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siamese_keras_inceptionmodel.py
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siamese_keras_inceptionmodel.py
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# -*- coding: utf-8 -*-
"""Siamese_Keras_InceptionModel
Automatically generated by Colaboratory.
Original file is located at
https://colab.research.google.com/drive/1PHjXGW-JyuVlc4qOh98M7J43pkZBC0Lc
"""
import numpy as np
import matplotlib.pyplot as plt
import os
import h5py
import tensorflow as tf
import keras
import PIL
import scipy
from keras.layers import *
from keras.regularizers import *
from keras.optimizers import *
from keras.models import *
from keras.initializers import *
from keras.backend import *
# %matplotlib inline
os.chdir('drive/My Drive/Dataset')
def Load_Data():
train_dataset = h5py.File('train_happy.h5','r')
test_dataset = h5py.File('test_happy.h5','r')
train_set_X_orig = np.array(train_dataset['train_set_x'][:])
train_set_Y_orig = np.array(train_dataset['train_set_y'][:])
test_set_X_orig = np.array(test_dataset['test_set_x'][:])
test_set_Y_orig = np.array(test_dataset['test_set_y'][:])
classes = np.array(test_dataset['list_classes'][:])
return train_set_X_orig,train_set_Y_orig,test_set_X_orig,test_set_Y_orig,classes
train_set_X_orig,train_set_Y_orig,test_set_X_orig,test_set_Y_orig,classes = Load_Data()
print(train_set_X_orig.shape,train_set_Y_orig.shape,test_set_X_orig.shape,test_set_Y_orig.shape)
i = 5
plt.imshow(train_set_X_orig[i])
print('It is Image_'+str(i)+' and the person is','Happy.' if classes[test_set_Y_orig[i]] else 'not Happy.')
def Reshape_And_Normalize(train_set_X_orig,train_set_Y_orig,test_set_X_orig,test_set_Y_orig):
train_set_X = train_set_X_orig/255
train_set_Y = train_set_Y_orig.reshape((train_set_Y_orig.shape[0],1))
test_set_X = test_set_X_orig/255
test_set_Y = test_set_Y_orig.reshape((test_set_Y_orig.shape[0],1))
return train_set_X,train_set_Y,test_set_X,test_set_Y
def Read_Outputs():
ptr = open('outputs_siamese.txt','r')
data = ptr.readlines()
train_Y = [float(c.strip()) for c in data]
train_Y = np.array(train_Y)
train_Y = train_Y.reshape((train_Y.shape[0],1))
return train_Y
def Create_Database():
database = {}
database["younes"] = "images/younes.jpg"
database["tian"] = "images/tian.jpg"
database["andrew"] = "images/andrew.jpg"
database["kian"] = "images/kian.jpg"
database["dan"] = "images/dan.jpg"
database["sebastiano"] = "images/sebastiano.jpg"
database["bertrand"] = "images/bertrand.jpg"
database["kevin"] = "images/kevin.jpg"
database["felix"] = "images/felix.jpg"
database["benoit"] = "images/benoit.jpg"
database["arnaud"] = "images/arnaud.jpg"
return database
def Data_Preprocessing():
train_set_X_orig,train_set_Y_orig,test_set_X_orig,test_set_Y_orig,classes = Load_Data()
train_set_X,train_set_Y,test_set_X,test_set_Y = Reshape_And_Normalize(train_set_X_orig,train_set_Y_orig,test_set_X_orig,test_set_Y_orig)
data = train_set_X_orig[0:21,:,:,:]
temp = np.zeros((1,64,64,3))
temp[0,:,:,:] = data[1,:,:,:]
data[1,:,:,:] = data[2,:,:,:]
data[2,:,:,:] = temp[0,:,:,:]
temp[0,:,:,:] = data[15,:,:,:]
data[15,:,:,:] = data[19,:,:,:]
data[19,:,:,:] = temp[0,:,:,:]
temp[0,:,:,:] = data[16,:,:,:]
