MatchNet: Keras implementation

[memon-aliraza]

Hi,

I am trying to re-write the network architecture in Keras. Please help me to review the following network architecture. The network contain two-different image types(i-e: the images taken from the camera (cam_input) and its corresponding CAD model image (cad_image)).

import os
import itertools
import numpy as np
from PIL import Image
from keras import backend as K
from keras.models import Model, Sequential
import matplotlib.pyplot as plt
from keras.optimizers import Adam
from sklearn.utils import shuffle
from keras.callbacks import TensorBoard
from keras.layers.merge import concatenate
from sklearn.metrics import confusion_matrix
from keras.layers import Input, Lambda, Dense, Flatten, Reshape, Conv2D, MaxPool2D

# input image size and channels
img_width, img_height, channels = 64, 64, 3

cam_input = Input(shape=(img_width,img_height,channels))
cad_input = Input(shape=(img_width,img_height,channels))

def feature_net(input_data):
    conv0 = Conv2D(24, kernel_size=7, strides=(1,1), padding = 'SAME', activation='relu')(input_data)
    pool0 = MaxPool2D((3, 3), strides=(2, 2), padding = 'SAME')(conv0)
    
    conv1 = Conv2D(64, kernel_size=5, strides=(1,1), padding = 'SAME', activation='relu')(pool0)
    pool1 = MaxPool2D((3, 3), strides=(2, 2), padding = 'SAME')(conv1)
    
    conv2 = Conv2D(96, kernel_size=3, strides=(1,1), padding = 'SAME', activation='relu')(pool1)
    
    conv3 = Conv2D(96, kernel_size=3, strides=(1,1), padding = 'SAME', activation='relu')(conv2)
    
    conv4 = Conv2D(64, kernel_size=3, strides=(1,1), padding = 'SAME',  activation='relu')(conv3)
    pool4 = MaxPool2D((3, 3), strides=(2, 2), padding = 'SAME')(conv4)
    
    return Reshape([4096])(pool4)

cam_fv = feature_net(cam_input)
cad_fv = feature_net(cad_input)

merge = concatenate([cam_fv, cad_fv])

# Similarity network
fc1 = Dense(1024, activation='relu', name='fc1')(merge)
fc2 = Dense(1024, activation='relu', name='fc2')(fc1)
output = Dense(2, activation='softmax', name='fc3')(fc2)

matchnet = Model(inputs=[cam_input, cad_input], outputs=output)
print(matchnet.summary())