Python Inception-v3的Keras实施没有BN辅助

我一直在实验中使用keras inception-v3模型。但是，当我打印模型摘要时，我看不到BN辅助层。我试图了解这是否是一个bug，或者是否因为特定的原因而被遗漏了？。根据Szegedy等人的“重新思考计算机视觉的Inception体系结构”，辅助层将top-1错误提高了0.4%，这是Inception-v2和Inception-v3之间的主要区别。我在这里附上了keras库中inception-v3的源代码。任何有价值的见解都可能对我的实验有用

def InceptionV3(include_top=True,
                weights='imagenet',
                input_tensor=None,
                input_shape=None,
                pooling=None,
                classes=1000):
    """Instantiates the Inception v3 architecture.

Optionally loads weights pre-trained
on ImageNet. Note that when using TensorFlow,
for best performance you should set
`image_data_format="channels_last"` in your Keras config
at ~/.keras/keras.json.
The model and the weights are compatible with both
TensorFlow and Theano. The data format
convention used by the model is the one
specified in your Keras config file.
Note that the default input image size for this model is 299x299.

Arguments:
    include_top: whether to include the fully-connected
        layer at the top of the network.
    weights: one of `None` (random initialization)
        or "imagenet" (pre-training on ImageNet).
    input_tensor: optional Keras tensor (i.e. output of `layers.Input()`)
        to use as image input for the model.
    input_shape: optional shape tuple, only to be specified
        if `include_top` is False (otherwise the input shape
        has to be `(299, 299, 3)` (with `channels_last` data format)
        or `(3, 299, 299)` (with `channels_first` data format).
        It should have exactly 3 inputs channels,
        and width and height should be no smaller than 139.
        E.g. `(150, 150, 3)` would be one valid value.
    pooling: Optional pooling mode for feature extraction
        when `include_top` is `False`.
        - `None` means that the output of the model will be
            the 4D tensor output of the
            last convolutional layer.
        - `avg` means that global average pooling
            will be applied to the output of the
            last convolutional layer, and thus
            the output of the model will be a 2D tensor.
        - `max` means that global max pooling will
            be applied.
    classes: optional number of classes to classify images
        into, only to be specified if `include_top` is True, and
        if no `weights` argument is specified.

Returns:
    A Keras model instance.

Raises:
    ValueError: in case of invalid argument for `weights`,
        or invalid input shape.
"""
if weights not in {'imagenet', None}:
    raise ValueError('The `weights` argument should be either '
                     '`None` (random initialization) or `imagenet` '
                     '(pre-training on ImageNet).')

if weights == 'imagenet' and include_top and classes != 1000:
    raise ValueError('If using `weights` as imagenet with `include_top`'
                     ' as true, `classes` should be 1000')

# Determine proper input shape
input_shape = _obtain_input_shape(
    input_shape,
    default_size=299,
    min_size=139,
    data_format=K.image_data_format(),
    include_top=include_top)

if input_tensor is None:
    img_input = Input(shape=input_shape)
else:
    img_input = Input(tensor=input_tensor, shape=input_shape)

if K.image_data_format() == 'channels_first':
    channel_axis = 1
else:
    channel_axis = 3

x = conv2d_bn(img_input, 32, 3, 3, strides=(2, 2), padding='valid')
x = conv2d_bn(x, 32, 3, 3, padding='valid')
x = conv2d_bn(x, 64, 3, 3)
x = MaxPooling2D((3, 3), strides=(2, 2))(x)

x = conv2d_bn(x, 80, 1, 1, padding='valid')
x = conv2d_bn(x, 192, 3, 3, padding='valid')
x = MaxPooling2D((3, 3), strides=(2, 2))(x)

# mixed 0, 1, 2: 35 x 35 x 256
branch1x1 = conv2d_bn(x, 64, 1, 1)

branch5x5 = conv2d_bn(x, 48, 1, 1)
branch5x5 = conv2d_bn(branch5x5, 64, 5, 5)

branch3x3dbl = conv2d_bn(x, 64, 1, 1)
branch3x3dbl = conv2d_bn(branch3x3dbl, 96, 3, 3)
branch3x3dbl = conv2d_bn(branch3x3dbl, 96, 3, 3)

branch_pool = AveragePooling2D((3, 3), strides=(1, 1), padding='same')(x)
branch_pool = conv2d_bn(branch_pool, 32, 1, 1)
x = layers.concatenate(
    [branch1x1, branch5x5, branch3x3dbl, branch_pool],
    axis=channel_axis,
    name='mixed0')

