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117 lines
4.5 KiB
117 lines
4.5 KiB
#!/usr/bin/python
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from __future__ import print_function
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import keras
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from keras.models import Sequential
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from keras.models import Model
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from keras.layers import Input
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from keras.layers import Dense
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from keras.layers import LSTM
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from keras.layers import GRU
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from keras.layers import SimpleRNN
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from keras.layers import Dropout
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from keras.layers import concatenate
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from keras import losses
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from keras import regularizers
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from keras.constraints import min_max_norm
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import h5py
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from keras.constraints import Constraint
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from keras import backend as K
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import numpy as np
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#import tensorflow as tf
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#from keras.backend.tensorflow_backend import set_session
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#config = tf.ConfigProto()
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#config.gpu_options.per_process_gpu_memory_fraction = 0.42
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#set_session(tf.Session(config=config))
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def my_crossentropy(y_true, y_pred):
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return K.mean(2*K.abs(y_true-0.5) * K.binary_crossentropy(y_pred, y_true), axis=-1)
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def mymask(y_true):
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return K.minimum(y_true+1., 1.)
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def msse(y_true, y_pred):
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return K.mean(mymask(y_true) * K.square(K.sqrt(y_pred) - K.sqrt(y_true)), axis=-1)
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def mycost(y_true, y_pred):
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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)
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def my_accuracy(y_true, y_pred):
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return K.mean(2*K.abs(y_true-0.5) * K.equal(y_true, K.round(y_pred)), axis=-1)
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class WeightClip(Constraint):
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'''Clips the weights incident to each hidden unit to be inside a range
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'''
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def __init__(self, c=2):
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self.c = c
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def __call__(self, p):
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return K.clip(p, -self.c, self.c)
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def get_config(self):
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return {'name': self.__class__.__name__,
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'c': self.c}
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reg = 0.000001
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constraint = WeightClip(0.499)
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print('Build model...')
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main_input = Input(shape=(None, 42), name='main_input')
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tmp = Dense(24, activation='tanh', name='input_dense', kernel_constraint=constraint, bias_constraint=constraint)(main_input)
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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)
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vad_output = Dense(1, activation='sigmoid', name='vad_output', kernel_constraint=constraint, bias_constraint=constraint)(vad_gru)
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noise_input = keras.layers.concatenate([tmp, vad_gru, main_input])
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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)
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denoise_input = keras.layers.concatenate([vad_gru, noise_gru, main_input])
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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)
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denoise_output = Dense(22, activation='sigmoid', name='denoise_output', kernel_constraint=constraint, bias_constraint=constraint)(denoise_gru)
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model = Model(inputs=main_input, outputs=[denoise_output, vad_output])
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model.compile(loss=[mycost, my_crossentropy],
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metrics=[msse],
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optimizer='adam', loss_weights=[10, 0.5])
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batch_size = 32
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print('Loading data...')
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with h5py.File('training.h5', 'r') as hf:
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all_data = hf['data'][:]
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print('done.')
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window_size = 2000
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nb_sequences = len(all_data)//window_size
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print(nb_sequences, ' sequences')
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x_train = all_data[:nb_sequences*window_size, :42]
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x_train = np.reshape(x_train, (nb_sequences, window_size, 42))
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y_train = np.copy(all_data[:nb_sequences*window_size, 42:64])
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y_train = np.reshape(y_train, (nb_sequences, window_size, 22))
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noise_train = np.copy(all_data[:nb_sequences*window_size, 64:86])
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noise_train = np.reshape(noise_train, (nb_sequences, window_size, 22))
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vad_train = np.copy(all_data[:nb_sequences*window_size, 86:87])
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vad_train = np.reshape(vad_train, (nb_sequences, window_size, 1))
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all_data = 0;
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#x_train = x_train.astype('float32')
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#y_train = y_train.astype('float32')
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print(len(x_train), 'train sequences. x shape =', x_train.shape, 'y shape = ', y_train.shape)
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print('Train...')
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model.fit(x_train, [y_train, vad_train],
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batch_size=batch_size,
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epochs=120,
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validation_split=0.1)
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model.save("weights.hdf5")
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