text: import tensorflow as tf
  import numpy as np
  import os
  from numpy import genfromtxt
  from keras.layers import Conv2D, ZeroPadding2D, Activation, Input, concatenate
  from keras.models import Model
  from keras.layers.normalization import BatchNormalization
  from keras.layers.pooling import MaxPooling2D, AveragePooling2D
  
  
  _FLOATX = 'float32'
  
  def variable(value, dtype=_FLOATX, name=None):
    v = tf.Variable(np.asarray(value, dtype=dtype), name=name)
    _get_session().run(v.initializer)
    return v
  
  def shape(x):
    return x.get_shape()
  
  def square(x):
    return tf.square(x)
  
  def zeros(shape, dtype=_FLOATX, name=None):
    return variable(np.zeros(shape), dtype, name)
  
  def concatenate(tensors, axis=-1):
    if axis < 0:
        axis = axis % len(tensors[0].get_shape())
    return tf.concat(axis, tensors)
  
  def LRN2D(x):
    return tf.nn.lrn(x, alpha=1e-4, beta=0.75)
  
  def conv2d_bn(
    x,
    layer=None,
    cv1_out=None,
    cv1_filter=(1, 1),
    cv1_strides=(1, 1),
    cv2_out=None,
    cv2_filter=(3, 3),
    cv2_strides=(1, 1),
    padding=None,
  ):
    num = '' if cv2_out == None else '1'
    tensor = Conv2D(cv1_out, cv1_filter, strides=cv1_strides, name=layer+'_conv'+num)(x)
    tensor = BatchNormalization(axis=3, epsilon=0.00001, name=layer+'_bn'+num)(tensor)
    tensor = Activation('relu')(tensor)
    if padding == None:
      return tensor
    tensor = ZeroPadding2D(padding=padding)(tensor)
    if cv2_out == None:
      return tensor
    tensor = Conv2D(cv2_out, cv2_filter, strides=cv2_strides, name=layer+'_conv'+'2')(tensor)
    tensor = BatchNormalization(axis=3, epsilon=0.00001, name=layer+'_bn'+'2')(tensor)
    tensor = Activation('relu')(tensor)
    return tensor
  
  weights = [
    'conv1', 'bn1', 'conv2', 'bn2', 'conv3', 'bn3',
    'inception_3a_1x1_conv', 'inception_3a_1x1_bn',
    'inception_3a_pool_conv', 'inception_3a_pool_bn',
    'inception_3a_5x5_conv1', 'inception_3a_5x5_conv2', 'inception_3a_5x5_bn1', 'inception_3a_5x5_bn2',
    'inception_3a_3x3_conv1', 'inception_3a_3x3_conv2', 'inception_3a_3x3_bn1', 'inception_3a_3x3_bn2',
    'inception_3b_3x3_conv1', 'inception_3b_3x3_conv2', 'inception_3b_3x3_bn1', 'inception_3b_3x3_bn2',
    'inception_3b_5x5_conv1', 'inception_3b_5x5_conv2', 'inception_3b_5x5_bn1', 'inception_3b_5x5_bn2',
    'inception_3b_pool_conv', 'inception_3b_pool_bn',
    'inception_3b_1x1_conv', 'inception_3b_1x1_bn',
    'inception_3c_3x3_conv1', 'inception_3c_3x3_conv2', 'inception_3c_3x3_bn1', 'inception_3c_3x3_bn2',
    'inception_3c_5x5_conv1', 'inception_3c_5x5_conv2', 'inception_3c_5x5_bn1', 'inception_3c_5x5_bn2',
    'inception_4a_3x3_conv1', 'inception_4a_3x3_conv2', 'inception_4a_3x3_bn1', 'inception_4a_3x3_bn2',
    'inception_4a_5x5_conv1', 'inception_4a_5x5_conv2', 'inception_4a_5x5_bn1', 'inception_4a_5x5_bn2',
    'inception_4a_pool_conv', 'inception_4a_pool_bn',
    'inception_4a_1x1_conv', 'inception_4a_1x1_bn',
    'inception_4e_3x3_conv1', 'inception_4e_3x3_conv2', 'inception_4e_3x3_bn1', 'inception_4e_3x3_bn2',
    'inception_4e_5x5_conv1', 'inception_4e_5x5_conv2', 'inception_4e_5x5_bn1', 'inception_4e_5x5_bn2',
    'inception_5a_3x3_conv1', 'inception_5a_3x3_conv2', 'inception_5a_3x3_bn1', 'inception_5a_3x3_bn2',
    'inception_5a_pool_conv', 'inception_5a_pool_bn',
    'inception_5a_1x1_conv', 'inception_5a_1x1_bn',
    'inception_5b_3x3_conv1', 'inception_5b_3x3_conv2', 'inception_5b_3x3_bn1', 'inception_5b_3x3_bn2',
    'inception_5b_pool_conv', 'inception_5b_pool_bn',
    'inception_5b_1x1_conv', 'inception_5b_1x1_bn',
    'dense_layer'
  ]
  
