Unverified Commit cf473ce2 authored by Jean-Marc Valin's avatar Jean-Marc Valin
Browse files

Keras training code

parent c6f563b3
#!/usr/bin/python
from __future__ import print_function
import numpy as np
import h5py
import sys
data = np.fromfile(sys.argv[1], dtype='float32');
data = np.reshape(data, (int(sys.argv[2]), int(sys.argv[3])));
h5f = h5py.File(sys.argv[4], 'w');
h5f.create_dataset('data', data=data)
h5f.close()
#!/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
import h5py
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 msse(y_true, y_pred):
return K.mean(K.square(K.sqrt(y_pred) - K.sqrt(y_true)), axis=-1)
def mycost(y_true, y_pred):
return K.mean(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)
reg = 0.0001
print('Build model...')
main_input = Input(shape=(None, 42), name='main_input')
tmp = Dense(12, activation='tanh', name='input_dense')(main_input)
vad_gru = GRU(12, activation='tanh', recurrent_activation='sigmoid', return_sequences=True, name='vad_gru', kernel_regularizer=regularizers.l2(reg), recurrent_regularizer=regularizers.l2(reg))(tmp)
vad_output = Dense(1, activation='sigmoid', name='vad_output')(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))(noise_input)
denoise_input = keras.layers.concatenate([vad_gru, noise_gru, main_input])
denoise_gru = GRU(128, activation='tanh', recurrent_activation='sigmoid', return_sequences=True, name='denoise_gru', kernel_regularizer=regularizers.l2(reg), recurrent_regularizer=regularizers.l2(reg))(denoise_input)
denoise_output = Dense(22, activation='sigmoid', name='denoise_output')(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 = 256
print('Loading data...')
with h5py.File('denoise_data4.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=300,
validation_split=0.1)
model.save("newweights3c.hdf5")
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