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study180314
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| { | |
| "nbformat": 4, | |
| "nbformat_minor": 0, | |
| "metadata": { | |
| "colab": { | |
| "name": "study180317.ipynb", | |
| "version": "0.3.2", | |
| "views": {}, | |
| "default_view": {}, | |
| "provenance": [] | |
| }, | |
| "kernelspec": { | |
| "name": "python3", | |
| "display_name": "Python 3" | |
| }, | |
| "accelerator": "GPU" | |
| }, | |
| "cells": [ | |
| { | |
| "metadata": { | |
| "id": "7NdWpOn8CQba", | |
| "colab_type": "text" | |
| }, | |
| "cell_type": "markdown", | |
| "source": [ | |
| "## 1. 헬로우 딥러닝 MNIST\n", | |
| "\n", | |
| "\n", | |
| "\n", | |
| "출처 : 밑바닥부터 시작하는 딥러닝\n", | |
| "\n", | |
| "MNIST란? 0-9까지의 손글씨로 쓴 숫자 데이터\n", | |
| "\n", | |
| "미니배치 : 데이터를 적당한 크기로 잘라서 학습\n", | |
| "\n", | |
| "ex) 60,000장의 훈련 데이터중에서 100장을 무작위 뽑아서 100장만을 학습 (밑바닥부터 시작하는 딥러닝에서 나온 내용)\n", | |
| "\n", | |
| "에포크 : 학습 데이터 전체를 한 바퀴 도는 것" | |
| ] | |
| }, | |
| { | |
| "metadata": { | |
| "id": "TwNKPhU_BlmP", | |
| "colab_type": "code", | |
| "colab": { | |
| "autoexec": { | |
| "startup": false, | |
| "wait_interval": 0 | |
| }, | |
| "output_extras": [ | |
| {} | |
| ], | |
| "base_uri": "https://localhost:8080/", | |
| "height": 374 | |
| }, | |
| "outputId": "d3ce8326-10d2-407c-c240-40e57e0a2d2b", | |
| "executionInfo": { | |
| "status": "ok", | |
| "timestamp": 1520754541511, | |
| "user_tz": -540, | |
| "elapsed": 42625, | |
| "user": { | |
| "displayName": "sj jin", | |
| "photoUrl": "https://lh3.googleusercontent.com/a/default-user=s128", | |
| "userId": "107618035827318999649" | |
| } | |
| } | |
| }, | |
| "cell_type": "code", | |
| "source": [ | |
| "import tensorflow as tf\n", | |
| "\n", | |
| "from tensorflow.examples.tutorials.mnist import input_data\n", | |
| "# 텐서플로우에 기본 내장된 mnist 모듈을 이용하여 데이터를 로드합니다.\n", | |
| "# 지정한 폴더에 MNIST 데이터가 없는 경우 자동으로 데이터를 다운로드합니다.\n", | |
| "# one_hot 옵션은 레이블을 동물 분류 예제에서 보았던 one_hot 방식의 데이터로 만들어줍니다.\n", | |
| "mnist = input_data.read_data_sets(\"./mnist/data/\", one_hot=True)\n", | |
| "\n", | |
| "#########\n", | |
| "# 신경망 모델 구성\n", | |
| "######\n", | |
| "# 입력 값의 차원은 [배치크기, 특성값] 으로 되어 있습니다.\n", | |
| "# 손글씨 이미지는 28x28 픽셀로 이루어져 있고, 이를 784개의 특성값으로 정합니다.\n", | |
| "X = tf.placeholder(tf.float32, [None, 784])\n", | |
| "# 결과는 0~9 의 10 가지 분류를 가집니다.\n", | |
| "Y = tf.placeholder(tf.float32, [None, 10])\n", | |
| "\n", | |
| "# 신경망의 레이어는 다음처럼 구성합니다.\n", | |
| "# 784(입력 특성값)\n", | |
| "# -> 256 (히든레이어 뉴런 갯수) -> 256 (히든레이어 뉴런 갯수)\n", | |
| "# -> 10 (결과값 0~9 분류)\n", | |
| "W1 = tf.Variable(tf.random_normal([784, 256], stddev=0.01))\n", | |
| "# 입력값에 가중치를 곱하고 ReLU 함수를 이용하여 레이어를 만듭니다.\n", | |
| "L1 = tf.nn.relu(tf.matmul(X, W1))\n", | |
| "\n", | |
