application_nasnet: Instantiates a NASNet model.
In dfalbel/keras: R Interface to 'Keras'

Note that only TensorFlow is supported for now, therefore it only works with the data format image_data_format='channels_last' in your Keras config at ~/.keras/keras.json.

application_nasnet(
  input_shape = NULL,
  penultimate_filters = 4032L,
  num_blocks = 6L,
  stem_block_filters = 96L,
  skip_reduction = TRUE,
  filter_multiplier = 2L,
  include_top = TRUE,
  weights = NULL,
  input_tensor = NULL,
  pooling = NULL,
  classes = 1000,
  default_size = NULL
)

application_nasnetlarge(
  input_shape = NULL,
  include_top = TRUE,
  weights = NULL,
  input_tensor = NULL,
  pooling = NULL,
  classes = 1000
)

application_nasnetmobile(
  input_shape = NULL,
  include_top = TRUE,
  weights = NULL,
  input_tensor = NULL,
  pooling = NULL,
  classes = 1000
)

nasnet_preprocess_input(x)

`input_shape`	Optional shape list, the input shape is by default `(331, 331, 3)` for NASNetLarge and `(224, 224, 3)` for NASNetMobile It should have exactly 3 inputs channels, and width and height should be no smaller than 32. E.g. `(224, 224, 3)` would be one valid value.
`penultimate_filters`	Number of filters in the penultimate layer. NASNet models use the notation `NASNet (N @ P)`, where: - N is the number of blocks - P is the number of penultimate filters
`num_blocks`	Number of repeated blocks of the NASNet model. NASNet models use the notation `NASNet (N @ P)`, where: - N is the number of blocks - P is the number of penultimate filters
`stem_block_filters`	Number of filters in the initial stem block
`skip_reduction`	Whether to skip the reduction step at the tail end of the network. Set to `FALSE` for CIFAR models.
`filter_multiplier`	Controls the width of the network. If `filter_multiplier` < 1.0, proportionally decreases the number of filters in each layer. If `filter_multiplier` > 1.0, proportionally increases the number of filters in each layer. - If `filter_multiplier` = 1, default number of filters from the paper are used at each layer.
`include_top`	Whether to include the fully-connected layer at the top of the network.
`weights`	`NULL` (random initialization) or `imagenet` (ImageNet weights)
`input_tensor`	Optional Keras tensor (i.e. output of `layer_input()`) to use as image input for the model.
`pooling`	Optional pooling mode for feature extraction when `include_top` is `FALSE`. - `NULL` means that the output of the model will be the 4D tensor output of the last convolutional layer. - `avg` means that global average pooling will be applied to the output of the last convolutional layer, and thus the output of the model will be a 2D tensor. - `max` means that global max pooling will be applied.
`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.
`default_size`	Specifies the default image size of the model
`x`	a 4D array consists of RGB values within `[0, 255]`.