application_nasnet {keras} | R Documentation

## 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

`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]`.

[Package keras version 2.2.4 Index]