(bottom)
return conv_1x1, conv_1x1_bn, conv_1x1_scale, conv_1x1_relu, conv_1x7_reduce, conv_1x7_reduce_bn, \
           conv_1x7_reduce_scale, conv_1x7_reduce_relu, conv_1x7, conv_1x7_bn, conv_1x7_scale, conv_1x7_relu, \
           conv_7x1, conv_7x1_bn, conv_7x1_scale, conv_7x1_relu, concat, conv_up, conv_up_bn, conv_up_scale, \
           residual_eltwise, residual_eltwise_relu
input:1088x17x17
    output:1088x17x17
    :param bottom: bottom layer
    :return: layers
input:1088x17x17
    output:1088x17x17
    :param bottom: bottom layer
    :return: layers
input : 1088x17x17 output : 1088x17x17 : param bottom : bottom layer : return : layers
def inception_resnet_v2_b(bottom):
    """
    input:1088x17x17
    output:1088x17x17
    :param bottom: bottom layer
    :return: layers
    """
    conv_1x1, conv_1x1_bn, conv_1x1_scale, conv_1x1_relu = \
        factorization_conv_bn_scale_relu(bottom, num_output=192, kernel_size=1)  # 192x17x17

    conv_1x7_reduce, conv_1x7_reduce_bn, conv_1x7_reduce_scale, conv_1x7_reduce_relu = \
        factorization_conv_bn_scale_relu(bottom, num_output=128, kernel_size=1)  # 128x17x17
    conv_1x7, conv_1x7_bn, conv_1x7_scale, conv_1x7_relu = \
        factorization_conv_mxn(conv_1x7_reduce, num_output=160, kernel_h=1, kernel_w=7, pad_h=0, pad_w=3)  # 160x17x17
    conv_7x1, conv_7x1_bn, conv_7x1_scale, conv_7x1_relu = \
        factorization_conv_mxn(conv_1x7, num_output=192, kernel_h=7, kernel_w=1, pad_h=3, pad_w=0)  # 192x17x17

    concat = L.Concat(conv_1x1, conv_7x1)  # 384(192+192)x17x17
    conv_up, conv_up_bn, conv_up_scale = \
        factorization_conv_bn_scale(concat, num_output=1088, kernel_size=1)  # 1088x17x17 

    residual_eltwise, residual_eltwise_relu = eltwise_relu(bottom, conv_up)  # 1088x17x17

    return conv_1x1, conv_1x1_bn, conv_1x1_scale, conv_1x1_relu, conv_1x7_reduce, conv_1x7_reduce_bn, \
           conv_1x7_reduce_scale, conv_1x7_reduce_relu, conv_1x7, conv_1x7_bn, conv_1x7_scale, conv_1x7_relu, \
           conv_7x1, conv_7x1_bn, conv_7x1_scale, conv_7x1_relu, concat, conv_up, conv_up_bn, conv_up_scale, \
           residual_eltwise, residual_eltwise_relu
def inception_resnet_v2_b ( bottom ) : conv_1x1 , conv_1x1_bn , conv_1x1_scale , conv_1x1_relu = factorization_conv_bn_scale_relu ( bottom , num_output = 192 , kernel_size = 1 ) # 192x17x17 conv_1x7_reduce , conv_1x7_reduce_bn , conv_1x7_reduce_scale , conv_1x7_reduce_relu = factorization_conv_bn_scale_relu ( bottom , num_output = 128 , kernel_size = 1 ) # 128x17x17 conv_1x7 , conv_1x7_bn , conv_1x7_scale , conv_1x7_relu = factorization_conv_mxn ( conv_1x7_reduce , num_output = 160 , kernel_h = 1 , kernel_w = 7 , pad_h = 0 , pad_w = 3 ) # 160x17x17 conv_7x1 , conv_7x1_bn , conv_7x1_scale , conv_7x1_relu = factorization_conv_mxn ( conv_1x7 , num_output = 192 , kernel_h = 7 , kernel_w = 1 , pad_h = 3 , pad_w = 0 ) # 192x17x17 concat = L . Concat ( conv_1x1 , conv_7x1 ) # 384(192+192)x17x17 conv_up , conv_up_bn , conv_up_scale = factorization_conv_bn_scale ( concat , num_output = 1088 , kernel_size = 1 ) # 1088x17x17  residual_eltwise , residual_eltwise_relu = eltwise_relu ( bottom , conv_up ) # 1088x17x17 return conv_1x1 , conv_1x1_bn , conv_1x1_scale , conv_1x1_relu , conv_1x7_reduce , conv_1x7_reduce_bn , conv_1x7_reduce_scale , conv_1x7_reduce_relu , conv_1x7 , conv_1x7_bn , conv_1x7_scale , conv_1x7_relu , conv_7x1 , conv_7x1_bn , conv_7x1_scale , conv_7x1_relu , concat , conv_up , conv_up_bn , conv_up_scale , residual_eltwise , residual_eltwise_relu