(bottom)
return conv_1x1, conv_1x1_bn, conv_1x1_scale, conv_1x1_relu, conv_1x3_reduce, conv_1x3_reduce_bn, \
           conv_1x3_reduce_scale, conv_1x3_reduce_relu, conv_1x3, conv_1x3_bn, conv_1x3_scale, conv_1x3_relu, \
           conv_3x1, conv_3x1_bn, conv_3x1_scale, conv_3x1_relu, concat, conv_up, conv_up_bn, conv_up_scale, \
           residual_eltwise, residual_eltwise_relu
input:2080x8x8
    output:2080x8x8
    :param bottom: bottom layer
    :return: layers
input:2080x8x8
    output:2080x8x8
    :param bottom: bottom layer
    :return: layers
input : 2080x8x8 output : 2080x8x8 : param bottom : bottom layer : return : layers
def inception_resnet_v2_c(bottom):
    """
    input:2080x8x8
    output:2080x8x8
    :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)  # 192x8x8

    conv_1x3_reduce, conv_1x3_reduce_bn, conv_1x3_reduce_scale, conv_1x3_reduce_relu = \
        factorization_conv_bn_scale_relu(bottom, num_output=192, kernel_size=1)  # 192x8x8
    conv_1x3, conv_1x3_bn, conv_1x3_scale, conv_1x3_relu = \
        factorization_conv_mxn(conv_1x3_reduce, num_output=224, kernel_h=1, kernel_w=3, pad_h=0, pad_w=1)  # 224x8x8
    conv_3x1, conv_3x1_bn, conv_3x1_scale, conv_3x1_relu = \
        factorization_conv_mxn(conv_1x3, num_output=256, kernel_h=3, kernel_w=1, pad_h=1, pad_w=0)  # 256x8x8

    concat = L.Concat(conv_1x1, conv_3x1)  # 448(192+256)x8x8
    conv_up, conv_up_bn, conv_up_scale = \
        factorization_conv_bn_scale(concat, num_output=2080, kernel_size=1)  # 2080x8x8 

    residual_eltwise, residual_eltwise_relu = eltwise_relu(bottom, conv_up)  # 2080x8x8  

    return conv_1x1, conv_1x1_bn, conv_1x1_scale, conv_1x1_relu, conv_1x3_reduce, conv_1x3_reduce_bn, \
           conv_1x3_reduce_scale, conv_1x3_reduce_relu, conv_1x3, conv_1x3_bn, conv_1x3_scale, conv_1x3_relu, \
           conv_3x1, conv_3x1_bn, conv_3x1_scale, conv_3x1_relu, concat, conv_up, conv_up_bn, conv_up_scale, \
           residual_eltwise, residual_eltwise_relu
def inception_resnet_v2_c ( bottom ) : conv_1x1 , conv_1x1_bn , conv_1x1_scale , conv_1x1_relu = factorization_conv_bn_scale_relu ( bottom , num_output = 192 , kernel_size = 1 ) # 192x8x8 conv_1x3_reduce , conv_1x3_reduce_bn , conv_1x3_reduce_scale , conv_1x3_reduce_relu = factorization_conv_bn_scale_relu ( bottom , num_output = 192 , kernel_size = 1 ) # 192x8x8 conv_1x3 , conv_1x3_bn , conv_1x3_scale , conv_1x3_relu = factorization_conv_mxn ( conv_1x3_reduce , num_output = 224 , kernel_h = 1 , kernel_w = 3 , pad_h = 0 , pad_w = 1 ) # 224x8x8 conv_3x1 , conv_3x1_bn , conv_3x1_scale , conv_3x1_relu = factorization_conv_mxn ( conv_1x3 , num_output = 256 , kernel_h = 3 , kernel_w = 1 , pad_h = 1 , pad_w = 0 ) # 256x8x8 concat = L . Concat ( conv_1x1 , conv_3x1 ) # 448(192+256)x8x8 conv_up , conv_up_bn , conv_up_scale = factorization_conv_bn_scale ( concat , num_output = 2080 , kernel_size = 1 ) # 2080x8x8  residual_eltwise , residual_eltwise_relu = eltwise_relu ( bottom , conv_up ) # 2080x8x8   return conv_1x1 , conv_1x1_bn , conv_1x1_scale , conv_1x1_relu , conv_1x3_reduce , conv_1x3_reduce_bn , conv_1x3_reduce_scale , conv_1x3_reduce_relu , conv_1x3 , conv_1x3_bn , conv_1x3_scale , conv_1x3_relu , conv_3x1 , conv_3x1_bn , conv_3x1_scale , conv_3x1_relu , concat , conv_up , conv_up_bn , conv_up_scale , residual_eltwise , residual_eltwise_relu