create_dense_net: Build the DenseNet model
In dfalbel/densenet: Densely Connected Convolutional Networks in Keras

Description Usage Arguments See Also

Build the DenseNet model

create_dense_net(nb_classes, img_input, include_top, depth = 40,
  nb_dense_block = 3, growth_rate = 12, nb_filter = -1,
  nb_layers_per_block = -1, bottleneck = FALSE, reduction = 0,
  dropout_rate = NULL, weight_decay = 1e-04, activation = "softmax")

`nb_classes`	number of classes
`img_input`	tuple of shape (channels, rows, columns) or (rows, columns, channels)
`include_top`	flag to include the final Dense layer
`depth`	total number of layers
`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 positive integer or a list. 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
`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.