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dl_models.py
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dl_models.py
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#============================================================
#
# Deep Learning BLW Filtering
# Deep Learning models
#
# author: Francisco Perdigon Romero
# email: [email protected]
# github id: fperdigon
#
#===========================================================
import keras
from keras.models import Sequential, Model
from keras.layers import Dense, Conv1D, Flatten, Dropout, BatchNormalization,\
concatenate, Activation, Input, Conv2DTranspose, Lambda, LSTM, Reshape, Embedding
import keras.backend as K
def Conv1DTranspose(input_tensor, filters, kernel_size, strides=2, activation='relu', padding='same'):
"""
https://stackoverflow.com/a/45788699
input_tensor: tensor, with the shape (batch_size, time_steps, dims)
filters: int, output dimension, i.e. the output tensor will have the shape of (batch_size, time_steps, filters)
kernel_size: int, size of the convolution kernel
strides: int, convolution step size
padding: 'same' | 'valid'
"""
x = Lambda(lambda x: K.expand_dims(x, axis=2))(input_tensor)
x = Conv2DTranspose(filters=filters,
kernel_size=(kernel_size, 1),
activation=activation,
strides=(strides, 1),
padding=padding)(x)
x = Lambda(lambda x: K.squeeze(x, axis=2))(x)
return x
##########################################################################
###### MODULES #######
def LFilter_module(x, layers):
LB0 = Conv1D(filters=int(layers / 4),
kernel_size=3,
activation='linear',
strides=1,
padding='same')(x)
LB1 = Conv1D(filters=int(layers / 4),
kernel_size=5,
activation='linear',
strides=1,
padding='same')(x)
LB2 = Conv1D(filters=int(layers / 4),
kernel_size=9,
activation='linear',
strides=1,
padding='same')(x)
LB3 = Conv1D(filters=int(layers / 4),
kernel_size=15,
activation='linear',
strides=1,
padding='same')(x)
x = concatenate([LB0, LB1, LB2, LB3])
return x
def NLFilter_module(x, layers):
NLB0 = Conv1D(filters=int(layers / 4),
kernel_size=3,
activation='relu',
strides=1,
padding='same')(x)
NLB1 = Conv1D(filters=int(layers / 4),
kernel_size=5,
activation='relu',
strides=1,
padding='same')(x)
NLB2 = Conv1D(filters=int(layers / 4),
kernel_size=9,
activation='relu',
strides=1,
padding='same')(x)
NLB3 = Conv1D(filters=int(layers / 4),
kernel_size=15,
activation='relu',
strides=1,
padding='same')(x)
x = concatenate([NLB0, NLB1, NLB2, NLB3])
return x
def LANLFilter_module(x, layers):
LB0 = Conv1D(filters=int(layers / 8),
kernel_size=3,
activation='linear',
strides=1,
padding='same')(x)
LB1 = Conv1D(filters=int(layers / 8),
kernel_size=5,
activation='linear',
strides=1,
padding='same')(x)
LB2 = Conv1D(filters=int(layers / 8),
kernel_size=9,
activation='linear',
strides=1,
padding='same')(x)
LB3 = Conv1D(filters=int(layers / 8),
kernel_size=15,
activation='linear',
strides=1,
padding='same')(x)
NLB0 = Conv1D(filters=int(layers / 8),
kernel_size=3,
activation='relu',
strides=1,
padding='same')(x)
NLB1 = Conv1D(filters=int(layers / 8),
kernel_size=5,
activation='relu',
strides=1,
padding='same')(x)
NLB2 = Conv1D(filters=int(layers / 8),
kernel_size=9,
activation='relu',
strides=1,
padding='same')(x)
NLB3 = Conv1D(filters=int(layers / 8),
kernel_size=15,
activation='relu',
strides=1,
padding='same')(x)
x = concatenate([LB0, LB1, LB2, LB3, NLB0, NLB1, NLB2, NLB3])
return x
def LANLFilter_module_dilated(x, layers):
LB1 = Conv1D(filters=int(layers / 6),
kernel_size=5,
activation='linear',
dilation_rate=3,
padding='same')(x)
LB2 = Conv1D(filters=int(layers / 6),
kernel_size=9,
activation='linear',
dilation_rate=3,
padding='same')(x)
LB3 = Conv1D(filters=int(layers / 6),
