DenseNet recreation based on Densely connected Convolutional Network(https://arxiv.org/pdf/1608.06993.pdf)
DenseNet(num_class, nb_blocks = 4, nb_filters = 128, depth = 40, growth_rate = 12, compression = 1,
input_shape = (150, 150), channel = 3, weight_decay = 1e-4,
include_top = False)
"""
num_class = number of classes of your label data,
nb_blocks = num of stages(num of dense blocks),
nb_filters = initial num of filters(compressed after)
denpth = L, growth_rate = k,
H(l) = composite function,
compression = 1(default),
input_shape = (150, 150)(default),
channel = image's RGB channel,
weight_decay = used in kernel_regularizer,
include_top = based on keras DenseNet121, if it is true, it includes
fully connected layer
"""
dataset : scene dataset(0 ~ 5, {'buildings' -> 0, 'forest' -> 1, 'glacier' -> 2, 'mountain' -> 3, 'sea' -> 4, 'street' -> 5 })
https://www.kaggle.com/puneet6060/intel-image-classification