data[16,:,:,:] = data[17,:,:,:]
data[17,:,:,:] = temp[0,:,:,:]
train_X1 = data[0:20,:,:,:]
train_X2 = data[1:21,:,:,:]
train_Y = Read_Outputs()
database = Create_Database()
return train_X1,train_X2,train_Y,database
def Inception_Block(X,channels,layers,XorY):
X_3x3 = Conv2D(channels[0],kernel_size=(1,1),strides=(1,1),padding='valid',name="Inception_Block"+str(layers)+"_Conv2D_1x1A"+XorY)(X)
X_3x3 = BatchNormalization(axis=3,name='Inception_Block'+str(layers)+'_BatchNorm_1'+XorY)(X_3x3)
X_3x3 = Activation('relu')(X_3x3)
X_3x3 = ZeroPadding2D((1,1))(X_3x3)
X_3x3 = Conv2D(channels[1],kernel_size=(3,3),strides=(1,1),padding='valid',name='Inception_Block'+str(layers)+'_Conv2D_3x3'+XorY)(X_3x3)
X_3x3 = BatchNormalization(axis=3,name='Inception_Block'+str(layers)+'_BatchNorm_2'+XorY)(X_3x3)
X_3x3 = Activation('relu')(X_3x3)
X_5x5 = Conv2D(channels[2],kernel_size=(1,1),strides=(1,1),padding='valid',name="Inception_Block"+str(layers)+"_Conv2D_1x1B"+XorY)(X)
X_5x5 = BatchNormalization(axis=3,name='Inception_Block'+str(layers)+'_BatchNorm_3'+XorY)(X_5x5)
X_5x5 = Activation('relu')(X_5x5)
X_5x5 = ZeroPadding2D((2,2))(X_5x5)
X_5x5 = Conv2D(channels[3],kernel_size=(5,5),strides=(1,1),padding='valid',name='Inception_Block'+str(layers)+'_Conv2D_5x5'+XorY)(X_5x5)
X_5x5 = BatchNormalization(axis=3,name='Inception_Block'+str(layers)+'_BatchNorm_4'+XorY)(X_5x5)
X_5x5 = Activation('relu')(X_5x5)
X_Pool = MaxPooling2D(pool_size=(2,2),strides=(1,1),padding='same',name='Inception_Block'+str(layers)+'_MaxPool'+XorY)(X)
X_Pool = Conv2D(channels[4],kernel_size=(1,1),strides=(1,1),padding='valid',name='Inception_Block'+str(layers)+'_Conv2D_1x1C'+XorY)(X_Pool)
X_Pool = BatchNormalization(axis=3,name="Inception_Block"+str(layers)+"_BatchNorm_6"+XorY)(X_Pool)
X_Pool = Activation('relu')(X_Pool)
X_1x1 = Conv2D(channels[5],kernel_size=(1,1),strides=(1,1),padding='valid',name='Inception_Block'+str(layers)+'_Conv2D_1x1D'+XorY)(X)
X_1x1 = BatchNormalization(axis=3,name='Inception_Block'+str(layers)+'_BatchNorm_5'+XorY)(X_1x1)
X_1x1 = Activation('relu')(X_1x1)
inception = Concatenate(axis=3)([X_3x3,X_5x5,X_Pool,X_1x1])
return inception
def Propagation(input_shape,output_neurons,channel_list):
X_input = Input(input_shape)
X = X_input
X = ZeroPadding2D((3,3))(X)
X = Conv2D(64,kernel_size=(3,3),strides=(1,1),padding='valid',kernel_initializer='glorot_uniform',name='Normal_Block_Conv2D_X')(X)
X = BatchNormalization(axis=3,name='Normal_Block_BatchNorm_X')(X)
X = Activation('relu')(X)
X = MaxPooling2D(pool_size=(2,2),strides=(2,2),padding='valid',name='Normal_Block_MaxPool1_X')(X)
'''X = Inception_Block(X,channel_list[0],1,'X')
X = Inception_Block(X,channel_list[1],2,'X')
X = Inception_Block(X,channel_list[2],3,'X')
X = MaxPooling2D(pool_size=(2,2),strides=(2,2),padding='valid',name="Normal_Block_MaxPool2_X")(X)
X = Inception_Block(X,channel_list[3],4,'X')
X = Inception_Block(X,channel_list[4],5,'X')
X = MaxPooling2D(pool_size=(2,2),strides=(2,2),padding='valid',name="Normal_Block_MaxPool3_X")(X)
X = Inception_Block(X,channel_list[5],6,'X')
X = Inception_Block(X,channel_list[6],7,'X')'''
X = AveragePooling2D(pool_size=(2,2),strides=(2,2),padding='valid',name='Normal_Block_AveragePool_X')(X)