kernel_size=15,
dilation_rate=3,
activation='linear',
padding='same')(x)
NLB1 = Conv1D(filters=int(layers / 6),
kernel_size=5,
activation='relu',
dilation_rate=3,
padding='same')(x)
NLB2 = Conv1D(filters=int(layers / 6),
kernel_size=9,
activation='relu',
dilation_rate=3,
padding='same')(x)
NLB3 = Conv1D(filters=int(layers / 6),
kernel_size=15,
dilation_rate=3,
activation='relu',
padding='same')(x)
x = concatenate([LB1, LB2, LB3, NLB1, NLB2, NLB3])
# x = BatchNormalization()(x)
return x
###### MODELS #######
def deep_filter_vanilla_linear(signal_size=512): # signal_size=None to use any size input
model = Sequential()
model.add(Conv1D(filters=64,
kernel_size=9,
activation='linear',
input_shape=(signal_size, 1),
strides=1,
padding='same'))
model.add(Conv1D(filters=64,
kernel_size=9,
activation='linear',
strides=1,
padding='same'))
model.add(Conv1D(filters=32,
kernel_size=9,
activation='linear',
strides=1,
padding='same'))
model.add(Conv1D(filters=32,
kernel_size=9,
activation='linear',
strides=1,
padding='same'))
model.add(Conv1D(filters=16,
kernel_size=9,
activation='linear',
strides=1,
padding='same'))
model.add(Conv1D(filters=16,
kernel_size=9,
activation='linear',
strides=1,
padding='same'))
model.add(Conv1D(filters=1,
kernel_size=9,
activation='linear',
strides=1,
padding='same'))
return model
def deep_filter_vanilla_Nlinear(signal_size=512):
model = Sequential()
model.add(Conv1D(filters=64,
kernel_size=9,
activation='relu',
input_shape=(signal_size, 1),
strides=1,
padding='same'))
model.add(Conv1D(filters=64,
kernel_size=9,
activation='relu',
strides=1,
padding='same'))
model.add(Conv1D(filters=32,
kernel_size=9,
activation='relu',
strides=1,
padding='same'))
model.add(Conv1D(filters=32,
kernel_size=9,
activation='relu',
strides=1,
padding='same'))
model.add(Conv1D(filters=16,
kernel_size=9,
activation='relu',
strides=1,
padding='same'))
model.add(Conv1D(filters=16,
kernel_size=9,
activation='relu',
strides=1,
padding='same'))
model.add(Conv1D(filters=1,
kernel_size=9,
activation='linear',
strides=1,
padding='same'))
return model
def deep_filter_I_linear(signal_size=512):
input_shape = (signal_size, 1)
input = Input(shape=input_shape)
tensor = LFilter_module(input, 64)
tensor = LFilter_module(tensor, 64)
tensor = LFilter_module(tensor, 32)
tensor = LFilter_module(tensor, 32)
tensor = LFilter_module(tensor, 16)
tensor = LFilter_module(tensor, 16)
predictions = Conv1D(filters=1,
kernel_size=9,
activation='linear',
strides=1,
padding='same')(tensor)
model = Model(inputs=[input], outputs=predictions)
return model
def deep_filter_I_Nlinear(signal_size=512):
input_shape = (signal_size, 1)
input = Input(shape=input_shape)
tensor = NLFilter_module(input, 64)
tensor = NLFilter_module(tensor, 64)
tensor = NLFilter_module(tensor, 32)
tensor = NLFilter_module(tensor, 32)
tensor = NLFilter_module(tensor, 16)
tensor = NLFilter_module(tensor, 16)
predictions = Conv1D(filters=1,
kernel_size=9,
activation='linear',
strides=1,
padding='same')(tensor)
model = Model(inputs=[input], outputs=predictions)
return model
def deep_filter_I_LANL(signal_size=512):
# TODO: Make the doc
input_shape = (signal_size, 1)
input = Input(shape=input_shape)
tensor = LANLFilter_module(input, 64)
tensor = BatchNormalization()(tensor)
tensor = LANLFilter_module(tensor, 64)
tensor = BatchNormalization()(tensor)
tensor = LANLFilter_module(tensor, 32)
tensor = BatchNormalization()(tensor)
tensor = LANLFilter_module(tensor, 32)
tensor = BatchNormalization()(tensor)
tensor = LANLFilter_module(tensor, 16)
tensor = BatchNormalization()(tensor)
tensor = LANLFilter_module(tensor, 16)
tensor = BatchNormalization()(tensor)
predictions = Conv1D(filters=1,
kernel_size=9,
activation='linear',
