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model.py~
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model.py~
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import tensorflow as tf
import tensorflow.nn as tfn
import tensorflow.keras as tfk
import tensorflow.keras.backend as K
import tensorflow.keras.layers as tfl
import tensorflow.keras.regularizers as tkr
class SiameseNet():
def __init__(self):
self.img_shape=(105,105,1)
#-------------------#
# Hyperparameters #
#-------------------#
self.filters={
"L1":64
,"L2":128
,"L3":128
,"L4":256
}
self.kernel_size={
"L1":(10,10)
,"L2":(7,7)
,"L3":(4,4)
,"L4":(4,4)
}
self.l2_regularizer={
"L1":2e-4
,"L2":2e-4
,"L3":2e-4
,"L4":2e-4
,"L5":2e-3
}
self.pool_size={
"L1":2
,"L2":2
,"L3":2
}
#----------------------#
# Model Architecture #
#----------------------#
# Left Side
self.left_input,self.left_output=self._load_architecture()
# Right Side
self.right_input,self.right_output=self._load_architecture()
L1_layer=tfl.Lambda(lambda tensors: K.abs(tensors[0]-tensors[1]))
L1_siamese_dist = L1_layer([self.left_output, self.right_output])
L1_siamese_dist = tfl.Dropout(0.4)(L1_siamese_dist)
# An output layer with Sigmoid activation function
prediction = tfl.Dense(1, activation='sigmoid',)(L1_siamese_dist)
siamese_net = tfk.Model(inputs=[self.left_input, self.right_input], outputs=prediction)
self.siamese_net = siamese_net
self.siamese_net.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])
def initialize_weights(self,shape,dtype=None):
return K.random_normal(shape,mean=0.0,stddev=0.01,dtype=dtype)
def initialize_bias(self,shape,dtype=None):
return K.random_normal(shape,mean=0.5,stddev=0.01,dtype=None)
def _load_architecture(self):
#-------------------#
# Input Layer #
# Shape (1,105,105) #
#-------------------#
input=tfl.Input(shape=self.img_shape)
#---------------------#
# Convolutional Layer #
# Shape (1,96,96) #
#---------------------#
x=tfl.Conv2D(
filters=self.filters["L1"]
,kernel_size=self.kernel_size["L1"]
,kernel_initializer=self.initialize_weights
,kernel_regularizer=tkr.l2(self.l2_regularizer["L1"])
,bias_initializer=self.initialize_bias
)(input)
#---------------------#
# Activation Layer #
#---------------------#
x=tfl.Activation('relu')(x)
#---------------------#
# Max Pooling Layer #
# Shape (1,48,48) #
#---------------------#
x=tfl.MaxPool2D(
pool_size=self.pool_size["L1"]
)(x)
#---------------------#
# Convolutional Layer #
# Shape (1,42,42) #
#---------------------#
x=tfl.Conv2D(
filters=self.filters["L2"]
,kernel_size=self.kernel_size['L2']
,kernel_regularizer=tkr.l2(self.l2_regularizer["L2"])
,kernel_initializer=self.initialize_weights
,bias_initializer=self.initialize_bias
)(x)
#---------------------#
# Activation Layer #
#---------------------#
x=tfl.Activation('relu')(x)
#---------------------#
# Max Pooling Layer #
# Shape (1,21,21) #
#---------------------#
x=tfl.MaxPool2D(
pool_size=self.pool_size["L2"]
)(x)
#---------------------#
# Convolutional Layer #
# Shape (1,18,18) #
#---------------------#
x=tfl.Conv2D(
filters=self.filters["L3"]
,kernel_size=self.kernel_size["L3"]
,kernel_regularizer=tkr.l2(self.l2_regularizer["L3"])
,kernel_initializer=self.initialize_weights
,bias_initializer=self.initialize_bias
)(x)
#---------------------#
# Activation Layer #
#---------------------#
x=tfl.Activation('relu')(x)
#---------------------#
# Max Pooling Layer #
# Shape (1,9,9) #
#---------------------#
x=tfl.MaxPool2D(
pool_size=self.pool_size["L3"]
)(x)
#-----------------------#
# Convolutational Layer #
# Shape (1,6,6) #
#-----------------------#
x=tfl.Conv2D(
filters=self.filters["L4"]
,kernel_size=self.kernel_size['L4']
,kernel_regularizer=tkr.l2(self.l2_regularizer['L4'])
,kernel_initializer=self.initialize_weights
,bias_initializer=self.initialize_bias
)(x)
#-----------------------#
# Flatten #
#-----------------------#
x=tfl.Flatten()(x)
#-----------------------#
# Fully Connected Layer #
#-----------------------#
x=tfl.Dense(
units=4096
,kernel_regularizer=tkr.l2(self.l2_regularizer['L5'])
,kernel_initializer=self.initialize_weights
,bias_initializer=self.initialize_bias
,activation='sigmoid'
)(x)
return input,x
def summary(self):
self.siamese_net.summary()