| "W2 = tf.Variable(tf.random_normal([256, 256], stddev=0.01))\n", | |
| "# L1 레이어의 출력값에 가중치를 곱하고 ReLU 함수를 이용하여 레이어를 만듭니다.\n", | |
| "L2 = tf.nn.relu(tf.matmul(L1, W2))\n", | |
| "\n", | |
| "W3 = tf.Variable(tf.random_normal([256, 10], stddev=0.01))\n", | |
| "# 최종 모델의 출력값은 W3 변수를 곱해 10개의 분류를 가지게 됩니다.\n", | |
| "model = tf.matmul(L2, W3)\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", | |
| "#########\n", | |
| "# 신경망 모델 학습\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", | |
| " # 텐서플로우의 mnist 모델의 next_batch 함수를 이용해\n", | |
| " # 지정한 크기만큼 학습할 데이터를 가져옵니다.\n", | |
| " batch_xs, batch_ys = mnist.train.next_batch(batch_size)\n", | |
| "\n", | |
| " _, cost_val = sess.run([optimizer, cost], feed_dict={X: batch_xs, Y: batch_ys})\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", | |
| "######\n", | |
| "# model 로 예측한 값과 실제 레이블인 Y의 값을 비교합니다.\n", | |
| "# tf.argmax 함수를 이용해 예측한 값에서 가장 큰 값을 예측한 레이블이라고 평가합니다.\n", | |
| "# 예) [0.1 0 0 0.7 0 0.2 0 0 0 0] -> 3\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,\n", | |
| " Y: mnist.test.labels}))" | |
| ], | |
| "execution_count": 0, | |
| "outputs": [ | |
| { | |
| "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 = 0.415\n", | |
| "Epoch: 0002 Avg. cost = 0.151\n", | |
| "Epoch: 0003 Avg. cost = 0.099\n", | |
| "Epoch: 0004 Avg. cost = 0.070\n", | |
| "Epoch: 0005 Avg. cost = 0.053\n", | |
| "Epoch: 0006 Avg. cost = 0.042\n", | |
| "Epoch: 0007 Avg. cost = 0.033\n", | |
| "Epoch: 0008 Avg. cost = 0.026\n", | |
| "Epoch: 0009 Avg. cost = 0.019\n", | |
| "Epoch: 0010 Avg. cost = 0.019\n", | |
| "Epoch: 0011 Avg. cost = 0.015\n", | |
| "Epoch: 0012 Avg. cost = 0.013\n", | |
| "Epoch: 0013 Avg. cost = 0.013\n", | |
| "Epoch: 0014 Avg. cost = 0.013\n", | |
| "Epoch: 0015 Avg. cost = 0.011\n", | |
| "최적화 완료!\n", | |
| "정확도: 0.9801\n" | |
| ], | |
| "name": "stdout" | |
| } | |
| ] | |
| }, | |
| { | |
| "metadata": { | |
| "id": "sKcwFGEJFHzI", | |
| "colab_type": "text" | |
| }, | |
| "cell_type": "markdown", | |
| "source": [ | |
| "\n", | |
| "\n", | |
| "출처 : 밑바닥부터 시작하는 딥러닝\n", | |
| "\n", | |
| "드롭아웃 : 학습 시 전체 신경망 중 일부만을 사용하도록 하는 것" | |
| ] | |
| }, | |
| { | |
| "metadata": { | |
| "id": "2_6zB320FvZn", | |
| "colab_type": "code", | |
| "colab": { | |
| "autoexec": { | |
| "startup": false, | |
| "wait_interval": 0 | |
| }, | |
| "output_extras": [ | |
| {}, | |
| {} | |
| ], | |
| "base_uri": "https://localhost:8080/", | |
| "height": 898 | |
| }, | |
| "outputId": "269c15b1-6388-4075-83ae-47134ba35806", | |
| "executionInfo": { | |
| "status": "ok", | |
| "timestamp": 1520755603043, | |
| "user_tz": -540, | |
| "elapsed": 98005, | |
| "user": { | |
| "displayName": "sj jin", | |
| "photoUrl": "https://lh3.googleusercontent.com/a/default-user=s128", | |
