application_nasnet_large: Instantiates a NASNet model in ImageNet mode.
In keras3: R Interface to 'Keras'

application_nasnet_large

R Documentation

Instantiates a NASNet model in ImageNet mode.

Description

Instantiates a NASNet model in ImageNet mode.

Usage

application_nasnet_large(
  input_shape = NULL,
  include_top = TRUE,
  weights = "imagenet",
  input_tensor = NULL,
  pooling = NULL,
  classes = 1000L,
  classifier_activation = "softmax",
  name = "nasnet_large"
)

Arguments

`input_shape`	Optional shape tuple, only to be specified if `include_top` is `FALSE` (otherwise the input shape has to be `⁠(331, 331, 3)⁠` for NASNetLarge. 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.
`include_top`	Whether to include the fully-connected layer at the top of the network.
`weights`	`NULL` (random initialization) or `imagenet` (ImageNet weights). For loading `imagenet` weights, `input_shape` should be (331, 331, 3)
`input_tensor`	Optional Keras tensor (i.e. output of `keras_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.
`classifier_activation`	A `str` or callable. The activation function to use on the "top" layer. Ignored unless `include_top=TRUE`. Set `classifier_activation=NULL` to return the logits of the "top" layer. When loading pretrained weights, `classifier_activation` can only be `NULL` or `"softmax"`.
`name`	The name of the model (string).

Value

A Keras model instance.

Reference

Learning Transferable Architectures for Scalable Image Recognition (CVPR 2018)

Optionally loads weights pre-trained on ImageNet. Note that the data format convention used by the model is the one specified in your Keras config at ⁠~/.keras/keras.json⁠.

Note

Each Keras Application expects a specific kind of input preprocessing. For NASNet, call application_preprocess_inputs() on your inputs before passing them to the model.