(bottom)
return pool_ave, conv_1x1, conv_1x1_bn, conv_1x1_scale, conv_1x1_relu, conv_1x1_2, conv_1x1_2_bn, conv_1x1_2_scale, \
           conv_1x1_2_relu, conv_3x3_reduce, conv_3x3_reduce_bn, conv_3x3_reduce_scale, conv_3x3_reduce_relu, conv_3x3, \
           conv_3x3_bn, conv_3x3_scale, conv_3x3_relu, conv_3x3_2_reduce, conv_3x3_2_reduce_bn, conv_3x3_2_reduce_scale, \
           conv_3x3_2_reduce_relu, conv_3x3_2, conv_3x3_2_bn, conv_3x3_2_scale, conv_3x3_2_relu, conv_3x3_3, \
           conv_3x3_3_bn, conv_3x3_3_scale, conv_3x3_3_relu, concat
input:384x35x35
    output:384x35x35
    :param bottom: bottom layer
    :return: layers
input:384x35x35
    output:384x35x35
    :param bottom: bottom layer
    :return: layers
input : 384x35x35 output : 384x35x35 : param bottom : bottom layer : return : layers
def inception_v4_a(bottom):
    """
    input:384x35x35
    output:384x35x35
    :param bottom: bottom layer
    :return: layers
    """
    pool_ave = L.Pooling(bottom, kernel_size=3, stride=1, pad=1, pool=P.Pooling.AVE)  # 384x35x35
    conv_1x1, conv_1x1_bn, conv_1x1_scale, conv_1x1_relu = \
        factorization_conv_bn_scale_relu(pool_ave, num_output=96, kernel_size=1)  # 96x35x35

    conv_1x1_2, conv_1x1_2_bn, conv_1x1_2_scale, conv_1x1_2_relu = \
        factorization_conv_bn_scale_relu(bottom, num_output=96, kernel_size=1)  # 96x35x35

    conv_3x3_reduce, conv_3x3_reduce_bn, conv_3x3_reduce_scale, conv_3x3_reduce_relu = \
        factorization_conv_bn_scale_relu(bottom, num_output=64, kernel_size=1)  # 64x35x35
    conv_3x3, conv_3x3_bn, conv_3x3_scale, conv_3x3_relu = \
        factorization_conv_bn_scale_relu(conv_3x3_reduce, num_output=96, kernel_size=3, pad=1)  # 96x35x35

    conv_3x3_2_reduce, conv_3x3_2_reduce_bn, conv_3x3_2_reduce_scale, conv_3x3_2_reduce_relu = \
        factorization_conv_bn_scale_relu(bottom, num_output=64, kernel_size=1)  # 64x35x35
    conv_3x3_2, conv_3x3_2_bn, conv_3x3_2_scale, conv_3x3_2_relu = \
        factorization_conv_bn_scale_relu(conv_3x3_2_reduce, num_output=96, kernel_size=3, pad=1)  # 96x35x35
    conv_3x3_3, conv_3x3_3_bn, conv_3x3_3_scale, conv_3x3_3_relu = \
        factorization_conv_bn_scale_relu(conv_3x3_2, num_output=96, kernel_size=3, pad=1)  # 96x35x35

    concat = L.Concat(conv_1x1, conv_1x1_2, conv_3x3, conv_3x3_3)  # 384(96+96+96+96)x35x35

    return pool_ave, conv_1x1, conv_1x1_bn, conv_1x1_scale, conv_1x1_relu, conv_1x1_2, conv_1x1_2_bn, conv_1x1_2_scale, \
           conv_1x1_2_relu, conv_3x3_reduce, conv_3x3_reduce_bn, conv_3x3_reduce_scale, conv_3x3_reduce_relu, conv_3x3, \
           conv_3x3_bn, conv_3x3_scale, conv_3x3_relu, conv_3x3_2_reduce, conv_3x3_2_reduce_bn, conv_3x3_2_reduce_scale, \
           conv_3x3_2_reduce_relu, conv_3x3_2, conv_3x3_2_bn, conv_3x3_2_scale, conv_3x3_2_relu, conv_3x3_3, \
           conv_3x3_3_bn, conv_3x3_3_scale, conv_3x3_3_relu, concat
def inception_v4_a ( bottom ) : pool_ave = L . Pooling ( bottom , kernel_size = 3 , stride = 1 , pad = 1 , pool = P . Pooling . AVE ) # 384x35x35 conv_1x1 , conv_1x1_bn , conv_1x1_scale , conv_1x1_relu = factorization_conv_bn_scale_relu ( pool_ave , num_output = 96 , kernel_size = 1 ) # 96x35x35 conv_1x1_2 , conv_1x1_2_bn , conv_1x1_2_scale , conv_1x1_2_relu = factorization_conv_bn_scale_relu ( bottom , num_output = 96 , kernel_size = 1 ) # 96x35x35 conv_3x3_reduce , conv_3x3_reduce_bn , conv_3x3_reduce_scale , conv_3x3_reduce_relu = factorization_conv_bn_scale_relu ( bottom , num_output = 64 , kernel_size = 1 ) # 64x35x35 conv_3x3 , conv_3x3_bn , conv_3x3_scale , conv_3x3_relu = factorization_conv_bn_scale_relu ( conv_3x3_reduce , num_output = 96 , kernel_size = 3 , pad = 1 ) # 96x35x35 conv_3x3_2_reduce , conv_3x3_2_reduce_bn , conv_3x3_2_reduce_scale , conv_3x3_2_reduce_relu = factorization_conv_bn_scale_relu ( bottom , num_output = 64 , kernel_size = 1 ) # 64x35x35 conv_3x3_2 , conv_3x3_2_bn , conv_3x3_2_scale , conv_3x3_2_relu = factorization_conv_bn_scale_relu ( conv_3x3_2_reduce , num_output = 96 , kernel_size = 3 , pad = 1 ) # 96x35x35 conv_3x3_3 , conv_3x3_3_bn , conv_3x3_3_scale , conv_3x3_3_relu = factorization_conv_bn_scale_relu ( conv_3x3_2 , num_output = 96 , kernel_size = 3 , pad = 1 ) # 96x35x35 concat = L . Concat ( conv_1x1 , conv_1x1_2 , conv_3x3 , conv_3x3_3 ) # 384(96+96+96+96)x35x35 return pool_ave , conv_1x1 , conv_1x1_bn , conv_1x1_scale , conv_1x1_relu , conv_1x1_2 , conv_1x1_2_bn , conv_1x1_2_scale , conv_1x1_2_relu , conv_3x3_reduce , conv_3x3_reduce_bn , conv_3x3_reduce_scale , conv_3x3_reduce_relu , conv_3x3 , conv_3x3_bn , conv_3x3_scale , conv_3x3_relu , conv_3x3_2_reduce , conv_3x3_2_reduce_bn , conv_3x3_2_reduce_scale , conv_3x3_2_reduce_relu , conv_3x3_2 , conv_3x3_2_bn , conv_3x3_2_scale , conv_3x3_2_relu , conv_3x3_3 , conv_3x3_3_bn , conv_3x3_3_scale , conv_3x3_3_relu , concat