{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"name":"03-6 주택 가격 예측 : 회귀 문제.ipynb","provenance":[],"collapsed_sections":[]},"kernelspec":{"name":"python3","display_name":"Python 3"}},"cells":[{"cell_type":"code","metadata":{"id":"tYoU_o5H974a","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":115},"outputId":"3e0a8c10-4716-473b-97cd-074f3586f4b5","executionInfo":{"status":"ok","timestamp":1577863108611,"user_tz":-540,"elapsed":3563,"user":{"displayName":"Yoon Jack","photoUrl":"","userId":"04923927567667044980"}}},"source":["import keras\n","keras.__version__"],"execution_count":1,"outputs":[{"output_type":"stream","text":["Using TensorFlow backend.\n"],"name":"stderr"},{"output_type":"display_data","data":{"text/html":["<p style=\"color: red;\">\n","The default version of TensorFlow in Colab will soon switch to TensorFlow 2.x.<br>\n","We recommend you <a href=\"https://www.tensorflow.org/guide/migrate\" target=\"_blank\">upgrade</a> now \n","or ensure your notebook will continue to use TensorFlow 1.x via the <code>%tensorflow_version 1.x</code> magic:\n","<a href=\"https://colab.research.google.com/notebooks/tensorflow_version.ipynb\" target=\"_blank\">more info</a>.</p>\n"],"text/plain":["<IPython.core.display.HTML object>"]},"metadata":{"tags":[]}},{"output_type":"execute_result","data":{"text/plain":["'2.2.5'"]},"metadata":{"tags":[]},"execution_count":1}]},{"cell_type":"code","metadata":{"id":"npAy37ii-B5r","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":52},"outputId":"58a326e5-d086-4967-e78c-be85571ab686","executionInfo":{"status":"ok","timestamp":1577863167204,"user_tz":-540,"elapsed":1783,"user":{"displayName":"Yoon Jack","photoUrl":"","userId":"04923927567667044980"}}},"source":["from keras.datasets import boston_housing\n","(train_data, train_targets), (test_data, test_targets) = boston_housing.load_data()"],"execution_count":2,"outputs":[{"output_type":"stream","text":["Downloading data from https://s3.amazonaws.com/keras-datasets/boston_housing.npz\n","57344/57026 [==============================] - 0s 2us/step\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"kVn4TMre-TWQ","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":34},"outputId":"31222022-a6c4-463e-f192-f8a9976dc692","executionInfo":{"status":"ok","timestamp":1577863171495,"user_tz":-540,"elapsed":1108,"user":{"displayName":"Yoon Jack","photoUrl":"","userId":"04923927567667044980"}}},"source":["train_data.shape"],"execution_count":3,"outputs":[{"output_type":"execute_result","data":{"text/plain":["(404, 13)"]},"metadata":{"tags":[]},"execution_count":3}]},{"cell_type":"code","metadata":{"id":"lwlTSFx2-UkE","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":34},"outputId":"3a3127dd-531e-43c9-d929-37949f278533","executionInfo":{"status":"ok","timestamp":1577863174876,"user_tz":-540,"elapsed":638,"user":{"displayName":"Yoon Jack","photoUrl":"","userId":"04923927567667044980"}}},"source":["test_data.shape"],"execution_count":4,"outputs":[{"output_type":"execute_result","data":{"text/plain":["(102, 13)"]},"metadata":{"tags":[]},"execution_count":4}]},{"cell_type":"code","metadata":{"id":"q_5ali_b-Vf_","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":674},"outputId":"9c3dce07-e2cf-4a6d-e2c4-b191d0b2c844","executionInfo":{"status":"ok","timestamp":1577863200464,"user_tz":-540,"elapsed":1394,"user":{"displayName":"Yoon Jack","photoUrl":"","userId":"04923927567667044980"}}},"source":["train_targets"],"execution_count":5,"outputs":[{"output_type":"execute_result","data":{"text/plain":["array([15.2, 