ref: b0e1a2eb95c684114431501cc1bb57fc3fb49842
dir: /scripts/rnn_train.py/
#!/usr/bin/python
from __future__ import print_function
from keras.models import Sequential
from keras.models import Model
from keras.layers import Input
from keras.layers import Dense
from keras.layers import LSTM
from keras.layers import GRU
from keras.layers import SimpleRNN
from keras.layers import Dropout
from keras import losses
import h5py
from keras import backend as K
import numpy as np
def binary_crossentrop2(y_true, y_pred):
return K.mean(2*K.abs(y_true-0.5) * K.binary_crossentropy(y_pred, y_true), axis=-1)
print('Build model...')
#model = Sequential()
#model.add(Dense(16, activation='tanh', input_shape=(None, 25)))
#model.add(GRU(12, dropout=0.0, recurrent_dropout=0.0, activation='tanh', recurrent_activation='sigmoid', return_sequences=True))
#model.add(Dense(2, activation='sigmoid'))
main_input = Input(shape=(None, 25), name='main_input')
x = Dense(16, activation='tanh')(main_input)
x = GRU(12, dropout=0.1, recurrent_dropout=0.1, activation='tanh', recurrent_activation='sigmoid', return_sequences=True)(x)
x = Dense(2, activation='sigmoid')(x)
model = Model(inputs=main_input, outputs=x)
batch_size = 64
print('Loading data...')
with h5py.File('features.h5', 'r') as hf:
all_data = hf['features'][:]
print('done.')
window_size = 1500
nb_sequences = len(all_data)/window_size
print(nb_sequences, ' sequences')
x_train = all_data[:nb_sequences*window_size, :-2]
x_train = np.reshape(x_train, (nb_sequences, window_size, 25))
y_train = np.copy(all_data[:nb_sequences*window_size, -2:])
y_train = np.reshape(y_train, (nb_sequences, window_size, 2))
all_data = 0;
x_train = x_train.astype('float32')
y_train = y_train.astype('float32')
print(len(x_train), 'train sequences. x shape =', x_train.shape, 'y shape = ', y_train.shape)
# try using different optimizers and different optimizer configs
model.compile(loss=binary_crossentrop2,
optimizer='adam',
metrics=['binary_accuracy'])
print('Train...')
model.fit(x_train, y_train,
batch_size=batch_size,
epochs=200,
validation_data=(x_train, y_train))
model.save("newweights.hdf5")