cnnadamRMS.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Extracting ./mnist/data/train-images-idx3-ubyte.gz\n",
      "Extracting ./mnist/data/train-labels-idx1-ubyte.gz\n",
      "Extracting ./mnist/data/t10k-images-idx3-ubyte.gz\n",
      "Extracting ./mnist/data/t10k-labels-idx1-ubyte.gz\n",
      "Epoch: 0001 Avg. cost = 0.472\n",
      "Epoch: 0002 Avg. cost = 0.143\n",
      "Epoch: 0003 Avg. cost = 0.101\n",
      "Epoch: 0004 Avg. cost = 0.078\n",
      "Epoch: 0005 Avg. cost = 0.066\n",
      "Epoch: 0006 Avg. cost = 0.054\n",
      "Epoch: 0007 Avg. cost = 0.045\n",
      "Epoch: 0008 Avg. cost = 0.041\n",
      "Epoch: 0009 Avg. cost = 0.036\n",
      "Epoch: 0010 Avg. cost = 0.034\n",
      "Epoch: 0011 Avg. cost = 0.030\n",
      "Epoch: 0012 Avg. cost = 0.027\n",
      "Epoch: 0013 Avg. cost = 0.026\n",
      "Epoch: 0014 Avg. cost = 0.025\n",
      "Epoch: 0015 Avg. cost = 0.022\n",
      "최적화 완료!\n",
      "정확도: 0.9866\n"
     ]
    }
   ],
   "source": [
    "import tensorflow as tf\n",
    "#MNIST 불러오기 \n",
    "from tensorflow.examples.tutorials.mnist import input_data\n",
    "mnist = input_data.read_data_sets(\"./mnist/data/\", one_hot=True)\n",
    "#입력층과 출력층 \n",
    "X = tf.placeholder(tf.float32, [None, 28, 28, 1])\n",
    "Y = tf.placeholder(tf.float32, [None, 10])\n",
    "keep_prob = tf.placeholder(tf.float32)\n",
    "\n",
    "#convolution layer 2개 \n",
    "#maxpooling 2X2 적용 \n",
    "W1 = tf.Variable(tf.random_normal([3, 3, 1, 32], stddev=0.01))\n",
    "L1 = tf.nn.conv2d(X, W1, strides=[1, 1, 1, 1], padding='SAME')\n",
    "L1 = tf.nn.relu(L1)\n",
    "L1 = tf.nn.max_pool(L1, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME')\n",
    "L1 = tf.nn.dropout(L1, keep_prob)\n",
    "\n",
    "W2 = tf.Variable(tf.random_normal([3, 3, 32, 64], stddev=0.01))\n",
    "L2 = tf.nn.conv2d(L1, W2, strides=[1, 1, 1, 1], padding='SAME')\n",
    "L2 = tf.nn.relu(L2)\n",
    "L2 = tf.nn.max_pool(L2, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME')\n",
    "L2 = tf.nn.dropout(L2, keep_prob)\n",
    "\n",
    "#FC 2개 구성, 각각 256,256\n",
    "#첫번째 \n",
    "W3 = tf.Variable(tf.random_normal([7 * 7 * 64, 256], stddev=0.01))\n",
    "L3 = tf.reshape(L2, [-1, 7 * 7 * 64])\n",
    "L3 = tf.matmul(L3, W3)\n",
    "L3 = tf.nn.relu(L3)\n",
    "L3 = tf.nn.dropout(L3, keep_prob)\n",
    "\n",
    "#두번째 \n",
    "W4 = tf.Variable(tf.random_normal([2*2*64, 256], stddev=0.01))\n",
    "L4 = tf.reshape(L3, [-1, 2 * 2 * 64])\n",
    "L4 = tf.matmul(L4, W4)\n",
    "L4 = tf.nn.relu(L4)\n",
    "L4 = tf.nn.dropout(L4, keep_prob)\n",
    "\n",
    "\n",
    "W5 = tf.Variable(tf.random_normal([256, 10], stddev=0.01))\n",
    "model = tf.matmul(L4, W5)\n",
    "\n",
    "cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=model, labels=Y))\n",
    "optimizer = tf.train.AdamOptimizer(0.001).minimize(cost)\n",
    "\n",
