GoogLeNet_V1_Model.py

import h5py
import numpy as np
import tensorflow as tf
import scipy.io
import collections
import random
import sys

class CNN_Triplet_Metric(object):
    def __init__(self,sess):
        np.set_printoptions(threshold=np.nan)
        self.var_dict = self.Variables_Dict()
        img_a = tf.placeholder(tf.float32, [None, 227, 227, 3])
        self.sess = sess

        print 'loding image mean file'
        image_mean = scipy.io.loadmat('imagenet_mean.mat')
        image_mean = image_mean['image_mean']
        image_mean = np.transpose(image_mean,(1,2,0))
        image_mean = np.expand_dims(image_mean,axis=0)
        print 'done loading image mean file'

        print 'constructing model'
        with tf.variable_scope("") as scope:
            a_output = self.CNN_Metric_Model(img_a)

        print 'done constructing model'

        print 'initialize variables '
        self.sess.run(tf.global_variables_initializer())
        print 'done initializing'


    def Variables_Dict(self):
        print 'Loading MAT file for pretrained'
        pretrained_weights = scipy.io.loadmat('tf_ckpt_from_caffe.mat')

        Conv2d_1a_7x7 = tf.constant(np.transpose(pretrained_weights['conv1/7x7_s2'],(2,3,1,0)))
        Conv2d_2b_1x1 = tf.constant(np.transpose(pretrained_weights['conv2/3x3_reduce'],(2,3,1,0)))
        Conv2d_2c_3x3 = tf.constant(np.transpose(pretrained_weights['conv2/3x3'],(2,3,1,0)))

