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]`

.