Jean-Marc Valin | af93fbd | 2017-07-12 16:55:28 -0400 | [diff] [blame] | 1 | #!/usr/bin/python |
| 2 | |
| 3 | from __future__ import print_function |
| 4 | |
| 5 | from keras.models import Sequential |
| 6 | from keras.models import Model |
| 7 | from keras.layers import Input |
| 8 | from keras.layers import Dense |
| 9 | from keras.layers import LSTM |
| 10 | from keras.layers import GRU |
| 11 | from keras.layers import SimpleRNN |
| 12 | from keras.layers import Dropout |
| 13 | from keras import losses |
| 14 | import h5py |
| 15 | |
| 16 | from keras import backend as K |
| 17 | import numpy as np |
| 18 | |
| 19 | def binary_crossentrop2(y_true, y_pred): |
| 20 | return K.mean(2*K.abs(y_true-0.5) * K.binary_crossentropy(y_pred, y_true), axis=-1) |
| 21 | |
| 22 | print('Build model...') |
| 23 | #model = Sequential() |
| 24 | #model.add(Dense(16, activation='tanh', input_shape=(None, 25))) |
| 25 | #model.add(GRU(12, dropout=0.0, recurrent_dropout=0.0, activation='tanh', recurrent_activation='sigmoid', return_sequences=True)) |
| 26 | #model.add(Dense(2, activation='sigmoid')) |
| 27 | |
| 28 | main_input = Input(shape=(None, 25), name='main_input') |
| 29 | x = Dense(16, activation='tanh')(main_input) |
| 30 | x = GRU(12, dropout=0.1, recurrent_dropout=0.1, activation='tanh', recurrent_activation='sigmoid', return_sequences=True)(x) |
| 31 | x = Dense(2, activation='sigmoid')(x) |
| 32 | model = Model(inputs=main_input, outputs=x) |
| 33 | |
| 34 | batch_size = 64 |
| 35 | |
| 36 | print('Loading data...') |
| 37 | with h5py.File('features.h5', 'r') as hf: |
| 38 | all_data = hf['features'][:] |
| 39 | print('done.') |
| 40 | |
| 41 | window_size = 1500 |
| 42 | |
| 43 | nb_sequences = len(all_data)/window_size |
| 44 | print(nb_sequences, ' sequences') |
| 45 | x_train = all_data[:nb_sequences*window_size, :-2] |
| 46 | x_train = np.reshape(x_train, (nb_sequences, window_size, 25)) |
| 47 | |
| 48 | y_train = np.copy(all_data[:nb_sequences*window_size, -2:]) |
| 49 | y_train = np.reshape(y_train, (nb_sequences, window_size, 2)) |
| 50 | |
| 51 | all_data = 0; |
| 52 | x_train = x_train.astype('float32') |
| 53 | y_train = y_train.astype('float32') |
| 54 | |
| 55 | print(len(x_train), 'train sequences. x shape =', x_train.shape, 'y shape = ', y_train.shape) |
| 56 | |
| 57 | # try using different optimizers and different optimizer configs |
| 58 | model.compile(loss=binary_crossentrop2, |
| 59 | optimizer='adam', |
| 60 | metrics=['binary_accuracy']) |
| 61 | |
| 62 | print('Train...') |
| 63 | model.fit(x_train, y_train, |
| 64 | batch_size=batch_size, |
| 65 | epochs=200, |
| 66 | validation_data=(x_train, y_train)) |
| 67 | model.save("newweights.hdf5") |