        Conv2d_1a_7x7_bias = tf.constant(pretrained_weights['conv1/7x7_s2_bias'].flatten())
        Conv2d_2b_1x1_bias = tf.constant(pretrained_weights['conv2/3x3_reduce_bias'].flatten())
        Conv2d_2c_3x3_bias = tf.constant(pretrained_weights['conv2/3x3_bias'].flatten())
        # first inception
        Mixed_3b_Branch_0_Conv2d_0a_1x1 = tf.constant(np.transpose(pretrained_weights['inception_3a/1x1'],(2,3,1,0)))
        Mixed_3b_Branch_1_Conv2d_0a_1x1 = tf.constant(np.transpose(pretrained_weights['inception_3a/3x3_reduce'], (2,3, 1, 0)))
        Mixed_3b_Branch_1_Conv2d_0b_3x3 = tf.constant(np.transpose(pretrained_weights['inception_3a/3x3'], (2,3, 1, 0)))
        Mixed_3b_Branch_2_Conv2d_0a_1x1 = tf.constant(np.transpose(pretrained_weights['inception_3a/5x5_reduce'], (2,3, 1, 0)))
        Mixed_3b_Branch_2_Conv2d_0b_5x5 = tf.constant(np.transpose(pretrained_weights['inception_3a/5x5'], (2,3, 1, 0)))
        Mixed_3b_Branch_3_Conv2d_0b_1x1 = tf.constant(np.transpose(pretrained_weights['inception_3a/pool_proj'], (2,3, 1, 0)))
        # first inception bias
        Mixed_3b_Branch_0_Conv2d_0a_1x1_bias = tf.constant(pretrained_weights['inception_3a/1x1_bias'].flatten())
        Mixed_3b_Branch_1_Conv2d_0a_1x1_bias = tf.constant(pretrained_weights['inception_3a/3x3_reduce_bias'].flatten())
        Mixed_3b_Branch_1_Conv2d_0b_3x3_bias = tf.constant(pretrained_weights['inception_3a/3x3_bias'].flatten())
        Mixed_3b_Branch_2_Conv2d_0a_1x1_bias = tf.constant(pretrained_weights['inception_3a/5x5_reduce_bias'].flatten())
        Mixed_3b_Branch_2_Conv2d_0b_5x5_bias = tf.constant(pretrained_weights['inception_3a/5x5_bias'].flatten())
        Mixed_3b_Branch_3_Conv2d_0b_1x1_bias = tf.constant(pretrained_weights['inception_3a/pool_proj_bias'].flatten())
        # second inception
        Mixed_3c_Branch_0_Conv2d_0a_1x1 = tf.constant(np.transpose(pretrained_weights['inception_3b/1x1'], (2,3, 1, 0)))
        Mixed_3c_Branch_1_Conv2d_0a_1x1 = tf.constant(np.transpose(pretrained_weights['inception_3b/3x3_reduce'], (2,3, 1, 0)))
        Mixed_3c_Branch_1_Conv2d_0b_3x3 = tf.constant(np.transpose(pretrained_weights['inception_3b/3x3'], (2,3, 1, 0)))
        Mixed_3c_Branch_2_Conv2d_0a_1x1 = tf.constant(np.transpose(pretrained_weights['inception_3b/5x5_reduce'], (2,3, 1, 0)))
        Mixed_3c_Branch_2_Conv2d_0b_5x5 = tf.constant(np.transpose(pretrained_weights['inception_3b/5x5'], (2,3, 1, 0)))
        Mixed_3c_Branch_3_Conv2d_0b_1x1 = tf.constant(np.transpose(pretrained_weights['inception_3b/pool_proj'], (2,3, 1, 0)))
        # second inception bias
        Mixed_3c_Branch_0_Conv2d_0a_1x1_bias = tf.constant(pretrained_weights['inception_3b/1x1_bias'].flatten())
        Mixed_3c_Branch_1_Conv2d_0a_1x1_bias = tf.constant(pretrained_weights['inception_3b/3x3_reduce_bias'].flatten())
        Mixed_3c_Branch_1_Conv2d_0b_3x3_bias = tf.constant(pretrained_weights['inception_3b/3x3_bias'].flatten())
        Mixed_3c_Branch_2_Conv2d_0a_1x1_bias = tf.constant(pretrained_weights['inception_3b/5x5_reduce_bias'].flatten())
        Mixed_3c_Branch_2_Conv2d_0b_5x5_bias = tf.constant(pretrained_weights['inception_3b/5x5_bias'].flatten())
        Mixed_3c_Branch_3_Conv2d_0b_1x1_bias = tf.constant(pretrained_weights['inception_3b/pool_proj_bias'].flatten())
        # third inception
        Mixed_4b_Branch_0_Conv2d_0a_1x1 = tf.constant(np.transpose(pretrained_weights['inception_4a/1x1'], (2,3, 1, 0)))
        Mixed_4b_Branch_1_Conv2d_0a_1x1 = tf.constant(np.transpose(pretrained_weights['inception_4a/3x3_reduce'], (2,3, 1, 0)))
        Mixed_4b_Branch_1_Conv2d_0b_3x3 = tf.constant(np.transpose(pretrained_weights['inception_4a/3x3'], (2,3, 1, 0)))
        Mixed_4b_Branch_2_Conv2d_0a_1x1 = tf.constant(np.transpose(pretrained_weights['inception_4a/5x5_reduce'], (2,3, 1, 0)))
        Mixed_4b_Branch_2_Conv2d_0b_5x5 = tf.constant(np.transpose(pretrained_weights['inception_4a/5x5'], (2,3, 1, 0)))
        Mixed_4b_Branch_3_Conv2d_0b_1x1 = tf.constant(np.transpose(pretrained_weights['inception_4a/pool_proj'], (2,3, 1, 0)))
        # third inception bias
        Mixed_4b_Branch_0_Conv2d_0a_1x1_bias = tf.constant(pretrained_weights['inception_4a/1x1_bias'].flatten())
        Mixed_4b_Branch_1_Conv2d_0a_1x1_bias = tf.constant(pretrained_weights['inception_4a/3x3_reduce_bias'].flatten())
        Mixed_4b_Branch_1_Conv2d_0b_3x3_bias = tf.constant(pretrained_weights['inception_4a/3x3_bias'].flatten())
        Mixed_4b_Branch_2_Conv2d_0a_1x1_bias = tf.constant(pretrained_weights['inception_4a/5x5_reduce_bias'].flatten())
        Mixed_4b_Branch_2_Conv2d_0b_5x5_bias = tf.constant(pretrained_weights['inception_4a/5x5_bias'].flatten())
        Mixed_4b_Branch_3_Conv2d_0b_1x1_bias = tf.constant(pretrained_weights['inception_4a/pool_proj_bias'].flatten())
        # fourth inception
        Mixed_4c_Branch_0_Conv2d_0a_1x1 = tf.constant(np.transpose(pretrained_weights['inception_4b/1x1'], (2,3, 1, 0)))
        Mixed_4c_Branch_1_Conv2d_0a_1x1 = tf.constant(np.transpose(pretrained_weights['inception_4b/3x3_reduce'], (2,3, 1, 0)))
        Mixed_4c_Branch_1_Conv2d_0b_3x3 = tf.constant(np.transpose(pretrained_weights['inception_4b/3x3'], (2,3, 1, 0)))
        Mixed_4c_Branch_2_Conv2d_0a_1x1 = tf.constant(np.transpose(pretrained_weights['inception_4b/5x5_reduce'], (2,3, 1, 0)))
        Mixed_4c_Branch_2_Conv2d_0b_5x5 = tf.constant(np.transpose(pretrained_weights['inception_4b/5x5'], (2,3, 1, 0)))
        Mixed_4c_Branch_3_Conv2d_0b_1x1 = tf.constant(np.transpose(pretrained_weights['inception_4b/pool_proj'], (2,3, 1, 0)))
        # fourth inception bias
        Mixed_4c_Branch_0_Conv2d_0a_1x1_bias = tf.constant(pretrained_weights['inception_4b/1x1_bias'].flatten())
        Mixed_4c_Branch_1_Conv2d_0a_1x1_bias = tf.constant(pretrained_weights['inception_4b/3x3_reduce_bias'].flatten())
        Mixed_4c_Branch_1_Conv2d_0b_3x3_bias = tf.constant(pretrained_weights['inception_4b/3x3_bias'].flatten())
        Mixed_4c_Branch_2_Conv2d_0a_1x1_bias = tf.constant(pretrained_weights['inception_4b/5x5_reduce_bias'].flatten())
        Mixed_4c_Branch_2_Conv2d_0b_5x5_bias = tf.constant(pretrained_weights['inception_4b/5x5_bias'].flatten())
        Mixed_4c_Branch_3_Conv2d_0b_1x1_bias = tf.constant(pretrained_weights['inception_4b/pool_proj_bias'].flatten())
        # fifth inception
        Mixed_4d_Branch_0_Conv2d_0a_1x1 = tf.constant(np.transpose(pretrained_weights['inception_4c/1x1'], (2,3, 1, 0)))
        Mixed_4d_Branch_1_Conv2d_0a_1x1 = tf.constant(np.transpose(pretrained_weights['inception_4c/3x3_reduce'], (2,3, 1, 0)))
        Mixed_4d_Branch_1_Conv2d_0b_3x3 = tf.constant(np.transpose(pretrained_weights['inception_4c/3x3'], (2,3, 1, 0)))
        Mixed_4d_Branch_2_Conv2d_0a_1x1 = tf.constant(np.transpose(pretrained_weights['inception_4c/5x5_reduce'], (2,3, 1, 0)))
        Mixed_4d_Branch_2_Conv2d_0b_5x5 = tf.constant(np.transpose(pretrained_weights['inception_4c/5x5'], (2,3, 1, 0)))
        Mixed_4d_Branch_3_Conv2d_0b_1x1 = tf.constant(np.transpose(pretrained_weights['inception_4c/pool_proj'], (2,3, 1, 0)))
        # fifth inception bias
        Mixed_4d_Branch_0_Conv2d_0a_1x1_bias = tf.constant(pretrained_weights['inception_4c/1x1_bias'].flatten())
        Mixed_4d_Branch_1_Conv2d_0a_1x1_bias = tf.constant(pretrained_weights['inception_4c/3x3_reduce_bias'].flatten())
        Mixed_4d_Branch_1_Conv2d_0b_3x3_bias = tf.constant(pretrained_weights['inception_4c/3x3_bias'].flatten())
        Mixed_4d_Branch_2_Conv2d_0a_1x1_bias = tf.constant(pretrained_weights['inception_4c/5x5_reduce_bias'].flatten())
        Mixed_4d_Branch_2_Conv2d_0b_5x5_bias = tf.constant(pretrained_weights['inception_4c/5x5_bias'].flatten())
        Mixed_4d_Branch_3_Conv2d_0b_1x1_bias = tf.constant(pretrained_weights['inception_4c/pool_proj_bias'].flatten())
        # sixth inception
        Mixed_4e_Branch_0_Conv2d_0a_1x1 = tf.constant(np.transpose(pretrained_weights['inception_4d/1x1'], (2,3, 1, 0)))
        Mixed_4e_Branch_1_Conv2d_0a_1x1 = tf.constant(np.transpose(pretrained_weights['inception_4d/3x3_reduce'], (2,3, 1, 0)))
        Mixed_4e_Branch_1_Conv2d_0b_3x3 = tf.constant(np.transpose(pretrained_weights['inception_4d/3x3'], (2,3, 1, 0)))
        Mixed_4e_Branch_2_Conv2d_0a_1x1 = tf.constant(np.transpose(pretrained_weights['inception_4d/5x5_reduce'], (2,3, 1, 0)))
        Mixed_4e_Branch_2_Conv2d_0b_5x5 = tf.constant(np.transpose(pretrained_weights['inception_4d/5x5'], (2,3, 1, 0)))
        Mixed_4e_Branch_3_Conv2d_0b_1x1 = tf.constant(np.transpose(pretrained_weights['inception_4d/pool_proj'], (2,3, 1, 0)))
        # sixth inception bias
        Mixed_4e_Branch_0_Conv2d_0a_1x1_bias = tf.constant(pretrained_weights['inception_4d/1x1_bias'].flatten())
        Mixed_4e_Branch_1_Conv2d_0a_1x1_bias = tf.constant(pretrained_weights['inception_4d/3x3_reduce_bias'].flatten())
        Mixed_4e_Branch_1_Conv2d_0b_3x3_bias = tf.constant(pretrained_weights['inception_4d/3x3_bias'].flatten())
        Mixed_4e_Branch_2_Conv2d_0a_1x1_bias = tf.constant(pretrained_weights['inception_4d/5x5_reduce_bias'].flatten())
        Mixed_4e_Branch_2_Conv2d_0b_5x5_bias = tf.constant(pretrained_weights['inception_4d/5x5_bias'].flatten())
        Mixed_4e_Branch_3_Conv2d_0b_1x1_bias = tf.constant(pretrained_weights['inception_4d/pool_proj_bias'].flatten())
        # seventh inception
        Mixed_4f_Branch_0_Conv2d_0a_1x1 = tf.constant(np.transpose(pretrained_weights['inception_4e/1x1'], (2,3, 1, 0)))
        Mixed_4f_Branch_1_Conv2d_0a_1x1 = tf.constant(np.transpose(pretrained_weights['inception_4e/3x3_reduce'], (2,3, 1, 0)))
        Mixed_4f_Branch_1_Conv2d_0b_3x3 = tf.constant(np.transpose(pretrained_weights['inception_4e/3x3'], (2,3, 1, 0)))
        Mixed_4f_Branch_2_Conv2d_0a_1x1 = tf.constant(np.transpose(pretrained_weights['inception_4e/5x5_reduce'], (2,3, 1, 0)))
        Mixed_4f_Branch_2_Conv2d_0b_5x5 = tf.constant(np.transpose(pretrained_weights['inception_4e/5x5'], (2,3, 1, 0)))
        Mixed_4f_Branch_3_Conv2d_0b_1x1 = tf.constant(np.transpose(pretrained_weights['inception_4e/pool_proj'], (2,3, 1, 0)))
        # seventh inception bias
        Mixed_4f_Branch_0_Conv2d_0a_1x1_bias = tf.constant(pretrained_weights['inception_4e/1x1_bias'].flatten())
        Mixed_4f_Branch_1_Conv2d_0a_1x1_bias = tf.constant(pretrained_weights['inception_4e/3x3_reduce_bias'].flatten())
        Mixed_4f_Branch_1_Conv2d_0b_3x3_bias = tf.constant(pretrained_weights['inception_4e/3x3_bias'].flatten())
        Mixed_4f_Branch_2_Conv2d_0a_1x1_bias = tf.constant(pretrained_weights['inception_4e/5x5_reduce_bias'].flatten())
        Mixed_4f_Branch_2_Conv2d_0b_5x5_bias = tf.constant(pretrained_weights['inception_4e/5x5_bias'].flatten())
        Mixed_4f_Branch_3_Conv2d_0b_1x1_bias = tf.constant(pretrained_weights['inception_4e/pool_proj_bias'].flatten())
        # eighth inception
        Mixed_5b_Branch_0_Conv2d_0a_1x1 = tf.constant(np.transpose(pretrained_weights['inception_5a/1x1'], (2,3, 1, 0)))
        Mixed_5b_Branch_1_Conv2d_0a_1x1 = tf.constant(np.transpose(pretrained_weights['inception_5a/3x3_reduce'], (2,3, 1, 0)))
        Mixed_5b_Branch_1_Conv2d_0b_3x3 = tf.constant(np.transpose(pretrained_weights['inception_5a/3x3'], (2,3, 1, 0)))
        Mixed_5b_Branch_2_Conv2d_0a_1x1 = tf.constant(np.transpose(pretrained_weights['inception_5a/5x5_reduce'], (2,3, 1, 0)))
        Mixed_5b_Branch_2_Conv2d_0b_5x5 = tf.constant(np.transpose(pretrained_weights['inception_5a/5x5'], (2,3, 1, 0)))
        Mixed_5b_Branch_3_Conv2d_0b_1x1 = tf.constant(np.transpose(pretrained_weights['inception_5a/pool_proj'], (2,3, 1, 0)))
        # eighth inception bias
        Mixed_5b_Branch_0_Conv2d_0a_1x1_bias = tf.constant(pretrained_weights['inception_5a/1x1_bias'].flatten())
        Mixed_5b_Branch_1_Conv2d_0a_1x1_bias = tf.constant(pretrained_weights['inception_5a/3x3_reduce_bias'].flatten())
        Mixed_5b_Branch_1_Conv2d_0b_3x3_bias = tf.constant(pretrained_weights['inception_5a/3x3_bias'].flatten())
        Mixed_5b_Branch_2_Conv2d_0a_1x1_bias = tf.constant(pretrained_weights['inception_5a/5x5_reduce_bias'].flatten())
        Mixed_5b_Branch_2_Conv2d_0b_5x5_bias = tf.constant(pretrained_weights['inception_5a/5x5_bias'].flatten())
        Mixed_5b_Branch_3_Conv2d_0b_1x1_bias = tf.constant(pretrained_weights['inception_5a/pool_proj_bias'].flatten())
        #ninth inception
        Mixed_5c_Branch_0_Conv2d_0a_1x1 = tf.constant(np.transpose(pretrained_weights['inception_5b/1x1'], (2,3, 1, 0)))
        Mixed_5c_Branch_1_Conv2d_0a_1x1 = tf.constant(np.transpose(pretrained_weights['inception_5b/3x3_reduce'], (2,3, 1, 0)))
        Mixed_5c_Branch_1_Conv2d_0b_3x3 = tf.constant(np.transpose(pretrained_weights['inception_5b/3x3'], (2,3, 1, 0)))
        Mixed_5c_Branch_2_Conv2d_0a_1x1 = tf.constant(np.transpose(pretrained_weights['inception_5b/5x5_reduce'], (2,3, 1, 0)))
        Mixed_5c_Branch_2_Conv2d_0b_5x5 = tf.constant(np.transpose(pretrained_weights['inception_5b/5x5'], (2,3, 1, 0)))
        Mixed_5c_Branch_3_Conv2d_0b_1x1 = tf.constant(np.transpose(pretrained_weights['inception_5b/pool_proj'], (2,3, 1, 0)))
        # ninth inception bias
        Mixed_5c_Branch_0_Conv2d_0a_1x1_bias = tf.constant(pretrained_weights['inception_5b/1x1_bias'].flatten())
        Mixed_5c_Branch_1_Conv2d_0a_1x1_bias = tf.constant(pretrained_weights['inception_5b/3x3_reduce_bias'].flatten())
        Mixed_5c_Branch_1_Conv2d_0b_3x3_bias = tf.constant(pretrained_weights['inception_5b/3x3_bias'].flatten())
        Mixed_5c_Branch_2_Conv2d_0a_1x1_bias = tf.constant(pretrained_weights['inception_5b/5x5_reduce_bias'].flatten())
        Mixed_5c_Branch_2_Conv2d_0b_5x5_bias = tf.constant(pretrained_weights['inception_5b/5x5_bias'].flatten())
        Mixed_5c_Branch_3_Conv2d_0b_1x1_bias = tf.constant(pretrained_weights['inception_5b/pool_proj_bias'].flatten())
        print 'Finished loading'
        variables = {
            'InceptionV1/Conv2d_1a_7x7/weights':tf.get_variable(name='InceptionV1/Conv2d_1a_7x7/weights',initializer=Conv2d_1a_7x7),
            'InceptionV1/Conv2d_2b_1x1/weights': tf.get_variable(name='InceptionV1/Conv2d_2b_1x1/weights',initializer=Conv2d_2b_1x1),
            'InceptionV1/Conv2d_2c_3x3/weights': tf.get_variable(name='InceptionV1/Conv2d_2c_3x3/weights',initializer=Conv2d_2c_3x3),