# mixed 1: 35 x 35 x 256
branch1x1 = conv2d_bn(x, 64, 1, 1)

branch5x5 = conv2d_bn(x, 48, 1, 1)
branch5x5 = conv2d_bn(branch5x5, 64, 5, 5)

branch3x3dbl = conv2d_bn(x, 64, 1, 1)
branch3x3dbl = conv2d_bn(branch3x3dbl, 96, 3, 3)
branch3x3dbl = conv2d_bn(branch3x3dbl, 96, 3, 3)

branch_pool = AveragePooling2D((3, 3), strides=(1, 1), padding='same')(x)
branch_pool = conv2d_bn(branch_pool, 64, 1, 1)
x = layers.concatenate(
    [branch1x1, branch5x5, branch3x3dbl, branch_pool],
    axis=channel_axis,
    name='mixed1')

# mixed 2: 35 x 35 x 256
branch1x1 = conv2d_bn(x, 64, 1, 1)

branch5x5 = conv2d_bn(x, 48, 1, 1)
branch5x5 = conv2d_bn(branch5x5, 64, 5, 5)

branch3x3dbl = conv2d_bn(x, 64, 1, 1)
branch3x3dbl = conv2d_bn(branch3x3dbl, 96, 3, 3)
branch3x3dbl = conv2d_bn(branch3x3dbl, 96, 3, 3)

branch_pool = AveragePooling2D((3, 3), strides=(1, 1), padding='same')(x)
branch_pool = conv2d_bn(branch_pool, 64, 1, 1)
x = layers.concatenate(
    [branch1x1, branch5x5, branch3x3dbl, branch_pool],
    axis=channel_axis,
    name='mixed2')

# mixed 3: 17 x 17 x 768
branch3x3 = conv2d_bn(x, 384, 3, 3, strides=(2, 2), padding='valid')

branch3x3dbl = conv2d_bn(x, 64, 1, 1)
branch3x3dbl = conv2d_bn(branch3x3dbl, 96, 3, 3)
branch3x3dbl = conv2d_bn(
    branch3x3dbl, 96, 3, 3, strides=(2, 2), padding='valid')

branch_pool = MaxPooling2D((3, 3), strides=(2, 2))(x)
x = layers.concatenate(
    [branch3x3, branch3x3dbl, branch_pool], axis=channel_axis, name='mixed3')

# mixed 4: 17 x 17 x 768
branch1x1 = conv2d_bn(x, 192, 1, 1)

branch7x7 = conv2d_bn(x, 128, 1, 1)
branch7x7 = conv2d_bn(branch7x7, 128, 1, 7)
branch7x7 = conv2d_bn(branch7x7, 192, 7, 1)

branch7x7dbl = conv2d_bn(x, 128, 1, 1)
branch7x7dbl = conv2d_bn(branch7x7dbl, 128, 7, 1)
branch7x7dbl = conv2d_bn(branch7x7dbl, 128, 1, 7)
branch7x7dbl = conv2d_bn(branch7x7dbl, 128, 7, 1)
branch7x7dbl = conv2d_bn(branch7x7dbl, 192, 1, 7)

branch_pool = AveragePooling2D((3, 3), strides=(1, 1), padding='same')(x)
branch_pool = conv2d_bn(branch_pool, 192, 1, 1)
x = layers.concatenate(
    [branch1x1, branch7x7, branch7x7dbl, branch_pool],
    axis=channel_axis,
    name='mixed4')

# mixed 5, 6: 17 x 17 x 768
for i in range(2):
    branch1x1 = conv2d_bn(x, 192, 1, 1)

    branch7x7 = conv2d_bn(x, 160, 1, 1)
    branch7x7 = conv2d_bn(branch7x7, 160, 1, 7)
    branch7x7 = conv2d_bn(branch7x7, 192, 7, 1)

    branch7x7dbl = conv2d_bn(x, 160, 1, 1)
    branch7x7dbl = conv2d_bn(branch7x7dbl, 160, 7, 1)
    branch7x7dbl = conv2d_bn(branch7x7dbl, 160, 1, 7)
    branch7x7dbl = conv2d_bn(branch7x7dbl, 160, 7, 1)
    branch7x7dbl = conv2d_bn(branch7x7dbl, 192, 1, 7)

    branch_pool = AveragePooling2D(
        (3, 3), strides=(1, 1), padding='same')(x)
    branch_pool = conv2d_bn(branch_pool, 192, 1, 1)
    x = layers.concatenate(
        [branch1x1, branch7x7, branch7x7dbl, branch_pool],
        axis=channel_axis,
        name='mixed' + str(5 + i))

# mixed 7: 17 x 17 x 768
branch1x1 = conv2d_bn(x, 192, 1, 1)

branch7x7 = conv2d_bn(x, 192, 1, 1)
branch7x7 = conv2d_bn(branch7x7, 192, 1, 7)
branch7x7 = conv2d_bn(branch7x7, 192, 7, 1)

branch7x7dbl = conv2d_bn(x, 192, 1, 1)
branch7x7dbl = conv2d_bn(branch7x7dbl, 192, 7, 1)
branch7x7dbl = conv2d_bn(branch7x7dbl, 192, 1, 7)
branch7x7dbl = conv2d_bn(branch7x7dbl, 192, 7, 1)
branch7x7dbl = conv2d_bn(branch7x7dbl, 192, 1, 7)

branch_pool = AveragePooling2D((3, 3), strides=(1, 1), padding='same')(x)
branch_pool = conv2d_bn(branch_pool, 192, 1, 1)
x = layers.concatenate(
    [branch1x1, branch7x7, branch7x7dbl, branch_pool],
    axis=channel_axis,
    name='mixed7')

