from tcn import TCN
from tcn.tcn import process_dilations
from keras.models import Input, Model
from keras.layers import Dense, Activation
from smd import config
def create_tcn(list_n_filters=[8],
kernel_size=4,
dilations=[1, 2],
nb_stacks=1,
activation='norm_relu',
n_layers=1,
dropout_rate=0.05,
use_skip_connections=True,
bidirectional=True):
if bidirectional:
padding = 'same'
else:
padding = 'causal'
dilations = process_dilations(dilations)
input_layer = Input(shape=(None, config.N_MELS))
for i in range(n_layers):
if i == 0:
x = TCN(list_n_filters[i], kernel_size, nb_stacks, dilations, activation,
padding, use_skip_connections, dropout_rate, return_sequences=True)(input_layer)
else:
x = TCN(list_n_filters[i], kernel_size, nb_stacks, dilations, activation,
padding, use_skip_connections, dropout_rate, return_sequences=True, name="tcn" + str(i))(x)
x = Dense(config.CLASSES)(x)
x = Activation('sigmoid')(x)
output_layer = x
return Model(input_layer, output_layer)