            'InceptionV1/Conv2d_1a_7x7/bias': tf.get_variable(name='InceptionV1/Conv2d_1a_7x7/bias',initializer=Conv2d_1a_7x7_bias),
            'InceptionV1/Conv2d_2b_1x1/bias': tf.get_variable(name='InceptionV1/Conv2d_2b_1x1/bias',initializer=Conv2d_2b_1x1_bias),
            'InceptionV1/Conv2d_2c_3x3/bias': tf.get_variable(name='InceptionV1/Conv2d_2c_3x3/bias',initializer=Conv2d_2c_3x3_bias),
            #first inception
            'InceptionV1/Mixed_3b/Branch_0/Conv2d_0a_1x1/weights': tf.get_variable(name='InceptionV1/Mixed_3b/Branch_0/Conv2d_0a_1x1/weights',initializer=Mixed_3b_Branch_0_Conv2d_0a_1x1),
            'InceptionV1/Mixed_3b/Branch_1/Conv2d_0a_1x1/weights': tf.get_variable(name='InceptionV1/Mixed_3b/Branch_1/Conv2d_0a_1x1/weights',initializer=Mixed_3b_Branch_1_Conv2d_0a_1x1),
            'InceptionV1/Mixed_3b/Branch_1/Conv2d_0b_3x3/weights': tf.get_variable(name='InceptionV1/Mixed_3b/Branch_1/Conv2d_0b_3x3/weights',initializer=Mixed_3b_Branch_1_Conv2d_0b_3x3),
            'InceptionV1/Mixed_3b/Branch_2/Conv2d_0a_1x1/weights': tf.get_variable(name='InceptionV1/Mixed_3b/Branch_2/Conv2d_0a_1x1/weights',initializer=Mixed_3b_Branch_2_Conv2d_0a_1x1),
            'InceptionV1/Mixed_3b/Branch_2/Conv2d_0b_5x5/weights': tf.get_variable(name='InceptionV1/Mixed_3b/Branch_2/Conv2d_0b_5x5/weights',initializer=Mixed_3b_Branch_2_Conv2d_0b_5x5),
            'InceptionV1/Mixed_3b/Branch_3/Conv2d_0b_1x1/weights': tf.get_variable(name='InceptionV1/Mixed_3b/Branch_3/Conv2d_0b_1x1/weights',initializer=Mixed_3b_Branch_3_Conv2d_0b_1x1),
            # first inception bias
            'InceptionV1/Mixed_3b/Branch_0/Conv2d_0a_1x1/bias': tf.get_variable(name='InceptionV1/Mixed_3b/Branch_0/Conv2d_0a_1x1/bias',initializer=Mixed_3b_Branch_0_Conv2d_0a_1x1_bias),
            'InceptionV1/Mixed_3b/Branch_1/Conv2d_0a_1x1/bias': tf.get_variable(name='InceptionV1/Mixed_3b/Branch_1/Conv2d_0a_1x1/bias',initializer=Mixed_3b_Branch_1_Conv2d_0a_1x1_bias),
            'InceptionV1/Mixed_3b/Branch_1/Conv2d_0b_3x3/bias': tf.get_variable(name='InceptionV1/Mixed_3b/Branch_1/Conv2d_0b_3x3/bias',initializer=Mixed_3b_Branch_1_Conv2d_0b_3x3_bias),
            'InceptionV1/Mixed_3b/Branch_2/Conv2d_0a_1x1/bias': tf.get_variable(name='InceptionV1/Mixed_3b/Branch_2/Conv2d_0a_1x1/bias',initializer=Mixed_3b_Branch_2_Conv2d_0a_1x1_bias),
            'InceptionV1/Mixed_3b/Branch_2/Conv2d_0b_5x5/bias': tf.get_variable(name='InceptionV1/Mixed_3b/Branch_2/Conv2d_0b_5x5/bias',initializer=Mixed_3b_Branch_2_Conv2d_0b_5x5_bias),
            'InceptionV1/Mixed_3b/Branch_3/Conv2d_0b_1x1/bias': tf.get_variable(name='InceptionV1/Mixed_3b/Branch_3/Conv2d_0b_1x1/bias',initializer=Mixed_3b_Branch_3_Conv2d_0b_1x1_bias),
            #second inception
            'InceptionV1/Mixed_3c/Branch_0/Conv2d_0a_1x1/weights': tf.get_variable(name='InceptionV1/Mixed_3c/Branch_0/Conv2d_0a_1x1/weights',initializer=Mixed_3c_Branch_0_Conv2d_0a_1x1),
            'InceptionV1/Mixed_3c/Branch_1/Conv2d_0a_1x1/weights': tf.get_variable(name='InceptionV1/Mixed_3c/Branch_1/Conv2d_0a_1x1/weights',initializer=Mixed_3c_Branch_1_Conv2d_0a_1x1),
            'InceptionV1/Mixed_3c/Branch_1/Conv2d_0b_3x3/weights': tf.get_variable(name='InceptionV1/Mixed_3c/Branch_1/Conv2d_0b_3x3/weights',initializer=Mixed_3c_Branch_1_Conv2d_0b_3x3),
            'InceptionV1/Mixed_3c/Branch_2/Conv2d_0a_1x1/weights': tf.get_variable(name='InceptionV1/Mixed_3c/Branch_2/Conv2d_0a_1x1/weights',initializer=Mixed_3c_Branch_2_Conv2d_0a_1x1),
            'InceptionV1/Mixed_3c/Branch_2/Conv2d_0b_5x5/weights': tf.get_variable(name='InceptionV1/Mixed_3c/Branch_2/Conv2d_0b_5x5/weights',initializer=Mixed_3c_Branch_2_Conv2d_0b_5x5),
            'InceptionV1/Mixed_3c/Branch_3/Conv2d_0b_1x1/weights': tf.get_variable(name='InceptionV1/Mixed_3c/Branch_3/Conv2d_0b_1x1/weights',initializer=Mixed_3c_Branch_3_Conv2d_0b_1x1),
            # second inception bias
            'InceptionV1/Mixed_3c/Branch_0/Conv2d_0a_1x1/bias': tf.get_variable(name='InceptionV1/Mixed_3c/Branch_0/Conv2d_0a_1x1/bias',initializer=Mixed_3c_Branch_0_Conv2d_0a_1x1_bias),
            'InceptionV1/Mixed_3c/Branch_1/Conv2d_0a_1x1/bias': tf.get_variable(name='InceptionV1/Mixed_3c/Branch_1/Conv2d_0a_1x1/bias',initializer=Mixed_3c_Branch_1_Conv2d_0a_1x1_bias),
            'InceptionV1/Mixed_3c/Branch_1/Conv2d_0b_3x3/bias': tf.get_variable(name='InceptionV1/Mixed_3c/Branch_1/Conv2d_0b_3x3/bias',initializer=Mixed_3c_Branch_1_Conv2d_0b_3x3_bias),
            'InceptionV1/Mixed_3c/Branch_2/Conv2d_0a_1x1/bias': tf.get_variable(name='InceptionV1/Mixed_3c/Branch_2/Conv2d_0a_1x1/bias',initializer=Mixed_3c_Branch_2_Conv2d_0a_1x1_bias),
            'InceptionV1/Mixed_3c/Branch_2/Conv2d_0b_5x5/bias': tf.get_variable(name='InceptionV1/Mixed_3c/Branch_2/Conv2d_0b_5x5/bias',initializer=Mixed_3c_Branch_2_Conv2d_0b_5x5_bias),
            'InceptionV1/Mixed_3c/Branch_3/Conv2d_0b_1x1/bias': tf.get_variable(name='InceptionV1/Mixed_3c/Branch_3/Conv2d_0b_1x1/bias',initializer=Mixed_3c_Branch_3_Conv2d_0b_1x1_bias),
            #third inception
            'InceptionV1/Mixed_4b/Branch_0/Conv2d_0a_1x1/weights': tf.get_variable(name='InceptionV1/Mixed_4b/Branch_0/Conv2d_0a_1x1/weights',initializer=Mixed_4b_Branch_0_Conv2d_0a_1x1),
            'InceptionV1/Mixed_4b/Branch_1/Conv2d_0a_1x1/weights': tf.get_variable(name='InceptionV1/Mixed_4b/Branch_1/Conv2d_0a_1x1/weights',initializer=Mixed_4b_Branch_1_Conv2d_0a_1x1),
            'InceptionV1/Mixed_4b/Branch_1/Conv2d_0b_3x3/weights': tf.get_variable(name='InceptionV1/Mixed_4b/Branch_1/Conv2d_0b_3x3/weights',initializer=Mixed_4b_Branch_1_Conv2d_0b_3x3),
            'InceptionV1/Mixed_4b/Branch_2/Conv2d_0a_1x1/weights': tf.get_variable(name='InceptionV1/Mixed_4b/Branch_2/Conv2d_0a_1x1/weights',initializer=Mixed_4b_Branch_2_Conv2d_0a_1x1),
            'InceptionV1/Mixed_4b/Branch_2/Conv2d_0b_5x5/weights': tf.get_variable(name='InceptionV1/Mixed_4b/Branch_2/Conv2d_0b_5x5/weights',initializer=Mixed_4b_Branch_2_Conv2d_0b_5x5),
            'InceptionV1/Mixed_4b/Branch_3/Conv2d_0b_1x1/weights': tf.get_variable(name='InceptionV1/Mixed_4b/Branch_3/Conv2d_0b_1x1/weights',initializer=Mixed_4b_Branch_3_Conv2d_0b_1x1),
            # third inception bias
            'InceptionV1/Mixed_4b/Branch_0/Conv2d_0a_1x1/bias': tf.get_variable(name='InceptionV1/Mixed_4b/Branch_0/Conv2d_0a_1x1/bias',initializer=Mixed_4b_Branch_0_Conv2d_0a_1x1_bias),
            'InceptionV1/Mixed_4b/Branch_1/Conv2d_0a_1x1/bias': tf.get_variable(name='InceptionV1/Mixed_4b/Branch_1/Conv2d_0a_1x1/bias',initializer=Mixed_4b_Branch_1_Conv2d_0a_1x1_bias),
            'InceptionV1/Mixed_4b/Branch_1/Conv2d_0b_3x3/bias': tf.get_variable(name='InceptionV1/Mixed_4b/Branch_1/Conv2d_0b_3x3/bias',initializer=Mixed_4b_Branch_1_Conv2d_0b_3x3_bias),
            'InceptionV1/Mixed_4b/Branch_2/Conv2d_0a_1x1/bias': tf.get_variable(name='InceptionV1/Mixed_4b/Branch_2/Conv2d_0a_1x1/bias',initializer=Mixed_4b_Branch_2_Conv2d_0a_1x1_bias),
            'InceptionV1/Mixed_4b/Branch_2/Conv2d_0b_5x5/bias': tf.get_variable(name='InceptionV1/Mixed_4b/Branch_2/Conv2d_0b_5x5/bias',initializer=Mixed_4b_Branch_2_Conv2d_0b_5x5_bias),
            'InceptionV1/Mixed_4b/Branch_3/Conv2d_0b_1x1/bias': tf.get_variable(name='InceptionV1/Mixed_4b/Branch_3/Conv2d_0b_1x1/bias',initializer=Mixed_4b_Branch_3_Conv2d_0b_1x1_bias),
            # fourth inception
            'InceptionV1/Mixed_4c/Branch_0/Conv2d_0a_1x1/weights': tf.get_variable(name='InceptionV1/Mixed_4c/Branch_0/Conv2d_0a_1x1/weights',initializer=Mixed_4c_Branch_0_Conv2d_0a_1x1),
            'InceptionV1/Mixed_4c/Branch_1/Conv2d_0a_1x1/weights': tf.get_variable(name='InceptionV1/Mixed_4c/Branch_1/Conv2d_0a_1x1/weights',initializer=Mixed_4c_Branch_1_Conv2d_0a_1x1),
            'InceptionV1/Mixed_4c/Branch_1/Conv2d_0b_3x3/weights': tf.get_variable(name='InceptionV1/Mixed_4c/Branch_1/Conv2d_0b_3x3/weights',initializer=Mixed_4c_Branch_1_Conv2d_0b_3x3),
            'InceptionV1/Mixed_4c/Branch_2/Conv2d_0a_1x1/weights': tf.get_variable(name='InceptionV1/Mixed_4c/Branch_2/Conv2d_0a_1x1/weights',initializer=Mixed_4c_Branch_2_Conv2d_0a_1x1),
            'InceptionV1/Mixed_4c/Branch_2/Conv2d_0b_5x5/weights': tf.get_variable(name='InceptionV1/Mixed_4c/Branch_2/Conv2d_0b_5x5/weights',initializer=Mixed_4c_Branch_2_Conv2d_0b_5x5),
            'InceptionV1/Mixed_4c/Branch_3/Conv2d_0b_1x1/weights': tf.get_variable(name='InceptionV1/Mixed_4c/Branch_3/Conv2d_0b_1x1/weights',initializer=Mixed_4c_Branch_3_Conv2d_0b_1x1),
            # fourth inception bias
            'InceptionV1/Mixed_4c/Branch_0/Conv2d_0a_1x1/bias': tf.get_variable(name='InceptionV1/Mixed_4c/Branch_0/Conv2d_0a_1x1/bias',initializer=Mixed_4c_Branch_0_Conv2d_0a_1x1_bias),
            'InceptionV1/Mixed_4c/Branch_1/Conv2d_0a_1x1/bias': tf.get_variable(name='InceptionV1/Mixed_4c/Branch_1/Conv2d_0a_1x1/bias',initializer=Mixed_4c_Branch_1_Conv2d_0a_1x1_bias),
            'InceptionV1/Mixed_4c/Branch_1/Conv2d_0b_3x3/bias': tf.get_variable(name='InceptionV1/Mixed_4c/Branch_1/Conv2d_0b_3x3/bias',initializer=Mixed_4c_Branch_1_Conv2d_0b_3x3_bias),
            'InceptionV1/Mixed_4c/Branch_2/Conv2d_0a_1x1/bias': tf.get_variable(name='InceptionV1/Mixed_4c/Branch_2/Conv2d_0a_1x1/bias',initializer=Mixed_4c_Branch_2_Conv2d_0a_1x1_bias),
            'InceptionV1/Mixed_4c/Branch_2/Conv2d_0b_5x5/bias': tf.get_variable(name='InceptionV1/Mixed_4c/Branch_2/Conv2d_0b_5x5/bias',initializer=Mixed_4c_Branch_2_Conv2d_0b_5x5_bias),
            'InceptionV1/Mixed_4c/Branch_3/Conv2d_0b_1x1/bias': tf.get_variable(name='InceptionV1/Mixed_4c/Branch_3/Conv2d_0b_1x1/bias',initializer=Mixed_4c_Branch_3_Conv2d_0b_1x1_bias),
            # fifth inception
            'InceptionV1/Mixed_4d/Branch_0/Conv2d_0a_1x1/weights': tf.get_variable(name='InceptionV1/Mixed_4d/Branch_0/Conv2d_0a_1x1/weights',initializer=Mixed_4d_Branch_0_Conv2d_0a_1x1),
            'InceptionV1/Mixed_4d/Branch_1/Conv2d_0a_1x1/weights': tf.get_variable(name='InceptionV1/Mixed_4d/Branch_1/Conv2d_0a_1x1/weights',initializer=Mixed_4d_Branch_1_Conv2d_0a_1x1),
            'InceptionV1/Mixed_4d/Branch_1/Conv2d_0b_3x3/weights': tf.get_variable(name='InceptionV1/Mixed_4d/Branch_1/Conv2d_0b_3x3/weights',initializer=Mixed_4d_Branch_1_Conv2d_0b_3x3),
            'InceptionV1/Mixed_4d/Branch_2/Conv2d_0a_1x1/weights': tf.get_variable(name='InceptionV1/Mixed_4d/Branch_2/Conv2d_0a_1x1/weights',initializer=Mixed_4d_Branch_2_Conv2d_0a_1x1),
            'InceptionV1/Mixed_4d/Branch_2/Conv2d_0b_5x5/weights': tf.get_variable(name='InceptionV1/Mixed_4d/Branch_2/Conv2d_0b_5x5/weights',initializer=Mixed_4d_Branch_2_Conv2d_0b_5x5),
            'InceptionV1/Mixed_4d/Branch_3/Conv2d_0b_1x1/weights': tf.get_variable(name='InceptionV1/Mixed_4d/Branch_3/Conv2d_0b_1x1/weights',initializer=Mixed_4d_Branch_3_Conv2d_0b_1x1),
            # fifth inception bias
            'InceptionV1/Mixed_4d/Branch_0/Conv2d_0a_1x1/bias': tf.get_variable(name='InceptionV1/Mixed_4d/Branch_0/Conv2d_0a_1x1/bias',initializer=Mixed_4d_Branch_0_Conv2d_0a_1x1_bias),
            'InceptionV1/Mixed_4d/Branch_1/Conv2d_0a_1x1/bias': tf.get_variable(name='InceptionV1/Mixed_4d/Branch_1/Conv2d_0a_1x1/bias',initializer=Mixed_4d_Branch_1_Conv2d_0a_1x1_bias),