42.3, 50. , 21.1, 17.7, 18.5, 11.3, 15.6, 15.6, 14.4, 12.1,\n","       17.9, 23.1, 19.9, 15.7,  8.8, 50. , 22.5, 24.1, 27.5, 10.9, 30.8,\n","       32.9, 24. , 18.5, 13.3, 22.9, 34.7, 16.6, 17.5, 22.3, 16.1, 14.9,\n","       23.1, 34.9, 25. , 13.9, 13.1, 20.4, 20. , 15.2, 24.7, 22.2, 16.7,\n","       12.7, 15.6, 18.4, 21. , 30.1, 15.1, 18.7,  9.6, 31.5, 24.8, 19.1,\n","       22. , 14.5, 11. , 32. , 29.4, 20.3, 24.4, 14.6, 19.5, 14.1, 14.3,\n","       15.6, 10.5,  6.3, 19.3, 19.3, 13.4, 36.4, 17.8, 13.5, 16.5,  8.3,\n","       14.3, 16. , 13.4, 28.6, 43.5, 20.2, 22. , 23. , 20.7, 12.5, 48.5,\n","       14.6, 13.4, 23.7, 50. , 21.7, 39.8, 38.7, 22.2, 34.9, 22.5, 31.1,\n","       28.7, 46. , 41.7, 21. , 26.6, 15. , 24.4, 13.3, 21.2, 11.7, 21.7,\n","       19.4, 50. , 22.8, 19.7, 24.7, 36.2, 14.2, 18.9, 18.3, 20.6, 24.6,\n","       18.2,  8.7, 44. , 10.4, 13.2, 21.2, 37. , 30.7, 22.9, 20. , 19.3,\n","       31.7, 32. , 23.1, 18.8, 10.9, 50. , 19.6,  5. , 14.4, 19.8, 13.8,\n","       19.6, 23.9, 24.5, 25. , 19.9, 17.2, 24.6, 13.5, 26.6, 21.4, 11.9,\n","       22.6, 19.6,  8.5, 23.7, 23.1, 22.4, 20.5, 23.6, 18.4, 35.2, 23.1,\n","       27.9, 20.6, 23.7, 28. , 13.6, 27.1, 23.6, 20.6, 18.2, 21.7, 17.1,\n","        8.4, 25.3, 13.8, 22.2, 18.4, 20.7, 31.6, 30.5, 20.3,  8.8, 19.2,\n","       19.4, 23.1, 23. , 14.8, 48.8, 22.6, 33.4, 21.1, 13.6, 32.2, 13.1,\n","       23.4, 18.9, 23.9, 11.8, 23.3, 22.8, 19.6, 16.7, 13.4, 22.2, 20.4,\n","       21.8, 26.4, 14.9, 24.1, 23.8, 12.3, 29.1, 21. , 19.5, 23.3, 23.8,\n","       17.8, 11.5, 21.7, 19.9, 25. , 33.4, 28.5, 21.4, 24.3, 27.5, 33.1,\n","       16.2, 23.3, 48.3, 22.9, 22.8, 13.1, 12.7, 22.6, 15. , 15.3, 10.5,\n","       24. , 18.5, 21.7, 19.5, 33.2, 23.2,  5. , 19.1, 12.7, 22.3, 10.2,\n","       13.9, 16.3, 17. , 20.1, 29.9, 17.2, 37.3, 45.4, 17.8, 23.2, 29. ,\n","       22. , 18. , 17.4, 34.6, 20.1, 25. , 15.6, 24.8, 28.2, 21.2, 21.4,\n","       23.8, 31. , 26.2, 17.4, 37.9, 17.5, 20. ,  8.3, 23.9,  8.4, 13.8,\n","        7.2, 11.7, 17.1, 21.6, 50. , 16.1, 20.4, 20.6, 21.4, 20.6, 36.5,\n","        8.5, 24.8, 10.8, 21.9, 17.3, 18.9, 36.2, 14.9, 18.2, 33.3, 21.8,\n","       19.7, 31.6, 24.8, 19.4, 22.8,  7.5, 44.8, 16.8, 18.7, 50. , 50. ,\n","       19.5, 20.1, 50. , 17.2, 20.8, 19.3, 41.3, 20.4, 20.5, 13.8, 16.5,\n","       23.9, 20.6, 31.5, 23.3, 16.8, 14. , 33.8, 36.1, 12.8, 18.3, 18.7,\n","       19.1, 29. , 30.1, 50. , 50. , 22. , 11.9, 37.6, 50. , 22.7, 20.8,\n","       23.5, 27.9, 50. , 19.3, 23.9, 22.6, 15.2, 21.7, 19.2, 43.8, 20.3,\n","       33.2, 19.9, 22.5, 32.7, 22. , 17.1, 19. , 15. , 16.1, 25.1, 23.7,\n","       28.7, 37.2, 22.6, 16.4, 25. , 29.8, 22.1, 17.4, 18.1, 30.3, 17.5,\n","       24.7, 12.6, 26.5, 28.7, 13.3, 10.4, 24.4, 23. , 20. , 17.8,  7. ,\n","       11.8, 24.4, 13.8, 19.4, 25.2, 19.4, 19.4, 29.1])"]},"metadata":{"tags":[]},"execution_count":5}]},{"cell_type":"markdown","metadata":{"id":"G0N4hjD5-dNK","colab_type":"text"},"source":["#데이터 준비"]},{"cell_type":"code","metadata":{"id":"MgjMPKUH-bgl","colab_type":"code","colab":{}},"source":["mean = train_data.mean(axis = 0)\n","train_data -= mean\n","std = train_data.std(axis = 0)\n","train_data /= std\n","\n","test_data -= mean\n","test_data /= std"],"execution_count":0,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"G9i68Y17-oTi","colab_type":"text"},"source":["#모델 구성"]},{"cell_type":"code","metadata":{"id":"wsBcvbX3-l9m","colab_type":"code","colab":{}},"source":["from