| #!/usr/bin/python |
| |
| from __future__ import print_function |
| |
| import keras |
| 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.layers import concatenate |
| from keras import losses |
| from keras import regularizers |
| from keras.constraints import min_max_norm |
| import h5py |
| |
| from keras.constraints import Constraint |
| from keras import backend as K |
| import numpy as np |
| |
| #import tensorflow as tf |
| #from keras.backend.tensorflow_backend import set_session |
| #config = tf.ConfigProto() |
| #config.gpu_options.per_process_gpu_memory_fraction = 0.42 |
| #set_session(tf.Session(config=config)) |
| |
| |
| def my_crossentropy(y_true, y_pred): |
| return K.mean(2*K.abs(y_true-0.5) * K.binary_crossentropy(y_pred, y_true), axis=-1) |
| |
| def mymask(y_true): |
| return K.minimum(y_true+1., 1.) |
| |
| def msse(y_true, y_pred): |
| return K.mean(mymask(y_true) * K.square(K.sqrt(y_pred) - K.sqrt(y_true)), axis=-1) |
| |
| def mycost(y_true, y_pred): |
| return K.mean(mymask(y_true) * (10*K.square(K.square(K.sqrt(y_pred) - K.sqrt(y_true))) + K.square(K.sqrt(y_pred) - K.sqrt(y_true)) + 0.01*K.binary_crossentropy(y_pred, y_true)), axis=-1) |
| |
| def my_accuracy(y_true, y_pred): |
| return K.mean(2*K.abs(y_true-0.5) * K.equal(y_true, K.round(y_pred)), axis=-1) |
| |
| class WeightClip(Constraint): |
| '''Clips the weights incident to each hidden unit to be inside a range |
| ''' |
| def __init__(self, c=2): |
| self.c = c |
| |
| def __call__(self, p): |
| return K.clip(p, -self.c, self.c) |
| |
| def get_config(self): |
| return {'name': self.__class__.__name__, |
| 'c': self.c} |
| |
| reg = 0.000001 |
| constraint = WeightClip(0.499) |
| |
| print('Build model...') |
| main_input = Input(shape=(None, 42), name='main_input') |
| tmp = Dense(24, activation='tanh', name='input_dense', kernel_constraint=constraint, bias_constraint=constraint)(main_input) |
| vad_gru = GRU(24, activation='tanh', recurrent_activation='sigmoid', return_sequences=True, name='vad_gru', kernel_regularizer=regularizers.l2(reg), recurrent_regularizer=regularizers.l2(reg), kernel_constraint=constraint, recurrent_constraint=constraint, bias_constraint=constraint)(tmp) |
| vad_output = Dense(1, activation='sigmoid', name='vad_output', kernel_constraint=constraint, bias_constraint=constraint)(vad_gru) |
| noise_input = keras.layers.concatenate([tmp, vad_gru, main_input]) |
| noise_gru = GRU(48, activation='relu', recurrent_activation='sigmoid', return_sequences=True, name='noise_gru', kernel_regularizer=regularizers.l2(reg), recurrent_regularizer=regularizers.l2(reg), kernel_constraint=constraint, recurrent_constraint=constraint, bias_constraint=constraint)(noise_input) |
| denoise_input = keras.layers.concatenate([vad_gru, noise_gru, main_input]) |
| |
| denoise_gru = GRU(96, activation='tanh', recurrent_activation='sigmoid', return_sequences=True, name='denoise_gru', kernel_regularizer=regularizers.l2(reg), recurrent_regularizer=regularizers.l2(reg), kernel_constraint=constraint, recurrent_constraint=constraint, bias_constraint=constraint)(denoise_input) |
| |
| denoise_output = Dense(22, activation='sigmoid', name='denoise_output', kernel_constraint=constraint, bias_constraint=constraint)(denoise_gru) |
| |
| model = Model(inputs=main_input, outputs=[denoise_output, vad_output]) |
| |
| model.compile(loss=[mycost, my_crossentropy], |
| metrics=[msse], |
| optimizer='adam', loss_weights=[10, 0.5]) |
| |
| |
| batch_size = 32 |
| |
| print('Loading data...') |
| with h5py.File('training.h5', 'r') as hf: |
| all_data = hf['data'][:] |
| print('done.') |
| |
| window_size = 2000 |
| |
| nb_sequences = len(all_data)//window_size |
| print(nb_sequences, ' sequences') |
| x_train = all_data[:nb_sequences*window_size, :42] |
| x_train = np.reshape(x_train, (nb_sequences, window_size, 42)) |
| |
| y_train = np.copy(all_data[:nb_sequences*window_size, 42:64]) |
| y_train = np.reshape(y_train, (nb_sequences, window_size, 22)) |
| |
| noise_train = np.copy(all_data[:nb_sequences*window_size, 64:86]) |
| noise_train = np.reshape(noise_train, (nb_sequences, window_size, 22)) |
| |
| vad_train = np.copy(all_data[:nb_sequences*window_size, 86:87]) |
| vad_train = np.reshape(vad_train, (nb_sequences, window_size, 1)) |
| |
| 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) |
| |
| print('Train...') |
| model.fit(x_train, [y_train, vad_train], |
| batch_size=batch_size, |
| epochs=120, |
| validation_split=0.1) |
| model.save("weights.hdf5") |