            'InceptionV1/Mixed_4d/Branch_1/Conv2d_0b_3x3/bias': tf.get_variable(name='InceptionV1/Mixed_4d/Branch_1/Conv2d_0b_3x3/bias',initializer=Mixed_4d_Branch_1_Conv2d_0b_3x3_bias),
            'InceptionV1/Mixed_4d/Branch_2/Conv2d_0a_1x1/bias': tf.get_variable(name='InceptionV1/Mixed_4d/Branch_2/Conv2d_0a_1x1/bias',initializer=Mixed_4d_Branch_2_Conv2d_0a_1x1_bias),
            'InceptionV1/Mixed_4d/Branch_2/Conv2d_0b_5x5/bias': tf.get_variable(name='InceptionV1/Mixed_4d/Branch_2/Conv2d_0b_5x5/bias',initializer=Mixed_4d_Branch_2_Conv2d_0b_5x5_bias),
            'InceptionV1/Mixed_4d/Branch_3/Conv2d_0b_1x1/bias': tf.get_variable(name='InceptionV1/Mixed_4d/Branch_3/Conv2d_0b_1x1/bias',initializer=Mixed_4d_Branch_3_Conv2d_0b_1x1_bias),
            # sixth inception
            'InceptionV1/Mixed_4e/Branch_0/Conv2d_0a_1x1/weights': tf.get_variable(name='InceptionV1/Mixed_4e/Branch_0/Conv2d_0a_1x1/weights',initializer=Mixed_4e_Branch_0_Conv2d_0a_1x1),
            'InceptionV1/Mixed_4e/Branch_1/Conv2d_0a_1x1/weights': tf.get_variable(name='InceptionV1/Mixed_4e/Branch_1/Conv2d_0a_1x1/weights',initializer=Mixed_4e_Branch_1_Conv2d_0a_1x1),
            'InceptionV1/Mixed_4e/Branch_1/Conv2d_0b_3x3/weights': tf.get_variable(name='InceptionV1/Mixed_4e/Branch_1/Conv2d_0b_3x3/weights',initializer=Mixed_4e_Branch_1_Conv2d_0b_3x3),
            'InceptionV1/Mixed_4e/Branch_2/Conv2d_0a_1x1/weights': tf.get_variable(name='InceptionV1/Mixed_4e/Branch_2/Conv2d_0a_1x1/weights',initializer=Mixed_4e_Branch_2_Conv2d_0a_1x1),
            'InceptionV1/Mixed_4e/Branch_2/Conv2d_0b_5x5/weights': tf.get_variable(name='InceptionV1/Mixed_4e/Branch_2/Conv2d_0b_5x5/weights',initializer=Mixed_4e_Branch_2_Conv2d_0b_5x5),
            'InceptionV1/Mixed_4e/Branch_3/Conv2d_0b_1x1/weights': tf.get_variable(name='InceptionV1/Mixed_4e/Branch_3/Conv2d_0b_1x1/weights',initializer=Mixed_4e_Branch_3_Conv2d_0b_1x1),
            # sixth inception bias
            'InceptionV1/Mixed_4e/Branch_0/Conv2d_0a_1x1/bias': tf.get_variable(name='InceptionV1/Mixed_4e/Branch_0/Conv2d_0a_1x1/bias',initializer=Mixed_4e_Branch_0_Conv2d_0a_1x1_bias),
            'InceptionV1/Mixed_4e/Branch_1/Conv2d_0a_1x1/bias': tf.get_variable(name='InceptionV1/Mixed_4e/Branch_1/Conv2d_0a_1x1/bias',initializer=Mixed_4e_Branch_1_Conv2d_0a_1x1_bias),
            'InceptionV1/Mixed_4e/Branch_1/Conv2d_0b_3x3/bias': tf.get_variable(name='InceptionV1/Mixed_4e/Branch_1/Conv2d_0b_3x3/bias',initializer=Mixed_4e_Branch_1_Conv2d_0b_3x3_bias),
            'InceptionV1/Mixed_4e/Branch_2/Conv2d_0a_1x1/bias': tf.get_variable(name='InceptionV1/Mixed_4e/Branch_2/Conv2d_0a_1x1/bias',initializer=Mixed_4e_Branch_2_Conv2d_0a_1x1_bias),
            'InceptionV1/Mixed_4e/Branch_2/Conv2d_0b_5x5/bias': tf.get_variable(name='InceptionV1/Mixed_4e/Branch_2/Conv2d_0b_5x5/bias',initializer=Mixed_4e_Branch_2_Conv2d_0b_5x5_bias),
            'InceptionV1/Mixed_4e/Branch_3/Conv2d_0b_1x1/bias': tf.get_variable(name='InceptionV1/Mixed_4e/Branch_3/Conv2d_0b_1x1/bias',initializer=Mixed_4e_Branch_3_Conv2d_0b_1x1_bias),
            # seventh inception
            'InceptionV1/Mixed_4f/Branch_0/Conv2d_0a_1x1/weights': tf.get_variable(name='InceptionV1/Mixed_4f/Branch_0/Conv2d_0a_1x1/weights',initializer=Mixed_4f_Branch_0_Conv2d_0a_1x1),
            'InceptionV1/Mixed_4f/Branch_1/Conv2d_0a_1x1/weights': tf.get_variable(name='InceptionV1/Mixed_4f/Branch_1/Conv2d_0a_1x1/weights',initializer=Mixed_4f_Branch_1_Conv2d_0a_1x1),
            'InceptionV1/Mixed_4f/Branch_1/Conv2d_0b_3x3/weights': tf.get_variable(name='InceptionV1/Mixed_4f/Branch_1/Conv2d_0b_3x3/weights',initializer=Mixed_4f_Branch_1_Conv2d_0b_3x3),
            'InceptionV1/Mixed_4f/Branch_2/Conv2d_0a_1x1/weights': tf.get_variable(name='InceptionV1/Mixed_4f/Branch_2/Conv2d_0a_1x1/weights',initializer=Mixed_4f_Branch_2_Conv2d_0a_1x1),
            'InceptionV1/Mixed_4f/Branch_2/Conv2d_0b_5x5/weights': tf.get_variable(name='InceptionV1/Mixed_4f/Branch_2/Conv2d_0b_5x5/weights',initializer=Mixed_4f_Branch_2_Conv2d_0b_5x5),
            'InceptionV1/Mixed_4f/Branch_3/Conv2d_0b_1x1/weights': tf.get_variable(name='InceptionV1/Mixed_4f/Branch_3/Conv2d_0b_1x1/weights',initializer=Mixed_4f_Branch_3_Conv2d_0b_1x1),
            # seventh inception bias
            'InceptionV1/Mixed_4f/Branch_0/Conv2d_0a_1x1/bias': tf.get_variable(name='InceptionV1/Mixed_4f/Branch_0/Conv2d_0a_1x1/bias',initializer=Mixed_4f_Branch_0_Conv2d_0a_1x1_bias),
            'InceptionV1/Mixed_4f/Branch_1/Conv2d_0a_1x1/bias': tf.get_variable(name='InceptionV1/Mixed_4f/Branch_1/Conv2d_0a_1x1/bias',initializer=Mixed_4f_Branch_1_Conv2d_0a_1x1_bias),
            'InceptionV1/Mixed_4f/Branch_1/Conv2d_0b_3x3/bias': tf.get_variable(name='InceptionV1/Mixed_4f/Branch_1/Conv2d_0b_3x3/bias',initializer=Mixed_4f_Branch_1_Conv2d_0b_3x3_bias),
            'InceptionV1/Mixed_4f/Branch_2/Conv2d_0a_1x1/bias': tf.get_variable(name='InceptionV1/Mixed_4f/Branch_2/Conv2d_0a_1x1/bias',initializer=Mixed_4f_Branch_2_Conv2d_0a_1x1_bias),
            'InceptionV1/Mixed_4f/Branch_2/Conv2d_0b_5x5/bias': tf.get_variable(name='InceptionV1/Mixed_4f/Branch_2/Conv2d_0b_5x5/bias',initializer=Mixed_4f_Branch_2_Conv2d_0b_5x5_bias),
            'InceptionV1/Mixed_4f/Branch_3/Conv2d_0b_1x1/bias': tf.get_variable(name='InceptionV1/Mixed_4f/Branch_3/Conv2d_0b_1x1/bias',initializer=Mixed_4f_Branch_3_Conv2d_0b_1x1_bias),
            # eighth inception
            'InceptionV1/Mixed_5b/Branch_0/Conv2d_0a_1x1/weights': tf.get_variable(name='InceptionV1/Mixed_5b/Branch_0/Conv2d_0a_1x1/weights',initializer=Mixed_5b_Branch_0_Conv2d_0a_1x1),
            'InceptionV1/Mixed_5b/Branch_1/Conv2d_0a_1x1/weights': tf.get_variable(name='InceptionV1/Mixed_5b/Branch_1/Conv2d_0a_1x1/weights',initializer=Mixed_5b_Branch_1_Conv2d_0a_1x1),
            'InceptionV1/Mixed_5b/Branch_1/Conv2d_0b_3x3/weights': tf.get_variable(name='InceptionV1/Mixed_5b/Branch_1/Conv2d_0b_3x3/weights',initializer=Mixed_5b_Branch_1_Conv2d_0b_3x3),
            'InceptionV1/Mixed_5b/Branch_2/Conv2d_0a_1x1/weights': tf.get_variable(name='InceptionV1/Mixed_5b/Branch_2/Conv2d_0a_1x1/weights',initializer=Mixed_5b_Branch_2_Conv2d_0a_1x1),
            'InceptionV1/Mixed_5b/Branch_2/Conv2d_0a_5x5/weights': tf.get_variable(name='InceptionV1/Mixed_5b/Branch_2/Conv2d_0a_5x5/weights',initializer=Mixed_5b_Branch_2_Conv2d_0b_5x5),
            'InceptionV1/Mixed_5b/Branch_3/Conv2d_0b_1x1/weights': tf.get_variable(name='InceptionV1/Mixed_5b/Branch_3/Conv2d_0b_1x1/weights',initializer=Mixed_5b_Branch_3_Conv2d_0b_1x1),
            # eighth inception bias
            'InceptionV1/Mixed_5b/Branch_0/Conv2d_0a_1x1/bias': tf.get_variable(name='InceptionV1/Mixed_5b/Branch_0/Conv2d_0a_1x1/bias',initializer=Mixed_5b_Branch_0_Conv2d_0a_1x1_bias),
            'InceptionV1/Mixed_5b/Branch_1/Conv2d_0a_1x1/bias': tf.get_variable(name='InceptionV1/Mixed_5b/Branch_1/Conv2d_0a_1x1/bias',initializer=Mixed_5b_Branch_1_Conv2d_0a_1x1_bias),
            'InceptionV1/Mixed_5b/Branch_1/Conv2d_0b_3x3/bias': tf.get_variable(name='InceptionV1/Mixed_5b/Branch_1/Conv2d_0b_3x3/bias',initializer=Mixed_5b_Branch_1_Conv2d_0b_3x3_bias),
            'InceptionV1/Mixed_5b/Branch_2/Conv2d_0a_1x1/bias': tf.get_variable(name='InceptionV1/Mixed_5b/Branch_2/Conv2d_0a_1x1/bias',initializer=Mixed_5b_Branch_2_Conv2d_0a_1x1_bias),
            'InceptionV1/Mixed_5b/Branch_2/Conv2d_0a_5x5/bias': tf.get_variable(name='InceptionV1/Mixed_5b/Branch_2/Conv2d_0a_5x5/bias',initializer=Mixed_5b_Branch_2_Conv2d_0b_5x5_bias),
            'InceptionV1/Mixed_5b/Branch_3/Conv2d_0b_1x1/bias': tf.get_variable(name='InceptionV1/Mixed_5b/Branch_3/Conv2d_0b_1x1/bias',initializer=Mixed_5b_Branch_3_Conv2d_0b_1x1_bias),
            # ninth inception
            'InceptionV1/Mixed_5c/Branch_0/Conv2d_0a_1x1/weights': tf.get_variable(name='InceptionV1/Mixed_5c/Branch_0/Conv2d_0a_1x1/weights',initializer=Mixed_5c_Branch_0_Conv2d_0a_1x1),
            'InceptionV1/Mixed_5c/Branch_1/Conv2d_0a_1x1/weights': tf.get_variable(name='InceptionV1/Mixed_5c/Branch_1/Conv2d_0a_1x1/weights',initializer=Mixed_5c_Branch_1_Conv2d_0a_1x1),
            'InceptionV1/Mixed_5c/Branch_1/Conv2d_0b_3x3/weights': tf.get_variable(name='InceptionV1/Mixed_5c/Branch_1/Conv2d_0b_3x3/weights',initializer=Mixed_5c_Branch_1_Conv2d_0b_3x3),
            'InceptionV1/Mixed_5c/Branch_2/Conv2d_0a_1x1/weights': tf.get_variable(name='InceptionV1/Mixed_5c/Branch_2/Conv2d_0a_1x1/weights',initializer=Mixed_5c_Branch_2_Conv2d_0a_1x1),
            'InceptionV1/Mixed_5c/Branch_2/Conv2d_0b_5x5/weights': tf.get_variable(name='InceptionV1/Mixed_5c/Branch_2/Conv2d_0b_5x5/weights',initializer=Mixed_5c_Branch_2_Conv2d_0b_5x5),
            'InceptionV1/Mixed_5c/Branch_3/Conv2d_0b_1x1/weights': tf.get_variable(name='InceptionV1/Mixed_5c/Branch_3/Conv2d_0b_1x1/weights',initializer=Mixed_5c_Branch_3_Conv2d_0b_1x1),
            # ninth inception bias
            'InceptionV1/Mixed_5c/Branch_0/Conv2d_0a_1x1/bias': tf.get_variable(name='InceptionV1/Mixed_5c/Branch_0/Conv2d_0a_1x1/bias',initializer=Mixed_5c_Branch_0_Conv2d_0a_1x1_bias),
            'InceptionV1/Mixed_5c/Branch_1/Conv2d_0a_1x1/bias': tf.get_variable(name='InceptionV1/Mixed_5c/Branch_1/Conv2d_0a_1x1/bias',initializer=Mixed_5c_Branch_1_Conv2d_0a_1x1_bias),
            'InceptionV1/Mixed_5c/Branch_1/Conv2d_0b_3x3/bias': tf.get_variable(name='InceptionV1/Mixed_5c/Branch_1/Conv2d_0b_3x3/bias',initializer=Mixed_5c_Branch_1_Conv2d_0b_3x3_bias),
            'InceptionV1/Mixed_5c/Branch_2/Conv2d_0a_1x1/bias': tf.get_variable(name='InceptionV1/Mixed_5c/Branch_2/Conv2d_0a_1x1/bias',initializer=Mixed_5c_Branch_2_Conv2d_0a_1x1_bias),
            'InceptionV1/Mixed_5c/Branch_2/Conv2d_0b_5x5/bias': tf.get_variable(name='InceptionV1/Mixed_5c/Branch_2/Conv2d_0b_5x5/bias',initializer=Mixed_5c_Branch_2_Conv2d_0b_5x5_bias),
            'InceptionV1/Mixed_5c/Branch_3/Conv2d_0b_1x1/bias': tf.get_variable(name='InceptionV1/Mixed_5c/Branch_3/Conv2d_0b_1x1/bias',initializer=Mixed_5c_Branch_3_Conv2d_0b_1x1_bias),
        }
        return variables
    def CNN_Metric_Model(self,x):
        #layer 1 - conv
        w_1 = self.var_dict['InceptionV1/Conv2d_1a_7x7/weights']
        b_1 = self.var_dict['InceptionV1/Conv2d_1a_7x7/bias']
        padding1 = tf.constant([[0,0],[3,3],[3,3],[0,0]])
        input_d = tf.pad(x,paddings=padding1)
        h_conv1 = tf.nn.conv2d(input_d, w_1, strides=[1, 2, 2, 1], padding='VALID') + b_1
        h_conv1 = tf.nn.relu(h_conv1)
        #layer 1 - max pool
        padding_format = tf.constant([[0,0],[0,1],[0,1],[0,0]])
        h_conv1 = tf.pad(h_conv1,paddings=padding_format)
        h_pool1 = tf.nn.max_pool(h_conv1, ksize=[1, 3, 3, 1],
                                 strides=[1, 2, 2, 1], padding='VALID')
        #h_pool1 = tf.nn.local_response_normalization(h_pool1,depth_radius=5,alpha=0.0001,beta=0.75)
        #layer 2 - conv
        w_2 = self.var_dict['InceptionV1/Conv2d_2b_1x1/weights']
        b_2 = self.var_dict['InceptionV1/Conv2d_2b_1x1/bias']
        h_conv2 = tf.nn.conv2d(h_pool1, w_2, strides=[1, 1, 1, 1], padding='VALID') + b_2
        h_conv2 = tf.nn.relu(h_conv2)

