diff options
Diffstat (limited to 'training/rnn_train.py')
| -rwxr-xr-x | training/rnn_train.py | 33 |
1 files changed, 18 insertions, 15 deletions
diff --git a/training/rnn_train.py b/training/rnn_train.py index 3917caf..e1b6935 100755 --- a/training/rnn_train.py +++ b/training/rnn_train.py @@ -19,37 +19,40 @@ 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)) +#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(K.square(K.sqrt(y_pred) - K.sqrt(y_true)), axis=-1) + 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(K.square(K.sqrt(y_pred) - K.sqrt(y_true)) + 0.01*K.binary_crossentropy(y_pred, y_true), axis=-1) + return K.mean(mymask(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) -reg = 0.0001 +reg = 0.000001 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) +tmp = Dense(24, activation='tanh', name='input_dense')(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))(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_gru = GRU(96, 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) @@ -60,10 +63,10 @@ model.compile(loss=[mycost, my_crossentropy], optimizer='adam', loss_weights=[10, 0.5]) -batch_size = 256 +batch_size = 32 print('Loading data...') -with h5py.File('denoise_data4.h5', 'r') as hf: +with h5py.File('denoise_data6.h5', 'r') as hf: all_data = hf['data'][:] print('done.') @@ -92,6 +95,6 @@ print(len(x_train), 'train sequences. x shape =', x_train.shape, 'y shape = ', y print('Train...') model.fit(x_train, [y_train, vad_train], batch_size=batch_size, - epochs=300, + epochs=60, validation_split=0.1) -model.save("newweights3c.hdf5") +model.save("newweights6a2a.hdf5") |