keras import models\n","from keras import layers\n","def build_model():\n","  model = models.Sequential()\n","  model.add(layers.Dense(64, activation = 'relu', input_shape=(train_data.shape[1],)))\n","  model.add(layers.Dense(64, activation = 'relu'))\n","  model.add(layers.Dense(1))\n","  model.compile(optimizer='rmsprop', loss= 'mse', metrics=['mae'])\n","  return model"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"MHlXf3fC_AUU","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":534},"outputId":"9a261cd8-c48d-496c-dbf9-2c0817229306","executionInfo":{"status":"ok","timestamp":1577863587029,"user_tz":-540,"elapsed":122711,"user":{"displayName":"Yoon Jack","photoUrl":"","userId":"04923927567667044980"}}},"source":["import numpy as np\n","\n","k = 4\n","num_val_samples = len(train_data) // k\n","num_epochs = 100\n","all_scores = []\n","for i in range(k):\n","    print('처리중인 폴드 #', i)\n","    # 검증 데이터 준비: k번째 분할\n","    val_data = train_data[i * num_val_samples: (i + 1) * num_val_samples]\n","    val_targets = train_targets[i * num_val_samples: (i + 1) * num_val_samples]\n","\n","    # 훈련 데이터 준비: 다른 분할 전체\n","    partial_train_data = np.concatenate(\n","        [train_data[:i * num_val_samples],\n","         train_data[(i + 1) * num_val_samples:]],\n","        axis=0)\n","    partial_train_targets = np.concatenate(\n","        [train_targets[:i * num_val_samples],\n","         train_targets[(i + 1) * num_val_samples:]],\n","        axis=0)\n","\n","    # 케라스 모델 구성(컴파일 포함)\n","    model = build_model()\n","    # 모델 훈련(verbose=0 이므로 훈련 과정이 출력되지 않습니다)\n","    model.fit(partial_train_data, partial_train_targets,\n","              epochs=num_epochs, batch_size=1, verbose=0)\n","    # 검증 세트로 모델 평가\n","    val_mse, val_mae = model.evaluate(val_data, val_targets, verbose=0)\n","    all_scores.append(val_mae)"],"execution_count":11,"outputs":[{"output_type":"stream","text":["처리중인 폴드 # 0\n","WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:66: The name tf.get_default_graph is deprecated. Please use tf.compat.v1.get_default_graph instead.\n","\n","WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:541: The name tf.placeholder is deprecated. Please use tf.compat.v1.placeholder instead.\n","\n","WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:4432: The name tf.random_uniform is deprecated. Please use tf.random.uniform instead.\n","\n","WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/optimizers.py:793: The name tf.train.Optimizer is deprecated. Please use tf.compat.v1.train.Optimizer instead.\n","\n","WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:1033: The name tf.assign_add is deprecated. Please use tf.compat.v1.assign_add instead.\n","\n","WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:1020: The name tf.assign is deprecated. Please use tf.compat.v1.assign instead.\n","\n","WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:3005: The name tf.Session is deprecated. Please use tf.compat.v1.Session instead.\n","\n","WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:190: The name tf.get_default_session is deprecated. Please use tf.compat.v1.get_default_session instead.\n","\n","WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:197: The name tf.ConfigProto is deprecated. Please use tf.compat.v1.ConfigProto instead.\n","\n","WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:207: The name tf.global_variables is deprecated. Please use tf.compat.v1.global_variables instead.