        #layer 3 - conv
        padding3 = tf.constant([[0, 0], [1, 1], [1, 1], [0, 0]])
        h_conv2 = tf.pad(h_conv2, paddings=padding3)
        w_3 = self.var_dict['InceptionV1/Conv2d_2c_3x3/weights']
        b_3 = self.var_dict['InceptionV1/Conv2d_2c_3x3/bias']
        h_conv3 = tf.nn.conv2d(h_conv2, w_3, strides=[1, 1, 1, 1], padding='VALID') + b_3
        h_conv3 = tf.nn.relu(h_conv3)
        #h_conv3 = tf.nn.local_response_normalization(h_conv3, depth_radius=5, alpha=0.0001, beta=0.75)

        #layer 3 - max pool
        h_pool3 = tf.nn.max_pool(h_conv3, ksize=[1, 3, 3, 1],
                                 strides=[1, 2, 2, 1], padding='VALID')
        #mixed layer 3b
        #first inception
        #branch 0
        w_4 = self.var_dict['InceptionV1/Mixed_3b/Branch_0/Conv2d_0a_1x1/weights']
        b_4 = self.var_dict['InceptionV1/Mixed_3b/Branch_0/Conv2d_0a_1x1/bias']
        branch1_0 = tf.nn.conv2d(h_pool3, w_4, strides=[1, 1, 1, 1], padding='VALID') + b_4
        branch1_0 = tf.nn.relu(branch1_0)

