Function conv_factory

DenseNet/densenet.py:12–38

Apply BatchNorm, Relu 3x3Conv2D, optional dropout :param x: Input keras network :param concat_axis: int -- index of contatenate axis :param nb_filter: int -- number of filters :param dropout_rate: int -- dropout rate :param weight_decay: int -- weight decay factor :returns:

(x, concat_axis, nb_filter,
                 dropout_rate=None, weight_decay=1E-4)

def conv_factory(x, concat_axis, nb_filter,
                 dropout_rate=None, weight_decay=1E-4):
    """Apply BatchNorm, Relu 3x3Conv2D, optional dropout

    :param x: Input keras network
    :param concat_axis: int -- index of contatenate axis
    :param nb_filter: int -- number of filters
    :param dropout_rate: int -- dropout rate
    :param weight_decay: int -- weight decay factor

    :returns: keras network with b_norm, relu and Conv2D added
    :rtype: keras network
    """

    x = BatchNormalization(axis=concat_axis,
                           gamma_regularizer=l2(weight_decay),
                           beta_regularizer=l2(weight_decay))(x)
    x = Activation('relu')(x)
    x = Conv2D(nb_filter, (3, 3),
               kernel_initializer="he_uniform",
               padding="same",
               use_bias=False,
               kernel_regularizer=l2(weight_decay))(x)
    if dropout_rate:
        x = Dropout(dropout_rate)(x)

    return x


def transition(x, concat_axis, nb_filter,