\n","\n","WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:216: The name tf.is_variable_initialized is deprecated. Please use tf.compat.v1.is_variable_initialized instead.\n","\n","WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:223: The name tf.variables_initializer is deprecated. Please use tf.compat.v1.variables_initializer instead.\n","\n","처리중인 폴드 # 1\n","처리중인 폴드 # 2\n","처리중인 폴드 # 3\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"zx3oF18N_Xn5","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":107},"outputId":"5a57dbd2-ca8f-4564-fb3c-724f7ce5cfbd","executionInfo":{"status":"ok","timestamp":1577863630343,"user_tz":-540,"elapsed":1147,"user":{"displayName":"Yoon Jack","photoUrl":"","userId":"04923927567667044980"}}},"source":["from keras import backend as K\n","\n","# 메모리 해제\n","K.clear_session()"],"execution_count":12,"outputs":[{"output_type":"stream","text":["WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:107: The name tf.reset_default_graph is deprecated. Please use tf.compat.v1.reset_default_graph instead.\n","\n","WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:111: The name tf.placeholder_with_default is deprecated. Please use tf.compat.v1.placeholder_with_default instead.\n","\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"Gb-bdIY1_pUE","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":87},"outputId":"63d3c898-05c2-471d-b893-10393c564aad","executionInfo":{"status":"ok","timestamp":1577864366532,"user_tz":-540,"elapsed":733060,"user":{"displayName":"Yoon Jack","photoUrl":"","userId":"04923927567667044980"}}},"source":["num_epochs = 500\n","all_mae_histories = []\n","for i in range(k):\n","    print('처리중인 폴드 #', i)\n","    # 검증 데이터 준비: k번째 분할\n","    val_data = train_data[i * num_val_samples: (i + 1) * num_val_samples]\n","    val_targets = train_targets[i * num_val_samples: (i + 1) * num_val_samples]\n","\n","    # 훈련 데이터 준비: 다른 분할 전체\n","    partial_train_data = np.concatenate(\n","        [train_data[:i * num_val_samples],\n","         train_data[(i + 1) * num_val_samples:]],\n","        axis=0)\n","    partial_train_targets = np.concatenate(\n","        [train_targets[:i * num_val_samples],\n","         train_targets[(i + 1) * num_val_samples:]],\n","        axis=0)\n","\n","    # 케라스 모델 구성(컴파일 포함)\n","    model = build_model()\n","    # 모델 훈련(verbose=0 이므로 훈련 과정이 출력되지 않습니다)\n","    history = model.fit(partial_train_data, partial_train_targets,\n","                        validation_data=(val_data, val_targets),\n","                        epochs=num_epochs, batch_size=1, verbose=0)\n","    mae_history = history.history['val_mean_absolute_error']\n","    all_mae_histories.append(mae_history)"],"execution_count":13,"outputs":[{"output_type":"stream","text":["처리중인 폴드 # 0\n","처리중인 폴드 # 1\n","처리중인 폴드 # 2\n","처리중인 폴드 # 3\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"xhEGP7ngAFmQ","colab_type":"code","colab":{}},"source":["average_mae_history = [\n","    np.mean([x[i] for x in all_mae_histories]) for i in range(num_epochs)]"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"Y-zdFmu3AVlm","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":278},"outputId":"268656b1-1fe9-494c-c5e9-d60fe71b352b","executionInfo":{"status":"ok","timestamp":1577864375677,"user_tz":-540,"elapsed":1933,"user":{"displayName":"Yoon Jack","photoUrl":"","userId":"04923927567667044980"}}},"source":["import matplotlib.pyplot as