        #branch 1
        w_5 = self.var_dict['InceptionV1/Mixed_3b/Branch_1/Conv2d_0a_1x1/weights']
        b_5 = self.var_dict['InceptionV1/Mixed_3b/Branch_1/Conv2d_0a_1x1/bias']
        branch1_1 = tf.nn.conv2d(h_pool3, w_5, strides=[1, 1, 1, 1], padding='VALID') + b_5
        branch1_1 = tf.nn.relu(branch1_1)

        padding6 = tf.constant([[0, 0], [1, 1], [1, 1], [0, 0]])
        branch1_1 = tf.pad(branch1_1, paddings=padding6)
        w_6 = self.var_dict['InceptionV1/Mixed_3b/Branch_1/Conv2d_0b_3x3/weights']
        b_6 = self.var_dict['InceptionV1/Mixed_3b/Branch_1/Conv2d_0b_3x3/bias']
        branch1_1 = tf.nn.conv2d(branch1_1, w_6, strides=[1, 1, 1, 1], padding='VALID') + b_6
        branch1_1 = tf.nn.relu(branch1_1)

        #branch 2
        w_7 = self.var_dict['InceptionV1/Mixed_3b/Branch_2/Conv2d_0a_1x1/weights']
        b_7 = self.var_dict['InceptionV1/Mixed_3b/Branch_2/Conv2d_0a_1x1/bias']
        branch1_2 = tf.nn.conv2d(h_pool3, w_7, strides=[1, 1, 1, 1], padding='VALID') + b_7
        branch1_2 = tf.nn.relu(branch1_2)

        padding7 = tf.constant([[0, 0], [2, 2], [2, 2], [0, 0]])
        branch1_2 = tf.pad(branch1_2, paddings=padding7)
        w_8 = self.var_dict['InceptionV1/Mixed_3b/Branch_2/Conv2d_0b_5x5/weights']
        b_8 = self.var_dict['InceptionV1/Mixed_3b/Branch_2/Conv2d_0b_5x5/bias']
        branch1_2 = tf.nn.conv2d(branch1_2, w_8, strides=[1, 1, 1, 1], padding='VALID') + b_8
        branch1_2 = tf.nn.relu(branch1_2)

        #branch 3
        padding7 = tf.constant([[0, 0], [1, 1], [1, 1], [0, 0]])
        branch1_3 = tf.pad(h_pool3, paddings=padding7)
        branch1_3 = tf.nn.max_pool(branch1_3, ksize=[1, 3, 3, 1],
                                 strides=[1, 1, 1, 1], padding='VALID')
        w_9 = self.var_dict['InceptionV1/Mixed_3b/Branch_3/Conv2d_0b_1x1/weights']
        b_9 = self.var_dict['InceptionV1/Mixed_3b/Branch_3/Conv2d_0b_1x1/bias']
        branch1_3 = tf.nn.conv2d(branch1_3, w_9, strides=[1, 1, 1, 1], padding='VALID') + b_9
        branch1_3 = tf.nn.relu(branch1_3)

        incpt = tf.concat(
            axis=3, values=[branch1_0, branch1_1, branch1_2, branch1_3])
        #second inception
        #branch 0
        w_10 = self.var_dict['InceptionV1/Mixed_3c/Branch_0/Conv2d_0a_1x1/weights']
        b_10 = self.var_dict['InceptionV1/Mixed_3c/Branch_0/Conv2d_0a_1x1/bias']
        branch2_0 = tf.nn.conv2d(incpt, w_10, strides=[1, 1, 1, 1], padding='VALID') + b_10
        branch2_0 = tf.nn.relu(branch2_0)

        #branch 1
        w_11 = self.var_dict['InceptionV1/Mixed_3c/Branch_1/Conv2d_0a_1x1/weights']
        b_11 = self.var_dict['InceptionV1/Mixed_3c/Branch_1/Conv2d_0a_1x1/bias']
        branch2_1 = tf.nn.conv2d(incpt, w_11, strides=[1, 1, 1, 1], padding='VALID') + b_11
        branch2_1 = tf.nn.relu(branch2_1)

        padding_format = tf.constant([[0, 0], [1, 1], [1, 1], [0, 0]])
        branch2_1 = tf.pad(branch2_1, paddings=padding_format)
        w_12 = self.var_dict['InceptionV1/Mixed_3c/Branch_1/Conv2d_0b_3x3/weights']
        b_12 = self.var_dict['InceptionV1/Mixed_3c/Branch_1/Conv2d_0b_3x3/bias']
        branch2_1 = tf.nn.conv2d(branch2_1, w_12, strides=[1, 1, 1, 1], padding='VALID') + b_12
        branch2_1 = tf.nn.relu(branch2_1)

        #branch 2
        w_13 = self.var_dict['InceptionV1/Mixed_3c/Branch_2/Conv2d_0a_1x1/weights']
        b_13 = self.var_dict['InceptionV1/Mixed_3c/Branch_2/Conv2d_0a_1x1/bias']
        branch2_2 = tf.nn.conv2d(incpt, w_13, strides=[1, 1, 1, 1], padding='VALID') + b_13
        branch2_2 = tf.nn.relu(branch2_2)

        padding_format = tf.constant([[0, 0], [2, 2], [2, 2], [0, 0]])
        branch2_2 = tf.pad(branch2_2, paddings=padding_format)
        w_14 = self.var_dict['InceptionV1/Mixed_3c/Branch_2/Conv2d_0b_5x5/weights']
        b_14 = self.var_dict['InceptionV1/Mixed_3c/Branch_2/Conv2d_0b_5x5/bias']
        branch2_2 = tf.nn.conv2d(branch2_2, w_14, strides=[1, 1, 1, 1], padding='VALID') + b_14
        branch2_2 = tf.nn.relu(branch2_2)

        #branch 3
        padding_format = tf.constant([[0, 0], [1, 1], [1, 1], [0, 0]])
        branch2_3 = tf.pad(incpt, paddings=padding_format)
        branch2_3 = tf.nn.max_pool(branch2_3, ksize=[1, 3, 3, 1],
                                 strides=[1, 1, 1, 1], padding='VALID')
        w_15 = self.var_dict['InceptionV1/Mixed_3c/Branch_3/Conv2d_0b_1x1/weights']
        b_15 = self.var_dict['InceptionV1/Mixed_3c/Branch_3/Conv2d_0b_1x1/bias']
        branch2_3 = tf.nn.conv2d(branch2_3, w_15, strides=[1, 1, 1, 1], padding='VALID') + b_15
        branch2_3 = tf.nn.relu(branch2_3)

        incpt = tf.concat(
            axis=3, values=[branch2_0, branch2_1, branch2_2, branch2_3])
        padding_format = tf.constant([[0,0],[0,1],[0,1],[0,0]])
        incpt = tf.pad(incpt,paddings=padding_format)
        incpt = tf.nn.max_pool(incpt, ksize=[1, 3, 3, 1],
                                   strides=[1, 2, 2, 1], padding='VALID')
        #third inception
        #branch 0
        w_16 = self.var_dict['InceptionV1/Mixed_4b/Branch_0/Conv2d_0a_1x1/weights']
        b_16 = self.var_dict['InceptionV1/Mixed_4b/Branch_0/Conv2d_0a_1x1/bias']
        branch3_0 = tf.nn.conv2d(incpt, w_16, strides=[1, 1, 1, 1], padding='VALID') + b_16
        branch3_0 = tf.nn.relu(branch3_0)

        #branch 1
        w_17 = self.var_dict['InceptionV1/Mixed_4b/Branch_1/Conv2d_0a_1x1/weights']
        b_17 = self.var_dict['InceptionV1/Mixed_4b/Branch_1/Conv2d_0a_1x1/bias']
        branch3_1 = tf.nn.conv2d(incpt, w_17, strides=[1, 1, 1, 1], padding='VALID') + b_17
        branch3_1 = tf.nn.relu(branch3_1)

        padding_format = tf.constant([[0, 0], [1, 1], [1, 1], [0, 0]])
        branch3_1 = tf.pad(branch3_1, paddings=padding_format)
        w_18 = self.var_dict['InceptionV1/Mixed_4b/Branch_1/Conv2d_0b_3x3/weights']
        b_18 = self.var_dict['InceptionV1/Mixed_4b/Branch_1/Conv2d_0b_3x3/bias']
        branch3_1 = tf.nn.conv2d(branch3_1, w_18, strides=[1, 1, 1, 1], padding='VALID') + b_18
        branch3_1 = tf.nn.relu(branch3_1)

        #branch 2
        w_19 = self.var_dict['InceptionV1/Mixed_4b/Branch_2/Conv2d_0a_1x1/weights']
        b_19 = self.var_dict['InceptionV1/Mixed_4b/Branch_2/Conv2d_0a_1x1/bias']
        branch3_2 = tf.nn.conv2d(incpt, w_19, strides=[1, 1, 1, 1], padding='VALID') + b_19
        branch3_2 = tf.nn.relu(branch3_2)

        padding_format = tf.constant([[0, 0], [2, 2], [2, 2], [0, 0]])
        branch3_2 = tf.pad(branch3_2, paddings=padding_format)
        w_20 = self.var_dict['InceptionV1/Mixed_4b/Branch_2/Conv2d_0b_5x5/weights']
        b_20 = self.var_dict['InceptionV1/Mixed_4b/Branch_2/Conv2d_0b_5x5/bias']
        branch3_2 = tf.nn.conv2d(branch3_2, w_20, strides=[1, 1, 1, 1], padding='VALID') + b_20
        branch3_2 = tf.nn.relu(branch3_2)

        #branch 3
        padding_format = tf.constant([[0, 0], [1, 1], [1, 1], [0, 0]])
        branch3_3 = tf.pad(incpt, paddings=padding_format)
        branch3_3 = tf.nn.max_pool(branch3_3, ksize=[1, 3, 3, 1],
                                 strides=[1, 1, 1, 1], padding='VALID')
        w_21 = self.var_dict['InceptionV1/Mixed_4b/Branch_3/Conv2d_0b_1x1/weights']
        b_21 = self.var_dict['InceptionV1/Mixed_4b/Branch_3/Conv2d_0b_1x1/bias']
        branch3_3 = tf.nn.conv2d(branch3_3, w_21, strides=[1, 1, 1, 1], padding='VALID') + b_21
        branch3_3 = tf.nn.relu(branch3_3)

        incpt = tf.concat(
            axis=3, values=[branch3_0, branch3_1, branch3_2, branch3_3])
        #fourth inception
        #branch 0
        w_22 = self.var_dict['InceptionV1/Mixed_4c/Branch_0/Conv2d_0a_1x1/weights']
        b_22 = self.var_dict['InceptionV1/Mixed_4c/Branch_0/Conv2d_0a_1x1/bias']
        branch4_0 = tf.nn.conv2d(incpt, w_22, strides=[1, 1, 1, 1], padding='VALID') + b_22
        branch4_0 = tf.nn.relu(branch4_0)

