# 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]` . |