plt\n","\n","plt.plot(range(1, len(average_mae_history) + 1), average_mae_history)\n","plt.xlabel('Epochs')\n","plt.ylabel('Validation MAE')\n","plt.show()"],"execution_count":15,"outputs":[{"output_type":"display_data","data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAAYIAAAEGCAYAAABo25JHAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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g3NeCxRFPZbyv+0DvkUA9AY2eO5vtB87UHU3xmyxb28Futu3D7ifg938Hw3M4gA/b7uLZAFQ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\npVSa00CglFJp7v8DhaNo8PUlcpYAAAAASUVORK5CYII=\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"tags":[]}}]},{"cell_type":"code","metadata":{"id":"eAHODA9vAuut","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":278},"outputId":"d88d19e6-00a0-425b-d967-40ad1871fb39","executionInfo":{"status":"ok","timestamp":1577864377716,"user_tz":-540,"elapsed":1278,"user":{"displayName":"Yoon Jack","photoUrl":"","userId":"04923927567667044980"}}},"source":["def smooth_curve(points, factor=0.9):\n","  smoothed_points = []\n","  for point in points:\n","    if smoothed_points:\n","      previous = smoothed_points[-1]\n","      smoothed_points.append(previous * factor + point * (1 - factor))\n","    else:\n","      smoothed_points.append(point)\n","  return smoothed_points\n","\n","smooth_mae_history = smooth_curve(average_mae_history[10:])\n","\n","plt.plot(range(1, len(smooth_mae_history) + 1), smooth_mae_history)\n","plt.xlabel('Epochs')\n","plt.ylabel('Validation MAE')\n","plt.show()"],"execution_count":16,"outputs":[{"output_type":"display_data","data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAAYkAAAEGCAYAAACQO2mwAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nhVmJZCbFMiEljo2l9VQ3tR8REGbmJPHBge7FZ/UtnaTER/HAG3spqW3hL6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4+d+ZUUuKi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RkTOCLjW5Z5fLyLPhqqewyU+pvtDvN3T3Xpo7nCCxJ0XzebbH54LQGeXEhEhrP2vC3niltMo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\nSBhjjAnKgoQxxpig/j+hdZ8W+B6rJwAAAABJRU5ErkJggg==\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"tags":[]}}]},{"cell_type":"code","metadata":{"id":"4lzjnXV8Aw-W","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":34},"outputId":"cc2c6dee-3ba1-4796-a1e6-e1cf9b0cf450","executionInfo":{"status":"ok","timestamp":1577864384733,"user_tz":-540,"elapsed":3871,"user":{"displayName":"Yoon Jack","photoUrl":"","userId":"04923927567667044980"}}},"source":["# 새롭게 컴파인된 모델을 얻습니다\n","model = build_model()\n","# 전체 데이터로 훈련시킵니다\n","model.fit(train_data, train_targets,\n","          epochs=80, batch_size=16, verbose=0)\n","test_mse_score, test_mae_score = model.evaluate(test_data, test_targets)"],"execution_count":17,"outputs":[{"output_type":"stream","text":["102/102 [==============================] - 0s 788us/step\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"AueNamVIC8El","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":34},"outputId":"e205ca8b-9783-4e0d-dc61-d4da9aca9b75","executionInfo":{"status":"ok","timestamp":1577864404083,"user_tz":-540,"elapsed":1449,"user":{"displayName":"Yoon Jack","photoUrl":"","userId":"04923927567667044980"}}},"source":["test_mae_score"],"execution_count":18,"outputs":[{"output_type":"execute_result","data":{"text/plain":["2.6418881042330873"]},"metadata":{"tags":[]},"execution_count":18}]},{"cell_type":"code","metadata":{"id":"tqC-WKi3DBYP","colab_type":"code","colab":{}},"source":[""],"execution_count":0,"outputs":[]}]}