2 minute read

Tags: , , , ,

超級好玩的兒~ AIA 在 Kaggle 創建的 Pokemon 比賽



  • 資料欄位

    • appearedTimeOfDay - time of the day of a sighting (night, evening, afternoon, morning)

    • appearedHour/appearedMinute - local hour/minute of a sighting (numeric)

    • terrainType - terrain where pokemon appeared described with help of GLCF Modis Land Cover (numeric)

    • closeToWater - did pokemon appear close (100m or less) to water (Boolean, same source as above)

    • city - the city of a sighting (nominal)

    • continent (not always parsed right) - the continent of a sighting (nominal)

    • weather - weather type during a sighting (Foggy Clear, PartlyCloudy, MostlyCloudy, Overcast, *Rain, BreezyandOvercast, LightRain, Drizzle, BreezyandPartlyCloudy, HeavyRain, ** BreezyandMostlyCloudy, Breezy, Windy, WindyandFoggy, Humid, Dry, WindyandPartlyCloudy, DryandMostlyCloudy, DryandPartlyCloudy, DrizzleandBreezy, LightRainandBreezy, HumidandPartlyCloudy, HumidandOvercast, RainandWindy)

    • temperature - temperature in celsius at the location of a sighting (numeric)

    • windSpeed - speed of the wind in km/h at the location of a sighting (numeric)

    • pressure - atmospheric pressure in bar at the location of a sighting (numeric)

    • weatherIcon - a compact representation of the weather at the location of a sighting (fog, clear-night, partly-cloudy-night, partly-cloudy-day, cloudy, clear-day, rain, wind)

    • population density - what is the population density per square km of a sighting (numeric, Source)

    • urban-rural - how urban is location where pokemon appeared (Boolean, built on Population density, =200 and =400 and 800 for urban)

    • gymDistanceKm, pokestopDistanceKm - how far is the nearest gym/pokestop in km from a sighting? (numeric, extracted from this dataset)

    • gymIn100m-pokestopIn5000m - is there a gym/pokestop in 100/200/etc meters? (Boolean)

    • cooc 1-cooc 151 - co-occurrence with any other pokemon (pokemon ids range between 1 and 151) within 100m distance and within the last 24 hours (Boolean) List of Pokémon by National Pokédex number

  • 練習方向

    • Data preprocessing and feature engineering
    • DNN model construction and hyper-parameter tuning
    • Metrics: Accuracy


  • 只有在大神教課的時候看到,真的自己跑出來的時候 超級開心~~:heart::heart::heart:,雖然是 overfitting XDD 但真的港決慢慢上軌道了! 哈哈 好險 前面近兩個月我有堅持住!!! GOGOG!! 繼續加油! :)

    • feature engineering 超級重要!!
    • 我是認真做 feature engineering 後,訓練才大大進步的兒
    • Gradient Descent in practice I - Feature: 我的筆記
  • 怎麼解!? 看 我的筆記


Keras 跑 early stop & 存取檔案!

  • 先做 callback funcs

     import tensorflow as tf
     from tensorflow.keras.callbacks import EarlyStopping
     from tensorflow.keras.callbacks import ModelCheckpoint
     from tensorflow.keras.layers import Dense, Dropout
     # early stop
     es = EarlyStopping(monitor='val_loss', mode='min', verbose=1, patience=200)
     # model checkpoint
     mc = ModelCheckpoint('best_model.h5', monitor='val_acc', mode='max', verbose=1, save_best_only=True)
  • 在 fit 中放入 callback function

     # 建模
     model = tf.keras.Sequential()
     model.add(Dense(50, activation='elu', input_shape=(225, ), ))
     model.add(Dense(25, activation='elu', kernel_regularizer=regularizers.l2(0.02),))
     model.add(Dense(12, activation='relu', kernel_regularizer=regularizers.l2(0.02)))
     model.add(Dense(6, activation='sigmoid'))
     # compile
     # fit
     model_history = model.fit(x=x_train_best, 
                               validation_split= 0.08,
                               callbacks=[es, mc]
  • 讀取

     # load saved h5 model
     best_model = tf.keras.models.load_model('best_model.h5')
     y_predict = best_model.predict_classes(x_test_best)