X = Flatten()(X)
if(output_neurons > 1):
activation = 'softmax'
else:
activation = 'sigmoid'
X = Dense(output_neurons,activation=activation,kernel_initializer='glorot_uniform',bias_initializer='zeros',name='Normal_Layer_FullyConnected_X',use_bias=True)(X)
Y_input = Input(input_shape)
Y = Y_input
Y = ZeroPadding2D((3,3))(Y)
Y = Conv2D(64,kernel_size=(3,3),strides=(1,1),padding='valid',kernel_initializer='glorot_uniform',name='Normal_Block_Conv2D_Y')(Y)
Y = BatchNormalization(axis=3,name='Normal_Block_BatchNorm_Y')(Y)
Y = Activation('relu')(Y)
Y = MaxPooling2D(pool_size=(2,2),strides=(2,2),padding='valid',name='Normal_Block_MaxPool1_Y')(Y)
'''Y = Inception_Block(Y,channel_list[0],1,'Y')
Y = Inception_Block(Y,channel_list[1],2,'Y')
Y = Inception_Block(Y,channel_list[2],3,'Y')
Y = MaxPooling2D(pool_size=(2,2),strides=(2,2),padding='valid',name="Normal_Block_MaxPool2_Y")(Y)
Y = Inception_Block(Y,channel_list[3],4,'Y')
Y = Inception_Block(Y,channel_list[4],5,'Y')
Y = MaxPooling2D(pool_size=(2,2),strides=(2,2),padding='valid',name="Normal_Block_MaxPool3_Y")(Y)
Y = Inception_Block(Y,channel_list[5],6,'Y')
Y = Inception_Block(Y,channel_list[6],7,'Y')'''
Y = AveragePooling2D(pool_size=(2,2),strides=(2,2),padding='valid',name='Normal_Block_AveragePool_Y')(Y)
Y = Flatten()(Y)
if(output_neurons > 1):
activation = 'softmax'
else:
activation = 'sigmoid'
Y = Dense(output_neurons,activation=activation,kernel_initializer='glorot_uniform',bias_initializer='zeros',name='Normal_Layer_FullyConnected_Y',use_bias=True)(Y)
A = Subtract()([X,Y])
A = Dense(50,activation='softmax',kernel_initializer='glorot_uniform', bias_initializer='zeros')(A)
A = Dense(1,activation='sigmoid',name="Siamese_Last_Layer",kernel_initializer='glorot_uniform', bias_initializer='zeros')(A)
model = Model(inputs=[X_input,Y_input],outputs=A,name='Siamese_Keras_InceptionModel')
return model
def Read_Image(image_path):
import skimage
image = plt.imread(image_path)
image = skimage.transform.resize(image,(64,64))
image = np.expand_dims(image,axis=0)
return image
def Reshape_And_Normalize_Happy(train_set_X_orig,train_set_Y_orig,test_set_X_orig,test_set_Y_orig):
m_train = train_set_X_orig.shape[0]
m_test = test_set_X_orig.shape[0]
train_set_X = train_set_X_orig.reshape(m_train,-1)
test_set_X = test_set_X_orig.reshape(m_test,-1)
train_set_Y = train_set_Y_orig.reshape((m_train,1))
test_set_Y = test_set_Y_orig.reshape((m_test,1))
U_train = np.sum(train_set_X,axis=1,keepdims=True)/m_train
U_test = np.sum(test_set_X,axis=1,keepdims=True)/m_test
train_set_X = train_set_X - U_train
test_set_X = test_set_X - U_test
sigma_train = np.sqrt(np.sum(np.square(train_set_X),axis=1,keepdims=True)/m_train)
sigma_test = np.sqrt(np.sum(np.square(test_set_X),axis=1,keepdims=True)/m_test)
train_set_X /= sigma_train
test_set_X /= sigma_test
return train_set_X,train_set_Y,test_set_X,test_set_Y
def Data_Preprocessing_Happy():
train_set_X_orig,train_set_Y_orig,test_set_X_orig,test_set_Y_orig,classes = Load_Data()
train_set_X,train_set_Y,test_set_X,test_set_Y = Reshape_And_Normalize_Happy(train_set_X_orig,train_set_Y_orig,test_set_X_orig,test_set_Y_orig)
return train_set_X,train_set_Y,test_set_X,test_set_Y
def Propagation_Happy(input_shape,output_neurons):