| "userId": "107618035827318999649" | |
| } | |
| } | |
| }, | |
| "cell_type": "code", | |
| "source": [ | |
| "import tensorflow as tf\n", | |
| "import numpy as np\n", | |
| "import matplotlib.pyplot as plt\n", | |
| "\n", | |
| "from tensorflow.examples.tutorials.mnist import input_data\n", | |
| "mnist = input_data.read_data_sets(\"./mnist/data/\", one_hot=True)\n", | |
| "\n", | |
| "#########\n", | |
| "# 신경망 모델 구성\n", | |
| "######\n", | |
| "X = tf.placeholder(tf.float32, [None, 784])\n", | |
| "Y = tf.placeholder(tf.float32, [None, 10])\n", | |
| "keep_prob = tf.placeholder(tf.float32)\n", | |
| "\n", | |
| "W1 = tf.Variable(tf.random_normal([784, 256], stddev=0.01))\n", | |
| "L1 = tf.nn.relu(tf.matmul(X, W1))\n", | |
| "# 텐서플로우에 내장된 함수를 이용하여 dropout 을 적용합니다.\n", | |
| "# 함수에 적용할 레이어와 확률만 넣어주면 됩니다. 겁나 매직!!\n", | |
| "L1 = tf.nn.dropout(L1, keep_prob)\n", | |
| "\n", | |
| "W2 = tf.Variable(tf.random_normal([256, 256], stddev=0.01))\n", | |
| "L2 = tf.nn.relu(tf.matmul(L1, W2))\n", | |
| "L2 = tf.nn.dropout(L2, keep_prob)\n", | |
| "\n", | |
| "W3 = tf.Variable(tf.random_normal([256, 10], stddev=0.01))\n", | |
| "model = tf.matmul(L2, W3)\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", | |
| "#########\n", | |
| "# 신경망 모델 학습\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(30):\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", | |
| " _, 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", | |
| "######\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,\n", | |
| " Y: mnist.test.labels,\n", | |
| " keep_prob: 1}))\n", | |
| "\n", | |
| "#########\n", | |
| "# 결과 확인 (matplot)\n", | |
| "######\n", | |
| "labels = sess.run(model,\n", | |
| " feed_dict={X: mnist.test.images,\n", | |
| " Y: mnist.test.labels,\n", | |
| " keep_prob: 1})\n", | |
| "\n", | |
| "fig = plt.figure()\n", | |
| "for i in range(10):\n", | |
| " subplot = fig.add_subplot(2, 5, i + 1)\n", | |
| " subplot.set_xticks([])\n", | |
| " subplot.set_yticks([])\n", | |
| " subplot.set_title('%d' % np.argmax(labels[i]))\n", | |
| " subplot.imshow(mnist.test.images[i].reshape((28, 28)),\n", | |
| " cmap=plt.cm.gray_r)\n", | |
| "\n", | |
| "plt.show()" | |
| ], | |
| "execution_count": 0, | |
| "outputs": [ | |
| { | |
| "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 = 0.418\n", | |
| "Epoch: 0002 Avg. cost = 0.164\n", | |
| "Epoch: 0003 Avg. cost = 0.114\n", | |
| "Epoch: 0004 Avg. cost = 0.090\n", | |
| "Epoch: 0005 Avg. cost = 0.072\n", | |
| "Epoch: 0006 Avg. cost = 0.061\n", | |
| "Epoch: 0007 Avg. cost = 0.053\n", | |
| "Epoch: 0008 Avg. cost = 0.046\n", | |
| "Epoch: 0009 Avg. cost = 0.040\n", | |
| "Epoch: 0010 Avg. cost = 0.038\n", | |
| "Epoch: 0011 Avg. cost = 0.034\n", | |
| "Epoch: 0012 Avg. cost = 0.033\n", | |
