ML_lab_10-4.ipynb

{
 "metadata": {
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.9-final"
  },
  "orig_nbformat": 2,
  "kernelspec": {
   "name": "python3",
   "display_name": "Python 3",
   "language": "python"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2,
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "import random\n",
    "from tensorflow.examples.tutorials.mnist import input_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "WARNING:tensorflow:From <ipython-input-2-1c72ebd49e13>:1: 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 C:\\Users\\user\\anaconda3\\envs\\tensorflow1\\lib\\site-packages\\tensorflow_core\\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 C:\\Users\\user\\anaconda3\\envs\\tensorflow1\\lib\\site-packages\\tensorflow_core\\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 MNIST_data/train-images-idx3-ubyte.gz\n",
      "WARNING:tensorflow:From C:\\Users\\user\\anaconda3\\envs\\tensorflow1\\lib\\site-packages\\tensorflow_core\\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 MNIST_data/train-labels-idx1-ubyte.gz\n",
      "WARNING:tensorflow:From C:\\Users\\user\\anaconda3\\envs\\tensorflow1\\lib\\site-packages\\tensorflow_core\\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 MNIST_data/t10k-images-idx3-ubyte.gz\n",
      "Extracting MNIST_data/t10k-labels-idx1-ubyte.gz\n",
      "WARNING:tensorflow:From C:\\Users\\user\\anaconda3\\envs\\tensorflow1\\lib\\site-packages\\tensorflow_core\\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": [
    "mnist = input_data.read_data_sets(\"MNIST_data/\", one_hot=True)\n",
    "\n",
    "learning_rate = 0.001\n",
    "training_epochs = 15\n",
    "batch_size = 100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "WARNING:tensorflow:\nThe TensorFlow contrib module will not be included in TensorFlow 2.0.\nFor more information, please see:\n  * https://github.com/tensorflow/community/blob/master/rfcs/20180907-contrib-sunset.md\n  * https://github.com/tensorflow/addons\n  * https://github.com/tensorflow/io (for I/O related ops)\nIf you depend on functionality not listed there, please file an issue.\n\n"
     ]
    }
   ],
   "source": [
    "X = tf.placeholder(tf.float32, [None, 784])\n",
    "Y = tf.placeholder(tf.float32, [None, 10])\n",
    "\n",
    "W1 = tf.get_variable(\"W1\", shape=[784, 512], initializer=tf.contrib.layers.xavier_initializer())     # layer 및 output 추가 (deep & wide)\n",
    "b1 = tf.Variable(tf.random_normal([512]))\n",
    "L1 = tf.nn.relu(tf.matmul(X, W1) + b1)\n",
    "\n",
    "W2 = tf.get_variable(\"W2\", shape=[512, 512], initializer=tf.contrib.layers.xavier_initializer())\n",
    "b2 = tf.Variable(tf.random_normal([512]))\n",
    "L2 = tf.nn.relu(tf.matmul(L1, W2) + b2)\n",
    "\n",
    "W3 = tf.get_variable(\"W3\", shape=[512, 512], initializer=tf.contrib.layers.xavier_initializer())\n",
    "b3 = tf.Variable(tf.random_normal([512]))\n",
    "L3 = tf.nn.relu(tf.matmul(L2, W3) + b3)\n",
    "\n",
    "W4 = tf.get_variable(\"W4\", shape=[512, 512], initializer=tf.contrib.layers.xavier_initializer())\n",
    "b4 = tf.Variable(tf.random_normal([512]))\n",
    "L4 = tf.nn.relu(tf.matmul(L3, W4) + b4)\n",
    "\n",
    "W5 = tf.get_variable(\"W5\", shape=[512, 10], initializer=tf.contrib.layers.xavier_initializer())\n",
    "b5 = tf.Variable(tf.random_normal([10]))\n",
    "hypothesis = tf.matmul(L4, W5) + b5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "WARNING:tensorflow:From <ipython-input-4-0c4e163566c8>:1: softmax_cross_entropy_with_logits (from tensorflow.python.ops.nn_ops) is deprecated and will be removed in a future version.\nInstructions for updating:\n\nFuture major versions of TensorFlow will allow gradients to flow\ninto the labels input on backprop by default.\n\nSee `tf.nn.softmax_cross_entropy_with_logits_v2`.\n\n"
     ]
    }
   ],
   "source": [
    "cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=hypothesis, labels=Y))\n",
    "optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate).minimize(cost)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Epoch: 0001 cost = 0.303496666\n",
      "Epoch: 0002 cost = 0.106412735\n",
      "Epoch: 0003 cost = 0.072868569\n",
      "Epoch: 0004 cost = 0.051846041\n",
      "Epoch: 0005 cost = 0.040919916\n",
      "Epoch: 0006 cost = 0.034472268\n",
      "Epoch: 0007 cost = 0.031869955\n",
      "Epoch: 0008 cost = 0.026142174\n",
      "Epoch: 0009 cost = 0.024068330\n",
      "Epoch: 0010 cost = 0.023334886\n",
      "Epoch: 0011 cost = 0.019205618\n",
      "Epoch: 0012 cost = 0.017361857\n",
      "Epoch: 0013 cost = 0.016451558\n",
      "Epoch: 0014 cost = 0.016671489\n",
      "Epoch: 0015 cost = 0.014372744\n",
      "Learning Finished!\n",
      "Accuracy: 0.9773\n"
     ]
    }
   ],
   "source": [
    "sess = tf.Session()\n",
    "sess.run(tf.global_variables_initializer())\n",
    "\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!')\n",
    "\n",
    "# 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={X: mnist.test.images, Y: mnist.test.labels}))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ]
}