X_input = Input(input_shape)
X = X_input
if(output_neurons > 1):
activation_type = 'softmax'
elif(output_neurons == 1):
activation_type = 'sigmoid'
X = BatchNormalization(axis=1,name='BatchNormalization')(X)
X = Dense(output_neurons,use_bias=True,activation=activation_type,kernel_initializer='glorot_uniform', bias_initializer='zeros',name='FullyConnected',kernel_regularizer=regularizers.l2(0.01))(X)
model = keras.models.Model(inputs=X_input,outputs=X,name="ANN_Keras")
return model
def Caller_Function_Happy():
train_set_X,train_set_Y,test_set_X,test_set_Y = Data_Preprocessing_Happy()
epochs = 10
mini_batch_size = 50
output_neurons = 1
if(output_neurons > 1):
train_set_Y = np.eye(output_neurons)[train_set_Y.T][0]
test_set_Y = np.eye(output_neurons)[test_set_Y.T][0]
loss_val = 'categorical_crossentropy'
elif(output_neurons == 1):
loss_val = 'binary_crossentropy'
input_shape = [train_set_X.shape[1]]
model = Propagation_Happy(input_shape,output_neurons)
model.compile(loss=loss_val,optimizer='adam',metrics=['accuracy'])
print("\n---------------------------------------------Happy House Start-------------------------------------------\n")
model.fit(x=train_set_X[0:50,:],y=train_set_Y[0:50,:],epochs=epochs,batch_size=mini_batch_size)
return model
def Caller_Function():
model_happy = Caller_Function_Happy()
train_X1,train_X2,train_Y,database = Data_Preprocessing()
epochs = 5
mini_batch_size = 2
output_neurons = 128
shape = train_X1.shape
shape = (shape[1],shape[2],shape[3])
channel_list = [[96,128,16,32,32,64],[96,128,32,64,64,64],[128,256,32,64,16,32],[96,192,32,64,128,256],[160,256,64,128,32,64],[96,128,64,128,192,256],[192,512,128,256,512,512]]
model = Propagation(shape,output_neurons,channel_list)
opt = Adam(lr=0.5)
model.compile(loss='mean_squared_error',optimizer=opt,metrics=['accuracy'])
print("\n---------------------------------------------Siamese Start-------------------------------------------\n")
model.fit(x=[train_X1,train_X2],y=train_Y,epochs=epochs,batch_size=mini_batch_size)
return model_happy,model,database,train_X1
#model.summary()
model_happy,model,database,train_X1 = Caller_Function()
def evaluate(model_happy,model,database,train_X1):
print('')
name = 'arnaud'
model_type = "verification"
if(model_type == 'verification'):
image_path = database[name]
anchor = Read_Image(image_path)
i = 10
person = np.zeros((1,64,64,3))
person[0,:,:,:] = train_X1[i,:,:,:]
out = model.predict(x=[anchor,person])
if(out[0][0] >= 0.4):
print('Same',out)
img = person.reshape((person.shape[0],-1))
ans = np.ceil(model_happy.predict(img))
print('Allowed' if ans else 'Rejected')
else:
print('Different',out)
else:
mx = -100.0
i = 5
person = np.zeros((1,64,64,3))
person[0,:,:,:] = train_X1[i,:,:,:]
for names in database:
image_path = database[names]
anchor = Read_Image(image_path)
out = model.predict(x=[anchor,person])
if(out[0][0] > mx):
mx = out[0][0]
person_name = names
if(mx >= 0.4):
print('Hello '+person_name)
img = person.reshape((person.shape[0],-1))
ans = np.ceil(model_happy.predict(img))
print('Allowed' if ans else 'Rejected')
else:
print('Not in Database')
evaluate(model_happy,model,database,train_X1)
model_happy.summary()
model.summary()