| "Epoch: 0013 Avg. cost = 0.030\n", | |
| "Epoch: 0014 Avg. cost = 0.029\n", | |
| "Epoch: 0015 Avg. cost = 0.026\n", | |
| "Epoch: 0016 Avg. cost = 0.026\n", | |
| "Epoch: 0017 Avg. cost = 0.023\n", | |
| "Epoch: 0018 Avg. cost = 0.020\n", | |
| "Epoch: 0019 Avg. cost = 0.022\n", | |
| "Epoch: 0020 Avg. cost = 0.022\n", | |
| "Epoch: 0021 Avg. cost = 0.022\n", | |
| "Epoch: 0022 Avg. cost = 0.018\n", | |
| "Epoch: 0023 Avg. cost = 0.018\n", | |
| "Epoch: 0024 Avg. cost = 0.021\n", | |
| "Epoch: 0025 Avg. cost = 0.020\n", | |
| "Epoch: 0026 Avg. cost = 0.016\n", | |
| "Epoch: 0027 Avg. cost = 0.016\n", | |
| "Epoch: 0028 Avg. cost = 0.016\n", | |
| "Epoch: 0029 Avg. cost = 0.018\n", | |
| "Epoch: 0030 Avg. cost = 0.015\n", | |
| "최적화 완료!\n", | |
| "정확도: 0.9833\n" | |
| ], | |
| "name": "stdout" | |
| }, | |
| { | |
| "output_type": "display_data", | |
| "data": { | |
| "image/png": 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MMZKkUaNG2babbrrJxiWtC40UepgAAHigYAIA4CHmZ8mazQqkgxsWuEMB7ozM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\n+3syRs6cSAAAAABJRU5ErkJggg==\n", | |
| "text/plain": [ | |
| "<matplotlib.figure.Figure at 0x7f1d7c6f89b0>" | |
| ] | |
| }, | |
| "metadata": { | |
| "tags": [] | |
| } | |
| } | |
| ] | |
| }, | |
| { | |
| "metadata": { | |
| "id": "rrPSE9e7lMU6", | |
| "colab_type": "text" | |
| }, | |
| "cell_type": "markdown", | |
| "source": [ | |
| "# 2. 붓꽃 데이터\n", | |
| "\n", | |
| "https://www.kaggle.com/uciml/iris/data\n", | |
| "\n", | |
| "캐글 데이터 참조 \n", | |
| "\n", | |
| "원본데이터 Iris.csv 참조\n", | |
| "\n", | |
| "https://thebook.io/006723/ch04/01/\n", | |
| "Iris 각 특성값들 참조" | |
| ] | |
| }, | |
| { | |
| "metadata": { | |
| "id": "uTGTyJLClNvX", | |
| "colab_type": "code", | |
| "colab": { | |
| "autoexec": { | |
| "startup": false, | |
| "wait_interval": 0 | |
| }, | |
| "output_extras": [ | |
| {} | |
| ], | |
| "base_uri": "https://localhost:8080/", | |
| "height": 251 | |
| }, | |
| "outputId": "b5604e9c-7bc3-42ec-a2fb-11ddc52eda55", | |
| "executionInfo": { | |
| "status": "ok", | |
| "timestamp": 1520847764215, | |
| "user_tz": -540, | |
| "elapsed": 3417, | |
| "user": { | |
| "displayName": "sj jin", | |
| "photoUrl": "https://lh3.googleusercontent.com/a/default-user=s128", | |
| "userId": "107618035827318999649" | |
| } | |
| } | |
| }, | |
| "cell_type": "code", | |
| "source": [ | |
| "import tensorflow as tf\n", | |
| "import numpy as np\n", | |
| "tf.set_random_seed(777)\n", | |
| "\n", | |
| "# 가공한 붓꽃 트레이닝 데이터 입력\n", | |
| "xy = np.loadtxt('https://raw.githubusercontent.com/Crpediem/deeprunning-study/master/180317/Iris_train.csv',delimiter=',',\n", | |
| " dtype=np.float32)\n", | |
| "x_data = xy[:,0:4]\n", | |
| "y_data = xy[:,4:7]\n", | |
| "\n", | |
| "# 가공한 붓꽃 테스트 데이터 입력\n", | |
