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model_config.py
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#Model architecture configuration file
import tensorflow as tf
from keras.models import Sequential,Model
from keras.layers import Conv1D,MaxPooling1D,Dense,Flatten,BatchNormalization,Dropout, Activation
from gammatone_init import GammatoneInit
class FeatureBlock1(Model):
def __init__(self):
super(FeatureBlock1,self).__init__()
self.l1 = Conv1D(filters=16,kernel_size=64,strides=2)
self.l2 = Activation(activation="relu")
self.l3 = BatchNormalization()
self.l4 = MaxPooling1D(pool_size=8,strides=8)
self.l5 = Activation(activation="relu")
self.l6 = Conv1D(filters=32,kernel_size=32,strides=2)
self.l7 = Activation(activation="relu")
self.l8 = BatchNormalization()
self.l9 = MaxPooling1D(pool_size=8,strides=8)
self.l10 = Activation(activation="relu")
self.l11 = Conv1D(filters=64,kernel_size=16,strides=2)
self.l12 = Activation(activation="relu")
self.l13 = BatchNormalization()
self.l14 = Conv1D(filters=128,kernel_size=8,strides=2)
self.l15 = Activation(activation="relu")
self.l16 = BatchNormalization()
self.l17 = Conv1D(filters=256,kernel_size=4,strides=2)
self.l18 = Activation(activation="relu")
self.l19 = BatchNormalization()
self.l20 = MaxPooling1D(pool_size=4,strides=4)
self.l21 = Activation(activation="relu")
def call(self,inputs):
x = self.l1(inputs)
x = self.l2(x)
x = self.l3(x)
x = self.l4(x)
x = self.l5(x)
x = self.l6(x)
x = self.l7(x)
x = self.l8(x)
x = self.l9(x)
x = self.l10(x)
x = self.l11(x)
x = self.l12(x)
x = self.l13(x)
x = self.l14(x)
x = self.l15(x)
x = self.l16(x)
x = self.l17(x)
x = self.l18(x)
x = self.l19(x)
x = self.l20(x)
x = self.l21(x)
return x
class FeatureBlock2(Model):
def __init__(self):
super(FeatureBlock2,self).__init__()
self.l1 = Conv1D(filters=16,kernel_size=64,strides=2)
self.l2 = Activation(activation="relu")
self.l3 = BatchNormalization()
self.l4 = MaxPooling1D(pool_size=8,strides=8)
self.l5 = Activation(activation="relu")
self.l6 = Conv1D(filters=32,kernel_size=32,strides=2)
self.l7 = Activation(activation="relu")
self.l8 = BatchNormalization()
self.l9 = MaxPooling1D(pool_size=8,strides=8)
self.l10 = Activation(activation="relu")
self.l11 = Conv1D(filters=64,kernel_size=16,strides=2)
self.l12 = Activation(activation="relu")
self.l13 = BatchNormalization()
self.l14 = Conv1D(filters=128,kernel_size=8,strides=2)
self.l15 = Activation(activation="relu")
self.l16 = BatchNormalization()
self.l17 = Conv1D(filters=256,kernel_size=4,strides=2)
self.l18 = Activation(activation="relu")
self.l19 = BatchNormalization()
self.l20 = MaxPooling1D(pool_size=4,strides=4)
self.l21 = Activation(activation="relu")
def call(self,inputs):
x = self.l1(inputs)
x = self.l2(x)
x = self.l3(x)
x = self.l4(x)
x = self.l5(x)
x = self.l6(x)
x = self.l7(x)
x = self.l8(x)
x = self.l9(x)
x = self.l10(x)
x = self.l11(x)
x = self.l12(x)
x = self.l13(x)
x = self.l14(x)
x = self.l15(x)
x = self.l16(x)
x = self.l17(x)
x = self.l18(x)
x = self.l19(x)
x = self.l20(x)
x = self.l21(x)
return x
class FeatureBlock3(Model):
def __init__(self):
super(FeatureBlock3,self).__init__()
self.l1 = Conv1D(filters=16,kernel_size=64,strides=2)
self.l2 = Activation(activation="relu")
self.l3 = BatchNormalization()
self.l4 = MaxPooling1D(pool_size=8,strides=8)
self.l5 = Activation(activation="relu")
self.l6 = Conv1D(filters=32,kernel_size=32,strides=2)
self.l7 = Activation(activation="relu")
self.l8 = BatchNormalization()
self.l9 = MaxPooling1D(pool_size=8,strides=8)
