application_nasnet

Instantiates a NASNet model.

Description

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.

Usage

 
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)

Arguments

Arguments	Description
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]`.