        #branch 1
        w_23 = self.var_dict['InceptionV1/Mixed_4c/Branch_1/Conv2d_0a_1x1/weights']
        b_23 = self.var_dict['InceptionV1/Mixed_4c/Branch_1/Conv2d_0a_1x1/bias']
        branch4_1 = tf.nn.conv2d(incpt, w_23, strides=[1, 1, 1, 1], padding='VALID') + b_23
        branch4_1 = tf.nn.relu(branch4_1)

        padding_format = tf.constant([[0, 0], [1, 1], [1, 1], [0, 0]])
        branch4_1 = tf.pad(branch4_1, paddings=padding_format)
        w_24 = self.var_dict['InceptionV1/Mixed_4c/Branch_1/Conv2d_0b_3x3/weights']
        b_24 = self.var_dict['InceptionV1/Mixed_4c/Branch_1/Conv2d_0b_3x3/bias']
        branch4_1 = tf.nn.conv2d(branch4_1, w_24, strides=[1, 1, 1, 1], padding='VALID') + b_24
        branch4_1 = tf.nn.relu(branch4_1)

        #branch 2
        w_25 = self.var_dict['InceptionV1/Mixed_4c/Branch_2/Conv2d_0a_1x1/weights']
        b_25 = self.var_dict['InceptionV1/Mixed_4c/Branch_2/Conv2d_0a_1x1/bias']
        branch4_2 = tf.nn.conv2d(incpt, w_25, strides=[1, 1, 1, 1], padding='VALID') + b_25
        branch4_2 = tf.nn.relu(branch4_2)

        padding_format = tf.constant([[0, 0], [2, 2], [2, 2], [0, 0]])
        branch4_2 = tf.pad(branch4_2, paddings=padding_format)
        w_26 = self.var_dict['InceptionV1/Mixed_4c/Branch_2/Conv2d_0b_5x5/weights']
        b_26 = self.var_dict['InceptionV1/Mixed_4c/Branch_2/Conv2d_0b_5x5/bias']
        branch4_2 = tf.nn.conv2d(branch4_2, w_26, strides=[1, 1, 1, 1], padding='VALID') + b_26
        branch4_2 = tf.nn.relu(branch4_2)

        #branch 3
        padding_format = tf.constant([[0, 0], [1, 1], [1, 1], [0, 0]])
        branch4_3 = tf.pad(incpt, paddings=padding_format)
        branch4_3 = tf.nn.max_pool(branch4_3, ksize=[1, 3, 3, 1],
                                 strides=[1, 1, 1, 1], padding='VALID')
        w_27 = self.var_dict['InceptionV1/Mixed_4c/Branch_3/Conv2d_0b_1x1/weights']
        b_27 = self.var_dict['InceptionV1/Mixed_4c/Branch_3/Conv2d_0b_1x1/bias']
        branch4_3 = tf.nn.conv2d(branch4_3, w_27, strides=[1, 1, 1, 1], padding='VALID') + b_27
        branch4_3 = tf.nn.relu(branch4_3)

        incpt = tf.concat(
            axis=3, values=[branch4_0, branch4_1, branch4_2, branch4_3])
        #fifth inception
        #branch 0
        w_28 = self.var_dict['InceptionV1/Mixed_4d/Branch_0/Conv2d_0a_1x1/weights']
        b_28 = self.var_dict['InceptionV1/Mixed_4d/Branch_0/Conv2d_0a_1x1/bias']
        branch5_0 = tf.nn.conv2d(incpt, w_28, strides=[1, 1, 1, 1], padding='VALID') + b_28
        branch5_0 = tf.nn.relu(branch5_0)

        #branch 1
        w_29 = self.var_dict['InceptionV1/Mixed_4d/Branch_1/Conv2d_0a_1x1/weights']
        b_29 = self.var_dict['InceptionV1/Mixed_4d/Branch_1/Conv2d_0a_1x1/bias']
        branch5_1 = tf.nn.conv2d(incpt, w_29, strides=[1, 1, 1, 1], padding='VALID') + b_29
        branch5_1 = tf.nn.relu(branch5_1)

        padding_format = tf.constant([[0, 0], [1, 1], [1, 1], [0, 0]])
        branch5_1 = tf.pad(branch5_1, paddings=padding_format)
        w_30 = self.var_dict['InceptionV1/Mixed_4d/Branch_1/Conv2d_0b_3x3/weights']
        b_30 = self.var_dict['InceptionV1/Mixed_4d/Branch_1/Conv2d_0b_3x3/bias']
        branch5_1 = tf.nn.conv2d(branch5_1, w_30, strides=[1, 1, 1, 1], padding='VALID') + b_30
        branch5_1 = tf.nn.relu(branch5_1)

        #branch 2
        w_31 = self.var_dict['InceptionV1/Mixed_4d/Branch_2/Conv2d_0a_1x1/weights']
        b_31 = self.var_dict['InceptionV1/Mixed_4d/Branch_2/Conv2d_0a_1x1/bias']
        branch5_2 = tf.nn.conv2d(incpt, w_31, strides=[1, 1, 1, 1], padding='VALID') + b_31
        branch5_2 = tf.nn.relu(branch5_2)

        padding_format = tf.constant([[0, 0], [2, 2], [2, 2], [0, 0]])
        branch5_2 = tf.pad(branch5_2, paddings=padding_format)
        w_32 = self.var_dict['InceptionV1/Mixed_4d/Branch_2/Conv2d_0b_5x5/weights']
        b_32 = self.var_dict['InceptionV1/Mixed_4d/Branch_2/Conv2d_0b_5x5/bias']
        branch5_2 = tf.nn.conv2d(branch5_2, w_32, strides=[1, 1, 1, 1], padding='VALID') + b_32
        branch5_2 = tf.nn.relu(branch5_2)

        #branch 3
        padding_format = tf.constant([[0, 0], [1, 1], [1, 1], [0, 0]])
        branch5_3 = tf.pad(incpt, paddings=padding_format)
        branch5_3 = tf.nn.max_pool(branch5_3, ksize=[1, 3, 3, 1],
                                 strides=[1, 1, 1, 1], padding='VALID')
        w_33 = self.var_dict['InceptionV1/Mixed_4d/Branch_3/Conv2d_0b_1x1/weights']
        b_33 = self.var_dict['InceptionV1/Mixed_4d/Branch_3/Conv2d_0b_1x1/bias']
        branch5_3 = tf.nn.conv2d(branch5_3, w_33, strides=[1, 1, 1, 1], padding='VALID') + b_33
        branch5_3 = tf.nn.relu(branch5_3)

        incpt = tf.concat(
            axis=3, values=[branch5_0, branch5_1, branch5_2, branch5_3])
        #sixth inception
        #branch 0
        w_34 = self.var_dict['InceptionV1/Mixed_4e/Branch_0/Conv2d_0a_1x1/weights']
        b_34 = self.var_dict['InceptionV1/Mixed_4e/Branch_0/Conv2d_0a_1x1/bias']
        branch6_0 = tf.nn.conv2d(incpt, w_34, strides=[1, 1, 1, 1], padding='VALID') + b_34
        branch6_0 = tf.nn.relu(branch6_0)

        #branch 1
        w_35 = self.var_dict['InceptionV1/Mixed_4e/Branch_1/Conv2d_0a_1x1/weights']
        b_35 = self.var_dict['InceptionV1/Mixed_4e/Branch_1/Conv2d_0a_1x1/bias']
        branch6_1 = tf.nn.conv2d(incpt, w_35, strides=[1, 1, 1, 1], padding='VALID') + b_35
        branch6_1 = tf.nn.relu(branch6_1)

        padding_format = tf.constant([[0, 0], [1, 1], [1, 1], [0, 0]])
        branch6_1 = tf.pad(branch6_1, paddings=padding_format)
        w_36 = self.var_dict['InceptionV1/Mixed_4e/Branch_1/Conv2d_0b_3x3/weights']
        b_36 = self.var_dict['InceptionV1/Mixed_4e/Branch_1/Conv2d_0b_3x3/bias']
        branch6_1 = tf.nn.conv2d(branch6_1, w_36, strides=[1, 1, 1, 1], padding='VALID') + b_36
        branch6_1 = tf.nn.relu(branch6_1)

        #branch 2
        w_37 = self.var_dict['InceptionV1/Mixed_4e/Branch_2/Conv2d_0a_1x1/weights']
        b_37 = self.var_dict['InceptionV1/Mixed_4e/Branch_2/Conv2d_0a_1x1/bias']
        branch6_2 = tf.nn.conv2d(incpt, w_37, strides=[1, 1, 1, 1], padding='VALID') + b_37
        branch6_2 = tf.nn.relu(branch6_2)

        padding_format = tf.constant([[0, 0], [2, 2], [2, 2], [0, 0]])
        branch6_2 = tf.pad(branch6_2, paddings=padding_format)
        w_38 = self.var_dict['InceptionV1/Mixed_4e/Branch_2/Conv2d_0b_5x5/weights']
        b_38 = self.var_dict['InceptionV1/Mixed_4e/Branch_2/Conv2d_0b_5x5/bias']
        branch6_2 = tf.nn.conv2d(branch6_2, w_38, strides=[1, 1, 1, 1], padding='VALID') + b_38
        branch6_2 = tf.nn.relu(branch6_2)

        #branch 3
        padding_format = tf.constant([[0, 0], [1, 1], [1, 1], [0, 0]])
        branch6_3 = tf.pad(incpt, paddings=padding_format)
        branch6_3 = tf.nn.max_pool(branch6_3, ksize=[1, 3, 3, 1],
                                 strides=[1, 1, 1, 1], padding='VALID')
        w_39 = self.var_dict['InceptionV1/Mixed_4e/Branch_3/Conv2d_0b_1x1/weights']
        b_39 = self.var_dict['InceptionV1/Mixed_4e/Branch_3/Conv2d_0b_1x1/bias']
        branch6_3 = tf.nn.conv2d(branch6_3, w_39, strides=[1, 1, 1, 1], padding='VALID') + b_39
        branch6_3 = tf.nn.relu(branch6_3)

        incpt = tf.concat(
            axis=3, values=[branch6_0, branch6_1, branch6_2, branch6_3])
        #seventh inception
        #branch 0
        w_40 = self.var_dict['InceptionV1/Mixed_4f/Branch_0/Conv2d_0a_1x1/weights']
        b_40 = self.var_dict['InceptionV1/Mixed_4f/Branch_0/Conv2d_0a_1x1/bias']
        branch7_0 = tf.nn.conv2d(incpt, w_40, strides=[1, 1, 1, 1], padding='VALID') + b_40
        branch7_0 = tf.nn.relu(branch7_0)