| "test_xy = np.loadtxt('https://raw.githubusercontent.com/Crpediem/deeprunning-study/master/180317/Iris_test.csv',delimiter=',',\n", | |
| " dtype=np.float32)\n", | |
| "\n", | |
| "x_test = test_xy[:,0:4]\n", | |
| "y_test = test_xy[:,4:7]\n", | |
| "\n", | |
| "#########\n", | |
| "# 신경망 모델 구성\n", | |
| "######\n", | |
| "X = tf.placeholder(tf.float32, [None, 4])\n", | |
| "Y = tf.placeholder(tf.float32, [None, 3])\n", | |
| "keep_prob = tf.placeholder(tf.float32)\n", | |
| "\n", | |
| "W1 = tf.Variable(tf.random_normal([4, 10], stddev=0.01))\n", | |
| "L1 = tf.nn.relu(tf.matmul(X, W1))\n", | |
| "L1 = tf.nn.dropout(L1, keep_prob)\n", | |
| "\n", | |
| "W2 = tf.Variable(tf.random_normal([10, 10], stddev=0.01))\n", | |
| "L2 = tf.nn.relu(tf.matmul(L1, W2))\n", | |
| "L2 = tf.nn.dropout(L2, keep_prob)\n", | |
| "\n", | |
| "W3 = tf.Variable(tf.random_normal([10, 3], stddev=0.01))\n", | |
| "model = tf.matmul(L2, W3)\n", | |
| "\n", | |
| "cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits_v2(logits=model, labels=Y))\n", | |
| "optimizer = tf.train.AdamOptimizer(learning_rate=0.01).minimize(cost)\n", | |
| "\n", | |
| "#########\n", | |
| "# 신경망 모델 학습\n", | |
| "######\n", | |
| "init = tf.global_variables_initializer()\n", | |
| "sess = tf.Session()\n", | |
| "sess.run(init)\n", | |
| "\n", | |
| "for step in range(1000):\n", | |
| " sess.run(optimizer, feed_dict={X: x_data, Y: y_data, keep_prob: 0.8})\n", | |
| "\n", | |
| " if (step + 1) % 100 == 0:\n", | |
| " print(step + 1, sess.run(cost, feed_dict={X: x_data, Y: y_data, keep_prob: 0.8}))\n", | |
| "\n", | |
| "#########\n", | |
| "# 결과 확인\n", | |
| "# 0: Iris-setosa 1: Iris-versicolor, 2: Iris-virginica\n", | |
| "######\n", | |
| "prediction = tf.argmax(model, 1)\n", | |
| "target = tf.argmax(Y, 1)\n", | |
| "print('예측값:', sess.run(prediction, feed_dict={X: x_test, keep_prob: 1}))\n", | |
| "print('실제값:', sess.run(target, feed_dict={Y: y_test, keep_prob: 1}))\n", | |
| "\n", | |
| "is_correct = tf.equal(prediction, target)\n", | |
| "accuracy = tf.reduce_mean(tf.cast(is_correct, tf.float32))\n", | |
| "print('정확도: %.2f' % sess.run(accuracy * 100, feed_dict={X: x_test, Y: y_test, keep_prob: 1}))" | |
| ], | |
| "execution_count": 0, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "text": [ | |
| "100 0.23863082\n", | |
| "200 0.23964703\n", | |
| "300 0.18831006\n", | |
| "400 0.23864982\n", | |
| "500 0.19743586\n", | |
| "600 0.19411115\n", | |
| "700 0.18276615\n", | |
| "800 0.19053736\n", | |
| "900 0.20052955\n", | |
| "1000 0.2167687\n", | |
| "예측값: [0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2]\n", | |
| "실제값: [0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2]\n", | |
| "정확도: 100.00\n" | |
| ], | |
| "name": "stdout" | |