# mixed 8: 8 x 8 x 1280
branch3x3 = conv2d_bn(x, 192, 1, 1)
branch3x3 = conv2d_bn(branch3x3, 320, 3, 3,
                      strides=(2, 2), padding='valid')

branch7x7x3 = conv2d_bn(x, 192, 1, 1)
branch7x7x3 = conv2d_bn(branch7x7x3, 192, 1, 7)
branch7x7x3 = conv2d_bn(branch7x7x3, 192, 7, 1)
branch7x7x3 = conv2d_bn(
    branch7x7x3, 192, 3, 3, strides=(2, 2), padding='valid')

branch_pool = MaxPooling2D((3, 3), strides=(2, 2))(x)
x = layers.concatenate(
    [branch3x3, branch7x7x3, branch_pool], axis=channel_axis, name='mixed8')

# mixed 9: 8 x 8 x 2048
for i in range(2):
    branch1x1 = conv2d_bn(x, 320, 1, 1)

    branch3x3 = conv2d_bn(x, 384, 1, 1)
    branch3x3_1 = conv2d_bn(branch3x3, 384, 1, 3)
    branch3x3_2 = conv2d_bn(branch3x3, 384, 3, 1)
    branch3x3 = layers.concatenate(
        [branch3x3_1, branch3x3_2], axis=channel_axis, name='mixed9_' + str(i))

    branch3x3dbl = conv2d_bn(x, 448, 1, 1)
    branch3x3dbl = conv2d_bn(branch3x3dbl, 384, 3, 3)
    branch3x3dbl_1 = conv2d_bn(branch3x3dbl, 384, 1, 3)
    branch3x3dbl_2 = conv2d_bn(branch3x3dbl, 384, 3, 1)
    branch3x3dbl = layers.concatenate(
        [branch3x3dbl_1, branch3x3dbl_2], axis=channel_axis)

    branch_pool = AveragePooling2D(
        (3, 3), strides=(1, 1), padding='same')(x)
    branch_pool = conv2d_bn(branch_pool, 192, 1, 1)
    x = layers.concatenate(
        [branch1x1, branch3x3, branch3x3dbl, branch_pool],
        axis=channel_axis,
        name='mixed' + str(9 + i))
if include_top:
    # Classification block
    x = GlobalAveragePooling2D(name='avg_pool')(x)
    x = Dense(classes, activation='softmax', name='predictions')(x)
else:
    if pooling == 'avg':
        x = GlobalAveragePooling2D()(x)
    elif pooling == 'max':
        x = GlobalMaxPooling2D()(x)

# Ensure that the model takes into account
# any potential predecessors of `input_tensor`.
if input_tensor is not None:
    inputs = get_source_inputs(input_tensor)
else:
    inputs = img_input
# Create model.
model = Model(inputs, x, name='inception_v3')

# load weights
if weights == 'imagenet':
    if K.image_data_format() == 'channels_first':
        if K.backend() == 'tensorflow':
            warnings.warn('You are using the TensorFlow backend, yet you '
                          'are using the Theano '
                          'image data format convention '
                          '(`image_data_format="channels_first"`). '
                          'For best performance, set '
                          '`image_data_format="channels_last"` in '
                          'your Keras config '
                          'at ~/.keras/keras.json.')
    if include_top:
        weights_path = get_file(
            'inception_v3_weights_tf_dim_ordering_tf_kernels.h5',
            WEIGHTS_PATH,
            cache_subdir='models',
            md5_hash='9a0d58056eeedaa3f26cb7ebd46da564')
    else:
        weights_path = get_file(
            'inception_v3_weights_tf_dim_ordering_tf_kernels_notop.h5',
            WEIGHTS_PATH_NO_TOP,
            cache_subdir='models',
            md5_hash='bcbd6486424b2319ff4ef7d526e38f63')
    model.load_weights(weights_path)
    if K.backend() == 'theano':
        convert_all_kernels_in_model(model)
return model

---

批量规范化辅助层作为Inception-v3体系结构的一部分引入，以缓解由于深度卷积层相互堆叠而产生的问题。与张量流版本相比，Keras中的Inception-v3是一个预先训练的模型，没有辅助层。由于Keras中的Inception-v3是用于直接预测任务（应用）的即插即用模型权重，因此可能会忽略此选项。链接：

Keras中的模型可以重新训练，但不会给出与原始论文报告相同的结果