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model.py
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model.py
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import tensorflow
from tensorflow import keras
from tensorflow.keras import Model, layers
from tensorflow.keras.layers import Dense, Dropout, Conv2D
from tensorflow.keras.layers import LSTM, TimeDistributed, Bidirectional
from tensorflow.keras.constraints import max_norm
class CNN_BLSTM(object):
def __init__(self):
print('CNN_BLSTM init')
def build(self):
_input = keras.Input(shape=(None, 257))
re_input = layers.Reshape((-1, 257, 1), input_shape=(-1, 257))(_input)
# CNN
conv1 = (Conv2D(16, (3,3), strides=(1, 1), activation='relu', padding='same'))(re_input)
conv1 = (Conv2D(16, (3,3), strides=(1, 1), activation='relu', padding='same'))(conv1)
conv1 = (Conv2D(16, (3,3), strides=(1, 3), activation='relu', padding='same'))(conv1)
conv2 = (Conv2D(32, (3,3), strides=(1, 1), activation='relu', padding='same'))(conv1)
conv2 = (Conv2D(32, (3,3), strides=(1, 1), activation='relu', padding='same'))(conv2)
conv2 = (Conv2D(32, (3,3), strides=(1, 3), activation='relu', padding='same'))(conv2)
conv3 = (Conv2D(64, (3,3), strides=(1, 1), activation='relu', padding='same'))(conv2)
conv3 = (Conv2D(64, (3,3), strides=(1, 1), activation='relu', padding='same'))(conv3)
conv3 = (Conv2D(64, (3,3), strides=(1, 3), activation='relu', padding='same'))(conv3)
conv4 = (Conv2D(128, (3,3), strides=(1, 1), activation='relu', padding='same'))(conv3)
conv4 = (Conv2D(128, (3,3), strides=(1, 1), activation='relu', padding='same'))(conv4)
conv4 = (Conv2D(128, (3,3), strides=(1, 3), activation='relu', padding='same'))(conv4)
re_shape = layers.Reshape((-1, 4*128), input_shape=(-1, 4, 128))(conv4)
# BLSTM
blstm1 = Bidirectional(
LSTM(128, return_sequences=True, dropout=0.3,
recurrent_dropout=0.3, recurrent_constraint=max_norm(0.00001)),
merge_mode='concat')(re_shape)
# DNN
flatten = TimeDistributed(layers.Flatten())(blstm1)
dense1=TimeDistributed(Dense(128, activation='relu'))(flatten)
dense1=Dropout(0.3)(dense1)
frame_score=TimeDistributed(Dense(1), name='frame')(dense1)
average_score=layers.GlobalAveragePooling1D(name='avg')(frame_score)
model = Model(outputs=[average_score, frame_score], inputs=_input)
return model
class CNN(object):
def __init__(self):
print('CNN init')
def build(self):
_input = keras.Input(shape=(None, 257))
re_input = layers.Reshape((-1, 257, 1), input_shape=(-1, 257))(_input)
# CNN
conv1 = (Conv2D(16, (3,3), strides=(1, 1), activation='relu', padding='same'))(re_input)
conv1 = (Conv2D(16, (3,3), strides=(1, 1), activation='relu', padding='same'))(conv1)
conv1 = (Conv2D(16, (3,3), strides=(1, 3), activation='relu', padding='same'))(conv1)
conv2 = (Conv2D(32, (3,3), strides=(1, 1), activation='relu', padding='same'))(conv1)
conv2 = (Conv2D(32, (3,3), strides=(1, 1), activation='relu', padding='same'))(conv2)
conv2 = (Conv2D(32, (3,3), strides=(1, 3), activation='relu', padding='same'))(conv2)
conv3 = (Conv2D(64, (3,3), strides=(1, 1), activation='relu', padding='same'))(conv2)
conv3 = (Conv2D(64, (3,3), strides=(1, 1), activation='relu', padding='same'))(conv3)
conv3 = (Conv2D(64, (3,3), strides=(1, 3), activation='relu', padding='same'))(conv3)
conv4 = (Conv2D(128, (3,3), strides=(1, 1), activation='relu', padding='same'))(conv3)
conv4 = (Conv2D(128, (3,3), strides=(1, 1), activation='relu', padding='same'))(conv4)
conv4 = (Conv2D(128, (3,3), strides=(1, 3), activation='relu', padding='same'))(conv4)
# DNN
flatten = TimeDistributed(layers.Flatten())(conv4)
dense1=TimeDistributed(Dense(64, activation='relu'))(flatten)
dense1=Dropout(0.3)(dense1)
frame_score=TimeDistributed(Dense(1), name='frame')(dense1)
average_score=layers.GlobalAveragePooling1D(name='avg')(frame_score)
model = Model(outputs=[average_score, frame_score], inputs=_input)
return model
class BLSTM(object):
def __init__(self):
print('BLSTM init')
def build(self):
_input = keras.Input(shape=(None, 257))
# BLSTM
blstm1 = Bidirectional(
LSTM(128, return_sequences=True, dropout=0.3,
recurrent_dropout=0.3, recurrent_constraint=max_norm(0.00001)),
merge_mode='concat')(_input)
# DNN
flatten = TimeDistributed(layers.Flatten())(blstm1)
dense1=TimeDistributed(Dense(64, activation='relu'))(flatten)
dense1=Dropout(0.3)(dense1)
frame_score=TimeDistributed(Dense(1), name='frame')(dense1)
average_score=layers.GlobalAveragePooling1D(name='avg')(frame_score)
model = Model(outputs=[average_score, frame_score], inputs=_input)
return model