Merge pull request #3 from 1eedaegon/master

gogo

codes

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코드(4월9일까지)/deep.ipynb

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+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\STU\\Anaconda3\\lib\\site-packages\\h5py\\__init__.py:36: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.\n",
+      "  from ._conv import register_converters as _register_converters\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Successfully downloaded train-images-idx3-ubyte.gz 9912422 bytes.\n",
+      "Extracting MNIST_data/train-images-idx3-ubyte.gz\n",
+      "Successfully downloaded train-labels-idx1-ubyte.gz 28881 bytes.\n",
+      "Extracting MNIST_data/train-labels-idx1-ubyte.gz\n",
+      "Successfully downloaded t10k-images-idx3-ubyte.gz 1648877 bytes.\n",
+      "Extracting MNIST_data/t10k-images-idx3-ubyte.gz\n",
+      "Successfully downloaded t10k-labels-idx1-ubyte.gz 4542 bytes.\n",
+      "Extracting MNIST_data/t10k-labels-idx1-ubyte.gz\n",
+      "WARNING:tensorflow:From <ipython-input-1-a8044be962d6>:61: softmax_cross_entropy_with_logits (from tensorflow.python.ops.nn_ops) is deprecated and will be removed in a future version.\n",
+      "Instructions for updating:\n",
+      "\n",
+      "Future major versions of TensorFlow will allow gradients to flow\n",
+      "into the labels input on backprop by default.\n",
+      "\n",
+      "See tf.nn.softmax_cross_entropy_with_logits_v2.\n",
+      "\n",
+      "Learning started. It takes sometime.\n",
+      "Epoch: 0001 cost = 0.345577185\n",
+      "Epoch: 0002 cost = 0.091736604\n",
+      "Epoch: 0003 cost = 0.068284046\n",
+      "Epoch: 0004 cost = 0.056339833\n",
+      "Epoch: 0005 cost = 0.047010720\n",
+      "Epoch: 0006 cost = 0.041194586\n",
+      "Epoch: 0007 cost = 0.036663712\n",
+      "Epoch: 0008 cost = 0.032757639\n",
+      "Epoch: 0009 cost = 0.027963868\n",
+      "Epoch: 0010 cost = 0.025047483\n",
+      "Epoch: 0011 cost = 0.022065875\n",
+      "Epoch: 0012 cost = 0.020263703\n",
+      "Epoch: 0013 cost = 0.016754853\n",
+      "Epoch: 0014 cost = 0.015507657\n",
+      "Epoch: 0015 cost = 0.013157484\n",
+      "Learning Finished!\n",
+      "Accuracy: 0.9883\n",
+      "Label:  [3]\n",
+      "Prediction:  [3]\n"
+     ]
+    }
+   ],
+   "source": [
+    "###################################################################################################################\n",
+    "#                               B A S I C   C O N V O L U S I O N  N U R A L _ N E T                              #\n",
+    "###################################################################################################################\n",
+    "import tensorflow as tf\n",
+    "import random\n",
+    "import csv\n",
+    "# import matplotlib.pyplot as plt\n",
+    "tf.set_random_seed(777)  # 랜덤이지만 항상 일정하게\n",
+    "\n",
+    "f = open('c:/tensorflow/train.csv')\n",
+    "\n",
+    "# 에폭, 배치크기\n",
+    "learning_rate = 0.001\n",
+    "training_epochs = 15\n",
+    "batch_size = 100\n",
+    "\n",
+    "# 한번에 넣을 placeholder\n",
+    "X = tf.placeholder(tf.float32, [None, 784])\n",
+    "X_img = tf.reshape(X, [-1, 28, 28, 1])   # img 28x28x1 (black/white)\n",
+    "Y = tf.placeholder(tf.float32, [None, 10])\n",
+    "\n",
+    "\n",
+    "W1 = tf.Variable(tf.random_normal([3, 3, 1, 32], stddev=0.01))\n",
+    "L1 = tf.nn.conv2d(X_img, 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],\n",
+    "                    strides=[1, 2, 2, 1], padding='SAME')\n",
+    "'''\n",
+    "Tensor(\"Conv2D:0\", shape=(?, 28, 28, 32), dtype=float32)\n",
+    "Tensor(\"Relu:0\", shape=(?, 28, 28, 32), dtype=float32)\n",
+    "Tensor(\"MaxPool:0\", shape=(?, 14, 14, 32), dtype=float32)\n",
+    "'''\n",
+    "\n",
+    "# L2 ImgIn shape=(?, 14, 14, 32)\n",
+    "W2 = tf.Variable(tf.random_normal([3, 3, 32, 64], stddev=0.01))\n",
+    "#    Conv      ->(?, 14, 14, 64)\n",
+    "#    Pool      ->(?, 7, 7, 64)\n",
+    "L2 = tf.nn.conv2d(L1, W2, strides=[1, 1, 1, 1], padding='SAME')\n",
+    "# 14 to 64\n",
+    "L2 = tf.nn.relu(L2)\n",
+    "# 14 to 643\n",
+    "L2 = tf.nn.max_pool(L2, ksize=[1, 2, 2, 1],\n",
+    "                    strides=[1, 2, 2, 1], padding='SAME')\n",
+    "# 7 to 64\n",
+    "L2_flat = tf.reshape(L2, [-1, 7 * 7 * 64])\n",
+    "\n",
+    "\n",
+    "# 숫자 10개로 아웃\n",
+    "W3 = tf.get_variable(\"W3\", shape=[7 * 7 * 64, 10],\n",
+    "                     initializer=tf.contrib.layers.xavier_initializer())\n",
+    "b = tf.Variable(tf.random_normal([10]))\n",
+    "logits = tf.matmul(L2_flat, W3) + b\n",
+    "\n",
+    "# 옵티마이저\n",
+    "cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(\n",
+    "    logits=logits, labels=Y))\n",
+    "optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate).minimize(cost)\n",
+    "\n",
+    "# 세션영역\n",
+    "sess = tf.Session()\n",
+    "sess.run(tf.global_variables_initializer())\n",
+    "##################################################### 배치 ############################################\n",
+    "print('Learning started. It takes sometime.')\n",
+    "for epoch in range(training_epochs):\n",
+    "    avg_cost = 0\n",
+    "    total_batch = # 로우 갯수 / 배치크기\n",
+    "\n",
+    "    for i in range(total_batch):\n",
+    "        batch_xs, batch_ys = #다음 배치(배치크기만큼)\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",
+    "##################################################### batch ############################################\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(logits, 1), tf.argmax(Y, 1))\n",
+    "accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))\n",
+    "print('Accuracy:', sess.run(accuracy, feed_dict={\n",
+    "      X: #테스트 이미지 x축, Y: #테스트 이미지 y축}))\n",
+    "\n",
+    "# Get one and predict\n",
+    "r = random.randint(0, mnist.test.num_examples - 1)\n",
+    "print(\"Label: \", sess.run(tf.argmax(mnist.test.labels[r:r + 1], 1)))\n",
+    "print(\"Prediction: \", sess.run(\n",
+    "    tf.argmax(logits, 1), feed_dict={X: mnist.test.images[r:r + 1]}))\n",
+    "\n",
+    "# plt.imshow(mnist.test.images[r:r + 1].\n",
+    "#           reshape(28, 28), cmap='Greys', interpolation='nearest')\n",
+    "# plt.show()\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": "3.6.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}