Guangning Yu's Blog

Regression using Keras

2017-07-18 11:24:12  |  DeepLearning Keras
  1. #!/usr/bin/env python
  2. import urllib2
  3. import numpy as np
  4. from keras.models import Sequential
  5. from keras.layers import Dense
  6. from keras.wrappers.scikit_learn import KerasRegressor
  7. from sklearn.model_selection import cross_val_score
  8. from sklearn.model_selection import KFold
  9. from sklearn.preprocessing import StandardScaler
  10. from sklearn.pipeline import Pipeline
  11. def load_data():
  12. X = []
  13. Y = []
  14. data_url = 'https://archive.ics.uci.edu/ml/machine-learning-databases/housing/housing.data'
  15. for line in urllib2.urlopen(data_url).readlines():
  16. line = map(float, line.split())
  17. X.append(line[0:13])
  18. Y.append(line[13])
  19. return X, Y
  20. def basic_model():
  21. # create model
  22. model = Sequential()
  23. model.add(Dense(13, input_dim=13, kernel_initializer='normal', activation='relu'))
  24. model.add(Dense(1, kernel_initializer='normal'))
  25. # compile model
  26. model.compile(loss='mean_squared_error', optimizer='adam')
  27. return model
  28. d