03-TensorFlow/03_Logistic_Regression_With_Mnist_Data.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "from tensorflow.examples.tutorials.mnist import input_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From <ipython-input-5-e0ce8097d3d7>:2: read_data_sets (from tensorflow.contrib.learn.python.learn.datasets.mnist) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Please use alternatives such as official/mnist/dataset.py from tensorflow/models.\n",
      "WARNING:tensorflow:From /Users/teddylee/miniconda3/lib/python3.6/site-packages/tensorflow/contrib/learn/python/learn/datasets/mnist.py:260: maybe_download (from tensorflow.contrib.learn.python.learn.datasets.base) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Please write your own downloading logic.\n",
      "WARNING:tensorflow:From /Users/teddylee/miniconda3/lib/python3.6/site-packages/tensorflow/contrib/learn/python/learn/datasets/mnist.py:262: extract_images (from tensorflow.contrib.learn.python.learn.datasets.mnist) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Please use tf.data to implement this functionality.\n",
      "Extracting /tmp/data/train-images-idx3-ubyte.gz\n",
      "WARNING:tensorflow:From /Users/teddylee/miniconda3/lib/python3.6/site-packages/tensorflow/contrib/learn/python/learn/datasets/mnist.py:267: extract_labels (from tensorflow.contrib.learn.python.learn.datasets.mnist) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Please use tf.data to implement this functionality.\n",
      "Extracting /tmp/data/train-labels-idx1-ubyte.gz\n",
      "WARNING:tensorflow:From /Users/teddylee/miniconda3/lib/python3.6/site-packages/tensorflow/contrib/learn/python/learn/datasets/mnist.py:110: dense_to_one_hot (from tensorflow.contrib.learn.python.learn.datasets.mnist) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Please use tf.one_hot on tensors.\n",
      "Extracting /tmp/data/t10k-images-idx3-ubyte.gz\n",
      "Extracting /tmp/data/t10k-labels-idx1-ubyte.gz\n",
      "WARNING:tensorflow:From /Users/teddylee/miniconda3/lib/python3.6/site-packages/tensorflow/contrib/learn/python/learn/datasets/mnist.py:290: DataSet.__init__ (from tensorflow.contrib.learn.python.learn.datasets.mnist) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Please use alternatives such as official/mnist/dataset.py from tensorflow/models.\n"
     ]
    }
   ],
   "source": [
    "# download mnist datas\n",
    "mnist = input_data.read_data_sets('/tmp/data/', one_hot=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train = mnist.train.images\n",
    "y_train = mnist.train.labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((55000, 784), (55000, 10))"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.shape, y_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# parameters\n",
    "learning_rate = 0.01\n",
    "batch_size = 100\n",
    "training_epochs = 25"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = tf.placeholder(tf.float32, shape=[None, 784])\n",
    "Y = tf.placeholder(tf.float32, shape=[None, 10])\n",
    "\n",
    "W = tf.Variable(tf.random_normal([784, 10]), name='weight')\n",
    "b = tf.Variable(tf.random_normal([10]), name='bias')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "hypothesis = tf.nn.softmax(tf.matmul(X, W) + b)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "cost = tf.reduce_mean(-tf.reduce_sum(Y * tf.log(hypothesis), reduction_indices=1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "train = tf.train.GradientDescentOptimizer(learning_rate=learning_rate).minimize(cost)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "cost_scalar = tf.summary.scalar('cost', cost)\n",
    "W_hist = tf.summary.histogram('W', W)\n",
    "summaries = tf.summary.merge_all()\n",
    "summary_writer = tf.summary.FileWriter('logs/03_logistic_regression_with_mnist_dataset', sess.graph)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "7.914185377467768\n",
      "4.781445711309255\n",
      "3.4321453462947495\n",
      "2.695776921619069\n",
      "2.2531482007286776\n",
      "1.9645995258201252\n",
      "1.7632157213037667\n",
      "1.614504184181039\n",
      "1.5002545454285359\n",
      "1.4092625564878631\n",
      "1.3348929220979873\n",
      "1.2729112621870922\n",
      "1.2201757449453532\n",
      "1.1746131080389028\n",
      "1.13468996757811\n",
      "1.0994269660928035\n",
      "1.0678009817275131\n",
      "1.0392743049426507\n",
      "1.0135599096254868\n",
      "0.9897475153207772\n",
      "0.9682238452001045\n",
      "0.948097512180156\n",
      "0.9296129375696183\n",
      "0.9123521485653793\n",
      "0.8962056561220775\n",
      "Accuracy:  0.788\n"
     ]
    }
   ],
   "source": [
    "with tf.Session() as sess:\n",
    "    sess.run(tf.global_variables_initializer())\n",
    "    for epoch in range(training_epochs):\n",
    "        total_batch = int(mnist.train.num_examples / batch_size)\n",
    "        avg_cost = 0\n",
    "        for i in range(total_batch):\n",
    "            batch_x, batch_y = mnist.train.next_batch(batch_size)\n",
    "            _, cost_, summary_ = sess.run([train, cost, summaries], feed_dict={X: batch_x, Y: batch_y})\n",
    "            avg_cost += cost_ / total_batch\n",
    "        print(avg_cost)\n",
    "        summary_writer.add_summary(summary_, global_step=epoch)\n",
    "    prediction = tf.equal(tf.argmax(hypothesis, 1), tf.argmax(Y, 1))\n",
    "    accuracy = tf.reduce_mean(tf.cast(prediction, tf.float32))\n",
    "    acc = accuracy.eval({X: mnist.test.images[:3000], Y: mnist.test.labels[:3000]})\n",
    "    print(\"Accuracy: \", acc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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