| } | |
| ] | |
| }, | |
| { | |
| "metadata": { | |
| "id": "JbV9NeOJl8_N", | |
| "colab_type": "text" | |
| }, | |
| "cell_type": "markdown", | |
| "source": [ | |
| "## csv 파일 데이터 분류\n", | |
| "\n", | |
| "#### 1단계. 데이터 다듬기 \n", | |
| "\n", | |
| "캐글의 원본 csv에서 train과 test에서 사용되지 않을 id값과 label값을 삭제하고\n", | |
| "분류되는 값인 Iris-setosa, Iris-versicolor, Iris-virginica을 one-hot 형식으로 변환시켜줍니다.\n", | |
| "예) Iris-setosa = 100, Iris-versicolor = 010, Iris-virginica = 001\n", | |
| "\n", | |
| "#### 2단계. train과 test 데이터 나누기\n", | |
| "\n", | |
| "분류한 값을 저장 후 test와 train할 값을 나누어 각각 다른 csv로 저장합니다.\n", | |
| "\n", | |
| "#### 3단계. python list split\n", | |
| "\n", | |
| "생성한 csv에서 학습할 값과 분류되는 값을 분류하여 x_data,y_data에 저장합니다.\n", | |
| "test할 데이터도 동일하게 진행합니다." | |
| ] | |
| }, | |
| { | |
| "metadata": { | |
| "id": "oZ-3RG9eM6mV", | |
| "colab_type": "text" | |
| }, | |
| "cell_type": "markdown", | |
| "source": [ | |
| "# 3. Wine data\n", | |
| "\n", | |
| "https://archive.ics.uci.edu/ml/machine-learning-databases/wine/wine.data\n", | |
| "\n", | |
| "위 주소에서 제공하는 데이터를 가지고 위의 소개한 방법으로 csv파일 분류을 동일하게 진행하여 학습해보도록 하겠습니다.\n" | |
| ] | |
| }, | |
| { | |
| "metadata": { | |
| "id": "AykS3QhdqsZJ", | |
| "colab_type": "code", | |
| "colab": { | |
| "autoexec": { | |
| "startup": false, | |
| "wait_interval": 0 | |
| }, | |
| "output_extras": [ | |
| { | |
| "item_id": 7 | |
| } | |
| ], | |
| "base_uri": "https://localhost:8080/", | |
| "height": 251 | |
| }, | |
| "outputId": "9ed3444a-2aa8-4be3-b9b3-ef83e4b99d6f", | |
| "executionInfo": { | |
| "status": "ok", | |
| "timestamp": 1520990309495, | |
| "user_tz": -540, | |
| "elapsed": 3186, | |
| "user": { | |
| "displayName": "sj jin", | |
| "photoUrl": "https://lh3.googleusercontent.com/a/default-user=s128", | |
| "userId": "107618035827318999649" | |
| } | |
| } | |
| }, | |
| "cell_type": "code", | |
| "source": [ | |
| "import tensorflow as tf\n", | |
| "import numpy as np\n", | |
| "\n", | |
| "# 가공한 트레이닝 데이터 입력\n", | |
| "xy = np.loadtxt('https://raw.githubusercontent.com/Crpediem/deeprunning-study/master/180317/wine_train.csv',delimiter=',',\n", | |
| " dtype=np.float32)\n", | |
| "x_data = xy[:,0:13]\n", | |
| "y_data = xy[:,13:16]\n", | |
| "\n", | |
| "# 가공한 테스트 데이터 입력\n", | |
| "test_xy = np.loadtxt('https://raw.githubusercontent.com/Crpediem/deeprunning-study/master/180317/wine_test.csv',delimiter=',',\n", | |
| " dtype=np.float32)\n", | |
| "\n", | |
| "x_test = test_xy[:,0:13]\n", | |
| "y_test = test_xy[:,13:16]\n", | |
| "\n", | |
| "# print(x_data)\n", | |
| "# print(y_data)\n", | |
| "\n", | |
| "#########\n", | |
| "# 신경망 모델 구성\n", | |
| "#####\n", | |
| "X = tf.placeholder(tf.float32, [None, 13])\n", | |
| "Y = tf.placeholder(tf.float32, [None, 3])\n", | |
