application_densenet: Instantiate the DenseNet architecture
In dfalbel/densenet: Densely Connected Convolutional Networks in Keras

Instantiate the DenseNet architecture, optionally loading weights pre-trained on CIFAR-10. 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 dimension ordering convention used by the model is the one specified in your Keras config file.

application_densenet(input_shape = NULL, depth = 40, nb_dense_block = 3,
  growth_rate = 12, nb_filter = 16, nb_layers_per_block = -1,
  bottleneck = FALSE, reduction = 0, dropout_rate = 0,
  weight_decay = 1e-04, include_top = TRUE, weights = NULL,
  input_tensor = NULL, classes = 10, activation = "softmax")

`input_shape`	optional shape tuple, only to be specified if 'include_top' is False (otherwise the input shape has to be '(32, 32, 3)' (with 'channels_last' dim ordering) or '(3, 32, 32)' (with 'channels_first' dim ordering). It should have exactly 3 inputs channels, and width and height should be no smaller than 8. E.g. '(200, 200, 3)' would be one valid value.
`depth`	number of layers in the DenseNet
`nb_dense_block`	number of dense blocks to add to end (generally = 3)
`growth_rate`	number of filters to add per dense block
`nb_filter`	initial number of filters. -1 indicates initial number of filters is 2 * growth_rate
`nb_layers_per_block`	number of layers in each dense block. Can be a -1, positive integer or a list. If -1, calculates nb_layer_per_block from the network depth. If positive integer, a set number of layers per dense block. If list, nb_layer is used as provided. Note that list size must be (nb_dense_block + 1)
`bottleneck`	flag to add bottleneck blocks in between dense blocks
`reduction`	reduction factor of transition blocks. Note : reduction value is inverted to compute compression.
`dropout_rate`	dropout rate
`weight_decay`	weight decay factor
`include_top`	whether to include the fully-connected layer at the top of the network.
`weights`	one of 'None' (random initialization) or cifar10' (pre-training on CIFAR-10)..
`input_tensor`	optional Keras tensor (i.e. output of 'layers.Input()') to use as image input for the model.
`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.
`activation`	Type of activation at the top layer. Can be one of 'softmax' or 'sigmoid'. Note that if sigmoid is used, classes must be 1.