| #!/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") |