6-DeepLearning_NLP/9일차/2019-08-19_CNN_RNN.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "from tensorflow.examples.tutorials.mnist import input_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "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"
     ]
    }
   ],
   "source": [
    "import tensorflow as tf\n",
    "\n",
    "from tensorflow.examples.tutorials.mnist import input_data\n",
    "\n",
    "mnist = input_data.read_data_sets(\"./mnist/data/\", one_hot=True)\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",
    "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",
    "\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",
    "\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",
    "W4 = tf.Variable(tf.random_normal([256, 10], stddev=0.01))\n",
    "model = tf.matmul(L3, W4)\n",
    "\n",
    "cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits_v2(logits=model, labels=Y))\n",
    "optimizer = tf.train.AdamOptimizer(0.001).minimize(cost)\n",
    "\n",
    "init = tf.global_variables_initializer()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 0001 Avg. cost = 0.342\n",
      "Epoch: 0002 Avg. cost = 0.109\n",
      "Epoch: 0003 Avg. cost = 0.076\n",
      "Epoch: 0004 Avg. cost = 0.061\n",
      "Epoch: 0005 Avg. cost = 0.051\n",
      "Epoch: 0006 Avg. cost = 0.041\n",
      "Epoch: 0007 Avg. cost = 0.036\n",
      "Epoch: 0008 Avg. cost = 0.030\n",
      "Epoch: 0009 Avg. cost = 0.027\n",
      "Epoch: 0010 Avg. cost = 0.024\n",
      "Epoch: 0011 Avg. cost = 0.020\n",
      "Epoch: 0012 Avg. cost = 0.019\n",
      "Epoch: 0013 Avg. cost = 0.018\n",
      "Epoch: 0014 Avg. cost = 0.017\n",
      "Epoch: 0015 Avg. cost = 0.014\n",
      "모델 완성!\n",
      "정확도: 0.9916\n"
     ]
    }
   ],
   "source": [
    "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",
    "        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: 0.7})\n",
    "        total_cost += cost_val\n",
    "\n",
    "    print('에폭:', '%04d' % (epoch + 1),\n",
    "          '평균 cost =', '{:.3f}'.format(total_cost / total_batch))\n",
    "\n",
    "print('모델 완성!')\n",
    "\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: 1}))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "#tf.layers 서브 패키지 활용한 cnn 모델링"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "x=tf.placeholder(tf.float32, [None, 28, 28, 1])\n",
    "y=tf.placeholder(tf.float32, [None, 10])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "in converted code:\n    relative to C:\\Users\\user\\Anaconda3\\lib\\site-packages\\tensorflow\\python:\n\n    keras\\layers\\convolutional.py:196 call *\n        outputs = self._convolution_op(inputs, self.kernel)\n    ops\\nn_ops.py:1079 __call__\n        return self.conv_op(inp, filter)\n    ops\\nn_ops.py:635 __call__\n        return self.call(inp, filter)\n    ops\\nn_ops.py:234 __call__\n        name=self.name)\n    ops\\nn_ops.py:1953 conv2d\n        name=name)\n    ops\\gen_nn_ops.py:1160 conv2d\n        data_format=data_format, dilations=dilations, name=name)\n    framework\\op_def_library.py:366 _apply_op_helper\n        g = ops._get_graph_from_inputs(_Flatten(keywords.values()))\n    framework\\ops.py:6135 _get_graph_from_inputs\n        _assert_same_graph(original_graph_element, graph_element)\n    framework\\ops.py:6071 _assert_same_graph\n        (item, original_item))\n\n    ValueError: Tensor(\"conv2d/kernel:0\", shape=(3, 3, 1, 32), dtype=float32_ref) must be from the same graph as Tensor(\"Placeholder:0\", shape=(?, 28, 28, 1), dtype=float32).\n",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-61-40741005f14a>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[0mtf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mreset_default_graph\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m \u001b[0mL1\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mtf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mlayers\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mconv2d\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m32\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;36m3\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m3\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mactivation\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mtf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mnn\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrelu\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      3\u001b[0m \u001b[0mL1\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mtf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mlayers\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmax_pooling2d\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mL1\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m2\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m2\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m2\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m2\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      4\u001b[0m \u001b[0mL1\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mtf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mlayers\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdropout\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mL1\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m0.7\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      5\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\tensorflow\\python\\util\\deprecation.py\u001b[0m in \u001b[0;36mnew_func\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m    322\u001b[0m               \u001b[1;34m'in a future version'\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mdate\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mNone\u001b[0m \u001b[1;32melse\u001b[0m \u001b[1;33m(\u001b[0m\u001b[1;34m'after %s'\u001b[0m \u001b[1;33m%\u001b[0m \u001b[0mdate\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    323\u001b[0m               instructions)\n\u001b[1;32m--> 324\u001b[1;33m       \u001b[1;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    325\u001b[0m     return tf_decorator.make_decorator(\n\u001b[0;32m    326\u001b[0m         \u001b[0mfunc\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mnew_func\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'deprecated'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\tensorflow\\python\\layers\\convolutional.py\u001b[0m in \u001b[0;36mconv2d\u001b[1;34m(inputs, filters, kernel_size, strides, padding, data_format, dilation_rate, activation, use_bias, kernel_initializer, bias_initializer, kernel_regularizer, bias_regularizer, activity_regularizer, kernel_constraint, bias_constraint, trainable, name, reuse)\u001b[0m\n\u001b[0;32m    422\u001b[0m       \u001b[0m_reuse\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mreuse\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    423\u001b[0m       _scope=name)\n\u001b[1;32m--> 424\u001b[1;33m   \u001b[1;32mreturn\u001b[0m \u001b[0mlayer\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mapply\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0minputs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    425\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    426\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\tensorflow\\python\\keras\\engine\\base_layer.py\u001b[0m in \u001b[0;36mapply\u001b[1;34m(self, inputs, *args, **kwargs)\u001b[0m\n\u001b[0;32m   1477\u001b[0m       \u001b[0mOutput\u001b[0m \u001b[0mtensor\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0ms\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1478\u001b[0m     \"\"\"\n\u001b[1;32m-> 1479\u001b[1;33m     \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__call__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0minputs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1480\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1481\u001b[0m   \u001b[1;33m@\u001b[0m\u001b[0mdoc_controls\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfor_subclass_implementers\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\tensorflow\\python\\layers\\base.py\u001b[0m in \u001b[0;36m__call__\u001b[1;34m(self, inputs, *args, **kwargs)\u001b[0m\n\u001b[0;32m    535\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    536\u001b[0m       \u001b[1;31m# Actually call layer\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 537\u001b[1;33m       \u001b[0moutputs\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0msuper\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mLayer\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__call__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0minputs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    538\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    539\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mcontext\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mexecuting_eagerly\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\tensorflow\\python\\keras\\engine\\base_layer.py\u001b[0m in \u001b[0;36m__call__\u001b[1;34m(self, inputs, *args, **kwargs)\u001b[0m\n\u001b[0;32m    632\u001b[0m                     \u001b[0moutputs\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mbase_layer_utils\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmark_as_return\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0moutputs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0macd\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    633\u001b[0m                 \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 634\u001b[1;33m                   \u001b[0moutputs\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcall_fn\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0minputs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    635\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    636\u001b[0m             \u001b[1;32mexcept\u001b[0m \u001b[0mTypeError\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\tensorflow\\python\\autograph\\impl\\api.py\u001b[0m in \u001b[0;36mwrapper\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m    147\u001b[0m       \u001b[1;32mexcept\u001b[0m \u001b[0mException\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[1;33m:\u001b[0m  \u001b[1;31m# pylint:disable=broad-except\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    148\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mhasattr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0me\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'ag_error_metadata'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 149\u001b[1;33m           \u001b[1;32mraise\u001b[0m \u001b[0me\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mag_error_metadata\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mto_exception\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtype\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0me\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    150\u001b[0m         \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    151\u001b[0m           \u001b[1;32mraise\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mValueError\u001b[0m: in converted code:\n    relative to C:\\Users\\user\\Anaconda3\\lib\\site-packages\\tensorflow\\python:\n\n    keras\\layers\\convolutional.py:196 call *\n        outputs = self._convolution_op(inputs, self.kernel)\n    ops\\nn_ops.py:1079 __call__\n        return self.conv_op(inp, filter)\n    ops\\nn_ops.py:635 __call__\n        return self.call(inp, filter)\n    ops\\nn_ops.py:234 __call__\n        name=self.name)\n    ops\\nn_ops.py:1953 conv2d\n        name=name)\n    ops\\gen_nn_ops.py:1160 conv2d\n        data_format=data_format, dilations=dilations, name=name)\n    framework\\op_def_library.py:366 _apply_op_helper\n        g = ops._get_graph_from_inputs(_Flatten(keywords.values()))\n    framework\\ops.py:6135 _get_graph_from_inputs\n        _assert_same_graph(original_graph_element, graph_element)\n    framework\\ops.py:6071 _assert_same_graph\n        (item, original_item))\n\n    ValueError: Tensor(\"conv2d/kernel:0\", shape=(3, 3, 1, 32), dtype=float32_ref) must be from the same graph as Tensor(\"Placeholder:0\", shape=(?, 28, 28, 1), dtype=float32).\n"
     ]
    }
   ],
   "source": [
    "tf.reset_default_graph()\n",
    "L1=tf.layers.conv2d(x, 32, [3,3], activation=tf.nn.relu)\n",
    "L1=tf.layers.max_pooling2d(L1,[2,2],[2,2])\n",
    "L1=tf.layers.dropout(L1,0.7)\n",
    "\n",
    "L2=tf.layers.conv2d(L1, 64, [3,3], activation=tf.nn.relu)\n",
    "L2=tf.layers.max_pooling2d(L2,[2,2],[2,2])\n",
    "L2=tf.layers.dropout(L2,0.7)\n",
    "\n",
    "L3=tf.contrib.layers.flatten(L2)\n",
    "L3=tf.layers.dense(L3,256, activation=tf.nn.relu)\n",
    "L3=tf.layers.dropout(L3,0.5)\n",
    "\n",
    "model=tf.layers.dense(L3,10, activation=None)\n",
    "cost=tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits_v2\n",
    "                    (logits=model, labels=y))\n",
    "train=tf.train.AdamOptimizer(0.001).minimize(cost)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "init = tf.global_variables_initializer()\n",
    "\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",
    "        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: 0.7})\n",
    "        total_cost += cost_val\n",
    "\n",
    "    print('에폭:', '%04d' % (epoch + 1),\n",
    "          '평균 cost =', '{:.3f}'.format(total_cost / total_batch))\n",
    "\n",
    "print('모델 완성!')\n",
    "\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: 1}))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "#keras에서 mnist cnn모델링"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "from keras import layers\n",
    "from keras import models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "W0819 14:45:46.436188  1084 deprecation_wrapper.py:119] From C:\\Users\\user\\Anaconda3\\lib\\site-packages\\keras\\backend\\tensorflow_backend.py:74: The name tf.get_default_graph is deprecated. Please use tf.compat.v1.get_default_graph instead.\n",