        #branch 1
        w_41 = self.var_dict['InceptionV1/Mixed_4f/Branch_1/Conv2d_0a_1x1/weights']
        b_41 = self.var_dict['InceptionV1/Mixed_4f/Branch_1/Conv2d_0a_1x1/bias']
        branch7_1 = tf.nn.conv2d(incpt, w_41, strides=[1, 1, 1, 1], padding='VALID') + b_41
        branch7_1 = tf.nn.relu(branch7_1)

        padding_format = tf.constant([[0, 0], [1, 1], [1, 1], [0, 0]])
        branch7_1 = tf.pad(branch7_1, paddings=padding_format)
        w_42 = self.var_dict['InceptionV1/Mixed_4f/Branch_1/Conv2d_0b_3x3/weights']
        b_42 = self.var_dict['InceptionV1/Mixed_4f/Branch_1/Conv2d_0b_3x3/bias']
        branch7_1 = tf.nn.conv2d(branch7_1, w_42, strides=[1, 1, 1, 1], padding='VALID') + b_42
        branch7_1 = tf.nn.relu(branch7_1)

        #branch 2
        w_43 = self.var_dict['InceptionV1/Mixed_4f/Branch_2/Conv2d_0a_1x1/weights']
        b_43 = self.var_dict['InceptionV1/Mixed_4f/Branch_2/Conv2d_0a_1x1/bias']
        branch7_2 = tf.nn.conv2d(incpt, w_43, strides=[1, 1, 1, 1], padding='VALID') + b_43
        branch7_2 = tf.nn.relu(branch7_2)

        padding_format = tf.constant([[0, 0], [2, 2], [2, 2], [0, 0]])
        branch7_2 = tf.pad(branch7_2, paddings=padding_format)
        w_44 = self.var_dict['InceptionV1/Mixed_4f/Branch_2/Conv2d_0b_5x5/weights']
        b_44 = self.var_dict['InceptionV1/Mixed_4f/Branch_2/Conv2d_0b_5x5/bias']
        branch7_2 = tf.nn.conv2d(branch7_2, w_44, strides=[1, 1, 1, 1], padding='VALID') + b_44
        branch7_2 = tf.nn.relu(branch7_2)

        #branch 3
        padding_format = tf.constant([[0, 0], [1, 1], [1, 1], [0, 0]])
        branch7_3 = tf.pad(incpt, paddings=padding_format)
        branch7_3 = tf.nn.max_pool(branch7_3, ksize=[1, 3, 3, 1],
                                 strides=[1, 1, 1, 1], padding='VALID')
        w_45 = self.var_dict['InceptionV1/Mixed_4f/Branch_3/Conv2d_0b_1x1/weights']
        b_45 = self.var_dict['InceptionV1/Mixed_4f/Branch_3/Conv2d_0b_1x1/bias']
        branch7_3 = tf.nn.conv2d(branch7_3, w_45, strides=[1, 1, 1, 1], padding='VALID') + b_45
        branch7_3 = tf.nn.relu(branch7_3)

        incpt = tf.concat(
            axis=3, values=[branch7_0, branch7_1, branch7_2, branch7_3])
        padding_format = tf.constant([[0, 0], [0, 1], [0, 1], [0, 0]])
        incpt = tf.pad(incpt, paddings=padding_format)
        incpt = tf.nn.max_pool(incpt, ksize=[1, 3, 3, 1],
                               strides=[1, 2, 2, 1], padding='VALID')
        #eighth inception
        #branch 0
        w_46 = self.var_dict['InceptionV1/Mixed_5b/Branch_0/Conv2d_0a_1x1/weights']
        b_46 = self.var_dict['InceptionV1/Mixed_5b/Branch_0/Conv2d_0a_1x1/bias']
        branch8_0 = tf.nn.conv2d(incpt, w_46, strides=[1, 1, 1, 1], padding='VALID') + b_46
        branch8_0 = tf.nn.relu(branch8_0)

        #branch 1
        w_47 = self.var_dict['InceptionV1/Mixed_5b/Branch_1/Conv2d_0a_1x1/weights']
        b_47 = self.var_dict['InceptionV1/Mixed_5b/Branch_1/Conv2d_0a_1x1/bias']
        branch8_1 = tf.nn.conv2d(incpt, w_47, strides=[1, 1, 1, 1], padding='VALID') + b_47
        branch8_1 = tf.nn.relu(branch8_1)

        padding_format = tf.constant([[0, 0], [1, 1], [1, 1], [0, 0]])
        branch8_1 = tf.pad(branch8_1, paddings=padding_format)
        w_48 = self.var_dict['InceptionV1/Mixed_5b/Branch_1/Conv2d_0b_3x3/weights']
        b_48 = self.var_dict['InceptionV1/Mixed_5b/Branch_1/Conv2d_0b_3x3/bias']
        branch8_1 = tf.nn.conv2d(branch8_1, w_48, strides=[1, 1, 1, 1], padding='VALID') + b_48
        branch8_1 = tf.nn.relu(branch8_1)

        #branch 2
        w_49 = self.var_dict['InceptionV1/Mixed_5b/Branch_2/Conv2d_0a_1x1/weights']
        b_49 = self.var_dict['InceptionV1/Mixed_5b/Branch_2/Conv2d_0a_1x1/bias']
        branch8_2 = tf.nn.conv2d(incpt, w_49, strides=[1, 1, 1, 1], padding='VALID') + b_49
        branch8_2 = tf.nn.relu(branch8_2)

        padding_format = tf.constant([[0, 0], [2, 2], [2, 2], [0, 0]])
        branch8_2 = tf.pad(branch8_2, paddings=padding_format)
        w_50 = self.var_dict['InceptionV1/Mixed_5b/Branch_2/Conv2d_0a_5x5/weights']
        b_50 = self.var_dict['InceptionV1/Mixed_5b/Branch_2/Conv2d_0a_5x5/bias']
        branch8_2 = tf.nn.conv2d(branch8_2, w_50, strides=[1, 1, 1, 1], padding='VALID') + b_50
        branch8_2 = tf.nn.relu(branch8_2)

        #branch 3
        padding_format = tf.constant([[0, 0], [1, 1], [1, 1], [0, 0]])
        branch8_3 = tf.pad(incpt, paddings=padding_format)
        branch8_3 = tf.nn.max_pool(branch8_3, ksize=[1, 3, 3, 1],
                                 strides=[1, 1, 1, 1], padding='VALID')
        w_51 = self.var_dict['InceptionV1/Mixed_5b/Branch_3/Conv2d_0b_1x1/weights']
        b_51 = self.var_dict['InceptionV1/Mixed_5b/Branch_3/Conv2d_0b_1x1/bias']
        branch8_3 = tf.nn.conv2d(branch8_3, w_51, strides=[1, 1, 1, 1], padding='VALID') + b_51
        branch8_3 = tf.nn.relu(branch8_3)

        incpt = tf.concat(
            axis=3, values=[branch8_0, branch8_1, branch8_2, branch8_3])
        #ninth inception
        #branch 0
        w_52 = self.var_dict['InceptionV1/Mixed_5c/Branch_0/Conv2d_0a_1x1/weights']
        b_52 = self.var_dict['InceptionV1/Mixed_5c/Branch_0/Conv2d_0a_1x1/bias']
        branch9_0 = tf.nn.conv2d(incpt, w_52, strides=[1, 1, 1, 1], padding='VALID') + b_52
        branch9_0 = tf.nn.relu(branch9_0)

        #branch 1
        w_53 = self.var_dict['InceptionV1/Mixed_5c/Branch_1/Conv2d_0a_1x1/weights']
        b_53 = self.var_dict['InceptionV1/Mixed_5c/Branch_1/Conv2d_0a_1x1/bias']
        branch9_1 = tf.nn.conv2d(incpt, w_53, strides=[1, 1, 1, 1], padding='VALID') + b_53
        branch9_1 = tf.nn.relu(branch9_1)

        padding_format = tf.constant([[0, 0], [1, 1], [1, 1], [0, 0]])
        branch9_1 = tf.pad(branch9_1, paddings=padding_format)
        w_54 = self.var_dict['InceptionV1/Mixed_5c/Branch_1/Conv2d_0b_3x3/weights']
        b_54 = self.var_dict['InceptionV1/Mixed_5c/Branch_1/Conv2d_0b_3x3/bias']
        branch9_1 = tf.nn.conv2d(branch9_1, w_54, strides=[1, 1, 1, 1], padding='VALID') + b_54
        branch9_1 = tf.nn.relu(branch9_1)

        #branch 2
        w_55 = self.var_dict['InceptionV1/Mixed_5c/Branch_2/Conv2d_0a_1x1/weights']
        b_55 = self.var_dict['InceptionV1/Mixed_5c/Branch_2/Conv2d_0a_1x1/bias']
        branch9_2 = tf.nn.conv2d(incpt, w_55, strides=[1, 1, 1, 1], padding='VALID') + b_55
        branch9_2 = tf.nn.relu(branch9_2)

        padding_format = tf.constant([[0, 0], [2, 2], [2, 2], [0, 0]])
        branch9_2 = tf.pad(branch9_2, paddings=padding_format)
        w_56 = self.var_dict['InceptionV1/Mixed_5c/Branch_2/Conv2d_0b_5x5/weights']
        b_56 = self.var_dict['InceptionV1/Mixed_5c/Branch_2/Conv2d_0b_5x5/bias']
        branch9_2 = tf.nn.conv2d(branch9_2, w_56, strides=[1, 1, 1, 1], padding='VALID') + b_56
        branch9_2 = tf.nn.relu(branch9_2)

        #branch 3
        padding_format = tf.constant([[0, 0], [1, 1], [1, 1], [0, 0]])
        branch9_3 = tf.pad(incpt, paddings=padding_format)
        branch9_3 = tf.nn.max_pool(branch9_3, ksize=[1, 3, 3, 1],
                                 strides=[1, 1, 1, 1], padding='VALID')
        w_57 = self.var_dict['InceptionV1/Mixed_5c/Branch_3/Conv2d_0b_1x1/weights']
        b_57 = self.var_dict['InceptionV1/Mixed_5c/Branch_3/Conv2d_0b_1x1/bias']
        branch9_3 = tf.nn.conv2d(branch9_3, w_57, strides=[1, 1, 1, 1], padding='VALID') + b_57
        branch9_3 = tf.nn.relu(branch9_3)

        nets = tf.concat(
            axis=3, values=[branch9_0, branch9_1, branch9_2, branch9_3])

        nets = tf.nn.avg_pool(nets, ksize=[1, 7, 7, 1],
                       strides=[1, 1, 1, 1], padding='VALID')
        nets = tf.reshape(nets, [-1, 1024])

        #fc layer
        # w_58 = tf.get_variable(shape=[1024,64],name='fc_layer_0/weights')
        # b_58 = tf.get_variable(shape=[64],name='fc_layer_0/bias')
        # nets = tf.reshape(nets,[-1,1024])
        # nets = tf.add(tf.matmul(nets,w_58),b_58)
        return nets


if __name__ == "__main__":
    config = tf.ConfigProto()
    config.gpu_options.allow_growth = True
    sess = tf.Session(config=config)
    cnn_triplet = CNN_Triplet_Metric(sess=sess)