  conv_shape = {
    'conv1': [64, 3, 7, 7],
    'conv2': [64, 64, 1, 1],
    'conv3': [192, 64, 3, 3],
    'inception_3a_1x1_conv': [64, 192, 1, 1],
    'inception_3a_pool_conv': [32, 192, 1, 1],
    'inception_3a_5x5_conv1': [16, 192, 1, 1],
    'inception_3a_5x5_conv2': [32, 16, 5, 5],
    'inception_3a_3x3_conv1': [96, 192, 1, 1],
    'inception_3a_3x3_conv2': [128, 96, 3, 3],
    'inception_3b_3x3_conv1': [96, 256, 1, 1],
    'inception_3b_3x3_conv2': [128, 96, 3, 3],
    'inception_3b_5x5_conv1': [32, 256, 1, 1],
    'inception_3b_5x5_conv2': [64, 32, 5, 5],
    'inception_3b_pool_conv': [64, 256, 1, 1],
    'inception_3b_1x1_conv': [64, 256, 1, 1],
    'inception_3c_3x3_conv1': [128, 320, 1, 1],
    'inception_3c_3x3_conv2': [256, 128, 3, 3],
    'inception_3c_5x5_conv1': [32, 320, 1, 1],
    'inception_3c_5x5_conv2': [64, 32, 5, 5],
    'inception_4a_3x3_conv1': [96, 640, 1, 1],
    'inception_4a_3x3_conv2': [192, 96, 3, 3],
    'inception_4a_5x5_conv1': [32, 640, 1, 1,],
    'inception_4a_5x5_conv2': [64, 32, 5, 5],
    'inception_4a_pool_conv': [128, 640, 1, 1],
    'inception_4a_1x1_conv': [256, 640, 1, 1],
    'inception_4e_3x3_conv1': [160, 640, 1, 1],
    'inception_4e_3x3_conv2': [256, 160, 3, 3],
    'inception_4e_5x5_conv1': [64, 640, 1, 1],
    'inception_4e_5x5_conv2': [128, 64, 5, 5],
    'inception_5a_3x3_conv1': [96, 1024, 1, 1],
    'inception_5a_3x3_conv2': [384, 96, 3, 3],
    'inception_5a_pool_conv': [96, 1024, 1, 1],
    'inception_5a_1x1_conv': [256, 1024, 1, 1],
    'inception_5b_3x3_conv1': [96, 736, 1, 1],
    'inception_5b_3x3_conv2': [384, 96, 3, 3],
    'inception_5b_pool_conv': [96, 736, 1, 1],
    'inception_5b_1x1_conv': [256, 736, 1, 1],
  }
  
  def load_weights():
    # Set weights path
    dirPath = './weights'
    fileNames = filter(lambda f: not f.startswith('.'), os.listdir(dirPath))
    paths = {}
    weights_dict = {}
  
    for n in fileNames:
      paths[n.replace('.csv', '')] = dirPath + '/' + n
  
    for name in weights:
      if 'conv' in name:
        conv_w = genfromtxt(paths[name + '_w'], delimiter=',', dtype=None)
        conv_w = np.reshape(conv_w, conv_shape[name])
        conv_w = np.transpose(conv_w, (2, 3, 1, 0))
        conv_b = genfromtxt(paths[name + '_b'], delimiter=',', dtype=None)
        weights_dict[name] = [conv_w, conv_b]     
      elif 'bn' in name:
        bn_w = genfromtxt(paths[name + '_w'], delimiter=',', dtype=None)
        bn_b = genfromtxt(paths[name + '_b'], delimiter=',', dtype=None)
        bn_m = genfromtxt(paths[name + '_m'], delimiter=',', dtype=None)
        bn_v = genfromtxt(paths[name + '_v'], delimiter=',', dtype=None)
        weights_dict[name] = [bn_w, bn_b, bn_m, bn_v]
      elif 'dense' in name:
        dense_w = genfromtxt(dirPath+'/dense_w.csv', delimiter=',', dtype=None)
        dense_w = np.reshape(dense_w, (128, 736))
        dense_w = np.transpose(dense_w, (1, 0))
        dense_b = genfromtxt(dirPath+'/dense_b.csv', delimiter=',', dtype=None)
        weights_dict[name] = [dense_w, dense_b]
  
    return weights_dict
  
  