self.l10 = Activation(activation="relu")
self.l11 = Conv1D(filters=64,kernel_size=16,strides=2)
self.l12 = Activation(activation="relu")
self.l13 = BatchNormalization()
self.l14 = Conv1D(filters=128,kernel_size=8,strides=2)
self.l15 = Activation(activation="relu")
self.l16 = BatchNormalization()
def call(self,inputs):
x = self.l1(inputs)
x = self.l2(x)
x = self.l3(x)
x = self.l4(x)
x = self.l5(x)
x = self.l6(x)
x = self.l7(x)
x = self.l8(x)
x = self.l9(x)
x = self.l10(x)
x = self.l11(x)
x = self.l12(x)
x = self.l13(x)
x = self.l14(x)
x = self.l15(x)
x = self.l16(x)
return x
class FeatureBlock4(Model):
def __init__(self):
super(FeatureBlock4,self).__init__()
self.l1 = Conv1D(filters=64,kernel_size=512,strides=1,kernel_initializer=GammatoneInit(16000,100,2))
self.l1.trainable=False
self.l2 = Activation(activation="relu")
self.l3 = BatchNormalization()
self.l4 = MaxPooling1D(pool_size=8,strides=8)
self.l5 = Activation(activation="relu")
self.l6 = Conv1D(filters=32,kernel_size=32,strides=2)
self.l7 = Activation(activation="relu")
self.l8 = BatchNormalization()
self.l9 = MaxPooling1D(pool_size=8,strides=8)
self.l10 = Activation(activation="relu")
self.l11 = Conv1D(filters=64,kernel_size=16,strides=2)
self.l12 = Activation(activation="relu")
self.l13 = BatchNormalization()
self.l14 = Conv1D(filters=128,kernel_size=8,strides=2)
self.l15 = Activation(activation="relu")
self.l16 = BatchNormalization()
def call(self,inputs):
x = self.l1(inputs)
x = self.l2(x)
x = self.l3(x)
x = self.l4(x)
x = self.l5(x)
x = self.l6(x)
x = self.l7(x)
x = self.l8(x)
x = self.l9(x)
x = self.l10(x)
x = self.l11(x)
x = self.l12(x)
x = self.l13(x)
x = self.l14(x)
x = self.l15(x)
x = self.l16(x)
return x
class FeatureBlock5(Model):
def __init__(self):
super(FeatureBlock5,self).__init__()
self.l1 = Conv1D(filters=16,kernel_size=64,strides=2)
self.l2 = Activation(activation="relu")
self.l3 = BatchNormalization()
self.l4 = MaxPooling1D(pool_size=8,strides=8)
self.l5 = Activation(activation="relu")
self.l6 = Conv1D(filters=32,kernel_size=32,strides=2)
self.l7 = Activation(activation="relu")
self.l8 = BatchNormalization()
self.l9 = MaxPooling1D(pool_size=8,strides=8)
self.l10 = Activation(activation="relu")
self.l11 = Conv1D(filters=64,kernel_size=16,strides=2)
self.l12 = Activation(activation="relu")
self.l13 = BatchNormalization()
def call(self,inputs):
x = self.l1(inputs)
x = self.l2(x)
x = self.l3(x)
x = self.l4(x)
x = self.l5(x)
x = self.l6(x)
x = self.l7(x)
x = self.l8(x)
x = self.l9(x)
x = self.l10(x)
x = self.l11(x)
x = self.l12(x)
x = self.l13(x)
return x
class FeatureBlock6(Model):
def __init__(self):
super(FeatureBlock6,self).__init__()
self.l1 = Conv1D(filters=16,kernel_size=32,strides=2)
self.l2 = Activation(activation="relu")
self.l3 = BatchNormalization()
self.l4 = MaxPooling1D(pool_size=2,strides=2)
self.l5 = Activation(activation="relu")
self.l6 = Conv1D(filters=32,kernel_size=16,strides=2)
self.l7 = Activation(activation="relu")
self.l8 = BatchNormalization()
self.l9 = MaxPooling1D(pool_size=2,strides=2)
self.l10 = Activation(activation="relu")
self.l11 = Conv1D(filters=64,kernel_size=8,strides=2)
self.l12 = Activation(activation="relu")
self.l13 = BatchNormalization()
def call(self,inputs):
x = self.l1(inputs)
x = self.l2(x)
x = self.l3(x)
x = self.l4(x)
x = self.l5(x)
x = self.l6(x)
x = self.l7(x)
x = self.l8(x)
x = self.l9(x)
x = self.l10(x)
x = self.l11(x)
x = self.l12(x)
x = self.l13(x)
x = self.l14(x)
x = self.l15(x)
x = self.l16(x)
return x