kerasで動的にlearning rateを構築する

keras_learning_rate.md

kerasのbackendで設定する必要があって、いくつか過去のレポジトリを修正しなくては行けない可能性がある

from sklearn.cross_validation import KFold
import swap_noise
from keras import backend as K 

for i in range(100):
    NFOLDS=100
    SEED=777
    kf = KFold(len(df.values), n_folds=NFOLDS, shuffle=True, random_state=SEED)
    for i, (train_index, test_index) in enumerate(kf):
        noised = swap_noise.noise(df.values)
        dae.fit(noised[train_index], df.values[train_index],
                        epochs=1,
                        validation_data=(noised[test_index], df.values[test_index]),
                        batch_size=6000,
                        shuffle=True,)
        lr = 0.01*( (NFOLDS - i)/NFOLDS )
        K.set_value(dae.optimizer.lr, lr)
    dae.save_weights('vars/dae.h5')