tf.argmax(hypothesis, 1), feed_dict={X: mnist.test.images[r:r + 1]}))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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