    "\n",
    "init = tf.global_variables_initializer()\n",
    "sess = tf.Session()\n",
    "sess.run(init)\n",
    "\n",
    "batch_size = 100\n",
    "total_batch = int(mnist.train.num_examples / batch_size)\n",
    "\n",
    "for epoch in range(15):\n",
    "    total_cost = 0\n",
    "\n",
    "    for i in range(total_batch):\n",
    "        batch_xs, batch_ys = mnist.train.next_batch(batch_size)\n",
    "\n",
    "        batch_xs = batch_xs.reshape(-1, 28, 28, 1)\n",
    "\n",
    "        _, cost_val = sess.run([optimizer, cost],\n",
    "                               feed_dict={X: batch_xs,\n",
    "                                          Y: batch_ys,\n",
    "                                          keep_prob: 0.8})\n",
    "        total_cost += cost_val\n",
    "\n",
    "    print('Epoch:', '%04d' % (epoch + 1),\n",
    "          'Avg. cost =', '{:.3f}'.format(total_cost / total_batch))\n",
    "\n",
    "print('최적화 완료!')\n",
    "\n",
    "\n",
    "# 결과 확인\n",
    "is_correct = tf.equal(tf.argmax(model, 1), tf.argmax(Y, 1))\n",
    "accuracy = tf.reduce_mean(tf.cast(is_correct, tf.float32))\n",
    "print('정확도:', sess.run(accuracy,\n",
    "                        feed_dict={X: mnist.test.images.reshape(-1, 28, 28, 1),\n",
    "                                   Y: mnist.test.labels,\n",
    "                                   keep_prob: 0.8}))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Extracting ./mnist/data/train-images-idx3-ubyte.gz\n",
      "Extracting ./mnist/data/train-labels-idx1-ubyte.gz\n",
      "Extracting ./mnist/data/t10k-images-idx3-ubyte.gz\n",
      "Extracting ./mnist/data/t10k-labels-idx1-ubyte.gz\n",
      "Epoch: 0001 Avg. cost = 1.177\n",
      "Epoch: 0002 Avg. cost = 0.118\n",
      "Epoch: 0003 Avg. cost = 0.076\n",
      "Epoch: 0004 Avg. cost = 0.059\n",
      "Epoch: 0005 Avg. cost = 0.049\n",
      "Epoch: 0006 Avg. cost = 0.046\n",
      "Epoch: 0007 Avg. cost = 0.039\n",
      "Epoch: 0008 Avg. cost = 0.037\n",
      "Epoch: 0009 Avg. cost = 0.033\n",
      "Epoch: 0010 Avg. cost = 0.032\n",
      "Epoch: 0011 Avg. cost = 0.031\n",
      "Epoch: 0012 Avg. cost = 0.031\n",
      "Epoch: 0013 Avg. cost = 0.029\n",
      "Epoch: 0014 Avg. cost = 0.028\n",
      "Epoch: 0015 Avg. cost = 0.028\n",
      "최적화 완료!\n",
      "정확도: 0.9866\n"
     ]
    }
   ],
   "source": [
    "import tensorflow as tf\n",
    "#MNIST 불러오기 \n",
    "from tensorflow.examples.tutorials.mnist import input_data\n",
    "mnist = input_data.read_data_sets(\"./mnist/data/\", one_hot=True)\n",
    "#입력층과 출력층 \n",
    "X = tf.placeholder(tf.float32, [None, 28, 28, 1])\n",
    "Y = tf.placeholder(tf.float32, [None, 10])\n",
    "keep_prob = tf.placeholder(tf.float32)\n",
    "\n",
    "#convolution layer 2개 \n",
    "#maxpooling 2X2 적용 \n",
    "W1 = tf.Variable(tf.random_normal([3, 3, 1, 32], stddev=0.01))\n",