strides=1,
padding='same')(tensor)
model = Model(inputs=[input], outputs=predictions)
return model
def deep_filter_model_I_LANL_dilated(signal_size=512):
# TODO: Make the doc
input_shape = (signal_size, 1)
input = Input(shape=input_shape)
tensor = LANLFilter_module(input, 64)
tensor = Dropout(0.4)(tensor)
tensor = BatchNormalization()(tensor)
tensor = LANLFilter_module_dilated(tensor, 64)
tensor = Dropout(0.4)(tensor)
tensor = BatchNormalization()(tensor)
tensor = LANLFilter_module(tensor, 32)
tensor = Dropout(0.4)(tensor)
tensor = BatchNormalization()(tensor)
tensor = LANLFilter_module_dilated(tensor, 32)
tensor = Dropout(0.4)(tensor)
tensor = BatchNormalization()(tensor)
tensor = LANLFilter_module(tensor, 16)
tensor = Dropout(0.4)(tensor)
tensor = BatchNormalization()(tensor)
tensor = LANLFilter_module_dilated(tensor, 16)
tensor = Dropout(0.4)(tensor)
tensor = BatchNormalization()(tensor)
predictions = Conv1D(filters=1,
kernel_size=9,
activation='linear',
strides=1,
padding='same')(tensor)
model = Model(inputs=[input], outputs=predictions)
return model
def FCN_DAE(signal_size=512):
# Implementation of FCN_DAE approach presented in
# Chiang, H. T., Hsieh, Y. Y., Fu, S. W., Hung, K. H., Tsao, Y., & Chien, S. Y. (2019).
# Noise reduction in ECG signals using fully convolutional denoising autoencoders.
# IEEE Access, 7, 60806-60813.
input_shape = (signal_size, 1)
input = Input(shape=input_shape)
x = Conv1D(filters=40,
input_shape=(512, 1),
kernel_size=16,
activation='elu',
strides=2,
padding='same')(input)
x = BatchNormalization()(x)
x = Conv1D(filters=20,
kernel_size=16,
activation='elu',
strides=2,
padding='same')(x)
x = BatchNormalization()(x)
x = Conv1D(filters=20,
kernel_size=16,
activation='elu',
strides=2,
padding='same')(x)
x = BatchNormalization()(x)
x = Conv1D(filters=20,
kernel_size=16,
activation='elu',
strides=2,
padding='same')(x)
x = BatchNormalization()(x)
x = Conv1D(filters=40,
kernel_size=16,
activation='elu',
strides=2,
padding='same')(x)
x = BatchNormalization()(x)
x = Conv1D(filters=1,
kernel_size=16,
activation='elu',
strides=1,
padding='same')(x)
x = BatchNormalization()(x)
# Keras has no 1D Traspose Convolution, instead we use Conv2DTranspose function
# in a souch way taht is mathematically equivalent
x = Conv1DTranspose(input_tensor=x,
filters=1,
kernel_size=16,
activation='elu',
strides=1,
padding='same')
x = BatchNormalization()(x)
x = Conv1DTranspose(input_tensor=x,
filters=40,
kernel_size=16,
activation='elu',
strides=2,
padding='same')
x = BatchNormalization()(x)
x = Conv1DTranspose(input_tensor=x,
filters=20,
kernel_size=16,
activation='elu',
strides=2,
padding='same')
x = BatchNormalization()(x)
x = Conv1DTranspose(input_tensor=x,
filters=20,
kernel_size=16,
activation='elu',
strides=2,
padding='same')
x = BatchNormalization()(x)
x = Conv1DTranspose(input_tensor=x,
filters=20,
kernel_size=16,
activation='elu',
strides=2,
padding='same')
x = BatchNormalization()(x)
x = Conv1DTranspose(input_tensor=x,
filters=40,
kernel_size=16,
activation='elu',
strides=2,
padding='same')
x = BatchNormalization()(x)
predictions = Conv1DTranspose(input_tensor=x,
filters=1,
kernel_size=16,
activation='linear',
strides=1,
padding='same')
model = Model(inputs=[input], outputs=predictions)
return model
def DRRN_denoising(signal_size=512):
# Implementation of DRNN approach presented in
# Antczak, K. (2018). Deep recurrent neural networks for ECG signal denoising.
# arXiv preprint arXiv:1807.11551.
model = Sequential()
model.add(LSTM(64, input_shape=(signal_size, 1), return_sequences=True))
model.add(Dense(64, activation='relu'))
model.add(Dense(64, activation='relu'))
model.add(Dense(1, activation='linear'))
return model