Skip to content

Instantly share code, notes, and snippets.

@ryoen
Created January 30, 2018 04:20
Show Gist options
  • Star 0 You must be signed in to star a gist
  • Fork 0 You must be signed in to fork a gist
  • Save ryoen/34501fc62a6e61e34229494ef7ea6b5d to your computer and use it in GitHub Desktop.
Save ryoen/34501fc62a6e61e34229494ef7ea6b5d to your computer and use it in GitHub Desktop.
"Deep Learning with Python" ボストンの住宅価格予測のサンプル
from keras.datasets import boston_housing
(train_data, train_targets), (test_data, test_targets) = boston_housing.load_data()
# 前処理
# データを標準化(平均0、分散1に変換)する
mean = train_data.mean(axis=0)
train_data -= mean
std = train_data.std(axis=0)
train_data /= std
test_data -= mean
test_data /= std
print(train_data)
from keras import models
from keras import layers
# モデル定義
#KFCV用に同じモデルを複数回作成するため、関数にする
def build_model():
model = models.Sequential()
model.add(layers.Dense(64, activation='relu',
input_shape=(train_data.shape[1],)))
model.add(layers.Dense(64, activation='relu'))
model.add(layers.Dense(1))
model.compile(optimizer='rmsprop', loss='mse', metrics=['mae'])
return model
import numpy as np
k = 4
num_val_samples = len(train_data) // k
num_epochs = 100
all_scores = []
# K-fold cross validation (K-分割交差検証; KFCV)
for i in range(k):
print('processing fold #', i)
val_data = train_data[i * num_val_samples: (i+1) * num_val_samples]
val_targets = train_targets[i * num_val_samples: (i+1) * num_val_samples]
partial_train_data = np.concatenate(
[train_data[:i * num_val_samples],
train_data[(i+1) * num_val_samples:]],
axis=0)
partial_train_targets = np.concatenate(
[train_targets[:i * num_val_samples],
train_targets[(i+1) * num_val_samples:]],
axis=0)
model = build_model()
model.fit(partial_train_data, partial_train_targets,
epochs=num_epochs, batch_size=1, verbose=0)
val_mse, val_mae = model.evaluate(val_data, val_targets, verbose=0)
all_scores.append(val_mae)
num_epochs = 500
all_mae_histories = []
for i in range(k):
print('processing fold #', i)
val_data = train_data[i * num_val_samples: (i + 1) * num_val_samples]
val_targets = train_targets[i * num_val_samples: (i + 1) * num_val_samples]
partial_train_data = np.concatenate(
[train_data[:i*num_val_samples],
train_data[(i+1) * num_val_samples:]],
axis=0)
partial_train_targets = np.concatenate(
[train_targets[:i * num_val_samples],
train_targets[(i+1) * num_val_samples:]],
axis=0)
model = build_model()
history = model.fit(partial_train_data, partial_train_targets,
validation_data = (val_data, val_targets),
epochs=num_epochs, batch_size=1, verbose=1)
mae_history = history.history['val_mean_absolute_error']
all_mae_histories.append(mae_history)
average_mae_history = [np.mean([x[i] for x in all_mae_histories]) for i in range(num_epochs)]
Sign up for free to join this conversation on GitHub. Already have an account? Sign in to comment