    "L1 = tf.nn.conv2d(X, W1, strides=[1, 1, 1, 1], padding='SAME')\n",
    "L1 = tf.nn.relu(L1)\n",
    "L1 = tf.nn.max_pool(L1, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME')\n",
    "L1 = tf.nn.dropout(L1, keep_prob)\n",
    "\n",
    "W2 = tf.Variable(tf.random_normal([3, 3, 32, 64], stddev=0.01))\n",
    "L2 = tf.nn.conv2d(L1, W2, strides=[1, 1, 1, 1], padding='SAME')\n",
    "L2 = tf.nn.relu(L2)\n",
    "L2 = tf.nn.max_pool(L2, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME')\n",
    "L2 = tf.nn.dropout(L2, keep_prob)\n",
    "\n",
    "#FC 2개 구성, 각각 256,256\n",
    "#첫번째 \n",
    "W3 = tf.Variable(tf.random_normal([7 * 7 * 64, 256], stddev=0.01))\n",
    "L3 = tf.reshape(L2, [-1, 7 * 7 * 64])\n",
    "L3 = tf.matmul(L3, W3)\n",
    "L3 = tf.nn.relu(L3)\n",
    "L3 = tf.nn.dropout(L3, keep_prob)\n",
    "\n",
    "#두번째 \n",
    "W4 = tf.Variable(tf.random_normal([2*2*64, 256], stddev=0.01))\n",
    "L4 = tf.reshape(L3, [-1, 2 * 2 * 64])\n",
    "L4 = tf.matmul(L4, W4)\n",
    "L4 = tf.nn.relu(L4)\n",
    "L4 = tf.nn.dropout(L4, keep_prob)\n",
    "\n",
    "\n",
    "W5 = tf.Variable(tf.random_normal([256, 10], stddev=0.01))\n",
    "model = tf.matmul(L4, W5)\n",
    "\n",
    "cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=model, labels=Y))\n",
    "optimizer = tf.train.RMSPropOptimizer(0.001,0.9).minimize(cost)\n",
    "\n",
    "\n",
    "init = tf.global_variables_initializer()\n",
    "sess = tf.Session()\n",
    "sess.run(init)\n",
    "\n",
    "batch_size = 100\n",
    "total_batch = int(mnist.train.num_examples / batch_size)\n",
    "\n",
    "for epoch in range(15):\n",
    "    total_cost = 0\n",
    "\n",
    "    for i in range(total_batch):\n",
    "        batch_xs, batch_ys = mnist.train.next_batch(batch_size)\n",
    "\n",
    "        batch_xs = batch_xs.reshape(-1, 28, 28, 1)\n",
    "\n",
    "        _, cost_val = sess.run([optimizer, cost],\n",
    "                               feed_dict={X: batch_xs,\n",
    "                                          Y: batch_ys,\n",
    "                                          keep_prob: 0.8})\n",
    "        total_cost += cost_val\n",
    "\n",
    "    print('Epoch:', '%04d' % (epoch + 1),\n",
    "          'Avg. cost =', '{:.3f}'.format(total_cost / total_batch))\n",
    "\n",
    "print('최적화 완료!')\n",
    "\n",
    "\n",
    "# 결과 확인\n",
    "is_correct = tf.equal(tf.argmax(model, 1), tf.argmax(Y, 1))\n",
    "accuracy = tf.reduce_mean(tf.cast(is_correct, tf.float32))\n",
    "print('정확도:', sess.run(accuracy,\n",
    "                        feed_dict={X: mnist.test.images.reshape(-1, 28, 28, 1),\n",
    "                                   Y: mnist.test.labels,\n",
    "                                   keep_prob: 0.8}))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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