      "\n",
      "W0819 14:45:46.436188  1084 deprecation_wrapper.py:119] From C:\\Users\\user\\Anaconda3\\lib\\site-packages\\keras\\backend\\tensorflow_backend.py:517: The name tf.placeholder is deprecated. Please use tf.compat.v1.placeholder instead.\n",
      "\n",
      "W0819 14:45:46.436188  1084 deprecation_wrapper.py:119] From C:\\Users\\user\\Anaconda3\\lib\\site-packages\\keras\\backend\\tensorflow_backend.py:4138: The name tf.random_uniform is deprecated. Please use tf.random.uniform instead.\n",
      "\n",
      "W0819 14:45:46.451572  1084 deprecation_wrapper.py:119] From C:\\Users\\user\\Anaconda3\\lib\\site-packages\\keras\\backend\\tensorflow_backend.py:3976: The name tf.nn.max_pool is deprecated. Please use tf.nn.max_pool2d instead.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "model=models.Sequential()\n",
    "model.add(layers.Conv2D(32, (3,3), activation='relu', \n",
    "                        input_shape=(28,28,1)))\n",
    "model.add(layers.MaxPooling2D((2,2)))\n",
    "\n",
    "model.add(layers.Conv2D(64, (3,3), activation='relu'))\n",
    "model.add(layers.MaxPooling2D((2,2)))\n",
    "\n",
    "model.add(layers.Conv2D(64, (3,3), activation='relu'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "conv2d_1 (Conv2D)            (None, 26, 26, 32)        320       \n",
      "_________________________________________________________________\n",
      "max_pooling2d_1 (MaxPooling2 (None, 13, 13, 32)        0         \n",
      "_________________________________________________________________\n",
      "conv2d_2 (Conv2D)            (None, 11, 11, 64)        18496     \n",
      "_________________________________________________________________\n",
      "max_pooling2d_2 (MaxPooling2 (None, 5, 5, 64)          0         \n",
      "_________________________________________________________________\n",
      "conv2d_3 (Conv2D)            (None, 3, 3, 64)          36928     \n",
      "=================================================================\n",
      "Total params: 55,744\n",
      "Trainable params: 55,744\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.add(layers.Flatten())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "conv2d_1 (Conv2D)            (None, 26, 26, 32)        320       \n",
      "_________________________________________________________________\n",
      "max_pooling2d_1 (MaxPooling2 (None, 13, 13, 32)        0         \n",
      "_________________________________________________________________\n",
      "conv2d_2 (Conv2D)            (None, 11, 11, 64)        18496     \n",
      "_________________________________________________________________\n",
      "max_pooling2d_2 (MaxPooling2 (None, 5, 5, 64)          0         \n",
      "_________________________________________________________________\n",
      "conv2d_3 (Conv2D)            (None, 3, 3, 64)          36928     \n",
      "_________________________________________________________________\n",
      "flatten_1 (Flatten)          (None, 576)               0         \n",
      "=================================================================\n",
      "Total params: 55,744\n",
      "Trainable params: 55,744\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.add(layers.Dense(64, activation='relu'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "conv2d_1 (Conv2D)            (None, 26, 26, 32)        320       \n",
      "_________________________________________________________________\n",
      "max_pooling2d_1 (MaxPooling2 (None, 13, 13, 32)        0         \n",
      "_________________________________________________________________\n",
      "conv2d_2 (Conv2D)            (None, 11, 11, 64)        18496     \n",
      "_________________________________________________________________\n",
      "max_pooling2d_2 (MaxPooling2 (None, 5, 5, 64)          0         \n",
      "_________________________________________________________________\n",
      "conv2d_3 (Conv2D)            (None, 3, 3, 64)          36928     \n",
      "_________________________________________________________________\n",
      "flatten_1 (Flatten)          (None, 576)               0         \n",
      "_________________________________________________________________\n",
      "dense_1 (Dense)              (None, 64)                36928     \n",
      "=================================================================\n",
      "Total params: 92,672\n",
      "Trainable params: 92,672\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "conv2d_1 (Conv2D)            (None, 26, 26, 32)        320       \n",
