logistic_regression.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 11,
   "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",
      "Media pas 0: 0.6896315354567307\n",
      "Media pas 1: 0.3692256230643857\n",
      "Media pas 2: 0.3252849845619468\n",
      "Media pas 3: 0.3042197634915372\n",
      "Media pas 4: 0.29183934507392223\n",
      "Media pas 5: 0.28329358164802854\n",
      "Media pas 6: 0.2767173498695269\n",
      "Media pas 7: 0.27292019118379046\n",
      "Media pas 8: 0.26790369399291375\n",
      "Media pas 9: 0.2646853335740127\n",
      "Media pas 10: 0.2619104111319655\n",
      "Media pas 11: 0.25943483117021326\n",
      "Media pas 12: 0.2589600896571344\n",
      "Media pas 13: 0.2553647763523466\n",
      "Media pas 14: 0.25261463936948\n",
      "Media pas 15: 0.25330560489412235\n",
      "Media pas 16: 0.25151073481097364\n",
      "Media pas 17: 0.25002436246071663\n",
      "Media pas 18: 0.24821962909403936\n",
      "Media pas 19: 0.24680659550053258\n",
      "Media pas 20: 0.24647978182647612\n",
      "Media pas 21: 0.24531820121206047\n",
      "Media pas 22: 0.24522136485243176\n",
      "Media pas 23: 0.24367254526117743\n",
      "Media pas 24: 0.2432292952855706\n",
      "Total time: 23.343068599700928 seconds\n",
      "Accuracy 0.9272\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'\n",
    "\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import tensorflow as tf\n",
    "from tensorflow.examples.tutorials.mnist import input_data\n",
    "import time\n",
    "\n",
    "learning_rate = 0.001\n",
    "batch_size = 128\n",
    "n_epochs = 25\n",
    "\n",
    "mnist = input_data.read_data_sets('/data/mnist', one_hot=True) \n",
    "#create placeholders for features and labels\n",
    "X = tf.placeholder(dtype=tf.float32, shape=(batch_size,784), name=\"X\")\n",
    "Y = tf.placeholder(dtype=tf.int32, shape=(batch_size,10), name=\"Y\")\n",
    "\n",
    "#weights, biases\n",
    "w = tf.Variable(tf.random_normal(shape=[784,10], stddev=0.01), name=\"weigths\")\n",
    "b = tf.Variable(tf.zeros(shape=[1,10]), name=\"biases\")\n",
    "\n",
    "#create logits\n",
    "logits = tf.add(tf.matmul(X,w), b)\n",
    "\n",
    "#create loss\n",
    "entropy = tf.nn.softmax_cross_entropy_with_logits(logits=logits, labels=Y, name=\"loss\")\n",
    "loss = tf.reduce_mean(entropy) # compute mean of all examples in batch\n",
    "\n",
    "#create optimizer\n",
    "optimizer = tf.train.AdamOptimizer(learning_rate).minimize(loss)\n",
    "\n",
    "with tf.Session() as sess:\n",
    "    sess.run(tf.global_variables_initializer())\n",
    "    writer = tf.summary.FileWriter('./graphs/logistic_reg', sess.graph)\n",
    "    start_time = time.time()\n",
    "    n_batches = int(mnist.train.num_examples/batch_size)\n",
    "    for i in range(n_epochs):\n",
    "        total_loss = 0\n",
    "        for j in range(n_batches):\n",
    "            X_batch, Y_batch = mnist.train.next_batch(batch_size)\n",
    "            _, loss_batch = sess.run([optimizer, loss], feed_dict={X: X_batch, Y: Y_batch}) \n",
    "            total_loss = total_loss + loss_batch\n",
    "        print(\"Media pas {0}: {1}\".format(i, total_loss/n_batches))\n",
    "    print('Total time: {0} seconds'.format(time.time() - start_time))\n",
    "    \n",
    "    preds = tf.nn.softmax(logits)\n",
    "    correct_preds = tf.equal(tf.argmax(preds,1), tf.argmax(Y,1))\n",
    "    accuracy = tf.reduce_sum(tf.cast(correct_preds, tf.float32))\n",
    "    \n",
    "    n_batches = int(mnist.test.num_examples/batch_size)\n",
    "    total_cor_preds = 0\n",
    "    \n",
    "    for i in range(n_batches):\n",
    "        X_batch, Y_batch = mnist.test.next_batch(batch_size)\n",
    "        accuracy_batch = sess.run(accuracy, feed_dict={X: X_batch, Y: Y_batch})\n",
    "        total_cor_preds += accuracy_batch\n",
    "        \n",
    "    print('Accuracy {0}'.format(total_cor_preds/mnist.test.num_examples))\n",
    "    \n",
    "    writer.close()\n",
    "        \n",
    "    "
   ]
  }
 ],
 "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": "3.5.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}