ref: 59b1a521f4909f959e6968c14dc7e618d0df461b
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")