def default_classification_model(
    num_classes,
    num_anchors,
    pyramid_feature_size=256,
    prior_probability=0.01,
    classification_feature_size=256,
    name='classification_submodel'
):
    """ Creates the default classification submodel.

    Args
        num_classes                 : Number of classes to predict a score for at each feature level.
        num_anchors                 : Number of anchors to predict classification scores for at each feature level.
        pyramid_feature_size        : The number of filters to expect from the feature pyramid levels.
        classification_feature_size : The number of filters to use in the layers in the classification submodel.
        name                        : The name of the submodel.

    Returns
        A keras.models.Model that predicts classes for each anchor.
    """
    options = {
        'kernel_size' : 3,
        'strides'     : 1,
        'padding'     : 'same',
    }

    if keras.backend.image_data_format() == 'channels_first':
        inputs  = keras.layers.Input(shape=(pyramid_feature_size, None, None))
    else:
        inputs  = keras.layers.Input(shape=(None, None, pyramid_feature_size))
    outputs = inputs
    for i in range(4):
        outputs = keras.layers.Conv2D(
            filters=classification_feature_size,
            activation='relu',
            name='pyramid_classification_{}'.format(i),
            kernel_initializer=keras.initializers.RandomNormal(mean=0.0, stddev=0.01, seed=None),
            bias_initializer='zeros',
            **options
        )(outputs)

    outputs = keras.layers.Conv2D(
        filters=num_classes * num_anchors,
        kernel_initializer=keras.initializers.RandomNormal(mean=0.0, stddev=0.01, seed=None),
        bias_initializer=initializers.PriorProbability(probability=prior_probability),
        name='pyramid_classification',
        **options
    )(outputs)

    # reshape output and apply sigmoid
    if keras.backend.image_data_format() == 'channels_first':
        outputs = keras.layers.Permute((2, 3, 1), name='pyramid_classification_permute')(outputs)
    outputs = keras.layers.Reshape((-1, num_classes), name='pyramid_classification_reshape')(outputs)
    outputs = keras.layers.Activation('sigmoid', name='pyramid_classification_sigmoid')(outputs)

    return keras.models.Model(inputs=inputs, outputs=outputs, name=name)
 Creates the default classification submodel.

    Args
        num_classes                 : Number of classes to predict a score for at each feature level.
        num_anchors                 : Number of anchors to predict classification scores for at each feature level.
        pyramid_feature_size        : The number of filters to expect from the feature pyramid levels.
        classification_feature_size : The number of filters to use in the layers in the classification submodel.
        name                        : The name of the submodel.

    Returns
        A keras.models.Model that predicts classes for each anchor.
    
keras_retinanet/models/retinanet.py