135 lines
4.8 KiB
Python
135 lines
4.8 KiB
Python
from __future__ import absolute_import
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from __future__ import print_function
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import numpy as np
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from speech_data import read_siamese_tfrecords_generator
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from keras.models import Model,load_model,model_from_yaml
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from keras.layers import Input,Concatenate,Lambda, BatchNormalization, Dropout
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from keras.layers import Dense, LSTM, Bidirectional, GRU
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from keras.losses import categorical_crossentropy
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from keras.utils import to_categorical
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from keras.optimizers import RMSprop
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from keras.callbacks import TensorBoard, ModelCheckpoint
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from keras import backend as K
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from keras.utils import plot_model
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from speech_tools import create_dir,step_count
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def create_base_rnn_network(input_dim):
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'''Base network to be shared (eq. to feature extraction).
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'''
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inp = Input(shape=input_dim)
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# ls0 = LSTM(512, return_sequences=True)(inp)
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ls1 = LSTM(128, return_sequences=True)(inp)
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bn_ls1 = BatchNormalization(momentum=0.98)(ls1)
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ls2 = LSTM(64, return_sequences=True)(bn_ls1)
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bn_ls2 = BatchNormalization(momentum=0.98)(ls2)
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# ls3 = LSTM(32, return_sequences=True)(ls2)
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ls4 = LSTM(32)(bn_ls2)
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# d1 = Dense(128, activation='relu')(ls4)
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#d2 = Dense(64, activation='relu')(ls2)
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return Model(inp, ls4)
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def compute_accuracy(y_true, y_pred):
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'''Compute classification accuracy with a fixed threshold on distances.
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'''
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pred = y_pred.ravel() > 0.5
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return np.mean(pred == y_true)
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def accuracy(y_true, y_pred):
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'''Compute classification accuracy with a fixed threshold on distances.
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'''
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return K.mean(K.equal(y_true, K.cast(y_pred > 0.5, y_true.dtype)))
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def dense_classifier(processed):
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conc_proc = Concatenate()(processed)
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d1 = Dense(64, activation='relu')(conc_proc)
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# dr1 = Dropout(0.1)(d1)
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bn_d1 = BatchNormalization(momentum=0.98)(d1)
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# d2 = Dense(128, activation='relu')(d1)
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d3 = Dense(8, activation='relu')(bn_d1)
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bn_d3 = BatchNormalization(momentum=0.98)(d3)
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# dr2 = Dropout(0.1)(d2)
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return Dense(2, activation='softmax')(bn_d3)
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def siamese_model(input_dim):
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base_network = create_base_rnn_network(input_dim)
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input_a = Input(shape=input_dim)
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input_b = Input(shape=input_dim)
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processed_a = base_network(input_a)
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processed_b = base_network(input_b)
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final_output = dense_classifier([processed_a,processed_b])
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model = Model([input_a, input_b], final_output)
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return model,base_network
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def write_model_arch(mod,mod_file):
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model_f = open(mod_file,'w')
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model_f.write(mod.to_yaml())
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model_f.close()
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def load_model_arch(mod_file):
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model_f = open(mod_file,'r')
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mod = model_from_yaml(model_f.read())
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model_f.close()
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return mod
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def train_siamese(audio_group = 'audio',resume_weights='',initial_epoch=0):
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batch_size = 128
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model_dir = './models/'+audio_group
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create_dir(model_dir)
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log_dir = './logs/'+audio_group
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create_dir(log_dir)
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tr_gen_fn,te_pairs,te_y,copy_read_consts = read_siamese_tfrecords_generator(audio_group,batch_size=batch_size,test_size=batch_size)
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n_step,n_features,n_records = copy_read_consts(model_dir)
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tr_gen = tr_gen_fn()
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input_dim = (n_step, n_features)
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model,base_model = siamese_model(input_dim)
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plot_model(model,show_shapes=True, to_file=model_dir+'/model.png')
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plot_model(base_model,show_shapes=True, to_file=model_dir+'/base_model.png')
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tb_cb = TensorBoard(
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log_dir=log_dir,
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histogram_freq=1,
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batch_size=32,
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write_graph=True,
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write_grads=True,
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write_images=True,
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embeddings_freq=0,
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embeddings_layer_names=None,
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embeddings_metadata=None)
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cp_file_fmt = model_dir+'/siamese_speech_model-{epoch:02d}-epoch-{val_loss:0.2f}\
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-acc.h5'
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cp_cb = ModelCheckpoint(
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cp_file_fmt,
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monitor='val_loss',
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verbose=0,
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save_best_only=True,
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save_weights_only=True,
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mode='auto',
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period=1)
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# train
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rms = RMSprop()
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model.compile(loss=categorical_crossentropy, optimizer=rms, metrics=[accuracy])
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write_model_arch(model,model_dir+'/siamese_speech_model_arch.yaml')
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epoch_n_steps = step_count(n_records,batch_size)
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if resume_weights != '':
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model.load_weights(resume_weights)
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model.fit_generator(tr_gen
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, epochs=10000
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, steps_per_epoch=epoch_n_steps
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, validation_data=([te_pairs[:, 0], te_pairs[:, 1]], te_y)
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, max_queue_size=8
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, callbacks=[tb_cb, cp_cb],initial_epoch=initial_epoch)
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model.save(model_dir+'/siamese_speech_model-final.h5')
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# y_pred = model.predict([te_pairs[:, 0], te_pairs[:, 1]])
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# te_acc = compute_accuracy(te_y, y_pred)
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# print('* Accuracy on test set: %0.2f%%' % (100 * te_acc))
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if __name__ == '__main__':
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train_siamese('test_5_words')
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