| "keep_prob = tf.placeholder(tf.float32)\n", | |
| "\n", | |
| "W1 = tf.Variable(tf.random_normal([13, 100], stddev=0.01))\n", | |
| "L1 = tf.nn.relu(tf.matmul(X, W1))\n", | |
| "L1 = tf.nn.dropout(L1, keep_prob)\n", | |
| "\n", | |
| "W2 = tf.Variable(tf.random_normal([100, 100], stddev=0.01))\n", | |
| "L2 = tf.nn.relu(tf.matmul(L1, W2))\n", | |
| "L2 = tf.nn.dropout(L2, keep_prob)\n", | |
| "\n", | |
| "W3 = tf.Variable(tf.random_normal([100, 3], stddev=0.01))\n", | |
| "model = tf.matmul(L2, W3)\n", | |
| "\n", | |
| "cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits_v2(logits=model, labels=Y))\n", | |
| "optimizer = tf.train.AdamOptimizer(learning_rate=0.001).minimize(cost)\n", | |
| "\n", | |
| "#########\n", | |
| "# 신경망 모델 학습\n", | |
| "######\n", | |
| "init = tf.global_variables_initializer()\n", | |
| "sess = tf.Session()\n", | |
| "sess.run(init)\n", | |
| "\n", | |
| "for step in range(1000):\n", | |
| " sess.run(optimizer, feed_dict={X: x_data, Y: y_data, keep_prob: 0.8})\n", | |
| "\n", | |
| " if (step + 1) % 100 == 0:\n", | |
| " print(step + 1, sess.run(cost, feed_dict={X: x_data, Y: y_data, keep_prob: 0.8}))\n", | |
| "\n", | |
| "#########\n", | |
| "# 결과 확인\n", | |
| "######\n", | |
| "prediction = tf.argmax(model, 1)\n", | |
| "target = tf.argmax(Y, 1)\n", | |
| "print('예측값:', sess.run(prediction, feed_dict={X: x_test, keep_prob: 1}))\n", | |
| "print('실제값:', sess.run(target, feed_dict={Y: y_test, keep_prob: 1}))\n", | |
| "\n", | |
| "is_correct = tf.equal(prediction, target)\n", | |
| "accuracy = tf.reduce_mean(tf.cast(is_correct, tf.float32))\n", | |
| "print('정확도: %.2f' % sess.run(accuracy * 100, feed_dict={X: x_test, Y: y_test, keep_prob: 1}))" | |
| ], | |
| "execution_count": 16, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "text": [ | |
| "100 0.5879292\n", | |
| "200 0.1710964\n", | |
| "300 0.11573343\n", | |
| "400 0.08524342\n", | |
| "500 0.07245682\n", | |
| "600 0.066718794\n", | |
| "700 0.082000956\n", | |
| "800 0.09255613\n", | |
| "900 0.0823765\n", | |
| "1000 0.080762655\n", | |
| "예측값: [0 0 0 0 0 1 1 1 1 1 2 2 2 2 2]\n", | |
| "실제값: [0 0 0 0 0 1 1 1 1 1 2 2 2 2 2]\n", | |
| "정확도: 100.00\n" | |
| ], | |
| "name": "stdout" | |
| } | |
| ] | |
| }, | |
| { | |
| "metadata": { | |
| "id": "4zc039MQG2FF", | |
| "colab_type": "text" | |
| }, | |
| "cell_type": "markdown", | |
| "source": [ | |
| "# 4. dropout 효과\n", | |
| "\n", | |
| "\n", | |
| "\n", | |
| "출처 : https://leonardoaraujosantos.gitbooks.io/artificial-inteligence/content/dropout_layer.html\n", | |
| "\n", | |
| "드롭아웃 정리 전, 후 에러율입니다.\n", | |
| "\n", | |
| "차이가 꽤나 나는 것을 보실 수 있습니다.\n", | |
| "\n", | |
| "\n", | |
| "\n" | |
| ] | |
| } | |
| ] | |
| } |
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