      "_________________________________________________________________\n",
      "max_pooling2d_1 (MaxPooling2 (None, 13, 13, 32)        0         \n",
      "_________________________________________________________________\n",
      "conv2d_2 (Conv2D)            (None, 11, 11, 64)        18496     \n",
      "_________________________________________________________________\n",
      "max_pooling2d_2 (MaxPooling2 (None, 5, 5, 64)          0         \n",
      "_________________________________________________________________\n",
      "conv2d_3 (Conv2D)            (None, 3, 3, 64)          36928     \n",
      "_________________________________________________________________\n",
      "flatten_1 (Flatten)          (None, 576)               0         \n",
      "_________________________________________________________________\n",
      "dense_1 (Dense)              (None, 64)                36928     \n",
      "_________________________________________________________________\n",
      "dense_2 (Dense)              (None, 10)                650       \n",
      "=================================================================\n",
      "Total params: 93,322\n",
      "Trainable params: 93,322\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.add(layers.Dense(10, activation='softmax'))\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [],
   "source": [
    "from keras.datasets import mnist\n",
    "from keras.utils import to_categorical"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [],
   "source": [
    "(train_images, train_labels), (test_images, test_labels)=\n",
    "mnist.load_data()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_images.shape\n",
    "train_images=train_images.reshape((60000, 28, 28, 1))\n",
    "train_images=train_images.astype('float32')/255\n",
    "test_images=test_images.reshape((10000, 28, 28, 1))\n",
    "test_images=test_images.astype('float32')/255"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_labels=to_categorical(train_labels)\n",
    "test_labels=to_categorical(test_labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "W0819 15:05:03.234409  1084 deprecation_wrapper.py:119] From C:\\Users\\user\\Anaconda3\\lib\\site-packages\\keras\\optimizers.py:790: The name tf.train.Optimizer is deprecated. Please use tf.compat.v1.train.Optimizer instead.\n",
      "\n",
      "W0819 15:05:03.250061  1084 deprecation_wrapper.py:119] From C:\\Users\\user\\Anaconda3\\lib\\site-packages\\keras\\backend\\tensorflow_backend.py:3295: The name tf.log is deprecated. Please use tf.math.log instead.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "model.compile(optimizer='rmsprop',\n",
    "             loss='categorical_crossentropy',\n",
    "             metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "W0819 15:05:58.091598  1084 deprecation.py:323] From C:\\Users\\user\\Anaconda3\\lib\\site-packages\\tensorflow\\python\\ops\\math_grad.py:1250: add_dispatch_support.<locals>.wrapper (from tensorflow.python.ops.array_ops) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Use tf.where in 2.0, which has the same broadcast rule as np.where\n",
      "W0819 15:05:58.195022  1084 deprecation_wrapper.py:119] From C:\\Users\\user\\Anaconda3\\lib\\site-packages\\keras\\backend\\tensorflow_backend.py:986: The name tf.assign_add is deprecated. Please use tf.compat.v1.assign_add instead.\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/5\n",
      "60000/60000 [==============================] - 27s 448us/step - loss: 0.1698 - acc: 0.9480\n",
      "Epoch 2/5\n",
      "60000/60000 [==============================] - 27s 448us/step - loss: 0.0476 - acc: 0.9852\n",
      "Epoch 3/5\n",
      "60000/60000 [==============================] - 26s 440us/step - loss: 0.0320 - acc: 0.99041s - loss: \n",
      "Epoch 4/5\n",
      "60000/60000 [==============================] - 26s 440us/step - loss: 0.0246 - acc: 0.9924\n",
      "Epoch 5/5\n",
      "60000/60000 [==============================] - 26s 440us/step - loss: 0.0194 - acc: 0.9941\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x1f29a14ad30>"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(train_images, train_labels, epochs=5, batch_size=64)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10000/10000 [==============================] - 1s 129us/step\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.9911"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_loss, test_acc=model.evaluate(test_images, test_labels)\n",
    "test_acc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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